The Impact of Variational Assimilation of SSM/I and Quikscat Satellite Observations on the Numerical Simulation of Indian Ocean Tropical Cyclones

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 improved 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, assimilation 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 hurricane 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

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°, respectively. 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 embedded. 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-

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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 radar. The SeaWinds measures the ocean surface winds using the relationship between the backscattered radar signal and the roughness of the ocean surface. The accuracy of the measured ocean surface wind reaches 2 msϪ1 in speed and 20° in direction for winds of 3–20 msϪ1 and 10% for winds of 20–30 m sϪ1 (Shirtliffe 1999), where help from independent information (e.g., numerical models) is needed to remove the ambiguity FIG. 1. Best track from IMD for the Orissa supercyclone in the direction determination. Rainfall can greatly af- (October 1999). fect the accuracy of the scatterometer wind measurements (Weissman et al. 2002; Hoffman and Leidner ment (IMD) observed track and minimum central sea 2005). Light winds can be overestimated by excess level pressure (MSLP) for the cyclone. The initial dis- backscatter from the hydrometers as they fall and be- turbance that eventually led to this development was cause they roughen the sea surface. Also, strong winds discerned in the Gulf of Thailand on 24 October. While can be underestimated by hydrometers attenuating the moving west-northwestward, it intensified through sev- signal. Overall, the impact of the rainfall is dependent eral stages of evolution, particularly on 28 October both on the rain rate and wind speed. Rain effects on when it slowed down its forward motion. It crossed the wind measurements must be consider when scatterom- Orissa coast of India close to Paradip (20.4°N, 86.5°E) eter data are applied on tropical cyclone analyses. between 0430 and 0630 UTC 29 October. The initial The QuikSCAT Operational Standard Data Prod- vortex was spotted over the Gulf of Thailand at 0000 ucts (L2B), which have been processed and distributed UTC 24 October. Moving westward across the Malay- sian Peninsula, it emerged in the north Andaman Sea as by the NASA Jet Propulsion Laboratory (JPL) Physical a well-marked low pressure area on the morning of 25 Oceanography Distributed Active Archive Center October. It concentrated into a depression in the eve- (PO.DAAC), contains two outputs of the wind data. ning of the same day and was centered at 13.5°N, 98°E One is the standard wind data, which has been pro- at 1200 UTC 25 October. The depression moved in a duced using a maximum likelihood estimator and me- west-northwesterly direction and intensified into a cy- dian filter ambiguity removal algorithm (Shaffer et al. clonic storm at 0300 UTC 26 October near 13.5°N, 1991) with the numerical weather product initialization. 95°E. The system further intensified as a severe cy- The other is enhanced wind data processed using the clonic storm at 0300 UTC 27 October near 16°N, 92°E. direction interval retrieval with threshold nudging While continuing to move north-westward, it deepened (DIRTH) algorithm. The wind vectors observed by the further. At 0300 UTC 28 October it was located near QuikSCAT satellite mission were validated by com- 17.5°N, 89.5°E. The system attained the stage of a su- parison with wind and wave data from ocean buoys. percyclonic storm at 1500 UTC 28 October near 19°N, Ebuchi et al. (2002) found that the wind speeds and 87.5°E. After crossing coast, the system moved very directions agree well with the buoy data. The root- slowly a little farther to the northwest, then weakened mean-square differences of the wind speed and direc- Ϫ and lay centered at 1200 UTC 29 October near 20.5°N, tion for the standard wind data products are 1.01 m s 1 86°E. The storm caused exceptionally heavy rain (20 and 23°, respectively. Therefore, we can reliably use cm in 24 h) over some stations in Orissa. The observed standard L2B QuikSCAT data near the tropical cy- MSLP (Fig. 1) was 998 hPa at 1200 UTC 26 October clone in this study. Another parameter available in the and dropped to 912 hPa after 60 h. The cyclone deep- JPL dataset, which can be used to remove potential ened by 46 hPa in 12 h (from 0000 to 1200 UTC 28 rain-contaminated data, is the probability of rainfall p October). for each wind vector cell (WVC). The parameter p is

