Downscaling Daily Satellite-Based Precipitation Estimates Using MODIS Cloud Optical and Microphysical Properties in Machine-Learning Models

• Published in: Atmosphere Vol. 14; no. 9; p. 1349
• Main Authors: Medrano, Sergio Callaú, Satgé, Frédéric, Molina-Carpio, Jorge, Zolá, Ramiro Pillco, Bonnet, Marie-Paule
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2023; MDPI
• Subjects: CHIRTS; Climate change; Cloud water; Clouds; Daily; Daily precipitation; Datasets; downscaling; Drought; Environmental aspects; Environmental Sciences; Estimates; Forecasts and trends; Gauges; Ground stations; IMERG; Machine learning; Methods; Minimum temperatures; Modelling; MODIS; MODIS cloud optical and microphysical properties; Numerical weather forecasting; Optical properties; Optical thickness; Physiographic features; Precipitation; Precipitation (Meteorology); Precipitation estimation; Rain; Rain gauges; random forest model; Regression analysis; Regression models; Reliability analysis; Satellites; Spatial discrimination; Spatial resolution; Stream flow; Training; Variables; France; Peru; Lake Titicaca; Downscaling Bias correction; Satellite precipitation; Machine leaning
• DOI: 10.3390/atmos14091349
• EISSN: 2073-4433

This study proposes a method for downscaling the spatial resolution of daily satellite-based precipitation estimates (SPEs) from 10 km to 1 km. The method deliberates a set of variables that have close relationships with daily precipitation events in a Random Forest (RF) regression model. The considered variables include cloud optical thickness (COT), cloud effective radius (CER) an cloud water path (CWP), derived from MODIS, along with maximum and minimum temperature (Tx, Tn), derived from CHIRTS. Additionally, topographic features derived from ALOS-DEM are also investigated to improve the downscaling procedure. The approach consists of two main steps: firstly, the RF model training at the native 10 km spatial resolution of the studied SPEs (i.e., IMERG) using rain gauge observations as targets; secondly, the application of the trained RF model at a 1 km spatial resolution to downscale IMERG from 10 km to 1 km over a one-year period. To assess the reliability of the method, the RF model outcomes were compared with the rain gauge records not considered in the RF model training. Before the downscaling process, the CC, MAE and RMSE metrics were 0.32, 1.16 mm and 6.60 mm, respectively, and improved to 0.48, 0.99 mm and 4.68 mm after the downscaling process. This corresponds to improvements of 50%, 15% and 29%, respectively. Therefore, the method not only improves the spatial resolution of IMERG, but also its accuracy.