USWRP WORKSHOP ON AIR QUALITY FORECASTING BY
WALTER F. DABBERDT, MARY ANNE CARROLL, WILLIAM APPLEBY, DARREL BAUMGARDNER, GREGORY CARMICHAEL, PAULA DAVIDSON, J. CHRISTOPHER DORAN, TIMOTHY S. DYE, SUSAN GRIMMOND, PAULETTE MIDDLETON, WILLIAM NEFF, AND YANG ZHANG*
T
here has recently been increased emphasis on air quality forecasting (AQF) and the research and development required to improve AQF skill and make AQF operational. In November 2001, the U.S. Weather Research Program (USWRP) charged Prospectus Development Team 11 with identification of the meteorological research needs for improved air quality forecasting (Dabberdt et al. 2004). Subsequently, the Interagency Working Group (IWG) of the USWRP tentatively adopted air quality as one of its principal scientific foci. In addition, National Oceanic and Atmospheric Administration (NOAA) and the U.S. Environmental Protection Agency (EPA) have deployed an operational air quality forecast system. With these activities as background, the lead scientist of the USWRP requested that a community workshop be conducted to further define and prioritize AQF research needs and opportunities. The results of the workshop would then be used in the development of an implementation plan that the AFFILIATIONS : DABBERDT—Vaisala, Inc., Boulder, Colorado,
CARROLL—University of Michigan, Ann Arbor, Michigan; APPLEBY— Meteorological Service Canada, Dartmouth, Nova Scotia, Canada; BAUMGARDNER—Universidad Nacional Autónoma de México, México DF, México; CARMICHAEL—University of Iowa, Iowa City, Iowa, DAVIDSON —National Oceanic and Atmospheric Administration, National Weather Service, Silver Spring, Maryland; DORAN — Pacific Northwest National Laboratory, Richland, Washington; DYE —Sonoma Technology, Inc., Petaluma, California; GRIMMOND — Indiana University, Bloomington, Indiana; MIDDLETON —Panorama Pathways, Boulder, Colorado; NEFF —National Oceanic and Atmospheric Administration, Oceanic and Atmospheric Research,
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USWRP: AIR QUALITY FORECASTING WORKSHOP WHAT:
USWRP invited a group of 50 scientists and stakeholders in air quality forecasting to identify priorities and help guide a research program. WHEN : 29 April–1 May 2003 WHERE : Houston, Texas
IWG would use to prioritize and support research directed at improving air quality knowledge, monitoring, and forecasting capabilities, and evaluating new air quality forecast products. The resulting USWRP Air Quality Forecasting Workshop was held in 2003 in Houston, Texas (complete workshop report is available online at http://box.mmm.ucar. edu/uswrp/reports/reports.html). The charge from the USWRP lead scientist to the 50 invited workshop participants was to identify and delineate critical meteorological issues related to the Boulder, Colorado; and ZHANG —Atmospheric and Environmental Research, Inc., San Ramon, California *CURRENT AFFILIATION : North Carolina State University, Raleigh, North Carolina CORRESPONDING AUTHOR : Walter F. Dabberdt, Vaisala Inc., P.O. Box 3659, Boulder, CO 80307-3659 E-mail:
[email protected] The abstract for this article can be found in this issue, following the table of contents. DOI:10.1175/BAMS-87-2-215 In final form 8 December 2005 ©2006 American Meteorological Society
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prediction of air quality. In this context, “air quality” refers to the chemical state of the atmosphere, including atmospheric constituents that pose a risk to health, those that may alter visibility, and any other aspects that have a high impact on human activities or the environment. “Prediction” is taken to include the depiction and communication of the present chemical state of the atmosphere in the urban zone and on the regional (meso-) scale, extrapolation (or “nowcasting”), and numerical prediction and chemical evolution on time scales up to several days. “High impact” refers to conditions that affect or alter behavior by the general public and private sector activities, including health and the environment, aviation, surface transportation, electric power, public events, broadcasting, and emergency response to and management of industrial accidents and terrorist actions. The emphasis is on the meteorological aspects of air quality, including depiction, nowcasting, and forecasting, and the development of prediction systems for air quality. The participants were also asked to consider advanced data assimilation systems that would be suitable for use with high-resolution models, taking advantage of existing data sources and those data sources that are likely to become available in the next 5 years; special emphasis was placed on methods that assimilate chemical observations into regional and global models. One goal of an AQF research program is to develop and assess various methods for observing the atmosphere (including its chemical state) on scales that are necessary to improve air quality forecasts. An AQF research program should also be charged with examining the value of the improved air quality forecasts and how the improved information is ultimately used. This should include research to develop measures of forecast improvements