CW3E Atmospheric River Landfall MET/MODE Verification Tool
MODE, the Method for Object-Based Diagnostic Evaluation, provides an object-based verification for comparing gridded forecasts to gridded observations. It has most commonly been applied to precipitation fields and radar reflectivity, but here it is applied to integrated water vapor transport (IVT). The objects identified by this tool serve as a simplified Atmospheric River (AR) detection tool. While the majority of the identified objects will be ARs, and the majority of ARs will be identified objects, there may be some exceptions.
The maps show AR objects that have a length greater than approximately 1500 km and a centroid latitude north of 20oN at a given threshold (250, 500, or 750 kg m-1 s-1). If there is more than one such AR object, the statistics will be performed on the larger one. For more information on MODE click here.
MODE, the Method for Object-Based Diagnostic Evaluation, provides an object-based verification for comparing gridded forecasts to gridded observations. MODE may be used in a generalized way to compare data from which objects may be well defined. It has most commonly been applied to precipitation fields and radar reflectivity. The steps performed in MODE consist of:
- Define objects in the forecast and observation fields based on user-defined parameters.
- Compute attributes for each of those objects: such as area, centroid, axis angle, and intensity.
- For each forecast/observation object pair, compute differences between their attributes: such as area ratio, centroid distance, angle difference, and intensity ratio.
- Use fuzzy logic to compute a total interest value for each forecast/observation object pair based on user-defined weights.
- Based on the computed interest values, match objects across fields and merge objects within the same field.
- Write output statistics summarizing the characteristics of the single objects, the pairs of objects, and the matched/merged objects.
- MODE may be configured to use a few different sets of logic with which to perform matching and merging.
It is not uncommon for the domain of the model to cut short what would have been an AR object, making it less likely that objects near the domain boundary will be identified. A curved AR object is also less likely to be identified, since the computation of the aspect ratio includes the area in the middle of the curve.