Identification of the sensitive area for targeted observation to improve vertical thermal structure prediction in summer in the Yellow Sea

Huiqin Hu, Jingyi Liu, Lianglong Da, Wuhong Guo, Kun Liu, Baolong Cui. Identification of the sensitive area for targeted observation to improve vertical thermal structure prediction in summer in the Yellow Sea[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1738-x
 Citation: Huiqin Hu, Jingyi Liu, Lianglong Da, Wuhong Guo, Kun Liu, Baolong Cui. Identification of the sensitive area for targeted observation to improve vertical thermal structure prediction in summer in the Yellow Sea[J]. Acta Oceanologica Sinica.

## Identification of the sensitive area for targeted observation to improve vertical thermal structure prediction in summer in the Yellow Sea

Funds: The National Natural Science Foundation of China under contract Nos 41705081 and 41906005; the Innovation Special Zone Project under contract No.18-H863-05-ZT-001-012-06; the Open Project Fund of the Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao) under contract No. 2019A05.
###### Corresponding author:Email: llda@qnlm.ac
• ╀ mean the authors contributed equally to this work and should be considered as co-first authors.
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╀ mean the authors contributed equally to this work and should be considered as co-first authors.
• Figure  1.  Model domain and topography (m) of model grids (a), and topography (m) of the mid-western part of the Yellow Sea (b). The black solid lines A and B in a indicate locations of the validation sections; the box with black solid line (Box A) in b indicates the location of the selected verification area.

Figure  2.  Monthly averaged SST in February, May, August, and November (from left to right) from the climatological simulation of model (upper row, a–d), and the multi-year averaged SST in the corresponding months from the satellite data of MODIS (bottom row, e–h).

Figure  3.  Vertical sections of monthly averaged temperature in August along 35°N (left column, a, c) and 124°E (right column, b, d), for observation from ocean atlas data (upper row, a, b) and model simulation (bottom row, c, d).

Figure  4.  Locations (colored dots) of the identified sensitive areas and the climatological background currents (vectors) for cases of the 21st (a), 23rd (b) and 25th (c) climatology years, respectively. The sensitive areas are identified based on CNOP-type errors of vertically-integrated temperature after being normalized with their maximum values.

Figure  5.  Temperature prediction errors (shaded) at water depth of 20 m over the verification area for experiments with adding initial random perturbations on four different areas at four different prediction times. Four different areas are the verification area (Exp_A_1, a1–4), sensitive area (Exp_A_2, b1–4), area to east of the verification area (Exp_A_3, c1–4), and area to northeast of the verification area (Exp_A_4, d1–4), respectively. Four different prediction times are the first, third, fifth and seventh prediction day, respectively.

Figure  6.  Temporal evolution of root mean square errors of area-averaged temperature profile over the verification area for experiments with adding initial random perturbations on the verification area (Exp_A_1, black line), sensitive area (Exp_A_2, red line), area to east of the verification area (Exp_A_3, blue line), area to northeast of the verification area (Exp_A_4, green line), respectively.

Figure  7.  Temporal evolution of root mean square errors of area-averaged temperature profile over the verification area (a), and corresponding prediction benefits (b) for sensitivity experiments based on removing different initial random errors. Exp_R_1 and Exp_R_2 denote experiment with removing initial random errors from the verification area and sensitive area, respectively. Ctrl Run in a denotes experiment without removing initial errors from any areas.

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##### 出版历程
• 收稿日期:  2020-06-23
• 录用日期:  2020-08-27
• 网络出版日期:  2021-05-07

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