The study of both the cloud structure and the atmosphere water vapor content is playing a central role in the development of the weather sciences. Traditional means of weather observation are limited in their spatial coverage, scarce over remote regions and unavailable over the sea. On the contrary, the use of satellite data is particularly valuable because of the wide coverage and the good temporal resolution. So, it is necessary to develop a conversion method of radiometric measurements into weather-related products. In particular, Passive Microwave (PMW) Radiometric data are very effective for remote sensing of weather phenomena, because the upwelling radiation is directly responsive to the precipitation structure. In this context the snowfall detection and retrieval is a particularly challenging subject; therefore, the development of a method for the snowfall detection is a central topic in the context of the climatological studies.
A present key point is related to the use of the so called observational "coincidence dataset", i. e. a dataset built from the coincident observations - in time and space - of a spaceborne microwave radiometer and a spaceborne cloud and precipitation RADAR.
The goal of the present research project is the analysis and exploitation of the coincidence dataset of the cross-track scanning radiometer Advanced Technology Microwave Sounder (ATMS) and CloudSat Cloud Profiling Radar (CPR) snowfall observations towards the development an algorithm for the snowfall detection and surface snowfall rate estimation based on ATMS. The research will focus on this instrument because: 1) it is carried by near-polar orbiting satellites, providing global coverage; 2) it is equipped with several channels suitable not only for snowfall retrieval, but also for the characterization of the background surface; 3) it is on board operational U.S. satellites guaranteeing continue observations for the next decades.
Until SLALOM development, an algorithm for the snowfall detection and retrieval from PMW data tuned against radar observations has not been just created. This approach is useful to skip the construction of a simulated database, that is influenced by the model approximation. Moreover, the use of ATMS instead of GMI will be allow to obtain also data over the polar region, that are not covered by GMI and other MW radiometers. The study of those areas are extremely challenging because accurate ground-based snowfall measurements require constant and effective instrument maintenance over the long durations needed to study precipitation. Data, instruments and techniques used affect and make difficult the spatial interpolation of such sparse data. Moreover, surface snowfall is often very light in polar climate, and so it is very difficult to detect and quantify using both ground-based instruments and satellite sensors. Nevertheless, quantifying atmospheric parameters in polar regions is essential for better understanding the global hydrologic cycle and energy budget, especially since certain higher latitude areas have experienced accelerated warming trends in recent years.
Finally, the development and the improvement of surface classifier based on remote sensing data will create a big dataset snow cover data, which will be very useful for climatological studies.