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The Outgoing Longwave Radiation (OLR) package was first developed as a stand-alone application, and then integrated into the National Oceanic and Atmospheric Administration (NOAA) Unique Combined Atmospheric Processing System (NUCAPS) hyperspectral sounding retrieval system. An objective of this package is to provide near-real-time OLR products derived from the Cross Track Infrared Sounder (CrIS) onboard the Joint Polar Satellite System (JPSS) satellites. It was initially developed and validated with CrIS onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite, and has been expanded to JPSS-1 (renamed NOAA-20 after launch) datasets that are currently available to the public. In this paper, we provide the results of detailed validation tests with NOAA-20 CrIS for large and wide representative conditions at a global scale. In our validation tests, the observations from Clouds and Earth’s Radiant Energy System (CERES) on Aqua were treated as the absolute reference or “truth”, and those from SNPP CrIS OLR were used as the transfer standard. The tests were performed on a 1°×1° global spatial grid over daily, monthly, and yearly timescales. We find that the CrIS OLR products from NOAA-20 agree exceptionally well with those from Aqua CERES and SNPP CrIS OLR products in all conditions: the daily bias is within ±0.6 Wm−2, and the standard deviation (STD) ranges from 4.88 to 9.1 Wm−2. The bias and the STD of OLR monthly mean are better, within 0.3 and 2.0 Wm−2, respectively. These findings demonstrate the consistency between NOAA-20 and SNPP CrIS OLR up to annual scales, and the robustness of NUCAPS CrIS OLR products.

Forests are critical in regulating the world’s climate and they maintain overall Earth’s energy balance. The variability in forest canopy structure, topography and underneath vegetation background condition creates uncertainty in the estimation and modelling of Earth’s surface radiation particularly for boreal regions in high latitude. We studied seasonal variation in surface reflectance with respect to land cover classes, canopy structures, and topography in a boreal region of Alaska by fusing together Landsat 8 surface reflectance and LiDAR-derived canopy matrices. Our study shows that canopy structure and topography interplay and influence surface reflectance in a complex way particularly during the snow season. Topographic aspect and elevation control vegetation growth, type and structure. The southern slope is featured with more deciduous and taller trees having greater rugosity than the northern slope. Higher elevation is associated with taller trees for both vegetation types, particularly in the southern slope. In general, surface reflectance shows similar relationships with canopy cover, height and rugosity, mainly due to close relationships between these parameters. Surface reflectance decreases with canopy cover, tree height, and rugosity especially for evergreen forest. Deciduous forest shows larger variability of surface reflectance, particularly in March, mainly due to the mixing effect of snow and vegetation. The relationship between vegetation structure and surface reflectance is greatly impacted by topography. The negative relationship between elevation and surface reflectance may be due to taller and denser vegetation distribution in higher elevation. Surface reflectance in the southern slope is slightly larger than the northern slope for both deciduous and evergreen forest. The shadow effect from topography and tree crowns on surface reflectance play a different role for deciduous and evergreen forests. For deciduous forest, topographic shadow effect on surface reflectance is stronger than from tree shadowing in all seasons. For evergreen forest, shadow effects from topography and tree crowns on surface reflectance are both equally dominant, however tree shadow effect is more significant in March than in May and August. The generalized additive models (GAM) based on non-linear relationships between response (surface reflectance) and predictor (canopy structures and topography) variables confirms such observations. Our study not only provides accurate quantification of surface radiation budget but also helps in parametrization of climate change models.

Incident surface shortwave radiation (ISR) is a key parameter in Earth&rsquo, s surface radiation budget. Many reanalysis and satellite-based ISR products have been developed, but they often have insufficient accuracy and resolution for many applications. In this study, we extended our optimization method developed earlier for the MODIS data with several major improvements for estimating instantaneous and daily ISR and net shortwave radiation (NSR) from Visible Infrared Imaging Radiometer Suite observations (VIIRS), including (1) an integrated framework that combines look-up table and parameter optimization, (2) enabling the calculation of net shortwave radiation (NSR) as well as daily values, and (3) extensive global validation. We validated the estimated ISR values using measurements at seven Surface Radiation Budget Network (SURFRAD) sites and 33 Baseline Surface Radiation Network (BSRN) sites during 2013. The root mean square errors (RMSE) over SURFRAD sites for instantaneous ISR and NSR were 83.76 W/m2 and 66.80 W/m2, respectively. The corresponding daily RMSE values were 27.78 W/m2 and 23.51 W/m2. The RMSE at BSRN sites was 105.87 W/m2 for instantaneous ISR and 32.76 W/m2 for daily ISR. The accuracy is similar to the estimation from MODIS data at SURFRAD sites but the computational efficiency has improved by approximately 50%. We also produced global maps that demonstrate the potential of this algorithms to generate global ISR and NSR products from the VIIRS data.

