Rain Estimation from Infrared and Visible GOES Satellite Data

O’Sullivan, Finbarr; Wash, Carlyle H.; Stewart, M. F.; Motell, Craig

doi:10.1175/1520-0450(1990)029<0209:refiav>2.0.co;2

Cited by 23 publications

(9 citation statements)

References 12 publications

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“…Instead, applications for forecasting snowfall in real-time are limited to qualitative methods identifying regions of low cloud-top temperatures (e.g., Beckman 1987;Johnston 1995). In contrast, satellite-derived rainfall techniques have tried to find relationships between cloud-top brightness temperature and rainfall rates (e.g., Scofield and Oliver 1977;Griffith et al 1978;Scofield 1987;O'Sullivan et al 1990;Vicente et al 1998;Ba et al 2003). In this note, we ask the question, is there a difference in the cloud-top brightness temperatures associated with different types of precipitation at the surface?…”

Section: Introductionmentioning

confidence: 99%

Cloud-Top Temperatures for Precipitating Winter Clouds

Hanna

Schultz

Irving

2008

Journal of Applied Meteorology and Climatology

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To explore the role of cloud microphysics in a large dataset of precipitating clouds, a sixmonth dataset of satellite-derived cloud-top brightness temperatures from GOES longwave infrared (channel 4) satellite data over precipitating surface observing stations is constructed, producing 144 738 observations of snow, rain, freezing rain, and sleet. The distributions of cloud-top brightness temperatures were constructed for each precipitation type, as well as light, moderate and heavy snow and rain. The light-snow distribution has a maximum at -16°C, whereas the moderate and heavy snow distributions have a bimodal distribution around -16° to -23°C and a secondary maximum at -35° to -45°C. The light, moderate, and heavy rain, as well as the freezing rain and sleet, distributions are also bimodal with roughly the same temperature maxima, although the colder mode dominates. The colder of the bimodal peaks trends to lower temperatures with increasing rainfall intensity: -45°C for light rain, -47°C for moderate rain, and -50°C for heavy rain. Like the distributions for snow, the colder bimodal peak increases in amplitude relative to the warmer bimodal peak at heavier rainfall intensities. The steep slope in the snow distribution for cloud-top brightness temperatures warmer than -15°C is due to the combined effect of the activation of ice nuclei and the maximum growth rate for ice crystals at temperatures near -15°C. In contrast, the rain distributions have a gentle slope toward higher cloud-top brightness temperatures (-5° to 0°C) due to the warm-rain process. Finally, satellitederived cloud-top brightness temperatures are compared to coincident radiosonde-derived cloudtop temperatures. Although most difference between these two are small amplitude, some are as large as +/-60°C. The cause of these differences remains unclear, and several hypotheses are offered.3

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Section: Introductionmentioning

confidence: 99%

Cloud-Top Temperatures for Precipitating Winter Clouds

Hanna

Schultz

Irving

2008

Journal of Applied Meteorology and Climatology

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“…Other approaches have used pattern recognition techniques applied to VIS/IR data sets. O'Sullivan et al (1990) used brightness and textural characteristics during daytime and IR temperature patterns to estimate rainfall over a 10 × 10 pixel array in three categories: no rain, light rain, and moderate/heavy rain.…”

Section: Levizzani Et Almentioning

confidence: 99%

Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation

Levizzani

Schmetz

Lutz

et al. 2001

Meteorological Applications

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The use of meteorological satellites for rainfall estimation and monitoring was introduced as a way of augmenting conventional ground-based rainfall data for hydrological models and weather forecasting. Today the primary scope of satellite rainfall monitoring is to provide information on rainfall occurrence, amount and distribution over the globe for a number of applications such as meteorology at all scales, climatology, hydrology and environmental sciences. The uneven distribution of raingauges and weather radars and the lack of rainfall data over the oceans have always been a concern and until now the rainfall, as a prominent branch of the global hydrological cycle, is not well understood. In this sense the problem is not different from the determination of wind, pressure, temperature and humidity fields although precipitation is by far the most variable in space and time. Furthermore, unlike many other atmospheric phenomena, precipitation (or the lack of it) has a direct impact on human life (e.g. flash floods, Barrett & Michell, 1991). Therefore, satellite monitoring is used to address the key questions of spatial and temporal coverage, which cannot be achieved by other observing systems.Among the challenges that face the science and technology of satellite remote sensing the quantitative determination of rainfall from the variety of precipitating systems is one of the most difficult and is largely unsolved. Barrett & Martin (1981) and Kidder & Vonder Haar (1995) give excellent reviews of the methods available. Petty (1995) has examined the status of satellite rainfall estimation over land. A recent review by Levizzani (1998a) has covered results and future perspectives from the geostationary orbit. The perspecMeteorol. Appl. 8, 23-41 (2001) Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation

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“…Validation and calibration of satellite methods should be based on rain gage network information, radar estimates of rainfall, or aviation weather reports (O'Sullivan et al, 1990). The major point is that satellite measurement techniques are verified against areal estimates of rainfall for areas around 10 5 km 2 (Bellon and Austin, 1986).…”

Section: Visible and Infrared Estimation Of Precipitation From Satellmentioning

confidence: 99%

Precipitation

1996

Hydrology Handbook

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Rain Estimation from Infrared and Visible GOES Satellite Data

Cited by 23 publications

References 12 publications

Cloud-Top Temperatures for Precipitating Winter Clouds

Cloud-Top Temperatures for Precipitating Winter Clouds

Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation

Precipitation

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