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Modeling Carbon Sequestration over the Large-Scale Amazon Basin, Aided by Satellite Observations. Part I: Wet- and Dry-Season Surface Radiation Budget Flux and Precipitation Variability Based on GOES Retrievals

机译:在卫星观测的帮助下,对大型亚马逊盆地的碳固存建模。第一部分:基于GOES检索的干季和湿季辐射预算通量和降水变化

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摘要

In this first part of a two-part investigation, large-scale Geostationary Operational Environmental Satellite (GOES) analyses over the Amazônia region have been carried out for March and October of 1999 to provide detailed information on surface radiation budget (SRB) and precipitation variability. SRB fluxes and rainfall are the two foremost cloud-modulated control variables that affect land surface processes, and they require specification at spacetime resolutions concomitant with the changing cloud field to represent adequately the complex coupling of energy, water, and carbon budgets. These processes ultimately determine the relative variations in carbon sequestration and carbon dioxide release within a forest ecosystem. SRB and precipitation retrieval algorithms using GOES imager measurements are used to retrieve surface downward radiation and surface rain rates at high spacetime resolutions for large-scale carbon budget modeling applications in conjunction with the Large-Scale BiosphereAtmosphere Experiment in Amazônia. To validate the retrieval algorithms, instantaneous estimates of SRB fluxes and rain rates over 8 km × 8 km areas were compared with 30-min-averaged surface measurements obtained from tower sites located near Ji-Paraná and Manaus in the states of Rondônia and Amazonas, respectively. Because of large aerosol concentrations originating from biomass burning during the dry season (i.e., September and October for purposes of this analysis), an aerosol index from the Total Ozone Mapping Spectrometer is used in the solar radiation retrieval algorithm. The validation comparisons indicate that bias errors for incoming total solar, photosynthetically active radiation (PAR), and infrared flux retrievals are under 4%, 6%, and 3% of the mean values, respectively. Precision errors at the analyzed space time scales are on the order of 20%, 20%, and 5%. The visible and infrared satellite measurements used for precipitation retrieval do not directly detect rainfall processes, and yet they are responsive to the rapidly changing cloud fields, which are directly associated with precipitation life cycles over the Amazon basin. In conducting the validation analysis at high spacetime scales, the comparisons indicate systematic bias uncertainties on the order of 25%. These uncertainties are comparable to published values from an independent assessment of bias uncertainties inherent to the current highest-quality satellite retrievals, that is, from the Tropical Rainfall Measuring Mission. Because precipitation is a weak direct control on photosynthesis for the Amazon ecosystem, that is, photosynthesis is dominated by the strong diurnal controls of incoming PAR and ambient air-canopy temperatures, such uncertainties are tolerable. By the same token, precipitation is a strong control on soil thermal properties and carbon respiration through soil moisture, but the latter is a time-integrating variable and thus inhibits introduction of modeling errors caused by random errors in the precipitation forcing. The investigation concludes with analysis of the monthly, daily, and diurnal variations intrinsic to SRB and rainfall processes over the Amazon basin, including explanations of how these variations arise during wet- and dry-season periods.
机译:在由两部分组成的调查的第一部分中,已于1999年3月和10月对整个亚马逊地区的地球静止地球同步运行环境卫星(GOES)进行了分析,以提供有关地表辐射预算(SRB)和降水变化的详细信息。 。 SRB通量和降雨量是影响陆地表面过程的两个最重要的云调制控制变量,它们需要以时空分辨率为指标,并与不断变化的云场相适应,以充分体现能源,水和碳预算的复杂耦合。这些过程最终决定了森林生态系统中固碳和二氧化碳释放的相对变化。使用GOES成像仪测量的SRB和降水量检索算法可在高时空分辨率下检索地表向下辐射和地表降雨率,以用于大规模碳预算建模应用程序,并与亚马孙州的大规模生物圈大气实验结合使用。为了验证该检索算法,将8 km×8 km区域的SRB通量和降雨率的瞬时估计值与从Rondônia和Amazonas州Ji-Paraná和Manaus附近塔楼站点获得的30分钟平均地面测量值进行了比较,分别。由于干旱季节(即本分析的目的是9月和10月)源自生物质燃烧的大量气溶胶浓度,在太阳辐射检索算法中使用了总臭氧测图仪的气溶胶指数。验证比较表明,入射的总太阳,光合有效辐射(PAR)和红外通量的反演误差分别低于平均值的4%,6%和3%。在所分析的时空尺度上,精度误差约为20%,20%和5%。用于降水获取的可见光和红外卫星测量值不能直接检测降雨过程,但是它们对迅速变化的云场作出了响应,而云场的变化与亚马逊盆地的降水生命周期直接相关。在高时空尺度上进行验证分析时,比较表明系统偏差不确定性约为25%。这些不确定性与独立评估当前最高质量卫星检索所固有的偏差不确定性(即热带雨量测量任务)所发表的值具有可比性。因为降水是亚马逊生态系统对光合作用的直接控制的弱项,也就是说,光合作用主要受传入PAR和周​​围空气冠层温度的昼夜控制,所以这种不确定性是可以容忍的。出于同样的原因,降水是对土壤热性质和通过土壤水分的碳呼吸的有力控制,但后者是时间积分变量,因此可以抑制由于降水强迫中的随机误差而引起的建模误差的引入。该调查以对亚马逊河流域SRB和降雨过程固有的每月,每日和每日变化的分析为结尾,包括对这些变化在干湿季期间如何产生的解释。

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