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NASA NTRS2020

Global Evaluation of Terrestrial Near-Surface Air Temperature and Specific Humidity Retrievals from the Atmospheric Infrared Sounder (AIRS)

Global observations of near-surface air temperature and specific humidity over land are needed for a variety of applications, including to constrain global estimates of evapotranspiration (ET). Spaceborne hyperspectral observations, such as those from NASA’s Atmospheric Infrared Sounder (AIRS) mission, show promise for meeting this need, yet there are surprisingly few validation studies of AIRS near-surface atmospheric state retrievals. In this study, we use triple collocation to validate AIRS Level 3 retrievals of near-surface atmospheric state over land using twelve years of gridded station observations and two reanalyses. Deseasonalized AIRS retrievals correlate well with deseasonalized ground observations outside the tropics, but correlate less well in the tropics. Lower temporal sensitivity near the surface in the tropics contributes to the lower correlation for near-surface air temperature and is consistent with known physics of the tropical atmosphere, in which temperatures outside the boundary layer (which dominate the AIRS retrieval signal) are poorly correlated with those near the surface. Retrievals in the tropics may also be more susceptible to errors in cloud-clearing algorithms, and to uncertainty in surface emissivity. Since ET is greatest in the tropics, and tropical measurement networks are particularly sparse, this work motivates new approaches for measuring ET in the tropics.

Evapotranspiration↗

NASA NTRS2016

Assessment of the SMAP Passive Soil Moisture Product

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational Level 2 soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 kilometer Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 cubic meter per cubic meter unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 cubic meter per cubic meter.

passive↗

DOE OSTI2020

A Satellite-Derived Upper-Ocean Stratification Data Set for the Tropical North Atlantic With Potential Applications for Hurricane Intensity Prediction

Upper-ocean stratification strongly impacts vertical mixing and the heat flux between the ocean and atmosphere, especially under extreme conditions of tropical cyclones (TCs). Knowledge of prestorm stratification is important for accurate TC intensity prediction. In situ observations of the tropical ocean have significantly increased in the past decade. However, they are still too sparse to resolve ocean stratification variability in near-real time and on small spatial scales. Here, in this study, based on long-term observations and an ocean reanalysis data set from 2004–2017, we investigate the possibility of retrieving upper-ocean stratification from sea surface temperature (SST), sea surface salinity (SSS), and sea surface height (SSH) using a simple regression method. It is found that more than 90% of the mean seasonal cycle and about 30% to 80% of temperature and salinity stratification anomalies can be reconstructed using surface data from either observations or an ocean reanalysis. Simple regression can be used with satellite observations to create a high-resolution, near-real-time-gridded ocean stratification data set that successfully reproduces both the large and mesoscale variability of ocean stratification. When used in a simple expression for TC-induced SST cooling, the satellite-derived stratification shows improvements over an ocean analysis in terms of variance explained of SST cooling, offering promise as a near-real-time indicator of the ocean's impact on TC intensification.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2010

Potential Utility of the Real-Time TMPA-RT Precipitation Estimates in Streamflow Prediction

We investigate the potential utility of the real-time Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA-RT) data for streamflow prediction, both through direct comparisons of TMPA-RT estimates with a gridded gauge product, and through evaluation of streamflow simulations over four tributaries of La Plata Basin (LPB) in South America using the two precipitation products. Our assessments indicate that the relative accuracy and the hydrologic performance of TMPA-RT-based streamflow simulations generally improved after February 2005. The improvements in TMPA-RT since 2005 are closely related to upgrades in the TMPA-RT algorithm in early February, 2005 which include use of additional microwave sensors (AMSR-E and AMSU-B) and implementation of different calibration schemes. Our work suggests considerable potential for hydrologic prediction using purely satellite-derived precipitation estimates (no adjustments by in situ gauges) in parts of the globe where in situ observations are sparse.

