日本海西南海域现场观测和卫星高度计获取的海面高度距平的比较研究

压力传感逆式回声仪(pressure-sensor-equipped inverted echo sounders,PIES)可以用来测量海底压力和声波从海底到海面的传播时间。海底压力和声波传播时间分别被用来估计水体质量变化(正压)和比容变化(斜压)对海面高度距平的贡献。对由PIES在日本海西南海域现场观测数据得到的海面高度距平(PIES SLA)与卫星高度计海面高度距平(Sat SLA)进行了比较研究。利用相关分析法,对PIES SLA和沿轨T/P卫星、沿轨ERS-2卫星测得的海面高度距平(TP SLA、ERS-2 SLA)进行了比较;对PIES SLA和AVISO网格化海面高度距平进行了比较,估计可能的误差来源,并分析PIES SLA正压部分和斜压部分对SLA的贡献。比较发现,PIES SLA和Sat SLA的相关系数较高,且均方根误差较小,并且对特定区域和特定站点产生误差可能的原因进行了进一步的探讨。通过研究,有以下结论:(1)相对于湾流和黑潮地区,这一区域正压部分对海面高度的贡献相对较大;(2)如果再考虑斜压变化对海面高度的贡献,PIES SLA和Sat SLA相关系数会有所提升;(3)在高能区PIES SLA和Sat SLA相关系数较高,符合得相对比较好。总的来说,在日本海地区,PIES SLA和Sat SLA相关系数较高,具有较高的一致性,能为我国海洋二号(HY-2)等卫星高度计的校验提供一种可靠的方式。该研究对于PIES的研发和设计以及对于PIES的布放位置的选择都有一定的借鉴意义。     

Sampling errors of the global mean sea level derived from TOPEX/Poseidon altimetry

Sampling errors of the global mean sea level derived from TOPEX/Poseidon (T/P) altimetry are explored using 31/ 4a of eddy-resolving numerical model outputs for sea level. By definition, the sampling errors would not exist if data were available everywhere at all times. Four problems with increasing and progressively added complexities are examined to understand the causes of the sampling errors. The first problem (P1) explores the error incurred because T/P with turning latitudes near 66° latitudes does not cover the entire globe. The second problem (P2) examines, in addition, the spatial sampling issue because samples are only available along T/P ground tracks. The third problem (P3) adds the additional complexity that sea level at any along track location is sampled only once every 10 d versus every 3 d for the model (i.e., the temporal sampling issue). The fourth problem (P4) incorporates the full complexity with the addition of real T/P data outages. The numerical model (Los Alamos POP model Run 11) conserves the total water volume, thus generating no global mean sea level variation. Yet when the model sea level is sampled in the four problems (with P4 using the real T/P sampling), variations occur as manifestations of the sampling errors. The results show root-mean-squares (rms) sampling errors for P1 of 0.67 (0.75) mm for 10 d (3 d) global mean sea level, 0.78 (0.86) mm for P2, 0.79 mm for P3, and 1.07 mm for P4, whereas the amplitudes of the sampling errors can be as large as 2.0 (2.7) mm for P1, 2.1 (2.7) mm for P2, 2.2 mm for P3, and 2.5 mm for P4. The results clearly show the largest source of the sampling errors to be the lack of global coverage (i.e., P1), which the model has actually underestimated due to its own less-than-global coverage (between latitudes about 77° latitudes). We have extrapolated that a truly global model would show the rms sampling error to be 1.14 (1.28) mm for P1, thus implying a substantially larger sampling error for P4.     

