Extended Abstract

June 8, 2010 at 15 UTC. Corresponding MODE objects (right) for observed field (blue line) and forecast field (red andblue solid objects).For the HMT West experiment, conducted during the winter of 2009-2010 in the westernUnited States, the ensemble mean forecasts were analyzed by MODE for the whole season. A singlecase is shown in Figure 3. Although it is best to analyze several cases together, looking at MODEresults for a single case can give the user a better feel for how object verification works. In this case, theforecast area is too small (497 grid squares) compared to the observed area (706 grid squares), but theoverlap is very good (intersecting area = 429 grid squares). The median precipitation value within theforecast object is about 7.5 mm, while the median observed precipitation within the object is about8mm. Thus, the typical amount of precipitation was well forecast. One benefit of using objects is thatthese amounts do not have to be forecast in the exact same locations to be compared. By comparison totraditional statistics, the probability of detection is 0.31 and the false alarm rate is 0.63, suggesting a notvery good forecast. An alternate interpretation, confirmed by the object based method, is that this is agood forecast, with a low level overforecasting bias (common for ensemble mean forecasts).Figure 3: Panels show 6-hour observed precipitation, ensemble mean forecast precipitation, and MODE objects(solid=forecast, outline=observation) for a single case.An example of object-based atmospheric river verification, completed as part of the HMT Westexperiment, is shown in Fig. 4 below. This case is from December 18, 2005. The forecast is forprecipitable water, while the observation is integrated water vapor (Ralph et al, 2006). The area of theforecast object is nearly twice that of the observed object (244 vs. 126 grid squares). However, theobserved object is almost nearly encompassed by the forecast object (intersection area: 114 gridsquares).6. ConclusionsMET provides a variety of methods for verifying precipitation forecasts, including diagnostic andspatial methods for ensemble and probability forecasts. The spatial verification in MODE seems toextend well to assessment of ensemble mean forecasts, probability forecasts, and other spatial fieldsrelated to precipitation, such as radar reflectivity, radar echo top, and atmospheric rivers. Object basedverification compensates for many deficiencies in traditional verification measures, providingassessments of forecast size and intensity accuracy, even when displacement errors are present.MET software simplifies and standardizes verification across projects while providing easyaccess to the most current verification capabilities. Short term updates to the software will includecapability to read CloudSat data, use of MODE object verification on vertical rather than just horizontalsurfaces, and capability to track MODE objects through time to better examine forecast timing errors.-192-