Surface 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.

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.

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).

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.

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.

Single-footprint retrieval of clear-sky surface longwave radiation from hyperspectral AIRS data

Surface longwave radiation (SLR) derived from remotely sensed data facilitates understanding of the SLR in global climate change. Hyperspectral infrared sounders aboard space platforms provide information on the surface and vertical structure of Earth’s atmosphere. However, currently, SLR products estimated from these observations are unavailable, which hampers their application potential for Earth’s radiation budget in the context of global warming. To address this issue, we developed simple and effective SLR model under clear-sky conditions using at-sensor spectral radiances from Atmospheric Infrared Sounder (AIRS). The model was found to be insensitive to AIRS instrument noise, and showed good performances based on a simulation dataset. The AIRS footprint geometrical model was proposed to match the AIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimate the cloud fraction. Validation against ground-based measurements found that the surface upward longwave radiation model has a bias of 3.18 W/m2, root-mean-square error (RMSE) of 30.51 W/m2, and R2 of 0.84; the surface downward longwave radiation model has a bias of 0.77 W/m2, RMSE of 29.09 W/m2, and R2 of 0.78. The large validation biases at two ground sites reflect the limited spatial representativeness for AIRS footprints. Terrain-induced altitude differences and spatial inhomogeneity can redistribute the spatial distributions of SLR. Moreover, the model performances were weakly dependent on seasonal variation. The results indicate that the proposed model provides a foundation for the further development of operational SLR products.

Retrieval of Daytime Surface Upward Longwave Radiation Under All-Sky Conditions With Remote Sensing and Meteorological Reanalysis Data

Surface upward longwave radiation (SULR) is a key parameter that regulates surface radiation budget balance and matter-energy exchange. However, the state-of-the-art SULR retrieval methods based on remotely sensed data are only effective under clear skies, which mean that existing methods are unable to generate spatiotemporal continuous SULR product at regional or global scale. Herein, taking the advantage of long-pending abundant ground-based radiation observations, satellite products and meteorological reanalysis data, a data-driven random forest (RF) method is proposed to retrieve the instantaneous SULR under all-sky conditions. Based on spectral samples of different surface types and simulation results from the moderate resolution atmospheric transmission (MODTRAN), spectral transformation is carried out to transform SULR of various measured domains into the defined 4~100 μm domain at first. SULR and surface downward shortwave radiation (SDSR) observations from seven stations of the Surface Radiation Budget Network (SURFRAD) and nine stations of the Baseline Surface Radiation Network (BSRN) are used in model’s training and testing procedures, and the RF model achieves a high accuracy with the root-mean-square error (RMSE) of 10.45 W/m² on test set. In model evaluation, ground measurements from 14 stations of FLUXNET have been used, and the overall RMSE is 18.40 W/m². In the actual application process, SDSR is estimated by remotely sensed data of Meteosat Second Generation (MSG). The accuracy of RF model has been validated with the observations from five stations of BSRN in 2021, and RMSEs are 17.00, 10.94, 12.17, 27.89 and 12.54 W/m², respectively. Validation result shows that the data-driven method is capable of estimating SULR under all-sky conditions with a high accuracy. Finally, sensitivity analysis has been carried out, and the established RF model keeps robust even though there are great uncertainties among input parameters.

Seasonal Changes in Arctic Cooling After Single Mega Volcanic Eruption

To investigate the pure long-term influence of single mega volcanic eruption (SMVE) of universal significance on Arctic temperature changes in summer and winter, the Samalas eruption in Indonesia which is the largest eruption over the past millennium is selected as an ideal eruption for simulation study based on Community Earth System Model. The significant Arctic cooling lasts for 16 years after the Samalas eruption. The obvious Arctic cooling shifts from summer to winter, and this seasonal change of cooling after the SMVE only exists in the high-latitude Arctic region. The cooling range in Arctic summer is larger than that in winter during the first 2 years, due to the strong weakening effect of volcanic aerosol on summer incident solar radiation and the snow-ice positive feedback caused by the rapid expansion of summer sea ice, while the winter sea ice in the same period doesn’t increase obviously. Starting from the third year, the Arctic winter cooling is more intense and lasting than summer cooling. The direct weakening effect of aerosol on solar radiation, which is the main heat source in Arctic summer, is greatly weakened during this period, making summer cooling difficult to sustain. However, as the main heat source in Arctic winter, the sea surface upward longwave radiation, sensible heat, and latent heat transport still maintain a large decrease. Furthermore, sea ice expansion and albedo increase result in the decrease in solar radiation and heat absorbed and stored by the ocean in summer. And the isolation effect of sea ice expansion on air-sea heat transfer in winter during this period makes the heat transfer from the ocean to the atmosphere correspondingly reduce in winter, thus intensifying the Arctic winter cooling. Additionally, the Arctic Oscillation (AO) changes from the negative phase to the positive phase in summer after the SMVE (such as Samalas), while it is reversed in winter. This phase change of AO is also one of the reasons for the seasonal changes in Arctic cooling.

