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Min Chen
,
Xiang-Yu Huang
, and
Wei Wang

Abstract

An incremental analysis update (IAU) scheme is successfully implemented into a WRF/WRFDA-based hourly cycling data assimilation system with the goal to reduce the imbalance introduced by the high-frequency intermittent data assimilation, especially when radar data are included. With the application of IAU, the analysis increment is smoothly introduced into the model integration over a time window centered at the analysis time. As in digital filter initialization (DFI), the IAU scheme is able to limit large shocks in the early part of a model forecast. Compared to DFI, IAU does better in hydrometeor spinup and produces more continuous precipitation forecasts from cycle to cycle. The run with IAU is shown to improve the precipitation forecast skills (10+% for CSI scores) compared to the regular cycling forecasts without IAU. The data assimilation system with IAU is also able to accept more observations due to balanced first-guess fields. Comparable results are obtained in IAU tests when the time-varying weights are used versus constant weights. Because of its better property, the IAU with the time-varying weights is implemented in the operational system.

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James W. Wilson
,
Yerong Feng
,
Min Chen
, and
Rita D. Roberts

Abstract

The Beijing 2008 Forecast Demonstration Project (B08FDP) included a variety of nowcasting systems from China, Australia, Canada, and the United States. A goal of the B08FDP was to demonstrate state-of-the-art nowcasting systems within a mutual operational setting. The nowcasting systems were a mix of radar echo extrapolation methods, numerical models, techniques that blended numerical model and extrapolation methods, and systems incorporating forecaster input. This paper focuses on the skill of the nowcasting systems to forecast convective storms that threatened or affected the Summer Olympic Games held in Beijing, China. The topography surrounding Beijing provided unique challenges in that it often enhanced the degree and extent of storm initiation, growth, and dissipation, which took place over short time and space scales. The skill levels of the numerical techniques were inconsistent from hour to hour and day to day and it was speculated that without assimilation of real-time radar reflectivity and Doppler velocity fields to support model initialization, particularly for weakly forced convective events, it would be very difficult for models to provide accurate forecasts on the nowcasting time and space scales. Automated blending techniques tended to be no more skillful than extrapolation since they depended heavily on the models to provide storm initiation, growth, and dissipation. However, even with the cited limitations among individual nowcasting systems, the Chinese Olympic forecasters considered the B08FDP human consensus forecasts to be useful. Key to the success of the human forecasts was the development of nowcasting rules predicated on the character of Beijing convective weather realized over the previous two summers. Based on the B08FDP experience, the status of nowcasting convective storms and future directions are presented.

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Jen-Ping Chen
,
Tzu-Chin Tsai
,
Min-Duan Tzeng
,
Chi-Shuin Liao
,
Hung-Chi Kuo
, and
Jing-Shan Hong

Abstract

Microphysical perturbation experiments were conducted to investigate the sensitivity of convective heavy rain simulation to cloud microphysical parameterization and its feasibility for ensemble forecasts. An ensemble of 20 perturbation members differing in either the microphysics package or process treatments within a single scheme was applied to simulate 10 summer-afternoon heavy-rain convection cases. The simulations revealed substantial disagreements in the location and amplitude of peak rainfall among the microphysics-package and single-scheme members, with an overall spread of 57%–161%, 66%–161%, and 65%–149% of the observed average rainfall, maximum rainfall, and maximum intensity, respectively. The single-scheme members revealed that the simulation of heavy convective precipitation is quite sensitive to factors including ice-particle fall speed parameterization, aerosol type, ice particle shape, and size distribution representation. The microphysical ensemble can derive reasonable probability of occurrence for a location-specific heavy-rain forecast. Spatial-forecast performance indices up to 0.6 were attained by applying an optimal fuzzy radius of about 8 km for the warning-area coverage. The forecasts tend to be more successful for more organized convection. Spectral mapping methods were further applied to provide ensemble forecasts for the 10 heavy rainfall cases. For most cases, realistic spatial patterns were derived with spatial correlation up to 0.8. The quantitative performance in average rainfall, maximum rainfall, and maximum intensity from the ensembles reached correlations of 0.83, 0.84, and 0.51, respectively, with the observed values.

Significance Statement

Heavy rainfall from summer convections is stochastic in terms of intensity and location; therefore, an accurate deterministic forecast is often challenging. We designed perturbation experiments to explore weather forecasting models’ sensitivity to cloud microphysical parameterizations and the feasibility of application to ensemble forecast. Promising results were obtained from simulations of 10 real cases. The cloud microphysical ensemble approach may provide reasonable forecasts of heavy rainfall probability and convincing rainfall spatial distribution, particularly for more organized convection.

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