Assimilation of Oceansat-2-Scatterometer-Derived Surface Winds in the Weather Research and Forecasting Model

Singh, Randhir; Kumar, Prashant; Pal, P. K.

doi:10.1109/tgrs.2011.2164410

Cited by 55 publications

(40 citation statements)

References 18 publications

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“…The impact of data assimilation on simulation of TCs over the NIO has been studied by several authors in the last decade (e.g. Singh et al, 2008Singh et al, , 2011Singh et al, , 2012aXiao et al, 2009;Krishna et al, 2010Krishna et al, , 2012Srinivas et al, 2010;Prashant et al, 2012;among others). However, most of these were individual case studies using various model configurations and with significant differences in the results due to the use of various data sources (satellite, radar, conventional, etc.)…”

Section: M Greeshma Et Al: Impact Of Local Data Assimilation On mentioning

confidence: 99%

“…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%

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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: M Greeshma Et Al: Impact Of Local Data Assimilation On mentioning

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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“…The rapid radiative transfer model [Mlawer et al, 1997] and Dudhia scheme [Dudhia, 1989] are used for longwave and shortwave radiation, respectively. Various sensitivity studies [Singh et al, 2012;Kumar et al, 2012] are performed to evaluate the performance of the above selected physics options over the Indian region.…”

Section: Wrf Modelmentioning

confidence: 99%

Impact of satellite rainfall assimilation on Weather Research and Forecasting model predictions over the Indian region

2014

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Rainfall is probably the most important parameter that is predicted by numerical weather prediction models, though the skill of rainfall prediction is the poorest compared to other parameters, e.g., temperature and humidity. In this study, the impact of rainfall assimilation on mesoscale model forecasts is evaluated during Indian summer monsoon 2011. The Weather Research and Forecasting (WRF) model and its four-dimensional variational data assimilation system are used to assimilate the Tropical Rainfall Measuring Mission 3B42 and Japan Aerospace Exploration Agency Global Satellite Mapping of Precipitation retrieved rainfall. A total of five experiments are performed daily with and without assimilation of rainfall data during the entire month of July 2011. Separate assimilation experiments are performed to assess the sensitivity of WRF model forecast with strict and less strict quality control. Assimilation of rainfall improves the forecast of temperature, specific humidity, and wind speed. Domain average improvement parameter of rainfall forecast is also improved over the Indian landmass when compared with NOAA Climate Prediction Center Morphing technique and Indian Meteorological Department gridded rainfall.

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“…At high wind speed, the retrieval model functions are limited by the relationship between the wind speed and the backscattered power [55]. For wind direction, the joint histograms (see Figures 3,5,7,9 and 11) show that there are some clusters around 180˝off, which is due to an imperfect ambiguity selection process. Regarding the wind direction at low wind speeds, H2SCAT (similar to QuikSCAT) showed a decentralized distribution in the residual analysis, which is attributed to the incompleteness of the ambiguity removal algorithm using the median filter technique [34], together with errors in the geophysical model at low wind speeds [13].…”

Section: Discussionmentioning

confidence: 97%

Evaluation of HY-2A Scatterometer Wind Vectors Using Data from Buoys, ERA-Interim and ASCAT during 2012–2014

et al. 2016

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Abstract:The first Chinese operational Ku-band scatterometer on board Haiyang-2A (HY-2A), launched in August 2011, is designed for monitoring the global ocean surface wind. This study estimates the quality of the near-real-time (NRT) retrieval wind speed and wind direction from the HY-2A scatterometer for 36 months from 2012 to 2014. We employed three types of sea-surface wind data from oceanic moored buoys operated by the National Data Buoy Center (NDBC) and the Tropical Atmospheric Ocean project (TAO), the European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis data (ERA-Interim), and the advanced scatterometer (ASCAT) to calculate the error statistics including mean bias, root mean square error (RMSE), and standard deviation. In addition, the rain effects on the retrieval winds were investigated using collocated Climate Prediction Center morphing method (CMORPH) precipitation data. All data were collocated with the HY-2A scatterometer wind data for comparison. The quality performances of the HY-2A NRT wind vectors data (especially the wind speeds) were satisfactory throughout the service period. The RMSEs of the HY-2A wind speeds relative to the NDBC, TAO, ERA-Interim, and ASCAT data were 1.94, 1.73, 2.25, and 1.62 m¨s´1, respectively. The corresponding RMSEs of the wind direction were 46.63˝, 43.11˝, 39.93˝, and 47.47˝, respectively. The HY-2A scatterometer overestimated low wind speeds, especially under rainy conditions. Rain exerted a diminishing effect on the wind speed retrievals with increasing wind speed, but its effect on wind direction was robust at low and moderate wind speeds. Relative to the TAO buoy data, the RMSEs without rain effect were reduced to 1.2 m¨s´1 and 39.68˝for the wind speed direction, respectively, regardless of wind speed. By investigating the objective laws between rain and the retrieval winds from HY-2A, we could improve the quality of wind retrievals through future studies.

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Assimilation of Oceansat-2-Scatterometer-Derived Surface Winds in the Weather Research and Forecasting Model

Cited by 55 publications

References 18 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

Impact of satellite rainfall assimilation on Weather Research and Forecasting model predictions over the Indian region

Evaluation of HY-2A Scatterometer Wind Vectors Using Data from Buoys, ERA-Interim and ASCAT during 2012–2014

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