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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 experiments 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 assimilation 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

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QuikSCAT data in tropical cyclone analysis and forecasting at the National Hurricane Center (NHC) is described 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. Like QuikSCAT, SSM/I data are available under both clear and cloudy conditions but can be contaminated by precipitation. The retrieved TPW and sea surface winds FIG. 1. The domain used in the WRF simulations. The locations of from the DMSP F13 satellite are used in this study. The the radiosonde data that are assimilated are marked by asterisks. TPW and the sea surface wind were both derived from brightness temperatures using Wentz’s algorithm (Wentz zation scheme; and the Yonsei University (YSU) planetary 1997) in rain-free areas. The resolution of SSM/I data is boundary layer scheme (Hong and Dudhia 2003). The 25 km, which is same as that of QuikSCAT winds. The Rapid Radiative Transfer Model (RRTM; Mlawer et al. SSM/I provides only wind speed, while QuikSCAT pro- 1997) and the Dudhia scheme (Dudhia 1989) were used vides both wind speed and direction. The positive impacts for longwave and shortwave radiation, respectively. All of SSM/I data on the NWP analysis and prediction have experiments were conducted with a single domain (Fig. 1) been shown by Gerard and Saunders (1999), Xiao et al. consisting of 170 3 170 grid points with 30-km horizontal (2000), Chen et al. (2004), and Kelly et al. (2008). grid resolution. The model had 28 vertical levels with the top of the model atmosphere located at 50 hPa. 3. WRF model and assimilation methodology a. WRF model b. Assimilation methodology The forecast model used is the Weather Research and The WRF three-dimensional variational data assim- Forecasting (Skamarock et al. 2005) model, version 2.2. ilation (3DVAR) system (Skamarock et al. 2005) was WRF is a next-generation mesoscale NWP system de- used in this study. The WRF 3DVAR evolved from the signed to serve both operational forecasting and atmo- MM5 3DVAR system (Barker et al. 2004), but the basic spheric research needs. It is a limited-area, nonhydrostatic, software interface and coordinate framework were fully primitive-equation model with multiple options for vari- updated for the WRF model. The background co- ous physical parameterization schemes. There are two variances matrix was estimated using the so-called Na- dynamics solvers within the WRF software framework: tional Meteorological Center (NMC, which is now the Advanced Research WRF (ARW) solver, developed known as NCEP) method (Parrish and Derber 1992; primarily at NCAR, and the Nonhydrostatic Mesoscale Wu et al. 2002). The observation errors were assumed Model (NMM) solver, developed at NCEP. We have used to be uncorrelated in space and time. Since observation the ARW dynamic solver for the present study. This errors were assumed to be uncorrelated, the observa- version employs Arakawa C-grid staggering for the hori- tional error covariances matrices were simple diagonal, zontal grid and a fully compressible system of equations. with QuikSCAT or SSM/I observation error variances The terrain-following hydrostatic pressure coordinate with as elements. In this study, these variances were taken as vertical grid stretching was followed in the vertical. The constant in space and time. The standard deviations for time-split integration uses a third-order Runge–Kutta the QuikSCAT wind speed and direction were 1.4 m s21 scheme with a smaller time step for acoustic and gravity and 208, respectively, while the standard deviations in wave modes. The WRF physical options used in this SSM/I winds and TPW were 2.5 m s21 and 0.2 g cm22, study consisted of the WRF single-moment six-class respectively. Prior to data assimilation, all satellite data (WSM6) graupel scheme for microphysics, which is underwent quality-checking processes in order to reduce similar to that used by Lin et al. (1983); the new Kain– the possibility of assimilating bad observations. First, rain- Fritsch (KF; Kain 2004) cumulus convection parameteri- contaminated data were excluded from the QuikSCAT and

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TABLE 1. Descriptions of the data sensitivity experiments. c. Design of numerical experiments Expt Assimilated data Five analyses (Table 1) were produced daily from 1 to CNT Radiosondes 31 July 2006 at 0000 UTC using the WRF 3DVAR. The QW QuikSCAT winds 1 radiosondes control 3DVAR experiments (CNT; Table 1), which only SW SSM/I winds 1 radiosondes assimilated conventional radiosonde observations, were SWV SSM/I TPW 1 radiosondes used for comparison purposes, as they did not assimilate SWWV SSM/I winds 1 SSM/I TPW 1 radiosondes any satellite data. The conventional radiosonde data were also assimilated in satellite data sensitivity experiments SSM/I data. The rainfall probability parameter (p) along (QW, SW, SWV, and SWWV; details are given in Table 1) with the QuikSCAT wind product are used to exclude in order to ensure the dynamical consistency among all the rain-contaminated observations from the QuikSCAT the analyses. Instead of directly using the NCEP final winds. The SSM/I retrievals were already flagged for (FNL) analysis for the first guess (FG), 6-h WRF fore- the rain-contaminated pixels by the SSM/I data products casts initialized using the NCEP FNL analysis were used generation team. Second, a gross error quality control was as the FG for all 3DVAR experiments. The NCEP FNL performed in which observations (from QuikSCAT and analysis with 18318 resolution was used for the model SSM/I) that differed from the model first guess by more boundary conditions for all the experiments. Forty-eight- than 5 times the observational errors were removed. hour forecasts were made daily from 1 to 31 July 2006 at

FIG. 2. Histogram of (a) wind speed departures of QuikSCAT observations from WRF FG winds (O 2 B), (b) wind speed departures of QuikSCAT observations from WRF 3DVAR analyzed winds (O 2 A), (c) TPW departures of SSM/I observations from WRF FG TPW (O 2 B), and (d) TPW departures of SSM/I observations from WRF 3DVAR analyses of TPW (O 2 A) at 0000 UTC 1 Jul 2006.

