Evaluation of satellite and reanalysis products of downward surface solar radiation over East Asia: Spatial and seasonal variations

Jia, Binghao; Xie, Zhenghui; Dai, Aiguo; Shi, Chunxiang; Chen, Feng

doi:10.1002/jgrd.50353

Cited by 102 publications

(57 citation statements)

References 45 publications

(74 reference statements)

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“…Figure 8 Lu et al (2011) with 20.4 % daily RMSE and 11.4 % monthly RMSE. Moreover, the daily mean RMSE of our study is obviously lower than that of Jia et al (2013), who estimate SSR with FY-2C and find a daily mean RMSE over China of about 49.3 W m −2 (or 27.5 %). These results suggest that our SSR estimation with MTSAT data works well for various climate regions, land cover types, and elevations.…”

Section: Validation Of Hourly Ssr In the Haihe River Basincontrasting

confidence: 73%

Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data

et al. 2016

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Abstract. Cloud parameters (cloud mask, effective particle radius, and liquid/ice water path) are the important inputs in estimating surface solar radiation (SSR). These parameters can be derived from MODIS with high accuracy, but their temporal resolution is too low to obtain high-temporalresolution SSR retrievals. In order to obtain hourly cloud parameters, an artificial neural network (ANN) is applied in this study to directly construct a functional relationship between MODIS cloud products and Multifunctional Transport Satellite (MTSAT) geostationary satellite signals. In addition, an efficient parameterization model for SSR retrieval is introduced and, when driven with MODIS atmospheric and land products, its root mean square error (RMSE) is about 100 W m −2 for 44 Baseline Surface Radiation Network (BSRN) stations. Once the estimated cloud parameters and other information (such as aerosol, precipitable water, ozone) are input to the model, we can derive SSR at high spatiotemporal resolution. The retrieved SSR is first evaluated against hourly radiation data at three experimental stations in the Haihe River basin of China. The mean bias error (MBE) and RMSE in hourly SSR estimate are 12.0 W m −2 (or 3.5 %) and 98.5 W m −2 (or 28.9 %), respectively. The retrieved SSR is also evaluated against daily radiation data at 90 China Meteorological Administration (CMA) stations. The MBEs are 9.8 W m −2 (or 5.4 %); the RMSEs in daily and monthly mean SSR estimates are 34.2 W m −2 (or 19.1 %) and 22.1 W m −2 (or 12.3 %), respectively. The accuracy is comparable to or even higher than two other radiation products (GLASS and ISCCP-FD), and the present method is more computationally efficient and can produce hourly SSR data at a spatial resolution of 5 km.

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Section: Validation Of Hourly Ssr In the Haihe River Basincontrasting

confidence: 73%

Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data

et al. 2016

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“…Previous studies have shown that R s from reanalysis data produces significantly less accuracy than satellite-based models [10,[19][20][21]. However, few studies have been focused on the comparison of operational NWP models with satellite-derived data [13,15,[23][24][25][26].…”

Section: Discussion and Outlookmentioning

confidence: 99%

“…Additionally, NWP models perform a comprehensive simulation of the whole atmospheric system, including ancillary variables such as wind, temperature or relative humidity [18]. However, previous studies have shown that the accuracy achieved using NWP models when computing the surface solar radiation is still significantly less than that obtained using satellite-based models [10,[19][20][21]. In these studies, reanalysis data has usually been used to be contrasted with satellite data, due to the fact that in reanalysis one NWP model with fixed parameters is employed for the entire time series, which improves the temporal consistency of retrieved climate variables [10].…”

Section: Introductionmentioning

confidence: 99%

Seasonal Characterization of Solar Radiation Estimates Obtained from a MSG-SEVIRI-Derived Dataset and a RAMS-Based Operational Forecasting System over the Western Mediterranean Coast

