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Abstract
Mesoscale models are often used to explicitly predict discrete, highly structured phenomena. Information regarding the ability of the model to predict events as coherent entities is thus a useful statement of performance. Observational constraints are a significant problem, though, as the shape, size, and intensity of any given event are often only partially known. Composite techniques offer an attractive approach because the full deterministic information about any one event need not be known. If enough quasi-random observations of a distribution of events exist, bulk properties of the distributions of forecasts and observations can be estimated. Composites are also useful in that the verification measures are based on conditional samples of events. Sample distributions contingent on event existence in either the forecasts or the observations can be compared to one another.
A verification technique in which meteorological events are located and composited on a relative grid centered on each event is described herein. This technique is described and demonstrated by comparing the 27-km Naval Research Laboratory's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) mistral wind forecasts to the Special Sensor Microwave Imager (SSM/I) observations for a 1-yr period. Diagnostic information regarding the forecast reliability, error type, and error spatial characteristics are derived. Also, statistics from the conditional distributions of both the observed and predicted events are compared. The difference between the two conditional biases (CBD) is found to reveal valuable information regarding the contribution of false alarms and missed forecasts to the forecast errors. The results indicate the mistral is remarkably predictable with high pattern correlations out to 66 h.
Abstract
Mesoscale models are often used to explicitly predict discrete, highly structured phenomena. Information regarding the ability of the model to predict events as coherent entities is thus a useful statement of performance. Observational constraints are a significant problem, though, as the shape, size, and intensity of any given event are often only partially known. Composite techniques offer an attractive approach because the full deterministic information about any one event need not be known. If enough quasi-random observations of a distribution of events exist, bulk properties of the distributions of forecasts and observations can be estimated. Composites are also useful in that the verification measures are based on conditional samples of events. Sample distributions contingent on event existence in either the forecasts or the observations can be compared to one another.
A verification technique in which meteorological events are located and composited on a relative grid centered on each event is described herein. This technique is described and demonstrated by comparing the 27-km Naval Research Laboratory's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) mistral wind forecasts to the Special Sensor Microwave Imager (SSM/I) observations for a 1-yr period. Diagnostic information regarding the forecast reliability, error type, and error spatial characteristics are derived. Also, statistics from the conditional distributions of both the observed and predicted events are compared. The difference between the two conditional biases (CBD) is found to reveal valuable information regarding the contribution of false alarms and missed forecasts to the forecast errors. The results indicate the mistral is remarkably predictable with high pattern correlations out to 66 h.
Abstract
The composite method is applied to verify a series of idealized and real precipitation forecasts as part of the Spatial Forecast Verification Methods Intercomparison Project. The test cases range from simple geometric shapes to high-resolution (∼4 km) numerical model precipitation output. The performance of the composite method is described as it is applied to each set of forecasts. In general, the method performed well because it was able to relay information concerning spatial displacement and areal coverage errors. Summary scores derived from the composite means and the individual events displayed relevant information in a condensed form. The composite method also showed an ability to discern performance attributes from high-resolution precipitation forecasts from several competing model configurations, though the results were somewhat limited by the lack of data. Overall, the composite method proved to be most sensitive in revealing systematic displacement errors, while it was less sensitive to systematic model biases.
Abstract
The composite method is applied to verify a series of idealized and real precipitation forecasts as part of the Spatial Forecast Verification Methods Intercomparison Project. The test cases range from simple geometric shapes to high-resolution (∼4 km) numerical model precipitation output. The performance of the composite method is described as it is applied to each set of forecasts. In general, the method performed well because it was able to relay information concerning spatial displacement and areal coverage errors. Summary scores derived from the composite means and the individual events displayed relevant information in a condensed form. The composite method also showed an ability to discern performance attributes from high-resolution precipitation forecasts from several competing model configurations, though the results were somewhat limited by the lack of data. Overall, the composite method proved to be most sensitive in revealing systematic displacement errors, while it was less sensitive to systematic model biases.
