Preprints
https://doi.org/10.5194/gmd-2022-30
https://doi.org/10.5194/gmd-2022-30
Submitted as: model evaluation paper
14 Apr 2022
Submitted as: model evaluation paper | 14 Apr 2022
Status: this preprint is currently under review for the journal GMD.

A preliminary evaluation of WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) in assimilating satellite visible radiance data for a cyclone case

Yongbo Zhou1,2, Yubao Liu1,2, Zhaoyang Huo1,2, and Yang Li1,2 Yongbo Zhou et al.
  • 1School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
  • 2Precision Regional Earth Modeling and Information Center (PREMIC), Nanjing University of Information Science and Technology, Nanjing, China

Abstract. Satellite visible (VIS) radiance data contain rich cloud information that are increasingly assimilated for improving cloud and precipitation forecasting of numerical weather prediction models. Recently, the Data Assimilation Research Testbed (DART), a widely used data assimilation resource that supports the Weather Research and Forecasting (WRF) model, was facilitated with an interface for the Radiative Transfer for TOVS (RTTOV), which supports radiance assimilation from visible (VIS) to microwave wavelength channels. This study evaluates the WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) system for assimilating the radiance data of channel 2 (0.55~0.75 μm) of the Advanced Geostationary Radiation Imager (AGRI) onboard FY-4. Observing System Simulation Experiments (OSSEs) were performed for a cyclone case. The results indicate that assimilating VIS radiance data improves cloud forecast skills in general. Best results were achieved for the data assimilation (DA) experiment with dense observations and high updating frequency. The best results could capture the “eye” structure of the cyclone system and significantly improves cloud water path and cloud coverage simulations. Nevertheless, three main problems were revealed. The first is its inability to improve cloud vertical distribution such as layered structures and cloud phases; The second is the its inability to influence atmosphere thermodynamic state variables positively; The third is a waste of up to 50 % observations during the filtering processes.

Yongbo Zhou et al.

Status: open (until 26 Jun 2022)

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Yongbo Zhou et al.

Data sets

WRF-DART/RTTOV input files for manuscript submitted to GMD Zhou et al., 2022 Zhou, Y., Liu, Y., Huo, Z., and Li, Y. https://doi.org/10.5281/zenodo.6417682

Model code and software

DART source code Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A. https://dart.ucar.edu/

RTTOV source code Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C. https://nwp-saf.eumetsat.int/site/software/rttov/

WRF-ARW source code Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Wang, X.-Y., Wang, W., and Power, J. G http://www2.mmm.ucar.edu/wrf/users/

Yongbo Zhou et al.

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Short summary
The study evaluates the performance of the Data Assimilation Research Testbed (DART), equipped with the recently-added forward operator Radiative Transfer for TOVS (RTTOV), in assimilating FY-4 visible image into the Weather Research and Forecasting (WRF) model. The ability of the WRF-DART/RTTOV system to improve the forecasting skills of a cyclone case over East Asia and Western Pacific is demonstrated using Observing System Simulation Experiments (OSSEs).