Multispectral Rainfall Algorithm Research Articles

COMPARISON ANALYSIS OF HIMAWARI 8, CHIRPS AND GSMaP DATA TO DETECT RAIN IN INDONESIA

The need for rainfall data, especially for areas where the number of observation stations is not very close, is very important for local climate analysis activities. This data need can be met, one of which is from remote sensing data, such as Himawari 8. The Himawari 8 rainfall data are data derived using the INSAT Multi-Spectral Rainfall Algorithm (IMSRA) method based on the infrared channel on the Himawari 8 satellite. However, research on the IMSRA method was carried out using a case study of a region in India. Thus, validation is needed to determine the ability of Himawari 8 rainfall data to detect rain in Indonesia. The data used for comparison are CHIRPS and GSMaP rainfall data. In addition, BMKG rainfall data are used as benchmark data. The technique used for validation is using the Contingency Table method. The results of the validation show that the rain detection ability for Himawari 8 rainfall data is relatively good, namely 66% for 2019 and 85% for 2020. In addition, the ability to detect rain using Himawari 8 rainfall data is quite good compared to the ability to detect rain using CHIRPS rainfall data and GSMaP rainfall data.

Estimation and evaluation of rainfall from INSAT-3D improved IMSRA algorithm during 2018 summer monsoon season

Indian National Satellite System (INSAT) multi-spectral Rainfall Algorithm (IMSRA) is an operational rainfall estimation technique of INSAT-3D at Indian Meteorological Department (IMD) and Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC) since 2014. The IMSRA rain estimates are widely used for various application studies. This algorithm is based on an empirical power-law relation between infrared brightness temperature and surface rain rate. Based on further researches, some more innovative elements are recognized and incorporated into the parent IMSRA algorithm, termed as improved IMSRA. The present study evaluates the potential of the two versions of IMSRA algorithms for estimation of accumulated rainfall and its comparison with Global Precipitation Measurement (GPM) based IMERG rainfall during the 2018 summer monsoon period. Studies suggest satellite rainfall data at monthly and seasonal scales are better suited for hydrological and meteorological applications. Much studies are not done towards the evaluation and characterizations of the two versions of IMSRA algorithms at monthly and seasonal scales, and the present study focuses on such comparisons. The results indicate that the overestimation tendencies of the older version of the IMSRA algorithm are reduced in the improved version. The statistical analysis suggests that the updated version show a reduction ~25% error for the improved version in the seasonal estimation of rainfall.

Synergy between INSAT-3D infra-red and GPM microwave radiometer for precipitation studies

The Indian National Satellite System (INSAT) based Multispectral Rainfall Algorithm (IMSRA) operationally provides precipitation estimates using measurements provided by the Kalpana satellite from 2008 onwards and the INSAT-3D from 2014 onwards over Indian land and oceanic regions for dissemination to the users. Here, an attempt is made to estimate rainfall over the Indian monsoon region by the synergistic use of geostationary INSAT-3D IMSRA derived rainfall estimates and rain data from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) using a suitable objective analysis method. The objective criteria of a successive correction method benefit from high spatial and temporal resolutions of the geostationary satellite and infrequent but more accurate microwave-based rainfall estimates, particularly during heavy rain conditions. The method is applied to various case studies for the southwest monsoon season of 2015 at both instantaneous and daily scale. A comparison is made with ground data and also with independent global concurrent rainfall products. The merged rainfall estimates shows noticeable improvement over the Infrared (IR)-based rainfall estimates over various parts of the Indian land and oceanic regions on a daily scale. Overall, results reveal that the GMI type radiometer in low inclination orbit like the Megha-Tropiques and Tropical Rainfall Measurement Mission (TRMM) could be more useful for synergistic use with INSAT-3D. It has the potential to predict more accurately near real-time tropical rainfall, particularly over the Indian monsoon region, which makes it a valuable source of information for flood forecasting, agriculture and water resource management, numerical weather prediction, and numerous other applications in the hydro-meteorological sciences.

