NOAA’s Air Resources Laboratory—75 Years of Research Linking Earth and Sky: A Historical Perspective

Ariel F. Stein NOAA/Air Resources Laboratory, College Park, Maryland;

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Bruce B. Hicks NOAA/Air Resources Laboratory, Oak Ridge, and MetCorps, Norris, Tennessee;

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LaToya Myles NOAA/Air Resources Laboratory, Oak Ridge, Tennessee

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Margaret Simon NOAA/Air Resources Laboratory, College Park, Maryland;

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Abstract

For over 75 years, the National Oceanic and Atmospheric Administration’s Air Resources Laboratory (NOAA ARL) has been at the forefront of federal meteorological and climate research. As the Special Projects Section (SPS) of the U.S. Weather Bureau (USWB), the laboratory pioneered the development of atmospheric trajectory modeling, initially used in studies related to nuclear weapons following World War II. Model development was guided by observations following weapons tests, assisted by later experiments using a wide variety of atmospheric tracers. Today’s familiar Gaussian plume dispersion model, previously in nascent form, was developed and promoted with ARL research, as was the much later and widely used HYSPLIT model. Much of ARL’s early research was focused on the challenges presented by the complex terrain surrounding nuclear installations, often addressed with high-spatial-resolution meteorological measurements, atmospheric tracers, and site-specific models. ARL has since extended boundary layer research to increasingly complex landscapes, such as forests, agricultural lands, and urban areas, and has expanded its research scope to air quality, weather, and climate applications based on the knowledge and experience developed throughout its long history. Examples of these research endeavors include the establishment of the U.S. Climate Reference Network, fundamental contributions to the development of the National Air Quality Forecast Capability, and foundational participation in the National Atmospheric Deposition Program. ARL looks forward to continuing to refine scientific understanding from field experiments, including coupling ground-based experimentation with modeling, and sustained observations, in order to facilitate the transfer of knowledge into practical applications of societal relevance.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ariel Stein, ariel.stein@noaa.gov

Abstract

For over 75 years, the National Oceanic and Atmospheric Administration’s Air Resources Laboratory (NOAA ARL) has been at the forefront of federal meteorological and climate research. As the Special Projects Section (SPS) of the U.S. Weather Bureau (USWB), the laboratory pioneered the development of atmospheric trajectory modeling, initially used in studies related to nuclear weapons following World War II. Model development was guided by observations following weapons tests, assisted by later experiments using a wide variety of atmospheric tracers. Today’s familiar Gaussian plume dispersion model, previously in nascent form, was developed and promoted with ARL research, as was the much later and widely used HYSPLIT model. Much of ARL’s early research was focused on the challenges presented by the complex terrain surrounding nuclear installations, often addressed with high-spatial-resolution meteorological measurements, atmospheric tracers, and site-specific models. ARL has since extended boundary layer research to increasingly complex landscapes, such as forests, agricultural lands, and urban areas, and has expanded its research scope to air quality, weather, and climate applications based on the knowledge and experience developed throughout its long history. Examples of these research endeavors include the establishment of the U.S. Climate Reference Network, fundamental contributions to the development of the National Air Quality Forecast Capability, and foundational participation in the National Atmospheric Deposition Program. ARL looks forward to continuing to refine scientific understanding from field experiments, including coupling ground-based experimentation with modeling, and sustained observations, in order to facilitate the transfer of knowledge into practical applications of societal relevance.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ariel Stein, ariel.stein@noaa.gov

ARL research, like many scientific endeavors of the modern era, has its roots in the world wars of the twentieth century. The gas warfare of World War I elevated issues of dispersion to a level that subsequently waned with the adoption of the Geneva Protocol in 1925. However, concerns about dispersion returned with the new atomic age. Radioactive debris from atomic explosions threatened all life forms, as well as potentially affecting climate. As the development and testing of nuclear weapons progressed, the ability to detect, trace, and predict the spread of nuclear materials became a national concern (Machta 1958). A small cadre assembled by Lester Machta within the U.S. Weather Bureau (USWB) was central in providing guidance to the federal administration on meteorological aspects of the nuclear programs emerging in the post-WWII period. The initial focus of these activities was to support the testing activities of the U.S. Department of Defense (DoD) and the Atomic Energy Commission (AEC). In this role, members of Machta’s team provided meteorological guidance for the series of nuclear tests conducted in the South Pacific.

In 1948, the importance of maintaining a crew of qualified meteorologists with high-level security clearances was recognized by Harry Wexler (research director of the USWB), with the designation of this specialized group as the Special Projects Section (SPS), the precursor of ARL (Wexler 1962). The initial group occupied humble quarters in what was once the stables of the Spanish Embassy in the suburbs of Washington, D.C. This provided easy access to the national security advisors of the Federal Government, a proximity that continued as a benefit to ARL’s research programs for many decades. The SPS operated closely with DoD and AEC scientists, also with those of the Public Health Service (PHS) and other federal entities as circumstances dictated (Slade 1966).

Following the ARL program supporting weapons tests in the South Pacific and starting with the initiation of weapons testing at the Nevada Test Site (NTS), the Weather Service Nuclear Support Office was established to provide meteorological guidance to test managers (Machta et al. 1957). Operating within the facilities of the AEC in Nevada, this group’s field work made use of dedicated installations on the NTS. Today, this group is the Nevada-based Special Operations and Research Division (SORD) of ARL (Fig. 1).

Fig. 1.
Fig. 1.

ARL Divisions span a range of landscapes across the United States; shown above are mesonet locations and towers for each division.

Citation: Bulletin of the American Meteorological Society 104, 12; 10.1175/BAMS-D-23-0006.1

The selection of a remote artillery test range in eastern Idaho for reactor development (the National Reactor Testing Station, NRTS) led to a need to study dispersion in complex terrain; the area is immediately downwind of the Rocky Mountains and concerns arose about the risk to populations if an accident were to occur (Wanta and White 1949). The Idaho Falls Weather Bureau office was expanded to address the concern and became a field component of the SPS (Yanskey 1960).

In 1957, as an outcome of collaboration with the PHS, the SPS was requested to set up a section collocated with PHS in Cincinnati, Ohio. This field operation of SPS subsequently relocated to Research Triangle Park, North Carolina, becoming the Atmospheric Sciences Modeling Division of ARL until it was transferred to the U.S. Environmental Protection Agency in 2008.

In 1965, the SPS and its affiliates in Idaho, Nevada, and Ohio were brought together as a consortium, directed by Lester Machta, and referred to collectively as the Air Resources Laboratories, with a mission “to advance the state of knowledge of the transport, dispersion and removal of those materials in the atmosphere usually regarded as pollutants.” Much early work involved the acquisition and/or analysis of classified data. Many of the reports on this work were not published and remain inaccessible even today. While some early scientists never received the recognition they deserved, their unpublished research often enabled ARL and partners across the federal government to develop methods and models that were ahead of their time; some examples are shown in Fig. 2.

Fig. 2.
Fig. 2.

Examples of ARL partnerships, collaborations, and programs spanning eight decades of ongoing research into boundary layer processes.

Citation: Bulletin of the American Meteorological Society 104, 12; 10.1175/BAMS-D-23-0006.1

In 1947, awareness of the complex terrain issue caused the AEC to request a meteorological survey of the Oak Ridge, Tennessee, area, one of the principal sites of the Manhattan Project, a World War II research and development effort that led to the world’s first atomic bombs (Gosling 1999). In response, the specialist group of the local Meteorological Office installed what was likely the first automatically telemetered mesoscale micrometeorological network (Green 1992). With Frank Gifford appointed as the leader of the Oak Ridge dispersion group in 1964, the organization was formally identified as the Atmospheric Turbulence and Diffusion Laboratory (ATDL). Funded by the AEC and USWB, ATDL operated independently from 1965 to 1971, when it was incorporated into the Air Resources Laboratories (Green 1992) and NOAA was formed.

The early years: Transport, dispersion, and complex terrain

Research on atmospheric trajectories and dispersion accelerated in 1949 when U.S. Air Force flights between Alaska and Japan detected fission products from the first Soviet nuclear test. SPS staff calculated back trajectories by hand to determine the most likely origin and future path of the radioactive debris (Machta 1992). Collaboration with the nuclear establishment continued as SPS used modeling to track emissions of radioactivity from sources worldwide. The initial development of such models was led by SPS researchers operating with access to classified data not available to others. These contributions will probably never be publicly acknowledged. However, some relevant documentation is among the Technical Reports and Memoranda available on the current ARL website and through the NOAA Institutional Repository. Real-time observations of nuclear tests and measurements of downwind concentrations and deposition constituted the foundations of the dispersion and deposition models developed by SPS and ARL researchers. Moreover, the plume rise descriptions produced from atomic cloud observations (Fig. 3) foreshadowed the well-known ARL formulations developed decades later, for different applications (Briggs 1969).

Fig. 3.
Fig. 3.

An example of early work, once classified, indicating how the height of an atomic cloud depends on the yield.

Citation: Bulletin of the American Meteorological Society 104, 12; 10.1175/BAMS-D-23-0006.1

Most AEC installations identified in the 1940s and 1950s were in locations affected by complex terrain. Meteorological data to initiate local dispersion models was a common requirement, so SPS advised and managed the installation of mesoscale networks at both Nevada and Idaho. At Nevada, dispersion forecasting required access to nuclear yield information, which was classified at that time, and to pilot balloon releases as well as network observations. The models used were continually updated (AMS 1977 Committee on Atmospheric Turbulence and Diffusion 1978) using the results from the tests that continued into the 1960s until 1992.

In place of the pilot balloon releases at the NTS, a subnetwork of 65 m tall towers was installed at NRTS in Idaho (Fig. 4), providing additional wind field information to initiate specialized site-specific dispersion models (Yanskey et al. 1966). Numerical modeling with digital computers was then in its infancy. The observing networks in Nevada and Idaho continue to operate, yielding an archive of more than 70 years of surface boundary layer meteorology.

Fig. 4.
Fig. 4.

