Downward Longwave Radiation Research Articles

A New Benchmark for Surface Radiation Products over the East Asia–Pacific Region Retrieved from the Himawari-8/AHI Next-Generation Geostationary Satellite

Abstract Surface downward radiation (SDR), including shortwave downward radiation (SWDR) and longwave downward radiation (LWDR), is of great importance to energy and climate studies. Considering the lack of reliable SDR data with a high spatiotemporal resolution in the East Asia–Pacific (EAP) region, we derived SWDR and LWDR at 10-min and 0.05° resolutions for this region from 2016 to 2020 based on the next-generation geostationary satellite Himawari-8 (H-8). The SDR product is unique in terms of its all-sky features, high accuracy, and high-resolution levels. The cloud effect is fully considered in the SDR product, and the influence of high aerosol loadings and topography on the SWDR are considered. Compared to benchmark products of the radiation, such as Clouds and the Earth’s Radiant Energy System (CERES) and the European Centre for Medium-Range Weather Forecasts (ECMWF) next-generation reanalysis (ERA5), and the Global Land Surface Satellite (GLASS), not only is the resolution of the new SDR product notably much higher, but the product accuracy is also higher than that of those products. In particular, hourly and daily root-mean-square errors of the new SWDR are 104.9 and 31.5 W m−2, respectively, which are much smaller than those of CERES (at 121.6 and 38.6 W m−2, respectively), ERA5 (at 176.6 and 39.5 W m−2, respectively), and GLASS (daily of 36.5 W m−2). Meanwhile, RMSEs of hourly and daily values of the new LWDR are 19.6 and 14.4 W m−2, respectively, which are comparable to that of CERES and ERA5, and even better over high-altitude regions.

Estimating the Clear-Sky Longwave Downward Radiation in the Arctic from FengYun-3D MERSI-2 Data

Surface longwave downward radiation (LWDR) plays a key role in determining the Arctic surface energy budget, especially in insolation-absent boreal winter. A reliable LWDR product is essential for understanding the intrinsic physical mechanisms of the rapid changes in the Arctic climate. The Medium-Resolution Spectral Imager (MERSI-2), a major payload of the Chinese second-generation polar-orbiting meteorological satellite, FengYun-3D (FY-3D), was designed similar to the NASA Moderate-Resolution Imaging Spectroradiometer (MODIS) in terms of the spectral bands. Although significant progress has been made in estimating clear-sky LWDR from MODIS observations using a variety of methods, few studies have focused on the retrieval of clear-sky LWDR from FY-3D MERSI-2 observations. In this study, we propose an advanced method to directly estimate the clear-sky LWDR in the Arctic from the FY-3D MERSI-2 thermal infrared (TIR) top-of-atmosphere (TOA) radiances and auxiliary information using the extremely randomized trees (ERT) machine learning algorithm. The retrieval accuracy of RMSE and bias, validated with the Baseline Surface Radiation Network (BSRN) in situ measurements, are 14.14 W/m2 and 4.36 W/m2, respectively, which is comparable and even better than previous studies. The scale effect in retrieval accuracy evaluation was further analyzed and showed that the validating window size could significantly influence the retrieval accuracy of the MERSI-2 clear-sky LWDR dataset. After aggregating to a spatial resolution of 9 km, the RMSE and bias of MERSI-2 retrievals can be reduced to 9.43 W/m2 and −0.14 W/m2, respectively. The retrieval accuracy of MERSI-2 clear-sky LWDR at the CERES SSF FOV spatial scale (approximately 20 km) can be further reduced to 8.64 W/m2, which is much higher than the reported accuracy of the CERES SSF products. This study demonstrates the feasibility of producing LWDR datasets from Chinese FY-3D MERSI-2 observations using machine learning methods.

