Albedo Data Research Articles

Two Decades of Fire‐Induced Albedo Change and Associated Short‐Wave Radiative Effect Over Sub‐Saharan Africa

AbstractWe present an analysis of 20 years of fire and albedo data in Africa. We show that, in the mean, the sub‐Saharan Africa post‐fire surface albedo anomaly can be parameterized using an exponential recovery function, recovering from a decrease of immediately after a fire with a time constant of days. Although the magnitude of albedo changes shows large spatial and temporal variations and a strong land cover type (LCT) dependency, exponential recovery is observed in the majority of LCTs. We show that fires cause long‐term surface brightening, with an Africa‐wide albedo increase of 10 months after a fire, but we find this is driven almost exclusively by slow vegetation recovery in the Kalahari region, confirming previous findings. Using downward surface shortwave flux (DSSF) estimates, we calculate the fire‐induced surface radiative forcing (RF), peaking at Wm−2 in the burn areas, albeit with a significantly smaller effect when averaged temporally and spatially. We find that the long‐term RF in months 5–10 after a burn averaged over the continent is negative because of the brightening observed. Despite a well‐documented reduction in burning in Africa in the recent decades, our temporal analysis does not indicate a decrease in the overall fire‐induced RF likely due to large interannual variability in albedo anomaly and DSSF data. However, we observe a decline in the short‐term RF in southern hemisphere Africa, driven by both a reduction in fires and changes in LCT distributions.

Evaluating land use ımpact on evapotranspiration in Yellow River Basin China through a novel GSEBAL model: a remote sensing perspective

Evapotranspiration (ET) is critical to surface water dynamics. Effective water resource management necessitates an accurate ET estimation. In the Yellow River Basin China, a study area, cutting-edge technologies are needed to improve large-scale ET estimates. This study estimates ET using GSEBAL, an advanced ET estimation algorithm. Google Earth Engine integrates the surface energy balance model-based GSEBAL. The technique includes the collection, preparation, and calculation of ET using Landsat imagery and ERA5-Land meteorological data from 1990 to 2020. The study examined satellite LST, albedo, and NDVI data. The GSEBAL model calculates soil heat flow, net radiation, and sensible heat flux. The study tested the GSEBAL model utilizing essential ET datasets such as ECOSTRESS, MOD16, and SSEBop. The study showed that the model effectively predicted daily and seasonal ET variations in different climates. Root mean squared error, bias, and Pearson's correlation coefficient verified the model's reliability. The study also analyzed land use and land cover (LULC) over 30 years using Random Forest classifiers. In the 1990–2020 YRBC ET, land use changes affect ET rates annually and seasonally. The study area experiences changes in LST, NDVI, and LULC. Maximum ET values rose from 214.217 mm in 1990 to 234.891 mm in 2000. The pattern flipped in 2020, decreasing to 221.456 mm. In 2010, Summer had the highest ET, 484.455 mm. 2020 spring ET is 314.727 mm. Low ET decreased from 24.652 mm in 1990 to 18.2 mm in 2020, reducing water loss. Fall ET peaks at 24.9 mm in 2020; winter ET is 18.75 mm.

Evaluation of the Duration of Albedo Measurement Campaigns

Bifacial PV modules are increasingly used in commercial solar PV projects. With the use of the solar energy available at the module’s rear surface, the correct estimation of the albedo during the pre-construction phase of commercial projects becomes more important. The intention of the study is to determine the impact of the duration of a short-term albedo measurement campaign to reduce the uncertainty with respect to satellite albedo, thus providing a ‘beneficial campaign’. Albedo measurements from seven sites in the USA have been used to estimate the impact of 1-day and 7-day measurements (short-term measurements) on the simulated annual electrical energy production. Simulations based on monthly albedo data obtained from a recognised satellite provider have been compared with simulations where the albedo has been corrected based on short-term measurements. The study found that a 1-day campaign is often not beneficial, causing an increase in uncertainty with respect to satellite albedo. Measurement campaigns in winter do not generally reduce the error of the annual electrical energy production simulation, however, a 7-day campaign undertaken in summer appears to often reduce the uncertainty. It should be noted that the climate conditions, ground vegetation and measurement setup of the analysed seven sites in the USA may not be representative for other sites in the rest of the world.