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21 FIG. 3. The RS and t values with respect to CNT in the initial 950-hPa wind speed (m s ), due to (a),(d) the assimilation of QuikSCAT surface winds, (b),(e) the assimilation of SSM/I surface winds, and (c),(f) the 950-hPa relative humidity (%) due to assimilation of SSM/I TPW. Points that fall above the 90% conﬁdence level (t values greater than 1.69) are shaded black in (d)–(f). The statistics are obtained from analyzing 30 samples.

0000 UTC with five (CNT, QW, SW, SWV, and SWWV) partures (O 2 B); hence, the analysis better matches the different sets of initial conditions. observations. This is illustrated in Fig. 2, showing his- tograms of first-guess and analysis departures for 4. Assimilation results QuikSCAT (Figs. 2a and 2b) and SWV (Figs. 2a and 2d) experiments. For QuikSCAT, the first-guess departures a. Overview of the fit to observations (O 2 B; Fig. 2a) have an RMSE of about 2.2 m s21, The first comparison that we made can be described as while the analysis departures (O 2 A; Fig. 2b) have an a sanity check; the mean and root-mean-square errors RMSE of about 0.5 m s21. The mean difference is re- (RMSEs) of the observed minus analysis (analysis de- duced from 20.31 m s21 (O 2 B) to 0.05 m s21 (O 2 A). partures), as well as the observed minus first guess In the SSM/I TPW assimilation (SWV) case, the mean (first-guess departures), were analyzed for the different difference is reduced from 0.245 g cm22 (O 2 B;Fig.2c) experiments. In a successful assimilation, the analysis to 20.002 g cm22 (O 2 A;Fig.2d),whileRMSEisre- departures (O 2 A) are smaller than the first-guess de- duced from 0.387 g cm22 (O 2 B; Fig. 2c) to 0.028 g cm22

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21 FIG. 4. (a) Spatial distribution of RMSE in 24-h forecasted 10-m wind speeds (m s ) by CNT, and FIs obtained for different experiments: (b) QW, (c) SW, (d) SWV, and (e) SWWV. The FIs are calculated only for the points where the CNT RMSEs are greater than 1 m s21. The values at the top-right corners in (b)–(e) show the domain-average values of the FIs. The statistics are obtained by comparing 30 samples of model forecasts valid at 0000 UTC with corresponding QuikSCAT observations.

(O 2 A; Fig. 2d). For SW (not shown), the RMSE of the experimental (with satellite data) and control (CNT; theanalysisdeparturesisoftheorderof1.5ms21 as op- without satellite data) cases. Figures 3a–c show the RS in posed to 3.2 m s21 for the ﬁrst-guess departures. Overall, initial wind speed between the QuikSCAT wind assimila- Fig. 2 conﬁrms that 3DVAR is successful in bringing the tion (QW) and CNT (Fig. 3a), SSM/I wind assimilation analysis closer to the observations than the background. (SW) and CNT (Fig. 3b), and SSM/I TPW assimilation (SWV) and CNT (Fig. 3c). These results are obtained by b. Impacts of QuikSCAT and SSM/I data on the comparing 30 samples of experimental analyses with the analysis corresponding control analyses. The change in 950-hPa To see the sensitivity of different satellite data in the wind speed due to the assimilation of QuikSCAT (QW) 3DVAR initial analysis, we computed the spatial distribu- and SSM/I (SW) data is mainly observed over the equation of root-mean-square sensitivity (RS; see the appendix) torial Indian Ocean and northern part of the Bay of Bengal in wind speed (m s21) and relative humidity (%) between (Figs. 3a and 3b). SW showed higher (of the order of

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21 FIG. 5. (a) Spatial distribution of RMSEs in 24-h forecasted 850-hPa wind speeds (m s ) by CNT, and FIs obtained for different experiments: (b) QW, (c) SW, (d) SWV, and (e) SWWV. The FIs are calculated only for the points where the CNT RMSEs are greater than 1 m s21. The values at the top-right corners (b)–(e) show the domain-average values of the FIs. The statistics are obtained by comparing 30 samples of model forecasts valid at 0000 UTC with NCEP analyses.

