Downward Longwave Radiation Research Articles

Toward User-Friendly All-Sky Surface Longwave Downward Radiation from Space: General Scheme and Product

Abstract Longwave downward radiation (LWDR) is an important driving parameter in climate and hydrological models. Compared to traditional ground-based measurements, remote sensing has unique advantages in estimating global LWDR. However, for current remote sensing missions, as the typical available satellite-derived LWDR product with global coverage and hourly temporal resolution, the Clouds and the Earth’s Radiant Energy System-Synoptic (CERES-SYN) top of atmosphere and surface fluxes and clouds has a low spatial resolution (1° × 1°). There is still much room for improvement of the existing remote sensing LWDR products in terms of accuracy, spatiotemporal resolutions, and the ability to explain and quantify the changes of longwave radiation at various scales. To overcome these limitations, this paper developed a new global LWDR product with improved accuracy (RMSE < 30 W m−2 over the globe), high temporal resolution (hourly), and spatial resolution (5 km) based on Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. It serves as a LWDR product within the Long-term Earth System spatiotemporally Seamless Radiation budget dataset (referred to as LessRad). As the first long-term high-resolution, spatiotemporally continuous LWDR product (2002–22, 1 h, 5 km), the LessRad reveals its advantages in studying the spatiotemporal variability of LWDR on finer scales. It also provides a valuable data source for various applications, such as analyzing land–atmosphere interactions and quantifying climate feedback, and thus is potentially helpful for understanding Earth’s energy budget and dynamics.

The temperature feedback in the intraseasonal Warm Arctic–Cold North America pattern

The feedback mechanism between air temperature (Ta) and skin temperature (Ts) and its origin is disclosed for the intraseasonal warm Arctic-cold North America pattern based on reanalysis data and simulations from an atmospheric global climate model. The change in Ta is caused by an anomalous anticyclonic circulation over Alaska and the resultant meridional temperature advection. It changes Ts through downward longwave radiation (DLWR), and the altered Ts further feedback to Ta via upward longwave radiation. While these processes decay the initial changes in Ta, they are important for the feedback between Ta and Ts. Compared with clouds, atmospheric moisture and the associated clear-sky DLWR dominate the changes in DLWR. Moisture thus plays a crucial role in the strength of the Ts-Ta feedback.

Sensitivity of surface downward longwave radiation to aerosol optical depth over the Lake Taihu region, China

Downward longwave radiation (DLR) is an important component of the global radiation budget, significantly influencing surface energy dynamics. Accurate quantification of aerosol impacts on surface DLR is crucial for refining numerical weather predictions and comprehending surface energy balance. While the impact of aerosols on downward shortwave radiation has garnered substantial attention, investigation into the longwave radiation effect of aerosols remains relatively limited. In this study, the impact of aerosols on surface DLR is quantified by using continuous radiation data, conventional meteorological observations, and aerosol optical depth (AOD) retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. These datasets span the period from 2011 to 2018 and are obtained from six stations of the Lake Taihu Flux Observational Network situated in Eastern China. The sensitivity of DLR to AOD (dDLR/dAOD) is determined through multiple linear regressions using both observational data and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. Additionally, a radiative transfer model is employed to derive the dDLR/dAOD. A three-variable linear regression of the observational data shows that DLR increases by 4.66 ± 1.99 W m−2 per unit increase in AOD (mean ± 1 standard deviation) under clear-sky conditions. The radiative transfer model and the three-variable linear regression of MERRA-2 reanalysis yield similar sensitivity values of 4.69 ± 1.23 W m−2 AOD−1 and 4.10 ± 0.25 W m−2 AOD−1, respectively. However, these sensitivity values are considerably smaller than those reported in previous studies. Plausible factors contributing to the observed variance in sensitivity values are discussed. These results help to improve our understanding on the aerosol longwave radiation effect over the Lake Taihu region in Eastern China.

