Formation of homogeneous regions for regional frequency analysis of extreme precipitation events in the Czech Republic

Extreme high precipitation amounts are among environmental events with the most disastrous consequences for human society. This paper deals with the identification of ‘homogeneous regions’ according to statistical characteristics of precipitation extremes in the Czech Republic, i.e. the basic and most important step toward the regional frequency analysis. Precipitation totals measured at 78 stations over 1961–2000 are used as an input dataset. Preliminary candidate regions are formed by the cluster analysis of site characteristics, using the average-linkage clustering and Ward’s method. Several statistical tests for regional homogeneity are utilized, based on the 10-yr event and the variation of L-moment statistics. In compliance with results of the tests, the area of the Czech Republic has been divided into four homogeneous regions. The findings are supported by simulation experiments proposed to evaluate stability of the test results. Since the regions formed reflect also climatological differences in precipitation regimes and synoptic patterns causing high precipitation amounts, their future application may not be limited to the frequency analysis of extremes.     

Implementation of a Single-Column Model for Fog and Low Cloud Forecasting at Central-Spanish Airports

Operations at Central-Spanish airports are often, especially in winter, affected by visibility reduction. The Instituto Nacional de Meteorología (INM), the Spanish Weather Service, has developed a single-column model (SCM) in order to improve short-term forecasts of fog, visibility and low-clouds. The SCM, called H1D, is a one-dimensional version of the HIRLAM limited-area model. It is operationally run for three airports in the region: Madrid-Barajas, Almagro and Albacete-Los Llanos. Since SCMs cannot deal with horizontal heterogeneity, the terms that depend on the horizontal structure of the atmosphere are estimated from the outputs of the three-dimensional (3-D) model and introduced into the SCM as external forcings. The systematic analysis of the meteorological situations has evidenced the existence of a close relationship between fog formation and the presence of drainage winds in the region. Since the 3-D model docs not have the necessary resolution to correctly simulate the main features of the drainage flow caused by the complex topography in the proximity of Madrid-Barajas, it cannot provide the SCM with the correct forcings. This problem has been partially overcome through the introduction of a module that, under certain conditions, substitutes the values computed from the 3-D model outputs by others that are based on a conceptual model of the phenomenon and have been empirically derived from climatological knowledge. This module improves the H1D verification scores for the basic meteorological variables—wind, temperature and humidity—and reduces the false alarm rate in fog forecast.     

Time-calibrated estimates of oceanographic profiles using empirical orthogonal functions and clustering

Oceanographic climatology is widely used in different applications, such as climate studies, ocean model validation and planning of naval operations. Conventional climatological estimates are based on historic measurements, typically by averaging the measurements and thereby smoothing local phenomena. Such phenomena are often local in time and space, but crucial to some applications. Here, we propose a new method to estimate time-calibrated oceanographic profiles based on combined historic and real-time measurements. The real-time measurements may, for instance, be SAR pictures or autonomous underwater vehicles providing temperature values at a limited set of depths. The method employs empirical orthogonal functions and clustering on a training data set in order to divide the ocean into climatological regions. The real-time measurements are first used to determine what climatological region is most representative. Secondly, an improved estimate is determined using an optimisation approach that minimises the difference between the real-time measurements and the final estimate.     

Analysis and modeling of the seasonal South China Sea temperature cycle using remote sensing

The present paper describes the analysis and modeling of the South China Sea (SCS) temperature cycle on a seasonal scale. It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is motivated by the possibility to significantly increase the model’s computational efficiency when neglecting tides. The goal is to develop a flexible and efficient tool for seasonal scenario analysis and to generate transport boundary forcing for local models. Given the significant spatial extent of the SCS basin and the focus on seasonal time scales, synoptic remote sensing is an ideal tool in this analysis. Remote sensing is used to assess the seasonal temperature cycle to identify the relevant driving forces and is a valuable source of input data for modeling. Model simulations are performed using a three-dimensional baroclinic-reduced depth model, driven by monthly mean sea surface anomaly boundary forcing, monthly mean lateral temperature, and salinity forcing obtained from the World Ocean Atlas 2001 climatology, six hourly meteorological forcing from the European Center for Medium range Weather Forecasting ERA-40 dataset, and remotely sensed sea surface temperature (SST) data. A sensitivity analysis of model forcing and coefficients is performed. The model results are quantitatively assessed against climatological temperature profiles using a goodness-of-fit norm. In the deep regions, the model results are in good agreement with this validation data. In the shallow regions, discrepancies are found. To improve the agreement there, we apply a SST nudging method at the free water surface. This considerably improves the model’s vertical temperature representation in the shallow regions. Based on the model validation against climatological in situ and SST data, we conclude that the seasonal temperature cycle for the deep SCS basin can be represented to a good degree. For shallow regions, the absence of tidal mixing and exchange has a clear impact on the model’s temperature representation. This effect on the large-scale temperature cycle can be compensated to a good degree by SST nudging for diagnostic applications.     

