Baseline Surface Radiation Network Site Research Articles

Evaluation and improvement of parameterization methods for estimating cloudy-sky downwelling surface longwave radiation from geostationary satellite data

ABSTRACT Estimating downwelling surface longwave radiation (DSLR) under cloudy-sky conditions presents a significant challenge, the parameterization methods used to estimate cloudy-sky DSLR may yield disparate results for the same scenario. Hence, it is imperative to undertake a comparative and validation study of these methods to comprehend their applicability. This study compares and validates five parameterized schemes for estimating cloudy-sky DSLR using data from the Fengyun-4A (FY-4A) and Himawari-8 geostationary satellites. Additionally, an improved algorithm for cloudy-sky DSLR estimation is proposed, integrating the radiation effect of the entire cloud layer and a nonlinear parameterization algorithm. The verification results demonstrate that the nonlinear parameterization algorithm exhibits higher accuracy compared to those reliant on cloud-base temperature. Among these, the improved algorithm has high accuracy similar to the nonlinear algorithms in BSRN (Baseline Surface Radiation Network) and TPDC (National Tibetan Plateau Data Center) sites, its RMSE are 29.62 and 30.09 W/m2, respectively. Sensitivity analysis reveals that cloud type and cloud-base temperature exert a pronounced influence on DSLR estimation, particularly within parameterization algorithms based on cloud-base temperature, warranting thorough consideration. Furthermore, the influence of land cover type and surface elevation, especially in high-altitude regions with bare surfaces, should not be disregarded in DSLR estimation.

Ten Years of VIIRS Land Surface Temperature Product Validation

The Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature (LST) has been operationally produced for a decade since the Suomi National Polar-orbiting Partnership (SNPP) launched in October 2011. A comprehensive evaluation of its accuracy and precision will be helpful for product users in climate studies and atmospheric models. In this study, the VIIRS LST is validated with ground observations from multiple high-quality radiation networks, including six stations from the Surface Radiation budget (SURFRAD) network, two stations from the Baseline Surface Radiation Network (BSRN), and 13 stations from the Atmospheric Radiation Measurement (ARM) network, to evaluate its performance over various land-cover types. The VNP21A1 LST was validated against the same ground observations as a reference. The results yield a close agreement between the SNPP VIIRS LST and ground LSTs with a bias of −0.4 K and a RMSE of 1.96 K over six SURFRAD sites; a bias of −0.2 K and a RMSE of 1.93 K over two BSRN sites; and a bias of −0.1 K and a RMSE of 1.7 K over the 13 ARM sites. The time series of the LST errors over individual sites indicate seasonal cycles. The data anomaly over the BSRN site in Cabauw and the SURFRAD site in Desert Rock is revealed and discussed in this study. In addition, a method using Landsat-8 data is applied to quantify the heterogeneity level of each ground station and the results provide promising insights. The validation results demonstrate the maturity of the JPSS VIIRS LST products and their readiness for various application studies.

Estimation of Land Surface Downward Shortwave Radiation Using Spectral-Based Convolutional Neural Network Methods: A Case Study From the Visible Infrared Imaging Radiometer Suite Images

Surface downward shortwave radiation (DSR) is a key parameter in Earth’s surface radiation budget. Many satellite products have been developed, but their accuracies need further improvements. This study proposed an innovative deep learning method that combines radiative-transfer (RT) modeling with convolutional neural network (CNN) learning for estimating instantaneous DSR from VIIRS observations. Unlike traditional CNN methods that rely on spatial contextual information and are not optimal for medium to coarse resolution satellite data, the proposed algorithm takes advantage of both spectral information as well as vertical information. The algorithm firstly estimates the atmospheric effective optical depth from TOA and surface reflectance by using the look-up table created by radiative transfer simulations. We then constructed a spectral-wised virtual matrix to train the CNN using surface DSR measurements at 34 Baseline Surface Radiation Network sites globally during 2013. The developed CNN was also compared with four traditional machine learning algorithms. The validation results showed that the root mean square error (RMSE) and the bias were 91.42 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and -0.94 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> respectively. This research is the first spectral-wised CNN application to estimate surface biophysical parameters from satellite remote sensing data quantitively. The comparison with previous look-up table and optimization-based algorithms shows that the proposed algorithm outperforms by around 10~20 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> We also explored how transfer learning can further improve the DSR estimation. Our results indicate that the universal model with local data transfer learning outperforms either the CNN with local data or the universal CNN by around 10~20 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

