Exploitable wave energy assessment based on ERA-Interim reanalysis data—A case study in the East China Sea and the South China Sea

Wave energy resources assessment is a very important process before the exploitation and utilization of the wave energy. At present, the existing wave energy assessment is focused on theoretical wave energy conditions for interesting areas. While the evaluation for exploitable wave energy conditions is scarcely ever performed. Generally speaking, the wave energy are non-exploitable under a high sea state and a lower sea state which must be ignored when assessing wave energy. Aiming at this situation, a case study of the East China Sea and the South China Sea is performed. First, a division basis between the theoretical wave energy and the exploitable wave energy is studied. Next, based on recent 20 a ERA-Interim wave field data, some indexes including the spatial and temporal distribution of wave power density, a wave energy exploitable ratio, a wave energy level, a wave energy stability, a total wave energy density, the seasonal variation of the total wave energy and a high sea condition frequency are calculated. And then the theoretical wave energy and the exploitable wave energy are compared each other; the distributions of the exploitable wave energy are assessed and a regional division for exploitable wave energy resources is carried out; the influence of the high sea state is evaluated. The results show that considering collapsing force of the high sea state and the utilization efficiency for wave energy, it is determined that the energy by wave with a significant wave height being not less 1 m or not greater than 4 m is the exploitable wave energy. Compared with the theoretical wave energy, the average wave power density, energy level, total wave energy density and total wave energy of the exploitable wave energy decrease obviously and the stability enhances somewhat. Pronounced differences between the theoretical wave energy and the exploitable wave energy are present. In the East China Sea and the South China Sea, the areas of an abundant and stable exploitable wave energy are primarily located in the north-central part of the South China Sea, the Luzon Strait, east of Taiwan, China and north of Ryukyu Islands; annual average exploitable wave power density values in these areas are approximately 10-15 kW/m; the exploitable coefficient of variation （COV） and seasonal variation （SV） values in these areas are less than 1.2 and 1, respectively. Some coastal areas of the Beibu Gulf, the Changjiang Estuary, the Hangzhou Bay and the Zhujiang Estuary are the poor areas of the wave energy. The areas of the high wave energy exploitable ratio is primarily in nearshore waters. The influence of the high sea state for the wave energy in nearshore waters is less than that in offshore waters. In the areas of the abundant wave energy, the influence of the high sea state for the wave energy is prominent and the utilization of wave energy is relatively difficult. The developed evaluation method may give some references for an exploitable wave energy assessment and is valuable for practical applications.

Uncertainties of ENSO-related Regional Hadley Circulation Anomalies within Eight Reanalysis Datasets

El Nino-Southern Oscillation(ENSO),the leading mode of global interannual variability,usually intensifies the Hadley Circulation(HC),and meanwhile constrains its meridional extension,leading to an equatorward movement of the jet system.Previous studies have investigated the response of HC to ENSO events using different reanalysis datasets and evaluated their capability in capturing the main features of ENSO-associated HC anomalies.However,these studies mainly focused on the global HC,represented by a zonal-mean mass stream function(MSF).Comparatively fewer studies have evaluated HC responses from a regional perspective,partly due to the prerequisite of the Stokes MSF,which prevents us from integrating a regional HC.In this study,we adopt a recently developed technique to construct the three-dimensional structure of HC and evaluate the capability of eight state-of-the-art reanalyses in reproducing the regional HC response to ENSO events.Results show that all eight reanalyses reproduce the spatial structure of HC responses well,with an intensified HC around the central-eastern Pacific but weakened circulations around the Indo-Pacific warm pool and tropical Atlantic.The spatial correlation coefficient of the three-dimensional HC anomalies among the different datasets is always larger than 0.93.However,these datasets may not capture the amplitudes of the HC responses well.This uncertainty is especially large for ENSO-associated equatorially asymmetric HC anomalies,with the maximum amplitude in Climate Forecast System Reanalysis(CFSR)being about 2.7 times the minimum value in the Twentieth Century Reanalysis(20CR).One should be careful when using reanalysis data to evaluate the intensity of ENSO-associated HC anomalies.

