(Invited Talk) Problems in operational prediction of return flow

Inaccuracy in the numerical prediction of the moisture content of return-flow air over the Gulf of Mexico continues to plague operational forecasters. At the Environmental Modeling Center/National Centers for Environmental Prediction (EMC/NCEP) in the United States, the prediction errors have exhibited bias — typically too dry in the early-1990s and too moist from the mid-1990s to present. This research explores the possible sources of bias by using a Lagrangian formulation of the classic mixed-layer model. Justification for use of this low-order model rests on careful examination of the upper-air thermodynamic structure in a well-observed event during the Gulf of Mexico Experiment (GUFMEX). The mixed-layer constraints are shown to be appropriate for the first phase of return flow, viz., the outflow phase.

The research begins by evaluating the model's estimates of sensitivity — the change in the model output (at the termination point of outflow) in response to inaccuracies in the elements of the control vector (the initial conditions, the boundary conditions, and the physical and empirical parameters). This evaluation is accomplished by recourse to a known analytic solution to the mixed-layer equations (a special case). Results indicate that the fourth-order Runge-Kutta integration scheme produces extremely accurate evolutions of the fields, and equally important, delivers accurate measures of the sensitivity. Further, the dynamical system is shown to be ‘weakly nonlinear', i. e., solutions that result from perturbations to the control vector are well approximated by the first-order terms in the Taylor series expansion about the base state.

The second stage of research examines the relative importance of elements of the control vector on the moisture forecast for the general case. Results indicate that inaccuracies in the initial conditions are significant, yet they are secondary to inaccuracies in the boundary conditions and physical/empirical parameters. The sea surface temperatures and associated parameters, the saturation mixing ratio at the sea surface and the turbulent transfer coefficient, exert the most influence on the moisture forecast. Inaccuracy in the translation speed of the atmospheric column is also shown to be a major source of systematic error in the forecast. By assuming errors in the elements of the control vector that reflect observational error and uncertainties in the parameters, the bias error in the moisture forecast is estimated. These bias errors are compared with random error through Monte Carlo experiments. Bias errors of in the moisture forecast are possible through a variety of systematic errors in the control vector. The sensitivity analysis also makes it clear that judiciously chosen incorrect specifications of the elements can offset each other and lead to a good moisture forecast. The paper ends with discussion of research approaches that hold promise for improved operational forecasts of moisture in return-flow events.