Assessment of theoretical approaches to derivation of internal solitary wave parameters from multi-satellite images near the Dongsha Atoll of the South China Sea

This study assesses the accuracy and the applicability of the Korteweg-de Vries(KdV)and the nonlinear Schr?dinger(NLS)equation solutions to derivation of dynamic parameters of internal solitary waves(ISWs)from satellite images.Visible band images taken by five satellite sensors with spatial resolutions from 5 m to 250 m near the Dongsha Atoll of the northern South China Sea(NSCS)are used as a baseline.From the baseline,the amplitudes of ISWs occurring from July 10 to 13,2017 are estimated by the two approaches and compared with concurrent mooring observations for assessments.Using the ratio of the dimensionless dispersive parameter to the square of dimensionless nonlinear parameter as a criterion,the best appliable ranges of the two approaches are clearly separated.The statistics of total 18 cases indicate that in each 50%of cases,the KdV and the NLS approaches give more accurate estimates of ISW amplitudes.It is found that the relative errors of ISW amplitudes derived from two theoretical approaches are closely associated with the logarithmic bottom slopes.This may be attributed to the nonlinear growth of ISW amplitudes as propagating along a shoaling thermocline or topography.The test results using three consecutive satellite images to retrieve the ISW propagation speeds indicate that the use of multiple satellite images(>2)may improve the accuracy of retrieved phase speeds.Meanwhile,repeated multi-satellite images of ISWs can help to determine the types of ISWs if mooring data are available nearby.

Multi-sensor monitoring of Ulva prolifera blooms in the Yellow Sea using different methods

The massive Ulva （U.）prolifera bloom in the Yellow Sea was first observed and reported in summer of 2008. After that, the green tide event occurred every year and influenced coastal areas of Jiangsu and Shandong provinces of China. Satellite remote sensing plays an important role in monitoring the floating macroalgae. In this paper, U. prolifera patches are detected from quasi- synchronous satellite images with different spatial resolu- tion, i.e., Aqua MODIS （Moderate Resolution Imaging Spectroradiometer）, HJ-1A/B （China Small Satellite Con- stellation for Environment and Disaster Monitoring and Forecasting）, CCD （Charge-Coupled Device）, Landsat 8 OLI （Operational Land Imager）, and ENVISAT （Environ- mental Satellite） ASAR （Advanced Synthetic Aperture Radar） images. Two comparative experiments are per- formed to explore the U. prolifera monitoring abilities by different data using detection methods such as NDVI （Normalized Difference Vegetation Index） with different thresholds. Results demonstrate that spatial resolution is an important factor affecting the extracted area of the floating macroalgae. Due to the complexity of Case II sea water characteristics in the Yellow Sea, a fixed threshold NDVI method is not suitable for U. prolifera monitoring. A method with adaptive ability in time and space, e.g., the threshold selection method proposed by Otsu （1979）, is needed here to obtain accurate information on the floating macroalgae.

odeling of oil spill beaching along the coast of the Bohai Sea, China

On June 4 and 17, 201 I, two separate oil spill accidents occurred at platforms B and C of the Penglai 19- 3 oilfield located in the Bohai Sea, China. Based on the initial oil spill locations detected from the first available Synthetic Aperture Radar （SAR） image acquired on June 11, 2011, we performed a numerical experiment to simulate the potential oil spill beaching area with the General NOAA Operational Modeling Environment （GNOME） model. The model was driven by ocean surface currents from an operational ocean model （Navy Coastal Ocean Model） and surface winds from operational scatterometer measurements （the Advanced Scatterom- eter）. Under the forcing of wind and ocean currents, some of the oil spills reached land along the coast of Qinhuangdao within 12 days. The results also demonstrate that the ocean currents are likely to carry the remaining oil spills along the Bohai coast towards the northeast. The predicted oil spill beaching area was verified by reported in-situ measurements and former studies based on MODIS observations.

Ensemble forecast of tropical cyclone tracks based on deep neural networks

A nonlinear artificial intelligence ensemble forecast model has been developed in this paper for predicting tropical cyclone(TC)tracks based on the deep neural network(DNN)by using the 24-h forecast data from the China Meteorological Administration(CMA),Japan Meteorological Agency(JMA)and Joint Typhoon Warning Center(JTWC).Data from a total of 287 TC cases over the Northwest Pacific Ocean from 2004 to 2015 were used to train and validate the DNN based ensemble forecast(DNNEF)model.The comparison of model results with Best Track data of TCs shows that the DNNEF model has a higher accuracy than any individual forecast center or the traditional ensemble forecast model.The average 24-h forecast error of 82 TCs from 2016 to 2018 is 63 km,which has been reduced by 17.1%,16.0%,20.3%,and 4.6%,respectively,compared with that of CMA,JMA,JTWC,and the error-estimation based ensemble method.The results indicate that the nonlinear DNNEF model has the capability of adjusting the model parameter dynamically and automatically,thus improving the accuracy and stability of TC prediction.