Comparative analysis of microwave brightness temperature data in Northeast China using AMSR-E and MWRI products

With such significant advantages as all-day observation, penetrability and all-weather coverage, passive microwave remote sensing technique has been widely applied in the research of global environmental change. As the satellite-based passive microwave remote sensor, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) loaded on NASA’s (National Aeronautics and Space Administration of USA) Aqua satellite has been popularly used in the field of microwave observation. The Microwave Radiation Imager (MWRI) loaded on the Chinese FengYun-3A (FY-3A) satellite is an AMSR-E-like conical scanning microwave sensor, but there are few reports about MWRI data. This paper firstly proposed an optimal spatial position matching algorithm from rough to exact for the position matching between AMSR-E and MWRI data, then taking Northeast China as an example, comparatively analyzed the microwave brightness temperature data derived from AMSR-E and MWRI. The results show that when the antenna footprints of the two sensors are filled with either full water, or full land, or mixed land and water with approximate proportion, the errors of brightness temperature between AMSR-E and MWRI are usually in the range from −10 K to +10 K. In general, the residual values of brightness temperature between the two microwave sensors with the same spatial resolution are in the range of ±3 K. Because the spatial resolution of AMSR-E is three times as high as that of MWRI, the results indicate that the quality of MWRI data is better. The research can provide useful information for the MWRI data application and microwave unmixing method in the future.     

Seasonal response of surface wind to SST perturbation in the Northern Hemisphere

The seasonal response of surface wind speed to sea surface temperature(SST)change in the Northern Hemisphere was investigated using 10 years(2002-2011)high-resolution satellite observations and reanalysis data.The results showed that correlation between surface wind speed perturbations and SST perturbations exhibits remarkable seasonal variation,with more positive correlation is stronger in the cold seasons than in the warm seasons.This seasonality in a positive correlation between SST and surface wind speed is attributable primarily to seasonal changes of oceanic and atmospheric background conditions in frontal regions.The mean SST gradient and the prevailing surface winds are strong in winter and weak in summer.Additionally,the eddy-induced response of surface wind speed is stronger in winter than in summer,although the locations and numbers of mesoscale eddies do not show obvious seasonal features.The response of surface wind speed is apparently due to stability and mixing within the marine atmospheric boundary layer(MABL),modulated by SST perturbations.In the cold seasons,the stronger positive(negative)SST perturbations are easier to increase(decrease)the MABL height and trigger(suppress)momentum vertical mixing,contributing to the positive correlation between SST and surface wind speed.In comparison,SST perturbations are relatively weak in the warm seasons,resulting in a weak response of surface wind speed to SST changes.This result holds for each individual region with energetic eddy activity in the Northern Hemisphere.     

An AMSR-E data unmixing method for monitoring flood and waterlogging disaster

Spectral remote sensing technique is usually used to monitor flood and waterlogging disaster. Although spectral remote sensing data have many advantages for ground information observation, such as real time and high spatial resolution, they are often interfered by clouds, haze and rain. As a result, it is very difficult to retrieve ground information from spectral remote sensing data under those conditions. Compared with spectral remote sensing technique, passive microwave remote sensing technique has obvious superiority in most weather conditions. However, the main drawback of passive microwave remote sensing is the extreme low spatial resolution. Considering the wide application of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data, an AMSR-E data unmixing method was proposed in this paper based on Bellerby’s algorithm. By utilizing the surface type classification results with high spatial resolution, the proposed unmixing method can obtain the component brightness temperature and corresponding spatial position distribution, which effectively improve the spatial resolution of passive microwave remote sensing data. Through researching the AMSR-E unmixed data of Yongji County, Jilin Provinc, Northeast China after the worst flood and waterlogging disaster occurred on July 28, 2010, the experimental results demonstrated that the AMSR-E unmixed data could effectively evaluate the flood and waterlogging disaster.     

