scholarly journals Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas

2020 ◽  
Vol 12 (2) ◽  
pp. 254 ◽  
Author(s):  
Akriti Masoom ◽  
Panagiotis Kosmopoulos ◽  
Ankit Bansal ◽  
Stelios Kazadzis
Keyword(s):  
Solar Energy ◽  
Solar Irradiance ◽  
Urban Areas ◽  
Surface Radiation ◽  
Atmospheric Composition ◽  
Coefficient Of Determination ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Energy Potential ◽  
Potential Conditions

Solar radiation ground data is available in poor spatial resolution, which provides an opportunity and demonstrates the necessity to consider solar irradiance modeling based on satellite data. For the first time, solar energy monitoring in near real-time has been performed for India. This study focused on the assessment of solar irradiance from the Indian Solar Irradiance Operational System (INSIOS) using operational cloud and aerosol data from INSAT-3D and Copernicus Atmosphere Monitoring Service (CAMS)-Monitoring Atmospheric Composition Climate (MACC), respectively. Simulations of the global horizontal irradiance (GHI) and direct normal irradiance (DNI) were evaluated for 1 year for India at four Baseline Surface Radiation Network (BSRN) stations located in urban regions. The INSIOS system outputs as per radiative transfer model results presented high accuracy under clear-sky and cloudy conditions for GHI and DNI. DNI was very sensitive to the presence of cloud and aerosols, where even with small optical depths the DNI became zero, and thus it affected the accuracy of simulations under realistic atmospheric conditions. The median BSRN and INSIOS difference was found to vary from −93 to −49 W/m2 for GHI and −103 to −76 W/m2 for DNI under high solar energy potential conditions. Clouds were able to cause an underestimation of 40%, whereas for various aerosol inputs to the model, the overall accuracy was high for both irradiances, with the coefficient of determination being 0.99, whereas the penetration of photovoltaic installation, which exploits GHI, into urban environments (e.g., rooftop) could be effectively supported by the presented methodology, as estimations were reliable during high solar energy potential conditions. The results showed substantially high errors for monsoon season due to increase in cloud coverage that was not well-predicted at satellite and model resolutions.

scholarly journals Solar Irradiance Assessment and Forecasting in Tropical Climates using Satellite Remote Sensing and Physical Modelling

2021 ◽  
Author(s):  
Akriti Masoom ◽  
Panagiotis Kosmopolous ◽  
Ankit Bansal
Keyword(s):  
Solar Energy ◽  
Optical Depth ◽  
Solar Irradiance ◽  
Distribution System ◽  
Wrf Model ◽  
Physical Modelling ◽  
Final Analysis ◽  
Surface Radiation ◽  
Atmospheric Composition ◽  
Energy Potential

<p>Poor resolution of solar irradiance ground data demonstrates the necessity and provides an opportunity for satellite data-based solar irradiance modeling. The study is focused on India due to its tropical climate that provides varied as well as extreme conditions for solar energy research. For solar energy monitoring in near real-time, the Indian Solar Irradiance Operational System (INSIOS) was developed using operational cloud and aerosol data from INSAT-3D and Copernicus Atmosphere Monitoring Service (CAMS)-Monitoring Atmospheric Composition Climate, respectively. It had high accuracy under clear-sky conditions for global horizontal irradiance (GHI) and direct normal irradiance (DNI) that were evaluated for a year at four Baseline Surface Radiation Network (BSRN) stations located in urban regions. The presented methodology could effectively support the penetration of photovoltaic installation as estimations were reliable during high solar energy potential conditions with median BSRN and INSIOS difference varying from 93 to 49 W/m<sup>2</sup> for GHI.</p><p>Further, an operational day-ahead solar irradiance forecasting system (WRF-CAMS) is presented that ingests CAMS aerosol optical depth (AOD) into the WRF model to better quantify the aerosol impact on solar energy long-term forecasts, and validation was done against ground-based measurements from BSRN stations. The analysis was carried out for forecast horizons varying from 24 h to 36 h for different seasons, varying solar zenith angles, and different cloud cover classifications based on calculated clearness index. The correlation coefficient was improved from 0.93 to 0.95 for GHI and 0.75 to 0.82 for DNI after the ingestion of CAMS AOD as compared to WRF default aerosol scheme. The annual root mean square error was observed to vary from 10 to 130 W/m<sup>2</sup> and 50 to 190 W/m<sup>2</sup> for GHI and DNI, respectively. This system is hoped to provide more accurate forecasts for better solar plant energy planning and improve day-to-day electricity exchange market supporting solar energy producers and distribution system operators.</p><p>In the final analysis, INSIOS and WRF-CAMS models were used for forecasting dust impact on solar irradiance during an extreme dust event using Aeronet measurements, satellite observations (MODIS and CALIPSO), and ModIs Dust AeroSol (MIDAS) dust database. WRF-CAMS model was used to examine dust impact on solar irradiance for a high-intensity dust storm with AOD and dust optical depth values reaching up to 2. The observed average decrease in GHI and DNI due to the dust plume was 76 W/m<sup>2</sup> and 275 W/m<sup>2</sup>, respectively, and a maximum reduction of 100 W/m<sup>2</sup> (10%) and 400 W/m<sup>2</sup> (40%), respectively. The proposed methodology can support solar energy producers, for optimum energy production forecasting, management, and maintenance as well as transmission and distribution system operators, as dust events of this extent significantly reduce solar irradiance and affect energy exploitation capacity due to solar aerosol-related extinction.</p>