Two examples of temperature records from an Idaho tall tower in 1952, showing the dawn–dusk transitions and the strong difference from the surface layer at higher elevations.

Citation: Bulletin of the American Meteorological Society 104, 12; 10.1175/BAMS-D-23-0006.1

To study the atmospheric processes affecting dispersion following a ground-level release (as from a leaking reactor), SPS made use of many gaseous and particulate agents in tracer experiments. The tracer materials ranged from smoke and oil fog to radioiodine, supported by aircraft sampling and radar-tracked constant-level balloons, which constituted very early use of radar in detailed meteorological research (Dickson and Pound 1961). Roll vortices were found to exist in the lower atmosphere where accidentally released pollutants would be confined. In later years, experiments used different tracers, such as Krypton-85 (85Kr, emitted when reactor fuel rods are reprocessed), sulfur hexafluoride (SF6), and perfluorocarbons (PFCs). PFCs are measurable in very low concentrations, enabling longer-range dispersion experiments than otherwise possible. In 1980, ARL was involved in a regional-scale feasibility study using five separate tracers (two PFCs and two isotopically tagged methane tracers, plus SF6) released from Norman, Oklahoma (Ferber et al. 1981). These tests combined the operational systems of several NOAA and Department of Energy (DOE) laboratories, notably including the sampling and analysis capabilities of Brookhaven National Laboratory and ARL (Idaho Falls). The Atlantic Coast Unique Regional Atmospheric Tracer Experiment (ACURATE) in 1982/83 (Heffter et al. 1984), Cross Appalachian Tracer Experiment (CAPTEX) in 1983 (Ferber 1986), and the Across North America Tracer Experiment (ANATEX) in 1985 (Draxler 1988) followed soon after. These studies demonstrated that the emitted materials are affected by wind shear, turbulence, and deposition so that surface ramifications of distant releases are more like sporadic islands of high concentration than any organized concentration field. In coastal areas, ARL scientists were involved in tracer releases which demonstrated that deterministic predictions of trajectories would be unreliable, since they do not account for turbulence or wind shear, except in situations of long, linear coastlines in stationary meteorological conditions; q.v. SF6 releases from the Ormond Beach facility in California in 1975 and the Krypton-85 ACURATE experiment of 1983.

Turbulence and micrometeorology

Early research necessarily extended to studies of atmospheric turbulence. Together, turbulent diffusion and velocity combine to result in dispersion—dispersion is the consequence of diffusion and transport. Classical studies relied on either wind tunnels (addressing diffusion) or on observations made in expansive, spatially homogeneous flat terrain (so minimizing such features as meander). The complex terrain of nuclear research establishments was clearly different and drew attention to the need to improve understanding of turbulence and the eddy fluxes associated with it (Gifford 1953; Start et al. 1973).

In the late 1960s, studies of the nocturnal pooling of radon (emitted from rocks and soil) and the reduction following sunrise of ground-level 222Rn concentrations were shown to be evidence of strong nighttime stratification followed by turbulent dilution after sunrise (Hosler 1966, 1969). Especially over vegetated surfaces, this atmospheric nocturnal pooling phenomenon is now a well-known characteristic of trace chemical species originating in the subsurface, including microbial carbon dioxide (CO2). In later years, this ARL research (at a site in Tysons Corner, Virginia) evolved into micrometeorological research that remains a central activity.

Research on various aspects of micrometeorology, diffusion, and dispersion at the different ARL divisions (including results from other researchers) was assembled into the “Meteorology and Atomic Energy” compendium (Slade 1968) and updated in “Atmospheric Sciences and Power Production” (Randerson 1984). Seminal studies of horizontal plume dispersion (including those from nuclear tests) in the 1960s have continued to inform transport and diffusion work in ARL and the broader research community (Heffter 1965).

At Oak Ridge, the emphasis was on improving the understanding of how turbulence and dispersion near the ground depend on synoptics, local topography, surface vegetation, meteorological intermittency, and nearby obstructions. Research on air–surface exchange (especially involving forests) culminated in the publication of The Forest-Atmosphere Interaction (Hutchison and Hicks 1985). In the 1980s and 1990s, studies of eddy fluxes led to their application in measuring the deposition of air pollutants (McMillen 1988; Hicks et al. 1989). The related field programs were frequently in collaboration with researchers from the academic and National Laboratory communities. The now-familiar infrared gas analyzer methodology for fast-response water vapor and CO2 measurement was developed at ARL (Auble and Meyers 1992) and utilized on towers and mobile platforms (Crawford et al. 1993). Recent research has focused on the use of eddy covariance instrumentation and specially developed subsurface sensors to explore spatial aspects of air–surface exchange and details of the surface heat budget, often reported to be imbalanced.

In the mid-1980s, aircraft began to be used to measure covariances, with emphasis on the exchanges of heat, moisture, ozone (O3), and CO2. Pressure-sphere anemometry instrumentation and measurement techniques were developed and installed on several aircraft—the Best Available Turbulence (BAT) probe (Crawford and Dobosy 1992). In Idaho, the same anemometry led to a new high-velocity sensor system for use in hurricane research (Eckman et al. 2007). The ARL experiments with aircraft improved sensing capabilities that eventually allowed it to be transferred to aircraft of the NOAA fleet. ARL aircraft studies of CO2 net ecosystem exchange focused on areas not suitable for tower studies—mainly coastal zones and wetlands. Idaho Falls also examined the turbulent wakes left by low-flying aircraft and in Washington, D.C., models were developed to predict the propagation of sonic booms.

Also, at Oak Ridge, uncrewed aerial systems (UASs) have been adapted for many investigations (Lee et al. 2019). Over the last few years, a research program has focused on surface temperature variability and the need for its consideration in conventional formulations of air–surface exchange, and ARL has been a key participant in a number of national research campaigns fairly recently as follows: Land-Atmosphere Feedback Experiment (LAFE; Lee and Buban 2020; Lee et al. 2021); Verification of the Origins of Rotation in Tornadoes Experiment-SE (VORTEX-SE; Lee et al. 2019); Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD; Butterworth et al. 2020), and Study of Precipitation, Lower Atmosphere and Surface for Hydrometeorology (SPLASH; de Boer et al. 2023).

In 1974, a Fluid Modeling Facility (FMF) was established in Research Triangle Park, North Carolina, as a joint venture with the U.S. Environmental Protection Agency (EPA) (Hosler 1975). One significant product of the FMF is enshrined in the Code of Federal Regulations, where the minimum height of pollution-emitting stacks is specified to be 2.5 times the height of structures in the surrounding area—a consequence of wind tunnel studies [40 CFR 51.100(ii); U.S. Environmental Protection Agency 2022]. Water-channel research at FMF enabled streamline deformation by obstacles to be formulated. ARL’s Idaho Falls division subsequently conducted a real-world (confirmatory) examination of a barrier like that modeled, using SF6 tracers (Finn et al. 2010). The models developed on the basis of the wind tunnel (and water channel) studies remain as mainstay pieces of dispersion lore.

The influence of buildings was of considerable concern because of the possibility of sheltering from or, alternatively, aggravating unwelcome exposure to dispersing and hazardous pollutants. Real-world validity was tested by tracer releases (e.g., at the Rancho Seco, California, nuclear plant in 1975), which demonstrated that complexities of turbulence, wind fields, and their modification by structures inhibit detailed deterministic attention to the matter (Start et al. 1977). Probabilistic descriptions of exposures were advocated.

A basic understanding of diffusion in the layer of the atmosphere closest to the ground was generated by the famous Prairie Grass experiment of 1956, sponsored by the DoD. At a site in Idaho in 2013, ARL conducted Project Sagebrush (Finn et al. 2015, 2017) to update the Prairie Grass results, using modern sensors and a more topographically diverse site. The results indicated a refinement of the older diffusion characteristics, as expected because of the change in surface, by as much as a factor of 2, depending on the circumstances (Ngan et al. 2018).

Atmospheric chemistry: Acid rain and air pollution

In its early dispersion research, ARL relied on radioactivity measurements made by elements of the AEC, such as the Health and Safety Laboratory (HASL) in New York City, NY. The HASL wet-dry collector was a favored tool of the atomic fallout era and subsequently was used to monitor deposition of other atmospheric pollutants under the National Atmospheric Deposition Program (NADP), still a leader of national environmental research. Measurement programs indicated that for radioactive fallout from distant origins, wet deposition exceeded dry by an order of magnitude. However, for pollutants with closer sources, the two deposition contributions were closely similar. At ARL, methods for deriving dry deposition rates from monitored site-specific observations were developed and deployed in a research network, the Atmospheric Integrated Research Monitoring Network (AIRMoN), to improve the Dry Deposition Inferential Method (DDIM; Hicks et al. 1987).

Studies of atmospheric deposition came to a head with the acid rain concerns of the 1980s. ARL played an essential role in the multiagency National Acid Precipitation Assessment Program (NAPAP), representing NOAA and eventually leading its Air Chemistry component. The final assessment produced by NAPAP (Irving 1991) relied heavily on the results of ARL’s Regional Acid Deposition Model (RADM; Chang et al. 1987), a precursor of the EPAs Community Model for Air Quality (CMAQ; Byun and Ching 1999). Both models were developed by ARL scientists with EPA support.

ARL’s air chemistry and deposition studies extended to a variety of hazardous pollutants, such as ammonia (Myles et al. 2007), dioxins/furans (Cohen et al. 2002), and mercury (Cohen et al. 2004), usually to elaborate on concerns about their ecological and water quality impacts on the Great Lakes and in agricultural and coastal environments. Global studies of wet deposition (via precipitation) were also pursued as part of ARL’s long involvement with the World Meteorological Organization (WMO; Vet et al. 2014).

