Placement of Observations to Correct Return-Flow Forecasts

Abstract

The continued presence of systematic errors in operational forecasts of return flow over the Gulf of Mexico has motivated an investigation into this problem. The theme of the work is use of a low-order mixed-layer model that is faithful to the phenomenon in the context of dynamic data assimilation. Data assimilation experiments in the identical-twin mode determine the best place to make observations that minimize the forecast error through adjustment of model controls. The emphasized controls are those associated with the fluxes of heat and moisture from sea to atmosphere. Results indicate that the best observations are at that time and place when the outflowing continental air passes over the warmest sea surface temperatures. In the case studied, this warmest zone is directly over the Loop Current. Observations at times long after the modified air leaves these warmest waters lead to relatively poor control adjustments and little improvement in the forecast. If input to data assimilation is restricted to observations of a single model variable over short intervals of time (the order of several hours), results are relatively poor. Yet, a significant improvement is forthcoming if one of the observations is replaced by an observation from another model variable. This result is understood through arguments based on forecast sensitivity to model control. The paper ends with discussion of steps to be taken that hold promise for correcting systematic error in return-flow forecasts.