On the Integrability of Limited-Area Numerical Weather Prediction Model ALADIN over Extended Time Periods

This note summarizes results of the first integration of regional numerical weather prediction model ALADIN in a climate mode. The ALADIN model, developed in an international cooperation led by Météo France, is operationally used for weather prediction. The grid step of the model is 12 km; the integration domain covers a major part of Europe. A one-month-long run has been performed with this model on observed boundary conditions (represented by assimilations by the global model ARPEGE). It is demonstrated that no excessive error is generated and accumulated in the model during the integration; hence the model is integrable for extended time periods and may serve a basis for a development towards a regional climate model.     

Regionalization of Europe based on a K-Means Cluster Analysis of the climate change of temperatures and precipitation

In order to study climate change on a regional scale using Earth System Models, it is useful to partition the spatial domain into regions according to their climate changes. The aim of this work is to divide the European domain into regions of similar projected climate changes using a simulation of daily total precipitation, minimum and maximum temperatures for the recent-past (1986–2005) and long-term future (2081–2100) provided by the Coupled Model Intercomparison Project (CMIP5). The difference between the long-term future and recent-past daily climatologies of these three variables is determined. Aiming to objectively identify the grid points with coherent climate changes, a K-Mean Cluster Analysis is applied to these differences. This method is performed for each variable independently (univariate version) and for the aggregation of the three variables (multivariate version). A mathematical approach to determine the optimal number of clusters is pursued. However, due to the method characteristics, a sensitivity test to the number of clusters is performed by analysing the consistency of the results. This is a novel method, allowing for the determination of regions based on the climate change of multiple variables. Results from the univariate application of this method are in accordance with results found in the literature, showing overall similar regions of changes. The regions obtained for the multivariate version are mainly defined by latitude over European land, with some features of land-sea interaction. Furthermore, all regions have statistically different distributions of at least one of the variables, providing confidence to the regions obtained.     

A general approach to advective–dispersive transport with multirate mass transfer

A numerical solution that is significantly more general than other semi-analytical solutions is presented for governing equations describing advective–dispersive transport with multirate mass transfer between mobile and immobile domains. The new solution approach is general in the sense that it does not impose any restrictive assumption on the spatial or temporal variability of advective and dispersive processes in the mobile domain. A single integro-differential equation (IDE) is developed for the concentration in the mobile domain by separating the concentration in the immobile domain from the set of two partial differential equations. The solution to the IDE requires the evaluation of a temporal integral of the concentration in the mobile domain, which is a function of the Laplace transform of the distribution of the mass transfer rate coefficient. The Laplace transform is not limited to flow fields with known constant velocities. The solutions for one- and two-dimensional examples obtained using the new approach agree with those obtained by existing semi-analytical and numerical approaches.     

Influence of high-resolution wind forcing on hydrodynamic modeling of the Gulf of Lions

The impact of the choice of high-resolution atmospheric forcing on ocean summertime circulation in the Gulf of Lions (GoL; Mediterranean Sea) is evaluated using three different datasets: AROME (2.5 km, 1 h), ALADIN (9.5 km, 3 h), and MM5 (9 km, 3 h). A short-term ocean simulation covering a 3-month summer period was performed on a 400-m configuration of the GoL. The main regional features of both wind and oceanic dynamics were well-reproduced by all three atmospheric models. Yet, at smaller scales and for specific hydrodynamic processes, some differences became apparent. Inertial oscillations and mesoscale variability were accentuated when high-resolution forcing was used. Sensitivity tests suggest a predominant role for spatial rather than temporal resolution of wind. The determinant influence of wind stress curl was evidenced, both in the representation of a mesoscale eddy structure and in the generation of a specific upwelling cell in the north-western part of the gulf.     