Abstract
The fractions skill score (FSS) belongs to a class of spatial neighborhood techniques that measures forecast skill from samples of gridded forecasts and observations at increasing spatial scales. Each sample contains the fraction of the predicted and observed quantities that exist above a threshold value. Skill is gauged by the rate that the observed and predicted fractions converge with increasing scale. In this study, neighborhood sampling is applied to diagnose the performance of high-resolution (1.67 km) precipitation forecasts over central Florida. Reliability diagrams derived from the spatial fractions indicate that the FSS can be influenced by small, low-predictability events. Further tests indicate the FSS is subtly affected by samples from points on and near the grid boundaries. Inclusion of these points tends to reduce the magnitude and sensitivity of the FSS, especially at large scales. An attempt to mine data from the set of neighborhood fractions was moderately successful at obtaining descriptive information about the precipitation fields. The width of the distribution of the fractions at each scale provided information concerning forecast resolution and sharpness. The rate at which the distribution of the fractions converged toward the domain mean with increasing scale was found to be sensitive to the uniformity of coverage of precipitation through the domain. Generally, the 6-h forecasts possessed greater spatial skill than those at 12 h. High-FSS values at 12 h were mostly associated with evenly distributed precipitation patterns, while the 6-h forecasts also performed well for several nonuniform cases.
Abstract
The fractions skill score (FSS) belongs to a class of spatial neighborhood techniques that measures forecast skill from samples of gridded forecasts and observations at increasing spatial scales. Each sample contains the fraction of the predicted and observed quantities that exist above a threshold value. Skill is gauged by the rate that the observed and predicted fractions converge with increasing scale. In this study, neighborhood sampling is applied to diagnose the performance of high-resolution (1.67 km) precipitation forecasts over central Florida. Reliability diagrams derived from the spatial fractions indicate that the FSS can be influenced by small, low-predictability events. Further tests indicate the FSS is subtly affected by samples from points on and near the grid boundaries. Inclusion of these points tends to reduce the magnitude and sensitivity of the FSS, especially at large scales. An attempt to mine data from the set of neighborhood fractions was moderately successful at obtaining descriptive information about the precipitation fields. The width of the distribution of the fractions at each scale provided information concerning forecast resolution and sharpness. The rate at which the distribution of the fractions converged toward the domain mean with increasing scale was found to be sensitive to the uniformity of coverage of precipitation through the domain. Generally, the 6-h forecasts possessed greater spatial skill than those at 12 h. High-FSS values at 12 h were mostly associated with evenly distributed precipitation patterns, while the 6-h forecasts also performed well for several nonuniform cases.
Abstract
The 19 July 1993 mesoscale convective system (MCS), discussed in Part I, was simulated using the Regional Atmospheric Modeling System (RAMS). The model was initialized with variable physiographic and atmospheric data with the goal of reproducing the convective system and its four-dimensional environment. Four telescopically nested, moving grids allowed for horizontal grid spacings down to 1.67 km on the cloud resolving grid. Comparisons with the analysis show that the propagation, evolution, and structure of this MCS were well simulated.
The simulation is used to further investigate the interactions between this MCS and its surrounding environment. In Part I, the Doppler-derived winds indicated that upshear (westward) propagating gravity waves left upper-tropospheric front-to-rear and midtropospheric rear-to-front flow perturbations in their wake. A similar flow structure developed in the simulated MCS, and unlike the Doppler results, the low-frequency waves that produced it were resolved in the data. In the simulation, much of the convectively generated temperature and momentum perturbations propagated westward with the waves, leaving a warm wake in the clear air trailing the system. Although the gravity waves traveled rearward, the perturbation flow in their wake was not strong enough to reverse the upper-tropospheric storm-relative winds. Thus, most of the anvil condensate advected ahead of the convective line.
