An ensemble approach for the analysis of extreme rainfall under climate change in Naples (Italy)

In the present paper, an ensemble approach is proposed to estimate possible modifications caused by climate changes in the extreme precipitation regime, with the rain gauge Napoli Servizio Idrografico (Naples, Italy) chosen as test case. The proposed research, focused on the analysis of extremes on the basis of climate model simulations and rainfall observations, is structured in several consecutive steps. In the first step, all the dynamically downscaled EURO‐CORDEX simulations at about 12 km horizontal resolution are collected for the current period 1971–2000 and the future period 2071–2100, for the RCP4.5 and the RCP8.5 concentration scenarios. In the second step, the significance of climate change effects on extreme precipitation is statistically tested by comparing current and future simulated data and bias‐correction is performed by means of a novel approach based on a combination of simple delta change and quantile delta mapping, in compliance with the storm index method. In the third step, two different ensemble models are proposed, accounting for the variabilities given by the use of different climate models and for their hindcast performances. Finally, the ensemble models are used to build novel intensity–duration–frequency curves, and their effects on the early warning system thresholds for the area of interest are evaluated.     

基于GPT3模型的ZTD及PWV反演精度分析

使用亚洲区域18个IGS测站和中国区域内16个探空站2016~2018年的数据,研究GPT3模型反演天顶对流层延迟（ZTD）和大气可降水量（PWV）的精度,并与其他GPT系列模型进行对比。结果表明,GPT3-1模型估计的ZTD的bias均值和最大值均最小,分别为1.34 mm和14.06 mm;GPT3模型整体精度略优于GPT2w模型,优于GPT2模型。探空站处GPT3模型反演的PWV的bias和RMSE均表现出较强的季节性特征;由GPT3模型反演的PWV的月均值可知,GPT3-1模型比GPT3-5模型具有更高的精度和稳定性。     

Guidelines on the use of structure-from-motion photogrammetry in geomorphic research

As a topographic modelling technique, structure-from-motion (SfM) photogrammetry combines the utility of digital photogrammetry with a flexibility and ease of use derived from multi-view computer vision methods. In conjunction with the rapidly increasing availability of imagery, particularly from unmanned aerial vehicles, SfM photogrammetry represents a powerful tool for geomorphological research. However, to fully realize this potential, its application must be carefully underpinned by photogrammetric considerations, surveys should be reported in sufficient detail to be repeatable (if practical) and results appropriately assessed to understand fully the potential errors involved. To deliver these goals, robust survey and reporting must be supported through (i) using appropriate survey design, (ii) applying suitable statistics to identify systematic error (bias) and to estimate precision within results, and (iii) propagating uncertainty estimates into the final data products. © 2019 John Wiley & Sons, Ltd.     

Evidence That Sea State Bias is Different for Ascending and Descending Tracks

Jason-1 and TOPEX/Poseidon (T/P) measured sea-surface heights (SSHs) are compared for five regions during the verification tandem phase. The five regions are of similar latitude and spatial extent and include the Gulf of Mexico, Arabian Sea, Bay of Bengal, and locations in the Pacific and Atlantic Oceans away from land. In all five regions, a bias, defined as Jason SSH—TOPEX-B SSH, exists that is different for ascending and descending tracks. For example, in the Gulf of Mexico the bias for ascending tracks was ?0.13 cm and the bias for descending tracks was 2.19 cm. In the Arabian Sea the bias for ascending tracks was ?2.45 cm and the bias for descending tracks was ?1.31 cm. The bias was found to depend on track orientation and significant wave height (SWH), indicating an error in the sea state bias (SSB) model for one or both altimeters. The bias in all five regions can be significantly reduced by calculating separate corrections for ascending and descending tracks in each region as a function of SWH. The correction is calculated by fitting a second-order polynomial to the bias as a function of SWH separately for ascending and descending tracks. An additional constraint is required to properly apply the correction, and we chose to minimize the sum of the TOPEX-B and Jason-1 root-mean-square (rms) crossover differences to be consistent with present SSB models. Application of this constraint shows that the correction, though consistent within each region, is different for each region and that each satellite contributes to the bias. One potential source that may account for a portion of the difference in bias is the leakage in the wave forms in TOPEX-B due to differing altitude rates for ascending and descending tracks. Global SSB models could be improved by separating the tracks into ascenders and descenders and calculating a separate SSB model for each track.     

