How do you tell if smoky quartz has been irradiated? I want to purchase a piece of smoky quartz crystal, but am not sure if it is irradiated.  Is there a way to tell if it has been irradiated?  Thank you.

It can be hard to distinguish man-made smoky quartz from natural smoky quartz. Traditional methods for irradiation to convert quartz to smoky quartz tends to leave the quartz crystals looking more black than translucent smoky brown.  This is caused in part by the rapid irradiation time for man-made smoky quartz.  I am sure a more patient man made process could produce more naturally smoky quartz.

The best way to determine if smoky quartz is man-made or not is to know the location for the quartz specimen.  Arkansas, USA produces mostly clear quartz and very little natural smoky quartz.  Dark smoky quartz from Arkansas is almost certainly artificially made.  I have a specimen of dark smoky quartz which is almost black from Bear Lodge mountain, Wyoming that is definitely natural.

Best practice, if smoky quartz specimen does not have a well known location, assume its man-made never pay alot for it.


What is the force imposed by underwater currents? I would like to calculate the force of an underwater current on a meter by meter plate (or any other unit that you may use to represent it) that is standing in front of the current.
How do I calculate it? What parameters do I need?

You are looking for the drag force imposed by the fluid flow on an object:

$$F = \dfrac{1}{2} \rho v^{2} C_D A$$

where $\rho$ is the density of the fluid, $v$ is the speed of the flow, $C_D$ is the drag coefficient depending on the shape of the object, and $A$ is the surface area normal to the fluid flow.

Let's plug in some characteristic numbers for scaling. For a current of 1 m s$^{-1}$, fluid density of 1000 kg m$^{-3}$, drag coefficient of 1, surface area of 1 m$^2$, the force imposed by the fluid on the object, according to the equation above, would be 500 N.

Note that this equation is valid only for high $Re$ flows that produce turbulence in the wake of the object. The drag coefficient is estimated empirically and takes into account both skin and form drag of the object.


Are there gases from the Earth useful to generate electricity besides "natural gas"? Natural gas:

  ...is a naturally occurring hydrocarbon gas mixture consisting primarily of methane, but commonly including varying amounts of other higher alkanes, and sometimes a small percentage of carbon dioxide, nitrogen, hydrogen sulfide, or helium.

I saw the graphic below in the BBC News article Trump climate: Challenges loom after Obama policies scrapped. It lists natural gas as the source of about 33% of the US electricity generation in 2015, but also lists "Other gases" as the source of less than 1%. Presumably it is a meaningful fraction of that 1% or else the BBC would not have included it. While the source of the data is listed as the US Energy Information Agency, the graphic itself was prepared by the BBC if I understand correctly.

Are thee other gases available from the Earth that could account for this fraction of 1%? I don't mean gases that are produced during an industrial refinement process, but perhaps gases that were simply separated. Just for example could it be natural propane? The same Wikipedia article mentions heavier hydrocarbons, but I don't understand if these are already present in the Earth and just being separated, or if they are produced primarily as reaction products.

EDIT: Based on comments, a substantially different source of gas that was chemically similar to natural gas would still be of interest. So just for example, methane from a bog should count in this case, since it does not involve many of the geological processes involved in the production of fossil fuels (e.g. the same timescales or temperatures).

The category "other gases" may not solely include other naturally occurring gases extracted from the Earth.

As you state natural gas is predominantly methane, extracted from the Earth.

Another type of gas commonly used is petroleum gas, which is prepared by refining petroleum of wet natural gas. Petroleum gas is propane or butane.

Coal gas, which was manufactured from coal and largely consists of hydrogen, carbon monoxide and methane, is no longer manufactured or used.

Another type of gas that could be included in the category is biogas, which is a manufactured gas made from biological waste products, such as: manure, sewage, food waste and green waste. Biogas is predominantly methane.


What was the storm surge height from Hurricane Harvey? The numbers bandied about have been 3.6 m (12 feet) and even 4.6 m (15 feet), but something told me that was unlikely.  As a guy who could "almost" slam dunk a basketball, the idea of a 3 m (10 feet) surge (height of a basketball goal) is formidable.  4-5 m (12-15 feet) is terrifying.

However, I have not found any statements on how high the storm surged ocean-wise.  I've seen plenty of homes wiped off of their foundations from wind, but if you look carefully homes right next to them are still quite intact.  My guess is the max "surge" was less than 1 m (<2 feet). Does anyone know this or where to find it?

For a general view of the surge in the region, the maps in u-surge give a pretty good idea of numbers and more affected regions.

The USGS provides a comprehensive view of High Water Marks (HWM) in the entire region. If you are looking for observations, then the HWM database gives you the best spatial coverage. While HWM do not provide surge information per se as it includes multiple processes (like ocean surge from the storm, waves and wave run-up, and flooding from rivers and precipitation), it gives an idea of the extreme values of water level in many locations. It also shows the large spatial variability of the effects of the storm. There are also tide gauge and rapid response stations in the area as part of the USGS database that provides more information of the temporal evolution of water levels.

Part of the problem is that storm surge is only the result of two processes: 1) wind piling water against the coast and 2) low atmospheric pressure that causes ocean level to go up. Additionally, you have many other processes affecting water levels during a storm or hurricane. The tides are still there as always and that adds complications because if the storm surge peak occurs during high tide, then the impacts are much worse. Tides in the Gulf of Mexico are not that high, but they can be a much larger factors in some other areas. The combination of storm surge and astronomical tides is often label "storm tide", which is a very poor name choice and leads to much confusion, as clearly the storm does not cause a tide. Another impact is that associated with the high winds, you have large wave heights. As these high waves approach the coast, they cause higher water levels as a result of wave run-up and wave setup. The magnitude of these wave effects can be pretty large in some locations especially in ocean-facing locations. The last main impact on water level is precipitation and the resulting increase river discharge. Clearly, Harvey had a catastrophic amount of precipitation and its impacts were much larger than the purely oceanic-driven storm surge in this storm.

Many agencies are working on developing a measure of water level that includes all these processes. For instance a good example is the USGS effort to develop Total Water Level measurements and forecast.


Can two earthquakes happen at the same time? I wanted to know if two earthquakes can happen at the same time in the same area? I "think" this can happen, but I'm not totally sure? I understand tectonic plates sliding against each other can cause an earthquake, but is it possible for two to happen at the same time in the same area?

The 2016 paper "A case for historic joint rupture of the San Andreas and San Jacinto faults" reports of research suggesting that simultaneous large earthquakes occurred in 1812 in California, the researchers used

  dynamic rupture modeling to show that paleoseismic and historic observations associated with the earthquake of 8 December 1812 can be explained by joint rupture of the SAF and SJF.

(SAF = San Andreas Fault and SJF = San Jacinto Fault)

But the author stresses that what they have is a model, rather than a certainty.

Alot of research centres around the notion of 'near-simultaneous earthquakes', such as the 2009 South Pacific earthquake, as described in the article "Double Shake: Multiple, Nearly Simultaneous Earthquakes Triggered Deadly 2009 Tsunami" where studies suggest that at least 2 quakes of similar magnitude occurred within 50km of eachother, separated by minutes - another similar study suggests that one of the quakes 'obscured' another nearby rupture of equal or higher magnitude.

Italy is another place where simultaneous or near-simultaneous earthquakes seem to occur and resulted in several studies, one of which being reported in the article "Likely near-simultaneous earthquakes complicate seismic hazard planning for Italy", stating that

  It's very important to consider this scenario of earthquakes, occurring possibly seconds apart, one immediately after another

Stating that this scenario would have an 'amplification' effect of the the effects of the shhaking (hence, the researchers' concern).  A particular example they cite is

  The 1980 Irpinia earthquakes included a sequence of three events, occurring at intervals within 20 seconds of each other.

Another compelling example, reported in the article "Sumatran strike-slip earthquakes challenge seismologists" that occurred in April, 2012 - a strike-slip fault induced quake of a magnitude of 8.6 on the richter Scale, one of the theories for the great magnitude is explained as

  Another apparent reason for the large magnitude, says Miaki Ishii, a sesimologist at Harvard University, is that multiple faults appear to have slipped almost simultaneously, creating a composite earthquake. “If it was just a single fault activation, it wouldn’t have been large as 8.6.

An important note the Harvard seismologist makes, that essentially answers your question regarding simultaneous (and near-simulatneous earthquakes), is that observations of this kind of fault activation, which is described as "a cascading failure of faults" are seen in a lot of large earthquakes.


Looking for fun and illuminating weather visualizations I'm looking for illuminating and fun weather data visualizations like e.g. EarthWindMap.

More specifically, I was hoping to find wind vectors overlayed onto a prognostic chart to visualize how the wind moves around pressure systems and fronts.

Try Windy.com  It looks very similar to the EarthWindMap, but appears to offer more controls.  It is simple to change the heights and what variables are of interest.


Why is colder seawater more compressible? I know compressibility is related to the pressure and temperature of the water parcel, but what is the physical/molecular reason as to why colder water is more compressible?

First, let's explain what compressibility is. Compressibility is defined as a measure of the relative volume change of a parcel of fluid as a response to a pressure change.

When I calculate the compressibility of seawater using Matlab and the seawater package (equivalent to Fofonoff & Millard, 1983), I obtained that the compressibility increases at cold temperatures for salty, brackish, and fresh water. In fact, water has a minimum compressibility at 46.5°C (Fine & Millero, 1973). As pressure increases the compressibility decreases for any of the salinities considered.

As can be seen in the figure the compressibility is maximum at low temperatures for any salinity (not just seawater) and pressure. So in general, the compressibility of water is maximum at low temperatures due to the internal structure of water. The inverse relationship with temperature seems well accepted (Fine & Millero, 1973) and has a direct relation with other properties like the speed of sound.

In a typical liquid, the compressibility at low temperatures is reduced as the structure becomes more compact. In water, there is a change at low temperatures toward more open-structure clusters (expanded icosahedral water cluster) versus denser clusters (collapsed icosahedral water cluster). As the water structure at lower temperatures is more open, the capacity for it to be compressed increases.

As cold liquid water is heated it shrinks, it becomes less easy to compress, the speed of sound within it increases, gases become less soluble and it is easier to heat.