scholarly journals Earth-Observation-Based Estimation and Forecasting of Particulate Matter Impact on Solar Energy in Egypt

2018 ◽  
Vol 10 (12) ◽  
pp. 1870 ◽  
Author(s):  
Panagiotis Kosmopoulos ◽  
Stelios Kazadzis ◽  
Hesham El-Askary ◽  
Michael Taylor ◽  
Antonis Gkikas ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Solar Energy ◽  
Forecast Accuracy ◽  
Earth Observation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Energy Potential ◽  
Surface Solar Radiation ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact

This study estimates the impact of dust aerosols on surface solar radiation and solar energy in Egypt based on Earth Observation (EO) related techniques. For this purpose, we exploited the synergy of monthly mean and daily post processed satellite remote sensing observations from the MODerate resolution Imaging Spectroradiometer (MODIS), radiative transfer model (RTM) simulations utilizing machine learning, in conjunction with 1-day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). As cloudy conditions in this region are rare, aerosols in particular dust, are the most common sources of solar irradiance attenuation, causing performance issues in the photovoltaic (PV) and concentrated solar power (CSP) plant installations. The proposed EO-based methodology is based on the solar energy nowcasting system (SENSE) that quantifies the impact of aerosol and dust on solar energy potential by using the aerosol optical depth (AOD) in terms of climatological values and day-to-day monitoring and forecasting variability from MODIS and CAMS, respectively. The forecast accuracy was evaluated at various locations in Egypt with substantial PV and CSP capacity installed and found to be within 5–12% of that obtained from the satellite observations, highlighting the ability to use such modelling approaches for solar energy management and planning (M&P). Particulate matter resulted in attenuation by up to 64–107 kWh/m2 for global horizontal irradiance (GHI) and 192–329 kWh/m2 for direct normal irradiance (DNI) annually. This energy reduction is climatologically distributed between 0.7% and 12.9% in GHI and 2.9% to 41% in DNI with the maximum values observed in spring following the frequent dust activity of Khamaseen. Under extreme dust conditions the AOD is able to exceed 3.5 resulting in daily energy losses of more than 4 kWh/m2 for a 10 MW system. Such reductions are able to cause financial losses that exceed the daily revenue values. This work aims to show EO capabilities and techniques to be incorporated and utilized in solar energy studies and applications in sun-privileged locations with permanent aerosol sources such as Egypt.

scholarly journals Impact of Aerosol and Cloud on the Solar Energy Potential over the Central Gangetic Himalayan Region

2021 ◽  
Vol 13 (16) ◽  
pp. 3248
Author(s):  
Umesh Chandra Dumka ◽  
Panagiotis G. Kosmopoulos ◽  
Shantikumar S. Ningombam ◽  
Akriti Masoom
Keyword(s):  
Solar Energy ◽  
Atmospheric Aerosols ◽  
Himalayan Region ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Energy Potential ◽  
Surface Solar Radiation ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact

