Improved Estimates of Wide-Ranging Sea Surface Temperature from GMS S-VISSR Data

We have developed an algorithm to estimate the wide-ranging Sea Surface Temperature (SST) data from the GMS-5 (Geostationary Meteorological Satellite) S-VISSR (Stretched-Visible Infrared Spin Scan Radiometer). Better SST estimates are realized by averaging the temporal variation of the VISSR calibration table and decreasing noise of the split-window terms using a spatial filter. The effects of the satellite zenith angle were examined in detail for better estimates, and VISSR-derived SSTs with root-mean-square (rms) error of 0.8 K were achieved using a new algorithm. The accuracy of SST estimates has been improved by using the temporal-spatial average of the split-window terms. Using the new techniques, we demonstrate that the hourly wide-ranging SST image data can be used to study the daily variations of SSTs in the Northern and Southern Pacific Oceans.     

Evaluation of the Diurnal Warming of Sea Surface Temperature Using Satellite-Derived Marine Meteorological Data

In order to produce a high-quality sea surface temperature (SST) data set, the daily amplitude of SST (ΔSST) should be accurately known. The purpose of this study was to evaluate the diurnal variation of sea surface temperature in a simple manner. The authors first simulated ΔSST with a one-dimensional numerical model using buoy-observed meteorological data and satellite-derived solar radiation data. When insolation is strong, the model-simulated 1-m-depth ΔSST becomes much smaller than the in situ value as wind speed decreases. By forcibly mixing the sea surface layer, the model ΔSST becomes closer to the in situ value. It can be considered that part of this difference is due to the turbulence induced by the buoy hull. Then, on the assumption that the model results were reliable, the authors derived a regression equation to evaluate ΔSST at the skin and 1-m depth from daily mean wind speed (U) and daily peak solar radiation (PS). ΔSST is approximately proportional to In(U) and (PS)², and the skin ΔSST estimated by the equation is not inconsistent with in situ observation results reported in past studies. The authors prepared maps of PS and U using only satellite data, and demonstrated the ΔSST evaluation over a wide area. The result showed that some wide patchy areas where the skin ΔSST exceeds 3.0 K can appear in the tropics and the mid-latitudes in summer. This revised version was published online in July 2006 with corrections to the Cover Date.     

Seasonal Variation of Space/Time Statistics of Short-Term Sea Surface Temperature Variability in the Kuroshio Region

Spatial and temporal scales of sea surface temperature (SST) variations in the Kuroshio region have been investigated using a satellite-based one-year merged SST product. Targeting short-term variations with temporal scales of less than a year, decorrelation scales, which are defined as the e-folding scale of SST variability, have been derived as functions of regional positions and calendar months. We assumed that the autocorrelation function of SST has anisotropic Gaussian characteristics in the space-time domain. Resultant spatial and temporal decorrelation scales range from 1 to 3° and 2 to 3 days, respectively. They are strongly inhomogeneous, anisotropic and time-dependent. These characteristics are attributed to the oceanic and atmospheric disturbances. Spatial decorrelation scales are determined mainly by strong atmospheric forcing in the study region. In the area with dominant atmospheric forcing, the spatial scales are larger than those in the other regions. Those in the regions with dynamical oceanographic disturbances are as small as 1°. Signal-to-noise ratios are also large where the atmospheric forcing is strong, while they are small where the oceanic signals are active.     

Generating time-series grid data of sea surface temperature from isotherms in the Northwestern Pacific Ocean using coupled interpolation