Evaluation of Surface Upward Longwave Radiation in the CMIP6 Models with Ground and Satellite Observations

Surface upward longwave radiation (SULR) is an indicator of thermal conditions over the Earth’s surface. In this study, we validated the simulated SULR from 51 Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) through a comparison with ground measurements and satellite-retrieved SULR from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF). Moreover, we improved the SULR estimations by a fusion of multiple CMIP6 GCMs using multimodel ensemble (MME) methods. Large variations were found in the monthly mean SULR among the 51 CMIP6 GCMs; the bias and root mean squared error (RMSE) of the individual CMIP6 GCMs at 133 sites ranged from −3 to 24 W m−2 and 22 to 38 W m−2, respectively, which were higher than those found between the CERES EBAF and GCMs. The CMIP6 GCMs did not improve the overestimation of SULR compared to the CMIP5 GCMs. The Bayesian model averaging (BMA) method showed better performance in simulating SULR than the individual GCMs and simple model averaging (SMA) method, with a bias of 0 W m−2 and an RMSE of 19.29 W m−2 for the 133 sites. In terms of the global annual mean SULR, our best estimation for the CMIP6 GCMs using the BMA method was 392 W m−2 during 2000–2014. We found that the SULR varied between 386 and 393 W m−2 from 1850 to 2014, exhibiting an increasing tendency of 0.2 W m−2 per decade (p < 0.05).

Clear-sky land surface upward longwave radiation dataset derived from the ABI onboard the GOES–16 satellite

ABSTRACT Surface upward longwave radiation (SULR) is one of the four components of the surface radiation budget, which is defined as the total surface upward radiative flux in the spectral domain of 4-100 μm. The SULR is an indicator of surface thermal conditions and greatly impacts weather, climate, and phenology. Big Earth data derived from satellite remote sensing have been an important tool for studying earth science. The Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite (GOES-16) has greatly improved temporal and spectral resolution compared to the imager sensor of the previous GOES series and is a good data source for the generation of high spatiotemporal resolution SULR. In this study, based on the hybrid SULR estimation method and an upper hemisphere correction method for the SULR dataset, we developed a regional clear-sky land SULR dataset for GOES-16 with a half-hourly resolution for the period from 1st January 2018 to 30th June 2020. The dataset was validated against surface measurements collected at 65 Ameriflux radiation network sites. Compared with the SULR dataset of the Global LAnd Surface Satellite (GLASS) longwave radiation product that is generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the polar-orbiting Terra and Aqua satellites, the ABI/GOES-16 SULR dataset has commensurate accuracy (an RMSE of 15.9 W/m2 vs 19.02 W/m2 and an MBE of −4.4 W/m2 vs −2.57 W/m2), coarser spatial resolution (2 km at nadir vs 1 km resolution), less spatial coverage (most of the Americas vs global), fewer weather conditions (clear-sky vs all-weather conditions) and a greatly improved temporal resolution (48 vs 4 observations a day). The published data are available at http://www.dx.doi.org/10.11922/sciencedb.j00076.00062.

Evaluation of Six High-Spatial Resolution Clear-Sky Surface Upward Longwave Radiation Estimation Methods with MODIS

