The impact of assimilation of AMSU data for the prediction of a tropical cyclone over India using a mesoscale model

Tropical cyclones form over the seas: a typical data‐sparse region for conventional observations. Therefore, satellites, especially with microwave sensors, are ideal for cyclone studies. The advanced microwave sounding unit (AMSU) , in addition to providing very valuable data over non‐precipitating cloudy regions, can provide very high horizontal resolution of the temperature and humidity soundings. Such high‐resolution microwave data can improve the poorly analysed cyclone. The objective of this study is to investigate the impact of ingesting and assimilating the AMSU data together with conventional upper air and surface meteorological observations over India on the prediction of a tropical cyclone which formed over the Arabian Sea during November 2003 using analysis nudging. The impact of assimilating the AMSU‐derived temperature and humidity vertical profiles in a mesoscale model has not been tested yet over the Indian region. Such studies are important as most weather systems over India form over the seas. The present study is unique in the sense that it addresses the impact of ingesting and assimilating microwave sounding data (together with conventional India Meteorological Department data) on the prediction of a tropical cyclone, which formed over the Arabian Sea during November 2003 using analysis nudging. Two sets of numerical experiments are designed in this study. While the first set utilizes the National Center for Environmental Prediction (NCEP) reanalysis (for the initial and lateral boundary conditions) only in the fifth‐generation mesoscale model simulation, the second set utilized the AMSU satellite and conventional meteorological upper air and surface data to provide an improved analysis through analysis nudging. The results of the two sets of model simulations are compared with one another as well as with the NCEP reanalysis and the observations.

The results of the study indicated that the impact of ingesting and assimilating microwave sounding data and the conventional meteorological data through nudging resulted in an improvement in the simulation of wind asymmetries and the warm temperature anomalies. The with‐assimilation run simulated stronger wind speeds and stronger vertical velocity motion as compared with the without‐assimilation run. The time series of the minimum sea level pressure (SLP) and maximum wind speed for the simulations with the microwave sounding data and conventional meteorological data show better agreement with the observations than the simulations without the assimilation. The central minimum pressure of the simulations with the modified analysis are lower by 7 hPa as compared with the simulations without the assimilations. Even though there is not much of a difference in the maximum wind speed between the two simulations at the initial forecast time, the results indicate that the simulations with microwave sounding data and conventional meteorological data reveal a marked (9 m/s) increase in the maximum wind speed over the simulations without the assimilation. While the lowest central pressure estimated from the satellite image is 988 hPa, the simulations with microwave sounding data and conventional meteorological data show a value of 999.5 hPa for the lowest central minimum pressure. One reason for the inability of the simulation with improved analysis to achieve the observed lowest SLP is that the NCEP reanalysis had manifested an extremely weak system in the first place and, despite assimilation with microwave sounding data and conventional meteorological data, only a moderate improvement in the lowest SLP could be achieved. A proper appreciation of the impact of the microwave sounding data can be obtained by comparing with the lowest SLP obtained from the simulation without assimilation which showed a value of 1007 hPa. The initial mis‐representation in the location of the centre of the cyclone in the NCEP reanalysis with respect to the observed location has led to marked errors in the track prediction of both the model simulations. The assimilation of microwave satellite data is yet to be implemented in the current operational regional model over India and hence the results of this study may be relevant to the operational tropical cyclone forecasting community.     

The impact of assimilating MeghaTropiques SAPHIR radiances in the simulation of tropical cyclones over the Bay of Bengal using the WRF model

