The Inference of GATE Convective Rainfall from SMS-1 Imagery

Woodley, William L.; Griffith, Cecilia Girz; Griffin, Joseph S.; Stromatt, Scott

doi:10.1175/1520-0450(1980)019<0388:tiogcr>2.0.co;2

Cited by 66 publications

(29 citation statements)

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“…For example, Griffith et al [6,9] analyzed the rainfall characteristics based on the lifecycle characteristics of clouds. Laurent et al [10] and Mathon et al [11] found that cloud life duration and propagation speed are positively related to the induced precipitation.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Application of the Relationship between Cumulonimbus (Cb) Cloud Features and Precipitation Based on FY-2C Image

Liu

et al. 2014

Atmosphere

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Although cumulonimbus (Cb) clouds are the main source of precipitation in south China, the relationship between Cb cloud characteristics and precipitation remains unclear. Accordingly, the primary objective of this study was to thoroughly analyze the relationship between Cb cloud features and precipitation both at the pixel and cloud patch scale, and then to apply it in precipitation estimation in the Huaihe River Basin using China's first operational geostationary meteorological satellite, FengYun-2C (FY-2C), and the hourly precipitation data of 286 gauges from 2007. First, 31 Cb parameters (14 parameters of three pixel features and 17 parameters of four cloud patch features) were extracted based on a Cb tracking method using an artificial neural network (ANN) cloud classification as a pre-processing procedure to identify homogeneous Cb patches. Then, the relationship between Cb cloud properties and precipitation was analyzed and applied in a look-up table algorithm to estimate precipitation. The results were as follows: (1) Precipitation increases first and then declines with increasing values for cold cloud and time evolution parameters, OPEN ACCESSAtmosphere 2014, 5 212 and heavy precipitation may occur not only near the convective center, but also on the front of the Cb clouds on the pixel scale. (2) As for the cloud patch scale, precipitation is typically associated with cold cloud and rough cloud surfaces, whereas the coldest and roughest cloud surfaces do not correspond to the strongest rain. Moreover, rainfall has no obvious relationship with the cloud motion features and varies significantly over different life stages. The involvement of mergers and splits of minor Cb patches is crucial for precipitation processes. (3) The correlation coefficients of the estimated rain rate and gauge rain can reach 0.62 in the cross-validation period and 0.51 in the testing period, which indicates the feasibility of the further application of the relationship in precipitation estimation.

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Section: Introductionmentioning

confidence: 99%

Analysis and Application of the Relationship between Cumulonimbus (Cb) Cloud Features and Precipitation Based on FY-2C Image

Liu

et al. 2014

Atmosphere

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“…Cloud-patch-based approaches estimate rainfall based on the cloud coverage under a specified temperature threshold, such as 253 K. The Griffith-Woodley Technique (Griffith et al 1978;Woodley et al 1980…”

Section: Introductionmentioning

confidence: 99%

Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System

Hong

Hsu

Sorooshian

et al. 2004

Journal of Applied Meteorology

733

444

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A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts local and regional cloud features from infrared (10.7 m) geostationary satellite imagery in estimating finescale (0.04Њ ϫ 0.04Њ every 30 min) rainfall distribution. This algorithm processes satellite cloud images into pixel rain rates by 1) separating cloud images into distinctive cloud patches; 2) extracting cloud features, including coldness, geometry, and texture; 3) clustering cloud patches into well-organized subgroups; and 4) calibrating cloud-top temperature and rainfall (T b -R) relationships for the classified cloud groups using gauge-corrected radar hourly rainfall data. Several cloud-patch categories with unique cloud-patch features and T b -R curves were identified and explained. Radar and gauge rainfall measurements were both used to evaluate the PERSIANN CCS rainfall estimates at a range of temporal (hourly and daily) and spatial (0.04Њ, 0.12Њ, and 0.25Њ) scales. Hourly evaluation shows that the correlation coefficient (CC) is 0.45 (0.59) at a 0.04Њ (0.25Њ) grid scale. The averaged CC of daily rainfall is 0.57 (0.63) for the winter (summer) season.

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“…Martin and Scherer (1973) gave a review of statellite rainfall estimation methods up to 1973. Many studies of convective rainfall estimation have been done using satellite data (Kilonsky and Ramage, 1976;Scofield and Oliver, 1977;Griffith et al, 1978;Stout et al, 1979;Arkin, 1979;Woodley et al, 1980;Richard and Arkin, 1981;Negri et al, 1984;Maruyama et al, 1986). These methods are based on the notion that deep convective clouds might produce more rain.…”

Section: Introductionmentioning

confidence: 99%

An Instantaneous Delineation of Convective Rainfall Areas Using Split Window Data of NOAA-7 AVHRR

Inoue¹

1987

Journal of the Meteorological Society of Japan

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A feasibility study of delineation of convective rainfall area is presented using the split window (11 µm and 12 µm) data measured by the Advanced Very High Resolution Radiometer (AVHRR) on board NOAA-7. Non-precipitating cirrus clouds and low-level cumulus clouds, which cause a erroneous rainfall area delineation, and cumulonimbus clouds are objectively distinguished by the split window data. The satellite rainfall areas, in this study, are defined as those of cumulus and cumulonimbus clouds whose tops are over 700mb level. The satellite rain-clouds are compared with concurrent radar data for eight cases. The study was performed over the area of 33°-39°N and 135°-14*E during the summer season in 1984 by taking into account the availability of digital radar data and the atmospheric condition for convective clouds.Statistics show that this method is better than the conventional single infrared method (utilizing -20* of brightness temperature as a threshold for rain clouds) by 12% for the probability of detection and 13% for the false alarm ratio. The false alarm ratio by our method is generally better than the conventional single infrared method for any cases studied. The improvement of the false alarm ratio by our method over the single infrared method is comparable to that shown by other authors who use the method combining visible and infrared data. Significant improvement in rain area delineation by our method in comparison with the single infrared method can be obtained in the situation where single layer cirrus clouds or low-level rain-clouds dominate over the study area.

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The Inference of GATE Convective Rainfall from SMS-1 Imagery

Cited by 66 publications

References 0 publications

Analysis and Application of the Relationship between Cumulonimbus (Cb) Cloud Features and Precipitation Based on FY-2C Image

Analysis and Application of the Relationship between Cumulonimbus (Cb) Cloud Features and Precipitation Based on FY-2C Image

Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System

An Instantaneous Delineation of Convective Rainfall Areas Using Split Window Data of NOAA-7 AVHRR

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