Downward Shortwave Research Articles

An Operational Method for Validating the Downward Shortwave Radiation Over Rugged Terrains

Estimation of downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. Although various algorithms have been proposed, effective validation methods are absent for rugged terrains due to the lack of rigorous methodology and reliable field measurements. We propose a two-step validation method for rugged terrains based on computer simulations. The first step is to perform point-to-point validation at local scale. Time-series measurements were applied to evaluate a three-dimensional (3-D) radiative transfer model. The second step is to validate the DSR at pixel-scale. A semiempirical model was built up to interpolate and upscale the DSR. Key terrain parameters were weighted by empirical coefficients retrieved from ground-based observations. The optimum number and locations of ground stations were designed by the 3-D radiative transfer model and Monte Carlo method. Four ground stations were selected to upscale the ground-based observations. Additional three ground stations were set up to validate the interpolated results. The upscaled DSR was finally applied to validate the satellite products provided by MODIS and Himawari-8. The results showed that the modeled and observed DSR exhibited good consistency at point scale with correlation coefficients exceeding 0.995. The average error was around 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 interpolated DSR and 10 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the upscaled DSR in theory. The accuracies of the satellite products were acceptable at most times, with correlation coefficients exceeding 0.94. From an operational point of view, our method has an advantage of using small amount of ground stations to upscale DSR with relatively high accuracy over rugged terrains.

2007~2014年中国区域地表辐射特征研究

2007~2014年中国区域地表辐射特征研究

Spatial Downscaling of MSG Downward Shortwave Radiation Product Under Clear-Sky Condition

Downward shortwave radiation (DSR) plays a very important role in land surface radiation budget and land-surface processes modeling. Although there are several radiation products developed based on satellite observations, the coarse spatial resolution greatly limits their applications in regional or local scales. To get high-resolution and accuracy-reliable DSR data, a practical downscaling method for clear-sky condition was proposed by using the scale-invariant relationship of the radiative transfer process to decompose the global radiation into direct and diffuse components at horizontal level and conducting topographic correction finally. Based on this method, the time series of Meteosat Second Generation (MSG) DSR product covering part of Navarre province in the northern Spain was disaggregated into 30-m level with the use of the ALOS World 3D-30m digital elevation model (DEM) data. The downscaled results not only presented high spatial heterogeneity with respect to the changes in surface topography but also showed reasonable values at different times over different days in one year. The in situ validation indicated that the hourly downscaled DSR had quite high correlation with the surface measurements at each day with the coefficient of determination above 0.97 and the root-mean-squared error lower than 35 W/m2. Overall, the evaluation allows concluding on the proposed method that can be a good way to get reliable and high-resolution DSR data from coarse-resolution DSR product under clear-sky condition.

An Operational Approach for Generating the Global Land Surface Downward Shortwave Radiation Product From MODIS Data

Surface shortwave net radiation (SSNR) and surface downward shortwave radiation (DSR) are the two surface shortwave radiation components in earth's radiation budget and the fundamental quantities of energy available at the earth's surface. Although several global radiation products from global circulation models, global reanalyses, and satellite observations have been released, their coarse spatial resolutions and low accuracies limit their application. In this paper, the Global LAnd Surface Satellite (GLASS) DSR product was generated from the Moderate Resolution Imaging Spectroradiometer top-of-atmosphere (TOA) spectral reflectance based on a direct-estimation method. First, the TOA reflectances were derived based on the atmospheric radiative transfer simulations under different solar/view geometries; second, a linear regression relationship between the TOA reflectance and SSNR was developed under various atmospheric conditions and surface properties for different solar/view geometries; third, the coefficients derived from the linear regression were used to compute the SSNR; and finally, the DSR was estimated using the SSNR estimates and broadband albedo at the surface. A 13-year (2003-2015) GLASS DSR product was generated at a 5-km spatial resolution and 1-day temporal resolution. Compared with the ground measurements collected from 525 stations from 2003 to 2005 around the world, the model-computed SSNR (DSR) had an overall bias of 8.82 (3.72) W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and a root mean square error of 28.83 (32.84) W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> at the daily time scale. Moreover, the global land annual mean of the DSR was determined to be 184.8 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> with a standard deviation of 0.8 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> over a 13-year (2003-2015) period.

