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.     

A new algorithm for sea-surface wind-speed retrieval based on the L-band radiometer onboard Aquarius

Aquarius is the second satellite mission to focus on the remote sensing of sea-surface salinity from space and it has mapped global sea-surface salinity for nearly 3 years since its launch in 2011. However,benefiting from the high atmospheric transparency and moderate sensitivity to wind speed of the L-band brightness temperature(TB),the Aquarius L-band radiometer can actually provide a new technique for the remote sensing of wind speed. In this article,the sea-surface wind speeds derived from TBs measured by Aquarius' L-band radiometer are presented,the algorithm for which is developed and validated using multisource wind speed data,including Wind Sat microwave radiometer and National Data Buoy Center buoy data,and the Hurricane Research Division of the Atlantic Oceanographic and Meteorological Laboratory wind field product. The error analysis indicates that the performance of retrieval algorithm is good. The RMSE of the Aquarius wind-speed algorithm is about 1 and 1.5 m/s for global oceans and areas of tropical hurricanes,respectively. Consequently,the applicability of using the Aquarius L-band radiometer as a near all-weather wind-speed measuring method is verified.     

Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model

WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer, which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this paper, a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat. The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model. Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation, by which a group of ambiguous wind directions was obtained. A median filter was then used to remove ambiguity of wind direction. Evaluated with sea surface wind speed and direction data from the U.S. National Data Buoy Center (NDBC), root mean square errors are 1.2 m/s and 30° for retrieved wind speed and wind direction, respectively. The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications, without reducing accuracy.     

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).     

HY-2散射计风速降雨影响的神经网络校正

海洋二号搭载的笔形圆锥扫描微波散射计(HY2-scat)是国内第一个业务化运行的,可提供大量实时海面风场数据的微波传感器。由于Ku波段散射计测风原理和微波传输特性,受到降雨影响的散射计反演风场数据准确度降低。降雨导致的微波传播路径衰减,雨滴对微波直接后向散射导致的回波能量增加和雨滴对海表面毛细波的干扰等综合效应,使得降雨条件下散射计测风风速计算值偏高,风向计算值偏差较大。针对散射计反演风速受降雨影响的特点引入神经网络模型,使用准确度较高的NWP数值预报模式风场数据作为参考,对受降雨影响的HY-2散射计反演L2B级标准风场数据产品进行校正,改进HY-2散射计反演风矢量在降雨条件下的准确度。与受降雨影响的散射计反演风场风速偏差相比较,经过神经网络校正后的风速偏差减小,说明该方法适用于改善受降雨影响的HY-2散射计测风风速精度。     

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.     

Brightness Temperature Model of Sea Foam Layer at L-band

Permittivity of a sea foam layer is very important in investigating ocean brightness temperature model. At microwave frequency, the Rayleigh method is developed to estimate the effective permittivity of the sea foam layer. To simplify the tedious calculation of sea foam effective permittivity at L band(1.4 GHz), Pade’ approximation is adopted to fit the sea foam effective permittivity computed by the Rayleigh method. With this fitting formula, a new brightness temperature model of sea foam layer defined by certain geophysical parameters, such as air volume fraction(AVF), sea surface temperature(SST), sea surface salinity(SSS) and thickness of foam layer d, is given. Furthermore, the sensitivities of the brightness temperature model to SST, SSS, d and AVF of a sea foam layer at L band are discussed. The sensitivities are ranked from most to least in the order:(1) d;(2) AVF;(3) SSS;(4) SST. This result indicates that the measurement errors of d and AVF have significant impacts on the retrievals of SSS and SST. With the experimental brightness temperature data, the SSS and AFV are retrieved by cost function.     

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.     

EXPERIMENTAL STUDY ON SOIL MOISTURE USING DUAL-FREQUENCY MICROWAVE RADIOMETER

1INTRODUCTIONSoil is the basis of human's living. Soil moisture plays asignificant role in studying the matter and energy ex-changes in global hydrology sphere. The evaporation ofsoil moisture has an influence on the water vapor cycle.Meanwhile soil moisture is also one of the firsthandmeasurable parameters in crop yield estimation and wa-ter resources management (JACKSON et al., 1993). Theinfluence of the interaction of land and atmosphere onsoil moisture can bring about anomalous cli…     

Evaluating and correcting rain effects on dual-frequency altimeter Jason-1 wind measurements

Rain is one of the main sources of error in dual-frequency altimeter Jason-1 wind measurement. In this study, a new radar altimeter backscatter model is proposed and validated to eliminate rain effects. The model takes into account attenuation, volume backscattering, and sea surface perturbation by raindrops under rain conditions. A match-up dataset is built to evaluate rain effects, in combination with the Jason-1 normalized radar cross section, precipitation radar data from the Tropical Rainfall Measuring Mission, and sea surface wind reanalysis data from the European Centre for Medium-Range Weather Forecasts. The results show that rain-induced surface perturbation backscatter increases with rain rate at Ku-band, but their correlation at C-band is poor. In addition, rain surface perturbation and attenuation have major effects onradar altimeter wind measurements. Finally, a rain correction model for Jason-1 winds is developed and validation results prove its ability to reduce rain-induced inaccuracies in wind retrievals.     

