The Potential Utility of High Resolution Ensemble Sensitivities During Weak Flow in Complex Terrain

Recent expansion in availability of re-locatable near-surface atmospheric observing sensors introduces the question of where placement maximizes gain in forecast accuracy. Here the potential for ensemble sensitivity analysis (ESA) is examined for high-resolution (Δx=4 km) predictions in complex terrain. The primary objective is to determine whether a mesoscale ESA applied at these scales is useful for identifying potential observing locations in weak flow. ESA can be inaccurate when the underlying assumptions of linear dynamics (and Gaussian statistics) are violated, or when the sensitivity cannot be robustly sampled. A case study of a fog event at the Salt Lake City airport (KSLC) provides a useful period for examining these issues, with the additional influence of complex terrain. A realistic upper-air observing network is used in perfect-model ensemble data assimilation experiments, providing the statistics for ESA. Results show that water vapor mixing ratios over KSLC are sensitive to temperature on the first model layer tens of km away, 6 h prior to verification and prior to the onset of fog. Sensitivity 12 h prior is weaker but leads to qualitatively similar results. Temperatures are shown to be a predictor of inversion strength in the Salt Lake basin; the ESA predicts southerly flow and strengthened inversions with warmer temperatures in a few locations. Simple linearity tests show that small perturbations do not lead to the expected forecast change, but larger perturbations do, suggesting that noise can dominate a small perturbation. Assimilating a perfect observation at the maximum sensitivity location produces forecasts more closely agreeing with the ESA. Sampling error evaluation show that similar conclusions can be reached with ensembles as small as 48 members, but smaller ensembles do not produce accurate sensitivity estimates.