As the MCS encountered the low-level jet, the midtropospheric upward mass flux increased, but gravity wave motions became less detectable. The upper-tropospheric anvil pushed westward into the strong flow as the system expanded into a characteristically oval shape. Temperature and momentum perturbations propagated rearward along with the anvil in a disturbance that resembled an advective outflow. Unlike the gravity waves, this disturbance became almost stationary with respect to the ground, and it retained its continuity through the rest of the simulation. Vertical cross sections indicate that a large slab of convectively processed air had detrained into the upper troposphere. Prior to this event, much of the warm temperature anomalies generated within the convective towers either remained in the updrafts, or propagated outward with the gravity waves. Early on, individual updrafts were relatively erect and although condensate did detrain eastward in the forward anvil, the temperature anomalies did not propagate with it. In contrast, convective updrafts associated with the expanding oval anvil disturbance were more continuous, and they tilted strongly westward with height.
Abstract
The 19 July 1993 mesoscale convective system (MCS), discussed in Part I, was simulated using the Regional Atmospheric Modeling System (RAMS). The model was initialized with variable physiographic and atmospheric data with the goal of reproducing the convective system and its four-dimensional environment. Four telescopically nested, moving grids allowed for horizontal grid spacings down to 1.67 km on the cloud resolving grid. Comparisons with the analysis show that the propagation, evolution, and structure of this MCS were well simulated.
The simulation is used to further investigate the interactions between this MCS and its surrounding environment. In Part I, the Doppler-derived winds indicated that upshear (westward) propagating gravity waves left upper-tropospheric front-to-rear and midtropospheric rear-to-front flow perturbations in their wake. A similar flow structure developed in the simulated MCS, and unlike the Doppler results, the low-frequency waves that produced it were resolved in the data. In the simulation, much of the convectively generated temperature and momentum perturbations propagated westward with the waves, leaving a warm wake in the clear air trailing the system. Although the gravity waves traveled rearward, the perturbation flow in their wake was not strong enough to reverse the upper-tropospheric storm-relative winds. Thus, most of the anvil condensate advected ahead of the convective line.
As the MCS encountered the low-level jet, the midtropospheric upward mass flux increased, but gravity wave motions became less detectable. The upper-tropospheric anvil pushed westward into the strong flow as the system expanded into a characteristically oval shape. Temperature and momentum perturbations propagated rearward along with the anvil in a disturbance that resembled an advective outflow. Unlike the gravity waves, this disturbance became almost stationary with respect to the ground, and it retained its continuity through the rest of the simulation. Vertical cross sections indicate that a large slab of convectively processed air had detrained into the upper troposphere. Prior to this event, much of the warm temperature anomalies generated within the convective towers either remained in the updrafts, or propagated outward with the gravity waves. Early on, individual updrafts were relatively erect and although condensate did detrain eastward in the forward anvil, the temperature anomalies did not propagate with it. In contrast, convective updrafts associated with the expanding oval anvil disturbance were more continuous, and they tilted strongly westward with height.
Abstract
When consulting a forecast, users often ask some variant of the following questions: Will an event of interest occur? If so, when will it occur? How long will it last? How intense will it be? Standard verification measures often do not directly communicate the ability of a forecast to answer these questions. Instead, quantitative scores typically address them indirectly or in some combined form. A more direct performance measure grew from what started as a project for a high-school intern. The challenge was to evaluate aspects of forecast quality from a set of convection-allowing (1.67 km) precipitation forecasts over Florida. Although the output was highly detailed, evaluation became manageable by simply adding a series of static landmarks with range rings and radials. Using the “targets” as a guide, the student and the two authors successfully obtained quantitative estimates of model tendencies that had heretofore only been reported anecdotally. What follows is a description of the method as well as the results from the analysis. It is hoped that this work will stimulate a broader discussion about how to extract performance information from very complex forecasts and present that information in terms that humans can readily perceive.