Unauthenticated | Downloaded 10/01/21 09:17 PM UTC JUNE 2008 SINGHETAL. 463 determined via the multi-dimensional histogram (Hud- dleston and Stiles 2000). The spatial resolution of the QuikSCAT winds is 25 km and the reference height of the wind vector is 10 m above the sea surface. There is one swath at 1154 UTC 26 October 1999, from Quik- SCAT, which passes exactly over the initial location of the cyclone (model initial condition, 1200 UTC). b. SSM/I The SSM/I (Hollinger 1989) is a conical-scanning, four-frequency, linearly polarized, seven-channel passive microwave radiometer. The first SSM/I instrument was launched aboard the Defense Meteorological Sat- ellite Program (DMSP) of the U.S. Navy in June 1987. This polar-orbiting satellite has a period of about 102 min. The instrument has a near-constant incidence angle of 53°, a mean altitude of approximately 830 km, and a swath width of about 1400 km. Like QuikSCAT, FIG. 2. Two nested domains used in the MM5 model simula- the SSM/I data are available under both clear and tions. The resolutions are 30 and 10 km for domains 1 and 2 cloudy oceanic conditions but can be contaminated by (shaded region), respectively. light as well as heavy precipitation. Detailed information about the SSM/I instrument may be found in Janjic´ 1994), explicit treatment (Goddard’s scheme) for Hollinger (1989). The resolution of the SSM/I data is 25 ice/graupel physics, Blackadar’s (Blackadar 1979) km, which is the same as that of the QuikSCAT winds. scheme for PBL parameterization, and the rapid radia- SSM/I observations are available at 1137 UTC 26 Oc- tive transfer model (RRTM) longwave and Dudhia tober 1999 exactly over the initial position of the cy- shortwave atmospheric radiation schemes. Except for clone. The retrieved total precipitable water (TPW) the cumulus parameterization, the inner domain has and sea surface winds from the DMSP F13 satellite are the same physics option as the outer domain. In the used in this study. The total precipitable water and the inner domain instead of the Betts–Miller scheme, we sea surface wind were both derived from brightness have used the Grell scheme for cumulus parameteriza- temperatures using Wentz’s algorithm (Wentz 1997). tions (Grell et al. 1991). The choice of different cumulus parameterizations for domains 1 and 2 is made based on our previous experience. For all of the experi- 4. Model descriptions and experimental design ments, the model was integrated for 72 h starting from a. Model description 1200 UTC 26 October 1999. The model initial conditions are chosen in such a way so that the cyclone is The fifth-generation Pennsylvania State University– fully covered by SSM/I and QuikSCAT satellite passes. National Center for Atmospheric Research Mesoscale Model (MM5) and its 3DVAR system (Barker et al b. Assimilation methodology 2004) are utilized in this study. The MM5 is a limited- area, nonhydrostatic primitive equation model with The three-dimensional variational data assimilation multiple options for various physical parameterization experiments were conducted by minimizing the cost schemes (Dudhia 1993; Grell et al. 1994). The model function. The MM5 3DVAR method (Barker et al. system is run with two nested domains (30 and 10 km). 2004) is based on the minimization of a cost function Figure 2 shows the two domains used for the simula- defined as (Ide et al. 1997) tions. The grid’s dimensions are 190 ϫ 190 and 220 ϫ 1 Ϫ 220 in the east–west and north–south directions in do- J͑x͒ ϭ Jb ϩ Jo ϭ ͑x Ϫ xb͒TB 1͑x Ϫ xb͒ 2 mains 1 and 2, respectively. The model has 28 vertical 1 Ϫ levels, with the top of the atmosphere located at 50 hPa. ϩ ͑y Ϫ yo͒TR 1͑y Ϫ yo͒, ͑1͒ 2 The major physics options in the experiments for the outer domain include the Betts–Miller–Janjic´ cumulus where x is the analysis variables vector (n dimensional), parameterization (Betts 1986; Betts and Miller 1986; xb is the background variables vector (n dimensional),

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o y the observation vector (m dimensional), B the back- TABLE 1. Assimilated data for each numerical experiment. ground error covariances matrix (n ϫ n), and R the Expt Assimilated data observation error covariances matrix (m ϫ m). In (1), the analyses x ϭ xa represent the a posteriori maximum CNT Conventional data (e.g., radiosondes, buoys, ships, and surface observations) likelihood (minimum variance) estimate of the true b o QWSD Conventional and QuikSCAT wind speeds and state of the atmosphere given two sources (x and y )of direction data. The analyses’ fits to these data are weighted by SWS Conventional and SSM/I wind speeds estimates of their errors (B, R). The cost function (1) QWS Conventional and QuikSCAT wind speeds assumes that the observation and background error co- SPW Conventional and SSM/I total precipitable water vapor variances are described using Gaussian probability den- QPW QuikSCAT winds and SSM/I total precipitable water sity functions with zero mean error. vapor The configuration of the MM5 3DVAR system is ALL Conventional, QuikSCAT winds, SSM/I TPW, and based on an incremental formulation producing a mul- wind speeds tivariate incremental (Courtier et al. 1994) analysis in the MM5 model space. The incremental cost function minimization is performed in a preconditioned control more studies are required to adequately define the vari- variables space. The preconditioned control variables ances in cyclone environment. are the streamfunction, velocity potential, unbalanced pressure, and relative humidity. The background cova- c. Experiment design riances matrix B is prescribed as monthly mean forecast error variances derived from the yearly NCAR MM5 We conducted seven forecast experiments. The dif- forecasts. Statistics for the differences between the 24- ferences between these experiments are in the initial and 12-h forecasts valid at 1200 UTC are used to esti- conditions, which are listed in Table 1. NCEP–NCAR mate background error covariances by applications of reanalysis data with 2.5° ϫ 2.5° resolution are used for the National Meteorological Center, NMC; now known the model’s boundary conditions. Instead of directly as the National Centers for Environmental Prediction, using NCEP–NCAR reanalysis data as the first guess in NCEP) method to the MM5 forecast. The representa- the 3DVAR experiments, an MM5 simulation was first tion of the horizontal components of the background integrated from 0600 UTC 26 October for6htopro- error is via horizontally isotropic and homogeneous re- vide a 3DVAR first guess at 1200 UTC 26 October. The cursive filters. The vertical component is applied 6-h forecast was used as the first guess for two reasons. through the projection onto climatologically averaged First, we were not sure whether the NCEP–NCAR eigenvectors of the vertical error estimated via the analysis used as the initial state contained information NMC method. Horizontal and vertical errors are non- from satellite observations like QuikSCAT and SSM/I separable, in that the horizontal scales vary with the via some data assimilation system. A 6-h forecast elimi- vertical eigenvectors. A detailed description of the nates these possibilities and provided an ideal testbed 3DVAR system can be found in Barker et al. (2004). for assessing the impacts of the satellite observations. In the MM5 3DVAR, all observation errors are as- Second, during the 6-h integration, the coarser- sumed to be uncorrelated in space and time. Since ob- resolution analysis is expected to adjust to the higher- servation errors are assumed to be uncorrelated, the resolution environment of the MM5, creating dynami- matrix R is a simple diagonal with SSM/I and Quik- cally balanced fields for assimilation experiments. Fig- SCAT observation error variances as elements. In this ure 3a shows the horizontal cross sections of the study, these variances are taken as constant in space synoptic-scale fields of the NCEP–NCAR reanalysis at and time. The variances for SSM/I wind speed and total 0600 UTC 26 October. The cyclone’s center (14.2°N, precipitable water are assigned as 2.52 m2 sϪ2 and 96.0°E), which is defined by the 950-hPa circulation 22 mm2, respectively. The variances for the u and ␷ com- center (or minimum wind speed), is about 134 km ponents of the QuikSCAT wind vector are 1.42 m2 sϪ2. northeast of the best-track position (13.5°N, 95°E). The These variances are defined by NCEP–NCAR based on observed MSLP at the cyclone’s center is 1002 hPa, the comparison of the QuikSCAT and SSM/I observa- while the MSLP is 1004 hPa in the NCEP–NCAR re- tions with ship and buoy data. The similar values of the analysis. After a 6-h simulation (i.e., at 1200 UTC 26 variances in the SSM/I and QuikSCAT observations October; see Fig. 3b), the cyclone’s center (14.1°N, have been used in earlier studies (Chen et al. 2004; 95.2°E) is about 163 km southeast of the best-track Chen 2007). However, the choice of the variances for position (14.6°N, 93.8°E; Fig. 1). The observed MSLP at the minimization processes is always a challenge, and 1200 UTC 26 October at cyclone’s center is 998 hPa,