In the 1980s, rising air quality concerns led to the development of real-time air quality models, evolving into complex 3D models by the 1990s and 2000s. In 2002, NOAA was tasked by Congress with providing a National Air Quality Forecast Capability (NAQFC), with collaboration from the EPA (Otte et al. 2005; Eder et al. 2010). First implemented in 2004, the NAQFC has been a continuing focus of ARL research. In 2019, the transition by NOAA’s National Weather Service (NWS) to a new dynamic core meteorological model enabled coupling between the weather model and the latest CMAQ version to improve air quality forecasting. ARL and its partners have continued to further develop the NAQFC and improve forecasting capabilities for dust, smoke, ozone, and other pollutants (Lee et al. 2017; Pan et al. 2020). More recently, ARL scientists have worked to develop and implement the use of the NOAA–EPA Atmosphere–Chemistry Coupler (NACC)-Cloud version 1 in order to provide the scientific community streamlined access to NOAA’s operational Global Forecast System (GFSv16) data and user-defined processing and download of model-ready, meteorological input for any regional CMAQ model domain worldwide (Campbell et al. 2023).

In conjunction with the NADP’s Atmospheric Mercury Initiative, ARL has been a leader in the observation and modeling of mercury in the environment, and it operates three stations for the long-term, research-grade monitoring of concentrations of mercury species and other trace pollutants in the atmosphere in Beltsville, Maryland; Utqiaġvik (formerly known as Barrow), Alaska; and Mauna Loa, Hawaii, that are key components of NADP’s Atmospheric Mercury Network (AMNet). A cornerstone of ARL’s mercury research is a state-of-the-art modeling system that tracks mercury emission sources and links these emissions to atmospheric transport, transformation, and deposition (Cohen et al. 2016).

Wildfires are not only dangerous, but also increasing in severity and prevalence, particularly on the West Coast of the United States. ARL has been a key participant in a number of NOAA research activities to better predict, respond, and recover to the increasing threat of wildfires. This work benefits from the laboratory’s early monitoring and modeling studies (e.g., Draxler et al. 1994) and the subsequent development of NOAA’s Smoke Forecasting System (Rolph et al. 2009; Stein et al. 2009). ARL has led efforts to improve the delivery of real-time fire weather, smoke, and air quality modeling and aerosol data assimilation.

Dust emissions and transport have also been active research areas throughout ARL’s history. Multiple emissions parameterizations (Gillette et al. 1997; Draxler et al. 2010) as well as modeling studies have been produced by ARL scientists over the past 30 years (e.g., Draxler et al. 2001; Stein et al. 2011). These research endeavors resulted in the implementation of the first operational dust forecasting at NOAA’s NWS in the 2010s. Moreover, ARL has made a sizable contribution to the integration of windblown dust to NOAA’s NAQFC and the Global Forecasting System. ARL continues to provide updates and improvements to these operational systems (Zhang et al. 2022).

Transport and dispersion model developments

ARL modeling research started with the prediction of radioactive fallout downwind of nuclear weapons tests and on dispersion from nuclear power installations, necessitating attention to aspects of plume rise and wind variability (Hanna and Gifford 1975). Predictive models were generated and adopted as the relevant operational capability within the U.S. Weather Bureau in 1957. The national security concerns of those times prohibited widespread dissemination of these products.

During the same period, research at Oak Ridge concentrated on the theoretical description of diffusion (Gifford 1955). The Gaussian plume description of near-field diffusion was refined and promoted as a product of collaboration with researchers from the United Kingdom in 1959. In Idaho, research centered on dispersion forecasts using mesoscale network observations. For risk assessment applications, statistical distributions of determining quantities and their consequences were examined. For real-time emergency response use, the mesoscale diffusion MESODIF model (Start and Wendell 1974) evolved, which was then further developed into the MDIFF (for short-term episodes) and MDIFFH (annual or long-term simulations) models (Sagendorf et al. 2001). Similar site-specific dispersion models are now common.

Extension to the case of emitted pollutants, in general, was subsequently the intent of the Air Resources Laboratories Atmospheric Transport and Dispersion Model (Heffter 1980). The development of dispersion simulations continued with the availability of observations from the extended program of ARL tracer experiments, culminating in the modern-day Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Hess 1997; Stein et al. 2015). Beginning in 1997, the ARL Real-time Environmental Applications and Display System (READY) was made available, to provide easy online access to HYSPLIT (Rolph et al. 2017) (Fig. 5).

Fig. 5.
Fig. 5.

As atmospheric transport and dispersion capabilities improved, products from 1949 to the present demonstrate increasing ability to pinpoint sources and emissions. (left) 1949, detection of first Soviet nuclear weapons test; originally hand drawn by ARL’s Lester Machta. (center) 2011, calculated Cs-137 deposition from the Fukushima incident using HYSPLIT with enhanced mesoscale analysis estimated for 31 Mar 2011 (WMO 2013). (right) 2023, HYSPLIT modeled plume from an August 2023 industrial accident near Dallas, Texas.

Citation: Bulletin of the American Meteorological Society 104, 12; 10.1175/BAMS-D-23-0006.1

The vulnerability of jet engines to ingestion of volcanic ash arose as an important issue in 1989 when all engines failed on an airliner traveling near Mt. Redoubt in Alaska (Casadevall 1994). The air traffic industry now relies on the provision of forecasts of volcanic clouds. The ARL volcanic ash product, based on HYSPLIT, has become a favored tool in this regard (Stunder et al. 2007).

Nuclear reactor accidents have repeatedly energized the ARL dispersion capabilities. International confusion following the Chernobyl event of 1986 caused the WMO to organize Regional Specialized Meteorological Centres (RSMCs) with ARL and NWS participation. ARL coordinated RSMC modeling results following the 2011 Fukushima, Japan, nuclear reactor event and contributed to the resulting United Nations report (WMO 2013). Further applications of the HYSPLIT model are described in Fig. 6.

Fig. 6.
Fig. 6.

HYSPLIT has been used for a variety of applications in the past three decades. Model evaluation and refinement are a continuing emphasis, especially as new applications arise.

Citation: Bulletin of the American Meteorological Society 104, 12; 10.1175/BAMS-D-23-0006.1

HYSPLIT continues to evolve and be used for a multitude of purposes not originally envisioned. In 2021, ARL was approached by the UN’s Food and Agriculture Organization (FAO) regarding a rather severe outbreak of locust swarms in Eastern Africa and the Arabian Peninsula in the Middle East. These areas are affected by outbreaks of desert locust swarms, creating significant threats to food security in these regions. Advance knowledge of where a given swarm might go, and/or where it might have come from, can aid efforts to mitigate the devastating impacts caused by these voracious pests. Locusts are believed to be relatively passive fliers, with movements primarily influenced by the wind. They also fly together in a swarm, making them ideal candidates for simulation using HYSPLIT’s air trajectory modeling capabilities, and as such an online capability was developed to assist the FAO in tracking these swarms.

Global/national monitoring and climate change

In the 1950s, concern about global cooling caused by volcanic eruptions and atomic fallout and, at much the same time, about warming due to emissions of carbon dioxide and other greenhouse gasses generated considerable scientific attention. ARL’s studies of the global spread of radioactive fallout, using upper-atmosphere measurements by balloons and aircraft, led to initial research into the seasonal pattern of stratospheric–tropospheric exchange and the role of the Hadley cell. These studies led to a focus on stratospheric temperature using a global network of radiosonde stations. Several recurring seasonal anomalies of zonal, hemispheric, and global temperatures were identified, as reported in the series of publications by the team led by Jim Angell, who introduced the term “quasi-biennial oscillation” (Angell and Korshover 1964). Research on the recurring features of the global climate continued with attention to sea surface temperature, water vapor pressure, precipitation, solar radiation, and ozone (Angell and Korshover 1973; Elliott and Gaffen 1991). The results contributed to several major global assessments conducted by the United Nations Environment Programme and the first (1990) Intergovernmental Panel on Climate Change (IPCC) report.

The International Geophysical Year (1957) brought an opportunity to consolidate ARL’s climate research by inaugurating a new global observing system (Price and Pales 1959). A first Geophysical Monitoring for Climate Change (GMCC) site was established on the upper slopes of Mauna Loa, in Hawaii, as an ARL entity (White 1982). Initial CO2 concentration observations were by Dr. David Keeling of Scripps Institution of Oceanography, followed by independent ARL measurements in 1964. ARL measurements of airborne radioactivity and dust started in 1958, the latter in recognition of the volcanic dust/atmospheric cooling scenario. Monitoring quickly expanded to ozone and other trace gases. In succession, additional monitoring stations were set up at Barrow (now Utqiaġvik) in Alaska, American Samoa, and the South Pole. To provide management of these sites and related research, the ARL-GMCC laboratory was organized in Boulder, Colorado. Annual meetings conducted in Boulder served to integrate the GMCC program describing time trends with the research of other ARL groups on how, when, and why the trending trace quantities entered and left the atmosphere. GMCC was a mainstay of ARL research until 1991, when it was transferred into what is now the Global Monitoring Laboratory, one of the NOAA OAR Boulder Laboratories. A component of GMCC, concentrating on monitoring incoming radiation and atmospheric turbidity, remained within ARL until it also transitioned.

To detect the surface repercussions of climate change across the United States, ARL worked with its partners at NOAA’s National Climatic Data Center in Asheville, North Carolina (now the National Center for Environmental Information, NCEI) to establish in the late 1990s the U.S. Climate Reference Network (USCRN). The USCRN provides long-term temperature, precipitation, soil moisture, and soil temperature measurements with high-quality instruments in stable and pristine settings far from societal sources of heat (Diamond et al. 2013). The USCRN network continues to grow, with (as of the end of 2022) 114 stations in the contiguous United States, 25 in Alaska (as of September 2023), and two in Hawaii. USCRN observations serve as a national climate standard against which other regional and local networks can be compared. ARL continues to build on the unique reference nature of the USCRN to look at furthering the science of boundary layer characterization (e.g., Kochendorfer et al. 2022) as well as in ensuring that the program can be a benefit to characterizing the nation’s climate, and as such, is developing a USCRN Aggregate Climate Extremes (ACE) index that will couple both air temperature and precipitation data from about 20 years of USCRN data in the conterminous United States to characterize the effects of changes in climate and their relationship to increasing extremes.

