Several GSD researchers presented their work at the European Geophysical Union (EGU) General Assembly in Vienna, Austria in early April. Their talks and posters are summarized below.
Forecasting smoke, visibility and smoke-weather interactions using a coupled meteorology-chemistry modeling system: Rapid Refresh andHigh-Resolution Rapid Refresh coupled with Smoke (RAP/HRRR-Smoke) - Ravan Ahmadov, Eric James, Georg Grell, Curtis Alexander, Stan Benjamin, Stuart McKeen, Gabriel Pereira, Saulo Freitas, Ivan Csiszar, Marina Tsidulko ), Shobha Kondragunta, Chuanyu Xu, Ka Yee Wong, and Steve Albers
The western US experienced one of the worst fire seasons in 2018. GSD and CIRES/CIRA researchers presented an experimental smoke forecasting system, which leverages the existing Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) numerical weather prediction models running operationally at NOAA/NWS. Researchers presented RAP/HRRR-Smoke simulations for August 2018 over the northwestern US. The smoke and visibility forecasts are evaluated using the available ground particulate matter and satellite based (e.g. VIIRS AOD) measurements. Additionally, a detailed verification of the meteorological forecasts for the case study is presented. We demonstrate the improvements in weather forecasting when smoke feedback on meteorological processes is enabled in the coupled HRRR-Smoke model.
Improvements to ceiling and visibility forecasts from the 2019-20 U.S. HRRR/RAP short-range forecast models - Stan Benjamin, Curtis Alexander, Stephen Weygandt, Ming Hu, and Terra Ladwig
Introduction of an hourly 3km ensemble data assimilation method and enhancements to boundary layer, cloud microphysics, and land surface schemes have resulted in more accurate ceiling, visibility, and near-surface forecasts in NOAA’s hourly-updated models. NOAA Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) forecasts for wind, moisture/cloud, and vertical motion fields are the backbone for the U.S. aviation hazard forecasts.
Development of the High-Resolution Rapid Refresh (HRRR) Version 4 and Transition to an FV3-Based Rapid Refresh Forecast System (RRFS) - Therese Ladwig, David Dowell, Curtis Alexander, Ming Hu, Jeffery Duda, Trevor Alcott, Jeffery Beck, John Brown, Stephen Weygandt, and Stan Benjamin
Researchers reported on the latest developments in HRRR version 4 including using Convection Allowing Model (CAM) ensemble covariance information that improves the deterministic forecast. The future of CAM systems like the HRRR will be to provide real-time ensemble analyses and forecasts to produce improved skill and uncertainty information. To this end, the development of an FV3-based CAM ensemble assimilation and prediction system known as the Rapid Refresh Forecast System (RRFS) has begun. A stand-alone regional (SAR) version of the FV3 is now running. This is being followed by work to build the HRRR physics modules and data assimilation features into the FV3-based SAR configuration. This SAR FV3 work is geared to a planned operation transition to the RRFS in the 2022 time frame.
Improved short-range cloud/surface forecasts from US models at 12-km and 3-km scale - Stan Benjamin, Curtis Alexander, Stephen Weygandt, Ming Hu, and Terra Ladwig
Researchers introduced 3-km ensemble data assimilation and better radar/cloud assimilation and boundary layer physics that have reduced biases in 2m temperature and downward shortwave radiation for short-range forecasts in the US. These refinements are critical for improved energy (solar and wind), aviation (cloud), and severe storm (convective environment) prediction. They also introduced a small lake model to avoid significant temperature biases in the spring and fall, and added a smoke treatment that improved 2m temperature forecasts under certain conditions. This physics suite is now found to be approximately equivalent in skill to that from current operational NOAA global model physics.
Aerosol Impact on Surface Radiation and Precipitation in the Subseasonal to Seasonal Prediction Using FIM-Chem-iHYCOM Coupled Model - Shan Sun, Stuart McKeen, Georg Grell, Li Zhang
GSD researchers investigated the direct impact of aerosols on sub-seasonal to seasonal (s2s) prediction using a global coupled atmosphere, chemistry, and ocean system. Several dozens of year-long experiments were carried out with prescribed sources and sinks for aerosols, fire, and anthropogenic emissions during the model integration. The resulting aerosol optical depths from the model are shown to be in good agreement with observations. They also compared the model sensitivity with and without fire emissions at different seasons against a control run using prescribed background aerosols. Overall, these multiple case studies show that the biggest aerosol direct impact from online chemistry appears to be on the reduction of surface radiation. Its impact on the cloudiness and precipitation is small and requires further study.
Experiments using climatological as well as predicted aerosols in both an aerosol-aware convective parameterization and a double moment microphysics scheme - Georg Grell, Haiqin Li, Li Zhang, Saulo R. Freitas, Dominikus Heinzeller, Stuart McKeen, Hannah Barnes, Ravan Ahmadov, and Joseph Olson
Researchers evaluated a physics package developed at ESRL that includes a unified scale-aware parameterization of subgrid cloudiness feedback to radiation (coupled PBL, microphysics, radiation, shallow convection), a scale- and aerosol-aware convective parameterization, and an aerosol aware microphysics scheme. They investigated sensitivities to aerosol concentrations for both resolved and non-resolved precipitation physics.
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