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already flagged for the rain-contaminated pixels by the SSM/I data products generation team and we have not done further flagging. Second, a gross error quality control was performed. Observations that differed from the model’s first guess by more than 8 times the observation error were removed. A value of 8 instead of 5 (default value in MM5 3DVAR) was used for gross error quality control. Initially, we experimented with this default value and found that too many useful observations were removed due to mismatches of the cyclone’s centers between the first guess and satellite observations. The control experiment, CNT, was used for comparison purposes since it did not assimilate any satellite observation. In CNT, we have assimilated only conventional data. The conventional data include surface station reports as well as upper-air observations. The surface and upper-air data over land are provided by the University of Wyoming (information online at http:// weather.uwyo.edu/upperair/sounding.html), while the surface marine observations (ships and buoys) are taken from the International Comprehensive Ocean– Atmosphere Dataset (ICOADS), provided by the Na- tional Climate Data Center (information online at www.ncdc.noaa.gov/oa/marine.html). Data sensitivity experiments were conducted to ex-

Ϫ amine the impacts of the QuikSCAT and SSM/I obser- FIG. 3. The 950-hPa wind field (m s 1) for the (a) NCEP– NCAR reanalysis valid at 0600 UTC 26 Oct 1999 and (b) the vations. The QWSD and SWS experiments, which as- MM5 6-h simulation valid at 1200 UTC 26 Oct 1999. similate the QuikSCAT and SSM/I wind vectors and wind speeds, respectively, at the initial time (1200 UTC 26 October), were conducted to assess the impact of which is 1004 hPa in the 6-h simulation using MM5 two types of satellite winds on cyclone simulations. The (valid at 1200 UTC 26 October). The availability of the QWS experiment assimilates only QuikSCAT-derived satellite data (i.e., full coverage of the initial cyclone wind speeds at 1200 UTC 26 October. The SPW ex- position) and the discrepancy in the cyclone’s initial periment assimilates only SSM/I-derived TPW at the position, as well as in the strength of the background initial time (1200 UTC 26 October). QPW is similar to field, provide a good opportunity to assess the impact of QWSD, but with the additional assimilation of SSM/I- satellite observations on this cyclone simulation. retrieved TPW. Another experiment, ALL, is also con- The MM5 3DVAR system was used to assimilate ducted to determine the combined effects of the SSM/I observations, including QuikSCAT wind vectors and and QuikSCAT data. SSM/I-retrieved total precipitable water and sea surface wind speeds. For the numerical experiments presented in Table 1, satellite data are assimilated on the 5. Results coarsest, 30-km-resolution domain (domain 1). The a. QuikSCAT and SSM/I observations model initial conditions for the second domain (10-km resolution) are interpolated from domain 1. Prior to Figure 4 shows the QuikSCAT data coverage near data assimilation, both the SSM/I and QuikSCAT data the model’s initial time (1200 UTC 26 October 1999). underwent quality-checking processes in order to re- The QuikSCAT data were available over the Bay of duce the possibility of assimilating bad observations. Bengal at 1154 UTC 26 October 2006. The observed First, data contaminated by heavy rain were excluded. oceanic surface wind field shows a clear picture of the The rainfall probability parameter p along with the cyclonic vortex embedded within an environmental QuikSCAT data are used to exclude the rain- flow. From the QuikSCAT observations, the cyclonic contaminated observations. The SSM/I retrievals were circulation is clearly seen near 14.7°N, 93.6°E, with