Assimilation of Satellite Albedo to Improve Simulations of Glacier Hydrology

ABSTRACTWildfires and heatwaves have recently affected the hydrological system in unprecedented ways due to climate change. In cold regions, these extremes cause rapid reductions in snow and ice albedo due to soot deposition and unseasonal melt. Snow and ice albedo dynamics control net shortwave radiation and the available energy for melt and runoff generation. Many albedo algorithms in hydrological models cannot accurately simulate albedo dynamics because they were developed or parameterised based on historical observations. Remotely sensed albedo data assimilation (DA) can potentially improve model performance by updating modelled albedo with observations. This study seeks to diagnose the effects of remotely sensed snow and ice albedo DA on the prediction of streamflow from glacierized basins during wildfires and heatwaves. Sentinel‐2 20‐m albedo estimates were assimilated into a glacio‐hydrological model created using the Cold Regions Hydrological Modelling Platform (CRHM) in two Canadian Rockies glacierized basins, Athabasca Glacier Research Basin (AGRB) and Peyto Glacier Research Basin (PGRB). The study was conducted in 2018 (wildfires), 2019 (soot/algae), 2020 (normal) and 2021 (heatwaves). DA was employed to assimilate albedo into CRHM to simulate streamflow and was compared to a control run (CTRL) using off‐the‐shelf albedo parameters. Albedo DA benefited streamflow predictions during wildfires for both basins, with a KGE coefficient improvement of 0.18 and 0.20 in AGRB and PGRB, respectively. Four‐year DA streamflow predictions were superior to CTRL in PGRB, but DA was slightly better in AGRB. DA was not beneficial to streamflow predictions during heatwaves. DA improved streamflow predictions by decreasing positive bias, showing that albedo DA can reveal unknown albedo and snowpack dynamics in remote glacier zones that are poorly simulated in models. These findings corroborate the power of observational tools to incorporate near real‐time information into hydrological models to better inform water managers of the streamflow response to wildfires and heatwaves.

Grassland albedo as a nature-based climate prospect: the role of growth form and grazing

Abstract Nature-based solutions for mitigating climate change focus largely on land management to reduce carbon emissions and enhance carbon sequestration. Tree planting, commonly advocated for carbon offset, threatens grassland biodiversity and may induce positive radiative forcing (warming) by lowering albedo. Before making decisions about land-use changes in grasslands, an understanding of the fine-scale albedo of grassy versus woody vegetation is needed. Existing satellite-based albedo products offer global coverage with temporally fine, but spatially coarse, resolution, whereas fine-scale in situ grassland albedo data are sparse. We examined the hypotheses that albedo varies seasonally between grass type patches, between shrub and grass patches, and with grazing at the patch scale. Using a tripod-mounted albedometer, we quantified albedo of seven distinct grassland patches in South Africa’s eastern Karoo during early and late dormancy and growing seasons. Patches included intensely-grazed grazing lawn (Cynodon dactylon), grazed and less-grazed red tussock grass (Themeda triandra), grazed and less-grazed white tussock grass (Eragrostis lehmanniana), shrub (Pentzia incana) encroached grass, and bare ground. Season influenced albedo in all patches and, additionally, we found strong differences for the same period between years due to varying rainfall and temperature patterns. For grass-dominated patches, albedo differences were most pronounced during early dormancy, likely due to an effect of grass inflorescences. Albedo of intensely-grazed grazing lawns was consistently higher than other patches, except during early dormancy when white tussock grass albedo was equally high. We found no albedo difference between grazed and less-grazed tussock patches of either red or white grass. Shrub-encroached patches exhibited consistently lower albedo than other patches. Our findings underscore the nuanced relationship between grassland patches and albedo, with shrub encroachment, proposed afforestation, and certain grasses possibly increasing warming potential through reduced albedo. As climate initiatives extend into grasslands, understanding these patterns is essential for climate change mitigation and grassland conservation.

