Improving Decision Support for the MQ-1/MQ-9 and Boeing X-37 Using a Rapidly Updating 1-km Ensemble, a GOES-based Convection Initiation Algorithm, and Service-Based Ensemble Products

Improving decision support for the MQ-1/MQ-9 and Boeing X-37 using a rapidly updating 1 km ensemble, a GOES-based convection initiation algorithm, and service-based ensemble products

Evan Kuchera, Scott Rentschler, Glenn Creighton, William Sedlacek, David Keller, and Michael Puskar; 16 WS, Offutt AFB, NE

William Clements; 49 OSS/OSW, Holloman AFB, NM
Jacob Jeffcoat; 432 OSS/OSW, Creech AFB, NV
Todd McNamara; 45 WS, Patrick AFB, FL

Weather hazards are often a key consideration when operational decisions are made. Complicating the decision making process is the high frequency of weather forecasts that have meaningful uncertainty. As such, producing skillful weather forecast information with an accurate description of uncertainty, and communicating that information in such a way that is relevant and understandable by decision makers is critical to ensuring the best decisions are made.

To attempt to improve decision making in hazardous weather, several new technologies have been developed and have undergone initial evaluation. The first is a 1 km ensemble that updates every hour using the most recent High Resolution Rapid Refresh (HRRR) short term forecasts as initial conditions, and three different WRF model configurations. A 12-member ensemble is generated by time-lagging the four most recent cycles together. Specialized variables are produced in the model to help predict hazards such as virga, large supercooled droplets, lightning, and large hail. Second is an algorithm developed by the University of Alabama-Huntsville that tracks changing cloud characteristics in GOES data along with modeled instability to estimate the probability of convective initiation in the next 120 minutes. Efforts have also been undertaken to statistically combine these two forecasts in an attempt to leverage the positive contributions of both.

The third technology is a data mining tool called the Interactive Point Ensemble Probability (iPEP). This tool enables users to create tailored probabilities by accessing the full database of ensemble members and performing on-demand calculations for unique thresholds, ranges (i.e. lightning with XX miles), joint probabilities, and runway crosswinds. The display visualizes the chosen variables for the chosen location in a red/yellow/green format based on the probabilities computed to visually summarize the model information for quick and easy comprehension by both forecasters and end users.