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Jing-Shan Hong

Abstract

During the 2001 Green Island Mesoscale Experiment (GIMEX), the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) was run at a horizontal resolution of 5 km twice a day and forced by initial and boundary conditions from the operational models of the Central Weather Bureau of Taiwan. The purpose of the paper is to evaluate the performance of the surface forecasts of the high-resolution numerical model and to verify quantitative precipitation forecasts (QPFs) over Taiwan Island within the 2-month period.

The major errors in the forecasts are surface warm and dry biases. The model also tends to predict a stronger surface wind speed and an inland wind component, which suggest that the model overpredicted the sea breeze, a result that is consistent with the surface warm bias. The underprediction of the precipitation and poor skill scores are possibly due to the inadequate description of the humidity in the initial condition, and a spinup problem due to the steep Central Mountain Range.

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Jing-Shan Hong, Chin-Tzu Fong, Ling-Feng Hsiao, Yi-Chiang Yu, and Chian-You Tzeng

Abstract

In this study, an ensemble typhoon quantitative precipitation forecast (ETQPF) model was developed to provide typhoon rainfall forecasts for Taiwan. The ETQPF rainfall forecast is obtained by averaging the pick-out cases, which are screened using certain criterion based on given typhoon tracks from an ensemble prediction system (EPS). Therefore, the ETQPF model resembles a climatology model. However, the ETQPF model uses the quantitative precipitation forecasts (QPFs) from an EPS instead of historical rainfall observations. Two typhoon cases, Fanapi (2010) and Megi (2010), are used to evaluate the ETQPF model performance. The results show that the rainfall forecast from the ETQPF model, which is qualitatively compared and quantitatively verified, provides reasonable typhoon rainfall forecasts and is valuable for real-time operational applications. By applying the forecast track to the ETQPF model, better track forecasts lead to better ETQPF rainfall forecasts. Moreover, the ETQPF model provides the “scenario” of the typhoon QPFs according to the uncertainty of the forecast tracks. Such a scenario analysis can provide valuable information for risk assessment and decision making in disaster prevention and reduction. Deficiencies of the ETQPF model are also presented, including that the average over the pick-out case usually offsets the extremes and reduces the maximum ETQPF rainfall, the underprediction is especially noticeable for weak phase-locked rainfall systems, and the ETQPF rainfall error is related to the model bias. Therefore, reducing model bias is an important issue in further improving the ETQPF model performance.

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I-Han Chen, Jing-Shan Hong, Ya-Ting Tsai, and Chin-Tzu Fong

Abstract

Recently, the Central Weather Bureau of Taiwan developed a WRF- and WRF data assimilation (WRFDA)-based convective-scale data assimilation system to increase model predictability toward high-impact weather. In this study, we focus on afternoon thunderstorm (AT) prediction and investigate the following questions: 1) Is the designation of a rapid update cycle strategy with a blending scheme effective? 2) Can surface data assimilation contribute positively to AT prediction under the complex geography of Taiwan island? 3) What is the relative importance between radar and surface observation to AT prediction? 4) Can we increase the AT forecast lead time in the morning through data assimilation? Consecutive ATs from 30 June to 8 July 2017 are investigated. Five experiments, each having 240 continuous cycles, are designed. Results show that employing continuous cycles with a blending scheme mitigates model spinup compared with downscaled forecasts. Although there are few radar echoes before AT initiation, assimilating radar observations is still crucial since it largely corrects model errors in cycles. However, assimilating surface observations is more important compared with radar in terms of extending forecast lead time in the morning. Either radar or surface observations contribute positively, and assimilating both has the highest QPF score. Assimilating surface observations systematically improves surface wind and temperature predictions based on 240 cases. A case study demonstrates that the model can capture the AT initiation and development by assimilating surface and radar observations. Its cold pool and outflow boundary prediction are also improved. In this case, the assimilation of surface wind and water vapor in the morning contributes more compared with temperature and pressure.

