Convective and stratiform cloud rainfall estimation from geostationary satellite data

Li, Jun; Wang, Luyi; Fengxian, Zhou

doi:10.1007/bf02656972

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Based on the experiences and work of Adler and Negri (1988), Goldenberg et al (1990), and Li et al (1993), a preliminary method of QPE for landfalling typhoons was proposed by Yue et al (2006a,b) using GMS5 infrared channel 1 (IR1) TBB data. Its main content includes five steps as follows:…”

Section: Gms5-tbb Datamentioning

confidence: 99%

Verification of Tropical Cyclone–Related Satellite Precipitation Estimates in Mainland China

Chen

et al. 2009

Journal of Applied Meteorology and Climatology

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To evaluate the abilities of satellite retrievals in reflecting precipitation features related to tropical cyclones (TCs) affecting mainland China, four years of 6-and 24-h precipitation retrievals from three datasets, namely the Tropical Rainfall Measuring Mission satellite algorithm 3B42, version 6 (3B42), Climate Prediction Center morphed (CMORPH) product, and one based on the Geostationary Meteorological Satellite-5 infrared brightness temperature (GMS5-TBB), are compared statistically with direct measurements from surface gauge rainfall data during the periods affected by TCs. The GMS5-TBB dataset was set up by a method of considering the GMS5-TBB characteristics, hourly precipitation intensity, and horizontal distribution for landfalling TCs. The results show that in a general sense, all three satellite-retrieved rainfall datasets give quite reasonable 6-and 24-h rainfall distributions, with skill decreasing with the increase in both latitude and rainfall amount. The 3B42 has a little bit better skill than CMORPH, which is likely related to the fact that the 3B42 product has a rain gauge adjustment and CMORPH does not. Further analyses show that both 3B42 and CMORPH considerably underestimate the moderate and heavy rainfall and overestimate the very light precipitation. The overestimation of the GMS5-TBB data for the light rain is larger than that for 3B42 and CMORPH, probably due to the fact that the GMS5-TBB method considers stratiform and convective rainfall separately with a fixed stratiform rain rate of 2 mm h 21 . For the heavy rainfall events, the GMS5-TBB data perform much better than the 3B42 and CMORPH data with an almost halved bias, owing to the fact that the GMS5-TBB method adopted the adjustment of the convective rain rate by considering TBB characteristics of landfalling TCs and using hourly gauge rainfall in the setup process. Since the heavy rainfall events associated with landfalling TCs are of the most concern, the compared GMS5-TBB data could be useful as an operational/research reference.

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Section: Gms5-tbb Datamentioning

confidence: 99%

Verification of Tropical Cyclone–Related Satellite Precipitation Estimates in Mainland China

Chen

et al. 2009

Journal of Applied Meteorology and Climatology

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“…MODIS atmospheric and surface parameter retrievals require cloud free measurements (Li et al 2001a), while cloud type information such as single/multi-layer or high/medium/low cloud information will greatly benefit cloud parameter retrievals (Frey et al 1999;Li et al 2001b) and the derivation of cloud motion vectors (Velden et al 1997). Cloud classification can also improve the monitoring of deep convective clouds and rainfall estimation from IR cloud imagery data (Li et al 1992(Li et al , 1993. MODIS cloud information can further the International Satellite Cloud Climatology Program (ISCCP) that was stimulated by research on several methods of cloud classification that have been tested in a systematic algorithm intercomparison (Rossow et al 1985).…”

Section: Introductionmentioning

confidence: 99%

High-Spatial-Resolution Surface and Cloud-Type Classification from MODIS Multispectral Band Measurements

Menzel

Yang

et al. 2003

J. Appl. Meteor.

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A method for automated classification of surface and cloud types using Moderate-Resolution Imaging Spectroradiometer (MODIS) radiance measurements has been developed. The MODIS cloud mask is used to define the training sets. Surface and cloud type classification is based on the maximum likelihood (ML) classification method. Initial classification results define training sets for subsequent iterations. Iterations end when the number of pixels switching classes becomes smaller than a predetermined number or when other criteria are met. The mean vector in the spectral and spatial domain within a class is used for class identification and a final 1km-resolution classification mask is generated for such field of view in a MODIS granule. This automated classification refines the output of the cloud mask algorithm, and enables further applications such as clear atmospheric profile or cloud parameter retrievals from MODIS and Atmospheric Infrared Sounder (AIRS) radiance measurements. The advantages of this method are that the automated surface and cloud type classifications are independent of radiance or brightness temperature threshold criteria, and that the interpretation of each class is based on the radiative spectral characteristics of different classes. This paper describes the ML classification algorithm and presents daytime MODIS classification results. The classification results are compared with the MODIS cloud mask, visible images, infrared window images, and other observations for an initial validation.

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Review of Chinese atmospheric science research over the past 70 years: Climate and climate change

Huang

Chen

Wen

et al. 2019

Sci. China Earth Sci.

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Convective and stratiform cloud rainfall estimation from geostationary satellite data

Cited by 5 publications

References 8 publications

Verification of Tropical Cyclone–Related Satellite Precipitation Estimates in Mainland China

Verification of Tropical Cyclone–Related Satellite Precipitation Estimates in Mainland China

High-Spatial-Resolution Surface and Cloud-Type Classification from MODIS Multispectral Band Measurements

Review of Chinese atmospheric science research over the past 70 years: Climate and climate change

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