Su, Fengge↗

NASA NTRS2021

Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations

The ocean mixed layer plays an important role in the coupling between the upper ocean and atmosphere across a wide range of time scales. Estimation of the variability of the ocean mixed layer is therefore important for atmosphere-ocean prediction and analysis. The increasing coverage of in situ Argo profile data allows for an increasingly accurate analysis of the mixed layer depth (MLD) variability associated with deviations from the seasonal climatology. However, sampling rates are not sufficient to fully resolve subseasonal (<90 day) MLD variability. Yet, many multivariate observations-based analyses include implicit modeled subseasonal MLD variability. One analysis method is optimal interpolation of in situ data, but the interior analysis can be improved by leveraging surface data with regression or variational approaches. Here, we demonstrate how machine learning methods and satellite sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid-latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2° gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We validate our methodology by applying it to ocean model simulations. We find that incorporating sea surface data through a machine learning model improves the performance of spatiotemporal MLD variability estimation compared to optimal interpolation of Argo observations alone. These preliminary results are a promising first step for the application of machine learning to MLD prediction.

Machine Learning↗

NASA NTRS2019

Historical (1700–2012) Global Multi-Model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP)

Fire emissions are critical for carbon and nutrient cycles, climate, and air quality. Dynamic Global Vegetation Models (DGVMs) with interactive fire modeling provide important estimates for long-term and large-scale changes of fire emissions. Here we present the first multi-model estimates of global gridded historical fire emissions for 1700-2012, including carbon and 33 species of trace gases and aerosols. The dataset is based on simulations of nine DGVMs with different state-of-the-art global fire models that participated in the Fire Modeling Intercomparison Project (FireMIP), using the same and standardized protocols and forcing data, and the most up-to-date fire emission factor table from field and laboratory studies over various land cover types. We evaluate the simulations of present-day fire emissions by comparing them with satellite-based products. Evaluation results show that most DGVMs simulate present-day global fire emission totals within the range of satellite-based products, and can capture the high emissions over the tropical savannas, low emissions over the arid and sparsely vegetated regions, and the main features of seasonality. However, most of the models fail to simulate the interannual variability, partly due to a lack of modeling peat fires and tropical deforestation fires. Historically, all models show only a weak trend in global fire emissions before ~1850s, consistent with multi-source merged historical reconstructions. The long-term trends among DGVMs are quite different for the 20th century, with some models showing an increase and others a decrease in fire emissions, mainly as a result of the discrepancy in their simulated responses to human population density change and land-use and land-cover change (LULCC). Our study provides a basic dataset for developing regional and global multi-source merged historical reconstructions and merging methods, and analyzing historical changes of fire emissions and their uncertainties as well as their role in the Earth system. It also highlights the importance of accurately modeling the responses of fire emissions to LULCC and population density change in reducing uncertainties in historical reconstructions of fire emissions and providing more reliable future projections.

Li, Fang↗

DOE OSTI2023

Transportation and electricity systems integration via electric vehicle charging-as-a-service: A review of techno-economic and societal benefits

Here, in support of global decarbonization efforts, the adoption of electric vehicles is proceeding rapidly across transportation sectors, population groups, and regions. However, access to electric vehicle charging infrastructure remains sparse and inequitable, and technical, economic, and procedural challenges have encumbered its expansion. Charging-as-a-service, which can mitigate cost and effort risks of charging equipment ownership, adds a means of meeting many of these challenges. As charging-as-a-service has not been analyzed or addressed in the scholarly literature, we synthesize media, marketing, and analogous scholarly publications to categorize and describe the functions asserted by existing charging-as-a-service vendors. We demonstrate and quantify the benefits of many of these functions via four example business cases, sited at a detached residence, a multi-unit dwelling, a consumer-facing business, and a commercial fleet depot. Outcomes show that savings and revenues realized via charging-as-a-service can reduce charging costs, beneficially shape grid loads, and make charging installation economical where it otherwise would not be. Discussion highlights the need to deploy charging-as-a-service in cooperation with initiatives promoting societal benefits, including charging access equity.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Non-linear relationships between daily temperature extremes and US agricultural yields uncovered by global gridded meteorological datasets