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FIG. 5. Distribution of SSM/I surface winds speeds (shaded; Ϫ Ϫ Ϫ FIG. 4. Distribution of QuikSCAT surface wind speeds (m s 1) ms 1) and total precipitable water vapor (contour line; g cm 2)at and vectors at 1154 UTC 26 Oct 1999. 1137 UTC 26 Oct 1999. maximum winds of more than 18 m sϪ1. Winds close to found that SSM/I winds derived from Wentz’s algo- the center of cyclone are flagged as missing because rithm are systematically overestimated in regions of microwave backscattering signals are highly contami- higher water vapor content (Halpern 1993; Waliser and nated due to heavy precipitation in this region. Due to Gautier 1993; Boutin and Etcheto 1996). Recently, the missing wind data over cyclone’s center, it is diffi- Chen (2007) made a comparison of these two winds and cult to find the exact location of the cyclone’s center, if found that SSM/I winds are higher than QuikSCAT we consider the center of the circulation (14.7°N, winds by 2 m sϪ1. Figure 6 shows the scatterplot of the 93.6°E) as a cyclone’s center. The center of the cyclonic SSM/I versus QuikSCAT wind speeds for this case. The vortex observed in the QuikSCAT winds matches up comparison is made only for this single pass, which is reasonably well with the observed position (14.6°N, shown in Figs. 4 and 5. The temporal separation for the 93.8°E) given by Indian Meteorological Department QuikSCAT (1154 UTC) and SSM/I (1137 UTC) mea- (IMD). Figure 5 shows the SSM/I data coverage near surements is 17 min. On average, the wind speeds mea- the model initial time. The SSM/I wind speeds and total sured by SSM/I were 1.7 m sϪ1 higher than those of precipitable water vapor are available over the Bay of QuikSCAT. This comparison supports the finding of Bengal at 1137 UTC 26 October 1999. Like the Quik- Chen (2007). SCAT data, the SSM/I observations close to the center of cyclone are missing because of contamination due to b. Analysis of the optimal initial conditions precipitation. It is to be noted that the SSM/I measure- 1) STATISTICS ANALYSIS ments are more sensitive to the precipitation as compared to the QuikSCAT. This fact is reflected in larger QuikSCAT observations are assimilated (observa- data gaps in the SSM/I observations as compared to tion distribution shown in Fig. 4) into the initial condi- QuikSCAT. tions by minimizing the cost function [Eq. (1)] that is Only a few studies (Chen 2007) have focused on the the measure of the distance between the QuikSCAT- comparison between SSM/I and QuikSCAT winds. observed and first-guess- (MM5 6-h forecast is taken as Boutin and Etcheto (1996) found that SSM/I-retrieved the first guess) derived surface winds in the QuikSCAT wind speeds are underestimated by more than 1 m sϪ1 data sensitivity experiment (QWSD). To see the agree- with respect to ship measurements at high latitudes. ment between QuikSCAT and the first-guess wind, we However, when compared with ERS-1, SSM/I winds have compared these two products. Comparison be- are overestimated by 0.5–1msϪ1 over regions where tween QuikSCAT and the first-guess 10-m wind speeds the atmospheric water content is high. It has also been indicates that these two products differ significantly

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FIG. 6. Scatterplot of QuikSCAT vs SSM/I wind speeds. The time difference between the QuikSCAT and SSM/I observations is 17 min.

(Fig. 7a) with an RMS difference and bias of the order of 3.4 and 2.1 m sϪ1, respectively. Figure 7b shows the scatterplot between the QuikSCAT-observed and 3DVAR-analyzed (optimal initial conditions; EXP QWSD) 10-m winds. Figure 7b clearly shows that the RMS difference between the observations (O) and the optimal analysis (A) (1.4 m sϪ1) is smaller than the RMS difference between the observations (O) and the first guess (B) (3.4 m sϪ1). The bias is also significantly reduced in case of the O Ϫ A (0.4 m sϪ1) as compared to O Ϫ B (2.1 m sϪ1). This shows that 3DVAR pro- duces an analysis that is consistent with the QuikSCAT observations. To see the closeness of the QWSD- analyzed winds with independent observations, we have compared the QWSD-analyzed wind speed with the SSM/I-observed wind speed. The comparison between the QWSD-analyzed wind and the SSM/I-observed wind (Fig. 7c) is similar to that of a comparison between QuikSCAT and SSM/I observations (Fig. 6), which shows that the assimilation of QuikSCAT winds brings the analysis closer to the observations (which is independent of the QuikSCAT data). However, due to large disagreements (Fig. 6) between SSM/I and Quik-

