Cooper Matthew J, Martin Randall V, Lyapustin Alexei I, McLinden Chris A
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada.
Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts, USA.
Atmos Meas Tech. 2018;11(5):2983-2994. doi: 10.5194/amt-11-2983-2018. Epub 2018 May 22.
Accurate representation of surface reflectivity is essential to tropospheric trace gas retrievals from solar backscatter observations. Surface snow cover presents a significant challenge due to its variability and thus snow-covered scenes are often omitted from retrieval data sets; however, the high reflectance of snow is potentially advantageous for trace gas retrievals. We first examine the implications of surface snow on retrievals from the upcoming TEMPO geostationary instrument for North America. We use a radiative transfer model to examine how an increase in surface reflectivity due to snow cover changes the sensitivity of satellite retrievals to NO2 in the lower troposphere. We find that a substantial fraction (>50%) of the TEMPO field of regard can be snow covered in January, and that the average sensitivity to the tropospheric NO2 column substantially increases (doubles) when the surface is snow covered. We then evaluate seven existing satellite-derived or reanalysis snow extent products against ground station observations over North America to assess their capability of informing surface conditions for TEMPO retrievals. The Interactive Multisensor Snow and Ice Mapping System (IMS) had the best agreement with ground observations (accuracy of 93%, precision of 87%, recall of 83%). Multiangle Implementation of Atmospheric Correction (MAIAC) retrievals of MODIS-observed radiances had high precision (90% for Aqua and Terra), but underestimated the presence of snow (recall of 74% for Aqua, 75% for Terra). MAIAC generally outperforms the standard MODIS products (precision of 51%, recall of 43% for Aqua; precision of 69%, recall of 45% for Terra). The Near-real-time Ice and Snow Extent (NISE) product had good precision (83%) but missed a significant number of snow-covered pixels (recall of 45%). The Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data set had strong performance metrics (accuracy of 91%, precision of 79%, recall of 82%). We use the score, which balances precision and recall, to determine overall product performance ( = 85%, 82(82)%, 81%, 58%, 46(54)% for IMS, MAIAC Aqua(Terra), CMC, NISE, MODIS Aqua(Terra) respectively) for providing snow cover information for TEMPO retrievals from solar backscatter observations. We find that using IMS to identify snow cover and enable inclusion of snow-covered scenes in clear-sky conditions across North America in January can increase both the number of observations by a factor of 2.1 and the average sensitivity to the tropospheric NO2 column by a factor of 2.7.
准确表示地表反射率对于从太阳后向散射观测中反演对流层痕量气体至关重要。地表积雪因其多变性带来了重大挑战，因此积雪场景在反演数据集中常常被忽略；然而，雪的高反射率对于痕量气体反演可能具有优势。我们首先研究地表积雪对即将用于北美的TEMPO地球静止仪器反演的影响。我们使用辐射传输模型来研究由于积雪导致的地表反射率增加如何改变卫星对对流层下部二氧化氮反演的灵敏度。我们发现，在1月份，TEMPO视场的很大一部分（>50%）可能被积雪覆盖，并且当表面被积雪覆盖时，对对流层二氧化氮柱的平均灵敏度会大幅增加（翻倍）。然后，我们将七种现有的卫星衍生或再分析积雪范围产品与北美的地面站观测进行对比，以评估它们为TEMPO反演提供地表状况信息的能力。交互式多传感器冰雪测绘系统（IMS）与地面观测的一致性最好（准确率93%，精度87%，召回率83%）。对MODIS观测辐射进行的多角度大气校正（MAIAC）反演具有高精度（Aqua和Terra的精度均为90%），但低估了积雪的存在（Aqua的召回率为74%，Terra的召回率为75%）。MAIAC总体上优于标准的MODIS产品（Aqua的精度为51%，召回率为43%；Terra的精度为69%，召回率为45%）。近实时冰雪范围（NISE）产品具有良好的精度（83%），但遗漏了大量积雪像素（召回率为45%）。加拿大气象中心（CMC）的每日积雪深度分析数据集具有很强的性能指标（准确率91%，精度79%，召回率82%）。我们使用平衡精度和召回率的F1分数来确定总体产品性能（IMS、MAIAC Aqua（Terra）、CMC、NISE、MODIS Aqua（Terra）的F1分数分别为85%、82（82）%、81%、58%、46（54）%），以从太阳后向散射观测中为TEMPO反演提供积雪覆盖信息。我们发现，使用IMS识别积雪覆盖并将积雪覆盖场景纳入1月份北美晴空条件下的观测中，可使观测次数增加2.1倍，对对流层二氧化氮柱的平均灵敏度增加2.7倍。