Comparison of nonlinear and linear PCA on surface wind, surface height, and SST in the South China Sea

We compared nonlinear principal component analysis (NLPCA) with linear principal component analysis (LPCA) with the data of sea surface wind anomalies (SWA), surface height anomalies (SSHA), and sea surface temperature anomalies (SSTA), taken in the South China Sea (SCS) between 1993 and 2003. The SCS monthly data for SWA, SSHA and SSTA (i.e., the anomalies with climatological seasonal cycle removed) were pre-filtered by LPCA, with only three leading modes retained. The first three modes of SWA, SSHA, and SSTA of LPCA explained 86%, 71%, and 94% of the total variance in the original data, respectively. Thus, the three associated time coefficient functions (TCFs) were used as the input data for NLPCA network. The NLPCA was made based on feed-forward neural network models. Compared with classical linear PCA, the first NLPCA mode could explain more variance than linear PCA for the above data. The nonlinearity of SWA and SSHA were stronger in most areas of the SCS. The first mode of the NLPCA on the SWA and SSHA accounted for 67.26% of the variance versus 54.7%, and 60.24% versus 50.43%, respectively for the first LPCA mode. Conversely, the nonlinear SSTA, localized in the northern SCS and southern continental shelf region, resulted in little improvement in the explanation of the variance for the first NLPCA.     

A Gaussian process regression-based sea surface temperature interpolation algorithm

The resolution of ocean reanalysis datasets is generally low because of the limited resolution of their associated numerical models.Low-resolution ocean reanalysis datasets are therefore usually interpolated to provide an initial or boundary field for higher-resolution regional ocean models.However,traditional interpolation methods(nearest neighbor interpolation,bilinear interpolation,and bicubic interpolation) lack physical constraints and can generate significant errors at land-sea boundarie s and around islands.In this paper,a machine learning method is used to design an interpolation algorithm based on Gaussian process regression.The method uses a multiscale kernel function to process two-dimensional space meteorological ocean processes and introduces multiscale physical feature information(sea surface wind stress,sea surface heat flux,and ocean current velocity).This greatly improves the spatial resolution of ocean features and the interpolation accuracy.The effectiveness of the algorithm was validated through interpolation experiments relating to sea surface temperature(SST).The root mean square error(RMSE)of the interpolation algorithm was 38.9%,43.7%,and 62.4% lower than that of bilinear interpolation,bicubic interpolation,and nearest neighbor interpolation,respectively.The interpolation accuracy was also significantly better in offshore area and around islands.The algorithm has an acceptable runtime cost and good temporal and spatial generalizability.     

A calibration method of Argo floats based on multiple regression analysis

1 INTRODUCTION Argo floats are instruments that move freely with the ocean current at fixed parking depths and cycle from a profiling depth to the sea surface at regular time intervals. While rising to the surface, these autonomous floats take profiles of…     

Evolution of surface cold patches in the North Yellow Sea based on satellite SST data

Ten years(from 2005 to 2014) of satellite sea surface temperature(SST) data from the Advanced Very High Resolution Radiometer(AVHRR) are analyzed to reveal the monthly changes in surface cold patches(SCPs) in the main areas of the Northern Yellow Sea(NYS). The Canny edge detection algorithm is used to identify the edges of the patches. The monthly changes are described in terms of location, temperature and area. The inter-annual variations, including changes in the location and area of the SCPs from 2010 to 2014, are briefly discussed. The formation mechanisms of the SCPs in different periods are systematically analyzed using both in situ data and numerical simulation. The results show that from May to October, the location and area of the SCPs remain stable, with a north-south orientation. The SCPs altogether cover about 1? of longitude(124?E–125?E) in width and 2? of latitude(37.5?N–39.5?N) in length. In November, the SCP separates from the Jangsan Cape and forms a closed, isolated, and approximately circular cold patch in the central NYS. From May to October, the upwelling that leads to the formation of the SCP is mainly triggered by the headland residual current, wind field, climbing movement of the current and secondary circulation at the tide front. In November, cyclonic circulation in the NYS is primarily responsible for generating the upwelling that leads to the formation of the closed and isolated SCP.     