Spatial models for probabilistic prediction of wind power with application to annual-average and high temporal resolution data

Producing accurate spatial predictions for wind power generation together with a quantification of uncertainties is required to plan and design optimal networks of wind farms. Toward this aim, we propose spatial models for predicting wind power generation at two different time scales: for annual average wind power generation, and for a high temporal resolution (typically wind power averages over 15-min time steps). In both cases, we use a spatial hierarchical statistical model in which spatial correlation is captured by a latent Gaussian field. We explore how such models can be handled with stochastic partial differential approximations of Matérn Gaussian fields together with Integrated Nested Laplace Approximations. We demonstrate the proposed methods on wind farm data from Western Denmark, and compare the results to those obtained with standard geostatistical methods. The results show that our method makes it possible to obtain fast and accurate predictions from posterior marginals for wind power generation. The proposed method is applicable in scientific areas as diverse as climatology, environmental sciences, earth sciences and epidemiology.     

Regionalisation of precipitation for the Iberian Peninsula and climate change

Temporal variability of precipitation over the Iberian Peninsula (IP) has high spatial gradients. Therefore, statistics of the temporal behaviour of precipitation and derived quantities over the IP must be estimated taking into account these spatial gradients. Some statistics can be displayed over a map. However there are statistics, such as Probability Density Functions at each location of the IP, that are impossible to display in a map. Because of this, it is mandatory to reduce the number of degrees of freedom which, in this case, consists of a reduction of the time series representative of the IP domain. In this work, we present a spatial partition of the IP region into areas of similar precipitation. For that, an observed dataset of daily-total precipitation for the years between 1951 and 2003 was used. The land-only high resolution data was obtained on a regular grid with 0.2° resolution in the IP domain. This data was subjected to a k-means Cluster Analysis in order to divide the IP into K regions. The clustering was performed using the squared Euclidean distance. Four clusters of IP grid points, defining 4 IP regions, were identified. The grid points in each region share the same time-varying behaviour which is different from region to region. The annual precipitation discriminates the following regions: (1) north Iberia, (2) a large region extending from the centre to the Mediterranean shores of the IP, (3) a large region ranging from the centre to the western and southwestern shores of the Iberia, and (4) northwest Iberia. The regions obtained for the four seasons of the year are similar. These results are consistent with the thermodynamic characteristics described in the available literature. These Iberian regions were used to assess climate change of seasonal precipitation from the multi-model ensemble of the fifteen simulations provided by the European project ENSEMBLES. Probability Density Functions of annual- and seasonal-total precipitation, consecutive dry days, and total precipitation above the 95th percentile, averaged in each region were estimated for a reference climate (1961–1960), a near-future climate (2021–2050), and a distant-future climate (2069–2098). Climate change projections are based on comparisons of these functions between each future climate and the reference climate.Finally, we emphasize that: (i) the methodology used here, based on Cluster Analysis, can be used to regionalise other areas of the world, and (ii) the identified regions of the IP can be used to represent the Iberian precipitation by four time series that can be subjected to further analysis, whose results can be presented in a concise manner.     

Modelling the spatio-temporal repartition of right-truncated data: an application to avalanche runout altitudes in Hautes-Savoie

In this paper, we propose a novel approach for generating avalanche hazard maps based on the spatial dependence of avalanche runout altitudes. The right-truncated data are described with a Bayesian hierarchical model in which the spatio-temporal process is assumed to be the sum of independent spatial and temporal terms. Topography is roughly taken into account according to valley altitude and path exposition, and the spatial dependence is modelled with a Matérn covariance function. An application is performed to the Haute-Savoie region, French Alps. A spatial dependence in runout altitudes is identified, and an effective range of about 10 km is inferred. The temporal trend extracted highlights the increase of avalanche runout altitudes from 1955, attributed to both anthropogenic factors and climate warming. In a cross validation scheme, spatial predictions are provided on undocumented paths using kriging equations. All in all, although our model is unable to take into account small topographic features, it is a first-ever approach that produces very encouraging results. It could be enhanced in future work by incorporating a numerical physically-based code into the modelling.     