Estimation of Land Surface Incident and Net Shortwave Radiation from Visible Infrared Imaging Radiometer Suite (VIIRS) Using an Optimization Method

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

Enterprise LST Algorithm Development and Its Evaluation with NOAA 20 Data

Satellite land surface temperatures (LSTs) have been routinely produced for decades from a variety of polar-orbiting and geostationary satellites, which makes it possible to generate LST climate data globally. However, consistency of the satellite LSTs from different satellite missions is a concern for such purpose; an enterprise satellite LST algorithm is desired for the LST production through different satellite missions, or at the least, through series satellites of a satellite mission. The enterprise LST algorithm employs the split window technique and uses the emissivity explicitly in its formula. This research focuses on the enterprise LST algorithm design, development and its evaluations with the National Oceanic and Atmospheric Administration’s (NOAA) 20 (N20) Visible Infrared Imaging Radiometer Suite (VIIRS) data available since 5 January 2018. In this study, the enterprise LST algorithm was evaluated using simulation dataset consisting of over 2000 profiles from SeeBor collection and the results show a bias of 0.19 K and 0.34 K and standard deviation of 0.48 K and 0.69 K for nighttime and daytime, respectively. The in situ observations from seven NOAA Surface Radiation budget (SURFRAD) sites and two Baseline Surface Radiation Network (BSRN) sites were used for LST validation. The results indicate a bias of −0.3 K and a root mean square error (RMSE) of 2.06 K for SURFRAD stations and a bias of 0.2 K and a RMSE of ~2 K for BSRN sites. Further, the cross-satellite analysis presents a bias of 0.7 K and an RMSE of 1.9 K for comparisons with AQUA MODIS LST (MYD11_L2, Collection 6). The enterprise N20 VIIRS LST product reached the provisional maturity in February 2019 and is ready for users to use in their applications.

The cloud-free global energy balance and inferred cloud radiative effects: an assessment based on direct observations and climate models

In recent studies we quantified the global mean Earth energy balance based on direct observations from surface and space. Here we infer complementary reference estimates for its components specifically under cloud-free conditions. While the clear-sky fluxes at the top of atmosphere (TOA) are accurately known from satellite measurements, the corresponding fluxes at the Earth’s surface are not equally well established, as they cannot be directly measured from space. This is also evident in 38 global climate models from CMIP5, which are shown to greatly vary in their clear-sky surface radiation budgets. To better constrain the latter, we established new clear-sky reference climatologies of surface downward shortwave and longwave radiative fluxes from worldwide distributed Baseline Surface Radiation Network sites. 33 out of the 38 CMIP5 models overestimate the clear-sky downward shortwave reference climatologies, whereas both substantial overestimations and underestimations are found in the longwave counterparts in some of the models. From the bias structure of the CMIP5 models we infer best estimates for the global mean surface downward clear-sky shortwave and longwave radiation, at 247 and 314 Wm−2, respectively. With a global mean surface albedo of 13.5% and net shortwave clear-sky flux of 287 Wm−2 at the TOA this results in a global mean clear-sky surface and atmospheric shortwave absorption of 214 and 73 Wm−2, respectively. From the newly-established diagrams of the global energy balance under clear-sky and all-sky conditions, we quantify the cloud radiative effects not only at the TOA, but also within the atmosphere and at the surface.

MODIS-derived global land products of shortwave radiation and diffuse and total photosynthetically active radiation at 5 km resolution from 2000