Evaluation of the Himawari-8 Shortwave Downward Radiation (SWDR) Product and its Comparison With the CERES-SYN, MERRA-2, and ERA-Interim Datasets

The land surface shortwave downward radiation (SWDR), also known as surface incident solar radiation, is usually defined as the total solar spectral energy between 300 nm and 3000 nm that arrives at the earth's surface. The SWDR is an essential variable for numerous land models and thus need to be accurately mapped using various approaches. Although numerous efforts have been made to assess the applicability and accuracy of the SWDR datasets produced by satellite and numeric models, few studies have evaluated the performance of the SWDR products from Himawari-8. In this paper, the Himawari-8 SWDR products with both 1 km and 5 km spatial scales were evaluated using ground measurements. The validation results indicate that both of the Himawari-8 SWDR products are in close agreement with ground measurements, even though obvious biases are found. Also, an in-depth intercomparison between the Himawari-8 SWDR product at a 5 km resolution and three other SWDR radiation datasets (CERES-SYN, MERRA-2, and ERA-Interim) was conducted. Here, the Himawari-8 showed the highest levels of accuracy among the four SWDR datasets. Considering its higher temporal and spatial resolutions, the Himawari-8 SWDR products are more detailed and provide the unprecedented potential to improve land surface modeling and reveal the cloud-radiation interactions, even at a 10-min temporal scale.

Estimation of surface downward shortwave radiation over China from AVHRR data based on four machine learning methods

Downward shortwave radiation (DSR) is one of the major driving forces of climate system. Knowledge of the Earth’s radiation budget is essential for improving our understanding of the Earth’s climate. Therefore, accurate estimation of DSR has great significance. Satellite remote sensing is a practical way to derive DSR with high spatial resolution and coverage. In this study, four machine learning methods, including gradient boosting regression tree (GBRT), random forest (RF), multivariate adaptive regression spline (MARS), and artificial neural network (ANN), were applied to estimate DSR at a spatial resolution of 5 km and a temporal resolution of 1 day using Advanced Very High Resolution Radiometer (AVHRR) data. The DSR estimates based on four machine learning methods were evaluated using ground measurements at 96 sites over China. The measurements were collected from the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA) from 2001 to 2003. The evaluation results showed that the GBRT method performed best at both daily and monthly time scales under both clear and cloudy sky conditions. The validation results at the daily time scale showed an overall root mean square error (RMSE) of 30.34 W m−2 and an R value of 0.90 under clear sky conditions, whereas these values were 42.03 W m−2 and 0.86, respectively, under cloudy sky conditions. The DSR estimates had an overall RMSE value of 16.93 W m−2 and an R value of 0.97 at the monthly time scale. The Clouds and Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) data sets were also used for comparison with the DSR estimates based on the GBRT method. The DSR estimates based on the GBRT method exhibited similar spatial distributions with those of the CERES-EBAF DSR product. Moreover, the DSR estimates based on the GBRT method did not show a clear overestimation tendency, as the CERES-EBAF DSR product did, at the CDC/CMA sites.

Temporal Extrapolation of Daily Downward Shortwave Radiation Over Cloud-Free Rugged Terrains. Part 1: Analysis of Topographic Effects

Estimation of daily downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. The combination of satellite-based instantaneous measurements and temporal extrapolation models is the most feasible way to capture daily radiation variations at large scales. However, previous studies did not pay enough attention to topographic effects and simple temporal extrapolation methods were applied directly to rugged terrains which cover a large amount of the land surface. This paper, divided into two parts, aims at analyzing the topographic uncertainties of existing models and proposing a better method based on a mountain radiative transfer (MRT) model to calculate daily DSR. As the first part, this paper analyze the spatiotemporal variations of DSR influenced by topographic effects and checks the applicability of three temporal extrapolation methods on cloud-free days. Considering that clouds also have a strong influence on solar radiation, cloud-free days are chosen for targeted analysis of topographic effects on DSR. Three indices, the coefficient of variation, entropy-based dispersion coefficient (CH), and sill of semivariogram, are put forward to give a quantitative description of spatial heterogeneity. Our results show that the topography can dramatically strengthen the spatial heterogeneity of DSR. The index, CH, has an advantage for quantifying spatial heterogeneity as it offers a tradeoff between accuracy and efficiency. Spatial heterogeneity distorts the daily variation of DSR. Application of extrapolation methods in rugged terrains leads to overestimation of daily average DSR up to 60 W/m2 and a maximum 200 W/m2 error of instantaneous DSR on cloud-free days. This paper makes a quantitative analysis of topographic effects under different spatiotemporal conditions, which lays the foundation for developing a new extrapolation method.

