Ensemble Sensitivity-based Observation Targeting OSSEs for Southern Plains Dryline Convection

A variety of observation targeting methods exist (e.g., adjoint sensitivity, singular vectors, ensemble transform) to estimate observation type and location that would enhance a specific forecast. Observation targeting methods have primarily been tested and applied on synoptic scales for lead times of 24 hours or greater using airborne platforms and dropwindsondes. In this study, the authors test 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 and then compared against forecasts that assimilate the targeted observations as well as those that assimilate observations from random locations. The primary goals of this study are to determine what impact observations may have in the presence of model and observational error and whether enhanced predictability may be gained through targeted observing for mesoscale severe convection forecasts.

Ten cases of dryline-initiated convection are simulated from 2011 to 2013 within an OSSE framework. Ensemble simulations are produced from a cycling system that utilizes the Weather Research and Forecasting (WRF) model v3.3.1 within the Data Assimilation Research Testbed (DART). A one-way nested configuration with three domains at 36, 12, and 4 km grid spacing is setup with EAKF data assimilation performed on all three. 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 4 km WRF run with GFS analyses and integrating the model forward ~72 hours from the beginning of cycling through the end of the forecast. Each nested domain extracts observations from the nature run, with random noise from a normal error distribution and variance consistent with the observation type, and assimilates every six hours during cycling. Target locations for surface and radiosonde observations are computed six hours into the forecast based on a chosen scalar forecast metric at the time CI occurs in each case. A new forecast is generated six hours after the prior forecast was initialized, assimilating observations based on the following three experiments: (1) Only the targeted observation is assimilated; (2) Only conventional observations are assimilated; (3) Both targeted and conventional observations are assimilated. Using these methods, a proper analysis of the impact from a single targeted observation is accomplished for dryline convection forecasts.