New NOAA Model Forecasts Another Sahara Dust Cloud Arriving at Florida, Gulf Coast This Week (Animation)
By NOAA As another cloud of Saharan dust swirls across the Atlantic Ocean towards the southern United States, one of NOAA’s newest models is providing more accurate forecasts of where the air quality impacts of the dust will be felt. The image above shows where the cloud is expected to be tomorrow, Tuesday, July 28. Read earlier story on this phenomenon. The coupled global weather and chemistry research model, dubbed FV3-Chem, produces seven-day forecasts for a host of air quality impacts, including where dust will deliver hazy days and colorful sunsets as well as potential breathing problems and other respiratory issues for sensitive populations. Running on the newest version of NOAA’s Global Forecast System, or GFS, the FV3-Chem forecasts the distribution of some primary air pollutants: smoke, soot, organic carbon, sulfate, and large and small particles of dust and sea salt--collectively known as aerosols. Because these aerosols affect the weather, the model also provides weather forecasts. The new model is also capable of predicting the atmospheric impact of volcanic eruptions, which can disperse quantities of ash and other particulates over wide areas. “Several NOAA laboratories worked with the National Weather Service to produce major improvements to NOAA’s air quality modeling capabilities,” said Georg Grell, chief of the Global Systems Laboratory’s Environmental Prediction Advancement Division. “Now the developers as well as the broader modeling research community can contribute towards further improving it.” When the experimental model goes into day-to-day operations as part of the Unified Forecast System later this year, FV3-Chem will be renamed GEFS-Aerosols, and deliver substantial improvement for both the composition and variability of aerosol distributions over those from the currently operational global aerosol prediction system. Biases and other errors are substantially reduced with the new model, a necessary condition for looking at the impact on weather prediction out to sub-seasonal and seasonal scales. |
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