Applicability Evaluation of NCEP/NCAR Reanalysis Temperature,Geopotential Height and Wind Field Data in the Upper Troposphere and Lower Stratosphere

By using the data in 169 sounding stations over the world,NCEP/NCAR reanalysis data were tested,and the distribution characteristics of standard errors of geopotential height,temperature and wind speed field from the upper troposphere to the lower stratosphere over the world(most were the land zone) were analyzed.The results showed that the standard error distribution of reanalysis wind speed field data was mainly affected by the jet stream zone.There existed the obvious difference between the jet stream zone and the actual wind field.The distribution of standard error in the wind speed field had the obvious seasonal difference in winter,summer,and the average deviation was larger near the coastline.The high value zones of standard errors of reanalysis geopotential height and temperature field mainly concentrated in the low-latitude region in the Eastern Hemisphere(Indian Ocean coast).The distribution of standard error was basically consistent with average error.Therefore,the standard error could be explained well by the average error.The standard errors of reanalysis temperature and geopotential height data in the inland zone were lower.The high value zone mainly distributed along the coastline,and the average error of wind speed field was bigger near the coastline.It closely related to the quality of data in the sounding stations,the regional difference and the fact that the land observation stations were dense,and the ocean observation stations were fewer.

Validation of the Relationship between Precipitable Water and Surface Vapor Pressure by Means of Reanalysis Data

By means of ERA-40, JRA-25, NCEP/NCAR and NCEP/DOE reanalysis data, empirical relations between precipitable water and surface vapor pressure in spatial and temporal scale were calculated. The reliabilities of precipitable water from reanalysis data were validated based on comparing different W-e empirical relations of various reanalysis data, in order to provide basis and reference for reasonable application. The results showed that W-e empirical relation of ERA-40 was closest to that of sounding data in China, and precipitable water from ERA-40 was the most credible. The worldwide comparison among W-e empirical relations of four reanalysis data showed that there was little difference in annual mean W-e empirical relations in the middle latitudes and great differences in low and high latitudes. Seasonal mean W-e empirical relations in the middle latitudes of the northern Hemisphere had little difference in spring, autumn and winter, but great difference in summer. Therefore, the reliabilities of precipitable water from reanalysis data in spring, autumn and winter in the middle latitudes of the northern hemisphere were higher than other areas and seasons. W-e empirical relations of NCEP/NCAR and NCEP/DOE had good stability in different years, while there was poor stability in ERA-40 and JRA-25.

Accumulation over the Greenland Ice Sheet as Represented in Reanalysis Data

Annual precipitation,evaporation,and calculated accumulation from reanalysis model outputs have been investigated for the Greenland Ice Sheet （GrIS）,based on the common period of 1989-2001.The ERA-40 and ERA-interim reanalysis data showed better agreement with observations than do NCEP-1 and NCEP-2 reanalyses.Further,ERA-interim showed the closest spatial distribution of accumulation to the observation.Concerning temporal variations,ERA-interim showed the best correlation with precipitation observations at five synoptic stations,and the best correlation with in situ measurements of accumulation at nine ice core sites.The mean annual precipitation averaged over the whole GrIS from ERA-interim （363 mm yr 1） and mean annual accumulation （319 mm yr 1） are very close to the observations.The validation of accumulation calculated from reanalysis data against ice-core measurements suggests that further improvements to reanalysis models are needed.

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.

Validation of ECMWF and NCEP–NCAR Reanalysis Data in Antarctica

The European Center for Medium-Range Weather Forecast （ECMWF） Re-Analysis （ERA-40） and the National Centers for Environmental Prediction-National Center for Atmospheric Research （NCEP-NCAR） ECMWF （ERA-40） and NCEP–NCAR reanalysis data were compared with Antarctic station observations, including surface-layer and upper-layer atmospheric observations, on intraseasonal and interannual timescales. At the interannual timescale, atmospheric pressure at different height levels in the ERA-40 data are in better agreement with observed pressure than that in the NCEP–NCAR reanalysis data. ERA-40 reanalysis also outperforms NCEP–NCAR reanalysis in atmospheric temperature, except in the surface layer where the biases are somewhat larger. The wind velocity fields in both datasets do not agree well with surface-and upper-layer atmospheric observations. At intraseasonal timescales, both datasets capture the observed intraseasonal variability in pressure and temperature during austral winter.

