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Abstract
Temperatures and pressures inferred from radio occultation data acquired by the Pioneer Venus orbiter between September 1982 and November 1983 are used to derive cyclostrophic zonal winds in the middle atmosphere of Venus (1350 to 2.1 mb, 10° to 70° latitude). The main feature of the wind field is a jet positioned just above the cloud tops at 70 km and ≈48° latitude. The maximum speed of the jet is about 130 m s−1. A comparison with results of similar analyses on Pioneer Venus radio occultation data obtained between December 1978 and October 1981 suggests an equatorward shift of the jet and a decrease in jet speed during this five-year time interval. It is proposed that the poleward transport of westward zonal momentum by the upper branch of the cloud level Hadley cell supplies the excess momentum of the jet and maintains it against dissipation. The location of the jet thereby provides a minimum estimate of the latitudinal extent of the Hadley cell. Cyclostrophic zonal wind velocities decrease with height above about 70–75 km. It is suggested that this deceleration of the superrotation in equatorial latitudes is due to the dissipation of vertically propagating thermal tides forced primarily at altitudes around 65 km.
Abstract
Temperatures and pressures inferred from radio occultation data acquired by the Pioneer Venus orbiter between September 1982 and November 1983 are used to derive cyclostrophic zonal winds in the middle atmosphere of Venus (1350 to 2.1 mb, 10° to 70° latitude). The main feature of the wind field is a jet positioned just above the cloud tops at 70 km and ≈48° latitude. The maximum speed of the jet is about 130 m s−1. A comparison with results of similar analyses on Pioneer Venus radio occultation data obtained between December 1978 and October 1981 suggests an equatorward shift of the jet and a decrease in jet speed during this five-year time interval. It is proposed that the poleward transport of westward zonal momentum by the upper branch of the cloud level Hadley cell supplies the excess momentum of the jet and maintains it against dissipation. The location of the jet thereby provides a minimum estimate of the latitudinal extent of the Hadley cell. Cyclostrophic zonal wind velocities decrease with height above about 70–75 km. It is suggested that this deceleration of the superrotation in equatorial latitudes is due to the dissipation of vertically propagating thermal tides forced primarily at altitudes around 65 km.
Abstract
Comparisons between measurements of a wind component by a Doppler lidar and by a conventional anemometer are presented. The two measurement techniques provided thirteen 15 min data sets which agreed within 0.04 m s−1 on the average. The maximum difference was 0.12 m s−1, which constitutes less than 3% discrepancy, referred to the period average. The results conclusively demonstrate the ability of Doppler lidar to measure winds with a high degree of velocity resolution and accuracy.
Abstract
Comparisons between measurements of a wind component by a Doppler lidar and by a conventional anemometer are presented. The two measurement techniques provided thirteen 15 min data sets which agreed within 0.04 m s−1 on the average. The maximum difference was 0.12 m s−1, which constitutes less than 3% discrepancy, referred to the period average. The results conclusively demonstrate the ability of Doppler lidar to measure winds with a high degree of velocity resolution and accuracy.
Abstract
Observations have shown that the mass of nitrogen dioxide decreased at both southern and northern midlatitudes in the year following the eruption of Mt. Pinatubo, indicating that the volcanic aerosol had enhanced nitrogen dioxide depletion via heterogeneous chemistry. In contrast, the observed ozone response showed a northern midlatitude decrease and a small southern midlatitude increase. Previous simulations that included an enhancement of heterogeneous chemistry by the volcanic aerosol but no other effect of this aerosol produce ozone decreases in both hemispheres, contrary to observations. The authors’ simulations show that the heating due to the volcanic aerosol enhanced both the tropical upwelling and Southern Hemisphere extratropical downwelling. This enhanced extratropical downwelling, combined with the time of the eruption relative to the phase of the Brewer–Dobson circulation, increased Southern Hemisphere ozone via advection, counteracting the ozone depletion due to heterogeneous chemistry on the Pinatubo aerosol.
