一种新的多通道GPS共视资料的处理算法

提出了一种按仰角加权的多通道GPS共视资料处理算法，充分利用了所有观测数据，同时抑制了多径等因素影响，从而可提高共视比对精度。对日本邮政省通讯研究所(CRL)和国家授时中心(NTSC)2002年5月～2003年3月的多通道共视比对的处理结果表明，新的处理算法结果更接近真实钟差。     

全球观测系统实地观测的内容和要求

全球性观测系统在检测，监测和预测地球系统的变化中发挥着越工重要的作用。地球系统的复杂性，多尺度性，非线性性，突变性，非平衡性等要求各种全球性观测系统之间必须加强联系与协调。评述了全球性观测系统联系与协调的发展趋势，并依据《全球性观测系统实地观测的内容和体系纲要》、介绍了全球性观测系统实地观测的内容和规范要求。     

Observations and simulations of an unsteady island wake in the Firth of Forth, Scotland

Beamer Rock, a 50-m-wide island in the Firth of Forth, produces a distinctive von Kármán vortex street wake, the characteristics of which depend on the speed and direction of the tidal flow. ADCP (acoustic Doppler current profiler), CTD (conductivity–temperature–depth) and aerial photograph data were collected from the region during flood and ebb tidal flow under neap and spring conditions. Good agreement was found between the observations and the results from a fixed-grid depth-averaged numerical tidal model. The island wake parameter correctly predicted the unsteady nature of the wake, and the Strouhal number (defined in terms of flow past a circular cylinder) was found to give excellent predictions of the wake wavelength when scaled on the island width. Contrary to published results, the study shows that it is possible to accurately simulate an unsteady island wake using a relatively coarse fixed-grid numerical model.Responsible Editor: Jens Kappenberg     

The Present Space-Time Motion and Deformation Features of the Northeastern Margin of the Qinghai-Xizang（Tibet） Block and Its Adjacent Area

On the basis of Discontinuous Deformation Analysis (DDA), and considering the moderate intrusion of specific block boundaries to different extents, the first-order block motion model is established for the northeastern margin of Qinghai-Xizang(Tibet) block and the kinematical model for depicting deformation of small regions as well by using GPS observations of three periods (1991, 1999 and 2001). By simulating, we obtained the motion features of the firstorder blocks between the large WWN faults on the sides of the studied region, the distribution features of the principal strain rate field and the inhomogeneous motion features with spacetime of the faults in the northern boundary of the Qinghai-Xizang (Tibet) block.     

Soil respiration in tropical seasonal rain forest in Xishuangbanna, SW China

With the static opaque chamber and gas chromatography technique, from January 2003 to January 2004 soil respiration was investigated in a tropical seasonal rain forest in Xishuangbanna, SW China. In this study three treatments were applied, each with three replicates: A (bare soil), B (soil+litter), and C (soil+litter+seedling). The results showed that soil respiration varied seasonally, low from December 2003 to February 2004, and high from June to July 2004. The annual average values of CO2 efflux from soil respiration differed among the treatments at 1% level, with the rank of C (14642 mgCO2· m-2. h-1)＞B (12807 mgCO2· m-2. h-1)＞A (9532 mgCO2· m-2. h-1). Diurnal variation in soil respiration was not apparent due to little diurnal temperate change in Xishuangbanna. There was a parabola relationship between soil respiration and soil moisture at 1% level. Soil respiration rates were higher when soil moisture ranged from 35% to 45%. There was an exponential relationship between soil respiration and soil temperature (at a depth of 5cm in mineral soil) at 1% level. The calculated Q1o values in this study,ranging from 2.03 to 2.36, were very near to those of tropical soil reported. The CO2 efflux in 2003was 5.34 kgCO2· m-2. a-1 from soil plus litter plus seedling, of them 3.48 kgCO2· m-2. a-1 from soil (accounting for 62.5%), 1.19 kgCO2· m-2. a-1 from litter (22.3%) and 0.67 kgCO2·m-2. a-1 from seedling (12.5%).     