Global agricultural commodity markets are highly integrated among major producers. Prices are driven by aggregate supply rather than what happens in individual countries in isolation. Furthermore, estimating the effects of weather-induced shocks on production, trade patterns and prices hence requires a globally representative weather data set. Recently, two data sets that provide daily or hourly records, GMFD and ERA5-Land, became available. Starting with the US, a data rich region, we formally test whether these global data sets are as good as more fine-scaled country-specific data in explaining yields and whether they estimate similar response functions. While GMFD and ERA5-Land have lower predictive skill for US corn and soybeans yields than the fine-scaled PRISM data, they still correctly uncover the underlying non-linear temperature relationship. All specifications using daily temperature extremes under any of the weather data sets outperform models that use a quadratic in average temperature. Correctly capturing the effect of daily extremes has a larger effect than the choice of weather data. In a second step, focusing on Sub Saharan Africa, a data sparse region, we confirm that GMFD and ERA5-Land have superior predictive power to CRU, a global weather data set previously employed for modeling climate effects in the region.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2000

Three-Dimensional Nacelle Aeroacoustics Code With Application to Impedance Education

A three-dimensional nacelle acoustics code that accounts for uniform mean flow and variable surface impedance liners is developed. The code is linked to a commercial version of the NASA-developed General Purpose Solver (for solution of linear systems of equations) in order to obtain the capability to study high frequency waves that may require millions of grid points for resolution. Detailed, single-processor statistics for the performance of the solver in rigid and soft-wall ducts are presented. Over the range of frequencies of current interest in nacelle liner research, noise attenuation levels predicted from the code were in excellent agreement with those predicted from mode theory. The equation solver is memory efficient, requiring only a small fraction of the memory available on modern computers. As an application, the code is combined with an optimization algorithm and used to reduce the impedance spectrum of a ceramic liner. The primary problem with using the code to perform optimization studies at frequencies above I1kHz is the excessive CPU time (a major portion of which is matrix assembly). The research recommends that research be directed toward development of a rapid sparse assembler and exploitation of the multiprocessor capability of the solver to further reduce CPU time.

Watson, Willie R.↗

NASA NTRS2021

Assessing Disaggregated SMAP Soil Moisture Products in the United States

A soil moisture (SM) disaggregation algorithm based on thermal inertia (TI) theory was implemented to downscale the Soil Moisture Active Passive (SMAP) Enhanced product(SPL2SMPE) from 9 km to 1 km over the continental United States. The algorithm applies land surface temperature and normalized difference vegetation index from Moderate Resolution Imaging Spectroradiometer (MODIS) at higher spatial resolution to estimate relative soil wetness within a coarse SMAP grid -this MODIS-derived relative wetness is then used to produce the downscaled SMAP SM. Results from the algorithm were evaluated in terms of their spatiotemporal coverage and accuracy using in situ measurements from SMAP Core Validation Sites (CVS), the US Department of Agriculture Soil Climate Analysis Network (USDA-SCAN), and the National Oceanic and Atmospheric Administration Climate Reference Network(NOAA-CRN). Results were also compared with the baselineSPL2SMPE and the SMAP/Sentinel-1 (SPL2SMAPS) 1kmproduct. Overall, the unbiased root mean square error (ubRMSE)of the disaggregated SM at the CVS using the TI approach is approximately 0.04 m3=m3, which is the SMAP mission requirement for the baseline products. The TI approach out performs the SMAP/Sentinel SL2SMAPS 1km product by approximately0.02 m3=m3. Over the agriculture/crop areas from SCAN and CRN sparse network stations, the TI approach exhibits better ubRMSE compared to SPL2SMPE andSPL2SMAPS byabout0.01 and 0.02 m3=m3, indicating its advantage in these areas. However, a drawback of this approach is that there are data gaps due to cloud cover as optical sensors cannot have a clear view of the land surface.

