Function-Based Troposphere Tomography Technique for Optimal Downscaling of Precipitation

Saeid Haji-Aghajany, Yazdan Amerian, Alireza Amiri-Simkooei

Remote Sensing · 26 May 2022

In one sentence. GNSS tomography humidity fields improve statistical and ANN precipitation downscaling, cutting monthly precipitation RMSE by about 38% on average.

Abstract

Precipitation is an important meteorological indicator that has a direct and significant impact on ecology, agriculture, hydrology, and other vital areas of human health and life. It is therefore essential to monitor variations of this parameter at a global and local scale. To monitor and predict long-term changes in climate elements, Global Circulation Models (GCMs) can provide simulated global-scale climatic processes. Due to the low spatial resolution of these models, downscaling methods are required to convert such large-scale information to regional-scale data for local applications. Among the downscaling methods, the Statistical DownScaling Model (SDSM) and the Artificial Neural Networks (ANNs) are widely used due to their low computational volume and suitable output. These models mainly require training data, and generally, the reanalysis data obtained from the National Center for Environmental Prediction (NCEP) and European Centre for Medium-range Weather Forecasts (ECMWF) are used for this purpose. With an optimal downscaling method, instead of applying the humidity indices extracted from ECMWF data, the outputs of the function-based tropospheric tomography technique obtained from the Global Navigation Satellite System (GNSS) will be used. The reconstructed function-based tropospheric data is then fed to the SDSM and ANN methods used for downscaling. The results of both methods indicate that the tomography can increase the accuracy of the downscaling process by about 20 mm in the wet months of the year. This corresponds to an average improvement of 38% with regard to the root mean square error (RMSE) of the monthly precipitation.

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