Microwave radiation with the inherent advantage of its ability to partially penetrate clouds is ideally suited for remote measurements of rainfall, especially over the oceanic regions. The retrieval problem is of great practical interest, as precipitation over the oceans has to be necessarily remotely sensed. More importantly, precipitation is also a crucial input in many weather and climate models. IIT Madras has developed over the last ten years an in-house polarized microwave radiation transfer model (Micro- Tropiques) along with candidate retrieval strategies for the retrieval of rainfall for possible use in the multi frequency (18.7 GHz, 23.8 GHz, 36.5 GHz, 89 GHz and 157 GHz) Microwave Analysis and Detection of Rain and Atmospheric Structures (MADRAS) microwave imager of the Indo-French mission Megha-Tropiques (MT) due to be launched shortly. This lecture will chronicle the efforts taken towards the development of robust rainfall retrieval techniques from the MT data. © 2011 IEEE.

C. Balaji

Department of Mechanical Engineering

MEGHA-TROPIQUES

MICROWAVE IMAGERS

MICROWAVE RADIATIONS

Multi frequency

OCEANIC REGIONS

REMOTE MEASUREMENT

RETRIEVAL STRATEGIES

RETRIEVAL TECHNIQUES

TROPIQUES

ATMOSPHERIC STRUCTURE

Climate models

Geology

Rain

Remote sensing

MICROWAVES

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Retrieval of rainfall from the MADRAS microwave imager of Megha-Tropiques

International Geoscience and Remote Sensing Symposium (IGARSS)

In this study, retrieval of temperature and humidity profiles of atmosphere from INSAT 3D-observed radiances has been accomplished. As the first step, a fast forward radiative transfer model using an Artificial neural network has been developed and it was proven to be highly effective, giving a correlation coefficient of 0.97. In order to develop this, a diverse set of physics-based clear sk profiles of pressure (P), temperature (T) and specific humidity (q) has been developed. The developed database was further used for geophysical retrieval experiments in two different frameworks, namely, an ANN and Bayesian estimation. The neural network retrievals were performed for three different cases, viz., temperature only retrieval, humidity only retrieval and combined retrieval. The temperature/humidity only ANN retrievals were found superior to combined retrieval using an ANN. Furthermore, Bayesian estimation showed superior results when compared with the combined ANN retrievals. © Indian Academy of Sciences.

Journal of Earth System Science

Retrieval of humidity and temperature profiles overthe oceans from INSAT 3D satellite radiances

This paper reports the radiative transfer simulations for the passive microwave radiometer onboard the proposed Indian climate research satellite Megha-Tropiques due to be launched in 2011. These simulations have been performed by employing an in-house polarized radiative transfer code for raining systems ranging from depression and tropical cyclones to the Indian monsoon. For the sake of validation and completeness, simulations have also been done for the Tropical Rainfall Measuring Mission (TRMM)'s Microwave Imager (TMI) of the highly successful TRMM mission of NASA and JAXA. The paper is essentially divided into two parts: (a) Radiometer response with specific focus on high frequency channels in both the radiometers is discussed in detail with a parametric study of the effect of four hydrometeors (cloud liquid water, cloud ice, precipitating water and precipitating ice) on the brightness temperatures. The results are compared with TMI measurements wherever possible. (b) Development of a neural network-based fast radiative transfer model is elucidated here. The goal is to speed up the computational time involved in the simulation of brightness temperatures, necessitated by the need for quick and online retrieval strategies. The neural network model uses hydrometeor profiles as inputs and simulates spectral microwave brightness temperature at multiple frequencies as output. A huge database is generated by executing the in-house radiative transfer code for seven different cyclones occurred in North Indian Ocean region during the period 2001-2006. A part of the dataset is used to train the network while the remainder is used for testing purposes. For the purpose of testing, a typical scene from the southwest monsoon rain is also considered. The results obtained are very encouraging and show that the neural network is able to mimic the underlying physics of the radiative transfer simulations with a correlation coefficient of over 99%. © Indian Academy of Sciences.