Incident shortwave radiation (SW), photosynthetically active radiation (PAR), and diffuse PAR (PARdif) at the land surface drive a multitude of processes related to biosphere-atmosphere interactions and play a critical role in the Earth climate system. Previous global solar radiation products were spatially coarse (>50-km resolution) or temporally short (a few years), which hindered scaling-up ground based observations of the land surface processes into regional, continental, and global scales across multiple time scales. Here, we report Breathing Earth System Simulator (BESS) SW, PAR, and PARdif products over the global land surface at a 5km resolution with 4day intervals between 2000 and 2016. We combined an atmospheric radiative transfer model with an artificial neural network (ANN) to compute SW, PAR, and PARdif. A series of MODerate Resolution Imaging Spectroradiometer (MODIS) atmosphere and land products were used as inputs to run the ANN. We test the performance of the products using data from 158 (SW), 77 (PAR), and 22 (PARdif) stations collected in the Baseline Surface Radiation Network (BSRN) and flux tower networks, which covered a range of climatic zones from polar to tropical zones. BESS had strong linear relationships with in-situ SW data (R2=0.95, relative bias=−2.3%), PAR (R2=0.94, relative bias=1.7%), and PARdif (R2=0.84, relative bias=0.2%). BESS captured the interannual variability of SW at both the site (a majority of long-term BSRN sites) and continental levels. Over the study period, global annual SW, PAR, and PARdif values did not show any dimming or brightening trends, although these trends appeared at regional levels, e.g. dimming in India. Mean annual SW over the global land surface was 184.8Wm−2 (875ZJyr−1, zetta=1021); 46% of SW was partitioned to PAR, which was further split into direct (59%) and diffuse (41%) components. The developed products will be useful in solar energy harvesting research and will improve water, carbon, and energy flux estimates of terrestrial ecosystems from local to the global scales.

The Austrian radiation monitoring network ARAD – best practice and added value

Abstract. The Austrian RADiation monitoring network (ARAD) has been established to advance the national climate monitoring and to support satellite retrieval, atmospheric modeling and the development of solar energy techniques. Measurements cover the downward solar and thermal infrared radiation using instruments according to Baseline Surface Radiation Network (BSRN) standards. A unique feature of ARAD is its vertical dimension of five stations, covering an altitude range between about 200 m a.s.l (Vienna) and 3100 m a.s.l. (BSRN site Sonnblick). The paper outlines the aims and scopes of ARAD, its measurement and calibration standards, methods, strategies and station locations. ARAD network operation uses innovative data processing for quality assurance and quality control, utilizing manual and automated control algorithms. A combined uncertainty estimate for the broadband shortwave radiation fluxes at all five ARAD stations, using the methodology specified by the Guide to the Expression of Uncertainty in Measurement indicates that relative accuracies range from 1.5 to 2.9 % for large signals (global, direct: 1000 W m−2, diffuse: 500 W m−2) and from 1.7 to 23 % (or 0.9 to 11.5 W m−2) for small signals (50 W m−2) (expanded uncertainties corresponding to the 95 % confidence level). If the directional response error of the pyranometers and the temperature response of the instruments and the data acquisition system (DAQ) are corrected, this expanded uncertainty reduces to 1.4 to 2.8 % for large signals and to 1.7 to 5.2 % (or 0.9–2.6 W m−2) for small signals. Thus, for large signals of global and diffuse radiation, BSRN target accuracies are met or nearly met (missed by less than 0.2 percentage points, pps) for 70 % of the ARAD measurements after this correction. For small signals of direct radiation, BSRN targets are achieved at two sites and nearly met (also missed by less than 0.2 pps) at the other sites. For small signals of global and diffuse radiation, targets are achieved at all stations. Additional accuracy gains can be achieved in the future through additional measurements, corrections and a further upgrade of the DAQ. However, to improve the accuracy of measurements of direct solar radiation, improved instrument accuracy is needed. ARAD could serve as a useful example for establishing state-of-the-art radiation monitoring at the national level with a multiple-purpose approach. Instrumentation, guidelines and tools (such as the data quality control) developed within ARAD are intended to increase monitoring capabilities of global radiation and thus designed to allow straightforward adoption in other regions, without high development costs.

An Efficient Method of Estimating Downward Solar Radiation Based on the MODIS Observations for the Use of Land Surface Modeling

Solar radiation is a critical variable in global change sciences. While most of the current global datasets provide only the total downward solar radiation, we aim to develop a method to estimate the downward global land surface solar radiation and its partitioned direct and diffuse components, which provide the necessary key meteorological inputs for most land surface models. We developed a simple satellite-based computing scheme to enable fast and reliable estimation of these variables. The global Moderate Resolution Imaging Spectroradiometer (MODIS) products at 1° spatial resolution for the period 2003–2011 were used as the forcing data. Evaluations at Baseline Surface Radiation Network (BSRN) sites show good agreement between the estimated radiation and ground-based observations. At all the 48 BSRN sites, the RMSE between the observations and estimations are 34.59, 41.98 and 28.06 W∙m−2 for total, direct and diffuse solar radiation, respectively. Our estimations tend to slightly overestimate the total and diffuse but underestimate the direct solar radiation. The errors may be related to the simple model structure and error of the input data. Our estimation is also comparable to the Clouds and Earth’s Radiant Energy System (CERES) data while shows notable improvement over the widely used National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) Reanalysis data. Using our MODIS-based datasets of total solar radiation and its partitioned components to drive land surface models should improve simulations of global dynamics of water, carbon and climate.

