Upward Longwave Radiation Research Articles

Land Surface Longwave Radiation Retrieval from ASTER Clear-Sky Observations

Surface longwave radiation (SLR) plays a pivotal role in the Earth’s energy balance, influencing a range of environmental processes and climate dynamics. As the demand for high spatial resolution remote sensing products grows, there is an increasing need for accurate SLR retrieval with enhanced spatial detail. This study focuses on the development and validation of models to estimate SLR using measurements from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Given the limitations posed by fewer spectral bands and data products in ASTER compared to moderate-resolution sensors, the proposed approach combines an atmospheric radiative transfer model MODerate resolution atmospheric TRANsmission (MODTRAN) with the Light Gradient Boosting Machine algorithm to estimate SLR. The MODTRAN simulations were performed to construct a representative training dataset based on comprehensive global atmospheric profiles and surface emissivity spectra data. Global sensitivity analyses reveal that key inputs influencing the accuracy of SLR retrievals should reflect surface thermal radiative signals and near-surface atmospheric conditions. Validated against ground-based measurements, surface upward longwave radiation (SULR) and surface downward longwave radiation (SDLR) using ASTER thermal infrared bands and surface elevation estimations resulted in root mean square errors of 17.76 W/m2 and 25.36 W/m2, with biases of 3.42 W/m2 and 3.92 W/m2, respectively. Retrievals show systematic biases related to extreme temperature and moisture conditions, e.g., causing overestimation of SULR in hot humid conditions and underestimation of SDLR in arid conditions. While challenges persist, particularly in addressing atmospheric variables and cloud masking, this work lays a foundation for accurate SLR retrieval from high spatial resolution sensors like ASTER. The potential applications extend to upcoming satellite missions, such as the Landsat Next, and contribute to advancing high-resolution remote sensing capabilities for an improved understanding of Earth’s energy dynamics.

Strengthened impact of late autumn Arctic sea ice on Asian winter cold extremes after 1999/2000

Winter cold extremes (WCEs) frequently plague densely populated areas of Asia, leading to substantial economic losses and even fatalities. It has been found that the late autumn sea ice concentration (SIC) anomalies in the northern (SICN) and southern Arctic (SICS) are significantly positively and negatively correlated with the occurrence frequency of WCE in Asia, respectively (Wang and Su 2024). Our study demonstrates that the impacts of SICN and SICS have strengthened after 1999/2000. Specifically, before 1999/2000, the influences of SICN and SICS on the Asian WCE (AWCE) were relatively weak, possibly related to the weak intensity of SICS and the limited correlation between SICN and SICS. After 1999/2000, the interannual variability of SICS became larger and anti-correlated with that of SICN, resulting in a stronger teleconnection between the Arctic SIC and AWCE. It is revealed that after 1999/2000, the greater loss of SICS modified atmospheric stability through changes in surface heat fluxes and surface upward longwave radiation fluxes. This alteration weakened the magnitudes of westerly winds and increased the frequency of blocking events over the northern Eurasian continent, leading directly to a higher occurrence of cold extremes in Asia. These interdecadal differences in the influence of Arctic SIC on AWCE may be associated with long-term climate change.

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.

Surface Albedo Dominates Radiation Efficiency of Alpine Grasslands Along an Elevation Profile of Qilian Mountains

AbstractQuantifying the elevation dependency of radiation partitioning in high‐altitude mountains is crucial for projecting regional energy balance while remains highly uncertain. We compared the surface radiation partitioning parameters across a meadow (3,200 m), shrub (3,400 m), and forb (3,600 m) along a southern slope of the Qilian Mountains. At a daily scale, the downward shortwave radiation (Rs) fluctuated minimally among the grassland types probably induced by similar site orientations. The greatest downward and upward longwave radiation (Ld and Lu) happened at the lowest meadow while the largest net shortwave (Sn) and longwave (Ln) radiation occurred at the deciduous shrub. The net all‐wave radiation (Rn) of the meadow and shrub was similar and exceeded that of the forb by ∼20%. The differences in Rn between the sites were jointly explained by those of upward shortwave radiation (Ru) and Lu, more than by Rs and Ld, suggesting the importance of surface attributes. The monthly normalized effective terrestrial radiation (λ, the ratio of Ln to Rs) varied insignificantly among the sites and averaged 0.25 ± 0.05, which was comparable to the global mean value (0.26). The smallest surface albedo (α, the ratio of Ru to Rs) and largest radiation efficiency (η, the ratio of Rn to Rs) were 0.13 ± 0.02 and 0.64 ± 0.09, respectively, both at the shrub. Grassland type dominated the spatial variations of monthly α and η. These findings highlighted the importance of grassland types to explain the spatiotemporal variations of radiation partitioning parameters in high‐altitude alpine grasslands.

