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Atmospheric oxygen levels control the oxidative side of key biogeochemical cycles and place limits on the development of high-energy metabolisms. Understanding Earth's oxygenation is thus critical to developing a clearer picture of Earth's long-term evolution. However, there is currently vigorous debate about even basic aspects of the timing and pattern of the rise of oxygen. Chemical weathering in the terrestrial environment occurs in contact with the atmosphere, making paleosols potentially ideal archives to track the history of atmospheric O 2 levels. Here we present stable chromium isotope data from multiple paleosols that offer snapshots of Earth surface conditions over the last three billion years. The results indicate a secular shift in the oxidative capacity of Earth's surface in the Neoproterozoic and suggest low atmospheric oxygen levels (<1% PAL pO 2 ) through the majority of Earth's history. The paleosol record also shows that localized Cr oxidation may have begun as early as the Archean, but efficient, modern-like transport of hexavalent Cr under an O 2 -rich atmosphere did not become common until the Neoproterozoic., (© 2019 John Wiley & Sons Ltd.)

Turbulence and winds below 328 m were measured on 5 successive nights in a program to study tracer transport in the nocturnal boundary layer at a site with moderately complex terrain and mixed land use. The instruments included sonic anemometers and CO2/H2O analyzers at four levels on a 328 m tall tower, a minisodar/RASS system, a midrange sodar, a ceilometer, and an array of 61 m towers. Preliminary simulations indicated satisfactory perfluorocarbon mixing to 68 m but insufficient transport to the 328 m level on both weakly stable and stable nights, possibly due to insufficient turbulence kinetic energy and/or small vertical mixing lengths, or the presence of meso-β fronts, e.g., sea-breeze fronts, that could transport trace chemicals efficiently to 328 m. To examine the problem further, time–height distributions of turbulence kinetic energy (TKE), mixing length, Richardson number, potential temperature, and winds were derived from the observations of mean winds and temperature and the TKE budget equation, interpolated to fit the observations, under the flux/gradient and z-less scaling assumptions, and displayed with aerosol profiles. The results indicated higher and more variable levels of TKE and mixing lengths above a typical turbulence maximum at 30–50 m. Oscillations with periods of ∼2 h were common and occasional meso-β fronts and shear zones between 75 and 150 m were seen, which increased TKE aloft and in some cases led to a poorly defined boundary layer top. Significance Statement: The atmosphere's boundary layer is the interface between the free atmosphere and natural and human activity near Earth's surface. The daytime boundary layer has been studied extensively and, because of vigorous sun-driven mixing, is well understood and readily parameterized in forecast and global climate models. In contrast, the nocturnal boundary layer is less well understood or predictable because turbulence is weak and tends to decouple it from the surface and the free atmosphere above. This paper focuses on the least-studied upper part of the nocturnal boundary layer over the southeastern United States where topography and land–sea contrast affect winds, turbulence, and chemical transport. [ABSTRACT FROM AUTHOR]

Microburst wind shear has caused or contributed to a significant number of aviation accidents. Since 1943, wind shear accidents have been responsible for more than 1,400 fatalities worldwide, including over 400 deaths in the United States between 1973 and 1985. In this paper, we describe one of the more successful and societally impactful research-to-operations (R2O) programs in atmospheric science history. The remarkable R2O journey included the discovery of microburst wind shear in the late 1970s and early 1980s, the scientific efforts to understand this phenomenon and its impact on aircraft operations, the development of a wind shear training program for pilots, and the rapid development, testing, and implementation of wind shear detection systems that successfully saved lives and property. The article includes a chronological description of the wind shear research and development program, key milestones toward implementation, and the research-to-operations best practices employed for successful technology transfer. [ABSTRACT FROM AUTHOR]

The highest correlative relations for air pollution levels are often with meteorological variables such as temperature and wind speed. Today, sophisticated gridded high-resolution meteorological models are used to produce meteorological fields that drive chemical transport models for air quality management. Errors in specification of the physical atmosphere such as temperature, clouds and winds can affect the air quality predictions. Additionally, the efficiency and efficacy of emission control strategies can be compromised by errors in the meteorological fields. In this paper, the role of meteorology in air quality behavior, primarily from the viewpoint of regional ozone modeling as carried out in the U.S., is reviewed. Particular attention is given to physics and new techniques for improving meteorological model performance. Uncertainties in model turbulent mixing in the nighttime boundary layer, where large model differences exist, are examined. The role of spatial mesoscale features such as topography and land/water systems in models are discussed. The nocturnal low-level jet, a mesoscale temporal and spatial feature, and its impact on air quality are examined. Traditional air quality concerns have focused on synoptic conditions at the center of high-pressure systems. However, high ozone levels have also been associated with stationary fronts. The ability of models to capture mesoscale structure and yet retain synoptic structure and its timing is challenging. Data assimilation and its ability to improve model performance are examined. Particular attention is given to vertical nudging strategies that can affect formation of the nocturnal low-level jets. Finally, clouds can have a major impact on air quality since insolation impacts temperature, biogenic emissions and photolysis rates and extremes in stability. Traditional techniques, which attempt to insert cloud water where there is not dynamical support, can lead to additional errors. New dynamical approaches for improving model cloud performance are discussed. Implications : This article shows that there has been a considerable improvement in meteorological models used for air quality simulations. In particular, improvement in the tools for incorporating both traditional observations and new satellite data for retrospective studies has been beneficial to air quality community. However, while this trend is continuing, many challenges remain. As an example, due to having many options available in configuring a model simulation, there is a need to evaluate and recommend sets of options that provide important performance measures.

In any discussion of forecast evaluation, it is tempting to fall back on statements reflecting unverified assumptions: "this tornado warning had lower skill because the underlying meteorology reflected a complicated or atypical scenario," or "that forecast performed worse than we would have expected given the straightforward setup." These statements of what is and is not a reasonable expectation for warning skill are particularly relevant as the meteorological community's focus has begun to emphasize non-classic storm environments (e.g., tornadoes spawned by quasi-linear convective systems). In this paper, we build a proof-of-concept methodology to quantify the effect of the near-storm environment on tornado warning skill, and we then test these methods on a 15-yr dataset composed of tens of thousands of tornado events and warnings over the contiguous United States. Our findings include that significant tornadoes rated (E)F2+ have a higher probability of detection (POD) than expected based on their near-storm environments, that nocturnal tornadoes have both worse POD and false alarm ratio (FAR) than even their marginal near-storm environments would suggest, and that tornadoes occurring during the summer months also show worse POD and FAR than their environment-based expectation. Quantifying these shifts in performance in an environmental skill score framework allows us to target the situations in which the greatest improvements may be possible, in terms of forecaster training and/or conceptual models. This work also highlights the essential question that should always be asked in the context of forecast verification: what, exactly, is the baseline standard to which we are comparing forecast performance? [ABSTRACT FROM AUTHOR]

Historical increases in emissions and atmospheric deposition of oxidized and reduced nitrogen (N) provided the impetus for extensive, global-scale research investigating the effects of excess N in terrestrial and aquatic ecosystems, with several regions within the Eastern Deciduous Forest of the United States found to be susceptible to negative effects of excess N. The Clean Air Act and associated rules have led to decreases in emissions and deposition of oxidized N, especially in eastern U.S., representing a research challenge and opportunity for ecosystem ecologists and biogeochemists. The purpose of this paper is to predict changes in the structure and function of North American forest ecosystems in a future of decreased N deposition. Hysteresis is a property of a system wherein output is not a strict function of corresponding input, incorporating lag, delay, or history dependence, particularly when the response to decreasing input is different from the response to increasing input. We suggest a conceptual hysteretic model predicting varying lag times in recovery of soil acidification, plant biodiversity, soil microbial communities, forest carbon (C) and N cycling, and surface water chemistry toward pre-N impact conditions. Nearly all of these can potentially respond strongly to reductions in N deposition. Most responses are expected to show some degree of hysteresis, with the greatest delays in response occurring in processes most tightly linked to "slow pools" of N in wood and soil organic matter. Because experimental studies of declines in N loads in forests of North America are lacking and because of the expected hysteresis, it is difficult to generalize from experimental results to patterns expected from declining N deposition. These will likely be long-term phenomena, difficult to distinguish from other, concurrent environmental changes, including elevated atmospheric CO 2 , climate change, reductions in acidity, invasions of new species, and long-term vegetation responses to past disturbance., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