Large-Scale Errors in ERS Altimeter Data

A method is described for mapping time-uncorrelated large-scale errors in satellite altimeter sea surface heights. Standard deviations of differences between pairs of successive measurements at track crossovers are computed, and the functional dependence of these deviations on absolute time difference is used to estimate the errors of individual measurements. This is first applied to all of ERS-1,2 altimeter data in the Pacific Ocean, yielding average errors of 3.2 cm in the deep ocean (>1 km) and 4.7 cm in the shallow seas (<1 km). The procedure is repeated for variable latitude bands, each with a full range of possible time differences, yielding a meridional profile of computed errors, ranging from 2.6 cm near the Antarctic continent (67–60S) and South Subtropical regions (25–5S) to 3.5 cm in the Antarctic Circumpolar Current (60–45S) and the Northern Hemisphere Subtropical and Subpolar Gyres. Finally, coarse-resolution maps of these errors are produced by subdividing the Pacific Ocean into latitude-longitude bins, each large enough to contain a sufficient number of samples for the functional fits. The larger errors are in Northwest and Subtropical Pacific, especially in South China Sea (4.3 to 4.5 cm) and off northern Australia (5.4 cm), while the smaller errors (2.5 to 3 cm) are in Northeast Pacific, central Tropical Pacific and near Antarctica in Southeast Pacific Ocean. These are lower bounds on altimeter errors, as they do not include contributions from time-correlated errors. We find that the computed error fields are not correlated with sea level standard deviations, thus disproving the notion that altimeter error variance can be scaled with the variance of sea surface height data.     

多源资料重构三维海温场的先验误差估计和诊断分析

针对遥感资料与实况观测融合重构三维海温场的问题，改进了最优插值中的先验统计信息估计，并研究了背景场和观测项的各自影响机制。在最优插值中，将背景场分别取为平均气候场（静态方案）和遥感合成场（动态方案）；在最优融合前，用后验诊断方法迭代优化了背景误差和观测误差协方差；融合完成后，用观测空间诊断误差和模式格点指标对比分析两种方案。主要结论:（1）动态方案相对于静态方案分析误差减小的绝对量值由二者的观测误差之差决定、减小的相对程度由二者的背景误差之比决定；（2）背景场的调整作用在中高纬占优势、在赤道海域与观测项调整作用相当，是由协方差相关尺度的准纬向分布特征决定的；（3）动态分析场的中尺度信号谱能量整体比静态分析场高1~3个量级，但在热带海域相当；（4）遥感资料通过分辨实况观测所无法分辨出来的海温中尺度特征，达到减小误差、提高有效分辨率的效果。     

Validation of ERS-1 and High-Resolution Satellite Gravity with in-situ Shipborne Gravity over the Indian Offshore Regions: Accuracies and Implications to Subsurface Modeling

Geoid and gravity anomalies derived from satellite altimetry are gradually gaining importance in marine geoscientific investigations. Keeping this in mind, we have validated ERS-1 (168 day repeat) altimeter data and very high-resolution free-air gravity data sets generated from Seasat, Geosat GM, ERS-1 and TOPEX/POSEIDON altimeters data with in-situ shipborne gravity data of both the Bay of Bengal and the Arabian Sea regions for the purpose of determining the consistencies and deviations. The RMS errors between high resolution satellite and ship gravity data vary from 2.7 to 6.0 mGal, while with ERS-1 data base the errors are as high as 16.5 mGal. We also have generated high resolution satellite gravity maps of different regions over the Indian offshore, which eventually have become much more accurate in extracting finer geological structures like 85° E Ridge, Swatch of no ground, Bombay High in comparison with ERS-1satellite-derived gravity maps. Results from the signal processing related studies over two specific profiles in the eastern and western offshore also clearly show the advantage of high resolution satellite gravity compared to the ERS-1 derived gravity with reference to ship gravity data.     