Wavelet-fusion image super-resolution model with deep learning for downscaling remotely-sensed, multi-band spectral albedo imagery

Generating granular-scale surface albedo data is extremely important for solar photovoltaic site planning and to optimise renewable energy yield of bifacial panel installations. The albedo effect brings about a significant increase in power in bifacial photovoltaic systems, compared to their mono-facial counterparts, since the spectral response of bifacial solar panels correlates with the incident solar radiation wavelength on the back of the panel, to provide additional power generation capacity. Thus, harnessing the albedo data at relatively local scales is critical towards boosting solar power generation and providing greater power density in local electricity grids. This paper develops novel modelling approaches to produce high-resolution spectral albedo imagery across the Visible and Near Infrared (VNIR) bands, using the Wavelet-Fusion super-resolution model (i.e., Wavelet-FusionSR) trained with the Learned Gamma Correction approach by applying satellite image enhancement methodology. The proposed Wavelet-FusionSR model utilises the low-resolution moderate-resolution Imaging Spectroradiometer (MODIS) as well as high-resolution multi-spectral Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) satellite images, as critical inputs and ground-truth imagery, respectively, in order to perform sensor-to-sensor deep downscaling, without employing any synthetic or low-resolution satellite imagery data pairs. To augment the proposed deep learning algorithm across the decomposed sub-images of low-resolution inputs, we integrate local and global feature representation learning to train the proposed Wavelet-FusionSR model with Cauchy loss functions. In comparison with five competing benchmark models, the proposed Wavelet-FusionSR model demonstrates performance superiority using quantitative image downscaling metrics and visual assessments of the downscaled images for the visible band of solar radiation. The proposed Wavelet-FusionSR model yielded a Mean Square Error (MSE) of 0.00017, Signal-to-noise-ratio (PSNR) of 37.80, Structural Similarity Index (SSIM) of 0.999 and combined loss, MS-SSIMLoss, based on Multi Structural Similarity and Mean Absolute Error of 2.354 for the Visible Band images, and an MSE of 0.0014, PSNR of 28.43, SSIM of 0.999 and MS-SSIMLoss of 7.426 for the NIR spectral bands, demonstrating high efficacy of the proposed Wavelet-FusionSR method. The Wavelet-FusionSR method therefore attains high-resolution spectral albedo imagery outputs.

Presenting a Model to Predict Changing Snow Albedo for Improving Photovoltaic Performance Simulation

As photovoltaic (PV) deployment increases worldwide, PV systems are being installed more frequently in locations that experience snow cover. The higher albedo of snow, relative to the ground, increases the performance of PV systems in northern and high-altitude locations by reflecting more light onto the PV modules. Accurate modeling of the snow’s albedo can improve estimates of PV system production. Typical modeling of snow albedo uses a simple two-value model that sets the albedo high when snow is present, and low when snow is not present. However, snow albedo changes over time as snow settles and melts and a binary model does not account for transitional changes, which can be significant. Here, we present and validate a model for estimating snow albedo as it changes over time. The model is simple enough to only require daily snow depth and hourly average temperature data, but can be improved through the addition of site-specific factors, when available. We validate this model to quantify its ability to more accurately predict snow albedo and compare the model’s performance against satellite imagery-based methods for obtaining historical albedo data. In addition, we perform modeling using the System Advisor Model (SAM) to show the impact of changes in albedo on energy modeling for PV systems. Overall, our albedo model has a significantly improved ability to predict the solar insolation on PV modules in real time, especially on bifacial PV modules where reflected irradiance plays a larger role in energy production.

Assimilating Satellite-Derived Snow Cover and Albedo Data to Improve 3-D Weather and Photochemical Models

During wintertime temperature inversion episodes, ozone in the Uinta Basin sometimes exceeds the standard of 70 ppb set by the US Environmental Protection Agency. Since ozone formation depends on sunlight, and less sunlight is available during winter, wintertime ozone can only form if snow cover and albedo are high. Researchers have encountered difficulties replicating high albedo values in 3-D weather and photochemical transport model simulations for winter episodes. In this study, a process to assimilate MODIS satellite data into WRF and CAMx models was developed, streamlined, and tested to demonstrate the impacts of data assimilation on the models’ performance. Improvements to the WRF simulation of surface albedo and snow cover were substantial. However, the impact of MODIS data assimilation on WRF performance for other meteorological quantities was minimal, and it had little impact on ozone concentrations in the CAMx photochemical transport model. The contrast between the data assimilation and reference cases was greater for a period with no new snow since albedo appears to decrease too rapidly in default WRF and CAMx configurations. Overall, the improvement from MODIS data assimilation had an observed enhancement in the spatial distribution and temporal evolution of surface characteristics on meteorological quantities and ozone production.

