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Abstract
A cloud-sensing Doppler radar is used with a vertically pointing antenna to measure the vertical air motion in clouds during the Atlantic Stratocumulus Transition Experiment. The droplet fall velocity contamination was made negligible by using only measurements during the time the reflectivity was below − 17 dBZ. During one day of measurements, the daytime character of the vertical velocity variance is different than that of the nighttime case. In the upper part of the cloud, the variance had a distinct maximum for both day and night; however, the nighttime maximum was about twice as large as the daytime case. Lower down in the cloud, there was a second maximum, with the daytime variance larger than the nighttime case. The skewness of the vertical velocity was negative near cloud top in both the day and night cases, changing to positive skewness in the lower part of the cloud. This behavior near cloud top indicates that the upper part of the cloud is behaving like an upside-down convective boundary layer, with the downdrafts smaller in area and more intense than the updrafts. In the lower part of the cloud, the behavior of the motion is more like a conventional convective boundary layer, with the updrafts smaller and more intense than the downdrafts. The upside-down convective forcing in the upper part of the cloud is due to radiative cooling, with the daytime forcing less because of shortwave warming.
Abstract
A cloud-sensing Doppler radar is used with a vertically pointing antenna to measure the vertical air motion in clouds during the Atlantic Stratocumulus Transition Experiment. The droplet fall velocity contamination was made negligible by using only measurements during the time the reflectivity was below − 17 dBZ. During one day of measurements, the daytime character of the vertical velocity variance is different than that of the nighttime case. In the upper part of the cloud, the variance had a distinct maximum for both day and night; however, the nighttime maximum was about twice as large as the daytime case. Lower down in the cloud, there was a second maximum, with the daytime variance larger than the nighttime case. The skewness of the vertical velocity was negative near cloud top in both the day and night cases, changing to positive skewness in the lower part of the cloud. This behavior near cloud top indicates that the upper part of the cloud is behaving like an upside-down convective boundary layer, with the downdrafts smaller in area and more intense than the updrafts. In the lower part of the cloud, the behavior of the motion is more like a conventional convective boundary layer, with the updrafts smaller and more intense than the downdrafts. The upside-down convective forcing in the upper part of the cloud is due to radiative cooling, with the daytime forcing less because of shortwave warming.
The U.S. Department of Agriculture's Ultraviolet (UV) Radiation Monitoring Program has been measuring UV radiation since 1994. The initial network of 12 stations employed broadband meters to measure UVB irradiance and included ancillary measurements of temperature, humidity, and irradiance at seven wavelengths in the visible produced by a Multi-Filter Rotating Shadowband Radiometer (MFRSR). Since that beginning the network has expanded to more than 20 stations and the broadband meters have been supplemented with a seven-wavelength Ultraviolet Multi-Filter Rotating Shadowband Radiometer (UV-MFRSR). The network has been designed to include 30 stations, each with a full complement of instrumentation. Annual characterizations of the network's filter radiometers indicate that gradual shifts in instrument response are manageable but must be accounted for to achieve accurate and precise measurements of UV irradiance. The characterization and calibration of the filter instruments is discussed along with filter stability and instrument precision. Broadband instruments are shown to be quite stable and collocated instruments are shown to agree to within 2.3% for zenith angles less than 80° under all sky conditions. Preliminary investigations into the accuracy of the UV-MFRSR calibrated with the Langley method are presented and successful column ozone retrievals are demonstrated with the UV-MFRSR under clear skies.
The U.S. Department of Agriculture's Ultraviolet (UV) Radiation Monitoring Program has been measuring UV radiation since 1994. The initial network of 12 stations employed broadband meters to measure UVB irradiance and included ancillary measurements of temperature, humidity, and irradiance at seven wavelengths in the visible produced by a Multi-Filter Rotating Shadowband Radiometer (MFRSR). Since that beginning the network has expanded to more than 20 stations and the broadband meters have been supplemented with a seven-wavelength Ultraviolet Multi-Filter Rotating Shadowband Radiometer (UV-MFRSR). The network has been designed to include 30 stations, each with a full complement of instrumentation. Annual characterizations of the network's filter radiometers indicate that gradual shifts in instrument response are manageable but must be accounted for to achieve accurate and precise measurements of UV irradiance. The characterization and calibration of the filter instruments is discussed along with filter stability and instrument precision. Broadband instruments are shown to be quite stable and collocated instruments are shown to agree to within 2.3% for zenith angles less than 80° under all sky conditions. Preliminary investigations into the accuracy of the UV-MFRSR calibrated with the Langley method are presented and successful column ozone retrievals are demonstrated with the UV-MFRSR under clear skies.
Abstract
This paper describes the use of a vertically pointing 8.6-mm-wavelength Doppler radar for measuring drop size spectra in clouds. The data used were collected in the Atlantic Stratocumulus Transition Experiment in 1992. This paper uses the full Doppler velocity spectrum from the time series of Doppler radial velocities to extract information farther into the small-drop regime than previously attempted. The amount of liquid residing in the cloud regime is compared with that found in the precipitation regime where drop fall velocities are resolvable. Total liquid is compared with that measured with a collocated three-channel microwave radiometer. Examples of number density spectra, liquid water spectra, and flux spectra are shown and compared with what is known of these quantities from various in situ measurements by aircraft in similar clouds. Error estimates and uncertainties are discussed. It is concluded that 8-mm Doppler radars have the potential for broader use in cloud and precipitation studies than generally realized.
Abstract
This paper describes the use of a vertically pointing 8.6-mm-wavelength Doppler radar for measuring drop size spectra in clouds. The data used were collected in the Atlantic Stratocumulus Transition Experiment in 1992. This paper uses the full Doppler velocity spectrum from the time series of Doppler radial velocities to extract information farther into the small-drop regime than previously attempted. The amount of liquid residing in the cloud regime is compared with that found in the precipitation regime where drop fall velocities are resolvable. Total liquid is compared with that measured with a collocated three-channel microwave radiometer. Examples of number density spectra, liquid water spectra, and flux spectra are shown and compared with what is known of these quantities from various in situ measurements by aircraft in similar clouds. Error estimates and uncertainties are discussed. It is concluded that 8-mm Doppler radars have the potential for broader use in cloud and precipitation studies than generally realized.
Abstract
The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.
Abstract
The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.