A Data-Driven Model for Estimating Clear-Sky Surface Longwave Downward Radiation Over Polar Regions

A Data-Driven Model for Estimating Clear-Sky Surface Longwave Downward Radiation Over Polar Regions

A Cloud Water Path-Based Model for Cloudy-Sky Downward Longwave Radiation Estimation from FY-4A Data

Clouds are a critical factor in regulating the climate system, and estimating cloudy-sky Surface Downward Longwave Radiation (SDLR) from satellite data is significant for global climate change research. The models based on cloud water path (CWP) are less affected by cloud parameter uncertainties and have superior accuracy in SDLR satellite estimation when compared to those empirical and parameterized models relying mainly on cloud fraction or cloud-base temperature. However, existing CWP-based models tend to overestimate the low SDLR values and underestimate the larger SDLR. This study found that this phenomenon was caused by the fact that the models do not account for the varying relationships between cloud radiative effects and key parameters under different Liquid Water Path (LWP) and Precipitable Water Vapor (PWV) ranges. Based upon this observation, this study utilized Fengyun-4A (FY-4A) cloud parameters and ERA5 data as data sources to develop a new CWP-based model where the model coefficients depend on the cloud phase and cloud water path range. The accuracy of the new model’s estimated SDLR is 20.8 W/m2 for cloudy pixels, with accuracies of 19.4 W/m2 and 23.5 W/m2 for overcast and partly cloudy conditions, respectively. In contrast, the accuracy of the old CWP-based model was 22.4, 21.2, and 24.8 W/m2, respectively. The underestimation and overestimation present in the old CWP-based model are effectively corrected by the new model. The new model exhibited higher accuracy under various station locations, cloud cover scenarios, and cloud phase conditions compared to the old one. Comparatively, the new model showcased its most remarkable improvements in situations involving overcast conditions, water clouds with low PWV and low LWP values, ice clouds with large PWV, and conditions with PWV ≥ 5 cm. Over a temporal scale, the new model effectively captured the seasonal variations in SDLR.

Atmospheric Latent Energy Transport Pathways into the Arctic and Their Connections to Sea Ice Loss during Winter over the Observational Period

Abstract To investigate patterns of horizontal atmospheric latent energy (LE) transport toward the Arctic, we applied the self-organizing maps (SOM) method to the daily vertically integrated horizontal LE flux from ERA5 in winter (January–March) during 1979–2021. A clear picture depicting the LE transport to the Arctic at a synoptic scale then emerged, with four primary transport pathways identified: the northern Europe, the Davis Strait, the Greenland Sea, and the Bering Strait pathways. The four primary pathways occurred at a comparable frequency, and noticeable interannual variability was observed in their time series of frequency during 1979–2021. Further analysis suggested that the northward LE transport through all these pathways is significantly modulated by cyclones, with the northern Europe and the Greenland Sea pathways being mostly affected. Generally, more frequent and stronger cyclones were observed near the entry regions of LE transport compared to other regions. Moreover, this study provides a comprehensive picture of how atmospheric LE transport is related to air temperature, moisture, surface heat flux, and sea ice anomalies over the Arctic Ocean in winter. Through a thermodynamic perspective, we argue that the deleterious impacts of poleward LE transport on Arctic sea ice are to a large extent attributable to the enhanced local atmosphere–ice interactions, which increase downward longwave radiation (DLR) plus turbulent fluxes, consequently warming the surface and promoting the loss of sea ice. According to the quantitative results, among the four primary pathways, LE transport through the Davis Strait and the Greenland Sea could cause the loss of Arctic sea ice most efficiently.

Sky emissivity model calibration with data from Brazil and building simulation sensitivity analysis

Downward long-wave radiation is an important parameter for building simulation because it strongly influences the thermal balance of the external surface. Brazil has several distinct climatic regions, and little formal work has been done with observed downward long-wave radiation to build thermal energy balancing due to a lack of continuous data. In this regard, selecting a model and adjusting local parameters is fundamental for reducing the uncertainty of the computational simulation process. In the present study, we use data from four different latitude sites to adjust the parameters of four models integrated into the EnergyPlus simulation program. The results obtained in the analysis indicated that with an appropriate adjustment of local parameters, all models achieved good predictive performance and provided lower errors values than previous versions, when using versions incorporated into the EnergyPlus, and when using original versions. In addition, it is recommended to use the Berdahl and Martin’s [10] model adjusted by this study in the building energy simulations carried out in Brazil, since it obtained the lowest rRMSE values, between 2.2% and 2.5%, while the standard model of EnergyPlus varies between 5.1% and 10.5%. After validating the adjustments and considering a single-family house of 43.25 m2, it was concluded that without proper adjustment, the models incorporated into the EnergyPlus, overestimate the total annual thermal load by 8% to 37% when compared to the model the proposed in the present work.