Estimation and Evaluation of 15 Minute, 40 Meter Surface Upward Longwave Radiation Downscaled from the Geostationary FY-4B AGRI

Surface upward longwave radiation (SULR) is one of the four components of surface net radiation. Geostationary satellites can provide high temporal but coarse spatial resolution SULR products. Downscaling coarse SULR to a higher resolution is important for fine-scale thermal condition monitoring. Statistical regression downscaling is widely used due to its simplicity and is built on the assumption that the thermal parameter like land surface temperature (LST) or SULR has a relationship with the related surface factors like the normalized difference vegetation index (NDVI), and the relationship remains unchanged in any scales. In this study, to establish the relationship between SULR and the related surface factors, we chose the multiple linear regression (MLR) model and five surface factors (i.e., the modified normalized difference water index (MNDWI), normalized difference built-up and soil index (NDBSI), NDVI, normalized moisture difference index (NMDI), and urban index (UI)) to drive the downscaling process. Additionally, a step-by-step downscaling strategy was applied to reach the 100-fold increase in spatial resolution, transitioning the estimated SULR from 4 km of the advanced geostationary radiation imager (AGRI) onboard FengYun-4B (FY-4B) satellite to 40 m of the visual and infrared multispectral imager (VIMI) in infrared spectrum onboard GaoFen5-02 (GF5-02). Finally, we evaluated the downscaling results by comparing the downscaled SULR values with the in situ measured SULR and GF5-02-calculated SULR, and the root mean square errors (RMSEs) were 19.70 W/m2 and 24.86 W/m2, respectively. Throughout this MLR-based step-by-step downscaling method (high-frequency data from FY-4B and high spatial resolution data from GF5-02), high spatiotemporal SULR (15 min temporal resolution, 40 m spatial resolution) were successfully generated instead of coarse spatial resolution ones from the FY-4B satellite or a coarse temporal resolution one from the GF5-02 satellite, relieving the above-mentioned conflict to some extent.

Long-term warming and interannual variability contributions’ to marine heatwaves in the Mediterranean

In the past 40 years, marine heatwaves (MHWs) have experienced a worldwide increase in duration, intensity, frequency and spatial extent. This trend has been particularly evident in the Mediterranean, where exceptional events were observed during the summers of 2022, 2018 and 2003. This study proposes a twofold analysis of MHWs in the Mediterranean, focusing on their statistical characteristics and physical causes. A satellite dataset is utilized to analyze MHWs via an index, called activity, which aggregates the occurrence, duration, intensity and spatial extent of events. Our results show that the trend toward more active summers for MHWs is strongest in the western Mediterranean basin and long-term warming is the main driver in the whole Mediterranean basin. We also show that in the western and Adriatic Mediterranean region, the increase of SST variability contributes about a third to the MHW activity long-term trend whereas in the central, eastern and Aegean basins, the variability of SST mostly acts to diminish this trend. Through principal component analysis (PCA) of MHW activity, we found that the three most severe summer MHW events in the Mediterranean occur at the same location where the overall trend is highest. Interannual variability increased MHW activity in 2022 around the Balearic Sea, in 2018 in the eastern basins and in 2003 in the central basins. A joint PCA revealed that the long-term trend in MHW activity co-varies with a positive geopotential height anomaly over the Mediterranean, which is consistent with the generation of atmospheric-driven MHWs and which, at the North Atlantic scale, resembles the positive phase of the summer East Atlantic. The additional interannual variability contribution to these three severe summers was associated with western warming and projected onto the positive phase of the summer North Atlantic Oscillation. The increase in MHW over the last 40 years is also associated in the western, central and Adriatic regions with increased downward short-wave radiation and in the eastern Mediterranean with decreased upward long-wave radiation. Increased upward latent heat flux partly compensated for the MHW long-term increase over the whole Mediterranean basin. The interannual variability of MHW activity is related in the western, central and Adriatic basins to increased downward sensible and decreased upward latent heat flux possibly due to warm and humid air intrusion.