The validation of the GEWEX SRB surface longwave flux data products using BSRN measurements

The longwave downward fluxes at the Earth׳s surface are a significant part of the products of the NASA GEWEX SRB (Surface Radiation Budget) project which has produced and archived a 24.5-year continuous record from July 1983 to December 2007 of global shortwave (SW) and longwave (LW) radiation fluxes at TOA and the surface from satellite measurements. The data are generated on a system of grid boxes ranging from 1° latitude by 1° longitude at lower latitudes to 1° latitude by 120° longitude next to the poles. The LW datasets, which are available as 3-hourly, 3-hourly–monthly, daily and monthly means, are produced from two sets of algorithms, the GEWEX LW (GLW) algorithm which is designated as primary and the Langley Parameterized LW (LPLA) algorithm which is designated as quality-check. The inputs of the latest versions, GLW (V3.1) and LPLA (V3.0), include the Geostationary Satellite system (GEOS) Version 4.0.3 meteorological information and cloud properties derived from the International Satellite Cloud Climatology Project (ISCCP) DX data. In this paper, we compare the LW downward fluxes at the Earth׳s surface from both algorithms against over 4000 site-months of the Baseline Surface Radiation Network (BSRN) data from among the 59 BSRN sites. The comparisons are made for the 3-hourly, daily and monthly means each for the entire record, and on a month-by-month basis as well as a site-by-site basis. It is found that the overall daily mean bias/RMS for the GLW (V3.1) and LPLA (V3.0) algorithms are, respectively, 1.1/22.1 and 4.6/22.8Wm−2, their monthly counterparts are, respectively, 0.9/11.1 and 4.5/12.9Wm−2. Anomaly time series for a subset of more continuous BSRN measurement data sets show a standard deviation of 2.3Wm−2 and a correlation of 0.82 indicating the accurate replication of month-to-month variability. Clusters of similar surface types are analyzed showing that the uncertainties are largest over the polar regions. Finally, Kolmogorov–Smirnov (KS) two-sample test and Cramér–von Mises (CvM) two-sample test are used to show that the GLW is able to replicate the cumulative frequency distribution of the measurements at the 0.01 significance level.

Estimating long‐wave radiation at the Earth's surface from measurements of specific humidity

ABSTRACTThe relationship between specific humidity (q) and down‐welling long‐wave radiation (El↓) was investigated on a daily, monthly and annual basis for 39 widely distributed sites of the Baseline Surface Radiation Network (BSRN) and validated using two additional BSRN sites. A power relationship between El↓ and q was fitted to the data for each site; the constant did not change significantly with time and geographic location. Neither the mean value of q nor the residual value of El↓ changed significantly between 1998 and 2011, suggesting the applicability of the relationship over periods without large changes in the Earth's temperature and/or concentration of H2O. The root mean square error for both the calibration and validation sites was comparable to that reported for both direct measurements and satellite‐derived estimates for daily, monthly and annual periods. The power relationships were weaker for three tropical sites located in the Pacific Ocean, where the seasonal variation in q was smaller than at the other sites. For the remaining stations, a common power relationship, El↓ = 212.64 q0.22, enabled monthly values of El↓ to be accurately estimated from widely available measurements of air temperature, humidity and pressure at the Earth's surface.

Improved estimates and understanding of global albedo and atmospheric solar absorption