We examine the impact of atmospheric aerosols and clouds on the surface solar radiation and solar energy at Nainital, a high-altitude remote location in the central Gangetic Himalayan region (CGHR). For this purpose, we exploited the synergy of remote-sensed data in terms of ground-based AERONET Sun Photometer and satellite observations from the MODerate Resolution Imaging Spectroradiometer (MODIS) and the Meteosat Second Generation (MSG), with radiative transfer model (RTM) simulations and 1 day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). Clouds and aerosols are one of the most common sources of solar irradiance attenuation and hence causing performance issues in the photovoltaic (PV) and concentrated solar power (CSP) plant installations. The outputs of RTM results presented with high accuracy under clear, cloudy sky and dust conditions for global horizontal (GHI) and beam horizontal irradiance (BHI). On an annual basis the total aerosol attenuation was found to be up to 105 kWh m−2 for the GHI and 266 kWh m−2 for BHI, respectively, while the cloud effect is much stronger with an attenuation of 245 and 271 kWh m−2 on GHI and BHI. The results of this study will support the Indian solar energy producers and electricity handling entities in order to quantify the energy and financial losses due to cloud and aerosol presence.

scholarly journals An improved cloud index for estimating downwelling surface solar irradiance from various satellite imagers in the framework of a Heliosat-V method

2021 ◽  
Author(s):  
Benoît Tournadre ◽  
Benoît Gschwind ◽  
Yves-Marie Saint-Drenan ◽  
Philippe Blanc
Keyword(s):  
Solar Irradiance ◽  
Surface Radiation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Bidirectional Reflectance ◽  
Clear Sky ◽  
Derived Data ◽  
Bidirectional Reflectance Distribution ◽  
Visible Imagery

Abstract. We develop a new way to retrieve the cloud index from a large variety of satellite instruments sensitive to reflected solar radiation, embedded on geostationary as non geostationary platforms. The cloud index is a widely used proxy for the effective cloud transmissivity, also called clear-sky index. This study is in the framework of the development of the Heliosat-V method for estimating downwelling solar irradiance at the surface of the Earth (DSSI) from satellite imagery. To reach its versatility, the method uses simulations from a fast radiative transfer model to estimate overcast (cloudy) and clear-sky (cloud-free) satellite scenes of the Earth’s reflectances. Simulations consider the anisotropy of the reflectances caused by both surface and atmosphere, and are adapted to the spectral sensitivity of the sensor. The anisotropy of ground reflectances is described by a bidirectional reflectance distribution function model and external satellite-derived data. An implementation of the method is applied to the visible imagery from a Meteosat Second Generation satellite, for 11 locations where high quality in situ measurements of DSSI are available from the Baseline Surface Radiation Network. Results from our preliminary implementation of Heliosat-V and ground-based measurements show a correlation coefficient reaching 0.948, for 15-minute means of DSSI, similar to operational and corrected satellite-based data products (0.950 for HelioClim3 version 5 and 0.937 for CAMS Radiation Service).

scholarly journals Fast Hyper-Spectral Radiative Transfer Model Based on the Double Cluster Low-Streams Regression Method

2021 ◽  
Vol 13 (3) ◽  
pp. 434
Author(s):  
Ana del Águila ◽  
Dmitry S. Efremenko
Keyword(s):  
Radiative Transfer ◽  
Single Scattering ◽  
Atmospheric Composition ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Absorption Bands ◽  
Acceleration Techniques ◽  
Scattering Approximation ◽  
Fine Resolution ◽  
Single Scattering Approximation

