# Tag Info

8

$M$ is the forward model you are using. It is the dynamical model used to solve whatever discrete equation evolves the field ($d?/dt=$). The best way to start with data assimilation and understand its nomenclature is to read Ide et al. (1997). In their work, they explain it as: A discrete model for the evolution of an atmospheric, oceanic, or coupled ...

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Grid Resolution of the NCEP/NCAR reanalysis data The data is available on different grids depending on the variables of interest: The original grid is a T62 Gaussian grid, which grid cells do not have a constant lon-lat spacing over the whole globe (see e.g. Gaussian grid at Wikipedia), with 28 sigma levels. The sigma levels are listed on this page, where ...

4

Provide a monthly average in every grid cell, and describe how many measurements were used for each cell. There is no fixed rule for the minimum number of days for reporting a monthly average. A reasonable threshold will depend on the geophysical quantity of interest y, in particular on how much y varies on short and long timescales. If y varies from ...

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You might try the RockFab package. I am not a structural geologist but I use other R packages for geological endevours. Documentation.

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SEG-Y files are a general purpose archival format. So they can be "anything". 2D seismic, 3D seismic, raw, processed, unstacked or even derivatives (seismic attributes). How to get information about your data in the SEG-Y file: SEG-Y contain 3 types of headers that describe the data. The EBCDIC header is a 40 by 80 character sheet that is a remnant from ...

4

The shift appears to match up to a large change in the number of cyclones that peak as tropical storms in the early part of the TC season (here defined as March-July), shown by the blue (tropical storms) versus purple (hurricanes) in this graph (made using the HURDAT2 dataset from NHC): It appears that a large part of the increase in tropical storms lines ...

4

My understanding is the following (NB: it could be wrong!). The assumption is that on a common shot gather, your travel time follows a hyperbolic curve: $$f(x)=t^2=t_0^2 + \frac{x^2}{v^2},$$ where $t_0$ is the zero-offset travel time, $x$ the offset and $v$ the speed of the medium above the interface. The hyperbolic travel-times, for example, create ...

3

You could fill the gaps with the analysis products of numerical weather prediction. NCAR for example (RDA UCAR) hosts the NCEP North American Regional Reanalysis (NARR) with data from 1979 to 2018 including snow cover (NARR) in 3-hourly intervals. But I would guess it depends on your goal: You can make an educated guess (bayesian approach, combining ...

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I believe that Leapfrog 3D will do this. They have academic licenses. I also seem to recall that Intrepid Geomodeller does what you are after. Both of these options are probably overkill though.

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OpendTect may do this, however getting to that stage would be a painful experience. Petrel can do this very easily, however, that is not anywhere near free... If you're a student, your department may have access to a few decent programs to achieve this. If you could find out what you have available, we might be of more help!

3

The SPI statistic can be considered a standard normally distributed random variable $SPI \sim N(0,1)$. Thus the expected value, or mean, of the SPI statistic is $0$, and its standard deviation and variance are $1$. If the mean of the distribution is zero, then by the central limit theorem, you would expect that the limit of your sample mean (the mean of ~...

3

The offset is measured in meters, but the depth is still measured in time. The extra distance the wave have traveled to a longer offset is therefor also seen as an increased time in the CMP-gather (Common Mid Point) and the extra time depends on the seismic velocity of the layers. If the velocities are high, the moveouts are short. The longer offset, the ...

3

You can compare verification scores using this application from ECMWF made from guidelines by the WMO. The example below fits nicely with what is widely known, namely that IFS (the ECMFWF global model) scores generally best. In recent year the UK Met Office global model have also scored better than GFS. The figure shows the root mean square error of MSLP. ...

2

Looking at the source you provided in the comment, there is some other information you may be able to accumulate to help your study. Control or Baseline. You need an the same actual set of data from a facility in the area that is meeting the above-mentioned standards. This needs to be as close to your target facility in size, structure and regional ...

2

Based on an answer from ECMWF support which OP can confirm to my best understanding relative vorticity in the ECMWF model is not calculated using grid points and finite differences (centered and forward and backward). Instead it is calculated using spectral approaches in meteorology. There is a package in Fortran called spherepack and a python wrapper as ...

