A variety of observation targeting methods exist (e.g., adjoint sensitivity, singular vectors, ensemble transform) to estimate the observation types and locations that, when assimilated, would improve a specific forecast. These methods have primarily been applied on synoptic scales for lead times of 24 hours or greater using airborne platforms and dropwindsondes. In this study, the authors test the ensemble-based sensitivity analysis (ESA) targeting methodology for forecasts of severe convection along drylines in the southern Plains using observing system simulation experiments (OSSEs). ESA is a computationally inexpensive technique that relates changes in forecast variables to changes in an initial state through linear regression. Forecast changes from targeted observations may be estimated by comparing control forecasts with those that assimilate the targeted observations. However, even with sophisticated objective targeting algorithms and data assimilation filters that properly spread observational information, positive forecast impacts are not always realized for mesoscale convection forecasts (Romine et al. 2016). This problem warrants further investigation into what factors play a role in accurate prediction of impacts as well as the realized impacts after assimilation of targeted observations. Our primary goals in this study are to ascertain: (1) If targeted observations hold more positive impact over non-targeted (i.e. randomly chosen) observations; (2) If there are lead-time constraints to targeting for convection; (3) How inflation, localization, and the assimilation filter influence prediction and realized results; (4) If there exist differences between targeted observations at the surface versus aloft; and (5) how physics errors and nonlinearity may augment observation impacts. By answering these questions, we can determine the quantitative impact observations may have and whether enhanced predictability may be gained through targeted observing for mesoscale convection forecasts in spite of the linear assumptions underpinning ESA.
Ten cases of dryline-initiated convection are simulated from 2011 to 2013 within a simplified OSSE framework. Ensemble simulations are produced from a cycling system that utilizes the Weather Research and Forecasting (WRF) model v3.8.1 within the Data Assimilation Research Testbed (DART). A one-way nested configuration with three domains at 27, 9, and 3-km grid spacing is setup using the ensemble adjustment Kalman filter data assimilation procedure. The ensemble is cycled for 48 hours prior to forecast initialization, with adaptive inflation and covariance localization utilized. A “truth” (nature) simulation is produced by supplying a 3-km WRF run with GFS analyses and integrating the model forward ~90 hours, from the beginning of ensemble initialization through the end of the forecast. Each nested domain extracts observations from the nature run, with random error from a normal distribution added, and assimilates the observations every six hours during cycling. Target locations for surface and radiosonde observations are computed 6, 12, and 18 hours into the forecast based on a chosen scalar forecast response metric (e.g., maximum reflectivity at convection initiation). A variety of experiments are designed to achieve the aforementioned goals and will be presented, along with their results, detailing the feasibility of targeting for mesoscale convection forecasts.