Soil moisture↗

DOE OSTI2023

Parallel Algebraic Multigrid for Fusion and Higher-Order PDEs

Multigrid methods play a key role in large-scale scientific simulation because they are among the fastest and most scalable approaches for solving the underlying sparse linear systems of equations that arise from a wide array of Partial Differential Equation (PDE) discretizations. Algebraic multigrid (AMG) is a special type of multigrid method that depends only on the description of the linear system, giving it better portability and broader applicability than geometric multigrid, as it requires no explicit knowledge of the problem geometry. Even though these methods are widely used today, there are still applications where further development is needed. In this report, we focus on PDEs with higher-order terms (e.g., fourth order), concentrating on a PDE that arises in tokamak edge plasma simulations (a tokamak is a machine that confines a plasma using magnetic fields and is believed to be the leading plasma confinement concept for future fusion power plants). General multigrid relaxes a linear system on coarser grids and reverses this process with interpolation, but standard AMG methods struggle with the aforementioned higher-order PDEs. We investigate cyclic coarsening and interpolation heuristics, as well as new iterative approximation methods of refining the solution at each grid to improve the existing multigrid approach. To this end, we ensure that these techniques are transferable to a parallelized setting with LLNL’s supercomputers.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2001

The Sensitivity of West African Squall Line Water Budgets to Land Cover

This study used a two-dimensional coupled land/atmosphere (cloud-resolving) model to investigate the influence of land cover on the water budgets of squall lines in the Sahel. Study simulations used the same initial sounding and one of three different land covers, a sparsely vegetated semi-desert, a grassy savanna, and a dense evergreen broadleaf forest. All simulations began at midnight and ran for 24 hours to capture a full diurnal cycle. In the morning, the latent heat flux, boundary layer mixing ratio, and moist static energy in the boundary layer exhibited notable variations among the three land covers. The broadleaf forest had the highest latent heat flux, the shallowest, moistest, slowest growing boundary layer, and significantly more moist static energy per unit area than the savanna and semi-desert. Although all simulations produced squall lines by early afternoon, the broadleaf forest had the most intense, longest-lived squall lines with 29% more rainfall than the savanna and 37% more than the semi-desert. The sensitivity of the results to vegetation density, initial sounding humidity, and grid resolution was also assessed. There were greater differences in rainfall among land cover types than among simulations of the same land cover with varying amounts of vegetation. Small changes in humidity were equivalent in effect to large changes in land cover, producing large changes in the condensate and rainfall. Decreasing the humidity had a greater effect on rainfall volume than increasing the humidity. Reducing the grid resolution from 1.5 km to 0.5 km decreased the temperature and humidity of the cold pools and increased the rain volume.

Mohr, Karen I.↗

DOE OSTI2020

General approaches for shear-correcting coordinate transformations in Bragg coherent diffraction imaging. Part I

This two-part article series provides a generalized description of the scattering geometry of Bragg coherent diffraction imaging (BCDI) experiments, the shear distortion effects inherent in the 3D image obtained from presently used methods and strategies to mitigate this distortion. Part I starts from fundamental considerations to present the general real-space coordinate transformation required to correct this shear, in a compact operator formulation that easily lends itself to implementation with available software packages. Such a transformation, applied as a final post-processing step following phase retrieval, is crucial for arriving at an undistorted, correctly oriented and physically meaningful image of the 3D crystalline scatterer. As the relevance of BCDI grows in the field of materials characterization, the available sparse literature that addresses the geometric theory of BCDI and the subsequent analysis methods are generalized here. This geometrical aspect, specific to coherent Bragg diffraction and absent in 2D transmission CDI experiments, gains particular importance when it comes to spatially resolved characterization of 3D crystalline materials in a reliable nondestructive manner. This series of articles describes this theory, from the diffraction in Bragg geometry to the corrections needed to obtain a properly rendered digital image of the 3D scatterer. Part I of this series provides the experimental BCDI community with the general form of the 3D real-space distortions in the phase-retrieved object, along with the necessary post-retrieval correction method. Part II builds upon the geometric theory developed in Part I with the formalism to correct the shear distortions directly on an orthogonal grid within the phase-retrieval algorithm itself, allowing more physically realistic constraints to be applied. Taken together, Parts I and II provide the X-ray science community with a set of generalized BCDI shear-correction techniques crucial to the final rendering of a 3D crystalline scatterer and for the development of new BCDI methods and experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Use of Machine Learning on PMU Data for Transmission System Fault Analysis

