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- Author or Editor: Donald L. Reinke x
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Abstract
This paper describes how the combination of a satellite-derived cloud classification with surface observations can improve analysis of cloud-base height. A cloud-base retrieval that combines a cloud classification derived from visible and infrared satellite data with surface reports of cloud base is investigated. A method using the satellite classification to interpret the surface data is compared with a more traditional distance-weighted approach of interpolating the surface data.
Cloud-height observations from the U.S. surface synoptic network were merged with a cloud classification of GOES-8 imager data for 235 test images from June 1996. Surface cloud-base height reports were withheld on a revolving basis and used as truth for the cloud-base height predictions from the satellite-based method. The comparison was limited to cloud-base heights of less than 10 000 feet because of biases in cloud-base height reporting at higher altitudes.
Results indicate that fusion of the satellite cloud classification with surface cloud-base height reports yields a superior estimate of cloud-base height versus an estimate using only interpolated surface data. This is true even though the surface-only method was given the advantage of always being spatially closer to the control site. Performance improvement is more significant for broken and overcast conditions. In addition, the use of a simple textural measure, derived from the satellite cloud classification, causes the satellite-assisted method to outperform the surface-only method by an even wider margin.
Abstract
This paper describes how the combination of a satellite-derived cloud classification with surface observations can improve analysis of cloud-base height. A cloud-base retrieval that combines a cloud classification derived from visible and infrared satellite data with surface reports of cloud base is investigated. A method using the satellite classification to interpret the surface data is compared with a more traditional distance-weighted approach of interpolating the surface data.
Cloud-height observations from the U.S. surface synoptic network were merged with a cloud classification of GOES-8 imager data for 235 test images from June 1996. Surface cloud-base height reports were withheld on a revolving basis and used as truth for the cloud-base height predictions from the satellite-based method. The comparison was limited to cloud-base heights of less than 10 000 feet because of biases in cloud-base height reporting at higher altitudes.
Results indicate that fusion of the satellite cloud classification with surface cloud-base height reports yields a superior estimate of cloud-base height versus an estimate using only interpolated surface data. This is true even though the surface-only method was given the advantage of always being spatially closer to the control site. Performance improvement is more significant for broken and overcast conditions. In addition, the use of a simple textural measure, derived from the satellite cloud classification, causes the satellite-assisted method to outperform the surface-only method by an even wider margin.
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In this paper, the authors describe experimental forecasting tools developed from high-resolution satellite cloud composites. The satellite data were extracted from the new 5-km, hourly, global satellite database called Climatological and Historical Analysis of Clouds for Environmental Simulations (CHANCES). Analysis was focused on a region over the former Yugoslavia and Adriatic Sea during summer 1994.
Cloud composite images were constructed using digital infrared data for each hour of the day. The value at each pixel in the cloud composites was the fractional coverage of cloud at that location for the season and represented its systematic variation. Composite images were also constructed for conditional probabilities of cloud 1–12 h past each hour of the day. The values at any particular pixel in the composites represented the conditional probability of cloud given an initial condition of cloudy or clear in that pixel. Data from both types of composite images were combined to produce a climatological forecasting tool. Forecast tables were constructed of values for the pixel centered over Sarajevo. These tables are similar to the conditional climatology statistics familiar to forecasters in any weather station.
A more sophisticated type of conditional probability was tested in which the initial condition was dependent on the average conditions of a region of pixels surrounding the Sarajevo pixel. Results demonstrate powerful operational applications of high-resolution satellite cloud climatologies.
Abstract
In this paper, the authors describe experimental forecasting tools developed from high-resolution satellite cloud composites. The satellite data were extracted from the new 5-km, hourly, global satellite database called Climatological and Historical Analysis of Clouds for Environmental Simulations (CHANCES). Analysis was focused on a region over the former Yugoslavia and Adriatic Sea during summer 1994.
Cloud composite images were constructed using digital infrared data for each hour of the day. The value at each pixel in the cloud composites was the fractional coverage of cloud at that location for the season and represented its systematic variation. Composite images were also constructed for conditional probabilities of cloud 1–12 h past each hour of the day. The values at any particular pixel in the composites represented the conditional probability of cloud given an initial condition of cloudy or clear in that pixel. Data from both types of composite images were combined to produce a climatological forecasting tool. Forecast tables were constructed of values for the pixel centered over Sarajevo. These tables are similar to the conditional climatology statistics familiar to forecasters in any weather station.