1ms21) sensitivity in the case of 950-hPa wind speed as greater is the confidence that there is a difference between compared to QW. Assimilation of SSM/I TPW (SWV) the CNT and EXP analyses. Our analysis for the 950-hPa caused a 4%–6% change in the 950-hPa relative humidity wind speed showed that in the CNT and QW analyses, and large sensitivity is seen over the northern part of the significant differences are observed over the southwest Arabian Sea, the Bay of Bengal, and the southwest equa- Arabian Sea and scattered areas near the Indian coast torial Indian Ocean (Fig. 3c). To complement the results (areas shaded in black show a difference that corresponds obtained in this section for the impacts of satellite data on to more than 90% confidence level; see Fig. 3d). In the initial analysis, we have carried out a significance test CNT and SW analyses, a large area of significant difference [Student’s t test (Dewberry 2004)] between the analysis (above 90% confidence level) is observed over the equa- from CNT and different assimilation experiments (Figs. torial Indian Ocean (Fig. 3e). Analysis of the 950-hPa 3d–f). It will give an idea about how significant the dif- relative humidity from CNT and SWV showed that a sig- ferences are in the analysis between CNT and the different nificant difference in humidity is observed over the Bay of assimilation experiments. The higher the value of t is, the Bengal and the Arabian Sea near the Indian coast (Fig. 3f).

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FIG. 6. Comparison between 24-h predicted wind speeds from different experiments and buoy/ship- observed wind speeds valid at 0000 UTC for (a) CNT, (b) QW, (c) SW, (d) SWV, and (e) SWWV.

c. Impacts of QuikSCAT and SSM/I data on the compared to CNT, while a negative value of FI indicates forecast degradation. The use of a normalized forecast impact parameter is advantageous for comparing the impacts of The bias or systematic errors are vital in diagnosing satellite data on different model-predicted variables irre- model discrepancies. In the forecasted ﬁelds, RMSE is spective of their original magnitudes. Hence, this param- considered to be a standard measure for evaluating eter is quite useful in quantitatively discriminating the model performance (Wang and Yongfu 2001). We an- prediction of which variable at a particular level was most alyzed the spatial distribution of the bias and RMSE in improved by the assimilation of a particular type of sat- the 24- and 48-h predicted wind speed, temperature, and ellite data. relative humidity. To see the impacts of different satellite data in WRF simulations, we computed the forecast 1) WIND SPEED impact (FI; see the appendix) based on the RMSEs of different assimilation experiments. A positive value of FI Spatial distributions of the biases and RMSEs in 24- indicates improvement due to satellite data assimilation as and 48-h wind speed forecasts valid at 0000 UTC are

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21 FIG. 7. Temporal variation of RMSEs of the predicted wind speeds (m s ) from different experiments as compared with buoy observations at two locations: (a) the Bay of Bengal (11.58N, 81.58E) and (b) the Arabian Sea (10.68N, 72.48E). The statistics are based on 30 samples. computed by comparing 30 samples of CNT forecasts speed by the WRF CNT simulation are increased by a with corresponding QuikSCAT observations. We used magnitude of the order of 0.5 m s21 in the 48-h forecasts the QuikSCAT observations falling in a time window of (not shown). Similar to the 24-h forecast, experiments 61 h near 0000 UTC for the comparison of model output. with the assimilation of satellite data improved the 48-h As compared with the observations, the 24-h WRF CNT wind speed prediction, and the largest improvement is forecasts underestimated the wind speed by 1.5–2 m s21 seen over the Bay of Bengal due to SSM/I TPW as- over the southern Arabian Sea near peninsular India similation (SWV; not shown). The assimilation of TPW and this weakening extends slightly eastward in the 48-h led to significant impacts on the winds. This change in forecasts (not shown). RMSEs of the order of 1–2 m s21 the wind field is likely due to the indirect forcing of are observed in the 24-h predicted wind speed from WRF winds by model physics. For example, by adding mois- CNT simulations over the oceanic region with larger ture to the lower troposphere or removing moisture values seen over the equatorial Indian Ocean south of from the upper troposphere, the TPW can lead to wind peninsular India (Fig. 4a). The spatial distribution of FI adjustments. from different assimilation experiments (Figs. 4b–e) To complement the results obtained from the verifica- shows that the assimilation of satellite data improved the tion of the model-predicted wind speed with QuikSCAT predicted wind speed (more positive area as compared to observations and considering that QuikSCAT observa- negative area in Figs. 4b–e). Among the assimilation tions are available only over the ocean, we compared the experiments, more positive area (representing improved model-predicted wind field at 850 hPa with the NCEP- prediction of wind speed) is seen in experiments in which analyzed field. Comparison with the NCEP analysis QuikSCAT wind (QW) or SSM/I TPW (SWV) results showed very high RMSEs (more than 4 m s21) in the are assimilated. The RMSEs in the 24-h forecasted wind WRF 24-h predicted wind speed near the southwest