This paper describes a forecasting configuration of the Geophysical Fluid Dynamics Laboratory (GFDL) High-resolution Atmospheric Model (HiRAM). HiRAM represents an early attempt in unifying, within a global modeling framework, the capabilities of GFDL's low-resolution climate models for Intergovernmental Panel on Climate Change (IPCC) type climate change assessments and high-resolution limited-area models for hurricane predictions. In this study, the potential of HiRAM as a forecasting tool is investigated by applying the model to the near-term and intraseasonal hindcasting of tropical cyclones (TCs) in the Atlantic basin from 2006 to 2009. Results demonstrate that HiRAM provides skillful near-term forecasts of TC track and intensity relative to their respective benchmarks from t == 48 h through t == 144 h. At the intraseasonal time scale, a simple HiRAM ensemble provides skillful forecasts of 21-day Atlantic basin TC activity at a 2-day lead time. It should be noted that the methodology used to produce these hindcasts is applicable in a real-time forecasting scenario. While the initial experimental results appear promising, the HiRAM forecasting system requires various improvements in order to be useful in an operational setting. These modifications are currently under development and include a data assimilation system for forecast initialization, increased horizontal resolution to better resolve the vortex structure, 3D ocean model coupling, and wave model coupling. An overview of these ongoing developments is provided, and the specifics of each will be described in subsequent papers. [ABSTRACT FROM AUTHOR]

This paper investigates the ability of the Weather Research and Forecasting (WRF) Model in simulating multiple small-scale precipitation bands (multibands) within the extratropical cyclone comma head using four winter storm cases from 2014 to 2017. Using the model output, some physical processes are explored to investigate band prediction. A 40-member WRF ensemble was constructed down to 2-km grid spacing over the Northeast United States using different physics, stochastic physics perturbations, different initial/boundary conditions from the first five perturbed members of the Global Forecast System (GFS) Ensemble Reforecast (GEFSR), and a stochastic kinetic energy backscatter scheme (SKEBS). It was found that 2-km grid spacing is adequate to resolve most snowbands. A feature-based verification is applied to hourly WRF reflectivity fields from each ensemble member and the WSR-88D radar reflectivity at 2-km height above sea level. The Method for Object-Based Diagnostic Evaluation (MODE) tool is used for identifying multibands, which are defined as two or more bands that are 5–20 km in width and that also exhibit a >2:1 aspect ratio. The WRF underpredicts the number of multibands and has a slight eastward position bias. There is no significant difference in frontogenetical forcing, vertical stability, moisture, and vertical shear between the banded versus nonbanded members. Underpredicted band members tend to have slightly stronger frontogenesis than observed, which may be consolidating the bands, but overall there is no clear linkage in ambient condition errors and band errors, thus leaving the source for the band underprediction motivation for future work. [ABSTRACT FROM AUTHOR]

Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months. [ABSTRACT FROM AUTHOR]

The article provides information on the undergraduate awards and scholarships of the American Meteorological Society. The purpose of the Father James B. Macelwane Annual Award is to stimulate interest in meteorology among college students through the encouragement of student papers concerned with some phase of the atmospheric phase of the atmospheric sciences. The Howard T. Orville Scholarship in Meteorology honors the late Howard T. Orville, head of the Naval Aerological Service. A donation by William J. Hartnett provides funds for the Howard H. Hanks Jr. Scholarship in Meteorology.

The CTP-HI[sub low] framework for describing atmospheric controls on soil moisture–boundary layer interactions is described in a companion paper,. In this paper, the framework is applied to the continental United States to investigate how differing atmospheric regimes influence local feedbacks between the land surface and the atmosphere. The framework was developed with a one-dimensional boundary layer model and is based on two measures of atmospheric thermodynamic properties: the convective triggering potential (CTP), a measure of the temperature lapse rate between approximately 1 and 3 km above the ground surface, and a low-level humidity index, HI[sub low] . These two measures are used to distinguish between three types of early-morning atmospheric conditions: those favoring moist convection over dry soils, those favoring moist convection over wet soils, and those that will allow or prevent deep convective activity, independent of the surface flux partitioning. Analyses of multiyear CTP-HI[sub low] scatterplots from radiosonde stations across the contiguous 48 United States reveal that during the summer months (June, July, and August) positive feedbacks between soil moisture and moist convection are likely in much of the eastern half of the country. Over the western half of the country, atmospheric conditions and the likelihood of moist convection are largely determined by oceanic influences, and land surface conditions in the summer are unlikely to impact convective triggering. The only area showing a potential negative feedback is in the dryline and monsoon region of the arid Southwest. This potential arises because of the topography of this and surrounding regions. A relatively narrow band of stations lies in between the eastern and western portions of the country, in some years behaving like the stations to the west and in other years behaving like the stations to the east. [ABSTRACT FROM AUTHOR]

This paper describes a new climate model and its potential application to the study of ENSO impacts. The model is a regional spectral model embedded within a global coupled ocean–atmosphere model. The atmospheric part of the model consists of a global spectral model with triangular truncation T63 and a nested regional spectral model. The regional model is a relocatable spectral perturbation model that can be run at any horizontal resolution. In this paper the regional model was run with a resolution of 40 km. The global atmosphere model is coupled to the Max Planck global ocean model. No flux correction or anomaly coupling is used. An ensemble of 120-day integrations was conducted using the coupled nested system for the boreal winters of 1987 and 1988. A control integration was also performed in which observed SSTs were used in both the global and regional models. Two domains were chosen for the regional model: the southeast United States and western North America. Results from the global models show that the models reproduce many of the large-scale ENSO climate variations including the shifts in the Pacific ITCZ and SPCZ along with a Pacific–North America response in the 500-hPa height field. These results are compared against the corresponding ECMWF and Global Precipitation Climatology Centre analysis. Over the southeast United States both the global and regional models captured the precipitation variations between the two years as compared with the monthly mean cooperative station data. It is shown that the regional model solution is consistent with the global model solution, but with more realistic detail. Finally prospects for using this coupled nested ocean–atmosphere regional spectral model for downscaling are discussed. [ABSTRACT FROM AUTHOR]

Restoring and preserving the world's forests are promising natural pathways to mitigate some aspects of climate change. In addition to regulating atmospheric carbon dioxide concentrations, forests modify surface and near‐surface air temperatures through biophysical processes. In the eastern United States (EUS), widespread reforestation during the 20th century coincided with an anomalous lack of warming, raising questions about reforestation's contribution to local cooling and climate mitigation. Using new cross‐scale approaches and multiple independent sources of data, we uncovered links between reforestation and the response of both surface and air temperature in the EUS. Ground‐ and satellite‐based observations showed that EUS forests cool the land surface by 1–2°C annually compared to nearby grasslands and croplands, with the strongest cooling effect during midday in the growing season, when cooling is 2–5°C. Young forests (20–40 years) have the strongest cooling effect on surface temperature. Surface cooling extends to the near‐surface air, with forests reducing midday air temperature by up to 1°C compared to nearby non‐forests. Analyses of historical land cover and air temperature trends showed that the cooling benefits of reforestation extend across the landscape. Locations surrounded by reforestation were up to 1°C cooler than neighboring locations that did not undergo land cover change, and areas dominated by regrowing forests were associated with cooling temperature trends in much of the EUS. Our work indicates reforestation contributed to the historically slow pace of warming in the EUS, underscoring reforestation's potential as a local climate adaptation strategy in temperate regions. Plain Language Summary: A century of eastern US reforestation has had a cooling effect that helps to explain a lack of regional warming in the 20th century, which stands in contrast to warming trends across the rest of North America during the same period. Our study shows that forests across much of the eastern United States have a substantial adaptive cooling benefit for surface temperature, and for the first time, we demonstrate that this benefit also extends to near‐surface air temperature. Therefore, reforestation in temperate zones could provide a complementary set of benefits: mitigating climate change by removing carbon dioxide from the atmosphere, while also helping with adaptation to rising temperatures by cooling surface and air temperatures over large areas. Key Points: Reforestation in the eastern United States (EUS) contributes to cooling the land surface and near‐surface air temperatureThe biophysical impacts of reforestation help explain the anomalous lack of 20th‐century warming in the EUSReforestation in temperate regions can provide biophysical climate adaptation benefits by cooling surface and air temperatures [ABSTRACT FROM AUTHOR]