Abstract
When consulting a forecast, users often ask some variant of the following questions: Will an event of interest occur? If so, when will it occur? How long will it last? How intense will it be? Standard verification measures often do not directly communicate the ability of a forecast to answer these questions. Instead, quantitative scores typically address them indirectly or in some combined form. A more direct performance measure grew from what started as a project for a high-school intern. The challenge was to evaluate aspects of forecast quality from a set of convection-allowing (1.67 km) precipitation forecasts over Florida. Although the output was highly detailed, evaluation became manageable by simply adding a series of static landmarks with range rings and radials. Using the “targets” as a guide, the student and the two authors successfully obtained quantitative estimates of model tendencies that had heretofore only been reported anecdotally. What follows is a description of the method as well as the results from the analysis. It is hoped that this work will stimulate a broader discussion about how to extract performance information from very complex forecasts and present that information in terms that humans can readily perceive.
Abstract
Numerical forecasts of heavy warm-season precipitation events are verified using simple composite collection techniques. Various sampling methods and statistical measures are employed to evaluate the general characteristics of the precipitation forecasts. High natural variability is investigated in terms of its effects on the relevance of the resultant statistics. Natural variability decreases the ability of a verification scheme to discriminate between systematic and random error. The effects of natural variability can be mitigated by compositing multiple events with similar properties. However, considerable sample variance is inevitable because of the extreme diversity of mesoscale precipitation structures.
The results indicate that forecasts of heavy precipitation were often correct in that heavy precipitation was observed relatively close to the predicted area. However, many heavy events were missed due in part to the poor prediction of convection. Targeted composites of the missed events indicate that a large percentage of the poor forecasts were dominated by convectively parameterized precipitation. Further results indicate that a systematic northward bias in the predicted precipitation maxima is related to the deficits in the prediction of subsynoptically forced convection.
Abstract
Numerical forecasts of heavy warm-season precipitation events are verified using simple composite collection techniques. Various sampling methods and statistical measures are employed to evaluate the general characteristics of the precipitation forecasts. High natural variability is investigated in terms of its effects on the relevance of the resultant statistics. Natural variability decreases the ability of a verification scheme to discriminate between systematic and random error. The effects of natural variability can be mitigated by compositing multiple events with similar properties. However, considerable sample variance is inevitable because of the extreme diversity of mesoscale precipitation structures.
The results indicate that forecasts of heavy precipitation were often correct in that heavy precipitation was observed relatively close to the predicted area. However, many heavy events were missed due in part to the poor prediction of convection. Targeted composites of the missed events indicate that a large percentage of the poor forecasts were dominated by convectively parameterized precipitation. Further results indicate that a systematic northward bias in the predicted precipitation maxima is related to the deficits in the prediction of subsynoptically forced convection.
Abstract
Operational cloud forecasts generated by the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) were verified over the eastern Pacific Ocean. The study focused on the accuracy of cloud forecasts associated with extratropical cyclone and convective activity during the late winter and spring of 2007. The condensed total water (liquid and solid) path was used as a proxy for cloud cover to compare the forecasts with retrievals from the Geostationary Operational Environmental Satellites (GOES). Analyses of the GOES retrievals indicate that deep cloud systems were generally well represented during daylight hours. Thus, the bulk of the verification focused on the general aspects of quality and timing of these deep systems. Multiple statistics were collected, ranging from simple correlations and histograms to more sophisticated fuzzy and composite statistics. The results show that synoptic-scale systems were generally well predicted to at least two days, with the primary error being an overestimation of deep cloud occurrence. Smaller subsynoptic-scale systems were subject to spatial and timing biases in that a number of the forecasts were lagged by 3–6 h. Despite the bias, 60%–70% of the forecasts of the mesoscale phenomena displayed useful skill.
Abstract
Operational cloud forecasts generated by the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) were verified over the eastern Pacific Ocean. The study focused on the accuracy of cloud forecasts associated with extratropical cyclone and convective activity during the late winter and spring of 2007. The condensed total water (liquid and solid) path was used as a proxy for cloud cover to compare the forecasts with retrievals from the Geostationary Operational Environmental Satellites (GOES). Analyses of the GOES retrievals indicate that deep cloud systems were generally well represented during daylight hours. Thus, the bulk of the verification focused on the general aspects of quality and timing of these deep systems. Multiple statistics were collected, ranging from simple correlations and histograms to more sophisticated fuzzy and composite statistics. The results show that synoptic-scale systems were generally well predicted to at least two days, with the primary error being an overestimation of deep cloud occurrence. Smaller subsynoptic-scale systems were subject to spatial and timing biases in that a number of the forecasts were lagged by 3–6 h. Despite the bias, 60%–70% of the forecasts of the mesoscale phenomena displayed useful skill.
