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

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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Typhoon Track Forecast with a Hybrid GSI-ETKF Data Assimilation System

Atmospheric and Oceanic Science Letters ◽

10.1080/16742834.2013.11447074 ◽

2013 ◽

Vol 6 (3) ◽

pp. 161-166 ◽

Cited By ~ 1

Author(s):

Luo Jing-Yao ◽

Chen Bao-De ◽

Li Hong ◽

Fan Guang-Zhou ◽

Wang Xiao-Feng

Keyword(s):

Data Assimilation ◽

Track Forecast ◽

Typhoon Track ◽

Data Assimilation System ◽

Assimilation System

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Impact Study of AMSR-E Radiances in the NCEP Global Data Assimilation System

Monthly Weather Review ◽

10.1175/2007mwr2147.1 ◽

2008 ◽

Vol 136 (2) ◽

pp. 541-559 ◽

Cited By ~ 25

Author(s):

Masahiro Kazumori ◽

Quanhua Liu ◽

Russ Treadon ◽

John C. Derber

Keyword(s):

Data Assimilation ◽

Geopotential Height ◽

Track Forecast ◽

Significant Difference ◽

Emissivity Model ◽

Global Data Assimilation System ◽

The Impact ◽

Data Assimilation System ◽

Global Data ◽

Assimilation System

Abstract The impact of radiance observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) was investigated in the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS). The GDAS used NCEP’s Gridpoint Statistical Interpolation (GSI) analysis system and the operational NCEP global forecast model. To improve the performance of AMSR-E low-frequency channels, a new microwave ocean emissivity model and its adjoint with respect to the surface wind speed and temperature were developed and incorporated into the assimilation system. The most significant impacts of AMSR-E radiances on the analysis were an increase in temperature of about 0.2 K at 850 hPa at the higher latitudes and a decrease in humidity of about 0.1 g kg−1 at 850 hPa over the ocean when the new emissivity model was used. There was no significant difference in the mean 6-h rainfall in the assimilation cycle. The forecasts made from the assimilation that included the AMSR-E data showed small improvements in the anomaly correlation of geopotential height at 1000 and 500 hPa in the Southern Hemisphere and reductions in the root-mean-square error (RMSE) for 500-hPa geopotential height in the extratropics of both hemispheres. Use of the new emissivity model resulted in improved RMSE for temperature forecasts from 1000 to 100 hPa in the extratropics of both hemispheres. The assimilation of AMSR-E radiances data using the emissivity model improved the track forecast for Hurricane Katrina in the 26 August 2005 case, whereas the assimilation using the NCEP operational emissivity model, FAST Emissivity Model, version 1 (FASTEM-1), degraded it.

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Evaluating Forecast Impact of Assimilating Microwave Humidity Sounder (MHS) Radiances with a Regional Ensemble Kalman Filter Data Assimilation System

Weather and Forecasting ◽

10.1175/waf-d-14-00091.1 ◽

2015 ◽

Vol 30 (4) ◽

pp. 964-983 ◽

Cited By ~ 9

Author(s):

Kathryn M. Newman ◽

Craig S. Schwartz ◽

Zhiquan Liu ◽

Hui Shao ◽

Xiang-Yu Huang

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Specific Humidity ◽

Lead Times ◽

Limited Area ◽

Microwave Humidity Sounder ◽

The Impact ◽

Data Assimilation System ◽

Assimilation System

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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The Influence of Assimilating Dropsonde Data on Typhoon Track and Midlatitude Forecasts

Monthly Weather Review ◽

10.1175/2010mwr3377.1 ◽

2011 ◽

Vol 139 (3) ◽

pp. 908-920 ◽

Cited By ~ 65

Author(s):

Martin Weissmann ◽

Florian Harnisch ◽

Chun-Chieh Wu ◽

Po-Hsiung Lin ◽

Yoichiro Ohta ◽

...

Keyword(s):

Data Assimilation ◽

Three Dimensional ◽

Wrf Model ◽

Variational Data Assimilation ◽

Japan Meteorological Agency ◽

Limited Area ◽

Track Forecast ◽

Forecast Errors ◽

Typhoon Track ◽

Environmental Prediction

Abstract A unique dataset of targeted dropsonde observations was collected during The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) in the autumn of 2008. The campaign was supplemented by an enhancement of the operational Dropsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR) program. For the first time, up to four different aircraft were available for typhoon observations and over 1500 additional soundings were collected. This study investigates the influence of assimilating additional observations during the two major typhoon events of T-PARC on the typhoon track forecast by the global models of the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the National Centers for Environmental Prediction (NCEP), and the limited-area Weather Research and Forecasting (WRF) model. Additionally, the influence of T-PARC observations on ECMWF midlatitude forecasts is investigated. All models show an improving tendency of typhoon track forecasts, but the degree of improvement varied from about 20% to 40% in NCEP and WRF to a comparably low influence in ECMWF and JMA. This is likely related to lower track forecast errors without dropsondes in the latter two models, presumably caused by a more extensive use of satellite data and four-dimensional variational data assimilation (4D-Var) of ECMWF and JMA compared to three-dimensional variational data assimilation (3D-Var) of NCEP and WRF. The different behavior of the models emphasizes that the benefit gained strongly depends on the quality of the first-guess field and the assimilation system.

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Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

Advances in Atmospheric Sciences ◽

10.1007/s00376-021-0326-5 ◽

2021 ◽

Author(s):

Magnus Lindskog ◽

Adam Dybbroe ◽

Roger Randriamampianina

Keyword(s):

Data Assimilation ◽

Positive Impact ◽

Weather Prediction ◽

Implementation Process ◽

Satellite System ◽

Limited Area ◽

Global Navigation Satellite ◽

Microwave Radiances ◽

Data Assimilation System ◽

Assimilation System

AbstractMetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

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