A Data Assimilation System for Lake Erie Based on the Local Ensemble Transform Kalman Filter

Abstract

Data assimilation (DA) is the process by which a model forecast is adjusted to account for recent observations, taking into account both forecast and observation uncertainties. Although DA is common in numerical weather prediction (NWP) and other applications at global and regional scales, DA for large lakes such as North America's Great Lakes is still at an early stage of research and is not yet used operationally. In particular, the use of an ensemble-based approach to DA has scarcely been explored for large lakes, despite its growing popularity in operational NWP centers worldwide due to its dynamic estimation of the forecast covariance. Using Lake Erie as a test case, this study investigates the potential of ensemble DA to i) propagate forecast improvements throughout the lake and across forecast variables, and ii) inform the design of in-situ observing systems. The local ensemble transform Kalman filter (LETKF) is an efficient, localized, flexible variant of the ensemble Kalman filter (EnKF) that is used in multiple operational NWP centers. This work presents the development of a DA system for Lake Erie, which uses the LETKF to adjust forecasts of temperatures, currents, and water levels throughout the lake, using only lake surface temperature (LST) and temperature profile (TP) observations. The impact of both types of observations on all three forecast variables is evaluated within the framework of observing system simulation experiments (OSSEs), in which a DA system attempts to reconstruct a nature run (NR) by assimilating simulated observations of the NR. Observing system design questions are explored by comparing three different TP configurations. Assimilation of LST observations alone produces strong improvements to temperatures throughout the epilimnion (upper layer), while assimilation of TP observations extends these improvements to the hypolimnion (lower layer) near each profile. TP assimilation also shows improved representation of strong gyre currents and associated changes to thermocline depth and surface height, particularly when profiles sample from locations and depths where the thermal stratification in the forecast has been strongly affected by erroneous gyre currents. This work shows that the LETKF can be an efficient and effective tool for improving both forecasts and observing systems for large lakes, two essential ingredients in predicting the onset and development of economically and ecologically important phenomena such as harmful algal blooms (HABs) and hypoxia.