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Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulation
Publication . Ribeiro, Sara; Caineta, Júlio; Costa, Ana Cristina; Henriques, Roberto; Soares, Amílcar; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS); Elsevier BV
Climate data homogenisation is of major importance in climate change monitoring, validation of weather forecasting, general circulation and regional atmospheric models, modelling of erosion, drought monitoring, among other studies of hydrological and environmental impacts. The reason is that non-climate factors can cause time series discontinuities which may hide the true climatic signal and patterns, thus potentially bias the conclusions of those studies. In the last two decades, many methods have been developed to identify and remove these inhomogeneities. One of those is based on a geostatistical simulation technique (DSS - direct sequential simulation), where local probability density functions (pdfs) are calculated at candidate monitoring stations using spatial and temporal neighbouring observations, which then are used for the detection of inhomogeneities. Such approach has been previously applied to detect inhomogeneities in four precipitation series (wet day count) from a network with 66 monitoring stations located in the southern region of Portugal (1980-2001). That study revealed promising results and the potential advantages of geostatistical techniques for inhomogeneity detection in climate time series. This work extends the case study presented before and investigates the application of the geostatistical stochastic approach to ten precipitation series that were previously classified as inhomogeneous by one of six absolute homogeneity tests (Mann-Kendall, Wald-Wolfowitz runs, Von Neumann ratio, Pettitt, Buishand range test, and standard normal homogeneity test (SNHT) for a single break). Moreover, a sensitivity analysis is performed to investigate the number of simulated realisations which should be used to infer the local pdfs with more accuracy. Accordingly, the number of simulations per iteration was increased from 50 to 500, which resulted in a more representative local pdf. As in the previous study, the results are compared with those from the SNHT, Pettitt and Buishand range tests, which were applied to composite (ratio) reference series. The geostatistical procedure also allowed us to fill in missing values in the climate data series. Finally, based on several experiments aimed at providing a sensitivity analysis of the procedure, a set of default and recommended settings is provided, which will help other users to apply this method.
Assessing the Performance of the Gsimcli Homogenisation Method with Precipitation Monthly Data from the COST-HOME Benchmark
Publication . Ribeiro, Sara; Caineta, J.; Costa, Ana Cristina; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management School
Nowadays, climate data series are used in so many different studies that their importance implies the essential need of good data quality. For this reason, the process of homogenisation became a hot topic in the last decades, and many researchers have focused on developing efficient methods for the detection and correction of inhomogeneities in climate data series. This study evaluates the efficiency of the gsimcli homogenisation method, which is based on a geostatistical simulation approach. For each instant in time, gsimcli uses the direct sequential simulation algorithm to generate several equally probable realisations of the climate variable at the candidate station’s location, disregarding its values. The probability density function estimated at the candidate station’s location (local probability density functions (PDF)), for each instant in time, is then used to verify the existence of inhomogeneities in the candidate time series. When an inhomogeneity is detected, that value is replaced by a statistical value (correction parameter) derived from the estimated local PDF. In order to assess the gsimcli efficiency with different implementation strategies, we homogenised monthly precipitation data from an Austrian network of the COST-HOME benchmark data set (COST Action ES0601, Advances in homogenization methods of climate series: an integrated approach – HOME). The following parameters were tested: grid cell size, candidate order in the homogenisation process, local radius parameter, detection parameter and correction parameter. Performance metrics were computed to assess the efficiency of gsimcli. The results show the high influence of the grid cell size and of the correction parameter in the method’s performance.
Review and discussion of homogenisation methods for climate data
Publication . Ribeiro, Sara; Caineta, Júlio; Costa, Ana Cristina; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management School; Elsevier
The quality of climate data is of extreme relevance, since these data are used in many different contexts. However, few climate time series are free from non-natural irregularities. These inhomogeneities are related to the process of collecting, digitising, processing, transferring, storing and transmitting climate data series. For instance, they can be caused by changes of measuring instrumentation, observing practices or relocation of weather stations. In order to avoid errors and bias in the results of analysis that use those data, it is particularly important to detect and remove those non-natural irregularities prior to their use. Moreover, due to the increase of storage capacity, the recent gathering of massive amounts of weather data implies also a toilsome effort to guarantee its quality. The process of detection and correction of irregularities is named homogenisation. A comprehensive summary and description of the available homogenisation methods is critical to climatologists and other experts, who are looking for a homogenisation method wholly considered as the best. The effectiveness of homogenisation methods depends on the type, temporal resolution and spatial variability of the climatic variable. Several comparison studies have been published so far. However, due to the absence of time series where irregularities are known, only a few of those comparisons indicate the level of success of the homogenisation methods. This article reviews the characteristics of the most important procedures used in the homogenisation of climatic variables based on a thorough literature research. It also summarises many methods applications in order to illustrate their applicability, which may help climatologists and other experts to identify adequate method(s) for their particular needs. This review study also describes comparison studies, which evaluated the efficiency of homogenisation methods, and provides a summary of conclusions and lessons learned regarding good practices for the use of homogenisation methods.
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Fundação para a Ciência e a Tecnologia
Programa de financiamento
3599-PPCDT
Número da atribuição
130692