This study integrates available surface‐based and satellite observations of solar radiation at the surface and the top of the atmosphere (TOA) with a comprehensive set of satellite observations of atmospheric and surface optical properties and a Monte Carlo Aerosol‐Cloud‐Radiation (MACR) model to estimate the three fundamental components of the planetary solar radiation budget: Albedo at the TOA; atmospheric solar absorption; and surface solar absorption. The MACR incorporates most if not all of our current understanding of the theory of solar radiation physics including modern spectroscopic water vapor data, minor trace gases, absorbing aerosols including its effects inside cloud drops, 3‐D cloud scattering effects. The model is subject to a severe test by comparing the simulated solar radiation budget with data from 34 globally distributed state‐of‐the art BSRN (Baseline Surface Radiation Network) land stations which began data collection in the mid 1990s. The TOA over these sites were obtained from the CERES (Cloud and Earth's Radiant Energy System) satellites. The simulated radiation budget was within 2 Wm−2for all three components over the BSRN sites. On the other hand, over these same sites, the IPCC‐2007 simulation of atmospheric absorption is smaller by 7–8 Wm−2. MACR was then used with a comprehensive set of model input from satellites to simulate global solar radiation budget. The simulated planetary albedo of 29.0% confirms the value (28.6%) observed by CERES. We estimate the atmospheric absorption to be 82 ± 8 Wm−2 to be compared with the 67 Wm−2 by IPCC models as of 2001 and updated to 76 Wm−2by IPCC‐2007. The primary reasons for the 6 Wm−2 larger solar absorption in our estimates are: updated water vapor spectroscopic database (∼1 Wm−2), inclusion of minor gases (∼0.5 Wm−2), black and brown carbon aerosols (∼4 Wm−2), the inclusion of black carbon in clouds (∼1 Wm−2) and 3‐D effect of clouds (∼1 Wm−2). The fundamental deduction from our study is the remarkable consistency between satellite measurements of the radiation budget and the parameters (aerosols, clouds and surface reflectivity) which determine the radiation budget. Because of this consistency we can account for and explain the global solar radiation budget of the planet within few Wm−2.

Remote sensing of solar surface radiation for climate monitoring — the CM-SAF retrieval in international comparison

Solar surface irradiance (SIS) and direct (SID) irradiance as well as effective cloud albedo (CAL) climate data records (CDR) derived from the Meteosat first generation satellites (Meteosats 2 to 7, 1983–2005) are presented. The CDRs are available free of charge for all purposes from wui.cmsaf.eu at monthly, daily and hourly means at a spatial resolution of 0.03∘.The processing employed a climate version of the Heliosat algorithm combined with a clear sky model using an eigenvector look-up table method. Modifications to the Heliosat method include a self-calibration algorithm as well as a running mean based clear sky retrieval algorithm.The datasets are validated using ground based observations from the Baseline Surface Radiation Network (BSRN) as a reference. The validation threshold for the mean absolute bias between satellite-derived and surface-measured radiation is given by the target accuracy for solar irradiance fields defined by the Global Climate Observing system (GCOS) and a measurement uncertainty for the surface data. The results demonstrate that the target accuracy is achieved for monthly and daily means. Furthermore, an intercomparison with similar datasets reveal a better performance and climate monitoring potential of the CM SAF SIS CDR at most BSRN sites compared to established data sets like e.g. ERA-reanalysis, GEWEX (Global Energy and Water Cycle Experiment) and ISCCP (International Satellite Cloud Climatology Project). Lastly, the realistic representation of both seasonal and inter-annual variability guarantees the applicability of the satellite-based climate data sets for climate monitoring and analysis of extremes.No trends in the normalized bias between the CM SAF and the BSRN datasets are detectable, which demonstrates the stability and homogeneity of the global and direct irradiance for the period covered by BSRN measurements. However, inconsistencies are detectable at few satellite transition dates for certain regions in earlier years.

Analysis of the biases in the downward shortwave surface flux in the GFDL CM2.1 general circulation model

[1] Simulations of downward shortwave surface fluxes by the coupled Geophysical Fluid Dynamics Laboratory (GFDL) CM2.1 general circulation model are compared against climatology derived from the Baseline Surface Radiation Network (BSRN), Global Energy Balance Archive, and International Satellite Cloud Climatology Project ISCCP-FD data sets. The spatial pattern of the model's biases is evaluated. An investigation is made of how these relate to accompanying biases in total cloud amount and aerosol optical depth and how they affect the surface temperature simulation. Comparing CM2.1's clear-sky fluxes against BSRN site values, for European, Asian, and North American locations, there are underestimates in the direct and overestimates in the diffuse, resulting in underestimates in the total flux. These are related to overestimates of sulfate aerosol optical depth, arising owing to the behavior of the parameterization function for hygroscopic growth of these aerosols at very high relative humidity. Contrastingly, flux overestimate biases at lower latitude locations are associated with underestimates in sea-salt and carbonaceous aerosol amounts. All-sky flux biases consist of underestimates for North America, Eurasia, southern Africa, and northern oceanic regions and overestimates for the Amazon region, equatorial Africa, off the west coast of the Americas, and southern oceanic regions. These biases show strong correlations with cloud amount biases. There are modest correlations of the flux biases with cool surface temperature biases over North America and Eurasia, warm biases over the Amazon region, and cool (warm) biases over the northern (southern) oceanic regions. Analyses assuming nonhygroscopicity illustrate that there is a reduction of surface temperature biases accompanying a reduction of sulfate aerosol optical depth biases, whereas a more significant improvement in the temperature simulation requires refining the model's simulation of cloudiness.