Fast radiative transfer models (RTMs) are required to process a great amount of satellite-based atmospheric composition data. Specifically designed acceleration techniques can be incorporated in RTMs to simulate the reflected radiances with a fine spectral resolution, avoiding time-consuming computations on a fine resolution grid. In particular, in the cluster low-streams regression (CLSR) method, the computations on a fine resolution grid are performed by using the fast two-stream RTM, and then the spectra are corrected by using regression models between the two-stream and multi-stream RTMs. The performance enhancement due to such a scheme can be of about two orders of magnitude. In this paper, we consider a modification of the CLSR method (which is referred to as the double CLSR method), in which the single-scattering approximation is used for the computations on a fine resolution grid, while the two-stream spectra are computed by using the regression model between the two-stream RTM and the single-scattering approximation. Once the two-stream spectra are known, the CLSR method is applied the second time to restore the multi-stream spectra. Through a numerical analysis, it is shown that the double CLSR method yields an acceleration factor of about three orders of magnitude as compared to the reference multi-stream fine-resolution computations. The error of such an approach is below 0.05%. In addition, it is analysed how the CLSR method can be adopted for efficient computations for atmospheric scenarios containing aerosols. In particular, it is discussed how the precomputed data for clear sky conditions can be reused for computing the aerosol spectra in the framework of the CLSR method. The simulations are performed for the Hartley–Huggins, O2 A-, water vapour and CO2 weak absorption bands and five aerosol models from the optical properties of aerosols and clouds (OPAC) database.

Assessment of a newly developed short-term forecasting system (nextSENSE) of Downwelling Surface Solar Irradiance (DSSI) and validation with ground-based measurements

2021 ◽  
Author(s):  
Kyriakoula Papachristopoulou ◽  
Ilias Fountoulakis ◽  
Panagiotis Kosmopoulos ◽  
Dimitris Kouroutsidis ◽  
Panagiotis I. Raptis ◽  
...  
Keyword(s):  
Solar Energy ◽  
Solar Irradiance ◽  
Surface Radiation ◽  
Solar Irradiation ◽  
Short Term ◽  
Large Spatial Scale ◽  
Infrared Imager ◽  
Flow Motion ◽  
Forecasting System ◽  
Short Term Forecasting

<p>Monitoring and forecasting cloud coverage is crucial for nowcasting and forecasting of solar irradiance reaching the earth surface, and it’s a powerful tool for solar energy exploitation systems.</p><p>In this study, we focused on the assessment of a newly developed short-term (up to 3h) forecasting system of Downwelling Surface Solar Irradiation (DSSI) in a large spatial scale (Europe and North Africa). This system forecasts the future cloud position by calculating Cloud Motion Vectors (CMV) using Cloud Optical Thickness (COT) data derived from multispectral images from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite and an optical flow motion estimation technique from the computer vision community. Using as input consecutive COT images, CMVs are calculated and cloud propagation is performed by applying them to the latest COT image. Using the predicted COT images, forecasted DSSI is calculated using Fast Radiative Transfer Models (FRTM) in high spatial (5 km over nadir) and temporal resolution (15 min time intervals intervals).</p><p>A first evaluation of predicted COT has been conducted, by comparing the predicted cloud parameter of COT with real observed values derived by the MSG/SEVIRI. Here, the DSSI is validated against ground-based measurements from three Baseline Surface Radiation Network (BSRN) stations, for the year 2017. Also, a sensitivity analysis of the effect on DSSI for different cloud and aerosol conditions is performed, to ensure reliability under different sky and climatological conditions.</p><p>The DSSI short-term forecasting system proposed, complements the existing short-term forecasting techniques and it is suitable for operational deployment of solar energy related systems</p><p>Acknowledgements</p><p>This study was funded by the EuroGEO e-shape (grant agreement No 820852).</p>

scholarly journals Description of the Baseline Surface Radiation Network (BSRN) station at the Izaña Observatory (2009–2017): measurements and quality control/assurance procedures

2019 ◽  
Vol 8 (1) ◽  
pp. 77-96 ◽  
Author(s):  
Rosa Delia García ◽  
Emilio Cuevas ◽  
Ramón Ramos ◽  
Victoria Eugenia Cachorro ◽  
Alberto Redondas ◽  
...  
Keyword(s):  
Quality Control ◽  
Research Programme ◽  
Surface Radiation ◽  
Shortwave Radiation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
High Mountain ◽  
Total Column Ozone ◽  
The World ◽  
Instantaneous Values

