Upper mantle structure of the South American continent and neighboring oceans from surface wave tomography

We present a new three-dimensional SV-wave velocity model for the upper mantle beneath South America and the surrounding oceans, built from the waveform inversion of 5850 Rayleigh wave seismograms. The dense path coverage and the use of higher modes to supplement the fundamental mode of surface waves allow us to constrain seismic heterogeneities with horizontal wavelengths of a few hundred kilometres in the uppermost 400 km of the mantle.The large scale features of our tomographic model confirm previous results from global and regional tomographic studies (e.g. the depth extent of the high velocity cratonic roots down to about 200–250 km).Several new features are highlighted in our model. Down to 100 km depth, the high velocity lid beneath the Amazonian craton is separated in two parts associated with the Guyana and Guapore shields, suggesting that the rifting episode responsible for the formation of the Amazon basin has involved a significant part of the lithosphere. Along the Andean subduction belt, the structure of the high velocity anomaly associated with the sudbduction of the Nazca plate beneath the South American plate reflects the along-strike variation in dip of the subducting plate. Slow velocities are observed down to about 100 km and 150 km at the intersection of the Carnegie and Chile ridges with the continent and are likely to represent the thermal anomalies associated with the subducted ridges. These lowered velocities might correspond to zones of weakness in the subducted plate and may have led to the formation of “slab windows” developed through unzipping of the subducted ridges; these windows might accommodate a transfer of asthenospheric mantle from the Pacific to the Atlantic ocean. From 150 to 250 km depth, the subducting Nazca plate is associated with high seismic velocities between 5°S and 37°S. We find high seismic velocities beneath the Paraná basin down to about 200 km depth, underlain by a low velocity anomaly in the depth range 200–400 km located beneath the Ponta Grossa arc at the southern tip of the basin. This high velocity anomaly is located southward of a narrow S-wave low velocity structure observed between 200 and 500–600 km depth in body wave studies, but irresolvable with our long period datasets. Both anomalies point to a model in which several, possibly diachronous, plumes have risen to the surface to generate the Paraná large igneous province (LIP).     

Development of a rain‐on‐snow detection algorithm using passive microwave radiometry

Currently observed climate warming in the Arctic has numerous consequences. Of particular relevance, the precipitation regime is modified where mixed and liquid precipitation can occur during the winter season leading to rain‐on‐snow (ROS) events. This phenomenon is responsible for ice crust formation, which has a significant impact on ecosystems (such as biological, hydrological, ecological and physical processes). The spatially and temporally sporadic nature of ROS events makes the phenomenon difficult to monitor using meteorological observations. This paper focuses on the detection of ROS events using passive microwave (PMW) data from a modified brightness temperature (T_B) gradient approach at 19 and 37 GHz. The approach presented here was developed empirically for observed ROS events with coincident ground‐based PMW measurements in Sherbrooke, Quebec, Canada. It was then tested in Nunavik, Quebec, with the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E). We obtained a detection accuracy of 57, 71 and 89% for ROS detection for three AMSR‐E grid cells with a maximum error of 7% when considering all omissions and commissions with regard to the total number of AMSR‐E passes throughout the winter period. Copyright © 2016 John Wiley & Sons, Ltd.     

Integrating ASTER and GLASS broadband emissivity products using a multi-resolution Kalman filter

In this study, the multi-resolution Kalman filter (MKF) algorithm, which can handle multi-resolution problems with high computational efficiency, was used to blend two emissivity products: the Global LAnd Surface Satellite (GLASS) (BBE) product and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) narrowband emissivity (NBE) product. The ASTER NBE product was first converted into a BBE product. A new detrending method was used to transfer the BBEs into a process suitable for the MKF. The new detrending method was superior to the two existing methods. Finally, both the de-trended GLASS and ASTER BBE products were incorporated into the MKF framework to obtain the optimal estimation at each scale. Field measurements collected in North America were used to validate the integrated BBEs. Visually, the fusion map showed good continuity, with the exception of the border areas, and the quality of the fusion map was better than that of the original maps. The validation results indicate that the MKF improved the BBE product accuracy at the coarse scale. In addition, the MKF was capable of recovering missing pixels at a finer scale.     

