ACMANT: homogenising temperature series with confidence and accuracy

Ponencia presentada en: VIII Congreso de la Asociación Española de Climatología celebrado en Salamanca entre el 25 y el 28 de septiembre de 2012.[EN]Changes in location, instrumentation or observing hours, and lot of other factors may cause artificial biases in observed climatic data. These undesired biases are often referred as inhomogeneities of time series, and homogenisation is applied for their elimination. For spatially dense networks of temperature observations the use of objective statistical methods is recommended. After a successful homogenisation the observable climatic characteristics (trend, variability, distribution function, etc.) are substantially closer to the reality than in the raw inhomogeneous series. ACMANT is a newly developed, fully automatic method for homogenising networks of monthly temperature series. Its excellent performance has been proved with a series of objective experiments, among others with the tests of the benchmark of the European project HOME. ACMANT is particularly good in the minimisation of root mean squared error, because it harmonises the work on different time scale in a unique way.[ES]Los cambios en la ubicación, la instrumentación o las horas de observación, y muchos otros factores pueden causar sesgos artificiales en las series temporales. Estos sesgos no deseados se refiere a menudo como non-homogeneidades de las series, y se aplica homogeneización para su eliminación. Para las redes espaciales densas de las observaciones de temperatura la utilización de métodos estadísticos objetivos se recomienda. Después de una homogeneización con éxito, las características observables (tendencia del clima, la variabilidad, la función de distribución, etc.) están mucho más cerca de la realidad que en la serie de datos originales. ACMANT es el resultado de nuevo desarrollo metodológico para homogeneizar las redes de las series mensuales de la temperatura. El funcionamiento de ACMANT es totalmente automático. Su excelente rendimiento ha sido probado con experimentos objetivos, entre otros, con las pruebas del proyecto europeo HOME. ACMANT es particularmente bueno en la minimización de error cuadrático medio, debido a la armonización del trabajo en las escalas temporales diferentes de una manera única.The research was supported by the European project EURO4M FP7-SPACE-2009-1/242093 and by the Spanish project “Tourism, Politics and Environment” ECO 2010-18158

Impact of missing data on the efficiency of homogenisation: experiments with ACMANTv3

The impact of missing data on the efficiency of homogenisation with ACMANTv3 is examined with simulated monthly surface air temperature test datasets. The homogeneous database is derived from an earlier benchmarking of daily temperature data in the USA, and then outliers and inhomogeneities (IHs) are randomly inserted into the time series. Three inhomogeneous datasets are generated and used, one with relatively few and small IHs, another one with IHs of medium frequency and size, and a third one with large and frequent IHs. All of the inserted IHs are changes to the means. Most of the IHs are single sudden shifts or pair of shifts resulting in platform-shaped biases. Each test dataset consists of 158 time series of 100 years length, and their mean spatial correlation is 0.68–0.88. For examining the impacts of missing data, seven experiments are performed, in which 18 series are left complete, while variable quantities (10–70%) of the data of the other 140 series are removed. The results show that data gaps have a greater impact on the monthly root mean squared error (RMSE) than the annual RMSE and trend bias. When data with a large ratio of gaps is homogenised, the reduction of the upper 5% of the monthly RMSE is the least successful, but even there, the efficiency remains positive. In terms of reducing the annual RMSE and trend bias, the efficiency is 54–91%. The inclusion of short and incomplete series with sufficient spatial correlation in all cases improves the efficiency of homogenisation with ACMANTv3

Efficiency tests for automatic homogenization methods of monthly temperature and precipitation series “MULTITEST”

Presentación realizada en: 10th EUMETNET Data Management Workshop celebrado en St. Gallen, Suiza, del 28 al 30 de octubre de 2015.Five years after the efficiency tests of the European COST ES0601 project (known as “HOME”) new tests are planned with the use of a new benchmark dataset. New tests are needed for three reasons: i) Several homogenization methods have newer versions since HOME, while some other methods were not tested by HOME; ii) The HOME benchmark represented only one climatic region (i.e. the central-western European climate) and a not very large selection of inhomogeneity problems; and iii) The number of networks in the HOME benchmark was small, and thus certain kinds of results with them have large statistical uncertainty. For all these reasons the representativeness of HOME results is limited. The new tests will be performed by researchers of the Centre for Climate Change of the University Rovira i Virgili and those of the Spanish Meteorological Agency (AEMET) with the support of a Spanish national project. We will develop and use a much larger benchmark dataset than the HOME benchmark, but we will test automatic methods only, thus the amount of the required work remains feasible. Primarily, the openly accessible automatic methods will be tested, which do not need the direct collaboration of the method developers. However, we intend to keep contact with the developers in order to test the best versions of the methods with the best possible parameterizations . Until the autumn of 2016 we will be accepting newly developed methods and versions for testing. In the development of the new benchmark, we will build on the knowledge gathered before the creation of HOME benchmark, but we aim to create an even more realistic benchmark including various segments representing particular climatic regions and inhomogeneity problems. For instance, the new benchmark will consider that a) The frequency of breaks of detectable size is generally much higher for temperature series than for precipitation series; b) The annual cycle of biases depends both on the examined variable and the geographical region; c) The frequent presence of short-term, platform-shaped inhomogeneities in observed temperature series. The efficiencies will be evaluated with the residual root mean square error of monthly and annual values in homogenized series, as well as with the residual trend bias errors for individual time series and network mean trends

