Abstract
When Monsoon depressions form over the seas, the Moderate Resolution Imaging Spectroradiometer (MODIS) provides humidity and high-horizontal resolution temperature details about the depressions. These high-resolution satellite data related to temperature and humidity can improve the poor predicting rate of depressions [1]. Using three-dimensional variational data assimilation (3DVAR) and with the help of humidity profiles along with MODIS temperature. We can achieve an advanced prospect of detection and a larger value of (ETS) equitable threat score observed over 48 hours collected precipitation with respect to the control run. The 3DVAR assimilation of Doppler Weather Radar wind data associated with Indian Meteorological Department (IMD) surface data and upper air data helped in the improvements in the simulation of strong gradients associated with horizontal wind speed ,higher warm core temperature , high vertical velocity & better precipitation and spatial distribution.[2]. The effect of Spectral sensor microwave imager (SSM/I), humidity profiles, use of Advanced TIROS Vertical Sounder (ATOVS) temperature and total precipitable water (TPW) helped in improving the ‘‘forecast impact’’ parameters of ‘‘bias score’’ and ‘‘equitable threat score’’ with respect to the assimilation of satellite observation[3] . In this paper we have discussed a comparative study of different proposed techniques to analyze its effects in improving the low prediction rates of depressions.
Highlights
The Indian summer monsoon rainfall is dependent on the longevity and frequency of monsoon disturbances or depressions which form over the Arabian Sea and the Bay of Bengal
Advanced TIROS vertical sounder: Advanced TIROS Vertical Sounder (ATOVS) is a sounding instrument package, and in the method validation by Rajan et al (2002) they found that the associated root means square error (RMSE), when validated against near radiosonde observations, was less than ten percentage.The data from ATOVS has undergone quality-control and has been assembled into discrete data sets
Model I: Figure 1 show that 3DVAR run having higher spatial correlation of SLP comparison to the control run .Due to assimilation of humidity profiles and Moderate Resolution Imaging Spectroradiometer (MODIS) temperature this model displays a noticeable improving in the space correlation of the SLP field
Summary
Agriculture is the most significant sector of the Indian Economy. The agricultural sector provides 18 per cent of the India’s GDP & offers employment to around 50% of the country’s workforce. We can get accurate and improved initial conditions by assimilating and ingesting high resolution meteorological observations with respect to the initial analysis using Three-Dimensional Variational data assimilation [1][6][7] These monsoon depressions are formulated in the data sparse oceanic areas, so it is highly important to assimilate the satellite observations processed into the numerical model to calculate better forecasts with better analysis. The main aim of the 3DVAR system is to calculate an optimal estimate of the correct state of the atmosphere at any specific time with the help of the cost function [4] Another method to enhance the initial parameters is by ingesting and assimilating non-conventional high-resolution observations like satellites along with DWR observations successfully processed into the numerical implemented model. The rest of the paper is prepared as follows: Section 2 is a review of the existing techniques, Section 3 explains the model descriptions and numerical experiments, Section 4 discusses the results, Section 5 is a comparative study and Section 6 is the conclusion
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