Particle filter (PF) is a very promising nonlinear data assimilation method. However, due to the particle degeneracy problem, it has not been widely used in large geophysical models. In contrast, the ensemble Kalman filter (EnKF) and its derivative methods have been widely used in operational data assimilation systems in recent years. A newly proposed local particle filter (LPF) which employs the localization technique in particle filter, can effectively avoid the degeneracy problem with low computational costs and has great potential for practical applications. In this paper, data assimilation experiments using LPF and EnKF are conducted in a fully coupled Community earth system model. The sythetic satellite sea surface temperature data are assimilated with each method. Different impact of local parameters on each method is investigated, and the data assimilation performances of LPF and EnKF are compared. The comparison results show that the performance of LPF is more sensitive to localization parameter. With the optimal localization strategy, it is shown that LPF can be better than EnKF, and have a potential to be further improved.

The hourly surface AWS data, conventional suface and radiosonde observation data and NCEP-FNL reanalysis data over the period of 2008−2018 were used for analyzing the cyclonic gale processes in the Bohai Sea and the Yellow Sea. The cyclone deepening rate and the pressure gradient were also discussed to analyze the relationship between themeteorological factors and the strength of the cyclone. Based on the Petterssen equation of the surface cyclone development, the effects of temperature advection, vorticity advection and diabatic heating in cyclone development were discussed. The results show: (1) 70.5% of the cyclones were strengthened after entering the sea, 14.7% of them became explosive cyclones, 17.6% of them were invariant, and 11.7% of them weakened. The extratropical cyclones which caused strong winds in the Yellow Sea and the Bohai Sea mainly occurred in autumn, secondly in spring and winter, and hardly appeared in summer. The enter sea cyclones were developed on the leftside of the exit of upper-level jet stream or the diverging region, and weakened on the right side of the exit of upper-level jet stream. (2) There are three kinds of cyclones which have effects on the Yellow Sea and the Bohai Sea: the Mongolian cyclones (17.6%) which moved from the northwest to the southeast; the Yellow River cyclones (49%) moved from the west to the east, the Changjiang-Huaihe cyclones (33.4%) moved from the southwest to the northeast which tend to develop into explosive cyclones in autumn. The maximum wind speed region often appears in the northwest (or the west) quadrant of the cyclone in the autumn and winter season, and the maximum wind speed region appears in the southeast quadrant of the cyclone in the spring. (3) The correlation coefficient of the temperature advection and cyclone deepening rate is higher than that of vorticity advection and adiabatic heating. The correlation coefficient temperature advection and cyclone deepening rate, vorticity advection and cyclone deepening rate are greater than the correlation coefficient of the barometric gradient and cyclone deepening rate. The temperature advection and vorticity advection in the four analyzed explosive cyclones events were stronger than in other cyclones events. The correlation of diabatic heating and the barometric gradient is stronger than it with the cyclone variation rate. (4) The temperature advection and diabatic heating have important effects on the Huang-huai and Jianghuai cyclone. The effects from the vorticity advection on the Yellow River cyclone are more important, and the effects from the vorticity advection and diabatic heat-ingon the Mongolian cyclone are the least.

The accurate classification of sea ice is of great significance for mastering the growth and development of sea ice and ensuring the safety of navigation. Due to the influence of data sources and classification methods, the improvement of sea ice classification accuracy is limited. In this paper, for high spatial resolution optical remote sensing images, an accurate sea ice classification method based on texture features and normalized difference vegetation index (NDVI) was proposed, and a random forest classifier was used to construct a sea ice classification method. Taking Jiaozhou Bay of Qingdao as the experimental area and GF-2 as the experimental data, the sea ice types were extracted and compared with other classification methods. The results show that for GF-2 high-resolution optical remote sensing data, compared with the traditional random forest, support vector machine, automatic classification and regression tree methods and maximum likelihood classification method of combining texture features, the overall classification accuracy was improved by 13.70%, 11.60%, 19.22% and 29.37%, respectively. The Kappa coefficient was increased by 0.16, 0.13, 0.22 and 0.44, respectively. Compared with the random forest method based on texture features and normalized difference water index, the overall classification accuracy was improved by 9.67% and Kappa coefficient was increased by 0.09. It shows that the sea ice classification method constructed in this paper can effectively improve the accuracy of sea ice classification, and provide an effective technical means for the accurate classification of sea ice.

Aiming at the problem of calculating overtopping of the composite slope breakwater, a prediction model of the overtopping for the composite slope based on the random forest method is proposed. Firstly, by filtering the European CLASH data set, the data consistent with the prediction of overtopping of the composite slope breakwater are selected. Secondly, after dimensionless processing of the data, overtopping prediction model is established based on random forest method, and improved by adjusting the model parameters according to GridSearchCV. Finally, the coefficient of determination R² is used to evaluate the accuracy of the model, and the prediction ability of the model is compared with the ensemble neural network model. The effect of each feature parameter of the random forest model on the prediction accuracy is assessed. The results show that the coefficient of determination of the random forest model is 92.7%, and the coefficient of determination of the ensemble neural network model is 87.7%, indicating the random forest model has a stronger prediction ability for predicting overtopping. Wall height with respect to static water level has the greatest influence on the prediction accuracy of the model, the height of the top of the embankment is the second, and the width of the foot of the embankment least.

