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In the US, meteorologists forecast the probability of ANY amount of precipitation falling. The minimum amount of that we deem acceptable to meet this criteria is .01". So, we are forecasting the probability of one hundredth of an inch of precipitation to fall at a location.
We look at observational data from ground stations, satellites, and computer ...
14
The first part of the association is that increasing levels of greenhouse gases in the atmosphere will cause rising temperatures on earth. Here is some information on why that is so, if you are interested. Now assuming that temperatures are currently going up (they are), there are two mechanisms that will connect this to rising sea level, melting of ice on ...
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You need a radiative transfer model and global climate model to do it with greenhouse gases. you can derive the temperature without greenhouse gases as discussed below:
The absorption is highly variable depending on wavelength and can be seen in this graphic:
Radiative transfer through the atmosphere is specific to pressure, temperature, and wavelength. ...
9
We have no evidence to suggest that the rate or magnitude of earthquakes has, in general, increased: while earthquakes are having a greater effect on humans, this is entirely - as best we can tell - due to increased population density, and the propensity of people to build cities immediately above major fault planes (because they channel water to the surface ...
8
The Köppen climate classification is a classic metric to classify climates:
Source: Wikimedia Commons
It classifies climates based on the number of months with temperature below and/or above certain thresholds, and on whether rainfall is evenly distributed or concentrated in a particular season. You will find details on the definitions on Wikipedia.
An ...
7
Rose diagrams, also called polar bar plots, are useful for showing azimuthal (directional) data. Any dataset consisting of lots of measurements of direction or orientation could be visualized this way.
A common application in sedimentology is visualizing measured cross-bedding azimuths. This can help work out the palaeocurrent direction (that is, the ...
7
You need a model. You could try to use Bayes theorem to build a model based upon conditional probabilities. See this reference from NOAA about weather forecasting: Probability Forecasting - Primer .
If I were to try to build a Bayesian model for weather forecasting, with the intention of using a limited data set of observations I had collected myself, I ...
6
It's a harder direct relation to show that it appears. On the surface, warmer = less ice, file under "duh", but while it probably is that simple, showing a causal relation is a more difficult.
The thermal expansion aspect of the question is easy enough. Warmer air warms the ocean surface which slowly warms the ocean. About 90% of the heat trapped by ...
5
Convergent cross mapping (CCM) is a recently developed tool to answer the question you've asked. It's based on tools developed in nonlinear time series analysis and dynamical systems theory. It allows you to:
1) determine if a causal relationship between two variables is present
2) establish the direction of causality
3) do so even in the presence of noise.
...
5
There are two solutions using the least square method for calculating $C$ and $D$. Both methods yield different results for your constants. There is no correct method.
Method of least Squares
We define the least square error as follows:
$$\text{lse} = \sum_{i}{\left(y_i - f(x_i)\right)^2}$$
The $y_i$ and $x_i$ are our data through that we want to fit a ...
5
First, don't take that graph too literally, it is just a cartoon version and isn't quantitatively correct.
The USSR heavily subsidized fertilizer until 1988. When they ended the subsidy, fertilizer prices increased to market prices and use of fertilizer decreased. Fertilizer use decreased further after the USSR fell a couple years later.
Fertilizer ...
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The source you mentioned tries to explain why CO2 lags by about 1000 years in historical records.
Henry's law tells you something about the equilibrium between the CO2 level in the surface layer and the atmosphere. However, this is not the reason for the 1000 year time-lag mentioned. There's not enough CO2 in the surface layer to cause these increases in ...
5
As mentioned, the term you are looking for is persistence. Generally its strength varies wildly depending on what variable you are looking at, the location, and by whether you also further select other factors like season and other meteorological variables.
I would expect daily precipitation persistence is a relatively bad predictor, given that ...
5
Your last question was easy to answer, this one is more difficult. You could look at correlation and dependence, but you'd be better off looking at variography. That's the easier part of this question dealt with. Most resource geologists do not deal with correlation & dependence, they do variography studies.
If you are dealing with a small data set to ...
4
To build on Drew's excellent answer, I would note the following:
Weather forecasts of the future are built on historical frequencies. A "forecast" of a X percent chance of rain (however defined), means, based on the current configuration of casual variables (temperature, humidity, air pressure etc.), there has historically been an X per cent chance of rain ...