Abstract
Observations have shown that the mass of nitrogen dioxide decreased at both southern and northern midlatitudes in the year following the eruption of Mt. Pinatubo, indicating that the volcanic aerosol had enhanced nitrogen dioxide depletion via heterogeneous chemistry. In contrast, the observed ozone response showed a northern midlatitude decrease and a small southern midlatitude increase. Previous simulations that included an enhancement of heterogeneous chemistry by the volcanic aerosol but no other effect of this aerosol produce ozone decreases in both hemispheres, contrary to observations. The authors’ simulations show that the heating due to the volcanic aerosol enhanced both the tropical upwelling and Southern Hemisphere extratropical downwelling. This enhanced extratropical downwelling, combined with the time of the eruption relative to the phase of the Brewer–Dobson circulation, increased Southern Hemisphere ozone via advection, counteracting the ozone depletion due to heterogeneous chemistry on the Pinatubo aerosol.
Abstract
The structure of barotropically unstable disturbances in the Tropics is studied with a two-level quasi-geostrophic model. An Ekman layer is attached to the lower boundary. The equations are linearized and the most unstable mode is found numerically by using the initial value technique. Computations are made for a shear-zone wind profile and a jet profile; these fields are independent of height. The disturbance structure is found to be critically dependent on the absolute vorticity gradient in the mean flow. The predicted disturbance structures contain a number of features that are observed in tropical wave disturbances.
Abstract
The structure of barotropically unstable disturbances in the Tropics is studied with a two-level quasi-geostrophic model. An Ekman layer is attached to the lower boundary. The equations are linearized and the most unstable mode is found numerically by using the initial value technique. Computations are made for a shear-zone wind profile and a jet profile; these fields are independent of height. The disturbance structure is found to be critically dependent on the absolute vorticity gradient in the mean flow. The predicted disturbance structures contain a number of features that are observed in tropical wave disturbances.
Abstract
On average, 2-m temperature forecasts over North America for lead times greater than two weeks have generally low skill in operational dynamical models, largely because of the chaotic, unpredictable nature of daily weather. However, for a small subset of forecasts, more slowly evolving climate processes yield some predictable signal that may be anticipated in advance, occasioning “forecasts of opportunity.” Forecasts of opportunity evolve seasonally, since they are a function of the seasonally varying jet stream and various remote forcings such as tropical heating. Prior research has demonstrated that for boreal winter, an empirical dynamical modeling technique called a linear inverse model (LIM), whose forecast skill is typically comparable to operational forecast models, can successfully identify forecasts of opportunity both for itself and for other dynamical models. In this study, we use a set of LIMs to examine how subseasonal North American 2-m temperature potential predictability and forecasts of opportunity vary from boreal winter through summer. We show how LIM skill evolves during the three phases of the spring transition of the North Pacific jet—late winter, spring, and early summer—revealing clear differences in each phase and a distinct skill minimum in spring. We identify a subset of forecasts with markedly higher skill in all three phases, despite LIM temperature skill that is somewhat low on average. However, skill improvements are only statistically significant during winter and summer, again reflecting the spring subseasonal skill minimum. The spring skill minimum is consistent with the skill predicted from theory and arises due to a minimum in LIM forecast signal-to-noise ratio.
Abstract
On average, 2-m temperature forecasts over North America for lead times greater than two weeks have generally low skill in operational dynamical models, largely because of the chaotic, unpredictable nature of daily weather. However, for a small subset of forecasts, more slowly evolving climate processes yield some predictable signal that may be anticipated in advance, occasioning “forecasts of opportunity.” Forecasts of opportunity evolve seasonally, since they are a function of the seasonally varying jet stream and various remote forcings such as tropical heating. Prior research has demonstrated that for boreal winter, an empirical dynamical modeling technique called a linear inverse model (LIM), whose forecast skill is typically comparable to operational forecast models, can successfully identify forecasts of opportunity both for itself and for other dynamical models. In this study, we use a set of LIMs to examine how subseasonal North American 2-m temperature potential predictability and forecasts of opportunity vary from boreal winter through summer. We show how LIM skill evolves during the three phases of the spring transition of the North Pacific jet—late winter, spring, and early summer—revealing clear differences in each phase and a distinct skill minimum in spring. We identify a subset of forecasts with markedly higher skill in all three phases, despite LIM temperature skill that is somewhat low on average. However, skill improvements are only statistically significant during winter and summer, again reflecting the spring subseasonal skill minimum. The spring skill minimum is consistent with the skill predicted from theory and arises due to a minimum in LIM forecast signal-to-noise ratio.
