Object-oriented verification methodology is becoming more and more common in the evaluation of model performance on high-resolution grids. The research herein describes an advanced version of an object-oriented approach that involves a combination of object identification on multiple scales with Procrustes shape analysis techniques. The multiscale object identification technique relies heavily on a novel Fourier transform approach to associate the signals within convection to different spatial scales. Other features of this new verification scheme include using a weighted cost function that can be user defined for object matching using different criteria, delineating objects that are more linear in character from those that are more cellular, and tagging object matches as hits, misses, or false alarms. Although the scheme contains a multiscale approach for identifying convective objects, standard minimum intensity and minimum size thresholds can be set when desirable. The method was tested as part of a spatial verification intercomparison experiment utilizing a combination of synthetic data and real cases from the Storm Prediction Center (SPC)/NSSL Weather Research and Forecasting (WRF) model Spring Program 2005. The resulting metrics, including error measures from differences in matched objects due to displacement, dilation, rotation, and intensity, from these cases run through this new, robust verification scheme are shown.
The Thunderstorm Observation by Radar (ThOR) algorithm is an objective and tunable Lagrangian approach to cataloging thunderstorms. ThOR uses observations from multiple sensors (principally multisite surveillance radar data and cloud-to-ground lightning) along with established techniques for fusing multisite radar data and identifying spatially coherent regions of radar reflectivity (clusters) that are subsequently tracked using a new tracking scheme. The main innovation of the tracking algorithm is that, by operating offline, the full data record is available, not just previous cluster positions, so all possible combinations of object sequences can be developed using all observed object positions. In contrast to Eulerian methods reliant on thunder reports, ThOR is capable of cataloging nearly every thunderstorm that occurs over regional-scale and continental United States (CONUS)-scale domains, thereby enabling analysis of internal properties and trends of thunderstorms. ThOR is verified against 166 manually analyzed cluster tracks and is also verified using descriptive statistics applied to a large (~35 000 tracks) sample. Verification also relied on a benchmark tracking algorithm that provides context for the verification statistics. ThOR tracks are shown to match the manual tracks slightly better than the benchmark tracks. Moreover, the descriptive statistics of the ThOR tracks are nearly identical to those of the manual tracks, suggesting good agreement. When the descriptive statistics were applied to the ~35 000-track dataset, ThOR tracking produces longer (statistically significant), straighter, and more coherent tracks than those of the benchmark algorithm. Qualitative assessment of ThOR performance is enabled through application to a multiday thunderstorm event and comparison to the behavior of the Storm Cell Identification and Tracking (SCIT) algorithm.
Ensemble sensitivity analysis (ESA) has been demonstrated for observation targeting of synoptic-scale and mesoscale phenomena, but could have similar applications for storm-scale observations with mobile platforms. This paper demonstrates storm-scale ESA using an idealized supercell simulated with a 101-member CM1 ensemble. Correlation coefficients are used as a measure of sensitivity and are derived from single-variable and multivariable linear regressions of pressure, temperature, humidity, and wind with forecast response variables intended as proxies for the strength of supercells. This approach is suitable for targeting observing platforms that simultaneously measure multiple base-state variables. Although the individual correlations are found to be noisy and difficult to interpret, averaging across small areas of the domain and over the duration of the simulation is found to simplify the analysis. However, it is difficult to identify physically meaningful results from the sensitivity calculations, and evaluation of the results suggests that the overall skill would be low in targeting observations at the storm scale solely based on these sensitivity calculations. The difficulty in applying ESA at the scale of an individual supercell is likely due to applying the linear model to an environment with highly nonlinear dynamics, rapidly changing forecast metrics, and autocorrelation.
ABSTRACT:The advanced algorithm for the tracking of objects (AALTO) constructs tracks from objects, such as thunderstorms or mesocyclones, detected by multiple weather radars at irregular time intervals. It is important to have high accuracy in tracking thunderstorms to generate skilful forecasts and high-quality climatologies and, fundamentally, to ensure that any derived product from tracks captures only that particular storm, and in its entirety. AALTO incorporates many of the best practices of existing tracking algorithms and techniques employed by meteorologists in constructing tracks. AALTO differs from existing algorithms designed to track meteorological phenomena that manifest in radar data in the following ways: (1) AALTO is designed to track objects from multiple radars, enabling analysis over a larger domain than if a single radar was used; (2) improved tracking is realized through improved initial motion estimates and directional thresholding and (3) AALTO looks both at the track history and at the subsequent possible positions along a track when constructing the best possible tracks, mimicking the approach that would be taken by a human meteorologist. Verification was done using metrics that were objectively determined to distinguish between good and degraded tracks; a description of the approach to determine the appropriate metrics is presented. An overview of the AALTO tracking procedure and an example case are presented in this study.
Radar-derived estimates of rainfall were compared against ten rain gages located in the Goodwater Creek for eight high-intensity one-hour rainfall events. Radar-estimated rainfall underestimated the rainfall totals observed by the gages. A bias correction factor was calculated by comparing the rainfall total at a single gage to the radar-estimated rainfall and was applied to the radar-estimated rainfall over the entire catchment. Although the bias was eliminated, there were still large differences between radar-estimated rainfall and gage observations at individual gages.An algorithm was devised to apply multiple Z-R relationships within a single domain, instead of applying only a single Z-R relationship to the entire domain. The algorithm did overestimate rainfall in many of the events. This demonstrates that a significantly different result can be obtained by estimating rainfall using more than one Z-R relationship. Rainfall tended to be overestimated during periods of light rain and underestimated during periods heavy rain. This suggests the need for using multiple Z-R relationships during a single rainfall event and the high variability of the suitable Z-R relationship even during a single event.
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