Climate impacts on tourism in Spain [Póster]

Póster presentado en: VIII Congreso de la Asociación Española de Climatología celebrado en Salamanca entre el 25 y el 28 de septiembre de 2012.The research was supported by the Spanish project “Tourism, Environment and Politics” ECO 2010-18158

Climate impacts on tourism in Spain

Ponencia presentada en: VIII Congreso de la Asociación Española de Climatología celebrado en Salamanca entre el 25 y el 28 de septiembre de 2012.[EN]Tourism is an important sector of the Spanish economy. The popularity of Mediterranean beaches does not decrease even during the European economic crisis. Rising temperature and extreme weather events may threat the popularity of summer tourism, both directly and indirectly. Unpleasant heat waves and severe persistent droughts can occur even without a systematic change of the climatic characteristics, but the growing frequency and severity of such events are expected due to the global warming process.[ES]El turismo es un importante sector de la economía española. La popularidad de las playas mediterráneas no decrece aún en los años de crisis económica europea. El aumento de la temperatura y fenómenos meteorológicos extremos pueden amenazar la popularidad del turismo de verano, tanto directa como indirectamente. Olas de calor y desagradables graves sequías persistentes pueden ocurrir aún sin cambio sistemático de las características climáticas, pero la creciente frecuencia y gravedad de tales eventos se espera debido al proceso de calentamiento global.The research was supported by the Spanish project “Tourism, Environment and Politics” ECO 2010-18158

An application of HOMER and ACMANT for homogenising monthly precipitation records in Ireland.

Climate change studies based only on raw long-term data are potentially flawed due to the many breaks introduced from non-climatic sources, consequently quality controlled and homogenised climate data is desirable for basing climate related decision making on. Seasonal cycles of precipitation in Ireland and the UK are projected to become more marked as the climate changes, and regional extremes in summer dry spells and winter precipitation have been recorded in recent years. Therefore to analyse and monitor the evolution of precipitation patterns across Ireland, quality controlled and homogenous climate series are needed

An application of HOMER and ACMANT for homogenising monthly precipitation records in Ireland.

Climate change studies based only on raw long-term data are potentially flawed due to the many breaks introduced from non-climatic sources, consequently quality controlled and homogenised climate data is desirable for basing climate related decision making on. Seasonal cycles of precipitation in Ireland and the UK are projected to become more marked as the climate changes, and regional extremes in summer dry spells and winter precipitation have been recorded in recent years. Therefore to analyse and monitor the evolution of precipitation patterns across Ireland, quality controlled and homogenous climate series are needed

Efficiency of time series homogenization: method comparison with 12 monthly temperature test datasets

The aim of time series homogenization is to remove nonclimatic effects, such as changes in station location, instrumentation, observation practices, and so on, from observed data. Statistical homogenization usually reduces the nonclimatic effects but does not remove them completely. In the Spanish ‘‘MULTITEST’’ project, the efficiencies of automatic homogenization methods were tested on large benchmark datasets of a wide range of statistical properties. In this study, test results for nine versions, based on five homogenization methods—the adapted Caussinus-Mestre algorithm for the homogenization of networks of climatic time series (ACMANT), ‘‘Climatol,’’ multiple analysis of series for homogenization (MASH), the pairwise homogenization algorithm (PHA), and ‘‘RHtests’’—are presented and evaluated. The tests were executed with 12 synthetic/surrogate monthly temperature test datasets containing 100–500 networks with 5–40 time series in each. Residual centered root-mean-square errors and residual trend biases were calculated both for individual station series and for network mean series. The results show that a larger fraction of the nonclimatic biases can be removed from station series than from network-mean series. The largest error reduction is found for the long-term linear trends of individual time series in datasets with a high signal-to-noise ratio (SNR), where the mean residual error is only 14%–36% of the raw data error. When the SNR is low, most of the results still indicate error reductions, although with smaller ratios than for large SNR. In general, ACMANT gave the most accurate homogenization results. In the accuracy of individual time series ACMANT is closely followed by Climatol, and for the accurate calculation of mean climatic trends over large geographical regions both PHA and ACMANT are recommended.This research was funded by the Spanish MULTITESTproject (Ministry of Economics and Competitiveness, CGL2014-52901-P)

Comparison of homogenization packages applied to monthly series of temperature and precipitation: the Multitest project

Comunicación realizada para: Ninth Seminar for Homogenization and Quality Control in Climatological Databases and Fourth Conference on Spatial Interpolation Techniques in Climatology and Meteorology celebrado del 3 al 7 de abril de 2017 en Budapest, Hungría.Project MULTITEST (CGL2014-52901-P) is funded by the Spanish Ministry of Economy and Competitiveness. Manola Brunet is also supported by the European Union-funded project "Uncertainties in Ensembles of Regional Reanalyses" (UERRA, FP7-SPACE-2013-1 project number 607193). Victor Venema is also supported by the DFG project Daily HUME (VE 366 - 8)