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Karaseva M.O.,Kyrgyz Russian Slavic University | Prakash S.,Atmospheric and Oceanic science Group | Gairola R.M.,Atmospheric and Oceanic science Group
Theoretical and Applied Climatology | Year: 2012

This paper presents the validation of monthly precipitation using Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA)-3B43 product with conventional rain gauge observations for the period 1998-2007 over Kyrgyzstan. This study is carried out to quantify the accuracy of TMPA-3B43 product over the high latitude and complex orographic region. The present work is quite important because it is highly desirable to compare the TMPA precipitation product with the ground truth data at a regional scale, so that the satellite product can be fine-tuned at that scale. For the validation, four different types of spatial collocation have been used: station wise, climatic zone wise, topographically and seasonal. The analysis has been done at the same spatial and temporal scales in order to eliminate the sampling biases in the comparisons. The results show that TMPA-3B43 product has statistically significant correlation (r = 0.36-0.88) with rain gauge data over the most parts of the country. The minimum linear correlation is observed around the large continental water bodies (e. g., Issyk-Kul lake; r = 0.17-0.19). The overall result suggests that the precipitation estimated using TMPA-3B43 product performs reasonably well over the plain regions and even over the orographic regions except near the big lake regions. Also, the negative bias suggests the systematic underestimation of high precipitation by TMPA-3B43 product. The analyses suggest the need of a better algorithm for precipitation estimation over this region separately to capture the different types of rain events more reliably. © 2011 Springer-Verlag.

Kumar P.,Atmospheric and Oceanic science Group | Bhattacharya B.K.,Space Applications Center | Pal P.K.,Atmospheric and Oceanic science Group
Agricultural and Forest Meteorology | Year: 2013

Indian economy is largely depending upon the agricultural productivity and thus influences the trade among the SAARC countries. High-resolution and good-quality regional weather forecasts are necessary for planners, resource managers, insurers and national agro-advisory services. In this study, high resolution updated land-surface state in terms of vegetation fraction (VF) from operational vegetation index products of Indian geostationary satellite (INSAT 3A) sensor (CCD) was utilized in numerical weather prediction (NWP) model (e.g. WRF) to investigate its impact on short-range weather forecast over the control run. Results showed that the updated vegetation fraction from INSAT 3A CCD improved the low-level 24. h temperature (∼18%) and moisture (∼10%) forecast in comparison to control run. The 24. h rainfall forecast was also improved (more than 5%) over central and southern India with the use of updated vegetation fraction compared to control experiment. INSAT 3A VF based experiment also showed a net improvement of 27% in surface sensible heat fluxes from WRF in comparison to control experiment when both were compared with area-averaged measurements from Large Aperture Scintillometer (LAS). This triggers the need of more and more use of realistic and updated land surface states through satellite remote sensing data as well as in situ micrometeorological measurements to improve the forecast quality, skill and consistency. © 2012 Elsevier B.V.

Kumar P.,Atmospheric and Oceanic science Group | Harish Kumar K.P.,AIR INDIA | Pal P.K.,Atmospheric and Oceanic science Group
IEEE Transactions on Geoscience and Remote Sensing | Year: 2013

The Indian Space Research Organisation launched the Oceansat-2 scatterometer (OSCAT) for atmospheric and oceanographic applications. In this paper, a case study has been performed to assess the impact of OSCAT-retrieved wind vectors on the simulation of tropical cyclone Phet over the Arabian Sea. Three-dimensional variational data assimilation of the Weather Research and Forecasting model is used for this purpose. In addition to OSCAT winds, wind speed and precipitable water derived from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are also used for assimilation to evaluate the impact of scatterometer and radiometer data on tropical cyclone prediction. Results show that an ∼60-km track error is observed in control and TMI experiments when compared with Joint Typhoon Warning Center observed cyclone center at 1800 UTC 01 June 2010. An approximately 40-km track error is determined in the initial center position of OSCAT experiments. The mean track error forecast is less in OSCAT experiments (∼ 80 km) in comparison with TMI experiments (∼ 110 km). Only OSCAT data experiments are able to predict the track of the cyclone toward the Oman coast. Assimilation of scatterometer wind direction improves the track forecast; but it degrades the forecast of the intensity, maximum magnitude, and evolution of the cyclone. None of these experiments are able to capture the observed minimum sea level pressure (964 hPa at 1200 UTC 02 June 2010) accurately. TMI experiments are slightly better than OSCAT experiments in capturing the intensity of cyclone Phet, whereas wind direction from OSCAT improves the track forecast of the cyclone. © 1980-2012 IEEE.

