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on the WSR-88D could be improved by reducing the effective antenna beamwidth and/or using finer azimuthal sampling ( Brown et al. 2005 ). Thus, in 2008 the NWS upgraded all WSR-88D radars to produce superresolution data at lower elevation angles, which are routinely used by forecasters as major inputs into the tornado warning decision process. The implementation of superresolution data on the WSR-88D entails producing reflectivity fields with finer sampling in range (250 m instead of 1 km) and
on the WSR-88D could be improved by reducing the effective antenna beamwidth and/or using finer azimuthal sampling ( Brown et al. 2005 ). Thus, in 2008 the NWS upgraded all WSR-88D radars to produce superresolution data at lower elevation angles, which are routinely used by forecasters as major inputs into the tornado warning decision process. The implementation of superresolution data on the WSR-88D entails producing reflectivity fields with finer sampling in range (250 m instead of 1 km) and
inconspicuous, although the XCTD data used in their study were collected in the subtropical and tropical ocean where vertical temperature gradients are relatively weak. Therefore, few studies of evaluation for the mismatch in the response time of the XCTD temperature and conductivity sensors have been done. To use XBT/XCTD data for climate change research, we must apply data processing and quality control measures that go beyond the manufacturer’s specifications ( Table 1 ). In this study, we propose a data
inconspicuous, although the XCTD data used in their study were collected in the subtropical and tropical ocean where vertical temperature gradients are relatively weak. Therefore, few studies of evaluation for the mismatch in the response time of the XCTD temperature and conductivity sensors have been done. To use XBT/XCTD data for climate change research, we must apply data processing and quality control measures that go beyond the manufacturer’s specifications ( Table 1 ). In this study, we propose a data
1. Introduction The Oceanscience underway CTD (UCTD) is a recently developed system for obtaining deep vertical profile CTD data from a moving ship ( Rudnick and Klinke 2007 ). We recently used the UCTD on two hydrographic survey cruises as a means to increase the spatial resolution of the survey without having to perform additional time-consuming CTD casts. Examination of the UCTD data after preliminary processing, using standard methodologies, suggested that data quality was not ideal in
1. Introduction The Oceanscience underway CTD (UCTD) is a recently developed system for obtaining deep vertical profile CTD data from a moving ship ( Rudnick and Klinke 2007 ). We recently used the UCTD on two hydrographic survey cruises as a means to increase the spatial resolution of the survey without having to perform additional time-consuming CTD casts. Examination of the UCTD data after preliminary processing, using standard methodologies, suggested that data quality was not ideal in
instrumental problems, concentrating on measurement principles, limitations, and uncertainties. Korolev et al. (2017 , chapter 5) examines issues related to mixed-phase clouds, concentrating on additional complications in measurements and related processing that arise when liquid and ice phases coexist. This current chapter concentrates on an additional source of uncertainty that has not received as much attention, namely, that introduced by algorithms used to process data. Such algorithms play a critical
instrumental problems, concentrating on measurement principles, limitations, and uncertainties. Korolev et al. (2017 , chapter 5) examines issues related to mixed-phase clouds, concentrating on additional complications in measurements and related processing that arise when liquid and ice phases coexist. This current chapter concentrates on an additional source of uncertainty that has not received as much attention, namely, that introduced by algorithms used to process data. Such algorithms play a critical
the data processing system The CALIPSO data downlink and data processing systems were designed to meet processing and archive requirements appropriate for climate research. Current data turnaround time for the acquisition, processing, and archival of nominal science data products is about 5 days. Using an approximate calibration, a limited set of “expedited” products is also produced, with a turnaround time of about 24 h. All CALIPSO payload science data are downlinked once per day to an X
the data processing system The CALIPSO data downlink and data processing systems were designed to meet processing and archive requirements appropriate for climate research. Current data turnaround time for the acquisition, processing, and archival of nominal science data products is about 5 days. Using an approximate calibration, a limited set of “expedited” products is also produced, with a turnaround time of about 24 h. All CALIPSO payload science data are downlinked once per day to an X
