Determination of Rainfall Rates from GOES Satellite Images by a Pattern Recognition Technique

Wu, Rongzhang; Weinman, J. A.; Chin, Roland T.

doi:10.1175/1520-0426(1985)002<0314:dorrfg>2.0.co;2

Cited by 57 publications

(19 citation statements)

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“…Wu et al (1985) used 24 brightness temperature texture features to retrieve rainfall within a neighborhood size of 20 km ϫ 20 km. Hsu et al (1997) developed the PERSIANN system, which calculates rain rate at 0.25Њ latitude ϫ 0.25Њ longitude resolution, based on the brightness temperature variations in the neighboring coverage of 1.25Њ ϫ 1.25Њ.…”

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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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

View full text Add to dashboard Cite

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“…The textural features computed on infrared sample arrays are features originally proposed by Haralick et al (1973) and Weska et al (1976) (a), modified to operate on grey-level difference histograms (Wu et al (1985), Parikh, (1977) (a)). The grey-level difference (GLD) spatial statistical features have been considered since they are computationally inexpensive and are known to perform well, compared to other methods (Weska et al (1976) …”

Section: Feature Descriptionmentioning

confidence: 99%

Physical and statistical approaches for cloud identification using Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager Data

Ricciardelli

Romano

Cuomo

2008

Remote Sensing of Environment

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“…Algorithms have used visible imagery most often to identify more clearly the type of cloud, such as cumulonimbus or cirrus, or to delineate the system more accurately. However, some recent applications (Wu et al, 1985;Garand, 1989) have used a pattern recognition approach in which textural parameters are utilized as predictors of rainfall without explicit physical justification. In such applications, the visible imagery presumably adds information both through its greater resolution and by its ability to depict small variations, too small to be seen in the IR, in the top of the system.…”

Section: Algorithmsmentioning

confidence: 99%

The estimation of global monthly mean rainfall using infrared satellite data: The GOES precipitation index (GPI)

Arkin

Joyce²,

Janowiak³

1994

Remote Sensing Reviews

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Algorithms for deriving estimates of monthly mean rainfall from infrared satellite observations are described and a classification scheme based on the amount of spatial and temporal information used is discussed. Estimates from a simple IR-only algorithm averaged over the period 1986-92 are compared with two longterm averages (climatologies). While the intensity and location of mean annual features are quite similar in all of these, the estimates are found to be systematically higher over land. Over the oceans, the differences between the estimates and the other data sets are similar in magnitude to the differences between the climatologies themselves. Tropical Pacific precipitation exhibited large interannual changes during the period, with greater/lesser amounts near the dateline during the warm/cold ENSO episodes that occurred during the period. The estimated tropical rainfall during this period exhibited most of the features found in historical studies based on rain gauge observations. Comparisons of the monthly estimates to observations of rainfall over the U.S. indicate that the large positive bias of the GPI has a systematic dependence on latitude and season, with largest values found during winter and to the north. Removal of this bias leaves residual errors of < 50% in monthly estimated rainfall over the eastern half of the country.

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Determination of Rainfall Rates from GOES Satellite Images by a Pattern Recognition Technique

Cited by 57 publications

References 0 publications

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

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

Physical and statistical approaches for cloud identification using Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager Data

The estimation of global monthly mean rainfall using infrared satellite data: The GOES precipitation index (GPI)

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