Evaluation of Wind Retrieval from Co-Polarization Gaofen-3 SAR Imagery Around China Seas

Gaofen-3(GF-3) is the first Chinese space-borne satellite to carry the C-band multi-polarization synthetic aperture radar(SAR). Marine applications, i.e., winds and waves retrieved from GF-3 SAR images, have been operational since January 2017. In this study, we have collected more than 1000 quad-polarization(vertical-vertical(VV); horizontal-horizontal(HH); vertical-horizontal(VH); horizontal-vertical(HV)) GF-3 SAR images, which were acquired around the China Seas from September 2016 to September 2017. Wind streaks were visible in these images in co-polarization(VV and HH) channel. Geophysical model functions(GMFs), including the CMOD5N together with polarization ratio(PR) model and C-SARMOD, were used to retrieve winds from the collected co-polarization GF-3 SAR images. Wind directions were directly obtained from GF-3 SAR images. Then, the SAR-derived wind speeds were compared with the measurements at a 0.25? grid from the Advanced Scatterometer on board the Metop-A/B and microwave radiometer WindSAT. Based on the analysis, empirical corrections are proposed to improve the performance of the two GMFs. Results of this study show that the standard deviation of wind speed is 1.63 m s~(-1) with a 0.19 m s~(-1) bias and 1.71 m s~(-1) with a 0.26 m s~(-1) bias for VV-and HH-polarization GF-3 SAR, respectively. Our work not only systematically evaluates wind retrieval by using the two advanced GMFs and PR models but also proposes empirical corrections to improve the accuracy of wind retrievals from GF-3 SAR images around the China Seas and thus enhance the accuracy of near real-time operational SAR-derived wind products.     

The Influence of wave state and sea spray on drag coefficient from low to high wind speeds

Ocean waves alter the roughness of sea surface, and sea spray droplets redistribute the momentum flux at the air-sea interface. Hence, both wave state and sea spray influence sea surface drag coefficient. Based on the new sea spray generation function which depends on sea surface wave, a wave-dependent sea spray stress is obtained. According to the relationship between sea spray stress and the total wind stress on the sea surface, a new formula of drag coefficient at high wind speed is acquired. With the analysis of the new drag coefficient, it is shown that the drag coefficient reduces at high wind speed, indicating that the sea spray droplets can limit the increase of drag coefficient. However, the value of high wind speed corresponding to the initial reduced drag coefficient is not fixed, and it depends on the wave state, which means the influence of wave cannot be ignored. Comparisons between the theoretical and measured sea surface drag coefficients in field and laboratory show that under different wave ages, the theoretical result of drag coefficient could include the measured data, and it means that the new drag coefficient can be used properly from low to high wind speeds under any wave state condition.     

Comparison of remote sensing data with in-situ wind observation during the development of the South China Sea monsoon

Wind measurements derived from QuikSCAT data were compared with those measured by anemometer on Yongxing Island in the South China Sea (SCS) for the period from April 2008 to November 2009. The comparison confirms that QuikSCAT estimates of wind speed and direction are generally accurate, except for the extremes of high wind speeds (>13.8m/s) and very low wind speeds (<1.5m/s) where direction is poorly predicted. In-situ observations show that the summer monsoon in the northern SCS starts between May 6 and June 1. From March 13, 2010 to August 31, 2010, comparisons of sea surface temperature (SST) and rainfall from AMSR-E with data from a buoy located at Xisha Islands, as well as wind measurements derived from ASCAT and observations from an automatic weather station show that QuikSCAT, ASCAT and AMSR-E data are good enough for research. It is feasible to optimize the usage of remote-sensing data if validated with in-situ measurements. Remarkable changes were observed in wind, barometric pressure, humidity, outgoing longwave radiation (OLR), air temperature, rainfall and SST during the monsoon onset. The eastward shift of western Pacific subtropical high and the southward movement of continental cold front preceded the monsoon onset in SCS. The starting dates of SCS summer monsoon indicated that the southwest monsoon starts in the Indochinese Peninsula and forms an eastward zonal belt, and then the belt bifurcates in the SCS, with one part moving northeastward into the tropical western North Pacific, and another southward into western Kalimantan. This largely determined the pattern of the SCS summer monsoon. Wavelet analysis of zonal wind and OLR at Xisha showed that intra-seasonal variability played an important role in the summer. This work improves the accuracy of the amplitude of intra-seasonal and synoptic variation obtained from remote-sensed data.     

