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Downloaded 10/03/21 11:32 PM UTC DECEMBER 2009 R a K E S H E T a L 1706 WEATHER AND FORECASTING VOLUME 24 Impacts of Satellite-Observed Winds and Total Precipitable Water on WRF Short-Range Forecasts over the Indian Region during the 2006 Summer Monsoon V. RAKESH CSIR Center for Mathematical Modeling and Computer Simulation, NAL Belur Campus, Bangalore, India RANDHIR SINGH,P.K.PAL, AND P. C. JOSHI Atmospheric Sciences Division, Meteorology and Oceanography Group, Space Applications Centre (ISRO), Ahmedabad, India (Manuscript received 8 December 2008, in final form 1 July 2009) ABSTRACT Assimilation experiments have been performed with the Weather Research and Forecasting (WRF) model’s three-dimensional variational data assimilation (3DVAR) scheme to assess the impacts of NASA’s Quick Scatterometer (QuikSCAT) near-surface winds, and Special Sensor Microwave Imager (SSM/I) wind speed and total precipitable water (TPW) on the analysis and on short-range forecasts over the Indian region. The control (without satellite data) as well as WRF 3DVAR sensitivity runs (which assimilated satellite data) were made for 48 h starting daily at 0000 UTC during July 2006. The impacts of assimilating the different satellite dataset were measured in comparison to the control run, which does not assimilate any satellite data. The spatial distribution of the forecast impacts (FIs) for wind, temperature, and humidity from 1-month assimilation ex- periments for July 2006 demonstrated that on an average, for 24- and 48-h forecasts, the satellite data provided useful information. Among the experiments, WRF wind speed prediction was improved by QuikSCAT surface wind and SSM/I TPW assimilation, while temperature and humidity prediction was improved due to the as- similation of SSM/I TPW. The rainfall prediction has also been improved significantly due to the assimilation of SSM/I TPW, with the largest improvement seen over the west coast of India. Through an improvement of the surface wind field, the QuikSCAT data also yielded a positive impact on the precipitation, particularly for day 1 forecasts. In contrast, the assimilation of SSM/I wind speed degraded the humidity and rainfall predictions. 1. Introduction been the focus of many modeling studies due to its anomalous characteristics in the tropical circulation Socioeconomic aspects of life in India are highly de- (Hahn and Manabe 1975; Fennessy et al. 1994; Ashok pendent on both the intensity and distribution of sum- et al. 1998; Chandrasekhar et al. 1999; Eitzen and Randall mer monsoon rainfall. Therefore, providing accurate 1999; Das et al. 2002; Ratnam and Kumar 2005). The weather forecasts using numerical weather prediction numerical weather forecasts exhibit uncertainties, which (NWP) models during the monsoon season is of primary can be due to errors in the initial conditions, the repre- importance within the scientific community. In recent sentation of physical processes, or the computational years, most of the meteorological agencies and re- precision used in the model. Even though progress has searchers have depended on guidance from NWP models been made in terms of computational speed, observation in issuing rainfall forecasts 1–2 days in advance. The networks, NWP techniques, and physical parameteri- simulation of the Indian summer monsoon (ISM) has zations, weather forecasts on the regional scale have not yet reached the required accuracy (Kalnay 2003). Understanding the errors in NWP models can only Corresponding author address: Rakesh V., CSIR Center for Mathematical Modeling and Computer Simulation, NAL Belur be achieved by extensive verification of these models Campus, Bangalore 560037, India. for various synoptic conditions and by conducting im- E-mail: [email protected] pact studies using better initial conditions (through DOI: 10.1175/2009WAF2222242.1 Ó 2009 American Meteorological Society Unauthenticated | Downloaded 10/03/21 11:32 PM UTC DECEMBER 2009 R A K E S H E T A L . 1707 data assimilation). Rakesh et al. (2007, 2009, manu- number. In light of this, we used the WRF for short- script submitted to Meteor. Appl.)evaluatedthepre- range forecast applications during the 2006 monsoon cipitation skill of the widely used fifth-generation over the Indian region for the satellite data impact study. Pennsylvania State University–National Center for At- The benefits to the modeling community of the present mospheric Research (PSU–NCAR) Mesoscale Model study, as compared to case studies, are due to the large (MM5) over the Indian region and found that the skill numbers of forecasts generated during this experiment, of the precipitation forecasts is still not satisfactory. They which make statistical evaluation an appropriate tool for also suggested the need for improvements in precipitation identifying model discrepancies. These results provide forecasts through the choice of better physics options by helpful information for further developments in nu- sensitivity studies and more accurate initial conditions by merical models. One of the objectives of the present the assimilation of