Chakraborty A.,Atmospheric and Oceanic science Group | Kumar R.,Atmospheric and Oceanic science Group | Stoffelen A.,Weather Research
Remote Sensing Letters | Year: 2013

Ocean surface winds from the OCEANSAT-2 scatterometer (OSCAT) were validated with equivalent neutral wind observations from 87 global buoys and winds from the European Centre for Medium Range Weather Forecasting (ECMWF) Numerical Weather Prediction (NWP) model using triple collocation for a period of 9 months. Functional relationship analysis (FRA) employing the error-in-variables method is found to be more 'exact' in comparison with classical linear regression analysis for the validation of the OSCAT data. Moreover, using the wind component domain for validation and error assessment rather than the speed and direction domain is confirmed to be favourable. The FRA method applied on the triple-collocated wind components shows that the error standard deviations of the OSCAT and buoy winds are quite similar. The calibration trends and biases for OSCAT, buoys and ECMWF are found to be close to unity and zero, respectively. © 2012 Taylor & Francis.

Chakraborty A.,Atmospheric and Oceanic science Group | Kumar R.,Atmospheric and Oceanic science Group
Remote Sensing Letters | Year: 2013

Daily and 12 hourly gridded analysed wind vectors (AWVs) over global ocean were generated using the simple spatial interpolation scheme of 'box averaging', with a horizontal resolution of 0.5° × 0.5°. For daily analysed winds, observations only from Oceansat-2 Scatterometer (O°CAT) were used. The 12 hourly AWVs were generated by combining the data from both O°CAT and Advanced Scatterometer (A°CAT). Apart from ocean wind vectors, an effort was made also to produce analysed wind stress, divergence and curl of wind stress. The daily and 12 hourly analysed winds were validated using in situ observations from 97 global moored buoys and data from European°Centre for Medium Range Weather Forecasting analyses for a period of 9 months. The validation result shows a good agreement between AWV products and the buoy and model analysis data, yielding a standard deviation of around 2 m s-1 in wind speed and around 20° in wind direction. © 2012 Taylor & Francis.

Ratheesh S.,Atmospheric and Oceanic science Group | Mankad B.,Atmospheric and Oceanic science Group | Basu S.,Atmospheric and Oceanic science Group | Kumar R.,Atmospheric and Oceanic science Group | Sharma R.,Atmospheric and Oceanic science Group
IEEE Geoscience and Remote Sensing Letters | Year: 2013

The study has been motivated by the desire to assess the performance of sea surface salinity (SSS) from the Soil Moisture and Ocean Salinity (SMOS) satellite launched by the European Space Agency. Daily Level 3 product on a0.25̂ × 0.25̂ grid for the year 2010 has been used for this assessment in the Indian Ocean. Various data sets, like the in situ data sets available from the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) buoys and Argo floats and also the data sets from modular ocean model version 3 simulations, have been utilized for this purpose. Comparison made at two buoy locations suggests good quality of SMOS SSS with root-mean-square error of the order of 0.36 and 0.34 psu. The triple collocation method, which explicitly takes into account the error characteristics of the SMOS, Argo, and model data sets, has been used for further validation of the SMOS data. Since the Indian Ocean exhibits characteristically different patterns of SSS in its different subregions, the study area has been divided into different such subregions. The SMOS-derived SSS appears to be of very good quality in the equatorial Indian Ocean and southern Indian Ocean, while the data are of poorer quality in the Arabian Sea and the Bay of Bengal possibly because of the errors in SSS retrieval due to the land contamination and strong winds. © 2004-2012 IEEE.

Singh R.,Atmospheric and Oceanic science Group | Kumar P.,Atmospheric and Oceanic science Group | Pal P.K.,Atmospheric and Oceanic science Group
IEEE Transactions on Geoscience and Remote Sensing | Year: 2012

This paper describes, for the first time, the impact of Oceansat-2 scatterometer (OSCAT) surface winds in the Weather Research and Forecasting (WRF) 3-D variational (3-D-Var) assimilation system. Before using OSCAT winds into WRF assimilation system, we compared OSCAT surface wind retrievals against National Centers for Environmental Prediction analyzed winds, Advanced Scatterometer retrievals, and buoy-measured winds. After the initial assessment of the quality of the OSCAT winds, the control (CNT) (without OSCAT) as well as experimental (which assimilated OSCAT surface winds) runs were made for 48 h starting daily at 0600 Universal Time Coordinated (UTC) during July 2010. The assimilation experiments demonstrated positive impact of OSCAT winds on both the analysis state as well as subsequent short-term forecasts of surface winds. Compared to CNT run, the assimilation of the OSCAT winds improved the surface wind analysis as large as 25%, when compared with Advanced Microwave Scanning Radiometer (AMSR-E) measured winds. The assimilation of OSCAT winds also showed small, but positive, impact on the forecast (particularly later hours of forecast) of midtropospheric moisture, temperature, and upper tropospheric winds. Compared to the CNT run, the assimilation of OSCAT winds improved precipitation forecast for moderate to heavy rainfall thresholds when validated against Tropical Rainfall Measuring Mission precipitation. © 2012 IEEE.