Sea surface temperature (SST) isoline charts that were manually mapped using in situ SST data and satellite-derived SST data are valuable because they incorporate oceanographers’ knowledge and experience. This type of SST data is useful for studying sea conditions of an area, for analyzing environmental factors that could affect fishing grounds, as a parameter for atmospheric or oceanic models, or as a diagnostic tool for comparison with the SSTs produced by ocean models. However, isoline maps must be digitized and interpolated into grid data in order to be used in these applications. Herein, we propose a coupled interpolation (CI), which couples improved multi-section interpolation and single-point change surface interpolation containing orientation, for generating grid data from SST isolines. We interpolated 1049 SST isoline maps (temperature interval 1°), which cover an area of the northwestern Pacific Ocean (125°E–180°E, 26°N–50°N) and were published by the Japan Fisheries Information Service Center (JAFIC) during 1990–2000, to grid datasets with 15′ grid resolution. We assessed the quality of grid datasets by checking noise points, RMSE analysis, checking offset errors, retrieving percentage of Kuroshio axes and visually comparing inverse isotherms with original isotherms. The quality analysis and comparison with four other interpolators showed the CI interpolator to be a good technique for generating SST grid data from isotherms. We also computed the SST anomaly (SSTA) using the SST grid datasets. The amplitude values of integral SSTA in the area of 31–46°N, 170–180°E were low, whereas they were high in the SW–NE rectangular area of 35–46°N, 142–160°E.     

Spatio-temporal variability in sea surface temperatures for the Yellow Sea based on MODIS dataset

The spatio-temporal variabilities in sea surface temperature (SST) were analyzed using a time series of MODIS datasets for four separate regions in the Yellow Sea (YS) that were located along a north-south axis. The space variant temporal anomaly was further decomposed using an empirical orthogonal function (EOF) for estimating spatially distributed SST. The monthly SSTs showed similar temporal patterns in each region, which ranged from 2.4°C to 28.4°C in the study years 2011 to 2013, with seasonal cycles being stronger at the higher latitudes and weaker at the lower latitudes. Spatially, although there were no significant differences among the four regions (p < 0.05) in any year, the geographical distribution of SST was characterized by an obvious gradient whereby SST decreased along the north-south axis. The monthly thermal difference among regions was largest in winter since the SST in the southeast was mainly affected by the Yellow Sea Warm Currents. The EOF1 mode accounted for 56% of the total spatial variance and exhibited a warming signal during the study period. The EOF2 mode accounted for 8% of the total variance and indicated the warm current features in the YS. The EOF3 mode accounted for 6% of the total variance and indicated the topographical features. The methodology used in this study demonstrated the spatio-temporal variabilities in the YS.     

Satellite-derived SST validation based on in-situ data during summer in the East China Sea and western North Pacific

Satellite-derived sea surface temperature (SST) is validated based on in-situ data from the East China Sea (ECS) and western North Pacific where most typhoons, which make landfall on the Korean peninsula, are formed and pass. While forecasting typhoons in terms of intensity and track, coupled ocean-typhoon models are significantly influenced by initial ocean condition. Potentially, satellite-derived SST is a very useful dataset to obtain initial ocean field because of its wide spatial coverage and high temporal resolution. In this study, satellite-derived SST from various sources such as Tropical Rainfall Measuring Mission Microwave Imager (TMI), Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and New Generation Sea Surface Temperature for Open Ocean (NGSST-O) datasets from merged SSTs were compared with in-situ observation data using an indirect method which is using near surface temperature for validation of satellite derived SST. In-situ observation data included shipboard measurements such as Expendable Bathythermograph (XBT), and Conductivity, Temperature, Depth (CTD), and Argo buoy data. This study shows that in-situ data can be used for microwave derived SST validation because homogeneous features of seawater prevail at water depths of 2 m to 10 m under favorable wind conditions during the summer season in the East China Sea. As a result of validation, root-mean-square errors (RMSEs) are shown to be 0.55 °C between microwave SST and XBT/CTD data mostly under weak wind conditions, and 0.7 °C between XBT/CTD measurement and NGSST-O data. Microwave SST RMSE of 0.55 °C is a potentially valuable data source for general application. Change of SST before and after typhoon passing may imply strength of ocean mixing due to upwelling and turbulent mixing driven by the typhoon. Based on SST change, ocean mixing, driven by Typhoon Nari, was examined. Satellite-derived SST reveals a significant SST drop around the track immediately following the passing of Typhoon Nari in October, 2007.     

