Inhomogeneous Anisotropic Analysis of the Available Water Content of the Upper Soil Layer According to Ground-Based and Remote Sensing on the Territory of Russia

The Hydrometeorological Center of Russia receives agrometeorological information from about 950 stations one time per ten days and the remote sensing Advanced Scatterometer (ASCAT) data from three Meteorological Operational (MetOp) satellites. We suggest a combined objective analysis (OA) of the available water content based on the available water content measurements at agrometeorological stations and on remote sensing data. The new version of OA is constructed using two neural networks and the backpropagation of error to learn it simultaneously. The first neural network is used to convert the ASCAT data into the available water content values, and the second network is used to estimate the inhomogeneities of soil moisture fields. We use the optimal interpolation (OI) method for assimilation of the ground-based data. In the new version, we evaluate the correlation functions (CFs) of inhomogeneous non-Gaussian fields, not from sample statistics but from machine learning methods. The method takes into account the combining of various datasets: ASCAT data, Food and Agriculture Organization (FAO) soil types, European Space Agency (ESA) GlobCover, and National Center for Atmospheric Research (NCAR) climate data.