2

An intrinsic assumption in using geostatistics is that one can approximate a distribution by a polynomial surface. In practice, maybe you can do this in a very simple geological situation, such as an isotropic shockwave arising from sedimentary compaction in horizontal sediments. When it comes to major seismic events at tectonic plate edges I think the ...

2

I typed in the commands as shown using your data in SAC. I've uploaded my result. The plot window shows the result of the commands run in the highlighted red area. Is this what you are trying to do? The plot xlim is set from 1750 s to 2700 s.

2

I had the same problem with surface DEM's. The best solution I found in Matlab was using the scatteredInterpolant class, it is inbuilt in Matlab. It allows Natural neighbour interpolation (that is a class of weighted distance interpolation as suggested in previous comments). The usage is like this: % IF X and Y are the coords of each grid cell (you can use ...

2

I have been using the routines in nctoolbox (also check here) that do all the slicing (vertical, horizontal, following a track). The repository is in GitHub. The one you should looking at is vsliceg.m. You can follow the logic there or develop your own.

2

It depends on your data, if the rainfall data is in a grid with temporal and spatial dimension, you can use a NetCDF format to analyse the data via R, matlab, IDL or Python. NetCDF is Network Common Data Form, which is a very common format of grided and temporal data. In Python, you can simply do several 10-year average map to see the long term decadal trend....

2

JT's and CR's suggestions in the comments following the "first answer" are incorporated in the gif's below. The new ones that correspond to the previously-displayed ones are a bit more "jittery" than those previous ones -- they had a little bug, as follows. The "baseline mean" for the year-by-year animations, with jan1-dec31 along the x-axis, is the mean/...

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Not really an answer, per se. However, as per JT's comment "you somehow need to incorporate the average" (which mirrors my own speculation in the original question), I've incorporated (subtracted out) the means, and that indeed exhibits a easily-seen trend in the graphs. So it's somewhat of an "answer" to that extent, anyway. And I've also created two new ...

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I would dispute the idea that the "vast majority" of the world's buildings have air con; in fact, considering that a large proportion of the world's buildings are either (a) in hitherto cool places; or (b) in undeveloped places, I would imagine that it's probably a minority. But I don't have figures on this. The main part of the question, though, is about ...

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(not an answer, too long for a comment) Strictly speaking, the answer is yes, since the temperature generally increases during the day and falls at night (and local time is a local variable), the barometric pressure and humidity can indicate if a storm is coming (there were some really old fashioned weather prediction "clocks" that did this). Realistically, ...

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Using your example of 10 AM EST and 9 AM CST, those are both exactly the same time and another way of specifying that time is 1500 UTC or 15Z (Z being historical for GMT, for your application the difference between UTC and GMT is negligible). If you know the location of your stations then you can know their timezone. To make inter-comparisons between ...

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You can build a machine learning algorithm with skill but you will need several parameters such as day of year, time of day, cloud cover, wind speed, and precipitation. However, keep in mind that your algorithm will need to be trained on several years of previous data. For projects like this, you typically want about 5 years of observations to "train" the ...

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Multiplicative decomposition as defined in the post is often called factorization. It is not always possible to achieve an exact factorization. There would be some residual difference that we could denote as eps(x, y, z, t). U(x, y, z, t) = uv(z) * uh(x, y, t) + eps(x, y, z, t) Such a decomposition is not unique, however you may choose a pair of ...

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You are right that upper air soundings are typically only done twice a day, and even at quite a coarse observation spacing. And that upper air data is definitely very important in models. I'd suggest that, firstly, it's important to note that even a broad, very hazy picture aloft really helps a great deal at the surface. That can be seen in the ...

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I would: Compute (U,V) transport on the model's C grid. This is easy if the model returns a batotropic velocity. Define a set of points S along the transect. The points should have spacing comparable to the model's horizontal resolution. Interpolate (U,V) transport to S. Find the component of (U,V) perpendicular to your transect. Iterate over model outputs....

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There is also the net function in the RFOC package

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