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1.
??????????????????????????????P????????L2????????????P?????????????P????λ/α??????1??-1??????-7??9?? ??????????????????????????????????????????????????α???·??Ч??仯??????P?????????????????????÷????? ??????α???ж?·??Ч????????仯?????????????????????????????????????????????·??????????????仯??1????????????????????Ч????????н??????  相似文献   

2.
???????λ???α??????????????????????????????????λ?????????????????????????????????????????C1??P2???????λ???????Ч???α??λ?????????????λ??????1 m????????t???????????????α????λ??????0.5 m???????P1??P2???????λ??????λ??????????????????????????λ????????0.6 m?????t??????????????????0.3 m???????????????У?????????????t????????????λ??????λ??????2 m??  相似文献   

3.
???????????????GLONASS??????棬?о??????????????????????????????????????????????????Ч????????г??????????????????????????????????????1~300 s???????Ч???????????????????????????????????????????????300 min???X??Y??Z????????????266.43??246??13??336.06 m?????????????24???????????2 ms?????????????????GLONASS??????档
??  相似文献   

4.
??????GPS/GALILEO??????λ????????????????????????????????????????????????????????????????????GPS/GALILEO??????λ???????????????????????GPS??????????????????1~2??GALILEO??????????????Ч????????GPS/GALILEO??λ????????GPS????????????????GPS/GALILEO??????GPS??????λ?????н??????????  相似文献   

5.
??GPS???????в????????????????ARMA?????????佨???????Box-Jenkins??????Pandit-Wu???????????????????????????????????,???в??????????????????????Ч??????????????????????????????Pandit-Wu???????Ч???????Щ,???в??????Box-Jenkins?????????1/3??????????????  相似文献   

6.
??????COMPASS???????????COMPASSα?????????????????????????????4GEO+5IGSO????????COMPASSα????λ???????????????COMPASSα???·??Ч???????????0??3 m???仯???侫???0.3??0.7 m??????????????·??Ч??????????С??GEO??????????·??Ч??????????????С??IGSO?????COMPASSα????λ?????RMS????N??E??U?????????3.33??3.45??8.84 m??  相似文献   

7.
????????SLR??2005??????3????????LAGEOS-1??BeaconC??Jason1????????ι?????????LAGEOS??1??????о?????????????????????????????????????????????????????ó?????SLR??3??????????????????2 cm??  相似文献   

8.
??OpenMP??????????Intel Math Kernel Library10.2??????????????????????????????????????????Ч??????????????1?????????????????γ??????????????????OpenMP????????????洢???????????????????????С??2???????????????????????????????γ?????????MKL?????????????Ч?????3??MKL??????Ч???????OpenMP????4?????????OpenMP??MKL??????????????????????????????????Ч???  相似文献   

9.
???????????????????????????????????????????PEM??????С????Э????????LSM????????120??GRACE???????????????????????????1??????????JPL??????GRACE???????????????????????120?????????PEM??LSM???????????λ?????????5.192??10-10????6.633??10-10????2????????PEM??LSM???120?????????λ???????????1?GFZ??????EIGEN-GRACE02S???????????????????????????????????????????PEM??LSM????????????????????????Ч???3?????????PEM??LSM?????????????λ????????????????????????????????????????????????????????????120??GRACE???????????????????  相似文献   

10.
?о???2003??2006???????????Sacks????????????峱??????????????????????????峱??????????????????????仯??????????????????????????????????????????????????0.533??????亯??????????????????????????????????????????????仯????????????????????????????????????????????????????6.28??10-11/Pa??????亯?????????????????????????????????????????????????????????????????????????Ч????£????????????????????????????????????????????????????????????????????????????????????????????????????й??  相似文献   

11.
Time series of sea surface temperature (SST),wind speed and significant wave height (SWH) from meteorologicalbuoys of the National Data Buoy Center (NDBC) are useful for studying the interannual variability and trend of these quantities at the buoy areas.The measurements from 4 buoys (B51001,B51002,B51003 and B51004) in the Hawaii area are used to study theresponses of the quantities to EI Nino and Southern Oscillation (ENSO).Long-term averages of these data reflect precise seasonaland climatological characteristics of SST,wind speed and SWH around the Hawaii area.Buoy observations from B51001 suggest asignificant warming trend which is,however,not very clear from the other three buoys.Compared with the variability of SST andSWH,the wind speeds from the buoy observations show an increasing trend.The impacts of El Nifio on SST and wind waves arealso shown.Sea level data observed by altimeter during October 1992 to September 2006 are analyzed to investigate the variabilityof sea level in the Hawaii area.The results also show an increasing trend in sea level anomaly (SLA).The low-passed SLA in theHawaii area is consistent with the inverse phase of the low-passed Sol (Southern Oscillation Index).Compared with the low-passedSOl and PDO (Pacific Decadal Oscillation),the low-passed PNA (Pacific-North America Index) has a better correlation with thelow-passed SLA in the Hawaii area.  相似文献   