Fulltext

Radiative transfer simulations for the MADRAS imager of Megha-Tropiques

Purpose - The purpose of this paper is to develop an algorithm, using PCA-based neural network, to retrieve the vertical rainfall structure in a precipitating atmosphere. The algorithm is powered by a rigorous solution to the plane parallel radiative transfer equation for the atmosphere with thermodynamically consistent vertical profiles of humidity, temperature and cloud structures, together with "measured" vertical profiles of the rain structure derived from a radar. Design/methodology/approach - The raining atmosphere is considered to be a plane parallel, radiatively participating medium.The atmospheric thermodynamic profiles such as pressure, temperature and relative humidity along with wind speed at sea surface and cloud parameters corresponding to Nargis, a category 4 tropical cyclone thatmade its landfall onMay 2, 2008 at the Republic ofMyanmar, are obtained by solving the flux form of Euler's equations in three-dimensional form. The state-of-the-art community softwareWeather Research and Forecasting has been used for solving the set of equations. The three-dimensional rain profiles for the same cyclone at the same instant of time are obtained fromNational Aeronautics and Space Administration's space borne Tropical RainfallMeasuringMission's precipitation radar over collocated pixels. An in-house Micro-Tropiques code is used to perform radiative transfer simulations for frequencies corresponding to a typical space borne radiometer, and hence to generate the databasewhich is later used for training the neural network.The back propagation-based neural network is optimized with reduced number of parameters using principal component analysis (PCA). Findings - The results show that neural network is capable of retrieving the vertical rainfall structure with a correlation coefficient of over 0.99. Further, reducing the ill-posedness in retrieving 56 parameters from just nine measurements using PCA has improved the root mean square error in the retrievals at reduced computational time. Originality/value - The paper shows that combining numerically generated atmospheric profiles together with radar measurements to serve as input to a radiative transfer model brings in the much-required synergy between numerical weather prediction, radar measurements and radiative transfer.This strategy can be gainfully used in satellitemeteorology.Using principal components to reduce the ill-posedness, thereby increasing the robustness in retrieving vertical rain structure, has been attempted for the first time.Awell-trained network can be used as one possible option for an operational algorithmfor the proposed Indian climate research satellite Megha-Tropiques, due to be launched in early 2011. © Emerald Group Publishing Limited.

International Journal of Numerical Methods for Heat and Fluid Flow

A new PCA-ANN algorithm for retrieval of rainfall structure in a precipitating atmosphere

In the present study, forward radiative transfer simulations are carried out for the tropical cyclone Fanoos that hit the coast off south India in December 2005. The in-house radiative transfer package used for this study employs the doubling and adding method to calculate radiances leaving the top of the one dimensional precipitating atmosphere. The particle drop size distribution is assumed to follow a modified gamma distribution in respect of the cloud liquid water and cloud ice water content. For precipitation, the Marshall-Palmer particle size distribution is used. All the hydrometeor particles are assumed to be spherical and Lorentz Mie theory is used to evaluate the interaction parameters like absorption, scattering coefficients and polarized scattering matrix. In order to validate the drop size distributions and inter Action parameter calculations, the simulated brightness temperatures are compared with the TMI measured brightness temperatures for all the channels. For carrying out this exercise, vertical hydrometeors retrieved by TMI are used as input. The differences between simulated and measured brightness temperatures are found to be within ±10%. The maximum difference in the brightness temperatures between the present work and the Eddington model which the TRMM algorithm employs is about 4.5 K. This may become significant when retrieval of precipitation is attempted by combining the forward model with a suitable retrieval strategy, under tropical conditions. © Printed in India.

Polarized microwave forward model simulations for tropical storm Fanoos

The objective of the present work is to develop a general purpose, polarized, microwave radiative transfer forward model, including calculation of interaction parameters for fast, simultaneous and accurate generation of radiances, for use in a wide variety of atmospheric retrieval applications. This complete polarized model includes the generation of atmospheric profiles, calculation of the interaction parameters and the solution of the vector radiative transfer equations. In the present work, the doubling and adding method is used to calculate radiances. Mie theory is used for spherical particles to evaluate the polarized scattering matrix. Two types of sea surface models applicable for specular and diffuse surfaces respectively are implemented. The model has been validated against standard benchmark cases and experimental data available in literature. While the model is itself generic, parametric studies are performed to study the influence of the vertical structure of the atmosphere, for two polarizations and a set of frequencies to be used on the microwave imager aboard the proposed Indo-French climate research satellite MEGHA-TROPIQUES. © 2007 Elsevier B.V. All rights reserved.

Atmospheric Research

A polarized microwave radiative transfer model for passive remote sensing

This study presents a new hybrid method that combines regression analysis with genetic algorithms for the retrieval of hydrometeors (cloud liquid water, ice and rain) in the atmosphere, from satellite microwave radiances. A three layered atmosphere model (divided into 30 sub-layers) is used to generate simulated profiles of hydrometeors. The equation governing the transfer of radiation is solved using the finite volume method to obtain radiances (brightness temperatures) in the microwave region. This is known as the forward problem and is solved repeatedly to create a database with which regression equations are developed for the monochromatic microwave radiances, for six typical frequencies ranging from 6.6 to 85 GHz. The regression is done using nonlinear parameter estimation techniques. The inverse problem of retrieving the hydrometeors characteristics from microwave radiances is accomplished by posing the parameter estimation problem as an optimization problem, wherein, minimization of the sum of squares of residuals between the estimated and known radiances, for the above mentioned six typical frequencies, is done. In this study, genetic algorithms have been used for solving the minimization problem. © 2007 Elsevier Ltd. All rights reserved.

Journal	International Geoscience and Remote Sensing Symposium (IGARSS)
Open Access	No