Assessing the Quality of Regional Ocean Reanalysis Data from ENSO Signals

The quality of regional ocean reanalysis data for ＂the joining area of Asia and the Indian-Pacific Ocean （AIPO）＂ has been assessed from the perspective of ENSO-related ocean signals. The results derived from the AIPO reanalysis, including SST, sea surface height （SSH）, and subsurface ocean temperature and currents, are compared with those of Hadley Center Sea Ice and Sea Surface Temperature （HadlSST） data set and Simple Ocean Data Assimilation （SODA） reanalysis data. Both the spatial pattern and the characteristics of evolution of the ENSO-related ocean temperature anomalies are well reproduced by the AIPO reanalysis data. The physical processes proposed to explain the life cycle of ENSO, including the delayed oscillator mechanism, recharge-discharge mechanism, and the zonal advection feedback, are reasonably represented in this dataset. However, the westward Rossby wave signal in 1992 is not obvious in the AIPO data, and the magnitude of the heat content anomalies is different from that of the SODA data. The reason for the discrepancies may lie in the different mod- els and methods for data assimilation and differences in wind stress forcing. The results demonstrate the high reliability of the AIPO reanalysis data in describing ENSO signals, implying its potential application value in ENSO- related studies.

An evaluation of new satellite-derived latent and sensible heat fluxes with moored buoy data, OAFlux and NCEP2 reanalysis products

New satellite-derived latent and sensible heat fluxes are performed by using Wind Sat wind speed, Wind Sat sea surface temperature, the European Centre for Medium-range Weather Forecasting（ECMWF） air humidity, and ECMWF air temperature from 2004 to 2014. The 55 moored buoys are used to validate them by using the 30 min and 25 km collocation window. Furthermore, the objectively analyzed air-sea heat fluxes（OAFlux） products and the National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis 2（NCEP2） products are also used for global comparisons. The mean biases of sensible and latent heat fluxes between Wind Sat flux results and buoy flux data are –0.39 and –8.09 W/m^2, respectively. In addition, the rootmean-square（RMS） errors of the sensible and latent heat fluxes between them are 5.53 and 24.69 W/m^2,respectively. The RMS errors of sensible and latent heat fluxes are observed to gradually increase with an increasing buoy wind speed. The difference shows different characteristics with an increasing sea surface temperature, air humidity, and air temperature. The zonal average latent fluxes have some high regions which are mainly located in the trade wind zones where strong winds carry dry air in January, and the maximum value centers are found in the eastern waters of Japan and on the US east coast. Overall, the seasonal variability is pronounced in the Indian Ocean, the Pacific Ocean, and the Atlantic Ocean. The three sensible and latent heat fluxes have similar latitudinal dependencies; however, some differences are found in some local regions.

Discharge Simulation in a Data-Scarce Basin Using Reanalysis and Global Precipitation Data: A Case Study of the White Volta Basin

Basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The purpose of this study was to examine the utility of reanalysis and global precipitation datasets in the river discharge simulation for a data-scarce basin. The White Volta basin of Ghana which is one of international rivers was selected as a study basin. NCEP1, NCEP2, ERA-Interim, and GPCP datasets were compared with corresponding observed precipitation data. Annual variations were not reproduced in NCEP1, NCEP2, and ERA-Interim. However, GPCP data, which is based on satellite and observed data, had good seasonal accuracy and reproduced annual variations well. Moreover, five datasets were used as input data to a hydrologic model with HYMOD, which is a water balance model, and with WTM, which is a river model;thereafter, the hydrologic model was calibrated for each datum set by a global optimization method, and river discharge were simulated. The results were evaluated by the root mean square error, relative error, and water balance error. As a result, the combination of GPCP precipitation and ERA-Interim evaporation data was the best in terms of most evaluations. The relative errors in the calibration and validation periods were 43.1% and 46.6%, respectively. Moreover, the results for the GPCP precipitation and ERA-Interim evaporation were better than those for the combination of observed precipitation and ERA-Interim evaporation. In conclusion, GPCP precipitation data and ERA-Interim evaporation data are very useful in a data-scarce basin water balance analysis.

