Observational Analysis and Retrieval of Snowfall Using Satellite Data at High Microwave Frequencies

In the high latitudes during cold seasons, a substantial portion of precipitation falls in the form of snow. Measuring snow precipitation has many applications such as forecasting hazardous weather, understanding hydrological water budget, and evaluating the cooling and freshening effects of snow onto ocean surface. However, unlike rainfall, snowfall measurement is extremely limited due to technical difficulties, especially over ocean and in the Polar Regions. The goal of this study is to assess the feasibility of measuring snowfall from satellite observations. From the temporal analysis of surface radar data, a diurnal variation of snowfall in the Wakasa Bay area is detected, which is not identified by satellite infrared data. Snowfall signatures are analyzed using satellite and airborne microwave radiometer measurements at frequencies ranging from 37 to 340 GHz. Data used in the analysis include satellite data from the Advanced Microwave Scanning Radiometer-EOS and the Advanced Microwave Sounding Unit-B, and airborne data from a millimeter-wave radiometer and a dual-frequency precipitation radar during January 2003 near Japan. An investigation of the sensitivity of microwave channels to snowfall associated with shallow convective clouds shows that microwave radiation at higher frequencies ( >= 150 GHz) is sensitive to scattering by snow/ice. Through data analysis and radiative transfer modeling, a snowfall retrieval algorithm based on Bayesian theorem is developed using high frequency satellite microwave data. In the Bayesian snowfall retrieval algorithm, observational data from airborne and surface-based radars are used to construct the a-priori database that is the most important component in the algorithm. Two size distributions for snowflakes and ten observed atmospheric sounding profiles are used to diversify the results and reduce the errors in the radiative transfer model. The scattering properties of the snowflakes are calculated based on realistic nonspherical shapes using discrete dipole approximation for the radiative transfer modeling. The algorithm is validated by independent surface radar/gauge data, subsequently applied to satellite AMSU-B data for winter snowstorms near Japan. The retrieved results show reasonable agreement with surface radar observations, which shows the possibility of applying this algorithm globally by expanding the database.= 150 GHz) is sensitive to scattering by snow/ice. Through data analysis and radiative transfer modeling, a snowfall retrieval algorithm based on Bayesian theorem is developed using high frequency satellite microwave data. In the Bayesian snowfall retrieval algorithm, observational data from airborne and surface-based radars are used to construct the a-priori database that is the most important component in the algorithm. Two size distributions for snowflakes and ten observed atmospheric sounding profiles are used to diversify the results and reduce the errors in the radiative transfer model. The scattering properties of the snowflakes are calculated based on realistic nonspherical shapes using discrete dipole approximation for the radiative transfer modeling. The algorithm is validated by independent surface radar/gauge data, subsequently applied to satellite AMSU-B data for winter snowstorms near Japan. The retrieved results show reasonable agreement with surface radar observations, which shows the possibility of applying this algorithm globally by expanding the database.