A preliminary evaluation of FY-4A visible radiance data assimilation by the WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) system for a tropical storm case
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Published:2022-10-05
Issue:19
Volume:15
Page:7397-7420
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ISSN:1991-9603
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Container-title:Geoscientific Model Development
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language:en
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Short-container-title:Geosci. Model Dev.
Author:
Zhou Yongbo,Liu Yubao,Huo Zhaoyang,Li Yang
Abstract
Abstract. Satellite visible radiance data that contain rich cloud
and precipitation information are increasingly assimilated to improve the
forecasts of numerical weather prediction models. This study evaluates the
Data Assimilation Research Testbed (DART, Manhattan release v9.8.0), coupled
with the Weather Research and Forecasting (WRF) model (ARW v4.1.1) and the
Radiative Transfer for TOVS (RTTOV, v12.3) package, for assimilating the
simulated visible imagery of the FY-4A geostationary satellite located over
Asia in an Observing System Simulation Experiment (OSSE) framework. The OSSE
was performed for the tropical storm Higos that occurred in 2020 and
contains multi-layer mixed-phase cloud and precipitation processes. The
advantages and limitations of DART for assimilating FY-4A visible imagery
were evaluated. Both single-observation experiments and cycled data assimilation (DA)
experiments were performed to study the impact of different filter
algorithms available in DART, variables being cycled, observation outlier
thresholds, observation errors, and observation thinning. The results show that assimilating visible radiance data significantly
improves the analysis of the cloud water path (CWP) and cloud coverage (CFC)
from first-guess forecasts. The rank histogram filter (RHF) allows WRF
to more accurately simulate CWP and CFC compared with the ensemble adjustment Kalman
filter (EAKF) although it took roughly twice as long as the latter. By cycling
both cloud and non-cloud variables, specifying large outlier threshold
values, or setting smaller observation errors without thinning of
observations, WRF achieved a better simulation of CWP and CFC. With model
integration, DA of the visible radiance data also generated a slightly
positive impact on non-cloud variables as they were adjusted through the
model dynamics and physics related to cloud processes. In addition, the DA
improved the representation of precipitation. However, the impact on the rain
rate is limited by the inabilities of the DA to improve cloud vertical
structures and cloud phases. A negative impact of the DA on cloud
variables was found due to the nature of the non-linear forward operator and
the non-Gaussian distribution of the prior. Future works should explore
faster and more accurate forward operators suitable for assimilating FY-4A
visible imagery, techniques to reduce the non-linear and non-Gaussian
errors, and methods to correct the location errors which correspond to the clouds underestimated by the first guess.
Funder
Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology Natural Science Foundation of Jiangsu Province National Key Research and Development Program of China
Publisher
Copernicus GmbH
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