Numerous studies have revealed that the sparse spatiotemporal distributions of ground-level PM_2.5 measurements affect the accuracy of PM_2.5 simulation, especially in large geographical regions. However, the high precision and stability of ground-level PM_2.5 measurements make their role irreplaceable in PM_2.5 simulations. This article applies a dynamically constrained interpolation methodology (DCIM) to evaluate sparse PM_2.5 measurements captured at scattered monitoring sites for national-scale PM_2.5 simulations and spatial distributions. The DCIM takes a PM_2.5 transport model as a dynamic constraint and provides the characteristics of the spatiotemporal variations of key model parameters using the adjoint method to improve the accuracy of PM_2.5 simulations. From the perspective of interpolation accuracy and effect, kriging interpolation and orthogonal polynomial fitting using Chebyshev basis functions (COPF), which have been proved to have high PM_2.5 simulation accuracy, were adopted to make a comparative assessment of DCIM performance and accuracy. Results of the cross validation confirm the feasibility of the DCIM. A comparison between the final interpolated values and observations show that the DCIM is better for national-scale simulations than kriging or COPF. Furthermore, the DCIM presents smoother spatially interpolated distributions of the PM_2.5 simulations with smaller simulation errors than the other two methods. Admittedly, the sparse PM_2.5 measurements in a highly polluted region have a certain degree of influence on the interpolated distribution accuracy and rationality. To some extent, adding the right amount of observations can improve the effectiveness of the DCIM around existing monitoring sites. Compared with the kriging interpolation and COPF, the results show that the DCIM used in this study would be more helpful for providing reasonable information for monitoring PM_2.5 pollution in China.

The wind stress drag coefficient plays an important role in storm surge models. This study reveals the influences of wind stress drag coefficients, which are given in form of formulas and inverted by the data assimilation method, on the storm surge levels in the Bohai Sea, Yellow Sea, and East China Sea during Typhoon 7008. In the process of data assimilation, the drag coefficient is based on the linear expression C_d = (a + b × U₁₀) × 10⁻³ (generally speaking, a and b are empirical parameters determined by observed data). The results showed that the performance of the data assimilation method was far superior to those of drag coefficient formulas. Additionally, the simulated storm surge levels obviously changed in the neighborhood of typhoon eye. Furthermore, the effect of initial values of a and b in the C_d expression on the storm surge levels was also investigated when employing the data assimilation method. The results indicated that the simulation of storm surge level was the closest to the observation when a and b were simultaneously equal to zero, whereas the simulations had slight differences when the initial values of a and b were separately equal to the drag coefficients from the work of Smith, Wu, and Geernaert et al.. Therefore, we should choose appropriate initial values for a and b by using the data assimilation method. As a whole, the data assimilation method is much better than drag coefficient parameterization formulas in the simulation of storm surges.