Comparative analysis of two rainfall retrieval algorithms during extreme rainfall event: a case study on cloudburst, 2010 over Ladakh (Leh), Jammu and Kashmir

Intense rainfall events lead to floods and landslides in the Western Himalayas (WH). These rainfall amounts are considered comparatively moderate over the plains. These events, called ‘cloudbursts,’ are convective triggered followed by orographically locked phenomena producing sudden high-intensity rainfall over a small area. Early warning and prediction of such severe local weather systems is important to mitigate societal impacts arising from the accompanying flash floods, landslides, etc. Due to lack of ground-based observations, cloudbursts over remote and unpopulated hilly areas often go unreported. Present study examines a cloudburst occurred at Ladakh (Leh) in the WH in the early hours of August 5, 2010, using remotely sensed rainfall data from Tropical Rainfall Measuring Mission (TRMM) and Kalpana-1. The storm lasted for 2 days starting from August 3, 2010, followed by flash floods. Rain-band propagation over the region is studied from Kalpana-1 3-hourly rainfall estimates using Indian Satellite (INSAT) multi-spectral rainfall algorithm (IMSRA) and TRMM rainfall estimates using TRMM 3B42 algorithm. Quantitative and qualitative assessment and comparison of these two products is made. It is observed that there is decrease in satellite brightness temperature (BT) during the rainfall event. Initiation of rainfall occurs at about < 255 K. Maximum of 16.75 mm/h rainfall is observed over the Jammu and Kashmir at 21 GMT from TRMM 3B42 estimates. In addition, it is observed that Kalpana-1 IMSRA underestimates the rainfall observations with respect to Indian Meteorological Department (IMD) rainfall estimates. Spatial correlation at 5% significant level is evaluated, and similarities in rainfall estimates based on rainfall retrieval algorithms are made during the cloudburst event. Mean and standard deviations depict that TRMM 3B42 and IMD rainfall estimates are closer in terms of spatial signature, but estimates of rainfall from Kalpana-1 underestimate the mean and standard deviation signature. In view of orographic contribution, it has been observed that linear fit is the better than nonlinear with less rainfall bias at 90% confidence bound over the region.

Comparison of different satellite-derived rainfall products with IMD gridded data over Indian meteorological subdivisions during Indian Summer Monsoon (ISM) 2016 at weekly temporal resolution

In this study, effort is made to estimate weekly rainfall over India at 36 meteorological subdivisions during monsoon 2016 (June to September) by using several precipitation products from different satellites which include Indian National Satellite System (INSAT) INSAT-3D derived Hydro Estimate (HE), INSAT Multi-Spectral Rainfall Algorithm (IMSRA) rainfall products, Integrated Multi-satellite Retrievals for GPM (IMERG) derived from JPL, NASA, and National Center for Medium Range Weather Forecasting (NCMRWF) Merged Satellite Gauge (NMSG) of IMD. Different satellite estimates, and rain gauge satellite merged products have been compared with gridded rainfall products prepared by India Meteorological Department (IMD). HE at 4 km pixel resolution and IMSRA at 0.100 × 0.100 resolution are derived rainfall products from Indian satellite INSAT-3D.GPM (IMERG) product is derived at 0.100 × 0.100 resolution from Global Precipitation Mission (GPM) satellite jointly operated by JAXA (Japan) and NASA (USA). NCMRWF Merged Satellite-Gauge (NMSG) rainfall data has been generated at a 0.25° × 0.25° grid resolution by IMD & NCMRWF. The data is analyzed preliminarily for the southwest monsoon season of 2016 at weekly temporal resolution. The overall comparison shows that GPM (IMERG) estimates better rainfall over the coastal and plain region, however, HE estimates better rainfall over the hilly region, in general, and for Kerala region, particularly. NMSG product needs improvement over the hilly region and considerable improvement is required in IMSRA product. For all 36 meteorological subdivisions, NMSG product has 0.56 average Nash-Sutcliffe efficiencies (NSE) for 17 weeks with IMD Gridded rainfall data. However, for satellite-derived rainfall by GPM (IMERG), HE and IMSRA have 0.29, 0.1 and −0.8 average NSE at 17 weeks, respectively. Based on this study, it is concluded that NMSG daily rainfall product is best, however, amongst satellite-derived rainfall, overall GPM gives a better result with a few exceptions and IMSRA need more modifications.

