Improving AHI Radiance Assimilation over Land with the Surface Skin Temperature Constrained by Station Observations and Its Impact for Quantitative Precipitation Forecasts

Author:

Li Xin1,Zou Xiaolei2,Zeng Mingjian1,Zhuge Xiaoyong1,Liu Weiguang1

Affiliation:

1. a Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Chinese Academy of Meteorological Sciences–Jiangsu Meteorological Service, Nanjing, China

2. b Center of Data Assimilation for Research and Application, Nanjing University of Information Science and Technology, Nanjing, China

Abstract

Abstract In this study, a new way to assimilate clear-sky Advanced Himawari Imager (AHI) surface-sensitive brightness temperature (TB) observations over land is investigated for improving quantitative precipitation forecasts (QPFs) in eastern China. To alleviate problems arising from inaccurate surface temperature in radiance simulations, surface station observations of land surface skin temperature (LSST) together with conventional and AMSU-A observations are assimilated to improve AHI surface-sensitive TB simulations of the Community Radiative Transfer Model (CRTM) before AHI data assimilation. First, the Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3DVar) system is updated with the additional control variable of surface temperature and its background error covariances. Second, surface temperature and emissivity sensitivity checks are designed for the quality control of the surface-sensitive AHI channels. Finally, the impacts of a two-time data assimilation strategy are assessed for a local convection rainfall case and a synoptic-scale precipitation case. The experiment in which AHI data are assimilated after assimilating LSST data (ExpL2) outperforms the traditional experiment in which the LSST is not updated (ExpL) in terms of its 24-h QPF skill score. A better description of atmospheric instability and moisture convergence forcing is obtained in ExpL2 than in ExpL. Both experiments show additional low-level temperature and humidity adjustments compared to the experiment that does not assimilate AHI surface-sensitive channels (ExpNL). Lower AHI TB simulation biases are found in the ExpL2 experiment, which improve the analyzed field and subsequent QPFs. The results in this study suggest the importance of proper utilization of LSST observations for AHI surface-sensitive TB assimilations over land.

Funder

National Key R&D Program of China

Publisher

American Meteorological Society

Subject

Atmospheric Science

Reference39 articles.

1. Adaptive bias correction for satellite data in a numerical weather prediction system;Auligné, T.,2007

2. Satellite cloud and precipitation assimilation at operational NWP centres;Bauer, P.,2011

3. An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites;Bessho, K.,2016

4. Borbas, E. E., and B. C. Ruston, 2010: The RTTOV UWiremis IR land surface emissivity module, version 1. NWP SAF Mission Rep. NWPSAF-MO-VS-042, 25 pp., https://nwpsaf.eu/vs_reports/nwpsaf-mo-vs-042.pdf.

5. Enhancing the impact of IASI observations through an updated observation-error covariance matrix;Bormann, N.,2016

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