Factors affecting air temperature in Bulgaria

The research period is 1984–2007 and includes data for air temperatures at 20 Bulgarian and Serbian stations. Also, data for four radiation fluxes and cloud fraction were used in the study. Atmospheric circulation is represented by six different circulation patterns and indices. Statistical methods such as principal component analysis, trend analysis, and correlation were employed in order to solve different problems. The main factor that affects air temperature in Bulgaria is solar radiation. Among different radiation fluxes, the most important is Surface Longwave Upward Flux followed by Surface Shortwave Downward Flux and radiation balance. Circulation factors also contribute to the values of air temperature in Bulgaria. The most significant is East Atlantic teleconnection pattern. Local physical geographical conditions such as relief and large water bodies are also important, especially by modeling and modifying the temperature field over Bulgaria. Average annual and most of average monthly air temperatures have increased in the period 1984–2007. The only exceptions are months of September and December. The observed increase in air temperature is due mainly to the increase in Surface Shortwave Downward Flux, which in turn is due to the decrease in cloudiness. The decrease in cloudiness leads to redistribution of radiation fluxes, increasing the net balance in shortwave spectrum and decreasing the net balance in longwave spectrum. The latter suggests a decreasing greenhouse effect in the studied period over the territory of Bulgaria.

Estimation of Daily Average Downward Shortwave Radiation over Antarctica

Surface shortwave (SW) irradiation is the primary driving force of energy exchange in the atmosphere and land interface. The global climate is profoundly influenced by irradiation changes due to the special climatic condition in Antarctica. Remote-sensing retrieval can offer only the instantaneous values in an area, whilst daily cycle and average values are necessary for further studies and applications, including climate change, ecology, and land surface process. When considering the large values of and small diurnal changes of solar zenith angle and cloud coverage, we develop two methods for the temporal extension of remotely sensed downward SW irradiance over Antarctica. The first one is an improved sinusoidal method, and the second one is an interpolation method based on cloud fraction change. The instantaneous irradiance data and cloud products are used in both methods to extend the diurnal cycle, and obtain the daily average value. Data from South Pole and Georg von Neumayer stations are used to validate the estimated value. The coefficient of determination (R2) between the estimated daily averages and the measured values based on the first method is 0.93, and the root mean square error (RMSE) is 32.21 W/m2 (8.52%). As for the traditional sinusoidal method, the R2 and RMSE are 0.68 and 70.32 W/m2 (18.59%), respectively The R2 and RMSE of the second method are 0.96 and 25.27 W/m2 (6.98%), respectively. These values are better than those of the traditional linear interpolation (0.79 and 57.40 W/m2 (15.87%)).

Estimating Surface Downward Shortwave Radiation over China Based on the Gradient Boosting Decision Tree Method

Downward shortwave radiation (DSR) is an essential parameter in the terrestrial radiation budget and a necessary input for models of land-surface processes. Although several radiation products using satellite observations have been released, coarse spatial resolution and low accuracy limited their application. It is important to develop robust and accurate retrieval methods with higher spatial resolution. Machine learning methods may be powerful candidates for estimating the DSR from remotely sensed data because of their ability to perform adaptive, nonlinear data fitting. In this study, the gradient boosting regression tree (GBRT) was employed to retrieve DSR measurements with the ground observation data in China collected from the China Meteorological Administration (CMA) Meteorological Information Center and the satellite observations from the Advanced Very High Resolution Radiometer (AVHRR) at a spatial resolution of 5 km. The validation results of the DSR estimates based on the GBRT method in China at a daily time scale for clear sky conditions show an R2 value of 0.82 and a root mean square error (RMSE) value of 27.71 W·m−2 (38.38%). These values are 0.64 and 42.97 W·m−2 (34.57%), respectively, for cloudy sky conditions. The monthly DSR estimates were also evaluated using ground measurements. The monthly DSR estimates have an overall R2 value of 0.92 and an RMSE of 15.40 W·m−2 (12.93%). Comparison of the DSR estimates with the reanalyzed and retrieved DSR measurements from satellite observations showed that the estimated DSR is reasonably accurate but has a higher spatial resolution. Moreover, the proposed GBRT method has good scalability and is easy to apply to other parameter inversion problems by changing the parameters and training data.