Toward an Improved Global Longwave Downward Radiation Product by Fusing Satellite and Reanalysis Data

Surface longwave downward radiation (LWDR) plays an important role in modulating greenhouse effect and climate change. Constructing a global longtime series LWDR dataset is greatly necessary to systematically and in-depth study the LWDR effect on the climate. However, the current multi-source LWDR products (satellite and reanalysis) show large differences in terms of both spatio-temporal resolutions and accuracy in various regions. Therefore, it is necessary to fuse multi-source datasets to generate more accurate LWDR with high spatio-temporal resolution on a global scale. To this end, a downscaling strategy is firstly proposed to generate LWDR dataset with 0.25° resolution from CERES-SYN data with 1° scale, by incorporating the Land Surface Temperature (LST), Total Column Water Vapor (TCWV) and Elevation. Then a machine learning-based fusion method is provided to generate a global hourly LWDR dataset with spatial resolution of 0.25° by combing three products (CERES-SYN, ERA5 and GLDAS). Compared with ground measurements, the performance of generated LWDR product reveals that the correlation coefficient (R), mean bias error (BIAS), and root mean square error (RMSE) were 0.97, -0.95 W/m2 and 22.38 W/m2 respectively over the land, and 0.99, -0.88 W/m2 and 10.96 W/m2 over the ocean. Specially, it shows improved accuracy in the low and middle latitude regions compared with other LWDR products. Considering its better accuracy and higher spatio-temporal resolution, the new LWDR product can provide essential data for deeply understanding the global energy balance and even the global warming. Moreover, the proposed fusion strategy can be enlightening for readers in the fields of multi-source data combination and big data analysis.

Integration of Multisource Data to Estimate Downward Longwave Radiation Based on Deep Neural Networks

Downward longwave radiation (DLR) at the surface is a key variable of interest in fields, such as hydrology and climate research. However, existing DLR estimation methods and DLR products are still problematic in terms of both accuracy and spatiotemporal resolution. In this article, we propose a deep convolutional neural network (DCNN)-based method to estimate hourly DLR at 5-km spatial resolution from top of atmosphere (TOA) brightness temperature (BT) of the Himawari-8/Advanced Himawari Imager (AHI) thermal channels, combined with near-surface air temperature and dew point temperature of ERA5 and elevation data. Validation results show that the DCNN-based method outperforms popular random forest and multilayer perceptron-based methods and that our proposed scheme integrating multisource data outperforms that only using remote sensing TOA observations or surface meteorological data. Compared with state-of-the-art CERES-SYN and ERA5-land DLR products, the estimated DLR by our proposed DCNN-based method with physical multisource inputs has higher spatiotemporal resolution and accuracy, with correlation coefficient (CC) of 0.95, root-mean-square error (RMSE) of 17.2 W/m², and mean bias error (MBE) of -0.8 W/m² in the testing period on the Tibetan Plateau.

Toward the Estimation of All-Weather Daytime Downward Longwave Radiation over the Tibetan Plateau

Downward longwave radiation (DLR) is a critical parameter for radiation balance, energy budget, and water cycle studies at regional and global scales. Accurate estimation of the all-weather DLR with a high temporal resolution is important for the estimation of the surface net radiation and evapotranspiration. However, most DLR products involve instantaneous DLR estimates based on polar orbiting satellite data under clear-sky conditions. To obtain an in-depth understanding of the performances of different models in the estimation of DLR over the Tibetan Plateau, which is a focus area of climate change study, this study tests eight methods for clear-sky conditions and six methods for cloudy conditions based on ground-measured data. It is found that the Dilley and O’Brien model and the Lhomme model are most suitable for clear-sky conditions and cloudy conditions, respectively. For the Dilley and O’Brien model, the average root mean square error (RMSE) of DLR under clear-sky conditions is approximately 22.5 W/m2 for nine ground sites; for the Lhomme model, the average RMSE is approximately 23.2 W/m2. Based on the estimated cloud fraction and meteorological data provided by the China Land Surface Data Assimilation System (CLDAS), hourly all-weather daytime DLR with a 0.0625° resolution over the Tibetan Plateau is estimated. Results demonstrate that the average RMSE of the estimated hourly all-weather DLR is approximately 26.4 W/m2. With the combined all-weather DLR model, the hourly all-weather daytime DLR dataset with a 0.0625° resolution from 2008 to 2016 over the Tibetan Plateau is generated. This dataset can contribute to studies associated with the radiation balance and energy budget, water cycle, and climate change over the Tibetan Plateau.