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

ABSTRACT 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.

Evaluation and inter-comparison of high-resolution multi-satellite rainfall products over India for the southwest monsoon period

ABSTRACTRainfall is one of the key drivers of the global hydrological cycle and has large socio-economic impacts. Tropical rainfall accounts for two-thirds of the global rainfall and is primarily associated with the monsoon. Multi-satellite rainfall products provide rainfall with high temporal and spatial resolutions; however, they exhibit regional and seasonal biases. Evaluation of these products against ground-based observations can improve the accuracy of the estimated rainfall. With the launch of the Global Precipitation Measurement (GPM) Core Observatory, two advanced high-resolution multi-satellite precipitation products namely; Integrated Multi-satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) are released. In the present study the spatial and temporal structures of rainfall in near real time and research versions of IMERG-V4 (near real-time (NRT) & Final (FNL)), GSMaP-V6 (NRT & moving vector with Kalman filter (MVK)), INSAT3D (Indian National Satellite System (INSAT) Multispectral Rainfall Algorithm (IMR) & Hydro-Estimator method (HEM)) and Indian Meteorological Department (IMD) – National Centre for Medium Range Weather Forecasting (NCMRWF) Merged product have been evaluated against gridded gauge-based IMD rainfall data on daily, monthly and seasonal scales. All the datasets show noticeable bias in producing rainfall over orographic regions (i.e. Western Ghats and foothills of Himalayas) and North-East India, though there exists significant difference among the satellite measurements. Different skill scores are computed for GSMaP, IMERG and INSAT3D data products to evaluate the performance of these satellite estimates. However in terms of biases IMD-NCMRWF Merged, GSMaP (NRT & MVK) and IMERG (NRT & FNL) underestimates rainfall (about 11%, 17%, 23%, 18% and 3%, respectively) and INSAT3D (IMR & HEM) overestimates (about 49% and 33%, respectively), for the India region as a whole. In a similar way, HEM product shows 15% better performance than IMR product in INSAT3D category. However, both NRT and MVK products of GSMaP show similar variations compared to observe rainfall. Overall IMD–NCMRWF merged and IMERG-FNL data products show better agreement with the gauge-based IMD data compared to GSMaP. The GPM-based products (IMERG and GSMaP) estimate rainfall much better than INSAT3D estimation.

A Case Study: Heavy Rainfall Event Comparison Between Daily Satellite Rainfall Estimation Products with IMD Gridded Rainfall Over Peninsular India During 2015 Winter Monsoon

India Meteorological department (IMD) used INSAT-3D Metrological Satellite Imager data to drive two type rainfall estimation products viz-Hydro Estimate (HE) and INSAT Multi-Spectral Rainfall Algorithm (IMSRA) on half hourly rainfall rate and daily accumulated rainfall in millimeter (mm). Integrated Multi-Satellite Retrieval for GPM (IMERG) product is being derived by NASA and JAXA by using Global Precipitation Mission (GPM) satellites data. IMSRA and GPM (IMERG) are gridded data at 10 km spatial resolution and HE is available at pixel level (4 km at Nadir). IMD provides gridded rainfall data at 0.25° × 0.25° resolution which is based on wide coverage of 6955 actual observation. In present study, validation of INSAT-3D based Hydro Estimator (HE), INSAT Multi-Spectral Rainfall Algorithm (IMSRA) and Integrated Multi-Satellite Retrieval for GPM (IMERG) of Global Precipitation Mission (GPM) satellites are carried out with IMD gridded data set for heavy rainfall event during winter monsoon, over peninsular India (November–December 2015). In validation, Nash–Sutcliffe efficiencies (NSE), RMSE, Correlation, Skilled scores are calculated at grid level for heavy and very heavy rain categories and the values of NSE of HE (− 32.36, − 3.12), GPM (− 68.67, − 2.39) and IMSRA (− 0.02, 0.28) on 16th November 2015 and HE (− 13.65, − 1.69), GPM (− 43.79, − 2.94) and IMSRA (− 1.08, − 1.60) on 2nd Dec 2015, for heavy and very heavy rainfall. On both days, HE is showing better rainfall estimate compare to GPM for Heavy rainfall and GPM showing better estimation for very heavy rainfall events. In all the cases IMSRA is underestimating, if daily rain fall exceeded 75 mm.

