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Evaluation of sea-surface photosynthetically available radiation algorithms under various sky conditions and solar elevations

机译:各种天空条件下海面光合作用辐射算法的评价与太阳升高

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摘要

In this study, we report on the performance of satellite-based photosynthetically available radiation (PAR) algorithms used in published oceanic primary production models. The performance of these algorithms was evaluated using buoy observations under clear and cloudy skies, and for the particular case of low sun angles typically encountered at high latitudes or at moderate latitudes in winter. The PAR models consisted of (i) the standard one from the NASA-Ocean Biology Processing Group (OBPG), (ii) the Gregg and Carder (GC) semi-analytical clear-sky model, and (iii) look-up-tables based on the Santa Barbara DISORT atmospheric radiative transfer (SBDART) model. Various combinations of atmospheric inputs, empirical cloud corrections, and semi-analytical irradiance models yielded a total of 13 (11 + 2 developed in this study) different PAR products, which were compared with in situ measurements collected at high frequency (15 min) at a buoy site in the Mediterranean Sea (the "BOUee pour l'acquiSition d'une Serie Optique a Long termE," or, "BOUSSOLE" site). An objective ranking method applied to the algorithm results indicated that seven PAR products out of 13 were well in agreement with the in situ measurements. Specifically, the OBPG method showed the best overall performance with a root mean square difference (RMSD) (bias) of 19.7% (6.6%) and 10% (6.3%) followed by the look-up-table method with a RMSD (bias) of 25.5% (6.8%) and 9.6% (2.6%) at daily and monthly scales, respectively. Among the four methods based on clear-sky PAR empirically corrected for cloud cover, the Dobson and Smith method consistently underestimated daily PAR while the Budyko formulation overestimated daily PAR. Empirically cloud-corrected methods using cloud fraction (CF) performed better under quasi-clear skies (CF 0.3) with an RMSD(bias) of 9.7%-14.8%(3.6%-11.3%) than under partially clear to cloudy skies (0.3 CF 0.7) with 16.1%-21.2% (-2.2%-8.8%). Under complete overcast conditions (CF 0.7), however, all methods showed larger RMSD differences (biases) ranging between 32% and 80.6% (-54.5%-8.7%). Finally, three methods tested for low sun elevations revealed systematic overestimation, and one method showed a systematic underestimation of daily PAR, with relative RMSDs as large as 50% under all sky conditions. Under partially clear to overcast conditions all the methods underestimated PAR. Model uncertainties predominantly depend on which cloud products were used. (C) 2018 Optical Society of America
机译:在这项研究中,我们报告了出版海洋主要生产模型中使用的卫星光合作用辐射(PAR)算法的性能。使用透明和阴天的天空下的浮标观察评估这些算法的性能,并且对于通常在高纬度或冬季中等纬度遇到的低阳光角度的特定情况。 PAR模型由(i)来自美国宇航局 - 海洋生物学处理组(OBPG),(ii)GREGG和卡特(GC)半分析清晰天模型,(iii)查找表基于Santa Barbara Sentort大气辐射转移(SBDART)模型。大气输入,经验云校正和半分析辐照模型的各种组合产生了总共13(11 + 2,在本研究中开发)不同的PAR产品,其与高频(15分钟)收集的原位测量相比地中海的一个浮标网站(“Bouee Pour L'收购D'Une Serie Optique一个长期,”或“或”Boussole“网站)。应用于算法结果的客观排名方法表明,13中的七种Par产品与原位测量一致。具体地,OBPG方法显示了具有19.7%(6.6%)和10%(6.3%)的根均值(RMSD)(偏置)的最佳整体性能,然后是具有RMSD的查找方法(偏置每日和每月尺度分别为25.5%(6.8%)和9.6%(2.6%)。在基于明确纠正云覆盖的明确纠正的四种方法中,多斯森和史密斯方法一直低估了每日低估,而Budyko制剂高估每日靶标准。使用云馏分(CF)在准透明的天空(CF <0.3)下进行的经验云矫正方法,RMSD(偏置)为9.7%-14.8%(3.6%-11.3%),而不是浑浊的天空。 (0.3&& 0.7),16.1%-21.2%(-2.2%-8.8%)。然而,在完全阴云中心(CF& 0.7)下,所有方法显示出较大的RMSD差异(偏差)在32%和80.6%之间(-54.5%-8.7%)。最后,测试低阳光升高的三种方法揭示了系统的高估,一种方法显示出每日PAR的系统低估,相对RMSD在所有天空条件下大约50%。在部分清楚地清楚的是阴天条件,所有低估的方法都受到了间隔。模型不确定因素主要取决于使用云产品。 (c)2018年光学学会

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  • 来源
    《Applied optics》 |2018年第12期|共18页
  • 作者单位

    Univ Laval Laval Univ Canada CNRS France Dept Biol &

    Quebec Ocean Takuvik Joint Int Lab Quebec City PQ G1V 0A6 Canada;

    Fisheries &

    Oceans Canada DFO MPO 1 Challenger Dr Dartmouth NS B2Y 4A2 Canada;

    Univ Quebec Rimouski Dept Biol Chim &

    Geog &

    BOREAS 300 Allee Ursulines Rimouski PQ G5L 3A1 Canada;

    Univ Paris 06 Sorbonne Univ Paris 06 UMR 7093 Lab Oceanog Villefranche Observ Oceanol F-06230 Villefranche Sur Mer France;

    Univ Paris 06 Sorbonne Univ Paris 06 UMR 7093 Lab Oceanog Villefranche Observ Oceanol F-06230 Villefranche Sur Mer France;

    Univ Laval Laval Univ Canada CNRS France Dept Biol &

    Quebec Ocean Takuvik Joint Int Lab Quebec City PQ G1V 0A6 Canada;

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  • 正文语种 eng
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