Decadal trends in precipitable water vapor over the Indus River Basin using ERA5 reanalysis data

Precipitable Water Vapor(PWV)constitutes a pivotal parameter within the domains of atmospheric science,and remote sensing due to its profound influence on Earth’s climate dynamics and weather patterns.It exerts a significant impact on atmospheric stability absorption and emission of radiation,thus engendering alterations in the Earth’s radiative equilibrium.As such,precise quantification of PWV holds the potential to enhance weather prognostication and fortify preparedness against severe meteorological phenomena.This study aimed to elucidate the spatial and temporal changes in seasonal and annual PWV across the Indus River Basin and its sub-basins using ERA5 reanalysis datasets.The present study used ERA5 PWV(entire atmospheric column),air temperature at 2 m(t2m)and 500 hPa(T_500hPa),evapotranspiration,and total cloud cover data from 1960 to 2021.Theil Sen slope estimator and Mann-Kendall test were used for trend analysis.Correlation and multiple regression methods were used to understand the association of PWV with other factors.The findings have unveiled the highest increase in mean PWV during the monsoon(0.40 mm/decade),followed by premonsoon(0.37 mm/decade),post-monsoon(0.27 mm/decade),and winter(0.19 mm/decade)throughout the study period.Additionally,the mean PWV exhibited the most pronounced positive trend in the sub-basin Lower Indus(LI),followed by Panjnad(P),Kabul(K),and Upper Indus(UI)across all seasons,except winter.Annual PWV has also risen in the Indus basin and its sub-basins over the last six decades.PWV exhibits a consistent upward trend up to an elevation of 3500 m within the basin which is most pronounced during the monsoon season,followed by the pre-monsoon.The escalating PWV within the basin is reasonably ascribed to increasing air temperatures,augmented evapotranspiration,and heightened cloud cover.These findings hold potential utility for pertinent authorities engaged in water resource management and planning.

Applicability evaluation of ERA5 wind and wave reanalysis data in the South China Sea

Wind and wave data are essential in climatological and engineering design applications.In this study,data from 15 buoys located throughout the South China Sea(SCS)were used to evaluate the ERA5 wind and wave data.Applicability assessment are beneficial for gaining insight into the reliability of the ERA5 data in the SCS.The bias range between the ERA5 and observed wind-speed data was-0.78-0.99 m/s.The result indicates that,while the ERA5 wind-speed data underestimation was dominate,the overestimation of such data existed as well.Additionally,the ERA5 data underestimated annual maximum wind-speed by up to 38%,with a correlation coefficient>0.87.The bias between the ERA5 and observed significant wave height(SWH)data varied from-0.24 to 0.28 m.And the ERA5 data showed positive SWH bias,which implied a general underestimation at all locations,except those in the Beibu Gulf and centralwestern SCS,where overestimation was observed.Under extreme conditions,annual maximum SWH in the ERA5 data was underestimated by up to 30%.The correlation coefficients between the ERA5 and observed SWH data at all locations were greater than 0.92,except in the central-western SCS(0.84).The bias between the ERA5 and observed mean wave period(MWP)data varied from-0.74 to 0.57 s.The ERA5 data showed negative MWP biases implying a general overestimation at all locations,except for B1(the Beibu Gulf)and B7(the northeastern SCS),where underestimation was observed.The correlation coefficient between the ERA5 and observed MWP data in the Beibu Gulf was the smallest(0.56),and those of other locations fluctuated within a narrow range from 0.82 to 0.90.The intercomparison indicates that during the analyzed time-span,the ERA5 data generally underestimated wind-speed and SWH,but overestimated MWP.Under non-extreme conditions,the ERA5 wind-speed and SWH data can be used with confidence in most regions of the SCS,except in the central-western SCS.

Applicability analysis of the sea surface pressure and wind speed of ERA5 reanalysis data in the Bohai Sea and the Northern Huanghai Seas

A preliminarily assessment of the applicability of the sea surface pressure and wind speed of ERA5 reanalysis data is carried out using the observation data at 10 m height observation data of 9 buoys in the Bohai Sea and the Northern Huanghai Sea.The results show that:the sea surface pressure and wind speed of ERA5 reanalysis data has high correlation coefficients with the observation data,the correlation between sea surface pressure and wind speed is different in different time scales.The correlation of monthly average is better than that of daily average and daily extreme value,and the correlation coefficient is the lowest in extreme weather.In generally,the deviation between statistical products of the ERA5 and the observed products is negative.It means that the high pressure is weaker than the observed data,and the low pressure is stronger than the observed data,and there is some systematic deviation between ERA5 reanalysis data and the observed data.The deviation varies with the different wind speed level,when the wind is high,the reanalysis wind speed is generally less than the measured.By analyzing the monthly average data,the reanalysis data reveal the seasonal variation of sea surface pressure in the study area,and the deviation from the observed data also show seasonal variation characteristics,the applicability in winter is better than in summer.The error of reanalysis data of sea surface pressure and wind speed is large under extreme weather conditions,especially the typhoon process,further evaluation and revision of the data are needed.

