我试图找到数据，我可以用来与第88页的图6.2中的图表进行比较来源，显示温度(从解决斜坡上平行侧板问题中发现)与冰川底部以上的高度。我通过NSIDC数据库找到了南极西部冰盖划分深钻孔(版本1,NSIDC -0550)的温度分布数据。数据说明说，该数据报告了2011年12月5日以来南极西部冰盖分水岭(WAIS-D)深钻孔中充满液体部分的深度与温度的关系。数据列出的空间覆盖率为spatial coverage: N: -79.4676 S: -79.4676 E: -112.0865 W: -112.0865。它看起来像我想要的，但我对冰川学了解不够，无法确定。我知道整个WAIS不能被建模为斜坡上的平板，但我希望它的这一部分可以。此外，我担心这些测量是在钻孔充满水的部分进行的。< / p >

Here are some links to the data that I described: link1, link 2.

Are these data a good to use for comparing the graph of temperature vs depth above surface found from the slab on slope problem?

I was originally trying to find data for the Mendenhall Glacier because I read something about it being a reasonable comparison for the slab on slope problem but I haven't found the type of data I want yet. Would this be a better glacier to pursue for my purposes?

*我想了解地面控制点(GCP)以东意味着什么，以及有x m的rmse和最小a m意味着什么。

GCP以东和北部的平均均方根误差(rmse)分别为'x'和'y' m，最小值为'a' m，最大值为'b' m。

我有一个数据集，给出了

1. 温度，
2. 相对湿度
3. 地球位能
4. 臭氧质量混合比
5. 位涡度
6. 云层分数
7. 特定云
8. 液态水含量
9. 特定湿度
10. 特定雨雪含量
11. 差异
12. v风分量
13. 垂直速度

哪些参数对耗电量的回归有意义?为了寻找这一点，我得到了这篇论文，https://www.sciencedirect.com/science/article/pii/S0306261914005674，它使用温度和相对湿度作为相同的参数，还有其他论文也只使用温度作为参数。你认为我还应该加入上面的其他参数吗?有没有人可以回答上面的问题，并对每个项目参数进行逐点推理，以及为什么要用它来计算用电量?< / p >

Phys.org的2022年4月23日迄今为止从行星远端记录的两个最大的火星地震链接到火星远端:InSight探测到的两个遥远的火星地震包含下面的图像。

回答如何仅使用一台地震仪确定火星地震的震中?告诉我们，s波和p波的存在以及它们不同的传播速度都有帮助。< / p >

But in the case of these to well publicized and studied events I'd like to ask more specifically:

Question: How well can they localize the sources of these marsquakes using only a single seismometer? Does being in the shadow zone of the core help or hinder?

Figure 1. Summary of Martian interior models from Stähler et al. (2021) and ray paths for seismic phases from events presented in this article. (a) The travel time curves are computed using the TauP package (Crotwell et al., 1999) for a source depth of 50 km. The phase picks that the Marsquake Service (MQS) identified for these events are indicated with crosses, with varying symbol sizes to schematically reflect the pick uncertainties. The distant events are S1000a and S0976a. For comparison, also shown is S0173a, an event at 30° that locates at Cerberus Fossae. For all three events, high-amplitude phase arrivals have been identified as direct P/S for S0173a and single free-surface reflections PP/SS for S1000a and S0976a. For S1000a, a weak Pdiff phase that is diffracted along the core–mantle boundary is also identified. The vertical dashed lines and gray shaded bars mark the event distances and uncertainties from Table S1, respectively. (b) The structural models are not constrained by observations for depths below ∼800 km for P waves (hatched region; Khan et al., 2021), hence the Pdiff travel times are purely from model predictions. (c) Illustrates the ray paths of the identified phases using a Mars model with a core radius of 1855 km.