Surface upward longwave radiation (SULR) is a critical component in the calculation of the Earth’s surface radiation budget. Multiple clear-sky SULR estimation methods have been developed for high-spatial resolution satellite observations. Here, we comprehensively evaluated six SULR estimation methods, including the temperature-emissivity physical methods with the input of the MYD11/MYD21 (TE-MYD11/TE-MYD21), the hybrid methods with top-of-atmosphere (TOA) linear/nonlinear/artificial neural network regressions (TOA-LIN/TOA-NLIN/TOA-ANN), and the hybrid method with bottom-of-atmosphere (BOA) linear regression (BOA-LIN). The recently released MYD21 product and the BOA-LIN—a newly developed method that considers the spatial heterogeneity of the atmosphere—is used initially to estimate SULR. In addition, the four hybrid methods were compared with simulated datasets. All the six methods were evaluated using the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Surface Radiation Budget Network (SURFRAD) in situ measurements. Simulation analysis shows that the BOA-LIN is the best one among four hybrid methods with accurate atmospheric profiles as input. Comparison of all the six methods shows that the TE-MYD21 performed the best, with a root mean square error (RMSE) and mean bias error (MBE) of 14.0 and −0.2 W/m2, respectively. The RMSE of BOA-LIN, TOA-NLIN, TOA-LIN, TOA-ANN, and TE-MYD11 are equal to 15.2, 16.1, 17.2, 21.2, and 18.5 W/m2, respectively. TE-MYD21 has a much better accuracy than the TE-MYD11 over barren surfaces (NDVI < 0.3) and a similar accuracy over non-barren surfaces (NDVI ≥ 0.3). BOA-LIN is more stable over varying water vapor conditions, compared to other hybrid methods. We conclude that this study provides a valuable reference for choosing the suitable estimation method in the SULR product generation.

Modeling surface longwave radiation over high-relief terrain

Thermal anisotropy is an important phenomenon in thermal infrared remote sensing as it restricts the retrieval accuracy of surface longwave radiation (SLR). Topography is an essential controlling factor for the directionality of SLR for high-relief regions (e.g., mountain regions) where there is land surface heterogeneity and non-isothermal properties at pixel scales. However, satellite sensors can only receive radiance from a specific surface object at a small number of simultaneous viewing angles, which makes the quantitative modeling of thermal anisotropy challenging. Therefore, we developed the topographic longwave radiation model (TLRM) to describe the directionality of SLR components taking into account the variability of both subpixel topography and thermal anisotropy in high-relief regions. The reliability of TLRM was validated using the Discrete Anisotropic Radiative Transfer (DART) model over two typical geomorphic areas: a valley scene and a peak scene. The preliminary validation shows good agreement in terms of surface upward longwave radiance, which confirms the potential of TLRM for capturing the anisotropic patterns of land surfaces. The radiance values simulated by the DART model were first revised for the spectral mismatch. Then, they are used to correct residual deviation for TLRM using linear regressions. The root mean square error (RMSE) and coefficient of determination (R2) were 0.830 W/(m2 ∙ sr) and 0.746 for the valley scene, respectively, and 0.239 W/(m2 ∙ sr) and 0.711 for the peak scene, respectively. Compared with TLRM, models that do not consider terrain effects generate significant discrepancies in high relief SLR components. The differences in downward longwave radiation can reach −60 W/m2 in valleys without considering terrain effects. Based on the reference of hemispherical upward longwave radiation, surface upward longwave radiation estimated by the direct estimation method had a bias of 11.41 W/m2 and standard deviation (STD) of 7.30 W/m2, while the directional upward longwave radiation had a bias of 5.99 W/m2 and STD of 4.08 W/m2, showing lower estimation variation. The discrepancy between surface net longwave radiation (NLR) and terrain-corrected NLR ranged between 50 and −130 W/m2 with clear negative biases mainly occurring in valleys. With higher spatial resolutions of remotely sensed imagery, the influence of complex terrain on land surface radiative flux has become more significant. This parameterization scheme is expected to better represent the topographic effects on SLR, enhance understanding of thermal anisotropy in non-isothermal mixed pixel areas of high relief, and improve the inversion accuracy of SLR.

Seasonal division of 850 hPa South China Sea based on multi-element atmospheric condition similarity

Comprehensive consideration of multiple meteorological elements for the objective identification and division of seasonal changes is important in the field of climate monitoring and diagnostic analysis. The development of relevant identification and classification methods will help us better understand the new characteristics of seasonal transitions against the background of climate change. Based on reanalysis data from the National Centers for Environmental Prediction from 1950 to 2015, we used the multi-element atmospheric condition similarity method to seasonally divide the average climatological conditions at 850 hPa over the South China Sea, and analyzed the annual mean results of the onset of seasons and annual mean seasonal variations of meteorological elements in this region. The results show the following. First, the seasonal division results based on multi-element atmospheric condition similarity coincide with the seasonal variation times of each meteorological element with atmospheric conditions comprising five meteorological elements. When the four seasons change, the meteorological elements at 850 hPa in the South China Sea have obvious seasonal variation, and atmospheric circulation and surface upward long-wave radiation change conspicuously with seasonal transformation. This confirms the validity of the method as applied to seasonal divisions in the South China Sea. Second, when the climate system translates from winter to summer in the South China Sea, thermal elements change greatly and rapidly. Changes to thermal elements in spring provide climate conditions for the onset of summer and the onset of the summer monsoon in the South China Sea. Third, when the climate system shifts from summer to winter in the South China Sea, changes to wind elements are obvious and rapid. In autumn, changes in thermal conditions between the Eurasian continent and the Pacific Ocean lead to major changes in atmospheric circulation and wind. In addition, seasonal divisions are no longer just nodes of time. Rather, they can be used as important indicators for further studies of atmospheric circulation changes, short-term climate predictions, and seasonal changes of other climate systems.