DIFFERENCES OF SOUTH CHINA SEA SUMMER MONSOON DERIVED BY NCEP AND ECMWF REANALYSIS DATA

Due to long-term time series and many elements, reanalysis data of National Centers for Environmental Prediction （NCEP） and European Center for MediumRange Weather Forecasts （ECMWF） are widely used in present climate studies. Even so, there are discrepancies between NCEP and ECMWF reanalysis. Some climate fields may be better reproduced by NCEP than by ECMWF. On the other hand, ECMWF may describe some climate characteristics more realistically than NCEP. Xu et al.pointed out that NCEP data are of uncertainty when used for studying long-term trends of climate change. By comparing temperatures and pressures from NCEP and observation, it can be seen that NCEP data show higher reliability in the east and lower-latitudes of China than in its west and higher latitudes, NCEP temperature is of more reality than pressure and NCEP data after 1979 are closer to the observations than before. Yang et al.also revealed some serious problems of NCEP data in the north of subtropical Asia. Regional differences of NCEP data in representation are also explored by other studiest. As for seasonal variability, NCEP simulates relatively real conditions of Chinese summer and annual mean but winter data are relatively bad, as in comparisons of NCEP data wity China surface station observations by Zhao et al.Moreover, Trenberth and Stepaniak showed that ECMWF data had better energy budgets than NCEP data for pure pressure coordinates are adopted by ECMWF. Renfrew et al. compared NCF, P data to ECMWF data in terms of surface fluxes and the results indicate that the time series of surface sensible and latent heating fluxes from ECMWF are 13% and 10% larger than the observations and those from NCEP would be 51% and 27% larger than the observations, respectively. So, Renfrew et al. suggested that it be more appropriate to drive ocean models by ECMWF data. Based on comparisons of multiple elements by some scientists, it seems that ECMWF data are better than NCEP data on global, hemispheric and regional scales. Whereas, reanalysis have big errors in some regions in contrast to observations, especially the variables related to humidity. Since that, researchers should compare the two sets of data and select a better one according to specific problems.

Study on the Applicability of ERA5 Reanalysis Data at Lake Taihu

Lakes are an important component of the earth climate system. They play an important role in the study of basin weather forecasting, air quality forecasting, and regional climate research. The accuracy of driving variables is the basic premise to ensure the rationality of lake mode simulation. Based on the in-situ observations at Bifenggang site of the Lake Taihu Eddy flux Network from 2012 to 2017, this paper investigated temporal variations in temperature, relative humidity, wind speed, radiation components at different time scales (hourly, seasonal and interannual). ERA5 reanalysis data were compared with in-situ observation to quantify the error and evaluate the performance of reanalysis data. The results show that: 1) On the hourly scale, the ERA5 reanalysis data described air temperature, and downward long-wave radiation more accurately. 2) On the seasonal variation scale, the ERA5 reanalysis data described air temperature, and downward long-wave radiation more accurately. However, the descriptions of wind speed, relative humidity and downward short-wave have large deviations. 3) On the interannual scale, the ERA5 reanalysis data show a good performance for temperature, followed by downward longwave radiation, downward shortwave radiation and relative humidity.

Evaluation of Reanalysis Data Based on the Three-dimensional High-density Sounding Data of the Qinghai-Tibet Plateau

Based on the data of the third Qinghai-Tibet Plateau atmospheric science experiment from 2015 to 2017,the applicability of plateau weather systems and meteorological elements of two commonly used reanalysis data(NCEP/NCAR reanalysis data set,and ERA-Interim reanalysis data set)in the plateau was evaluated.Some conclusions are obtained as follows.Compared with EC reanalysis data,NCEP reanalysis data are more consistent with the scientific experimental data.The correlation of geopotential height is above 0.99,followed by temperature;The correlation of specific humidity is the worst.Seen from average deviation,geopotential height and temperature are both lower;for EC,the westerly and southerly winds are both weaker;for NCEP,westerly wind is weaker,while southerly wind is stronger;specific humidity is higher.From the perspective of monthly and seasonal distribution characteristics,the average deviation of geopotential height is larger in spring and summer,and that of temperature is slightly worse in late spring and early summer.In terms of wind field,EC deviation is more obvious in winter,while NCEP deviation is more obvious in late spring and early summer.Seen from spatial distribution,the deviations of geological height and temperature in the north of the plateau are smaller than those in the south of the plateau.For wind field,the westerly wind in the Qaidam Basin is weaker,and the southerly wind in the southern plateau is weaker.In vertical profile,the deviation of geopotential height at high levels is greater than that of low levels.The deviation of temperature and wind field is larger near the ground.The temperature at middle levels and the westerly wind at middle and high levels are smaller,and southerly wind is stronger for NCEP.The establishment of the three sounding stations(Gaize,Shenzha and Shiquanhe)is conducive to the discovery of plateau vortex and plateau shear line in the western plateau.The western plateau vortex and plateau shear line mostly appeared in the flood season.Most plateau weather systems were maintained within 24 h,and mainly appeared and disappeared in situ.The objective recognition rate of EC for plateau weather systems is higher than NCEP,so EC is more conducive to the diagnosis and analysis of evolution characteristics of plateau weather systems.