Positive Low Cloud Feedback Primarily Caused by Increasing Longwave Radiation From the Sea Surface in Two Versions of a Climate Model

AbstractLow cloud feedback in global warming projections by climate models is characterized by its positive sign, the mechanism of which is not well understood. Here we propose that the positive sign is primarily caused by the increase in upward longwave radiation from the sea surface. We devise numerical experiments that enable separation of the feedback into components coming from physically distinct causes. Results of these experiments with a climate model indicate that increases in upward longwave radiation from the sea surface cause warming and absolute drying in the boundary layer, leading to the positive low cloud feedback. The absolute drying results from decrease in surface evaporation, and also from decrease in inversion strength which enhances vertical mixing of drier free tropospheric air into the boundary layer. This mechanism is different from previously proposed understanding that positive low cloud feedback is caused by increases in surface evaporation or vertical moisture contrast.

Sea Surface Energy Fluxes' Response to the Quasi‐Biweekly Oscillation: A Case Study in the South China Sea

AbstractThe South China Sea (SCS) owns the world's strongest quasi‐biweekly oscillation (QBWO) in boreal summer, but the mechanism is still unclear. This case study summarizes two modes of QBWO over the summer SCS in 2019 by using empirical orthogonal function on the 10–20‐day bandpass‐filtered outgoing longwave radiation fields. The maximum positive irradiance anomalies for the two modes are 90 W m−2. The upward solar and downward longwave radiation anomalies own about 4%–8% of the irradiance magnitude, and the surface upward longwave radiation shows a weak response. Sea surface turbulent heat fluxes' responses to QBWO display different spatial patterns compared to radiation fluxes. Their changes are mainly ascribed to the surface wind in Mode1 and the air‐sea thermal contrast in Mode2. We also discuss the cause and impact of sea surface turbulent heat fluxes on QBWO.

Sea Surface Cooling by Rainfall Modulates Earth’s Heat Energy Flow

Abstract Characterizing the physical processes that modulate the continuous partitioning of heat between the ocean and overlying atmosphere is important for monitoring the subsequent flow of the heat accumulating in the ocean because of anthropogenic climate change. Oceanic rainfall sensible heat flux (Qp), whereby rainwater cools the sea surface, is computed and compared to the sea surface heat energy balance in the 60°N–60°S region. Contrary to popular belief, the results show that Qp is large at both short and long time scales, accounting for up to 22.5% of sea surface net heat flux around the 5.8°N line of latitude, 10.1% in the tropical 20°N–20°S region, and 5.7% in the global 60°N–60°S region. In the mixed layer of these same regions, area-average temperature change owing to a 10-yr accumulated Qp is up to −2.6° and −1.4°C, respectively. Further analysis reveals a previously unspecified rainfall–evaporation negative feedback between successive evaporation–rainfall cycles at the sea surface. The Qp depresses sea surface temperature and thus inhibits evaporation (latent heat flux), which in turn inhibits rainfall owing to decrease in water vapor supply to the atmosphere. The decrease in sea surface temperature also inhibits heat conduction from the ocean to the atmosphere (sensible heat flux). To compensate for the weaker latent and sensible heat fluxes, sea surface upward longwave radiation flux strengthens. We conclude that Qp acts like a modulator of Earth’s heat energy flow by controlling the partition of upper-ocean heat energy and the cycle of heat flow in the ocean and between the ocean and the atmosphere. Significance Statement Upper-ocean heat energy is partitioned between the ocean and the overlying atmosphere. Characterizing the physical processes that modulate this continuous partitioning is important for monitoring the subsequent flow of the heat energy accumulating in the ocean because of anthropogenic climate change. Here, we identify sea surface cooling by rainwater (oceanic rainfall sensible heat flux) as a modulator of this partition: this cooling depresses sea surface temperature and thus inhibits evaporation (latent heat flux), which in turn inhibits rainfall owing to the decrease in water vapor supply to the atmosphere; depressing sea surface temperature also inhibits heat conduction from the ocean to the atmosphere (sensible heat flux). To compensate for the weaker latent and sensible heat fluxes, sea surface upward longwave radiation flux strengthens.