Estimation of Surface Upward Longwave Radiation Using a Direct Physical Algorithm

Surface upward longwave radiation (SULR) is a significant component of the surface radiation budget and is closely linked with evapotranspiration, soil moisture, and surface cooling on clear nights. Therefore, accurately estimating SULR is essential to better understand its spatiotemporal dynamics or to characterize the thermal environment of a given land surface. Currently, most methods for estimating SULR (including the physical and hybrid methods) fail to account for the thermal anisotropy, which can introduce significant errors into the calculation. We previously proposed the combined algorithm that considers the thermal anisotropy to more accurately estimate the SULR. However, this proposed method has several shortcomings. For example, it considers the directionality of the emissivity and the effective temperature separately under the support of a parametric directional emissivity model. However, the directional emissivity model is not maturely developed for different land surface types, especially on nonvegetated surfaces. And the separation of land surface temperature and emissivity may undermine the estimation accuracy. Furthermore, this proposed method requires a series of input parameters that is not always available, limiting its applicability. In this paper, we present a refined algorithm that uses a kernel-driven model and the technique of band conversion to calculate the SULR directly based on surface-leaving radiances. This direct physical algorithm is then applied to the Wide-angle infrared Dual-mode line/area Array Scanner data set and validated using longwave radiation data collected by automatic meteorological stations from the Heihe Watershed Allied Telemetry Experimental Research experiment. The results of these tests suggest that the direct algorithm works effectively. The root-mean-square error (RMSE) and mean bias error (MBE) of the direct algorithm on maize surfaces are 4.417 and 0.474 $\text {W}\cdot ~\text {m}^{-2}$ , respectively. When the thermal anisotropy is incorporated, the RMSE and absolute MBE decrease by a maximum of 4.734 and 7.414 ${\mathrm{ W}}\cdot {\mathrm{ m}}^{\mathrm {-2}}$ , respectively. Different land types yield different results: for vegetable surfaces, the estimation biases of the direct model are approximately $-2{\mathrm{ W}}\cdot {\mathrm{ m}}^{\mathrm {-2}}$ , whereas orchard surfaces yield biases are between −2 and $-3.5{\mathrm{ W}}\cdot {\mathrm{ m}}^{-2}$ , and village surfaces yield biases exceeding $- 10~{\mathrm{ W}}\cdot {\mathrm{ m}}^{-2}$ . These differences can be attributed to the varying effects of the kernel-driven model across different types of land surfaces. The RMSE and absolute MBE obtained using the direct algorithm are slightly smaller (0.587 and 1.685 ${\mathrm{ W}}\cdot {\mathrm{ m}}^{\mathrm {-2}}$ , respectively) than those obtained using the combined algorithm; they are also smaller than the results of the traditional temperature–emissivity algorithm (by 8.7 and 11.7 ${\mathrm{ W}}\cdot {\mathrm{ m}}^{-2}$ , respectively).

Estimation of Upward Longwave Radiation From Vegetated Surfaces Considering Thermal Directionality