Estimating temperature and salinity profiles using empirical orthogonal functions and clustering on historical measurements

Oceanographic climatology is normally estimated by dividing the world’s oceans into geographical boxes of fixed shape and size, where each box is represented by a climatological salinity and temperature profile. The climatological profile is typically an average of historical measurements from that region. Since an arbitrarily chosen box may contain different types of water masses both in space and time, an averaged profile may be a statistically improbable or even non-physical representation. This paper proposes a new approach that employs empirical orthogonal functions in combination with a clustering technique to divide the world’s oceans into climatological regions. Each region is represented by a cluster that is determined by minimising the variance of the state variables within each cluster. All profiles contained in a cluster are statistically similar to each other and statistically different from profiles in other clusters. Each cluster is then represented by mean temperature and salinity profiles and a mean position. Methods for estimating climatological profiles from the cluster information are examined, and their performances are compared to a conventional method of estimating climatology. The comparisons show that the new methods outperform conventional methods and are particularly effective in areas where oceanographic fronts are present.     

Temporal and spatial rainfall analysis across a humid tropical catchment

Temporal and spatial rainfall patterns were analysed to describe the distribution of daily rainfall across a medium‐sized (379km²) tropical catchment. Investigations were carried out to assess whether a climatological variogram model was appropriate for mapping rainfall taking into consideration the changing rainfall characteristics through the wet season. Exploratory, frequency and moving average analyses of 30 years' daily precipitation data were used to describe the reliability and structure of the rainfall regime. Four phases in the wet season were distinguished, with the peak period (mid‐August to mid‐September) representing the wettest period. A low‐cost rain gauge network of 36 plastic gauges with overflow reservoirs was installed and monitored to obtain spatially distributed rainfall data. Geostatistical techniques were used to develop global and wet season phase climatological variograms. The unscaled climatological variograms were cross‐validated and compared using a range of rainfall events. Ordinary Kriging was used as the interpolation method. The global climatological variogram performed better, and was used to optimize the number and location of rain gauges in the network. The research showed that although distinct wet season phases could be established based on the temporal analysis of daily rainfall characteristics, the interpolation of daily rainfall across a medium‐sized catchment based on spatial analysis was better served by using the global rather than the wet season phase climatological variogram model. Copyright © 2001 John Wiley & Sons, Ltd.     

Space–time clustering of case–control data with residential histories: insights into empirical induction periods, age-specific susceptibility, and calendar year-specific effects

Our research group recently developed Q-statistics for evaluating space–time clustering in case–control studies with residential histories. This technique relies on time-dependent nearest-neighbor relationships to examine clustering at any moment in the life-course of the residential histories of cases relative to that of controls. In addition, in place of the widely used null hypothesis of spatial randomness, each individual’s probability of being a case is based instead on his/her risk factors and covariates. In this paper, we extend this approach to illustrate how alternative temporal orientations (e.g., years prior to diagnosis/recruitment, participant’s age, and calendar year) influence a spatial clustering pattern. These temporal orientations are valuable for shedding light on the duration of time between clustering and subsequent disease development (known as the empirical induction period), and for revealing age-specific susceptibility windows and calendar year-specific effects. An ongoing population-based bladder cancer case–control study is used to demonstrate this approach. Data collection is currently incomplete and therefore no inferences should be drawn; we analyze these data to demonstrate these novel methods. Maps of space–time clustering of bladder cancer cases are presented using different temporal orientations while accounting for covariates and known risk factors. This systematic approach for evaluating space–time clustering has the potential to generate novel hypotheses about environmental risk factors and provides insights into empirical induction periods, age-specific susceptibility, and calendar year-specific effects.     

