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- Author or Editor: T. W. Christian x
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Abstract
Observations taken during the Convection Initiation and Downburst Experiment (CINDE) are used to describe the formation and structure of an orographically induced mesoscale vortex that frequently occurs in northeastern Colorado. This vortex, known locally as the Denver Cyclone due to its proximity to the Denver metropolitan area, is frequently associated with severe weather. We present a case study of the Denver Cyclone of 25 June 1987, that formed during the late morning hours and remained nearly stationary for over 24 hours.
Interesting features of the case study vortex are: low-level convergence into the center of the cyclone during nighttime hours but divergence at the center when daytime heating becomes significant; a very shallow initial vertical extent at night, growing to nearly 1500 m during the daytime hours; a cold pool of air on the west side of the vortex, with highest surface potential temperatures present in a warm plume on the east side; a perturbation low pressure of ∼150 Pa in the region of warmest potential temperatures; a sloping zone of low-level convergence, in the region of lower pressure, that triggers intense convective activity, and an upwind tilt of the center axis of the vortex.
Abstract
Observations taken during the Convection Initiation and Downburst Experiment (CINDE) are used to describe the formation and structure of an orographically induced mesoscale vortex that frequently occurs in northeastern Colorado. This vortex, known locally as the Denver Cyclone due to its proximity to the Denver metropolitan area, is frequently associated with severe weather. We present a case study of the Denver Cyclone of 25 June 1987, that formed during the late morning hours and remained nearly stationary for over 24 hours.
Interesting features of the case study vortex are: low-level convergence into the center of the cyclone during nighttime hours but divergence at the center when daytime heating becomes significant; a very shallow initial vertical extent at night, growing to nearly 1500 m during the daytime hours; a cold pool of air on the west side of the vortex, with highest surface potential temperatures present in a warm plume on the east side; a perturbation low pressure of ∼150 Pa in the region of warmest potential temperatures; a sloping zone of low-level convergence, in the region of lower pressure, that triggers intense convective activity, and an upwind tilt of the center axis of the vortex.
Abstract
Soil moisture is an important variable for numerous scientific disciplines, and therefore provision of accurate and timely soil moisture information is critical. Recent initiatives, such as the National Soil Moisture Network effort, have increased the spatial coverage and quality of soil moisture monitoring infrastructure across the contiguous United States. As a result, the foundation has been laid for a high-resolution, real-time gridded soil moisture product that leverages data from in situ networks, satellite platforms, and land surface models. An important precursor to this development is a comprehensive, national-scale assessment of in situ soil moisture data fidelity. Additionally, evaluation of the United States’s current in situ soil moisture monitoring infrastructure can provide a means toward more informed satellite and model calibration and validation. This study employs a triple collocation approach to evaluate the fidelity of in situ soil moisture observations from over 1200 stations across the contiguous United States. The primary goal of the study is to determine the monitoring stations that are best suited for 1) inclusion in national-scale soil moisture datasets, 2) deriving in situ–informed gridded soil moisture products, and 3) validating and benchmarking satellite and model soil moisture data. We find that 90% of the 1233 stations evaluated exhibit high spatial consistency with satellite remote sensing and land surface model soil moisture datasets. In situ error did not significantly vary by climate, soil type, or sensor technology, but instead was a function of station-specific properties such as land cover and station siting.
Abstract
Soil moisture is an important variable for numerous scientific disciplines, and therefore provision of accurate and timely soil moisture information is critical. Recent initiatives, such as the National Soil Moisture Network effort, have increased the spatial coverage and quality of soil moisture monitoring infrastructure across the contiguous United States. As a result, the foundation has been laid for a high-resolution, real-time gridded soil moisture product that leverages data from in situ networks, satellite platforms, and land surface models. An important precursor to this development is a comprehensive, national-scale assessment of in situ soil moisture data fidelity. Additionally, evaluation of the United States’s current in situ soil moisture monitoring infrastructure can provide a means toward more informed satellite and model calibration and validation. This study employs a triple collocation approach to evaluate the fidelity of in situ soil moisture observations from over 1200 stations across the contiguous United States. The primary goal of the study is to determine the monitoring stations that are best suited for 1) inclusion in national-scale soil moisture datasets, 2) deriving in situ–informed gridded soil moisture products, and 3) validating and benchmarking satellite and model soil moisture data. We find that 90% of the 1233 stations evaluated exhibit high spatial consistency with satellite remote sensing and land surface model soil moisture datasets. In situ error did not significantly vary by climate, soil type, or sensor technology, but instead was a function of station-specific properties such as land cover and station siting.