Abstract
This paper is the first in a two part series in which the interactions between a growing mesoscale convective system (MCS) and its surrounding environment are investigated. The system studied here developed in northeastern Colorado on 19 July 1993 and propagated into Kansas as a long-lived nocturnal MCS. High-resolution dual-Doppler and surface mesonet data collected from this system are discussed in Part I, while the results of a numerical simulation are discussed in Part II.
The observations show that organized mesoscale surface pressure and flow features appeared very early in the lifetime of this system, long before the development of any trailing stratiform precipitation. Most of the stratiform anvil advected ahead of the convective line in the strong upper-tropospheric westerlies. In accordance with this, most of the mid- and upper-tropospheric storm-relative flow behind the line remained westerly, or rear-to-front.
Despite the westerlies, the strongest flow perturbations with respect to the ambient winds developed to the rear of the line. The structure of these perturbations was similar to the upper-tropospheric front-to-rear and midtropospheric rear-to-front flows typically found in more mature leading-line/trailing-stratiform systems. The presence of these perturbations on the upwind side of the convective line indicates that gravity wave propagation was primarily responsible for their development.
Abstract
This paper is the first in a two part series in which the interactions between a growing mesoscale convective system (MCS) and its surrounding environment are investigated. The system studied here developed in northeastern Colorado on 19 July 1993 and propagated into Kansas as a long-lived nocturnal MCS. High-resolution dual-Doppler and surface mesonet data collected from this system are discussed in Part I, while the results of a numerical simulation are discussed in Part II.
The observations show that organized mesoscale surface pressure and flow features appeared very early in the lifetime of this system, long before the development of any trailing stratiform precipitation. Most of the stratiform anvil advected ahead of the convective line in the strong upper-tropospheric westerlies. In accordance with this, most of the mid- and upper-tropospheric storm-relative flow behind the line remained westerly, or rear-to-front.
Despite the westerlies, the strongest flow perturbations with respect to the ambient winds developed to the rear of the line. The structure of these perturbations was similar to the upper-tropospheric front-to-rear and midtropospheric rear-to-front flows typically found in more mature leading-line/trailing-stratiform systems. The presence of these perturbations on the upwind side of the convective line indicates that gravity wave propagation was primarily responsible for their development.
Abstract
Dual-Doppler radar, surface mesonet, satellite, and upper-air sounding data from the 1985 Preliminary Regional Experiment for STORM-Central field experiment are used to analyze the early growth stages of a mesoscale convective complex (MCC) that developed in the network on 3 June 1985. This MCC was characterized by a complex distribution of convective clusters and intervening stratiform echo as it grew from its initial stage to the typical meso-α-scale cloud shield structure at its mature stage. The MCC exhibited two very different states of organization as it grew. The early state was characterized by a relatively weak and disorganized surface pressure pattern and a highly variable three-dimensional mesoscale flow structure. The later state was characterized by a well-developed mesohigh-wake-low surface pressure pattern and more organized mososcale flow fields. The evolution between these two regimes occurred about 1 h after the upper-level cloud shield reached MCC proportions and manifested itself as a rapid, almost discrete transition that took place over a period of about 30 win.
The flow structure in this system was highly complex compared to the two-dimensional squall-line conceptual model. Five separate flow branches coexisted and interacted with one another throughout the observed development of the MCC, and the structure of some of them changed considerably as the system evolved. Notably, the rear inflow evolved from a highly variable westerly flow that ascended in its northern half and descended in the south, to a more uniformly descending rear-inflow jet. This transition was dynamically linked to the development of an upper-tropospheric mesohigh, which we hypothesize blocked the upper-trapospheric flow and partially forced the descent of the rear inflow.
