Blending Model and Observations for a Ceiling and Visbility Product for Alaska

Poor weather conditions, particularly restricted visibility and low cloud tops, were the leading cause of fatal general aviation (GA) accidents in Alaska from 2001-2012. Traditional weather observations from Alaska’s widely dispersed airfields inadequately forewarn of weather likely to be encountered along routes between stations or, in particular, through hazardous mountain passes with localized conditions. Under FAA sponsorship, NCAR/RAL, MIT/LL, and the Alaska Aviation Weather Unit (AAWU), are involved in a collaborative effort to produce rapidly-updated, high resolution, gridded ceiling and visibility (C&V) and Flight Category products for Alaska (CVA-AK).

As a first step in producing the CVA-AK product, ceiling and visibility information from the NCEP RAP model 2 h forecasts are blended with METAR observations of C & V using data fusion techniques. A cloud mask algorithm is run on the GOES 3.9 and 11 µm imagery to identify cloud/no cloud regions. This information is then blended together with the RAP and METAR information. This integrated product of RAP model, METARs and GOES retrieved cloud mask has been installed at the AAWU and provide forecasters with “first guess” fields of ceiling, visibility and flight category. The challenges associated with the blending of these data include data quality control and optimization of limited observational data in Alaska, correcting biases in the model C&V fields, and limited GOES coverage of northern Alaska. Future versions of the CVA-AK product will include Suomi NPP VIIRS and AVHRR polar orbital satellite observations and visibility estimates retrieved from the FAA web camera imagery collected in Alaska (Matthews and Hallowell, 2018). A transition from the RAP to the Alaska HRRR model is also planned once the HRRR becomes operationally available at the AAWU.

In this presentation we will provide examples of the CVA-AK products and discuss the research efforts associated with the blending of the various datasets and providing a reliable and accurate product for the AAWU forecasters.

**Disclaimer: This research is in response to requirements and funding by the Federal Aviation Administration (FAA). The views expressed are those of the authors and do not necessarily represent the official policy or position of the FAA.