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Juanzhen Sun, Ying Zhang, Junmei Ban, Jing-Shan Hong, and Chung-Yi Lin

Abstract

Radar and surface rainfall observations are two sources of operational data crucial for heavy rainfall prediction. Their individual values on improving convective forecasting through data assimilation have been examined in the past using convection-permitting numerical models. However, the benefit of their simultaneous assimilations has not yet been evaluated. The objective of this study is to demonstrate that, using a 4D-Var data assimilation system with a microphysical scheme, these two data sources can be assimilated simultaneously and the combined assimilation of radar data and estimated rainfall data from radar reflectivity and surface network can lead to improved short-term heavy rainfall prediction. In our study, a combined data assimilation experiment is compared with a rainfall-only and a radar-only (with or without reflectivity) experiments for a heavy rainfall event occurring in Taiwan during the passage of a mei-yu system. These experiments are conducted by applying the Weather Research and Forecasting (WRF) 4D-Var data assimilation system with a 20-min time window aiming to improve 6-h convective heavy rainfall prediction. Our results indicate that the rainfall data assimilation contributes significantly to the analyses of humidity and temperature whereas the radar data assimilation plays a crucial role in wind analysis, and further, combining the two data sources results in reasonable analyses of all three fields by eliminating large, unphysical analysis increments from the experiments of assimilating individual data only. The results also show that the combined assimilation improves forecasts of heavy rainfall location and intensity of 6-h accumulated rainfall for the case studied.

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Ling-Feng Hsiao, Der-Song Chen, Ying-Hwa Kuo, Yong-Run Guo, Tien-Chiang Yeh, Jing-Shan Hong, Chin-Tzu Fong, and Cheng-Shang Lee

Abstract

In this paper, the impact of outer loop and partial cycling with the Weather Research and Forecasting Model’s (WRF) three-dimensional variational data assimilation system (3DVAR) is evaluated by analyzing 78 forecasts for three typhoons during 2008 for which Taiwan’s Central Weather Bureau (CWB) issued typhoon warnings, including Sinlaku, Hagupit, and Jangmi. The use of both the outer loop and the partial cycling approaches in WRF 3DVAR are found to reduce typhoon track forecast errors by more than 30%, averaged over a 72-h period. The improvement due to the outer loop approach, which can be more than 42%, was particularly significant in the early phase of the forecast. The use of the outer loop allows more observations to be assimilated and produces more accurate analyses. The assimilation of additional nonlinear GPS radio occultation (RO) observations over the western North Pacific Ocean, where traditional observational data are lacking, is particularly useful. With the lack of observations over the tropical and subtropical oceans, the error in the first-guess field (which is based on a 6-h forecast of the previous cycle) will continue to grow in a full-cycling limited-area data assimilation system. Even though the use of partial cycling only shows a slight improvement in typhoon track forecast after 12 h, it has the benefit of suppressing the growth of the systematic model error. A typhoon prediction model using the Advanced Research core of the WRF (WRF-ARW) and the WRF 3DVAR system with outer loop and partial cycling substantially improves the typhoon track forecast. This system, known as Typhoon WRF (TWRF), has been in use by CWB since 2010 for operational typhoon predictions.

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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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Guo-Yuan Lien, Chung-Han Lin, Zih-Mao Huang, Wen-Hsin Teng, Jen-Her Chen, Ching-Chieh Lin, Hsu-Hui Ho, Jyun-Ying Huang, Jing-Shan Hong, Chia-Ping Cheng, and Ching-Yuang Huang

Abstract

The FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) Radio Occultation (RO) satellite constellation was launched on June 2019 as a successor of the FORMOSAT-3/COSMIC mission. The Central Weather Bureau (CWB) of Taiwan has received FORMOSAT-7/COSMIC-2 GNSS RO data in real time from Taiwan Analysis Center for COSMIC. With the global numerical prediction system at CWB, a parallel semi-operational experiment assimilating the FORMOSAT-7/COSMIC-2 bending angle data with all other operational observation data has been conducted to evaluate the impact of the FORMOSAT-7/COSMIC-2 data. The first seven-month results show that the quality of the early FORMOSAT-7/COSMIC-2 data has been satisfactory for assimilation. Consistent and significant positive impacts on global forecast skills have been observed since the start of the parallel experiment, with the most significant impact found in the tropical region, reflecting the low-inclination orbital design of the satellites. The impact of the FORMOSAT-7/COSMIC-2 RO data is also estimated using the Ensemble Forecast Sensitivity to Observation Impact (EFSOI) method, showing an average positive impact per observation similar to other existing GNSS RO datasets, while the total impact is impressive by virtue of its large amount. Sensitivity experiments suggest that the quality control processes built in the Gridpoint Statistical Interpolation (GSI) system for RO data work well to achieve a positive impact by the low-level FORMOSAT-7/COSMIC-2 RO data, while more effort on observation error tuning should be focused to obtain an optimal assimilation performance. This study demonstrates the usefulness of the FORMOSAT-7/COSMIC-2 RO data in global numerical weather prediction during the calibration/validation period and leads to the operational use of the data at CWB.

Open access