Search Results
You are looking at 1 - 4 of 4 items for
- Author or Editor: Roderick A. Scofield x
- Refine by Access: All Content x
Abstract
This paper presents the NESDIS operational technique for estimating convective rainfall from GOES data. Estimates and 3-hour outlooks are computed on the Interactive Flash Flood Analyzer (IFFA) and transmitted via the Automation of Field Operations and Services (AFOS) computer systems to Weather Service Forecast Offices, Weather Service Offices, and River Forecast Centers. The technique consists of two steps: (1) locating the active portion of the convective system and (2) computing half-hourly rainfall estimates based on cloud-top temperature and cloud growth or divergence aloft, overshooting tops, mergers, saturated environment (stationary storms), and a moisture correction factor. A warm-top modification is used for cloud tops warmer than −62°C. Three-hour outlooks are based on persistence and extrapolation and trend and expectancy guidelines. Verification results show the average error of the rainfall estimates for a storm total precipitation event is about30%.
Abstract
This paper presents the NESDIS operational technique for estimating convective rainfall from GOES data. Estimates and 3-hour outlooks are computed on the Interactive Flash Flood Analyzer (IFFA) and transmitted via the Automation of Field Operations and Services (AFOS) computer systems to Weather Service Forecast Offices, Weather Service Offices, and River Forecast Centers. The technique consists of two steps: (1) locating the active portion of the convective system and (2) computing half-hourly rainfall estimates based on cloud-top temperature and cloud growth or divergence aloft, overshooting tops, mergers, saturated environment (stationary storms), and a moisture correction factor. A warm-top modification is used for cloud tops warmer than −62°C. Three-hour outlooks are based on persistence and extrapolation and trend and expectancy guidelines. Verification results show the average error of the rainfall estimates for a storm total precipitation event is about30%.
Abstract
Flash floods are among the most devastating natural weather hazards in the United States, causing an average of more than 225 deaths and $4 billion in property damage annually. As a result, prediction of flash floods in an accurate and timely fashion is one of the most important challenges in weather prediction. Data from geostationary and polar-orbiting satellites are significant sources of information for the diagnosis and prediction of heavy precipitation and flash floods. Geostationary satellites are especially important for their unique ability simultaneously to observe the atmosphere and its cloud cover from the global scale down to the storm scale at high resolution in both time (every 15 min) and space (1–4 km). This capability makes geostationary satellite data ideally suited for estimating and predicting heavy precipitation, especially during flash-flood events. Presented in this paper are current and future efforts in the National Environmental Satellite, Data, and Information Service that support National Weather Service River Forecast Centers and Weather Forecast Offices during extreme-precipitation events.
Abstract
Flash floods are among the most devastating natural weather hazards in the United States, causing an average of more than 225 deaths and $4 billion in property damage annually. As a result, prediction of flash floods in an accurate and timely fashion is one of the most important challenges in weather prediction. Data from geostationary and polar-orbiting satellites are significant sources of information for the diagnosis and prediction of heavy precipitation and flash floods. Geostationary satellites are especially important for their unique ability simultaneously to observe the atmosphere and its cloud cover from the global scale down to the storm scale at high resolution in both time (every 15 min) and space (1–4 km). This capability makes geostationary satellite data ideally suited for estimating and predicting heavy precipitation, especially during flash-flood events. Presented in this paper are current and future efforts in the National Environmental Satellite, Data, and Information Service that support National Weather Service River Forecast Centers and Weather Forecast Offices during extreme-precipitation events.