Analysis of sea surface height variabilities in the Kuroshio Current region by using Geosat altimeter data

INTRODUCTIONBeing a current of high temperature and high salinity, the Kuroshio carries a large amount ofheat from low latitude tropical ocean to high latitude ocean, and plays an imPOrtant role in theheat balance in East Asia. The variability of the Kurosl,io can affect the climate of East Asia, aswell as the ocean environment and the fishery resources. A lot of studies showed that the variabilitiies of the Kuroshio were related to the global changes especially to the onset of ENSO.…     

Onboard correction of mispointing errors in satellite altimeterreturns [oceanographic measurement]

An investigation to determine whether useful onboard mispointing corrections to satellite altimeter measurements for errors in antenna mispointing can be made with the European Research Satellite (ERS-1) altimeter is reported. An analytic model of the nonlinear mispointing control loop is developed and the step response and signal-to-noise ratio (SNR) behavior of the loop in isolation are derived. The ERS-1 altimeter is expected to have a maximum static mispointing error of 0.2° and a maximum harmonic error of 0.1°. Taking these values as typical, it is concluded that with a loop time response of about one minute, it is not possible to correct the biased estimate of backscattering coefficient without decreasing its SNR. However, it is possible to achieve an unbiased estimate with a noise level significantly less than the uncorrected bias, but a successful implementation would require very accurately calibrated range gate samplers     

Sea Surface Height Variability in the Indian Ocean from TOPEX/POSEIDON Altimetry and Model Simulations

Sea Surface Height (SSH) variability in the Indian Ocean during 1993-1995 is studied using TOPEX/POSEIDON (T/P) altimetry data. Strong interannual variability is seen in the surface circulation of the western Arabian Sea, especially in the Somali eddy structure. During the Southwest (SW) monsoon, a weak monsoon year is characterized by a single eddy system off Somalia, a strong or normal monsoon year by several energetic eddies. The Laccadive High (LH) and Laccadive Low (LL) systems off southwest India are observed in the altimetric SSH record. The variability of the East India Coastal Current (EICC), the western boundary current in the Bay of Bengal, is also detected. Evidence is found for the propagation of Kelvin and Rossby waves across the northern Indian Ocean; these are examined in the context of energy transfer to the western boundary currents, and associated eddies. A simple wind-driven isopycnal model having three active layers is implemented to simulate the seasonal changes of surface and subsurface circulation in the North Indian Ocean and to examine the response to different wind forcing. The wind forcing is derived from the ERS-1 scatterometer wind stress for the same period as the T/P altimeter data, enabling the model response in different (active/weak) monsoon conditions to be tested. The model output is derived in 10-day snapshots to match the time period of the T/P altimeter cycles. Complex Principal Component Analysis (CPCA) is applied to both altimetric and model SSH data. This confirms that long Rossby waves are excited by the remotely forced Kelvin waves off the southwest coast of India and contribute substantially to the variability of the seasonal circulation in the Arabian Sea.     

The impact of ocean data assimilation on seasonal predictions based on the National Climate Center climate system model

An ensemble optimal interpolation (EnOI) data assimilation method is applied in the BCC_CSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin experiment framework. Pseudo-observations of sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), temperature and salinity (T/S) profiles were first generated in a free model run. Then, a series of sensitivity tests initialized with predefined bias were conducted for a one-year period; this involved a free run (CTR) and seven assimilation runs. These tests allowed us to check the analysis field accuracy against the “truth”. As expected, data assimilation improved all investigated quantities; the joint assimilation of all variables gave more improved results than assimilating them separately. One-year predictions initialized from the seven runs and CTR were then conducted and compared. The forecasts initialized from joint assimilation of surface data produced comparable SST root mean square errors to that from assimilation of T/S profiles, but the assimilation of T/S profiles is crucial to reduce subsurface deficiencies. The ocean surface currents in the tropics were better predicted when initial conditions produced by assimilating T/S profiles, while surface data assimilation became more important at higher latitudes, particularly near the western boundary currents. The predictions of ocean heat content and mixed layer depth are significantly improved initialized from the joint assimilation of all the variables. Finally, a central Pacific El Ni?o was well predicted from the joint assimilation of surface data, indicating the importance of joint assimilation of SST, SSH, and SSS for ENSO predictions.     