Error modelling for modified-INSAT multi-spectral rainfall algorithm

ABSTRACTConsidering the importance of the error estimates for satellite rainfall products in various applications, the present article deals with the development of an Error Model for Modified-INSAT Multi-Spectral Rainfall Algorithm (M-IMSRA) Estimates (EMME), a recently developed climate region scale rainfall algorithm across India. A non-parametric framework has been adopted to model all the four error components: Correct No-Rain Detection, Miss Rain, False Rain, and Hit Rain in M-IMSRA estimates at climate region scale. The developed error model generated convincing realization of reference rainfall for the estimated rainfall from M-IMSRA algorithm across all the climate regions of India. Exceptions are the high intensity hit rain events across arid Thar Desert and arid Himalayan regions and miss rain events across arid Himalayan region. Overall, the developed error model showed promising results in modelling hit, miss, and false error components of daily M-IMSRA estimates and thus can be associated with the M-IMSRA estimates.

Modified-INSAT Multi-Spectral Rainfall Algorithm (M-IMSRA) at climate region scale: Development and validation

Abstract The present article reports an improvement in the INSAT Multispectral Rainfall Algorithm which is currently operational in the Indian Meteorological Department (IMD). The proposed Modified-IMSRA (M-IMSRA) algorithm deviates from original IMSRA in two ways: first is by improvement in rain/no-rain area detection scheme using a Multi-Index Rain Detection (MIRD) index; second is based on the climate region-wise correction through Least Absolute Shrinkage and Selection Operator (LASSO) models developed for each climate regions using rainfall (obtained based on first improvement) and static topographic variables extracted from Digital Elevation Model (DEM). The overall results indicate that the M-IMSRA is performing better than the IMSRA in all climatic regions when compared with the IMD gridded gauge data. However, the improvement is not uniform in all the regions. The inclusion of the MIRD index led to considerable improvement in M-IMSRA-based rainfall estimates mainly in the arid regions. Likewise, the results obtained after the LASSO regression corrections indicate that they are necessary only for the orographic regions where significant improvements are observed in the rainfall estimates. Finally, the inter-comparison of the simple hybrid M-IMSRA estimates with Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and TRMM 3B42-RT V7 illustrates that the M-IMSRA performs nearly as well as even better (except in terms of Correlation Coefficient) than the complex multi-satellite-based rainfall estimates in all the climate regions of India. Considering the above results, it can be said that the performance of simple hybrid algorithms such as IMSRA can be improved to match the quality or even outperform complex multi-satellite rainfall estimates by incorporating appropriate corrections.

Assessment of Kalpana-1 Rainfall Product over Indian Meteorological Sub-Divisions During the Summer Monsoon Season