Urban meteorology

The meteorological complexity of cities and urban areas was a widely recognized issue confronting dispersion predictions and risk assessments of the 1950s and 1960s when fears of nuclear confrontations and accidents dominated atmospheric research. Several extensive tracer experiments were conducted, starting with the tracking of tetroons over Atlantic City (1965), New York City (1965), Columbus (1969), and Los Angeles (1969, 1973) and culminating with studies using PFCs in Washington, D.C. (1984, q.v. METREX) (Angell and Pack 1965; Hass et al. 1967; Pack et al. 1970; Draxler 1985). More recent intensive tracer studies led by DOE national laboratories (notably at Los Angeles, Oklahoma City, Salt Lake City, and New York) have made extensive use of ARL-developed technologies. In addition, wind tunnel studies conducted at the FMF also focused on dispersion near specific buildings, such as the Pentagon and the World Trade Center (Perry et al. 2004).

Uncertainties about dispersion in cities assumed new importance with the 11 September 2001 terrorist attacks and the awareness that population centers lacked guidance on how to protect people following a chem/bio/radiological event. Many studies have demonstrated that prediction of local risk at street level is strongly affected by the distribution of buildings and streets (Hosker and Pendergrass 1987). To identify downwind areas of major risk, wind information above the city buildings is needed, as demonstrated by ARL tall-tower studies in New York (in the mid-1960s). In 2002, a trial rooftop network, DCNet, was established in Washington, D.C., to develop a methodology to predict dispersion using city-specific wind flow observations made above the influence of particular streets and buildings and at a height compatible with where dispersion models are often initiated (Hicks et al. 2005). DCNet continued for more than a decade, yielding evidence that a single rooftop station can yield widely applicable data and demonstrating how local heat sources can control atmospheric stability (Lichiheb et al. 2023). DCNet research led to the development of a new city-scale dispersion methodology using local observations.

Beginning in 2023, DCNet has been redirected and restructured in light of the changed Washington, D.C., urban landscape and has been renamed UrbanNet. In the near future, the UrbanNet system will add new and upgraded sensors to improve the quantification of the atmospheric environment experienced by the population, including in areas downwind, and the renaming of the system opens the possibility for possible expansion into other urban areas. Improved measurements of precipitation will be made—detection of urban precipitation trends is crucial for an array of planning activities. The central scientific aspect of this research is, once again, the mainstream ARL specialty—atmospheric dispersion as influenced by synoptic meteorological processes, in the context of temporal and spatial variability. In recent years, ARL initiated a mobile monitoring effort in the Washington, D.C., region that pairs an instrumented sport utility vehicle for on-the-ground measurements and a leased, similarly instrumented aircraft for observations up to ∼2,500 m above ground level with our partners at the University of Maryland to quantify urban greenhouse gases (GHGs) emissions (Ren et al. 2018).

Urban emissions of GHGs represent a large portion of anthropogenic greenhouse gas contributions to the global carbon budget, and UrbanNet will play a role here as well. As the global population increasingly lives in cities, the proportion of anthropogenic emissions in urban areas relative to rural regions is increasing. ARL has undertaken a new research direction to identify biases and reduce uncertainties in GHG sources and sinks estimates in order to improve GHG monitoring in urban areas. This urban GHG monitoring system is based on a top-down approach utilizing a variety of field observations of GHG and an inverse modeling system based on the HYSPLIT model. This scientific endeavor will contribute to the evaluation and improvement of existing GHG emissions/sinks inventories, the assessment of how greenhouse gas emissions/sinks are changing over time, and the identification of sources and sinks for possible GHG mitigation strategies in a sustained and systematic way. The final objective of this work is to build a prototype operational GHG monitoring system for urban areas with the possibility of expansion to a regional and national coverage.

Conclusions

Throughout its 75-yr history, ARL has made significant and pioneering contributions to the understanding of the physical and chemical processes that occur in the atmosphere. Most notable advancements were done in the areas of micrometeorology [e.g., Best Aircraft Turbulence (BAT) probe and Infrared Absorption Gas Analyzer (IRGA)], especially over complex terrain; modeling of transport and dispersion of hazardous materials (e.g., Gaussian and HYSPLIT models); wet and dry deposition parameterizations, modeling (e.g., RADM), and monitoring (e.g., NADP); air quality model development and forecasting (e.g., CMAQ); and climate studies and monitoring (e.g., USCRN).

Today, ARL is a highly integrated organization addressing particular aspects of boundary layer research in a cooperative and synergistic manner. Current research focuses on surface–atmosphere exchange, atmospheric transport and dispersion, and boundary layer characterization with the goal of improving weather prediction, air quality forecasts, and climate science and services. ARL research is conducted in support of NOAA’s mission with specific attention to the improvement of its predictions by either further developing existing models (e.g., NAQFC) or anticipating the requirements of models in development (e.g., Unified Forecast System Air Quality Model, UFS-AQM). ARL’s approach fosters a flexible and adaptive research environment to address new challenges, in service to the nation. This has been the strength of ARL since its inception in 1948 and our capabilities and research efforts continue to evolve well into the twenty-first century.

Acknowledgments.

This presentation highlights some of the achievements of ARL (and its precursor groups) since 1948. It is not intended to be a comprehensive review of all research activities and relies on information gleaned from available resources and recollections. The authors wish to thank former and current ARL staff for sharing archival documents and papers. We thank Ella Hunter for assistance with historical photos. Brief biographies of key individuals mentioned in the text, as well as further insight into ARL’s historical evolution, are available at https://www.arl.noaa.gov/about/history/.

Data availability statement.

Data sharing is not applicable. No datasets were generated or analyzed during the current study.

References

  • AMS 1977 Committee on Atmospheric Turbulence and Diffusion, 1978: Accuracy of dispersion models. Bull. Amer. Meteor. Soc., 59, 10251026, https://doi.org/10.1175/1520-0477-59.8.1025.

    • Search Google Scholar
    • Export Citation
  • Angell, J. K., and J. Korshover, 1964: Quasi-biennial variations in temperature, total ozone and tropopause height. J. Atmos. Sci., 21, 479492, https://doi.org/10.1175/1520-0469(1964)021<0479:QBVITT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Angell, J. K., and D. H. Pack, 1965: A study of the sea breeze at Atlantic City, New Jersey using tetroons as Lagrangian tracers. Mon. Wea. Rev., 93, 475493, https://doi.org/10.1175/1520-0493(1965)093<0475:ASOTSB>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Angell, J. K., and J. Korshover, 1973: Quasi-biennial and long-term fluctuations in total ozone. Mon. Wea. Rev., 101, 426443, https://doi.org/10.1175/1520-0493(1973)101<0426:QALFIT>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Auble, D. L., and T. P. Meyers, 1992: An open path, fast response infrared absorption gas analyzer for H2O and CO2. Bound.-Layer Meteor., 59, 243256, https://doi.org/10.1007/BF00119815.

    • Search Google Scholar
    • Export Citation
  • Briggs, G. A., 1969: Optimum formulas for buoyant plume rise. Philos. Trans. Roy. Soc., A265, 197203, https://doi.org/10.1098/rsta.1969.0048.

    • Search Google Scholar
    • Export Citation
  • Butterworth, B. J. A., and Coauthors, 2020: Connecting land–atmosphere interactions to surface heterogeneity in CHEESEHEAD19. Bull. Amer. Meteor. Soc., 102, E421E445, https://doi.org/10.1175/BAMS-D-19-0346.1.

    • Search Google Scholar
    • Export Citation
  • Byun, D. W., and J. K. S. Ching, 1999: Science algorithms of the EPA MODELS-2 Community Multiscale Air Quality (CMAQ) modeling system. EPA/600/R-99/030, U.S. EPA Office of Research and Development, 22 pp., https://cfpub.epa.gov/si/si_public_file_download.cfm?p_download_id=524687&Lab=NERL.

  • Campbell, P. C., W. Jiang, Z. Moon, S. Zinn, and Y. Tang, 2023: NOAA’s Global Forecast System data in the cloud for community air quality modeling. Atmosphere, 14, 1110, https://doi.org/10.3390/atmos14071110.

    • Search Google Scholar
    • Export Citation
  • Casadevall, T. J., 1994: The 1989–1990 eruption of Redoubt Volcano, Alaska: Impacts on aircraft operations. J. Volcanol. Geotherm. Res., 62, 301316, https://doi.org/10.1016/0377-0273(94)90038-8.

    • Search Google Scholar
    • Export Citation
  • Chang, J. S., R. A. Brost, I. S. A. Isaksen, S. Madronich, P. Middleton, W. R. Stockwell, and C. J. Walcek, 1987: A three-dimensional Eulerian acid deposition model: Physical concepts and formulation. J. Geophys. Res., 92, 14 68114 700, https://doi.org/10.1029/JD092iD12p14681.

    • Search Google Scholar
    • Export Citation
  • Cohen, M., and Coauthors, 2002: Modeling the atmospheric transport and deposition of PCDD/F to the Great Lakes. Environ. Sci. Technol., 36, 48314845, https://doi.org/10.1021/es0157292.

    • Search Google Scholar
    • Export Citation
  • Cohen, M., R. Artz, R. Draxler, P. Miller, and L. Poissant, 2004: Modeling the atmospheric transport and deposition of mercury to the Great Lakes. Environ. Res., 95, 247265, https://doi.org/10.1016/j.envres.2003.11.007.

    • Search Google Scholar
    • Export Citation
  • Cohen, M., and Coauthors, 2016: Modeling the global atmospheric transport and deposition of mercury to the Great Lakes. Elementa, 4, 000118, https://doi.org/10.12952/journal.elementa.000118.

    • Search Google Scholar
    • Export Citation
  • Crawford, T. L., and R. J. Dobosy, 1992: A sensitive fast-response probe to measure turbulence and heat flux from any airplane. Bound.-Layer Meteor., 59, 257278, https://doi.org/10.1007/BF00119816.

    • Search Google Scholar
    • Export Citation
  • Crawford, T. L., R. T. McMillen, T. P. Meyers, and B. B. Hicks, 1993: Spatial and temporal variability of heat, water vapor, carbon dioxide, and momentum air-sea exchange in a coastal environment. J. Geophys. Res., 98, 12 86912 880, https://doi.org/10.1029/93JD00628.

