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Discussion papers | Copyright
https://doi.org/10.5194/amt-2018-96
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 11 Apr 2018

Research article | 11 Apr 2018

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Measurement Techniques (AMT) and is expected to appear here in due course.

Towards variational retrieval of warm rain from passive microwave observations

David Ian Duncan1, Christian D. Kummerow2, Brenda Dolan2, and Veljko Petković2 David Ian Duncan et al.
  • 1Department of Earth, Space, and Environment, Chalmers University of Technology, SE-41296, Gothenburg, Sweden
  • 2Department of Atmospheric Science, Colorado State University, Fort Collins, CO

Abstract. An experimental retrieval of oceanic warm rain is presented, extending a previous variational algorithm to provide a suite of retrieved variables spanning non-raining through predominantly warm raining conditions. The warm rain retrieval is underpinned by hydrometeor covariances and drizzle onset data derived from CloudSat. Radiative transfer modelling and analysis of drop size variability from disdrometer observations permit state-dependent observation error covariances that scale with columnar rainwater during iteration. The state-dependent errors and nuanced treatment of drop distributions in precipitating regions are novel and may be applicable for future retrievals and all-sky data assimilation methods. This retrieval method can effectively increase passive microwave sensors' sensitivity to light rainfall that might otherwise be missed.

Comparisons with space-borne and ground radar estimates are provided as a proof of concept, demonstrating that a passive-only variational retrieval can be sufficiently constrained from non-raining through warm rain conditions. Significant deviations from forward model assumptions cause non-convergence, usually a result of scattering hydrometeors above the freezing level. However, for cases with liquid-only precipitation, this retrieval displays greater sensitivity than a benchmark operational retrieval. Analysis against passive and active products from the Global Precipitation Measurement (GPM) satellite shows substantial discrepancies in precipitation frequency, with the experimental retrieval observing more frequent light rain. This approach may be complementary to other precipitation retrievals, and its potential synergy with the operational passive GPM retrieval is briefly explored. There are also implications for data assimilation, as all 13 channels on the GPM Microwave Imager (GMI) are simulated over ocean with fidelity in warm raining conditions.

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David Ian Duncan et al.
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
David Ian Duncan et al.
Data sets

CSU 1DVAR -- GMI Warm Rain Retrieval David I. Duncan https://doi.org/10.5281/zenodo.1098212

Model code and software

CSU 1DVAR -- GMI Warm Rain Retrieval David I. Duncan https://doi.org/10.5281/zenodo.1098212

David Ian Duncan et al.
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Latest update: 17 Jul 2018
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Short summary
Satellites are fairly good at detecting and quantifying rainfall over oceans, but light rainfall characteristic of high latitudes and stratocumulus areas is harder to sense for passive sensors. The method presented extends the sensitivity of passive measurements to light rain by leveraging radar data and measurements of raindrop distributions. This method may help to close the gap between global precipitation estimates at high latitudes and maximise the utility of passive sensors.
Satellites are fairly good at detecting and quantifying rainfall over oceans, but light rainfall...
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