A more sophisticated type of conditional probability was tested in which the initial condition was dependent on the average conditions of a region of pixels surrounding the Sarajevo pixel. Results demonstrate powerful operational applications of high-resolution satellite cloud climatologies.
Abstract
The relationship of the rainfall from convective clouds to area-time integrals determined from satellite infrared data using a fixed infrared-temperature threshold is investigated. Concurrent radar and rapid-scan satellite data obtained during field projects in the northern High Plains and the southeastern United States were used in this study. The fixed IR threshold appropriate for each region was determined by an optimization procedure that identified the brightness threshold that yields the strongest relationship between estimated rainfall from a cloud cluster and its satellite area-time integral (ATI) for each dataset. For the North Dakota–Montana area the optimization procedure indicated that the area enclosed by the −22.5°C isotherm provides satellite ATI values most closely related to the estimated rainfalls. For the southeastern United States project, the optimized temperature threshold was 8.5°C. The difference between the thresholds determined for the two geographic areas suggests that a different “calibration” for each distinct area may be needed to make use of this relationship. Slopes of the two log–log rainfall-ATI regressions are less than unity, indicating that a relative horizontal expansion and/or increase in persistence of a cloud cluster exceeds the associated increase in precipitation. Implications for the Geostationary Operational Environmental Satellite precipitation index are discussed. New results concerning the rain volume-radar ATI relationship for the southeastern United States are also appended to the paper.
Abstract
The relationship of the rainfall from convective clouds to area-time integrals determined from satellite infrared data using a fixed infrared-temperature threshold is investigated. Concurrent radar and rapid-scan satellite data obtained during field projects in the northern High Plains and the southeastern United States were used in this study. The fixed IR threshold appropriate for each region was determined by an optimization procedure that identified the brightness threshold that yields the strongest relationship between estimated rainfall from a cloud cluster and its satellite area-time integral (ATI) for each dataset. For the North Dakota–Montana area the optimization procedure indicated that the area enclosed by the −22.5°C isotherm provides satellite ATI values most closely related to the estimated rainfalls. For the southeastern United States project, the optimized temperature threshold was 8.5°C. The difference between the thresholds determined for the two geographic areas suggests that a different “calibration” for each distinct area may be needed to make use of this relationship. Slopes of the two log–log rainfall-ATI regressions are less than unity, indicating that a relative horizontal expansion and/or increase in persistence of a cloud cluster exceeds the associated increase in precipitation. Implications for the Geostationary Operational Environmental Satellite precipitation index are discussed. New results concerning the rain volume-radar ATI relationship for the southeastern United States are also appended to the paper.
GOES digital imagery has been collected and processed using new techniques over portions of the United States since March 1988. High spatial and temporal resolution satellite cloud composite climatologies (SCCCs) have been produced that represent cloud frequency maps for each season. For each month studied, the cloud composite products represent the cloud occurrence frequency for each GOES pixel location and depict the overall spatial distribution of cloud cover over large portions of the United States.
The satellite composites present a new cloud climatology at a greater spatial and temporal resolution than previously available. Composites with ground resolutions of 2.5 km at hourly time intervals show striking patterns of cloud cover that are not detected in preexisting cloud climatologies.
A comparison between the SCCCs and climatologies produced from conventional surface observations is presented. The comparison is quite good for most stations, yet some significant differences are noted and discussed. Cloud occurrence in the vast areas between surface observing sites can now be analyzed using the new SCCC tool.
GOES digital imagery has been collected and processed using new techniques over portions of the United States since March 1988. High spatial and temporal resolution satellite cloud composite climatologies (SCCCs) have been produced that represent cloud frequency maps for each season. For each month studied, the cloud composite products represent the cloud occurrence frequency for each GOES pixel location and depict the overall spatial distribution of cloud cover over large portions of the United States.
The satellite composites present a new cloud climatology at a greater spatial and temporal resolution than previously available. Composites with ground resolutions of 2.5 km at hourly time intervals show striking patterns of cloud cover that are not detected in preexisting cloud climatologies.
A comparison between the SCCCs and climatologies produced from conventional surface observations is presented. The comparison is quite good for most stations, yet some significant differences are noted and discussed. Cloud occurrence in the vast areas between surface observing sites can now be analyzed using the new SCCC tool.
