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首页> 外文期刊>Atmospheric Measurement Techniques >An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals
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An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals

机译：用于过滤，校正和聚合MODIS Collection 5光学深度检索数据的陆上气溶胶光学深度数据集

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摘要

MODIS Collection 5 retrieved aerosol optical depth (AOD) over land (MOD04/MYD04) was evaluated using 4 years of matching AERONET observations, to assess its suitability for aerosol data assimilation in numerical weather prediction models. Examination of errors revealed important sources of variation in random errors (e.g., atmospheric path length, scattering angle "hot spot"), and systematic biases (e.g., snow and cloud contamination, surface albedo bias). A set of quality assurance (QA) filters was developed to avoid conditions with potential for significant AOD error. An empirical correction for surface boundary condition using the MODIS 16-day albedo product captured 25percent of the variability in the site mean bias at low AOD. A correction for regional microphysical bias using the AERONET fine/coarse partitioning information increased the global correlation between MODIS and AERONET from r~(2) velence 0.62-0.65 to r~(2) velence 0.71-0.73. Application of these filters and corrections improved the global fraction of MODIS AOD within (0.05 +- 20percent) of AERONET to 77percent, up from 67percent using only built-in MODIS QA. The compliant fraction in individual regions was improved by as much as 20percent (South America). An aggregated Level 3 product for use in a data assimilation system is described, along with a prognostic error model to estimate uncertainties on a per-observation basis. The new filtered and corrected Level 3 product has improved performance over built-in MODIS QA with less than a 15percent reduction in overall data available for data assimilation.
机译：使用4年匹配的AERONET观测值对MODIS Collection 5检索到的陆地气溶胶光学深度（AOD）（MOD04 / MYD04）进行了评估，以评估其在数值天气预报模型中对气溶胶数据同化的适用性。对误差的检查揭示了随机误差（例如，大气路径长度，散射角“热点”）和系统偏差（例如，雪和云污染，表面反照率偏差）的重要变化来源。开发了一组质量保证（QA）过滤器，以避免可能出现严重AOD误差的条件。使用MODIS 16天反照率积对表面边界条件进行的经验校正，在低AOD时捕获了站点平均偏差的25％。使用AERONET细/粗分配信息对区域微物理偏差的校正将MODIS和AERONET之间的全局相关性从r〜（2）速度0.62-0.65提高到r〜（2）速度0.71-0.73。这些过滤器和校正的应用将仅在内置MODIS QA中的MODIS AOD的整体比例（从AERONET的0.05％到20％）提高到77％，从67％提高到77％。各个地区的合规率提高了20％（南美）。描述了用于数据同化系统的汇总3级产品，以及预测误差模型，用于根据每个观测来估计不确定性。新的经过过滤和校正的3级产品与内置的MODIS QA相比，性能得到了改善，可用于数据同化的总体数据减少了不到15％。

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