DOI: 10.5061/dryad.h70rxwdqn
title: Data archive for: Exploring the use of machine learning to improve vertical profiles of temperature and moisture

description: Vertical profiles of temperature and dewpoint are useful in predicting
 deep convection that leads to severe weather that threatens property and
 lives. Currently, forecasters rely on observations from radiosonde
 launches and numerical weather prediction (NWP) models. Radiosonde
 observations are, however, temporally and spatially sparse, and NWP models
 contain inherent errors that influence short-term predictions of
 high-impact events. This work explores using machine learning (ML) to
 postprocess NWP model forecasts, combining them with satellite data to
 improve vertical profiles of temperature and dewpoint. We focus on
 different ML architectures, loss functions, and input features to optimize
 predictions. Because we are predicting vertical profiles at 256 levels in
 the atmosphere, this work provides a unique perspective at using ML for
 1-D tasks. Compared to baseline profiles from the Rapid Refresh (RAP), ML
 predictions offer the largest improvement for dewpoint, particularly in
 the mid- and upper-atmosphere.  emperature improvements are
 modest, but CAPE values are improved by up to 40%. Feature importance
 analyses indicate that the ML models are primarily improving incoming RAP
 biases. While additional model and satellite data offer some improvement
 to the predictions, architecture choice is more important than feature
 selection in fine-tuning the results. Our proposed deep residual UNet
 performs the best by leveraging spatial context from the input RAP
 profiles; however, the results are remarkably robust across model
 architecture. Further, uncertainty estimates for every level are
 well-calibrated and can provide useful information to forecasters.
description: This dataset was collected for the corresponding publication in
 <em>Artificial Intelligence for the Earth Systems</em>, and
 the processing methodology is outlined in that publication.
description: # Title of Dataset: Data Archive for Exploring the Use of Machine Learning
 to Improve Vertical Profiles of Temperature and Moisture This dataset
 contains a combination of four data sources that allow for the training
 and testing of using machine learning to predict temperature and dewpoint
 vertical profiles. The data consists of matched Radiosonde Observations
 (RAOB), Rapid Refresh (RAP) output, Real-Time Mesoscale Analysis (RTMA),
 and Geostationary Operational Environmental Satellite (GOES)-16 data at
 sites over the central U.S. Tornado Alley from January 2017 through May
 2020. All data sources have been collocated to corresponding site, height
 (256 vertical levels), and time. ## Description of the data The data are
 in a single netCDF file, mlsoundings\_dataset.nc. The file consists of
 five different groups. The groups correspond to the different data
 sources, with an additional group that has the indices that were used for
 training, validation, and testing. ## Sharing/Access information These
 data were derived from the following sources: * Radiosonde Observations
 are from the National Oceanic and Atmospheric Administration (NOAA) Earth
 System Research Laboratory radiosonde archive
 (https://ruc.noaa.gov/raobs/) * The Rapid Refresh (RAP) are from NOAA
 Global Systems Laboratory Assimilation and Verification Innovation
 Division (https://rapidrefresh.noaa.gov/) * The Real-Time Mesoscale
 Analysis (RTMA) data are from NOAA National Centers for Environmental
 Prediction (NCEP) Central Operations
 (https://www.nco.ncep.noaa.gov/pmb/products/rtma/ and
 https://noaa-rtma-pds.s3.amazonaws.com/index.html) * The satellite data
 are from the Advanced Baseline Imager (ABI) onboard the Geostationary
 Operational Environmental Satellite (GOES)-16 and are available from NOAA
 and NASA on Amazon Web Services
 (https://www.ncei.noaa.gov/products/goes-terrestrial-weather-abi-glm and
 https://noaa-goes16.s3.amazonaws.com/index.html)