The present study aims to investigate the impact of assimilating SAPHIR (Sounder for Probing Vertical Profiles of Humidity) radiances in the simulation of tropical cyclones over the Indian region by the Weather Research and Forecasting (WRF) model. Three tropical cyclones which formed over the Bay of Bengal are chosen as the case studies. Since SAPHIR is a humidity microwave sensor, it is interesting to assess the impact of these observations in simulating cyclones which depend significantly on moist-convective processes. The study makes use of the three-dimensional variational (3DVar) assimilation technique of the WRF variational assimilation system. The results of the study indicate that the assimilation of SAPHIR radiances do have a positive impact on the simulation of tropical cyclones considered here. Two model simulations are performed – a control run (Ctrl) with only conventional and satellite wind observations assimilated, and a SAPH run (SAPH) where SAPHIR radiances are also assimilated in addition to conventional and satellite wind observations. Both these simulations are compared to each other and to observations from the India Meteorological Department (IMD), Joint Typhoon Warning Centre (JTWC), and Tropical Rainfall Measurement Mission (TRMM), as well as analysis fields from Global Forecast System (GFS) from the National Centres for Environmental Prediction (NCEP). Comparison of minimum sea level pressure and maximum wind speed simulated by the model with the IMD and JTWC observations shows that the SAPHIR assimilation has a moderate impact on the simulation of these features by the model. Track prediction of the model is also improved at initial forecast times, as evidenced by the reduced track errors in the model run with SAPHIR radiances assimilated. The warm core structure, as well as the relative vorticity structure of the cyclones, are also impacted in a moderate manner by the assimilation of SAPHIR radiances. The assimilation also positively impacted the rainfall simulation of the model. This is seen from the higher equitable threat score, lower false alarm ratio, and higher probability of detection estimated with respect to TRMM observations, in the SAPH run as compared to the Ctrl run.     

Importance of satellite-retrieved rain/no-rain information on short-range weather predictions

The impact of assimilating rain (satellite-retrieved rainfall is greater than zero) and no-rain (satellite-retrieved rainfall is equal to zero) information retrieved from the Tropical Rainfall Measuring Mission (TRMM) 3B42 precipitation is assessed during Indian summer monsoon 2013 using the weather research and forecasting (WRF) model. Daily three parallel experiments are performed with and without satellite rainfall assimilation for short-range weather forecasts. Additional two experiments are performed daily to evaluate the sensitivity of cumulus parameterization on the WRF model predictions when precipitations are used for assimilation. Precipitation assimilation improves the 48 h low-level temperature, moisture, and winds predictions. Rainfall prediction is also improved over central India when satellite-retrieved rainfall information are assimilated compared to without rainfall assimilation (CNT) experiments. More improvements are seen in moisture forecasts when the Kain–Fritsch (KF) cumulus convection parameterization scheme is used against the Grell–Devenyi ensemble (GD) scheme, whereas for temperature and wind speed forecasts the Grell convection parameterization scheme performed better over the Indian region. Overall, precipitation assimilation improved the WRF model analysis and subsequent model forecasts compared with without precipitation assimilation experiments. Results show that no-rain observations also have a significant positive impact on short-range weather forecasts.     

Multisatellite observations and numerical simulation of an along‐coast cumulus cloud line induced by sea‐breeze circulation

A coastal cumulus cloud‐line formation along the east coast of the USA was observed on a National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellite (POES) Advanced Very High Resolution Radiometer (AVHRR) satellite image from 17 August 2001. The cloud line starts to form at about 16:00 UTC (local 12:00 noon) and follows the coastline from Florida to North Carolina. The length and width of the cloud line are about 850 km and 8.5 km, respectively. A 15‐min interval sequence of NOAA Geostationary Operational Environmental Satellite (GOES) images shows that the cloud line maintains the shape of the coastline and penetrates inland for more than 20 km over the next 6‐h timespan. Model simulation with actual atmospheric conditions as inputs shows that the cloud line is formed near the land–sea surface temperature (SST) gradient. The synoptic flow at all model levels is in the offshore direction prior to 16:00 UTC whereas low‐level winds (below 980 hPa) reverse direction to blow inland after 16:00 UTC. This reversal is due to the fact that local diurnal heating over the land takes place on shorter time‐scales than over the ocean. The vertical wind at these levels becomes stronger as the land–SST increases during the summer afternoon, and the leading edge of the head of the inland wind ascends from 920 hPa to about 850 hPa in the 3 h after 16:00 UTC. Model simulation and satellite observations show that the cloud line becomes very weak after 21:00 UTC when the diurnal heating decreases.     