Downward shortwave radiation estimation and spatial assessment on sites over complex terrain applying integrative approach of MTCLIM-XL, interpolation, RS and GIS

Downward shortwave radiation (DSR) is a highly variable solar source on spatiotemporal basis and essential for energy and agriculture systems, while its calculations are helpful in the environment-related studies, climatology, and monitoring fire risk. Statistical methods developed to extrapolate values of climatic variables and radiation could fail to generate reliable findings of DSR over a complex terrain without considering local topographic factors. In the present study, we proposed an integrative approach of MTCLIM-XL extrapolation with remote sensing (RS) and geographic information system (GIS) to estimate real-time DSR and its spatial potential over surfaces of contrasting elevated sites on a mountainous terrain of Quetta (Pakistan).Based on methodological approach, remote sensing data product of high-resolution DEM (SRTM 30m) was processed to extract topographic data, and meteorological data were obtained from a base site, Subsequently, MTCLIM-XL executed the simulation to calculate the daily-based DSR (W/m2).Spatial distribution of DSR was generated by applying deterministic interpolation with complementing quantification of Hillshade analysis for spatially obstructive surfaces, and resultant spatial hotspot-based potential was assessed on basis of specified threshold level (above 250 W/m2 = 2 kW h/m2) over the specified area. We observed usable potential of DSR at target sites and its spatial distribution during the study period of 2015 to April 2016. Using EUMETSAT CMSAF data as a standard, the validation demonstrates agreeable results of low RMSE and high correlation coefficient values for selected sites, except some sites with relatively high elevations and irregular gradients. Analysis of solar zenith angle to evaluate its inverse relation with increment in DSR values shows agreeable high inverse relation, while the negative trend for only some sites features relatively high rugged topography. In conclusion, MTCLIM-XL with RS and GIS integration manifests as a reliable approach for estimation and spatial potential assessment-based exploration of DSR over complex terrain having no ground data, while prospectively it will complement to the environment-related studies on local to mesoscale.

A 5 km/daily Downward Shortwave Radiation Data Product over China-ASEAN (2013)

A 5 km/daily Downward Shortwave Radiation Data Product over China-ASEAN (2013)

Evaluation of the Spatial and Temporal Uncertainties Distribution of Daily-Integrated Shortwave Downward Radiation Estimated from Polar-Orbiting Satellite Observation

Abstract The polar-orbiting satellite sensor, which can observe the entire Earth’s surface with good spatial and spectral resolution, is a potential tool for estimation of global downward shortwave radiation (DSR). However, it can only provide a couple of snapshots in one day, which should be extended to daily-integrated DSR in order to drive the ecosystem models. In this paper, the temporal and spatial uncertainties for estimating of daily-integrated DSR from instantaneous measurements of polar-orbiting satellites are evaluated using geostationary Geosynchronous Meteorological Satellite-5 (GMS-5) observations over East Asia. Those overpass times—including 1030, 1200, and 1330 local time (LT), which correspond to Terra/Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and Aqua/MODIS—are evaluated. The combinations of multiobservations are also assessed. The results show that the daily-integrated DSR from polar-orbiting satellite observations of 1030, 1200, and 1330 underestimate solar radiation by 2.16% (0.46 MJ m−2), 5.44% (1.16 MJ m−2), and 5.54% (1.09 MJ m−2), with an RMSE of 2.05 MJ m−2 (12.92%), 2.50 MJ m−2 (13.33%), and 2.34 MJ m−2 (13.95%) in East Asia with large spatial and seasonal variations. In general, the bias is higher in the southern than in the northern part of East Asia. It is low in January, February, and March, then increases from April and reaches the maximum in June and July, and decreases rapidly to lower than 1.0 MJ m−2 in October. The uncertainties of daily-integrated DSR could be reduced by averaging multiday observations or combining of multitime observations. These uncertainties’ distributions are important for evaluation the usability of daily-integrated DSR from polar-orbiting satellite data and probably can be used for its calibration.