基于贝叶斯模型的中国未来气温变化预估及不确定性分析

利用第5次耦合模式比较计划(CMIP5)中35个全球气候模式历史模拟与RCP4.5预估结果,通过贝叶斯模型平均(Bayesian Model Averaging,BMA)对中国气温进行多模式集合研究,给出了中国未来气温变化预估及其不确定性的时空分布。结果表明,中国21世纪冬夏将持续升温,且升温具有冬季高于夏季,北方高于南方的特点。初期(2016—2035年)北方有很大可能(80%)升温超过0.7℃,南方升温相同幅度的概率则超过50%;中期(2046—2065年)北方和南方升温超过1.5℃的概率分别为80%和50%;末期(2081—2100年),北方(南方)有80%(50%)的可能的升温超过2℃。气温预估的不确定性研究发现,无论冬夏,21世纪不同时期升温相对较弱的塔里木盆地、青藏高原南侧和中国东南地区为不确定性低值区,基本低于0.6℃,对应可信度较高,如21世纪初期信噪比超过4;而不确定性的高值区则主要分布在新疆北部、东北平原北部和青藏高原东南侧等升温相对较大的地区,普遍高于1℃,对应可信度较低,如初期信噪比低于2.5。此外,基于信噪比对比发现除青藏高原东部外,其他区域夏季预估的可信度均高于冬季,21世纪末期高于初期,且空间分布特征一致。     

强度尺度分解方法在气候温度场检验中的应用

本文首次将Casati等提出的强度尺度分解方法应用到气候地表温度场的检验中,探讨此方法用于评估气候模拟场误差的详细空间信息的适用性。以新一代气候系统模式模拟的月平均地表温度为例,传统的统计分析方法（较为常用的是空间相关系数和均方根误差、EOF分析等）不能完全反映模拟场误差的空间信息;强度尺度分解方法可计算不同阈值和空间尺度上的均方误差和模拟技巧,评估对应的模拟能力,定量给出模拟场主要误差的空间信息（误差范围即温度阈值及对应的空间尺度）,例如,亚洲东部地区1月单年及多年平均的模拟场在230 K阈值1600 km模拟技巧非常低。本研究表明强度尺度分解方法适用于气候温度场的检验评估,能定量给出误差的空间信息。     

Heterogeneous surface displacement pattern at the Hatchobaru geothermal field inferred from SAR interferometry time-series

We estimated surface displacements using persistent scatterer SAR interferometry (PS-InSAR) around the Hatchobaru geothermal field, Japan, from 18 ALOS/PALSAR images acquired from July 2007 to December 2010. Generally, geothermal fields, covered with natural targets such as rocky terrain and vegetation, have been one of the difficult targets for PS-InSAR analysis. However, we applied space adaptive filtering to increase the number of pixels for measuring surface displacement. The results of our analysis demonstrate ground subsidence with decaying velocity over the observation period around the geothermal field. The spatial pattern of ground subsidence includes sharp boundaries of subsidence that can be interpreted as fault traces. We demonstrated the usefulness of PS-InSAR analysis with the space adaptive filtering to estimate surface displacements with high spatial resolution and high spatial density around a geothermal field.     

Classification of forest land attributes using multi-source remotely sensed data

The aim of the study was to (1) examine the classification of forest land using airborne laser scanning (ALS) data, satellite images and sample plots of the Finnish National Forest Inventory (NFI) as training data and to (2) identify best performing metrics for classifying forest land attributes. Six different schemes of forest land classification were studied: land use/land cover (LU/LC) classification using both national classes and FAO (Food and Agricultural Organization of the United Nations) classes, main type, site type, peat land type and drainage status. Special interest was to test different ALS-based surface metrics in classification of forest land attributes. Field data consisted of 828 NFI plots collected in 2008–2012 in southern Finland and remotely sensed data was from summer 2010. Multinomial logistic regression was used as the classification method. Classification of LU/LC classes were highly accurate (kappa-values 0.90 and 0.91) but also the classification of site type, peat land type and drainage status succeeded moderately well (kappa-values 0.51, 0.69 and 0.52). ALS-based surface metrics were found to be the most important predictor variables in classification of LU/LC class, main type and drainage status. In best classification models of forest site types both spectral metrics from satellite data and point cloud metrics from ALS were used. In turn, in the classification of peat land types ALS point cloud metrics played the most important role. Results indicated that the prediction of site type and forest land category could be incorporated into stand level forest management inventory system in Finland.     