Estimating high-spatial resolution surface daily longwave radiation from the instantaneous Global LAnd Surface Satellite (GLASS) longwave radiation product

ABSTRACT In this paper, time extension methods, originally designed for clear-sky land surface conditions, are used to estimate high-spatial resolution surface daily longwave (LW) radiation from the instantaneous Global LAnd Surface Satellite (GLASS) longwave radiation product. The performance of four time methods were first tested by using ground based flux measurements that were collected from 141 global sites. Combined with the accuracy of daily LW radiation estimated from the instantaneous GLASS LW radiation, the linear sine interpolation method performs better than the other methods and was employed to estimate the daily LW radiation as follows: The bias/Root Mean Square Error (RMSE) of the linear sine interpolation method were −6.30/15.10 W/m2 for the daily longwave upward radiation (LWUP), −1.65/27.63 W/m2 for the daily longwave downward radiation (LWDN), and 4.69/26.42 W/m2 for the daily net longwave radiation (LWNR). We found that the lengths of the diurnal cycle of LW radiation are longer than the durations between sunrise and sunset and we proposed increasing the day length by 1.5 h. The accuracies of daily LW radiation were improved after adjusting the day length. The bias/RMSE were −4.15/13.74 W/m2 for the daily LWUP, −1.3/27.52 W/m2 for the daily LWDN, and 2.85/25.91 W/m2 for the daily LWNR. We are producing long-term surface daily LW radiation values from the GLASS LW radiation product.

Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018

It is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (Ld, 4–100 μm) dataset. Although a number of global Ld datasets are available, their low accuracies and coarse spatial resolutions limit their applications. This study generated a daily Ld dataset with a 5-km spatial resolution over the global land surface from 2000 to 2018 using atmospheric parameters, which include 2-m air temperature (Ta), relative humidity (RH) at 1000 hPa, total column water vapor (TCWV), surface downward shortwave radiation (Sd), and elevation, based on the gradient boosting regression tree (GBRT) method. The generated Ld dataset was evaluated using ground measurements collected from AmeriFlux, AsiaFlux, baseline surface radiation network (BSRN), surface radiation budget network (SURFRAD), and FLUXNET networks. The validation results showed that the root mean square error (RMSE), mean bias error (MBE), and correlation coefficient (R) values of the generated daily Ld dataset were 17.78 W m−2, 0.99 W m−2, and 0.96 (p < 0.01). Comparisons with other global land surface radiation products indicated that the generated Ld dataset performed better than the clouds and earth’s radiant energy system synoptic (CERES-SYN) edition 4.1 dataset and ERA5 reanalysis product at the selected sites. In addition, the analysis of the spatiotemporal characteristics for the generated Ld dataset showed an increasing trend of 1.8 W m−2 per decade (p < 0.01) from 2003 to 2018, which was closely related to Ta and water vapor pressure. In general, the generated Ld dataset has a higher spatial resolution and accuracy, which can contribute to perfect the existing radiation products.

Estimation of Long-term Surface Downward Longwave Radiation over the Global Land from 2000 to 2007

Estimation of Long-term Surface Downward Longwave Radiation over the Global Land from 2000 to 2007

Influences of Summertime Arctic Dipole Atmospheric Circulation on Sea Ice Concentration Variations in the Pacific Sector of the Arctic during Different Pacific Decadal Oscillation Phases

