Retrieval of Cloud Water Variables by 1D-Var Algorithm

Qun-Bo Huang,Bai-Nian Liu,Xiao-Gang Zhang,Wei-Feng Wang,De Xing,Xiang Xing,Wei-Min Zhang,Teng-Ling Luo

doi:10.1051/itmconf/20171203007

Abstract

Data assimilation of satellite observations in cloud and rain regions is a worldwide problem. In the paper, we designed the background field and their error covariance of three cloud water variables (cloud cover, cloud liquid water and cloud ice water) at first. The experiment results demonstrate that 1D-Var (One-dimensional variational data assimilation) can retrieve the three variables efficiently. The cloud liquid water and cloud ice water can be obviously improved by 1D-Var. However, the retrieved effect of low cloud cover is still to be improved. It has the positive potential to assimilate the satellite observations in cloud and rain regions.

Highlights

Clouds and precipitation are produced under certain weather conditions, which are related to the majority of the important weather phenomena
Ma et al analyzed the error of simulated brightness temperature and the sensitivity to cloud parameters of the infrared instrument channels on NOAA14 [2]
Based on the 1D-Var software developed by ECMWF, This paper presents a method of retrieving the cloud water variables by satellite observations

Summary

Introduction

Clouds and precipitation are produced under certain weather conditions, which are related to the majority of the important weather phenomena (such as thunderstorms, hail, tornadoes, heavy rain and so on). The assimilation of satellite data in cloud and rain regions will improve the accuracy of numerical weather forecast, and which is currently a hotspot problem in the world. Chevallier and Bauer simulated satellite observations under cloud and rain condition by RTTOV, and compared with Special Sensor Microwave/Imager (SSM/I) observations [1]. 1D+4D-Var system process which is employed to assimilate the cloud and rain affected SSM/I data [5]. Based on the 1D-Var software developed by ECMWF, This paper presents a method of retrieving the cloud water variables by satellite observations. Minimization algorithms are generally the conjugate gradient method or quasi-Newton iteration method [6]

Background

Experiment Results

Conclusions