Abstract
The sources of predictability for the February 2021 cold air outbreak (CAO) over the central United States, which led to power grid failures and water delivery shortages in Texas, are diagnosed using a machine learning–based prediction model called a linear inverse model (LIM). The flexibility and low computational cost of the LIM allows its forecasts to be used for identifying and assessing the predictability of key physical processes. The LIM may also be run as a climate model for sensitivity and risk analysis for the same reasons. The February 2021 CAO was a subseasonal forecast of opportunity, as the LIM confidently predicted the CAO’s onset and duration four weeks in advance, up to two weeks earlier than other initialized numerical forecast models. The LIM shows that the February 2021 CAO was principally caused by unpredictable internal atmospheric variability and predictable La Niña teleconnections, with nominally predictable contributions from the previous month’s sudden stratospheric warming and the Madden–Julian oscillation. When run as a climate model, the LIM estimates that the February 2021 CAO was in the top 1% of CAO severity and suggests that similarly extreme CAOs could be expected to occur approximately every 20–30 years.
Abstract
The sources of predictability for the February 2021 cold air outbreak (CAO) over the central United States, which led to power grid failures and water delivery shortages in Texas, are diagnosed using a machine learning–based prediction model called a linear inverse model (LIM). The flexibility and low computational cost of the LIM allows its forecasts to be used for identifying and assessing the predictability of key physical processes. The LIM may also be run as a climate model for sensitivity and risk analysis for the same reasons. The February 2021 CAO was a subseasonal forecast of opportunity, as the LIM confidently predicted the CAO’s onset and duration four weeks in advance, up to two weeks earlier than other initialized numerical forecast models. The LIM shows that the February 2021 CAO was principally caused by unpredictable internal atmospheric variability and predictable La Niña teleconnections, with nominally predictable contributions from the previous month’s sudden stratospheric warming and the Madden–Julian oscillation. When run as a climate model, the LIM estimates that the February 2021 CAO was in the top 1% of CAO severity and suggests that similarly extreme CAOs could be expected to occur approximately every 20–30 years.
Abstract
Surface meteorological analyses serve a wide range of research and applications, including forcing inputs for hydrological and ecological models, climate analysis, and resource and emergency management. Quantifying uncertainty in such analyses would extend their utility for probabilistic hydrologic prediction and climate risk applications. With this motivation, we enhance and evaluate an approach for generating ensemble analyses of precipitation and temperature through the fusion of station observations, terrain information, and numerical weather prediction simulations of surface climate fields. In particular, we expand a spatial regression in which static terrain attributes serve as predictors for spatially distributed 1/16° daily surface precipitation and temperature by including forecast outputs from the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model as additional predictors. We demonstrate the approach for a case study domain of California, focusing on the meteorological conditions leading to the 2017 flood and spillway failure event at Lake Oroville. The approach extends the spatial regression capability of the Gridded Meteorological Ensemble Tool (GMET) and also adds cross validation to the uncertainty estimation component, enabling the use of predictive rather than calibration uncertainty. In evaluation against out-of-sample station observations, the HRRR-based predictors alone are found to be skillful for the study setting, leading to overall improvements in the enhanced GMET meteorological analyses. The methodology and associated tool represent a promising method for generating meteorological surface analyses for both research-oriented and operational applications, as well as a general strategy for merging in situ and gridded observations.