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.     

融入土壤湿度指标的青藏高原近地表土壤冻融机器学习监测算法

青藏高原作为中低纬度地区最大的高山冻土区,多年冻土和季节冻土广泛分布。高精度的地表冻融监测结果对研究该区域的水热交换、碳氮循环和土壤冻融侵蚀非常重要。本文基于4个青藏高原典型地区的土壤温湿度观测网数据,开展利用LightGBM算法和随机森林算法进行土壤冻融循环监测的研究。在构建土壤冻融监测模型的过程中,发现土壤湿度是影响冻融判别的一个关键因子。使用AMSR2亮温数据和ERA5-Land土壤湿度数据,基于两种机器学习算法判别地表冻融状态,将结果与传统冻融判别式算法进行对比分析。结果表明：相比冻融判别式算法,LightGBM算法在白天和夜间的总体判对率提高了12.09%;14.45%,随机森林算法在白天和夜间的总体判对率提高了13.23%和14.96%。近80%的错分样本分布在-4.0 ℃~4.0 ℃之间,说明2个机器学习算法能够识别出稳定的土壤冻结状态和融化状态。另外,LightGBM算法和随机森林算法得到的日冻融转换天数的平均RMSE降低了112.82和117.00;冻结天数的平均RMSE降低了47.87和53.96;融化天数的平均RMSE降低了37.10和39.80。同时,基于随机森林算法计算了2014年7月—2015年6月青藏高原冻结天数、融化天数、日冻融转换天数。得到的青藏高原冻结天数图,以中国冻土区划图为参考进行精度评价,总体分类精度为96.78%。     

基于高斯曲线拟合-FFT方法的南海风场反演

海面风场是海洋学的基本参量,获取海面风场对了解海洋的物理过程以及海洋与大气之间的相互作用至关重要。宽阔的海域面积及复杂的海面状况通常使南海海面上的风场信息很难被及时获取。ENVISAT ASAR是一种全天候全天时监测海面的微波雷达传感器,可实时获取海面风场数据。本文基于已有ASAR数据对南海海面风场进行反演实验,首先将结合高斯曲线拟合的FFT风向反演方法应用于南海风向反演,并参考Cross-Calibrated Multi-Platform （CCMP）风场数据去除180o方向模糊获得海面风向。然后,将高斯曲线拟合-FFT风向与传统的峰值-FFT风向进行对比,最后将准确率较高的高斯曲线拟合-FFT风向分别输入CMOD4模型和CMOD5模型获得海面风速大小。实验结果与CCMP参考数据的比较结果表明,在风条纹不明显的情况下,利用结合高斯曲线的FFT风向反演方法和CMOD4模型风速反演方法可有效地进行南海海面风场反演。该成果对利用SAR数据实时获取南海大面积海面风场信息,尤其是观测点缺乏海域的风场信息,具有重要的指导意义。     

Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network

It is more difficult to retrieve land surface temperature(LST) from passive microwave remote sensing data than from thermal remote sensing data, because the emissivities in the passive microwave band can change more easily than those in the thermal infrared band. Thus, it is very difficult to build a stable relationship. Passive microwave band emissivities are greatly influenced by the soil moisture, which varies with time. This makes it difficult to develop a general physical algorithm. This paper proposes a method to utilize multiple-satellite, sensors and resolution coupled with a deep dynamic learning neural network to retrieve the land surface temperature from images acquired by the Advanced Microwave Scanning Radiometer 2(AMSR2), a sensor that is similar to the Advanced Microwave Scanning Radiometer Earth Observing System(AMSR-E). The AMSR-E and MODIS sensors are located aboard the Aqua satellite. The MODIS LST product is used as the ground truth data to overcome the difficulties in obtaining large scale land surface temperature data. The mean and standard deviation of the retrieval error are approximately 1.4° and 1.9° when five frequencies(ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used. This method can effectively eliminate the influences of the soil moisture, roughness, atmosphere and various other factors. An analysis of the application of this method to the retrieval of land surface temperature from AMSR2 data indicates that the method is feasible. The accuracy is approximately 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations.     