    • Search Google Scholar
    • Export Citation
  • de Boer, G., and Coauthors, 2023: Supporting advancement in weather and water prediction in the upper Colorado River basin: The SPLASH campaign. Bull. Amer. Meteor. Soc., 104, E1853E1874, https://doi.org/10.1175/BAMS-D-22-0147.1.

    • Search Google Scholar
    • Export Citation
  • Diamond, H. J., and Coauthors, 2013: U.S. Climate Reference Network after one decade of operations: Status and assessment. Bull. Amer. Meteor. Soc., 94, 485498, https://doi.org/10.1175/BAMS-D-12-00170.1.

    • Search Google Scholar
    • Export Citation
  • Dickson, C. R., and E. F. Pound, 1961: A radar transponder for determining meteorological trajectories [in “Program of the Ninth Weather Radar Conference October 23–26, 1961, Kansas City, Missouri”]. Bull. Amer. Meteor. Soc., 42, 652, https://doi.org/10.1175/1520-0477-42.9.640.

    • Search Google Scholar
    • Export Citation
  • Draxler, R. R., 1985: Metropolitan tracer experiment. NOAA Tech. Memo. ERL ARL-140, 43 pp., https://www.arl.noaa.gov/wp_arl/wp-content/uploads/2017/08/arl-140.pdf.

  • Draxler, R. R., 1988: Across North America Tracer Experiment (ANATEX), weather maps and tracer concentrations. NOAA Tech. Memo. ERL ARL-165, 91 pp., https://www.arl.noaa.gov/wp_arl/wp-content/uploads/2017/08/arl-165.pdf.

  • Draxler, R. R., and G. D. Hess, 1997: Description of the HYSPLIT_4 modeling system. NOAA Tech. Memo. ERL ARL-224, 31 pp., https://www.arl.noaa.gov/wp_arl/wp-content/uploads/documents/reports/arl-224.pdf.

  • Draxler, R. R., J. T. McQueen, and B. J. B. Stunder, 1994: An evaluation of air pollutant exposures due to the 1991 Kuwait oil fires using a Lagrangian model. Atmos. Environ., 28, 21972210, https://doi.org/10.1016/1352-2310(94)90360-3.

    • Search Google Scholar
    • Export Citation
  • Draxler, R. R., D. A. Gillette, J. S. Kirkpatrick, and J. Heller, 2001: Estimating PM10 air concentrations from dust storms in Iraq, Kuwait, and Saudi Arabia. Atmos. Environ., 35, 43154330, https://doi.org/10.1016/S1352-2310(01)00159-5.

    • Search Google Scholar
    • Export Citation
  • Draxler, R. R., P. Ginoux, and A. F. Stein, 2010: An empirically derived emission algorithm for wind-blown dust. J. Geophys. Res., 115, D16212, https://doi.org/10.1029/2009JD013167.

    • Search Google Scholar
    • Export Citation
  • Eckman, R. M., R. J. Dobosy, D. L. Auble, T. W. Strong, and T. L. Crawford, 2007: A pressure-sphere anemometer for measuring turbulence and fluxes in hurricanes. J. Atmos. Oceanic Technol., 24, 9941007, https://doi.org/10.1175/JTECH2025.1.

    • Search Google Scholar
    • Export Citation
  • Eder, B., and Coauthors, 2010: Using National Air Quality Forecast guidance to develop local air quality index forecasts. Bull. Amer. Meteor. Soc., 91, 313326, https://doi.org/10.1175/2009BAMS2734.1.

    • Search Google Scholar
    • Export Citation
  • Elliott, W. P., and D. J. Gaffen, 1991: On the utility of radiosonde humidity archives for climate studies. Bull. Amer. Meteor. Soc., 72, 15071520, https://doi.org/10.1175/1520-0477(1991)072<1507:OTUORH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ferber, G. J., 1986: Cross-Appalachian Tracer Experiment (CAPTEX’83). NOAA Tech. Memo. ERL ARL-142, 62 pp., https://www.arl.noaa.gov/wp_arl/wp-content/uploads/2017/08/arl-142.pdf.

  • Ferber, G. J., K. Telegadas, J. L. Heffter, C. R. Dickson, R. N. Dietz, and P. W. Krey, 1981: Demonstration of a long-range atmospheric tracer system using perfluorocarbons. NOAA Tech. Memo. ERL ARL-1O1, 83 pp., https://www.arl.noaa.gov/wp_arl/wp-content/uploads/2017/08/ARL-101.pdf.

  • Finn, D., K. L. Clawson, R. G. Carter, J. D. Rich, R. M. Eckman, S. G. Perry, V. Isakov, and D. K. Heist, 2010: Tracer studies to characterize the effects of roadside noise barriers on near-road pollutant dispersion under varying atmospheric stability conditions. Atmos. Environ., 44, 204214, https://doi.org/10.1016/j.atmosenv.2009.10.012.

    • Search Google Scholar
    • Export Citation
  • Finn, D., and Coauthors, 2015: Project Sagebrush Phase 1. NOAA Tech. Memo. OAR ARL-268, 362 pp., https://doi.org/10.7289/V5VX0DHV.

  • Finn, D., and Coauthors, 2017: Project Sagebrush Phase 2. NOAA Tech. Memo. OAR ARL-275, 391 pp., https://doi.org/10.7289/V5/TM-OAR-ARL-275.

  • Gifford, F., 1953: A study of low level air trajectories at Oak Ridge, Tenn. Mon. Wea. Rev., 81, 179192, https://doi.org/10.1175/1520-0493(1953)081<0179:ASOLLA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gifford, F., 1955: A simultaneous Lagrangian–Eulerian turbulence experiment. Mon. Wea. Rev., 83, 293301, https://doi.org/10.1175/1520-0493(1955)083<0293:ASLTE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gillette, D. A., D. W. Fryrear, T. E. Gill, T. Ley, T. A. Cahill, and E. A. Gearhart, 1997: Relation of vertical flux of particles smaller than 10 μm to total aeolian horizontal mass flux at Owens Lake. J. Geophys. Res., 102, 26 00926 015, https://doi.org/10.1029/97JD02252.

    • Search Google Scholar
    • Export Citation
  • Gosling, F. G., 1999: The Manhattan Project: Making the atomic bomb. U.S. Department of Energy History Division Tech. Rep. DOE/MA-0001, 76 pp., https://doi.org/10.2172/303853.

  • Green, R. A., 1992: History, atmospheric turbulence and diffusion division: 1948–June 1992. NOAA, 33 pp., https://www.atdd.noaa.gov/wp-content/uploads/2017/02/History-of-ATDD.pdf.

  • Hanna, S. R., and F. A. Gifford, 1975: Meteorological effects of energy dissipation at large power parks. Bull. Amer. Meteor. Soc., 56, 10691077, https://doi.org/10.1175/1520-0477(1975)056<1069:MEOEDA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hass, W. A., W. H. Hoecker, D. H. Pack, and J. K. Angell, 1967: Analysis of low-level, constant volume balloon (tetroon) flights over New York City. Quart. J. Roy. Meteor. Soc., 93, 483493, https://doi.org/10.1002/qj.49709339807.

    • Search Google Scholar
    • Export Citation
  • Heffter, J. L., 1965: The variation of horizontal diffusion parameters with time for travel periods of one hour or longer. J. Appl. Meteor., 4, 153156, https://doi.org/10.1175/1520-0450(1965)004<0153:TVOHDP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Heffter, J. L., 1980: Air Resources Laboratories Atmospheric Transport and Dispersion Model (ARL-ATAD). NOAA Tech. Memo. ERL ARL-81, 29 pp., https://repository.library.noaa.gov/view/noaa/13189.

  • Heffter, J. L., J. F. Schubert, and G. A. Mead, 1984: Atlantic Coast Unique Regional Atmospheric Tracer Experiment (ACURATE). NOAA Tech. Memo. OAR ARL-130, 67 pp., https://repository.library.noaa.gov/view/noaa/9474/noaa_9474_DS1.pdf.

  • Hicks, B., K. Clawson, W. Pendergrass, and R. Eckman, 2005: Applying local data to urban dispersion forecasting. Environ. Manage., 132, 2630.

    • Search Google Scholar
    • Export Citation
  • Hicks, B. B., D. D. Baldocchi, T. P. Meyers, D. R. Matt, and R. P. Hosker Jr., 1987: A preliminary multiple resistance routine for deriving dry deposition velocities from measured quantities. Water Air Soil Pollut., 36, 311330, https://doi.org/10.1007/BF00229675.

    • Search Google Scholar
    • Export Citation
  • Hicks, B. B., and Coauthors, 1989: A field investigation of sulfate fluxes to a deciduous forest. J. Geophys. Res., 94, 13 00313 011, https://doi.org/10.1029/JD094iD10p13003.

    • Search Google Scholar
    • Export Citation
  • Hosker, R. P., and W. R. Pendergrass, 1987: Flow and dispersion near clusters of buildings. NOAA Tech. Memo. ERL ARL-153, 107 pp., https://repository.library.noaa.gov/view/noaa/19705.

  • Hosler, C. R., 1966: Natural radioactivity (radon-222) and air pollution measurements in Washington, D.C. J. Appl. Meteor., 5, 653662, https://doi.org/10.1175/1520-0450(1966)005<0653:NRAAPM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hosler, C. R., 1969: Vertical diffusivity from radon profiles. J. Geophys. Res., 74, 70187026, https://doi.org/10.1029/JC074i028p07018.

    • Search Google Scholar
    • Export Citation
  • Hosler, C. R., 1975: The meteorology program of the Environmental Protection Agency. Bull. Amer. Meteor. Soc., 56, 12611270, https://doi.org/10.1175/1520-0477(1975)056<1261:TMPOTE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hutchison, B. A., and B. B. Hicks, Eds., 1985: The Forest–Atmosphere Interaction. D. Reidel, 684 pp.

  • Irving, P. A., 1991: Acidic Deposition: State of Science and Technology, Vols I–IV. U.S. National Acid Precipitation Assessment Program, 265 pp.