and to estimate the impact of the improvements. It may also include research to determine how best to convey forecast information to decision makers. Identifying user needs and priorities should be done in close cooperation with agencies, organizations, and companies that provide air quality information, as well as in concert with the general public and other end users. At the Houston AQF workshop, participants affirmed that improved air quality forecasts would increase the protection of human health from a range of threats and would aid in the long-term management of mobile and fixed emission sources. In addition, improved forecasts would reduce the impacts on agriculture (crop stresses) and ecosystems (oxidant and acid stress), as well as facilitate short-term targeted restrictions on emission sources to reduce pollution 216 |
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severity. These same improvements in monitoring and modeling would directly benefit emergency responders charged with minimizing impacts from transportation and industrial accidents and terrorism involving the airborne transport and diffusion of chemical, biological, and radiological agents. Five work groups were constituted to focus on the following areas: 1) 2) 3) 4) 5)
stakeholder and societal impacts, boundary layer dynamics, clouds and aerosols, measurements and modeling, and operational air quality forecasting.
The following summarizes the many complementary recommendations from each of the five work groups; see Table 1 for a summary of the specific recommendations organized according to the nine research themes. STAKEHOLDERS AND SOCIETAL IMPACTS. Given the growing importance of reliable air quality forecast information, the Stakeholders and Impacts Work Group identified the following areas of research needs:
• Assess user needs by working with external stakeholder and user communities and thus bringing together the diverse user groups. • Develop educational materials for interpreting and using AQF products and promoting AQF curricula in academia to ensure adequately trained professionals. • Use existing communications channels to increase public awareness of the value of AQF by enhancing and collaborating with existing efforts and creating instructional media kits for those who communicate AQF to the public. • Evaluate public perception and response to available AQF to obtain qualitative feedback and quantitative statistics and assess how well forecast products meet the real needs of the public, decision makers, and health-related organizations. BOUNDARY LAYER DYNAMICS. Research needs are in three principal areas of boundary layer dynamics: atmospheric structure and dynamics, lower boundary conditions, and special parameterization issues. Particular attention is needed to address the difficulties associated with light wind conditions, the structure of turbulence and dispersion in stable and transitional boundary layers, and
low-level jets. There also is a need for significant effort to describe urban effects as well as to develop nearsurface turbulence parameterizations, consider issues of terrain complexity and scaling, and improve cloud parameterizations and turbulence parameterizations in response to improvements in model resolution. To implement this needed research, three steps are suggested:
• Establish several testbeds, that is, well-instrumented sites suitable for making measurements over extended periods that can be used to test, evaluate, and develop models. • Develop new instruments to advance boundary layer characterization. • Develop new and improved data assimilation techniques to make the best use of currently available and anticipated new data to improve air quality model forecasts. CLOUDS AND AEROSOLS. There are numerous reasons to include clouds and aerosols in AQF models. A number of high-priority research and implementation activities must be initiated, however, prior to and in parallel with the incorporation of aerosol and cloud processes in these models. The most important research needs are the following:
• Develop high-resolution emission inventories for • •
•
•
•
primary aerosols and precursors of secondary aerosols. Improve numerical techniques for aerosol microphysical simulations in terms of accuracy and computational efficiency. Develop and deploy accurate and comprehensive (spatially and temporally) near-real-time meteorological and chemical (both gaseous and particulate species) measurements to better characterize source–receptor relationships. Conduct focused field experiments (e.g., closure studies) to study aerosol–cloud interactions and influential processes or factors and to derive aerosol activation parameterizations. Improve measurement techniques to characterize microphysical properties [e.g., cloud droplet number, particulate matter (PM) number, mass concentrations and size distributions, optical properties of aerosols and cloud droplets] of clouds and aerosols in urban areas [e.g. very high concentrations of cloud droplets and cloud condensation nuclei (CCN)]. Deploy dense surface sampling to characterize local variability of the physical and chemical properties of gases and aerosols.