Experimental study on soil CO2 emission in the alpine grassland ecosystem on Tibetan Plateau

The Tibetan Plateau, the Roof of the World, is the highest plateau with a mean elevation of 4000 m. It is characterized by high levels of solar radiation, low air temperature and low air pressure compared to other regions around the world. The alpine grassland, a typical ecosystem in the Tibetan Plateau, is distributed across regions over the elevation of 4500 m. Few studies for carbon flux in alpine grassland on the Tibetan Plateau were conducted due to rigorous natural conditions. A study of soil respiration under alpine grassland ecosystem on the Tibetan Plateau from October 1999 to October 2001 was conducted at Pangkog County, Tibetan Plateau (31.23°N, 90.01°E, elevation 4800 m). The measurements were taken using a static closed chamber technique, usually every two weeks during the summer and at other times at monthly intervals. The obvious diurnal variation of CO2 emissions from soil with higher emission during daytime and lower emission during nighttime was discovered. Diurnal CO2 flux fluctuated from minimum at 05:00 to maximum at 14:00 in local time. Seasonal CO2 fluxes increased in summer and decreased in winter, representing a great variation of seasonal soil respiration. The mean soil CO2 fluxes in the alpine grassland ecosystem were 21.39 mgCO2 · m-2 · h-1, with an average annual amount of soil respiration of 187.46 gCO2 · m-2 · a-1. Net ecosystem productivity is also estimated, which indicated that the alpine grassland ecosystem is a carbon sink.     

Chemical weathering and CO2 consumption in the glaciated Karuxung River catchment,Tibetan Plateau

Climate factors play critical roles in controlling chemical weathering, while chemically weathered surface material can regulate climate change. To estimate global chemical weathering fluxes and CO₂ balance, it is important to identify the characteristics and driving factors of chemical weathering and CO₂ consumption on the Tibetan Plateau, especially in glaciated catchments. The analysis of the hydro-geochemical data indicated that silicate weathering in this area was inhibited by low temperatures, while carbonate weathering was promoted by the abundant clastic rocks with fresh surfaces produced by glacial action. Carbonate weathering dominated the riverine solute generation (with a contribution of 58%, 51%, and 43% at the QiangYong Glacier (QYG), the WengGuo Hydrological Station (WGHS), and the lake estuary (LE), respectively). The oxidation of pyrite contributed to 35%, 42%, and 30% of the riverine solutes, while silicate weathering contributed to 5%, 6%, and 26% of the riverine solutes at the QYG, WGHS, and LE, respectively. The alluvial deposit of easily weathering fine silicate minerals, the higher air temperature, plant density, and soil thickness at the downstream LE in comparison to upstream and midstream may lead to longer contact time between pore water and mineral materials, thus enhancing the silicate weathering. Because of the involvement of sulfuric acid produced by the oxidation of pyrite, carbonate weathering in the upstream and midstream did not consume atmospheric CO₂, resulting in the high rate of carbonate weathering (73.9 and 75.6 t km⁻² yr⁻¹, respectively, in maximum) and potential net release of CO₂ (with an upper constraint of 35.6 and 35.2 t km⁻² yr⁻¹, respectively) at the QYG and WGHS. The above results indicate the potential of the glaciated area of the Tibetan Plateau with pyrite deposits being a substantial natural carbon source, which deserves further investigation.     

Ocean current estimation using a Multi-Model Ensemble Kalman Filter during the Grand Lagrangian Deployment experiment (GLAD)

In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4 km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter’s observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories.     

Evaluation of NLDAS‐2 evapotranspiration against tower flux site observations

The North American Land Data Assimilation System project phase 2 (NLDAS‐2) has run four land surface models for a 30‐year (1979–2008) retrospective period. Land surface evapotranspiration (ET) is one of the most important model outputs from NLDAS‐2 for investigating land–atmosphere interaction or to monitor agricultural drought. Here, we evaluate hourly ET using in situ observations over the Southern Great Plains (Atmospheric Radiation Measurement/Cloud and Radiation Testbed network) for 1 January 1997–30 September 1999 and daily ET u‐sing in situ observations at the AmeriFlux network over the conterminous USA for an 8‐year period (2000–2007). The NLDAS‐2 models compare well against observations, with the National Centers for Environmental Prediction's Noah land surface model performing best, followed, in order, by the Variable Infiltration Capacity, Sacramento Soil Moisture Accounting, and Mosaic models. Daily evaluation across the AmeriFlux network shows that for all models, performance depends on season and vegetation type; they do better in spring and fall than in winter or summer and better for deciduous broadleaf forest and grasslands than for croplands or evergreen needleleaf forest. Copyright © 2014 John Wiley & Sons, Ltd.     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.