This study investigates the performance of the latter NCEP Unified Forecast System (UFS) Coupled Model prototype simulations (P5–P8) during boreal summer 2011–17 in regard to coupled land–atmosphere processes and their effect on model bias. Major land physics updates were implemented during the course of model development. Namely, the Noah land surface model was replaced with Noah-MP and the global vegetation dataset was updated starting with P7. These changes occurred along with many other UFS improvements. This study investigates UFS's ability to simulate observed surface conditions in 35-day predictions based on the fidelity of model land surface processes. Several land surface states and fluxes are evaluated against flux tower observations across the globe, and segmented coupling processes are also diagnosed using process-based multivariate metrics. Near-surface meteorological variables generally improve, especially surface air temperature, and the land–atmosphere coupling metrics better represent the observed covariance between surface soil moisture and surface fluxes of moisture and radiation. Moreover, this study finds that temperature biases over the contiguous United States are connected to the model's ability to simulate the different balances of coupled processes between water-limited and energy-limited regions. Sensitivity to land initial conditions is also implicated as a source of forecast error. Above all, this study presents a blueprint for the validation of coupled land–atmosphere behavior in forecast models, which is a crucial model development task to assure forecast fidelity from day one through subseasonal time scales. [ABSTRACT FROM AUTHOR]

A unique storm identification and tracking method is analyzed in varying storm environments within the United States spanning 273 hours in 2018. The methodology uses a quantity calculated through fusion of radar-based vertically integrated liquid (VIL) and satellite-based GLM flash rate density (FRD) called VILFRD to identify storms in space and time. This research analyzes GLM data use within VILFRD for the first time (method original: O), assesses four modifications to VILFRD implementation to find a more stable storm size with time (method new: N), larger storms (method original dilated: OD), or both (method new dilated: ND), and compares VILFRD methods with storm tracking using the 35-dBZ isosurface at −10°C (method non-VILFRD: NV). A case study analysis from 2019 is included to assess methods on a smaller scale and introduce a "lightning only" (LO) version of VILFRD. Large study results highlight that VILFRD-based storm identification produces smaller storms with more lightning than the NV method, and the NV method produces larger storms with a more stable size over time. Methods N and ND create smaller storm size fluctuations, but size changes more often. Dilation (OD, ND) creates larger storms and almost double the number of storms identified relative to nondilated methods (O, N, NV). The case study results closely resemble the large sample results and show that the LO method identifies storms with more lightning and shorter durations. Overall, these findings can aid in choice of storm tracking method based on desired user application and promote further testing of a lightning-only version of VILFRD. [ABSTRACT FROM AUTHOR]

Cloud-to-ground (CG) lightning substantially impacts human health and property. However, the relations between U.S. lightning activity and the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO), two predictable drivers of global climate variability, remain uncertain, in part because most lightning datasets have short records that cannot robustly reveal MJO- and ENSO-related patterns. To overcome this limitation, we developed an empirical model of 6-hourly lightning flash count over the contiguous United States (CONUS) using environmental variables (convective available potential energy and precipitation) and National Lightning Detection Network data for 2003–16. This model is shown to reproduce the observed daily and seasonal variability of lightning over most of CONUS. Then, the empirical model was applied to construct a proxy lightning dataset for the period 1979–2021, which was used to investigate the summer MJO–lightning relationship at daily resolution and the winter–spring ENSO–lightning relationship at seasonal resolution. Overall, no robust relationship between MJO phase and lightning patterns was found when seasonality was taken into consideration. El Niño is associated with increased lightning activity over the coastal Southeast United States during early winter, the Southwest during winter through spring, and the Northwest during late spring, whereas La Niña is associated with increased lightning activity over the Tennessee River valley during winter. Significance Statement: Cloud-to-ground lightning is dangerous for humans via direct strikes or through triggering wildfires, generating air pollution, etc. How lightning activity can be affected by climate remains unclear, and it is challenging to study their links because the data record for lightning is short. With the available lightning record, we developed a model that relates lightning flash counts over the United States to environmental factors. This model well represents observed fluctuations in daily and seasonal lightning over most of the United States. Because the model only needs environmental information to predict lightning flash counts, we were able to construct a longer record of predicted lightning based on the longer data record of environmental variables. With this dataset, we investigated the links between lightning and climate and found that the state of sea surface temperatures in the tropical Pacific (El Niño–Southern Oscillation) is linked to changes in U.S. lightning patterns in winter and spring. [ABSTRACT FROM AUTHOR]

The historical rise of irrigation has profoundly mitigated the effect of drought on agriculture in many parts of the United States. While irrigation directly alters soil moisture, meteorological drought indices ignore the effects of irrigation, since they are often based on simple water balance models that neglect the irrigation input. Reanalyses also largely neglect irrigation. Other approaches estimate the evaporative fraction (EF), which is correlated with soil moisture under water-limited conditions typical of droughts, with lower values corresponding to drier soils. However, those approaches require satellite observations of land surface temperature, meaning they cannot be used to study droughts prior to the satellite era. Here, we use a recent theory of land-atmosphere coupling--surface flux equilibrium (SFE) theory--to estimate EF from readily available observations of near-surface air temperature and specific humidity with long historical records. In contrast to EF estimated from a reanalysis that largely neglects irrigation, the SFE-predicted EF is greater at irrigated sites than at nonirrigated sites during droughts, and its historical trends are typically consistent with the spatial distribution of irrigation growth. Two sites at which SFE-predicted EF unexpectedly rises in the absence of changes in irrigation can be explained by increased flooding due to human interventions unrelated to irrigation (river engineering and the expansion of fish hatcheries). This work introduces a new method for quantifying agricultural drought prior to the satellite era. It can be used to provide insight into the role of irrigation in mitigating drought in the United States over the twentieth century. [ABSTRACT FROM AUTHOR]

Accurate representation of mesoscale scale convective systems (MCSs) in climate models is of vital importance to understanding global energy, water cycles, and extreme weather. In this study, we evaluate the simulated MCS features over the United States from the newly developed GFDL global high-resolution (∼50 km) AM4 model by comparing them with the observations during spring to early summer (April–June) and late summer (July–August). The results show that the spatial distribution and seasonality of occurrence and genesis frequency of MCSs are reasonably simulated over the central United States in both seasons. The model reliably reproduces the observed features of MCS duration, translation speed, and size over the central United States, as well as the favorable large-scale circulation pattern associated with MCS development over the central United States during spring and early summer. However, the model misrepresents the amplitude and the phase of the diurnal cycle of MCSs during both seasons. In addition, the spatial distribution of occurrence and genesis frequency of MCSs over the eastern United States is substantially overestimated, with larger biases in early spring and summer. Furthermore, while large-scale circulation patterns are reasonably simulated in spring and early summer, they are misrepresented in the model during summer. Finally, we examine MCS-related precipitation, finding that the model overestimates MCS-related precipitation during spring and early summer, but this bias is insufficient to explain the significant dry bias observed in total precipitation over the central United States. Nonetheless, the dry biases in MCS-associated precipitation during late summer likely contribute to the overall precipitation deficit in the model. [ABSTRACT FROM AUTHOR]