Semi-Empirical Algorithm for Wind Speed Retrieval from Gaofen-3 Quad-Polarization Strip Mode SAR Data

Synthetic aperture radar(SAR)is a suitable tool to obtain reliable wind retrievals with high spatial resolution.The geophysical model function(GMF),which is widely employed for wind speed retrieval from SAR data,describes the relationship between the SAR normalized radar cross-section(NRCS)at the copolarization channel(vertical-vertical and horizontal-horizontal)and a wind vector.SAR-measured NRCS at cross-polarization channels(horizontal-vertical and vertical-horizontal)correlates with wind speed.In this study,a semi-empirical algorithm is presented to retrieve wind speed from the noisy Chinese Gaofen-3(GF-3)SAR data with noise-equivalent sigma zero correction using an empirical function.GF-3 SAR can acquire data in a quad-polarization strip mode,which includes cross-polarization channels.The semi-empirical algorithm is tuned using acquisitions collocated with winds from the European Center for Medium-Range Weather Forecasts.In particular,the proposed algorithm includes the dependences of wind speed and incidence angle on cross-polarized NRCS.The accuracy of SAR-derived wind speed is around 2.10ms−1 root mean square error,which is validated against measurements from the Advanced Scatterometer onboard the Metop-A/B and the buoys from the National Data Buoy Center of the National Oceanic and Atmospheric Administration.The results obtained by the proposed algorithm considering the incidence angle in a GMF are relatively more accurate than those achieved by other algorithms.This work provides an alternative method to generate operational wind products for GF-3 SAR without relying on ancillary data for wind direction.     

The formation of wind curl in the marine atmosphere boundary layer over the East China Sea Kuroshio in spring

Various data are used to investigate the characteristics of the surface wind field and rainfall on the East China Sea Kuroshio (ESK) in March and April, 2011. In March, the wind speed maximum shows over the ESK front (ESKF) in the 10 meter wind field, which agrees with the thermal wind effect. A wind curl center is generated on the warm flank of the ESKF. The winds are much weaker in April, so is the wind curl. A rainband exists over the ESKF in both the months. The Weather Research and Forecasting (WRF) model is used for further researches. The winds on the top of the marine atmosphere boundary layer (MABL) indicate that in March, a positive wind curl is generated in the whole MABL over the warm flank of the ESKF. The thermal wind effect forced by the strong SST gradient overlying the background wind leads to strong surface northeasterly winds on the ESKF, and a positive shearing vorticity is created over the warm flank of the ESKF to generate wind curl. In the smoothed sea surface temperature experiment, the presence of the ESKF is responsible for the strong northeast winds in the ESKF, and essential for the distribution of the rainfall centers in March, which confirms the mechanism above. The same simulation is made for April, 2011, and the responses from the MABL become weak. The low background wind speed weakens the effect of the thermal wind, thus no strong Ekman pumping is helpful for precipitation. There is no big difference in rainfall between the control run and the smooth SST run. Decomposition of the wind vector shows that local wind acceleration induced by the thermal wind effect along with the variations in wind direction is responsible for the pronounced wind curl/divergence over the ESKF.     

Comparison of CloudSat cloud liquid water paths in arctic summer using ground-based microwave radiometer

Arctic clouds strongly influence the regional radiation balance, temperature, melting of sea ice, and freezing of sea water. Despite their importance, there is a lack of systematic and reliable observations of Arctic clouds. The CloudSat satellite launched in 2006 with a 94 GHz Cloud Profiling Radar (CPR) may contribute to close this gap. Here we compare one of the key parameters, the cloud liquid water path (LWP) retrieved from CloudSat observations and from microwave radiometer (MWR) data taken during the ASCOS (Arctic Summer Cloud Ocean Study) cruise of the research vessel Oden from August to September 2008. Over the 45 days of the ASCOS cruise, collocations closer than 3 h and 100 km were found in only 9 d, and collocations closer than 1 h and 30 km in only 2 d. The poor correlations in the scatter plots of the two LWP retrievals can be explained by the patchiness of the cloud cover in these two days (August 5th and September 7th), as confirmed by coincident MODIS (Moderate-resolution Imaging Spectroradiometer) images. The averages of Oden-observed LWP values are systematically higher (40–70 g m⁻²) than the corresponding CloudSat observations (0–50 g m⁻²). These are cases of generally low LWP with presumably small droplets, and may be explained by the little sensitivity of the CPR to small droplets or by the surface clutter.