La descente et la montée étendues: the spatially <Emphasis Type="Bold">d</Emphasis>-anisotropic and the spatio-temporal case

There is a great demand for statistical modeling of phenomena that evolve in both space and time, and thus, there is a growing literature on correlation function models for spatio-temporal processes. In particular, various properties of these correlation functions have been studied only for the merely spatial or temporal case, fact that constitutes a strong motivation for our work. The goal of this paper is to inspect some properties, obtained with respect to partial differentiation and integration, of stationary spatio-temporal correlation functions for which anisotropy is obtained through isotropy between components as in Fernández-Casal et al. (Stat Comput 13(2):127–136, 2003). We show that through partial differentiation and integration it is possible to obtain permissible spatio-temporal correlation functions in the space–time domain. Other new results regard specific classes of space–time correlations introduced in recent literature. A curious result arises by differentiating scale mixtures of Euclid’s hat. Work partially funded by grant MTM2004-06231 from the Spanish Ministery of Science and Education.     

Spatial and temporal synthesized probability gain for middle and long-term earthquake forecast and its preliminary application

IntroductionThemodelofspatialandtemporalsynthesizedgainforastrongearthquakeoccurrenceisdevelopedasthebasisofTheDynamicSoftwareSystemforMiddle-andLong-termEarthquakeForecast.Themodelisestablishedaccordingbothtothestatisticalprincipleandtotheobservatio...     

IMAGE: a multivariate multi-site stochastic weather generator for European weather and climate

Capturing the spatial and temporal correlation of multiple variables in a weather generator is challenging. A new massively multi-site, multivariate daily stochastic weather generator called IMAGE is presented here. It models temperature and precipitation variables as latent Gaussian variables with temporal behaviour governed by an auto-regressive model whose residuals and parameters are correlated through resampling of principle component time series of empirical orthogonal function modes. A case study using European climate data demonstrates the model’s ability to reproduce extreme events of temperature and precipitation. The ability to capture the spatial and temporal extent of extremes using a modified Climate Extremes Index is demonstrated. Importantly, the model generates events covering not observed temporal and spatial scales giving new insights for risk management purposes.     

Spatial uncertainty in bias corrected climate change projections and hydrogeological impacts

The question of which climate model bias correction methods and spatial scales for correction are optimal for both projecting future hydrological changes as well as removing initial model bias has so far received little attention. For 11 climate models (CMs), or GCM/RCM – Global/Regional Climate Model pairing, this paper analyses the relationship between complexity and robustness of three distribution‐based scaling (DBS) bias correction methods applied to daily precipitation at various spatial scales. Hydrological simulations are forced by CM inputs to assess the spatial uncertainty of groundwater head and stream discharge given the various DBS methods. A unique metric is devised, which allows for comparison of spatial variability in climate model bias and projected change in precipitation. It is found that the spatial variability in climate model bias is larger than in the climate change signals. The magnitude of spatial bias seen in precipitation inputs does not necessarily correspond to the magnitude of biases seen in hydrological outputs. Variables that integrate basin responses over time and space are more sensitive to mean spatial biases and less so on extremes. Hydrological simulations forced by the least parameterized DBS approach show the highest error in mean and maximum groundwater heads; however, the most highly parameterised DBS approach shows less robustness in future periods compared with the reference period it was trained in. For hydrological impacts studies, choice of bias correction method should depend on the spatial scale at which hydrological impacts variables are required and whether CM initial bias is spatially uniform or spatially varying. Copyright © 2015 John Wiley & Sons, Ltd.     