Related and potentially helpful in Earth Science SE:

• What could be causing microseisms on Mars?

and in Space Exploration SE:

• Just how sensitive is the Insight seismometer?
• What is VBB recentering (InSight lander's Very Broad Band seismic pendulums)?
• How are the most sensitive seismometers on Mars protected from the most powerful jackhammer on Mars just a few feet away?
• What are InSight's barometric and seismic digitization rates and frequency responses when it "listens to the wind"?
• Plot of the thermally-induced tilt in Martian surface during solar eclipse by Phobos? (slope of 1 atom per meter?)
• Has Mars quaked yet? Any scientific speculation when it might?
• How was InSight's vertical seismometer (accelerometer) tested in Earth's stronger gravity?

我有高达300米海洋深度的地下温度数据(在不同的深度间隔)。我想计算0-300 m的海洋热含量。

公式是:

OHC =海水密度*比热容*对该深度的温度积分。

但是，深度不是在相同的间隔。所以我读到有必要使用加权温度。我想问两个问题。< / p >

1. Why do we have to use weighted temperature?
2. Is there any module or code available to do this? So that it will help me to compute the OHC?

我正在寻找一种气象措施(公式)的东西，结合以下2或更多:

<强>温度，湿度，云，风，降水，UV

我已经搜索过，例如，发现热指数结合温度和湿度，但不幸的是，热指数只有在温度高的时候才有意义，我将需要一种措施，总是“有意义”;

(背景信息:我想建立一个数学模型，其中一些气候信息也包括作为一个辅助措施，因此想“合并”;一个新的度量的可用时间序列，并有更多的信息作为输入，否则我总是只包括一个单一的信息。< / p >

Further background information: Would like to measure the effect of those variables (single variables, or better yet, combination of some of those) on the time-varying infection rates of COVID infections, the reason those were chosen is because in papers surrounding the topic "meterological factors" and "Covid infections" those were chosen most often)

In my processing I create a climatology of tasmax for the modeled historical data (1990-2010) and do some other 'tinkering'.

How can I check that my final tasmax 'product' is still realistic (i.e. how well does it match up to observations?)? Should I directly difference it from the observations to see how far my climatology differs? Are there any other methods that will highlight spatial anomalies in my new data set, if they exist?

在冰芯中测量和分析了大量数据，并可以绘制出与深度的关系，例如

• 层年龄
• 层厚度
• 杂生气体浓度
• 固体浓度
• delta信号

我正在寻找我可以找到的来源-理想情况下在一个地方-给出这些量与冰芯深度的值的曲线，例如，像这样

是否可能有一个互动网站，我甚至可以放大到这样的曲线?

最后我想知道，是否有一套“方程式”;其中给出了估计的(地表?)温度和CO₂浓度以及降水量作为测量数据的函数。我想这样的方程一定存在——还是说它们太复杂了，写不出来?(方程也应该给出误差范围!附加问题:误差范围是否随着时间而减小(即随着深度而增加)?)

我正在寻找一份阵风气候数据的世界地图，其具体目的是用于建筑设计负荷的粗略比较，无论位置如何，都具有一致的度量标准。

理想情况下，它将非常类似于建筑规范中使用的内容，例如“基本风速”;在ASCE 7美国&国际建筑规范，以及其他地区的可比规范:具有50年回报周期的最大值的等高线图或热图(或类似的统计数据，不同风险概况的不同级别)。参见美国地图这里和印度地图(下面，来源)。

我已经找到了其他气气学和极端值数据的世界地图，但没有高百分比的风。大多数全球风能数据图都是面向发电的。我不确定我需要什么分辨率，可能1-或2-度就可以了，但它必须来自一个更高分辨率的源来捕获局部最大值。< / p >

What I've already tried/considered:

• The closest I've found is this Dlubal seismic/wind/snow load map explorer - which is super useful and impressive, but doesn't fully serve my need because it's (a) a limited-click web app of a paid program, not a static map or set of maps, (b) does not have all countries, and (c) uses local codes which vary, making initial comparisons hard -although they would be needed eventually.
• I would make my own if I had more time to spend. The data is there at a useful level in several climate re-analysis products, such as MERRA-2 and ERA-5. In particular the Copernicus CDS not only lets you slice and download datasets, but even do the analysis online. I tried to use Panoply to create a view for a single timestep, but simply don't have the experience and time to efficiently do the 24 hr x 365 day x 30 year x 10⁶ gridpoints analysis.

