Short-range forecasts of global precipitation using deep learning-augmented numerical weather prediction

Precipitation governs Earth's hydroclimate, and its daily spatiotemporal fluctuations have major socioeconomic effects. Advances in Numerical weather prediction (NWP) have been measured by the improvement of forecasts for various physical fields such as temperature and pressure; however, large biases exist in precipitation prediction. We augment the output of the well-known NWP model CFSv2 with deep learning to create a hybrid model that improves short-range global precipitation at 1-, 2-, and 3-day lead times. To hybridise, we address the sphericity of the global data by using modified DLWP-CS architecture which transforms all the fields to cubed-sphere projection. Dynamical model precipitation and surface temperature outputs are fed into a modified DLWP-CS (UNET) to forecast ground truth precipitation. While CFSv2's average bias is +5 to +7 mm/day over land, the multivariate deep learning model decreases it to within -1 to +1 mm/day. Hurricane Katrina in 2005, Hurricane Ivan in 2004, China floods in 2010, India floods in 2005, and Myanmar storm Nargis in 2008 are used to confirm the substantial enhancement in the skill for the hybrid dynamical-deep learning model. CFSv2 typically shows a moderate to large bias in the spatial pattern and overestimates the precipitation at short-range time scales. The proposed deep learning augmented NWP model can address these biases and vastly improve the spatial pattern and magnitude of predicted precipitation. Deep learning enhanced CFSv2 reduces mean bias by 8x over important land regions for 1 day lead compared to CFSv2. The spatio-temporal deep learning system opens pathways to further the precision and accuracy in global short-range precipitation forecasts.

GLOBUS: GLObal Building heights for Urban Studies

Urban weather and climate studies continue to be important as extreme events cause economic loss and impact public health. Weather models seek to represent urban areas but are oversimplified due to data availability, especially building information. This paper introduces a novel Level of Detail-1 (LoD-1) building dataset derived from a Deep Neural Network (DNN) called GLObal Building heights for Urban Studies (GLOBUS). GLOBUS uses open-source datasets as predictors: Advanced Land Observation Satellite (ALOS) Digital Surface Model (DSM) normalized using Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), Landscan population density, and building footprints. The building information from GLOBUS can be ingested in Numerical Weather Prediction (NWP) and urban energy-water balance models to study localized phenomena such as the Urban Heat Island (UHI) effect. GLOBUS has been trained and validated using the United States Geological Survey (USGS) 3DEP Light Detection and Ranging (LiDAR) data. We used data from 5 US cities for training and the model was validated over 6 cities. Performance metrics are computed at a spatial resolution of 300-meter. The Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) were 5.15 meters and 28.8 %, respectively. The standard deviation and histogram of building heights over a 300-meter grid are well represented using GLOBUS.