A first attempt at assimilating microwave-derived SST to improve the predictive capability of a coupled,high-resolution Eta-POM forecasting system

This study implements the assimilation of sea surface temperature (SST) data acquired by passive microwave remote sensing to a high-resolution, primitive-equation ocean model. The aim was to improve a forecasting tool capable of predicting the surface ocean processes linked to the air–sea interactions at sub-mesoscale level using one-way coupled, atmosphere–ocean modelling. An assimilation scheme based on a Newtonian relaxation scheme was fine-tuned to improve the forecasting skill of the ocean model. The ocean model was driven by predicted, synchronous air–sea fluxes derived by an overlying atmosphere model, remotely sensed SST and lateral boundary conditions derived from its previous run. The estimation of the model forecasting error was based on statistical and spatial comparison with remotely sensed observations. The optimal nudging coefficient was found to be 5 × 10^?4 for 12 hours, giving a mean bias of ?0.07°C. Forecast validation was done against calibrated AVHRR scenes using a new approach to calibrate region-specific scenes based on the split-window technique. This work demonstrates the benefit of using passive microwave remote sensing to improve high-resolution ocean forecasting systems. It also shows the high complementarity of infrared and passive microwave satellite sensors to provide information on the surface thermodynamics of the Ionian Sea.     

同化FY-3C/MWHTS观测资料反演的海面气压场对台风数值预报的影响

由中国风云三号C星（FY-3C）搭载的微波温湿探测仪（MWHTS）的亮温观测资料能够实时反演得到高分辨率、高精度的海面气压场。基于三维变分同化方法将FY-3C/MWHTS观测资料反演的海面气压场同化进入中尺度天气研究与预报（Weather Research and Forecasting, WRF）模式,以台风“Maria”和“Noru”为例,通过控制实验和同化试验的对比分析,探讨了同化反演的海面气压场对台风数值预报的影响。初始化敏感性试验结果表明,同化海面气压场使初始时刻台风中心气压与位置更接近实况,并且调整了台风初始温度场和风场的结构和分布。台风的数值预报结果表明:同化反演的海面气压场能够改进台风的路径和强度预报精度。     

Influence of Assimilating the Sea Surface Pressure Fields Retrieved from FY-3C/MWHTS Data on Typhoon Forecasting

Using measurements with the Microwave Temperature and Humidity Sounder (MWHTS) onboard the Chinese Fengyun-3C satellite, real-time and high resolution sea surface pressure information can be retrived. Based on the three-dimensional variational assimilation (3DVAR) method, the retrieved sea pressure fields from FY-3C/MWHTS observations are assimilated into the Weather Research and Forecasting (WRF) model. The influence of the retrieved pressure fields on typhoon forecasting is discussed through the comparison between control experiment and assimilation experiment. Sensitivity experiments of typhoon Maria and Noru show that the assimilation of sea surface pressure fields makes the central pressure and central location closer to the actual value, and adjusts the structure and distribution of initial temperature fields and wind fields. The numerical prediction results show that the assimilation of the sea surface pressure fields can improve the accuracy of typhoon track and intensity prediction.     

Evaluation and assimilation of various satellite-derived rainfall products over India

Accurate prediction of rainfall from the numerical weather prediction model is one of the major objectives over tropical regions. In this study, four different satellite-derived rainfall products (viz. merged-rainfall product from TRMM (Tropical Rainfall Measuring Mission) 3B42 and IMERG (Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement)), and Indian meteorological satellite INSAT-3D retrieved HEM (Hydro-Estimator Method) and IMSRA (INSAT Multi-Spectral Rainfall Algorithm) rainfall) are assimilated in the Weather Research and Forecasting (WRF) model using variational method. Before assimilation of satellite retrieved rainfall product in the WRF model, selected rainfall products are compared with ground rainfall from India Meteorological Department during Indian summer monsoon (June–September) 2015. Preliminary validation results show root-mean-square-difference (mean difference) of 18.1 (2.1), 21.3 (2.1), 15.4 (?0.72), and 14.4 (0.5) mm day^?1 in IMSRA, HEM, IMERG, and TRMM 3B42 rainfall, respectively. Further, the four-dimensional variational data assimilation method is used daily to assimilate selected rainfall products in the WRF model during the entire month of August 2015. Results suggest that assimilation of satellite rainfall improved the WRF model analyses and subsequent temperature and moisture forecasts. Moreover, rainfall prediction is also improved with the maximum positive impact from TRMM rainfall assimilation followed by IMERG rainfall assimilation. Similar nature of improvements is also seen in rainfall prediction when INSAT-3D retrieved rainfall products (HEM and IMSRA) are used for assimilation.     