    • Search Google Scholar
    • Export Citation
  • Kochendorfer, J., and Coauthors, 2022: How well are we measuring snow post-SPICE? Bull. Amer. Meteor. Soc., 103, E370E388, https://doi.org/10.1175/BAMS-D-20-0228.1.

    • Search Google Scholar
    • Export Citation
  • Lee, P., and Coauthors, 2017: NAQFC developmental forecast guidance for fine particulate matter (PM2.5). Wea. Forecasting, 32, 343360, https://doi.org/10.1175/WAF-D-15-0163.1.

    • Search Google Scholar
    • Export Citation
  • Lee, T. R., and M. Buban, 2020: Evaluation of Monin–Obukhov and bulk Richardson parameterizations for surface–atmosphere exchange. J. Appl. Meteor. Climatol., 59, 10911107, https://doi.org/10.1175/JAMC-D-19-0057.1.

    • Search Google Scholar
    • Export Citation
  • Lee, T. R., M. Buban, E. Dumas, and C. B. Baker, 2019: On the use of rotary-wing aircraft to sample near-surface thermodynamic fields: Results from recent field campaigns. Sensors, 19, 10, https://doi.org/10.3390/s19010010.

    • Search Google Scholar
    • Export Citation
  • Lee, T. R., M. S. Buban, and T. P. Meyers, 2021: Application of bulk Richardson parameterizations of surface fluxes to heterogeneous land surfaces. Mon. Wea. Rev., 149, 32433264, https://doi.org/10.1175/MWR-D-21-0047.1.

    • Search Google Scholar
    • Export Citation
  • Lichiheb, N., B. B. Hicks, and L. Myles, 2023: An evaluation of meteorological data prediction over Washington, D.C.: Comparison of DCNet observations and NAM model outputs. Urban Climate, 48, 101410, https://doi.org/10.1016/j.uclim.2023.101410.

    • Search Google Scholar
    • Export Citation
  • Machta, L., 1958: The use of radioactive tracers in meteorology. Annals of the International Geophysical Year 1957–1958, Vol. V, Part V, Pergamon Press, 309312.

    • Search Google Scholar
    • Export Citation
  • Machta, L., 1992: Finding the site of the first Soviet nuclear test in 1949. Bull. Amer. Meteor. Soc., 73, 17971806, https://doi.org/10.1175/1520-0477(1992)073<1797:FTSOTF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Machta, L., H. L. Hamilton, L. F. Hubert, R. J. List, and K. M. Nagler, 1957: Airborne measurements of atomic debris. J. Meteor., 14, 165175, https://doi.org/10.1175/1520-0469(1957)014<0165:AMOAD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • McMillen, R. T., 1988: An eddy correlation technique with extended applicability to non-simple terrain. Bound.-Layer Meteor., 43, 231245, https://doi.org/10.1007/BF00128405.

    • Search Google Scholar
    • Export Citation
  • Myles, L., T. P. Meyers, and L. Robinson, 2007: Relaxed eddy accumulation measurements of ammonia, nitric acid, sulfur dioxide, and particulate sulfate dry deposition near Tampa, FL, USA. Environ. Res. Lett., 2, 034004, https://doi.org/10.1088/1748-9326/2/3/034004.

    • Search Google Scholar
    • Export Citation
  • Ngan, F., A. F. Stein, D. Finn, and R. Eckman, 2018: Dispersion simulations using HYSPLIT for the Sagebrush Tracer Experiment. Atmos. Environ., 186, 1831, https://doi.org/10.1016/j.atmosenv.2018.05.012.

    • Search Google Scholar
    • Export Citation
  • Otte, T. L., and Coauthors, 2005: Linking the Eta Model with the Community Multiscale Air Quality (CMAQ) modeling system to build a National Air Quality Forecasting System. Wea. Forecasting, 20, 367384, https://doi.org/10.1175/WAF855.1.

    • Search Google Scholar
    • Export Citation
  • Pack, D. H., J. K. Angell, M. Hodges, W. Hoecker, and C. R. Dickson, 1970: Tetroon flights in Los Angeles, California – 1969. NOAA Tech. Memo. ERL ARL-19, 31 pp., https://www.arl.noaa.gov/wp_arl/wp-content/uploads/2017/08/ARL-19.pdf.

  • Pan, L., and Coauthors, 2020: Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign. Geosci. Model Dev., 13, 21692184, https://doi.org/10.5194/gmd-13-2169-2020.

    • Search Google Scholar
    • Export Citation
  • Perry, S. G., D. K. Heist, R. S. Thompson, W. H. Synder, and R. E. Lawson Jr., 2004: Wind tunnel simulation of flow and pollutant dispersal around the World Trade Center site. EM: The Air & Waste Management Association Magazine. Air & Waste Management Association, 3134.

  • Price, S., and J. C. Pales, 1959: The Mauna Loa high-altitude observatory. Mon. Wea. Rev., 87, 114, https://doi.org/10.1175/1520-0493(1959)087<0001:TMLHO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Randerson, D., 1984: Atmospheric science and power production. U.S. Department of Energy Tech. Rep. DOE/TIC-27601, 859 pp., https://doi.org/10.2172/6503687.

  • Ren, X., and Coauthors, 2018: Methane emissions from the Baltimore-Washington area based on airborne observations: Comparison to emissions inventories. J. Geophys. Res. Atmos., 123, 88698882, https://doi.org/10.1029/2018JD028851.

    • Search Google Scholar
    • Export Citation
  • Rolph, G. D., and Coauthors, 2009: Description and verification of the NOAA Smoke Forecasting System: The 2007 fire season. Wea. Forecasting, 24, 361378, https://doi.org/10.1175/2008WAF2222165.1.

    • Search Google Scholar
    • Export Citation
  • Rolph, G. D., A. Stein, and B. Stunder, 2017: Real-time Environmental Applications and Display sYstem: READY. Environ. Modell. Software, 95, 210228, https://doi.org/10.1016/j.envsoft.2017.06.025.

    • Search Google Scholar
    • Export Citation
  • Sagendorf, J. F., R. G. Carter, and K. L. Clawson, 2001: MDIFF transport and diffusion models. NOAA Tech. Memo. ARL-238, 32 pp., https://www.arl.noaa.gov/wp_arl/wp-content/uploads/2017/08/arl-238.pdf.

  • Slade, D. H., 1966: Estimates of dispersion from pollutant releases of a few seconds to 8 hours in duration. NOAA ESSA ARL Tech. Note 39-ARL-3, 34 pp., https://www.arl.noaa.gov/wp_arl/wp-content/uploads/2017/08/TN-39-ARL-3.pdf.

  • Slade, D. H., Ed., 1968: Meteorology and atomic energy. TID-24190, U.S. Atomic Energy Commission, 445 pp., https://doi.org/10.2172/4492043.

  • Start, G. E., and L. L. Wendell, 1974: Regional effluent dispersion calculations considering spatial and temporal meteorological variations. NOAA Tech. Memo. ERL ARL-44, 70 pp., https://www.arl.noaa.gov/wp_arl/wp-content/uploads/2017/08/ARL-44.pdf.

  • Start, G. E., C. R. Dickson, and L. L. Wendell, 1973: Diffusion in a canyon within rough mountainous terrain. NOAA Tech. Memo. ERL ARL-38, 55 pp., https://www.arl.noaa.gov/wp_arl/wp-content/uploads/2017/08/ARL-38.pdf.

  • Start, G. E., J. H. Cate, C. R. Dickson, N. R. Ricks, G. R. Ackermann, and J. F. Sagendorf, 1977: Rancho Seco building wake effects on atmospheric diffusion. NOAA Tech. Memo. ERL ARL-69, 189 pp., https://repository.library.noaa.gov/view/noaa/19660/noaa_19660_DS1.pdf.

  • Stein, A. F., G. D. Rolph, R. R. Draxler, B. Stunder, and M. Ruminski, 2009: Verification of the NOAA Smoke Forecasting System: Model sensitivity to the injection height. Wea. Forecasting, 24, 379394, https://doi.org/10.1175/2008WAF2222166.1.

    • Search Google Scholar
    • Export Citation
  • Stein, A. F., Y. Wang, J. D. de la Rosa, A. M. Sanchez de la Campa, N. Castell, and R. R. Draxler, 2011: Modeling PM10 originated from dust intrusions in the southern Iberian Peninsula using HYSPLIT. Wea. Forecasting, 26, 236242, https://doi.org/10.1175/WAF-D-10-05044.1.

    • Search Google Scholar
    • Export Citation
  • Stein, A. F., R. R. Draxler, G. D. Rolph, B. J. B. Stunder, M. D. Cohen, and F. Ngan, 2015: NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Amer. Meteor. Soc., 96, 20592077, https://doi.org/10.1175/BAMS-D-14-00110.1.

    • Search Google Scholar
    • Export Citation
  • Stunder, B. J. B., J. L. Heffter, and R. R. Draxler, 2007: Airborne volcanic ash forecast area reliability. Wea. Forecasting, 22, 11321139, https://doi.org/10.1175/WAF1042.1.

    • Search Google Scholar
    • Export Citation
  • U.S. Environmental Protection Agency, 2022: “Air Programs” Code of Federal Regulations, title 40. Government Printing Office, 183184, https://www.ecfr.gov/current/title-40/chapter-I/subchapter-C/part-51/subpart-F?toc=1.

  • Vet, R., and Coauthors, 2014: A global assessment of precipitation chemistry and deposition of sulfur, nitrogen, sea salt, base cations, organic acids, acidity and pH, and phosphorus. Atmos. Environ., 93, 3100, https://doi.org/10.1016/j.atmosenv.2013.10.060.

    • Search Google Scholar
    • Export Citation
  • Wanta, R. C., and F. D. White, 1949: Meteorology of the reactor test site, Arco, Idaho. U.S. Weather Bureau, Scientific Services Division Tech. Rep., 41 pp.

  • Wexler, H., 1962: Harry Wexler papers. Library of Congress Manuscript Division, https://lccn.loc.gov/mm79045229.