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FIG. 8. (a) Spatial distribution of RMSEs of 18-h forecasted 850-hPa temperature (K) by CNT, and FIs obtained for different experiments: (b) QW, (c) SW, (d) SWV, and (e) SWWV. The FIs are calculated only for the points where the CNT RMSEs are greater than 1 K. The values at the top-right corners in (b)–(e) show the domain-average values of the FIs. The statistics are obtained by comparing 30 samples of model forecasts valid at 1800 UTC with AIRS observations. coast of India and the Bay of Bengal near Sri Lanka (Fig. speed at 10-m height with buoy/ship-observed wind speeds 5a). Among the experiments, the one in which only the available from the International Comprehensive Ocean– SSM/I TPW was assimilated (SWV), improved the 24-h Atmosphere Dataset (ICOADS; Woodruff et al. 1998; predicted wind speed at 850 hPa (Figs. 5b–e). It is seen Worley et al. 2005). The conventional buoy/ship obser- that the assimilation of SSM/I wind speeds resulted in vations at 2-m height are converted to 10-m heights using a large degradation of wind speed prediction over the the logarithmic wind proﬁle relation followed by Mears equatorial Indian Ocean and northeast India (Figs. 5c et al. (2001) before using them to verify the model- and 5e). Assimilation of SSMI TPW (SWV) showed predicted wind speed. It is observed from the scatterplot large improvements (not shown) in the 48-h wind speed of 24-h predicted wind speeds by different experiments predictions over the equatorial Indian Ocean. and buoy/ship observed wind speeds (Fig. 6) that the With the aim of verifying the model-predicted wind WRF forecasts showed an average RMSE of 3.5 m s21 speed with an independent observation (which is not as- and a positive bias of 0.65 m s21 over the oceanic region. similated in this study), we compared the predicted wind The RMSE in forecasted wind speed slightly increased in

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FIG. 9. As in Fig. 8 but for 500-hPa temperature (K). The FIs arecalculated only for the points where the CNT RMSEs are greater than 0.8 K. The values in the top-left corners in (b)–(e) show the domain-average values of the FIs. the case of the 48-h forecast, while a significant change is the forecast intervals over the Bay of Bengal while the not observed in the bias (not shown). Temporal vari- experiments showed mixed results over the Arabian Sea ations in the forecasted wind speeds are analyzed by (Fig. 7). verifying them with buoy/ship observations over two se- 2) TEMPERATURE lected locations: one over the Bay of Bengal (11.58N, 81.58E) and the other over the Arabian Sea (10.68N, The model-predicted temperature is verified with the 72.48E). It is clear that the RMSEs in the forecasted wind independent (not assimilated) observations from the speeds from the WRF are larger over the Bay of Bengal Atmospheric Infrared Sounder (AIRS; Aumann et al. (Fig. 7a) as compared to those over the Arabian Sea (Fig. 2003). When the AIRS-retrieved thermodynamic profiles 7b). The RMSEs in the predicted wind speeds are slightly are compared to radiosondes (Fetzer et al. 2003; Tobin less in the satellite data assimilation experiments as et al. 2006; Divakarla et al. 2006), RMSEs of 1 K in 1-km compared to the CNT at some forecast intervals over layers for temperature and 10%–15% in 2-km layers for both areas (Fig. 7). The experiments in which SSM/I relative humidity are found. The AIRS data used in this TPW are assimilated (SWV and SWWV) showed the study are level 2 version 4.0 atmospheric temperature and lowest RMSEs in the predicted wind speeds for most of moisture profiles of the clear-sky condition at a spatial

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FIG. 10. (a) Spatial distribution of the RMSEs in 24-h forecasted 850-hPa temperature (K) by CNT, and the FIs obtained for different experiments: (b) QW, (c) SW, (d) SWV, and (e) SWWV. The FIs are calculated only for the points where the CNT RMSEs are greater than 1 K. The values in the top-right corners in (b)–(e) show the domain- average values of the FIs. The statistics are obtained by comparing 30 samples of model forecasts valid at 0000 UTC with NCEP analyses. resolution of 50 km. We have compared the 18- and 42-h FI is computed for the assimilation experiments to assess predicted temperatures at lower (850 hPa), middle the impacts of satellite data. The spatial distribution of the (500 hPa), and upper (200 hPa) levels with AIRS obser- biases in the 18-h temperature forecasts at 850 and vations around (61.5 h) 1800 UTC. The 1800 UTC AIRS 200 hPa from CNT show that the WRF produced a neg- observations are selected for the veriﬁcation because of ative bias (warming) of the order 1–1.5 K, whereas at the greater spatial coverage of observations around this 500 hPa it showed a positive bias (cooling) of the same time. All the statistics shown in this section were obtained order in magnitude (not shown). The 18-h predicted by comparing 30 samples of model forecasts with a cor- temperature from WRF CNT showed RMSEs of the or- responding sample of 30 AIRS observations. The spatial der of 1.5 K at 850 hPa (Fig. 8a), 1 K at 500 hPa (Fig. 9a), distribution of the biases and RMSEs in the predicted and 2 K at 200 hPa (not shown) with the higher values temperatures is computed for CNT to verify the temper- found over the northwest of Bay of Bengal and the In- ature prediction of the WRF and the spatial distribution of dian landmass. The spatial distribution of FI in 18-h