A thermal radiation directionality correction method for the surface upward longwave radiation of geostationary satellite based on a time-evolving kernel-driven model

Thermal radiation directionality (TRD) characterizes the anisotropic signature of most surface targets in the thermal infrared domain. It causes significant uncertainties in estimating surface upward longwave radiation (SULR) from space observations. In this regard, kernel-driven models (KDMs) are suitable to remove TRD effects from remote sensing dataset as they are computationally efficient. However, KDMs requires simultaneous multi-angle observations as inputs to be well calibrated, which yields a difficulty with geostationary satellites as they can only provide a single-angle observation. To overcome this issue, we proposed a six-parameter time-evolving KDM that combines a four-parameter SULR diurnal variation model and a two-parameter TRD amplitude model to correct the TRD effect for single-angle estimated SULR dataset of geostationary satellites. The significant daytime TRD effect when solar zenith angle is within 60° can be effectively eliminated. The modeling accuracy of the time-evolving KDM is evaluated using a simulated SULR dataset generated by the 3D Discrete Anisotropic Radiative Transfer (DART) model; the TRD correction method based on the new time-evolving KDM is validated using a two-year single-angle estimated SULR dataset derived from data of the Advanced Baseline Imager (ABI) onboard Geostationary Operational Environmental Satellite-16 (GOES-16) against in situ measurements at 20 AmeriFlux sites. Results show that the proposed time-evolving KDM has a high accuracy with an R2 > 0.999 and a small RMSE = 1.5 W/m2; the TRD correction method based on the time-evolving KDM can greatly reduce the GOES-16 SULR uncertainty caused by the TRD effect with an RMSE decrease of 4.5 W/m2 (22.1%) and mean bias error decrease of 7.9 W/m2 (62.7%). Hence, the proposed TRD correction method is practically efficient for the operational TRD correction of SULR products generated from the geostationary satellites (e.g., GOES-16, FY-4A, Himawari-8, MSG).

Green roof effects on urban building surface processes and energy budgets

Green roofs, as a nature-based solution, can be an effective tool for cities to improve the thermal environment, save energy, and combat climate change. However, it remains unclear how green roofs change the energy budget of urban building surfaces. Therefore, we modeled the surface energy balance of bare and green roofs based on experimental observations over a full calendar year to compare the physical transport processes of energy flows on bare and green roofs. Observational data were used to quantify the seasonal influence of green and bare roof surfaces on the energy flows and to document how roof vegetation alteres surface energy flow via physical and biochemical processes. On the bare roof, the incident solar radiation was partitioned largely into heating the building structure, with approximately 32.3% of the solar radiation absorbed. The green roof intercepted and consumed nearly 89% of incident solar radiation. The green roof effectively reduced convective energy by 6.7% compared to the bare roof, while longwave radiation emitted by green roof surfaces was reduced by 24.89 Wm−2. Evapotranspiration was the primary form of energy consumption on the green roof, accounting for 23.7% of the energy output. Results show that seasonal green roof evapotranspiration ranks as follows: winter > summer > spring > autumn. Reflected solar radiation, upward longwave radiation, and latent heat flux accounted for almost 95% of the total energy output of the green roof. Quantifying the energy balance impacts of green roofs on urban building surfaces improves the accuracy of urban climate models and supports adopting green roofs to foster sustainable urban development.

Understanding two key processes associated with alpine lake ice phenology using a coupled atmosphere-lake model

Study regionLake Nam-Co, a typical deep alpine lake in the central of Tibetan Plateau. Study focusThis study investigates the role of surface turbulent fluxes in simulating lake freeze-up and the role of solar radiation transfer (when lake ice exists) in simulating the lake ice break-up. New hydrological insightsIn the coupled model, the realistic representation of surface turbulent heat fluxes is crucial to simulate the lake freeze-up. This is because turbulent heat fluxes, especially the latent heat, directly controlling the lake water temperature through energy exchange between water and atmosphere. Additionally, the partitioning of solar radiation transfers when lake ice exist is crucial in simulating lake ice break-up. The proportion absorbed by the ice surface will be released associated with upward longwave radiation and turbulent heat fluxes, and only a fraction is used for surface ice-water phase change. The proportion absorbed by the subsurface layer ice is directly used for ice-water phase changes. The proportion absorbed by the water, through ice penetration, is temporarily stored and used for ice melting through heat exchange between the ice and water. The offline FLake model is much less sensitive to the above two processes, implying the importance and necessity in improving the model physics in coupled model.