Abstract
Dual-Doppler radar, surface mesonet, satellite, and upper-air sounding data from the 1985 Preliminary Regional Experiment for STORM-Central field experiment are used to analyze the early growth stages of a mesoscale convective complex (MCC) that developed in the network on 3 June 1985. This MCC was characterized by a complex distribution of convective clusters and intervening stratiform echo as it grew from its initial stage to the typical meso-α-scale cloud shield structure at its mature stage. The MCC exhibited two very different states of organization as it grew. The early state was characterized by a relatively weak and disorganized surface pressure pattern and a highly variable three-dimensional mesoscale flow structure. The later state was characterized by a well-developed mesohigh-wake-low surface pressure pattern and more organized mososcale flow fields. The evolution between these two regimes occurred about 1 h after the upper-level cloud shield reached MCC proportions and manifested itself as a rapid, almost discrete transition that took place over a period of about 30 win.
The flow structure in this system was highly complex compared to the two-dimensional squall-line conceptual model. Five separate flow branches coexisted and interacted with one another throughout the observed development of the MCC, and the structure of some of them changed considerably as the system evolved. Notably, the rear inflow evolved from a highly variable westerly flow that ascended in its northern half and descended in the south, to a more uniformly descending rear-inflow jet. This transition was dynamically linked to the development of an upper-tropospheric mesohigh, which we hypothesize blocked the upper-trapospheric flow and partially forced the descent of the rear inflow.
Abstract
Lagrangian parcel models are often used to predict the fate of airborne hazardous material releases. The atmospheric input for these integrations is typically supplied by surrounding surface and upper-air observations. However, situations may arise in which observations are unavailable and numerical model forecasts may be the only source of atmospheric data. In this study, the quality of the atmospheric forecasts for use in dispersion applications is investigated as a function of the horizontal grid spacing of the atmospheric model. The Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) was used to generate atmospheric forecasts for 14 separate Dipole Pride 26 trials. The simulations consisted of four telescoping one-way nested grids with horizontal spacings of 27, 9, 3, and 1 km, respectively. The 27- and 1-km forecasts were then used as input for dispersion forecasts using the Hazard Prediction Assessment Capability (HPAC) modeling system. The resulting atmospheric and dispersion forecasts were then compared with meteorological and gas-dosage observations collected during Dipole Pride 26. Although the 1-km COAMPS forecasts displayed considerably more detail than those on the 27-km grid, the RMS and bias statistics associated with the atmospheric observations were similar. However, statistics from the HPAC forecasts showed the 1-km atmospheric forcing produced more accurate trajectories than the 27-km output when compared with the dosage measurements.
Abstract
Lagrangian parcel models are often used to predict the fate of airborne hazardous material releases. The atmospheric input for these integrations is typically supplied by surrounding surface and upper-air observations. However, situations may arise in which observations are unavailable and numerical model forecasts may be the only source of atmospheric data. In this study, the quality of the atmospheric forecasts for use in dispersion applications is investigated as a function of the horizontal grid spacing of the atmospheric model. The Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) was used to generate atmospheric forecasts for 14 separate Dipole Pride 26 trials. The simulations consisted of four telescoping one-way nested grids with horizontal spacings of 27, 9, 3, and 1 km, respectively. The 27- and 1-km forecasts were then used as input for dispersion forecasts using the Hazard Prediction Assessment Capability (HPAC) modeling system. The resulting atmospheric and dispersion forecasts were then compared with meteorological and gas-dosage observations collected during Dipole Pride 26. Although the 1-km COAMPS forecasts displayed considerably more detail than those on the 27-km grid, the RMS and bias statistics associated with the atmospheric observations were similar. However, statistics from the HPAC forecasts showed the 1-km atmospheric forcing produced more accurate trajectories than the 27-km output when compared with the dosage measurements.