A Regional Ocean Reanalysis System for Coastal Waters of China and Adjacent Seas

A regional ocean reanalysis system for the coastal waters of China and adjacent seas has been developed by the National Marine Data and Information Service（NMDIS）.It produces a dataset package called CORA （China ocean reanalysis）.The regional ocean model used is based on the Princeton Ocean Model with a generalized coordinate system（POMgcs）.The model is parallelized by NMDIS with the addition of the wave breaking and tidal mixing processes into model parameterizations.Data assimilation is a sequential three-dimensional variational（3D-Var） scheme implemented within a multigrid framework.Observations include satellite remote sensing sea surface temperature（SST）,altimetry sea level anomaly（SLA）,and temperature/salinity profiles.The reanalysis fields of sea surface height,temperature,salinity,and currents begin with January 1986 and are currently updated every year. Error statistics and error distributions of temperature,salinity and currents are presented as a primary evaluation of the reanalysis fields using sea level data from tidal gauges,temperature profiles,as well as the trajectories of Argo floats.Some case studies offer the opportunity to verify the evolution of certain local circulations.These evaluations show that the reanalysis data produced provide a good representation of the ocean processes and phenomena in the coastal waters of China and adjacent seas.

Comparison of wind data from ERA-Interim and buoys in the Yellow and East China Seas

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.

An Ocean Reanalysis System for the Joining Area of Asia and Indian-Pacific Ocean

An ocean reanalysis system for the joining area of Asia and Indian-Pacific Ocean (AIPO) has been developed and is currently delivering reanalysis data sets for study on the air-sea interaction over AIPO and its climate variation over China in the inter-annual time scale.This system consists of a nested ocean model forced by atmospheric reanalysis,an ensemble-based multivariate ocean data assimilation system and various ocean observations.The following report describes the main components of the data assimilation system in detail.The system adopts an ensemble optimal interpolation scheme that uses a seasonal update from a free running model to estimate the background error covariance matrix.In view of the systematic biases in some observation systems,some treatments were performed on the observations before the assimilation.A coarse resolution reanalysis dataset from the system is preliminarily evaluated to demonstrate the performance of the system for the period 1992 to 2006 by comparing this dataset with other observations or reanalysis data.

Evaluation of the China Ocean Reanalysis (CORA) in the South China Sea

The daily regional reanalysis product of the China Ocean Reanalysis(CORA)product was released in website in 2018.Using in situ observational data including Argo profiling floats,drifters,and cruise data,the performance of CORA in the South China Sea in terms of temperature,salinity,current and mixed layer depths is evaluated based on timescale(seasonal and interannual)and spatial distribution characteristics.The CORA temperature,salinity,and mixed layer depth show certain seasonal and interannual variations.In 50-400 m depth in the SCS,the CORA temperature is colder in winter and warmer in summer and autumn.In 0-150 m in the SCS,the CORA salinity is higher in most time of the year.However,in the second half of the year,the salinity is slightly weaker in 100-150 m depth.In most years,the CORA mixed layer depths tend to be shallower,and in season,shallower in winter and deeper in summer.In spatial distribution,the closer the area is to the coast,the greater the CORA errors would be.The CORA temperature is colder in the western side and warmer in the eastern side,resulting in a weaker SCS western boundary current(SCSwbc).In most areas,the CORA mixed layer depths are shallower.In the area close to the coast,the CORA mixed layer depths change rapidly,and the deviations in the mixed layer depths are larger.In the central SCS,the CORA mixed layer depths change slowly,and the deviations in the mixed layer depths are also small.