Atmospheric dispersion computer programs are widely used to simulate air and surface exposures from airborne and deposited radionuclides. For longer-term emissions (i.e., occurring for a year or longer), many of these models use joint frequency tables, which summarize the frequency of occurrence of specific ranges of wind speed and direction as a function of stability categories to define the atmospheric dispersion conditions. This paper addresses directional limitations of a computer code, STAR (STability ARray), that has been historically used to generate these joint frequency tables. As part of an effort to provide an updated version of the STAR code, directional limitations were found stemming from the manner in which the reported direction data are stored in joint frequency data tables. The STAR code provides tabular frequency summaries based on sixteen 22.5-degree wind direction sectors. Surface observation data in the CD-144 format from the United States National Oceanographic and Oceanic Administration (NOAA) is read by the STAR program. For 1964 and later years the wind direction has been reported in CD-144 datasets as 10-degree increments instead of the 22.5-degree sector direction codes used in prior years. When 10-degree data are input, the process by which the STAR code puts each wind direction occurrence into the 22.5-degree sectors results in a consistent positive bias for cardinal direction sectors (north, south, east, and west), and a consistent negative bias for all the other sectors. Individual entries in the joint frequencies summaries with high wind frequencies tend to be overestimated by up to about 30% or underestimated down to about -10%. A larger range of changes is seen for entries with lower wind frequencies. To avoid these errors, it is recommended that joint frequencies generated by the STAR program be checked for this directional bias. If the NOAA observational data are for 1964 or later years and generated by the original EPA STAR program, it is likely that the bias will be in the dataset. To be certain, it is best to regenerate the STAR data summary using a revised version of the STAR program (STARR) or an alterative program that better handles the binning of wind directions.

From 1945 to 1980, over 500 weapons tests were conducted in the atmosphere at a number of locations around the world. These tests resulted in the release of substantial quantities of radioactive debris to the environment. Local, intermediate, and global fallout deposition densities downwind from test sites depended on the heights of bursts, the yields, and the half-lives and volatilities of the particular fission or activation products, as well as on the meteorological conditions. A number of national and international monitoring programs were established to trace the fallout through the atmosphere and biosphere. These programs included continuous monitoring of ground-level air, exposure rates, and deposition as well as periodic sampling of food, bone, water, soil, and stratospheric air. Although data for specific high-yield tests are still classified, the fission and fusion yields of the various tests and test series have been estimated and from this information the quantities of specific fission and activation products released into the atmosphere have been determined. The geographic and temporal variations in the fallout deposition of specific radionuclides based on both actual measurements and model calculations are discussed in this paper. A feasibility study to estimate the deposition density (deposition per unit area) of particular radionuclides from both Nevada Test Site and "global" fallout on a county-by-county scale for the continental United States is described. These deposition estimates provide a basis for reconstructing population exposure and dose. They support the feasibility of a more detailed evaluation of the population doses that resulted from fallout from atmospheric tests to document the experience fully and to report results more systematically and completely to the world community. The impact of weapons fallout will continue to be felt for years to come since a contaminant baseline has been imposed on the ambient radiation environment that will be an important factor in the assessment of past and future releases of radioactive materials into the biosphere.

Widely distributed (sparse) ground-based antenna arrays are being considered for deep space communications applications with the development of the proposed Next Generation Deep Space Network. However, atmospheric-induced phase fluctuations can impose daunting restrictions on the performance of such an array, particularly during transmit and particularly at Ka-band frequencies, which have yet to be successfully resolved. In this paper, an analysis of the uncompensated performance of a sparse antenna array, in terms of its directivity and pattern degradation, is performed utilizing real data. The theoretical derivation for array directivity degradation is validated with interferometric measurements (for a 2-element array) recorded at Goldstone, CA, from May 2007—May 2008. With the validity of the model established, an arbitrary 27-element array geometry is defined at Goldstone, CA, to ascertain its theoretical performance in the presence of phase fluctuations based on the measured data. Therein, a procedure in which array directivity performance can be determined based on site-specific interferometric measurements is established. It is concluded that a combination of compact array geometry and atmospheric compensation is necessary to minimize array loss impact for deep space communications. [ABSTRACT FROM PUBLISHER]

The expansion and advancement of businesses, cities, municipalities, and populations in Canada are increasingly demanding the effective usage of land and resources in building development. Companies, organizations, and individuals are seeking to construct more environmentally responsible buildings to help minimize the negative effects that structures place on the natural environment, as well as on the people that have either direct or indirect contact with them. In an effort to set a benchmark for what constitutes a green building, the Canada Green Building Council provides a certification system called Leadership in Energy and Environmental Design (LEED), which is a point scheme that seeks to measure sustainability. Many stakeholders in the construction industry in Canada are increasingly employing this system to validate their achievement in sustainable development. This paper reviews the usage of the LEED system in Canadian building construction and the potential challenges and barriers associated with LEED implementation that specifically pertain to Canadian circumstances. Information from 42 new construction projects across Canada that achieved LEED certification were collected and analyzed. The points most often awarded, as well as those that are granted the least frequently, were tabulated. The percentages of points obtained were compared to American projects. Credit frequency indicators (CFIs), which were used to depict the achievement of credits by each project, can provide decision makers with information on credits awarded in the past and insight into credit implementation in future projects with similar goals. The energy and atmosphere and materials and resources categories proved to have the lowest CFIs, whereas the innovation and design process and water efficiency categories yielded the highest CFIs. The study also found that there were differences in the points obtained by LEED projects in Canada and the United States as some credits are influenced by climate, such as weather and temperature, as well as regional location. [ABSTRACT FROM AUTHOR]

The Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Earth Observing System (EOS) Terra and Aqua platforms provides unique measurements for deriving global and regional cloud properties. MODIS has spectral coverage combined with spatial resolution in key atmospheric bands, which is not available on previous imagers and sounders. This increased spectral coverage/spatial resolution, along with improved onboard calibration, enhances the capability for global cloud property retrievals. MODIS operational cloud products are derived globally at spatial resolutions of 5 km (referred to as level-2 products) and are aggregated to a 1° equal-angle grid (referred to as level-3 product), available for daily, 8-day, and monthly time periods. The MODIS cloud algorithm produces cloud-top pressures that are found to be within 50 hPa of lidar determinations in single-layer cloud situations. In multilayer clouds, where the upper-layer cloud is semitransparent, the MODIS cloud pressure is representative of the radiative mean between the two cloud layers. In atmospheres prone to temperature inversions, the MODIS cloud algorithm places the cloud above the inversion and hence is as much as 200 hPa off its true location. The wealth of new information available from the MODIS operational cloud products offers the promise of improved cloud climatologies. This paper 1) describes the cloud-top pressure and amount algorithm that has evolved through collection 5 as experience has been gained with in-flight data from NASA Terra and Aqua platforms; 2) compares the MODIS cloud-top pressures, converted to cloud-top heights, with similar measurements from airborne and space-based lidars; and 3) introduces global maps of MODIS and High Resolution Infrared Sounder (HIRS) cloud-top products. [ABSTRACT FROM AUTHOR]