Surface upward longwave radiation (SULR) is an important component of the surface energy balance and is closely related to land surface temperature and emissivity. The estimation of SULR plays an important role in the study of surface energy circulation and climate change. State-of-the-art methods to estimate SULR, including the physical method and the hybrid method, are conducted without considering directional thermal radiation (DTR), which may induce a large error in the estimation, particularly over sparsely vegetated surfaces. In this paper, we modified the physical temperature–emissivity algorithm by combining a directional emissivity model (FRA97) and a kernel-driven DTR model to estimate the SULR of vegetated surfaces while considering the thermal directionality of the land surface. The most suitable kernel-driven model and an angle combination of the DTR were selected from six kernel-driven models and five angular combinations. The sensitivity of the proposed algorithm to the input parameters was also analyzed. The proposed algorithm was then validated with the Wide-angle infrared Dual-mode line/area Array Scanner (WiDAS) data set and longwave radiation data of automatic meteorological stations from the Heihe Watershed Allied Telemetry Experimental Research experiment. The results showed that the five-angle combination with large-angle intervals performs the best. When the leaf area index (LAI) is less than 1.2, the RossThick-LiSparseR model performs the best; when LAI is larger than 1.2, the RossThick-LiDenseR model is the most accurate. The SULR is not sensitive to surface downward longwave radiation and LAI, is slightly sensitive to leaf and soil emissivity at certain LAIs, and is highly sensitive to DTR, which may greatly affect the accuracy of the estimated SULR. The root-mean-square error (RMSE) and the mean bias error (MBE) of the SULR estimated using the WiDAS data and the proposed algorithm are 5.618 and −1.642 W/m2, respectively, thereby improving the estimation accuracy by as much as 7.479 and 10.511 W/m2 at most in terms of RMSE and MBE, respectively, compared with the results calculated without considering the DTR.

Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

Abstract. Sea ice plays an important role in the air–ice–ocean interaction, but it is often represented simply in many regional atmospheric models. The Noah sea ice scheme, which is the only option in the current Weather Research and Forecasting (WRF) model (version 3.6.1), has a problem of energy imbalance due to its simplification in snow processes and lack of ablation and accretion processes in ice. Validated against the Surface Heat Budget of the Arctic Ocean (SHEBA) in situ observations, Noah underestimates the sea ice temperature which can reach −10 °C in winter. Sensitivity tests show that this bias is mainly attributed to the simulation within the ice when a time-dependent ice thickness is specified. Compared with the Noah sea ice model, the high-resolution thermodynamic snow and ice model (HIGHTSI) uses more realistic thermodynamics for snow and ice. Most importantly, HIGHTSI includes the ablation and accretion processes of sea ice and uses an interpolation method which can ensure the heat conservation during its integration. These allow the HIGHTSI to better resolve the energy balance in the sea ice, and the bias in sea ice temperature is reduced considerably. When HIGHTSI is coupled with the WRF model, the simulation of sea ice temperature by the original Polar WRF is greatly improved. Considering the bias with reference to SHEBA observations, WRF-HIGHTSI improves the simulation of surface temperature, 2 m air temperature and surface upward long-wave radiation flux in winter by 6, 5 °C and 20 W m−2, respectively. A discussion on the impact of specifying sea ice thickness in the WRF model is presented. Consistent with previous research, prescribing the sea ice thickness with observational information results in the best simulation among the available methods. If no observational information is available, we present a new method in which the sea ice thickness is initialized from empirical estimation and its further change is predicted by a complex thermodynamic sea ice model. The ice thickness simulated by this method depends much on the quality of the initial guess of the ice thickness and the role of the ice dynamic processes.

Surface energy budget changes over Central Australia during the early 21st century drought

Satellite observations are used to investigate surface energy budget variability over central Australia during the early 21st century drought. Over a large expanse of open shrubland and savanna, surface albedo exhibits a multiyear increase of 0.06 during the drought followed by a sharp decline of 0.08 after heavy rainfall in 2010 broke the drought. The surface albedo variations are associated with increased normalized difference vegetation index (NDVI) during wet years before and after the drought and decreased NDVI during drought years. During the worst drought years (2002–2009), the surface albedo increase is most pronounced in the shortwave infrared region (wavelengths between 1 and 3 µm), implying soil moisture content variability is the likely cause of the albedo changes. At interannual timescales, surface albedo variability is associated with near-surface soil moisture, controlled by episodic precipitation events, whereas the multiyear increase in surface albedo is more closely linked with decreases in soil moisture in deeper surface layers. In addition to a higher surface albedo and lower soil moisture content during the drought, the observations show less evaporation, enhanced reflected shortwave radiation, increased upward emission of thermal infrared radiation, lower downwelling longwave (LW) radiation, reduced net total downward radiation, and higher sensible heating compared with the rainy period following the drought. Upward emission of thermal infrared radiation decreases sharply after the drought with increased surface evaporation. However, the surface energy budget changes during the worst drought years show a stronger relationship between upward emission of thermal radiation and reflected shortwave flux. During this period, evaporative fraction is extremely low and surface albedo is steadily increasing. In such extreme conditions, the surface albedo appears to modulate surface upward LW radiation, preventing it from getting too high. The change in upward LW radiation thus represents a negative feedback as it offsets further decreases in surface net radiation.