The compressibility above is calculated using the formula in the figure (same as in the wiki article) and I have included the code to reproduce the results:

T=-2:1:30;
P=0:10:1000;
S=35;
for i=1:length(T)
    for j=1:length(P)
        dens(i,j)=sw_dens(S,T(i),P(j));
        dens15(i,j)=sw_dens(15,T(i),P(j));
        dens0(i,j)=sw_dens(0,T(i),P(j));
    end
end
spvo=1./dens;
spvo15=1./dens15;
spvo0=1./dens0;
for i=1:length(T)
    for j=1:length(P)-1
%        Ev2(i,j)=-(1./spvo(i,j))*(spvo(i,j+1)-spvo(i,j))./(P(j+1)-P(j));
        Ev35(i,j)=(1-spvo(i,j+1)/spvo(i,j))/(P(j+1)-P(j));
        Ev15(i,j)=(1-spvo15(i,j+1)/spvo15(i,j))/(P(j+1)-P(j));
        Ev0(i,j)=(1-spvo0(i,j+1)/spvo0(i,j))/(P(j+1)-P(j));
    end
end
figure(1);clf
for j=[1,20,100]
    line(T,Ev35(:,j),'Color','k','LineWidth',2);
    line(T,Ev15(:,j),'Color','b','LineWidth',2);
    line(T,Ev0(:,j),'Color','r','LineWidth',2);axtt
end
text(T(20),Ev35(20,1),['P=',num2str(P(2)),'dbar'])
text(T(20),Ev35(20,20)*.99,['P=',num2str(P(21)),'dbar'])
text(T(20),Ev35(20,100)*.98,['P=',num2str(P(101)),'dbar'])
text(T(15),Ev0(15,1),['P=',num2str(P(2)),'dbar'],'Color','r')
text(T(15),Ev0(15,20)*.99,['P=',num2str(P(21)),'dbar'],'Color','r')
text(T(15),Ev0(15,100)*.99,['P=',num2str(P(101)),'dbar'],'Color','r')
xlabel('Temperature (degC) ')
ylabel('Compressibility (1/dbar) ')
legend('35 PSU','15 PSU','0 PSU')
text(7,5e-6,'$c_w=-{1\over V}({dV\over dp})$','Interpreter','latex','FontSize',20,'FontWeight','bold')


About an alternative type of wind energy facility Many people seem to complain about large wind farms. Instead of all that, there are many cities with all their tall buildings that actually 'magnify' the wind or augment it. Even corridors through a building can be a virtual wind tunnel. Why not put a lot of medium size wind-to-energy devices or small ones all over a city, wherever there is a known spot or area that augments the wind because of buildings or corridors etc.? (I even saw an old Popular Science magazine with a sci-fi style picture of a huge building with a big fan to generate energy built right in it..)

This has been tried to some extent; Strata in London was a skyscraper that was built with three wind turbines at the top, with a deliberate design to funnel wind into them. There are other examples.

Siting renewable energy generation in cities is very attractive, partly because many people feel that the "industrial" feel fits better there, but also because it locates sources of generation close to sources demand. For some other technologies, such as photovoltaics, this is indeed a good approach. However, it doesn't tend to work out that way for wind.

There are three main disadvantages to urban wind:

"Dirty air" : Not what it literally sounds like (that's also true in cities, but wind turbines don't mind). The wind that is funnelled through, around and above buildings is sometimes accelerated, but it is also made turbulent, and wind turbines do not like turbulent flow. Turbulence increases the unwanted forces that turbines experience (i.e. forces that do not help them make electricity), and makes them less efficient.
Economies of scale: The amount of power that a wind turbine can generate, in a given wind speed, is proportional to the square of its diameter. Which is to say, size matters. In the countryside, onshore rotors commonly reach 80m diameter (offshore are much larger). In cities, this size would not be practical or acceptable. That means that a much larger number of turbines would be needed to generate as much power, which would be more expensive and have more visual impact.
It isn't all that windy: The power that a turbine of a given diameter can generate is proportional to the cube of the wind speed. Because of this, small differences in wind speed are very important to wind farms. Most cities simply do not experience such high windspeeds as rural hilltops do.

A fourth reason may be public acceptance; while people may view a few token turbines on rooftops as a good thing, the quantity needed to make a substantial contribution to national demand would drastically change a city's skyline, and might meet with disapproval similar to many rural wind farms. However, I have no evidence for this.


CO2's rise up into the atmosphere CO2 is heavier than air (1.51 while oxygen is 1.1 and nitrogen is 0.97), however when combusted, it will initially rise until it's temperature gets closer to that of air (about 1000 feet), then it will tend to fall. CO2 is a reluctant defusser and mixer, so how does it get from 1000 to 10,000 feet - what lifts CO2 up?

...so how does it get from 1000 to 10,000 feet - what lifts CO2 up?

Hmm... how does it get to the mid-troposphere and higher?
Well, first, to understand that one may find some helpful information in comments below the question. Second, it always helps to review our own assumptions.
In the question, there's an implicit assumption that CO2 does not mix with the rest of the atmosphere at the molecular level. As it turns out, it does!
Also, the primary mixing mechanism in the troposphere takes place through eddy diffusion, not molecular diffusion, and that's an important distinction. Storms and jet streams, convection, and turbulence (from storms and clouds down to the nearly molecular size) all contribute to mixing.
As one goes higher in the atmosphere, the balance between the magnitude of eddy diffusion and molecular diffusion shifts. This happens above the tropopause.


Are there any areas on Earth with purplish-colored soil/sand/rock/land? This sort of comes from my related question, but only sort of. Are there any areas on Earth with purplish-colored soil? The purple tint would come from some mineral, perhaps deposited by rivers or volcanic activity.

I don't know exactly what mineral. The only purple mineral I know for sure is amethyst. It does not hafta be pure purple. A purple-brown mix would be just fine.

The line "purple mountains majesty" comes to mind here, but that appears to be just a reddish/purplish sunset reflecting off of brown mountains. I'm looking for something real in the soil/rock, not an illusion.

Purplish sand and purplish rock are okay too. Really whatever the land is, that's what I'm asking for purple color.

One more semi-reference: Apollo 17 actually discovered orange soil on the Moon.

Soil color is highly dependant on the oxides and other minerals in the composition.  Purplish tones appear to be possible by inclusion of manganese oxide compounds.  There are locations in China that have markedly violet soils.

If you broaden your search to sands as well as soils, there are plenty of examples of garnet-based sands that can appear markedly purple.  California's Pfeiffer Beach is one such location.


Why wouldn't the earth be "always daytime" if the "daily cycle of sunlight" was drawn full-scale? The question is clear enough in my opinion but, I've found out that if you redraw this picture here:

You'll notice that the Sun is as big as the Earth. But if you scale up the Sun to it's real size compared to the Earth, you would realize that the Earth would covered in daylight ALL THE TIME. So what's going on here?

The sun is really far away. Thus its rays are essentially parallel at the earth's orbit. So, while the diagram you posted is clearly a bit off in terms of the relative size and distance between the sun and the earth, the parallel rays are about right.


Is the maximum human population carrying capacity of Earth higher or lower than the current total human population? Not sure if this is the right stack for this but:
I have been arguing with friends on overpopulation, several are convinced that Earth cannot sustain the current population.  Without getting into all of the ways that we are destroying various ecosystems and polluting, I believe that if we were actively working to optimize the environment and how we use resources, Earth could easily handle many, many more humans.
Such as renewable energy, reducing waste, committing to reversing desertification, and so much more.  But regardless of the behaviors we specifically change, looking at the raw chemistry of the planet, i.e. how much water, oxygen, carbon, plant life, and so on, there is some maximum amount of humans that can live healthy lives.

The FAO estimate that we use 11% of the Earth's surface area for agriculture,  and that that represents approximately a third of the land that 'could' be used (http://www.fao.org/3/y4252e/y4252e06.htm). If we accept that current agricultural production is at a level that allows the current population to live healthy lives that implies that we could triple our population and not starve.
In this very simplistic view I am ignoring the very real questions of inequity in distribution, what constitutes 'healthy' and the potential of changing efficiency in agriculture or transitioning populations away from expensive meat based diets.
However, and it's a really big however, you couldn't make that sort of increase in the area of globe used for agriculture without destroying natural ecosystems, and it's arguable that already much of global agriculture is unsustainable in it's present form, as aquifers are overexploited and soil quality degrades.


Waves in very thin layer Elastic waves in the earth are described by the elastodynamic equations
\begin{align}
    \rho \frac{\partial^2 u}{\partial t^2} &= (2 \mu + \lambda) \frac{\partial^2 u}{\partial x^2} + \mu \left( \frac{\partial^2 u}{\partial y^2} +  \frac{\partial^2 u}{\partial z^2} \right) + (\lambda+\mu) \left( \frac{\partial^2 v}{\partial x \partial y} + \frac{\partial^2 w}{\partial x \partial z} \right) \\
    \rho \frac{\partial^2 v}{\partial t^2} &= (2 \mu + \lambda) \frac{\partial^2 v}{\partial y^2} + \mu \left( \frac{\partial^2 v}{\partial x^2} +  \frac{\partial^2 v}{\partial z^2} \right) + (\lambda+\mu) \left( \frac{\partial^2 u}{\partial x \partial y} + \frac{\partial^2 w}{\partial y \partial z} \right) \\
    \rho \frac{\partial^2 w}{\partial t^2} &= (2 \mu + \lambda) \frac{\partial^2 w}{\partial z^2} + \mu \left( \frac{\partial^2 w}{\partial x^2} +  \frac{\partial^2 w}{\partial y^2} \right) + (\lambda+\mu) \left( \frac{\partial^2 u}{\partial x \partial z} + \frac{\partial^2 v}{\partial y \partial z} \right)
\end{align}

Is it possible to have a very thin and stiff layer in a surrounding soft material? If yes, is it possible to approximate the thin layer by a thin film with free boundaries and a uniform displacement over the thickness direction ($\partial / \partial z = 0$)? In this case, the equations of motion become
\begin{align}
    \rho \frac{\partial^2 u}{\partial t^2} &= (2 \mu + \lambda) \frac{\partial^2 u}{\partial x^2} + \mu \frac{\partial^2 u}{\partial y^2} + (\lambda+\mu) \frac{\partial^2 v}{\partial x \partial y}  \\
    \rho \frac{\partial^2 v}{\partial t^2} &= (2 \mu + \lambda) \frac{\partial^2 v}{\partial y^2} + \mu \frac{\partial^2 v}{\partial x^2} + (\lambda+\mu)\frac{\partial^2 u}{\partial x \partial y}  \\
    \rho \frac{\partial^2 w}{\partial t^2} &= \mu \left( \frac{\partial^2 w}{\partial x^2} +  \frac{\partial^2 w}{\partial y^2} \right)
\end{align}
Do these equations describe a realistic case? Does this state have a specific name (like plane stress or antiplane shear ...)?