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FIG. 11. (a) Spatial distribution of RMSEs in 18-h forecasted 850-hPa relative humidity (%) by CNT, and FIs obtained for different experiments: (b) QW, (c) SW, (d) SWV, and (e) SWWV. The FIs are calculated only for the points where the CNT RMSEs are greater than 10%. The values in the top-right corners in (b)–(e) show the domain- averages values of the FIs. The statistics are obtained by comparing 30 samples of model forecasts valid at 1800 UTC with AIRS observations. temperature forecasts at 850 hPa showed that SSM/I different experiments at different pressure levels showed TPW assimilation (SWV) produced the greatest im- that assimilation of satellite data improved the tempera- provement in temperature prediction, while the SSM/I ture prediction and the largest improvement was due to wind speed (SW) showed slight degradation (Figs. 8b–e). QuikSCAT wind (QW) or SSM/I TPW (SWV) assimila- Assimilation of satellite data showed improvement (more tion (not shown). positive area as compared to negative area) in 18-h tem- Since complete spatial coverage of AIRS observations perature forecasts at 500 (Figs. 9b–e) and 200 hPa (not over the model domain was not available for veriﬁcation, shown). Analyses of 42-h temperature forecasts from we also compared the model-predicted temperature ﬁeld

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FIG. 12. As in Fig. 11 but for 500-hPa relative humidity (%). The FIs are calculated only for the points where the CNT RMSEs are greater than 15%. The values in the top-left corners in (b)–(e) show the domain-averages values of the FIs. with the NCEP analysis. The 24-h forecast from the WRF wind (QW) improved the 24- (Fig. 10b) and 48-h (not CNT simulation at 850 hPa showed RMSEs of the order shown) temperature forecasts at 850 hPa over the similar to the comparison with AIRS, with larger RMSEs southern part of the Arabian Sea. The experiments, in observed over the regions to the north and west of which SSM/I wind speed is assimilated (SW and SWWV), northwest India (Fig. 10a). Among the assimilation ex- degraded the 24- (Figs. 10c and 10e) and 48-h (not shown) periments, the largest improvements in the 24- (Figs. temperature forecasts at 850 hPa over most of the region. 10b–e) and 48-h (not shown) temperature forecasts at The 24-h temperature forecasts at 500 hPa by WRF CNT 850 hPa are observed to be due to the assimilation of showed RMSEs of the order of 1 K and the largest values SSM/I TPW (SWV), which is similar to the comparison were distributed over the north and northwest of India with AIRS observations. The assimilation of QuikSCAT (not shown). The assimilation of satellite data improved

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FIG. 13. (a) Spatial distribution of RMSEs in 24-h forecasted 850-hPa relative humidity (%) by CNT, and forecast impact (FI) obtained for different experiments: (b) QW, (c) SW, (d) SWV, and (e) SWWV. The FIs are calculated only for the points where the CNT RMSEs are greater than 10%. The values in the top-right corners in (b)–(e) show the domain-averages values of the FIs. The statistics are obtained by comparing 30 samples of model forecasts valid at 0000 UTC with NCEP analysis. the 24- and 48-h temperature forecasts at 500 hPa with sphere (;5%–10%) at 850 hPa as compared to AIRS, the largest improvement due to SSM/I TPW (SWV) as- but underpredicted the humidity over the western part similation (not shown). of the Bay of Bengal (not shown). Similarly, 18- and 42-h forecasts by WRF CNT overpredicted the humidity on 3) RELATIVE HUMIDITY the order of 10%–15% at 500 hPa (also not shown). The Similar to temperature, 18- and 42-h forecasts of rel- 18-h forecasts by WRF CNT showed a RMSE on the ative humidity at different pressure levels are compared order of 10% at 850 hPa (Fig. 11a) and 20% at 500 hPa with AIRS observations valid at 1800 UTC. The 18- and (Fig. 12a). The RMSE for the predicted humidity at 42-h forecasts by WRF CNT simulated a moist atmo- 500 hPa over the Indian landmass is larger compared