Any pointers are welcome (including on this, my first StackExchange question). Thanks.

我有一个地震数据的问题，镜头的最小偏移量不在最近的接收器的中心，这种偏移可以是3个轨迹2或一个，可能在两个方面更多!!

地球是否表现出可测量的重力波?注意:我不是在问引力波。< / p >

Some recent experiments looking at low-frequency (3$\times 10^{-5}$Hz) accelerometer data are showing some features that might be explained by this. The signal that I and others have measured shows a signal at least 10 to 15 dB above the noise floor that appears to change somewhat based on local geology as determined between measurements in Japan, Hawaii, and the continental US.

Any insight into crustal gravity waves with a period of roughly 9 hours, or other phenomena that might result in reproducible signals like this in accelerometers is appreciated.

Any suggestions for further analysis that might differentiate among different possible explanations is also welcome!

我开始一个天气项目，我使用ERA5小时数据集(ref: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form)。在python中使用这个数据集，我现在有一个如下所示的数据框架。< / p >

As you can see, for one State I have n observations considering latitude|longitude and Time of observation. So, now I would like to aggregate this dataset daily, considering Admin2 and Admin1, in order to have Max Temp, Min Temp and Total precipitation. Is there a right way to do this aggregation or is this simple the max / min values from 2m temp and sum of precipitation? As you can see, by the content of my question, I'm not a meterologist. I'm just trying to build a reliable database for past historical weather data from some cities around the world.

(These descriptions denoted by '*' are referenced the Power's Angularity Index of Smoothness)

我有许多描述地理空间数据(例如降水强度，云场…)的预测和观测的数组。其中包括四个动画动图，每个动图在观察(降水)和amp之间来回循环;对观测期间的预测这四种预测在质量上存在显著差异(从第一种预测的极好到第四种预测的糟糕)。< / p >

I would like to have some way of systematically quantifying the similarity between two such arrays (e.g. between an observation and its associated forecast). I don't really know any more sophisticated way than to simply take the difference between two arrays (normalized somehow, e.g. by dividing by the mean of one of the first).

The problem is that 'scoring' the forecasts in this way gives case #3 & #4 equally bad marks - even though from visual inspection case #3 appears substantially better than #4. Clouds which are in slightly the wrong place (e.g. the ones in central Germany, or in Switzerland & Western Austria, in case #3) score equally poorly to those which are in entirely the wrong place, or simply absent.

Ideally, I'd like some measure of the minimum amount by which one image needs to be 'deformed' to yield another one, but I have no idea how to approach that in anything like a systematic manner.

我正在试图估计森林林分的木材量，我使用可变样地抽样方法进行实地清查，使用BAF-10棱镜测量每种树木的种类、胸径和高度。在我的领域，国际1/4对数规则被用来估计产量。我发现一个表，一般，但不特定于物种。在任何情况下，根据胸径&我所记录的高度，因为在大约50英亩的森林中，21个地块大约有220棵树。

专业人员如何有效地应用胸径和高度来估算体积?我想象有一个体积的数字查找表，理想情况下是每个物种，并且有一种方法可以通过编程(在电子表格中)根据与胸径对应的行和与高度对应的列来查找体积。然而，我还没能找到物种的数字查找表(只有PDF或纸质的)，或者Excel中那种行-col查找的方法。

我在美国的纽约，主要的物种是东部铁杉，红枫树，黄桦树，黑樱桃，红云杉和香脂冷杉

I am working on a remote sensing tool that uses only one input dataset (containing multiple fields) which accomplishes a task that has previously required more than one dataset with quite a bit of uncertainty coming from the other datasets. I think that's kind of novel.

