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The Areal Averaged Albedo VAP yields areal averaged surface spectral albedo estimates from MFRSR measurements collected under fully overcast conditions via a simple one-line equation (Barnard et al., 2008), which links cloud optical depth, normalized cloud transmittance, asymmetry parameter, and areal averaged surface albedo under fully overcast conditions.
Here, the surface urban heat island (SUHI) effect, assessed through remotely sensed land surface temperature (LST), remains a focal point in urban climate research. Conventional indicators like SUHI intensity (SUHII) and footprint (SUHIF) capture peak values and spatial extent but fail to account for the cumulative thermal load—a critical dimension reflecting the total heat exposure imposed by spatially continuous warming, which directly limits a holistic assessment of ecological and societal impacts of the SUHI effect. Therefore, this study introduces an indicator termed SUHI capacity (SUHIC), designed to quantify the aggregated SUHI effect by integrating the magnitude of the warming signal across all affected areas, thereby enabling a more comprehensive evaluation of urban thermal environments. Furthermore, a direction-enhanced adaptive synchronous extraction (DEASE) method is proposed for the quantification of SUHIC. This method can dynamically identify the optimal background reference area based on the urban-rural LST gradients in various directions within the city, without relying on predefined mathematical models as previously. The results from 102 European cities first confirm that the directional variations in urban-rural LST gradients, and the DEASE method can effectively capture these distinctions for the simultaneous estimation of SUHII, SUHIF, and SUHIC. Secondly, the spatial patterns of absolute SUHIC values show strong associations with those of SUHIF (R2>0.86), while its relative values (normalized by the area of urban) align more closely with SUHII (R2 > 0.64). More importantly, SUHIC can serve as a crucial reference for assessing the urban thermal signal when SUHII and SUHIF diverge. The proposed method and framework contribute to standardizing the quantification of the SUHI effect.
The Surface Atmosphere Integrated Field Laboratory (SAIL) campaign, conducted from September 2021 to June 2023 in Crested Butte, Colorado, aimed to characterize precipitation processes in the Upper Colorado River Basin (UCRB). This increased observations of snowfall accumulation in this hydrologically significant watershed would be useful for quantitative precipitation estimates (QPE). Therefore, the Surface Quantitative Precipitation Estimate (SQUIRE) product was developed using the ARM-supported Colorado State University (CSU) X-band Precipitation Radar. Although SQUIRE will be only released for snowfall, by categorizing precipitation types, users can effectively utilize relevant datasets under diverse meteorological conditions. Moreover, the dataset facilitates validation of the QPE product and the analysis of seasonal variations in precipitation types at the surface. Hydrometeors classes are organized based on their phase and physical characteristics mapping the CSU (both winter Summer) and Py-ART classifications into four groups. 1. Liquid Precipitation: includs drizzle, rain, and large raindrops. 2. Frozen Snow and Ice : Pure Snow, combining ice crystals, aggregates, and vertically oriented ice structures. 3. Dense and Large frozen hydrometeors: including low- and high-density graupel and dry hail. 4.Melting: Wet Snow and Melting Hail, hydrometeors exhibiting both liquid and frozen characteristics.
Many current gridded surface meteorological datasets are inadequate for quantifying near surface spatiotemporal variability because they do not fully represent the impacts of land surface heterogeneity. Of note, explicit representation of the spatial structure and magnitude of local urban warming are usually lacking. Here we enhance the representation of spatial meteorological variability over urban areas in the conterminous United States (CONUS) by employing the High-Resolution Land Data Assimilation System (HRLDAS), which accounts for the fine-scale impacts of spatiotemporally varying land surfaces on weather. We also synthesize in situ meteorological data including local mesonets to create a 1 km grid spacing model-observation fusion product spanning 1981-2018 over the CONUS. Daily maximum, minimum, and mean values for a variety of temperature estimates, humidity, and surface energy budget terms, among others, are included. This High-resolution Urban Meteorology for Impacts Dataset (HUMID) will be useful for studies examining spatial variability of near surface meteorology and the impacts of urban heat islands across many disciplines including epidemiology, ecology, and climatology.