Effects of local aerosol and transported dust pollution on the surface energy balance over farmland in eastern China during spring

Compared with the urban boundary layer, air pollution–surface energy budget interactions in farmland areas are relatively sparse. To investigate the roles of local aerosol and transported dust pollution on the surface energy balance over farmland, a field experiment was conducted during spring 2016 in eastern China. The heavy pollution episodes during the measurement period were typically accompanied by easterly winds, low wind speeds, and high relative humidity. On polluted days, relative to clean days, both downward and upward shortwave radiation decreased by 46%, while downward and upward longwave radiation increased by 4% and 1%. The combined effects of the four radiation components resulted in a 33% reduction in net radiation on polluted days, and thus, the surface energy budget changed. Polluted days had 15% (58%) less sensible (latent) heat flux than clean days. This resulted in a higher daytime Bowen ratio on polluted days (0.83) compared to clean days (0.44). In a case analysis of dust pollution, high wind speeds and continuous high pressure induced the outbreak of the dust pollution episodes. Compared to local pollution events, the latent (sensible) heat flux was reduced more (less) than that in transported dust pollution events. Generally, this work has important implications for understanding the surface energy balance across land–atmosphere interactions over farmland regions, which are experiencing frequent aerosol pollution from local emissions and transboundary dust events.

Estimating All-Weather Surface Longwave Radiation from Satellite Passive Microwave Data

Surface longwave radiation (SLR) is an essential geophysical parameter of Earth’s energy balance, and its estimation based on thermal infrared (TIR) remote sensing data has been extensively studied. However, it is difficult to estimate cloudy SLR from TIR measurements. Satellite passive microwave (PMW) radiometers measure microwave radiation under the clouds and therefore can estimate SLR in all weather conditions. We constructed SLR retrieval models using brightness temperature (BT) data from an Advanced Microwave Scanning Radiometer 2 (AMSR2) based on a neural network (NN) algorithm. SLR from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) product was used as the reference. NN-based models were able to reproduce well the spatial variability of SLR from ERA5 at the global scale. Validations indicate a reasonably good performance was found for land sites, with a bias of 1.32 W/m2, root mean squared error (RMSE) of 35.37 W/m2, and coefficient of determination (R2) of 0.89 for AMSR2 surface upward longwave radiation (SULR) data, and a bias of −2.26 W/m2, RMSE of 32.94 W/m2, and R2 of 0.82 for AMSR2 surface downward longwave radiation (SDLR) data. AMSR2 SULR and SDLR retrieval accuracies were higher for oceanic sites, with biases of −2.98 and −4.04 W/m2, RMSEs of 6.50 and 13.42 W/m2, and R2 values of 0.83 and 0.66, respectively. This study provides a solid foundation for the development of a PMW SLR retrieval model applicable at the global scale to generate long-term continuous SLR products using multi-year satellite PMW data and for future research with a higher spatiotemporal resolution.

Regional atmospheric circulation patterns driving consecutive fog events in the United Arab Emirates

In this study, the link between the occurrence of consecutive fog days in the United Arab Emirates (UAE) and the associated synoptic-scale circulation is investigated. This is particularly pertinent, as such a link may provide an important predictive skill for a phenomenon that has a pronounced impact on road and air traffic but is still poorly simulated by numerical models. A cluster analysis of all consecutive fog days from January 1983 to December 2021 indicated that the positive phase of the East Atlantic/Western Russia teleconnection pattern, Eastern Pacific La Nina events, and the circumglobal wavenumber 5 pattern promote the occurrence of multiple fog days in the UAE. The fog's radiative impacts, as estimated from satellite data, revealed that the fog in the UAE is more optically thick than that observed elsewhere. A trend analysis over the period 1983 to 2021 revealed that consecutive fog events have become more frequent and longer-lasting but less intense (i.e., associated with higher values of visibility). The fog's spatial extent over the UAE at its mature stage has also decreased over time. An analysis of the trends in the surface and top of atmosphere (TOA) radiative fluxes indicated that over the period 2000–2021, the fog clouds have likely become less reflective, with a statistically significant decrease in the surface downward shortwave and TOA upward longwave radiation fluxes. Long-term measurements of fog microphysics in the region are needed to better understand the variability in the properties of the fog cloud droplets.

Numerical simulation of the surface flux of an alpine grassland in the source region of the Yellow River by the land surface model

The simulation performance of the land surface model Community Land Model 5.0 (CLM5.0) is tested by using the eddy correlation system and micrometeorological tower observational data at the Maqu observation field in 2016. The results show that the CLM5.0 model has a good simulation effect on the soil temperature and can well reflect the seasonal variation characteristics of soil temperature. In addition, the model can well simulate the seasonal variation in shallow soil moisture (5 cm and 10 cm). However, the deviations between the simulated and observed values are large. CLM5.0 cannot simulate the water holding capacity of shallow soil well, resulting in its underestimation. The simulation performance of the upward longwave radiation and net radiation is substantially better than that of the upward shortwave radiation due to the change in surface albedo caused by precipitation from March to July. CLM5.0 can also well reproduce the variation trend of the latent heat flux and 5 cm soil heat flux (heat exchange of soil at 5 cm). When the leaf area index (LAI) and Medlyn slope values are changed to 10%, 20%, and 50% of the default values, the simulation effects of soil moisture at depths of 5 cm and 10 cm are significantly improved, and the best simulation effects are achieved at 0.5 times the default values. This result indicates that lower LAI and Medlyn slope values are more suitable for simulating shallow soil moisture.

