SMOS and SMAP combined SSS L3 maps over the Chukchi and the Beaufort seas during summer 2019

sss weekly fields are derived from smos and smap measurements from june to september 2019. smos sss is from a modified version of the cec-locean l3 arctic, distributed by the “centre aval de traitement des données smos” (catds; supply et al., 2020). in this new version, the smos level 2 sss are from the esa climate change initiative (cci) v3.2 reprocessing described in perrot et al. (2021). in comparison with level 2 sss used in supply et al. (2020): sss is computed with an updated dielectric constant parametrization (boutin et al., 2020). smos vicarious calibration, the so-called ocean target transformation, is derived using argo optimal interpolated sss (gaillard et al., 2016) instead of a climatology. sst and wind speed used as priors in the sss retrieval are taken from european centre for medium-range weather forecasts (ecmwf) era5 instead of ecmwf forecasted fields. updated sea ice filtering derived from the difference between smos retrieved pseudo dielectric constant (acard parameter) and the one expected from retrieved sss and sst: instead of being applied only at level 3, sea ice filtering is applied both at level 2 (swath product) and at level 3 (weekly averaged product).sss is derived with a correction of sst-induced bias using remote sensing systems (rss) sst.smos sss are averaged over 9 days on a 25km equal-area scalable earth (ease) 2.0 grid adapted to polar areas. these fields have an effective spatial resolution close to 50 km, corresponding to the original resolution of smos sss (no spatial interpolation is applied from level 2 to level 3). given the difficulty to find a reference to adjust absolute sss values in the arctic ocean, this product does not contain any land sea contamination (lsc) correction; nevertheless, in the averaging process, smos sss is weighted by the chi2 of the retrieval, and we expect it to be degraded on smos dwell lines largely contaminated by lsc. smap sss is from the jet propulsion laboratory (jpl; fore et al., 2020) version 4.3 8-day averaged sss provided on a 0.25° regular grid, with a spatial interpolation from level 2 to level 3. in this product that uses a lsc correction, the effective spatial resolution of sss is close to 60 km. the use of jpl smap sss instead of smap sss distributed by rss is motivated by a less restrictive ice mask in the polar regions. between 19 june 19th and july 23rd 2019, smap was in safe mode and did not provide sss estimates. smos, smap and combined sss are provided with an uncertainty estimated by the sss retrieval algorithm.additional sea ice filtering applied to smos and smap sss: only sss estimates with an uncertainty lower than 0.6 pss are considered in the following. sss estimates from both satellites (smap interpolated in the 25 km ease 2.0 grid) are intercalibrated using summer 2019 saildrones measurements (saildrone (2020), vazquez-cuervo et al., 2021) and finally combined. video: illustration of sss evolution (smos, smap and combined sss) during summer sea ice retreat between june and september 2019 (ocean and sea ice satellite application facility (osi-saf) sea ice concentration (sic) derived from amsr-2 measurements, provided by the danish and the norwegian meteorological institute).

每周的sss字段数据源自2019年6月至9月期间收集的SMOS和SMAP测量数据。SMOS的sss数据来自经过修改的CEC-LOOCEAN L3北极数据版本，由“SMOS数据处理中心”（CATDS；Supply等人，2020年）分发。在新版本中，SMOS Level 2 sss数据来源于欧洲空间局气候变化倡议（CCI）v3.2再处理，具体内容见Perrot等人（2021年）的描述。与Supply等人（2020年）使用的Level 2 sss相比：sss的计算采用了更新的介电常数参数化（Boutin等人，2020年）。SMOS的替代校准，所谓的海洋目标转换，是通过使用Argo最优插值sss（Gaillard等人，2016年）而非气候学数据来获得的。在sss反演中作为先验的SST和风速数据取自欧洲中期天气预报中心（ECMWF）的ERA5，而非ECMWF预报场。海冰过滤的更新基于从SMOS反演的伪介电常数（acard参数）与从反演的sss和SST中预期的值之间的差异：海冰过滤不仅应用于Level 3，也应用于Level 2（扫描产品）和Level 3（周平均产品）。sss是在一个25公里等面积可扩展地球（Ease）2.0网格上，针对极地地区进行9天平均，该网格的平均有效空间分辨率接近50公里，与SMOS sss的原分辨率相匹配（从Level 2到Level 3未进行空间插值）。鉴于在北极海洋中难以找到参考值以调整绝对sss值，该产品不包含任何陆地与海洋混合（LSC）校正；尽管如此，在平均过程中，SMOS sss是根据反演的卡方值进行加权的，我们预期在SMOS停留线上受LSC大量污染的地区其质量会降低。SMAP的sss数据来自喷气推进实验室（JPL；Fore等人，2020年）的4.3版本，提供的是基于0.25°规则网格的8天平均sss，从Level 2到Level 3进行了空间插值。在此使用LSC校正的产品中，sss的有效空间分辨率接近60公里。使用JPL的SMAP sss而非RSS分发的SMAP sss的动机在于在极地地区具有更宽松的冰掩膜。在2019年6月19日至7月23日之间，SMAP处于安全模式，未提供sss估计值。SMOS、SMAP以及组合sss均提供了由sss反演算法估计的不确定性。对SMOS和SMAP sss应用了额外的海冰过滤：仅考虑不确定性低于0.6 pss的sss估计值。来自两颗卫星（SMAP在25公里Ease 2.0网格上插值）的sss估计值在2019年夏季使用Saildrones测量值（Saildrone，2020年；Vazquez-Cuervo等人，2021年）进行互校准，并最终合并。视频：展示2019年6月至9月夏季海冰消退期间sss（SMOS、SMAP和组合sss）的演变（OSI-SAF从amsr-2测量数据中得出的海冰浓度（SIC），由丹麦和挪威气象研究所提供）。