Abstract
Surface meteorological analyses serve a wide range of research and applications, including forcing inputs for hydrological and ecological models, climate analysis, and resource and emergency management. Quantifying uncertainty in such analyses would extend their utility for probabilistic hydrologic prediction and climate risk applications. With this motivation, we enhance and evaluate an approach for generating ensemble analyses of precipitation and temperature through the fusion of station observations, terrain information, and numerical weather prediction simulations of surface climate fields. In particular, we expand a spatial regression in which static terrain attributes serve as predictors for spatially distributed 1/16° daily surface precipitation and temperature by including forecast outputs from the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model as additional predictors. We demonstrate the approach for a case study domain of California, focusing on the meteorological conditions leading to the 2017 flood and spillway failure event at Lake Oroville. The approach extends the spatial regression capability of the Gridded Meteorological Ensemble Tool (GMET) and also adds cross validation to the uncertainty estimation component, enabling the use of predictive rather than calibration uncertainty. In evaluation against out-of-sample station observations, the HRRR-based predictors alone are found to be skillful for the study setting, leading to overall improvements in the enhanced GMET meteorological analyses. The methodology and associated tool represent a promising method for generating meteorological surface analyses for both research-oriented and operational applications, as well as a general strategy for merging in situ and gridded observations.
Climatologists from the climate centers of 12 states of the upper Midwest contributed temperature, precipitation, and related data for December 1982, January and February 1983. Analyses present the month-to-month spatial anomaly patterns of these parameters. Mean monthly temperatures were much above normal (30-year means) during the three months in virtually the entire region, with maximum magnitudes (+4 to +9°C) extending from the Dakotas to Iowa, and to Indiana (December) and Missouri (January and February).
December precipitation was also above normal with anomalies of + 100 mm in much of Missouri, Illinois, extreme southwest Michigan, and Indiana. The maximum anomaly was over +250 mm in southern Illinois. January and February precipitation anomalies showed only little deviation from normal.
Impacts of the mild winter were generally favorable to consumers in that heating demand was reduced from normal, and particularly reduced from that of the previous year. Costs for urban snow removal were much under budget, as well. The only potentially negative impact was a relatively high survival rate of insect larvae, which is usually controlled by normally colder winter temperatures.
The 1982 peach crop of southern Illinois was essentially lost during the 1981–82 winter due to record cold temperatures. The 1983 crop was also lost largely by a late spring frost, even though the winter was one of the warmest on record.
Climatologists from the climate centers of 12 states of the upper Midwest contributed temperature, precipitation, and related data for December 1982, January and February 1983. Analyses present the month-to-month spatial anomaly patterns of these parameters. Mean monthly temperatures were much above normal (30-year means) during the three months in virtually the entire region, with maximum magnitudes (+4 to +9°C) extending from the Dakotas to Iowa, and to Indiana (December) and Missouri (January and February).
December precipitation was also above normal with anomalies of + 100 mm in much of Missouri, Illinois, extreme southwest Michigan, and Indiana. The maximum anomaly was over +250 mm in southern Illinois. January and February precipitation anomalies showed only little deviation from normal.
Impacts of the mild winter were generally favorable to consumers in that heating demand was reduced from normal, and particularly reduced from that of the previous year. Costs for urban snow removal were much under budget, as well. The only potentially negative impact was a relatively high survival rate of insect larvae, which is usually controlled by normally colder winter temperatures.
The 1982 peach crop of southern Illinois was essentially lost during the 1981–82 winter due to record cold temperatures. The 1983 crop was also lost largely by a late spring frost, even though the winter was one of the warmest on record.
The review of the climate of the summer of 1983 and associated economic impacts were collated by the state climatologists of 12 states of the Upper Midwest. Their data archives and facilities permitted relatively fast analysis of cooperative station data.
Whereas June temperature was near normal across the region, July and August temperatures were generally higher than the 1951-80 normal, with anomalies of +2°C common, and some August anomalies representing a departure greater than 4σ. Cooling degree days were 50% greater than normal over about 1/3 of the 12- state area.
Precipitation was mixed over the area in June, with the greatest anomalies (ca. 200% of normal) in Illinois, Iowa, Minnesota, and Nebraska. July and August precipitation anomalies were similar to each other, and generally negative. Twenty-five percent of normal precipitation was not uncommon. Indeed, two stations in Nebraska and Missouri recorded no precipitation in August.