Additive manufacturing via selective laser melting can result in variable levels of internal porosity both between build plates and within components from the same build. In this work, sample porosity levels were compared to tensile properties for 176 samples spanning eight different build plates. Sample porosity was measured both by Archimedes density, which provided an estimation of overall porosity, and by observation of voids in the fracture surface, which provided an estimation of the porosity at the failure plane. The porosity observed at the fracture surface consistently demonstrated higher porosity than that suggested by Archimedes density. The porosity values obtained from both methods were compared against the mechanical results. Sample porosity appears to have some correlation to the ultimate tensile strength, yield strength, and modulus, but the strongest relationship is observed between porosity and ductility. Three different models were used to relate the fracture surface porosity to the ductility. The first method was a simple linear regression analysis, while the other two models have been used to relate porosity to ductility in cast alloys. It is shown that all three models fit the data well over the observed porosity ranges, suggesting that the models taken from casting theory can extend to additively manufactured metals. Finally, it is proposed that the non-destructive Archimedes method could be used to estimate an approximate sample ductility through the use of correlations realized here. Such a relationship could prove useful for design and for a deeper understanding of the impact of pores on tensile behavior.
The National Solar Radiation Database (NSRDB) is an extensively used dataset that furnishes satellite-retrieved solar resource data across the United States and an expanding list of other countries. Although the NSRDB uses a physical model to compute global horizontal irradiance (GHI), it currently employs an empirical approach based on surface observations to estimate cloudy-sky direct normal irradiance (DNI). Recently, a new physics-based approach, known as the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to improve DNI forecasting. FARMS-DNI integrates direct and scattered solar radiances within the circumsolar region, resulting in improved day-ahead forecasting of DNI by incorporating it into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study incorporates FARMS-DNI into the NSRDB algorithm to produce high-spatiotemporal-resolution DNI data from satellite data. The accuracy of the NSRDB based on FARMS-DNI is analyzed using surface observations from 19 sites situated within the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation Budget (SURFRAD) and Solar Radiation (SOLRAD) networks, the University of Oregon (UO) network, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) network, and at the National Renewable Energy Laboratory (NREL). The results demonstrate that FARMS-DNI reduces the significant overestimation of DNI in the conventional NSRDB at all surface sites, particularly in cloud overcast conditions classified using both satellite retrievals and surface observations. Consequently, this new model can effectively improve the overall accuracy of the NSRDB. The results also suggest that further improvement of DNI estimates at individual time steps, however, requires advanced satellite techniques and precise identification of clouds and retrieval of cloud properties.
Nanostructures in the form of ellipsoids, prolate spheroids, rings, and cylinders are known to exhibit resonant surface and cavity modes with applications in nanophotonics and plasmonics and, more recently, in novel quantum experiments, in which control of plasmons and their interactions with plasmons, photons, phonons, excitons, and quantum emitters are desired. Nanorods and nanowires are examples of plasmonic structures with spectral properties of potential use as interconnects and circuit components. Additionally, estimates of the surface properties of these components are needed in circuit design and integrated systems. Here, we present a quantum Hamiltonian for the cylindrical surface charge density. We then study the photon excitation of plasmons on the cylindrical surface and calculate their scattering and radiative decay rate. Nonradiative decay of plasmons induces an efficient heating of the nanoparticle and can photoacoustically excite mechanical oscillations. Computational calculations are also presented for the plasmonic modes and the ensuing excitations of nanomechanical eigenmodes of nanoparticles with near-cylindrical symmetries.
In this work, generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We perform inverse modeling and compute the posterior distribution of the critical hydrological parameters that are subject to great uncertainty in the Community Land Model (CLM) for a given value of the output LH. The unknown parameters include those that have been identified as the most influential factors on the simulations of surface and subsurface runoff, latent and sensible heat fluxes, and soil moisture in CLM4.0. We set up the inversion problem in the Bayesian framework in two steps: (i) building a surrogate model expressing the input–output mapping, and (ii) performing inverse modeling and computing the posterior distributions of the input parameters using observation data for a given value of the output LH. The development of the surrogate model is carried out with a Bayesian procedure based on the variable selection methods that use gPC expansions. Our approach accounts for bases selection uncertainty and quantifies the importance of the gPC terms, and, hence, all of the input parameters, via the associated posterior probabilities.
Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral reflectance coupled with machine learning, can provide rapid, efficient, and cost-effective quantification of SOC. However, how to exploit soil hyperspectral reflectance to predict SOC concentration, and the potential performance of airborne and satellite data for predicting surface SOC at large scales remain relatively underknown. Here, this study utilized a continental-scale soil laboratory spectral library (37,540 full-pedon 350–2500 nm reflectance spectra with SOC concentration of 0–780 g·kg –1 across the US) to thoroughly evaluate seven machine learning algorithms including Partial-Least Squares Regression (PLSR), Random Forest (RF), K-Nearest Neighbors (KNN), Ridge, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) along with four preprocessed spectra, i.e. original, vector normalization, continuum removal, and first-order derivative, to quantify SOC concentration. Furthermore, by using the coupled soil-vegetation-atmosphere radiative transfer model, we simulated twelve airborne and spaceborne hyper/multi-spectral remote sensing data from surface bare soil laboratory spectra to evaluate their potential for estimating SOC concentration of surface bare soils. Results show that LSTM achieved best predictive performance of quantifying SOC concentration for the whole data sets (R 2 = 0.96, RMSE = 30.81 g·kg –1 ), mineral soils (SOC ≤ 120 g·kg –1 , R 2 = 0.71, RMSE = 10.60 g·kg –1 ), and organic soils (SOC > 120 g·kg –1 , R 2 = 0.78, RMSE = 62.31 g·kg –1 ). Spectral data preprocessing, particularly the first-order derivative, improved the performance of PLSR, RF, Ridge, KNN, and ANN, but not LSTM or CNN. We found that the SOC models of mineral and organic soils should be distinguished given their distinct spectral signatures. Finally, we identified that the shortwave infrared is vital for airborne and spaceborne hyperspectral sensors to monitor surface SOC. This study highlights the high accuracy of LSTM with hyperspectral/multispectral data to mitigate a certain level of noise (soil moisture <0.4 m 3 ·m –3 , green leaf area < 0.3 m 2 ·m –2 , plant residue <0.4 m 2 ·m –2 ) for quantifying surface SOC concentration. Forthcoming satellite hyperspectral missions like Surface Biology and Geology (SBG) have a high potential for future global soil carbon monitoring, while high-resolution satellite multispectral fusion data can be an alternative.
Heterogeneity in surface fuels produced by overstory trees and understory vegetation is a major driver of fire behavior and ecosystem dynamics. Previous attempts at predicting tree leaf and needle litter accumulation over time have been constrained in scope to probabilistic models that consider a limited number of key factors influencing tree litter dispersal patterns and decomposition processes. Here we present a mechanistic model for estimating variation in surface fuels called the Distribution of Understory using Elliptical Transport (DUET). DUET uses a pre-generated voxelated canopy array and distributes the leaf litter from the trees in an elliptical shape based on tree species characteristics, wind data, and location-specific features. DUET then calculates grass growth between trees and around the litter patterns and sets moisture levels based on relative humidity. In this study, we use DUET to simulate spatial distributions of leaf litter and illustrate model sensitivity to several parameters, providing inputs for testing fuel spatial heterogeneity impacts on fire behavior using process-based models.
Reactive transport modeling is an essential tool to simulate complex geochemical reactions in porous media that can impact formation properties including porosity and permeability. However, simulating these reactions is challenging due to uncertainties in model parameters, particularly mineral surface areas. Imaging has emerged as a powerful means of estimating model parameters including porosity and mineral abundance, accessibility and accessible surface area. However, these parameters, particularly mineral accessible surface area, vary with image resolution. This work aims to enhance understanding of the impact of image resolution and other means of estimating mineral reactive surface area on simulated mineral reactions and reaction rates. Mineral surface areas calculated from images with resolutions of 0.34 μm and 5.71 μm were used to simulate mineral reactions in the context of geologic CO 2 sequestration in the Paluxy formation at the continuum scale. Additional simulations were carried out using BET surface areas collected from the literature and geometric surface areas. Simulations were run for 7300 days and mineral volume fractions and effluent ion concentrations tracked and compared. Variations in mineral surface areas measured from images are within 1 order of magnitude and yield similar simulation results, indicating the impact of image resolution on simulated reactions and reaction rates is minimum for the resolutions and sample considered. In comparison, surface areas obtained from BET and geometric approaches are 1–4 orders of magnitude higher than image-obtained surface areas and result in greater simulated reaction rates and extents. Minerals with high reaction rates (calcite and siderite) are most impacted by surface area values at short times where simulated mineral volume fractions at longer times agree relatively well, even for simulations with several orders of magnitude variation in surface area. Phases with lower reaction rates, such as K-feldspar and muscovite, are predominantly impacted over longer times where variations in surface areas impact reaction extents and porosity evolution. Overall, variations in surface areas due to image resolution are small and result in little variation in simulated reactions and reaction rates while there are significant variations in simulation results when other surface area estimates are used. These variations, however, depend on the reactivity of the mineral phase where surface areas of fast-reacting phases largely impact simulations at short (hours to days) timescales and surface areas of slower-reactive phases impact longer term simulations (years).