Parameterization of downward long-wave radiation based on long-term baseline surface radiation measurements in China

Abstract. Downward long-wave radiation (DLR) affects energy exchange between the land surface and the atmosphere and plays an important role in weather forecasting, agricultural activities, and the development of climate models. Because DLR is seldom observed at conventional radiation stations, numerous empirical parameterizations have been presented to estimate DLR from screen-level meteorological variables. The reliability and representativeness of parameterization depend on the coefficients regressed from the simultaneous observations of DLR and meteorological variables. Only a few previous studies have attempted to build parameterizations over regions in China such as the Tibetan Plateau and East China. In this study, a long-term (2011–2022) hourly dataset of DLR and meteorological elements, obtained from seven stations of the China Baseline Surface Radiation Network, was used to recalculate the coefficients of the Brunt and Weng models and to develop a new model. Results showed that the mean bias error (MBE) and relative MBE (rMBE) between the measured clear-sky DLR and that estimated using the Brunt, Weng, and new models were −4.3, −5.1, and 3.7 W m−2 and −1.5 %, −1.8 %, and 1.3 %, respectively. The root mean square errors (RMSEs) were in the range of 13.8–14.3 W m−2, and the relative RMSEs (rRMSEs) were approximately 5.0 %. The MBEs (rMBEs) of the Brunt, Weng, and new models under all-sky conditions were −2.8 W m−2 (−1.0 %), −6.1 W m−2 (−2.1 %), and −1.5 W m−2 (−0.5 %), respectively. The RMSE (rRMSE) of the parameterization models in retrieving all-sky DLR was ∼ 17.5 W m−2 (∼ 6.1 %). Therefore, the models are considered suitable for retrieval of DLR over China.

Estimation of Surface Downward Longwave Radiation and Cloud Base Height Based on Infrared Multichannel Data of Himawari-8

Surface downward longwave radiation (SDLR) is significant with regard to surface energy budgets and climate research. The uncertainty of cloud base height (CBH) retrieval by remote sensing induces the vast majority of SDLR estimation errors under cloudy conditions; reliable CBH observation and estimation are crucial for determining the cloud radiative effect. This study presents a CBH retrieval methodology built from 10 thermal spectral data from Himawari-8 (H-8) observations, utilizing the random forest (RF) algorithm to fully account for each band’s contribution to CBH. The algorithm utilizes only infrared band data, making it possible to obtain CBH 24 h a day. Considering some factors that can significantly affect the CBH estimation, RF models are trained for different clouds using inputs from multiple H-8 channels together with geolocation information to target CBH derived from CloudSat/CALIPSO combined measurements. The validation results reveal that the new methodology performs well, with a root-mean-square error (RMSE) of only 1.17 km for all clouds. To evaluate the effect of CBH on SDLR estimation, an all-sky SDLR estimation algorithm based on previous CBH predictions is proposed. The new SDLR product not only has a resolution that is noticeably higher than that of benchmark products of the SDLR, such as the Clouds and the Earth’s Radiant Energy System (CERES) and the next-generation reanalysis (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF), but it also has greater accuracy, with an RMSE of 21.8 W m−2 for hourly surface downward longwave irradiance (SDLI).

