Glenn D. Rolph

NOAA’s HYSPLIT Atmospheric Transport and Dispersion Modeling System

AbstractThe Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), developed by NOAA’s Air Resources Laboratory, is one of the most widely used models for atmospheric trajectory and dispersion calculations. We present the model’s historical evolution over the last 30 years from simple hand-drawn back trajectories to very sophisticated computations of transport, mixing, chemical transformation, and deposition of pollutants and hazardous materials. We highlight recent applications of the HYSPLIT modeling system, including the simulation of atmospheric tracer release experiments, radionuclides, smoke originated from wild fires, volcanic ash, mercury, and wind-blown dust.

Sensitivity of Three-Dimensional Trajectories to the Spatial and Temporal Densities of the Wind Field

Abstract Initialization and forecast fields from the National Weather Services (NWS) Nested Grid Model (NOM) were archived on the 90 km calculational grid at 2-hour intervals out to 12 hours twice per day, for the 3-month period of January–March 1987. The resulting time series of meteorological data were used to determine the sensitivity of calculated trajectories to changes in temporal and spatial density of meteorological data during a wide range of synoptic conditions. Trajectories were started from 63 evenly spaced locations, twice per day, for a duration of 4 days each over the 74-day period. The 9324 separate trajectories were computed using the meteorological data at 90, 180, and 360 km grid spacing and at 2-, 4-, 6-, and 12-hour time intervals. Calculated trajectories were compared with the base “truth” case of 2-hour data on the 90 km grid. Trajectories were most sensitive to changes in temporal resolution when the grid resolution was 90 and 180 km. Trajectories computed on the coarser 360 km gr...

Description and Verification of the NOAA Smoke Forecasting System: The 2007 Fire Season

Abstract An overview of the National Oceanic and Atmospheric Administration’s (NOAA) current operational Smoke Forecasting System (SFS) is presented. This system is intended as guidance to air quality forecasters and the public for fine particulate matter (≤2.5 μm) emitted from large wildfires and agricultural burning, which can elevate particulate concentrations to unhealthful levels. The SFS uses National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS), which is based on satellite imagery, to establish the locations and extents of the fires. The particulate matter emission rate is computed using the emission processing portion of the U.S. Forest Service’s BlueSky Framework, which includes a fuel-type database, as well as consumption and emissions models. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model is used to calculate the transport, dispersion, and deposition of the emitted particulate matter. The model evaluation is carried out...

Verification of the NOAA Smoke Forecasting System: Model Sensitivity to the Injection Height

Abstract A detailed evaluation of NOAA’s Smoke Forecasting System (SFS) is a fundamental part of its development and further refinement. In this work, particulate matter with a diameter less than or equal to 2.5-μm (PM2.5) concentration levels, simulated by the SFS, have been evaluated against satellite and surface measurements. Four multiday forest fire case studies, one covering the continental United States, two in California, and one near the Georgia–Florida border, have been analyzed. The column-integrated PM2.5 concentrations for these cases compared to the satellite measurements showed a similar or better statistical performance than the mean performance of the SFS for the period covering 1 September 2006–1 November 2007. However, near the surface, the model shows a tendency to overpredict the measured PM2.5 concentrations in the western United States and underpredict concentrations for the Georgia–Florida case. Furthermore, a sensitivity analysis of the model response to changes in the smoke relea...

Long-Range Forecast Trajectories of Volcanic Ash from Redoubt Volcano Eruptions

The Redoubt Volcano in Alaska began a series of eruptions on 14 December 1989. Volcanic ash was often reported to reach heights where, as it moved with the upper-level flow, it could affect aircraft operations thousands of km from the eruption. In an agreement between the National Oceanic and Atmospheric Administration (NOAA) and the Federal Aviation Administration, the NOAA Air Resources Laboratory (ARL) was assigned responsibility for providing long-range forecast trajectories of volcanic ash during a volcanic hazards alert. An ARL immediate-response program was implemented for the Redoubt Volcano eruptions. The response products, in the form of tables, maps, and written messages are discussed. An evaluation of the forecast trajectories is included. The evaluation is based on after-the-fact trajectories from analyzed wind fields and on actual ash cloud sightings. For 90% of the cases verified at 300 mb, the average forecast error was less than 25% of the downwind distance from the eruption (this often i...

Long-Range Forecast Trajectories of Volcanic Ash from Redoubt Ash from Redoubt Volcano Eruptions

The Redoubt Volcano in Alaska began a series of eruptions on 14 December 1989. Volcanic ash was often reported to reach heights where, as it moved with the upper-level flow, it could affect aircraft operations thousands of km from the eruption. In an agreement between the National Oceanic and Atmospheric Administration (NOAA) and the Federal Aviation Administration, the NOAA Air Resources Laboratory (ARL) was assigned responsibility for providing long-range forecast trajectories of volcanic ash during a volcanic hazards alert. An ARL immediate-response program was implemented for the Redoubt Volcano eruptions. The response products, in the form of tables, maps, and written messages are discussed. An evaluation of the forecast trajectories is included. The evaluation is based on after-the-fact trajectories from analyzed wind fields and on actual ash cloud sightings. For 90% of the cases verified at 300 mb, the average forecast error was less than 25% of the downwind distance from the eruption (this often i...

Chapter 22 Regional Real-Time Smoke Prediction Systems

Abstract Several real-time smoke prediction systems have been developed worldwide to help land managers, farmers, and air quality regulators balance land management needs against smoke impacts. Profiled here are four systems that are currently operational for regional domains for North America and Australia, providing forecasts to a well-developed user community. The systems link fire activity data, fuels information, and consumption and emissions models, with weather forecasts and dispersion models to produce a prediction of smoke concentrations from prescribed fires, wildfires, or agricultural fires across a region. The USDA Forest Services BlueSky system is operational for regional domains across the United States and obtains prescribed burn information and wildfire information from databases compiled by various agencies along with satellite fire detections. The U.S. National Oceanic and Atmospheric Administration (NOAA) smoke prediction system is initialized with satellite fire detections and is operational across North America. Washington State Universitys ClearSky agricultural smoke prediction system is operational in the states of Idaho and Washington, and burn location information is input via a secure Web site by regulators in those states. The Australian Bureau of Meteorology smoke prediction system is operational for regional domains across Australia for wildfires and prescribed burning. Operational uses of these systems are emphasized as well as the approaches to evaluate their performance given the uncertainties associated with each systems subcomponents. These real-time smoke prediction systems are providing a point of interagency understanding between land managers and air regulators from which to negotiate the conflicting needs of ecological fire use while minimizing air quality health impacts.