北太平洋柔鱼不同阶段角质颚微量元素含量差异及洄游路径推测的应用

大洋性鱿鱼的洄游路径能够给我们重要的信息,帮助我们了解其时空分布的变化。标记-重捕和电子标记方法在施行过程中仍存在一些问题。头足类的硬组织,如角质颚,有着稳定的形态特征和类似耳石的连续生长纹,同时包含丰富的生态信息,可以为我们研究物种的时空分布提供相关信息。本研究中,我们以北太平洋柔鱼为研究对象,基于不同角质颚生长阶段进行取样来重建鱿鱼的洄游路径。研究结果认为,通过电感耦合等离子质谱仪（LA-ICP-MS）取样,发现角质颚的喙端矢状平面（RSS）检测到9种微量元素。针对上述几种元素,发现不同生长阶段的磷（P）、铜（Cu）和锌（Zn）存在显著差异。利用钠（Na）、磷和锌与海表面温度（SST）建立线性回归模型。基于贝叶斯模型,计算出不同时期柔鱼出现的较高概率的海域。结合不同时期的高概率分布海域,建立起柔鱼的洄游路径,该结果与前人研究结果一致。本研究也证实了角质颚可以为研究大洋性柔鱼的洄游路径提供有效的信息。     

A review of satellite-based microwave observations of sea surface temperatures

Satellite-based microwave radiometers can measure sea surface temperature (SST) over wide areas, even under cloud cover, owing to the weak absorption of microwaves by cloud droplets. This advantage is not available in the case of infrared observations, hence SST data derived from microwave radiometers have been widely used for operational and research purposes in recent years. This paper reviews the significant algorithms, validations, and applications related to microwave observation of SST. The history and specifications of past and present microwave radiometers are also documented. Various physical properties, including sea surface salinity, sea surface wind, molecules in the atmosphere, and clouds, affect the accuracy of SST data estimated by satellite-based microwave radiometers. Estimation algorithms are designed to correct these effects by using microwave measurements in several frequency channels and by using data of ancillary geophysical parameters. Validation studies have shown that microwave radiometer SST data have high accuracy that is comparable to the accuracy of data obtained from infrared measurements. However, certain persistent problems, such as sea-surface wind correction, remain to be solved.     

基于GAM的西北太平洋日本鲭资源丰度预测模型建立

日本鲭(Scomber japonicus)是西北太平洋重要的鱼类资源之一,科学预测日本鲭的资源丰度有利于其资源的合理开发和利用。本研究依据日本渔业机构提供的1987–2012年日本鲭太平洋群体的资源量数据,结合产卵场和渔场的海洋环境数据以及气候因子,使用广义加性模型对影响日本鲭太平洋群体的海洋环境和气候因子进行分析,筛选出有显著影响的因子并建立该群体的资源量预测模型。结果表明,与该群体资源量有显著关系的影响因子有:北极涛动指数、太平洋年代际振荡指数、渔场海表面高度、渔场海表面盐度和渔场海表面温度。基于赤池信息准则筛选出的4个资源量预测模型分析表明,包含北极涛动指数、渔场海表面高度和渔场海表面温度的模型有较好的预测效果,该模型的验证结果也通过了t检验(P<0.05),可用于日本鲭太平洋群体资源量的预测。     

不同气候模态下西北太平洋秋刀鱼资源丰度预测模型建立

秋刀鱼（Cololabis saira）资源对海洋环境因素极为敏感,不同气候模态可能对秋刀鱼资源丰度产生不同的影响。根据1990-2014年西北太平洋日本的秋刀鱼渔业中单位捕捞努力量渔获量（CPUE,以此作为资源丰度）,以及相应产卵场、索饵场的海表温（SST）遥感数据,探讨太平洋年际震荡（PDO）指数冷、暖年下,秋刀鱼资源丰度CPUE变化与产卵场、索饵场SST的关系,并分别建立资源丰度的预测模型。研究表明,PDO冷年索饵场4月SST与年CPUE显著相关（P<0.05）,PDO暖年索饵场11月的SST与年标准化CPUE显著相关（P<0.05）。PDO冷、暖年的秋刀鱼资源丰度的预测模型中,CPUE均与索饵场11月的SST、索饵场4月SST呈现正相关的关系,统计学上为显著相关（P<0.05）。PDO冷年（2012年）和PDO暖年（2014年）的CPUE预测值与实际值相对误差分别为14.03%、-16.26%,具有较好的拟合效果。研究认为,不同气候模态下,可用于秋刀鱼资源丰度预测的环境因子不同,上述建立资源丰度模型可用于业务化运行。     