The problem with the cancelling you suggested is that it is imposed globally for the PDE and not locally (just on the thin layer).

Usually when we talk about vertically layered media we use a plane wave $\exp(i(\mathbf{k}\cdot\mathbf{x}-wt))$ trial solution (you can plug this in to confirm that it satisfies the PDE) and match boundary conditions at the interface of two layers to determine reflection and transmission coefficients. If you are not familiar with this, Aki and Richards (1980) is a good reference. See also the Zoeppritz equations https://en.wikipedia.org/wiki/Zoeppritz_equations

Introducing a very thin, stiff layer is equivalent to placing a 0 displacement (Dirichlet) boundary condition at the location of the layer. Physically, this models an infinite impedance reflector and will generate a total reflection. Waves on a string with both ends fixed are modeled in the same way.

Setting vertical derivatives in displacement to 0 is imposing a Neumann B.C. on the location of the thin layer. This is exactly how the free surface is modeled. Physically, this models vanishing vertical tractions at the boundary location.

So, if you want to model seismic wave propagation with an extremely stiff layer, then use the Dirichelet 0 displacement boundary condition. If the thin layer is only 'very stiff', use the reflection and transmission coefficients provided by the Zoeppritz equations. If you want tractions to disappear, use the Neumann boundary conditions. But, beware; this doesn't make physical sense if the thin layer is sandwiched between two other layers.

Hope this helps.
-D


Understanding the Mercator Projection To illustrate my problem, let's say I have lattitude and longitude coordinates, such as 38.871105, -77.056042.
I want to be able to calculate the new lattitude and longitude if, say, I moved 300m eastwords, or 400m northwards, in a way that google maps will like.
Now, from what I can understand, this would mean converting it from lat/long to mercator pixel coordinates, getting new mercator pixel coordinates then converting back to lat/long...

Here's what Wikipedia has to say on Google's Mercator projection:

Why do the above equations not actually take into account the longitude? And what is a? If I could understand these equations then theoretically the rest should be easy...
Thanks!
-B

The Mercator Projection has been used and it is used since the XVI century due to it maintain the course, rhumb or route. So it is useful for navigation.

As the Earth is approached by a ellipsoid, then you get the formulas that you saw on Mercator Projection. This transformation it is complicated for a ellipsoid, as the Earth curvature it is not a constant, so in the example you gave (moving 300m eastwords, or 400m northwards), you need to transform the first point from geographical coordinates to rectangular coordinates, then shift the point 300 meters east, check the correction on distance due to the different curvature on each point, apply the correction, then shift the point 400 meters northwards, check the correction, apply the correction and bring the coordinates back to geographical coordinate.

That is to much computing time and there are a lost of precision loss on the different shiftings and transformation geographical <-> rectangular coordinates. Web Mercator is just a Mercator that considers the Earth approach by an sphere, not a ellipsoid. That is much easier, needs less computation as, the curvature in a sphere is constant it is not required to consider.

Mercator projection is ok for navigation (that is what you are doing with Google Earth or Google Maps). Check this link.

Regarding the constants... a is the semi-major axis of an ellipsoid, amap is the semi-major axis on the map (as the Mercator Projection is distorting the distances, you should make a distance correction, that it is become bigger as far as you are from the Equator) e is the eccentricity of the ellipsoid.

Regarding the latitude issue... Thing a little bit on this question: In the Mercator Projection it is consider, as Earth approach a sphere and a revolution ellipsoid. Both surfaces are revolution surfaces so, there is no variation along the longitude way. That is why the longitude is not required for Y calculation.

Anyhow, is something is not clear, just let me know. Hope it helps!


Does being in the air provide any protection from an asteroid? How does radiation and shock waves from a radioactive asteroid or atomic explosion disperse throughout Earth's atmosphere?

Are there specific areas on Earth that are less likely to receive the shock wave or radiation by being on the other side of the Earth and/or land or wind characteristics like being in a deep valley?

I'm trying to go with the worst case survivable scenario if the asteroid hit with the possibility of hitting a nuclear reactor, arsenal or deposit of uranium.

Related:

  This is from a broad question that I am breaking up in 3 questions. Feel free to edit.

Which of these scenarios would have a smaller chance of being struck?

In the air of some type blimp or aircraft on the opposite side of the Earth?

Does being under water provide any protection from a asteroid?

To answer the radiation question, it is both absorbed and reflected into space. A lot of these processes are complex relations between atmosphere, magnetosphere, and solar radiation. Solar radiation does penetrate through as it drives everything we need or any creature needs to live, whether directly or indirectly. Aurora Borealis is caused by Solar radiation trapped in our atmosphere. This is why Ozone (O3) is so important as it does a massive support function of helping block those rays. Some of this radiation is reflected by the earth as thermal radiation. The magnetosphere also plays a role in capturing and diffusing such particles. A little more research may help, it is a very fascinating process. For more:

https://en.wikipedia.org/wiki/Aurora

This was from courses in astronomy, hydrology, and my brain. Use wisely.


What are these lines that I frequently see on doppler radar? I frequently see lines like this (the red arrows in the image below point to what I am referring to) on doppler radars. They are usually propagating outward from a line of thunderstorms.
What are they, what do they indicate, and how do they form?

If they propagate outward from thunderstorms, they are outflow boundaries. Cold air flows down from the thunderstorm with precipitation and spreads outward upon hitting the ground. This air moves ahead of the storm and forces warmer air upward, suspending clouds and other particles. That's what is visible on radar.
More: https://en.wikipedia.org/wiki/Outflow_boundary


Why is the weather in San Francisco so different from that in San Jose? San Francisco has around 5 months a year of intermittent rain. San Jose, just 50 miles south and nestled between mountains instead of between waters, has perhaps only 1 month of rain. San Francisco also has notably colder winters and summers than San Jose, to a fairly shocking degree.
Geographical differences are three, primarily. First of all, San Francisco is further north, and closer to the Pacific Northwest, but only by 50 miles, or 80 kilometers. Second, San Francisco is surrounded by water, with the San Francisco Bay to the east and the Pacific Ocean to the West, while San Jose is isolated from the Pacific by the Santa Cruz mountains. Third, San Francisco has a far higher population density, with more smog and greenhouse gases as a result, which may or may not be a significant contributor to rain (nucleation sites?).
Why do these two so proximate cities have such different climates?

San Francisco is more exposed the maritime westerly weather, whereas San Jose is in a rain shadow region with a mountain range between it and the Pacific Ocean.

San Jose, California, and adjacent cities are usually drier than the rest of the San Francisco Bay Area because of the rain shadow cast by the highest part of the Santa Cruz Mountains.

The exposure of San Francisco to westerly maritime weather and being surrounded by water on three sides are significant reasons why it has a milder climate. It's climate is largely influenced by the cold California Current.

Due to the Bay Area's diverse topography, the result of the clashing tectonic plates, the region is home to numerous microclimates that lead to pronounced differences in climate and temperature over short distances.

San Jose however is slightly inland and is surround by mountains of three sides, has less rain and an average of 301 sunshine days each year.


Why is WRF most often configured at 3:1 nesting ratio? In WRF application, nested domain has always been used for weather/air quality modeling for the place of interest in a better resolution. The figure below illustrates this:

I have noticed that the ratio of grid resolution between outer (coarser) domain and inner (finer) domain are always set at 3:1 ratio.

Is there any reason in the aspects of physical process or computation? Is the ratio of 3:1 helpful for the interaction between the boundary?

To understand why the nesting ratio of 3 is preferred to the nesting ratio of 2, it is important to understand the following two features of WRF:

1) Grids are Arakawa C-staggered: mass points are at cell centers,
 u-velocities are at east-west cell edges, v-velocities are at
 north-south cell edges. See Mesinger and Arakawa 1976, Chapter 4
 for a good description and explanation of Arakawa staggering. WRF's
 grid in computer memory looks like this:

   +-------+-------+-------+
   |     v |     v |     v |
j=3|       |       |       |
   | u   p | u   p | u   p |
   +-------+-------+-------+
   |     v |     v |     v |
j=2|       |       |       |
   | u   p | u   p | u   p |
   +-------+-------+-------+
   |     v |     v |     v |
j=1|       |       |       |
   | u   p | u   p | u   p |
   +-------+-------+-------+
      i=1     i=2     i=3

Thus, p(1,1) is geographically located $\Delta x /2$ East of u(1,1) and  $\Delta x /2$ south of v(1,1).

2) For computational efficiency and accuracy, the data from the child (nested) domain is copied to the parent domain. This is opposite from the data going from parent to child, which is interpolated. For more specifics, see the WRF Registry documentation by Michalakes and Schaffer.