The Earth Radiation Budget (ERB) at the top of the atmosphere describes how the Earth gains energy from the Sun and loses energy to space through the reflection of solar radiation and the emission of thermal radiation. The ERB is measured from space with dedicated remote sensing instruments. Its long-term monitoring is of fundamental importance for understanding climate change. This Special Issue contains contributions focusing on ERB remote sensing instruments for either (1) the establishment of past and current ERB Climate Data Records (CDRs), (2) insights in climate change gained from the analysis of ERB CDRs, and 3) the outlook for continued or improved future ERB monitoring.

The anticipated climate change during the next decades is posing crucial challenges to ecosystems. In order to decrease the vulnerability of forests, introducing tree species&rsquo, mixtures are a viable strategy, with deep-rooting native Silver fir (Abies alba) being a primary candidate for admixture into current pure stands of European beech (Fagus sylvatica) especially in mountainous areas. Such a change in forest structure also has effects on the regional scale, which, however, have been seldomly quantified. Therefore, we measured and modeled radiative balance and air chemistry impacts of admixing Silver fir to European beech stands, including changes in biogenic volatile organic compound emissions. An increased fraction of Silver fir caused a smaller albedo and a (simulated) larger evapotranspiration, leading to a dryer and warmer forest. While isoprene emission was negligible for both species, sesquiterpene and monoterpene emissions were larger for fir than for beech. From these differences, we derived that ozone concentration as well as secondary organic aerosols and cloud condensation nuclei would increase regionally. Overall, we demonstrated that even a relatively mild scenario of tree species change will alter the energy balance and air quality in a way that could potentially influence the climate on a landscape scale.

The Clouds and the Earth’s Radiant Energy System (CERES) instruments have enabled the generation of a multi-decadal Earth radiation budget (ERB) climate data record (CDR) at the top of the Earth’s atmosphere, within the atmosphere, and at the Earth’s surface. Six CERES instruments have been launched over the course of twenty years, starting in 1999. To seamlessly continue the data record into the future, there is a need to radiometrically scale observations from newly launched instruments to observations from the existing data record. In this work, we describe a methodology to place the CERES Flight Model (FM) 5 instrument on the Suomi National Polar-orbiting Partnership (SNPP) spacecraft on the same radiometric scale as the FM3 instrument on the Aqua spacecraft. We determine the required magnitude of radiometric scaling by using spatially and temporally matched observations from these two instruments and describe the process to radiometrically scale SNPP/FM5 to Aqua/FM3 through the instrument spectral response functions. We also present validation results after application of this radiometric scaling and demonstrate the long-term consistency of the SNPP/FM5 record in comparison with the CERES instruments on Aqua and Terra.

Sea surface emissivity (SSE) is a key variable in the estimation of sea surface temperature and the sea surface radiation budget. A physical base SSE model with adequate accuracy and acceptable computational efficiency is highly desired. This paper develops a Monte Carlo ray-tracing model to compute the SSE of a wind-roughened sea surface. The adoption of a two-dimensional continuous surface model and averaging the two polarization components in advance before ray-tracing gives the model acceptable computational efficiency. The developed model can output the contributions of direct emission and the reflected component to the effective emissivity. The contribution of the reflected component to the effective emissivity reaches 0.035 at an 80&deg, emission angle for a wind speed larger than 10 m/s. The emissivity spectra and channel emissivities collected from two field campaigns and one set of outdoor measurements are used to validate the developed model. Statistical results indicate that the absolute value of bias or difference is less than 0.5% when the view angle is less than 65&deg, which means the retrieval accuracy of sea surface temperature (SST) is guaranteed from the view of SSE. When the view angle increases, the accuracy of the developed model degraded, especially at the view angle of 85&deg, Without considering this view angle, the absolute value of bias or difference is less than 0.016, and the root mean square difference (RMSD) is less than 0.018.

Satellite-derived estimates of downward surface shortwave radiation (SSR) and photosynthetically active radiation (PAR) are a part of the surface radiation budget, an essential climate variable (ECV) required by climate and vegetation models. Ground measurements are insufficient for generating long-term, global measurements of surface radiation, primarily due to spatial limitations; however, remotely sensed Earth observations offer freely available, multi-day, global coverage of radiance that can be used to derive SSR and PAR estimates. Satellite-derived SSR and PAR estimates are generated by computing the radiative transfer inversion of top-of-atmosphere (TOA) measurements, and require ancillary data on the atmospheric condition. To reduce computational costs, often the radiative transfer calculations are done offline and large look-up tables (LUTs) are generated to derive estimates more quickly. Recently studies have begun exploring the use of machine-learning techniques, such as neural networks, to try to improve computational efficiency. Here, nine machine-learning methods were tested to model SSR and PAR using minimal input data from the Moderate Resolution Imaging Spectrometer (MODIS) observations at 1 km spatial resolution. The aim was to reduce the input data requirements to create the most robust model possible. The bootstrap aggregated decision tree (Bagged Tree), Gaussian Process Regression, and Neural Network yielded the best results with minimal training data requirements: an R 2 of 0.77, 0.78, and 0.78 respectively, a bias of 0 ± 6, 0 ± 6, and 0 ± 5 W / m 2 , and an RMSE of 140 ± 7, 135 ± 8, and 138 ± 7 W / m 2 , respectively, for all-sky condition total surface shortwave radiation and viewing angles less than 55°. Viewing angles above 55° were excluded because the residual analysis showed exponential error growth above 55°. A simple, robust model for estimating SSR and PAR using machine-learning methods is useful for a variety of climate system studies. Future studies may focus on developing high temporal resolution direct and diffuse estimates of SSR and PAR as most current models estimate only total SSR or PAR.