An ongoing effort looking at bioblendstocks to function as drop-in replacements or blending components for gasoline has identified a large number of candidate fuels. This work documents an approach for rapid screening of candidate fuels using relatively small sample sizes ($\mathcal{O}(\mu\,{\rm l})$) that targets two key physical properties of liquid fuels—surface tension and viscosity. The approach utilizes shape oscillation dynamics of single droplets generated by a piezo-electric device and their decay over time. Strobed imaging of the oscillation process is used along with image processing, edge detection, and data analysis to capture the decay of the oscillation over time. The time constant of the decay process along with oscillation frequency are then used to estimate viscosity and surface tension using a theory for small amplitude droplet oscillations. Measurements are obtained for primary reference fuels (isooctane and n-heptane) as well as candidate fuels from four bio-derived functional groups of interest. Measurement results for surface tension and viscosity are correlated with literature data as well as measurements from standard reference instruments. The measurement results show that the droplet oscillation based approach is capable of reproducing surface tension and viscosity values for the tested fuels within deviations of 7% and 13% respectively from literature data. Results are obtained using an average of 5 µl per fuel within about 20 s, thus demonstrating a small-volume, high-throughput approach that can be used for screening of candidate bioblendstock fuels.
Forest litter cover significantly alters the ground surface resistance, which influences the water and energy transfer processes between the land surface and atmosphere. Penman-Monteith (PM) model is widely used to estimate land surface evapotranspiration (ET). However, it was shown to overestimate ET during the period when foliar litter covers the ground surface. Therefore, incorporating the effect of foliar litter on ground surface resistance can potentially improve ET estimates. Here, in this study, we proposed a foliar litter surface resistance model to describe the effect of litter cover on water vapor transport, then incorporated the litter surface resistance into the PM model (noted as PM-EL model). The performance of the PM-EL model was evaluated using observations from 18 deciduous broadleaf forest (DBF) flux sites across global FLUXNET2015 datasets. Results showed that: (1) both the PM model and the PM-EL model are most sensitive to ground surface resistance among resistance parameters; (2) the proposed litter resistance model is capable of describing the seasonal dynamic of litter in DBF ; (3) the PM-EL model, has been shown to improve ET estimates with the coefficient of determination increased by 8.5 %, the mean absolute error and relative root mean square error decreased by 22.1 % and 20.9 %, respectively. The study contributes to our understanding of the effect of litter cover on ET estimation and provides insights into forest ecohydrology and land surface mass and energy transfer processes.
The surface albedo plays an important role in the Earth's radiation balance. This report provides information on how to estimate areal-averaged surface albedo from ground-based measurements of solar radiation at five wavelengths (415,500,615,675, and 870 nm). The report first explains why this estimation is important and challenging. Then the report defines both the required and complimentary inputs for the corresponding Areal-Averaged Surface Albedo (ArealAveAlb) Value-Added Product (VAP) and highlights the major outputs of this VAP. The following section explains how estimation of the areal-averaged surface albedo at four wavelengths (500,615,675, and 870 nm) can be performed for different surface types, including those partly covered by snow. The final section provides three examples that illustrate the VAP’s performance and emphasizes the benefits of complementary inputs, such as distinct cloud types and precipitation.
The mobilization and land-to-ocean transfer of dissolved organic carbon (DOC) in Arctic watersheds is intricately linked with the region's climate and water cycle, and furthermore at risk of changes from climate warming and associated impacts. This study quantifies model-simulated estimates of runoff, surface and active layer leachate DOC concentrations and loadings to western Arctic rivers, specifically for basins that drain into coastal waters between and including the Yukon and Mackenzie Rivers. Model validation leverages data from other field measurements, synthesis studies, and modeling efforts. The simulations effectively quantify DOC leaching in surface and subsurface runoff and broadly capture the seasonal cycle in DOC concentration and mass loadings reported from other studies that use river-based measurements. A marked east-west gradient in simulated spring and summer DOC concentrations of 24 drainage basins on the North Slope of Alaska is captured by the modeling, consistent with independent data derived from river sampling. Simulated loadings for the Mackenzie and Yukon show reasonable agreement with estimates of DOC export for annual totals and four of the six seasonal comparisons. Nearly equivalent loading occurs to rivers which drain north to the Beaufort Sea and west to the Bering and Chukchi Seas. The modeling framework provides a basis for understanding carbon export to coastal waters and for assessing impacts of hydrological cycle intensification and permafrost thaw with ongoing warming in the Arctic.