Jaiswal N.,Atmospheric and Oceanic science Group | Kishtawal C.M.,Atmospheric and Oceanic science Group
IEEE Transactions on Geoscience and Remote Sensing | Year: 2011

In the present work, a new technique based on the data mining approach has been discussed for the prediction of tropical cyclogenesis using the ocean wind vectors derived from the sea-winds scatterometer on QuikScat satellite. The technique is based on similarity of given wind pattern with wind vector signatures of developing systems, available from the past observations. A database has been formed in this work using the QuikScat-observed vector wind patterns associated with the early stages of tropical cyclones that developed in the North Indian basin during the years 2000-2008. The prediction of possibility of cyclogenesis, in a given scene, is determined by matching it with all the archived scenes in a database using vector block matching algorithm. The system is predicted as developing, if its matching index exceeds the predetermined threshold value (0.5). The prediction of cyclogenesis can be made 24-96 h prior to its classification as a tropical storm by the Joint Typhoon Warning Centre. The probability of detection of the technique is determined using the equivalent of the Jackknife approach in the database as 0.93. The algorithm is tested with a continuous wind data of years 2007-2009 for active cyclone months (excluding the scenes archived in a database). All the 14 tropical disturbances that developed into tropical storms during the test period were predicted using the discussed method, and two false alarms were found. © 2011 IEEE.

Prakash S.,Atmospheric and Oceanic science Group | Sathiyamoorthy V.,Atmospheric and Oceanic science Group | Mahesh C.,Atmospheric and Oceanic science Group | Gairola R.M.,Atmospheric and Oceanic science Group
International Journal of Remote Sensing | Year: 2014

To date, more than half a dozen merged rainfall data sets are available to the research community. These data sets use different approaches for rainfall retrieval and combine different satellites or/and ground-based rainfall observations. However, these data sets appear to differ among themselves and deviate from in situ observations at regional and seasonal scales. Hence, it is becoming difficult to choose a suitable data set from these products for regional rainfall analyses. In the present study, four independently developed multisatellite high-resolution precipitation products (HRPPs), namely Climate Prediction Center Morphing (CMORPH) version 1.0, Naval Research Laboratory (NRL)-blended, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)-3B42 version 7 are compared with quality-controlled gridded rain gauge data over India developed by the India Meteorological Department (IMD). A preliminary analysis is carried out for a 6 year period from 2004 to 2009 at daily scale for the summer monsoon season of June to September. Comparison of all-India seasonal (June to September) mean rainfall with rain gauge data shows a considerable underestimation by all HRPPs, although the underestimation is comparatively less for TMPA. Moreover, all the HRPPs are able to capture the important characteristic features of the summer monsoon rainfall such as intra-seasonal (active/break spells) and inter-annual (excess/deficient) variabilities reasonably well. Regional differences between observed rainfall and the HRPPs are also analysed. Results suggest that TMPA is comparatively closer to the ground-truth, possibly due to the incorporation of rain gauge observations. Furthermore, all the HRPPs show high probability of detection, low false alarm ratio, and high threat score in detection of rainfall events over most parts of India. It is also observed that all these HRPPs have certain issues in rainfall detection over the rain-shadow region of southeast peninsular India, semi-arid northwest parts of India, and hilly northern parts. Hence, results of the 6 year analysis over a region with a dense network of surface observations of rainfall suggest that the TMPA merged rainfall product is better than the other HRPPs due to (1) lower underestimation of rainfall, (2) higher correlation and lower root-mean-square error (RMSE), and (3) better performance over the west coast. Therefore, TMPA can be used with confidence as compared to other HRPPs for monsoon studies, particularly over the Indian land region with a considerable rain gauge network. This study also clarifies the fact that the merged satellite rainfall products with sufficient ground-truths can be the ideal product for monsoon and hydrological studies. © 2014 Taylor & Francis.

Prakash S.,Atmospheric and Oceanic science Group | Gairola R.M.,Atmospheric and Oceanic science Group
Theoretical and Applied Climatology | Year: 2014

In the present study, an attempt has been made to validate the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)-3B42 recently released version 7 product over the tropical Indian Ocean using surface rain gauges from the National Oceanic and Atmospheric Administration/Pacific Marine Environmental Laboratory Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction buoy array available since late 2004. The validation exercise is carried out at daily scale for an 8-year period of 2004-2011. Results show statistically significant linear correlation between these two precipitation estimates ranging from 0.40 to 0.89 and the root-mean-square error varies from about 1 to 22 mm day-1. Although systematic overestimation of precipitation by the TMPA product is evident over most of the buoy locations, the TMPA noticeably underestimates higher (more than 100 mm day-1) and light (less than 0.5 mm day-1) precipitation events. The highest correlation is observed during the southwest monsoon season (June-September) even though bias is the maximum possibly due to relatively lower fraction of stratiform precipitation during the monsoon season than other seasons. Furthermore, the TMPA estimates slightly underestimate or misses intermittent warm precipitation events as compared to the precipitation radar derived precipitation rates. © 2013 Springer-Verlag Wien.

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