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The Clouds and Earth Radiant Energy System (CERES) project’s objectives are to measure the reflected solar radiance (shortwave) and Earth-emitted (longwave) radiances and from these measurements to compute the shortwave and longwave radiation fluxes at the top of the atmosphere (TOA) and the surface and radiation divergence within the atmosphere. T...
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... We utilize the synoptic TOA and surface fluxes, and clouds data set (SYN1deg-Ed4.1-Level 3) at 1°× 1°r esolution from the Clouds and the Earth's Radiant Energy System (CERES) (Smith et al., 2011), accessible at https://ceres.larc.nasa.gov/data/, to derive daily LW and SW CRE for the period of 2001-2020. It's important to note that the long-term global and monthly mean values of TOA radiative fluxes from CERES-SYN1deg-Ed4.1 are not identical to those from the CERES Energy Balanced and Filled data (CERES-EBAF-Ed4.1) ...
Using high temporal resolution satellite observations and reanalysis data, we classify daily weather into distinct regimes and quantify their associated cloud radiative effect (CRE) to better understand the roles of various weather systems in affecting Earth's top‐of‐atmosphere radiation budget. These regimes include non‐precipitation, drizzle, wet non‐storm, and storm days, which encompass atmospheric rivers (AR), tropical storms (TS), and mesoscale convection systems (MCS). We find that precipitation (wet) days account for roughly 80% (60%) of global longwave (LW) and shortwave (SW) CREs due to their large frequency and high intensity in CRE. Despite being rare globally (13%), AR, TS, and MCS days together account for 32% of global LW CRE and 27% of SW CRE due to their higher intensity in LW and SW CRE. These results enhance our understanding of how various weather systems, particularly severe storms, influence Earth's radiative balance, and will help to better constrain climate models.
... The CERES is a key component of the Earth Observing System EOS, Suomi National Polar-Orbiting Partnership S-NPP, and National Oceanic and Atmospheric Administration NOAA-20 observatories. It also consists of Terra and Aqua satellites with a three-channel radiometer: a shortwave channel, a long-wave channel, and a total channel that measures both solar-reflected radiation at the top of the atmosphere and Earth-emitted radiation from the Earth's surface (Smith et al., 2011;Loeb et al., 2018;Parkinson, 2022). ...
The aerosol-cloud-precipitation interactions – ACPIs - are uncertain that show a large spatiotemporal variability in their magnitude. These things happen because of the effects of aerosol particles on precipitation and cloud parameters, environmental and meteorological conditions, industrial and agricultural influences, and other human influences and natural factors in each ecological functional area. For this study, aerosol and cloud data were retrieved from the Moderate Resolution Imaging Spectroradiometer MODIS sensors. These comprised of the aerosol optical depth AOD, Ångström exponent AET, atmospheric water vapor AWV, mean cloud fraction CFM, cloud top pressure CTP, and cloud top temperature CTT. Precipitation data is comprised of 3B43 products sourced from Tropical Rainfall Measuring Mission TRMM and the outgoing long-wave radiation OLR flux is comprised of Clouds and Earth‘s Radiant Energy System CERES satellite instruments. The study covers sixteen sites in East Africa-Ethiopia with neighboring countries - Eritrea, Djibouti, and South Sudan clustered into four regions for the periods of 2001–2022 to provide detailed information on the aerosol particles spatiotemporal effects on clouds and precipitation. The increase-decrease AWV, CFM and PPT fluctuations are with AOD opposing OLR, CTP and CTT. The parameters are oriented towards western part mostly in the southwest region of the study area. The minima values were found at the southeast cluster in 2022 for all AOD, AWV and CFM; in 2010 for PPT and OLR and in 1999 and for OLR with their maxima at northeast cluster in 2010 for AOD, AWV and CFM; in 2009 for PPT; and in 2011 and 2022 for CTP and CTT from both instruments. Accordingly, the AOD, AWV, CFM, PPT, CTP and CTT minimum values are 0.22, 1.90, 0.21, 1.15, 253.86, 504.53 and 257.73 for Terra and 0.18, 1.91, 0.27, 252.14, 533.43 and 262.94 for Aqua, and the maxima are 0.35, 2.33, 0.33, 2.26, 271.23, 619.08 and 268.49 for Terra and 0.35, 2.35, 0.41, 272.22, 640.07 and 272.58 for Aqua, respectively. The parameters OLR and AWV had the lowest optimum significant PCs at the Humera and Dahlak sites whereas the PCs retained based on AET at the Awassa site and AWV at the Dangote site were the highest. Differences in retained PCs point to different atmospheric dynamics responsible for the behavior of climate during various seasons of the year and the spatial coherence arising from both interannual and intraseasonal variability. And our observation using the HYSPLIT model and fire map confirms that transported aerosol particles in the atmosphere show varied source regions, mostly the Arabian desert and the southwest Indian ocean, at different levels.
