College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China
College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China
The National Key R&D Program of China under contract No. 2017YFC1405600; the National Natural Science Foundation of China under contract Nos 61931025, 41974144 and 41976173; the Graduate Innovation Project of China University of Petroleum (East China) under contract No. YCX2021124; the Shandong Provincial Natural Science Foundation, China under contract No. ZR2019MD016.
The significant wave height (SWH) is one of the main parameters that describe wave characteristics and is widely used in wave research fields. Wave parameters measured by radar are influenced by the offshore distance and sea state. Validation and calibration are of great significance for radar data applications. The nadir beam of surface wave investigation and monitoring (SWIM) detects the global-ocean-surface SWH. To determine the product quality of SWIM SWH, this paper carried out time-space matching between SWIM and buoy data. The data qualities were evaluated under different offshore distances and sea states. An improved calibration method was proposed based on sea state segmentation, which considered the distribution of the point collocation numbers in various sea states. The results indicate that (1) the SWIM SWH accuracy at offshore distances greater than 50 km is higher than that at distances less than 50 km, with an RMSE of 0.244 4 m, SI of 0.115 6 and RE of 9.97% at distances greater than 50 km and those of 0.446 0 m, 0.223 0 and 18.66% at distances less than 50 km. (2) SWIM SWH qualities are better in moderate and rough sea states with RMSEs of 0.284 8 m and 0.316 9 m but are worse in slight and very rough sea states. (3) The effect of the improved calibration method is superior to the traditional method in each sea state and overall data, and the RMSE of SWIM SWH is reduced from the raw 0.313 5 m to 0.285 9 m by the traditional method and 0.198 2 m by the improved method. The influence of spatiotemporal window selection on data quality evaluation was analyzed in this paper. This paper provides references for SWIM SWH product applications.
Figure 1. Location of 64 NDBC buoys selected. (▲less than 50 km offshore, ● greater than 50 km offshore).
Figure 2. Flowchart of time-space matching.
Figure 3. Scatterplots for SWH comparisons between SWIM and NDBC buoys at different offshore distances with validation parameters and data sizes (dotted line is reference line). a. Less than 50 km offshore and b. greater than 50 km offshore.
Figure 4. Scatterplot and comparisons of validation results in different sea states. a. Scatterplot for SWH comparisons between SWIM and NDBC buoys (dotted line is reference line) and b. comparisons of validation parameters and data sizes under different sea states.
Figure 5. Scatterplots and comparisons of validation results in different sea states using two calibration methods. Calibration results scatterplot by a. traditional method (dotted line is reference line), b. improved method (dotted line is reference line), and c. comparisons of validation parameters using two methods separately and data sizes in different sea states.
Figure 6. Validation results of SWIM SWH product based on buoy data classification. Buoys a. less than 50 km offshore and b. greater than 50 km offshore (to facilitate presentation of results, all REs are 1/50 times the results).