SCAT, the final analysis (QWSD) may not be expected FIG. 7. Comparison between (a) QuikSCAT and MM5 first- to match the SSM/I observations perfectly. guess 10-m wind speed, (b) QuikSCAT and MM5 3DVAR- SSM/I wind speeds are assimilated in the SSM/I data analyzed (QWSD) 10-m wind speed, and (c) MM5 3DVAR- sensitivity experiment (SWS). The SSM/I (O) and first- analyzed (QWSD) and SSM/I-observed 10-m wind speed at 1200 UTC 26 Oct 1999. guess (B) wind speeds show that these products differ (not shown) with RMS difference and bias on the order

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best-track position). The 950-hPa wind field at 1200 UTC 26 October 1999 with the QuikSCAT data assimilation (QWSD) shows (Fig. 8b) a cyclonic circulation with a maximum wind speed of 21 m sϪ1 and the center of circulation at 14.3°N, 93.8°E, a 33-km position error compared with the best-track position (14.6°N, 93.8°E). This shows a dramatic impact of QuikSCAT wind vectors in terms of the placement of the cyclone’s center. The higher wind speed in the CNT analysis is seen in the southeast sector, while in the case of QWSD the higher winds are seen in the north and northeast sectors of the cyclone. The minimum sea level pressure (MSLP) at the storm center is 1002 hPa (not shown) in the case of QWSD, which is closer to the observed (998 hPa) than the CNT-analyzed result (1004 hPa). The 950-hPa wind field at 1200 UTC 26 October 1999 with the SSM/I wind speed assimilation (SWS) shows (Fig. 8c) a cyclonic circulation with a maximum wind speed of 19 m sϪ1 (higher than in CNT) and the circulation center at 14.7°N, 95.1°E, a 143-km position error compared with the best-track position (14.6°N, 93.8°E). MSLP at the cyclone’s center is 1003 hPa in the case of SWS, which is slightly better than in the CNT analysis (1004 hPa). The lack of wind direction (Chen 2007) in the case of the SWS experiment might be the reason for not showing a positive impact on the cyclone’s center placement, as is shown in the case of QWSD. To con- firm this, we have conducted another experiment in which we have assimilated only QuikSCAT-derived wind speeds (QWS). In the QWS case, the storm’s ini- Ϫ FIG. 8. The 950-hPa wind fields (m s 1) from (a) CNT, (b) tial position (Fig. 9a) is slightly to the south (14.3 °N, QWSD, and (c) SWS valid at 1200 UTC 26 Oct 1999. 95.1°E) of the SWS analysis (Fig. 8c). The initial position errors of the cyclone’s center (Table 2) from these two experiments (QWS, SWS) are very similar. This of 4.4 and 3.1 m sϪ1, respectively. The comparison of shows that the QuikSCAT wind directions are very im- SSM/I (O) and 3DVAR-analyzed (A) wind speed portant for the placement of the storm’s initial position shows an RMS difference and a bias of the order of 2.0 in the QWSD experiment. As expected, the assimila- and1msϪ1, which demonstrates that SSM/I winds get tion of the total precipitable water vapor (SPW) does accepted by the 3DVAR procedure. Assimilation of not show any significant impact on the wind circulation. total precipitable water vapor (experiment SPW) also The wind circulation looks similar to that in CNT, indicates (not shown) that the 3DVAR-analyzed TPW though significant changes are noted in the TPW field. (A) is closer (RMS difference of 0.06) to the SSM/I- As compared to the first guess, assimilation of the observed TPW (O) than the first guess (B) (RMS dif- Ϫ SSM/I TPW shows a moistening of the lower tropo- ference of 0.6 g cm 2). The bias is also drastically re- Ϫ2 sphere over most of the Bay of Bengal (Fig. 9b; positive duced in the case of O Ϫ A (0.05 g cm ) as compared to O Ϫ B (0.3 g cmϪ2). differences), except over cyclonic region, where the assimilation of the SSM/I TPW reduces the lower- troposphere moisture (Fig. 9b; negative differences). 2) CIRCULATION FEATURES This large change in the initial moisture over the cy- The 950-hPa wind field at 1200 UTC 26 October in clone can influence the storm intensity. In the cases of the CNT analysis shows (Fig. 8a) a cyclonic circulation QPW and ALL, the wind circulation pattern (not with a maximum wind speed of 14 m sϪ1 and the cy- shown) is similar to that of QWSD, while the TPW clone’s center at 14.1°N, 95.2°E (163 km away from the distribution is the same as in SPW.