Abstract
On 2 July 1987 a nonmesocyclone tornado was observed in northeastern Colorado during the Convection Initiation and Downburst Experiment (CINDE). This tornado, reaching FI–F2 intensity, developed under a rapidly growing convective cell, without a preceding supercell or midlevel mesocyclone being present.
The pretornado environment on 2 July is described, including observations from a triangle of wind profilers, a dense surface mesonet array, and a special balloon sounding network. Important features contributing to tornado generation include the passage of a 700-mb short-wave trough; the formation of an ∼70-km diameter, terrain-induced mesoscale vortex (the Denver Cyclone) and its associated baroclinic zone; the presence of a stationary low-level convergence boundary; and the presence of low-level azimuthal sheer maxima (misovortices) along the boundary.
Vorticity budget terms are calculated in the lowest 2 km AGL using a multiple-Doppler radar analysis. These terms and their spatial distributions are compared with observations of mesocyclone-associated supercell tornadoes. Results show that vorticity associated with the 2 July nonsupercell tornado was generated in a more complicated manner than that proposed by previous nonsupercell tornadogenesis theory. In particular, tilting of baroclinically generated streamwise horizontal vorticity into the vertical was important for the formation of low-level rotation, in a manner similar to that previously proposed for supercell tornadic storms.
Abstract
On 2 July 1987 a nonmesocyclone tornado was observed in northeastern Colorado during the Convection Initiation and Downburst Experiment (CINDE). This tornado, reaching FI–F2 intensity, developed under a rapidly growing convective cell, without a preceding supercell or midlevel mesocyclone being present.
The pretornado environment on 2 July is described, including observations from a triangle of wind profilers, a dense surface mesonet array, and a special balloon sounding network. Important features contributing to tornado generation include the passage of a 700-mb short-wave trough; the formation of an ∼70-km diameter, terrain-induced mesoscale vortex (the Denver Cyclone) and its associated baroclinic zone; the presence of a stationary low-level convergence boundary; and the presence of low-level azimuthal sheer maxima (misovortices) along the boundary.
Vorticity budget terms are calculated in the lowest 2 km AGL using a multiple-Doppler radar analysis. These terms and their spatial distributions are compared with observations of mesocyclone-associated supercell tornadoes. Results show that vorticity associated with the 2 July nonsupercell tornado was generated in a more complicated manner than that proposed by previous nonsupercell tornadogenesis theory. In particular, tilting of baroclinically generated streamwise horizontal vorticity into the vertical was important for the formation of low-level rotation, in a manner similar to that previously proposed for supercell tornadic storms.
Abstract
This paper describes the latest improvements applied to the Goddard profiling algorithm (GPROF), particularly as they apply to the Tropical Rainfall Measuring Mission (TRMM). Most of these improvements, however, are conceptual in nature and apply equally to other passive microwave sensors. The improvements were motivated by a notable overestimation of precipitation in the intertropical convergence zone. This problem was traced back to the algorithm's poor separation between convective and stratiform precipitation coupled with a poor separation between stratiform and transition regions in the a priori cloud model database. In addition to now using an improved convective–stratiform classification scheme, the new algorithm also makes use of emission and scattering indices instead of individual brightness temperatures. Brightness temperature indices have the advantage of being monotonic functions of rainfall. This, in turn, has allowed the algorithm to better define the uncertainties needed by the scheme's Bayesian inversion approach. Last, the algorithm over land has been modified primarily to better account for ambiguous classification where the scattering signature of precipitation could be confused with surface signals. All these changes have been implemented for both the TRMM Microwave Imager (TMI) and the Special Sensor Microwave Imager (SSM/I). Results from both sensors are very similar at the storm scale and for global averages. Surface rainfall products from the algorithm's operational version have been compared with conventional rainfall data over both land and oceans. Over oceans, GPROF results compare well with atoll gauge data. GPROF is biased negatively by 9% with a correlation of 0.86 for monthly 2.5° averages over the atolls. If only grid boxes with two or more atolls are used, the correlation increases to 0.91 but GPROF becomes positively biased by 6%. Comparisons with TRMM ground validation products from Kwajalein reveal that GPROF is negatively biased by 32%, with a correlation of 0.95 when coincident images of the TMI and Kwajalein radar are used. The absolute magnitude of rainfall measured from the Kwajalein radar, however, remains uncertain, and GPROF overestimates the rainfall by approximately 18% when compared with estimates done by a different research group. Over land, GPROF shows a positive bias of 17% and a correlation of 0.80 over monthly 5° grids when compared with the Global Precipitation Climatology Center (GPCC) gauge network. When compared with the precipitation radar (PR) over land, GPROF also retrieves higher rainfall amounts (20%). No vertical hydrometeor profile information is available over land. The correlation with the TRMM precipitation radar is 0.92 over monthly 5° grids, but GPROF is positively biased by 24% relative to the radar over oceans. Differences between TMI- and PR-derived vertical hydrometeor profiles below 2 km are consistent with this bias but become more significant with altitude. Above 8 km, the sensors disagree significantly, but the information content is low from both TMI and PR. The consistent bias between these two sensors without clear guidance from the ground-based data reinforces the need for better understanding of the physical assumptions going into these retrievals.