Upscaling of longwave downward radiation from instantaneous to any temporal scale: Algorithms, validation, and comparison

Surface longwave downward radiation (LWDR) is a key factor affecting the surface energy balance. The daily LWDR and the diurnal variations of LWDR are of great significance for studies of climate change and surface processes. How to obtain LWDR at an averaged temporal scale from instantaneous LWDR is one of the long-standing problems in the field of radiation budget from remote sensing. In this paper, two temporal upscaling methods are introduced, namely, a method based on the diurnal variations of LWDR (diurnal variation based, DVB) and a method based on random forest regression (RFR). The results reveal that: (1) The DVB method has a global hourly and daily LWDR root-mean-square error (RMSE) of less than 21 W/m2 and 15 W/m2, respectively, and the RMSE of the daily LWDR based on RFR is less than 7 W/m2; (2) When compared with four existing statistical interpolation methods, the DVB method can not only ensure the accuracy, but also can overcome the problem of missing samples and/or an abnormal samples during upscaling; (3) Except for directly predict daily LWDR, the DVB methods can also obtain more accurate LWDR diurnal variations such as hourly, half-hourly etc. The RFR method enables high-efficiency and accurate estimation of daily averaged LWDR from instantaneous measurements. Compared with existing methods and products, the proposed methods are not only efficient, but also have a superior applicability and reliable accuracy. The proposed strategies provide new ideas for the community in estimating LWDR at continuous temporal scales from remotely sensed measurements.

Improved Algorithm to Derive All-Sky Longwave Downward Radiation From Space: Application to Fengyun-4A Measurements

Longwave downward radiation (LWDR) is an important parameter that modulates the earth’s radiation and energy balance, and is also a key variable that affects the global warming. Currently, although many reanalysis LWDR products and satellite-based algorithms are available, their coarse spatio-temporal resolutions as well as the difficulties in organizing the corresponding driving parameters seriously limit their applications. As China’s new generation geostationary satellite, Fengyun-4A (FY-4A) provides higher spatial and temporal resolutions (4 km @nadir, 15min at full disk mode) at longwave infrared channels which can routinely monitor the changes of the earth’s radiation in near real-time and therefore provide great potentials in generating various high-accuracy radiation products. Unfortunately, the existing official LWDR products of FY-4A can only provide estimates under clear skies, and its accuracy still has much room for improvement. For above-mentioned points, an improved general all-sky parameterization algorithm is proposed based on readily available input variables, such as land surface temperature (LST), column water vapor (CWV) and cloud-top temperature (CTT). Then the new algorithm is applied to FY-4A aiming to derive believable all-sky LWDR. The validation results show that, the new algorithm does show a noticeable improvement over the original one by reducing the relatively large errors in LWDR under conditions of extremely cold and dry (flux range <150 W/m²), as well as the large bias in the polar and high altitude regions. Moreover, the new method can generate more reliable LWDR than that of FY-4A official product in terms of both spatio-temporal continuity and accuracy, with RMSE less than 22 W/m² and bias less than 0.5 W/m² under all-sky conditions. The easy-to-use and believable performance of the new algorithm provide an opportunity to accurately derive all-sky LWDR from FY-4A and similar satellite missions with high resolutions.

Errata on “Improved Algorithm to Derive All-Sky Longwave Downward Radiation From Space: Application to Fengyun-4A Measurements”

Errata on “Improved Algorithm to Derive All-Sky Longwave Downward Radiation From Space: Application to Fengyun-4A Measurements”

Near-surface heat transfer at two gentle slope sites with differing aspects, Qinghai-Tibet Plateau

The slope aspect effect is widely distributed on the Qinghai-Tibet Plateau and has an important impact on the permafrost environment. The differences in surface heat exchange characteristics of different slope aspects in the permafrost region of Gu Mountain in the Beiluhe Basin were compared and analyzed based on observations of the south slope (sunny slope) and north slope (shadowy slope) from 2019 to 2021. The air-ground heat transfer process on the slopes was simulated using the Monin-Obukhov similarity theory. Then, the simulation results of the sensible and latent heat fluxes on the slopes were corrected and analyzed using the Bowen ratio correction method. The results show that under the influence of the solar altitude angle and subsurface conditions, the downward shortwave radiation (DR), upward shortwave radiation (UR), and upward longwave radiation (ULR) were higher on the sunny slope than those on the shadowy slope, whereas the downward longwave radiation (DLR) was lower than that on the shadowy slope. Jointly, the net radiation energy on the sunny slope was smaller than that on the shadowy slope, and the annual average net radiation difference reached 16.7 W·m−2. The annual and daily variations in soil heat flux on the sunny slope were higher than those on the shadowy slope. The energy closure rate on the sunny slope was high with a confinement rate of 0.85, whereas that on the shadowy slope was poor with a confinement rate of 0.51. The air-ground energy transfer patterns on the sunny and shadowy slopes showed obvious seasonal differences. Both slopes are dominated by the sensible heat exchange transfer mode in the cold season, whereas the shadowy slope is dominated by latent heat exchange in the warm season. This study improves our understanding of the distribution, development, and environmental effects of permafrost, under the influence of local factors.

