Results of an automated comparison between winds and virtual temperatures from radiosonde and profilers.
01 Jan 1996-
TL;DR: In this article, the radar wind profiler Radio Acoustic Sounding System (RASS) and balloon borne sounding system (BBSS) provide a useful tandem for scientific studies, instrument comparison, and instrument maintenance at the Southern Great Plains (SGP) Cloud and Radiation Testbed (CART) central facility.
Abstract: Data from the radar wind profiler Radio Acoustic Sounding System (RASS) and balloon borne sounding system (BBSS) provide a useful tandem for scientific studies, instrument comparison, and instrument maintenance at the Southern Great Plains (SGP) Cloud and Radiation Testbed (CART) central facility. The RASS measures virtual temperature profiles remotely from, nominally, 100 m to 1500 m above the surface 10 min out of every hour and wind speed and direction for the remaining 50 min. In comparison, the BBSS measures profiles of wind, wind direction, temperature, and moisture from the surface to 30 km or more with samples of winds approximately every 50 min and temperature and moisture every 10 m. Because the BBSS data are sampled with a freeflying balloon, the vertical profile is a Lagrangian measure, at least in the horizontal; they are affected by horizontal and A time series of the mean and standard deviation of the differtemporal differences in the meteorological fields. The profiler ences is shown in Figure 1. Differences of about 1 m/s, 5 deg winds and temperatures are true vertical profiles anchored to and 0.5 K for wind speed, wind direction, and virtual temthe central facility and are Eulerian measures. perature, respectively, appear to be “normal” for these two
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TL;DR: In this article, a signal processing procedure outlined by Angevine et al. was adapted and further built upon to derive vertical velocity variance, w′2¯, from 915 MHz wind profiler measurements in the mixed layer.
Abstract: Vertical velocity variance data were derived from remotely sensed mixed layer turbulence measurements at the Atmospheric Boundary Layer Experiments (ABLE) facility in Butler County, Kansas. These measurements and associated data were provided by a collection of instruments that included two 915 MHz wind profilers, two radio acoustic sounding systems, and two eddy correlation devices. The data from these devices were available through the Atmospheric Boundary Layer Experiment (ABLE) database operated by Argonne National Laboratory. A signal processing procedure outlined by Angevine et al. was adapted and further built upon to derive vertical velocity variance, w′2¯, from 915 MHz wind profiler measurements in the mixed layer. The proposed procedure consisted of the application of a height-dependent signal-to-noise ratio (SNR) filter, removal of outliers plus and minus two standard deviations about the mean on the spectral width squared, and removal of the effects of beam broadening and vertical shearing of ...
6 citations
TL;DR: In this article, a bulk ABL similarity approach was used to make regional estimates of the sensible heat flux by combining surface temperature measurements with mixed layer temperature and wind speed profiles, and the mixed layer profiles were measured by a 915 MHz Profiler/Radio Acoustic Sounding System and by radiosondes in north-central Oklahoma at the ARM Southern Great Plains CART Central Facility.
6 citations
Cites background or methods or result from "Results of an automated comparison ..."
...Coulter and Lesht [7] found that the average wind speed dierence (radiosonde minus pro®ler) was 0....
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...07 C. Coulter and Lesht [7] found that the average wind speed di erence (radiosonde minus pro®ler) was 0.69 m sÿ1 and the temperature di erence was 0.04 K at the SGP CART Central Facility....
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...This dierence is somewhat larger than the results of [12,11,7]....
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...The data used were obtained in an experiment that was conducted at an uncalibrated site with the identical site and instrumentation already analyzed in Coulter and Lesht [7]....
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18 Mar 2005
TL;DR: Within the Atmospheric Radiation Measurement (ARM) instrument network, several different systems often measure the same quantity at the same site as mentioned in this paper, and they have been extended to the development of methods for automated quality control (QC) of ARM datastreams.
Abstract: Within the Atmospheric Radiation Measurement (ARM) instrument network, several different systems often measure the same quantity at the same site. For example, several ARM instruments measure time-series profiles of the atmosphere that were previously available only from balloon-borne radiosonde systems. These instruments include the Radar Wind Profilers (RWP) with Radio-Acoustic Sounding Systems (RASS), the Atmospheric Emitted Radiance Interferometer (AERI), the Microwave Radiometer Profiler (MWRP), and the Raman Lidar (RL). ARM researchers have described methods for direct cross-comparison of time-series profiles (Coulter and Lesht 1996; Turner et al. 1996) and we have extended this concept to the development of methods for automated quality control (QC) of ARM datastreams.
1 citations
Cites methods from "Results of an automated comparison ..."
...ARM researchers have described methods for direct cross-comparison of time-series profiles (Coulter and Lesht 1996; Turner et al. 1996) and we have extended this concept to the development of methods for automated quality control (QC) of ARM datastreams....
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14 Dec 2004
TL;DR: A system to comprehensively generate long time-series plots, frequency distributions, and other relevant statistics for scientific and engineering data in most high-level, publicly available ARM data streams is embarked on.
Abstract: ATK Mission Research develops analyst tools and automated quality control software in order to assist the Atmospheric Radiation Measurement (ARM) Data Quality Office with their data inspection tasks. We have developed a web-based data analysis and visualization tool, called NCVweb, that allows for easy viewing of ARM NetCDF files. NCVweb, along with our library of sharable Interactive Data Language procedures and functions, allows even novice ARM researchers to be productive with ARM data with only minimal effort. We also contribute to the ARM Data Quality Office by analyzing ARM data streams, developing new quality control metrics, new diagnostic plots, and integrating this information into DQ HandS - the Data Quality Health and Status web-based explorer. We have developed several ways to detect outliers in ARM data streams and have written software to run in an automated fashion to flag these outliers.