This paper uses the ADCIRC model to establish a block-scale storm surge inundation model in the Beilun District, Ningbo City, with a land resolution of 5−10 m in the Beilun District. The simulation results show the overall process of inundation flooding urban street buildings, and detail description of the flow of water in the complex distribution of blocks and buildings, as well as changes in water depth near different buildings, which are more refined than previous inundation simulations, reflecting the advantages of block scale grid. Numerical simulation results show that this model can simulate the storm surge process of No. 1211 Typhoon “Haikui” and No. 5612 Typhoon “Wanda” well. Satellite remote sensing images are used to classify features in the Beilun District, and the influence of changes in the friction of different underlying surfaces on inundation simulation is considered. Comparing the results of the simulation experiment without considering the underlying surface friction, the submerged area is reduced by 21.4%, and the water depth in most areas is reduced to 0.1−0.2 m. The block-scale grid shows the degree of water depth reduction and the change of submerged area in different areas.

The interface of assimilating radiance on a new satellite sensor GMI (Global Precipitation Measurement (GPM) microwave imager) was constructed in the framework of the mesoscale numerical model WRF (Weather Research and Forecasting Model) and its three-dimensional variational assimilation system (3DVAR). The assimilation of GMI radiance data is applied for the typhoon system based on the case of typhoon Matmo in the Pacific typhoon season in 2014 before its landing. The results show that, after assimilating the GMI radiance data under the clear sky condition, the typhoon position in the background of the model is effectively corrected. The GMI data are able to improve the warm core structure of the typhoon when compared with the control experiment without assimilation and enhanced the typhoon vortex circulation structure at the same time. Data assimilating of GMI data further improves the forecast skills of the typhoon track.

Wave breaking is the most prominent feature of the ocean surface and it induces huge loads on structures. In this study, laboratory experiments in a large model scale are carried out to study wave forces on a vertical pile induced by breaking waves and non-breaking extreme waves, and to get insight the magnitude and the distribution of the wave forces, pressure transducers are installed in the face of the tested pile. The results show that in repeated experiments the wave pressures induced by the breaking waves present a wider variance compared with non-breaking waves; as the breaking density at the pile increases and the measuring point gets closer to wave crest, the variance of pressures increases; the maximum measured pressures can reach three times of the maximum hydrostatic pressure and occur at around 1.2 times of the maximum water surface. Based on the continuous wavelet spectrums of the wave pressures, different breaking phases show different characteristics: when the wave breaks far in front of the pile, its spectrum has a high frequency range with a wider vertical distribution, indicting more complicated pressures; when the wave breaks at the front of the pile, the wave force reaches a highest value.

The formation of vertical sedimentary rhythmic layers of tidal flat mainly depends on periodic tidal conditions, including flood and ebb tide, spring and neap tide, seasonal and longer term scale tidal characteristics. In order to investigate the distribution and mechanism of sediment bedding on tidal flats, a one-dimensional numerical model was used to simulate the rhythmic layers of long-term tidal flat bedding layers under spring and neap tidal cycles. Results indicate that the periodicity of spring-neap tide is the main reason for the rhythmicity of sedimentary bedding. One couplet in the rhythm layer corresponds to the spring-neap tidal period, which is formed by the mud-dominated layer during the neap tide and the sand-dominated layer during the spring tide. The thicknesses of layers also show a cyclical change: bedding layers are thicker during the spring tide and thinner during the neap tide. The boundary sediment concentration is also an important factor affecting the structure of tidal couplets. An increasing boundary concentration of silt makes the tidal rhythm layer coarser and increases the overall thickness of the sand-dominated layer. When the boundary sediment concentration significantly increases, the vertical tidal rhythm layers on the tidal flat are more intact with an evident increase in layer thickness. The formation and characteristics of tidal bedding layers are the result of the joint action of many factors (e.g., waves, storms, biological factors and etc.), which await further research effort in the future.

The elevation, current, salinity and suspended sediment concentration (SSC) data were observed during spring and neap tides in the Jiaojiang Estuary. The spatial and temporal characteristics of tides, salinity and SSC in the main tidal channel of the estuary were studied, and the stratification physical mechanism under the action of high turbidity and strong tides was explained, using the field data. SSC and salinity during spring tides were higher than those during neap tides, and SSC during ebb periods was higher than the flood periods along the main channel. Salinity varied with the tidal currents, and the salinity water front appeared around the Station S2. A turbidity maximum zone appeared near the salt water front. SSC decreased and the salinity increased towards the sea. SSC and salinity increased with the water depth. According to the Richardson number and mixing parameters, stratification caused by salinity and SSC changes with tides. Stratification during flood periods was stronger than during ebb periods. Stratification lasted the longest time and was more extensive during neap tides. The mixing parameter varied with the tidal periods, and the value was high/below the critical value of 1.0 during spring/neap tides. The tidal strain term was an important role for the change rate of the potential energy anomaly. The stratified state changed to a mixed state during neap tides, while the opposite change occurred during flood tides.

Using the 3-D numerical model Ecom-si, saltwater intrusion in the Changjiang Estuary and water take in the Qingcaosha Reservoir under different wind speeds are studied. Model results show that the high saline water in Subei is transported southward to the Changjiang Estuary. Ekman transport drives a surge along the coast and a horizontal circulation in the Changjiang Estuary, which enters the North Channel (NC) and exit through the South Channel. This process increases salinity in the NC. Under the mean river discharge in the dry season (11 900 m³/s), the net water flux across the mouth of the NC is landward when wind speed exceeds 10 m/s, and the saltwater in the NC can spill over into the South Channel when wind speed exceeds 11 m/s. When wind speed is 0, the 15-day averaged salinity in the NC is only 0.97 and the elevation at the mouth of the NC is only 0.13 m. When wind speed increases to 14 m/s, salinity in the NC increases to 27.4 and the elevation at the mouth of the NC increases to 0.42 m. North wind reduces the number of days that the Qingcaosha Reservoir could take water from Changjiang within a month. When wind speed is 0, the number of days is 29.4 and when wind speed exceeds 10 m/s, the number of days is 0. The salinity in the NC increases with the strength of north wind, which is unfavorable to water take in the Qingcaosha Reservoir.