... CERES has three main data products: Single Scanner Footprint (SSF), Synaptic TOA and surface fluxes and clouds (SYN), and Energy Balanced and Filled (EBAF). These data help scientists understand the Earth's energy balance and how clouds, aerosols, and greenhouse gases can affect this balance [32]. These data are available from the official CERES website (https://ceres.larc.nasa.gov/data/, ...
The distribution and variation of top-of-atmosphere longwave cloud radiative forcing (LCRFTOA) has drawn a significant amount of attention due to its importance in understanding the energy budget. Advancements in sensor and data processing technology, as well as a new generation of geostationary satellites, such as the FengYun-4A (FY-4A), allow for high spatiotemporal resolutions that are crucial for real-time radiation monitoring. Nevertheless, there is a distinct lack of official top-of-atmosphere outgoing longwave radiation products under clear-sky conditions (OLRclear). Consequently, this study addresses the challenge of constructing LCRFTOA data with high spatiotemporal resolution over the full disk region of FY-4A. After simulating the influence of atmospheric parameters on OLRclear based on the SBDART radiation transfer model (RTM), we developed a model for estimating OLRclear using infrared channels from the advanced geosynchronous radiation imager (AGRI) onboard the FY-4A satellite. The OLRclear results showed an RMSE of 5.05 W/m² and MBE of 1.59 W/m² compared to ERA5. The corresponding RMSE and MBE value compared to CERES was 6.52 W/m² and 2.39 W/m². Additionally, the calculated LCRFTOA results were validated against instantaneous, daily average, and monthly average ERA5 and CERES LCRFTOA products, supporting the validity of the algorithm proposed in this paper. Finally, the changes in LCRFTOA due to varied cloud heights (high, medium, and low cloud) were analyzed. This study provides the basis for comprehensive studies on the characteristics of top-of atmosphere radiation. The results suggest that high-height clouds exert a greater degree of radiative forcing more frequently, while low-height clouds are more frequently found in the lower forcing range.
... Nowadays, satellite and station data can complement each other to a certain extent, helping to provide a deeper understanding of regional solar radiation characteristics. So far, there have been SSR datasets available, such as the Global Energy and Water Exchanges-Surface Radiation Budget (GEWEX-SRB) [15,16], the International Satellite Cloud Climatology Project Flux Data (ISCCP-FD) [17,18], and the Clouds and the Earth's Radiant Energy System (CERES) [19,20]. However, the satellite products mentioned above either have coarse spatial resolutions or limited time spans, which do not support long-term analyses [21]. ...
Xizang boasts a vast and geographically complex landscape with an average elevation surpassing 4000 m. Understanding the spatiotemporal distribution of surface solar radiation is indispensable for simulating surface processes, studying climate change, and designing photovoltaic power generation and solar heating systems. A multi-dimensional, long-term, spatial, and temporal investigation of solar radiation in Xizang was conducted using three unique datasets, including the difference in surface solar radiation (SSR) between high-resolution satellite and ground station data, the annual and monthly distribution of SSR, and the interannual–monthly–daily variation and the coefficient of hourly variability. Combined with high-resolution elevation data, a strong linear correlation was shown between the radiation and the elevation below 4000 m. Furthermore, analysis reveals greater differences in data between east and west compared to the center, as well as between summer and winter seasons. SSR levels vary in steps, reaching the highest from Ngari to Shigatse and the lowest in a U-shaped area formed by southeastern Shannan and southern Nyingchi. In June, high monthly SSR coverage was the highest of the year. Since 1960, the annual mean SSR has generally exhibited a declining trend, displaying distinctive trends across various seasons and datasets. Owing to intricate meteorological factors, some regions exhibited double peaks in monthly SSR. Finally, we have introduced a solar resource assessment standard, along with a multidimensional evaluation of the resources, and categorized all townships. We offer a thorough analysis of Xizang’s solar radiation to provide a comprehensive understanding, which will help to prioritize recommendations for PV construction in Xizang.