Abstract. The Baseline Surface Radiation Network (BSRN) was implemented by the World Climate Research Programme (WCRP) starting observations with nine stations in 1992, under the auspices of the World Meteorological Organization (WMO). Currently, 59 BSRN stations submit their data to the WCRP. One of these stations is the Izaña station (station IZA, no. 61) that enrolled in this network in 2009. This is a high-mountain station located in Tenerife (Canary Islands, Spain, at 28.3∘ N, 16.5∘ W; 2373 m a.s.l.) and is a representative site of the subtropical North Atlantic free troposphere. It contributes with basic-BSRN radiation measurements, such as global shortwave radiation (SWD), direct radiation (DIR), diffuse radiation (DIF) and longwave downward radiation (LWD), and extended-BSRN measurements, including ultraviolet ranges (UV-A and UV-B), shortwave upward radiation (SWU) and longwave upward radiation (LWU), and other ancillary measurements, such as vertical profiles of temperature, humidity and wind obtained from radiosonde profiles (WMO station no. 60018) and total column ozone from the Brewer spectrophotometer. The IZA measurements present high-quality standards since more than 98 % of the data are within the limits recommended by the BSRN. There is an excellent agreement in the comparison between SWD, DIR and DIF (instantaneous and daily) measurements with simulations obtained with the LibRadtran radiative transfer model. The root mean square error (RMSE) for SWD is 2.28 % for instantaneous values and 1.58 % for daily values, while the RMSE for DIR is 2.00 % for instantaneous values and 2.07 % for daily values. IZA is a unique station that provides very accurate solar radiation data in very contrasting scenarios: most of the time under pristine sky conditions and periodically under the effects of the Saharan air layer characterized by a high content of mineral dust. A detailed description of the BSRN program at IZA, including quality control and quality assurance activities, is given in this work.

The versatile Heliosat-V method for estimating downwelling surface solar irradiance from satellite imagery

2020 ◽  
Author(s):  
Benoit Tournadre ◽  
Benoit Gschwind ◽  
Yves-Marie Saint-Drenan ◽  
Philippe Blanc
Keyword(s):  
Radiative Transfer ◽  
Solar Irradiance ◽  
Data Retrieval ◽  
Surface Radiation ◽  
Model Parameters ◽  
Transfer Model ◽  
Electromagnetic Spectrum ◽  
Geostationary Satellites ◽  
Spatial Coverage ◽  
Mean Square Errors

<p>Downwelling surface solar irradiance (DSSI) is one of the Essential Climate Variables defined by the Global Climate Observing System. The knowledge of its space and time variabilities is of primary importance for different applications, including Earth sciences, agriculture and renewable solar energies. To characterize such variabilities, the retrieval of long time series and of a dense kilometric global spatial coverage is required. The Heliosat methods are developed by Mines ParisTech since the mid-1980’s to estimate DSSI from the imagery produced by geostationary meteorological satellites. A challenge today is to use imagery from different satellites, including non-geostationary. This raises a number of issues, related among others to the different viewing geometries and spectral sensitivities of the sensors. These issues motivate the evolution of the Heliosat methods toward a more flexible version: the versatile Heliosat-V method. Other difficulties, mainly of operational types, such as massive data retrieval/processing, geometric correction, radiometric cross-calibration, missing data, seamless mosaicking, etc. are out of the scope of this communication.</p><p>Heliosat-V is designed to produce estimates of DSSI that can cover a wide variety of satellite optical sensors that have at least one radiometric channel with sensitivity in the 400-1000-nm part of the electromagnetic spectrum. The method is capable of using calibrated imagery from geostationary and also non-geostationary satellites. External remote-sensed data of surface reflectance anisotropy (Ross-Li model parameters derived from the imagery of the Moderate-Resolution Imaging Spectroradiometer (MODIS)) and atmospheric composition (ozone, water vapour and aerosol types and optical depths) from coupled meteorological and chemical transport models (Copernicus Atmospheric Monitoring Services) are used to produce fast radiative transfer simulations. Typical reflectances of cloudy scenes at the top of the atmosphere are produced via look-up tables derived from a radiative transfer model (libRadtran). They can adapt to the spectral sensitivity of the satellite channel, and to the solar and viewing geometries. This algorithm setup allows its use without past data, which were necessary for previous Heliosat methods. This is a real asset for its implementation to non-geostationary satellites.</p><p>We test the validity of the method, by comparing DSSI estimates derived from one year of Meteosat Second Generation 0° imagery, with ground-based pyranometer measurements from 10 stations of the Baseline Surface Radiation Network, on different continents and environments. Our results show root-mean square errors of 15-min averaged DSSI between 12% and 35% (71 and 133 W m<sup>-2</sup> in absolute value), similarly to existing surface irradiance products based on Heliosat-2 or Heliosat-4.</p>

scholarly journals Effects of total solar eclipse of 29 March 2006 on surface radiation

