Evaluation of the Reanalysis Surface Incident Shortwave Radiation Products from NCEP, ECMWF, GSFC, and JMA Using Satellite and Surface Observations

Zhang, Xiaolin; Liang, Shunlin; Wang, Guoxin; Yao, Yunjun; Jiang, Bo; Cheng, Jie

doi:10.3390/rs8030225

Cited by 136 publications

(129 citation statements)

References 60 publications

(109 reference statements)

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“…Current reanalysis systems calculate cloud properties based on the assimilation of water vapor and temperature profiles from radiosonde and satellite observations. However, spatial biases in these data have been found, by comparing R s data derived from reanalysis datasets with field observations and satellite data in numerous validation studies [10,[34][35][36]. Wang et al [10] find that the R s data derived from the Modern Era Retrospective-analysis for Research and Applications (MERRA) reanalysis data and ERA-interim (ERAI) products have a large spatial bias of 29.8 W·m −2 and 19.1 W·m −2 in China, respectively.…”

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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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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“…The earliest assessment of reanalysis fluxes using GEBA data is documented in Wild et al (1998a), while the most upto-date assessment of six recent reanalyses was performed by Zhang et al (2016). Similarly to climate models, reanalyses were shown to have difficulties in representing the multidecadal variations in surface shortwave radiation seen in the GEBA data (Wild and Schmucki, 2011).…”

Section: Validation Of Surface Energy Fluxes From Climate Models Reamentioning

confidence: 99%

The Global Energy Balance Archive (GEBA) version 2017: a database for worldwide measured surface energy fluxes

Wild

Ohmura

Schär

et al. 2017

Earth Syst. Sci. Data

110

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Abstract. The Global Energy Balance Archive (GEBA) is a database for the central storage of the worldwide measured energy fluxes at the Earth's surface, maintained at ETH Zurich (Switzerland). This paper documents the status of the GEBA version 2017 dataset, presents the new web interface and user access, and reviews the scientific impact that GEBA data had in various applications. GEBA has continuously been expanded and updated and contains in its 2017 version around 500 000 monthly mean entries of various surface energy balance components measured at 2500 locations. The database contains observations from 15 surface energy flux components, with the most widely measured quantity available in GEBA being the shortwave radiation incident at the Earth's surface (global radiation). Many of the historic records extend over several decades. GEBA contains monthly data from a variety of sources, namely from the World Radiation Data Centre (WRDC) in St. Petersburg, from national weather services, from different research networks (BSRN, ARM, SURFRAD), from peer-reviewed publications, project and data reports, and from personal communications. Quality checks are applied to test for gross errors in the dataset. GEBA has played a key role in various research applications, such as in the quantification of the global energy balance, in the discussion of the anomalous atmospheric shortwave absorption, and in the detection of multi-decadal variations in global radiation, known as "global dimming" and "brightening". GEBA is further extensively used for the evaluation of climate models and satellite-derived surface flux products. On a more applied level, GEBA provides the basis for engineering applications in the context of solar power generation, water management, agricultural production and tourism. GEBA is publicly accessible through the internet via http://www.geba.ethz.ch. Supplementary data are available at https://doi.org/10.1594/PANGAEA.873078.

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“…These data were obtained from the NASA Langley Research Center CERES ordering tool (http://ceres.larc.nasa.gov/). CERES-EBAF has shown greater accuracy compared to other gridded radiation products as it incorporates detailed cloud and aerosol information (Ma et al 2015;Wild et al 2013Zhang et al 2015Zhang et al , 2016. Figure 8 also depicts additional ground-based measurements from independent sites (black dots; Abisko, Sweden; Atqasuk, Alaska; Sondankyla, Finland; and Summit, Greenland) and others that are part of the World Climate Research Programme Baseline Surface Radiation Network (BSRN; Hegner et al 1998;Ohmura et al 1998).…”

Section: Nares Strait Flowmentioning

confidence: 99%

The Arctic System Reanalysis, Version 2

Bromwich

Wilson

Bai

et al. 2018

Bulletin of the American Meteorological Society

109

107

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The Arctic is a vital component of the global climate, and its rapid environmental evolution is an important element of climate change around the world. To detect and diagnose the changes occurring to the coupled Arctic climate system, a state-of-the-art synthesis for assessment and monitoring is imperative. This paper presents the Arctic System Reanalysis, version 2 (ASRv2), a multiagency, university-led retrospective analysis (reanalysis) of the greater Arctic region using blends of the polar-optimized version of the Weather Research and Forecasting (Polar WRF) Model and WRF three-dimensional variational data assimilated observations for a comprehensive integration of the regional climate of the Arctic for 2000–12. New features in ASRv2 compared to version 1 (ASRv1) include 1) higher-resolution depiction in space (15-km horizontal resolution), 2) updated model physics including subgrid-scale cloud fraction interaction with radiation, and 3) a dual outer-loop routine for more accurate data assimilation. ASRv2 surface and pressure-level products are available at 3-hourly and monthly mean time scales at the National Center for Atmospheric Research (NCAR). Analysis of ASRv2 reveals superior reproduction of near-surface and tropospheric variables. Broadscale analysis of forecast precipitation and site-specific comparisons of downward radiative fluxes demonstrate significant improvement over ASRv1. The high-resolution topography and land surface, including weekly updated vegetation and realistic sea ice fraction, sea ice thickness, and snow-cover depth on sea ice, resolve finescale processes such as topographically forced winds. Thus, ASRv2 permits a reconstruction of the rapid change in the Arctic since the beginning of the twenty-first century–complementing global reanalyses. ASRv2 products will be useful for environmental models, verification of regional processes, or siting of future observation networks.

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Evaluation of the Reanalysis Surface Incident Shortwave Radiation Products from NCEP, ECMWF, GSFC, and JMA Using Satellite and Surface Observations

Cited by 136 publications

References 60 publications

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

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

The Global Energy Balance Archive (GEBA) version 2017: a database for worldwide measured surface energy fluxes

The Arctic System Reanalysis, Version 2

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