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Aiming at the difficulties in the quantitative verification and evaluation of underwater countermeasures for our underwater platform and the lack of a guided maneuvering evasive combat model, a prediction model of underwater countermeasures based on big data learning is designed. Firstly, the underwater countermeasure modeling of the underwater platform is carried out, and the large data set of avoidance probability is obtained by several rounds of simulation based on Monte Carlo method. At the same time, in order to solve the problem of poor time efficiency under massive simulation, BP neural network prediction algorithm is proposed for big data learning to provide accurate, fast and visual antagonistic results. The test results show that under the test environment set in this paper, the average prediction error of BP neural network prediction algorithm is 7.28% respectively, which can effectively predict the avoidance probability of the underwater platform and provide data support for the commander’s command decision.

Crucial to the assessment of future water security is how the land model component of Earth System Models partition precipitation into evapotranspiration and runoff, and the sensitivity of this partitioning to climate. This sensitivity is not explicitly constrained in land models nor the model parameters important for this sensitivity identified. Here, we seek to understand parametric controls on runoff sensitivity to precipitation and temperature in a state‐of‐the‐science land model, the Community Land Model version 5 (CLM5). Process‐parameter interactions underlying these two climate sensitivities are investigated using the sophisticated variance‐based sensitivity analysis. This analysis focuses on three snow‐dominated basins in the Colorado River headwaters region, a prominent exemplar where land models display a wide disparity in runoff sensitivities. Runoff sensitivities are dominated by indirect or interaction effects between a few parameters of subsurface, snow, and plant processes. A focus on only one kind of parameters would therefore limit the ability to constrain the others. Surface runoff exhibits strong sensitivity to parameters of snow and subsurface processes. Constraining snow simulations would require explicit representation of the spatial variability across large elevation gradients. Subsurface runoff and soil evaporation exhibit very similar sensitivities. Model calibration against the subsurface runoff flux would therefore constrain soil evaporation. The push toward a mechanistic treatment of processes in CLM5 have dampened the sensitivity of parameters compared to earlier model versions. A focus on the sensitive parameters and processes identified here can help characterize and reduce uncertainty in water resource sensitivity to climate change. Key Points: Parametric controls on runoff sensitivity to climate in the land component of an Earth System Model are investigated in a snowmelt runoff regimeRunoff sensitivities are dominated by interactions between certain parameters of subsurface, snow, and plant processesHigh sensitivity is found for soil porosity and depth, saturated fraction, snow cover and densification, stomatal conductance, and photosynthetic capacity [ABSTRACT FROM AUTHOR]

Multisource meteorological re-analyses provide the most reliable forcing data for driving hydrological models to simulate streamflow. We aimed to assess different hydrological responses through hydrological modeling in the upper Lancang-Mekong River Basin (LMRB) using two gridded meteorological datasets, Climate Forecast System Re-analysis (CFSR) and the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS). We selected the Pearson's correlation coefficient (R), percent bias (PBIAS), and root mean square error (RMSE) indices to compare the six meteorological variables of the two datasets. The spatial distributions of the statistical indicators in CFSR and CMADS, namely, the R, PBIAS, and RMSE values, were different. Furthermore, the soil and water assessment tool plus (SWAT+) model was used to perform hydrological modeling based on CFSR and CMADS meteorological re-analyses in the upper LMRB. The different meteorological datasets resulted in significant differences in hydrological responses, reflected by variations in the sensitive parameters and their optimal values. The differences in the calibrated optimal values for the sensitive parameters led to differences in the simulated water balance components between the CFSR- and CMADS-based SWAT+ models. These findings could help improve the understanding of the strengths and weaknesses of different meteorological re-analysis datasets and their roles in hydrological modeling. [ABSTRACT FROM AUTHOR]

Land–atmosphere coupling (LAC) is a critical process in the climate system, and has great effects on surface air temperature over the humid–dry transition zones. Using ERA5/ERA5‐land reanalysis datasets, we show that the summer temperature anomalies over Northwest China (NWC), known as one of the most important LAC hotpots in China, are significantly correlated with the LAC strength during 1981–2020. The stronger the coupling strength between air temperature and land surface, the more pronounced decrease in air temperature and extreme hot days over NWC. Among the four pathways of land–air temperature coupling, including soil moisture (SM)/soil temperature–latent heat flux/sensible heat flux–air temperature coupling, SM–sensible heat flux–air temperature pathway corresponds most closely with the temperature changes in summer. By analyzing the possible local and non‐local physical links in this relationship, we show that the wave trains composed of the intensification of Ural ridge and Central–Asia trough stimulate a meridional fluctuation of westerly airflow over the mid–high latitudes of Eurasia. Large moisture, therefore, converges into NWC, leading to excessive precipitation and deficient incident solar radiation reaching the land surface. In such case, wetter and cooler soil states accelerate the cooling of the environment through feedbacks between the lands, land–atmosphere exchanging fluxes and air temperature locally, accompanied by a significant enhancement in LAC strength. Our study provides insight into the physical processes causing variations in summer temperature over a typical LAC hotspot and suggests that the role of land surface–atmosphere interactions needs to be considered when studying temperature extremes under climate change. Plain Language Summary: There exist strong interactions between land surface and air temperature over Northwest China (NWC) in summer. In the meantime, NWC region (especially Xinjiang province) has undergone severe hot extremes recently and suffered great loss of life and property. In this case, this study is presented to investigate the relationship of variation in surface air temperature with the land–air temperature coupling strength using reanalysis‐based datasets. We revealed that the stronger the coupling strength, the more pronounced decrease in air temperature and extreme hot days there. Both local and non‐local factors contribute to this process. The large‐scale anomalous Arctic Oscillation–East Atlantic/West Russia‐like patterns are conducive to extra water vapor convergence in southern Xinjiang, accompanied by reduced net solar radiation. Such changes further result in significant variations in land states, and local land–air temperature interactions had also been exacerbated, collectively leading to air temperature decreases. These findings are expected to deepen our understanding of the variation of surface air temperature and related extreme temperature events in NWC. Key Points: The stronger the land–atmosphere coupling (LAC), the more decrease of air temperature and extreme hot days (HDs) during summer in Northwest China (NWC)Anomalous circulation patterns correlate with the LAC strength and air temperature variations in NWCThe local land–air temperature interactions enhance the impacts from non‐local atmospheric forcings [ABSTRACT FROM AUTHOR]