Development of a neural network algorithm for the retrieval of TPW from NOAA16 AMSU measurements

A neural‐network‐based algorithm for the retrieval of Total Precipitable Water (TPW) using Advanced Microwave Sounding Unit (AMSU) data available in real time from NOAA16 satellite has been developed. The retrieval method benefits from reliable surface observations, which includes the skin temperature and ocean surface wind speed and direction. The algorithm uses the simulated brightness temperatures at four frequencies, 23.4 Ghz, 31.4 Ghz, 50.3 Ghz, and 89.0 Ghz, of AMSU‐A as input and TPW derived from Radiosonde Observations (RAOB) profiles as output. The pairs of input and output are restricted to the homogeneous emitting areas, e.g. over Bay of Bengal and Arabian Sea. The performance of the algorithm is assessed using independent RAOB measurements. The bias and rms differences are found to be 0.21 mm and 2.03 mm respectively over the range of 15 and 75 mm. Further, extensive comparisons are made between the TPW obtained from the neural network algorithm and those obtained using other satellite instruments like the Tropical Rainfall Measuring Mission Microwave Imager, Advanced Infrared Sounder, and Moderate Resolution Imaging Spectroradiometer, in which the results are found to be in close agreement.     

Enhancement of OMI aerosol optical depth data assimilation using artificial neural network

A regional chemical transport model assimilated with daily mean satellite and ground-based aerosol optical depth (AOD) observations is used to produce three-dimensional distributions of aerosols throughout Europe for the year 2005. In this paper, the AOD measurements of the Ozone Monitoring Instrument (OMI) are assimilated with Polyphemus model. In order to overcome missing satellite data, a methodology for preprocessing AOD based on neural network (NN) is proposed. The aerosol forecasts involve two-phase process assimilation and then a feedback correction process. During the assimilation phase, the total column AOD is estimated from the model aerosol fields. The main contribution is to adjust model state to improve the agreement between the simulated AOD and satellite retrievals of AOD. The results show that the assimilation of AOD observations significantly improves the forecast for total mass. The errors on aerosol chemical composition are reduced and are sometimes vanished by the assimilation procedure and NN preprocessing, which shows a big contribution to the assimilation process.     

Sensitivity analysis of high-resolution Oceansat-2 scatterometer winds on Thane cyclone simulation

Ocean surface wind vectors retrieved from the Oceansat-2 scatterometer (OSCAT) are used in this study to evaluate their impact on Thane cyclone simulation. The Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-Var) data assimilation system are adapted to evaluate the sensitivity of OSCAT observations. Simulated track error and landfall forecast are considered as standard measurements to assess the impact of 50 km and ~15 km spacing grid OSCAT winds along and across the swath. Significant improvement is obtained in track forecasting, when high-resolution vector winds (HVW; composite slice-level winds, ~15 km) are used for assimilation rather than coarser-resolution (50 km) operational OSCAT winds. Forecasting sensitivity to observations (OSCAT winds) using WRF tangent linear and adjoint modelling is used to quantify the impact of two different resolutions of OSCAT winds. WRF adjoint modelling is used here as a diagnostic tool, which indicates that high-resolution OSCAT winds have a more positive impact on the track prediction of Thane tropical cyclone.     