As a consequence of the above two, it is more computationally efficient and accurate to use a nesting ratio of 3, rather than 2. In the example below, for 2:1 nesting ratio and 1-d case for simplicity, u values are simply copied from child to parent array because these points are guaranteed to overlap between two grids. Notice that the p points do not overlap and child p values must be interpolated to the parent. This induces computational overhead and numerical damping of the $2\Delta x$ wavelength in mass:

child  u   p   u   p   u   p   u
       +-------+-------+-------+
       |    \     /    |
       |     \   /     |
       |      \ /      |
       v       v       v

parent U       P       U
       +---------------+

In a 3:1 nesting ratio however, both the velocity and mass points overlap, and a simple copy is feasible:

child  u   p   u   p   u   p   u
       +-------+-------+-------+
       |           |           |
       |           |           |
       v           v           v

parent U           P           U
       +-----------------------+


Why do P waves have a higher velocity in the lower mantle than in the core even though the core has a much higher density? I've just started teaching 9th grade Earth science and my students and I have noticed something about P and S wave depth vs velocity graphs. In the typical graph,
eg: https://www.researchgate.net/figure/Earths-P-wave-velocity-S-wave-velocity-and-density-profiles-as-a-function-of-depth_fig8_280835886

With the p wave's velocity plotted from a depth of 0 km to a depth of 6371 km it shows the p wave velocity reaching about 14 km/sec by the time it reaches the bottom of the mantle.  Then of course it drops precipitously through the mantle/outer core boundary. However, even when the wave reaches the inner core, it's velocity never gets close to that 14 km/sec speed, it's speeds in the inner/outer core range only from 8 to 12 km/sec even though the core has a much higher density than the mantle. You would expect a much higher wave velocity.

tldr; Increased density corresponds with decreased P-wave velocity

The P-wave velocity for an isotropic medium is:

$$
V_p = \sqrt\frac{K+4/3\mu}{\rho}
$$

where $K$ is the Bulk Modulus, $\mu$ is the shear modulus, and $\rho$ is the density (https://en.wikipedia.org/wiki/Elastic_modulus, see $M$ the P-wave modulus). Isotropy just means that the strain on the rock caused by a stress is irrespective of the stress orientation. Examining the equation you can see that increased density corresponds with decreased velocity.

In the mantle, $\rho$ increases with depth, but the numerator $K+4/3\mu$ increases faster than the $\rho$, so the net effect is an increase in velocity.

In the fluid outer core, $\mu = 0$. This is because fluids do not support shear stresses. Correspondingly, $V_p$ drops precipitously.

In the solid inner core, $\mu$ is nonzero hence there is a jump in velocity at the inner/outer core boundary. The combination of $K$, $\mu$, and $\rho$ in the inner core is such that $V_p$ doesn't exceed the lower mantle values.


"Five of the Solomon Islands disappeared" due to sea level rise, how is this possible so quickly? The text of the introduction to the BBC Podcast Sea Levels Rise; The Compass, Living on the Edge Episode 1 of 4 says:

Five of the Solomon Islands have disappeared, many more are becoming uninhabitable. For Kerry and Sally, climate change is not a theory - it is what has made them abandon their island and the graves of their ancestors. They see themselves as lucky - they had family land to move to and the skills to build new homes on stilts - but they are resigned to moving again.
Award-winning journalist Didi Akinyelure visits her home city of Lagos to find out the latest solution to sea level rise in West Africa. The glass towers of the new financial district of Eko Atlantic are protected from the waves by state of the art sea defences. The residents of the luxury apartments should keep their feet dry whatever the climate throws at them. That may be small comfort for their unprotected neighbours in the shanty town on the lagoon, Makoko, but they’re experts in survival against the odds.

Certainly sea level is rising, on the order of perhaps 15 centimeters in the last century judging from this plot, and the New Scientist article Five Pacific islands vanish from sight as sea levels rise certainly adds credence to this. Answers to the question Sea Level Rise due to Climate Change shed some light on human-induced climate change.
Between about 04:00 and 06:00 in the podcast, Simon Albert, a climate change scientist from University of Queensland describes the situation in the Solomons.
Here is my best attempt at a transcription of a small part of the podcast:

The Solomons have really over the last couple of decades been a global “hot spot” for sea level rise. So the rates of sea level rise we’ve observed in the Solomons have been in the order of three times the global average.
Whilst globally the seas have been rising approximately 3 millimeters per year for the last couple of decades, the Solomons have seen a rise of 7 to 10 millimeters per year over that time frame.
A large part of that is a result of human-induced climate change, and then on top of that we’ve also had “the perfect storm” if you like, of a series of natural cycles in sea-level rise and weather conditions, such as El Nino, intensification of trade winds, which effectively have pushed water into the Western Pacific, resulting in a significantly higher than would be normally experienced.
Over the last 20 years we’ve seen sea level in the Solomons rise by 15 to 20 centimeters. When you translate that over these very low-lying islands, that can translate to the coast line receding by several hundreds of meters.

Question: Could someone help me better understand the phenomenon that have lead to this faster rate of local sea level rise in the Solomon islands?

edit: My original post had these four points. Once I replayed a few more times I realized they don't perfectly reflect the breakdown in the podcast, but since @John has taken the time to write such a great answer using them I'll include them here to maintain continuity:

Human induced climate change
Series of natural cycles in sea level rise
Weather cycles such as El Niño
Intensification of trade winds, which have pushed water into the pacific

below: The sea encroaches on a tropical island. Credit: Getty Images. From here. (click for full size)

below: Credit: Chris Roelfsema, from NewScientist. (click for full size)

The sea is not the only thing that rises, the sea floor can also rise and fall in accordance with the underlying geology. Oceanic tectonic plates sink as they age (and thus get colder and denser), the Solomon islands happen to be on a  sinking section of the ocean floor and at the same time the sea level is rising. Keep in mind many of those islands have a very shallow slope, so a few rising millimeters are many feet of inland horizontal motion. Of course once the wave base reaches  new "virgin" sediment (often loose sand), it often washes it away moving the tide line even further inland.
Storms cause a storm surge which can wash away sediment. A rising sea level means the storm surge is reaching sediments they could not reach before, so they are causing faster erosion. In addition, salt water kills a lot of plants further eroding the sediment, so a higher surge means more dead plants and more settling of sediment. Storms can cause a tremendous amount of erosion on unprotected soil, a single storm washed away two entire city blocks in the Atlantic City history.
I am not entirely sure, but prevailing winds can cause some displacement of water (the ocean is not completely leveled because it is moving) so a change in the trade winds could lead to a minor effect of local sea level, how much this contributes, I have no idea.

Note: the OP started with a numbered list and specifically asked about the highlighted numbers, hence only 2-4 were referenced in my answer (the first one was a normal sea level rise).


Where do I get hourly temperature data for cities with a high resolution? I want to do a project with very high resolution temperature data from a city (any city) with very high resolution (<10 m), in an attempt to create an animated 3D microclimate map for that city.
The data would probably have to be raster data and it should be a time series of any kind (could be hourly but could also daily, the goal here is to test the 3D animated presentation). Where could I get such data?

We used this site in one of the projects I did in a geoscience class. https://www.ncdc.noaa.gov/data-access The current site lists that there is a problem accessing information, but from what I recall the information can be obtained from any weather station across the globe under your parameters, and is compiled in a Microsoft Xcel spreadsheet that was easy to convert to ArcGIS, so would most likely be compatible in open-sourced ones if that is what you are using. Hope this helps.


What is the definition of wind gust? I'm currently building a weather station from scratch. It measures temperature, pressure, humidity, wind speed and direction. The end result shall be a weather station that sends out METAR weather messages so pilots can see live weather in our area.

Now I'm at the point where I'm writing the software and I have only little knowledge about meteorology. More specifically I ask myself when a meteorologist speaks about "gusts". I know it's a wind speed peak, but I'm a bit puzzled how much faster compared to the average wind speed a gust must be to be actually called gust?
I was searching for some definitions, but I didn't really find anything useful.

A US definition is:

  Wind Gust: Is a sudden, brief increase in speed of the wind. According to U.S. weather observing practice, gusts are reported when the peak wind speed reaches at least 16 knots and the variation in wind speed between the peaks and lulls is at least 9 knots. The duration of a gust is usually less than 20 seconds.

So it must be 9 knots above the non-gust background.


How will cloud feedback effects on the climate change as the result of global warming? So global warming will increase the tropospheric temperatures, which, in turn, would make the clouds higher than they otherwise would be.

And higher clouds (especially cirrus clouds) tend to trap in more long-wave radiation. It's the low clouds (like marine stratus) that reflect away incoming shortwave radiation while not trapping in more long-wave radiation.

So my question is this: would global warming increase these feedback effects due to increasing the cloud heights? (and perhaps decreasing the amount of marine stratus clouds?) If status clouds gave way to cumulus clouds, what would be their net effects on Earth's radiative budget?

We don't really know.

Climate models agree that the feedback is profound.  Significant.  Unfortunately, they do not agree about the magnitude of the feedback.  Nor about the sign.  The problem is that there are competing feedbacks.  Clouds act similar to greenhouse gases, because they absorb and re-emit terrestrial radiation.  But they also reflect solar radiation.  These feedbacks compete, and both have a large uncertainty.  The net effect is the difference between the two, and the difference between two quantities with a large uncertainty, has a very large uncertainty.

This is a major topic of research and it has been a major source of uncertainty in global climate models for... decades.  A major paper on the topic, a bit old but still mostly true, is:

Stephens, Graeme L., 2005: Cloud Feedbacks in the Climate System: A Critical Review. J. Climate, 18, 237–273. doi: http://dx.doi.org/10.1175/JCLI-3243.1

Quoting from this article, published in 2005:

  Thus we have no clear theory that suggests the accumulated effects of cloud feedbacks are in any way a function of global-mean temperature or, as posed, ΔT.

And from IPCC AR4 WG1, published in 2007:

  Cloud feedbacks remain the largest source of uncertainty in climate sensitivity estimates.

The most state-of-the-art knowledge at the time of writing is summarised in IPCC AR5 WG1 chapter 7 (PDF), which stated in 2013:

  Owing to the large magnitudes of the SWCRE [short wave cloud radiative effect] and LWCRE [long wave cloud radiative effect], clouds have the potential to cause significant climate feedback (Section 7.2.5). The sign of this feedback on climate change cannot be determined from the sign of CRE [cloud radiative effect] in the current climate, but depends instead on how climate-sensitive the properties are that govern the LWCRE and SWCRE.

Does that mean we can say nothing?  Well, it's not quite so pessimistic.  There are many attempts to answer this question.  The figure below shows estimates for the regional distribution of the shortwave and longwave cloud radiative effect, again from AR5 WG1:

Figure 7.7 | Distribution of annual-mean top of the atmosphere (a) shortwave, (b) longwave, (c) net cloud radiative effects averaged over the period 2001–2011 from the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Ed2.6r data set (Loeb et al., 2009) and (d) precipitation rate (1981–2000 average from the GPCP version 2.2 data set; Adler et al., 2003).  Figure reference:  Boucher, O., D. Randall, P. Artaxo, C. Bretherton, G. Feingold, P. Forster, V.-M. Kerminen, Y. Kondo, H. Liao, U. Lohmann, P. Rasch, S.K. Satheesh, S. Sherwood, B. Stevens and X.Y. Zhang, 2013: Clouds and Aerosols. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

This suggests the effect is negative, but the debate goes on.  To be continued!