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.

A coupled WRF-PV mesoscale model simulating the near-surface climate of utility-scale photovoltaic plants

To simulate the local or regional climate of utility-scale photovoltaic (PV) plants, a new PV-associated energy balance model was developed for the Weather Research and Forecasting Model (WRF). The resultant tool can be used to improve our understanding of the interaction between utility-scale PV plants and land-surface processes. The effects of PV panels were represented by transferring the solar radiation into electricity and heat flux and imposing an extra drag force on the mean flow in the model domain. The proposed PV model is more advanced than previous models in the following ways: (1) The model is universal based on its analytical expression of the sensible heat of PV panels and the influence of the physical shielding of the panels on the upward longwave radiation. (2) The photoelectric conversion efficiency (ε) in the model is temperature dependent and restrained between seasons. (3) The model includes the soil and latent heat flux parameterizations under observation constraints in a novel way. (4) The model bases the dynamic roughness length (Z0) on the eddy covariance flux observation. This new PV model was then incorporated with the WRF mesoscale meteorological model, and the model evaluation in two utility-scale PV plants demonstrated acceptable performance of the new WRF-PV mesoscale model in simulating the max/mean daily surface temperature, downward shortwave radiation, and wind speed with correlation coefficients of 0.61–0.99, and the hourly sensible/latent heat fluxes, with correlation coefficients of 0.59–0.93 during different seasons. The consistency of the performance of the model across these near-surface meteorological variables confirms the versatility and reliability of the new WRF-PV model as a suitable tool for simulating the climate of utility-scale PV plants.

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.

Analysis of Surface Energy Changes over Different Underlying Surfaces Based on MODIS Land-Use Data and Green Vegetation Fraction over the Tibetan Plateau

To better predict and understand land–atmospheric interactions in the Tibetan Plateau (TP), we used Moderate Resolution Imaging Spectroradiometer (MODIS)-based land-use data and the MODIS-derived green vegetation fraction (GVF) to analyze the variation trend over the TP. The in situ observations from six flux stations (“BJ” (the BJ site of Nagqu Station of Plateau Climate and Environment), “MAWORS” (the Muztagh Ata Westerly Observation and Research Station), “NADORS” (the Ngari Desert Observation and Research Station), “NAMORS” (the Nam Co Monitoring and Research Station for Multisphere Interactions), “QOMS” (the Qomolangma Atmospheric and Environmental Observation and Research Station), and “SETORS” (the Southeast Tibet Observation and Research Station for the Alpine Environment)) at the Chinese TP Scientific Data Center were used to study the surface energy variation characteristics and energy distribution over different underlying surfaces. Finally, we used observation data to verify the applicability of the ERA-5 land reanalysis data to the TP. The results showed that the annual GVF steadily declined from the southeast parts to the northwest parts of the TP, and the vegetation coverage rate was highest from June to September. The sensible heat flux (H), latent heat flux (LE), net surface radiation (Rn), and four-component radiation (solar downward shortwave radiation (Rsd), surface upward shortwave radiation (Rsu), atmospheric downward longwave radiation (Rld), and surface upward longwave radiation (Rlu)) reached their maxima in summer at each station. Rld did not change significantly with time; all other variables increased during the day and decreased at night. The interannual variation in H and LE shows that latent heat exchange was the dominant form of energy transfer in BJ, MAWORS, NAMORS, and SETORS. By contrast, sensible heat exchange was the main form of energy transfer in NADORS and QOMS. The Bowen ratio was generally low in summer, and some sites had a maximum in spring. The surface albedo exhibited a “U” shape, decreasing in spring and summer, and increasing in autumn and winter, and reaching the lowest value at noon. Except for SETORS, ERA-5 Land data and other flux stations had high simulation accuracy and correlation. Regional surface energy changes were mainly observed in the eastern and western parts of the TP, except for the maximum of H in spring; the maximum values of other heat fluxes were concentrated in summer.