위성자료를 이용한 몽골의 일사량 분포 특성

몽골의 태양-기상자원지도는 위성자료 및 재분석 자료를 이용하여 개발되었다. 태양복사량은 단층 태양복사모델을 이용하였으며 입력자료는 SRTM, MODIS, OMI, MTSAT-1R 등의 위성관측자료와 전구모델의 재분석자료를 이용하였다. 계산된 결과는 NCEP/NCAR 재분석 DSWRF 자료를 이용하여 계산된 일사량을 검증하였다. 몽골은 서부의 산악지역과 중남부의 사막 및 반사막지대로 이루어져 있으며 대륙 내부에 위치하여 강수량이 적고 맑은 날이 많아 동일 위도상의 다른 지역과 비교하여 높은 일사량이 나타난다. 서부 산악지역은 고도가 높아 태양에너지가 많이 도달되는 곳임에도 불구하고 일사량이 낮게 나타난다. 그 이유는 산악지역에 존재하는 연중 적설이 위성자료의 구름탐지 알고리즘에서 구름으로 오탐지 되기 때문이다. 따라서 청천지수뿐만 아니라 일사량 또한 낮게 계산된다. 남부지역은 상대적으로 높은 가강수량과 에어로솔 광학두께가 나타났으나 다른 지역에 비해 위도가 낮고 청천지수가 높아 일사량이 높게 나타나는 것으로 분석된다. 계산된 월 누적 일사량은 547.59 MJ로써 전 지점에서 약 2.89 MJ로 높게 계산되었으며 상관성은 0.99였고 평방근오차(Root Mean Square Error; RMSE)는 6.17 MJ 이었다. 월별 통계 값을 계산하였을 때 상관성이 가장 높은 월은 10월로 0.94였고 3월은 0.62로 가장 낮게 나타났다. Mongolia's solar-meteorological resources map has been developed using satellite data and reanalysis data. Solar radiation was calculated using solar radiation model, in which the input data were satellite data from SRTM, TERA, AQUA, AURA and MTSAT-1R satellites and the reanalysis data from NCEP/NCAR. The calculated results are validated by the DSWRF (Downward Short-Wave Radiation Flux) from NCEP/NCAR reanalysis. Mongolia is composed of mountainous region in the western area and desert or semi-arid region in middle and southern parts of the country. South-central area comprises inside the continent with a clear day and less rainfall, and irradiation is higher than other regions on the same latitude. The western mountain region is reached a lot of solar energy due to high elevation but the area is covered with snow (high albedo) throughout the year. The snow cover is a cause of false detection from the cloud detection algorithm of satellite data. Eventually clearness index and solar radiation are underestimated. And southern region has high total precipitable water and aerosol optical depth, but high solar radiation reaches the surface as it is located on the relatively lower latitude. When calculated solar radiation is validated by DSWRF from NCEP/NCAR reanalysis, monthly mean solar radiation is 547.59 MJ which is approximately 2.89 MJ higher than DSWRF. The correlation coefficient between calculation and reanalysis data is 0.99 and the RMSE (Root Mean Square Error) is 6.17 MJ. It turned out to be highest correlation (r=0.94) in October, and lowest correlation (r=0.62) in March considering the error of cloud detection with melting and yellow sand.

Inter-Model Differences in the Relationships between Downward Shortwave Radiation and Air Temperatures Derived from Dynamical and Statistical Downscaling Models

Inter-Model Differences in the Relationships between Downward Shortwave Radiation and Air Temperatures Derived from Dynamical and Statistical Downscaling Models

Evaluation of a relationship between aerosols and surface downward shortwave flux through an integrative analysis of modeling and observation

Although aerosols have great impacts on Surface Downward Shortwave Flux (SDSF), the relationship between aerosol loading and SDSF in global models has not yet been adequately investigated. In this study, we attempt to investigate the effects of aerosol optical thickness (AOT) and single scattering albedo (SSA) on SDSF through an integrative analysis of modeling and observation. At first, we compared the results obtained by a global aerosol model, SPRINTARS, with in-situ measurements, AERONET and BSRN. And then we estimated the impacts of AOT and SSA on SDSF through an offline radiative transfer model, Rstar. Through this study, we found that the difference in SDSF between SPRINTARS and BSRN is much larger over heavy aerosol regions than those over regions. Using the Rstar radiative transfer model, we demonstrated that the AOT difference usually has the strongest impact on the SDSF difference and the SSA difference has a moderate impact over heavy aerosol loading regions, whereas the effect of water vapor can be ignored. Finally, we generated a contour plot to demonstrate the relationships between AOT-SSA-SDSF. For example, at low AOT (e.g., 0.15), the 20 W m−2 changes in SDSF are required to make more than 0.2 changes of SSA, whereas at high AOT (e.g., 0.85), the same changes in SDSF are required to have only 0.05 changes of SSA.

