Advancements in Hurricane Prediction With NOAA's Next‐Generation Forecast System

Jan‐Huey Chen; Shian‐Jiann Lin; Linus Magnusson; Morris Bender; Xi Chen; Linjiong Zhou; Baoqiang Xiang; Shannon Rees; Matthew Morin; Lucas Harris

doi:10.1029/2019gl082410

Improving global chemical weather forecast with modern online-coupled models for the U.S. Next Generation Global Prediction System (NGGPS)

10.5194/egusphere-egu2020-11807 ◽

2020 ◽

Author(s):

Raffaele Montuoro ◽

Georg Grell ◽

Li Zhang ◽

Stuart McKeen ◽

Gregory Frost ◽

...

Keyword(s):

Air Quality ◽

Environmental Modeling ◽

Coupled Systems ◽

Prediction System ◽

Next Generation ◽

Forecast System ◽

Coupled Models ◽

Air Quality Forecast ◽

Recent Advances ◽

The U.S

<p>Significant progress has been made within the last couple of years towards developing online coupled systems aimed at providing more accurate descriptions of atmospheric chemistry processes to improve performance of global aerosol and air quality forecasts. Operating within the U.S. National Weather Service (NWS) research-to-operation initiative to implement the fully-coupled Next Generation Global Prediction System (NGGPS), cooperative development efforts have delivered two integrated online global prediction systems for aerosols (GEFS-Aerosols) and air quality (FV3GFS-AQM). These systems include recent advances in aerosol convective transport and wet deposition processes introduced into the SAS scheme of the National Center for Environmental Prediction&#8217;s (NCEP) latest Global Forecast System (GFS) based on the Finite-Volume cubed-sphere dynamical core (FV3). GEFS-Aerosols is slated to become the new control member of the NWS Global Ensemble Forecast System (GEFS). The model features an online-coupled version of the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model with a biomass-burning, plume-rise model and recent advances from NOAA Earth System Research Laboratory (ESRL), along with a state-of-the-art FENGSHA dust scheme from NOAA Air Resource Laboratory (ARL). FV3GFS-AQM incorporates a coupled, single-column adaptation of the U.S. Environmental Protection Agency&#8217;s (EPA) Community Multiscale Air Quality (CMAQ) model to improve NOAA&#8217;s current National Air Quality Forecast Capability (NAQFC). Both coupled systems&#8217; design and development benefited from the use of the National Unified Operational Prediction Capability (NUOPC) Layer, which provided a common model architecture for interoperable, coupled model components within the framework of NOAA&#8217;s Environmental Modeling System (NEMS). Results from each of the described coupled systems will be discussed.</p>

Download Full-text

Evaluation of the offline-coupled GFSv15–FV3–CMAQv5.0.2 in support of the next-generation National Air Quality Forecast Capability over the contiguous United States

Geoscientific Model Development ◽

10.5194/gmd-14-3969-2021 ◽

2021 ◽

Vol 14 (6) ◽

pp. 3969-3993

Author(s):

Xiaoyang Chen ◽

Yang Zhang ◽

Kai Wang ◽

Daniel Tong ◽

Pius Lee ◽

...

Keyword(s):

United States ◽

Air Quality ◽

Organic Compounds ◽

Next Generation ◽

Aerosol Formation ◽

Forecast System ◽

Southeastern Us ◽

Air Quality Forecast ◽

Time Specific ◽

Quality Forecast

Abstract. As a candidate for the next-generation National Air Quality Forecast Capability (NAQFC), the meteorological forecast from the Global Forecast System with the new Finite Volume Cube-Sphere dynamical core (GFS–FV3) will be applied to drive the chemical evolution of gases and particles described by the Community Multiscale Air Quality modeling system. CMAQv5.0.2, a historical version of CMAQ, has been coupled with the North American Mesoscale Forecast System (NAM) model in the current operational NAQFC. An experimental version of the NAQFC based on the offline-coupled GFS–FV3 version 15 with CMAQv5.0.2 modeling system (GFSv15–CMAQv5.0.2) has been developed by the National Oceanic and Atmospheric Administration (NOAA) to provide real-time air quality forecasts over the contiguous United States (CONUS) since 2018. In this work, comprehensive region-specific, time-specific, and categorical evaluations are conducted for meteorological and chemical forecasts from the offline-coupled GFSv15–CMAQv5.0.2 for the year 2019. The forecast system shows good overall performance in forecasting meteorological variables with the annual mean biases of −0.2 ∘C for temperature at 2 m, 0.4 % for relative humidity at 2 m, and 0.4 m s−1 for wind speed at 10 m compared to the METeorological Aerodrome Reports (METAR) dataset. Larger biases occur in seasonal and monthly mean forecasts, particularly in spring. Although the monthly accumulated precipitation forecasts show generally consistent spatial distributions with those from the remote-sensing and ensemble datasets, moderate-to-large biases exist in hourly precipitation forecasts compared to the Clean Air Status and Trends Network (CASTNET) and METAR. While the forecast system performs well in forecasting ozone (O3) throughout the year and fine particles with a diameter of 2.5 µm or less (PM2.5) for warm months (May–September), it significantly overpredicts annual mean concentrations of PM2.5. This is due mainly to the high predicted concentrations of fine fugitive and coarse-mode particle components. Underpredictions in the southeastern US and California during summer are attributed to missing sources and mechanisms of secondary organic aerosol formation from biogenic volatile organic compounds (VOCs) and semivolatile or intermediate-volatility organic compounds. This work demonstrates the ability of FV3-based GFS in driving the air quality forecasting. It identifies possible underlying causes for systematic region- and time-specific model biases, which will provide a scientific basis for further development of the next-generation NAQFC.