Abstract: A prototype high altitude balloon payload dedicated to measuring wind characteristics, pressure and temperature in terrestrial atmospheric environments was developed as part of a National Aeronautics and Space Administration (NASA) Workforce Development Educational Grant. The WindSat as it became known was one of 15 conceptual projects suggested by various NASA agencies with this specific concept suggested by the Jet Propulsion Laboratory (JPL). The WindSat project was designed, built, and tested by the University of Colorado at Colorado Springs (UCCS) Space Grant program to determine the feasibility of obtaining atmospheric weather in the upper and lower atmosphere for entry, descent, and landing of planetary missions. In the first phase of the project described in this paper, the payload was designed for and tested in the terrestrial environment of Earth. The prototype is similar to a radiosonde in function; however, the prototype logs data at a frequency of 1Hz to determine relative gusts and also records values on both ascent and descent through the atmosphere. Some preliminary results from the first two test flights of WindSat are given in this paper. [Copyright &y& Elsevier]

The Climate Forecast System (CFS), the fully coupled ocean–land–atmosphere dynamical seasonal prediction system, which became operational at NCEP in August 2004, is described and evaluated in this paper. The CFS provides important advances in operational seasonal prediction on a number of fronts. For the first time in the history of U.S. operational seasonal prediction, a dynamical modeling system has demonstrated a level of skill in forecasting U.S. surface temperature and precipitation that is comparable to the skill of the statistical methods used by the NCEP Climate Prediction Center (CPC). This represents a significant improvement over the previous dynamical modeling system used at NCEP. Furthermore, the skill provided by the CFS spatially and temporally complements the skill provided by the statistical tools. The availability of a dynamical modeling tool with demonstrated skill should result in overall improvement in the operational seasonal forecasts produced by CPC. The atmospheric component of the CFS is a lower-resolution version of the Global Forecast System (GFS) that was the operational global weather prediction model at NCEP during 2003. The ocean component is the GFDL Modular Ocean Model version 3 (MOM3). There are several important improvements inherent in the new CFS relative to the previous dynamical forecast system. These include (i) the atmosphere–ocean coupling spans almost all of the globe (as opposed to the tropical Pacific only); (ii) the CFS is a fully coupled modeling system with no flux correction (as opposed to the previous uncoupled “tier-2” system, which employed multiple bias and flux corrections); and (iii) a set of fully coupled retrospective forecasts covering a 24-yr period (1981–2004), with 15 forecasts per calendar month out to nine months into the future, have been produced with the CFS. These 24 years of fully coupled retrospective forecasts are of paramount importance to the proper calibration (bias correction) of subsequent operational seasonal forecasts. They provide a meaningful a priori estimate of model skill that is critical in determining the utility of the real-time dynamical forecast in the operational framework. The retrospective dataset also provides a wealth of information for researchers to study interactive atmosphere–land–ocean processes. [ABSTRACT FROM AUTHOR]

Mass spectrometry is a versatile research tool that has proved to be extremely useful for exploring the fundamental nature of the earth's atmosphere and ionosphere and in helping to solve operational problems facing the Air Force and the Department of Defense. In the past 40 years, our research group at the Air Force Research Laboratory has flown quadrupole mass spectrometers of many designs on nearly 100 sounding rockets, nine satellites, three Space Shuttles and many missions of high-altitude research aircraft and balloons. We have also used our instruments in ground-based investigations of rocket and jet engine exhaust, combustion chemistry and microwave breakdown chemistry. This paper is a review of the instrumentation and techniques needed for space research, a summary of the results from many of the experiments, and an introduction to the broad field of atmospheric and space mass spectrometry in general., (Copyright 1999 John Wiley & Sons, Ltd.)

Objective: In this investigation we sought to summarize the atmospheric surveys that were published in two United States allergy journals (and subsequent series) during a 31-year period beginning in 1966., Data Sources: All original articles published in the Annals of Allergy (and its subsequent series) and the Journal of Allergy (and its subsequent series) were cataloged in a computer database beginning with the first issues of 1966. Publications were classified in a manner reflecting their aerobiologic content., Study Selection: From this database of articles (n = 7,403), atmospheric surveys for pollen, spores, and other aeroflora were identified and summarized according to standard criteria. For each study we documented the sampling instruments used, the height of these instruments off of the ground, the study period, and the sampling schedule., Results: Sixty-one atmospheric surveys were summarized: 30 from locations in the United States and 31 from outside of the country. Various volumetric and nonvolumetric samplers were used; the height of instruments above the ground and the sampling protocols varied widely., Conclusions: This investigation can serve as a companion to earlier compilations. These data should prove more useful than information available via electronic and paper indexes.

This paper deals with the systematic evolution of the North American monsoon system by analyzing the evolution of tropospheric circulation, precipitation, moisture fluxes and the adjoining sea surface temperatures from observations and the Florida State University Nested Regional Spectral Model embedded within a coupled atmosphere-ocean coupled model. The atmospheric part is a global spectral model with triangular truncation T63 and a nested regional spectral model. The global atmospheric model is coupled with the Hamburg's Global Ocean Model. This model was integrated for six-months with 50 km resolution in the North American monsoon domain. The NCEP/NCAR reanalysis were used as the observed state for comparison. The analyzed model results shows that the above model has the capability of capturing some of the features of the North American monsoon system. Besides the evolution of the monsoon system, the out-of-phase relationship between the Mexican monsoon with south-central United States precipitation and the largest moisture supply from the Gulf of Mexico are the principal findings of this study. It is also noted that during the mature phase of the monsoon, the Gulf of California and the Pacific Ocean dominates the Gulf of Mexico in order of moisture supply but immediately after the mature phase, the Gulf of Mexico dominates. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of soil moisture on the development and triggering of convection in different early-morning atmospheric conditions. A one-dimensional model of the atmospheric boundary layer (BL) is initialized with atmospheric sounding data from Illinois and with the soil moisture set to either extremely wet (saturated) or extremely dry (20% of saturation) conditions. Two measures are developed to assess the low-level temperature and humidity structure of the early-morning atmosphere. These two measures are used to distinguish between four types of soundings, based on the likely outcome of the model: 1) those soundings favoring deep convection over dry soils, 2) those favoring deep convection over wet soils, 3) those unlikely to convect over any land surface, and 4) those likely to convect over any land surface. Examples of the first two cases are presented in detail. The early-morning atmosphere is characterized in this work by the newly developed convective triggering potential (CTP) and a low-level humidity index, HI[sub low] . The CTP measures the departure from a moist adiabatic temperature lapse rate in the region between 100 and 300 mb (about 1–3 km) above the ground surface (AGS). This region is the critical interface between the near-surface region, which is almost always incorporated into the growing BL, and free atmospheric air, which is almost never incorporated into the BL. Together, these two measures form the CTP-HI[sub low] framework for analyzing atmospheric controls on soil moisture–boundary layer interactions. Results show that in Illinois deep convection is trigged in the model 22% of the time over wet soils and only 13% of the time over dry soils. Additional testing varying the radiative conditions in Illinois and also using the 1D model with soundings from four additional stations confirm that the CTP-HI[sub low] framework is valid for regions far removed from Illinois. [ABSTRACT FROM AUTHOR]

The paper deals with an alternative formulation of the so-called NMC (National Meteorological Center, now National Centers for Environmental Prediction) statistics to compute the background error covariance matrix to be used in a mesoscale variational analysis. While the standard method uses differences of forecasts valid for the same time, but starting from different analysis times, the new formulation required the recomputation of the short-term forecast with the initial and lateral boundary data that come from the long-term run. In the frame of a limited-area model, this approach forces the error variances at large scales to decrease drastically, because those scales are controlled by the (constant data) lateral boundary coupling. As a result, the background cost function acts more scale selectively, with an emphasis on medium scales. The analysis increments obtained from the 3D-VAR system show that the analysis increments are sharper and more concentrated with the new formulation, both in single observation and in full observation experiments. This work is part of a wider project for building a variational assimilation system inside the ALADIN model. The complete system should concentrate on mesoscale features and it should not reanalyse those scales that were already treated by the global model (ARPEGE). Some difficulties and perspectives are drawn in the concluding discussion. [ABSTRACT FROM AUTHOR]