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• Utilize less expensive and more easily deployable measurement techniques to characterize aerosol properties in real time. • Conduct systematic and rigorous model evaluations (including operational, diagnostic, mechanistic and probabilistic) and intercomparisons for PM models. MEASUREMENTS AND MODELING. Numerous detailed recommendations were made for improved measurements, predictive models, and data assimilation models for meteorology and chemistry. The work group also identified the need for test beds to evaluate and demonstrate alternative methods. A wide range of chemical and physical data is required to design comprehensive air quality forecast models, provide input data for operational and diagnostic forecast models, and determine the skill level of air quality forecasts. Advancing measurement technology allows the deployment of real-time in situ sensors in dense surface networks and on mobile platforms, and remote sensing instruments at the surface and on satellite platforms that can acquire needed data with sufficient temporal and spatial resolution. A significant investment in advanced instruments is required to properly support air quality forecast models. Specific recommendations follow:
• Develop advanced chemical modules and targeted model-nesting capabilities.
• Develop improved emissions inventories and land surface characterizations.
• Develop and apply new and existing parameteriza-
• •
• •
tion approaches for subgrid-scale processes where boundary layer parameterizations may be the most important aspect. Incorporate, improve, and verify aerosol modules in AQF models. Explore the suitability of other modeling approaches, such as different numerical techniques, chemical modules (e.g. improved photolysis modules, chemical mechanisms), grid structures, and nesting methods. Develop and apply ensemble methods and statistical methods, such as model output statistics (MOS), to quantify and reduce model error. Establish regional test beds to design and test measurement systems and strategies, as well as to develop improved meteorological and chemical models and data assimilation models.
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TABLE 1. Detailed recommendations, by theme. Boundary layer structure and modeling Develop advanced knowledge of stable and transitional boundary layer properties (e.g., turbulence, low-level jet, intermittency) BLD* Develop new meteorological instruments/techniques for volumetric sampling of PBL (see below, Instrumentation and measurements) BLD, M&M Develop advanced turbulence parameterizations for operational AQF (see below, Models and modeling) OAQF Surface–atmosphere interface and emissions Improve parameterizations of urban surface, building morphology, and anthropogenic energy fluxes BLD Develop high-resolution emission inventories for aerosols and aerosol precursors C&A Develop improved high-resolution methods to estimate emission rates C&A, M&M Explore coupling land surface models with air quality (AQ) models M&M Strengthen emission inventory methods, both top-down and bottom-up M&M Harmonize emission estimation methods OAQF Clouds and aerosol microphysics Improve parameterizations of cloud formation and aerosol/chemical processing BLD, M&M, OAQF Improve physical and numerical modeling of particle formation by developing a generic nucleation module (see below, Models and modeling) C&A Conduct field studies of aerosol–cloud interactions (see below, Establish AQ regional test beds) BLD, C&A Improve 3D particle and cloud droplet distributions on urban and regional scales C&A, M&M Develop and test algorithms for modeling smoke from burning vegetation, including emissions OAQF Establish AQ regional test beds Evaluate coupled meteorological and chemical forecast models BLD Conduct field research to further understand source–receptor relationships for O3 and PM BLD, C&A, M&M Conduct field studies of aerosol–cloud interactions (see above, Clouds and aerosol microphysics) BLD, C&A Specify method for routine CCN measurements in urban areas (see below, Instrumentation and measurements) BLD Deploy high-density surface sampling networks for chemical and physical properties of aerosols and gases BLD, C&A, M&M Instrumentation and measurements Develop new meteorological instruments/techniques for volumetric sampling of PBL (see above, Boundary layer structure and modeling) BLD, M&M Develop methods for assimilating new PBL meteorological measurements (see below, Data assimilation) BLD Improve microphysical characterization techniques for clouds and aerosols in urban areas BLD Specify method for routine CCN measurements in urban areas (see above, Establish AQ regional test beds) BLD Develop an “aerosol sonde” C&A Develop low-cost, easily deployable techniques for real-time aerosol characterization C&A, M&M Explore innovative approaches to upper-air measurements C&A Design optimal monitoring networks for gases, PM, and UV radiance M&M, OAQF Develop improved vertical profiling methods for O3 and aerosols M&M Improve methods for using satellite chemical measurements M&M Develop ACARS-like aircraft system for chemical and PM measurements OAQF Data assimilation Develop methods for assimilating new PBL meteorological measurements (see above, Instrumentation and measurements) BLD Assimilate satellite chemical observations C&A Develop and test DA techniques for gases and particles for AQ forecasting C&A, M&M Develop and test DA techniques for gases and particles for designing optimal measurement networks M&M, OAQF
