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- Author or Editor: Matthew S. Jones x
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Abstract
A short-range ensemble forecast system was constructed over the northeast United States down to 12-km grid spacing using 18 members from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The ensemble consisted of 12 physics members with varying planetary boundary layer schemes and convective parameterizations as well as seven different initial conditions (ICs) [five National Centers for Environmental Prediction (NCEP) Eta-bred members at 2100 UTC and the 0000 UTC NCEP Global Forecast System (GFS) and Eta runs]. The full 18-member ensemble (ALL) was verified at the surface for the warm (May–September 2003) and cool (October 2003–March 2004) seasons. A randomly chosen subset of seven physics (PHS) members at each forecast hour was used to quantitatively compare with the seven IC members. During the warm season, the PHS ensemble predictions for surface temperature and wind speed had more skill than the IC ensemble and a control (shared PHS and IC member) run initialized 12 h later (CTL12). During the cool and warm seasons, a 14-day running-mean bias calibration applied to the ALL ensemble (ALLBC) added 10%–30% more skill for temperature, wind speed, and sea level pressure, with the ALLBC far outperforming the CTL12. For the 24-h precipitation, the PHS ensemble had comparable probabilistic skill to the IC ensemble during the warm season, while the IC subensemble was more skillful during the cool season. All ensemble members had large diurnal surface biases, with ensemble variance approximating ensemble uncertainty only for wind direction. Selection of ICs was also important, because during the cool season the NCEP-bred members introduced large errors into the IC ensemble for sea level pressure, while none of the subensembles (PHS, IC, or ALL) outperformed the GFS–MM5 for sea level pressure.
Abstract
A short-range ensemble forecast system was constructed over the northeast United States down to 12-km grid spacing using 18 members from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The ensemble consisted of 12 physics members with varying planetary boundary layer schemes and convective parameterizations as well as seven different initial conditions (ICs) [five National Centers for Environmental Prediction (NCEP) Eta-bred members at 2100 UTC and the 0000 UTC NCEP Global Forecast System (GFS) and Eta runs]. The full 18-member ensemble (ALL) was verified at the surface for the warm (May–September 2003) and cool (October 2003–March 2004) seasons. A randomly chosen subset of seven physics (PHS) members at each forecast hour was used to quantitatively compare with the seven IC members. During the warm season, the PHS ensemble predictions for surface temperature and wind speed had more skill than the IC ensemble and a control (shared PHS and IC member) run initialized 12 h later (CTL12). During the cool and warm seasons, a 14-day running-mean bias calibration applied to the ALL ensemble (ALLBC) added 10%–30% more skill for temperature, wind speed, and sea level pressure, with the ALLBC far outperforming the CTL12. For the 24-h precipitation, the PHS ensemble had comparable probabilistic skill to the IC ensemble during the warm season, while the IC subensemble was more skillful during the cool season. All ensemble members had large diurnal surface biases, with ensemble variance approximating ensemble uncertainty only for wind direction. Selection of ICs was also important, because during the cool season the NCEP-bred members introduced large errors into the IC ensemble for sea level pressure, while none of the subensembles (PHS, IC, or ALL) outperformed the GFS–MM5 for sea level pressure.
Abstract
The Along Track Scanning Radiometer (ATSR) was launched in July 1991 on the European Space Agency's first remote sensing satellite ERS-1. ATSR has the potential to measure sea surface temperature (SST) to a precision of 0.3 K, which is more than double the accuracy of any previously flown infrared radiometer. A key factor limiting ATSR's performance is remnant cloud contamination. Examination of the 0.5° spatially averaged ATSR SST data (version 500) from the South Atlantic for the whole of 1992 and 1993 shows the presence of regional cloud contamination in the night SST measurements. The authors establish a figure of 5.7% as a lower limit for this nighttime cloud contamination. The contamination leads to differences between day and night mean SSTs and to poor comparisons with in situ thermosalinograph SST data. A new cloud filtering process designed for postprocessing of the data is proposed to remove the contamination. The algorithm presented here relies on assumptions that the day data are less cloud contaminated than the night data and that a large proportion of the SST variability can he explained by an annual and semiannual model. Testing the filtering algorithm shows that differences between the day and night SST signals are substantially reduced and that comparisons with the thermosalinograph SST data improve by a factor of 3 in rms scatter and by 0.3 K in the mean difference.