The topic generally known as acid rain presents a series of data and information challenges to researchers, policy makers, educators, and librarians and information specialists. Acid rain became one of the first and, at the time, one of the largest and most vexing environmental problems of a global scale in terms of both its origins and its solutions. The transport of air pollutants over long distances had been postulated as early as the 17th century. Scientific study of the chemical composition of the Earth's atmosphere began in earnest in the later half of the 19th century and continues today. This paper examines the development of a national network of environmental data-gathering activities revealed from decades of acid rain research in the United States, ranging from nationally coordinated, interagency research programs to state sponsored ecosystem and sub-ecosystem studies and data monitoring activities. [ABSTRACT FROM AUTHOR]

Reliably quantifying uncertainty in precipitation forecasts remains a critical challenge. This work examines the application of a deep learning (DL) architecture, Unet, for postprocessing deterministic numerical weather predictions of precipitation to improve their skills and for deriving forecast uncertainty. Daily accumulated 0–4-day precipitation forecasts are generated from a 34-yr reforecast based on the West Weather Research and Forecasting (West-WRF) mesoscale model, developed by the Center for Western Weather and Water Extremes. The Unet learns the distributional parameters associated with a censored, shifted gamma distribution. In addition, the DL framework is tested against state-of-the-art benchmark methods, including an analog ensemble, nonhomogeneous regression, and mixed-type meta-Gaussian distribution. These methods are evaluated over four years of data and the western United States. The Unet outperforms the benchmark methods at all lead times as measured by continuous ranked probability and Brier skill scores. The Unet also produces a reliable estimation of forecast uncertainty, as measured by binned spread–skill relationship diagrams. Additionally, the Unet has the best performance for extreme events (i.e., the 95th and 99th percentiles of the distribution) and for these cases, its performance improves as more training data are available. Significance Statement: Accurate precipitation forecasts are critical for social and economic sectors. They also play an important role in our daily activity planning. The objective of this research is to investigate how to use a deep learning architecture to postprocess high-resolution (4 km) precipitation forecasts and generate accurate and reliable forecasts with quantified uncertainty. The proposed approach performs well with extreme cases and its performance improves as more data are available in training. [ABSTRACT FROM AUTHOR]

International water resources have been a source of contention in many parts of the world over the last few decades and such conflicts may grow in frequency and severity as future climatic changes alter the quantity or quality of limited water resources. Indeed, some future climatic changes appear inevitable due to growing atmospheric concentrations of carbon dioxide and other trace gases. Unless mechanisms for incorporating climatic changes into water agreement can be worked out, these changes may provoke further frictions and conflicts. One region with a history of political conflict over shared water resources is the Colorado River Basin in the United States and Mexico. While past disagreements over water have mostly been resolved, future climatic changes that adversely affect the existing hydrologic regime of the basin cannot be discounted. This paper examines the possibility that future long-term climatic changes may exacerbate shortages in the Colorado River. Political conflicts and tensions that arise from reductions in water supply in both the United States and Mexico are evaluated and discussed, together with recommendations for incorporating the issue of climatic change into existing international treaties and agreements. [ABSTRACT FROM AUTHOR]

This paper introduces the term "landscape meteorology" to cover a variety of beliefs associating significant atmospheric processes and results with culturally induced features of the landscape. These beliefs, which became prominent during the period of discovery, played an important role in early America and during the settlement period of the Plains. In the forested East, weather and climate modification centered largely on deforestation and reforestation. The concept ot the tree-rainfall syndrome was also transferred to the West, where it received much attention. New and largely indigenous Western concepts regarding landscape forms of weather and climate modification stressed plowing the hard sod, facilitating electrical expression by means, and storing water and (including irrigation) to humidify and cool the air. [ABSTRACT FROM AUTHOR]

The radiative effects of the large‐scale air traffic slowdown during April and May 2020 due to the international response to the COVID‐19 pandemic are estimated by comparing the coverage (CC), optical properties, and radiative forcing of persistent linear contrails over the conterminous United States and two surrounding oceanic air corridors during the slowdown period and a similar baseline period during 2018 and 2019 when air traffic was unrestricted. The detected CC during the slowdown period decreased by an area‐averaged mean of 41% for the three analysis boxes. The retrieved contrail optical properties were mostly similar for both periods. Total shortwave contrail radiative forcings (CRFs) during the slowdown were 34% and 42% smaller for Terra and Aqua, respectively. The corresponding differences for longwave CRF were 33% for Terra and 40% for Aqua. To account for the impact of any changes in the atmospheric environment between baseline and slowdown periods on detected CC amounts, the contrail formation potential (CFP) was computed from reanalysis data. In addition, a filtered CFP (fCFP) was also developed to account for factors that may affect contrail formation and visibility of persistent contrails in satellite imagery. The CFP and fCFP were combined with air traffic data to create empirical models that estimated CC during the baseline and slowdown periods and were compared to the detected CC. The models confirm that decreases in CC and radiative forcing during the slowdown period were mostly due to the reduction in air traffic, and partly due to environmental changes. Plain Language Summary: Contrails produced by aircraft flying in cold but humid air both warm the atmosphere by reducing infrared radiation emitted back into space and cool it by increasing reflected sunlight. Due to the decrease in air traffic during the first months of the COVID pandemic, fewer satellite‐detectable contrails were produced compared to pre‐pandemic times, and thus the radiative effects of contrails were also diminished. But changes in the overall temperature and humidity at aircraft cruise altitudes also affect contrail formation and might explain at least some of the observed decrease in contrail coverage during April and May 2020. Analysis of satellite imagery showed that the thickness and ice‐crystal size of the contrails during the COVID period did not change much from pre‐pandemic contrails. The regional contrail coverage was accurately simulated from a combination of the estimated air traffic activity at cruise altitude and the probable frequency of when atmospheric conditions were favorable for contrail formation. This simulation confirms that most of the decrease in contrails and their radiative effects during the COVID‐related slowdown period were due to the reduction in air traffic, and to a lesser extent to changes in temperature and humidity at cruise altitude during April and May 2020. Key Points: Contrail coverage during the start of the COVID pandemic was reduced significantly over the study area, mostly due to less air trafficMonthly mean coverage was accurately estimated from a simple relation between air traffic and contrail formation probabilityThe relation confirms that the decrease in contrail radiative effects is greater than that expected from the decrease in air traffic alone [ABSTRACT FROM AUTHOR]

The mass concentration of fine particulate matter (PM2.5; diameters less than 2.5 μm) estimated from geostationary satellite aerosol optical depth (AOD) data can supplement the network of ground monitors with high temporal (hourly) resolution. Estimates of PM2.5 over the United States were derived from NOAA's operational geostationary satellites' Advanced Baseline Imager (ABI) AOD data using a geographically weighted regression with hourly and daily temporal resolution. Validation versus ground observations shows a mean bias of −21.4% and −15.3% for hourly and daily PM2.5 estimates, respectively, for concentrations ranging from 0 to 1000 μg m−3. Because satellites only observe AOD in the daytime, the relation between observed daytime PM2.5 and daily mean PM2.5 was evaluated using ground measurements; PM2.5 estimated from ABI AODs were also examined to study this relationship. The ground measurements show that daytime mean PM2.5 has good correlation (r > 0.8) with daily mean PM2.5 in most areas of the United States, but with pronounced differences in the western United States due to temporal variations caused by wildfire smoke; the relation between the daytime and daily PM2.5 estimated from the ABI AODs has a similar pattern. While daily or daytime estimated PM2.5 provides exposure information in the context of the PM2.5 standard (>35 μg m−3), the hourly estimates of PM2.5 used in nowcasting show promise for alerts and warnings of harmful air quality. The geostationary satellite based PM2.5 estimates inform the public of harmful air quality 10 times more than standard ground observations (1.8 versus 0.17 million people per hour). Significance Statement: Fine particulate matter (PM2.5; diameters less than 2.5 μm) are generated from smoke, dust, and emissions from industrial, transportation, and other sectors. They are harmful to human health and even lead to premature mortality. Data from geostationary satellites can help estimate surface PM2.5 exposure by filling in gaps that are not covered by ground monitors. With this information, people can plan their outdoor activities accordingly. This study shows that availability of hourly PM2.5 observations covering the entire continental United States is more informative to the public about harmful exposure to pollution. On average, 1.8 million people per hour can be informed using satellite data compared to 0.17 million people per hour based on ground observations alone. [ABSTRACT FROM AUTHOR]

