|Item type||Location||Call number||Copy||Status||Date due|
|REPORT||Mesa Lab||03706 (Browse shelf)||1||Available|
The spatial forecast methods intercomparison project (ICP, http://www.ral.ucar.edu/projects/icp) was formed in 2007 with the aim of better understanding the rapidly increasing literature introducing new spatial verification methods. Some questions addressed were: How does each method inform about forecast performance overall? Does the method inform about location errors? If so, how? Which methods yield identical information to each other? Which methods provide complementary information? The initial phase of the project focused on prescribed errors and quantitative precipitation forecasts over the central United States.
The second phase, called the Mesoscale Verification Inter-Comparison over Complex Terrain (MesoVICT) has been established to further explore the new methods for more realistic meteorological scenarios. Test cases in- clude more variables in addition to precipitation, such as winds. In addition, the cases represent interesting meteorological events that develop over time rather than single snapshots. The cases also include ensembles of forecasts as well as observations, and, as the name suggests, they are provided on a region associated with complex terrain over Europe.
The aim of this note is to describe the test cases and to describe how to participate in the project. Like its predecessor, the ICP, MesoVICT is largely unfunded, and relies on voluntary participation in order for it to be successful. To that end, a bare minimum test case has been established in the hope that each method will be applied to at least this one test case in order to enhance comparisons of the methods. Tiers of increasingly involved test cases are set up in order to allow those who are able to conduct more interest- ing experiments. We anticipate several methods will undergo tests on these higher tiers. We also hope that new creative insights into further method development may be facilitated by these higher tier cases; in particular, that address realistic needs for weather forecast verification.