In this paper, Kalpana-1 derived INSAT Multispectral Rainfall Algorithm (IMSRA) rainfall estimates are compared with two multisatellite rainfall products namely, TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 and Global Satellite Mapping of Precipitation (GSMaP), and India Meteorological Department (IMD) surface rain gauge (SRG)-based rainfall at meteorological sub-divisional scale over India. The performance of the summer monsoon rainfall of 2013 over Indian meteorological sub-divisions is assessed at different temporal scales. Comparison of daily accumulated rainfall over India from IMSRA shows a linear correlation of 0.72 with TMPA-3B42 and 0.70 with GSMaP estimates. IMSRA is capable to pick up daily rainfall variability over the monsoon trough region as compared to TMPA-3B42 and GSMaP products, but underestimates moderate to heavy rainfall events. Satellite-derived rainfall maps at meteorological sub-divisional scales are in reasonably good agreement with IMD-SRG based rainfall maps with some exceptions. However, IMSRA performs better than GSMaP product at meteorological sub-divisional scale and comparable with TMPA data. All the satellite-derived rainfall products underestimate orographic rainfall along the west coast, the Himalayan foothills and over the northeast India and overestimate rainfall over the southeast peninsular India. Overall results suggest that IMSRA estimates have potential for monsoon rainfall monitoring over the Indian meteorological sub-divisions and can be used for various hydro-meteorological applications.

Improved rainfall estimation over the Indian monsoon region by synergistic use of Kalpana-1 and rain gauge data

In this paper, an attempt has been made to estimate rainfall over the Indian monsoon region by the synergistic use of the geostationary Kalpana-1 satellite-derived INSAT Multispectral Rainfall Algorithm (IMSRA) rainfall estimates and rain gauge data, using a successive correction method in order to further refine the operational IMSRA rainfall estimates. The successive correction method benefits from high spatial and temporal resolutions of the Kalpana-1 satellite and accurate rainfall estimates from rain gauges. A preliminary analysis is done for the southwest monsoon season of 2010 at a daily scale. The successive correction method is applied at a 1° latitude×1° longitude resolution determined by the spatial autocorrelation analysis. Results are compared with four independent global multisatellite rainfall products, namely the Global Satellite Mapping of Precipitation, the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis near-real time and research version rainfall monitoring products, and the Global Precipitation Climatology Project rainfall product. The objectively analyzed rainfall estimates show noticeable improvement over the satellite-based rainfall estimates alone over southern India. Comparison with independent rain gauge observations shows a considerable improvement in terms of correlation, bias and root-mean-square error after objective analysis, especially over the regions where density of rain gauge is fairly good. Overall results reveal that the synergistic use of satellite and in situ observations has potential for more accurate rainfall estimations over the Indian monsoon region.

Multi-Index Rain Detection: A New Approach for Regional Rain Area Detection from Remotely Sensed Data

Abstract In this article, a new approach called Multi-Index Rain Detection (MIRD) is suggested for regional rain area detection and was tested for India using Kalpana-1 satellite data. The approach was developed based on the following hypothesis: better results should be obtained for combined indices than an individual index. Different combinations (scenarios) were developed by combining six commonly used rain detection indices using AND and OR logical connectives. For the study region, an optimal rain area detection scenario and optimal threshold values of the indices were found through a statistical multi-decision-making technique called the Technique for Order Preference by Similarity Ideal Solution (TOPSIS). The TOPSIS analysis was carried out based on independent categorical statistics like probability of detection, probability of no detection, and Heidke skill score. It is noteworthy that for the first time in literature, an attempt has been made (through sensitivity analysis) to understand the influence of the proportion of rain/no-rain pixels in the calibration/validation dataset on a few commonly used statistics. Thus, the obtained results have been used to identify the above-mentioned independent categorical statistics. Based on the results obtained and the validation carried out with different independent datasets, scenario 8 (TIRt &amp;lt; 260 K and TIRt − WVt &amp;lt; 19 K, where TIRt and WVt are the brightness temperatures from thermal IR and water vapor, respectively) is found to be an optimal rain detection index. The obtained results also indicate that the texture-based indices [standard deviation and mean of 5 × 5 pixels at time t (mean5)] did not perform well, perhaps because of the coarse resolution of Kalpana-1 data. It is also to be noted that scenario 8 performs much better than the Roca method used in the Indian National Satellite (INSAT) Multispectral Rainfall Algorithm (IMSRA) developed for India.