Connection between radiative imbalance and radiative forcing There seems to be a general consensus that the Earth is in radiative imbalance, i.e., it is absorbing more solar energy than it radiates.  One figure I've seen bandied about for this imbalance is that it is about +0.6 W/m2 averaged over Earth's surface.
We also have a fairly good grasp of the various radiative forcings influencing Earth's climate, with a sum total of something like 2.2 watts per square meter.  These forcings are measured relative to 1750, i.e., the Earth is effectively receiving 2.2 W/m2 more than it was in 1750 due to these effects.
Does this mean that the Earth was losing net energy in 1750, at a rate of 0.6 W/m2 - 2.2 W/m2 ≈ -1.6 W/m2?  If so, does this imply that the Earth was not in radiative equilibrium in pre-industrial times?  Was it cooling off?
Or have I misunderstood some important ideas in here, and this this is too simplistic of a way to look at things?

That +2.2 W/m2 is the effective radiative forcing, which is the difference in net top of atmosphere radiation in response to a changed set of conditions (e.g., greenhouse gas and aerosol concentrations, land use) after allowing fast system feedbacks to equilibrate (e.g., tropospheric humidity, stratospheric cooling) but not allowing for slow system feedbacks (ocean heat uptake, land- and sea-ice change).  That latter point is important because relatively little energy is stored in the atmosphere and land, so it’s the slow components that would adjust the system state to remove any net radiative imbalance.
But note that it’s a theoretical, diagnostic property of the system; +2.2 W/m2 is not an energy imbalance that was ever actually expressed between the pre-industrial period and the present.  In reality, the forcing conditions and radiation imbalance occurred gradually, and the system responded gradually along with them, mainly by storing heat in the ocean and increasing the ocean surface temperature.  That has kept the expressed, measurable imbalance down to much lower values, giving us the +0.6 W/m2 that we have today.
As for the pre-industrial imbalance, well, to quote Andrews and Forster (2020):

Since heat uptake was not observed in the nineteenth century, climate and energy balance models must be used to infer the pre-industrial energy imbalance. Forster summarized this to be around 0.10 W/m2 …

My experience from climate models is that 0.1 W/m2 is the typical variability in the decadal mean radiation balance in pre-industrial control simulations, so that observed estimate is probably not distinguishable from noise for a system that’s basically in radiative balance.  In the absence of any solid evidence to the contrary, we normally use a target of radiative balance and no drift in long term model state when spinning-up pre-industrial model climates.


Feldspar content of coarse-grained igneous rocks From the chart, I would say that plagioclase feldspar is more abundant in coarse-grained igneous rocks than alkali feldspar, as it is found in all three listed, whereas alkali feldspar is only present in diorite and granite.
Apparently I am wrong, can anyone please explain to me why?

You can't really generalize like that for all igneous rock ranges.  The chart actually tells you what the type and (approximate) mineral composition of a rock are as functions of the chemical composition, specifically silica content.  You should be looking at how the minerals are distributed as a function of silica content.
To understand this diagram fully, note the following:

The horizontal axis is actually a continuous variable, typically the silica content you would obtain if you were to separate all the individual oxide components.  This diagram seems to go roughly from 50% to 70% from left to right; the one quoted here which includes rocks found predominantly in the mantle goes down to 40%.

The rocks are classified according to silica content and crystal or grain size; thus granite and rhyolite are similar in silica content and are regarded in the diagram as having similar mineral compositions; only the grain size is really different.

Thus the curved lines define how much of each mineral is typically in a rock having some level of silica content, which varies across the chart as indicated in point (1).  When we have a high silica content more than about 65% (felsic rocks) in a granite or rhyolite, there can be more alkali feldspar than plagioclase feldspar (the alkali feldspar composition range is higher in silica).  In contrast, a low-silica (mafic) rock won't have any alkali feldspar and even the more flexible plagioclase feldspar is limited; mafic rocks are higher in other minerals such as pyroxenes.


Calculating 30% less solar energy. Provided the math is right, would there have been liquid water on Earth? The Sun is  estimated   to  have    had a   solar   luminosity  about   30% lower   when    the solar   system  was formed.

Now if I assumed that Earth's albedo was the same as it is today, what would have been the effective radiating temperature of the Earth at that time.

My solution:
T_e = S/4*(1 - A)

I know that Earth's current albedo is A = 0.3.

Hence,

T_e = S/4*(1 - 0.3)

T_e = ( (0.7)*(1370) ) / (4 (5.67*10^-8))  * (1-0.3) = 233K

233K <- would have been Earth's effective radiating temperature

Now if one assumes the magnitude of the greenhouse effect was the same then (now it is 33k) Earth's temperature would be:

233K + 33K = 266K

Assuming the math is right between those two would there have been liquid water on Earth? If there would be, how does one resolve this paradox? that is, given that albedo was the same, what element of the climate system could explain this?

Thanks

Quick and simple answer: A greater greenhouse effect.

There is an equilibrium in place. CO2 weathering rates depend on temperature, so at lower temperatures, we see CO2 levels rise, warming the planet. At higher temperatures, higher weathering rates mean more CO 2 drawdown, so the planet cools.

This is a very long term process. But this CO2 thermometer effect is believed to be the reason why we have had liquid oceans for all of known earth history.

As far as the black body effect goes; this is correct - from the viewpoint of an observer at distance, Earth would 'appear' colder. However, this is because the emission height changes (see https://andthentheresphysics.wordpress.com/2014/03/05/effective-emission-height/ ) for more of an explanation. Think of it as 'the height at which the atmosphere becomes IR-opaque' (although this is a simplification); the temperature then follows the lapse rate down to the ground. So raising the emission height raises the surface temperature.

(NB late at night, add refs later..)


Can an igneous rock have a Vp of less than 1000 m/s and need blasting to excavate? I have run a 60 m refraction seismic line on a mountain summit. The environment was noisy (wind would be the principal agent) and quite difficult to pick first arrivals. It resulted in 800 m/s.

A borehole has shown the profile stands the same after 0.5 m depth of biologic weathering with no fault nor joints to 10 m. Sample taken only at 1 m shows UCS 5 MPa, 10% water absorption which leads to 0.16 porosity, 17.5 kN/m$^3$ bulk density. My geologist partner has identified it as rhyolite or andesite, and attributes the low density to weathering action that can extend to several meters.

A backhoe couldn't excavate more than 1.0 m. So my concern of rippability expected based on Seismic velocity Vp.

Rock type has been estimated visually and according to a general geologic map (I'm sure it is an igneous rock. It could be also ignimbrite) . All tests results has been shown above, and I'm confident about them.

Can an igneous rock have a Vp of less than 1000 m/s on this situation and need blasting?

That seems way too low, even for crustal rocks. Recall: Vp = $\sqrt{[(K + 4G/3)/]}$

If you have a rough idea of the mineralogy of the rock (you do if you know that it's andesite or rhyolite), you can estimate the bulk modulus (K) and shear modulus (G) with a Voigt-Reuss-Hill average and use a reasonable density value (maybe 2500 kg/m3) to get a reasonable number for Vp.

This Wikipedia page has a table of velocities for common rocks that might be helpful.


How can I test the Air Quality? I'm unable to find good sources to make myself an AQI(Air quality Index) measuring device. All I was able to find was the way it is calculated in different regions. But I need some help with the technology used to measure the concentrations of different pollutants like SO2, NOx, PM10, PM2.5, etc.

Are there any good papers or patents on the technology used to measure the AQI?

Any help is appreciated. Thanks.

The AQI calculation does not require that all pollutants be measured.  Instead, it uses the pollutants that are available and then uses a function (chosen by the overarching regulatory agency) that equates health risk categories to the pollutant concentrations.  The pollutant with the highest health risk (at present concentrations) will effectively determine the corresponding AQI.  See here for how the USA EPA computes their AQI.

In order to detect the most commonly monitored pollutants, you would need an ozone analyzer and a PM2.5 monitor.  This is quite expensive.  A good single-pollutant monitor will cost thousands of dollars to purchase and many continuing costs.  You will also need calibration equipment and standards.  Depending on what AQI network you will be using as a reference, the model of instrument that you use will have to meet certain specifications in order for the data to be valid.  Teledyne is an example of a company that makes equipment that would meet the qualifications for the USA AirNOW network.  You may also find of interest their nice AQI calculator.


How to sample for K-Ar dating? How much sample of volcanic rock is required to undertake a K-Ar date measurement, and does it matter if the rock contains vesicles? Are there tricks of the trade to get a good sample?

I run an argon lab which does also K-Ar measurements.
The sample amount depends on the age because you need enough signal strength to measure the radiogenic argon component precisely. Young rocks have very low 40Ar* and to get enough volts on the detector you need more sample. Having the incorrect amount is akin to trying to measure micrometers with a yardstick.

For K measurements 30 mg in our lab is routine.
For the argon measurement we usually use about 2 mg; for very young rocks up to 200mg for rocks with ca. 2% K. We can date volcanic rocks of less than 20000 years using this technique and have dated historic eruptions successfully in our lab.

Usually customers can say if the rocks are very young, so I usually prepare the sample amount accordingly. I had a couple of completely blind samples where I had no idea of the age of the sample a priori, I ran a test with a ca 4 mg which yielded almost no gas, so I re-ran with more material.

Regarding sampling technique itself- look for minerals with more potassium, such as muscovite, biotite, amphiboles, or more felsic rocks. If the rocks are obviously weathered, they are not suitable.

I also have to say that Ar/Ar is a more accurate tool for most rocks and minerals, and then just a few mg is enough if the samples are Mesozoic or older. If they are very young, higher sample amounts are required as with the K-Ar technique.


Post-stack seismic impedance (and amplitude) inversion confusion Yilmaz (2016) briefly explains - along with a nice accompanied image/diagram - the main categories of seismic inversion. He claims there are four: travel-time, waveform, hybrid, and join inversion. Now, I have heard the phrase "amplitude inversion," too.