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FIG. 10. Temporal variation of (a) MSLP (hPa) at the cyclone’s center and (b) the maximum low-level winds (m sϪ1) from IMD Ϫ FIG. 9. (a) The 950-hPa wind field (m s 1) for QWS and (b) the (OBS) and model simulations. differences between SPW and CNT analyzed total precipitable water (shaded region; g cmϪ2) and 950-hPa streamlines for SPW valid at 1200 UTC 26 Oct 1999. observed intensity. The initial MSLPs in the cases of QWSD (1002 hPa) and QWS (1001 hPa) are closer to the observed than they are in the other experiments. c. Simulation results The simulated MSLP in CNT indicates a weak cyclone 1) STORM INTENSITY during the first 60 h of the simulation, which intensified during next 12 h, as shown in Fig. 10a. After 72 h of Figures 10a and 10b show the temporal variation of simulation, the MSLP dropped to 994 hPa, which is the observed and simulated cyclone’s minimum central about 74 hPa higher than the observed MSLP (920 sea level pressure (MSLP) and low-level (950 hPa) hPa). The observed low-level maximum wind speed is maximum winds. The observed MSLP was 998 hPa at 60 m sϪ1 at 1200 UTC 29 October but it is only 23 m sϪ1 the model’s initial time (1200 UTC 26 October) and in the CNT simulated cyclone. The positive impact of dropped to 912 hPa 72 h later. During the first 24 h, the QuikSCAT-derived winds is clearly seen in the pre- MSLP decreased slowly (deepened only 6 hPa within 24 dicted intensity of the cyclone. The MSLP from the h). During the next 24 h, the MSLP decreased dramati- QWSD and QWS experiments closely matches the ob- cally (deepened by 46 hPa). Between 48 and 60 h, the servations for the first 36 h; however, the observed rate storm deepened by 28 hPa. The initial intensity for all of the MSLP fall is higher than in QWSD and QWS of the experiments (Fig. 10a) is slightly weaker than the after 36 h. At the end of the simulation, the simulated MSLP is 948 hPa (955 hPa) for QWSD (QWS), which

TABLE 2. The cyclone center position error (km) at the model is higher than the observed MSLP. The increase in initial time. simulated MSLP, after 66 h, as seen in the observations, is not seen in the simulations because the simulated Expt CNT QWSD SWS QWS SPW QPW ALL cyclone could not make landfall. The low-level (950 Error 163 33 143 147 147 33 44 hPa) maximum wind speeds (Fig. 10b) have increased (km) to 61 and 52 m sϪ1 for QWSD and QWS, respectively,

Unauthenticated | Downloaded 10/01/21 09:17 PM UTC 470 WEATHER AND FORECASTING VOLUME 23 which are still lower than the observed intensity. The assimilation of SSM/I wind speeds (SWS) also shows slightly better intensity than does CNT. After 72 h, the simulated MSLP at the cyclone’s center from SWS is 985 hPa (Fig. 10a), which is about 65 hPa higher than the observed MSLP (920 hPa). At the end of the simulation the maximum wind speed (Fig. 10b) in the case of SWS reaches up to 30 m sϪ1. This demonstrates that the inclusion of satellite winds (QSCAT or SSM/I) posi- tively impacted the cyclone’s intensity prediction. As compared to SSM/I, the QuikSCAT winds show greater potential for making a positive impact on the intensity prediction. This may be due to the following reasons. First, the QuikSCAT measurements are slightly less sensitive to precipitation (particularly light precipitation) as compared to the SSM/I measurements. As a result, QuikSCAT observations are available over a larger area of the cyclone than are those from SSM/I (Figs. 4 and 5). Second, QuikSCAT has additional information of the wind direction. The assimilation of the SSM/I TPW (SPW) shows a negative impact on the intensity prediction of the cyclone. In the case of SPW, the simulated MSLP is always higher than in CNT (Fig. 10a). This decrease in the intensity in case of SPW is due to the decrease of the initial moisture over the cyclonic region (Fig. 9b). The initial moisture over the cyclone is crucial for pro- viding the upper-level heating over the cyclone. Re- cently, Chen (2007) arrived at a similar conclusion from

SSM/I TPW assimilation. It is possible that SSM/I may FIG. 11. RMS difference (RMSD) of the simulated (a) MSLP underestimate TPW in the cyclonic environment for the (hPa) at the cyclone’s center and (b) the low-level maximum wind following two reasons. The first reason pertains to the speed (m sϪ1) during the first, second, and third days of the simu- nonrepresentative sampling of the training dataset lation, where error is based on four samples for each day. (e.g., radiosonde) used for deriving the regression co- efficients in the statistical algorithm, and it may be pos- The simulated cyclone intensity in the case of QPW sible that a small number of in situ soundings from the (Fig. 10a) is similar to that of SWS. The assimilation of cyclonic environment have gone into the training SSM/I TPW creates drier conditions within the cyclone, dataset during the algorithm development process. The which in turn negatively impacts the precipitation and second reason may be that, due to heavy precipitation diabetic heating field, resulting in a weaker storm in the within the cyclone, the upwelling radiances at lower case of QPW as compared to QWSD. This shows that microwave frequencies (e.g., 19 and 22 GHz) are domi- the large error in the SSM/I moisture suppressed the nated by the process of scattering. This influence might positive improvement due to the QuikSCAT wind as- have been underemphasized in the development of the similation. The statistical results (RMS difference) for retrieval algorithm. A preliminary comparison of these experiments are shown in Fig. 11. The RMS dif- NCEP–NCAR and SSM/I data indicates (figure not ferences between the MSLP and winds are based on the shown) that for NCEP–NCAR values of TPW larger 6-hourly data of cyclone’s MSLP and maximum low- than 6.4 g cmϪ2, the corresponding SSM/I measure- level (950 hPa) winds. Thus, there are four samples in ments remain saturated. At the initial time (1200 UTC each day’s statistics for MSLP and maximum winds. 26 October) the cyclone is already in the mature stage From Fig. 11, it is very clear that best intensity is ob- (MSLP of 998 hPa) and hence must have a significant tained from QWSD and QWS for all days. amount of precipitable water (i.e., more than saturation Air–sea interactions including heat, momentum, and value of SSM/I). In that case SSM/I will always under- moisture fluxes play a vital role in the formation and estimate the TPW as compared to the actual value. intensification of the tropical cyclones. Winds play a