Water‐vapour retrieval from Meteosat 8/SEVIRI observations

This paper aims to propose operational algorithms to retrieve the total atmospheric water vapour content (W) using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on‐board Meteosat 8. MODTRAN3.5 was used to obtain simulated data in the thermal infrared channels IR10.8 and IR12.0, in order to determine the numerical values of the coefficients of the algorithms. The algorithm proposed for land pixels takes into account the SEVIRI observation geometry and the radiometric temperatures obtained in the split‐window channels at two different times during a day and requires a minimum difference of 10 K in terms of temperature between the two situations. Comprehensive error analyses gave rms errors lower than 0.5 g cm^?2 when observations were taken between the nadir and 50°. The algorithm is validated with in situ values, i.e. radiosondes and W measurements with a CIMEL CE318 sun photometer, both obtained from a field campaign, with rms validation errors of 0.2 and 0.7 g cm^?2, respectively. Additionally, six stations all over the SEVIRI field of view were selected to validate the algorithm from radiosondes data, providing an rms error of 0.4 g cm^?2. Concerning sea pixels, the linear atmosphere–surface temperature relation is adapted to SEVIRI and takes into account the sea‐surface temperature, the atmospheric effective temperature, and the radiometric temperature in the IR10.8 channel. The total error obtained from this methodology has a value between 0.8 and 1.1 g cm^?2, and the validation is carried out using radiosonde data from four stations near the sea, providing rms errors lower than 0.6 g cm^?2.     

Impact of SSM/I retrieval data on the systematic bias of analyses and forecasts of the Indian summer monsoon using WRF assimilation system

Assimilation and forecast experiments have been carried out in this study using conventional observations as well as total precipitable water and surface wind data retrieved from the Special Sensor for Microwave Imaginary (SSM/I) sensors. The main objectives of this study were to document the bias in short-range predictions of the Weather Research and Forecasting (WRF) version 3.1 model over the Indian region during the summer monsoon season and the impact of SSM/I data. All the experiments were carried out in the monsoon seasons of 2001 as a part of pilot phase studies for the South Asian Regional Reanalysis (SARR) project. It is seen that the model has strong bias in wind forecasts over the Arabian Sea and the Indian Ocean. A cyclonic bias in the forecasts exists over south-west India. Over the equatorial Indian Ocean, a strong southerly bias towards the Bay of Bengal is noticed. The model has a systematic bias to increase moisture over most parts of the equatorial Indian Ocean. Except over the Gangetic plains, the model exhibits dry bias with reduced moisture over most parts of India in 24 hour forecasts. The impact of assimilation of SSM/I products has been to increase the moisture over the Bay of Bengal, where the model has shown dry bias. The moisture content over the equatorial Indian Ocean (western sector) reduced significantly after assimilation of SSM/I data, where the model has a tendency to enhance moisture. Major rainfall zones during the monsoon season are brought out well in 6 hour forecasts by the model; however, the rainfall amount increased over the Bay of Bengal due to the assimilation of SSM/I data. These features are consistent with the moisture and wind differences between the two assimilation experiments. A quantitative verification of model rainfall in terms of equitable threat scores indicate that the accuracy of rainfall products is higher when SSM/I data are assimilated. It is seen that the general pattern of rainfall tendency in 24 hour forecasts remains the same irrespective of whether the forecast initial conditions are with or without SSM/I data. Examination of a case of monsoon depression showed that assimilation of SSM/I data improved the analysis.     

Validation of Kalpana-1 atmospheric motion vectors against upper air observations and numerical model derived winds