Spatio-temporal variation of land surface temperature and temperature lapse rate over mountainous Kashmir Himalaya

In this study, Land Surface Temperature(LST) and its lapse rate over the mountainous Kashmir Himalaya was estimated using MODIS data and correlated with the observed in-situ air temperature(Tair) data. Comparison between the MODIS LST and Tair showed a close agreement with the maximum error of the estimate ±1°C and the correlation coefficient 0.90. Analysis of the LST data from 2002-2012 showed an increasing trend at all the selected locations except at a site located in the southeastern part of Kashmir valley. Using the GTOPO30 DEM, MODIS LST data was used to estimate the actual temperature lapse rate(ATLR) along various transects across Kashmir Himalaya, which showed significant variations in space and time ranging from 0.3°C to 1.2°C per 100 m altitude change. This observation is at variance with the standard temperature lapse rate(STLR) of 0.65°C used universally in most of the hydrological and other land surface models. Snowmelt Runoff Model(SRM) was used to determine the efficacy of using the ATLR for simulating the stream flows in one of the glaciated and snow-covered watersheds in Kashmir. The use of ATLR in the SRM model improved the R2 between the observed and predicted streamflows from 0.92 to 0.97.It is hoped that the operational use of satellite-derived LST and ATLR shall improve the understanding and quantification of various processes related to climate, hydrology and ecosystem in the mountainous and data-scarce Himalaya where the use of temperature and ATLR are critical parameters for understanding various land surface and climate processes.     

New methods for calculating bare soil land surface temperature over mountainous terrain

Land surface temperature(LST) causes the phase change of water, links to the partitioning of surface water and energy budget, and becomes an important parameter to hydrology, meteorology, ecohydrology, and other researches in the high mountain cold regions. Unlike air temperature, which has common altitudinal lapse rates in the mountainous regions, the influence of terrain leads to complicated estimation for soil LST. This study presents two methods that use air temperature and solar position,to estimate bare LST with high temporal resolution over horizontal sites and mountainous terrain with a random slope azimuth. The data from three horizontal meteorological stations and fourteen LST observation fields with different aspects and slopes were used to test the proposed LST methods. The calculated and measured LST were compared in a range of statistical analysis, and the analysis showed that the average RMSE(root mean square error),MAD(mean absolute deviation), and R~2(correlation coefficient) for three horizontal sites were 5.09℃,3.66℃, 0.92, and 5.03℃, 3.52℃, 0.85 for the fourteen complex terrain sites. The proposed methods showed acceptable accuracy, provide a simple way to estimate LST, and will be helpful for simulating the water and energy cycles in alpine mountainous terrain.     

Representing surface wind stress response to mesoscale SST perturbations in western coast of South America using Tikhonov regularization method

Journal of Oceanology and Limnology - Interaction between mesoscale perturbations of sea surface temperature (SSTmeso) and wind stress (WSmeso) has great influences on the ocean upwelling system...     

Spatial and seasonal variability of global ocean diapycnal transport inferred from Argo profiles

The global diapycnal transport in the ocean interior is one of the significant branches to return the deep water back toward near-surface. However, the amount of the diapycnal transport and the seasonal variations are not determined yet. This paper estimates the dissipation rate and the associated diapycnal transports at 500 m, 750 m and 1 000 m depth throughout the global ocean from the wide-spread Argo profiles, using the finescale parameterizations and classic advection-dif fusion balance. The net upwelling is ～5.2±0.81 Sv(Sverdrup) which is approximately one fifth in magnitude of the formation of the deep water. The Southern Ocean is the major region with the upward diapycnal transport, while the downwelling emerges mainly in the northern North Atlantic. The upwelling in the Southern Ocean accounts for over 50%of the amount of the global summation. The seasonal cycle is obvious at 500 m and vanishes with depth,indicating the energy source at surface. The enhancement of diapycnal transport occurs at 1 000 m in the Southern Ocean, which is pertinent with the internal wave generation due to the interaction between the robust deep-reaching flows and the rough topography. Our estimates of the diapycnal transport in the ocean interior have implications for the closure of the oceanic energy budget and the understanding of global Meridional Overturning Circulation.     

