Publication date
May 2023
DOIAbstract
Data and pre-trained random forest models necessary to correct GFS forecast data and produce improved Everest Forecasts. Please find the associated code at: github.com/MaxVWDV/Everest_wind_forecas
Data and pre-trained random forest models necessary to correct GFS forecast data and produce improved Everest Forecasts. Please find the associated code at: github.com/MaxVWDV/Everest_wind_forecas
This is the companion data for the manuscript titled as 'The June 2012 North American Derecho: A tes...
The tropospheric mapping function (MF) plays an important role in estimating the delay of electromag...
Code and data products associated with "Bias correction and statistical modeling of variable oceanic...
Improved gridded wind speed forecasts by statistical postprocessing of numerical models with block r...
These are data are python scripts for creating figures in Doane et al., 2022 (Hillslope Roughness Re...
This code was used for the following publications: Blaker, A. T., J. J.-M. Hirschi, M. J. Bell and ...
Profile of the dataset The GGWS-PCNN is a global gridded monthly dataset of 10-m wind speed based...
In the previous version, I forgot to include the trained random forest model. This version adds that...
The dataset contains two files, which are "GCAM_input" , "Windqueries_code". The "GCAM_input "f...
Processing code for generating the datasets used for creating the figures in "Evaluating wind profil...
Code and data products associated with "Bias correction and statistical modeling of variable oceanic...
Code and model output data for the manuscript entitled "Intercomparison of the weather and climate p...
This folder contains the random forest model used in the manuscript Modeling Equatorial Ionospheric ...
International audienceThe relationship between the wind speed derived from the outputs of a numerica...
This dataset is intended as a machine learning dataset, to train a model to predict the occurrence ...
This is the companion data for the manuscript titled as 'The June 2012 North American Derecho: A tes...
The tropospheric mapping function (MF) plays an important role in estimating the delay of electromag...
Code and data products associated with "Bias correction and statistical modeling of variable oceanic...
Improved gridded wind speed forecasts by statistical postprocessing of numerical models with block r...
These are data are python scripts for creating figures in Doane et al., 2022 (Hillslope Roughness Re...
This code was used for the following publications: Blaker, A. T., J. J.-M. Hirschi, M. J. Bell and ...
Profile of the dataset The GGWS-PCNN is a global gridded monthly dataset of 10-m wind speed based...
In the previous version, I forgot to include the trained random forest model. This version adds that...
The dataset contains two files, which are "GCAM_input" , "Windqueries_code". The "GCAM_input "f...
Processing code for generating the datasets used for creating the figures in "Evaluating wind profil...
Code and data products associated with "Bias correction and statistical modeling of variable oceanic...
Code and model output data for the manuscript entitled "Intercomparison of the weather and climate p...
This folder contains the random forest model used in the manuscript Modeling Equatorial Ionospheric ...
International audienceThe relationship between the wind speed derived from the outputs of a numerica...
This dataset is intended as a machine learning dataset, to train a model to predict the occurrence ...
This is the companion data for the manuscript titled as 'The June 2012 North American Derecho: A tes...
The tropospheric mapping function (MF) plays an important role in estimating the delay of electromag...
Code and data products associated with "Bias correction and statistical modeling of variable oceanic...
Add to ORKG