Abstract
Abstract. Satellite remote sensing of rain is important for quantifying the hydrological cycle, atmospheric energy budget, and cloud and precipitation processes; however, radar retrievals of rain rate are sensitive to assumptions about the raindrop size distribution. The upcoming EarthCARE satellite will feature a 94 GHz Doppler radar alongside lidar and radiometer instruments, presenting opportunities for enhanced retrievals of the raindrop size distribution.
Highlights
Satellite remote sensing of rain is important for quantifying the global water and energy cycles
The thickness of the melting layer and the total attenuation may depend on the local temperature profile: as sufficient information to retrieve the total melting layer attenuation may be available from the path-integrated attenuation (PIA) and the attenuation inferred from the radar reflectivity gradient, we include the variable Xm in the retrieval to represent the effect of melting layer thickness on radar attenuation; in this study Xm is held constant with a value of 1.0 km, allowing us to capture the effect of this uncertainty on the retrieved variables and their errors without retrieving Xm
In this study we have used an airborne 94 GHz Doppler radar to investigate the prospects for making improved rain retrievals by assimilating mean Doppler velocity measurements with a focus on improving upon radar rain retrievals from CloudSat in two key respects: (1) to facilitate rain rate estimates over land and (2) to reduce uncertainties in rain rate estimates by retrieving an additional parameter of the raindrop size distribution (DSD)
Summary
Satellite remote sensing of rain is important for quantifying the global water and energy cycles. Improved instrumentation and retrieval algorithms for the satellite remote sensing of rain are priorities for earth observation, model evaluation, and an understanding of cloud and precipitation processes. The more sensitive 94 GHz cloud-profiling radar aboard CloudSat (Stephens et al, 2002) is capable of measuring light rainfall not detected by TRMM, which is very frequent and amounts to 10 % of total tropical maritime precipitation (Berg et al, 2010). Two approaches have been made to improve rain retrievals with additional observations from satellite radars, both to assist in estimating rain rate over land and to better constrain the rain DSD. In this study we use a variational retrieval methodology developed for EarthCARE to investigate improved estimates of rain rate by exploiting mean Doppler velocity measurements to retrieve drop size and drop number concentration parameters of the DSD. We briefly consider applications of the retrieval framework to dual-frequency radar retrievals (Sect. 5) and the retrieval of more complex variations in the DSD through the vertical profile (Sect. 6) before summarizing our key findings with a view to applications to EarthCARE retrievals (Sect. 7)
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