Validation of Kalpana-1 atmospheric motion vectors (AMVs) against upper air radiosonde (RS) winds and numerical model-derived winds (National Centre for Medium Range Weather Forecasting's (NCMRWF's) T382L64 first guess) during the monsoon season of 2011 was attempted in this study. This was the first attempt to compare Kalpana-1 AMVs with model-derived winds. An AMV validation against RS winds showed that the mean AMV speed is always higher than that of the mean RS speed, except in high-level cloud motion vectors (CMVs). In the southwest monsoon season of 2011, the maximum speed bias in Kalpana-1 AMV with respect to RS winds was observed in the middle level (～5 m s^?1). The root mean square vector difference (RMSVD) of Kalpana-1 AMV with respect to the collocated RS winds (～5–7 m s^?1) has been found to be in the same range as those of other geostationary satellites, especially over the northern hemisphere and the tropics. The validation of Kalpana-1 AMVs against first guess revealed more erroneous low-level and middle-level AMVs, but the vector difference in the high-level winds was found to be smaller than the same in the low- and middle-level winds. The uncertainty in the empirical genetic algorithm (GA) used to derive the Kalpana-1 AMVs, which does not use model background fields, may be the reason for the high RMSVD of Kalpana-1 AMVs with respect to RS winds and high bias with respect to first guess. The mean observed AMV clearly depicted monsoonal features such as low-level westerly jet (LLWJ) and tropical easterly jet (TEJ). The speed bias density plots of Kalpana-1 high-level CMVs (400–100 hPa) and water vapour channel winds (WVWs) (above ～500 hPa) with respect to first guess showed that the bias was higher for WVWs; however, the standard deviations of high-level CMVs and WVWs are comparable.     

Development of a coupled land-atmosphere satellite data assimilation system for improved local atmospheric simulations

This study developed a coupled land-atmosphere satellite data assimilation system as a new physical downscaling approach, by coupling a mesoscale atmospheric model with a land data assimilation system (LDAS). The LDAS consists of a land surface scheme as the model operator, a radiative transfer model as the observation operator, and the simulated annealing method for minimizing the difference between the observed and simulated microwave brightness temperature. The atmospheric model produces forcing data for the LDAS, and the LDAS produces better initial surface conditions for the modelling system. This coupled system can take into account land surface heterogeneities through assimilating satellite data for a better precipitation prediction. To assess the effectiveness of the new system, 3-dimensional numerical experiments were carried out in a mesoscale area of the Tibetan Plateau during the wet monsoon season. The results show significant improvement compared with a no assimilation regional atmospheric model simply nested from the global model. The surface soil moisture content and its distribution from the assimilation system were more consistent to in situ observations. These better surface conditions affect the land-atmosphere interactions through convection systems and lead to better atmospheric predictability as confirmed by satellite-based cloud observations and in situ sounding observations. Through the use of satellite brightness temperature, the developed coupled land-atmosphere assimilation system has shown potential ability to provide better initial surface conditions and its inputs to the atmosphere and to improve physical downscaling through regional models.     

East Hainan upwelling fronts detected by remote sensing and modelled in summer

Using the Belkin and O’Reilly algorithm and high-resolution (1 km) satellite sea surface temperature (SST) and chlorophyll-a (chl-a) data from 2002 to 2011, fronts were detected off the east/northeast coast of Hainan Island, South China Sea. These fronts were mainly produced by upwelling off eastern Hainan Island, through which cold, high-salinity, high-density, and nutrient-rich bottom water was brought to the surface and subsurface and then transported to the northeast of Hainan Island by the along-shore currents. The fronts are anisotropic, with a dominant orientation SSW–NNE. A three-dimensional ocean model forced by the Quick Scatterometer (QuikSCAT) winds was employed to study the three-dimensional structure of these fronts as well as the relationship between the fronts and upwelling or summer monsoon. The results show that the front intensity (cross-frontal gradient) is strongly correlated with the along-shore local winds, and has a strong seasonal and a weak inter-annual variation with a maximum of about 0.5°C km^–1 at the subsurface (about 15 m) rather than the surface.     

Coastally trapped atmospheric gravity waves on SAR,AVHRR and MODIS images

Alternative dark–bright patterns on two ENVISAT Advanced Synthetic Aperture Radar (ASAR) images of the east coast of the Korean Peninsula acquired on 18 and 19 May 2004 are interpreted as atmospheric gravity waves (AGWs) on the basis of simultaneous multi‐satellite observations and atmospheric gravity wave theory. The AGWs appeared in the form of a wave packet containing several waves located between 50 and 200 km offshore. The wavelengths were ranging from 13 to 20 km. The lengths of AGW crests were from 20 to 150 km. An NOAA‐17 pass was received about 30 min after the ASAR pass. Its channel 4 infrared (IR) image clearly shows wave‐like moisture patterns. However, the sea surface temperature (SST) image derived after applying nonlinear calibration and split‐window atmospheric correction shows no wave patterns. A daytime true‐colour MODIS image taken about 14 h later still shows a cloud band of same AGWs, indicating the lifespan of the standing AGWs can be over half a day. Although oceanic internal waves (IWs) may also cause similar wave patterns imaged by spaceborne SAR as they modulate the ocean surface roughness, we provide evidence to eliminate this possibility in this case. The characteristics of satellite observed AGWs are in good agreement with these simulated by a linear coastal AGW model.     