Improving Error Estimates for Evaluating Satellite-Based Atmospheric CO2 Measurement Concepts through Numerical Simulations

To assess the accuracy of satellite monitoring of anthropogenic CO2 emissions, inversions of satellite data in SWIR are usually combined with the assimilation of the total CO2 column into a Kalman filter that reconstructs the sources and sinks of atmospheric CO2. To provide error estimates of the total CO2 column for multi-month assimilation experiments of simulated satellite data, we parametrise these errors using linear regressions. These regression are obtained from a database that links meteorological situations, albedos, and aerosols to the errors in the inversion of the total CO2 column based on simulated satellite data for those conditions. The errors in this database are explicitly computed using the Bayesian estimation formalism, and the linear regressions are optimised by selecting appropriate predictors and predictants. For different levels of measurement noise, error simulations are performed over a period of several months using the albedo and aerosol data from MODIS.

Detection of land surface albedo changes over Iran using remote sensing data

AbstractAlbedo is one of the key parameters in climatic studies. Investigating its temporal and spatial behavior can be a tool for understanding environmental changes. The MODIS sensor continuously produces the land surface albedo on a global scale and with the appropriate spatial resolution and makes it available to researchers. In this study, to analyze Iran's surface albedo trend, first, the daily albedo data of the MODIS on Iran in the period from January 1, 2001 to December 30, 2021 with a spatial resolution of 500 m were prepared from the NASA website. After the necessary pre‐processing, the long‐term seasonal and annual trend of Iran's albedo was calculated at the 90% confidence level using the non‐parametric Mann–Kendall test. The findings showed that the albedo trend is positive in the lowland interior areas of Iran and negative in the highland areas. Since the decreasing trend of albedo in highland areas indicates the reduction of snow cover in these areas, this issue can challenge the life and water resources of these areas that rely on the accumulation of snow.

Snow cover duration delays spring green-up in the northern hemisphere the most for grasslands

Snow is an important factor controlling vegetation functions in high latitudes/altitudes. However, due to the lack of reliable in-situ measurements, the effects of snow on vegetation phenology remains poorly understood. Here, we examine the effects of snow cover duration (SCD) on the start of growing season (SOS) for different vegetation types. SOS and SCD were extracted from in-situ carbon flux and albedo data, respectively, at 51 eddy covariance flux sites in the northern mid-high latitudes. The effects of SCD on SOS vary substantially among different vegetation types. For grassland, preseason SCD outperforms other factors controlling grassland SOS. However, for forests and cropland, the preseason air temperature is the dominant factor in controlling SOS. Preseason SCD mainly influences the SOS by regulating preseason air and soil temperature rather than soil moisture. The CMIP6 Earth system models (ESMs) fail to capture the effect of SCD on SOS. Thus, Random Forest (RF) models were established to predict future SOS changing trends considering the effect of SCD. For grassland and evergreen needleleaf forest, the projected SOS advance rate is slower when SCD is considered. These findings can help us better understand impacts of snow on vegetation phenology and carbon-climate feedbacks in the warming world.

Comparison of optical spectra between asteroids Ryugu and Bennu: I. Cross calibration between Hayabusa2/ONC-T and OSIRIS-REx/MapCam

