|官莉||南京信息工程大学气象灾害预报预警与评估协同创新中心中国气象局气溶胶-云-降水重点开放实验室, 南京 email@example.com|
|李依鸿||南京信息工程大学气象灾害预报预警与评估协同创新中心中国气象局气溶胶-云-降水重点开放实验室, 南京 210044|
|张思勃||南京信息工程大学气象灾害预报预警与评估协同创新中心中国气象局气溶胶-云-降水重点开放实验室, 南京 210044|
The amount of hydrometeors in clouds plays an important role in the Earth's radiation balance. It is also an important parameter in representing clouds in global circulation models used for climate study and weather forecasting. Satellite data have been widely used to estimate global atmospheric parameters. In this study, we introduce in detail a 1D-Var retrieval algorithm and assess the quality of the devived hydrometeor products. This algorithm could provide an estimate of the geophysical state, especially hydrometeor profiles, which are used as first guess and/or background before starting data assimilation. This algorithm is beneficial to the assimilation of satellite measurements under cloudy and rainy conditions. A one-dimensional variational retrieval algorithm is developed to retrieve hydrometeor parameters (profiles of liquid cloud, liquid precipitation, and ice cloud) from spaceborne microwave AMSR-E/2 measurements. The algorithm is an iterative physical inversion system that finds a consistent geophysical solution to fit all radiometric measurements simultaneously. It inverts the radiative transfer equation by finding radiometrically appropriate profiles of geophysical parameters. In addition, the retrieved parameters include a set of derived products that are a simple vertical integration of fundamental profiles, such as total precipitable water, cloud liquid water, ice water path, and rainfall rate. AMSR-2 measurements from Halong Typhoon in 2014 were used as examples, and all of the retrieved products were assessed These hydrometeor profiles were integrated into the radiative transform model (observation operators), in which cloud absorption and scattering effect were measured. The simulated and observed brightness temperatures were consistent in all microwave channels. The retrieved hydrometeor profiles were validated using the observed reflectivities of the Cloud Profiling Radar uploaded on CloudSat satellite. Comparison results showed that areas with high radar reflectivity matched the cloud water content and liquid precipitation regions at high amounts, proving the high precision of hydrometeor retrievals from the 1D-Var algorithm. However, the AMSR-E/2 observations were not sensitive to small-scale shallow clouds because of its few channels and poor spatial resolution. In addition, the inversion ability of satellite microwave measurments was limited to overcast or layered clouds with a high optical thickness. These hydrometeor parameters are extremely difficult to assess because of the lack of effective ways to measure these quantities (either from ground-based or satellite sensors). Mutual validation of these hydrometeor products from different sensors for long periods of time is still needed.