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- Author or Editor: Phil Rosenberg x
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Abstract
In situ observations of cloud properties made by airborne probes play a critical role in ice cloud research through their role in process studies, parameterization development, and evaluation of simulations and remote sensing retrievals. To determine how cloud properties vary with environmental conditions, in situ data collected during different field projects processed by different groups must be used. However, because of the diverse algorithms and codes that are used to process measurements, it can be challenging to compare the results. Therefore it is vital to understand both the limitations of specific probes and uncertainties introduced by processing algorithms. Since there is currently no universally accepted framework regarding how in situ measurements should be processed, there is a need for a general reference that describes the most commonly applied algorithms along with their strengths and weaknesses. Methods used to process data from bulk water probes, single-particle light-scattering spectrometers and cloud-imaging probes are reviewed herein, with emphasis on measurements of the ice phase. Particular attention is paid to how uncertainties, caveats, and assumptions in processing algorithms affect derived products since there is currently no consensus on the optimal way of analyzing data. Recommendations for improving the analysis and interpretation of in situ data include the following: establishment of a common reference library of individual processing algorithms, better documentation of assumptions used in these algorithms, development and maintenance of sustainable community software for processing in situ observations, and more studies that compare different algorithms with the same benchmark datasets.
Abstract
In situ observations of cloud properties made by airborne probes play a critical role in ice cloud research through their role in process studies, parameterization development, and evaluation of simulations and remote sensing retrievals. To determine how cloud properties vary with environmental conditions, in situ data collected during different field projects processed by different groups must be used. However, because of the diverse algorithms and codes that are used to process measurements, it can be challenging to compare the results. Therefore it is vital to understand both the limitations of specific probes and uncertainties introduced by processing algorithms. Since there is currently no universally accepted framework regarding how in situ measurements should be processed, there is a need for a general reference that describes the most commonly applied algorithms along with their strengths and weaknesses. Methods used to process data from bulk water probes, single-particle light-scattering spectrometers and cloud-imaging probes are reviewed herein, with emphasis on measurements of the ice phase. Particular attention is paid to how uncertainties, caveats, and assumptions in processing algorithms affect derived products since there is currently no consensus on the optimal way of analyzing data. Recommendations for improving the analysis and interpretation of in situ data include the following: establishment of a common reference library of individual processing algorithms, better documentation of assumptions used in these algorithms, development and maintenance of sustainable community software for processing in situ observations, and more studies that compare different algorithms with the same benchmark datasets.
Abstract
The Fennec automatic weather station (AWS) network consists of eight stations installed across the Sahara, with four in remote locations in the central desert, where no previous meteorological observations have existed. The AWS measures temperature, humidity, pressure, wind speed, wind direction, shortwave and longwave radiation (upwelling and downwelling), ground heat flux, and ground temperature. Data are recorded every 3 min 20 s, that is, at 3 times the temporal resolution of the World Meteorological Organization’s standard 10-min reporting for winds and wind gusts. Variations in wind speeds on shorter time scales are recorded through the use of second- and third-order moments of 1-Hz data. Using the Iridium Router-Based Unrestricted Digital Internetworking Connectivity Solutions (RUDICS) service, data are transmitted in near–real time (1-h lag) to the United Kingdom, where calibrations are applied and data are uploaded to the Global Telecommunications System (GTS), for assimilation into forecast models.
This paper describes the instrumentation used and the data available from the network. Particular focus is given to the engineering applied to the task of making measurements in this remote region and challenging climate. The communications protocol developed to operate over the Iridium RUDICS satellite service is described. Transmitting the second moment of the wind speed distribution is shown to improve estimates of the dust-generating potential of observed winds, especially for winds close to the threshold speed for dust emission of the wind speed distribution. Sources of error are discussed and some preliminary results are presented, demonstrating the system’s potential to record key features of this region.