On the nonlinear triggering of VLF emissions by power line harmonic radiation

VLF ground data from Porojarvi in N. Finland has been presented. Spectrograms reveal frequent occurrence of power line harmonic radiation (PLHR), originating from the Finnish power system and from heavy industrial plant. The radiation is seen to penetrate the magnetosphere since numerous occurrences of PLHR triggered emissions are seen. Risers predominate but fallers and hooks are also observed. A well-established 1D Vlasov simulation code has been used to simulate these emissions, using plausible magnetospheric data for a range of L values from L = 4 to L = 5.5. The code is able to reproduce risers fallers and hooks in close agreement with observations. The results shed considerable insight into the generation structure of both risers and fallers.     

Functional data clustering using K-means and random projection with applications to climatological data

In this paper, an efficient pattern recognition method for functional data is introduced. The proposed method works based on reproducing kernel Hilbert space (RKHS), random projection and K-means algorithm. First, the infinite dimensional data are projected onto RKHS, then they are projected iteratively onto some spaces with increasing dimension via random projection. K-means algorithm is applied to the projected data, and its solution is used to start K-means on the projected data in the next spaces. We implement the proposed algorithm on some simulated and climatological datasets and compare the obtained results with those achieved by K-means clustering using a single random projection and classical K-means. The proposed algorithm presents better results based on mean square distance (MSD) and Rand index as we have expected. Furthermore, a new kernel based on a wavelet function is used that gives a suitable reconstruction of curves, and the results are satisfactory.     

Evaluation of efficiency of different estimation methods for missing climatological data

Reliable estimation of missing data is an important task for meteorologists, hydrologists and environment protection workers all over the world. In recent years, artificial intelligence techniques have gained enormous interest of many researchers in estimating of missing values. In the current study, we evaluated 11 artificial intelligence and classical techniques to determine the most suitable model for estimating of climatological data in three different climate conditions of Iran. In this case, 5 years (2001–2005) of observed data at target and neighborhood stations were used to estimate missing data of monthly minimum temperature, maximum temperature, mean air temperature, relative humidity, wind speed and precipitation variables. The comparison includes both visual and parametric approaches using such statistic as mean absolute errors, coefficient of efficiency and skill score. In general, it was found that although the artificial intelligence techniques are more complex and time-consuming models in identifying their best structures for optimum estimation, but they outperform the classical methods in estimating missing data in three distinct climate conditions. Moreover, the in-filling done by artificial neural network rivals that by genetic programming and sometimes becomes more satisfactory, especially for precipitation data. The results also indicated that multiple regression analysis method is the suitable method among the classical methods. The results of this research proved the high importance of choosing the best and most precise method in estimating different climatological data in Iran and other arid and semi-arid regions.     

Artificial intelligence for identifying hydrologically homogeneous regions: A state‐of‐the‐art regional flood frequency analysis

Due to the severity related to extreme flood events, recent efforts have focused on the development of reliable methods for design flood estimation. Historical streamflow series correspond to the most reliable information source for such estimation; however, they have temporal and spatial limitations that may be minimized by means of regional flood frequency analysis (RFFA). Several studies have emphasized that the identification of hydrologically homogeneous regions is the most important and challenging step in an RFFA. This study aims to identify state‐of‐the‐art clustering techniques (e.g., K ‐means, partition around medoids, fuzzy C‐means, K ‐harmonic means, and genetic K ‐means) with potential to form hydrologically homogeneous regions for flood regionalization in Southern Brazil. The applicability of some probability density function, such as generalized extreme value, generalized logistic, generalized normal, and Pearson type 3, was evaluated based on the regions formed. Among all the 15 possible combinations of the aforementioned clustering techniques and the Euclidian, Mahalanobis, and Manhattan distance measures, the five best were selected. Several watersheds' physiographic and climatological attributes were chosen to derive multiple regression equations for all the combinations. The accuracy of the equations was quantified with respect to adjusted coefficient of determination, root mean square error, and Nash–Sutcliffe coefficient, whereas, a cross‐validation procedure was applied to check their reliability. It was concluded that reliable results were obtained when using robust clustering techniques based on fuzzy logic (e.g., K ‐harmonic means), which have not been commonly used in RFFA. Furthermore, the probability density functions were capable of representing the regional annual maximum streamflows. Drainage area, main river length, and mean altitude of the watershed were the most recurrent attributes for modelling of mean annual maximum streamflow. Finally, an integration of all the five best combinations stands out as a robust, reliable, and simple tool for estimation of design floods.     

