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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 1
Atmos. Meas. Tech., 8, 369–384, 2015
https://doi.org/10.5194/amt-8-369-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 8, 369–384, 2015
https://doi.org/10.5194/amt-8-369-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 22 Jan 2015

Research article | 22 Jan 2015

Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations

K. Ramesh1, A. P. Kesarkar2, J. Bhate2, M. Venkat Ratnam2, and A. Jayaraman2 K. Ramesh et al.
  • 1Department of Computer Applications, Anna University, Regional Center, Tirunelveli, Tamil Nadu 627 005, India
  • 2National Atmospheric Research Laboratory, Gadanki 517 112, Chittoor District, Andhra Pradesh, India

Abstract. The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods.

The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and ANFIS) has limited success.

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The study of atmospheric convection is important for the understanding of evolution of diurnal cycles of rainfall. High-resolution observations of vertical profiles of temperature and relative humidity are very useful for understanding the behaviour of these convections. Microwave radiometers are becoming useful tools for it. In this paper, we propose a new method to retrieve these profiles based on adaptive neuro-fuzzy interface systems and find that this method has a better skill of retrieval.
The study of atmospheric convection is important for the understanding of evolution of diurnal...
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