Normalized GEFS Week 4 Forecasts and ERA5 VPD Labels for ML

Machine Learning dataset from 2000-2019, specifically used to train UNET neural networks, containing the following training data processed to a CONUS-like domain, 10.5 to 59.5 latitude and -159.5 to -60.5 longitude, on half degree resolution from 11 ensemble members of 6-hourly GEFS data and vapor pressure deficit (VPD) labels created on the same domain from ERA5. Training data are from years: 2000, 2001, 2003-2006, 2009-2012, 2016, 2017, and 2019 Validation data are from years: 2002, 2008, 2014, and 2018 Blind testing data are from years 2007, 2013, and 2015 The input data are created from week 4 forecasting data produced by the GEFS initialized on the first Wednesdays of the year. Input data included in this dataset are: 'soilw' # Weekly mean volumetric soil moisture content @ bgrnd_depth 'pwat' # Weekly mean precipitable water '2t' # Weekly mean 2 metre temperature 't' # Weekly mean temperature at pressure (note two values at 850mb and 200mb) sine of the number of the week of the year Finally, the files are normalized by z-score normalization by pressure height (or surface) and variable. They are then saved into npy matrices sized [99, 199, 6] in the above order for NN training purposes. The VPD labels are the coresponding weekly mean VPD per gridcell derived form ERA5 data, and stored in npy files sized [99, 199, 1] for NN label purposes, intended to represent "observed" vpd on the corresponding week three forecast. They have an identical name to the input but are stored in the label directory. The zip files has been zipped in a way that contains subdirectories directories storing the npy files and identical data-label names as the following: naming example - nn_dataset_YYYY_week_WW_ens_E_f_3.npy where YYYY = year (2019) where WW = week (1 through up to 48) where E = GEFS ensemble number (0-10) where f_3 means forecast week three (0-4 included in initial GEFS dataset) Directories are named to divide npy files into: training data training labels validation data validation labels testing data testing labels Lastly, an additional file called "norm_inference_vars" is concluded and contains the validation and testing input variable datasets standard deviations and means.