Application of WRF 3DVAR to Operational Typhoon Prediction in Taiwan: Impact of Outer Loop and Partial Cycling Approaches

Hsiao, Ling-Feng; Chen, Der-Song; Kuo, Ying‐Hwa; Guo, Yong-run; Yeh, Tien-Chiang; Hong, Jimin; Fong, Chin–Tzu; Lee, Cheng-Shang

doi:10.1175/waf-d-11-00131.1

Cited by 88 publications

(44 citation statements)

References 33 publications

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“…Then, observations are assimilated in two forecast cycles (06:00, 12:00 UTC) and WRF is integrated from 12:00 UTC to produce 84 h forecasts for the five cyclones Laila, Jal, Thane, Phailin and Madi, 60 h forecasts for Khaimuk and Nilam and 108 h for Lehar. Recent studies (Kuni et al, 2010;Singh et al, 2012a;Hsiao et al, 2012) considered 12 h duration of assimilation as sufficient enough for the mesoscale model to warm up, i.e. develop its own small-scale features for cyclone prediction.…”

Section: Data and Model Initializationmentioning

confidence: 99%

“…It has been pointed out that the 24 h cycle cannot give much improvement compared to the 12 h cycle because of less satellite data coverage, which is the main source of data over the oceans. Hsiao et al (2012) considered a two-cycle 12 h assimilation duration as a warm-up period for reducing the model imbalance and derive better initial condition for cyclone predictions. A 6 h warm-up period is used at NCAR (http://www.dtcenter.…”

Section: Data and Model Initializationmentioning

confidence: 99%

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Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model

et al. 2015

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Abstract. The tropical cyclone (TC) track and intensity predictions over Bay of Bengal (BOB) using the Advanced Research Weather Research and Forecasting (ARW) model are evaluated for a number of data assimilation experiments using various types of data. Eight cyclones that made landfall along the east coast of India during 2008-2013 were simulated. Numerical experiments included a control run (CTL) using the National Centers for Environmental Prediction (NCEP) 3-hourly 0.5 × 0.5 • resolution Global Forecasting System (GFS) analysis as the initial condition, and a series of cycling mode variational assimilation experiments with Weather Research and Forecasting (WRF) data assimilation (WRFDA) system using NCEP global PrepBUFR observations (VARPREP), Atmospheric Motion Vectors (VARAMV), Advanced Microwave Sounding Unit (AMSU) A and B radiances (VARRAD) and a combination of PrepBUFR and RAD (VARPREP+RAD). The impact of different observations is investigated in detail in a case of the strongest TC, Phailin, for intensity, track and structure parameters, and finally also on a larger set of cyclones. The results show that the assimilation of AMSU radiances and Atmospheric Motion Vectors (AMV) improved the intensity and track predictions to a certain extent and the use of operationally available NCEP PrepBUFR data which contains both conventional and satellite observations produced larger impacts leading to improvements in track and intensity forecasts. The forecast improvements are found to be associated with changes in pressure, wind, temperature and humidity distributions in the initial conditions after data assimilation. The assimilation of mass (radiance) and wind (AMV) data showed different impacts. While the motion vectors mainly influenced the track predictions, the radiance data merely influenced forecast intensity. Of various experiments, the VARPREP produced the largest impact with mean errors (India Meteorological Department (IMD) observations less the model values) of 78, 129, 166, 210 km in the vector track position, 10.3, 5.8, 4.8, 9.0 hPa deeper than IMD data in central sea level pressure (CSLP) and 10.8, 3.9, −0.2, 2.3 m s −1 stronger than IMD data in maximum surface winds (MSW) for 24, 48, 72, 96 h forecasts respectively. An improvement of about 3-36 % in track, 6-63 % in CSLP, 26-103 % in MSW and 11-223 % in the radius of maximum winds in 24-96 h lead time forecasts are found with VARPREP over CTL, suggesting the advantages of assimilation of operationally available PrepBUFR data for cyclone predictions. The better predictions with PrepBUFR could be due to quality-controlled observations in addition to containing different types of data (conventional, satellite) covering an effectively larger area. The performance degradation of VARPREP+RAD with the assimilation of all available observations over the domain after 72 h could be due to poor area coverage and bias in the radiance data.

