The effect of satellite and conventional meteorological data assimilation on the mesoscale modeling of monsoon depressions over India

Abstract

Copyright 2008 Elsevier B.V., All rights reserved.The Fifth Generation Mesoscale Model (MM5) is used to study the effect of assimilated satellite and conventional data on the prediction of three monsoon depressions over India using analysis nudging. The satellite data comprised the vertical profiles of temperature and humidity (NOAA-TOVS: - National Oceanic and Atmospheric Administration-TIROS Operational Vertical Sounder; MODIS: - MODerate resolution Imaging Spectroradiometer) and the surface wind vector over the sea (QuikSCAT: - Quick Scatterometer); the conventional meteorological data included the upper-air and surface data from the India Meteorological Department (IMD). Two sets of numerical experiments are performed for each case: the first set, NOFDDA (no nudging), utilizes NCEP reanalysis (for the initial conditions and lateral boundary conditions) in the simulation, the second set, FDDA, employs the satellite and conventional meteorological data for an improved analysis through analysis nudging. Two additional experiments are performed to study the effect of increased vertical and horizontal resolution as well as convective parameterization for one of the depressions for which special fields observations were available. The results from the simulation are compared with each other and with the analysis and observations. The results show that the predicted sea level pressure (SLP), the lower tropospheric cyclonic circulation, and the precipitation of the FDDA simulation reproduced the large-scale structure of the depression as manifested in the NCEP reanalysis. The simulation of SLP using no assimilation high-resolution runs (HRSKF10KM, HRSKF3.3KM) with the Kain-Fritsch cumulus parameterization scheme appeared poor in comparison with the FDDA run, while the no assimilation high-resolution runs (HRSGR10KM, HRSGR3.3KM) with the Grell cumulus scheme provided better results. However, the space correlation and the root mean square (rms) error of SLP for the HRSKF10KM was better than the FDDA; the largest and smallest space correlation for HRSKF10KM, FDDA, and HRSGR10KM were 0.894 and 0.623, 0.663 and 0.195, and 0.733 and 0.338 respectively; the smallest and largest rms error for HRSKF10KM, FDDA and HRSGR10KM were 1.879 and 5.245, 2.308 and 4.242, and 2.055 and 4.909 respectively. The precipitation simulations with the 3.3 km high-resolution, no assimilation runs performed no better than the precipitation simulation with the FDDA run. Thus, a significant finding of this study is that over the Indian monsoon region, the improvements in the simulation using nudging in the FDDA run are of similar magnitude (or better) than the improvements in the simulation due to high-resolution and to cumulus parameterization sensitivity. The improvements in the FDDA run due to analysis nudging were also verified in two more depression cases. The current operational regional models in India do not incorporate the data assimilation of NOAA-TOVS/MODIS and QuikSCAT satellite data, and hence the results of this study are relevant to ...

Improvement of Mesoscale Forecasts of Monsoon Depressions Through Assimilation of QuikSCAT Wind Data: Two Case Studies Over India

“…A large part of the monsoon rainfall over the eastern and central plains of India is associated with low pressure systems, especially, monsoon depressions; hence monsoon depressions need to be better understood and simulated [2]. However, the above still remains a challenging task to simulate realistically these low pressure systems and depressions and their associated spatial and temporal distribution of rainfall by using available mesoscale models [3]. For the short-range prediction, it is extremely important to have accurate initial conditions for better model performance because all the mesoscale models, in general, are very sensitive to the small perturbations in the initial fields and errors in model-predicted fields are due to errors in the initial conditions as well as deficiencies in the model physics [4].…”

“…The study demonstrated the benefits of ingesting and assimilating different satellite and conventional observations using FASDAS and FDDA. Xavier et al (2008) [3] used the MM5 model to study the effect of assimilated satellite and conventional data on the prediction of three monsoon depressions over India using analysis nudging. The satellite data included the vertical profiles of temperature and humidity (from NOAA-TOVS and MODIS) and the surface wind vector over the sea (from QuikSCAT).…”

Role of Four-Dimensional Data Assimilation on Track and Intensity of Severe Cyclonic Storms

“…The results suggested that improvement of monsoon 2 ISRN Meteorology depression simulations over BoB was equivalent, or better than that of increasing the model resolution from 30 km to 10 km grid spacing. Xavier et al [4] studied the effect of assimilated satellite and conventional data on the prediction of three monsoon depressions over India using analysis nudging with MM5 (5th generation NCAR/Penn State Mesoscale Model) and found a positive overall impact on the model performance. An analysis of the status and developments of the four-dimensional variational data assimilation for mesoscale/storm-scale applications has been provided by Park and Zupanski [5].…”

A Comparative Study on Performance of Analysis Nudging and 3DVAR in Simulation of a Heavy Rainfall Event Using WRF Modeling System

“…The study in [9] found positive impact of ingesting special observations available from the Arabian Sea Monsoon Experiment (ARMEX-II) on the operational India Meteorological Department (IMD) limited area forecast system. The authors in [10] studied the effect of assimilated satellite and conventional data on the prediction of three monsoon depressions over the Indian region using nudging technique with MM5 and found an overall positive impact on the model performance. Similarly, the authors in [5] carried out a study with MM5 to find out the impact of nudging technique in the modeling system and its performance to simulate the convective episodes leading to heavy rainfall events over the AS off the west coast of India during the ARMEX-2002.…”