12.
The 21st century Maritime Silk Road(MSR) proposed by China strongly promotes the maritime industry. In this paper, we use wind and ocean wave datasets from 1979 to 2014 to analyze the spatial and temporal distributions of the wind speed, significant wave height(SWH), mean wave direction(MWD), and mean wave period(MWP) in the MSR. The analysis results indicate that the Luzon Strait and Gulf of Aden have the most obvious seasonal variations and that the central Indian Ocean is relatively stable. We analyzed the distributions of the maximum wind speed and SWH in the MSR over this 36-year period. The results show that the distribution of the monthly average frequency for SWH exceeds 4 m(huge waves) and that of the corresponding wind speed exceeds 13.9 ms~(-1)(high wind speed). The occurrence frequencies of huge waves and high winds in regions east of the Gulf of Aden are as high as 56% and 80%, respectively. We also assessed the wave and wind energies in different seasons. Based on our analyses, we propose a risk factor(RF) for determining navigation safety levels, based on the wind speed and SWH. We determine the spatial and temporal RF distributions for different seasons and analyze the corresponding impact on four major sea routes. Finally, we determine the spatial distribution of tropical cyclones from 2000 to 2015 and analyze the corresponding impact on the four sea routes. The analysis of the dynamic characteristics of the MSR provides references for ship navigation as well as ocean engineering.  相似文献   

13.
Wave parameters, such as wave height and wave period, are important for human activities, such as navigation, ocean engineering and sediment transport, etc. In this study, wave data from six buoys around Chinese waters, are used to assess the quality of wave height and wave period in the ERA5 reanalysis of the European Centre for Medium-Range Weather Forecasts. Annual hourly data with temporal resolution are used. The difference between the significant wave height(SWH) of ERA 5 and that of the buoy varies from-0.35 m to 0.30 m for the three shallow locations;for the three deep locations, the variation ranges from-0.09 m to 0.09 m. The ERA5 SWH data show positive biases, indicating an overall overestimation for all locations, except for E2 and S1 where underestimation is observed. During the tropical cyclone period, a large(about 32%) underestimation of the maximum SWH in the ERA5 data is observed. Hence, the ERA5 SWH data cannot be used for design applications without site-specific validation. The difference between the annual wave period from ERA5 and the mean wave period from the buoys varies from-1.31 s to 0.4 s. Inter-comparisons suggest that the ERA5 dataset is consistent with the annual mean SWH. However, for the average period, the performance is not good, and half of the correlation coefficients in the four points are less 50%. Overall, the deep water area simulation effect is better than that in the shallow water.  相似文献   

14.
Directional wave spectra and integrated wave parameters can be derived from X-band radar sea surface images.A vessel on the sea surface has a significant influence on wave parameter inversions that can be seen as intensive backscatter speckles in X-band wave monitoring radar sea surface images.A novel algorithm to eliminate the interference of vessels in ocean wave height inversions from X-band wave monitoring radar is proposed.This algorithm is based on the characteristics of the interference.The principal components(PCs) of a sea surface image sequence are extracted using empirical orthogonal function(EOF)analysis.The standard deviation of the PCs is then used to identify vessel interference within the image sequence.To mitigate the interference,a suppression method based on a frequency domain geometric model is applied.The algorithm framework has been applied to OSMAR-X,a wave monitoring system developed by Wuhan University,based on nautical X-band radar.Several sea surface images captured on vessels by OSMAR-X are processed using the method proposed in this paper.Inversion schemes are validated by comparisons with data from in situ wave buoys.The root-mean-square error between the significant wave heights(SWH) retrieved from original interference radar images and those measured by the buoy is reduced by 0.25 m.The determinations of surface gravity wave parameters,in particular SWH,confirm the applicability of the proposed method.  相似文献   