Technologies and approaches that remove and sequester carbon dioxide (CO2) from Earth's atmosphere are likely to play a significant role in mitigating anthropogenic climate disruption in the coming century. Enhanced rock weathering (ERW) on the land surface is one extensively discussed approach toward carbon dioxide removal (CDR), but the capacity of rivers to carry dissolved products derived from ERW without CO2 re‐release is largely unexplored, hindering a full understanding of the life cycle of ERW and its associated maximum CDR potential. Here, we use a conceptual model built upon river/stream carbonate chemistry to estimate the upper limits on the carbon transport potential (CTP) of rivers and groundwaters. Our model yields a riverine CTP ranging between 0.26–0.89 GtCO2 yr−1 for the United States and 7.1–21.3 GtCO2 yr−1 globally for accelerated silicate weathering, and between 0.090–0.37 GtCO2 yr−1 for the United States and 2.5–8.8 GtCO2 yr−1 globally for accelerated carbonate weathering. Although these limits will be challenging to achieve in practice, our results support the notion that the transport of dissolved constituents in surface waters is unlikely to be a primary bottleneck limiting the CDR potential of ERW. This supports the notion that accelerated mineral weathering should be considered as an additional component of greenhouse gas mitigation portfolios. However, future research on the kinetics of ERW reactions and river responses, along with consideration of possible added CO2 emissions by activities needed to accomplish ERW, are needed for a more realistic quantification of the net CDR via ERW in the land–soil–river system. [ABSTRACT FROM AUTHOR]

The Emirates Mars Mission (EMM) was launched to Mars in the summer of 2020, and is the first interplanetary spacecraft mission undertaken by the United Arab Emirates (UAE). The mission has multiple programmatic and scientific objectives, including the return of scientifically useful information about Mars. Three science instruments on the mission's Hope Probe will make global remote sensing measurements of the Martian atmosphere from a large low-inclination orbit that will advance our understanding of atmospheric variability on daily and seasonal timescales, as well as vertical atmospheric transport and escape. The mission was conceived and developed rapidly starting in 2014, and had aggressive schedule and cost constraints that drove the design and implementation of a new spacecraft bus. A team of Emirati and American engineers worked across two continents to complete a fully functional and tested spacecraft and bring it to the launchpad in the middle of a global pandemic. EMM is being operated from the UAE and the United States (U.S.), and will make its data freely available. [ABSTRACT FROM AUTHOR]

Abstract: The present work aims at studying the local to regional atmospheric pollution transport and transformation processes over Bulgaria and at tracking and characterizing the main pathways and processes that lead to atmospheric composition formation in the region. The US EPA Models-3 system is chosen as a modeling tool. As the NCEP Global Analysis Data with 1 degree resolution is used as meteorological background, the MM5 and CMAQ nesting capabilities are applied for downscaling the simulations to a 9 km resolution over Balkans and 3 km over Bulgaria. The TNO emission inventory is used as emission input. Special pre-processing procedures are created for introducing temporal profiles and speciation of the emissions. The study is based on a large number of numerical simulations carried out day by day for the years 2000–2007 and four emission scenarios—with all the emissions and with biogenic emissions, emissions from energetics and road transport excluded. Results from the numerical simulations concerning the main features of the atmospheric composition in Bulgaria and the contribution of the different emission categories are demonstrated in the paper. Some results from the CMAQ “Integrated Process Rate Analysis” are also given. [Copyright &y& Elsevier]

Abstract: Space weather affects global technological systems and societies. Space weather, or the dynamic conditions on the Sun and in the space environment and their impacts on technological systems, can produce coronal mass ejections, solar energetic particles, and geomagnetic disturbances. These space weather events can cause extreme currents in the electric grid, widespread blackouts, and phone and internet communication failures both in space and within the Earth''s atmosphere. Severe space weather can damage satellites used for global positioning, communications, and weather forecasting. It also creates a risk of radiation exposure to astronauts and commercial airline crews and passengers. Accurate forecast knowledge of the space weather threat and timing of events is critical to planning and preparation to minimize socioeconomic impacts. This paper reviews the scope of the space weather threat on the global community today. It describes current international and US public policy initiatives to mitigate risk to infrastructure, public safety, and human life. It examines recent international reports and US federal disaster reduction plans to meet these challenges and provides recommendations to increase public awareness and implement public policies to prepare, prevent, and recover from possible catastrophic failures of commercial and government infrastructures caused by a major space weather event. [Copyright &y& Elsevier]

Abstract: Information and communication technologies (ICTs) devices operate within the atmosphere around the earth as part of their global network. They are therefore subjected to all the natural activities that take place in this global space. Solar storm activities continue to increase in intensity and NASA has issued a warning that a powerful solar storm may hit the earth sometime in 2012. This has the potential to lead to ICTs devices not functioning at all if this happens. In this paper a worst case scenario of this storm, if it was to happen, is discussed with its pros and cons. [Copyright &y& Elsevier]

Satellites in low Earth orbit ultimately reenter the Earth's atmosphere at the end of the mission due to orbital decay. Although most of the mass of a typical space vehicle is destroyed and rendered harmless, a significant portion survives to ground impact. As the number of reentry events has increased in recent years, some surviving components have impacted near populated areas and drawn attention to the casualty risk. In response to this hazard, the U.S. government developed guidelines to mitigate the danger posed by randomly reentering space objects that survive to surface impact. If an upper bound for casualty expectation is exceeded, a controlled deorbit/reentry into a sparsely populated region is recommended. This paper develops the mathematical methods needed to compute risk to people and property from uncontrolled reentries. The impact probability density function for surviving debris objects associated with an uncontrolled reentry is derived. The impact probability density function is used with the population density function to compute the casualty expectation. Examples of casualty expectation and risk of damage to property are provided. [ABSTRACT FROM AUTHOR]

In the first part of this paper, a simple coupled dynamic soil–atmosphere model for studying the gaseous exchange of pesticide soil residues with the atmosphere is described and evaluated by comparing model results with published measurements of pesticide concentrations in air and soil. In Part II, the model is used to study the concentration profiles of pesticide residues in both undisturbed and annually tilled agricultural soils. Future trends are estimated for the measured air and soil concentrations of lindane and six highly persistent pesticides (toxaphene, p,p′-DDE, dieldrin, cis-and trans-chlordane and trans-nonachlor) over a twenty-year period due to volatilization and leaching into the deeper soil. Wet deposition and particle associated pesticide deposition (that increase soil residue concentrations) and soil erosion, degradation in the soil (other than for lindane) and run-off in precipitation are not considered in this study. Estimates of the rain deposition fluxes are reported that show that, other than for lindane, net volatilization fluxes greatly exceed rain deposition fluxes. The model shows that the persistent pesticides studied are highly immobile in soil and that loss of these highly persistent residues from the soil is by volatilization rather than leaching into the deeper soil. The soil residue levels of these six pesticides are currently sources of net volatilization to the atmosphere and will remain so for many years. The maximum rate of volatilization from the soil was simulated by setting the atmospheric background concentration to zero; these simulations show that the rates of volatilization will not be significantly increased since soil resistance rather than the atmospheric concentration controls the volatilization rates. Annual tilling of the soils increases the volatilization loss to the atmosphere. Nonetheless, the model predicts that, if only air–soil exchange is considered, more than 76% of current persistent pesticide residues will remain after 20 years in the top 7 cm of annually tilled soils. In contrast, lindane is relatively mobile in soil due to weaker binding to soil carbon and leaching of lindane into soil is the main removal route for current lindane residues near the soil surface. The model predicts that the soil is a sink for lindane in the atmosphere and that soil residue levels of lindane in the surface soil are determined by a balance between dry gaseous deposition to the soil from the atmosphere and leaching from the surface soil into the deeper soil where degradation is the dominant loss route. The model suggests that deposition of lindane from the atmosphere will sustain residues in the soil and, in the absence of fresh applications of lindane to the soil, eliminating lindane from the atmosphere would lead to a rapid decline of lindane residues in agricultural soils of the southern U.S. [Copyright &y& Elsevier]