I am now left wondering what exactly is or what constitutes amplitude inversion...? Also, is impedance inversion a type of waveform inversion? Finally, is amplitude inversion also a type of waveform inversion. Obviously, I'm putting a lot in here, so I would greatly appreciate if someone could resolve my confusion.

Reference

Öz Yilmaz (2016). ”Image-based near-surface modeling for statics corrections.” The Leading Edge 35 (11), 961–967.
https://doi.org/10.1190/tle35110961.1

Here is the figure in question:

"Traveltime inversion" is a very broad category that includes any inverse problem where the input data is a time-delay from a seismic source impulse to when it is received at a seismometer. This includes refraction tomography, reflection tomography, and most implementations of full-waveform inversion (i.e. using diving waves). The output (i.e. what you are solving for) here is a velocity model that best explains all time-delay data simultaneously given the known physics of wave propagation.

"Waveform inversion" is the category that includes all post-migration reflection seismic inverse problems where the input is a single trace or an offset gather (constant X,Y for all input reflections). The output in these are the vertically-varying elastic properties (acoustic impedance, vp/vs, density, etc) that best explain the observed seismic trace/gather (i.e. waveform) given the physics of seismic reflection. This is commonly called "amplitude inversion" because amplitude is one of the main inputs here (although phase and frequency are involved as well).

"Hybrid inversion" is what Yilmaz is calling the recent methods that try to use both the seismic time-delays and ampltidues/waveforms to solve for the best elastic property model that simultaneously explain both types of observations. He cites some recent examples in that paper. The first one I saw on this was Misra and Sacchi (2008). The problem with this stuff is that things become highly nonlinear and therefore require global optimization techniques which are way slower.

"Joint inversion" is just combining different types of geophysical data. For example, let's say you are trying to map the 3D structure of a salt dome. You can use 3D seismic reflection data to map the top of a salt dome easily, but the base is usually really tough due to imaging problems below the salt. Gravity data only gives you the cumulative response of the entire 3D structure below so many salt dome shapes could give you the same gravity response. However, you can use the seismic data to map the top of the salt, then the gravity data to constrain the base, then use that model to re-migrate the seismic data and you should have a good top and base of salt. Similarly, you could pose the problem as a joint-inverse problem where you are looking for the best 3D structural model that simultaneously properly migrates the seismic data and also explains the gravity response.

Hope this helps!


Phase changes deep in planetary interiors and their implications for surface tectonics Earth's core mantle boundary represents a phase change from a solid (though convecting) mantle to a liquid Iron/Nickel core. This dramatic transition is likely the origin of the D" boundary, as well as thermodynamic perturbations and deep mineral heterogeneities in the mantle, which cause tectonic features on Earth's surface.

It is well documented that Europa and Enceladus have subsurface oceans, which represents a phase change inside the satellite's interior. Is this phase change responsible for the chaos terrain  on Europa, the Tiger Stripes and volcanism on Enceladus? Or do these tectonic features originate from exterior forces, such as gravitational tides and bombardments from Jupiter/Saturn's magnetosphere?

The tiger stripes of Enceladus are thought to be a result of this moon's unusual chemical composition, as opposed to any mineral heterogeneity or thermodynamic perturbations occurring due to the phase change near the rocky core.

Ammonia has been found indirectly to exist on Enceladus and the unique ammonia-water composition of Enceladus is thought to give rise to the cracks in the tectonic plates on the surface, which we know as the "tiger stripes".  Enceladus began as a frozen shell of an ammonia and water ice mixture surrounding a rocky core.  Through interaction with other moons, heat was generated above the rocky silicate core, resulting in the ice shell separating into an ammonia-enriched liquid layer directly on top of the core, and a thin layer of ice (water) above it.

Research by Dr Dave Stegman, a Centenary Research Fellow in the School of Earth Sciences at the University of Melbourne, has found that if the water ice formed near the core, then it would give rise to the "tiger stripes" which are thought to be cracks caused by tectonic activity on the south pole of the moon's surface

  "We found that if a layer of pure water ice formed near the core, it
  would have enough buoyancy to rise upwards, and such a redistribution
  of mass can generate large tectonic stresses at the surface,"

  "However, the pure water ice rising up is also slightly warmer which
  causes the separation to occur again, this time forming an
  ammonia-enriched ocean just under the surface. The presence of
  ammonia, which acts as an anti-freeze, then helps keep the ocean in
  its liquid state."

So in this case, while one could say that the boundary may have played a role, it is certainly not analogous to the role the phase transition plays in the formation of features on Earth's surface.  The linked paper provides more details as to how the ammonia-water composition can give rise to the observed terrain.

With regards to the chaotic terrain of Europa, it is an active area of research and is very poorly understood.  No model so far has been able to explain why features such as Conamara Chaos stand above surrounding terrain and contain matrix domes.

According to one paper however,

  We conclude that Europa's ocean dynamics can modulate the exchange of
  heat and materials between the surface and interior and explain the
  observed distribution of chaos terrains.

It therefore seems unlikely given current research that the chaotic terrain of Europa is a result of a phase change boundary.


How is phosphorus getting into lakes and rivers? Apparently there is so much phosphorus in waste water streams that states are passing laws to exempt water treatment facilities from having to spend billions removing the phosphorus.

I don't get this. I thought that phosphates had been banned in detergents and fertilizer years ago, so where is the phosphorus coming from?

Phosphorus & phosphates are a key component of agricultural and garden fertilizers. Most plants need nitrogen (N), phosphorus (P) and potassium (K). They are generally combined into one product called NPK fertilizers.

Nitrogen is needed for leaf growth. Phosphorus is needed for roots, flowers, seeds and fruit and potassium is needed for stem growth and water movement.

The trouble with the use of fertilizers is they get dumped onto plants periodically and the plants cannot use all of the elements in the fertilizer at that time. What isn't absorbed by plants eventually is leached from soil via rain or irrigation water and it ends up in rivers, streams and sometimes lakes.

In Australia, phosphorus can  be toxic to some native plants because over millennia Australia's, so called, soil, have been depleted of phosphorus and plants evolved to live with either no or very little phosphorus,


Ice Water Content from GFS file I am working my way through this paper: http://downloads.hindawi.com/journals/amete/2017/8707234.pdf

Equations 10 and 11 require the ice water content.  I can't find it in the GFS grib2 file variables.  Did I miss it, or is possible to derive it from the available information.  Right now I have pressure, temperature, and relative humidity.

There is a parameter ice water mixing ratio in the gfs.t<hour>z.pgrb2.0p25.f<step> file given at isobaric levels.


OpenEEW - earthquake early warning by everyone? This site
https://grillo.io/
and here
https://github.com/openeew/openeew/
advertises such a system. But from the make-up of the site and no geo-science related work that they link to (at least what I could find) I assume it is just an advertisement for the internet of things (IoT) and economic interests outweigh a scientific usefulness.
But opinion aside, does a denser (home made) sensor network than the status quo of earthquake watchers in the hot spots and ODAS buoys really shorten warning times? Hasn't this been worked on in the wake of the 2004 Indian Ocean tsunami?
It would be interesting to know if home made sensors and IoT really could contribute to shorten warning times from official sensor networks.

In fact it is possible, the early warning system is not something more than a series of accelerometers (seismometers). There is always an epicenter for an earthquake. closer sensors to the epicenter shorten the time for occurrence announcement and more time for far locations to get ready.
There are even some efforts to use personal cellphones accelerometer sensors as a part of broad early warning system. for example here and here . RaspberryShake home sensors can also contribute in a global effort to shorten seismic events announcements. you can follow some live data shared by this home instruments in Iris jamaseis software, freely.
This should be noted that all these efforts are still in early stages. Currently some seismically active cities are equipped with early warning systems but not based on home sensors but on seismic networks.


Classification of rivers according to width I'm looking for a simple classification of rivers according to their width. In which size categories should I split them? This may usually be done according to catchment area but I'm more interested in the width at one spot of the river. I'm interested in the width at a given spot and point in time, not in the variability in width along the river or in different seasons.

Ideally this classification would make some sense (e.g. rivers smaller than x meter width are usually not used for ship traffic, rivers larger than x meter are commonly not crossed by bridges, etc.) or is commonly used in official documents (preferably in Europe).

Intuitively I would classified them according to the following but without much reasoning behind it:

below 2m (can be jumped)
3-10m (may be used for leisure water sport)
11-25m (smaller commercial vessels?)
26-50m (large commercial vessels?)
51-100m (may still be bridged)
over 100m (very large rivers, close to flowing into the sea)

The Rosgen river classification contains - besides slope, entrenchment and sinuosity - the width. But it is mostly applicable to natural rivers, not so much artificial waterways ...

http://extension.umass.edu/riversmart/fgm-assessment-methods/rosgen-classification-natural-rivers-natural-channel-design

https://en.wikipedia.org/wiki/Rosgen_Stream_Classification

https://content.ces.ncsu.edu/application-of-the-rosgen-stream-classification-to-north-carolina

https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_024290.pdf


What causes a volcano? Inspired by the question, How does one measure what causes earthquakes?, I'd like to know what causes another great geological phenomenon - volcanoes?

Most volcanism occurs at three major tectonic features: Subduction Zones, Rifting Centers (Like East Pacific Rise) and under hotspots.

I will start with the most contentious, hotspots. The clearest example of hotspot volcanism is the island chain Hawaii. Underneath the island, there is a chaotic discontinuity caused by the boundary conditions at the core-mantle boundary. This causes highly viscous and hot mantle to upwell rapidly and push through the lithosphere, forming a volcano. The orgin of hotspots is not well known, and it is even a debate if they exist (they do, imo). Some papers worth looking at are by Paul Hall (BU) and Christopher Kincaid(URI), and maybe Dave Stegman (Scripps) who publish on melting caused by mantle plumes.

As a consequence of a subducting slab, at a distance in front of the trench, Arc Volcanoes form. The distance in front of the trench is often thought to be where the overriding plate is 100-120km higher than the suducting slab. The cold, oceanic plate plunges underneath the continental plate in the image above, adding water to the mantle. This water lowers the melting temperature (solidus) of the mantle inside the wedge of the subduction zone, causing  partial melting (4-6%). This melt, more viscous and buoyant than the solid mantle, floats to tho the top of the mantle and eventually penetrates the lithosphere and forms a volcano. This question is also relevant.