Asteroids (162173) Ryugu and (101955) Bennu observed by Hayabusa2 and Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) share many global properties, but high-spatial-resolution spectral observations by the telescopic Optical Navigation Camera (ONC-T) and MapCam detected subtle but significant differences (e.g., opposite space weathering trends), which may reflect differences in their origin and evolution. Comparing these differences on the same absolute scale is necessary for understanding their causes and obtaining implications for C-complex asteroids. However, ONC-T and MapCam have a large imager-to-imager systematic error of up to 15% caused by the difference in radiometric calibration targets. To resolve this problem, we cross calibrated albedo and color data between the two instruments using the Moon as the common calibration standard. The images of the Moon taken by ONC-T and MapCam were compared with those simulated using photometry models developed from lunar orbiter data. Our results show that the cross-calibrated reflectance of Ryugu and Bennu can be obtained by upscaling the pre-cross-calibrated reflectance of Bennu by 13.3 ± 1.6% at b band, 13.2 ± 1.5% at v band, 13.6 ± 1.7% at w band, and 14.8 ± 1.8% at x band, while those for Ryugu are kept the same. These factors compensate for the imager-to-imager bias caused by differences in targets used for radiometric calibration and solar irradiance models used for data reduction. Need for such large upscaling underscore the importance of using the cross-calibrated data for accurately comparing the Ryugu and Bennu data. The uncertainty in these factors show that the reflectance of Ryugu and Bennu can be compared with <2% accuracy after applying our results. By applying our cross calibration, the geometric albedo of Bennu became consistent with those observed by ground-based telescopes and the OSIRIS-REx Visible and InfraRed Spectrometer (OVIRS). Our result can be simply applied by multiplying a constant to the publicly available data and enables accurate comparison of the optical spectra of Ryugu and Bennu in future studies.

Quantifying desertification in the Qinghai Lake Basin

Desertification in the context of global change and intensified anthropogenic activities poses a huge challenge to the sustainable development of the Earth’s systems, including the Qinghai Lake Basin, which is located in the Tibetan Plateau. However, we know little about desertification in the Qinghai Lake Basin, which is an urgent and important issue. To that end, this study used the enhanced vegetation index (EVI) and the albedo data from MODIS satellites to explore this issue. Results based on vegetation cover (FVC) calculated from EVI indicated that desertification in the Qinghai Lake Basin had reversed over the last 20 years, and the heavily desertified areas were concentrated along the northwestern to northern basin boundaries and on the sandy eastern shore of Qinghai Lake. More interestingly, we found that the relationship between albedo and EVI showed a “V” shape instead of the traditional monotonic trapezoid, which may be related to multiple factors such as soil moisture, soil type, and spatial scale of sampling. This study unfolds the spatial and temporal distribution of desertification in the Qinghai Lake Basin quantitatively and emphasizes the threshold of the desertification process revealed in the EVI-albedo feature space, which sheds light on the monitoring of desertification in alpine areas.

The influence of Urmia Lake desiccation on an extreme snowfall event: A case study using the WRF-Lake model

Urmia Lake in the northwest Iran has undergone significant desiccation in recent decades, shrinking to a much smaller size than its original. This study implements the WRF-Lake coupled modeling system to examine the impacts of the observed physical changes over the Lake during a heavy snowfall event occurred February 1–2, 2017. Modifications are introduced to represent the shrunken, salt-encrusted lake surface and surrounding arid environment through updating land use, soil characteristics, lake size and depth, and albedo data. Experiments incorporating lake-atmosphere coupling and land surface refinements (Lake_CTL, Lake_LULC, Lake_AL) outperform simulations without a lake (WRF_noLake) in reproducing observed precipitation patterns at stations surrounding the Urmia Lake. All simulations underestimate the extreme 290 mm snowfall recorded at the Urmia station; however, the Lake_LULC and Lake_AL improve snowfall representation at other stations around the lake. Regarding snow cover validation, Lake_LULC and Lake_AL exhibit a higher correlation with satellite observations and show a significant improvement in snow detection near Urmia Lake. They have a Probability of Detection (POD) of 0.94 and a Critical Success Index (CSI) of 0.91, compared to a POD of 0.79–0.80 and a CSIs of ∼0.77 for the default lake model (Lake_CTL) and the WRF_noLake experiments, respectively. Additionally, adjusting the initial lake surface temperature to match observations substantially reduces cold biases in near-surface air temperatures over Urmia Lake. The lack of lake temperature updating in WRF-Lake poses ongoing challenges in the model, though. In summary, this study demonstrates that refining the representations of desiccated lakes and their surroundings in high-resolution coupled models would improve simulations of meteorological processes influenced by lake-atmosphere interactions.