Sea Surface Height Variability in the Indian Ocean from TOPEX/POSEIDON Altimetry and Model Simulations

Sea Surface Height (SSH) variability in the Indian Ocean during 1993-1995 is studied using TOPEX/POSEIDON (T/P) altimetry data. Strong interannual variability is seen in the surface circulation of the western Arabian Sea, especially in the Somali eddy structure. During the Southwest (SW) monsoon, a weak monsoon year is characterized by a single eddy system off Somalia, a strong or normal monsoon year by several energetic eddies. The Laccadive High (LH) and Laccadive Low (LL) systems off southwest India are observed in the altimetric SSH record. The variability of the East India Coastal Current (EICC), the western boundary current in the Bay of Bengal, is also detected. Evidence is found for the propagation of Kelvin and Rossby waves across the northern Indian Ocean; these are examined in the context of energy transfer to the western boundary currents, and associated eddies. A simple wind-driven isopycnal model having three active layers is implemented to simulate the seasonal changes of surface and subsurface circulation in the North Indian Ocean and to examine the response to different wind forcing. The wind forcing is derived from the ERS-1 scatterometer wind stress for the same period as the T/P altimeter data, enabling the model response in different (active/weak) monsoon conditions to be tested. The model output is derived in 10-day snapshots to match the time period of the T/P altimeter cycles. Complex Principal Component Analysis (CPCA) is applied to both altimetric and model SSH data. This confirms that long Rossby waves are excited by the remotely forced Kelvin waves off the southwest coast of India and contribute substantially to the variability of the seasonal circulation in the Arabian Sea.     

Comparisons of sea surface height variability observed by pressure-recording inverted echo sounders and satellite altimetry in the Kuroshio Extension

Satellite-measured along-track and gridded sea surface height (SSH) anomaly products from AVISO are compared with in situ SSH anomaly measurements from an array of 43 pressure-recording inverted echo sounders (PIESs) in the Kuroshio Extension. PIESs measure bottom pressure (P _bot) and round-trip acoustic travel time from the sea floor to the sea surface (τ). The P _bot and τ measurements are used to estimate, respectively, the mass-loading and steric height variations in SSH anomaly. All comparisons are made after accurate removal of tidal components from all data. Overall good correlations are found between along-track and PIES-derived SSH anomalies with mean correlation coefficient of 0.97. Comparisons between the two measurements reveal that the mass-loading component estimated from P _bot is relatively small in this geographical region. It improves regression coefficients about 5?% and decreases mean root-mean-squared (rms) differences from 7.8 to 6.4?cm. The AVISO up-to-date gridded product, which merges all available satellite measurements of Jason-1, Envisat, Geosat Follow-On, and TOPEX/Poseidon interlaced, shows better correlations and smaller rms differences than the AVISO reference gridded product, which merges only Jason-1 and Envisat. Especially, the up-to-date gridded product reveals 6.8?cm rms improvement on average at sites away from Jason-1 ground tracks. Gridded products exhibit low correlation (0.75–0.9) with PIES-derived SSH in a subregion where the SSH fluctuations have relatively high energy at periods shorter than 20?days.     

Quality control methods for KOOS operational sea surface temperature products

Sea surface temperature SST obtained from the initial version of the Korea Operational Oceanographic System(KOOS) SST satellite have low accuracy during summer and daytime. This is attributed to the diurnal warming effect. Error estimation of SST data must be carried out to use the real-time forecasting numerical model of the KOOS. This study suggests two quality control methods for the KOOS SST system. To minimize the diurnal warming effect, SSTs of areas where wind speed is higher than 5 m/s were used. Depending on the wind threshold value, KOOS SST data for August 2014 were reduced by 0.15°C. Errors in SST data are considered to be a combination of random, sampling, and bias errors. To estimate bias error, the standard deviation of bias between KOOS SSTs and climatology SSTs were used. KOOS SST data yielded an analysis error standard deviation value similar to OSTIA and NOAA NCDC(OISST) data. The KOOS SST shows lower random and sampling errors with increasing number of observations using six satellite datasets. In further studies, the proposed quality control methods for the KOOS SST system will be applied through more long-term case studies and comparisons with other SST systems.     