Simulation of microphysical structure associated with tropical cloud clusters using mesoscale model and comparison with TRMM observations

An attempt has been made in the present study to examine the microphysical structure of a non‐squall Tropical Cloud Cluster (TCC). Three‐dimensional model simulations of cloud microphysical structure associated with a non‐squall TCC occurred on 26 October 2005 over the South Bay of Bengal have been carried out. The initial conditions for the model simulations were improved by incorporating upper air radiosonde observations and Indian Mesosphere Stratosphere Troposphere (MST) radar wind observations through analysis nudging. The horizontal and vertical distribution of the cloud hydrometeor fields observed from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are compared to those simulated by a mesoscale model using a sophisticated microphysical scheme. Substantial differences are noticed in the amounts of cloud microphysical parameters, with simulated values of hydrometeors being higher than TMI retrievals. Spatial distribution of Cloud Liquid Water (CLW) and Rain Water (RNW) from TMI and model simulations correspond well with each other. The cloud microphysical structure during the initial and mature phases of the storm is also investigated. Comparisons of horizontal and vertical reflectivity structure from the TRMM‐Precipitation Radar (PR) and those simulated by the model show reflectivity cores of values greater than 30 dBZ. The TRMM‐PR echo tops are 3–4 km higher than the simulated echo tops. The 24 hr accumulated precipitation from model simulations are then verified with the combined rainfall product from the TRMM observations.     

Comparison of wind data from QuikSCAT and buoys in the Indian Ocean

The performance of QuikSCAT‐derived wind vectors is evaluated using in‐situ data from moored buoys over the Indian Ocean. The results show that the mean differences for wind speed and wind direction are 0.37 ms^?1 and 5.8°, root mean square deviations are 1.57 ms^?1 and 44.1° and corresponding coefficients of correlation are 0.87 and 0.75, respectively. The matching between in‐situ and satellite estimates seems to be better in the North Indian Ocean than in the Equatorial Indian Ocean. The effects of sea surface temperature and air–sea temperature difference on wind residuals were also investigated. In general, QuikSCAT is found to overestimate the winds. It is speculated that low wind speed during rain‐free conditions and high wind speed, normally associated with rain, may be the reason for the less accurate estimation of the wind vector from QuikSCAT over the Indian Ocean.     

Comparison of Oceansat-2 scatterometer- to buoy-recorded winds and spatial distribution over the Arabian Sea during the monsoon period

For this wind resource assessment (WRA) study, wind speed and direction are the fundamental inputs. Also, these studies are data driven and require large historical wind speed data sets available on the site. This work explores the application of space-based scatterometer winds for assimilation into WRA studies towards the development of offshore wind energy. This article focuses on estimating the performance of Oceansat-2 scatterometer (OSCAT)-derived wind vector using in situ data from buoys at different locations in the Arabian Sea. A comparative study between three methods for estimating the equivalent neutral winds (ENW) for buoys is carried out. OSCAT winds were closest to ENW estimated by the Liu–Katsaros–Businger (LKB) method. The spatial and temporal windows for comparison were 0.5° and ±60 minutes, respectively. The monsoon months (June–September) of 2011 were selected for study. The root mean square deviation for wind speed is less than 2.5 m s^?1 and wind direction is less than 20°, and a small positive bias is observed in the OSCAT wind values. From the analysis, the OSCAT wind values are consistent with in situ-observed values. Furthermore, wind atlas maps were developed with OSCAT winds, representing the spatial distribution of winds at a height of 10 m over the Arabian Sea.