Improving Winter Storm Forecasts with Observing System Simulation Experiments (OSSEs). Part I: An Idealized Case Study of Three U.S. Storms

Abstract

Severe weather events can have a significant impact on local communities because of the loss of life and property. Forecast busts associated with high-impact weather events have been attributed to initial condition errors over data-sparse regions, such as the Pacific Ocean. Numerous flight campaigns have found that targeted observations over these areas can improve forecasts. To better understand the impacts of measurement type and sampling domains on forecast performance, observing system simulation experiments are performed using the National Centers for Environmental Prediction Global Forecast System (GFS) with hybrid 3DEnVar data assimilation and the ECMWF T511 nature run. First, three types of simulated perfect dropsonde observations (temperature, specific humidity, and wind) are assimilated into the GFS over a large idealized sampling domain covering the Pacific Ocean. For the three winter storms studied, forecast error was found to be significantly reduced with all three types of measurements providing the most benefit (%–15% reduction in error). Instances when forecasts are not improved are investigated and concluded to be due to challenging meteorological structures, such as cutoff lows and interactions with atmospheric structures outside the sampling domain. Second, simulated dropsondes are assimilated over sensitive areas and flight tracks established using the ensemble transform sensitivity (ETS) technique. For all three winter storms, forecast error is reduced up to 5%, which is less than that found using an idealized domain. These results suggest that targeted observations over the Pacific Ocean may provide a small improvement to winter storm forecasts over the United States.

Improving Winter Storm Forecasts With Observing System Simulation Experiments (OSSEs). Part 2: Evaluating a Satellite Gap With Idealized and Targeted Dropsondes

“…This OSSE framework consisting of the T511 NR with the global GFS model has been previously developed and validated (Errico et al, ; Privé et al, ) and utilized for several applications including the impacts of rawinsondes (Privé, Errico, & Tai, ). More details on this OSSE framework are also provided in Part 1 of this project (Peevey et al, ).…”

“…The Flight Path domain assumes that the simulated aircraft flies only over ocean and samples for ~ 24 hr, which is comparable to an operational Global Hawk aircraft. More details on the three dropsonde domains are provided in Peevey et al (). All dropsonde experiments assimilate temperature, wind, and humidity, since in Part 1 of this project we found that assimilating all three measurements provides the largest forecast improvement (Peevey et al, ).…”

“…More details on the three dropsonde domains are provided in Peevey et al (). All dropsonde experiments assimilate temperature, wind, and humidity, since in Part 1 of this project we found that assimilating all three measurements provides the largest forecast improvement (Peevey et al, ). Additionally, all dropsonde experiments assimilate data in all grid boxes simultaneously for all designated cycles.…”

“…This study is Part 2 of a two‐part OSSE research project. In Part 1 (Peevey et al, ), OSSEs were completed to evaluate the impact of three types of dropsonde data (temperature, humidity, and wind) over three sampling domains on the forecast accuracy of the same three winter storms present in the NR. Sampling all three types of measurements over a large idealized sampling domain produced the largest forecast improvement.…”

“…Investigation into the causes of individual forecast degradations were concluded to be due to challenging meteorological features such as cutoff low‐pressure systems or interactions with meteorological features outside of the sampling domains. More details on other OSE and OSSE targeted observations studies and reasons for the possible differences between them are also covered in Part 1 of this project (Peevey et al, ).…”