  • White, R. M., 1982: Science, politics, and international atmospheric and oceanic programs. Bull. Amer. Meteor. Soc., 63, 924933, https://doi.org/10.1175/1520-0477-63.8.924.

    • Search Google Scholar
    • Export Citation
  • WMO, 2013: Report on the third meeting of the WMO Task Team on meteorological analyses for the Fukushima-Daiichi nuclear power plant accident, annex III. WMO-1120, 68 pp., https://library.wmo.int/doc_num.php?explnum_id=7835.

  • Yanskey, G. R., 1960: Weather Bureau operations and research at the National Reactor Testing Station. Weatherwise, 13, 187203, https://doi.org/10.1080/00431672.1960.9940978.

    • Search Google Scholar
    • Export Citation
  • Yanskey, G. R., E. H. Markee Jr., and A. P. Richter, 1966: Climatography of the National Reactor Testing Station. Idaho National Engineering Laboratory Rep. IDO-12048, 213 pp.

  • Zhang, L., and Coauthors, 2022: Development and evaluation of the Aerosol Forecast Member in the National Center for Environment Prediction (NCEP)’s Global Ensemble Forecast System (GEFS-aerosols v1). Geosci. Model Dev., 15, 53375369, https://doi.org/10.5194/gmd-15-5337-2022.

    • Search Google Scholar
    • Export Citation
Save
  • AMS 1977 Committee on Atmospheric Turbulence and Diffusion, 1978: Accuracy of dispersion models. Bull. Amer. Meteor. Soc., 59, 10251026, https://doi.org/10.1175/1520-0477-59.8.1025.

    • Search Google Scholar
    • Export Citation
  • Angell, J. K., and J. Korshover, 1964: Quasi-biennial variations in temperature, total ozone and tropopause height. J. Atmos. Sci., 21, 479492, https://doi.org/10.1175/1520-0469(1964)021<0479:QBVITT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Angell, J. K., and D. H. Pack, 1965: A study of the sea breeze at Atlantic City, New Jersey using tetroons as Lagrangian tracers. Mon. Wea. Rev., 93, 475493, https://doi.org/10.1175/1520-0493(1965)093<0475:ASOTSB>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Angell, J. K., and J. Korshover, 1973: Quasi-biennial and long-term fluctuations in total ozone. Mon. Wea. Rev., 101, 426443, https://doi.org/10.1175/1520-0493(1973)101<0426:QALFIT>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Auble, D. L., and T. P. Meyers, 1992: An open path, fast response infrared absorption gas analyzer for H2O and CO2. Bound.-Layer Meteor., 59, 243256, https://doi.org/10.1007/BF00119815.

    • Search Google Scholar
    • Export Citation
  • Briggs, G. A., 1969: Optimum formulas for buoyant plume rise. Philos. Trans. Roy. Soc., A265, 197203, https://doi.org/10.1098/rsta.1969.0048.

    • Search Google Scholar
    • Export Citation
  • Butterworth, B. J. A., and Coauthors, 2020: Connecting land–atmosphere interactions to surface heterogeneity in CHEESEHEAD19. Bull. Amer. Meteor. Soc., 102, E421E445, https://doi.org/10.1175/BAMS-D-19-0346.1.

    • Search Google Scholar
    • Export Citation
  • Byun, D. W., and J. K. S. Ching, 1999: Science algorithms of the EPA MODELS-2 Community Multiscale Air Quality (CMAQ) modeling system. EPA/600/R-99/030, U.S. EPA Office of Research and Development, 22 pp., https://cfpub.epa.gov/si/si_public_file_download.cfm?p_download_id=524687&Lab=NERL.

  • Campbell, P. C., W. Jiang, Z. Moon, S. Zinn, and Y. Tang, 2023: NOAA’s Global Forecast System data in the cloud for community air quality modeling. Atmosphere, 14, 1110, https://doi.org/10.3390/atmos14071110.

    • Search Google Scholar
    • Export Citation
  • Casadevall, T. J., 1994: The 1989–1990 eruption of Redoubt Volcano, Alaska: Impacts on aircraft operations. J. Volcanol. Geotherm. Res., 62, 301316, https://doi.org/10.1016/0377-0273(94)90038-8.

    • Search Google Scholar
    • Export Citation
  • Chang, J. S., R. A. Brost, I. S. A. Isaksen, S. Madronich, P. Middleton, W. R. Stockwell, and C. J. Walcek, 1987: A three-dimensional Eulerian acid deposition model: Physical concepts and formulation. J. Geophys. Res., 92, 14 68114 700, https://doi.org/10.1029/JD092iD12p14681.

    • Search Google Scholar
    • Export Citation
  • Cohen, M., and Coauthors, 2002: Modeling the atmospheric transport and deposition of PCDD/F to the Great Lakes. Environ. Sci. Technol., 36, 48314845, https://doi.org/10.1021/es0157292.

    • Search Google Scholar
    • Export Citation
  • Cohen, M., R. Artz, R. Draxler, P. Miller, and L. Poissant, 2004: Modeling the atmospheric transport and deposition of mercury to the Great Lakes. Environ. Res., 95, 247265, https://doi.org/10.1016/j.envres.2003.11.007.

    • Search Google Scholar
    • Export Citation
  • Cohen, M., and Coauthors, 2016: Modeling the global atmospheric transport and deposition of mercury to the Great Lakes. Elementa, 4, 000118, https://doi.org/10.12952/journal.elementa.000118.

    • Search Google Scholar
    • Export Citation
  • Crawford, T. L., and R. J. Dobosy, 1992: A sensitive fast-response probe to measure turbulence and heat flux from any airplane. Bound.-Layer Meteor., 59, 257278, https://doi.org/10.1007/BF00119816.

    • Search Google Scholar
    • Export Citation
  • Crawford, T. L., R. T. McMillen, T. P. Meyers, and B. B. Hicks, 1993: Spatial and temporal variability of heat, water vapor, carbon dioxide, and momentum air-sea exchange in a coastal environment. J. Geophys. Res., 98, 12 86912 880, https://doi.org/10.1029/93JD00628.

    • Search Google Scholar
    • Export Citation
  • de Boer, G., and Coauthors, 2023: Supporting advancement in weather and water prediction in the upper Colorado River basin: The SPLASH campaign. Bull. Amer. Meteor. Soc., 104, E1853E1874, https://doi.org/10.1175/BAMS-D-22-0147.1.

    • Search Google Scholar
    • Export Citation
  • Diamond, H. J., and Coauthors, 2013: U.S. Climate Reference Network after one decade of operations: Status and assessment. Bull. Amer. Meteor. Soc., 94, 485498, https://doi.org/10.1175/BAMS-D-12-00170.1.

    • Search Google Scholar
    • Export Citation
  • Dickson, C. R., and E. F. Pound, 1961: A radar transponder for determining meteorological trajectories [in “Program of the Ninth Weather Radar Conference October 23–26, 1961, Kansas City, Missouri”]. Bull. Amer. Meteor. Soc., 42, 652, https://doi.org/10.1175/1520-0477-42.9.640.

    • Search Google Scholar
    • Export Citation
  • Draxler, R. R., 1985: Metropolitan tracer experiment. NOAA Tech. Memo. ERL ARL-140, 43 pp., https://www.arl.noaa.gov/wp_arl/wp-content/uploads/2017/08/arl-140.pdf.

  • Draxler, R. R., 1988: Across North America Tracer Experiment (ANATEX), weather maps and tracer concentrations. NOAA Tech. Memo. ERL ARL-165, 91 pp., https://www.arl.noaa.gov/wp_arl/wp-content/uploads/2017/08/arl-165.pdf.

  • Draxler, R. R., and G. D. Hess, 1997: Description of the HYSPLIT_4 modeling system. NOAA Tech. Memo. ERL ARL-224, 31 pp., https://www.arl.noaa.gov/wp_arl/wp-content/uploads/documents/reports/arl-224.pdf.

  • Draxler, R. R., J. T. McQueen, and B. J. B. Stunder, 1994: An evaluation of air pollutant exposures due to the 1991 Kuwait oil fires using a Lagrangian model. Atmos. Environ., 28, 21972210, https://doi.org/10.1016/1352-2310(94)90360-3.

    • Search Google Scholar
    • Export Citation
  • Draxler, R. R., D. A. Gillette, J. S. Kirkpatrick, and J. Heller, 2001: Estimating PM10 air concentrations from dust storms in Iraq, Kuwait, and Saudi Arabia. Atmos. Environ., 35, 43154330, https://doi.org/10.1016/S1352-2310(01)00159-5.

    • Search Google Scholar
    • Export Citation
  • Draxler, R. R., P. Ginoux, and A. F. Stein, 2010: An empirically derived emission algorithm for wind-blown dust. J. Geophys. Res., 115, D16212, https://doi.org/10.1029/2009JD013167.

    • Search Google Scholar
    • Export Citation
  • Eckman, R. M., R. J. Dobosy, D. L. Auble, T. W. Strong, and T. L. Crawford, 2007: A pressure-sphere anemometer for measuring turbulence and fluxes in hurricanes. J. Atmos. Oceanic Technol., 24, 9941007, https://doi.org/10.1175/JTECH2025.1.

    • Search Google Scholar
    • Export Citation
  • Eder, B., and Coauthors, 2010: Using National Air Quality Forecast guidance to develop local air quality index forecasts. Bull. Amer. Meteor. Soc., 91, 313326, https://doi.org/10.1175/2009BAMS2734.1.

    • Search Google Scholar
    • Export Citation
  • Elliott, W. P., and D. J. Gaffen, 1991: On the utility of radiosonde humidity archives for climate studies. Bull. Amer. Meteor. Soc., 72, 15071520, https://doi.org/10.1175/1520-0477(1991)072<1507:OTUORH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ferber, G. J., 1986: Cross-Appalachian Tracer Experiment (CAPTEX’83). NOAA Tech. Memo. ERL ARL-142, 62 pp., https://www.arl.noaa.gov/wp_arl/wp-content/uploads/2017/08/arl-142.pdf.