Quantification of Spatial and Thematic Uncertainty in the Application of Underwater Video for Benthic Habitat Mapping

This study presents an analysis of the application of underwater video data collected for training and validating benthic habitat distribution models. Specifically, we quantify the two major sources of error pertaining to collection of this type of reference data. A theoretical spatial error budget is developed for a positioning system used to co-register video frames to their corresponding locations at the seafloor. Second, we compare interpretation variability among trained operators assessing the same video frames between times over three hierarchical levels of a benthic classification scheme. Propagated error in the positioning system described was found to be highly correlated with depth of operation and varies from 1.5m near the surface to 5.7m in 100m of water. In order of decreasing classification hierarchy, mean overall observer agreement was found to be 98% (range 6%), 82% (range 12%) and 75% (range 17%) for the 2, 4, and 6 class levels of the scheme, respectively. Patterns in between-observer variation are related to the level of detail imposed by each hierarchical level of the classification scheme, the feature of interest, and to the amount of observer experience.     

Comparison of the Ku-Band Range Noise Level and the Relative Sea-State Bias of the Jason-1, TOPEX,and Poseidon-1 Radar Altimeters Special Issue: Jason-1 Calibration/Validation

TOPEX/Poseidon is a well known success, with the operational altimeter (TOPEX) and the experimental one (Poseidon-1), providing data of unprecedented quality. However, there are two major differences between the TOPEX and Poseidon-1 radar altimeters on board TOPEX/Poseidon. The first is related to the estimated range noise; the second is linked to the sea-state bias (SSB) model estimates. Since the recent launch of the Jason-1 radar altimeter (also called Poseidon-2), we have been cross-comparing these three systems to better characterize each of them. Analyzing standard user products, we have found that Jason-1 is behaving like Poseidon-1 and thus shows the same observed differences when compared with TOPEX. A comparative analysis of their features was performed, starting from the on-board acquisition of the ocean return and ending with the ground generation of the high level accuracy oceanographic product. The results lead us to believe that the sources for these differences lie in both the waveform tracking processing and the presence or abscence of a retracking procedure whether on-board or on ground. Because Poseidon-1 and Jason-1 waveforms are retracked while TOPEX waveforms are not in the products distributed to the users, we have applied the same ground retracking algorithm to the waveforms of the three radar altimeters to get consistent data sets. The analysis of the outputs has shown that: (a) the noise level for the three radar altimeters is definitively the same, and (b) the source of the relative SSB between Jason-1 and TOPEX lies in the different behavior of the on-board tracking softwares.     

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Improved estimators of correlation and R2 for skewed hydrologic data

ABSTRACT

The coefficient of determination R² and Pearson correlation coefficient ρ = R are standard metrics in hydrology for the evaluation of the goodness of fit between model simulations and observations, and as measures of the degree of dependence of one variable upon another. We show that the standard product moment estimator of ρ, termed r, while well-behaved for bivariate normal data, is upward biased and highly variable for bivariate non-normal data. We introduce three alternative estimators of ρ which are nearly unbiased and exhibit much less variability than r for non-normal data. We also document remarkable upward bias and tremendous increases in variability associated with r using both synthetic data and daily streamflow simulations from 905 calibrated rainfall–runoff models. We show that estimators of ρ = R accounting for skewness are needed for daily streamflow series because they exhibit high variability and skewness compared to, for example, monthly/annual series, where r should perform well.     

基于GNSS和SLR观测数据的北斗卫星(GEO/IGSO)精密定轨

研究了联合GNSS和卫星激光测距（satellite laser ranging,SLR）观测数据对北斗卫星定轨精度的影响。仅利用GNSS观测值对北斗卫星进行定轨时,C01、C08和C10卫星的SLR检核结果显示,这3颗卫星的SLR残差均存在一定的偏差且不相同。加入SLR观测值联合定轨时,分别对估计和不估计系统偏差的卫星轨道结果进行分析。结果显示,对C01（GEO）卫星加入SLR观测值时必须引入系统偏差,否则会使轨道偏离;C08和C10（IGSO）卫星估计和不估计系统偏差时对预报轨道的精度均有一定的改善。最后统计了这3颗卫星的系统偏差,C01卫星的系统偏差达到50 cm,C08和C10卫星的系统偏差在5 cm以内。     

一种顾及GNSS系统间偏差的伪距单点定位方法

对系统间偏差的成分和影响因素进行分析,发现各项改正后残余误差对系统间偏差估值的影响可归结为与卫星编号和广播星历更新相关。提出一种新的顾及参与解算卫星构成的系统间偏差估计方案,新方案能够在仅有4颗多模GNSS卫星可见时提供可靠的位置服务。