Clouds play a critical role in adjusting the global radiation budget and hydrological cycle; however, obtaining accurate information on the cloud base height (CBH) is still challenging. In this study, based on Lidar and aircraft soundings, we investigated the features of the CBH and determined the thresholds of the environmental relative humidity (RH) corresponding to the observed CBHs over Southeast China from October 2017 to September 2018. During the observational period, the CBHs detected by Lidar/aircraft were commonly higher in cold months and lower in warm months; in the latter, 75.91% of the CBHs were below 2000 m. Overall, the RHs at the cloud base were mainly distributed between 70 and 90% for the clouds lower than 1000 m, in which the most concentrated RH was approximately 80%. In addition, for the clouds with a cloud base higher than 1000 m, the RH thresholds decreased dramatically with increasing CBH, where the RH thresholds at cloud bases higher than 2000 m could be lower than 60%. On average, the RH thresholds for determining the CBHs were the highest (72.39%) and lowest (63.56%) in the summer and winter, respectively, over Southeast China. Therefore, to determine the CBH, a specific threshold of RH is needed. Although the time period covered by the collected CBH data from Lidar/aircraft is short, the above analyses can provide some verification and evidence for using the RH threshold to determine the CBH.

Natural and anthropogenic aerosols over the Kingdom of Saudi Arabia (KSA) play a major role in affecting the regional radiation budget. The long-term variability of these aerosols&rsquo, physical and optical parameters, including aerosol optical depth (AOD) and &Aring, ngstr&ouml, m exponent (&alpha, ), were measured at a location near central KSA using the Solar Village (SV) AERONET (Aerosol Robotic Network) station during the period December 1999&ndash, January 2013. The AERONET measurements show an overall increase in AOD on an annual basis. This upward trend is mainly attributed to a prolonged increase in the monthly/seasonal mean AOD during March&ndash, June and during August&ndash, September. In contrast, lower AOD values were observed during November&ndash, December. This can be attributed to a low frequency of dust outbreaks and higher precipitation rates. An overall, weak declining trend in &alpha, was observed, except during the summer. The spring and summer seasons experienced a pronounced increase in the number of coarse particles (~2 &micro, m) during April 2006&ndash, January 2013 as compared to December 1999&ndash, March 2006, suggesting an increase in natural aerosol loadings. Using the HYSPLIT model, it was found that the March 2009 dust storm contributed to the mixing of long-transported dust with anthropogenic local emissions near the SV. The results suggest that extensive industrial activity contributed to the increase of anthropogenic emissions over KSA during the period April 2006&ndash, January 2013.

The CM SAF Top of Atmosphere (TOA) Radiation MVIRI/SEVIRI Data Record provides a homogenised satellite-based climatology of TOA Reflected Solar (TRS) and Emitted Thermal (TET) radiation in all-sky conditions over the Meteosat field of view. The continuous monitoring of these two components of the Earth Radiation Budget is of prime importance to study climate variability and change. Combining the Meteosat MVIRI and SEVIRI instruments allows an unprecedented temporal (30 min/15 min) and spatial (2.5 km/3 km) resolution compared to, e.g., the CERES products. It also opens the door to the generation of a long data record covering a 32 years time period and extending from 1 February 1983 to 30 April 2015. The retrieval method used to process the CM SAF TOA Radiation MVIRI/SEVIRI Data Record is discussed. The overlap between the MVIRI and GERB instruments in the period 2004–2006 is used to derive empirical narrowband to broadband regressions. The CERES TRMM angular dependency models and theoretical models are respectively used to compute the TRS and TET fluxes from the broadband radiances. The TOA radiation products are issued as daily means, monthly means and monthly averages of the hourly integrated values (diurnal cycle). The data is provided on a regular grid at a spatial resolution of 0.05 degrees and covers the region 70 ∘ N–70 ∘ S and 70 ∘ W–70 ∘ E. The quality of the data record has been evaluated by intercomparison with several references. In general, the stability in time of the data record is found better than 4 Wm − 2 and most products fulfill the predefined accuracy requirements.