Consequently, I am wondering if there is an existing name for an algorithm which uses only one dataset that distinguishes it from previous algorithms using multiple input sets?

My instincts (read: "love of coffee") tell me to refer to it as a "single-origin" algorithm, but surely there must be a name for this already.

Any insight is greatly appreciated.

我把这个反照率转换写下来:$$\alpha = 0.53\alpha {VIS}+0.47\alpha {NIR}$$在我的笔记上，这个方程是来自Collins等人(2002)。然而，我根本找不到这是从哪里来的。希望你们能帮帮我!< / p >

1. The output of the FK analysis algorithm is the Slowness,the back-azimuth and the corresponding power. This tells us the angle at which the source is present taking the center of the array geometry as the origin and the velocity of the wave which was received on site. My question is that, how to convert the Slowness vs Backazimuth plot to a X distance vs Y distance graph.

2. Where do we use the information that there are surface waves too since we only look at the vertical components and not the radial ones of the seismometer?

Any help, clarification or resource provision would be highly appreciated! Forgive me for my insufficient knowledge about the topic since this isn't my major.

Thank you :).

您使用什么软件、库、框架或工具来执行这些任务?我对处理来自传感器的时间序列数据的东西最感兴趣，但我欢迎为任何现场数据处理和清理任务提供解决方案的答案

我将在下面编译答案，从我找到的几个答案开始:

Libraries

meteoio

Python

DIT

I'm currently processing some oceanographic data such as salinity, oxigen concentration and conductivity, using SeaDataNet's NEMO software, version 1.6.7. I don't know if anybody has used previously NEMO, but I'll describe some basic steps in order to get to the step in which I'm stuck.

The original data file that I'm using is written in CNV format (not CSV), I provide this screenshot and this one since I can't upload any data file.

This file won't work so that I've given exactly the same data but formatted in another way, this is the other one , with this data The first file's header contains some information written in Spanish, since the institution for which I work is Instituto Español de Oceanografía. I'm using the latter file.

I'll skip right to the data tab, since the question might get too long and I haven't had any problem in validating every previous step.

In data tab, in the table below in I select all necessary parameters: pressure (PRES), temperature (TEMP), conductivity (CNDC), oxigen concentration (DOX1) and salinity (PSAL). Data starts at line 35.

After that I go with pressure data: I select the first line of the pressure data (1.0 in this image) and then right clicking choose the start/end option so that NEMO stores the position of that data on that line. Units and format are automatically stored because there is already information about them on the new data file. I repeat the process with output def.value choosing the -999.9 value at the bottom, like this:[

I do the same with the rest of the parameters. Now in order to validate this step, I first have to test every value and for that I choose the update test option, so in principle in the test column (next to conversion column) those first parameter values should appear according to the output format (format column).

The problem is that nothing appears in the test column. I've tried several times repeating the process from scratch without getting any error message when validating steps, but every time I get to the data tab, choose parameters, select the right start/end and try to update test, it continues appearing nothing. Besides, if I try anyway to validate the step, I get no error or succes message in the log tab at the bottom of the program screen. At this step the program doesn't let me change nothing else.

Am I doing something wrong though I don't get error messages? I have read several times the manual, and there is no any troubleshooting tip about this issue. Apart from that, is there any program to accomodate data so that I convert data as on image 2 to that appearing in image 4?

Any help would be greatly appreciated as I really need to go on. Thank you again for your patience and for taking your time to read the whole post.

I've been oriented toward NOAA's site mostly, but having seen some mentions about 'European models' of weather forecasting in various articles over the past year, I wonder if it would be better to look toward mining the ECMWF.