利用海表盐度等遥感观测产品重构三维盐度场的性能评估

Several remotely sensed sea surface salinity(SSS) retrievals with various resolutions from the soil moisture and ocean salinity(SMOS) and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles(S) using multilinear regressions. The performance is evaluated using a total root mean square(RMS) error, different error sources, and the feature resolutions of the retrieved S fields. In the mixed layer of the salinity, the SSS-S regression coefficients are uniformly large. The SSS inputs yield smaller RMS errors in the retrieved S with respect to Argo profiles as their spatial or temporal resolution decreases. The projected SSS errors are dominant, and the retrieved S values are more accurate than those of climatology in the tropics except for the tropical Atlantic, where the regression errors are abnormally large. Below that level, because of the influence of a sea level anomaly, the areas of high-accuracy S values shift to higher latitudes except in the high-latitude southern oceans, where the projected SSS errors are abnormally large. A spectral analysis suggests that the CATDS-0.25° results are much noisier and that the BEC-L4-0.25° results are much smoother than those of the other retrievals. Aquarius-CAP-1° generates the smallest RMS errors, and Aquarius-V2-1° performs well in depicting large-scale phenomena. BEC-L3-0.25°,which has small RMS errors and remarkable mesoscale energy, is the best fit for portraying mesoscale features in the SSS and retrieved S fields. The current priority for retrieving S is to improve the reliability of satellite SSS especially at middle and high latitudes, by developing advanced algorithms, combining both sensors, or weighing between accuracy and resolutions.     

Monitoring the total atmospheric ozone content using data collected by the Elektro-L Russian geostationary meteorological satellite

The possibility of remotely monitoring the total atmospheric ozone content (TAOC) using data from the multichannel geostationary scanning instrument (MGSI) aboard the Elektro-L no. 1 Russian meteorological satellite is explored. In addition to the MGSI measurements in three channels (8.2–9.2, 9.2–10.2, and 10.2–11.2 μm), data on the vertical temperature distributions in the ozone layer and the temperature and pressure at the underlying terrain level (satellite sensing results or forecast data) are used as additional predictors in the process of TAOC estimation. The TAOC estimates are constructed with the use of a regularized regression algorithm (ridge regression). The learning and check samples are formed using independent TAOC estimates based on the data gathered by the OMI instrument aboard the EOS Aura satellite. Numerical experiments in processing the actual MGSI data gathered over certain periods within the interval from November 2011 to August 2012 reveal the possibility of arranging regular monitoring of the TAOC fields with high spatial and temporal resolutions and an acceptable precision: the absolute value of relative mean deviations and the relative root-mean-square deviations of the estimates based on the MGSI data from the estimates based on the OMI data lie within intervals of 1–2% and 5–7%, respectively, depending on the underlying terrain type.     

利用卫星遥感海面温度、高度联合反演南海北部三维温度场

利用历史观测得到的温度剖面数据,通过严格筛选和插值,建立了南海北部的气候态垂向温度剖面。随后,利用回归统计分析的方法构建了海面温度异常(SSTA)、海面高度异常(SSHA)联合扩展温度剖面的经验回归模型,并采用卫星遥感得到的SST和SSH数据扩展了南海北部的三维海洋温度场,其时间分辨率为天,空间分辨率为0.25°×0.25°。通过与观测数据的对比研究,扩展得到的温度场可以较为准确地反映南海北部温度剖面的结构特征,并且能有效地体现出一些中尺度变化过程。结果表明,本研究反演得到的三维温度扩展场是较为可靠的,它可以作为海洋数值模型的初始场,实现现场观测数据和卫星遥感数据的互补,有助于更好地分析南海北部温度场的三维结构及变化特征。     