15.
The validation and assessment of Envisat advanced synthetic aperture radar (ASAR) ocean wave spectra products are important to their application in ocean wave numerical predictions. Six-year ASAR wave spectra data are compared with one-dimensional (1D) wave spectra of 55 co-located moored buoy observations in the northern Pacific Ocean. The ASAR wave spectra data are firstly quality control filtered and spatio-temporal matched with buoy data. The comparisons are then performed in terms of 1D wave spectra, significant wave height (SWH) and mean wave period (MWP) in different spatio-temporal offsets respectively. SWH comparison results show the evident dependence of SWH biases on wind speed and the ASAR SWH saturation effect. The ASAR wave spectra tend to underestimate SWH at high wind speeds and overestimate SWH at low wind speeds. MWP comparison results show that MWP has a systematic bias and therefore it should be bias-modified before used. The comparisons of 1D wave spectra show that both wave spectra agree better at low frequencies than at high frequencies, which indicates the ASAR data cannot resolve the high frequency waves.  相似文献   

16.
以CCMP(Cross—Calibrated,Multi—Platfoml)风场为驱动场,分别驱动目前国际先进的第3代海浪模式ww3(WAVEWATCH—III)、SWAN(Simulating WAves Nearshore),对2010年9月发生在东中国海的台风“圆规”所致的台风浪进行数值模拟,就台风浪的特征进行分析,并对比分析两个海浪模式的模拟效果。结果表明:1)以CCMP风场分别驱动WW3、SWAN海浪模式,可以较好地模拟发生在东中国海的台风浪,风向与波向保持了大体一致,波高与风速的分布特征保持了很好的一致性;2)综合相关系数、偏差、均方根误差、平均绝对误差来看,两个模式模拟的有效波高(SWH—Significant Wdve Height)都具有较高精度,SWAN模拟的SWH略低于观测值,WW3模拟的SWH与观测值更为接近;3)台风浪可给琉球群岛海域带来5m左右的大浪,台风浪进入东海后,波高、风速都有一定程度的增加,当台风沿西北路径穿越朝鲜半岛时,受到半岛地形的巨大影响,风速和波高都明显降低。  相似文献   

17.
The multi-scale characteristics of wave significant height (Hss) in eastern China seas were revealed by multi-scale wavelet analysis. In order to understand the relation between wave and wind, the TOPEX/Poseidon measurements of Hs and wind speed were analyzed. The result showed that Hs and wind speed change in multi-scale at one-, two-month, half-, one- and two-year cycles. But in a larger time scale, the variations in Hs and wind speed are different. Hs has a five-year cycle similar to the cycle of ENSO variation, while the wind speed has no such cycle. In the time domain, the correlation between Hs and ENSO is unclear.  相似文献   

18.
???????????????????????????жΣ?????????????????????й?????????Ч????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????  相似文献   

19.
We compared data of sea surface wind from the European Centre for Medium-Range Weather Forecasts Interim Reanalysis(ERA-Interim) with that collected from eight buoys deployed in the Yellow and East China seas.The buoy data covered a period from 2010 to 2011,during which the longest time series without missing data extended for 329 days.Results show that the ERA-Interim wind data agree well with the buoy data.The regression coefficients between the ERA-Interim and observed wind speed and direction are greater than 0.7 and 0.79,respectively.However,the ERA-Interim wind data overestimate wind speed at most of the buoy stations,for which the largest bias is 1.8 m/s.Moreover,it is found from scatter plots of wind direction that about 13%of the ERA-Interim wind data can be classified as bad for wind speeds below6 m/s.Overall,the ERA-Interim data forecast both the wind speed and direction well,although they are not very representative of our observations,especially those where the wind speed is below 6 m/s.  相似文献   

20.
In this work, we examined long-term wave distributions using a third-generation numerical wave model called WAVE- WATCH-Ⅲ(WW3) (version 6.07). We also evaluated the influence of sea ice on wave simulation by using eight parametric switches. To select a suitable ice-wave parameterization, we validated the simulations from the WW3 model in March, May, September, and December 2017 against the measurements from the Jason-2 altimeter at latitudes of up to 60?N. Generally, all parameterizations ex-hibited slight differences, i.e., about 0.6 m root mean square error (RMSE) of significant wave height (SWH) in May and September and about 0.9 m RMSE for the freezing months of March and December. The comparison of the results with the SWH from the European Centre for Medium-Range Weather Forecasts for December 2017 indicated that switch IC4_M1 performed most effec-tively (0.68 m RMSE) at high latitudes (60?– 80?N). Given this finding, we analyzed the long-term wave distributions in 1999 – 2018 on the basis of switch IC4_M1. Although the seasonal variability of the simulated SWH was of two types, i.e., 'U' and 'sin' modes, our results proved that fetch expansion prompted the wave growth. Moreover, the interannual variability of the specific regions in the 'U' mode was found to be correlated with the decade variability of wind in the Arctic Ocean.  相似文献   

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