The impact of high temperatures and low rainfall resulted in substantially less corn and bean yields than expected, but yields of wheat in Kansas, and corn in Wisconsin were greater than last summer. Electrical demand was generally higher than one year earlier, with increases of +15% to +25% common, and 60% greater this July than July 1982 in South Dakota.
New climatological records of high temperatures, low rainfall, and number of days with high temperatures were established and re-established during the summer, primarily in the southwestern Upper Midwest.
The review of the climate of the summer of 1983 and associated economic impacts were collated by the state climatologists of 12 states of the Upper Midwest. Their data archives and facilities permitted relatively fast analysis of cooperative station data.
Whereas June temperature was near normal across the region, July and August temperatures were generally higher than the 1951-80 normal, with anomalies of +2°C common, and some August anomalies representing a departure greater than 4σ. Cooling degree days were 50% greater than normal over about 1/3 of the 12- state area.
Precipitation was mixed over the area in June, with the greatest anomalies (ca. 200% of normal) in Illinois, Iowa, Minnesota, and Nebraska. July and August precipitation anomalies were similar to each other, and generally negative. Twenty-five percent of normal precipitation was not uncommon. Indeed, two stations in Nebraska and Missouri recorded no precipitation in August.
The impact of high temperatures and low rainfall resulted in substantially less corn and bean yields than expected, but yields of wheat in Kansas, and corn in Wisconsin were greater than last summer. Electrical demand was generally higher than one year earlier, with increases of +15% to +25% common, and 60% greater this July than July 1982 in South Dakota.
New climatological records of high temperatures, low rainfall, and number of days with high temperatures were established and re-established during the summer, primarily in the southwestern Upper Midwest.
The international experiment called the European Aqua Thermodynamic Experiment (EAQUATE) was held in September 2004 in Italy and the United Kingdom to validate Aqua satellite Atmospheric Infrared Sounder (AIRS) radiance measurements and derived products with certain groundbased and airborne systems useful for validating hyperspectral satellite sounding observations. A range of flights over land and marine surfaces were conducted to coincide with overpasses of the AIRS instrument on the Earth Observing System Aqua platform. Direct radiance evaluation of AIRS using National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed-Interferometer (NAST-I) and the Scanning High-Resolution Infrared Sounder has shown excellent agreement. Comparisons of level-2 retrievals of temperature and water vapor from AIRS and NAST-I validated against high-quality lidar and dropsonde data show that the 1-K/l-km and 10%/1-km requirements for temperature and water vapor (respectively) are generally being met. The EAQUATE campaign has proven the need for synergistic measurements from a range of observing systems for satellite calibration/validation and has paved the way for future calibration/validation activities in support of the Infrared Atmospheric Sounding Interferometer on the European Meteorological Operational platform and Cross-Track Infrared Sounder on the U.S. NPOESS Prepatory Project platform.
The international experiment called the European Aqua Thermodynamic Experiment (EAQUATE) was held in September 2004 in Italy and the United Kingdom to validate Aqua satellite Atmospheric Infrared Sounder (AIRS) radiance measurements and derived products with certain groundbased and airborne systems useful for validating hyperspectral satellite sounding observations. A range of flights over land and marine surfaces were conducted to coincide with overpasses of the AIRS instrument on the Earth Observing System Aqua platform. Direct radiance evaluation of AIRS using National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed-Interferometer (NAST-I) and the Scanning High-Resolution Infrared Sounder has shown excellent agreement. Comparisons of level-2 retrievals of temperature and water vapor from AIRS and NAST-I validated against high-quality lidar and dropsonde data show that the 1-K/l-km and 10%/1-km requirements for temperature and water vapor (respectively) are generally being met. The EAQUATE campaign has proven the need for synergistic measurements from a range of observing systems for satellite calibration/validation and has paved the way for future calibration/validation activities in support of the Infrared Atmospheric Sounding Interferometer on the European Meteorological Operational platform and Cross-Track Infrared Sounder on the U.S. NPOESS Prepatory Project platform.
