460 WEATHER AND FORECASTING VOLUME 23 The Impact of Variational Assimilation of SSM/I and QuikSCAT Satellite Observations on the Numerical Simulation of Indian Ocean Tropical Cyclones RANDHIR SINGH,P.K.PAL,C.M.KISHTAWAL, AND P. C. JOSHI Atmospheric Sciences Division, Meteorology and Oceanography Group, Space Applications Centre (ISRO), Ahmedabad, India (Manuscript received 13 February 2007, in final form 12 June 2007) ABSTRACT In this study, the fifth-generation Pennsylvania State University–National Center for Atmospheric Re- search Mesoscale Model (MM5) with three-dimensional variational data assimilation (3DVAR) is utilized to investigate the influence of Special Sensor Microwave Imager (SSM/I) and Quick Scatterometer (Quik- SCAT) observations on the prediction of an Indian Ocean tropical cyclone. The 3DVAR sensitivity runs were conducted separately with QuikSCAT wind vectors, SSM/I wind speeds, and total precipitable water (TPW) to investigate their individual impact on cyclone intensity and track. The Orissa supercyclone over the Bay of Bengal during October 1999 was used for simulation and assimilation experiments. Assimilation of the QuikSCAT wind vector improves the initial position of the cyclone’s center with a position error of 33 km, which was 163 km in the background analysis. Incorporation of QuikSCAT winds was found to increase the air–sea heat fluxes over the cyclonic region, which resulted in the improved simulated intensity when compared with the simulation made without QuikSCAT winds in the initial conditions. The cyclone track improved significantly with assimilation of QuikSCAT wind vectors. The track improvement resulted from relocation of the initial cyclonic vortex after assimilation of QuikSCAT wind vectors. Like QuikSCAT, assimilation of SSM/I wind speeds strengthened the cyclonic circulation in the initial conditions. This increase in the low-level wind speeds enhanced the air–sea exchange processes and im- proved the simulated intensity of the cyclone. The lack of information about the wind direction from SSM/I prevented it from making much of an impact on track prediction. As compared to the first guess, assimi- lation of the SSM/I TPW shows a moistening of the lower troposphere over most of the Bay of Bengal except over the central region of the cyclone, where the assimilation of SSM/I TPW reduces the lower- tropospheric moisture. This decrease of moisture in the TPW assimilation experiment resulted in a weak cyclone intensity. 1. Introduction ing data from satellites is crucial for improving hurri- cane initial analyses and forecasts (Zou and Xiao 2000; The tropical cyclone is one of the greatest natural Xiao et al. 2000, 2002; Pu and Braun 2001; Chen et al. hazards on Earth. Therefore, forecasting of tropical cy- 2004; Chen 2007). It is now widely recognized that sat- clones is one of the most important components in nu- ellite products have a positive impact on operational merical weather prediction. Tropical cyclone forecasts analyses and forecasts, especially in the data-sparse have improved steadily over the last decade primarily oceanic areas (English et al. 2000). due to the increased use of remote sensing data, par- The accurate initial condition is the foremost prereq- ticularly over the oceans in the tropical cyclone initial- uisite to accurate numerical simulations and prediction. izations in NWP models. Since there are limited con- Data assimilation has been recognized as a useful way ventional observations over the ocean where tropical to obtain better “consistent” initial conditions for nu- cyclones occur and evolve, effective use of remote sens- merical weather prediction (NWP). One of the most attractive and effective methods of data assimilation is the variational technique, which is based on an estima- Corresponding author address: Randhir Singh, Atmospheric Sciences Division, Meteorology and Oceanography Group, Space tion theory that constructs a theoretical basis for varia- Applications Centre (ISRO), Ahmedabad 380015, India. tional analyses in the minimization of the bias of the E-mail: [email protected] analyzed data (Gelb et al. 1974). A current area of DOI: 10.1175/2007WAF2007014.1 © 2008 American Meteorological Society Unauthenticated | Downloaded 10/01/21 09:17 PM UTC WAF2007014 JUNE 2008 SINGHETAL. 461 interest in data assimilation is how to properly ity observations in the model initialization process. The and effectively assimilate large amounts of data avail- conventional observational network of the World Me- able from current satellites. The synthesis of different teorological Organization (WMO; e.g., upper-air radio- sources of data has become one of the most important sondes and surface stations) provides moisture obser- issues of data assimilation (Zhu et al. 2002; Zhao et al. vations, but these are mostly distributed over land with 2005; Chen 2007; Zhang et al. 2007). soundings usually available only twice a day. This is Air–sea interactions including heat, momentum, and insufficient to sample the rapidly evolving environment moisture fluxes play a vital