AbstractAtmospheric circulation associated with the Arctic dipole (AD) pattern plays a crucial role in modulating the variations of summertime sea ice concentration (SIC) within the Pacific Arctic sector (PAS). Based on reanalysis data and satellite observations, we found that the impacts of atmospheric circulation associated with a positive AD (AD+) on SIC change over different regions of the PAS [including the East Siberian Sea (ESS), Beaufort and Chukchi Seas (BCS), and Canadian Arctic Archipelago (CAA)] are dependent on the phase shifts of Pacific decadal oscillation (PDO). Satellite observations reveal that SIC anomalies, influenced by AD+ during PDO− relative to that during PDO+, varies significantly in summer by 4.9%, −7.3%, and −6.4% over ESS, BCS, and CAA, respectively. Overall, the atmospheric anomalies over CAA and BCS in terms of specific humidity, air temperature, and thereby downward longwave radiation (DLR), are enhanced (weakened) in the atmospheric conditions associated with AD+ during PDO− (PDO+). In these two regions, the larger (smaller) increases in specific humidity and air temperature, associated with AD+ during PDO− (PDO+), are connected to the increased (decreased) poleward moisture flux, strengthened (weakened) convergence of moisture and heat flux, and in part to adiabatic heating. As a consequence, the DLR and surface net energy flux anomalies over the two regions are reinforced in the atmospheric scenarios associated with AD+ during PDO− compared with that during PDO+. Therefore, smaller SIC anomalies are identified over CAA and BCS in the cases related to AD+ during PDO− than during PDO+. Essentially, the changes of the DLR anomaly in CAA and BCS are in alignment with geopotential height anomalies, which are modulated by the anticyclonic circulation pattern in association with AD+ during varying PDO phases. In contrast, the SIC changes over ESS is primarily attributed to the variations in mechanical wind forcing and sea surface temperature (SST) anomalies. The cloud fraction anomalies associated with AD+ during different PDO phases are found not to be a significant contributor to the variations of sea ice anomaly in the studied regions. Given the oscillatory nature of PDO, we speculate that the recent shift to the PDO+ phase may temporarily slow the observed significant decline trend of the summertime SIC within PAS of the Arctic.

Trends and Variability of Atmospheric Downward Longwave Radiation Over China From 1958 to 2015

AbstractSurface downward longwave radiation (SDLR) is a major component of the energy budget. Although studies have reported the spatiotemporal variations of SDLR in China, the spatiotemporal coverage of the situ measurements used is always limited. In this study, the gradient boosting regression tree (GBRT) was developed to reconstruct SDLR based on air temperature (Ta), relative humidity (RH), and downward shortwave radiation (DSR). Ground measurements collected at the Baseline Surface Radiation Network (BSRN) and the Arid and Semi‐arid Region Collaborative Observation Project (ASRCOP) were used to build and evaluate the GBRT model. The evaluation results showed that the daily SDLR estimates are correlated well with the SDLR in situ, with an overall root mean square errors (RMSE) of 16.5 Wm−2 and a correlation coefficient (R) value of 0.91 for the validation data set. Comparison with existing SDLR products showed that accuracy and trends of the SDLR estimates based on the GBRT method are reasonable. To obtain long‐term SDLR data for spatiotemporal analysis over China, densely distributed reconstructed DSR and ground measured Ta and RH collected at 756 Chinese Meteorological Administration (CMA) stations were used as input to estimate the SDLR based on the GBRT method over China during 1958–2015. The long‐term estimated SDLRs at the selected 563 stations showed that SDLR increased at an average rate of 1.3 Wm−2 per decade over China from 1958 to 2015. The trend of SDLR is positively correlated with the trend in Ta and water vapor pressure, whereas negatively correlated with the trend in DSR.

Does ERA5 outperform satellite products in estimating atmospheric downward longwave radiation at the surface?

Atmospheric downward longwave radiation (DLR) is a key component of the surface energy budget in the Earth system. Satellite retrievals and atmospheric reanalysis estimates are the two typical approaches to obtaining a spatio-temporally continuous DLR product. In this study, we evaluated the DLR product from the latest ERA5 atmospheric reanalysis and the well-known Clouds and Earth's Radiant Energy System (CERES) satellite retrievals, against high-quality observations collected at 46 Baseline Surface Radiation Network (BSRN) stations over land surfaces and at 9 Global Tropical Moored Buoy Array (GTMBA) buoy stations. The accuracy of the ERA5 DLR product over land was found to be higher on average than that of CERES at hourly to monthly time scales. Conversely, ERA5 performed slightly worse than CERES-SYN when estimating DLR over the ocean surface. This is the first time that atmospheric reanalysis has performed better than satellite retrievals in estimating DLR over the land surface, demonstrating the potentially extensive application prospects for ERA5 as well as setting new challenges for quantitative remote sensing research.

