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Abstract
A brief review is presented of the methods available for airborne cloud-droplet measurements. A foil technique for measuring liquid precipitation particles (greater than 100 µ diameter) is discussed with respect to development, calibration, flight testing, and application.
Abstract
A brief review is presented of the methods available for airborne cloud-droplet measurements. A foil technique for measuring liquid precipitation particles (greater than 100 µ diameter) is discussed with respect to development, calibration, flight testing, and application.
Abstract
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Abstract
Precipitation particle measurements from the upper levels of cumulus congestus clouds are analyzed with regard to general cloud characteristics, liquid water content, and precipitation water content as related to the theoretical radar reflectivity. Conclusions are: (1) the majority of the cumulus congestus clouds examined, whose tops exceed 14,000 ft, contained precipitation particles (250-microns diameter) in the upper levels sometime during their life cycle, (2) particle concentrations in excess of 1000 per m3 were found in about 20 per cent of the clouds examined, (3) the relationship Z=1.6×10−2M1.46 for radar reflectivity is applicable for cumulus congestus in the early stages of precipitation development.
Abstract
Precipitation particle measurements from the upper levels of cumulus congestus clouds are analyzed with regard to general cloud characteristics, liquid water content, and precipitation water content as related to the theoretical radar reflectivity. Conclusions are: (1) the majority of the cumulus congestus clouds examined, whose tops exceed 14,000 ft, contained precipitation particles (250-microns diameter) in the upper levels sometime during their life cycle, (2) particle concentrations in excess of 1000 per m3 were found in about 20 per cent of the clouds examined, (3) the relationship Z=1.6×10−2M1.46 for radar reflectivity is applicable for cumulus congestus in the early stages of precipitation development.
Abstract
Precipitation particles in the 100- to 6.50-µ-diam range were sampled in a large number of tropical convective clouds. These samples permit one to trace the development of precipitation in these clouds. Liquid-water-content measurements were made simultaneously with some of the particle measurements. From these data, it is shown that the large concentrations of large drops are associated with low liquid-water contents and, conversely, that the large values of liquid water are associated with small numbers of droplets greater than 150 µ in diam. The computed relationship between radar reflectivity, water-content, and median-volume diameter is very similar to that which has been reported for other cloud types.
Abstract
Precipitation particles in the 100- to 6.50-µ-diam range were sampled in a large number of tropical convective clouds. These samples permit one to trace the development of precipitation in these clouds. Liquid-water-content measurements were made simultaneously with some of the particle measurements. From these data, it is shown that the large concentrations of large drops are associated with low liquid-water contents and, conversely, that the large values of liquid water are associated with small numbers of droplets greater than 150 µ in diam. The computed relationship between radar reflectivity, water-content, and median-volume diameter is very similar to that which has been reported for other cloud types.
Uncertainty information from ensemble prediction systems can enhance and extend the suite of tropical cyclone (TC) forecast products. This article will review progress in ensemble prediction of TCs and the scientific issues in ensemble system development for TCs. Additionally, it will discuss the needs of forecasters and other users for TC uncertainty information and describe some ensemble-based products that may be able to be disseminated in the near future. We hope these proposals will jump-start a community-wide discussion of how to leverage ensemble-based uncertainty information for TC prediction.
A supplement to this article is available online (10.1175/2011BAMS3106.2)
Uncertainty information from ensemble prediction systems can enhance and extend the suite of tropical cyclone (TC) forecast products. This article will review progress in ensemble prediction of TCs and the scientific issues in ensemble system development for TCs. Additionally, it will discuss the needs of forecasters and other users for TC uncertainty information and describe some ensemble-based products that may be able to be disseminated in the near future. We hope these proposals will jump-start a community-wide discussion of how to leverage ensemble-based uncertainty information for TC prediction.
A supplement to this article is available online (10.1175/2011BAMS3106.2)
Abstract
The Hurricane Forecast Improvement Project (HFIP; renamed the “Hurricane Forecast Improvement Program” in 2017) was established by the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 with a goal of improving tropical cyclone (TC) track and intensity predictions. A major focus of HFIP has been to increase the quality of guidance products for these parameters that are available to forecasters at the National Weather Service National Hurricane Center (NWS/NHC). One HFIP effort involved the demonstration of an operational decision process, named Stream 1.5, in which promising experimental versions of numerical weather prediction models were selected for TC forecast guidance. The selection occurred every year from 2010 to 2014 in the period preceding the hurricane season (defined as August–October), and was based on an extensive verification exercise of retrospective TC forecasts from candidate experimental models run over previous hurricane seasons. As part of this process, user-responsive verification questions were identified via discussions between NHC staff and forecast verification experts, with additional questions considered each year. A suite of statistically meaningful verification approaches consisting of traditional and innovative methods was developed to respond to these questions. Two examples of the application of the Stream 1.5 evaluations are presented, and the benefits of this approach are discussed. These benefits include the ability to provide information to forecasters and others that is relevant for their decision-making processes, via the selection of models that meet forecast quality standards and are meaningful for demonstration to forecasters in the subsequent hurricane season; clarification of user-responsive strengths and weaknesses of the selected models; and identification of paths to model improvement.
Significance Statement
The Hurricane Forecast Improvement Project (HFIP) tropical cyclone (TC) forecast evaluation effort led to innovations in TC predictions as well as new capabilities to provide more meaningful and comprehensive information about model performance to forecast users. Such an effort—to clearly specify the needs of forecasters and clarify how forecast improvements should be measured in a “user-oriented” framework—is rare. This project provides a template for one approach to achieving that goal.
Abstract
The Hurricane Forecast Improvement Project (HFIP; renamed the “Hurricane Forecast Improvement Program” in 2017) was established by the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 with a goal of improving tropical cyclone (TC) track and intensity predictions. A major focus of HFIP has been to increase the quality of guidance products for these parameters that are available to forecasters at the National Weather Service National Hurricane Center (NWS/NHC). One HFIP effort involved the demonstration of an operational decision process, named Stream 1.5, in which promising experimental versions of numerical weather prediction models were selected for TC forecast guidance. The selection occurred every year from 2010 to 2014 in the period preceding the hurricane season (defined as August–October), and was based on an extensive verification exercise of retrospective TC forecasts from candidate experimental models run over previous hurricane seasons. As part of this process, user-responsive verification questions were identified via discussions between NHC staff and forecast verification experts, with additional questions considered each year. A suite of statistically meaningful verification approaches consisting of traditional and innovative methods was developed to respond to these questions. Two examples of the application of the Stream 1.5 evaluations are presented, and the benefits of this approach are discussed. These benefits include the ability to provide information to forecasters and others that is relevant for their decision-making processes, via the selection of models that meet forecast quality standards and are meaningful for demonstration to forecasters in the subsequent hurricane season; clarification of user-responsive strengths and weaknesses of the selected models; and identification of paths to model improvement.
Significance Statement
The Hurricane Forecast Improvement Project (HFIP) tropical cyclone (TC) forecast evaluation effort led to innovations in TC predictions as well as new capabilities to provide more meaningful and comprehensive information about model performance to forecast users. Such an effort—to clearly specify the needs of forecasters and clarify how forecast improvements should be measured in a “user-oriented” framework—is rare. This project provides a template for one approach to achieving that goal.
