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

2013 ◽  
Vol 6 (3) ◽  
pp. 161-166 ◽  
Author(s):  
Luo Jing-Yao ◽  
Chen Bao-De ◽  
Li Hong ◽  
Fan Guang-Zhou ◽  
Wang Xiao-Feng
2012 ◽  
Vol 27 (5) ◽  
pp. 1249-1263 ◽  
Author(s):  
Ling-Feng Hsiao ◽  
Der-Song Chen ◽  
Ying-Hwa Kuo ◽  
Yong-Run Guo ◽  
Tien-Chiang Yeh ◽  
...  

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.


2008 ◽  
Vol 136 (2) ◽  
pp. 541-559 ◽  
Author(s):  
Masahiro Kazumori ◽  
Quanhua Liu ◽  
Russ Treadon ◽  
John C. Derber

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.


2021 ◽  
pp. 1-6
Author(s):  
Hao Luo ◽  
Qinghua Yang ◽  
Longjiang Mu ◽  
Xiangshan Tian-Kunze ◽  
Lars Nerger ◽  
...  

Abstract To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.


Author(s):  
Magnus Lindskog ◽  
Adam Dybbroe ◽  
Roger Randriamampianina

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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