The seasonal variations in the significant wave height and sea surface wind speed of the China's seas

Long-term variations in a sea surface wind speed （WS） and a significant wave height （SWH） are associated with the global climate change, the prevention and mitigation of natural disasters, and an ocean resource exploitation, and other activities. The seasonal characteristics of the long-term trends in China＇s seas WS and SWH are determined based on 24 a （1988-2011） cross-calibrated, multi-platform （CCMP） wind data and 24 a hindcast wave data obtained with the WAVEWATCH-III （WW3） wave model forced by CCMP wind data. The results show the following. （1） For the past 24 a, the China＇s WS and SWH exhibit a significant increasing trend as a whole, of 3.38 cm/（s.a） in the WS, 1.3 cm/a in the SWH. （2） As a whole, the increasing trend of the China＇s seas WS and SWH is strongest in March-April-May （MAM） and December-January-February （DJF）, followed by June-July-August （JJA）, and smallest in September-October-November （SON）. （3） The areal extent of significant increases in the WS was largest in MAM, while the area decreased in JJA and DJF; the smallest area was apparent in SON. In contrast to the WS, almost all of China＇s seas exhibited a significant increase in SWH in MAM and DJF; the range was slightly smaller in JJA and SON. The WS and SWH in the Bohai Sea, the Yellow Sea, East China Sea, the Tsushima Strait, the Taiwan Strait, the northern South China Sea, the Beibu Gull and the Gulf of Thailand exhibited a significant increase in all seasons. （4） The variations in China＇s seas SWH and WS depended on the season. The areas with a strong increase usually appeared in DJF.

Evaluation on monthly sea surface wind speed of four reanalysis data sets over the China seas after 1988

This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution are used: Cross-Calibrated Multi-Platform data set(CCMP), NCEP climate forecast system reanalysis data set(CFSR),ERA-interim reanalysis data set(ERA-int) and Japanese 55-year reanalysis data set(JRA55). The monthly sea surface wind speeds of four major reanalysis data sets have been investigated through comparisons with the longterm and homogeneous observation wind speeds data recorded at ten stations. The results reveal that(1) the wind speeds bias of CCMP, CFSR, ERA-int and JRA55 are 0.91 m/s, 1.22 m/s, 0.62 m/s and 0.22 m/s, respectively.The wind speeds RMSE of CCMP, CFSR, ERA-int and JRA55 are 1.38 m/s, 1.59 m/s, 1.01 m/s and 0.96 m/s,respectively;(2) JRA55 and ERA-int provides a realistic representation of monthly wind speeds, while CCMP and CFSR tend to overestimate observed wind speeds. And all the four data sets tend to underestimate observed wind speeds in Bohai Sea and Yellow Sea;(3) Comparing the annual wind speeds trends between observation and the four data sets at ten stations for 1988-1997, 1988–2007 and 1988–2015, the result show that ERA-int is superior to represent homogeneity monthly wind speeds over the China seaes.

A preliminary assessment of the sea surface wind speed production of HY-2 scanning microwave radiometer

A scanning microwave radiometer（RM） was launched on August 16,2011,on board HY-2 satellite.The six-month long global sea surface wind speeds observed by the HY-2 scanning microwave radiometer are preliminarily validated using in-situ measurements and WindSat observations,respectively,from January to June 2012.The wind speed root-mean-square（RMS） difference of the comparisons with in-situ data is 1.89 m/s for the measurements of NDBC and 1.72 m/s for the recent four-month data measured by PY30-1 oil platform,respectively.On a global scale,the wind speeds of HY-2 RM are compared with the sea surface wind speeds derived from WindSat,the RMS difference of 1.85 m/s for HY-2 RM collocated observations data set is calculated in the same period as above.With analyzing the global map of a mean difference between HY-2 RM and WindSat,it appears that the bias of the sea surface wind speed is obviously higher in the inshore regions.In the open sea,there is a relatively higher positive bias in the mid-latitude regions due to the overestimation of wind speed observations,while the wind speeds are underestimated in the Southern Ocean by HY-2 RM relative to WindSat observations.

Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods

Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed(SSWS)from HH-polarized Sentinel-1(S1)SAR images.The Polarization Ratio(PR)models combined with the CMOD5.N Geophysical Model Function(GMF)is used for SSWS retrieval from the HH-polarized SAR data.We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data.The recently proposed CMODH,i.e.,retrieving SSWS directly from the HHpolarized S1 data is also validated.The results indicate that the CMODH model performs better than results achieved using the PR models.We proposed a neural network method based on the backward propagation(BP)neural network to retrieve SSWS from the S1 HH-polarized data.The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods.Compared to the buoy measurements,the bias,root mean square error(RMSE)and scatter index(SI)of wind speed retrieved by the BP neural network model are 0.10 m/s,1.38 m/s and 19.85%,respectively,while compared to the ASCAT dataset the three parameters of training set are–0.01 m/s,1.33 m/s and 15.10%,respectively.It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.