... It is derived by resampling the MODIS Level 1B Calibrated Radiances product (MOD021KM, MYD021KM) to 5 km. This product offers a significant advantage for validation: the MODIS and CERES sensors are mounted on the same satellite platform (Aqua and Terra) (Smith et al., 2011), and their imaging times align precisely. Therefore, there is no issue of temporal matching when compared with instantaneous CERES OLR products. ...
A R T I C L E I N F O Edited by Jing M. Chen Keywords: Outgoing longwave radiation (OLR) Radiation transfer simulation Multi-Dimensional matrix MAPping (MDMAP) Polynomial regression Moderate resolution imaging Spectroradiometer (MODIS) Cloud and Earth's radiant energy system (CERES) A B S T R A C T Outgoing Longwave Radiation (OLR) is an important component of the Earth's radiation budget and a key parameter for coupled models of the atmosphere, ocean, land, and other systems. It is of significant importance in studies related to Earth sciences such as weather forecasting, climate research, and disaster monitoring. Since narrowband sensors are more widely available and have higher spatial resolutions than broadband sensors, high-resolution OLR data are currently frequently estimated using narrowband sensors. This study proposes a novel physical method, namely the Multi-Dimensional matrix MAPping algorithm (MDMAP) framework, inspired by the scene classification ideas of Cloud and Earth's Radiant Energy System (CERES) and the differential absorption theory. The new framework aims to accurately retrieve OLR from the multi-channel infrared sensor, such as Moderate Resolution Imaging Spectroradiometer (MODIS). Corresponding to traditional algorithms, such as the polynomial regression algorithm (POLY) and lookup table algorithm (LUT), the new framework provides two distinct implementations of the MDMAP algorithm framework (MDMAP:POLY and MDMAP:LUT). The performances of both the traditional and the newly proposed algorithms are evaluated based on the radiative transfer simulation dataset and CERES SSF OLR products. The results show that the MDMAP algorithms behave more accurately than the traditional ones under most conditions, especially under clear-sky conditions. Specifically, a comprehensive analysis indicates that the new algorithms demonstrate smaller RMSEs than the traditional ones under various conditions, particularly in desert regions with the RMSE reduction exceeding 3 W/m 2 (>30%). Moreover, the two new algorithms reveal enhanced robustness to noise uncertainties, and demonstrate remarkable generality and computational efficiency, implying their potential and better applicability in deriving believable OLR from most infrared sensors.
... CERES has three main data products: Single Scanner Footprint (SSF), Synaptic TOA and surface fluxes and clouds (SYN), and Energy Balanced and Filled (EBAF). These data help scientists understand the Earth's energy balance and how clouds, aerosols, and greenhouse gases can affect this balance [30]. These data are available from the official CERES website (https://ceres.larc.nasa.gov/data/). ...
The distribution and variation of top-of-atmosphere longwave cloud radiative forcing (LCRFTOA) has drawn a significant amout of attention due to its importance in understanding the energy budget. Advancements in sensor and data processing technology, as well as a new generation of geostationary satellites, such as the FengYun-4A (FY-4A), allow for high spatial resolutions that are crucial for real-time radiation monitoring. Nevertheless, there is a distinct lack of official top-of-atmosphere outgoing longwave radiation products under clear-sky conditions (OLRclear). Consequently, this study addresses the challenge of constructing LCRFTOA data with high spatiotemporal resolution over the full disk region of FY-4A. After simulating the influence of atmospheric parameters on OLRclear based on SBDART radiation transfer model (RTM), we developed a model for estimating OLRclear using infrared channels from the advanced geosynchronous radiation imager (AGRI) onboard the FY-4A satellite. The OLRclear results showed an RMSE of 5.05 W/m2 and MBE of 1.59 W/m2 compared to ERA5. Besides, the calculated LCRFTOA results were validated against instantaneous, daily average, and monthly average ERA5 and CERES LCRFTOA products, supporting the validity of the algorithm proposed in this paper. Finally, the changes in LCRFTOA due to variaty cloud heights (high, medium, and low cloud) were analyzed. This study provides the basis for comprehensive studies on the characteristics of top-of atmosphere radiation.