NAVDAS-AR is the 4DVAR extension of the United States Navy's operational 3DVAR data assimilation system, NAVDAS (Naval Research Laboratory Atmospheric Variational Data Assimilation System), where AR stands for accelerated representer. Like NAVDAS, NAVDAS-AR is cast in observation space, and is an observation space formulation of the 4DVAR algorithm, in contrast to the European Centre for Medium-Range Weather Forecasts 4DVAR system, which is an analysis space algorithm. In this paper we show how an inner loop linear solution can be augmented by an outer loop iteration procedure that introduces non-linear effects in the form of a first-order Taylor series expansion. The non-linear problem is then solved iteratively as a sequence of linear problems. We show results of the outer loop strategy, where the first loop, the ‘linear’ solution, is modified in the second outer loop by both a Taylor series term in the calculation of the observation innovations, and a linearized form of the background model that is linearized about an analysis ‘trajectory’ from the solution of the first loop. We also show results of how the outer loop strategy allows better assimilation of observations of surface wind speed and precipitable water, compared to the operational NAVDAS. [ABSTRACT FROM AUTHOR]

For the first time, calculations for the more than 3000 counties of the US have been completed that estimate the average deposition density (Bq m−2) of more than 40 radionuclides in fallout from atmospheric nuclear weapons tests conducted in the US (1951–1962) and 19 radionuclides from tests conducted elsewhere in the world (1952–1963). The geographic pattern of deposition across the US, as well as the amount of fallout deposited, varied significantly depending on whether the tests were conducted within or outside of the US. Fallout deposited from the Nevada Test Site (NTS) varied geographically as a result of dispersion and dilution in the atmosphere, the wind patterns following each test, and the occurrence of localized rainfall events. In general, states immediately east of the NTS received the highest deposition from tests conducted there. In contrast, the variation in deposition across the country from global fallout was less than for NTS fallout primarily reflecting variations in annual precipitation across larger regions. Hence, in the eastern and mid-western US, where rainfall is above the national average, higher levels of global fallout were deposited than in the more arid southwestern states. This paper presents a summary of the methods used and findings of our studies on fallout from NTS and global fallout, with emphasis on two of the most important radionuclides, 131I and 137Cs. [Copyright &y& Elsevier]

Precipitation scavenging can effectively remove particulates from the atmosphere. Interest in the phenomena waxed in the 1980s, but models developed at that time remain limited by the lack of both detailed, time-resolved wet deposition pattern measurements for model confirmation and real-time rain data for model execution. Recently, new rain products have become available that can revolutionize real-time use of precipitation scavenging models on the regional scale. We have utilized a 4-km, hourly resolution precipitation data set from the Arkansas Red-Basin River Forecast Center. A standard below-cloud aerosol scavenging model has been modified to incorporate the potentially larger scavenging in heavy rain events. This paper demonstrates the model on a sample rainfall data set. The simulations demonstrate the concentrating effect of rainfall, especially heavy rain, on deposition patterns. Wet deposition played an important role in the simulated fate and transport, removing as much as 70% of the released aerosol. [Copyright &y& Elsevier]

In the hydrological sciences, the outstanding challenge of regional modeling requires to capture common and event-specific hydrologic behaviors driven by rainfall spatial variability and catchment physiography during floods. The overall objective of this study is to develop robust understanding and predictive capability of how rainfall spatial variability influences flood peak discharge relative to basin physiography. A machine-learning approach is used on a high-resolution dataset of rainfall and flooding events spanning 10 years, with rainfall events and basins of widely varying characteristics selected across the continental United States. It overcomes major limitations in prior studies that were based on limited observations or hydrological model simulations. This study explores first-order dependencies in the relationships between peak discharge, rainfall variability, and basin physiography, and it sheds light on these complex interactions using a multidimensional statistical modeling approach. Among different machine-learning techniques, XGBoost is used to determine the significant physiographical and rainfall characteristics that influence peak discharge through variable importance analysis. A parsimonious model with low bias and variance is created that can be deployed in the future for flash flood forecasting. The results confirm that, although the spatial organization of rainfall within a basin has a major influence on basin response, basin physiography is the primary driver of peak discharge. These findings have unprecedented spatial and temporal representativeness in terms of flood characterization across basins. An improved understanding of subbasin scale rainfall spatial variability will aid in robust flash flood characterization as well as with identifying basins that could most benefit from distributed hydrologic modeling. Significance Statement: To improve understanding of the effect of precipitation on floods, a machine-learning workflow is designed to scrutinize hydrological processes and is applied on a database of flood events over the United States. The model accurately reproduces observed maximum streamflow. It reflects physical hydrologic behavior that is consistent across basins, thereby addressing the challenge of regional modeling and improving upon traditional hydrological models. Rainfall spatial variability has a major influence on flood peak discharge, although basin geomorphology is the primary driver. This grants an improved understanding of the conditions under which floods are generated, allowing for better predictions and warning. [ABSTRACT FROM AUTHOR]

Hundreds of supercell proximity soundings obtained for field programs over the central United States are analyzed to reconcile differences in recent studies and to refine our knowledge of supercell environments. The large, storm-centric observation-based dataset and high vertical resolution of the sounding data provide an unprecedented look at supercell environments. Not surprisingly, storm-relative environmental helicity (SRH) is found to be larger in tornadic soundings than in nontornadic soundings. The primary finding that departs from previous studies is that storm-relative winds contribute substantially to the larger SRH. Stronger ground-relative winds and more rightward-deviant storm motions contribute to the larger storm-relative winds for the tornadic soundings. Spatial analyses of the soundings reveal lower near-ground pressure perturbations and stronger low- to midlevel cyclonic flow for the tornadic soundings, which suggests stronger mesocyclones, perhaps explaining the more rightward-deviant motions. Differences in the mean critical angle between the tornadic and nontornadic soundings are small and do not contribute to the larger mean SRH, but the tornadic soundings do have fewer instances of smaller (<60°) critical angles. Furthermore, the critical angle is shown to be a function of azimuth from the updraft. Other results include a low-to-the-ground (~250 m on average) hodograph kink for both the tornadic and nontornadic soundings and few notable differences in thermodynamic quantities, except for the expected lower LCLs related to higher RH for the tornadic soundings, somewhat smaller 0–3 km lapse rates in tornadic environments related to weaker/shallower capping inversions, and larger 0–3 km CAPE in near-field environments. Significance Statement: We analyze hundreds of high-resolution radiosonde observations taken close to supercell thunderstorms during field programs over the last 25 years, resulting in an unprecedented look at supercell environments. There are many results that support, clarify, or refute past hypotheses, but the primary finding that departs from previous studies is that low-level winds relative to the storm are substantially stronger around the time of tornado production compared to nontornadic supercells. These results help to refine our understanding of the conditions that support tornado formation, which provides guidance on environmental cues that can improve the prediction of supercell tornadoes. Future research should explore how to more routinely sample the environments closer to storms to help forecasters better exploit these environmental cues. [ABSTRACT FROM AUTHOR]