  • Ferber, G. J., K. Telegadas, J. L. Heffter, C. R. Dickson, R. N. Dietz, and P. W. Krey, 1981: Demonstration of a long-range atmospheric tracer system using perfluorocarbons. NOAA Tech. Memo. ERL ARL-1O1, 83 pp., https://www.arl.noaa.gov/wp_arl/wp-content/uploads/2017/08/ARL-101.pdf.

  • Finn, D., K. L. Clawson, R. G. Carter, J. D. Rich, R. M. Eckman, S. G. Perry, V. Isakov, and D. K. Heist, 2010: Tracer studies to characterize the effects of roadside noise barriers on near-road pollutant dispersion under varying atmospheric stability conditions. Atmos. Environ., 44, 204214, https://doi.org/10.1016/j.atmosenv.2009.10.012.

    • Search Google Scholar
    • Export Citation
  • Finn, D., and Coauthors, 2015: Project Sagebrush Phase 1. NOAA Tech. Memo. OAR ARL-268, 362 pp., https://doi.org/10.7289/V5VX0DHV.

  • Finn, D., and Coauthors, 2017: Project Sagebrush Phase 2. NOAA Tech. Memo. OAR ARL-275, 391 pp., https://doi.org/10.7289/V5/TM-OAR-ARL-275.

  • Gifford, F., 1953: A study of low level air trajectories at Oak Ridge, Tenn. Mon. Wea. Rev., 81, 179192, https://doi.org/10.1175/1520-0493(1953)081<0179:ASOLLA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gifford, F., 1955: A simultaneous Lagrangian–Eulerian turbulence experiment. Mon. Wea. Rev., 83, 293301, https://doi.org/10.1175/1520-0493(1955)083<0293:ASLTE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gillette, D. A., D. W. Fryrear, T. E. Gill, T. Ley, T. A. Cahill, and E. A. Gearhart, 1997: Relation of vertical flux of particles smaller than 10 μm to total aeolian horizontal mass flux at Owens Lake. J. Geophys. Res., 102, 26 00926 015, https://doi.org/10.1029/97JD02252.

    • Search Google Scholar
    • Export Citation
  • Gosling, F. G., 1999: The Manhattan Project: Making the atomic bomb. U.S. Department of Energy History Division Tech. Rep. DOE/MA-0001, 76 pp., https://doi.org/10.2172/303853.

  • Green, R. A., 1992: History, atmospheric turbulence and diffusion division: 1948–June 1992. NOAA, 33 pp., https://www.atdd.noaa.gov/wp-content/uploads/2017/02/History-of-ATDD.pdf.

  • Hanna, S. R., and F. A. Gifford, 1975: Meteorological effects of energy dissipation at large power parks. Bull. Amer. Meteor. Soc., 56, 10691077, https://doi.org/10.1175/1520-0477(1975)056<1069:MEOEDA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hass, W. A., W. H. Hoecker, D. H. Pack, and J. K. Angell, 1967: Analysis of low-level, constant volume balloon (tetroon) flights over New York City. Quart. J. Roy. Meteor. Soc., 93, 483493, https://doi.org/10.1002/qj.49709339807.

    • Search Google Scholar
    • Export Citation
  • Heffter, J. L., 1965: The variation of horizontal diffusion parameters with time for travel periods of one hour or longer. J. Appl. Meteor., 4, 153156, https://doi.org/10.1175/1520-0450(1965)004<0153:TVOHDP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Heffter, J. L., 1980: Air Resources Laboratories Atmospheric Transport and Dispersion Model (ARL-ATAD). NOAA Tech. Memo. ERL ARL-81, 29 pp., https://repository.library.noaa.gov/view/noaa/13189.

  • Heffter, J. L., J. F. Schubert, and G. A. Mead, 1984: Atlantic Coast Unique Regional Atmospheric Tracer Experiment (ACURATE). NOAA Tech. Memo. OAR ARL-130, 67 pp., https://repository.library.noaa.gov/view/noaa/9474/noaa_9474_DS1.pdf.

  • Hicks, B., K. Clawson, W. Pendergrass, and R. Eckman, 2005: Applying local data to urban dispersion forecasting. Environ. Manage., 132, 2630.

    • Search Google Scholar
    • Export Citation
  • Hicks, B. B., D. D. Baldocchi, T. P. Meyers, D. R. Matt, and R. P. Hosker Jr., 1987: A preliminary multiple resistance routine for deriving dry deposition velocities from measured quantities. Water Air Soil Pollut., 36, 311330, https://doi.org/10.1007/BF00229675.

    • Search Google Scholar
    • Export Citation
  • Hicks, B. B., and Coauthors, 1989: A field investigation of sulfate fluxes to a deciduous forest. J. Geophys. Res., 94, 13 00313 011, https://doi.org/10.1029/JD094iD10p13003.

    • Search Google Scholar
    • Export Citation
  • Hosker, R. P., and W. R. Pendergrass, 1987: Flow and dispersion near clusters of buildings. NOAA Tech. Memo. ERL ARL-153, 107 pp., https://repository.library.noaa.gov/view/noaa/19705.

  • Hosler, C. R., 1966: Natural radioactivity (radon-222) and air pollution measurements in Washington, D.C. J. Appl. Meteor., 5, 653662, https://doi.org/10.1175/1520-0450(1966)005<0653:NRAAPM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hosler, C. R., 1969: Vertical diffusivity from radon profiles. J. Geophys. Res., 74, 70187026, https://doi.org/10.1029/JC074i028p07018.

    • Search Google Scholar
    • Export Citation
  • Hosler, C. R., 1975: The meteorology program of the Environmental Protection Agency. Bull. Amer. Meteor. Soc., 56, 12611270, https://doi.org/10.1175/1520-0477(1975)056<1261:TMPOTE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hutchison, B. A., and B. B. Hicks, Eds., 1985: The Forest–Atmosphere Interaction. D. Reidel, 684 pp.

  • Irving, P. A., 1991: Acidic Deposition: State of Science and Technology, Vols I–IV. U.S. National Acid Precipitation Assessment Program, 265 pp.

    • Search Google Scholar
    • Export Citation
  • Kochendorfer, J., and Coauthors, 2022: How well are we measuring snow post-SPICE? Bull. Amer. Meteor. Soc., 103, E370E388, https://doi.org/10.1175/BAMS-D-20-0228.1.

    • Search Google Scholar
    • Export Citation
  • Lee, P., and Coauthors, 2017: NAQFC developmental forecast guidance for fine particulate matter (PM2.5). Wea. Forecasting, 32, 343360, https://doi.org/10.1175/WAF-D-15-0163.1.

    • Search Google Scholar
    • Export Citation
  • Lee, T. R., and M. Buban, 2020: Evaluation of Monin–Obukhov and bulk Richardson parameterizations for surface–atmosphere exchange. J. Appl. Meteor. Climatol., 59, 10911107, https://doi.org/10.1175/JAMC-D-19-0057.1.

    • Search Google Scholar
    • Export Citation
  • Lee, T. R., M. Buban, E. Dumas, and C. B. Baker, 2019: On the use of rotary-wing aircraft to sample near-surface thermodynamic fields: Results from recent field campaigns. Sensors, 19, 10, https://doi.org/10.3390/s19010010.

    • Search Google Scholar
    • Export Citation
  • Lee, T. R., M. S. Buban, and T. P. Meyers, 2021: Application of bulk Richardson parameterizations of surface fluxes to heterogeneous land surfaces. Mon. Wea. Rev., 149, 32433264, https://doi.org/10.1175/MWR-D-21-0047.1.

    • Search Google Scholar
    • Export Citation
  • Lichiheb, N., B. B. Hicks, and L. Myles, 2023: An evaluation of meteorological data prediction over Washington, D.C.: Comparison of DCNet observations and NAM model outputs. Urban Climate, 48, 101410, https://doi.org/10.1016/j.uclim.2023.101410.

    • Search Google Scholar
    • Export Citation
  • Machta, L., 1958: The use of radioactive tracers in meteorology. Annals of the International Geophysical Year 1957–1958, Vol. V, Part V, Pergamon Press, 309312.

    • Search Google Scholar
    • Export Citation
  • Machta, L., 1992: Finding the site of the first Soviet nuclear test in 1949. Bull. Amer. Meteor. Soc., 73, 17971806, https://doi.org/10.1175/1520-0477(1992)073<1797:FTSOTF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Machta, L., H. L. Hamilton, L. F. Hubert, R. J. List, and K. M. Nagler, 1957: Airborne measurements of atomic debris. J. Meteor., 14, 165175, https://doi.org/10.1175/1520-0469(1957)014<0165:AMOAD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • McMillen, R. T., 1988: An eddy correlation technique with extended applicability to non-simple terrain. Bound.-Layer Meteor., 43, 231245, https://doi.org/10.1007/BF00128405.

    • Search Google Scholar
    • Export Citation
  • Myles, L., T. P. Meyers, and L. Robinson, 2007: Relaxed eddy accumulation measurements of ammonia, nitric acid, sulfur dioxide, and particulate sulfate dry deposition near Tampa, FL, USA. Environ. Res. Lett., 2, 034004, https://doi.org/10.1088/1748-9326/2/3/034004.

    • Search Google Scholar
    • Export Citation
  • Ngan, F., A. F. Stein, D. Finn, and R. Eckman, 2018: Dispersion simulations using HYSPLIT for the Sagebrush Tracer Experiment. Atmos. Environ., 186, 1831, https://doi.org/10.1016/j.atmosenv.2018.05.012.

    • Search Google Scholar
    • Export Citation
  • Otte, T. L., and Coauthors, 2005: Linking the Eta Model with the Community Multiscale Air Quality (CMAQ) modeling system to build a National Air Quality Forecasting System. Wea. Forecasting, 20, 367384, https://doi.org/10.1175/WAF855.1.

    • Search Google Scholar
    • Export Citation
  • Pack, D. H., J. K. Angell, M. Hodges, W. Hoecker, and C. R. Dickson, 1970: Tetroon flights in Los Angeles, California – 1969. NOAA Tech. Memo. ERL ARL-19, 31 pp.,