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  • Author or Editor: Xiang-Yu Huang x
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Hongli Wang
,
Juanzhen Sun
,
Shuiyong Fan
, and
Xiang-Yu Huang

Abstract

An indirect radar reflectivity assimilation scheme has been developed within the Weather Research and Forecasting model three-dimensional data assimilation system (WRF 3D-Var). This scheme, instead of assimilating radar reflectivity directly, assimilates retrieved rainwater and estimated in-cloud water vapor. An analysis is provided to show that the assimilation of the retrieved rainwater avoids the linearization error of the Zqr (reflectivity–rainwater) equation. A new observation operator is introduced to assimilate the estimated in-cloud water vapor. The performance of the scheme is demonstrated by assimilating reflectivity observations into the Rapid Update Cycle data assimilation and forecast system operating at Beijing Meteorology Bureau. Four heavy-rain-producing convective cases that occurred during summer 2009 in Beijing, China, are studied using the newly developed system. Results show that on average the assimilation of reflectivity significantly improves the short-term precipitation forecast skill up to 7 h. A diagnosis of the analysis fields of one case shows that the assimilation of reflectivity increases humidity, rainwater, and convective available potential energy in the convective region. As a result, the analysis successfully promotes the developments of the convective system and thus improves the subsequent prediction of the location and intensity of precipitation for this case.

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Hongli Wang
,
Xiang-Yu Huang
,
Juanzhen Sun
,
Dongmei Xu
,
Man Zhang
,
Shuiyong Fan
, and
Jiqin Zhong

Abstract

Background error modeling plays a key role in a variational data assimilation system. The National Meteorological Center (NMC) method has been widely used in variational data assimilation systems to generate a forecast error ensemble from which the climatological background error covariance can be modeled. In this paper, the characteristics of the background error modeling via the NMC method are investigated for the variational data assimilation system of the Weather Research and Forecasting (WRF-Var) Model. The background error statistics are extracted from short-term 3-km-resolution forecasts in June, July, and August 2012 over a limited-area domain. It is found 1) that background error variances vary from month to month and also have a feature of diurnal variations in the low-level atmosphere and 2) that u- and υ-wind variances are underestimated and their autocorrelation length scales are overestimated when the default control variable option in WRF-Var is used. A new approach of control variable transform (CVT) is proposed to model the background error statistics based on the NMC method. The new approach is capable of extracting inhomogeneous and anisotropic climatological information from the forecast error ensemble obtained via the NMC method. Single observation assimilation experiments show that the proposed method not only has the merit of incorporating geographically dependent covariance information, but also is able to produce a multivariate analysis. The results from the data assimilaton and forecast study of a real convective case show that the use of the new CVT improves synoptic weather system and precipitation forecasts for up to 12 h.

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Yaodeng Chen
,
Hongli Wang
,
Jinzhong Min
,
Xiang-Yu Huang
,
Patrick Minnis
,
Ruizhi Zhang
,
Julie Haggerty
, and
Rabindra Palikonda

Abstract

Analysis of the cloud components in numerical weather prediction models using advanced data assimilation techniques has been a prime topic in recent years. In this research, the variational data assimilation (DA) system for the Weather Research and Forecasting (WRF) Model (WRFDA) is further developed to assimilate satellite cloud products that will produce the cloud liquid water and ice water analysis. Observation operators for the cloud liquid water path and cloud ice water path are developed and incorporated into the WRFDA system. The updated system is tested by assimilating cloud liquid water path and cloud ice water path observations from Global Geostationary Gridded Cloud Products at NASA. To assess the impact of cloud liquid/ice water path data assimilation on short-term regional numerical weather prediction (NWP), 3-hourly cycling data assimilation and forecast experiments with and without the use of the cloud liquid/ice water paths are conducted. It is shown that assimilating cloud liquid/ice water paths increases the accuracy of temperature, humidity, and wind analyses at model levels between 300 and 150 hPa after 5 cycles (15 h). It is also shown that assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300 and 150 hPa. The precipitation forecast skills are improved as well. One reason that leads to the improved analysis and forecast is that the 3-hourly rapid update cycle carries over the impact of cloud information from the previous cycles spun up by the WRF Model.

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