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Models and modeling Explore methods to estimate upwind gaseous and particulate boundary conditions for small-area models C&A, M&M Improve numerical techniques for aerosol microphysics simulations C&A Determine uncertainty in cloud and aerosol parameterizations C&A Develop state-of-the-art parameterizations for hydrophobic and hydrophilic organic formation C&A Improve cloud modeling for urban areas C&A Improve physical and numerical modeling of particle formation => develop generic nucleation module (see above, Clouds and aerosol microphysics) C&A Improve subgrid treatment of PBL, wet deposition, convection, and microphysics in AQ models C&A, M&M Test sensitivity of AQF to mesoscale dynamical processes (e.g., sea-breeze, low-level jet, mountain–valley circulations) OAQF Improve advection schemes C&A Evaluate and compare particulate matter models and wet deposition estimates C&A Develop and test ensemble methods using the Weather Research Forecast (WRF) model M&M, OAQF Develop and test statistical methods, including MOS M&M Develop and apply operationally multiple diagnostic tools M&M Couple chemical processes with radiation and microphysics parameterizations M&M Develop and test targeted grid-nesting methods M&M Conduct exploratory research on possible WRF model improvements (numerics, chemistry, gridding, nesting) M&M Evaluate coupled online meteorological–chemical model OAQF Develop advanced turbulence parameterizations for operational AQF (see above, Boundary layer structure and modeling) OAQF Forecaster and end-user products Improve postprocessing of forecast products Æ value-added products OAQF Develop forecaster guidance products OAQF, S&SI Outreach Assess current products and improve postprocessing of forecast products => new value-added products (see above, Forecaster and end-user products) OAQF, S&SI Conduct a user needs assessment and a “public perception” survey with periodic updates S&SI Establish an external “AQF Science Committee” S&SI Establish partnerships with AQ impacts and management groups S&SI Develop partnerships with commercial weather providers S&SI Establish outreach partnerships with the American Meteorological Society S&SI Create a media AQ information package and increase public awareness S&SI Develop public service announcements for broadcast media S&SI Promote student education through capacity building with academia S&SI Assess effectiveness of AQF to reduce air pollution (AP) and AP impacts S&SI * Key to the work groups and priorities: BLD: Boundary layer dynamics C&A: Clouds and aerosols M&M: Measurements and modeling OAQF: Operational air quality forecasting S&SI: Stakeholders and societal impacts Color/typographic priority code: URGENT, PRESSING, IMPORTANT
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OPERATIONAL AIR QUALITY FORECASTING. A number of key areas were identified as needing research to advance and improve operational AQF capabilities. Near-term (< 5 year) research needs are based primarily on numerical prediction of groundlevel ozone, while longer-term (5–10 year) needs pertain to forecasting of finescale PM. In the near-term, local, state, and national emissions inventories need to be harmonized. Emissionsprocessing steps need to be streamlined and uncertainties must be quantified. Methods are required to quantify emissions from special events and upset conditions. Improvement of PM speciation and size distribution information, evaluation, and improvement of quality assurance protocols that are used with national emissions inventories are additional near-term needs. Air quality data latency must be improved. There is a pressing need to improve air quality monitoring for specification of model boundary conditions. A complementary need is to develop and implement methods for obtaining real-time air quality data aloft from instrumented aircraft and cost-effective ozone sounding systems. There is a compelling need to investigate optimal integrated observational bases for chemical data assimilation and to develop and apply chemical data assimilation methods. Regularly acquired, near-real-time profiles of ozone, oxides of nitrogen (NOx), and hydrocarbons would significantly supplement surface monitors in specifying appropriate initial conditions and boundary-condition inputs for air quality forecast model runs. An Aircraft Commission Addressing and Reporting System (ACARS)1-like real-time air quality aircraft measurement package would add important observational data for AQF in the United States; an enhanced real-time measurement system patterned after the European Measurement of Ozone on Airbus In-Service Aircraft (MOZAIC) Program, but capable of providing data in real time, should be explored. The use of satellite data to constrain air-chemistry to specify initial conditions and boundary conditions for AQF modeling has considerable potential. Because the vertical and horizontal scales of satellite retrievals of air chemistry species vary greatly, the optimum use of various satellite products that could provide model constraints on column totals of air-chemistry species needs to be explored.