An incremental Knox test for the determination of the serial interval between successive cases of an infectious disease

An estimate of the interval between successive infections is essential for surveillance, control, and modeling of infectious diseases. This paper proposes a method for determining the serial interval when the location and time of onset of illness are known. The theoretical underpinning of this method is the intrinsically spatial nature of disease transmission. Successive infections tend to be closer than unrelated cases of disease and, therefore, exhibit spatial clustering. An incremental Knox type analysis of cases is introduced. Cases occurring at a range of time intervals are examined to determine the serial interval. The significance of clustering is determined using a permutation approach under the null hypothesis of space-time independence. The power of this method is evaluated using an individual level, spatially explicit epidemic simulation. The time increment Knox test is robust to multiple introductions and incomplete sampling. Finally, the increment Knox statistic is used to analyze an outbreak of dengue fever in the city of Florida, Puerto Rico during 1991. Results indicate that the likely interval between successive cases during this outbreak is at least 18–19 days.     

Performance of operational systems with respect to water level forecasts in the Gulf of Finland

This paper is devoted to the validation of water level forecasts in the Gulf of Finland. Daily forecasts produced by four setups of operational, three-dimensional Baltic Sea oceanographic models are analyzed using statistical means and are compared with water level observations at three Finnish stations located on the northern coast of the Gulf of Finland. The overall conclusion is that the operational systems were skillful in forecasting water level variations during the study period from November 1, 2003, to January 31, 2005. The factors causing differences between the water level forecasts of different models are discussed as well. An important task of operational sea level forecasting services is to provide accurate and early information about extreme water levels, both positive and negative surges. During the study period, two major winter storms occurred which caused coastal flooding in the region. According to our analysis, the operational models forecast the rise of water levels during these events rather successfully. Nowadays, operational forecasts can provide early warnings of extreme water levels at least 1 day in advance, which may be regarded as a minimum requirement for an operational forecasting system. The paper concludes that the models generally performed very well, with over 93% of the hourly water level forecasts found to be within the range of ±15 cm of the observed water levels, and with the timing of the water level peaks accurately predicted. Further discussion and studies dealing with the assessment of the skills of both operational meteorological and oceanographic forecasts, especially in connection with rare surge events, will be necessary. Skill assessment of operational oceanographic models would be relatively easy if acceptable error limits or a quality system was developed for the Baltic Sea operational models.     

A multi‐source precipitation estimation approach to fill gaps over a radar precipitation field: a case study in the Colorado River Basin

The present work develops an approach to seamlessly blend satellite, available radar, climatological and gauge precipitation products to fill gaps in ground‐based radar precipitation field. To mix different precipitation products, the error of any of the products relative to each other should be removed. For bias correction, the study uses an ensemble‐based method that aims to estimate spatially varying multiplicative biases in SPEs using a radar precipitation product. A weighted successive correction method (SCM) is used to make the merging between error corrected satellite and radar precipitation estimates. In addition to SCM, we use a combination of SCM and Bayesian spatial model for merging the rain gauges (RGs) and climatological precipitation sources with radar and SPEs. We demonstrated the method using a satellite‐based hydro‐estimator; a radar‐based, stage‐II; a climatological product, Parameter‐elevation Regressions on Independent Slopes Model and a RG dataset for several rain events from 2006 to 2008 over an artificial gap in Oklahoma and a real radar gap in the Colorado River basin. Results show that: the SCM method in combination with the Bayesian spatial model produced a precipitation product in good agreement with independent measurements. The study implies that using the available radar pixels surrounding the gap area, RG, Parameter‐elevation Regressions on Independent Slopes Model and satellite products, a radar‐like product is achievable over radar gap areas that benefit the operational meteorology and hydrology community. Copyright © 2013 John Wiley & Sons, Ltd.     

