Comparison of aircraft observations with ensemble forecast model results in terms of the microphysical characteristics of stratiform precipitation

The prediction of the particle number concentration and liquid/ice water content of cloud is significant for many aspects of atmospheric science. However, given the uncertainties in the initial and boundary conditions and imperfections of microphysical schemes, the accurate prediction of these micro...

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Main Authors: Yuan FU, Hengchi LEI, Jiefan YANG, Jiaxu GUO, Jiangshan ZHU
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2020-09-01
Series:Atmospheric and Oceanic Science Letters
Subjects:
Online Access:http://dx.doi.org/10.1080/16742834.2020.1780902
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spelling doaj-16e30b7955e6451693f6fdeabec39d9e2021-04-02T14:17:26ZengKeAi Communications Co., Ltd.Atmospheric and Oceanic Science Letters1674-28342376-61232020-09-0113545246110.1080/16742834.2020.17809021780902Comparison of aircraft observations with ensemble forecast model results in terms of the microphysical characteristics of stratiform precipitationYuan FU0Hengchi LEI1Jiefan YANG2Jiaxu GUO3Jiangshan ZHU4Institute of Atmospheric Physics, Chinese Academy of SciencesInstitute of Atmospheric Physics, Chinese Academy of SciencesInstitute of Atmospheric Physics, Chinese Academy of SciencesInstitute of Atmospheric Physics, Chinese Academy of SciencesInstitute of Atmospheric Physics, Chinese Academy of SciencesThe prediction of the particle number concentration and liquid/ice water content of cloud is significant for many aspects of atmospheric science. However, given the uncertainties in the initial and boundary conditions and imperfections of microphysical schemes, the accurate prediction of these microphysical properties of cloud is still a big challenge. The ensemble approach may be a viable way to reduce forecast uncertainties. In this paper, a large-scale stratiform cloud precipitation process is studied by comparing results of a 10-member ensemble forecast model with aircraft observation data. By means of the ensemble average, the prediction of bulk parameters such as liquid water content and ice water content can be improved in comparison with the control member, but the particle number concentrations are still one to two orders of magnitude less than those from observations. Intercomparison of raindrop size spectra reveals a big distinction between observations and predictions for particles with a diameter less than 1000 μm.http://dx.doi.org/10.1080/16742834.2020.1780902aircraft observationensemble forecast modelparticle number concentrationliquid/ice water content
collection DOAJ
language English
format Article
sources DOAJ
author Yuan FU
Hengchi LEI
Jiefan YANG
Jiaxu GUO
Jiangshan ZHU
spellingShingle Yuan FU
Hengchi LEI
Jiefan YANG
Jiaxu GUO
Jiangshan ZHU
Comparison of aircraft observations with ensemble forecast model results in terms of the microphysical characteristics of stratiform precipitation
Atmospheric and Oceanic Science Letters
aircraft observation
ensemble forecast model
particle number concentration
liquid/ice water content
author_facet Yuan FU
Hengchi LEI
Jiefan YANG
Jiaxu GUO
Jiangshan ZHU
author_sort Yuan FU
title Comparison of aircraft observations with ensemble forecast model results in terms of the microphysical characteristics of stratiform precipitation
title_short Comparison of aircraft observations with ensemble forecast model results in terms of the microphysical characteristics of stratiform precipitation
title_full Comparison of aircraft observations with ensemble forecast model results in terms of the microphysical characteristics of stratiform precipitation
title_fullStr Comparison of aircraft observations with ensemble forecast model results in terms of the microphysical characteristics of stratiform precipitation
title_full_unstemmed Comparison of aircraft observations with ensemble forecast model results in terms of the microphysical characteristics of stratiform precipitation
title_sort comparison of aircraft observations with ensemble forecast model results in terms of the microphysical characteristics of stratiform precipitation
publisher KeAi Communications Co., Ltd.
series Atmospheric and Oceanic Science Letters
issn 1674-2834
2376-6123
publishDate 2020-09-01
description The prediction of the particle number concentration and liquid/ice water content of cloud is significant for many aspects of atmospheric science. However, given the uncertainties in the initial and boundary conditions and imperfections of microphysical schemes, the accurate prediction of these microphysical properties of cloud is still a big challenge. The ensemble approach may be a viable way to reduce forecast uncertainties. In this paper, a large-scale stratiform cloud precipitation process is studied by comparing results of a 10-member ensemble forecast model with aircraft observation data. By means of the ensemble average, the prediction of bulk parameters such as liquid water content and ice water content can be improved in comparison with the control member, but the particle number concentrations are still one to two orders of magnitude less than those from observations. Intercomparison of raindrop size spectra reveals a big distinction between observations and predictions for particles with a diameter less than 1000 μm.
topic aircraft observation
ensemble forecast model
particle number concentration
liquid/ice water content
url http://dx.doi.org/10.1080/16742834.2020.1780902
work_keys_str_mv AT yuanfu comparisonofaircraftobservationswithensembleforecastmodelresultsintermsofthemicrophysicalcharacteristicsofstratiformprecipitation
AT hengchilei comparisonofaircraftobservationswithensembleforecastmodelresultsintermsofthemicrophysicalcharacteristicsofstratiformprecipitation
AT jiefanyang comparisonofaircraftobservationswithensembleforecastmodelresultsintermsofthemicrophysicalcharacteristicsofstratiformprecipitation
AT jiaxuguo comparisonofaircraftobservationswithensembleforecastmodelresultsintermsofthemicrophysicalcharacteristicsofstratiformprecipitation
AT jiangshanzhu comparisonofaircraftobservationswithensembleforecastmodelresultsintermsofthemicrophysicalcharacteristicsofstratiformprecipitation
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