Evaluation of Atmospheric Downward Longwave Radiation in the Brazilian Pampa Region

Atmospheric downward longwave radiation flux (L↓) is a variable that directly influences the surface net radiation and consequently, weather and climatic conditions. Measurements of L↓ are scarce, and the use of classical models depending on some atmospheric variables may be an alternative. In this paper, we analyzed L↓ measured over the Brazilian Pampa biome. This region is located in a humid subtropical climate zone and characterized by well defined seasons and well distributed precipitation. Furthermore, we evaluated the performance of the eleven classical L↓ models for clear sky with one-year experimental data collected in the Santa Maria experimental site (SMA) over native vegetation and high relative humidity throughout the year. Most of the L↓ estimations, using the original coefficients, underestimated the experimental data. We performed the local calibration of the L↓ equations coefficients over an annual period and separated them into different sky cover classifications: clear sky, partly cloudy sky, and cloudy sky. The calibrations decreased the errors, especially in cloudy sky classification. We also proposed the joint calibration between the clear sky emissivity equations and cloud sky correction function to reduce errors and evaluate different sky classifications. The results found after these calibrations presented better statistical indexes. Additionally, we presented a new empirical model to estimate L↓ based on multiple regression analysis using water vapor pressure and air temperature. The new equation well represents partial and cloudy sky, even without including the cloud cover parameterization, and was validated with the following five years in SMA and two years in the Cachoeira do Sul experimental site (CAS). The new equation proposed herein presents a root mean square error ranging from 13 to 21 Wm−2 and correlation coefficient from 0.68 to 0.83 for different sky cover classifications. Therefore, we recommend using the novel equation to calculate L↓ over the Pampa biome under these specific climatic conditions.

Evaluation and Calibration of Downward Longwave Radiation Models under Cloudless Sky at Ile-Ife, Nigeria

Downward longwave radiation flux is an important variable for estimating net radiation, in order to investigate the surface energy budget and carry out climatic studies. The flux is usually estimated using empirical models based on the information of meteorological parameters such as relative humidity, surface and air temperature, and water vapor pressure. This paper presents the assessment of 11 widely used empirical models for estimating downward longwave radiation using a ground-based dataset acquired from January 2016 to December 2017 at Ile-Ife, a tropical city in Nigeria. The original Idso and Niemela et al. models performed better than other models with errors less than 5.0% when compared to measured values. The performances of all the models improved greatly after calibration. The Guest model, which gave low errors (MBE = 0.65 Wm–2, RMBE = 0.15%, RMSE = 9.38 Wm–2, RRMSE = 2.14%, MAE = 7.84 Wm–2, RMAE = 1.79%), performed best followed by the Dilley and O’Brien, Idso, Prata, Brutsaert, Garratt, Niemela et al., and Ångström models. The calibrated models presented in this study can be used to estimate the flux under cloudless sky conditions at Ile-Ife and at other places with similar meteorological conditions, where this flux is not measured due to technological problems and the high cost of purchasing and maintaining the needed sensors. The proposed model for estimating the flux showed better performance with lower statistical errors than all the existing empirical models tested, and conform greatly (R2 = 0.88) to the measured data.

Observed Relationships Between the Urban Heat Island, Urban Pollution Island, and Downward Longwave Radiation in the Beijing Area

AbstractWe used the mean air temperature and particulate matter concentration at northern and southern rural stations as rural background values to calculate the urban heat island intensity (UHII) and urban pollution island intensity (UPII) for Beijing. The correlation between UHII and UPII is significantly negative in winter during the daytime and nighttime when selecting southern rural background stations but significantly positive in spring during both daytime and nighttime and in winter during the nighttime when selecting northern rural background stations. The downward longwave radiation (DLR) is highly correlated with surface air temperature and water vapor, and with particulate matter concentration in winter and summer. Water vapor also has a high correlation with particulate matter concentration in winter and summer. Winter data were used to investigate the particulate matter contribution to DLR to minimize the effect of humidity. The results indicate that in winter the urban area DLR and net radiation increased more than rural area under polluted conditions compared with clean conditions, which may lead to an increase in UHII. But in other seasons with more moisture, the aerosol effect on DLR is smaller than water vapor. Our results imply that the contribution of air pollutants to DLR had been overestimated in recent studies without removing water vapor effects on the longwave radiation. We suggest that the interaction between the urban heat island and the urban pollution island and related mitigation strategies needs to be carefully studied in the future by considering different climate zone and seasons.

