Comparison of sea surface wind field measured by HY-2A scatterometer and WindSat in global oceans

In this study, we present a comprehensive comparison of the sea surface wind ?eld measured by scatterometer(Ku-band scatterometer) aboard the Chinese HY-2 A satellite and the full-polarimetric radiometer WindSat aboard the Coriolis satellite. The two datasets cover a four-year period from October2011 to September 2015 in the global oceans. For the sea surface wind speed, the statistical comparison indicates good agreement between the HY-2 A scatterometer and WindSat with a bias of nearly 0 m/s and a root mean square error(RMSE) of 1.13 m/s. For the sea surface wind direction, a bias of 1.41° and an RMSE of 20.39° were achieved after excluding the data collocated with opposing directions. Furthermore,discrepancies in sea surface wind speed measured by the two sensors in the global oceans were investigated.It is found that the larger dif ferences mainly appear in the westerlies in the both hemispheres. Both the bias and RMSE show latitude dependence, i.e., they have signi?cant latitudinal ?uctuations.     

Verification and recalibration of HY-2A microwave radiometer brightness temperature

HY-2A is the first one of the Chinese HY-2 ocean satellite series carrying a microwave radiometer(RM) to measure sea surface temperature,sea surface wind speed,atmospheric water vapor,cloud liquid water content, and rain rate. We verified the RM level 1B brightness temperature(T_B) to retrieve environmental parameters. In the verification, TB that simulated using the ocean-atmosphere radiative transfer model(RTM) was used as a reference. The total bias and total standard deviation(SD) of the RM level 1B T_B, with reference to the RTM simulation, ranged-20.6-4.38 K and 0.7-2.93 K, respectively. We found that both the total bias and the total SD depend on the frequency and polarization, although the values for ascending and descending passes are different. In addition, substantial seasonal variation of the bias was found at all channels. The verification results indicate the RM has some problems regarding calibration, e.g.,correction of antenna spillover and antenna physical emission, especially for the 18.7-GHz channel. Based on error analyses, a statistical recalibration algorithm was designed and recalibration was performed for the RM level 1B T_B. Validation of the recalibrated TB indicated that the quality of the recalibrated RM level 1B T_B was improved significantly. The bias of the recalibrated TB at all channels was reduced to 0.4 K, seasonal variation was almost eradicated, and SD was diminished(i.e., the SD of the 18.7-GHz channel was reduced by more than 0.5 K).     

Absolute calibration of HY-2A and Jason-2 altimeters for sea surface height using GPS buoy in Qinglan,China

GPS buoy methodology is one of the main calibration methodologies for altimeter sea surface height calibration.This study introduces the results of the Qinglan calibration campaign for the HY-2 A and Jason-2 altimeters.It took place in two time slices;one was from August to September 2014,and the other was in July 2015.One GPS buoy and two GPS reference stations were used in this campaign.The GPS data were processed using the real-time kinematic(RTK)technique.The final error budget estimate when measuring the sea surface height(SSH)with a GPS buoy was better than 3.5 cm.Using the GPS buoy,the altimeter bias estimate was about-2.3 cm for the Jason-2 Geophysical Data Record(GDR)Version 'D' and from-53.5 cm to-75.6 cm for the HY-2 A Interim Geophysical Data Record(IGDR).The bias estimates for Jason-2 GDR-D are similar to the estimates from dedicated calibration sites such as the Harvest Platform,the Crete Site and the Bass Strait site.The bias estimates for HY-2 A IGDR agree well with the results from the Crete calibration site.The results for the HY-2 A altimeter bias estimated by the GPS buoy were verified by cross-calibration,and they agreed well with the results from the global analysis method.     

Application of nonlinear multi-channel algorithms for estimating sea surface temperature with NOAA-14 AVHRR data

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Polar Sea Ice Identification and Classification Based on HY-2A/SCAT Data

In this paper,a Bayesian sea ice detection algorithm is first used based on the HY-2A/SCAT data,and a backpropagation(BP)neural network is used to classify the Arctic sea ice type.During the implementation of the Bayesian sea ice detection algorithm,linear sea ice model parameters and the backscatter variance suitable for HY-2A/SCAT were proposed.The sea ice extent obtained by the Bayesian sea ice detection algorithm was projected on a 12.5 km grid sea ice map and validated by the Advanced Microwave Scanning Radiometer 2(AMSR2)15%sea ice concentration data.The sea ice extent obtained by the Bayesian sea ice detection al-gorithm was found to be in good agreement with that of the AMSR2 during the ice growth season.Meanwhile,the Bayesian sea ice detection algorithm gave a wider ice edge than the AMSR2 during the ice melting season.For the sea ice type classification,the BP neural network was used to classify the Arctic sea ice type(multi-year and first-year ice)from January to May and October to De-cember in 2014.Comparison results between the HY-2A/SCAT sea ice type and Equal-Area Scalable Earth Grid(EASE-Grid)sea ice age data showed that the HY-2A/SCAT multi-year ice extent variation had the same trend as the EASE-Grid data.Classification errors,defined as the ratio of the mismatched sea ice type points between HY-2A/SCAT and EASE-Grid to the total sea ice points,were less than 12%,and the average classification error was 8.6%for the study period,which indicated that the BP neural network classification was a feasible algorithm for HY-2A/SCAT sea ice type classification.     