... were used (Rutan et al., 2015, NASA/LARC/SD/ASDC, 2017). The "SYN"(Synoptic Radiative Fluxes and Clouds) means that this version provides radiation data on clear and all-sky conditions, the "1deg" means it has a 1-degree spatial resolution, and the "Ed3A" is the version number (Smith et al., 2011;Jia et al. 2016). Considering that the main driving forces on the ET process are available energy, aerodynamic effects, and soil water storage (for ET a ), we chose as input variables the CERES products mentioned in Table 2. ...
A key aspect in agricultural zones, such as the Pampean Plain of Argentina, is to accurately estimate evapotranspiration rates to optimize crops and irrigation requirements and the floods and droughts prediction. In this sense, we evaluate six machine learning approaches to estimate the reference and actual evapotranspiration (ET0 and ETa) through CERES satellite products data. The results obtained applying machine learning techniques were compared with values obtained from ground-based information. After training and validating the algorithms, we observed that Support Vector machine-based Regressor (SVR) showed the best accuracy. Then, with an independent dataset, the calibrated SVR were tested. For predicting the reference evapotranspiration, we observed statistical errors of MAE = 0.437 mm d−1, and RMSE = 0.616 mm d−1, with a determination coefficient, R2, of 0.893. Regarding actual evapotranspiration modelling, we observed statistical errors of MAE = 0.422 mm d−1, and RMSE =0.599 mm d−1, with a R2 of 0.614. Comparing the results obtained with the machine learning models developed another studies in the same field, we understand that the results are promising and represent a baseline for future studies. Combining CERES data with information from other sources may generate more specific evapotranspiration products, considering the different land covers.
... were used (Rutan et al., 2015, NASA/LARC/SD/ASDC, 2017). The "SYN"(Synoptic Radiative Fluxes and Clouds) means that this version provides radiation data on clear and all-sky conditions, the "1deg" means it has a 1-degree spatial resolution, and the "Ed3A" is the version number (Smith et al., 2011;Jia et al. 2016). Considering that the main driving forces on the ET process are available energy, aerodynamic effects, and soil water storage (for ET a ), we chose as input variables the CERES products mentioned in Table 2. ...
A key aspect in agricultural zones, such as the Pampean Plain of Argentina, is to accurately estimate evapotranspiration rates to optimize crops and irrigation requirements and the floods and droughts prediction. In this sense, we evaluate six machine learning approaches to estimate the reference and actual evapotranspiration (ET0 and ETa) through CERES satellite products data. The results obtained applying machine learning techniques were compared with values obtained from ground-based information. After training and validating the algorithms, we observed that Support Vector machine-based Regressor (SVR) showed the best accuracy. Then, with an independent dataset, the calibrated SVR were tested. For predicting the reference evapotranspiration, we observed statistical errors of MAE = 0.437 mm d−1, and RMSE = 0.616 mm d−1, with a determination coefficient, R2, of 0.893. Regarding actual evapotranspiration modelling, we observed statistical errors of MAE = 0.422 mm d−1, and RMSE =0.599 mm d−1, with a R2 of 0.614. Comparing the results obtained with the machine learning models developed another studies in the same field, we understand that the results are promising and represent a baseline for future studies. Combining CERES data with information from other sources may generate more specific evapotranspiration products, considering the different land covers.
... The outgoing Earth's radiation includes reflected Short-Wave (SW: 0.2-5 µm) Radiation (OSR) and Long-Wave (LW: 5-200 µm) infrared Radiation (OLR) [3][4][5][6]. At present, the OLR and OSR are obtained with dedicated ERB satellite instruments, such as Earth Radiation Budget Experiment (ERBE) [6][7][8], the Clouds and the Earth's Radiant Energy System (CERES) [9], the Geostationary Earth Radiation Budget (GERB) [10,11], and the Deep Space Climate Observatory (DSCOVR) [12]. Until now, satellite-based data products have enhanced our understanding of the ERB and many important climatic features, such as the role of clouds and aerosols in the ERB [6,13]. ...