Retrieval of Sea Surface Temperature from TRMM VIRS

The Visible and Infrared Scanner (VIRS) aboard the Tropical Rainfall Measuring Mission (TRMM) is a five-channel radiometer with wavelength from 0.6 to 12 μm. Daily 0.125° sea surface temperature (SST) data from VIRS were first produced at the National Space Development Agency (NASDA) for comparison with SST from TRMM Microwave Imager (TMI). In order to obtain accurate high spatial resolution SST for the merging of SST from infrared and microwave measurements, new SST retrieval coefficients of the Multichannel SST (MCSST) algorithm were generated using the global matchups from VIRS brightness temperature (BT) and Global Telecommunications System (GTS) SST. Cloud detection was improved and striping noise was eliminated. One-year global VIRS level-1B data were reprocessed using the MCSST algorithm and the advanced cloud/noise treatments. The bias and standard deviation between VIRS split-window SST and in situ SST are 0.10°C and 0.63°C, and for triple-window SST, are 0.06°C and 0.48°C. The results indicate that the reprocessing algorithm is capable of retrieving high quality SST from VIRS data. This revised version was published online in July 2006 with corrections to the Cover Date.     

变分伴随数据同化在海表面温度预报中的应用研究

将变分伴随数据同化技术应用于海表面温度(SST)数值预报.采用中国近海海表面温度短期数值预报模式,将船舶测报海表面温度同化到该模型中,对SST初始场进行优化.文中给出了中国近海SST数值预报同化模型5d试报结果与观测值的比较,整个区域的均绝差由同化前的2.71℃降至0.87℃,即变分伴随数据同化对改进SST数值预报的效果是比较明显的,表明它可成为SST数值预报初始化的新方法.     

基于深度卷积嵌入式聚类（DCEC）的海洋环境特征提取对渔情预报模型的改进研究

In order to improve the forecasting ability of the fishery forecast model for the longline bigeye tuna (Thunnus obesus), we proposed a marine environment feature extraction method based on deep convolutional embedded clustering (DCEC), combined with generalized additive model (GAM) for forecasting the longline bigeye tuna fishing grounds in the Southwest Indian Ocean. We used the MODIS-Aqua and MODIS-Terra sea surface temperature (SST) three-level inversion image data (in days) from January to December in 2018 at 0.041 6°×0.041 6° to construct a DCEC model, determined the optimal number of clusters based on the Davies-Bouldi index (DBI), and extracted the category feature value (F_M) of each month’s sea surface temperature (SST); we used monthly 1°×1° bigeye tuna longline fishery data from January to December in 2018 generated from the Indian Ocean Tuna Commission (IOTC), and calculated the catch per unit effort (CPUE); we matched the monthly category feature value F_M and the monthly average value of Chl a concentration with the CPUE data to construct an improved GAM; we matched the monthly average SST, the monthly average Chl a concentration and CPUE data to build a basic GAM; we used the joint hypothesis test (F test) to verify the influence of model explanatory variables; we used akaike information criterion (AIC), mean square error (MSE), and draw the frequency distribution diagrams and box diagrams of measured and predicted values, etc., to analysis the improvement effect of the improved GAM compared to the basic GAM. The results showed that: (1) the category feature value (F_M) extracted based on the DCEC model could better reflect the temporal and spatial dynamic characteristics of SST in the Southwest Indian Ocean, and was related with the climatic conditions, monsoon conditions, and hydrological characteristics in the Southwest Indian Ocean; (2) the factor interpretation of F_M was higher than that of the monthly average SST in GAM, which means F_M had more significant impact on the CPUE of bigeye tuna. The high catch rate was concentrated in the areas where the F_M category was 2, 10, 24 with intersections between the warm and cold currents; (3) the AIC of the improved GAM was reduced by 9.17% than that of the basic GAM and MSE of the improved GAM was reduced by 26.7% than that of the basic GAM; the frequency distribution of the CPUE logarithmic value predicted by the improved GAM was closer to the normal distribution, and the high frequency distribution interval was closer to that of the measured value; the scatter plot showed that the CPUE predicted by the improved GAM had a significant correlation with the measured CPUE, with r equaled to 0.60. This study proves the effectiveness of the DCEC model in extracting marine environmental features, and can provide a reference for the further study on the bigeye tuna fishery forecast.     