Preliminary validation of SMOS sea surface salinity measurements in the South China Sea

The SMOS(soil moisture and ocean salinity) mission undertaken by the European Space Agency(ESA) has provided sea surface salinity(SSS) measurements at global scale since 2009.Validation of SSS values retrieved from SMOS data has been done globally and regionally.However,the accuracy of SSS measurements by SMOS in the China seas has not been examined in detail.In this study,we compared retrieved SSS values from SMOS data with in situ measurements from a South China Sea(SCS) expedition during autumn 2011.The comparison shows that the retrieved SSS values using ascending pass data have much better agreement with in situ measurements than the result derived from descending pass data.Accuracy in terms of bias and root mean square error(RMS) of the SSS retrieved using three different sea surface roughness models is very consistent,regardless of ascending or descending orbits.When ascending and descending measurements are combined for comparison,the retrieved SSS using a semi-empirical model shows the best agreement with in situ measurements,with bias-0.33 practical salinity units and RMS 0.74.We also investigated the impact of environmental conditions of sea surface wind and sea surface temperature on accuracy of the retrieved SSS.The SCS is a semi-closed basin where radio frequencies transmitted from the mainland strongly interfere with SMOS measurements.Therefore,accuracy of retrieved SSS shows a relationship with distance between the validation sites and land.     

Impact of land cover change on land surface temperature: A case study of Spiti Valley

Land surface temperature(LST) is the skin temperature of the earth surface. LST depends on the amount of sunlight received by any geographical area. Apart from sun light, LST is also affected by the land cover, which leads to change in land surface temperature. Impact of land cover change(LCC) on LST has been assessed using Landsat TM5, Landsat 8 TIRS/OLI and Digital Elevation Model(ASTER) for Spiti Valley, Himachal Pradesh, India. In the present study, Spiti valley was divided into three altitudinal zones to check the pattern of changing land cover along different altitudes and LST was calculated for all the four land cover categories extracted from remote sensing data for the years of 1990 and 2015. Matrix table was used as a technique to evaluate the land cover change between two different years. Matrix table shows that as a whole, about 2,151,647 ha(30%) area of Spiti valley experienced change in land cover in the last 25 years. The result also shows vegetation and water bodies increased by 107,560.2 ha(605.87%) and 45 ha(0.98%), respectively. Snow cover and barren land decreased by 19,016.5 ha(23.92%) and 88,589(14.14%), during the study period. A significant increase has been noticed in vegetation amongst all land cover types. Minimum, maximum and mean LST for three altitudinal zones have been calculated. The mean LST recorded was 11℃ in 1990 but it rose by 2℃ and reached to 13℃ in 2015. Changes in LST were obtained for each land cover categories. The mean temperature of different land cover types was calculated by averaging value of all pixels of a given land cover types. The mean LST of vegetation, barren land, snow cover and water body increased by 6℃, 9℃, 1℃, and 7℃, respectively. Further, relationships between LST, Normalized Difference Snow Index(NDSI), and Normalised Difference Vegetation Index(NDVI) were established using Linear Regression.     

重庆地面最高气温与最大风速年极值的渐近分布

利用重庆1951-1990年间地面最高气温和1956-1990年间地面最大风速年极值的记录,采用韦伯分布和耿贝尔分布分别对其渐近分布进行拟合.通过统计推断,找出重庆地面最高气温和地面最大风速年极值遵循的最佳渐近分布--韦伯分布.     