Unauthenticated | Downloaded 10/01/21 09:17 PM UTC JUNE 2008 SINGHETAL. 471 dominant role in the air–sea interaction processes. The the assimilation of winds results in an improved simu- stronger low-level winds can significantly increase the lated track throughout (only in QuikSCAT winds) the air–sea heat, momentum, and moisture fluxes (Singh et integration, when compared to CNT. The improvement al. 2006; Singh 2004; Chou et al. 2003, Bentamy et al. is quite large in QWSD. This may be due to the im- 2003; Esbensen et al. 1993; Liu 1988). Simulation stud- provement of the initial cyclone’s center position (Fig. ies by Singh (2004) show that in the absence of surface 8b) for QWSD. Although none of the experiment is heat flux (particularly latent heat flux) the simulated able to predict the landfall accurately, all of them show cyclone is very weak. The better simulation of the air– the slow westward movement of the tropical cyclone. sea heat and momentum exchange may be one of the QWSD predicts the landfall with the least error. reasons for the better intensity prediction in the case of the wind data assimilation (particularly in the case of 6. Conclusions QuikSCAT). Figures 12 and 13 show the simulated heat fluxes (latent and sensible) after 24 h of model integra- The present study examines the impacts of assimilat- tion. Large latent heat fluxes (more than 700 W mϪ2) ing sea surface winds from QuikSCAT, and sea surface are transferred from the ocean to the atmosphere, par- winds and precipitable water from SSM/I, on the pre- ticularly in the case of the QuikSCAT winds assimila- diction of an Indian Ocean tropical cyclone’s track and tion experiments (Figs. 12b and 12d). This is due to the intensity. MM5 3DVAR sensitivity runs were con- assimilation of winds, which strengthen the initial wind ducted separately with QuikSCAT surface winds, field. Sensible heat fluxes on the order of more than 150 SSM/I surface wind speeds, and total precipitable water WmϪ2 (Figs. 13b and 13d) are seen in the wind assim- vapor to investigate their individual impacts on cyclone ilation experiments. These fluxes in the wind assimila- track and intensity. The results were compared against tion experiments are very large as compared to CNT, in a simulation initialized with only conventional data. which the initial wind field is very weak. The conclusions that emerged from the present study are as follows. 2) STORM TRACK There are very few studies that compare SSM/I and Figure 14 shows the predicted cyclone tracks from QuikSCAT observations (Chen 2007). These two sat- the CNT and satellite data assimilation experiments ellites (QuikSCAT and SSM/I) observed the Orissa su- starting from 1200 UTC 26 October 1999. The IMD- percyclone simultaneously (17-min difference) at the observed track is also plotted for comparison. Starting model initial time. So we decided to compare these two from 1200 UTC 26 October, the observed cyclone wind speed datasets for this case. From comparisons of moved north-northwestward, and landed at about 0600 almost 5000 collocated SSM/I and QuikSCAT observa- UTC 29 October at the Orissa coast. The CNT- tions, it was found that the SSM/I wind speed was ap- simulated cyclone track is on the northeast side of the proximately 1.7 m sϪ1 stronger than QuikSCAT. The observed track and the position errors (Fig. 15) are 235, results of this comparison are similar to those obtained 275, and 345 km for first, second, and third days of the by Chen (2007). integration, respectively. On the contrary, the cyclone The results show that the wind observations, particu- track from QWSD is closer to the observations (Fig. larly from QuikSCAT, have great potential for improv- 14). On the first day of the model integration, the track ing cyclone prediction. Through an improvement in the forecast made by QWSD has a position error of about surface wind field, the SSM/I or QuikSCAT data yield 131 km (Fig. 15). This position error increased to 170 a positive impact on the simulated storm intensity and 234 km for the second- and third-day forecasts, (MSLP) and the maximum low-level wind. This impact respectively. The SWS-simulated cyclone track is on likely occurs through a change of the PBL moisture and the southeast side of the observed track and the posi- heat fluxes. These fluxes are strongly coupled to the tion errors (Fig. 15) are 155, 205, and 394 km for first, low-level winds. The wind assimilation experiments second, and third days of the integration, respectively. strengthen the cyclonic circulation in the initial condi- When only SSM/I TPW was assimilated (SPW), the tions. The stronger low-level winds enhanced air–sea cyclone track did not shows any significant improve- exchange, which is critical to tropical cyclone intensifi- ment. In fact, compared to CNT, the track error was cation–evolution over the ocean. large (later stage of simulation) in the TPW and SSW QuikSCAT data show a more positive impact than assimilations. When SSM/I TPW is assimilated along do SSM/I because they contain both wind speeds and with winds (QPW, ALL), the track errors are similar to directional information. When the cyclone’s center is those of QWSD for the first 2 days but on the third day misrepresented at the model initial time (as in this the error are larger than in CNT. The results show that case), good quality information on wind direction can

