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A novel synergistic retrieval approach to enable tropospheric temperature and humidity profiling under all weather conditions for an improved quantification of evaporation rates

Applicant Dr. Andreas Foth
Subject Area Atmospheric Science
Term from 2020 to 2024
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 438108095
 
Final Report Year 2024

Final Report Abstract

The constant further development and improvement of weather and climate models poses a major challenge for remote sensing. Increasingly better resolved measurements and methods are needed to evaluate the models. Conventional approaches fail here primarily due to the lack of continuous observations of temperature and humidity in all weather conditions and especially during rain. However, a wind profiler could be able to observe vertical information of temperature and humidity gradients under such conditions. Unfortunately, the novel approach proposed in the project to derive temperature and humidity profiles using an artificial neural network failed. The inaccuracies of the derived profiles were too high to use them in further studies. As a result, the planned synergy of wind profiler (including radio acoustic sounding system), Raman lidar, microwave radiometer and cloud radar was no longer possible for the automated and continuous generation of temperature and humidity profiles, even during precipitation. For this purpose, a different temperature profile retrieval method was developed and published, which is based on microwave radiometer observations and enables the derivation of temperature profiles up to a height of 1.5 km at rainfall rates of up to 2.5 mm h^-1 . The derived temperature profiles should subsequently be used to determine evaporation rates and the resulting cooling, particularly in the case of convective rain. For this purpose, two algorithms were developed and published at the beginning of the project, which can differentiate between stratiform and convective rain with the help of micro-rain radar observations. One algorithm is based on probability density functions (PDFs) in combination with a confidence function, the other is a classification with an artificial neural network. Furthermore, a software tool (VirgaSniffer) was developed and published during the project period to classify evaporating precipitation from a combination of remote sensing instruments. Initial studies on the determination of evaporative cooling based on ship-borne measurements in the subtropical Atlantic show promising results. Unfortunately, towards the end of the project it was no longer possible to intensify the studies on evaporative cooling due to the time-consuming failed attempts to derive temperature and humidity profiles from wind profiler data.

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