Improved ENSO simulation in regional coupled GCM using regressive correction method

A regressive correction method is presented with the primary goal of improving ENSO simulation in regional coupled GCM. It focuses on the correction of ocean-atmosphere exchanged fluxes. On the basis of numerical experiments and analysis, the method can be described as follows: first, driving the ocean model with heat and momentum flux computed from a long-term observation data set; the pro-duced SST is then applied to force the AGCM as its boundary condition; after that the AGCM’s simula-tion and the corresponding observation can be correlated by a linear regressive formula. Thus the re-gressive correction coefficients for the simulation with spatial and temporal variation could be obtained by linear fitting. Finally the coefficients are applied to redressing the variables used for the calculation of the exchanged air-sea flux in the coupled model when it starts integration. This method together with the anomaly coupling method is tested in a regional coupled model, which is composed of a global grid-point atmospheric general circulation model and a high-resolution tropical Pacific Ocean model. The comparison of the results shows that it is superior to the anomaly coupling both in reducing the coupled model ‘climate drift’ and in improving the ENSO simulation in the tropical Pacific Ocean.     

Deciphering orogenic evolution

Deciphering orogenic evolution requires the integration of a growing number of geological and geophysical techniques on various spatial and temporal scales. Contrasting visions of mountain building and lithospheric deformation have been proposed in recent years. These models depend on the respective roles assigned to the mantle, the crust or the sediments. This article summarizes the contents of the Special Issue dedicated to ‘Geodynamics and Orogenesis’ following the ‘Réunion des Sciences de la Terre’ 2010 conference held in Bordeaux, France. Further, based on the example of the Western Alps-Mediterranean domain we emphasize the possibility to integrate long and short term, plate- to sample-scale, datasets in order to constrain orogenic evolution.     

Complexity‐reduction modelling for assessing the macro‐scale patterns of historical soil moisture in the Euro‐Mediterranean region

Complexity‐reduction modelling can be useful for increasing the understanding of how the climate affects basin soil moisture response upon historical times not covered by detailed hydrological data. For this purpose, here is presented and assessed an empirical regression‐based model, the European Soil Moisture Empirical Downscaling (ESMED), in which different climatic variables, easily available on the web, are addressed for simplifying the inherent complexity in the long‐time studies. To accommodate this simplification, the Palmer Drought Severity Index, the precipitation, the elevation and the geographical location were used as input data in the ESMED model for predicting annual soil moisture budget. The test area was a large region including central Europe and Mediterranean countries, and the spatial resolution was initially set at 50 km. ESMED model calibration was made according to the soil moisture values retrieved from the Terrestrial Water Budget Data archive by selecting randomly 285 grid points (out of 2606). Once parameterized, ESMED model was performed at validation stage both spatially and temporally. The spatial validation was made for the grid points not selected in the calibration stage while the comparison with the soil moisture outputs of the Global Land Data Assimilation System–NOAH10 simulations upon the period 1950–2010 was carried out for the temporal validation. Moreover, ESMED results were found to be in good agreement with a root‐zone soil moisture product obtained from active and passive microwave sensors from various satellite missions. ESMED model was thus found to be reliable for both the temporal and spatial validations and, hence, it might represent a useful tool to characterize the long‐term dynamics of soil moisture–weather interaction. Copyright © 2013 John Wiley & Sons, Ltd.     

Integrating TRMM and MODIS satellite with socio-economic vulnerability for monitoring drought risk over a tropical region of India

Drought is a recurring feature of the climate, responsible for social and economic losses in India. In the present work, attempts were made to estimate the drought hazard and risk using spatial and temporal datasets of Tropical Rainfall Measuring Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS) in integration with socio-economic vulnerability. The TRMM rainfall was taken into account for trend analysis and Standardized Precipitation Index (SPI) estimation, with aim to investigate the changes in rainfall and deducing its pattern over the area. The SPI and average rainfall data derived from TRMM were interpolated to obtain the spatial and temporal pattern over the entire South Bihar of India, while the MODIS datasets were used to derive the Normalized Difference Vegetation Index (NDVI) deviation in the area. The Geographical Information System (GIS) is taken into account to integrate the drought vulnerability and hazard, in order to estimate the drought risk over entire South Bihar. The results indicated that approximately 36.90% area is facing high to very high drought risk over north-eastern and western part of South Bihar and need conservation measurements to combat this disaster.     