Spatiotemporal Evolution of the Land Cover over Deception Island, Antarctica, Its Driving Mechanisms, and Its Impact on the Shortwave Albedo

The aim of this work is to provide a full description of how air temperature and solar radiation induce changes in the land cover over an Antarctic site. We use shortwave broadband albedo (albedo integrated in the range 300–3000 nm) from a spaceborne sensor and from field surveys to calculate the monthly relative abundance of landscape units. Field albedo data were collected in January 2019 using a portable albedometer over seven landscape units: clean fresh snow; clean old snow; rugged landscape composed of dirty snow with disperse pyroclasts and rocky outcrops; dirty snow; stripes of bare soil and snow; shallow snow with small bare soil patches; and bare soil. The MODIS MCD43A3 daily albedo products were downloaded using the Google Earth Engine API from the 2000–2001 season to the 2020–2021 season. Each landscape unit was characterized by an albedo normal distribution. The monthly relative abundances of the landscape units were calculated by fitting a linear combination of the normal distributions to a histogram of the MODIS monthly mean albedo. The monthly relative abundance of the landscape unit consisting of rugged landscape composed of dirty snow with dispersed clasts and small rocky outcrops exhibits a high positive linear correlation with the monthly mean albedo (R2 = 0.87) and a high negative linear correlation with the monthly mean air temperature (R2 = 0.69). The increase in the solar radiation energy flux from September to December coincides with the decrease in the relative abundance of the landscape unit composed of dirty snow with dispersed clasts and small rocky outcrops. We propose a mechanism to describe the evolution of the landscape: uncovered pyroclasts act as melting centers favoring the melting of surrounding snow. Ash does not play a decisive role in the melting of the snow. The results also explain the observed decrease in the thaw depth of the permafrost on the island in the period 2006–2014, resulting from an increase in the snow cover over the whole island.

Emission-Based Machine Learning Approach for Large-Scale Estimates of Black Carbon in China

Tremendous efforts have been made to construct large-scale estimates of aerosol components. However, Black Carbon (BC) estimates over large spatiotemporal scales are still limited. We proposed a novel approach utilizing machine-learning techniques to estimate BC on a large scale. We leveraged a comprehensive gridded BC emission database and auxiliary variables as inputs to train various machine learning (ML) models, specifically a Random Forest (RF) algorithm, to estimate high spatiotemporal BC concentration over China. Different ML algorithms have been applied to a large number of potential datasets and detailed variable importance and sensitivity analysis have also been carried out to explore the physical relevance of variables on the BC estimation model. RF algorithm showed the best performance compared with other ML models. Good predictive performance was observed for the training cases (R2 = 0.78, RMSE = 1.37 μgm−3) and test case databases (R2 = 0.77, RMSE = 1.35 μgm−3) on a daily time scale, illustrating a significant improvement compared to previous studies with remote sensing and chemical transport models. The seasonal variation of BC distributions was also evaluated, with the best performance observed in spring and summer (R2 ≈ 0.7–0.76, RMSE ≈ 0.98–1.26 μgm−3), followed by autumn and winter (R2 ≈ 0.7–0.72, RMSE ≈ 1.37–1.63 μgm−3). Variable importance and sensitivity analysis illustrated that the BC emission inventories and meteorology showed the highest importance in estimating BC concentration (R2 = 0.73, RMSE = 1.88 μgm−3). At the same time, albedo data and some land cover type variables were also helpful in improving the model performance. We demonstrated that the emission-based ML model with an appropriate auxiliary database (e.g., satellite and reanalysis datasets) could effectively estimate the spatiotemporal BC concentrations at a large scale. In addition, the promising results obtained through this approach highlight its potential to be utilized for the assessment of other primary pollutants in the future.

Four decades of global surface albedo estimates in the third edition of the CM SAF cLoud, Albedo and surface Radiation (CLARA) climate data record

Abstract. We present the surface albedo data in the third edition of the CM SAF cLoud, Albedo and surface Radiation (CLARA) data record family. The temporal coverage of this edition is extended from 1979 until the near-present day. The core algorithms and data format remain unchanged from previous editions, but now white- and blue-sky albedo estimates are also available for the first time in CLARA data. We present an overview of the retrieval, followed by an assessment of the accuracy and stability of the data record, based on collocated comparisons with reference surface albedo measurements and intercomparisons with preceding satellite-based albedo data records. Specific attention is paid to addressing the spatial representativeness problem inherent in the “point-to-pixel” validation of satellite-based coarse surface albedo estimates against in situ measurements. We find the CLARA-A3 albedo data to match or improve upon the accuracy and robustness of the predecessor record (CLARA-A2), with good agreement found when compared to in situ measurements. In cases of a large bias, the spatial representativeness of the measurement site typically explains most of the increase. We conclude with a summarizing discussion on the observed strengths and weaknesses of the new data record, including guidance for potential users. The data are available at https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003 (Karlsson et al., 2023b).