Synchrophasor technology has been used for monitoring, control, and protection of bulk power system for over 10 years. Deployment of phasor measurement units (PMUs) in the USA power system has surpassed 3000 units installed in the transmission substations as stand-alone intelligent electronic devices (IEDs) or as a software add-on to other devices such as digital protective relays (DPRs) or digital fault recorders (DFRs). By now, thousands of terabytes of PMU data may have been captured and stored by various transmission system operators (TSOs) and independent system operators (ISOs). This creates an opportunity to deploy advanced machine learning (ML) techniques to detect and classify faults recorded by PMUs automatically to be used by the system operators for rapid, critical decision-making when manual analysis of the past or unfolding events is not feasible. In this paper we offer a brief background on how the automated fault analysis may be done using DPR and/or DFR data, and compare some of the legacy approaches to the new ML approaches in the context of the system-wide PMU recordings. We then offer insights from developing practical ML solutions that have been applied on field recordings captured by close to 450 PMUs from all three US interconnections (Western, Eastern and ERCOT) over two years (2016-2017). We identify and illustrate ML challenges we addressed: inaccurate data, data with scarce and temporally imprecise fault labels, data recorded by PMUs sparsely located at substations resulting in the fault records taken afar from the ends of the faulted lines, data containing only positive sequence values, and data taken at different voltage levels. We then illustrate the ML model results for fault analysis under different application scenarios. The novelty of this study is not only in the design, implementation, and performance analysis of the ML algorithms, but also in the use of advanced fault modelling and simulation approaches to improve the training results when developing supervised ML models for fault detection and classification. Extensive simulations of faults were conducted on a 14-bus power system to create a training dataset with over 1400 accurately labelled faults. This dataset was applied to enhance the accuracy of fault detection and classification of machine learning-based models trained with small number of labelled faults in large datasets recorded in the grid interconnections ranging from 5,000 to 70,000 buses.

Synchrophasors, Machine Learning, Fault Analysis, ↗

NASA NTRS1999

Incorporating TRMM and Other High-Quality Estimates into the One-Degree Daily (1DD) Global Precipitation Product

The One-Degree Daily (1DD) precipitation dataset was recently developed for the Global Precipitation Climatology Project (GPCP). The IDD provides a globally-complete, observation-only estimate of precipitation on a daily 1 deg x 1 deg grid for the period 1997 through late 1999 (by the time of the conference). In the latitude band 40 N - 40 S the IDD uses the Threshold-Matched Precipitation Index (TMPI), a GPI-like IR product with the T(sub b) threshold and (single) conditional rain rate determined locally for each month by the frequency of precipitation in the GPROF SSNU product and by the precipitation amount in the GPCP satellite-gauge (SG) combination. Outside 40 N - 40 S the 1DD uses a scaled TOVS precipitation estimate that has adjustments based on the TMPI and the SG. This first-generation 1DD has been in beta test preparatory to release as an official GPCP product. In this paper we discuss further development of the 1DD framework to allow the direct incorporation of TRMM and other high-quality precipitation estimates. First, these data are generally sparse (typically from low-orbit satellites), so a fair amount of work was devoted to data boundaries. Second, these data are not the same as the original 1DD estimates, so we had to give careful consideration to the best scheme for forcing the 1DD to sum to the SG for the month. Finally, the non-sun-synchronous, low-inclination orbit occupied by TRMM creates interesting variations against the sun-synchronous, high-inclination orbits occupied by the Defense Meteorological Satellite Program satellites that carry the SSM/I. Examples will be given of each of the development issues, then comparisons will be made to daily raingauge analyses.