Assessment of Three Satellite-Derived Surface Downward Longwave Radiation Products in Polar Regions

The radiation budget in polar regions plays an important role in global climate change study. This study investigates the performance of downward longwave radiation (DLR) of three satellite radiation products in polar regions, including GEWEX-SRB, ISCCP-FD, and CERES-SYN. The RMSEs are 35.8, 40.5, and 26.9 W/m2 at all polar sites for GEWEX-SRB, ISCCP-FD, and CERES-SYN. The results in the Arctic are much better than those in the Antarctic, RMSEs of the three products are 34.7 W/m2, 36.0 W/m2, and 26.2 W/m2 in the Arctic and are 38.8 W/m2 and 54.8 W/m2, and 28.6 W/m2 in the Antarctic. Both GEWEX-SRB and CERES-SYN underestimate DLRs at most sites, while ISCCP-FD overestimates DLRs at most sites. CERES-SYN and GEWEX-SRB DLR products can capture most of the DLR seasonal variation in both the Antarctic and Arctic. Though CERES-SYN has the best results that RMSE within 30 W/m2 in most polar sites, the accuracy of satellite products in polar regions still cannot meet the requirement of climate research. The improvement of satellite DLR products in polar regions mainly depends on the quality of improving input atmospheric parameters, the accuracy of improving cloud detection over the snow and ice surface and cloud parameters, and better consideration of spatial resolution and heterogeneity.

ПАРАМЕТРИЗАЦИЯ ПРОТИВОИЗЛУЧЕНИЯ АТМОСФЕРЫ В ГЛЯЦИОЛОГИЧЕСКИХ ПРИЛОЖЕНИЯХ

Downward longwave radiation (atmospheric counter-radiation) plays a significant role in the formation of the net radiation regime of a mountain glacier. For this reason, its role should not be underestimated in glaciological calculations, especially in the warm period of the year, during the melting season. Glaciological models often do not take into account individual components of the longwave radiation flux, for example, radiation reflected from the mountain slopes surrounding the glacier. The choice of the algorithm for the parameterization of counter-radiation and cloudiness affects the accuracy of model calculations. The well-known parameterization algorithms for atmospheric counter-radiation are analyzed from the point of view of their applicability in glaciological modeling in a specific geographical region: the Inner Tien Shan. The calculation results are compared with the observational data for the Karabatkak glacier (the northern slope of the Terskey Ala-Too range). The choice of the optimum algorithms of a simple cloud parameterization scheme is substantiated taking into account the influence of surrounding relief.

Parameterization of Downward Long-wave Radiation in Glaciological Applications

Parameterization of Downward Long-wave Radiation in Glaciological Applications

Generating 5 km resolution 1981–2018 daily global land surface longwave radiation products from AVHRR shortwave and longwave observations using densely connected convolutional neural networks