近20年渤海叶绿素a浓度时空变化

浮游植物作为食物链的基础,对海洋生态系统具有重要作用。渤海作为我国最大的内海和重要渔业生物的产卵场、育幼场和索饵场,该区浮游植物研究具有重要意义。叶绿素a浓度是反映浮游植物生物量的重要指标。利用Google Earth Engine平台,对1997–2010年的宽视场海洋观测传感器(SeaWiFS)叶绿素a浓度数据和2002–2018年的水色卫星中分辨率成像光谱仪传感器(MODIS Aqua)叶绿素a浓度数据进行合并,并研究其时空变化特征。研究表明,近20年来,渤海全年叶绿素a浓度增加了14.1%,且增加显著。叶绿素a浓度在所有季节都呈现增加趋势;除11月外,其他各月都呈现稳定或增加趋势。从滦河入河口沿岸至渤海海峡的渤海中部,叶绿素a浓度增加较明显。同时也分析了海洋表面温度、风速和降水量数据。夏季渤海周边区域降水量和风速增加以及秋季海表温度的降低都有助于同季叶绿素a浓度的升高。渤海浮游植物可能受陆源营养物质输入影响较大。     

Estimation of annual variation of water vapor in the Arctic Ocean between 80°–87°N using shipborne GPS data based on kinematic precise point positioning

The measurement of atmospheric water vapor(WV) content and variability is important for meteorological and climatological research. A technique for the remote sensing of atmospheric WV content using ground-based Global Positioning System(GPS) has become available, which can routinely achieve accuracies for integrated WV content of 1–2 kg/m2. Some experimental work has shown that the accuracy of WV measurements from a moving platform is comparable to that of(static) land-based receivers. Extending this technique into the marine environment on a moving platform would be greatly beneficial for many aspects of meteorological research, such as the calibration of satellite data, investigation of the air-sea interface, as well as forecasting and climatological studies. In this study, kinematic precise point positioning has been developed to investigate WV in the Arctic Ocean(80°–87°N) and annual variations are obtained for 2008 and 2012 that are identical to those related to the enhanced greenhouse effect.     

Calibration of JASON-1 Altimeter over Lake Erie Special Issue: Jason-1 Calibration/Validation

This article describes absolute calibration results for both JASON-1 and TOPEX Side B (TSB) altimeters obtained at the Lake Erie calibration site, Marblehead, Ohio, USA. Using 15 overflights, the estimated JASON altimeter bias at Marblehead is 58 ± 38 mm, with an uncertainty of 19 mm based on detailed error analysis. Assuming that the TSB bias is negligible, relative bias estimates using both data from the TSB-JASON formation flight period and data from 48 water level gauges around the entire Great Lakes confirmed the Marblehead results. Global analyses using both the formation flight data and dual-satellite (TSB and JASON) crossovers yield a similar relative bias estimate of 146 ± 59 mm, which agrees well with open ocean absolute calibration results obtained at Harvest, Corsica, and Bass Strait (e.g., Watson et al. 2003). We find that there is a strong dependence of bias estimates on the choice of sea state bias (SSB) models. Results indicate that the invariant JASON instrument bias estimated oceanwide is 71 mm, with additional biases of 76 mm or 28 mm contributed by the choice of Collecte Localisation Satellites (CLS) SSB or Center for Space Research (CSR) SSB model, respectively. Similar analysis in the Great Lakes yields the invariant JASON instrument bias at 19 mm, with the SSB contributed biases at 58 mm or 13 mm, respectively. The reason for the discrepancy is currently unknown and warrants further investigation. Finally, comparison of the TOPEX/POSEIDON mission (1992–2002) data with the Great Lakes water level gauge measurements yields a negligible TOPEX altimeter drift of 0.1 mm/yr.     