Retrieve sea surface salinity using principal component regression model based on SMOS satellite data

The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity(SMOS) satellite data. Based on the principal component regression(PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea(in the area of 4?–25?N, 105?–125?E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu(practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.     

基于卫星观测的两例青藏高原低涡结构的初步分析

应用风云2C卫星云图和配有云顶亮温的MTSAT卫星红外云图,描述了两例青藏高原低涡形成、发展及消亡过程,云图清晰显示了高原低涡具有涡眼和暖心结构的特征,并利用水汽图分析揭示了缺乏水汽供应是导致这类低涡不能进一步发展并移出高原的重要原因.最后用1°×1°NCEP分析资料对高原低涡形成眼结构时的基本物理场进行了诊断计算.结果表明:低涡整体为上升气流,而涡眼区在近地层为下沉气流,上层为相对涡区较弱的上升气流;低层辐合,高层辐散.进而印证了动力学研究工作得出的高原低涡具有与热带气旋类低涡(TCLV, tropical cyclone-like vortices)类似的涡眼和暖心结构特征的结论.     

全球海水质量季节变化研究

海平面变化是海水密度和海水总质量变化的结果,是全球气候变化研究的重要内容之一。利用GRACE卫星观测得到的重力场系数变化资料及重力卫星测高得到的海平面变化扣除由模式得到的热容海平面变化,研究了海水的质量变化。研究结果显示,由这两种方法得到的海水质量变化有非常明显的季节性特征,且其周年振幅和位相非常接近。     

Chaos particle swarm optimization combined with circular median filtering for geophysical parameters retrieval from Windsat

This paper established a geophysical retrieval algorithm for sea surface wind vector, sea surface temperature, columnar atmospheric water vapor, and columnar cloud liquid water from WindSat, using the measured brightness temperatures and a matchup database. To retrieve the wind vector, a chaotic particle swarm approach was used to determine a set of possible wind vector solutions which minimize the difference between the forward model and the WindSat observations. An adjusted circular median filtering function was adopted to remove wind direction ambiguity. The validation of the wind speed, wind direction, sea surface temperature, columnar atmospheric water vapor, and columnar liquid cloud water indicates that this algorithm is feasible and reasonable and can be used to retrieve these atmospheric and oceanic parameters. Compared with moored buoy data, the RMS errors for wind speed and sea surface temperature were 0.92 m s~(-1) and 0.88℃, respectively. The RMS errors for columnar atmospheric water vapor and columnar liquid cloud water were 0.62 mm and 0.01 mm, respectively, compared with F17 SSMIS results. In addition, monthly average results indicated that these parameters are in good agreement with AMSR-E results. Wind direction retrieval was studied under various wind speed conditions and validated by comparing to the Quik SCAT measurements, and the RMS error was 13.3?. This paper offers a new approach to the study of ocean wind vector retrieval using a polarimetric microwave radiometer.     

Impact of sea surface temperature front on stratus-sea fog over the Yellow and East China Seas — a case study with implications for climatology

A stratus-sea fog event that occurred over the Yellow and East China Seas on 3 June 2011 is investigated using observa-tions and a numerical model, with a focus on the effects of background circulation and Sea Surface Temperature Front (SSTF) on the transition of stratus into sea fog. Southerly winds of a synoptic high-pressure circulation transport water vapor to the Yellow Sea, creating conditions favorable for sea fog/stratus formation. The subsidence from the high-pressure contributes to the temperature inversion at the top of the stratus. The SSTF forces a secondary circulation within the ABL (Atmospheric Boundary Layer), the sink-ing branch of which on the cold flank of SSTF helps lower the stratus layer further to reach the sea surface. The cooling effect over the cold sea surface counteracts the adiabatic warming induced by subsidence. The secondary circulation becomes weak and the fog patches are shrunk heavily with the smoothed SSTF. A conceptual model is proposed for the transition of stratus into sea fog over the Yellow and East China Seas. Finally, the analyses suggest that sea fog frequency will probably decrease due to the weakened SSTF and the reduced subsidence of secondary circulation under global warming.     