observations. study is to explore the short-range forecast skill of the A continuing difficulty with respect to the improve- WRF model over the Indian region. The work described ments in forecasts at smaller spatial scales by mesoscale in this paper is also relevant in quantifying the potential models relates to the fact that observational in- impacts of QuikSCAT and SSM/I observations on the formation is limited and inaccurate, especially in data- WRF short-range forecasts. The paper is structured as sparse areas such as large oceans and deserts. Data follows: section 2 describes the satellite data assimilated assimilation has been recognized as a useful way to in this study; descriptions of the WRF model, the as- obtain better ‘‘consistent’’ initial conditions for NWP similation methodology, and the design of the numerical (Kalnay 2003). Recent improvements in remote sens- experiments are given in section 3; the initialization and ing technology make it possible to observe the atmo- simulation results of the study are shown in section 4; sphere in areas where conventional observations are section 5 discusses the sensitivity of assimilation results sparse. A number of case studies (Zou and Xiao 2000; to the cumulus parameterization; and the paper is Pu et al. 2002; Harasti et al. 2004; Chen 2007; Zhang summarized in section 6. et al. 2007; Singh et al. 2008a) have shown that remote sensing of data over the oceans can improve tropical 2. Data used for assimilation cyclone initialization and prediction. Similarly, other a. QuikSCAT surface winds studies (Fan and Tilley 2005; Chou et al. 2006; Powers 2007; Singh et al. 2008b; Rakesh et al. 2009) have shown Launched in June 1999, the QuikSCAT orbits the earth that satellite data assimilation improved the forecasted at an altitude of 800 km once every 101 min (Shirtliffe meteorological features associated with various weather 1999). Having a swath width of approximately 1800 km, systems. A major drawback of such case studies is the the SeaWinds instrument aboard the QuikSCAT satellite limited number of forecast samples and the statistics operates at the 13.4-GHz Ku band. The accuracy of the resulting from them may not be robust enough to reach measured ocean surface wind reaches 2 m s21 in speed a firm conclusion. Zapotocny et al. (2007) studied the and 208 in direction for winds of 3–20 m s21 and 10% for impacts of various satellite and in situ data in the Na- winds of 20–30 m s21 (Shirtliffe 1999). Information from tional Centers for Environmental Prediction (NCEP) independent data sources (e.g., numerical models) is Global Data Assimilation System (GDAS) for two dif- needed to remove the ambiguity in the direction deter- ferent seasons. Their results showed a positive impact mination. While QuikSCAT observations can be con- from both conventional in situ and remotely sensed taminated by a rainy atmosphere (Weissman et al. 2002; satellite data during both seasons in both hemispheres. Sharp et al. 2002; Pasch et al. 2003; Leidner et al. 2003; The present study used the maturing Weather Research Hoffman and Leidner 2005), recent assimilation studies and Forecasting (WRF; Skamarock et al. 2005) model, (Atlas et al. 2001; Leidner et al. 2003; Goerss and Hogan which is the successor to MM5, to investigate the potential 2006; Zhang et al. 2007; Chen 2007; Zapotocny et al. impacts of the National Aeronautics and Space Admin- 2007; Singh et al. 2008a) have shown that QuikSCAT istration’s (NASA’s) Quick Scatterometer (QuikSCAT) data have a positive impact on the analysis and pre- surface winds, Special Sensor Microwave Imager (SSM/I) diction of weather systems. The utility of QuikSCAT wind speed, and total precipitable water (TPW) on short- winds in extratropical cyclone forecasting and marine range forecasts. The WRF model was used due to its weather prediction at the National Oceanographic and vastly growing popularity within the mesoscale model- Atmospheric Administration’s (NOAA) Ocean Pre- ing community and the fact that it is in the develop- diction Center (OPC) is documented by Von Ahn et al. mental stage. The developmental applications of WRF (2006), while Chelton et al. (2006) and Atlas et al. (2001) have primarily been in the midlatitudes, and to date the described the utility of scatterometer winds in gen- studies over tropical regions are comparatively less in eral marine weather forecasting applications. The use of Unauthenticated | Downloaded 10/03/21 11:32 PM UTC 1708 WEATHER AND FORECASTING VOLUME 24 QuikSCAT data in tropical cyclone analysis and fore- casting at the National Hurricane Center (NHC) is de- scribed by Brennan et al. (2009). b. SSM/I wind and TPW The SSM/I (Hollinger 1989) is a conical scanning, four-frequency, linearly polarized, seven-channel pas- sive microwave radiometer, which has operated on board Defense Meteorological Satellite Program (DMSP) sat- ellites since June 1987. This polar-orbiting satellite has a period of approximately 102 min, a near-constant in- cidence angle of 538, a mean altitude of approximately 830 km, and a swath width of about 1400 km.
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