2007 ◽  
Vol 7 (22) ◽  
pp. 5775-5783 ◽  
Author(s):  
S. Kazadzis ◽  
A. Bais ◽  
M. Blumthaler ◽  
A. Webb ◽  
N. Kouremeti ◽  
...  
Keyword(s):  
Solar Eclipse ◽  
Total Ozone ◽  
Diffuse Radiation ◽  
Black Body ◽  
Black Body Radiation ◽  
Total Solar Eclipse ◽  
Surface Radiation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Limb Darkening

Abstract. Solar irradiance spectral measurements were performed during a total solar eclipse. The spectral effect of the limb darkening to the global, direct irradiance and actinic flux measurements was investigated. This effect leads to wavelength dependent changes in the measured solar spectra showing a much more pronounced decrease in the radiation at the lower wavelengths. Radiative transfer model results were used for the computation of a correction for the total ozone measurements due to the limb darkening. This correction was found too small to explain the large decrease in total ozone column derived from the standard Brewer measurements, which is an artifact in the measured irradiance due to the increasing contribution of diffuse radiation against the decreasing direct irradiance caused by the eclipse. Calculations of the Extraterrestrial spectrum and the effective sun's temperatures, as measured from ground based direct irradiance measurements, showed an artificial change in the calculations of both quantities due to the fact that radiation coming from the visible part of the sun during the eclipse phases differs from the black body radiation described by the Planck's law.

scholarly journals Aerosol trends as a potential driver of regional climate in the central United States: evidence from observations

2017 ◽  
Vol 17 (22) ◽  
pp. 13559-13572 ◽  
Author(s):  
Daniel H. Cusworth ◽  
Loretta J. Mickley ◽  
Eric M. Leibensperger ◽  
Michael J. Iacono
Keyword(s):  
United States ◽  
Radiative Transfer ◽  
Southeastern United States ◽  
Radiant Energy ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky

Abstract. In situ surface observations show that downward surface solar radiation (SWdn) over the central and southeastern United States (US) has increased by 0.58–1.0 Wm−2 a−1 over the 2000–2014 time frame, simultaneously with reductions in US aerosol optical depth (AOD) of 3.3–5.0  ×  10−3 a−1. Establishing a link between these two trends, however, is challenging due to complex interactions between aerosols, clouds, and radiation. Here we investigate the clear-sky aerosol–radiation effects of decreasing US aerosols on SWdn and other surface variables by applying a one-dimensional radiative transfer to 2000–2014 measurements of AOD at two Surface Radiation Budget Network (SURFRAD) sites in the central and southeastern United States. Observations characterized as clear-sky may in fact include the effects of thin cirrus clouds, and we consider these effects by imposing satellite data from the Clouds and Earth's Radiant Energy System (CERES) into the radiative transfer model. The model predicts that 2000–2014 trends in aerosols may have driven clear-sky SWdn trends of +1.35 Wm−2 a−1 at Goodwin Creek, MS, and +0.93 Wm−2 a−1 at Bondville, IL. While these results are consistent in sign with observed trends, a cross-validated multivariate regression analysis shows that AOD reproduces 20–26 % of the seasonal (June–September, JJAS) variability in clear-sky direct and diffuse SWdn at Bondville, IL, but none of the JJAS variability at Goodwin Creek, MS. Using in situ soil and surface flux measurements from the Ameriflux network and Illinois Climate Network (ICN) together with assimilated meteorology from the North American Land Data Assimilation System (NLDAS), we find that sunnier summers tend to coincide with increased surface air temperature and soil moisture deficits in the central US. The 1990–2015 trends in the NLDAS SWdn over the central US are also of a similar magnitude to our modeled 2000–2014 clear-sky trends. Taken together, these results suggest that climate and regional hydrology in the central US are sensitive to the recent reductions in aerosol concentrations. Our work has implications for severely polluted regions outside the US, where improvements in air quality due to reductions in the aerosol burden could inadvertently pose an enhanced climate risk.

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