不同BDS星历反演大气可降水量精度分析

基于陕西地区3个GNSS观测站2018年1～6月数据,利用北斗卫星导航系统进行水汽反演。首先利用不同星历产品计算水汽结果,分别与利用IGF解算的水汽结果、探空数据探测结果进行比较;再将不同星历得到的水汽结果进行相互对比。结果表明,利用3种星历都能获得精度和可靠性较高的水汽结果,其中精密星历和快速星历反演水汽的精度相当,各测站偏差均优于1 mm,标准差和均方根误差均优于3.5 mm;利用超快速星历（预报部分）反演水汽在各测站的偏差约为1 mm,标准差和均方根误差均优于5 mm。综合3种产品反演水汽的对应精度可知,利用3种星历产品均可反演大气可降水量,且超快速星历（预报部分）计算的水汽值可为气象预报提供参考依据。     

Satellite retrieval of hurricane wind speeds using the AMSR2 microwave radiometer

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

水体表面温度反演研究综述

水体表面温度是研究全球或区域气候变化、数值天气预报的重要参数,是控制水体与大气热量、水分交换的重要变量,对理解水体生物物理过程具有重要意义。卫星观测水表温度具有传统手段不可比拟的技术优势,同时也存在精度和质量上的限制和挑战。本文总结了观测水表温度常用的红外、微波传感器及其分辨率特征,并比较分析了各类传感器的优势、劣势和分辨率适用的时空尺度;在区别不同手段观测的水表温度基础上,分别概述了红外遥感和微波遥感反演水表温度的理论基础,以及常用的算法模型;基于水表温度反演的原理和过程,系统分析了云、水汽、气溶胶、比辐射率等不确定性因素,对反演精度的影响及解决方法,并对精度验证方法做了简单介绍;最后,对水体表面温度反演的发展趋势进行了展望,并指出多源数据的同化融合、优势互补是提升水温反演精度的重要途径。     

A comparison of Argo nominal surface and near-surface temperature for validation of AMSR-E SST

Satellite SST(sea surface temperature) from the Advanced Microwave Scanning Radiometer for the Earth Observing System(AMSR-E) is compared with in situ temperature observations from Argo profiling floats over the global oceans to evaluate the advantages of Argo NST(near-surface temperature: water temperature less than 1 m from the surface). By comparing Argo nominal surface temperature(~5 m) with its NST, a diurnal cycle caused by daytime warming and nighttime cooling was found, along with a maximum warming of 0.08±0.36°C during 14:00–15:00 local time. Further comparisons between Argo 5-m temperature/Argo NST and AMSR-E SST retrievals related to wind speed, columnar water vapor, and columnar cloud water indicate warming biases at low wind speed(5 m/s) and columnar water vapor 28 mm during daytime. The warming tendency is more remarkable for AMSR-E SST/Argo 5-m temperature compared with AMSR-E SST/Argo NST, owing to the effect of diurnal warming. This effect of diurnal warming events should be excluded before validation for microwave SST retrievals. Both AMSR-E nighttime SST/Argo 5-m temperature and nighttime SST/Argo NST show generally good agreement, independent of wind speed and columnar water vapor. From our analysis, Argo NST data demonstrated their advantages for validation of satellite-retrieved SST.     

Arctic multiyear sea ice variability observed from satellites: a review

In comparison with seasonal sea ice(first-year ice,FY ice),multiyear(MY) sea ice is thicker and has more opportunity to survive through the summer melting seasons.Therefore,the variability of wintertime MY ice plays a vital role in modulating the variations in the Arctic sea ice minimum extent during the following summer.As a response,the ice-ocean-atmosphere interactions may be significantly affected by the variations in the MY ice cover.Satellite observations are characterized by their capability to capture the spatiotemporal changes of Arctic sea ice.During the recent decades,many active and passive sensors onboard a variety of satellites(QuikSCAT,ASCAT,SSMIS,ICESat,CryoSat-2,etc.) have been used to monitor the dramatic loss of Arctic MY ice.The main objective of this study is to outline the advances and remaining challenges in monitoring the MY ice changes through the utilization of multiple satellite observations.We summarize the primary satellite data sources that are used to identify MY ice.The methodology to classify MY ice and derive MY ice concentration is reviewed.The interannual variability and trends in the MY ice time series in terms of coverage,thickness,volume,and age composition are evaluated.The potential causes associated with the observed Arctic MY ice loss are outlined,which are primarily related to the export and melting mechanisms.In addition,the causes to the MY ice depletion from the perspective of the oceanic water inflow from Pacific and Atlantic Oceans and the water vapor intrusion,as well as the roles of synoptic weather,are analyzed.The remaining challenges and possible upcoming research subjects in detecting the rapidly changing Arctic MY ice using the combined application of multisource remote sensing techniques are discussed.Moreover,some suggestions for the future application of satellite observations on the investigations of MY ice cover changes are proposed.     