A revisiting of the parametrization of downward longwave radiation in summer over the Tibetan Plateau based on high-temporal-resolution measurements

Abstract. The Tibetan Plateau (TP) is one of the research hot spots in the climate change research due to its unique geographical location and high altitude. Downward longwave radiation (DLR), as a key component in the surface energy budget, has practical implications for radiation budget and climate change. A couple of attempts have been made to parametrize DLR over the TP based on hourly or daily measurements and crude clear-sky discrimination methods. This study uses 1 min shortwave and longwave radiation measurements at three stations over the TP to parametrize DLR during summer months. Three independent methods are used to discriminate clear sky from clouds based on 1 min radiation and lidar measurements. This guarantees the strict selection of clear-sky samples that is fundamental for the parametrization of clear-sky DLR. A total of 11 clear-sky and 4 cloudy DLR parametrizations are examined and locally calibrated. Compared to previous studies, DLR parametrizations here are shown to be characterized by smaller root-mean-square errors (RMSEs) and higher coefficients of determination (R2). Clear-sky DLR can be estimated from the best parametrization with a RMSE of 3.8 W m−2 and R2>0.98. Systematic overestimation of clear-sky DLR by the locally calibrated parametrization in one previous study is found to be approximately 25 W m−2 (10 %), which is very likely due to potential residual cloud contamination on previous clear-sky DLR parametrization. The cloud base height under overcast conditions is shown to play an important role in cloudy DLR parametrization, which is considered in the locally calibrated parametrization over the TP for the first time. Further studies on DLR parametrization during nighttime and in seasons except summer are required for our better understanding of the role of DLR in climate change.

Impact of Air Temperature Inversion on the Clear-Sky Surface Downward Longwave Radiation Estimation

Parameterization schemes for estimating clear-sky surface downward longwave radiation (SDLR) are well recognized for their simplicity and acceptable accuracy, especially at the local scale. The near-surface temperature and/or water vapor are usually used to predict the clear-sky SDLR in a parameterization scheme. Air temperature inversion (ATI) alters the atmospheric state at the near-surface boundary layer and affects the accuracy of the clear-sky SDLR estimation. However, few studies have investigated the impact of ATI on the estimate of the clear-sky SDLR. This article investigated the impact of ATI on the estimate of the clear-sky SDLR using six widely used parameterization schemes. According to the evaluation results using ATI profiles from the Thermodynamic Initial Guess Retrieval (TIGR) database and the Surface Radiation Budget Network (SURFRAD) sites, all the parameterization schemes are sensitive to ATI, and their accuracy is degraded greatly as a whole. The SDLR is underestimated for the ATI profile both in the TIGR database and SURFRAD sites. The best three schemes can achieve the accuracy with bias values of approximately -10 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and root-mean-square errors (RMSEs) less than 20 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the ATI profiles in the TIGR database. The reason the SDLR is underestimated for the ATI profiles is provided by a simulation study. An empirical method is proposed to correct the impact of ATI. The accuracy of all the parameterization schemes is remarkably improved at SURFRAD sites after correcting the impact of ATI, with the absolute values of bias and RMSEs less than 10 and 20 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> at SURFRAD sites.

Calculation Algorithm of Upward Longwave Radiation Based on Surface Types

Upward longwave radiation (ULR) is an important element for climate change analysis. We calculated ULR using land surface temperature (LST), sea surface temperature (SST), and downward longwave radiation (DLR) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud and the Earth’s Radiant Energy System (CERES), along with broadband emissivity calculated using four multiple linear regression models designed to consider specific land-cover categories along with MODIS data and reflectivity data for 241 materials from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Our broadband emissivity values were compared with those of Wang et al. Journal of Geophysical Research: Atmospheres, 110(D11), (2005); all correlation coefficients were higher than 0.90 for the period from January to December 2016, and the root mean square error (RMSE) was 0.00750–0.00825. We then calculated ULR using the broadband emissivity and compared the results with CERES data, resulting in smaller errors relative to CERES than found by Wang et al. (2005). Furthermore, the calculated ULR compared with the observational data of Baseline Surface Radiation Network (BSRN) and the resultant statistical values of R(correlation coefficient), RMSE, and bias were 0.93, 21.22 W m−2, and 0.29 respectively.

