Can turbulence within the field of view cause significant biases in radiative transfer modeling at the 183 GHz band?

Xavier Calbet,Emil Robert Kursinski,Dale Ward,Rigel Kivi,Angel Otarola,Niobe Peinado-Galan,Pedro Oria,Pilar Rípodas,Sergio Desouza-Machado

doi:10.5194/amt-11-6409-2018

Abstract

Abstract. The hypothesis whether turbulence within the passive microwave sounders field of view can cause significant biases in radiative transfer modeling at the 183 GHz water vapor absorption band is tested. A novel method to calculate the effects of turbulence in radiative transfer modeling is presented. It is shown that the turbulent nature of water vapor in the atmosphere can be a critical component of radiative transfer modeling in this band. Radiative transfer simulations are performed comparing a uniform field with a turbulent one. These comparisons show frequency dependent biases which can be up to several kelvin in brightness temperature. These biases can match experimentally observed biases, such as the ones reported in Brogniez et al. (2016). Our simulations show that those biases could be explained as an effect of high-intensity turbulence in the upper troposphere. These high turbulence phenomena are common in clear air turbulence, storm or cumulus cloud situations.

Highlights

Radiative transfer models (RTMs) are key tools in the microwave and infrared atmospheric remote sensing of the atmosphere
The hypothesis whether turbulence within the passive microwave sounders field of view can cause significant biases in radiative transfer modeling at the 183 GHz water vapor absorption band is tested
It is shown that the turbulent nature of water vapor in the atmosphere can be a critical component of radiative transfer modeling in this band

Summary

Introduction

Radiative transfer models (RTMs) are key tools in the microwave and infrared atmospheric remote sensing of the atmosphere They are used to model the radiances at the top of the atmosphere as measured from satellites. By applying inversion techniques to these RTMs, such as Optimal Estimation (Rodgers, 2000), the physical parameters of the atmosphere can be obtained from radiances observed from satellites. These inversion techniques are commonly used in retrieval processes, where atmospheric properties are directly estimated from the measurements. RTMs are, elements that bridge the gap between measured satellite radiances and atmospheric physical parameters such as profiles of temperature and water vapor concentration

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Conclusion