4
Yes, the most commonly used distribution for wind speed is the Weibull - at least when it comes to predicting wind speeds at prospective wind farm locations.
Yes, the parameters vary.
See, for example, the book "Wind Energy - The Facts", the output of an EU research programme (ISBN 9781844077106, also freely available online).
Or Seguro & Lambert's ...
4
I can give some examples from atmospheric science:
Wind and temperature in the vertical direction: as you increase in height, the temperature decreases due to the conservation of geopotential energy. Also the wind speed increases, due to the lack of friction.
In data assimilation, spurious correlations are quite common, especially for large distances. ...
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C is purely empirical for any given situation. BEWARE of such equations! Using coefficients to six significant figures gives the illusion of high precision when, in fact, the whole approach is extremely 'rubbery', and highly dependent upon the local geology / soil type / local hydrology. The expression is only valid for the location in which it was ...
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Part of the complication goes to two separate definitions for the word:
prevailing:
To be frequent (Miriam-Webster)
The most frequent, the most occurring wind direction in your example, even if weak, is east. So that would be the prevailing wind, as that's the definition we generally use in meteorology.
So don't think of it as
Having superior power ...
3
As an alternative to Convergent Cross Mapping (CCM), the recent Tigramite is a fast python library for causal discovery that promises to ...
... outperforms current approaches in detection power and scales up to high-dimensional datasets. It overcomes detection biases, especially when strong autocorrelations are present, and allows ranking associations in ...
3
The argument and science behind this idea is well summarized on the Union of Concerned Scientist web page on global warming as it causes sea levels to rise. There is a link to a white paper which lists various information sources and studies.
Basically, the claim is that CO2 makes the earth warmer by insulating the earth and preventing the escape of long-...
3
Count data.
Quantitative data that varies discretely and arbitrarily along some scale can be called count data.
Nick Chrisman (1995; Beyond Stevens: A revised approach to measurement for geographic information, presented at Auto-Carto; see this PDF for a transcript) pointed out that there are many more typologies of measurement than the 'classic' four ...
3
I find the following method very straightforward and elegant. So maybe I can convince you to consider it.
For the South American (SA) monsoon, Silva et al. (2007) and Carvalho et al. (2012) proposed the Long-scale South American Monsoon Index (LISAM), based on the annual cycle of some fields.
For the 2012 paper, where several precipitation datasets are ...
3
I recommend you read Monahan et al. (1990) for a thorough explanation of Empirical Orthogonal Functions. I suggest you pay attention to the way the principal components are extracted. Ultimately, there is no guarantee that a sample belongs to a specific component. What you are working with is eigenvalues/eigenvectors and thus a specific data point can have ...
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Pearson publish a data set for A-level students (high school) that includes weather data from various locations. While the data set is (deliberately) flawed [one task for students is to identify flaws in the data set] it is "real" data and questions of persistence can be investigated with it.
For example, in Heathrow, May-October 2015, there were 80 days on ...
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In addition to satellite measurements of carbon dioxide, there are numerous ground based stations that also measure carbon dioxide: Italy, Hawaii, Australia, just to name a few.
If you want to catch carbon dioxide emitting cheaters, a dense network of such ground based stations would be needed around the world. The measurements would need to combined with ...
3
Welcome to Earth Science SE!
Given that the crisis is still ongoing, I doubt we know the full extent of the impact (forecasting the weather is hard enough, forecasting the impact of a pandemic is outside my expertise). But, at least one conference was supposed t
o go to was cancelled. And I know the lack of airplane obs is impacting data assimilation. ...
2
Hurricanes are tropical cyclones (TC) and these storms form in the ocean over warm waters when the sea surface temperature (SST) is above 27C or 28C. The higher the SST the greater the probability that a TC will form and the greater the probability that once formed the TC will intensify. These relationships provide the theoretical basis for the idea that ...
2
To be sure about why it appears to be centered around -0.2°C, you'd have to go to the original source of the graph. However, as an educated guess, I would imagine that they are showing the anomaly compared to some recent period, such as 1961-1990 or 1971-2000, as is commonly done. From your Wikimedia Commons link, this figure appears to be original work by ...
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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....
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