High-Frequency Doppler Radars for a Polar Precipitation Mission

Lead Organisation: University of Leicester

Despite the well-recognized role played by clouds and precipitation in affecting our climate, gaps in the remote sensing observational capabilities of their vertically resolved microphysics significantly hamper progress in understanding the physical processes within them, whose parameterizations underpin numerical weather and climate models. Accurate measurement of solid precipitation remains particularly challenging and accurate large-scale estimations of the snowfall are not yet available. While the CloudSat mission has paved the way towards the use of millimetre wave radars (94 GHz) for monitoring snow and for providing vertically-resolved precipitating cloud microphysical measurements, it has become clear that multi-frequency radar observations are irreplaceable assets to overcome the snow microphysical deadlock, i.e. the dependence of the snow rate on the snow microphysical characteristics: particle habit, fall velocity and size distribution.

This study aims at better quantifying the information content coming from dual-frequency reflectivity ratio measurements and at identifying the optimal frequency pair for discriminating between snow habits and for narrowing down uncertainties in snow-rate estimates. Besides considering the 35-94 GHz pair, initially proposed for the EE8 Polar Precipitation Mission radar, higher frequencies (140 and 220 GHz) will be examined. Since the deployment of space-borne radars at such high frequencies is challenging, the critical technology development requirements required for an EE9-like space-mission will be assessed. Specifically, a subsystem to component level study of the high power millimetre wave frequency multipliers required to drive the radar transmitter’s output stage will be undertaken. The resulting design guidelines will accelerate eventual hardware development, and will provide an input to a technology development roadmap, which will include schedule and ROM cost estimates.