Abstract
The Fennec automatic weather station (AWS) network consists of eight stations installed across the Sahara, with four in remote locations in the central desert, where no previous meteorological observations have existed. The AWS measures temperature, humidity, pressure, wind speed, wind direction, shortwave and longwave radiation (upwelling and downwelling), ground heat flux, and ground temperature. Data are recorded every 3 min 20 s, that is, at 3 times the temporal resolution of the World Meteorological Organization’s standard 10-min reporting for winds and wind gusts. Variations in wind speeds on shorter time scales are recorded through the use of second- and third-order moments of 1-Hz data. Using the Iridium Router-Based Unrestricted Digital Internetworking Connectivity Solutions (RUDICS) service, data are transmitted in near–real time (1-h lag) to the United Kingdom, where calibrations are applied and data are uploaded to the Global Telecommunications System (GTS), for assimilation into forecast models.
This paper describes the instrumentation used and the data available from the network. Particular focus is given to the engineering applied to the task of making measurements in this remote region and challenging climate. The communications protocol developed to operate over the Iridium RUDICS satellite service is described. Transmitting the second moment of the wind speed distribution is shown to improve estimates of the dust-generating potential of observed winds, especially for winds close to the threshold speed for dust emission of the wind speed distribution. Sources of error are discussed and some preliminary results are presented, demonstrating the system’s potential to record key features of this region.
Abstract
The Convective Precipitation Experiment (COPE) was a joint U.K.–U.S. field campaign held during the summer of 2013 in the southwest peninsula of England, designed to study convective clouds that produce heavy rain leading to flash floods. The clouds form along convergence lines that develop regularly as a result of the topography. Major flash floods have occurred in the past, most famously at Boscastle in 2004. It has been suggested that much of the rain was produced by warm rain processes, similar to some flash floods that have occurred in the United States. The overarching goal of COPE is to improve quantitative convective precipitation forecasting by understanding the interactions of the cloud microphysics and dynamics and thereby to improve numerical weather prediction (NWP) model skill for forecasts of flash floods. Two research aircraft, the University of Wyoming King Air and the U.K. BAe 146, obtained detailed in situ and remote sensing measurements in, around, and below storms on several days. A new fast-scanning X-band dual-polarization Doppler radar made 360° volume scans over 10 elevation angles approximately every 5 min and was augmented by two Met Office C-band radars and the Chilbolton S-band radar. Detailed aerosol measurements were made on the aircraft and on the ground. This paper i) provides an overview of the COPE field campaign and the resulting dataset, ii) presents examples of heavy convective rainfall in clouds containing ice and also in relatively shallow clouds through the warm rain process alone, and iii) explains how COPE data will be used to improve high-resolution NWP models for operational use.
Abstract
The Convective Precipitation Experiment (COPE) was a joint U.K.–U.S. field campaign held during the summer of 2013 in the southwest peninsula of England, designed to study convective clouds that produce heavy rain leading to flash floods. The clouds form along convergence lines that develop regularly as a result of the topography. Major flash floods have occurred in the past, most famously at Boscastle in 2004. It has been suggested that much of the rain was produced by warm rain processes, similar to some flash floods that have occurred in the United States. The overarching goal of COPE is to improve quantitative convective precipitation forecasting by understanding the interactions of the cloud microphysics and dynamics and thereby to improve numerical weather prediction (NWP) model skill for forecasts of flash floods. Two research aircraft, the University of Wyoming King Air and the U.K. BAe 146, obtained detailed in situ and remote sensing measurements in, around, and below storms on several days. A new fast-scanning X-band dual-polarization Doppler radar made 360° volume scans over 10 elevation angles approximately every 5 min and was augmented by two Met Office C-band radars and the Chilbolton S-band radar. Detailed aerosol measurements were made on the aircraft and on the ground. This paper i) provides an overview of the COPE field campaign and the resulting dataset, ii) presents examples of heavy convective rainfall in clouds containing ice and also in relatively shallow clouds through the warm rain process alone, and iii) explains how COPE data will be used to improve high-resolution NWP models for operational use.
