Towards variational retrieval of warm rain from passive microwave observations
<p>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 driz...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-07-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://www.atmos-meas-tech.net/11/4389/2018/amt-11-4389-2018.pdf |
| Summary: | <p>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.</p><p>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.</p> |
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| ISSN: | 1867-1381 1867-8548 |