Surface longwave radiation (SLWR) components, including downward longwave radiation (DLR), upward longwave radiation (ULR), and net longwave radiation (NLR), are major contributors to the Earth's surface radiation budget and play important roles in ecological, hydrological, and atmospheric processes. Previous SLWR products have different drawbacks, such as being temporally short (after 2000), spatially coarse (≥ 25 km), and instantaneous values, which hinder their in-depth applications in land surface process modeling and climate trends analysis. Here, we reported the Advanced Very High-Resolution Radiometer (AVHRR)-based Global LAnd Surface Satellites (GLASS-AVHRR) SLWR products over the global land surface at a 5 km spatial resolution and 1 day temporal resolution between 1981 and 2018. These products were generated using multiple densely connected convolutional neural networks (DesCNNs) from the AVHRR top-of-atmosphere (TOA) reflected and emitted observations and European Centre for Medium-Range Weather Forecasts (ECMWF) fifth generation reanalysis (ERA5) near-surface meteorological data. DesCNNs were trained using integrated SLWR samples derived from the Moderate Resolution Imaging Spectroradiometer (MODIS)-based GLASS, Clouds and the Earth's Radiant Energy System Synoptic (CERES-SYN), and ERA5 SLWR products. In situ measurements from 231 globally distributed sites were used to evaluate the GLASS-AVHRR SLWR estimates. The results illustrated the overall high accuracies of GLASS-AVHRR SLWR products with root-mean-square-errors (RMSEs) of 18.66, 14.92, and 16.29 Wm−2, and mean bias errors (MBEs) of −2.69, −3.77, and 0.49 Wm−2 for all-sky DLR, ULR, and NLR, respectively. We found good correlation and consistency between GLASS-AVHRR and both CERES-SYN and ERA5 in terms of spatial patterns, latitudinal gradient, and temporal evolution. Our results revealed the significant contribution of shortwave observations to SLWR estimation owing to the high amounts of clouds over polar regions and water vapor and clouds in tropical areas, which was not previously widely recognized by the remote sensing community. GLASS-AVHRR SLWR products were updated, documented, and made available to the public at www.glass.umd.edu and www.geodata.cn.

Estimation Downward Longwave Radiation Using Neural Networks in Baghdad City

The aim of the present study is to compare the accuracy and precision of the traditional method and artificial neural network (ANN) to estimate downward longwave radiation (DLR) under clear-sky conditions. DLR is useful for agriculture, global warming detection, and ecology. In order to train and estimate DLR in Baghdad, Iraq, an artificial neural network (ANN) model was developed. The activation functions (Sigmoid, Hyperbolic secant, Hyperbolic tangent), hidden layers (1, 2, 3, and 4), alteration 10000 to 50000 with interval 10000 were employed for both hidden and output layers. ANN best model was compared to eight other models based on root mean square error (RMSE) and correlation coefficient (R2). The comparative statistic for performance of the DLR model calculations during day-time and night-time have shown that the parameterization has the best results compared to the measured data. It was found that the optimum model is the (105) model where R and RMSE were 0.999 and 0.6 respectively. The results in this study indicate that the ANN can successfully be used for the estimation of DLR for Baghdad city and is more suitable than the traditional method.

Turbulent Heat Flux, Downward Longwave Radiation, and Large-Scale Atmospheric Circulation Associated with Wintertime Barents–Kara Sea Extreme Sea Ice Loss Events

AbstractWe investigate wintertime extreme sea ice loss events on synoptic to subseasonal time scales over the Barents–Kara Sea, where the largest sea ice variability is located. Consistent with previous studies, extreme sea ice loss events are associated with moisture intrusions over the Barents–Kara Sea, which are driven by the large-scale atmospheric circulation. In addition to the role of downward longwave radiation associated with moisture intrusions, which is emphasized by previous studies, our analysis shows that strong turbulent heat fluxes are associated with extreme sea ice melting events, with both turbulent sensible and latent heat fluxes contributing, although turbulent sensible heat fluxes dominate. Our analysis also shows that these events are connected to tropical convective anomalies. A dipole pattern of convective anomalies with enhanced convection over the Maritime Continent and suppressed convection over the central to eastern Pacific is consistently detected about 6–10 days prior to extreme sea ice loss events. This pattern is associated with either the Madden–Julian oscillation (MJO) or El Niño–Southern Oscillation (ENSO). Composites show that extreme sea ice loss events are connected to tropical convection via Rossby wave propagation in the midlatitudes. However, tropical convective anomalies alone are not sufficient to trigger extreme sea ice loss events, suggesting that extratropical variability likely modulates the connection between tropical convection and extreme sea ice loss events.

Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation

The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m−2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.