Abstract
This paper describes the latest improvements applied to the Goddard profiling algorithm (GPROF), particularly as they apply to the Tropical Rainfall Measuring Mission (TRMM). Most of these improvements, however, are conceptual in nature and apply equally to other passive microwave sensors. The improvements were motivated by a notable overestimation of precipitation in the intertropical convergence zone. This problem was traced back to the algorithm's poor separation between convective and stratiform precipitation coupled with a poor separation between stratiform and transition regions in the a priori cloud model database. In addition to now using an improved convective–stratiform classification scheme, the new algorithm also makes use of emission and scattering indices instead of individual brightness temperatures. Brightness temperature indices have the advantage of being monotonic functions of rainfall. This, in turn, has allowed the algorithm to better define the uncertainties needed by the scheme's Bayesian inversion approach. Last, the algorithm over land has been modified primarily to better account for ambiguous classification where the scattering signature of precipitation could be confused with surface signals. All these changes have been implemented for both the TRMM Microwave Imager (TMI) and the Special Sensor Microwave Imager (SSM/I). Results from both sensors are very similar at the storm scale and for global averages. Surface rainfall products from the algorithm's operational version have been compared with conventional rainfall data over both land and oceans. Over oceans, GPROF results compare well with atoll gauge data. GPROF is biased negatively by 9% with a correlation of 0.86 for monthly 2.5° averages over the atolls. If only grid boxes with two or more atolls are used, the correlation increases to 0.91 but GPROF becomes positively biased by 6%. Comparisons with TRMM ground validation products from Kwajalein reveal that GPROF is negatively biased by 32%, with a correlation of 0.95 when coincident images of the TMI and Kwajalein radar are used. The absolute magnitude of rainfall measured from the Kwajalein radar, however, remains uncertain, and GPROF overestimates the rainfall by approximately 18% when compared with estimates done by a different research group. Over land, GPROF shows a positive bias of 17% and a correlation of 0.80 over monthly 5° grids when compared with the Global Precipitation Climatology Center (GPCC) gauge network. When compared with the precipitation radar (PR) over land, GPROF also retrieves higher rainfall amounts (20%). No vertical hydrometeor profile information is available over land. The correlation with the TRMM precipitation radar is 0.92 over monthly 5° grids, but GPROF is positively biased by 24% relative to the radar over oceans. Differences between TMI- and PR-derived vertical hydrometeor profiles below 2 km are consistent with this bias but become more significant with altitude. Above 8 km, the sensors disagree significantly, but the information content is low from both TMI and PR. The consistent bias between these two sensors without clear guidance from the ground-based data reinforces the need for better understanding of the physical assumptions going into these retrievals.
Abstract
The second Meteor Crater Experiment (METCRAX II) was conducted in October 2013 at Arizona’s Meteor Crater. The experiment was designed to investigate nighttime downslope windstorm−type flows that form regularly above the inner southwest sidewall of the 1.2-km diameter crater as a southwesterly mesoscale katabatic flow cascades over the crater rim. The objective of METCRAX II is to determine the causes of these strong, intermittent, and turbulent inflows that bring warm-air intrusions into the southwest part of the crater. This article provides an overview of the scientific goals of the experiment; summarizes the measurements, the crater topography, and the synoptic meteorology of the study period; and presents initial analysis results.
Abstract
The second Meteor Crater Experiment (METCRAX II) was conducted in October 2013 at Arizona’s Meteor Crater. The experiment was designed to investigate nighttime downslope windstorm−type flows that form regularly above the inner southwest sidewall of the 1.2-km diameter crater as a southwesterly mesoscale katabatic flow cascades over the crater rim. The objective of METCRAX II is to determine the causes of these strong, intermittent, and turbulent inflows that bring warm-air intrusions into the southwest part of the crater. This article provides an overview of the scientific goals of the experiment; summarizes the measurements, the crater topography, and the synoptic meteorology of the study period; and presents initial analysis results.
Abstract
The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.
Abstract
The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.