All-sky longwave downward radiation from satellite measurements: General parameterizations based on LST, column water vapor and cloud top temperature

Remotely sensed surface longwave downward radiation (LWDR) plays an essential role in studying the surface energy budget and greenhouse effect. Most existing satellite-based methods or products depend on variables that are not readily available from space such as, liquid water path, air temperature, vapor pressure and/or cloud-base temperature etc., which seriously restrict the wide applications of satellite data. In this paper, new nonlinear parameterizations and a machine learning-based model for deriving all-sky LWDR are proposed based only on land surface temperature (LST), column water vapor and cloud-top temperature (CTT), that are relatively readily available day and night for most satellite missions. It is the first time to incorporate the CTT in the parameterizations for estimating LWDR under the cloudy-sky conditions. The results reveal that the new models work well and can derive all-sky global LWDR with reasonable accuracies (RMSE <23 W/m2, bias <2.0 W/m2). The convenience of input data makes the new models easy to use, and thus will definitely expand the applicability of remotely sensed measurements in radiation budget fields and many land applications.

Assessment of the Long-Term High-Spatial-Resolution Global LAnd Surface Satellite (GLASS) Surface Longwave Radiation Product Using Ground Measurements

In this article, we comprehensively assessed the newly released long-term high-spatial-resolution Global LAnd Surface Satellite (GLASS) surface longwave (LW) radiation product using site measurements of LW fluxes. In total, three years of ground-measured LW fluxes (surface longwave upward radiation (LWUP), surface longwave downward radiation (LWDN), and surface longwave net radiation (LWNR) collected from 141 sites in six independent networks (AmeriFlux, AsiaFlux, BSRN, CEOP, HiWATER-MUSOEXE, and TIPEX-III) are used to evaluate the GLASS LW radiation product. These sites cover various land cover types, surface elevations, and climatic types. According to the evaluation results, the biases are -4.33, -3.77, and 0.70 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and the RMSEs are 18.15, 26.94, and 26.70 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for clear-sky LWUP, LWDN, and LWNR, respectively. The GLASS LW radiation product performs well in climate-change-sensitive areas such as poleward areas, semiarid areas, and the “third pole”, namely, the Tibetan Plateau. The accuracy of the GLASS LW product is higher or comparable to that of available LW products and studies but has a high-spatial-resolution of 1 km and a time span of 19 years. In conclusion, the overall accuracy of the clear-sky GLASS LW radiation product can satisfy the requirements of the hydrological, meteorological, and agricultural research communities on a global scale. We will continue to improve the retrieval algorithms and update the products accordingly.

What Drives the North Atlantic Oscillation’s Temperature Anomaly Pattern? Part II: A Decomposition of the Surface Downward Longwave Radiation Anomalies

AbstractRadiative transfer calculations are conducted to determine the contribution of temperature and water vapor anomalies toward the surface clear-sky downward longwave radiation (DLR) anomalies of the NAO. These calculations are motivated by the finding that the NAO’s skin temperature anomalies are driven primarily by changes in surface DLR. The clear-sky radiative transfer calculations follow the result that the clear-sky surface DLR anomalies can account for most of the all-sky surface DLR anomalies of the NAO. The results of the radiative transfer calculations prompt an analysis of the thermodynamic energy and total column water (TCW) budget equations, as water vapor and temperature anomalies are found to be equally important drivers of the surface DLR anomalies of the NAO. Composite analysis of the thermodynamic energy equation reveals that the temperature anomalies of the NAO are wind driven: the advection of climatological temperature by the anomalous wind drives the NAO’s temperature anomalies at all levels except for those in the upper troposphere–lower stratosphere where the advection of anomalous temperature by the climatological wind becomes dominant. A similar analysis of the TCW budget reveals that changes in TCW are driven by water flux convergence. In addition to determining the drivers of the temperature and TCW anomalies, the thermodynamic energy and water budget analyses reveal that the decay of the temperature anomalies occurs primarily through vertical mixing, and that of the water anomalies mostly by evaporation minus precipitation.