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 andassess 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 Cloud Sat 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.