三亚崖州湾海域开发建设适宜性定量分析评价

三亚崖州湾是海南自贸港科技发展的前沿阵地,摸清其地质环境是科技发展规划的基础条件。选取水深、坡度、沉积物环境质量、古河道或古湖泊面积、沙波面积、沙脊面积、软土面积和软土厚度共8个影响因素,采用K-means聚类法、层次分析法和熵权法对研究区适宜性进行定量分区,共划分适宜性好、较好、中等、较差和差五个等级,分析不同影响因素对崖州湾适宜性的影响,总结五个分区的地质环境特征。研究结果表明适宜性最好区位于研究区中北部,其次是中南部,两翼地质环境质量最差,中等区镶嵌分布于崖州湾的外缘;建议优先考虑适宜性最好区进行规划开发,其次是适宜性较好区。文章先用K-means聚类法对影响因素分级,再用层次分析法和熵权法计算评价因子的主客观权重,形成一种主客观结合的科学评价方法,并运用到地质环境开发适宜性评价中,很好地划分出优选区。评价结果可为研究区开发规划及防灾减灾提供基础地质依据,同时具有良好的借鉴作用。     

Testing fractal coefficients sensitivity on real and simulated earthquake data

We examined the behavior of different fractal dimensions when applied to study features of earthquake spatial distribution on different types of data. We first examined simulated spatial fields of points of different clustering level, following the so called Soneira-Peebles model. The model was chosen because it displays some similarity to the real clustering structure of earthquakes occurring on hierarchically ordered faults. The analysis of the capacity, clustering and correlation dimensions revealed that their behavior did not completely correlate with the clustering level of the simulated data sets. We also studied temporal variations of the fractal coefficients, characterizing the spatial distribution of the 1999 İzmit-Düzce aftershock sequence. The calculated coefficient values demonstrated analogous behavior like for the simulated data. They exposed different variability in time, but for all of them a systematic fluctuation was observed before the occurrence of the Düzce earthquake. Our analysis revealed that although fractal coefficients could be applied to measure earthquake clustering, they should be used with caution, trying to figure out the best coefficient for a certain data set.     

Estimation of the nutrient load on the Gulf of Finland from the Russian part of its catchment

The nutrient load on the Gulf of Finland, the Baltic Sea, is estimated taking into account the export of nutrients from Lake Ladoga with Neva runoff, from the Chudsko-Pskovskoe Lake with Narva runoff, from a partial watershed of the Gulf of Finland, and wastewater discharges from St. Petersburg. The data used include the materials of state monitoring of water bodies and state statistical reports on northwestern Russia, materials of GUP Vodokanal Sankt Peterburga, the results of earlier researches of water quality formation in Lake Ladoga, the Gulf of Finland, and on their catchment, and the results of calculation of nutrient load on the gulf with the use of a model developed in the Institute of Limnology, RAS. Currently, the annual nutrient load on the Gulf of Finland is ∼5200 t P_tot and 70800 t N_tot. The phosphorus load exceeds the admissible levels recommended by the Helsinki Commission, thus suggesting the need to search for real ways to reduce the load in the future.     

On the variation of frequency-based rainfall amounts: a case study for evaluating recent extreme rainfalls in Korea

Extreme rainfall events recently occurring in Korea have been shown to change frequency-based rainfall amounts quite significantly. Regardless of the reason for these extremes, the general concern of most hydrologists is how to handle these events for practical applications in Hydrology. Our study aim is to evaluate these extremes with their effect on frequency-based rainfall amounts, especially if they can be assumed to be within normal levels. As there is no commonly accepted methodology to be applied to this kind of study, we follow simplified steps such as: (1) estimation of the climatological variance of frequency-based rainfall amounts, (2) estimation of confidence intervals of frequency-based rainfall amounts (lower and upper bounds for the 5 and 1% significance levels estimated using the climatological variance), and (3) evaluation of the effect of extra rainfall events on the frequency-based rainfall amounts. Twelve stations on the Korean peninsula are selected as they have relatively longer data length. The annual maximum rainfall data collected from 1954 to 1998 are used. From this study we concluded that (1) at least 30 years of data length should be used for the frequency analysis in order to assure the stability of the variance of frequency-based rainfall amounts, (2) the climatological variances estimated all range from 5 to 8% of the frequency-based rainfall amounts, and (3) even though the frequency-based rainfall amount seems to become extreme with seemingly abnormal events, it still remains under its upper bound for the 5 or 1% significance levels estimated using the climatological variance, as well as it decays exponentially to the normal level as extra events are added. Thus, we conclude that we do not need to panic over seemingly abnormal events occurring so far, but just need to consider the variability inherent in frequency-based rainfall amounts.