MEAN SEA LEVEL AND SEA SURFACE VARIABILITY OF NQRTHWEST PACIFIC OCEAN AND EASTERN CHINA SEAS FROM GEOSAT ALTIMETRY

Collinear analysis technique is widely used for determining sea surface variability with Geosat altimeterdata from its Exact Repeat Mission(ERM).But most of the researches have been only on global scaleor in oceans deeper than 2000 m.In shallow shelf waters this method is hampered by the inaccuracy ofocean tide data supplied with Geosat Geophysical Data Records(GDRs).This work uses a modified collinearanalysis technique characterized by simultaneous separation of mean sea level and ocean tide with theleast squares method,to compute sea surface variability in the Northwest Pacific Ocean and eastern ChinaSeas.The mean sea level map obtained contains not ouly bathymetric but also dynamic features such asamphidromes,indicating considerable improvement over previous works.Our sea surface variability mapsshow clearly the main current system,the well-known Zhejiang coastal upwelling,and a northern East Chi-na Sea meso-scale eddy in good agreement with satellite sea surface temperature(SST)observation and his-to     

Toward development of the 4Dvar data assimilation system in the Bering Sea： reconstruction of the mean dynamic ocean topography

The Bering Sea circulation is derived as a variational inverse of hydrographic profiles（ temperature and salinity） , atmospheric climatologies and historical observation of ocean curents. The important result of this study is estimate of the mean climatological sea surface height （SSH） that can be used as a reference for satellite altimetry sea level anomaly data in the Bering Sea region. Numerical experiments reveal that, when combined with satellite altimetry, the obtained reference SSH effectively constrains a realistic reconstruction of the Amukta Pass circulation.     

Parallel-Dynamic Interpolation Algorithm of Sea Surface Height for Future 2D Altimetry Mapping of Sea Surface Height

The sea surface height data volume of the future wide-swath two-dimensional(2 D) altimetric satellite is thousands of times greater than that of nadir altimetric satellites. The time complexity of the 2 D altimetry mapping reaches O(n~3). It is challenging to map the global grid products of future 2 D altimetric satellites. In this study, to improve the efficiency of global data mapping, a new algorithm called parallel-dynamic interpolation(PA-DI) was designed. Through the use of 2 D data segmentation and fine-grained data mosaic methods, the parallel along-track DI processes were accelerated, and a fast and efficient spatial-temporal high-resolution and low-error enhanced mapping method was obtained. As determined from a comparison of the single-threaded DI with the PA-DI,the new algorithm optimized the time complexity from O(n~3) to O(n~3/KL), which improved the mapping efficiency and achieved the expected results. According to the test results of the observing system simulation experiments, the PA-DI algorithm may provide an efficient and reliable method for future wide-swath 2 D altimetric satellite mapping.     

Tidal current observation in the southern Yellow Sea in the summers of 2001 and 2003

Direct current observations in the Yellow Sea interior are very scarce due to intense fishing and trawling activities. Most previous studies on tides in the area were based on coastal measurements or satellite altimeter sea levels and have not been rigorously compared with direct measurements. In this paper, tidal currents are studied with current profiles from three bottom-moored Sontek Acoustic Doppler Profilers （ADPs） deployed in the southern Yellow Sea in summer of 2001 and 2003. The measured current series were dominated by tidal currents. Maximum velocities are between 40-80 cm/s at the mooring stations. M2 current is the most dominant primary tidal constituent, while MS4 and M4 are the most significant shallow water tides with much smaller amplitudes than the primary tides.     

The Evaporation Effect on the Isotopes in the Yellow River Water

Based on the isotope analysis result of water samples in the 18 sections of the Yellow River, the variation of δ18Oand δD have been analyzed. From near the source to the entrance to the sea, the Yellow River has a general trend that the ratios of the stable isotope increase progressively; The main factors affecting the isotopes in the river water are mixing of external water bodies, evaporation and others; In the river segment between Lanzhou and Baotou and in lower reaches, the extent of the evaporation effect on the isotope fractionation from the river water surface is limited but the evaporation from the irrigated river water and the return flow is one of the main factors affecting the isotopes in river water.