Unauthenticated | Downloaded 10/03/21 11:32 PM UTC DECEMBER 2009 R A K E S H E T A L . 1721 to the oceanic region (Fig. 12a). The RMSEs in 42-h forecasts by WRF CNT show a spatial distribution similar to the 18-h forecast with a slightly larger magnitude (not shown). The FIs due to the assimilation of satellite data are computed for those grid points where CNT RMSEs exceed a minimum threshold (10% at 850 hPa and 15% at 500 hPa). It is seen that the assimilation of satellite data improved the 18- (Figs. 11b–e) and 42-h (not shown) humidity forecasts at 850 hPa, and among the experiments, the largest improvement is observed to be due to SSM/I TPW (SWV) assimilation. Even though there are small pockets of negative area (degradation) observed in the 18- (Figs. 12b–e) and 42-h (not shown) humidity forecasts at 500 hPa from different assimilation experiments, a large positive area (improvement) is seen over the Indian landmass and the Bay of Bengal, particularly for the experiments in which SSM/I TPW is assimilated. The 24- and 48-h forecasts of relative humidity at 850 and 500 hPa are also verified against the NCEP analysis. An RMSE of the order of 10% is observed in the 24-h predicted relative humidity (Fig. 13a) by WRF CNT at 850 hPa and, as expected, the RMSE is increased (;5%) at 48 h (not shown). A large area of very high RMSE in humidity prediction is observed over the Arabian Sea in the 24- (Fig. 13a) and 48-h (not shown) forecasts. The assimilation of SSM/I TPW (SWV) improved the 24- (Fig. 13d) and 48-h (not shown) humidity forecasts, but the assimilation of wind speed (QuikSCAT or SSM/I) did not show much improvement (Figs. 13b and 13c). At 500 hPa, the RMSEs in the 24- and 48-h predicted humidity by WRF CNT are on the order of 20% (not shown). Similar to 850 hPa, assimilation of SSM/I FIG. 14. Accumulated rainfall (cm) for the month of July 2006 TPW (SWV) showed significant improvement in humidity (a) from TRMM, (b) from CNT, and (c) from (a) minus (b). prediction at 500 hPa whereas wind speed (QuikSCAT or SSM/I) assimilation did not show much improvement (not shown). northwest India and the rainshadow areas of the eastern coast of southern peninsular India. The observed monthly 4) RAINFALL accumulated rainfall maxima and rainshadow regions The Tropical Rainfall Measuring Mission (TRMM) were qualitatively reproduced by accumulated rainfall 3B42 version 6 (3B42V6) product (Haddad et al. 1997; prediction from WRF CNT day 1 forecasts (Fig. 14b). Adler et al. 2000) was used for the validation of the model- The difference plot (Fig. 14c) of the accumulated rain- predicted rainfall. Here, we examine the spatial distribu- fall prediction from day 1 forecasts by WRF CNT with tion of the monthly accumulated rainfall from day 1 (ac- TRMM-observed rainfall showed that WRF CNT un- cumulated rainfall during first 24-h forecast) and day 2 derpredicted rainfall over the Western Ghat near pen- (accumulated rainfall from 24- to 48-h forecasts) forecasts, insular India and in the foothills of the Himalayas. The the improvement parameter (h), the statistical skill scores rainfall over the northern fringes of the Western Ghat at various rainfall thresholds, and the impact ratio (IR) and the head of the Bay of Bengal is overpredicted in from the FIs based on the RMSEs in the rainfall forecasts. WRF CNT day 1 forecasts. The accumulated rainfall The observed monthly accumulated rainfall distribu- from day 2 forecasts by WRF CNT showed an increased tion from TRMM (Fig. 14a) showed the July monsoon overprediction of rainfall throughout the model domain rainfall maxima over the west coast of India and the as compared to the day 1 forecast (not shown). For the northeast Bay of Bengal near the Arakan coast. Much quantitative assessment of improvement/degradation due less precipitation is observed over semiarid regions of to the assimilation of satellite data as compared to CNT,

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FIG. 15. Spatial distribution of the improvement parameter (h) in July accumulated rainfall from day 1 forecasts by different experiments: (a) QW, (b) SW, (c) SWV, and (d) SWWV. we have computed the spatial distribution of h in the 0.5, 1, 2, 3, 4, 5, 6, 7, and 8 cm). A detailed description of monthly accumulated rainfall prediction from the day 1 the rain contingency table and formulas used for calcu- and 2 forecasts. It is clear from the spatial distribution of lating the skill scores are available in Yang and Tung h (Fig. 15) that the assimilation of SSM/I TPW (SWV (2003). The variations of BSs and ETSs with rainfall and SWWV) improved (positive area in Figs. 15c and thresholds obtained for day 1 and 2 forecasts are pre- 15d) the day 1 rainfall prediction over the west coast of sented in Figs. 16 and 17, respectively. The day 1 forecasts India and the Bay of Bengal. Similarly, the spatial dis- from all the experiments overpredicted (underpredicted) tribution of h for the accumulated rainfall from day 2 the area of 24-h accumulated rainfall for low (high) forecasts also showed that the SSM/I TPW assimilation thresholds (Fig. 16a). The assimilation of SSM/I TPW improved the rainfall prediction in this period (not showed comparatively low bias (less overprediction) in shown). This impact is likely due to the improvement in predicting light (less than 2 cm) rainfall while it showed the lower-level moisture ﬁelds due to the assimilation of high bias (more underprediction) for heavy (more than SSM/I TPW. 2 cm) rainfall (Fig. 16a). The comparatively higher bias To examine the skill of the different experiments in (underprediction) due to SSM/I TPW assimilation for reproducing the frequencies of occurrence of rainfall high rainfall thresholds may be due to the saturation events at or above a precipitation threshold, we have problem in the SSM/I TPW data (Singh et al. 2008a). computed statistical skill scores [such as the bias scores The day 2 forecasts by all the WRF experiments showed (BSs) and equitable threat scores (ETSs); Anthes et al. large overprediction (more than double that observed) (1989); Wilks (2006)] for 24-h accumulated rainfall in the area of occurrence of 24-h accumulated rainfall for predictions from day 1 and 2 forecasts. These statistics all rainfall thresholds, and among the experiments, the are obtained by comparing 30 samples each of daily overprediction is comparatively less in SWV (Fig. 16b). It accumulated rainfall predictions from different experi- can be seen in Fig. 17 that day 1 and 2 forecasts from the ments with corresponding observed rainfall for the pe- WRF showed poor skill in reproducing the frequency of riod 2–31 July 2006 at various rainfall thresholds (0.2, occurrence of the daily accumulated rainfall at higher

