The impact of assimilation of MODIS data for the prediction of a tropical low‐pressure system over India using a mesoscale model

Xavier, V. F.; Chandrasekar, A.; Singh, Randhir; Simon, B.

doi:10.1080/01431160500207302

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…Satellite data provide the ability to monitor atmospheric information from outer space, and have been incorporated into weather predictions using a data assimilation approach. In previous studies, the Four-Dimensional Data Assimilation (FDDA) approach was used to incorporate MODIS data in MM5 (hereafter, MODIS-MM5 FDDA), which caused the enhancement of weather predictions (Xavier et al, 2006;Yamazaki & Orgaz, 2005). One of the byproducts of the MODIS-MM5 FDDA is the ability to provide missing data (i.e., meteorological data) from satellite observations during cloudy condition, which thus enables ET to be estimates under cloudy conditions or during any temporal gaps in the data.…”

Section: Introductionmentioning

confidence: 99%

Mapping evapotranspiration using MODIS and MM5 Four-Dimensional Data Assimilation

Jang

Kang

Kim

et al. 2010

Remote Sensing of Environment

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Section: Introductionmentioning

confidence: 99%

Mapping evapotranspiration using MODIS and MM5 Four-Dimensional Data Assimilation

Jang

Kang

Kim

et al. 2010

Remote Sensing of Environment

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“…A number of recent studies have employed mesoscale models to study monsoon depressions and low pressure systems over India (Roy Bhowmik 2003;Potty et al 2000;Vaidya et al 2004;Mukhopadhyay et al 2005;Xavier et al 2006). The present study used MM5 version 3.5 (Grell et al 1994).…”

Section: Numerical Experimentsmentioning

confidence: 99%

The effect of a surface data assimilation technique and the traditional four-dimensional data assimilation on the simulation of a monsoon depression over India using a mesoscale model

et al. 2007

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The objective of this study is to investigate the impact of a surface data assimilation (SDA) technique, together with the traditional four-dimensional data assimilation (FDDA), on the simulation of a monsoon depression that formed over India during the field phase of the 1999 Bay of Bengal Monsoon Experiment (BOBMEX). The SDA uses the analyzed surface data to continuously assimilate the surface layer temperature as well as the water vapor mixing ratio in the mesoscale model. The depression for the greater part of this study was offshore and since successful application of the SDA would require surface information, a method of estimating surface temperature and surface humidity using NOAA-TOVS satellites was used. Three sets of numerical experiments were performed using a coupled mesoscale model. The first set, called CONTROL, uses the NCEP (National Center for Environmental Prediction) reanalysis for the initial and lateral boundary conditions in the MM5 simulation. The second and the third sets implemented the SDA of temperature and moisture together with the traditional FDDA scheme available in the MM5 model. The second set of MM5 simulation implemented the SDA scheme only over the land areas, and the third set extended the SDA technique over land as well as sea. Both the second and third sets of the MM5 simulation used theNOAA-TOVS and QuikSCAT satellite and conventional upper air and surface meteorological data to provide an improved analysis. The results of the three sets of MM5 simulations are compared with one another and with the analysis and the BOBMEX 1999 buoy, ship, and radiosonde observations. The predicted sea level pressure of both the model runs with assimilation resembles the analysis closely and also captures the large-scale structure of the monsoon depression well. The central sea level pressures of the depression for both the model runs with assimilation were 2-4 hPa lower than the CONTROL. The results of both the model runs with assimilation indicate a larger spatial area as well as increased rainfall amounts over the coastal regions after landfall compared with the CONTROL. The impact of FDDA and SDA, the latter over land, resulted in reduced errors of the following: 1.45 K in temperature, 0.39 m s -1 in wind speed, and 14°in wind direction compared with the BOBMEX buoy observation, and 1.43 m s -1 in wind speed, 43°in wind direction, and 0.75% in relative humidity compared with the CONTROL. The impact of SDA over land and sea compared with SDA over land only showed a further marginal reduction of errors: 0.23 K in air temperature (BOBMEX buoy) and 1.33 m s -1 in wind speed simulations.

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“…We tested several model settings (Chang et al, 2005) and selected a configuration with the MRF planetary boundary layer (PBL) scheme, the Dudhia shortwave radiation scheme, and the Goddard longwave radiation scheme with the Grell-Devenyi ensemble convective parameterization (CP) scheme as the control simulation. The choice of the control configuration was also guided by some recent high resolution model simulations over the Indian region (Azadi et al, 2002;Xavier et al, 2006).…”

Section: Model Configuration and Experimental Set Upmentioning

confidence: 99%

The role of land surface processes on the mesoscale simulation of the July 26, 2005 heavy rain event over Mumbai, India

Chang

Kumar

Niyogi

et al. 2009

Global and Planetary Change

100

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The impact of assimilation of MODIS data for the prediction of a tropical low‐pressure system over India using a mesoscale model

Cited by 12 publications

References 12 publications

Mapping evapotranspiration using MODIS and MM5 Four-Dimensional Data Assimilation

Mapping evapotranspiration using MODIS and MM5 Four-Dimensional Data Assimilation

The effect of a surface data assimilation technique and the traditional four-dimensional data assimilation on the simulation of a monsoon depression over India using a mesoscale model

The role of land surface processes on the mesoscale simulation of the July 26, 2005 heavy rain event over Mumbai, India

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