Impact of sub-pixel heterogeneity on modelled brightness temperature for an agricultural region

Knowledge of sub-pixel heterogeneity, particularly at the passive microwave scale, can improve the brightness temperature (and ultimately the soil moisture) estimation. However, the impact of surface heterogeneity (in terms of soil moisture, soil temperature and vegetation water content) on brightness temperature in an agricultural setting is relatively unknown. The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) provided an opportunity to evaluate sub-pixel heterogeneity at the scale of a Soil Moisture Ocean Salinity (SMOS) or the Soil Moisture Active Passive (SMAP) radiometer footprint using field measured data. The first objective of this study was to determine if accounting for surface heterogeneity reduced the error between estimated brightness temperature (T_b) and T_b measured by SMOS. It was found that when accounting for variation in surface soil moisture, temperature and vegetation water content within the pixel footprint, the error between the modelled T_b and the measured T_b was less than if a homogeneous pixel were modelled. The correlation between the surface parameters and the error associated with not accounting for surface heterogeneity were investigated. It was found that there was low to moderate correlation between the error and the coefficient of variance associated with the measured soil moisture, soil temperature and vegetation volumetric water content during the field campaign. However, it was found that the correlations changed depending on the stage of vegetation growth and the amount of time following a precipitation event. At the start of the field campaign (following a precipitation event), there was strong correlation between the error and all three surface parameters (r ≥ 0.75). Following a precipitation event close to the middle of the field campaign (during which there was rapid growth in vegetation), there was strong correlation between the error and the variability in vegetation water content (r = 0.89), moderate correlation with soil moisture (r = 0.61) and low correlation with soil temperature (r = 0.26).     

Integrated approach of using ASTER-derived emissivity and pixel temperature for delineating different granitoids – a case study in parts of Dharwar Craton,India

We have delineated different granitoids based on variation in emissivity and relative surface temperature recorded in thermal bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor of EO-1 satellite. In this regard, we have used emissivity normalization algorithm to derive broadband emissivity from thermal bands of ASTER sensor to delineate different lithounits of the granitoid family. We have compared emissivity and radiance image composites in terms of delineation of different granitoids. We have also used false colour composite (FCC) image derived using two emissivity bands and temperature (derived using emissivity normalisation method) bands to delineate different granitoids. We could differentiate different granitoids in the three-dimensional (3D) data space of ASTER-derived emissivity bands (second and third bands) and temperature bands. Based on the analysis of 3D scatter plot, we also proposed a ternary diagram of emissivity and temperature, which can be used to delineate different granitoids.     

Development of a new method for efficiently calculating of evaporation from the phreatic aquifer in variably saturated flow modeling

In this paper, we proposed a new method that has been developed based on the surface soil moisture content (SSMC) to more efficiently calculate the groundwater evaporation in variably saturated flow modeling. In this method, the empirical formula to calculate evaporation was modified and the value of the formula varies from zero to one as a closed interval. In addition, the simulation code for calculating the groundwater evaporation based on the SSMC method was incorporated into the EOS9 module of Tough2, a variably saturated flow modeling code. Finally, two numerical tests and a case simulation were conducted to verify the feasibility and accuracy of the SSMC method. Simulation results indicate that the SSMC method is capable of appropriately simulating the characteristics of water flow in vadose zone and the amount of evaporation with the variable water table. And such results are in coincidence with the value calculated by the logistic function method, and fit well with the measured data globally rather than locally.