Evaluation of short-period rainfall estimates from Kalpana-1 satellite using MET software

The INSAT Multispectral Rainfall Algorithm (IMSRA) technique for rainfall estimation, has recently been developed to meet the shortcomings of the Global Precipitation Index (GPI) technique of rainfall estimation from the data of geostationary satellites; especially for accurate short period rainfall estimates. This study evaluates the 3-hourly precipitation estimates by this technique as well as the rainfall estimates by the GPI technique using data of the Kalpana-1 satellite, over the Indian region for the south-west monsoon season of 2010 to understand their relative strengths and weaknesses in estimating short period rainfall. The gridded 3 hourly accumulated TRMM satellite (3B42 V6 product or TMPA product) and surface raingauge data for stations over the Indian region for the same period is used as the standard measure of rainfall estimates. The Method for Object-based Diagnostic Evaluation (MODE) utility of the METv3.0 software, has been used for the evaluation purpose. The results show that the new IMSRA technique is closer to the TMPA rainfall estimate, in terms of areal spread, geometric shape and location of rainfall areas, as compared to the GPI technique. The overlap of matching rainfall areas with respect to TMPA rainfall patches is also higher for the IMSRA estimates as compared to the GPI values. However, both satellite rainfall estimates are observed to be generally higher compared to the TMPA measurements. However, the values for the highest 10% of the rainfall rates in any rainfall patch, is generally higher for rainfall measured by the IMSRA technique, as compared to the estimates by the GPI technique. This may partly be due to the capping maximum limit of 3 mm/hr for rainfall measured by the GPI technique limits the total 3-hour accumulation to 9 mm even during heavy rainfall episodes. This is not so with IMSRA technique, which has no such limiting value. However, this general overestimation of the rainfall amount, measured by both techniques, and the greater error in case of IMSRA estimates, is also validated independently with respect to surface raingauge observations. Hence the observed overestimation by the IMSRA technique for the highest 10th percentile rainfall rates in rainfall episodes, is real. This overestimation by the latter technique may become a significant source of error, if the IMSRA estimate is used for monitoring very heavy rainfall episodes. In all other respects, since the IMSRA method shows significant improvement over the GPI, the rainfall estimates by the IMSRA method may be used for operational short period rainfall estimation.

Estimation of Indian summer monsoon rainfall using Kalpana-1 VHRR data and its validation using rain gauge and GPCP data

In the present study, an attempt has been made to estimate and validate the daily and monthly rainfall during the Indian summer monsoon seasons of 2008 and 2009 using INSAT (Indian National Satellite System) Multispectral Rainfall Algorithm (IMSRA) technique utilizing Kalpana-1 very high resolution radiometer (VHRR) measurements. In contrary to infrared (IR), microwave (MW) rain rates are based on measurements that sense precipitation in clouds and do not rely merely on cloud top temperature. Geostationary satellites provide broad coverage and frequent refresh measurements but microwave measurements are accurate but sparse. IMSRA technique is the combination of the infrared and microwave measurements which make use of the best features of both IR- and MW-based rainfall estimates. The development of this algorithm included two major steps: (a) classification of rain-bearing clouds using proper cloud classification scheme utilizing Kalpana-1 IR and water vapor (WV) brightness temperatures (Tb) and (b) collocation of Kalpana-1 IR brightness temperature with Tropical Rainfall Measuring Mission (TRMM)-Precipitation Radar (PR) surface rain rate and establishment of a regression relation between them. In this paper, the capability of IMSRA as an operational algorithm has been tested for the two monsoon seasons 2008 and 2009. For this, IMSRA has been used to estimate daily and monthly rainfall and has been intercompared on daily and monthly scales with TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 V6 product and Global Precipitation Climatology Project (GPCP) rain product during these two monsoon years. The daily and monthly IMSRA rainfall has also been validated against ground-based observations from Automatic Weather Station (AWS) Rain Gauge and Buoy data. The algorithm proved to be in good correlation with AWS data over land up to 0.70 for daily rain estimates except orographic regions like North-East and South-West India and 0.72 for monthly rain estimates. The validation with Buoys gives the reasonable correlation of 0.49 for daily rain estimates and 0.66 for monthly rain estimates over Tropical Indian Ocean.