role in the formation and of marine weather systems, such as tropical cyclones, intensification of the tropical cyclones (Kuo and Low- adequately. Nonconventional observations, such as sat- Nam 1990; Kuo et al. 1991: Miller and Katsaros 1992; ellite data, on the other hand, can provide spatially Singh 2004). Near-surface winds play a dominant role dense information with high frequency. Satellite mea- in air–sea interactions processes (Liu 1988; Geernaert surements of water vapor could be a useful source of 1990; Chou et al. 2003; Bao et al. 2000; Singh et al. additional humidity information in analyses over 2006). Fortunately, several satellites have observed oceans, where the conventional measurements (radio- winds over the ocean during the last two decades, sonde and meteorological station) are very sparse. such as the Geosat altimeter, the National Aeronautics There are several satellite instruments that measure at- and Space Administration (NASA) Scatterometer mospheric moisture, including the Advanced Micro- (NSCAT), the Quick Scatterometer (QuikSCAT), the wave Sounding Unit (AMSU-B) humidity sounder Special Sensor Microwave Imager (SSM/I), the Euro- (English et al. 1994; Rosenkranz 2001), the High Reso- pean Space Agency’s first and second European Re- lution Infrared Radiation Sounder (HIRS; McNally mote Sensing satellites (ERS-1/2; Isakessen and Stof- and Vesperini 1996; Derber and Wu 1998; Engelen and felen 2000; Leidner et al. 2003), and water vapor or Stephens 1999; Escoffier et al. 2001), the SSM/I, and the cloud-derived satellite winds. The scatterometer pro- Moderate Resolution Imaging Spectrometer (MODIS). vides both surface wind speed and direction, which sig- In this study, fifth-generation Pennsylvania State nificantly increases the tropical cyclone (TC) forecast- University–National Center for Atmospheric Research er’s knowledge of TC formation and TC surface wind Mesoscale Model (MM5) three-dimensional variational structure. Global coverage of scatterometer data has data assimilation (3DVAR) (Barker et al. 2004) sensi- been routinely available (Tomassini et al. 1998) to fore- tivity experiments are conducted separately with Quik- casters and researchers since 1991 from the ERS-1 and SCAT and SSM/I data to investigate their individual ERS-2. However, ERS satellites often did not provide impacts on storm track and intensity. The case of the adequate coverage (Andrews and Bell 1998; Rufenach Orissa supercyclone over the Bay of Bengal during Oc- 1998) of TCs due to the narrow swaths (ϳ500 km) of tober 1999 was chosen for this study to explore the these satellites. This situation changed following the impact of assimilated QuikSCAT and SSM/I observa- launch of the QuikSCAT satellite in 1999, with its Sea- tions on model simulation. The paper is arranged as Winds instrument offering near-continuous daily cov- follows. In section 2, we briefly describe the Orissa su- erage of over 90% of the tropical oceans with a wide percyclone. The satellite data used in this study are swath of 1800 km. Ebuchi et al. (2002) found that the summarized in section 3. The MM5 model and its wind speeds and directions observed by QuikSCAT 3DVAR formalism, as well as the experimental design, agree well with the buoy data. The root-mean-squared are presented in section 4. Results are discussed in sec- differences of the wind speed and direction for the stan- tion 5. The paper is concluded in section 6. dard wind data products are 1.01 m sϪ1 and 23°, respec- tively. Katsaros et al. (2001) demonstrated that Quik- 2. Synoptic overview of the Orissa supercyclone SCAT provides valuable information about ambient over the Bay of Bengal (26–29 October 1999) surface wind fields in which tropical cyclones were em- bedded. Mohanty et al. (2004) and Singh et al. (2005) describe It is well known now (Xiao et al. 2002; Chen et al. the supercyclone of 26–29 October 1999 as the most 2004) that atmospheric moisture is crucial to the evo- intense tropical cyclone in the history of Orissa (India) lution of severe weather systems because of its poten- in the last 114 yr, or since the false-point cyclone of tial to release large amounts of latent heat. Therefore, September 1885. The state of Orissa, located on India’s uncertainties in the initial conditions of the humidity east coast, was battered for more than 2 days by fierce field in numerical weather prediction models could winds and intense rain. The storm also produced a huge have a significant impact on weather forecasts. These storm surge and catastrophic floods. uncertainties can be reduced through the use of humid- Figure 1 shows the Indian Meteorological Depart- Unauthenticated | Downloaded 10/01/21 09:17 PM UTC 462 WEATHER AND FORECASTING VOLUME 23 3. Satellite datasets a. QuikSCAT The NASA Quick Scatterometer (QuikSCAT) was launched in June 1999. It circles the earth at an altitude of 800 km once every 101 min. The QuikSCAT satellite (Shirtliffe 1999) swath width is about 1800 km. The SeaWinds instrument aboard the QuikSCAT satellite is a 13.4-GHz Ku-band conical-scanning microwave ra- dar.