Estimation and Verification of Daily Surface Shortwave Flux over China

Downward shortwave flux at the surface over China was derived from sunshine duration data using parameters of Jordan sunshine recorders. The sunshine duration data were obtained as routine meteorological data and ad- justed as necessary for the eects of topography, station surroundings, altitude, and atmospheric turbidity. Calculated flux was verified by in situ observations. Daily flux was calculated at more than 190 stations in China, Climatic maps (statistical period: 1971-2000) of surface solar radiation were produced and have been made public. The sunshine duration-based shortwave flux was used to check satellite observation-based datasets. Two ver- sions of satellite-based surface radiation budget datasets (SRB 2.0 and SRB 3.0) from NASA's Langley Research Center were investigated. Compared with the SRB 2.0 dataset, the SRB 3.0 dataset showed improved shortwave flux, especially over western China and the Tibetan Plateau.

Surface radiative fluxes over the pan‐Arctic land region: Variability and trends

Surface radiative fluxes are a key driver of the land surface hydrological cycle, but because in situ measurements are sparse, relatively little attention has been focused on their space‐time variations. Recent satellite data and atmospheric model reanalysis products have resulted in data sets that predict most or all terms in the surface energy budget and offer the opportunity to investigate variations in surface radiative fluxes. This study contributes the NASA Energy and Water Cycle Study (NEWS), which is intended to improve estimates of the terrestrial energy budget components for the hydrologic and ecological communities. We analyzed surface downward shortwave (DSW) and longwave radiation (DLW) and albedo over the pan‐Arctic domain from (1) the European Centre for Medium‐Range Weather Forecast (ECMWF) 40‐Year Reanalysis (ERA‐40), (2) the ECMWF Interim Reanalysis (ERA‐Interim), (3) the International Satellite Cloud Climatology Project‐Flux Data (ISCCP‐FD), and (4) a Temperature Index (TIND) scheme for the period from 1984 to 2006. As compared with in situ measurements, the reanalysis products provide better estimates of the DSW diurnal cycle than do the satellite product and temperature index scheme simulation. At the regional scale, all data sets have similar temporal patterns except for DLW in ISCCP and snow season albedo. In terms of dominant spatial variability, all data sets show large variability in the pan‐Arctic. In addition, DSW and DLW show a similar latitudinal gradient. However, the difference in albedo suggests a need of improvement for the reanalysis products. For a small number of stations with relatively long records, we analyzed long‐term trends in DSW and found a turning point between 1985 and 1990. Before that, a dimming period exists, whereas brightening occurred thereafter.

The Spatio-temporal Variation of Downward Shortwave Radiation over China from 1998 to 2002

The Spatio-temporal Variation of Downward Shortwave Radiation over China from 1998 to 2002

Spatial and Temporal Scaling Behavior of Surface Shortwave Downward Radiation Based on MODIS and In Situ Measurements

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this letter, a new Moderate Resolution Imaging Spectroradiometer (MODIS)-based 5-km surface shortwave downward (SWD) radiation data set is used to examine the spatial scaling behavior of the surface radiative fluxes. <emphasis emphasistype="boldital">In situ</emphasis> measurements of SWD radiation at time scales from 5 to 55 min from the Oklahoma MesoNet and the Baseline Surface Radiation Network sites over the U.S. are employed to examine the temporal scaling behavior of surface downward radiation and to evaluate the estimates based on MODIS observations from both Terra and Aqua platforms. Analysis reveals that the surface SWD radiation derived from MODIS Terra is in better agreement with ground observations than those from MODIS Aqua. Spatial scaling behavior is investigated based on the MODIS-derived SWD radiation at scales varying from 5 to 100 km. The root mean square difference and the mean standard deviation are found to be highly correlated with the scale exhibiting a strong log–linear relationship. This is a first study on the scaling behavior for surface SWD. Such information is expected to provide linkage between different satellite-based radiation data sets including those derived from geostationary satellites usually of coarser spatial resolutions. </para>