Ebullitive methane (CH 4 ) emissions in lentic ecosystems tend to concentrate at river-lake interfaces and within shallow littoral zones. However, inconsistent definitions of the littoral zone and static representations of the lake or reservoir surface area contribute to major uncertainties in greenhouse gas (GHG) emissions estimates, particularly in reservoirs with large water-level fluctuations. This study examines temporal variation in littoral and total surface areas of US reservoirs and demonstrates how different methods and data sources lead to discrepencies in reservoir GHG emissions at large scales and over time. We also explore variability in remotely sensed water occurrence according to maximum surface area, reservoir purposes, and hydrologic regions. Notably, the largest relative variability in surface area is exhibited by small reservoirs with a maximum surface area <1 km 2 and non-hydroelectric reservoirs. Additionally, we use a case study of measured CH 4 emissions from the southeastern United States (Douglas Reservoir) to illustrate the effects of varying surface area on reservoir-wide GHG estimates. Upscaled CH 4 emissions in Douglas Reservoir differed by nearly two-fold depending on the source of total surface area data and whether estimates accounted for seasonal fluctuations in surface area. During seasonal drawdown in Douglas Reservoir, relative littoral area varies non-linearly; periods of lower pool elevation (and thus larger relative littoral area) likely contribute disproportionately high CH 4 emission rates compared to the commonly sampled summer season when water levels are at full-pool elevation. Improved GHG monitoring and upscaling techniques require accounting for temporal variability in reservoir surface extent and littoral area.
Eddy covariance measurements of land-atmosphere energy, carbon, and water exchange now span multiple decades at some sites, supporting an improved understanding of flux interannual variability (IAV) and its ecophysiological and physical controls. Most eddy covariance IAV studies have focused on temperate forest ecosystems, where carbon fluxes are large and flux records are longest – but also where IAV is much lower than in dryland regions, which have been identified as an essential driver of the trend and variability in the global terrestrial carbon sink. In this study, we leveraged 19 years of continuous micrometeorological measurements at the AmeriFlux US-SRM mesquite savanna site in southern Arizona, USA to quantify the IAV, trends, and drivers of carbon fluxes during the distinct spring and summer growing seasons. We also assessed the ability of modern satellite and land surface models to capture the IAV of seasonal water and carbon fluxes. Annual net ecosystem production (NEP) was small and highly variable (23 +/- 64 gC m –2 yr –1 ). Precipitation and associated measures of water availability determined most of the variability in NEP, largely through their influence on annual and seasonal gross ecosystem productivity (GEP) as opposed to ecosystem respiration (ER). Root-zone soil moisture captured between 73% (spring) and 85% (summer) of GEP variability and between 73% (spring) and 58% (summer) of ER variability. Throughout the study period, soil moisture and greenness increased with associated increases in GEP, ER and NEP. These trends were strongly influenced by very productive and wet summer growing seasons during the last two years, which were characterized by abundant understory grass cover. Typically, less than half of the variability in growing season GEP and evapotranspiration was captured by satellite-based estimates and land surface model simulations with local site forcing and calibration, highlighting the ongoing utility of long-term datasets to support careful model testing and improvement.
Abstract Large volcanic eruptions are one of the dominant perturbations to global and regional atmospheric temperatures on timescales of years to decades. Discrepancies remain, however, in the estimated magnitude and persistence of the surface temperature cooling caused by volcanic eruptions, as characterized by paleoclimatic proxies and climate models. We investigate these discrepancies in the context of large tropical eruptions over the Last Millennium using two state‐of‐the‐art data assimilation products, the Paleo Hydrodynamics Data Assimilation product (PHYDA) and the Last Millennium Reanalysis (LMR), and simulations from the National Center for Atmospheric Research Community Earth System Model‐Last Millennium Ensemble (NCAR CESM‐LME). We find that PHYDA and LMR estimate mean global and hemispheric cooling that is similar in magnitude and persistence once effects from eruptions occurring in short succession are removed. The estimates also compare well to Northern‐Hemisphere reconstructions based solely or partially on tree‐ring density, which have been proposed as the most accurate proxy estimates of surface cooling due to volcanism. All proxy‐based estimates also agree well with the magnitude of the mean cooling simulated by the CESM‐LME. Differences remain, however, in the spatial patterns of the temperature responses in the PHYDA, LMR, and the CESM‐LME. The duration of cooling anomalies also persists for several years longer in the PHYDA and LMR relative to the CESM‐LME. Our results demonstrate progress in resolving discrepancies between proxy‐ and model‐based estimates of temperature responses to volcanism, but also indicate these estimates must be further reconciled to better characterize the risks of future volcanic eruptions.