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ACARS is the term used by NOAA’s Forecast Systems Laboratory to designate the automated weather reports that it archives from commercial aircraft. FEBRUARY 2006
Among modeling improvements, there is the need to develop efficient yet advanced turbulence parameterizations; improve cloud parameterizations for both microphysics and bulk parameterizations; conduct air quality sensitivity studies for various mesoscale phenomena; harmonize physics and grid designs for meteorological and AQF models; improve calculations of actinic flux; reduce uncertainties due to limited mesonet observations and flux estimates; and improve the treatment of precipitation, especially at scales ≤ 10 km. In the near term, ensemble research should be focused on the uncertainty in the meteorology (initial conditions and boundary conditions), while in the longer term it should expand to address uncertainties in the chemistry. Various ensemble forecasting techniques are needed, including mean solutions, probabilistic forecasts, and most-likely meteorological ensemble member(s) to drive air quality simulations. Research is needed to determine the proper horizontal resolution of ensemble members for AQF modeling. Improved techniques are also needed to validate probabilistic forecasts (e.g., Talagrand plots, skill scores). Of primary importance is the need to demonstrate the value added by coupled online modeling both for next-day and longer-term forecasts. Studies are needed to investigate simplifying the model(s) using parameterizations or fewer species to reduce computational demands, where model performance is not unduly compromised. Prediction algorithms must be designed for the most processing-intensive components of the system. The most time-consuming components should be analyzed and optimized. The currently available AQF models should be optimized on the presently available computer architecture(s). Forecasting other pollutants. Beyond PM and O3, species already embodied in the current modeling system, but not provided as explicit forecast products, include volatile organic compounds (VOC), sulfur oxides (SOx), and oxides of nitrogen (NO x). Additional species that would need to be added include toxics, size-resolved and speciated PM, smoke, ammonia, and the pH of precipitation. Providing explicit forecasts for these species is a long-term goal and the urgency of each depends on the identified health risk and the maturity of the modeling capability. The most challenging aspect of operational AQF is the overwhelming focus on skill in the extremes of the concentration distribution and the evaluation
of forecast skill in terms of peak concentrations scattered across a metropolitan-wide region. Forecast products that should be evaluated include: MOS products, bias and error information, forecast trends, back-trajectory and residual-layer estimates, and measures of the stagnation/ventilation index. OVERARCHING RECOMMENDATION. The consensus of the workshop is that the USWRP is especially well positioned to make substantial research contributions that can significantly improve AQF by focusing on the critical meteorological processes that have major impacts on the concentration and
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distribution of ozone, particulate matter, and other chemical species. The research needs and various recommendations identified at the workshop form the basis for developing a comprehensive implementation plan for a USWRP focus on AQF.
REFERENCE Dabberdt, W. F., and Coauthors, 2004: Meteorological research needs for improved air quality forecasting: Report of the 11th Prospectus Development Team of the U.S. Weather Research Program. Bull. Amer. Meteor. Soc., 85, 563–586.
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