Overall, the DC3 field phase was considered successful in achieving many of its scientific objectives. Due to the successful nature of the field campaign, the forecast process used may become a model for future campaigns and is thus worthy of further study. Examining how well this process worked for DC3 will be useful in determining more efficient forecast processes for future field campaigns.
The first author participated directly in operations in Alabama during the first two weeks of the campaign, as well as participated in a forecasting internship conducted by the second author that lasted the duration of the campaign. Due to the first author's direct exposure to Alabama operations, the work presented here focuses solely on forecasts for the Alabama region.
Forecast parameters examined in the study include the location of forecast convection, the timing of the convection and the mode (isolated, supercell or mesoscale convective systems (MCS)) of convection forecast. The location of convection was considered accurate if it fell within the northern Alabama domain (as defined in the DC3 Science Plan). If a cell is located on the edge of the aforementioned domain, for that cell to be considered accurately forecast, more than half of its areal coverage must be within the domain. With respect to convective mode, scattered showers and multicells would be included in the isolated convection category'. Convection was considered supercelluar if rotation could be seen within the cell and all other convective systems fell into the MCS category. The timing of convection was considered accurate if convection occurred within a window that extended from 30 minutes prior to the earliest convection to one hour late of forecasted convection. The discrepancies in the timing window are to account for the DC3 science goal of sampling preconvective environments.
With parameters set that determine what constitutes as an accurate forecast, contingency-table-based statistics can be computed and analyzed for the entirety of the DC3 forecast period. The statistical analysis we present provides the basis for describing strengths and weaknesses of the forecast process used in this field campaign as well as provides recommendations as to how the process might be improved for future field campaigns.