Cloudy sky shortwave radiative closure for a Baseline Surface Radiation Network site

[1] A shortwave radiative closure analysis for cloudy skies is presented for the Cabauw Baseline Surface Radiation Network (BSRN) site (51.97°N, 4.93°E). The cloudy cases are carefully selected to be overcast, single-layer, homogeneous, nonprecipitating water clouds. We selected in total 639 cases on 9 days between May 2008 and May 2009 and on 30 January 2007. The Doubling-Adding KNMI (DAK) code is used to simulate global irradiances. The cloud optical thickness is derived from the cloud liquid water path from microwave radiometer (MWR) measurements and the MODIS L2 cloud effective radius product. The scattering phase matrix of the cloud particles is calculated using a Mie code with the two-parameter Gamma size distribution. The MWR integrated water vapor column and an aerosol climatology are also used in the simulations. The cloudy cases cover a large range of liquid water path (30–400 g/m2), water vapor column (0.7–3.1 cm), and solar zenith angle (41°–75°). The mean difference between simulated global irradiances and BSRN measurements is 6 W/m2 (5%), with a standard deviation of 14 W/m2 (13%). This difference is within the uncertainties of the model input parameters and measurement errors. The correlation coefficient between the measured and simulated global irradiances is 0.95. The good closure results demonstrate the high quality of the MODIS effective radius data and MWR liquid water path data and the accuracy of the DAK model for the selected water cloud cases. Furthermore, the effects of clouds, aerosols, water vapor, and surface albedo on the global irradiance have been analyzed carefully. The sensitivity study shows that in order to achieve the closure with an uncertainty of a few W/m2, more frequent effective radius data, simultaneous aerosol and cloud measurements, and surface albedo measurements are essential.

Assessment of BSRN radiation records for the computation of monthly means

Abstract. The integrity of the Baseline Surface Radiation Network (BSRN) radiation monthly averages are assessed by investigating the impact on monthly means due to the frequency of data gaps caused by missing or discarded high time resolution data. The monthly statistics, especially means, are considered to be important and useful values for climate research, model performance evaluations and for assessing the quality of satellite (time- and space-averaged) data products. The study investigates the spread in different algorithms that have been applied for the computation of monthly means from 1-min values. The paper reveals that the computation of monthly means from 1-min observations distinctly depends on the method utilized to account for the missing data. The intra-method difference generally increases with an increasing fraction of missing data. We found that a substantial fraction of the radiation fluxes observed at BSRN sites is either missing or flagged as questionable. The percentage of missing data is 4.4%, 13.0%, and 6.5% for global radiation, direct shortwave radiation, and downwelling longwave radiation, respectively. Most flagged data in the shortwave are due to nighttime instrumental noise and can reasonably be set to zero after correcting for thermal offsets in the daytime data. The study demonstrates that the handling of flagged data clearly impacts on monthly mean estimates obtained with different methods. We showed that the spread of monthly shortwave fluxes is generally clearly higher than for downwelling longwave radiation. Overall, BSRN observations provide sufficient accuracy and completeness for reliable estimates of monthly mean values. However, the value of future data could be further increased by reducing the frequency of data gaps and the number of outliers. It is shown that two independent methods for accounting for the diurnal and seasonal variations in the missing data permit consistent monthly means to within less than 1 W m−2 in most cases. The authors suggest using a standardized method for the computation of monthly means which addresses diurnal variations in the missing data in order to avoid a mismatch of future published monthly mean radiation fluxes from BSRN. The application of robust statistics would probably lead to less biased results for data records with frequent gaps and/or flagged data and outliers. The currently applied empirical methods should, therefore, be completed by the development of robust methods.