Moon-Based Earth Radiation Observation (MERO) is expected to improve and enrich the current Earth radiation budget (ERB). For the design of MERO’s instrument and the interpretation of Moon-based data, evaluating the uncertainty of the instrument’s Entrance Pupil Irradiance (EPI) is an important part. In this work, by analyzing the effect of the Angular Distribution Models (ADMs), Earth’s Top of Atmosphere (TOA) flux, and the Earth–Moon distance on the EPI, the uncertainty of EPI is finally studied with the help of the theory of errors. Results show that the ADMs have a stronger influence on the Short-Wave (SW) EPI than those from the Long-Wave (LW). For the change of TOA flux, the SW EPI could keep the attribute of varying hourly time scales, but the LW EPI will lose its hourly-scale variability. The variation in EPI caused by the hourly change of the Moon–Earth distance does not exceed 0.13 mW∙m−2 (1σ). The maximum hourly combined uncertainty reveals that the SW and LW combined uncertainties are about 5.18 and 1.08 mW∙m−2 (1σ), respectively. The linear trend extraction of the EPI demonstrates that the Moon-based data can effectively capture the overall linear change trend of Earth’s SW and LW outgoing radiation, and the uncertainty does not change the linear trend of data. The variation of SW and LW EPIs in the long term are 0.16 mW∙m−2 (SW) and 0.23 mW∙m−2 (LW) per decade, respectively. Based on the constraint of the uncertainty, a simplified dynamic response model is built for the cavity radiometer, a kind of MERO instrument, and the results illuminate that the Cassegrain optical system and electrical substitution principle can realize the detection of Earth’s outing radiation with the sensitivity design goal 1 mW∙m−2.
... Then, based on the grid visibility, the established model is used to obtain the Earth's outward radiative heat flow. (2) The simulated EPIs of the MWFVR: Based on the radiation transfer model, the ERBE ADMs, and the CERES flux datasets [38,39], the MWFVR's EPI can be obtained. Due to the actual MWFVR not being placed on the lunar surface, the actual measurements have not been obtained. ...
A Moon-based radiometer can provide continuous measurements for the Earth’s full-disk broadband irradiance, which is useful for studying the Earth’s Radiation Budget (ERB) at the height of the Top of the Atmosphere (TOA). The ERB describes how the Earth obtains solar energy and emits energy to space through the outgoing broadband Short-Wave (SW) and emitted thermal Long-Wave (LW) radiation. In this work, a model for estimating the Earth’s outgoing radiative flux from the measurements of a Moon-based radiometer is established. Using the model, the full-disk LW and SW outgoing radiative flux are gained by converting the unfiltered entrance pupil irradiances (EPIs) with the help of the anisotropic characteristics of the radiances. Based on the radiative transfer equation, the unfiltered EPI time series is used to validate the established model. By comparing the simulations for a Moon-based radiometer with the satellite-based data from the National Institute of Standards and Technology Advanced Radiometer (NISTAR) and the Clouds and the Earth’s Radiant Energy System (CERES) datasets, the simulations show that the daytime SW fluxes from the Moon-based measurements are expected to vary between 194 and 205 Wm−2; these simulations agree well with the CERES data. The simulations are about 5 to 20 Wm−2 smaller than the NISTAR data. For the simulated Moon-based LW fluxes, the range is 251~287 Wm−2. The Moon-based and NISTAR fluxes are consistently 5~15 Wm−2 greater than CERES LW fluxes, and both of them also show larger diurnal variations compared with the CERES fluxes. The correlation coefficients of SW fluxes for Moon-based data and NISTAR data are 0.97, 0.63, and 0.53 for the months of July, August, and September, respectively. Compared with the SW flux, the correlation of LW fluxes is more stable for the same period and the correlation coefficients are 0.87, 0.69, and 0.61 for July to September 2017.
