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Advancements in weather forecast models and their enhanced resolution have led to substantially improved and more realistic-appearing forecasts for some variables. However, traditional verification scores often indicate poor performance because of the increased small-scale variability so that the true quality of the forecasts is not always characterized well. As a result, numerous new methods for verifying these forecasts have been proposed. These new methods can mostly be classified into two overall categories: filtering methods and displacement methods. The filtering methods can be further delineated into neighborhood and scale separation, and the displacement methods can be divided into features based and field deformation. Each method gives considerably more information than the traditional scores, but it is not clear which method(s) should be used for which purpose.A verification methods intercomparison project has been established in order to glean a better understanding of the proposed methods in terms of their various characteristics and to determine what verification questions each method addresses. The study is ongoing, and preliminary qualitative results for the different approaches applied to different situations are described here. In particular, the various methods and their basic characteristics, similarities, and differences are described. In addition, several questions are addressed regarding the application of the methods and the information that they provide. These questions include (i) how the method(s) inform performance at different scales; (ii) how the methods provide information on location errors; (iii) whether the methods provide information on intensity errors and distributions; (iv) whether the methods provide information on structure errors; (v) whether the approaches have the ability to provide information about hits, misses, and false alarms; (vi) whether the methods do anything that is counterintuitive; (vii) whether the methods have selectable parameters and how sensitive the results are to parameter selection; (viii) whether the results can be easily aggregated across multiple cases; (ix) whether the methods can identify timing errors; and (x) whether confidence intervals and hypothesis tests can be readily computed.

A large-domain explicit convection simulation is used to investigate the life cycle of nocturnal convection for a one-week period of successive zonally propagating heavy precipitation episodes occurring over the central United States. Similar to climatological studies of phase-coherent warm-season convection, the longest-lived precipitation episodes initiate during the late afternoon over the western Great Plains (105°-100°W), reach their greatest intensity at night over the central Great Plains (100°-95°W), and typically weaken around or slightly after sunrise over the Midwest (95°-85°W). The longest-lived episodes exhibit average zonal phase speeds of ϳ20 m s Ϫ1 , consistent with radar observations during the period.Composite analysis of the life cycle of five long-lived nocturnal precipitation episodes indicates that convection both develops and then propagates eastward along an east-west-oriented lower-tropospheric frontal zone. An elevated ϳ2-km-deep layer of high-e air helps sustain convection during its period of greatest organization overnight. Trajectory analysis for individual episodes reveals that the high-e air originates both from within the frontal zone and to its south where, in this latter case, it is transported northward by the nocturnal low-level jet (LLJ).The mature (nocturnal) stage composite evinces a thermally direct cross-frontal circulation, within which the trajectories ascend 0.5-2 km to produce the elevated conditionally unstable layer. This transverse vertical circulation is forced by deformation frontogenesis, which itself is supported by the intensification of the nocturnal LLJ. The frontal zone also provides an environment of strong vertical shear, which helps organize the zonally propagating component of convection. Overnight the convection exhibits squall-line characteristics, where its phase speed is typically consistent with that which arises from deep convectively induced buoyancy perturbations combined with the opposing environmental surface flow. In a large majority of cases convection weakens as it reaches the Midwest around sunrise, where environmental thermodynamic stability is greater, and environmental vertical shear, frontogenesis, and vertical motions are weaker than those located farther west overnight.

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A multiradar network, operated in the southern Gulf of California (GoC) region during the 2004 North American Monsoon Experiment, is used to analyze the spatial and temporal variabilities of local precipitation. Based on the initial findings of this analysis, it is found that terrain played a key role in this variability, as the diurnal cycle was dominated by convective triggering during the afternoon over the peaks and foothills of the Sierra Madre Occidental (SMO). Precipitating systems grew upscale and moved WNW toward the gulf. Distinct precipitation regimes within the monsoon are identified. The first, regime A, corresponded to enhanced precipitation over the southern portions of the coast and GoC, typically during the overnight and early morning hours. This was due to precipitating systems surviving the westward trip (ϳ7 m s Ϫ1; 3-4 m s Ϫ1 in excess of steering winds) from the SMO after sunset, likely because of enhanced environmental wind shear as diagnosed from local soundings. The second, regime B, corresponded to the significant northward/along-coast movement of systems (ϳ10 m s Ϫ1; 4-5 m s Ϫ1 in excess of steering winds) and often overlapped with regime A. The weak propagation is explainable by shallow-weak cold pools. Reanalysis data suggest that tropical easterly waves were associated with the occurrence of disturbed regimes. Gulf surges occurred during a small subset of these regimes, so they played a minor role during 2004. Mesoscale convective systems and other organized systems were responsible for most of the rainfall in this region, particularly during the disturbed regimes.