Finally, at divergent plate boundaries, the process is fairly straight forward. A break in the lithosphere caused by rifting plates allows the pressurized mantle to flow towards the opening. This causes and abundance of hot, fertile mantle which can be melted.  This melted mantle creeps through the lithosphere and forms ocean floor volcanoes (sometimes called smokers).


Can volcanos cause isostatic depression Isostatic depression is normally associated with glaciation, but can other geologic processes result in similar behavior of the lithosphere? Especially, can the weight of a volcano cause the surrounding lithosphere to sink.

Yes, they can. Hawaiian Trough is a good example. The impact on thin oceanic crust is naturally larger/faster than on stiff continental crust.

Generally the lithosphere will always respond to load, even if it's sediments, ice or water. The process also works the other way, exhumation by erosion ease the load and cause uplift.


Persistence time of ozone Ozone is formed in the upper atmosphere by ultraviolet light, and broken down by ultraviolet, spontaneous reactions and artificial pollutants among other things.

Suppose there were no artificial pollutants, and the sun was magically replaced with a giant heat lamp that put out no ultraviolet but kept the current temperature, how long would it take for the ozone to decay of its own accord or by reacting with natural pollutants?

The accepted answer for the other question is three days. That's wildly inconsistent with everything else I've heard said about ozone depletion taking years to recover. Maybe that's because the other question asks about ozone released near sea level, and I'm asking about ozone in the stratosphere?

Let's first consider the scenario in the absence of free radicals that can act as catalysts of $O_3$ destruction.
In such scenario, added to the absence of UV radiation, the photolysis of ozone ($O_3 + UV → O_2 + O$) would not be possible. Therefore, the only way to destroy ozone would be by the reaction

$O_3 + O· → 2 O_2$

But the lack of UV radiation would also stop the production of atomic oxygen by photolysis, therefore only the preexisting atomic oxygen would be available for that reaction.
Then, for simplicity we can consider two extreme scenarios:

All the atomic Oxygen forms $O_2$ and the Ozone level remains constant at the same level it has prior to the shut down of UV radiation.
All the atomic Oxygen is used to destroy Ozone

In the second scenario the final concentration of $O_3$ will depend on the initial concentration of it and atomic oxygen. But as seen in the following image, the stratospheric concentration of atomic oxygen is much lower than the one of $O_3$, therefore the ozone levels would never fall to zero.

(Figure taken from here)

Let's now consider the more realistic case, on which there are other "polutants" that can break down ozone. The moste common ones are hydroxyl radical (OH·), nitric oxide radical (NO·), chlorine radical (Cl·) and bromine radical (Br·) (source), and the first two are the only ones naturally occurring. Therefore, the ozone remaining from our first scenario would be reduced to zero by reactions like:

$OH + O_3 → {HO}_2 + O_2$

or

${NO}_2 + O_3 → {NO}_3 + O_2$

Among others.

The exact time that will take will depend on the elevation, as the abundance of ozone as well as the radicals can vary orders of magnitude trough the stratosphere. It will also depend on the exact spectrum of your "sustitute Sun", as some of those reactions are also mediated by light and might not happen in the absence of light of the right wavelength.

I know this answer might be disappointing, as it seem that a model would need to be run to really say how long it would take to destroy all stratospheric ozone. But still I thought would be an informative answer after 11 month without one. I hope, it motivate a better answer or the improvement of this one.


The significance of peak period in surface wave models The peak period or peak frequency is considered to be an important quantity in characterizing ocean surface waves, including wave models [e.g., 1]. But practically, why are we interested in modelling the peak period accurately?

I'm thinking the peak frequency directly translates to how fast the dominant waves propagate, and so is important to predicting when the waves will reach coastal locations of interest. Is this thinking correct?

The peak period is only one of multiple parameters that is used to describe the spectral shape of ocean waves. Most commonly used parameters are significant wave height, mean period, and dominant (peak) period. Significant wave height is proportional to the square-root of total wave energy and it tells us how much bulk wave energy is there at any given location. Mean period tells us how long are waves on average, and peak period indicates how long are the waves with maximum energy. These are just statistical parameters describing the shape of the spectrum. In reality, accurate prediction of the whole spectrum is important for wave prediction.

What happens if we vary the peak wave period while keeping the total wave energy, and thus significant wave height, constant? These are some of the effects that can expect with longer peak period:

Swell propagates faster and reaches the shore sooner, as you mentioned in your question;
Because swell propagates faster, wave dissipation from swell outrunning wind will increase;
Longer waves are more dissipated due to bottom friction, i.e. longer waves will feel the bottom sooner as they propagate toward shallow water;
Longer waves induce Stokes drift that decays more slowly with depth compared to that of shorter waves, inducing stronger Lagrangian transport at depth and enhanced Langmuir circulations;
Longer waves with same wave energy are less steep, effectively decreasing the dissipation of short waves that "ride" on the forward faces of swell waves;
Different interaction with structures, as mentioned by Wolfgang Bangerth.

Because of the above physical processes, waves at peak period affect the evolution of the spectral shape both locally and along their propagation line.


Term for the Process of the Early Earth's Crust's Changing Mineral Composition In the book, Story of Earth-The First 4.5 Billion Years, From Stardust to Living Planet, by Robert Hazen, Chapter 3: He discusses the changing mineral composition of the early Earth's crust from continuous heating, cooling, sinking, and remelting. From mafic magnesium rich dunite, anorthite, pyroxene, peridotite, on to today's predominant more felsic silicon enriched basalt and granite/rhyolite.
As a book directed at laymen, like myself, he does not mention a term for this process of decomposition of the Earth's early crust.
My question then is: What is the term for the process of the early Earth's crust to change its mineral composition over many cycles of melting, rising, cooling and sinking?
Thank you.

The term you are searching for is differentiation. In Early Earth the Crust was mafic. Remelting cycles created an enrichment in silica and alkali elements, forming basalts and granites.
As it happens on Continental Crust it is commonly referred as Differentiation of the Continental Crust.
Hacker, B.R. et al (2011): "Differentiation of the continental crust by relamination",
Earth and Planetary Science Letters, Volume 307, Issues 3â€“4, Pages 501-516, ISSN 0012-821X, https://doi.org/10.1016/j.epsl.2011.05.024.


Is my understanding of saturation and cloud formation correct? I'm trying to see if I understand the concepts of saturation, vapor pressure and cloud formation correctly.

When, at a given temperature and height in the atmosphere, the rate of evaporation and condensation are equal, then the air is said to be saturated and clouds form (assuming the presence of nuclei).

If the rate of evaporation is greater than the rate of condensation, a state of undersaturation is reached and no clouds form.

When the rate of evaporation is less than the rate of condensation, a state of supersaturation is reached and clouds form.

Is my understanding of this correct?

The height at which clouds form is called the cloud condensation level (CCL), where air lifted by convection powered by temperature differences cools and condensates. When it comes to basic physics, yes, evaporation would need to be less (i.e. zero) for condensation to occur.
However, this is not necessarily the correct way to look at it, as relative humidity is the determining factor for cloud formation. Relative humidity depends on temperature and pressure.
As a volume of unsaturated air cools, its relative humidity increases. If sufficiently cooled, the relative humidity becomes 100%, the temperature equals the dew point, and clouds form. So it's less to do with evaporation and condensation than the amount of humidity, and the temperature (and temp gradient) and pressure of an air mass.


How much of the Earth's heat is caused by the hot centre? What is the percentage of the Earth's warmth that the Earth's hot centre is responsible for? What would be the temperature of the atmosphere if the Earth's centre was 18° C (average temperature on the surface)?

Given the insulating effect of 6370 km of rock (the average radius of the Earth) & its low thermal conductivity, if the temperature at the center of the earth was 18 °C it contribution to warming the atmosphere would be negligible.

The temperature at the center of the Earth is about 7000 °C, while the average temperature of the surface is about 10 °C. So if the temperature at the Earth's center was 18 °C its contribution to the surface temperature would be exceedingly small.


What geological processes would 'restart' subduction processes? According to the Science Daily article Plate Tectonics May Grind To A Halt, Then Start Again (Carnegie Institution), one main aspect of the research being presented is the hypothesis that

  suggests that plate tectonics may have ground to a halt at least once in our planet's history -- and may do so again

Specifically, the article states that geochemical evidence of igneous rocks from about a billion years ago suggests there was a lull in subduction processes, coinciding with evidence of the closing of a Pacific-like basin, forming the supercontinent Rodinia.  The lulls also, according to the article's authors, in part explain that

  By periodically clamping the lid on heat flow, intermittent plate tectonics may explain why the Earth has lost heat slower than current models predict. And the buildup of heat beneath stagnant plates may explain the occurrence of certain igneous rocks in the middle of continents away from their normal locations in subduction zones.

However, the article rather vaguely states that

  Rodinia eventually split apart when subduction and plate tectonics resumed.

What geological mechanism would cause a 'restart' of plate tectonics, specifically subduction?

Subduction is ultimately driven by the heat flow process of mantle convection. A mantle plume beneath a stable plate will ultimately cause that plate to split, a la the African rift zone we observe today.

The crust of the earth is very thin compared to the mass of flowing material beneath it. As my global geophysics prof taught us, the fact that the earth has a solid outer "skin" is a consequence of the thermal conditions around the earth's surface and geochemistry, but that has little to no effect on the thermal conditions within the planet that ultimately control thermal convection that expresses itself on the surface as plate motions. I.E., the thermal mass of the crust is inconsequential when considering the thermal mechanisms that drive the mantle and outer core. Heat makes the plate go!

On the surface, plates may move, be created or destroyed, but thermodynamics tells us these are consequences, not drivers. We may observe evidence of past situations where plates appear to be static with respect to the underlying mantle motions, but this does not reveal any new mechanism that would suggest plate tectonic theory would not predict such circumstances.


Is there a difference between rock and stone? I couldn't find the definitions from either source:

Geology Dictionary

Glossary of Geologic Terms

I'm quoting from my old The Penguin Dictionary of Geology by D. G. A Whitten &
J. R. V. Brooks, published in 1979.

  Rock (1) To the geologist any mass of mineral matter, whether consolidated or not, which forms part of the Earth's crust ... (2) The
  civil engineer regards rock as something hard, consolidated, and/or
  load bearing, which, where necessary, has to be removed by blasting.
  This concept also accords with the popular idea of the meaning of the
  word.