Using Field Measurements Across Land Cover Types to Evaluate Albedo‐Based Wind Friction Velocity and Estimate Sediment Transport

AbstractThe soil surface wind friction velocity (us*) is an essential parameter for predicting sediment transport on rough surfaces. However, this parameter is difficult and time‐consuming to obtain over large areas due to its spatiotemporal heterogeneity. The albedo‐based approach calibrates normalized shadow retrieved from any source of albedo data with laboratory measurements of aerodynamic properties. This enables direct and cross‐scale us* retrieval but has not been evaluated against field measurements for different cover types. We evaluated the approach's performance using wind friction velocity (u*) measurements from ultrasonic anemometers. We retrieved coincident field pyranometer and satellite albedo at 48 sites that were spread over approximately 1,800 km on the Inner Mongolia Plateau, including grassland, artificial shrubland, open shrubland, and gobi land. For all cover types, u* estimates from ultrasonic anemometers were close to the albedo‐based results approach. Our results confirm and extend the findings that the approach works across scales from lab to field measurements and permits large‐area assessments using satellite albedo. We compared the seasonal sediment transport across the region calculated from albedo‐based us* with results from an exemplar traditional transport model (QT) driven by u* with aerodynamic roughness length varying with land cover type and fixed over time. The traditional model could not account for spatiotemporal variation in roughness elements and considerably over‐estimated sediment transport, particularly in partially vegetated and gravel‐covered central and western parts of the Inner Mongolia Plateau. The albedo‐based sediment transport (QA) estimates will enable dynamic monitoring of the interaction between wind and surface roughness to support Earth System models.

Global Observations of Tropospheric Bromine Monoxide (BrO) Columns From TROPOMI

AbstractBromine monoxide (BrO) plays an important role in tropospheric chemistry. The state‐of‐the‐science TROPOspheric Monitoring Instrument (TROPOMI) offers the potential to monitor atmospheric composition with a fine spatial resolution of up to 5.5 × 3.5 km2. We present here the retrieval of tropospheric BrO columns from TROPOMI. We implement a stratospheric correction scheme using a climatological approach based on the latest GEOS‐Chem High Performance chemical transport model, and improve the tropospheric air mass factor calculation with TROPOMI surface albedo data accounting for the geometrical dependency. Our product presents a good level of consistency in comparison with measurements from ground‐based zenith‐sky differential optical absorption spectroscopy (r = 0.67), aircrafts (r = 0.46), and satellites (similar spatial distributions of BrO columns). Furthermore, our retrieval captures BrO enhancements in the polar springtime with values up to 7.8 × 1013 molecules cm−2 and identifies small‐scale emission sources such as volcanoes and salt marshes. Based on TROPOMI data, we probe a blowing snow aerosol bromine mechanism in which the snow salinity is reduced to better match simulation and observation. Our TROPOMI tropospheric BrO product contributes high‐resolution global information to studies investigating atmospheric bromine chemistry.

An statistical model for the short-term albedo estimation applied to PV bifacial modules

Albedo estimation is an essential input parameter for bifacial PV modules. However, there was no clear agreement on which albedo values should be used and how they should be measured, either with satellite measurements or using onsite measurements which can be obtained from nearby meteorological stations. Since long-term measurements are not always available for new PV system locations, short-term albedo measurements are also used as input parameters in PV system performance estimation models. Short-term albedo presents high variability due to factors such as weather, seasonality or changes in the surrounding surface among others. In addition, apparently random albedo variations of 60% can be observed, even during consecutive days or within the same day. Therefore, this study presents a two-parameter exponential model that modelates the albedo data statistical distribution with an error of less than 5% in all cases and from which it is possible to determine the reliability of the obtained data. This model has been evaluated at a set of locations, with different surface types and climates, and assessed using onsite and satellite data. The impact of the methodology used to estimate the albedo data uncertainty and its reliability has also been studied as a function of several parameters such as the global horizontal irradiance and measurement time, allowing the process optimization and enhancing its reliability.