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FIG. 16. BSs in rainfall prediction for different experiments calculated by verifying 30 samples each of 24-h accumulated rainfall predictions by day (a) 1 and (b) 2 forecasts with corresponding TRMM observations for various rainfall thresholds. thresholds. Model skill deteriorated rapidly with rainfall for daily accumulated rainfall prediction from day 1 and threshold and the experiments in which SSM/I TPW is 2 forecasts are shown in Fig. 18. Assimilation of satellite assimilated (SWV and SWWV) showed comparatively data (QuikSCAT and SSM/I TPW) produced consistent better skill in predicting the area of rainfall for most of the positive impact (IR values greater than 100 for high FI thresholds (Fig. 17). As expected, the skill in predicting ranges) on rainfall prediction (Fig. 18), while SSM/I wind the area of rainfall by WRF is poorer for the day 2 fore- speed assimilation shows mixed (negative and positive) casts as compared to the day 1 forecasts (Figs. 16 and 17). results. The assimilation of QuikSCAT winds yielded the The BSs and ETSs based on the contingency table maximum positive impact for day 1 forecasts. This impact only measure model accuracy based on the frequency of likely occurs through a change of the boundary layer occurrence at or above a precipitation threshold and, processes (e.g., moisture ﬂux and convergence ﬁelds; thus, do not determine the quantitative rainfall prediction because QuikSCAT contains wind speed as well as di- skill of the model (Colle et al. 1999). Therefore, it is im- rection). Among the experiments, the assimilation of portant to calculate the model skill based on RMSEs in the SSM/I TPW produced highest positive impacts in the model forecasts using the actual quantitative precipitation day 2 rainfall predictions (Fig. 18b). from the model. To assess the percentage improvement– degradation due to the assimilation of satellite data as 5. Sensitivity of assimilation results to the cumulus compared to CNT (without satellite data), we calculated parameterization the IR (see the appendix) based on quantitative FIs (see the appendix) for day 1 and 2 forecasted rainfall by The sensitivities of model biases and RMSEs to the comparing with the TRMM observed rainfall. The IRs convective parameterization scheme (CPS) used (KF of different assimilation experiments against the FI range scheme is used in this study) are explored by

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FIG. 17. As in Fig. 16 but for ETSs.

conducting some additional experiments. We have re- WRF CNT simulations and the FIs from different satellite peated a portion of the study (for the ﬁrst week of July) data assimilation experiments using the GDE scheme for using another CPS, namely the Grell–Devenyi ensem- 18-h temperature and relative humidity prediction at ble (GDE; Grell and Devenyi 2002), in WRF. The re- 850 hPa are shown in Figs. 20 and 21, respectively. The sults from these experiments are compared with the WRF CNT simulations using the GDE scheme showed previous results that used the KF scheme. Such a com- RMSEs of about 1.5 K (Fig. 20a) in the 18-h tempera- parison can answer two questions: 1) to what extend ture prediction at 850 hPa, which is similar to the result can the model biases and RMSEs described in this from the experiments using the KF scheme (Fig. 8a) in paper be attributed to the CPS used in the study and 2) this study. Similarly, the magnitudes of the RMSEs in how sensitive are the assimilation results to the CPS the 18-h temperature prediction at mid- and upper levels used. A comparison of model biases in 18-h tempera- by WRF CNT using the GDE scheme (not shown) are ture predictions by WRF CNT at various levels using similar to those obtained using the KF scheme earlier in the KF and GDE CPSs is presented in Fig. 19. It is clear this study. The impacts of the satellite data on the WRF that the simulation using GDE is similar to that with experiments with the GDE scheme (Figs. 20b–e) are in KF, except for the warming seen in the midlevels. As agreement with the simulations using the KF scheme compared to KF, the magnitude of the biases is slightly (Figs. 8b–e). In both cases, the largest positive impacts larger in experiments using the GDE scheme. As seen are a result of the SSM/I TPW assimilation and deg- earlier in the KF case, the 18-h humidity prediction using radation due to the SSM/I wind speed assimilation. the GDE scheme also showed a moist atmosphere as Similarly, the largest positive impacts in the 18-h hu- compared to observations in the lower and midlevels (not midity prediction at 850 hPa in the experiments using shown). Similar results are also observed for 42-h tem- the GDE scheme are observed to be due to SSM/I TPW perature and humidity predictions. The RMSEs in the assimilation while the least positive impacts are seen in

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FIG. 18. IR vs FI range in 24-h accumulated rainfall prediction (a) day 1 and (b) day 2 forecasts by different experiments.

