Ruohan Li;Dongdong Wang;Sadashiva Devadiga;Sudipta Sarkar;Miguel O. Román
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本研究介绍了MODIS自适应处理系统(MODAPS)利用NASA MODIS陆地科学团队开发的最新版科学算法生成的新版MODIS/Terra + Aqua Surface Radiation Daily/3-h向下短波辐射(DSR)(MCD18A1 V6.2)和光合有效辐射(PAR)(MCD18A2 V6.2)产品。新算法的主要改进包括:使用了涵盖可见光、近红外和短波红外的多个波段,以增强描述云光学特征的能力,尤其是在积雪表面;采用线性插值法进行时间扩展,从瞬时检索到每3小时检索一次。与 MCD18 V6.1 和云与地球辐射能系统综合(CERES-SYN)的比较验证表明,V6.2 显著提高了瞬时、3 小时和每日尺度的精度,尤其是在积雪覆盖地区。V6.2 的均方根误差(RMSE)(相对 RMSE:rRMSE)在瞬时 DSR 和 PAR 方面分别达到 101.9 W/m2 (18.8%)和 48.4 W/m2 (20.8%)。每日 DSR 和 PAR 的均方根误差(rRMSE)分别达到 29.9 W/m2 (16.9%) 和 14.1 W/m2 (18.4%)。汇总到 100 公里,V6.2 与仅使用极轨卫星数据的 CERES-SYN 精确度相匹配。这项研究还探讨了未来通过整合地球静止观测数据进一步提高精度的可能性。
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MCD18 V6.2: A New Version of MODIS Downward Shortwave Radiation and Photosynthetically Active Radiation Products
This study presents the new version of MODIS/Terra + Aqua Surface Radiation Daily/3-h downward shortwave radiation (DSR) (MCD18A1 V6.2) and photosynthetic active radiation (PAR) (MCD18A2 V6.2) product generated by MODIS adaptive processing system (MODAPS) using the latest version of the science algorithm developed by the NASA MODIS land science team. Key improvements in the new algorithm include using multiple bands covering visible, near-infrared, and shortwave infrared to enhance the capability of characterizing cloud optical characteristics, especially over snow-covered surfaces, and adopting linear interpolation for temporal scaling from instantaneous to 3-hourly retrievals. Comparative validation against MCD18 V6.1 and clouds and the Earth’s radiant energy system synoptic (CERES-SYN) demonstrates that V6.2 significantly improves accuracy at instantaneous, 3-hourly, and daily scales, particularly in snow-covered regions. The root mean square error (RMSE) (relative RMSE: rRMSE) of V6.2 reaches 101.9 W/m2 (18.8%) and 48.4 W/m2 (20.8%) for instantaneous DSR and PAR. The RMSE (rRMSE) reaches 29.9 W/m2 (16.9%) and 14.1 W/m2 (18.4%) for daily DSR and PAR, respectively. Aggregated to 100 km, V6.2 matches CERES-SYN accuracy using only polar-orbiting satellite data. This study also explores the potential for future improvement by integrating geostationary observations to enhance accuracy further.
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