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.     

西北太平洋气候和海洋环境变化对柔鱼空间分布的影响

Ommastrephes bartramii is an ecologically dependent species and has great commercial values among the AsiaPacific countries. This squid widely inhabits the North Pacific, one of the most dynamic marine environments in the world, subjecting to multi-scale climatic events such as the Pacific Decadal Oscillation(PDO). Commercial fishery data from the Chinese squid-jigging fleets during 1995–2011 are used to evaluate the influences of climatic and oceanic environmental variations on the spatial distribution of O. bartramii. Significant interannual and seasonal variability are observed in the longitudinal and latitudinal gravity centers(LONG and LATG) of fishing ground of O. bartramii. The LATG mainly occurred in the waters with the suitable ranges of environmental variables estimated by the generalized additive model. The apparent north-south spatial shift in the annual LATG appeares to be associated with the PDO phenomenon and is closely related to the sea surface temperature(SST)and sea surface height(SSH) on the fishing ground, whereas the mixed layer depth(MLD) might contribute limited impacts to the distribution pattern of O. bartramii. The warm PDO regimes tend to yield cold SST and low SSH, resulting in a southward shift of LATG, while the cold PDO phases provid warm SST and elevated SSH,resulting in a northward shift of LATG. A regression model is developed to help understand and predict the fishing ground distributions of O. bartramii and improve the fishery management.     

Estimation of surface chlorophyll on an exposed mudflat using digital colour-infrared (CIR) photography

The usefulness of field-based digital Colour-InfraRed (CIR) photography to quantify concentrations of chlorophyll on the surface of exposed mudflats is investigated. Multiple images, each 626 mm by 467 mm, were acquired during Austral summertime using a Duncantech three-band CIR camera from two areas of mudflat in the upper reaches of Sydney Harbour. Sediment samples were obtained from within the field of view of the camera and their chlorophyll concentration was estimated spectrophotometrically. After the camera images were normalised to compensate for the effects of variations in the intensity of downwelling solar radiation, chlorophyll was estimated for each 0.9 mm square pixel using a suite of five different vegetation indices. Regression analysis was used to determine the strength of the relationship between the index values and the estimates of chlorophyll from the in situ samples. Indices constructed from near-infrared and red bands were found to have the strongest relationships with in situ chlorophyll estimates (R² ranging from 0.28 to 0.79) and indices derived from near-infrared and green bands the weakest (R² ranging from 0.16 to 0.22). The vegetation indices highlighted complex small-scale variability in chlorophyll distribution that was not evident in the original camera images. These findings indicate that field-based CIR photography will provide a useful tool for the non-destructive determination of benthic chlorophyll.     

Reconstruction of pH in the Surface Seawater over the North Pacific Basin for All Seasons Using Temperature and Chlorophyll-<Emphasis Type="Italic">a</Emphasis>

We found a simple function of pH that relates to sea surface temperature (SST, K) and chlorophyll-a (Chl, µg l⁻¹) using measured surface seawater pH, SST and Chl data sets over the North Pacific: pH (total hydrogen scale at 2°C) = 0.01325 SST − 0.0253 Chl + 4.150 (R² = 0.95, p < 0.0001, n = 483). Moreover, evaluating the seasonal variation of pH based on this algorithm, we compared the measured pH with the predicted pH at the observational time series stations in subpolar and subtropical regions. The average of ΔpH (measured - predicted, n = 52) was 0.006 ± 0.022 pH. Therefore, the combination of SST and Chl can allow us to determine the spatiotemporal distribution of pH over the North Pacific. Using the climatological data sets of SST and Chl with our pH algorithms, we have described the seasonal distributions of pH at 25°C (pH₍₂₅₎) and pH in situ temperature (pH_(T)) over the North Pacific surface water.