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.

C. Balaji

Department of Mechanical Engineering

C. V. Krishnamoorthy

Artificial neural network

Maximum likelihood analysis

Radiative transfer

Relative humidity

satellite data

TEMPERATURE PROFILE

THREE-DIMENSIONAL MODELING

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

Journal of Earth System Science

The first step in developing any algorithm to retrieve the atmospheric temperature and humidity parameters at various pressure levels is the simulation of the top of the atmosphere radiances that can be measured by the satellite. This study reports the results of radiative transfer simulations for the multichannel infrared sounder of the proposed Indian satellite INSAT-3D due to be launched shortly. Here, the widely used community software k Compressed Atmospheric Radiative Transfer Algorithm (kCARTA) is employed for performing the radiative transfer simulations. Though well established and benchmarked, kCARTA is a line-by-line solver and hence takes enormous computational time and effort for simulating the multispectral radiances for a given atmospheric scene. This necessitates the development of a much faster and at the same time, equally accurate RT model that can drive a real-time retrieval algorithm. In the present study, a fast radiative transfer model using neural networks is proposed to simulate radiances corresponding to the wavenumbers of INSAT-3D. Realistic atmospheric temperature and humidity profiles have been used for training the network. Spectral response functions of GOES-13, a satellite similar in construction, purpose and design and already in use are used. The fast RT model is able to simulate the radiances for 1200 profiles in 18 ms for a 15-channel GOES profile, with a correlation coefficient of over 99%. Finally, the robustness of the model is tested using additional synthetic profiles generated using empirical orthogonal functions (EOF). © Indian Academy of Sciences.

Fulltext

An artificial neural network based fast radiative transfer model for simulating infrared sounder radiances

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.

International Geoscience and Remote Sensing Symposium (IGARSS)

Retrieval of rainfall from the MADRAS microwave 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

Optics and Photonics News

Optics in 2018

The Cambridge Quarterly

David Christopher Gervais 12 November 1943–13 May 2015

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

Journal	Journal of Earth System Science
Publisher	Indian Academy of Sciences
ISSN	23474327
Open Access	No