Our project focuses on the evaluation and advancement of the representation of lake-atmosphere interactions and resulting lake-effect snowstorms across the Laurentian Great Lakes Basin in the National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) model, coupled to the Land Information System (LIS). Traditionally, NU-WRF lacked a lake model and instead represented large lakes through observed lake-surface temperatures as boundary conditions, until the latest version 9 patch 1, which now includes an option for coupling to the 1D lake model from the Community Land Model. We are addressing the traditional deficient treatment of large lakes and lake-atmosphere interactions in NU-WRF by (1) testing the regional performance of NU-WRF coupled to the pre-existing 1D lake model across the Great Lakes Basin and (2) developing a two-way coupling option with a 3D lake model, in recognition of the notable deficiencies of 1D lake models for representing large lakes with well defined circulations, such as Superior. Initially, we have produced tests to assess the optimal configuration of NU-WRF for simulating regional climate and lake-effect snow in the Great Lakes region, using version 8 patch 5 without a lake model. This optimal configuration, which minimizes regional climate biases, will later be applied in all subsequent NU-WRF runs, once coupling to a 3D lake model is complete. The applied configuration consists of one-way nested domains with outer- and inner-domain grid spacing of 15- and 3-km, 61 vertical levels, Global Data Assimilation System (GDAS) 0-hour forecast data as lateral boundary conditions, Kain-Fritsch convective scheme in the outer domain and resolved convection in the inner domain, and RRTMG radiation scheme. We have tested options for spectral nudging (with versus without nudging), in which we nudge the model’s large-scale fields to a wavelength of »600 km. The nudged model appears to simulate more intense lake-effect snowfall. We are currently exploring options in mixed-phase microphysics schemes and planetary boundary layer (PBL) schemes. Overall, the model at 3-km grid spacing realistically captures observed snow band structures. This does not nullify the need to couple NU-WRF to a lake model; for example, future applications of NU-WRF for developing downscaled climate projections for the Great Lakes Basin will require an accurate treatment of changing lake temperatures, ice cover, evaporation, and resulting lake-effect snow associated with well-represented coupled lake-atmosphere interactions. Substantial progress has been made in terms of the coupling of NU-WRF to both 1D and 3D lake models. In terms of assessing the performance of NU-WRF recently coupled to 1D thermal diffusion lake physics, we have performed tests focused on the impacts of lake treatment, PBL scheme, and microphysics scheme on simulated lake-effect snowfall during 19-23 January 2007. For an in-development 3D option, we are coupling NU-WRF to the Finite Volume Community Ocean Model (FVCOM) using the OASIS Model Coupling Toolkit. NU-WRF-simulated wind fields and heat fluxes are provided as forcings to drive FVCOM, while FVCOM-simulated LSTs and ice cover are transferred to NU-WRF/LIS as surface boundary conditions for the Great Lakes. Planned evaluation of NU-WRF’s representation of lake-effect snowfall will be based on CloudSat and Global Precipitation Measurement (GPM) data, while evaluating spaceborne snowfall retrievals against station observations. We are assessing the GPM mission Core Observatory’s ability to detect and provide quantitative precipitation estimates for lake-effect snow events with its Dual-frequency Precipitation Radar and GPM Microwave Imager (GMI). We initially focused on lower Great Lakes single-band, shore parallel lake-effect snow events. We have compiled and analyzed a multi-year dataset of GPM overpasses, with preliminary results indicating mixed success using the GMI precipitation retrieval product generated from the Goddard Profiling precipitation algorithm. We are likewise developing an objective pattern recognition-based method for identifying lake-effect morphology patterns, using remotely sensed, radar-based, and NU-WRF-simulated data, while applying the method to deepen understanding of the environmental regulators of LES morphology. We developed a program that can distinguish in-lake clouds, non-clouds (open water), long-lake-axis-parallel lake-effect clouds, and some non-lake-effect clouds from sample Geostationary Operational Environmental Satellite images.
|Original language||English (US)|
|Title of host publication||AMS - 99th American Meteorological Society Annual Meeting|
|State||Published - 2019|