Three well-observed Atlantic tropical weather systems that occurred during the 2010 hurricane season are analyzed. One case was former Tropical Storm Gaston that failed to redevelop into a tropical cyclone; the other two cases were developing storms Karl and Matthew. Geostationary satellite, multisensor-derived precipitation, and dropsondes from the National Science Foundation (NSF)-NCAR Gulfstream V (GV), NASA DC-8, and the NOAA Gulfstream IV (G-IV) and WP-3D Orion (P-3) aircraft are analyzed in a system-following frame to quantify the mesoscale dynamics of these systems.Gaston featured extensive dry air surrounding an initially moist core. Vertical shear forced a misalignment of midtropospheric and lower-tropospheric circulation centers. This misalignment allowed dry air to intrude above the lower-tropospheric center and severely limited the area influenced by deep moist convection, thus providing little chance of maintaining or rebuilding the vortex in sheared flow. By contrast, Karl and Matthew developed in a moister environment overall, with moisture increasing with time in the middle and upper troposphere. Deep moist convection was quasi-diurnal prior to genesis. For Karl, deep convection was initially organized away from the lower-tropospheric circulation center, creating a misalignment of the vortex. The vortex gradually realigned over several days and genesis followed this realignment within roughly one day. Matthew experienced weaker shear, was vertically aligned through most of its early evolution, and developed more rapidly than Karl. The evolutions of the three cases are interpreted in the context of recent theories of tropical cyclone formation.

Several spatial forecast verification methods have been developed that are suited for high-resolution precipitation forecasts. They can account for the spatial coherence of precipitation and give credit to a forecast that does not necessarily match the observation at any particular grid point. The methods were grouped into four broad categories (neighborhood, scale separation, features based, and field deformation) for the Spatial Forecast Verification Methods Intercomparison Project (ICP). Participants were asked to apply their new methods to a set of artificial geometric and perturbed forecasts with prescribed errors, and a set of real forecasts of convective precipitation on a 4-km grid. This paper describes the intercomparison test cases, summarizes results from the geometric cases, and presents subjective scores and traditional scores from the real cases.All the new methods could detect bias error, and the features-based and field deformation methods were also able to diagnose displacement errors of precipitation features. The best approach for capturing errors in aspect ratio was field deformation. When comparing model forecasts with real cases, the traditional verification scores did not agree with the subjective assessment of the forecasts.

An international project tests new spatial forecast verification methods to find out how they handle different types of forecast error and what they tell us about forecast performance.V erification of a forecast field presents many challenges, especially at higher resolutions. When assessing forecast performance at a single point, straightforward summary statistics [e.g., root-mean-square error (RMSE)] are meaningful because they give an intuitive notion of how well the forecasts matched the observations at that point. It is also straightforward to identify hits, misses, false alarms, and correct negatives, all of which give rise to numerous useful summary statistics [e.g., the probability of detection (POD) and the Gilbert skill score (GSS)] and diagnostics (e.g., the relative operating characteristic) of forecast performance (for more on traditional verification, see, e.g., Wilks 1995;Jolliffe and Stephenson 2003). However, when interest is in spatially coherent structures, these notions are not as simple to determine. Further, several new types of errors become relevant, which bring about a new set of verification questions. Are there spatial displacement errors? Does the scale-dependent variance of the forecast field match the spatial structure that was observed? Did the forecast under-or overpredict the spatial extent of a storm system? Are there orientation errors for specific structures in the field? At what scales do the forecasts have skill?Numerous methods have been proposed in recent years to address these issues, and some are already used regularly at meteorological centers around the world. Because the methods are relatively new, however, it is not always clear which approaches are best suited for answering particular questions about forecast performance. Which ones should be used for specific purposes, and which provide analogous information? How can uncertainty information be determined about the results? Do methods have any unintuitive characteristics about which a user should know? Such questions provided the impetus for the Spatial Forecast Verification Methods InterComparison project (ICP; details at www.ral.ucar. edu/projects/icp). Although not every approach is currently under study in the ICP, a reasonably representative subset is included, in most cases by the researchers who originally proposed them.test CAses. Several test cases have been provided in order to make head-to-head comparisons of how each method addresses forecast performance. So far, three sets of cases focused on quantitative precipitation forecasts (QPFs) are being used, with plans to incorporate more varied cases in the future. The cases VERIfyING fORECAStS SpAtIALLyBy eric Gilleland, david a. ahijevych, BarBara G. Brown, and elizaBeth e. eBert 1365 october 2010 AMerIcAN MeteoroLoGIcAL SocIetY | under study now include 24-h forecasts of 1-h precipitation from three configurations of the Weather Research and Forecasting (WRF) model, known perturbations of one of these cases, and simple geometric cases with prescribed spati...