验后平差方法在Geosat/GM卫星测高数据处理中的应用

为了消除多代卫星测高数据之间的不协调性,利用两步处理法对Geosat/GM正常点海面高数据进行自交叉点平差及与T/P卫星测高数据联合平差,发现误差模型的系数计算出现异常,通过分析误差模型及正常点海面高数据的分布特征,找出了出现异常的原因,对计算方法进行改进后,大大提高了计算结果的稳定性和可靠性。     

Wind-driven interannual variability over the northeast Pacific Ocean

Interannual variability of the sea surface height (SSH) over the northeast Pacific Ocean is hindcast with a reduced-gravity, quasi-geostrophic model that includes linear damping. The model is forced with monthly Ekman pumping fields derived from the NCEP reanalysis wind stresses. The numerical solution is compared with SSH observations derived from satellite altimeter data and gridded at a lateral resolution of 1 degree. Provided that the reduced gravity parameter is chosen appropriately, the results demonstrate that the model has significant hindcast skill over interior regions of the basin, away from continental boundaries. A damping time scale of 2 to 3 years is close to optimal, although the hindcast skill is not strongly dependent on this parameter.A simplification of the quasi-geostrophic model is considered in which Rossby waves are eliminated, yielding a Markov model driven by local Ekman pumping. The results approximately reproduce the hindcast skill of the more complete quasi-geostrophic model and indicate that the interannual SSH variability is dominated by the local response to wind forcing. There is a close correspondence the two leading empirical orthogonal modes of the local model and those of the observed SSH anomalies. The latter account for over half of the variance of the interannual signal over the region.     

On model validation for meso/submesoscale currents: Metrics and application to ROMS off Central California

ROMS with horizontal grid spacing of 3.5 km for the region off Central California was compared to RAFOS float observations and satellite altimetry on meso/submesoscales. The approach introduced and used two new metrics for model-data comparison, as well as suggested how to calculate these metrics for different spatio-temporal scales. The first metric consisted of the first two moments of exit time and was used to compare ROMS against RAFOS float observations at mid-depths (between 300 m and 350 m). Exit time is the time a float launched at a point takes to leave a domain for the first time. The second metric was spectral entropy and was used to estimate how well ROMS reproduced variability of the sea surface height (SSH) anomaly field extracted from an AVISO data set (1992–2007) for specified temporal and spatial scales. Calculations showed that ROMS reproduced the mid-depth mesoscale/submesoscale currents next to the coast in a very accurate manner (low-order exit time statistics of floats were reproduced by ROMS with an accuracy better than 95%); but ROMS overestimated the speed of westward drift of floats by as much as 20–30% at distances greater than 350 km from the coastline. ROMS predicted the variability of the mesoscale (100–400 km) SSH anomaly field for temporal scales of 1–12 months with a reasonable accuracy. A wavelet transform modulus maxima technique applied to the spectral entropy of SSH anomaly also demonstrated good agreement between ROMS and satellite altimetry for mesoscales characterized by singular exponents and multi-fractal spectra for 1–12 month time scales.     

Construction of surface wind stress fields with high temporal resolution using ERS-1 scatterometer data

Wind stress fields with high temporal resolution over the North Pacific have been constructured by using ERS-1 scatterometer data. A simple objective analysis, a successive correction method, was used to construct the fields. Several necessary parameters used in the method are examined by a simulation based on the climatological data. The meridional decorrelation scale of the wind stress depends strongly on the season, while the zonal decorrelation scale is highly stable. We determined the decorrelation scale depending on the location and the time and applied to the successive correction method. The monthly mean field constructed by averaging the daily mean data is free from an aliasing error, which is a serious problem if a simple monthly averaging is applied. The daily wind stress data obtained in the present study represent small time- and spatial-scale variation and large amplitudes compared with data interpolated from simple monthly mean data. The satellite-derived data are also compared with in situ data obtained by meteorological buoys. The satellite wind speeds are lower than in situ wind speeds for every buoy. This underestimation is not due to the present objective analysis, but due to the original data, the ERS-1 Scatterometer Value-Added Product.