Remote Sensing of Snow for Improvement of Weather Forecasts

// However, atmospheric data from microwave sounding is disregarded in weather predictions due to contaminated signals deriving from ever-changing snow properties. Therefore, the spatial variability of snow emission and atmospheric noise driven by crystal behaviour, snow depth and density need to be elucidated in order to assimilate microwave data into models (Picard et al., 2018). By simulating these variables, comparisons can be made to current satellite data and thus relax constraints for Numerical Weather Predictions (Brucker et al., 2010). Thus, integrating valuable microwave sounding into NWPs would potentially change the way in which weather in eastern Europe, northern Asia, North America, the mid-latitudes and the Arctic is forecasted (Jung et al. 2014). // The following aims and objectives have been proposed for this project. // This project aims to improve ways in which satellite data is assimilated into Numerical Weather Prediction (NWP) and provide more accurate Arctic weather forecasts. // To achieve these aims, the following objectives have been proposed: // * To estimate the spatial variability of microwave emission from snow properties such as snow depth, density, grainsize and temperature to tighten constraints for microwave scattering models. // * To simulate microwave emissions represented in satellite and Met Office campaign data with the Snow Microwave Radiative Transfer (SMRT) model and attempt to replicate data at the satellite scale. // Proposed Methods. // * Passive Satellite Observations of Snow. // * Advanced Microwave Sounding Units (AMSU-A and AMSU-B). // * The Snow Microwave Radiative Transfer Model (SMRT). * Met Office MACSSIMIZE Field Campaign Data. //