Huffman, George J.↗

NASA NTRSDate not supplied

Cloud-Precipitation Hybrid Regimes and their Projection onto IMERG Precipitation Data

We extend and enhance the concept of the Cloud Regimes (CRs) developed from two-dimensional joint histograms of cloud optical thickness and cloud top pressure from the Moderate Resolution Imaging Spectroradiometer (MODIS), by adding precipitation information in order to better understand cloud-precipitation relationships. Taking advantage of the high-resolution Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation dataset, cloud-precipitation “hybrid” regimes are derived by implementing the k-means clustering algorithm with advanced initialization and objective measures to determine the most optimal clusters. By expressing precipitation rates within 1-degree grid cell as histograms and making choices on the relative weight of cloud and precipitation, we could obtain several editions of hybrid cloud-precipitation regimes (CPRs), and examine their characteristics. In the deep tropics, when precipitation is weighted weakly, the cloud part of the hybrid entroids resembles the centroid of cloud-only regimes, but still tightens the cloud-precipitation relationship by decreasing the precipitation variability of each regime. As precipitation weight progressively increases, the shape of the cloudy part of the hybrid centroids becomes blunter, while the precipitation part of the centroids sharpens. In the case where cloud and precipitation are weighted equally, the CPRs representing high clouds with intermediate to heavy precipitation exhibit distinct features in the precipitation parts of the centroids, which allows us to project them onto the 30-minly IMERG domain. Such a projection can be used to overcome the temporal sparseness of MODIS cloud observations, which leads to great application potential for various convection-focused studies, including diurnal cycle analysis.

cloud-precipitation↗

DOE OSTI2020

Spatial Mapping of Riverbed Grain-Size Distribution Using Machine Learning

Recent alluvial sediments in riverbeds play a significant role in controlling hydrologic exchange flows (HEFs) in river systems. The alluvial layer is usually associated with strong heterogeneity in physical properties (e.g., permeability and hydraulic conductivity), which affects local HEFs and therefore biogeochemical processes. The spatial distribution of these physical properties needs to be determined to inform the numerical models used to reveal the realistic hydro-biogeochemical behaviors. Such information can be obtained based on the intrinsic link between sediment grain-size distribution and hydraulic properties where sediment texture information is available. However, grain-size measurements are usually spatially sparse and do not have adequate coverage and resolution, particularly for a relatively large domain such as the Hanford Reach of the Columbia River. In this paper, we adopted machine learning (ML) approaches for categorizing and mapping the spatial distributions of riverbed substrate grain size and filling in missing areas of substrate data using the ML models along the reach. Such ML models for substrate size mapping were trained at 13,372 locations using measured substrate sizes along with observed and simulated attributes, including bathymetric attributes (e.g., elevation, slope, and aspect ratio) from LIDAR and bathymetric surveys, and hydrodynamic properties (e.g., water depth, velocity, shear stress, and their statistical moments). An ensemble bagging-based ML technique, Random Forest, was adopted to identify the most influential factors as predictors to develop the predictive models with over-fitting issues addressed. The models were evaluated with respect to each individual substrate size class and the lumped group, and then used to generate the final substrate size maps covering all the grid cells in the numerical modeling domain.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Influence of background sources and topographic resolution in the Weather Research and Forecasting Model on xenon plume characteristics at monitoring stations

For many atmospheric monitoring applications, networks of measurement sites—such as the radionuclide stations of the International Monitoring System—can be sparse. With measurement locations potentially hundreds to thousands of kilometers from a release it is important to quantify the effects of physical processes on transport and dispersion of plumes between source and measurement locations. This study addresses the effects of background sources and topography resolution near the release location of radionuclides. We use the Weather Research and Forecasting (WRF) model with inline chemistry to investigate (1) how an additional, time-varying source of 133 Xe, such as an operational medical isotope production facility, contributes to activity concentration measurements at monitoring sites, and (2) how complex topography influences on atmospheric conditions near emission sources impact plume concentrations at varying distances from the source. Two 133 Xe emission sources, including (1) a high flux rate of short duration representing an explosive event, and (2) a variable and continuous background source, are simulated. The continuous background source contributes significantly to total 133 Xe concentrations at several monitoring stations. Further, a WRF simulation at 9 km horizontal resolution is compared with a nested grid simulation, where the innermost domain has a resolution of 1 km. Increased topographic resolution leads to an improved representation of plume responses to local winds, with topographic influences greatest at locations closest to the sources. Differences between the two domain resolutions decrease at greater distances from the sources, as plumes have time to spread and mix and are influenced by synoptic scale circulation patterns that are represented similarly in both simulations.

54 ENVIRONMENTAL SCIENCES↗