This paper presents a description, sensitivity analyses, sample results, validation, and the recent progress done on the development of a new satellite rainfall estimation technique in the National Environmental Satellite Data and Information Service (NESDIS) at the National Oceanic and Atmospheric Administration (NOAA). The technique, called the auto-estimator, runs in real time for applications to flash flood forecasting, numerical modeling, and operational hydrology. The auto-estimator uses the Geoestationary Operational Environmental Satellite-8 and -9 in the infrared (IR) 10.7μm band to compute real-time precipitation amounts based on a power-law regression algorithm. This regression is derived from a statistical analysis between surface radar–derived instantaneous rainfall estimates and satellite-derived IR cloud-top temperatures collocated in time and space. The rainfall rate estimates are adjusted for different moisture regimes using the most recent fields of precipitable water and relative humidity generated by the National Centers for Environmental Prediction Eta Model. In addition, a mask is computed to restrict rain to regions satisfying two criteria: (a) the growth rate of the cloud as a function of the temperature change of the cloud tops in two consecutive IR images must be positive, and (b) the spatial gradients of the cloud-top temperature field must show distinct and isolated cold cores in the cloud-top surface. Both the growth rate and the gradient corrections are useful for locating heavy precipitation cores. The auto-estimator has been used experimentally for almost 3 yr to provide real-time instantaneous rainfall rate estimates, average hourly estimates, and 3-, 6-, and 24-h accumulations over the conterminous 48 United States and nearby ocean areas. The NOAA/NESDIS Satellite Analyses Branch (SAB) has examined the accuracy of the rainfall estimates daily for a variety of storm systems. They have determined that the algorithm produces useful 1–6-h estimates for flash flood monitoring but exaggerates the area of precipitation causing overestimation of 24-h rainfall total associated with slow-moving, cold-topped mesoscale convective systems. The SAB analyses have also shown a tendency for underestimation of rainfall rates in warm-top stratiform cloud systems. Until further improvements, the use of this technique for stratiform events should be considered with caution. The authors validate the hourly rainfall rates of the auto-estimator using gauge-adjusted radar precipitation products (with radar bias removed) in three distinct cases. Results show that the auto-estimator has modest skill at 1-h time resolution for a spatial resolution of 12 km. Results improve with larger grid sizes (48 by 48 km or larger).
This paper presents a description, sensitivity analyses, sample results, validation, and the recent progress done on the development of a new satellite rainfall estimation technique in the National Environmental Satellite Data and Information Service (NESDIS) at the National Oceanic and Atmospheric Administration (NOAA). The technique, called the auto-estimator, runs in real time for applications to flash flood forecasting, numerical modeling, and operational hydrology. The auto-estimator uses the Geoestationary Operational Environmental Satellite-8 and -9 in the infrared (IR) 10.7μm band to compute real-time precipitation amounts based on a power-law regression algorithm. This regression is derived from a statistical analysis between surface radar–derived instantaneous rainfall estimates and satellite-derived IR cloud-top temperatures collocated in time and space. The rainfall rate estimates are adjusted for different moisture regimes using the most recent fields of precipitable water and relative humidity generated by the National Centers for Environmental Prediction Eta Model. In addition, a mask is computed to restrict rain to regions satisfying two criteria: (a) the growth rate of the cloud as a function of the temperature change of the cloud tops in two consecutive IR images must be positive, and (b) the spatial gradients of the cloud-top temperature field must show distinct and isolated cold cores in the cloud-top surface. Both the growth rate and the gradient corrections are useful for locating heavy precipitation cores. The auto-estimator has been used experimentally for almost 3 yr to provide real-time instantaneous rainfall rate estimates, average hourly estimates, and 3-, 6-, and 24-h accumulations over the conterminous 48 United States and nearby ocean areas. The NOAA/NESDIS Satellite Analyses Branch (SAB) has examined the accuracy of the rainfall estimates daily for a variety of storm systems. They have determined that the algorithm produces useful 1–6-h estimates for flash flood monitoring but exaggerates the area of precipitation causing overestimation of 24-h rainfall total associated with slow-moving, cold-topped mesoscale convective systems. The SAB analyses have also shown a tendency for underestimation of rainfall rates in warm-top stratiform cloud systems. Until further improvements, the use of this technique for stratiform events should be considered with caution. The authors validate the hourly rainfall rates of the auto-estimator using gauge-adjusted radar precipitation products (with radar bias removed) in three distinct cases. Results show that the auto-estimator has modest skill at 1-h time resolution for a spatial resolution of 12 km. Results improve with larger grid sizes (48 by 48 km or larger).