Download Full-text

Evaluation of the offline-coupled GFSv15-FV3-CMAQv5.0.2 in support of the next-generation National Air Quality Forecast Capability over the contiguous United States

10.5194/gmd-2020-272 ◽

2020 ◽

Author(s):

Xiaoyang Chen ◽

Yang Zhang ◽

Kai Wang ◽

Daniel Tong ◽

Pius Lee ◽

...

Keyword(s):

United States ◽

Air Quality ◽

Fine Particles ◽

Scientific Basis ◽

Next Generation ◽

Aerosol Formation ◽

Forecast System ◽

Air Quality Forecast ◽

Time Specific ◽

Quality Forecast

Abstract. The next-generation National Air Quality Forecast Capability (NAQFC) will use the meteorology from Global Forecast System with the new Finite Volume Cube-Sphere dynamic core (GFS-FV3) to drive the chemical evolution of gases and particles described by the Community Multiscale Air Quality modelling system version 5.3 (CMAQ v5.3). CMAQ v5.0.2, a historical version of CMAQ, has been coupled with the North American Mesoscale Forecast System (NAM) model in the current operational NAQFC. An experimental version of the NAQFC based on the offline-coupled GFS-FV3 version 15 with CMAQv5.0.2 modeling system (GFSv15-CMAQv5.0.2), has been developed by the National Oceanic and Atmospheric Administration (NOAA) to provide real-time air quality forecasts over the contiguous United States (CONUS) since 2018. In this work, comprehensive region-specific, time-specific, and categorical evaluations are conducted for meteorological and chemical forecasts from the offline-coupled GFSv15-CMAQv5.0.2 for the year 2019. The forecast system shows good overall performance in forecasting meteorological variables with the annual mean biases of−0.2 °C for temperature at 2-m, 0.4 % for relative humidity at 2-m, and 0.4 m s−1 for wind speed at 10-m against the METeorological Aerodrome Reports (METAR) dataset. Larger biases occur in seasonal and monthly mean forecasts, particularly in spring. Although the monthly accumulated precipitation forecasts show generally consistent spatial distributions with those from the remote sensing and ensemble datasets, moderate-to-large biases exist in hourly precipitation forecasts against the Clean Air Status and Trends Network (CASTNET) and METAR. While the forecast system performs well in forecasting ozone (O3) throughout the year and fine particles with a diameter of 2.5 μm or less (PM2.5) for warm months (May-September), it significantly overpredicts annual-mean concentrations of PM2.5. This is due mainly to the high predicted concentrations of fine fugitive, coarse-mode, and nitrate particle components. Underpredictions in the southeastern U.S. and California during summer are attributed to missing sources and mechanisms of secondary organic aerosol formation from biogenic volatile organic compounds (VOCs) and semi- or intermediate-VOCs. This work identifies possible underlying causes for systematic region- and time-specific model biases, which will provide a scientific basis for further development of the next-generation NAQFC, in particular, derivation of the science-based bias correction methods to improve forecasting skill for O3 and PM2.5.

Download Full-text

Ice Forecasting in the Next-Generation Great Lakes Operational Forecast System (GLOFS)

Journal of Marine Science and Engineering ◽

10.3390/jmse6040123 ◽

2018 ◽

Vol 6 (4) ◽

pp. 123 ◽

Cited By ~ 12

Author(s):

Eric Anderson ◽

Ayumi Fujisaki-Manome ◽

James Kessler ◽

Gregory Lang ◽

Philip Chu ◽

...

Keyword(s):

Great Lakes ◽

Ice Cover ◽

State Variables ◽

Next Generation ◽

Forecast System ◽

Modeling Framework ◽

Operational Forecast ◽

Lake Ecology ◽

Ice Conditions ◽

Forecast Guidance

Ice Cover in the Great Lakes has significant impacts on regional weather, economy, lake ecology, and human safety. However, forecast guidance for the lakes is largely focused on the ice-free season and associated state variables (currents, water temperatures, etc.) A coupled lake-ice model is proposed with potential to provide valuable information to stakeholders and society at large about the current and near-future state of Great Lakes Ice. The model is run for three of the five Great Lakes for prior years and the modeled ice cover is compared to observations via several skill metrics. Model hindcasts of ice conditions reveal reasonable simulation of year-to-year variability of ice extent, ice season duration, and spatial distribution, though some years appear to be prone to higher error. This modeling framework will serve as the basis for NOAA’s next-generation Great Lakes Operational Forecast System (GLOFS); a set of 3-D lake circulation forecast modeling systems which provides forecast guidance out to 120 h.

Download Full-text