Abstract
The Along Track Scanning Radiometer (ATSR) was launched in July 1991 on the European Space Agency's first remote sensing satellite ERS-1. ATSR has the potential to measure sea surface temperature (SST) to a precision of 0.3 K, which is more than double the accuracy of any previously flown infrared radiometer. A key factor limiting ATSR's performance is remnant cloud contamination. Examination of the 0.5° spatially averaged ATSR SST data (version 500) from the South Atlantic for the whole of 1992 and 1993 shows the presence of regional cloud contamination in the night SST measurements. The authors establish a figure of 5.7% as a lower limit for this nighttime cloud contamination. The contamination leads to differences between day and night mean SSTs and to poor comparisons with in situ thermosalinograph SST data. A new cloud filtering process designed for postprocessing of the data is proposed to remove the contamination. The algorithm presented here relies on assumptions that the day data are less cloud contaminated than the night data and that a large proportion of the SST variability can he explained by an annual and semiannual model. Testing the filtering algorithm shows that differences between the day and night SST signals are substantially reduced and that comparisons with the thermosalinograph SST data improve by a factor of 3 in rms scatter and by 0.3 K in the mean difference.
Abstract
This paper develops a definition of a supercell reflectivity feature called the descending reflectivity core (DRC). This is a reflectivity maximum pendant from the rear side of an echo overhang above a supercell weak-echo region. Examples of supercells with and without DRCs are presented from two days during the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX), as well as one day with tornadic high-precipitation supercell storms in central Kansas. It was found that in all cases, tornado formation was preceded by the descent of a DRC. However, the sample reported herein is much too small to allow conclusions regarding the overall frequency of DRC occurrence in supercells, or the frequency with which DRCs precede tornado formation. Although further research needs to be done to establish climatological frequencies, the apparent relationship observed between DRCs and impending tornado formation in several supercells is important enough to warrant publication of preliminary findings.
Abstract
This paper develops a definition of a supercell reflectivity feature called the descending reflectivity core (DRC). This is a reflectivity maximum pendant from the rear side of an echo overhang above a supercell weak-echo region. Examples of supercells with and without DRCs are presented from two days during the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX), as well as one day with tornadic high-precipitation supercell storms in central Kansas. It was found that in all cases, tornado formation was preceded by the descent of a DRC. However, the sample reported herein is much too small to allow conclusions regarding the overall frequency of DRC occurrence in supercells, or the frequency with which DRCs precede tornado formation. Although further research needs to be done to establish climatological frequencies, the apparent relationship observed between DRCs and impending tornado formation in several supercells is important enough to warrant publication of preliminary findings.
ABSTRACT
Two reports of Antarctic region potential new record high temperature observations (18.3°C, 6 February 2020 at Esperanza station and 20.8°C, 9 February 2020 at a Brazilian automated permafrost monitoring station on Seymour Island) were evaluated by a World Meteorological Organization (WMO) panel of atmospheric scientists. The latter figure was reported as 20.75°C in the media. The panel considered the synoptic situation and instrumental setups. It determined that a large high pressure system over the area created föhn conditions and resulted in local warming for both situations. Examination of the data and metadata of the Esperanza station observation revealed no major concerns. However, analysis of data and metadata of the Seymour Island permafrost monitoring station indicated that an improvised radiation shield led to a demonstrable thermal bias error for the temperature sensor. Consequently, the WMO has accepted the 18.3°C value for 1200 LST 6 February 2020 (1500 UTC 6 February 2020) at the Argentine Esperanza station as the new “Antarctic region (continental, including mainland and surrounding islands) highest temperature recorded observation” but rejected the 20.8°C observation at the Brazilian automated Seymour Island permafrost monitoring station as biased. The committee strongly emphasizes the permafrost monitoring station was not badly designed for its purpose, but the project investigators were forced to improvise a nonoptimal radiation shield after losing the original covering. Second, with regard to media dissemination of this type of information, the committee urges increased caution in early announcements as many media outlets often tend to sensationalize and mischaracterize potential records.
ABSTRACT
Two reports of Antarctic region potential new record high temperature observations (18.3°C, 6 February 2020 at Esperanza station and 20.8°C, 9 February 2020 at a Brazilian automated permafrost monitoring station on Seymour Island) were evaluated by a World Meteorological Organization (WMO) panel of atmospheric scientists. The latter figure was reported as 20.75°C in the media. The panel considered the synoptic situation and instrumental setups. It determined that a large high pressure system over the area created föhn conditions and resulted in local warming for both situations. Examination of the data and metadata of the Esperanza station observation revealed no major concerns. However, analysis of data and metadata of the Seymour Island permafrost monitoring station indicated that an improvised radiation shield led to a demonstrable thermal bias error for the temperature sensor. Consequently, the WMO has accepted the 18.3°C value for 1200 LST 6 February 2020 (1500 UTC 6 February 2020) at the Argentine Esperanza station as the new “Antarctic region (continental, including mainland and surrounding islands) highest temperature recorded observation” but rejected the 20.8°C observation at the Brazilian automated Seymour Island permafrost monitoring station as biased. The committee strongly emphasizes the permafrost monitoring station was not badly designed for its purpose, but the project investigators were forced to improvise a nonoptimal radiation shield after losing the original covering. Second, with regard to media dissemination of this type of information, the committee urges increased caution in early announcements as many media outlets often tend to sensationalize and mischaracterize potential records.
