Improving The Retrieval Of Atmospheric Stability Indices By Combining Ground-based And Satellite Remote Sensing

Abstract:

A new generation of high-resolution (~1km) weather forecast models now becoming operational over Europe promises to revolutionize predictions of severe weather, specifically by explicitly resolving convection. For this, a dense observing network is required, focusing especially on the lowest few km of the atmosphere, so that forecast models have the most realistic state of the atmosphere for initialization, continuous assimilation and verification. In this context, the current European COST action TOPROF (ES1303) deals with operational networking of three existing but so far under-exploited, ground-based remote sensing instruments throughout Europe: i) Several hundreds of ceilometers, ii) more than 20 Doppler lidars, and iii) About 30 microwave profilers (MWP) giving profiles of temperature and humidity in the lowest few km every 10 minutes.

Specifically, MWP are highly suited for continuously monitoring the temporal development of atmospheric stability (i.e. Cimini et al. 2015, AMT) before the initiation of deep convection. However, the vertical resolution of MWP temperature profiles is best in the lowest kilometer above the surface, decreasing rapidly with increasing height. In addition, humidity profile retrievals typically cannot be resolved with more than two degrees of freedom for signal, resulting in a rather poor vertical resolution throughout the troposphere. Typical stability indices (i.e. K-index, Lifted Index, Showalter Index, CAPE,..) rely on temperature and humidity values not only in the region of the boundary layer (850 hPa) but also at 700 hPa, 500 hPa, in between these levels or even higher above. In this study, for clear sky cases, satellite remote sensing (i.e. SEVIRI radiances from the geostationary METEOSAT ) is used to complement the ground-based MWP information. The theoretical basis of the combined retrieval is highlighted, error reductions resulting from the sensor synergy are discussed and applications to real data are shown. The study generally underlines the need for an enhanced ground-based and satellite synergy. Such techniques may possess high potential for improving weather forecasting and/or nowcasting application, however have not even begun to be exploited.