Improvement of mono-window algorithm for retrieving land surface temperature from HJ-1B satellite data

The thermal infrared channel (IRS4) of HJ-1B satellite obtains view zenith angles (VZA) up to ±33°. The view angle should be taken into account when retrieving land surface temperature (LST) from IRS4 data. This study aims at improving the mono-window algorithm for retrieving LST from IRS4 data. Based on atmospheric radiative transfer simulations,a model for correcting the VZA effects on atmospheric transmittance is proposed. In addition,a generalized model for calculating the effective mean atmospheric temperature is developed. Validation with the simulated dataset based on standard atmospheric profiles reveals that the improved mono-window algorithm for IRS4 obtains high accuracy for LST retrieval,with the mean absolute error (MAE) and root mean square error (RMSE) being 1.0 K and 1.1 K,respectively. Numerical experiment with the radiosonde profile acquired in Beijing in winter demonstrates that the improved mono-window algorithm exhibits excellent ability for LST retrieval,with MAE and RMSE being 0.6 K and 0.6 K,respectively. Further application in Qinghai Lake and comparison with the Moderate-Resolution Imaging Spectroradiometer (MODIS) LST product suggest that the improved mono-window algorithm is applicable and feasible in actual conditions.     

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

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Effect of altitude and latitude on surface air temperature across the Qinghai-Tibet Plateau

The correlation between mean surface air temperature and altitude is analyzed in this paper based on the annual and monthly mean surface air temperature data from 106 weather stations over the period 1961–2003 across the Qinghai-Tibet Plateau. The results show that temperature variations not only depend on altitude but also latitude, and there is a gradual decrease in temperature with the increasing altitude and latitude. The overall trend for the vertical temperature lapse rate for the whole plateau is approximately linear. Three methods, namely multivariate composite analysis, simple correlation and traditional stepwise regression, were applied to analyze these three correlations. The results assessed with the first method are well matched to those with the latter two methods. The apparent mean annual near-surface lapse rate is −4.8 °C /km and the latitudinal effect is −0.87 °C /^olatitude. In summer, the altitude influences the temperature variations more significantly with a July lapse rate of -4.3°C /km and the effect of latitude is only −0.28°C /^olatitude. In winter, the reverse happens. The temperature decrease is mainly due to the increase in latitude. The mean January lapse rate is −5.0°C /km, while the effect of latitude is −1.51°C /^olatitude. Comparative analysis for pairs of adjacent stations shows that at a small spatial scale the difference in altitude is the dominant factor affecting differences in mean annual near-surface air temperature, aided to some extent by differences of latitude. In contrast, the lapse rate in a small area is greater than the overall mean value for the Qinghai-Tibet Plateau (5 to 13°C /km). An increasing trend has been detected for the surface lapse rate with increases in altitude. The temperature difference has obvious seasonal variations, and the trends for the southern group of stations (south of 33° latitude) and for the more northerly group are opposite, mainly because of the differences in seasonal variation at low altitudes. For yearly changes, the temperature for high-altitude stations occurs earlier clearly. Temperature datasets at high altitude stations are well-correlated, and those in Nanjing were lagged for 1 year but less for contemporaneous correlations. The slope of linear trendline of temperature change for available years is clearly related to altitude, and the amplitude of temperature variation is enlarged by high altitude. The change effect in near-surface lapse rate at the varying altitude is approximately 1.0°C /km on the rate of warming over a hundred-year period.     

重庆地面最低气温年极值的渐近分布及参数估计

利用重庆 1951-1996 年间 46 年地面气温年极小值的记录,采用韦伯分布和耿贝尔分布分别进行拟合试验.通过统计推断和对比,找出重庆地面最低气温年极值遵循的最佳渐近分布--韦伯分布.     

基于MODIS和AMSR-E反演京津冀地区大气水汽含量及比较

为了长时间、大范围获取水汽数值,利用2005~2008年光学遥感的MODIS近红外、红外水汽产品,以及微波遥感AMSR-E数据,2种方法反演水汽。微波AMSR-E亮温数据采用Merritt N.Deeter(2007)亮温极化差方法,选取18.7GHz和23.8GHz 2个波段,得到AMSR-E升轨、降轨大气水汽数值。以京津冀地区为研究区域,通过地统计相关性分析、时间序列分析、年际间变化分析,可知2种方法4种资料反演的大气水汽数值的R2都达到0.95,时间分布符合中国雨带移动规律,空间分布不均。MODIS数据反演值比AMSR-E值要低,得到2种方法反演水汽的各自优缺点。