Issues in Very High Resolution Numerical Weather Prediction Over Complex Terrain in Juneau, Alaska Don Morton 1,2, Delia Arnold 3,4, Irene Schicker 3, Kayla Harrison 1, Carl Dierking 5, Gene Petrescu 6 1 Arctic Region Supercomputing Center, University of Alaska Fairbanks 2 Developmental Testbed Center, National Center for Atmospheric Research, Boulder, Colorado 3 Institute of Meteorology, University of Natural Resources and Life Sciences, Vienna, Austria 4 Institute of Energy Technologies, Technical University of Catalonia, Barcelona, Spain 5 Juneau Weather Forecast Office, NOAA National Weather Service, Juneau, Alaska 6 Alaska Region Headquarters, NOAA National Weather Service, Anchorage, Alaska Introduction This study examines the viability of the Weather Research and Forecasting (WRF) model as a tool to model high wind events in areas with complex topography. By manipulating different parameterizations within the model and using high resolution orography, we examine the accuracy and reliability of the output data. Juneau International Airport in Alaska is surrounded by steep terrain, often presenting challenging conditions for departing aircraft. Under strong wind conditions characterized by post-frontal topographically enhanced wind shear, aircraft following general aviation departure procedures may encounter turbulence or severe wind shear. In January 1993, a Boeing 727 aircraft encountered extreme crosswinds resulting in departure from controlled flight, with successful recovery occurring within 50 meters of the ground. Carl Dierking (Dierking et al. 2011) has performed initial high-resolution WRF simulations of the January 1993 case, providing a basis for our current study. In this work, we focus on a post-frontal wind shear event at Juneau from December Dierking's simulation of this event was reviewed and provided a basis for enhanced modeling with new initial and boundary conditions, model parameterizations, and higher resolution. The various modeling runs were compared amongst themselves and with local observations from the Juneau Airport Wind System (JAWS) in order to determine which set-up performed better and had potential to be used in the early warning systems of airports similarly located in complex terrain like the Juneau area. Method The WRF model was used to simulate this high wind event because of its high resolution capabilities and parameter options. To compare the effects of different input data and namelist parameterizations on the forecast product, the following options were selected to create 6 simulation profiles: Input data: Two different types of data were chosen to supply WRF with atmospheric conditions. ECMWF – European Center for Medium-range Weather Forecasts produces global reanalysis data with 25 km resolutionECMWF – European Center for Medium-range Weather Forecasts produces global reanalysis data with 25 km resolution NAM – North American Mesoscale model produces 45 km forecast dataNAM – North American Mesoscale model produces 45 km forecast data Namelist options: Two different namelists were used to determine the effect of simulation parameters on the model output. Dierking’s namelist – From Dierking et. al (2011) setup: 3 domains (D1, 9 km; D2, 3 km; D3, 1 km), 40 vertical levels, WRF Single Moment 3-class scheme microphysics, Yonsei University Non-Local-K PBL scheme, MM5 similarity surface layer, 5-layer land surface thermal diffusion and no dampening options.Dierking’s namelist – From Dierking et. al (2011) setup: 3 domains (D1, 9 km; D2, 3 km; D3, 1 km), 40 vertical levels, WRF Single Moment 3-class scheme microphysics, Yonsei University Non-Local-K PBL scheme, MM5 similarity surface layer, 5-layer land surface thermal diffusion and no dampening options. Modified namelist – The same domain grid spacing, 75 vertical levels, Lin et. al scheme microphysics, MYNN level 2.5 PBL scheme, MYNN surface layer, Noah land surface model, an implicit dampening layer, and orographic shadowingModified namelist – The same domain grid spacing, 75 vertical levels, Lin et. al scheme microphysics, MYNN level 2.5 PBL scheme, MYNN surface layer, Noah land surface model, an implicit dampening layer, and orographic shadowing An additional domain (D4, m) was introduced to study the influence of horizontal resolution on the simulation output.An additional domain (D4, m) was introduced to study the influence of horizontal resolution on the simulation output. Fig 2a. Wind direction plots on Pederson Hill Fig 2b. Wind speed plots on Mount Roberts ECMWF data with Dierking’s namelist (left) and the Modified namelist (right) – 3 domains NAM data with the Modified namelist - 4 domains Results The simulations with 3 domains show a time offset between the predicted wind direction and the recorded wind direction at the time of high wind velocity between 14 and 16 Z. The 4 domain simulation did not have a time offset. Results The simulations with 3 domains under-represent the wind velocity at the time of high wind velocity between 14 and 16 Z. The 4 domain simulation shows this increased velocity best. NAM data with Dierking’s namelist (left) and the Modified namelist (right) – 3 domains NAM data with the Modified namelist - 4 domains NAM data with Dierking’s namelist (left) and the Modified namelist (right) – 3 domains ECMWF data with Dierking’s namelist (left) and the Modified namelist (right) – 3 domains 1430 UTC 1510 UTC 1550 UTC 1630 UTC Initial Results As shown in the wind plots, the namelist parameters do not significantly influence the modeled winds in the vicinity of the airport. The simulation with the fourth domain (333m resolution) provided results closest to the sensor data. The wind velocity is better represented by the high resolution WRF simulation, and closely resembles anemometer readings at the time of the front. However, extension to a fifth nest of 111m resolution provided results that did not match the observations well at all, often a poorer match than the parent nest. Further analysis revealed that at these resolutions, topographic features were approximately 300 meters shifted from their known positions, and it’s believed that in such complex terrain, this may offer an explanation for the poor matches with observations. This is currently being investigated in depth. WRF discrete 111m topography plotted over Google Earth in Gastineau Channel, near Juneau, Alaska Coghlan Island at WRF 111m resolution. Was specified to be at center of domain. Coghlan Island with 111m WRF grid overlayed