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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. ...


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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 ...


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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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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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