Shortwave radiative fluxes from MODIS: Model development and implementation

A forward inference scheme driven with satellite‐based information is developed to derive top of the atmosphere, surface, and atmospheric spectral shortwave radiative fluxes at global scale. The model takes into account all major atmospheric constituents, the characteristics of water and ice clouds, and the variation of cloud particle effective radius. The multilayered structure accounts for surface elevation effects and for the vertical distribution of the radiative fluxes. Spectral fluxes such as photosynthetically active radiation and near‐infrared radiation can also be estimated. The model is implemented with products from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor both on Terra and Aqua at 1° spatial resolution for a 3‐year period (2003–2005) and at global scale. The derived surface fluxes are evaluated against the globally distributed Baseline Surface Radiation Network (BSRN) measurements and compared with products from independent sources. It is demonstrated that the twice a day MODIS‐based estimates of daily radiative fluxes adjusted for the diurnal cycle and the ground observations have a correlation coefficient greater than 0.96, a relative root‐mean‐square error less than 18%, and a relative bias between 0 and 6% for BSRN sites located in the Antarctic, the Atlantic (islands), North America, Europe, and other continental sites.

Clear‐sky shortwave radiative closure for the Cabauw Baseline Surface Radiation Network site, Netherlands

In this paper a clear‐sky shortwave closure analysis is presented for the Baseline Surface Radiation Network (BSRN) site of Cabauw, Netherlands (51.97°N, 4.93°E). The analysis is based on an exceptional period of fine weather during the first half of May 2008, resulting in a selection of 72 comparisons, on 6 days, between BSRN measurements and Doubling Adding KNMI (DAK) model simulations of direct, diffuse, and global irradiances. The data span a wide range of aerosol properties, water vapor columns, and solar zenith angles. The model input consisted of operational Aerosol Robotic Network (AERONET) aerosol products and radiosonde data. The wavelength dependence of the aerosol optical thickness, single scattering albedo, and asymmetry parameter was taken into account. On the basis of these data, excellent closure was obtained: the mean differences between model and measurements are 2 W/m2 (+0.2%) for the direct irradiance, 1 W/m2 (+0.8%) for the diffuse irradiance, and 2 W/m2 (+0.3%) for the global irradiance. The good results were obtained because of proper specification of the DAK model input and the high quality of the AERONET and BSRN measurements. The sensitivity of the achieved closure to uncertainties in the aerosol optical thickness, single scattering albedo, and asymmetry parameter was examined. Furthermore, several sensitivity experiments related to the wavelength dependence of the aerosol optical properties and the treatment of water vapor were performed. It appeared that a correct description of the wavelength dependence of the aerosol optical properties is important for achieving broadband closure. However, broadband closure can also be obtained by means of using spectrally averaged values of the single scattering albedo and the asymmetry parameter. Cancellation of errors in different parts of the solar spectrum also contributes to the achieved closure.

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>

Use of Moderate‐Resolution Imaging Spectroradiometer bidirectional reflectance distribution function products to enhance simulated surface albedos

Moderate‐Resolution Imaging Spectroradiometer (MODIS) surface albedo at high spatial and spectral resolution is compared with other remotely sensed climatologies, ground‐based data, and albedos simulated with the European Center/Hamburg 4 (ECHAM4) global climate model at T42 resolution. The study demonstrates the importance of MODIS data in assessing and improving albedo parameterizations in weather forecast and climate models. The remotely sensed PINKER surface albedo climatology follows the MODIS estimates fairly well in both the visible and near‐infrared spectra, whereas ECHAM4 simulates high positive albedo biases over snow‐covered boreal forests and the Himalayas. In contrast, the ECHAM4 albedo is probably too low over the Sahara sand desert and adjacent steppes. The study clearly indicates that neglecting albedo variations within T42 grid boxes leads to significant errors in the simulated regional climate and horizontal fluxes, mainly in mountainous and/or snow‐covered regions. MODIS surface albedo at 0.05 resolution agrees quite well with in situ field measurements collected at Baseline Surface Radiation Network (BSRN) sites during snow‐free periods, while significant positive biases are found under snow‐covered conditions, mainly due to differences in the vegetation cover at the BSRN site (short grass) and the vegetation within the larger MODIS grid box. Black sky (direct beam) albedo from the MODIS bidirectional reflectance distribution function model captures the diurnal albedo cycle at BSRN sites with sufficient accuracy. The greatest negative biases are generally found when the Sun is low. A realistic approach for relating albedo and zenith angle has been proposed. Detailed evaluations have demonstrated that ignoring the zenith angle dependence may lead to significant errors in the surface energy balance.