  Stone In geology the word 'stone' is admissible only in combinations such as limestone, sandstone, etc., or where it is used
  as the name for extracted material - building stone, stone road. It
  should not be used as a synonym for rock or pebble.


Nitrogen cycle semantics; nitrification/nitrogen fixation Is 'nitrification' and 'nitrogen fixation' used interchangeably to describe biological reduction of $\ce{N2}$ gas to $\ce{NH3}$? I know $\ce{NH4} \to \ce{NO3}$ is called nitrification too.

I went to do a bit of research on this, and think that you can get a satisfactory answer just from the relevant wikipedia articles. Here are some select quotes:

Nitrogen fixation is a process by which nitrogen in the Earth's
atmosphere is converted into ammonia (NH3) or other molecules
available to living organisms.

Nitrogen fixation has the chemical process:

N$_2$ + 8 H$^+$ + 8 e$^−$ $\rightarrow$ 2 NH$_3$ + H$_2$

and is performed by Diazotroph bacteria or archaea. Some of these are symbiotic with plant roots, especially of the family Fabaceae.

Nitrification is the biological oxidation of ammonia or ammonium to
nitrite followed by the oxidation of the nitrite to nitrate.

An example of a nitrification chemical reaction is:

2NH$_4^+$ +  3O$_2$ $\rightarrow$ 2NO$_2^−$ + 2 H$_2$O + 4 H$^+$
2NO$_2^−$ + O$_2$ $\rightarrow$ 2 NO$_3^−$

There are a wide variety of bacteria that peform these steps, and some only do one step or the other. There are also other pathways for reducing ammonia instead of ammonium, for example.
In conclusion, these are two different biologically driven steps in the nitrogen cycle, driven by two different sets of microorganims. There other biologically driven processes as well, such as ammonification, where organic nitrogen compounds in formerly living tissue (or waste) is broken down into ammonia or ammonium.


Can I use radar data to estimate snowfall, or is there a better way? The problem:  There are lots of websites that provide snow forecasts for various ski resorts.  The area of interest to me is Japan, specifically the Nagano area, where I use snow-forecast.com and www.windyty.com to get fairly accurate snow forecasts at elevation for the resorts I'm interested in, and in the case of Windy, also for other mountain peaks outside of the ski resorts.

However, unsurprisingly, often in the morning it turns out that the forecasts are not always perfect.  In fact, they can often be way off.  Sometimes a big storm never materializes, and sometimes it affects only certain areas.

The Japanese Meteorological Agency (JMA) has public hourly weather station data, but the weather stations tend to be down in towns, rather than on mountain peaks, and are not necessarily near the ski resorts in question.

My solution at the moment is to frequently check the radar during storms, watching hour by hour to see which areas are being affected by the storm, correlate it with the weather station data, and try to make a prediction about which resorts are getting the most snow.  Needless to say, this is fairly time consuming and often misses some things (if the storm is a surprise, if there is a big storm somewhere that I'm not checking).

My proposed solution:  Write a script to screen scrape the radar images from the JMA and watch the pixel values of the areas in question, considering the pixel values to be a function of mm precip/hour, and taking the integral of the pixel function over time to get a rough estimate of total mm precip over the past 24 hours.  I can probably then use other data at the weather stations to fit a function to calculate estimated snowfall from mm precip, elevation, and temperature.  It obviously will be pretty inaccurate, but at least it should be able to tell me with a decent degree of certainty whether there is no snow, a little bit, a lot, or tons, which is the only degree of accuracy I need.

Questions about my proposal:

Is it more or less a sound idea?
Does this data ("snow accumulation in the past 12-24 hours at an arbitrary lat/lon position") already exist somewhere that I don't know about?
Is there more accurate source of radar data somewhere?  I'm currently screen scraping the JMA website to download the images, then sampling the image pixel values to get the mm precip at a given position, but the data has already been discretized, presumably for aesthetic reasons.  I would way rather just pay someone for access to a web service that will take the lat/lon and a timestamp and spit out a more accurate number.
Is there an easier way to do this that I'm missing?

References:

JMA website where I get the data:  https://www.jma.go.jp/en/highresorad/
Sample radar image:  https://www.jma.go.jp/en/highresorad/highresorad_tile/HRKSNC/201801111000/201801111045/zoom6/34_28.png
Sample weather station page:  http://www.jma.go.jp/en/amedas_h/today-48141.html?areaCode=000&groupCode=33

Full disclosure: Being in Florida, I don't spend as much time watching winter weather, and don't have great experience correlating radar precipitation totals to actual snowfall.  Is it spring yet?

But I do at least some background and information, and so hopefully can add some insight.

Using radar is certain to have imperfections...

Smaller Radar Caveats:

Radar beams increase in height with distance from the radar.  They really don't tell you what is actually happening at the ground, but instead up at some height aloft... this may mean that, especially further from the dish, light precipitation shown on radar can actually be evaporating before hitting the ground... or you may not get any precipitation showing up at all because sometimes convection can be so shallow that it is only below the radar beam and thus undetected (example [2.5 km ≈ 8200 ft]))
Radar beams are also blocked by elevated terrain, such that some areas in mountainous regions may not return any data.  See for example this map of the regions that are visible to US weather radars, and note the jagged/non-circular shapes common in the west indicative of mountains.

The exact radar setup is very important in determining the utility of the data.  In addition to the range and terrain issues, the beam angle and width alter what returns mean, some frequencies attenuate or reflect certain drop/flake sizes better than others, etc... so exact radar details are very crucial.

I was able to dig up this site that shows the range outlines of nine Japanese radars and then the full JMA radar specs page.  Looks like probably the radar spacing is pretty quality generally, but the frequencies are quite different than the radars I'm used to.  In the end, understanding the nuances and return characteristics of a radar's setup can be a very intensive life-long process for even the best of meteorologist!

Greater Complications of Estimating Snow

Also note that winter precipitation adds an extra complication because the particles are lighter in weight and can thus be blown about more by vertical and horizontal winds.  Raindrops (and hail) are quite likely to fall unless extreme updrafts exist because they are heavy.  But drizzle, snow, and sleet may be blown around quite a bit.  Without a time-intensive dual-Doppler analysis, you cannot know the wind motion in the storm thoroughly, and therefore will have varying results at times.
And finally, the big wrench is unfortunate inherent to how radars work.  They measure the percentage of their sent energy that is reflected back to them.  That's great because that's directly connected to the diameter of the item falling (to the 6th power).  But unfortunately the grand problem is that in a storm, there is a huge variety of drop/flake sizes mixed together at once... such that we can't extract which combination of particle sizes created it (and thus can't calculate volume to actually know the rain/snow amount that falls).  It could be like 6 medium size flakes causing the 10 dBZ echo... or 2 large flakes and 10 small flakes... and each combination is a different volume/snow total.  (to see the nitty-gritty math details on this, read more here.)  So we can never know for sure the exact rain/snow falling using just radar.  The good news is we've at least done lots of experiments and come up with some fairly useful best-practice formulas for using the Z-R ratio in different scenarios.  Good, but not perfect.
But when it comes to winter precip, it gets even uglier.  For one, the reflectivity of ice is way more than water (making it look like a ton is falling!)... but we typically can determine where the changeover line to ice is and work from that.  But the bigger issue is that ice crystals form an amazing range of shapes.  And you can imagine that each of those is going to reflect a radar beam in different percentages... and then take up different amounts of room on the ground.  In the US, the switch to polarimetric radar has helped with the shape issue a fair bit.  But if you don't have that in Japan, you could have quite a bit of error in some formation regimes.  Regardless, we have certainly come up with a few fair rules of thumb for winter precip, such as discussed in this article, but they're far from perfect.

Now that doesn't mean working from radar isn't helpful.  The "precipitation nowcasts" are probably better for you, taking out the complexity of all that Z-R relationship stuff just mentioned to just give you a mm/hr (though the site may well not be taking enough into account to give worthwhile values in all situations).  But you can have some comfort from the fact that in the US we do regularly use basically the same idea to what you're doing to identify floods and even roughly track snowfall areas.  But unfortunately it's a real pity it doesn't seem most countries like Japan offer as many detailed radar products for free as we get.  In the US, the NWS gives us storm-total precipitation plots directly, which provides us the total return accumulation (since last reset) so we don't have to track the aggregation ourselves.  Plus we have direct access to the full radar data files, which would remove the overcome the value stratification issues from the imagery and simplify the process.  But unfortunately I couldn't find anything about any rawer data files in my searching other than for the US site in Okinawa.

Your method does sounds fairly decent, particularly for Nagano since it appears to have it's own radar (based upon that earlier list).  I do imagine it'll still have some errors at times, being off by a fairly decent percentage sometimes.  Even here in the US, we're only just starting to cautiously venture more into more high resolution official snow analyses.  So it won't be nearly perfect, but it'll probably have some reasonable utility, and perhaps is your best method on this avenue.

However, I want to give you a couple more options that may well end up being better for you, depending upon your interest:

Initial model plots:
Global models, like the American GFS, use large amounts of data to create their initial conditions.  And so among their products, they offer current snow cover plots.  Pivotal Weather offers a useful tool to mouseover and get values at any point in a plot like this one:

Don't know if it's perfect for you, as the GFS resolution is around 30 km, so even at maximum resolution it's going to lump some mountains and lower areas together.  A pity Pivotal Weather also is a little zoomed out in your area.  If you were really wanting to go all in, you could definitely dig up the data files to download the data directly out of the model for peak resolution and information.  Otherwise if content, you can just check each day (or every 6 hours) and compare to the previous image (which you can pull up in the dropdown on Pivotal's site).

Satellite data:
I was able to come across one satellite that does offer a snow analysis product, the NASA TERRA/MODIS pair.  On that same page they have a further link to download the data at 0.05 degree resolution, which would be less than 1 km!!!  I have no idea how good these plots are, but the potential sounds great.

So just maybe model and satellite can offer to bypass some of the flaws of radar data (though just may add their own challenges).  All of it is likely better than relying on the town's reporting station... it's unfortunate there's a lack of reliable reporting stations on mountains (but it's a problem we have here too, and a matter of simple economics/utility).  But hopefully those help you better get the data you wish for!  Happy skiing :-p