This paper uses the Argo sea surface salinity (SSSArgo) before and after the passage of 25 tropical cyclones (TCs) in the Bay of Bengal from 2015 to 2019 to evaluate the sea surface salinity (SSS) of the Soil Moisture Active Passive (SMAP) remote sensing satellite (SSSSMAP). First, SSSArgo data were used to evaluate the accuracy of the 8-day SMAP SSS data, and the correlations and biases between SSSSMAP and SSSArgo were calculated. The results show good correlations between SSSSMAP and SSSArgo before and after TCs (before: SSSSMAP = 1.09SSSArgo−3.08 (R2 = 0.69); after: SSSSMAP = 1.11SSSArgo−3.61 (R2 = 0.65)). A stronger negative bias (−0.23) and larger root-mean-square error (RMSE, 0.95) between the SSSSMAP and SSSArgo were observed before the passage of 25 TCs, which were compared to the bias (−0.13) and RMSE (0.75) after the passage of 25 TCs. Then, two specific TCs were selected from 25 TCs to analyze the impact of TCs on the SSS. The results show the significant SSS increase up to the maximum 5.92 psu after TC Kyant (2016), which was mainly owing to vertical mixing and strong Ekman pumping caused by TC and high-salinity waters in the deep layer that were transported to the sea surface. The SSSSMAP agreed well with SSSArgo in both coastal and offshore waters before and after TC Roanu (2016) and TC Kyant (2016) in the Bay of Bengal.

中文翻译：

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基于SMAP和Argo数据的热带气旋对孟加拉湾海面盐度的影响

本文利用2015-2019年孟加拉湾25个热带气旋（TCs）经过前后的Argo海面盐度（SSSArgo）评估了Soil Moisture Active Passive（SMAP）的海面盐度（SSS）遥感卫星（SSSSMAP）。首先，利用SSSSArgo数据对8天SMAP SSS数据的准确性进行评估，计算SSSSMAP与SSSSArgo的相关性和偏差。结果显示 TCs 前后 SSSSMAP 和 SSSArgo 之间具有良好的相关性（之前：SSSSMAP = 1.09SSSArgo−3.08 (R2 = 0.69)；之后：SSSSMAP = 1.11SSSArgo−3.61 (R2 = 0.65)）。在通过 25 个 TC 之前观察到 SSSSMAP 和 SSArgo 之间更强的负偏差 (-0.23) 和更大的均方根误差 (RMSE, 0.95)，与偏差 (-0.13) 和 RMSE (0.75) 相比经过 25 个 TC 后。然后，从 25 个 TC 中选择了两个特定的 TC 来分析 TC 对 SSS 的影响。结果表明，在 TC Kyant (2016) 之后，SSS 显着增加，达到最大值 5.92 psu，这主要是由于 TC 和深层高盐度海水输送到海面引起的垂直混合和强烈的 Ekman 泵送. SSSSMAP 在孟加拉湾 TC Roanu (2016) 和 TC Kyant (2016) 之前和之后的沿海和近海水域都与 SSSSARgo 一致。