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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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Craig S. Schwartz
,
Zhiquan Liu
,
Yongsheng Chen
, and
Xiang-Yu Huang

Abstract

Two parallel experiments were designed to evaluate whether assimilating microwave radiances with a cyclic, limited-area ensemble adjustment Kalman filter (EAKF) could improve track, intensity, and precipitation forecasts of Typhoon Morakot (2009). The experiments were configured identically, except that one assimilated microwave radiances and the other did not. Both experiments produced EAKF analyses every 6 h between 1800 UTC 3 August and 1200 UTC 9 August 2009, and the mean analyses initialized 72-h Weather Research and Forecasting model forecasts. Examination of individual forecasts and average error statistics revealed that assimilating microwave radiances ultimately resulted in better intensity forecasts compared to when radiances were withheld. However, radiance assimilation did not substantially impact track forecasts, and the impact on precipitation forecasts was mixed. Overall, net positive results suggest that assimilating microwave radiances with a limited-area EAKF system is beneficial for tropical cyclone prediction, but additional studies are needed.

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Kathryn M. Newman
,
Craig S. Schwartz
,
Zhiquan Liu
,
Hui Shao
, and
Xiang-Yu Huang

Abstract

This study examines the impact of assimilating Microwave Humidity Sounder (MHS) radiances in a limited-area ensemble Kalman filter (EnKF) data assimilation system. Two experiments spanning 11 August–13 September 2008 were run over a domain featuring the Atlantic basin using a 6-h full cycling analysis and forecast system. Deterministic 72-h forecasts were initialized at 0000 and 1200 UTC for a comparison of forecast impact. The two experiments were configured identically with the exception of the inclusion of the MHS radiances (AMHS) in the second to isolate the impacts of the MHS radiance data. The results were verified against several sources, and statistical significance tests indicate the most notable differences are in the midlevel moisture fields. Both configurations were characterized by high moisture biases when compared to the European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim, also known as ERA-I) specific humidity fields, as well as precipitable water vapor from an observationally based product. However, the AMHS experiment has midlevel moisture fields closer to the ERA-I and observation datasets. When reducing the verification domain to focus on the subtropical and easterly wave regions of the North Atlantic Ocean, larger improvements in midlevel moisture at nearly all lead times is seen in the AMHS simulation. Finally, when considering tropical cyclone forecasts, the AMHS configuration shows improvement in intensity forecasts at several lead times as well as improvements at early to intermediate lead times for minimum sea level pressure forecasts.

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Feng Gao
,
Xiaoyan Zhang
,
Neil A. Jacobs
,
Xiang-Yu Huang
,
Xin Zhang
, and
Peter P. Childs

Abstract

Tropospheric Airborne Meteorological Data Reporting (TAMDAR) observations are becoming a major data source for numerical weather prediction (NWP) because of the advantages of their high spatiotemporal resolution and humidity measurements. In this study, the estimation of TAMDAR observational errors, and the impacts of TAMDAR observations with new error statistics on short-term forecasts are presented. The observational errors are estimated by a three-way collocated statistical comparison. This method employs collocated meteorological reports from three data sources: TAMDAR, radiosondes, and the 6-h forecast from a Weather Research and Forecasting Model (WRF). The performance of TAMDAR observations with the new error statistics was then evaluated based on this model, and the WRF Data Assimilation (WRFDA) three-dimensional variational data assimilation (3DVAR) system. The analysis was conducted for both January and June of 2010. The experiments assimilate TAMDAR, as well as other conventional data with the exception of non-TAMDAR aircraft observations, every 6 h, and a 24-h forecast is produced. The standard deviation of the observational error of TAMDAR, which has relatively stable values regardless of season, is comparable to radiosondes for temperature, and slightly smaller than that of a radiosonde for relative humidity. The observational errors in wind direction significantly depend on wind speeds. In general, at low wind speeds, the error in TAMDAR is greater than that of radiosondes; however, the opposite is true for higher wind speeds. The impact of TAMDAR observations on both the 6- and 24-h WRF forecasts during the studied period is positive when using the default observational aircraft weather report (AIREP) error statistics. The new TAMDAR error statistics presented here bring additional improvement over the default error.

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Xiangming Sun
,
Xiang-Yu Huang
,
Chris Gordon
,
Marion Mittermaier
,
Rebecca Beckett
,
Wee Kiong Cheong
,
Dale Barker
,
Rachel North
, and
Allison Semple

Abstract

Sumatra squalls are important rain-bearing weather systems that affect Singapore and southern Peninsular Malaysia. The performance of forecasts for 63 past squall events is evaluated using a subjective evaluation by forecasters and an objective evaluation based on the fractions skill score (FSS). The purpose of this study is to investigate whether an objective procedure can reproduce the main results of the subjective evaluation. A convection permitting version of the Met Office (UKMO) Unified Model (UM), configured for a limited domain in the southern region of the South China Sea, is used with two driving global deterministic models: the UM and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. Subjective and objective evaluation scoring methods for the two limited-area forecasts of the UM are compared, and it is shown that the objective procedure can reasonably emulate the scores produced by the forecasters in the context of parameters that are of direct relevance to the forecast process. This indicates that automated objective verification methods may be a reasonable alternative to resource intensive subjective evaluations for some cases. The robustness of the objective results is investigated using 7 months of data, and issues of statistical significance are considered.

Open access
Ling-Feng Hsiao
,
Xiang-Yu Huang
,
Ying-Hwa Kuo
,
Der-Song Chen
,
Hongli Wang
,
Chin-Cheng Tsai
,
Tien-Chiang Yeh
,
Jing-Shan Hong
,
Chin-Tzu Fong
, and
Cheng-Shang Lee

Abstract

A blending method to merge the NCEP global analysis with the regional analysis from the WRF variational data assimilation system is implemented using a spatial filter for the purpose of initializing the Typhoon WRF (TWRF) Model, which has been in operation at Taiwan’s Central Weather Bureau (CWB) since 2010. The blended analysis is weighted toward the NCEP global analysis for scales greater than the cutoff length of 1200 km, and is weighted toward the WRF regional analysis for length below that. TWRF forecast experiments on 19 typhoons from July to October 2013 over the western North Pacific Ocean show that the large-scale analysis from NCEP GFS is superior to that of the regional analysis, which significantly improves the typhoon track forecasts. On the other hand, the regional WRF analysis provides a well-developed typhoon structure and more accurately captures the influence of the Taiwan topography on the typhoon circulation. As a result, the blended analysis takes advantage of the large-scale analysis from the NCEP global analysis and the detailed mesoscale analysis from the regional WRF analysis. In additional to the improved track forecast, the blended analysis also provides more accurate rainfall forecasts for typhoons affecting Taiwan. Because of the improved performance, the blending method has been implemented in the CWB operational TWRF typhoon prediction system.

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