data archive available to NCDC, and they provide the only source of updates for many stations. 3. Data integration As shown in Table 4 , the process of integrating data from multiple sources into the GHCN-Daily dataset takes place in three steps: 1) eliminating source data for stations whose location is unknown or questionable; 2) classifying each station in a source dataset either as one that is already represented in GHCN-Daily or as a new site; and 3) combining the data from the different
data archive available to NCDC, and they provide the only source of updates for many stations. 3. Data integration As shown in Table 4 , the process of integrating data from multiple sources into the GHCN-Daily dataset takes place in three steps: 1) eliminating source data for stations whose location is unknown or questionable; 2) classifying each station in a source dataset either as one that is already represented in GHCN-Daily or as a new site; and 3) combining the data from the different
1. Introduction Traditional observational analyses of vorticity, divergence, and deformation fields usually rely on interpolating observations to either a Cartesian or spherical grid and then evaluating the appropriate finite-difference equations. While this approach has the benefit of creating a set of gridded data that is easily processed by uniform spacing finite-difference methods, it has been shown that greater accuracy can be obtained by using a line-integration method, which employs
1. Introduction Traditional observational analyses of vorticity, divergence, and deformation fields usually rely on interpolating observations to either a Cartesian or spherical grid and then evaluating the appropriate finite-difference equations. While this approach has the benefit of creating a set of gridded data that is easily processed by uniform spacing finite-difference methods, it has been shown that greater accuracy can be obtained by using a line-integration method, which employs
times (dwell times). Theoretical and simulation studies demonstrating the advantages of these techniques ( Torres and Zrnić 2003a , b ) have been successfully verified on weather data collected with experimental setups and offline signal processing ( Ivić et al. 2003b ; Torres and Ivić 2005 ; Choudhury and Chandrasekar 2007 ; Hefner and Chandrasekar 2008 ). However, a real-time implementation of range oversampling processing on weather radars has not been reported to date. Although appealing, a
times (dwell times). Theoretical and simulation studies demonstrating the advantages of these techniques ( Torres and Zrnić 2003a , b ) have been successfully verified on weather data collected with experimental setups and offline signal processing ( Ivić et al. 2003b ; Torres and Ivić 2005 ; Choudhury and Chandrasekar 2007 ; Hefner and Chandrasekar 2008 ). However, a real-time implementation of range oversampling processing on weather radars has not been reported to date. Although appealing, a
reduction when meteorological variable estimates are averaged along range to gain data precision at the cost of reduced range resolution. Finally, the RWF can also affect the performance of algorithms that process meteorological data. For example, changes in the effective resolution volume in angular and/or range extents can affect tornado detection algorithms that utilize Doppler velocity signatures ( Wood and Brown 1997 ; Torres and Curtis 2006 ). These effects can be further complicated by the fact
reduction when meteorological variable estimates are averaged along range to gain data precision at the cost of reduced range resolution. Finally, the RWF can also affect the performance of algorithms that process meteorological data. For example, changes in the effective resolution volume in angular and/or range extents can affect tornado detection algorithms that utilize Doppler velocity signatures ( Wood and Brown 1997 ; Torres and Curtis 2006 ). These effects can be further complicated by the fact
simulations, followed by analyses of experimental data obtained at the Fire Science Laboratory (FSL) test site located in mountainous terrain, approximately 30 km west of the city of Missoula, Montana, at an altitude of ∼1000 m. The measurements were made in clear atmospheres. The paper is structured in three parts: an improved measurement methodology and data-processing technique for the Kano–Hamilton method is introduced first, followed by the determination of the lidar overlap function, and closing
simulations, followed by analyses of experimental data obtained at the Fire Science Laboratory (FSL) test site located in mountainous terrain, approximately 30 km west of the city of Missoula, Montana, at an altitude of ∼1000 m. The measurements were made in clear atmospheres. The paper is structured in three parts: an improved measurement methodology and data-processing technique for the Kano–Hamilton method is introduced first, followed by the determination of the lidar overlap function, and closing