SSM/I wind speed assimilation (Figs. 21b–e) and are in 6. Summary agreement with the earlier results using the KF scheme (Figs. 11b–e). A month-long series of numerical simulations was It is seen from the spatial distribution of the RMSEs in conducted using the Advanced Research version of the temperature and humidity prediction using the KF and Weather Research and Forecasting (WRF-ARW) model GR schemes that the former showed comparatively to assess the impacts of the assimilation of satellite data isolated and extreme values of RMSEs as compared to on model forecasts over the Indian region during the 2006 the latter. One of the reasons for isolated and extreme summer monsoon. The WRF and its 3DVAR system are values of RMSEs in experiments using the GR scheme used to investigate the impacts of the QuikSCAT near- may be due to the false precipitation rate simulated by surface winds, SSM/I-derived winds, and total precipi- this scheme, particularly the convective precipitation table water (TPW). The control (without satellite data rates. The GR scheme had less active convective pre- assimilation) and experimental (with satellite data as- cipitation and might have failed in stabilizing the at- similation) forecasts are made for 48 h each day starting at mosphere through removing the instability locally by the 0000 UTC from 1 to 31 July 2006. The control run served transfer of heat and moisture. Similar results were also as a baseline for verifying the assimilation experiments. reported by previous investigators (Wang and Seaman The 24- and 48-h forecasts from the WRF CNT simu- 1997; Ratnam and Cox 2006). Even though slight dif- lations showed a weakening of the cross-equatorial ﬂow ferences are found in the satellite data assimilation ex- over the southern Arabian Sea near peninsular India. The periments using different CPSs, on average the satellite WRF CNT simulated a warm and moist atmosphere at data produced similar impacts to the WRF simulations the lower (850 hPa) levels, a cooling and moistening at in this study. the middle (500 hPa) levels, and a warming at the upper

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FIG. 19. Biases in 18-h predicted temperature at different levels in the WRF CNT simulation using the KF and GDE schemes.

(200 hPa) levels. The 18–24-h forecasts at lower levels assimilation experiments, the largest positive impacts in from WRF CNT showed RMSEs in predicted wind, the temperature and humidity predictions are observed temperature, and humidity to be on the order of 2 m s21, to be due to SSM/I TPW assimilation. The assimilation 1.5 K, and 10%–15%, respectively, with the magnitude of SSM/I wind speeds resulted in a degradation of of the RMSEs being slightly larger in the 42–48-h fore- the temperature and humidity predictions at lower casts. The errors in the wind speed predictions of the WRF levels. are larger over the Bay of Bengal as compared to the The day 1 forecasts by WRF CNT showed an un- Arabian Sea. Assimilation of satellite data (QuikSCAT derprediction of rainfall over the Western Ghat near wind and SSM/I TPW) improved the 24- and 48-h hour peninsular India, as well as over the foothills of the predicted wind speeds, with the largest improvement Himalayas. The spatial distribution of the improvement observed due to SSM/I TPW assimilation. Among the parameter (h) from the day 1 and 2 forecasts showed

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FIG. 20. As in Fig. 8 but from the WRF experiments using the GDE cumulus scheme. The statistics are obtained by comparing seven forecast samples valid at 1800 UTC with AIRS observations for the first week of July. significant improvement in the rainfall prediction over the day 2 rainfall predictions. The SSM/I TPW assimila- the west coast of India due to the assimilation of SSM/I tion resulted in improved rainfall prediction skill for both TPW. The day 1 forecasts of WRF overpredicted (un- day 1 and 2 forecasts. Across most of the thresholds, the derpredicted) the area of light (heavy) rainfall in the assimilation of SSM/I wind speeds alone degraded the 24-h accumulated rainfall predictions. The experiments in day 1 and 2 rainfall prediction skill of the WRF. which SSM/I TPW is assimilated showed comparatively better skill in predicting the areas of rainfall for most of Acknowledgments. WRF is made publicly available the thresholds tested. The assimilation of QuikSCAT and supported by the Mesoscale and Microscale Me- winds significantly improved the quantitative rainfall teorology (MMM) division at the National Center prediction skill level for the day 1 forecasts, but significant for Atmospheric Research (NCAR). Their dedication improvement due to QuikSCAT data was not observed in and hard work is gratefully acknowledged. The authors

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FIG. 21. As in Fig. 11 but from the WRF experiments using the GDE cumulus scheme. The statistics are obtained by comparing seven forecast samples valid at 1800 UTC with AIRS observations for the first week of July. acknowledge the National Centers for Environmental of research fellowship by the University Grant Commis- Prediction (NCEP) for making analysis data available at sion (UGC), India. their site. The SSM/I and QuikSCAT data were obtained online (ftp.ssmi.com). The ICOADS data were also ob- APPENDIX tained online (ftp.dss.ucar.edu). The AIRS and TRMM data were obtained from NASA Web sites and NASA Definition of the Parameters Used for Assessing is acknowledged thankfully. The authors thank the the Impacts of Satellite Data anonymous reviewers for their critical and insightful comments and suggestions, which were helpful in sub- 1) The root-mean-square sensitivity (RS) of the assim- stantially improving the content and quality of the man- ilated satellite data in the 3DVAR analysis (initial uscript. One of the authors (RV) acknowledges the award conditions) is defined as

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