Basically, I'm looking at current surface temperature and wind speeds, then trying to get a daily forecast out to 15-20 days forward. Haven't dug into ECMWF yet, but on NOAA's site, they do provide probabilistic outlooks, albeit on a monthly frequency (example). I could always check weather.com's 10-Day or Monthly, but again, I'm wondering about the accuracy.

Anyone with experience comparing forecasted vs actual results on a thorough basis, and can share some perspective?

首先，问题!:

是否有可能仅使用本地数据来预测未来几个小时(比如2-3小时)的本地天气趋势?如果是，需要哪些数据来做出这样的预测，哪些预测是可能的?

我不是在谈论非常精确的预测，只是一些基本的东西，比如:“温度会上升/下降”或“如果湿度上升，气压下降，可能会下雨”等。你懂的。< / p >

I was thinking of using data for temperature, air pressure, humidity, time of day and wind speed/direction.. maybe something else?

Some Background :

for a little DIY Home-Project I decided it would be an interesting idea to set up a little "weather station" in front of my home and collect all sorts of weather related data in short intervalls (e.g. temperature, air pressure, humidity, wind speed/direction, time of day etc.)

The idea is to collect data over the period of a few months (or more) and to develop an algorithm that is able to predict the upcoming weather with a certain probability. The result should be a little program that was trained on past data and is then able to predict weather tendencies (Temperature, rain, etc..) using the data of the last couple of hours. Let's say the temperature and the air pressure drop and the humidty rises over a certain period of time, then it should tell me that it will likely rain.

I will not bother you with the details on how to write such a self-learning algorithm, but since I'm not very well educated regarding meteorology I'm not even sure if such predictions are possible or to what extent.

I think you've understood the idea. Please give me your Ideas about it. What's possible what's not. Maybe experiences, which data I need, what predictions i could possibly make and which not and so on.

Thanks for any help!

1. Magnitude spatial pattern
2. Magnitude time series
3. Phase spatial pattern
4. Phase time series

I would like to ask that the spatial pattern in Magnitude and Phase are corresponding to "Phase velocity" or "Group velocity" ?

Thank you!

不幸的是它已经卖完了，我想当它再次上市的时候会很贵。所以作为一个业余爱好者，我想也许我可以自己做一个这样的东西?所以我想知道他们是如何在土壤中测量这些东西的?唯一可能有帮助的是this但它没有说他们是否使用了准备好的土壤样本或只是从他们的花园或其他地方的普通土壤。有没有人知道他们是怎么做到的?他们使用了什么技术?< / p >

我有一个关于变量可分离方法的实用性的查询。通常我们将变量w.r.t分量分解为不同维数对应的分量，用这种方法解析求解微分方程。当涉及到实际应用时，我们通常已经以模型输出的形式提供了解决方案。有可能沿着维度分解它们吗?例如，我可以将纬向速度写成

 U(x, y, z, t) = uv(z) * uh(x, y, t) 

这个分解是唯一的吗?我很无知，因为它看起来就是一个矩阵乘法。我想知道这些矩阵的形式是怎样的。是否可以使用任何软件工具从数据变量中获得uv和uh ?< / p >

Edit: An example of such a decomposition is provided here at page number 7 (equation 2.6). I wonder if it is possible to obtain the decomposed RHS values practically from a variable.

Moreover I often see a PDE associated with the decomposition which is absolutely logical. The equation is solved analytically by substituting the decomposed variables and different software tools are able to plot the analytical solution. The situation is little different in the study attached herewith. I wonder how to obtain the decomposed values if I am not getting any way to solve it analytically.

我正在寻找集合预测和相关的真实观测值的数据集。例如，数据集包含$n$集合预报24小时内的温度和24小时后的观测温度。ECMWF数据集看起来很有希望，但作为一名数学家，我不熟悉术语。点击一个数据集，此页为例，显示“分析”和“预测”数据可用。这些是集合成员还是观测结果?下载数据也没有帮助