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Studies on the Impacts of 3D-VAR Assimilation of Satellite Observations on the Simulation of Monsoon Depressions over India

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Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II)

Abstract

Variational data assimilation provides a convenient means of optimally combining the “first-guess” or “background” meteorological fields with the observations. The background fields are typically obtained from the numerical weather prediction output of a model while the observations can be either the meteorological model variables or even non-model variables. In the three-dimensional variational (3D-VAR) method the analysis state is obtained by optimally combining the “first-guess” and the “observations” at the same analysis time.The present article begins with a brief overview of the characteristics of the monsoon disturbances that form over the Indian region during the summer monsoon season. Subsequently, the 3D-VAR method is briefly introduced together with details of the mesoscale model employed in this study. The next section outlines the results of the impact of the 3D-VAR assimilation of satellite observations in the simulation of a few monsoon disturbances over India using the Weather Research and Forecast (WRF) model. The satellite observations utilized in the 3D-VAR assimilation study presented in this article include (1) temperature and humidity profiles from Moderate Resolution Imaging Spectroradiometer (MODIS), (2) temperature and humidity profiles from Advanced TIROS Vertical Sounder (ATOVS), and (3) total precipitable water from Special Sensor microwave imager (SSMI), respectively. In order to discern the impact of 3D-VAR assimilation of satellite observations a (base or control) numerical experiment called “control run” is performed, which is identical to the assimilated run (called “3D-VAR run”) except that no observations are assimilated in the control run. The results of the simulation between the assimilated run and the control run are compared with one another as well as with global analysis and Tropical Rainfall Measurement Mission (TRMM) and Quick Scatterometer (QuikSCAT) observations.The results of the study indicate that the assimilation of satellite observations, in general, does improve the simulation of the various monsoon disturbances over India, although the improvements are not uniformly very marked for all the monsoon disturbances and for all the satellite observations. Assimilating MODIS temperature and humidity profiles have yielded better results for two of the depressions as compared to the ATOVS and SSM/I assimilations. Also the results of the study indicate that assimilating total precipitable water from SSM/I has lower impact as compared to assimilating temperature and humidity profiles from ATOVS and MODIS.

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Acknowledgements

The authors acknowledge NASA, NOAA and NCEP for providing access to the various satellite (MODIS, ATOVS, SSM/I, QuikSCAT and TRMM) observations as well as the NCEP GFS and FNL fields. The first author acknowledges funding support for this work from Space Application Centre, Ahmadabad, India. The second author acknowledges the CSIR and IIT Kharagpur, India for providing research fellowship and all research facilities to undertake this work. The above work includes a small portion of the PhD work of the second author.

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Authors and Affiliations

  1. Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Valiamala, Thiruvananthapuram, 695547, India

    A. Chandrasekar

  2. Center for Analysis & Prediction of Storms, National Weather Centre, University of Oklahoma, Oklahoma, 73072, USA

    M. Govindan Kutty

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  1. A. Chandrasekar
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  2. M. Govindan Kutty
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Correspondence to A. Chandrasekar .

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  1. , Environmental Science and Engineering, Ewha Womans University, Ewhayeodae-gil, Seodaemun-gu 52, Seoul, 120-750, Korea, Republic of (South Korea)

    Seon Ki Park

  2. Naval Research Laboratory, Grace Hopper Ave. 7, Monterey, 93943-5502, California, USA

    Liang Xu

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Chandrasekar, A., Kutty, M.G. (2013). Studies on the Impacts of 3D-VAR Assimilation of Satellite Observations on the Simulation of Monsoon Depressions over India. In: Park, S., Xu, L. (eds) Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35088-7_26

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