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Ϫ FIG. 12. Latent heat flux (W m 2) after 24 h of integration from (a) CNT, (b) QWSD, (c) SWS, (d) QWS, (e) SPW, and (f) QPW. potentially correct the initial position of the cyclone, best track. After the QuikSCAT wind assimilation, the thus affecting the simulated cyclone track. In this study, optimal initial conditions (QWSD) place the cyclone’s the assimilation of QuikSCAT wind vectors (QWSD) center about 33 km away from the best-track position, helped to correct the cyclone initial position toward the while it is 163 km away in the background analysis. This

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Ϫ FIG. 13. Sensible heat flux (W m 2) after 24 h of integration from (a) CNT, (b) QWSD, (c) SWS, (d) QWS, (e) SPW, and (f) QPW.

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FIG. 15. RMSD of the simulated cyclone track (km) during the first, second, and third days of the simulation, where the error is based on the 6-hourly position of the cyclone’s center.

the cyclone intensity. Actually, Chen (2007) assimilated the TPW when the cyclone was very weak and was not having much precipitation. During mild precipitation the scattering does not exert much influence on the upwelling radiances of the microwave channels sensitive to the atmospheric water vapor. However, in an environment of heavy precipitation, the scattering may affect these upwelling radiances and hence the retrieval of the water vapor. Chen (2007) assimilated the TPW when the cyclone was stronger and was dominated by FIG. 14. The 12-hourly cyclone center positions for the 72-h heavy precipitation at the initial time. In this case the period ending at 1200 UTC 29 Oct 1999 for (a) the observed position (O), CNT (C), QWSD (asterisk), SWS (pound sign), and actual TPW content of the atmosphere may be ex- SPW (ampersand), and (b) the observed (O), QWS (X), QPW pected to be considerably higher than the moisture re- (dollar sign), and ALL (caret). flected by the SSM/I measurements due to the reasons mentioned above. So, when TPW is assimilated in a results in a drastic improvement in the QWSD- weak cyclone, the moisture content of the SSM/I is simulated track during the entire simulation period. As- closer to reality and the innovations are correct. But similating both wind speed and direction is better than when TPW is assimilated in a strong cyclone, the inno- only wind speed in terms of track prediction. The im- vations (observation–background) are negative. That is portance of wind direction is further confirmed by the what exactly happened in the Chen et al. (2004) and QWS experiment, which assimilated the QuikSCAT Chen (2007) papers and in this study. It can be argued wind speed only. that the impact of passive-microwave-based TPW is SSM/I total precipitable water vapor has a negative sensitive to the stage of the cyclone during which the impact, particularly on the intensity prediction. Re- TPW fields are assimilated. cently, Chen et al. (2004) and Chen (2007) assimilated Our results indicate that both error in SSM/I TPW the SSM/I TPW in two cyclones and got different re- and the sensitivity of TC intensification to TPW are sults. Chen et al. (2004) obtained a positive impact of causes of the negative impacts of the SPW and QPW TPW on the cyclone intensity, while Chen (2007) ob- experiments. The drying up of the TC core due to TPW tained a negative impact of the TPW assimilation on assimilation offsets the gain due to QuikSCAT winds,

Unauthenticated | Downloaded 10/01/21 09:17 PM UTC JUNE 2008 SINGHETAL. 475 and weakens the cyclone, showing the stronger role of and ERS-1 global surface wind speeds—Comparison with in TPW compared to the wind, particularly in the regions situ data. J. Atmos. Oceanic Technol., 13, 183–197. near the center of the cyclone. Chen, S. H., 2007: The impact of assimilating SSM/I and Quik- SCAT satellite winds on Hurricane Isidore simulation. Mon. The inferences from the present study thus comple- Wea. Rev., 135, 549–566. ment the findings from other studies for the assessment ——, F. Vandenberghe, G. W. Petty, and J. F. Bresch, 2004: Ap- of the impact of satellite data on the prediction of tropi- plication of SSM/I satellite data to a hurricane simulation. cal cyclones over different basins of the globe. Further Quart. J. Roy. Meteor. Soc., 130, 801–825. investigation should be undertaken to study the com- Chou, S. H., E. Nelkin, J. Ardizzone, R. M. Atlas, J. Ardizzone, and C. L. Shie, 2003: Surface turbulent heat and momentum bined effects of various satellite data sources on the fluxes over global ocean based on Goddard satellite retriev- cyclone tracks and the prediction of their intensity with als, version 2 (GSSTF2). J. Climate, 16, 3256–3273. more cyclones cases over different basins of the globe. Courtier, P., J. N. Thepaut, and A. Hollingsworth, 1994: A strat- egy for operational implementation of 4D-Var using an in- Acknowledgments. The MM5 data used in this paper cremental approach. Quart. J. Roy. Meteor. Soc., 120, 1367– were made publicly available and supported by the Me- 1387. Derber, J. C., and W. S. Wu, 1998: The use of TOVS cloud- soscale and Microscale Meteorology division at the Na- cleared radiances in the NCEP SSI analysis system. Mon. tional Center for Atmospheric Research (NCAR). The Wea. Rev., 126, 2287–2299. authors sincerely thank Dr. S. 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