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Section: Data and Model Initializationmentioning

confidence: 99%

Section: Data and Model Initializationmentioning

confidence: 99%

Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model

et al. 2015

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show abstract

“…The WRFDA system has DA options such as three-dimensional variational data assimilation (3D-Var), 4D-Var, and hybrid variational-ensemble DA that permit assimilating a wide range of observations including in situ measurements, Doppler radar reflectivity, precipitation, and radiances (Barker et al 2012;Wang et al 2013). For example, the 3D-Var assimilation of conventional ground-based data and radiance observations has been used for improving precipitation forecasts at various spatial resolutions (Ha et al 2011;Ha and Lee 2012;Hsiao et al 2012;Liu et al 2012;Routray et al 2010;Schwartz et al 2012;Xu and Powell 2012).…”

Section: Introductionmentioning

confidence: 99%

Dynamical Precipitation Downscaling for Hydrologic Applications Using WRF 4D-Var Data Assimilation: Implications for GPM Era

Lin

Ebtehaj

Bras

et al. 2015

Journal of Hydrometeorology

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The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacingrelevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.

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“…The experiment began in 2010 and was the first attempt to design a high-resolution numerical ensemble weather model in Taiwan. The experiment collects worldwide observation data, including temperature, wind, surface pressure, and relative humidity, from satellites, atmospheric sounding devices, buoys, aviation routine weather reports, ships, and other available sources (e.g., Hsiao et al, 2012Hsiao et al, , 2013. TAPEX uses the outputs from the Global Forecast System (GFS) produced by the National Centers for Environment Prediction (NCEP), along with observation data, as the initial and boundary conditions for its forecasts.…”

Section: Ensemble Precipitation Forecasts For System Inputmentioning

confidence: 99%

Using rainfall thresholds and ensemble precipitation forecasts to issue and improve urban inundation alerts

Yang

Hwang

Tsai

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Urban inundation forecasting with extended lead times is useful in saving lives and property. This study proposes the integration of rainfall thresholds and ensemble precipitation forecasts to provide probabilistic urban inundation forecasts. Utilization of ensemble precipitation forecasts can extend forecast lead times to 72 h, predicting peak flows and to allow response agencies to take necessary preparatory measures. However, ensemble precipitation forecasting is time-and resource-intensive. Using rainfall thresholds to estimate urban areas' inundation risk can decrease this complexity and save computation time. This study evaluated the performance of this system using 352 townships in Taiwan and seven typhoons during the period 2013-2015. The levels of forecast probability needed to issue inundation alerts were addressed because ensemble forecasts are probability based. This study applied six levels of forecast probability and evaluated their performance using five measures. The results showed that this forecasting system performed better before a typhoon made landfall. Geography had a strong impact at the start of the numerical weather modeling, resulting in the underestimation of rainfall forecasts. Regardless of this finding, the inundation forecast performance was highly contingent on the rainfall forecast skill. This study then tested a hybrid approach of on-site observations and rainfall forecasts to decrease the influence of numerical weather predictions and improve the forecast performance. The results of this combined system showed that forecasts with a 24 h lead time improved significantly. These findings and the hybrid approach can be applied to other hydrometeorological early warning systems to improve hazard-related forecasts.

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Application of WRF 3DVAR to Operational Typhoon Prediction in Taiwan: Impact of Outer Loop and Partial Cycling Approaches

Cited by 88 publications

References 33 publications

Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model

Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model

Dynamical Precipitation Downscaling for Hydrologic Applications Using WRF 4D-Var Data Assimilation: Implications for GPM Era

Using rainfall thresholds and ensemble precipitation forecasts to issue and improve urban inundation alerts

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