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

Abstract: The sensitive area of targeted observations for shortterm (7d) prediction of vertical thermal structure (VTS) in summer in the Yellow Sea was investigated. We applied the Conditional Nonlinear Optimal Perturbation (CNOP) method and an adjointfree algorithm with the Regional Ocean Modeling System (ROMS). We used vertical integration of CNOPtype temperature errors to locate the sensitive areas, where reduction of initial errors is expected to yield the greatest improvement in VTS prediction for the selected verification area. The identified sensitive areas were northeastsouthwest orientated northeast to the verification area, which were possibly related to the southwestward background currents. Then, we performed a series of sensitivity experiments to evaluate the effectiveness of the identified sensitive areas. Results show that initial errors in the identified sensitive areas had the greatest negative effect on VTS prediction in the verification area compared to errors in other areas (e.g., the verification area and areas to its east and northeast). Moreover, removal of initial errors through deploying simulated observations in the identified sensitive areas led to more refined prediction than correction of initial conditions in the verification area itself. Our results suggest that implementation of targeted observation in the CNOPbased sensitive areas is an effective method to improve shortterm prediction of VTS in summer in the Yellow Sea.

Key words:
 targeted observation /
 sensitive area /
 vertical thermal structure (VTS) /
 Conditional Nonlinear Optimal Perturbation (CNOP)

Figure 1. Model domain and topography (m) of model grids (a), and topography (m) of the midwestern 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 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 CNOPtype errors of verticallyintegrated 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 areaaveraged 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 areaaveraged 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.
Table 1. Three different cases for identifying sensitive areas
Case name Case 1 Case 2 Case 3 Initial time August of the 21st year August of the 23rd year August of the 25th year Table 2. Sensitivity experiments based on adding initial random perturbations
Experiment
nameInitial
conditionsLocation of adding
perturbationsTrue Run IC_{true} no Exp_A_1 IC_{true} verification area Exp_A_2 IC_{true} sensitive area Exp_A_3 IC_{true} east of the verification area Exp_A_4 IC_{true} northeast of the verification area Table 3. Sensitivity experiments based on removing initial random errors
Experiment
nameInitial
conditionsLocation of removing initial
random errors (replace IC_{pg} with IC_{true})True Run IC_{true} no Ctrl Run IC_{pg} no Exp_R_1 IC_{pg} verification area Exp_R_2 IC_{pg} sensitive area Table 4. Experimental design of balanced/imbalanced initial conditions (ICs)
Experiment name IC_mean IC_23 IC_25 IC_comb IC climatological mean of model
output from 6th to 25thmodel output of the
23rd climatic yearmodel output of the
25th climatic yearcombined ICs from
IC_23 and IC_25^{1)}Note: ^{1)} The ICs are from model output of the 23rd climatic year at the model grid points (i, j) where (i+j) are odd and are from model output of the 25th climatic year at the model grid points (i, j) where (i+j) are even. 
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