Retrieval of Sea Surface Wind Speed by One-Dimensional Synthetic Aperture Microwave Radiometer

One-dimensional synthetic aperture microwave radiometers have higher spatial resolution and record measurements at multiple incidence angles.In this paper,we propose a multiple linear regression method to retrieve sea surface wind speed at an incidence angle between 0°65°.We assume that a one-dimensional synthetic aperture microwave radiometer operates at frequencies of 6.9,10.65,18.7,23.8 and 36.5 GHz.Then,the microwave radiative transfer forward model is used to simulate the measured brightness temperatures.The sensitivity of the brightness temperatures at 0°65°to the sea surface wind speed is calculated.Then,vertical polarization channels(VR),horizontal polarization channels(HR)and all channels(AR)are used to retrieve the sea surface wind speed via a multiple linear regression algorithm at 0°65°,and the relationship between the retrieval error and incidence angle is obtained.The results are as follows:(1)The sensitivity of the vertical polarization brightness temperature to the sea surface wind speed is smaller than that of the horizontal polarization.(2)The retrieval error increases with Gaussian noise.The retrieval error of VR first increases and then decreases with increasing incidence angle,the retrieval error of HR gradually decreases with increasing incidence angle,and the retrieval error of AR first decreases and then increases with increasing incidence angle.(3)The retrieval error of AR is the lowest and it is necessary to retrieve the sea surface wind speed at a larger incidence angle for AR.

Sea Surface Roughness Derivation from Wind Speed Estimated by Satellite Altimeter

For open sea conditions the sea surface roughness is described as a function of surface stress and wind speed over sea surface by Charnock relation. The sea surface roughnessn in the North-west Pacific Ocean is derived successfully using wind speed data estimated by the TOPEX satellite altimeter. From the results we find that： （1） the mean sea surface roughness in winter is greater than in summer; （2） compared with other sea areas, the sea surface roughness in the sea area east of Japan （ N30°- 40°, E135°- 150°） is larger than in other sea areas; （3） sea surface roughness in the South China Sea changes more greatly than that in the Bohai Sea, Yellow Sea and East China Sea.

Evaluation of ENVISAT ASAR data for sea surface wind retrieval in Hong Kong coastal waters of China

The C-band wind speed retrieval models, CMOD4, CMOD - IFR2, and CMOD5 were applied to retrieval of sea surface wind speeds from ENVISAT （European environmental satellite） ASAR （advanced synthetic aperture radar） data in the coastal waters near Hong Kong during a period from October 2005 to July 2007. The retrieved wind speeds are evaluated by comparing with buoy measurements and the QuikSCAT （quick scatterometer） wind products. The results show that the CMOD4 model gives the best performance at wind speeds lower than 15 m/s. The correlation coefficients with buoy and QuikSCAT winds are 0.781 and 0.896, respectively. The root mean square errors are the same 1.74 m/s. Namely, the CMOD4 model is the best one for sea surface wind speed retrieval from ASAR data in the coastal waters near Hong Kong.

Retrieval of sea surface winds under hurricane conditions from GNSS-R observations

Reflected signals from global navigation satellite systems（GNSSs） have been widely acknowledged as an important remote sensing tool for retrieving sea surface wind speeds.The power of GNSS reflectometry（GNSS-R）signals can be mapped in delay chips and Doppler frequency space to generate delay Doppler power maps（DDMs）,whose characteristics are related to sea surface roughness and can be used to retrieve wind speeds.However,the bistatic radar cross section（BRCS）,which is strongly related to the sea surface roughness,is extensively used in radar.Therefore,a bistatic radar cross section（BRCS） map with a modified BRCS equation in a GNSS-R application is introduced.On the BRCS map,three observables are proposed to represent the sea surface roughness to establish a relationship with the sea surface wind speed.Airborne Hurricane Dennis（2005） GNSS-R data are then used.More than 16 000 BRCS maps are generated to establish GMFs of the three observables.Finally,the proposed model and classic one-dimensional delay waveform（DW） matching methods are compared,and the proposed model demonstrates a better performance for the high wind speed retrievals.

Satellite retrieval of hurricane wind speeds using the AMSR2 microwave radiometer

The AMSR2 microwave radiometer is the main payload of the GCOM-W1 satellite,launched by the Japan Aerospace Exploration Agency in 2012. Based on the pre-launch information extraction algorithm,the AMSR2 enables remote monitoring of geophysical parameters such as sea surface temperature,wind speed,water vapor,and liquid cloud water content. However,rain alters the properties of atmospheric scattering and absorption,which contaminates the brightness temperatures measured by the microwave radiometer. Therefore,it is difficult to retrieve AMSR2-derived sea surface wind speeds under rainfall conditions. Based on microwave radiative transfer theory,and using AMSR2 L1 brightness temperature data obtained in August 2012 and NCEP reanalysis data,we studied the sensitivity of AMSR2 brightness temperatures to rain and wind speed,from which a channel combination of brightness temperature was established that is insensitive to rainfall,but sensitive to wind speed. Using brightness temperatures obtained with the proposed channel combination as input parameters,in conjunction with HRD wind field data,and adopting multiple linear regression and BP neural network methods,we established an algorithm for hurricane wind speed retrieval under rainfall conditions. The results showed that the standard deviation and relative error of retrievals,obtained using the multiple linear regression algorithm,were 3.1 m/s and 13%,respectively. However,the standard deviation and relative error of retrievals obtained using the BP neural network algorithm were better(2.1 m/s and 8%,respectively). Thus,the results of this paper preliminarily verified the feasibility of using microwave radiometers to extract sea surface wind speeds under rainfall conditions.

Physical mechanism and numerical simulations of surface layer temperature inversion in tropical ocean

The one-dimensional Kraus- Turner mixed layer model improved by Liu is developed to consider the effect of salinity and the equa- tions of temperature and salinity under the mixed layer. On this basis, the processes of growth and death of surface layer temperature inversion is numerically simulated under different environmental parameters. At the same time, the physical mechanism is preliminari- ly discussed combining the observations at the station of TOGA- COARE 0°N, 156°E. The results indicate that temperature inversion sensitively depends on the mixed layer depth, sea surface wind speed and solar shortwave radiation, etc., and appropriately meteoro- logical and hydrological conditions often lead to the similarly periodical occurrence of this inversion phenomenon.