2016

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Solar radiation is a key factor in the Earth's energy balance and it is used as a crucial input parameter in many disciplines such as ecology, agriculture, solar energy and hydrology. Thus, accurate information of the global downward surface shortwave flux integration into the grid is of significant importance. From the different strategies used for grid integration of the surface solar radiation estimates, satellite-derived and numerical weather prediction forecasts are two interesting alternatives. In the current work, we present a comprehensive evaluation of the global downward solar radiation forecasts provided by the Regional Atmospheric Modeling System (RAMS) and the Downwelling Surface Shortwave Flux (DSSF) product, derived from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Both solar radiation estimates are compared to thirteen ground-based weather station measurements for the winter 2010-2011 and the summer 2011 seasons. For these periods, the most recent versions of RAMS (4.4 and 6.0) were running in parallel within the real-time weather forecasting system implemented over the Valencia Region. The solar radiation performance and accuracy are evaluated for these datasets segmented into two atmospheric conditions (clear and cloudy skies) and two terrain classes (flat and hilly). DSSF shows a very good agreement over the study area. Statistical daily evaluations show that corresponding errors vary between seasons, with absolute bias ranging from´30 to 40 W¨m´2, absolute root mean square errors (RMSE) from 25 to 60 W¨m´2, relative bias ranging from´11% to 7% and relative RMSE from 7% to 22%, depending on the sky condition and the terrain location as well, thus reproducing the observations more faithfully than RAMS, which produces higher errors in comparison to the measurements. In this regard, statistical daily evaluations show absolute bias values varying from´50 to 160 W¨m´2, absolute RMSE from 60 to 240 W¨m´2, relative bias ranging from´30% to 40% and relative RMSE from 10% to 80%, also depending on the daily initialization and the forecast horizon. This bias variability demonstrates that there is a different trend in the deviation of the model results in relation to the observations, both for the DSSF product and RAMS forecasts, and considering the summer and the winter seasons independently. In this regard, although there is an overestimation of the observed solar radiation within the summer months, this magnitude is underestimated during the winter. Finally, comparing this solar radiation estimates for different atmospheric conditions and different terrain classes, the best results are found under clear skies over flat terrain. This result is achieved using both methodologies.

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“…Boilley and Wald [35] conclude that overestimations of R s by MERRA are probably due to the underestimation of cloudy conditions. Jia et al [37] find that R s from the National Centers for Environmental Prediction and National Center for Atmospheric Research Reanalysis (NCEP1) has a poor correlation with field observations, showing a root mean square error of 56.5 W·m −2 over East Asia.…”

Section: Introductionmentioning

confidence: 99%

Merging Satellite Retrievals and Reanalyses to Produce Global Long-Term and Consistent Surface Incident Solar Radiation Datasets

Feng

Wang

2018

Remote Sensing

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Surface incident solar radiation (R s ) is a key parameter in many climatic and ecological processes. The data from satellites and reanalysis have been widely used. However, for reanalysis, R s data has been shown to have substantial spatial bias, and the time span of reliable satellite R s is too short for climatic and ecological studies. Combining reanalysis and satellite data would be an effective method for generating long-term and consistent R s datasets. Here, we apply a cumulative probability density function-based (CPDF) method to merge eight reanalyses with the latest available satellite R s data from Clouds and Earth's Radiant Energy System Energy Balanced and Filled (CERES EBAF) surface retrievals. The CPDF method not only reduces the spatial bias of the reanalysis R s data, but also makes the R s datasets in a global, long-term and consistent way. The observed R s data collected at 54 Baseline Surface Radiation Network (BSRN) stations from 1992 to 2016 are used to evaluate the method. Results show that the CPDF method could reduce the mean absolute biases (MAB) of the reanalysis R s effectively by 21.24-64.36%. The European Centre for Medium-Range Weather Forecasts Re-Analysis interim (ERA-interim) reanalysis R s data, which are available for 1979 onward, perform the best before MAB = 13.20 W·m −2 and after MAB = 10.40 W·m −2 merging. This small post-merging MAB of the ERA-interim reanalysis is caused by the MAB of 9.90 W·m −2 in the satellite R s retrievals. The Japanese 55-year reanalysis provides R s values back to 1958, and CPDF can reduce its MAB by 32.87%, to 11.17 W·m −2 . The National Oceanic and Atmospheric Administration (NOAA)-CIRES twentieth-century reanalysis (CIRES) and the ECMWF twentieth-century reanalysis (ERA20CM) provide century-long R s estimates. CIRES performs better after merging. The MAB of CIRES can be reduced by 32.10%, to 12.99 W·m −2 , while ERA20CM's can be reduced by 12.51%, to 16.40 W·m −2 .

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Evaluation of satellite and reanalysis products of downward surface solar radiation over East Asia: Spatial and seasonal variations

Cited by 102 publications

References 45 publications

Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data

Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data

Seasonal Characterization of Solar Radiation Estimates Obtained from a MSG-SEVIRI-Derived Dataset and a RAMS-Based Operational Forecasting System over the Western Mediterranean Coast

Merging Satellite Retrievals and Reanalyses to Produce Global Long-Term and Consistent Surface Incident Solar Radiation Datasets

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