最优可分表示法三维波场延拓

适于复杂介质的高精度波场延拓算子是叠前深度偏移研究的重要内容。本文采用最优可分表示方法,运用正反傅立叶变换构造了三维单程波场延拓算子,算子实现了波数域变量与空间(速度)域变量分离。波数域内进行相移计算,在空间域对因介质横向变速引起的时移作修正。脉冲响应显示在区域内各速度的脉冲计算值与理论值基本一致,说明最优可分表示法叠前深度偏移可适用于强变速条件下复杂介质的成像需求。SEG／EAGE模型和实测数据的成像结果验证了本文方法对复杂构造的成像能力。     

边坡振动台模型实验动位移的加速度时程积分探讨

受数据采集条件限制,振动台模型实验的速度和动位移时程一般由实测加速度时程信号进行积分得到。通过理论分析和实验实测数据计算,认为动位移的频域积分法误差来源是低频截止频率的选取,特别当低频频率接近零时,传统二阶频域积分可产生较大的动位移低频振荡和峰值误差。提出利用时域与频域混合的方法,即频域一时域混合积分法,来克服积分过程带来的误差,其在对加速度两次积分中,分别进行一次频域积分和一次时域积分。地震模拟振动实验的台面实测加速度数据积分与台面动位移计实测数据的对比表明,较之传统的二阶频域积分,采用频域一时域混合积分时误差可明显减小。     

3D wavefield extrapolation with optimal separable approximation

An accurate and wide-angle one-way propagator for wavefield extrapolation is an important topic for research on wave-equation prestack depth migration in the presence of large and rapid velocity variations. Based on the optimal separable approximation presented in this paper, the mixed domain algorithm with forward and inverse Fourier transforms is used to construct the 3D one-way wavefield extrapolation operator. This operator separates variables in the wavenumber and spatial domains. The phase shift operation is implemented in the wavenumber domain while the time delay for lateral velocity variation is corrected in the spatial domain. The impulse responses of the one-way wave operator show that the numeric computation is consistent with the theoretical value for each velocity, revealing that the operator constructed with the optimal separable approximation can be applied to lateral velocity variations for the case of small steps. Imaging results of the SEG/EAGE model and field data indicate that the new method can be used to image complex structure.     

Performance of ALADIN-Climate/CZ over the area of the Czech Republic in comparison with ENSEMBLES regional climate models

Nowadays Regional Climate Models (RCMs) are increasingly used for downscaling of information from the coarse resolution of global climate models (GCMs) and they represent a more and more popular tool for assessment of future climate changes and their impacts at regional scales. In spite of continual progress of RCMs, their outputs still suffer from many uncertainties and biases. Therefore, it is necessary to assess their ability to simulate observed climate characteristics and uncertainties in their outputs before they are applied in subsequent studies. In the present study, the assessment of RCM performance in simulating climate in the reference period of 1961–1990 over the area of Czech Republic is presented. Furthermore, we focused on the intercomparison of the models’ results, mainly on the comparison of the Czech model ALADIN-Climate/CZ with outputs of other RCMs. Simulation of ALADIN-Climate/CZ in 25-km horizontal resolution, and thirteen RCM simulations from the ENSEMBLES project were assessed. Attention was paid especially to comparison of simulated and observed spatial and temporal variability of several climatic variables. The monthly and seasonal values of surface air temperature, precipitation totals and relative humidity were examined for evaluation of temporal variability and 30-year seasonal and monthly values with respect to spatial variability. Climate model performance was evaluated in several ways, namely by boxplots, maps of variability characteristics, skill scores based on mean square error and Taylor diagrams. Model errors detected by model evaluation depend on many factors (e.g. considered variables and their characteristics, area of analysis, time scale of interest and the method of assessment). On the basis of incorporated performance criteria model ALADIN-Climate/CZ belonged to a better group of RCMs in most cases. However, it was definitely the worst in simulating spring monthly means of air temperature and relative humidity in all seasons.