k-Gaps: a novel technique for clustering incomplete climatological time series

In this paper, we show a new clustering technique (k-gaps) aiming to generate a robust regionalization using sparse climate datasets with incomplete information in space and time. Hence, this method provides a new approach to cluster time series of different temporal lengths, using most of the information contained in heterogeneous sets of climate records that, otherwise, would be eliminated during data homogenization procedures. The robustness of the method has been validated with different synthetic datasets, demonstrating that k-gaps performs well with sample-starved datasets and missing climate information for at least 55% of the study period. We show that the algorithm is able to generate a climatically consistent regionalization based on temperature observations similar to those obtained with complete time series, outperforming other clustering methodologies developed to work with fragmentary information. k-Gaps clusters can therefore provide a useful framework for the study of long-term climate trends and the detection of past extreme events at regional scales

Spatial clustering and meteorological drivers of summer ozone in Europe

We have applied the k-means clustering technique on a maximum daily 8-h running average near-surface ozone (MDA8 O3) gridded dataset over Europe at 1° × 1° resolution for summer 1998–2012. This has resulted in a spatial division of nine regions where ozone presents coherent spatiotemporal patterns. The role of meteorology in the variability of ozone at different time scales has been investigated by using daily meteorological fields from the NCEP-NCAR meteorological reanalysis. In the five regions of central-southern Europe ozone extremes (exceedances of the summer 95th percentile) occur mostly under anticyclonic circulation or weak sea level pressure gradients which trigger elevated temperatures and the recirculation of air masses. In the four northern regions extremes are associated with high-latitude anticyclones that divert the typical westerly flow at those latitudes and cause the advection of aged air masses from the south. The impact of meteorology on the day-to-day variability of ozone has been assessed by means of two different types of multiple linear models. These include as predictors meteorological fields averaged within the regions (“region-based” approach) or synoptic indices indicating the degree of resemblance between the daily meteorological fields over a large domain (25°–70° N, 35° W – 35° E) and their corresponding composites for extreme ozone days (“index-based” approach). With the first approach, a reduced set of variables, always including daily maximum temperature within the region, explains 47–66% of the variability (adjusted R2) in central-southern Europe, while more complex models are needed to explain 27–49% of the variability in the northern regions. The index-based approach yields better results for the regions of northern Europe, with adjusted R2 = 40–57%. Finally, both methodologies have also been applied to reproduce the interannual variability of ozone, with the best models explaining 66–88% of the variance in central-southern Europe and 45–66% in the north. Thus, the regionalisation carried out in this work has allowed establishing clear distinctions between the meteorological drivers of ozone in northern Europe and in the rest of the continent. These drivers are consistent across the different time scales examined (extremes, day-to-day and interannual), which gives confidence in the robustness of the results.This work was supported by the Juan de la Cierva and Ramón y Cajal Programmes of the Spanish Ministerio de Economía y Competitividad [grant numbers FJCI-2014-20838 and RYC-2014-15036, respectively].Peer reviewe

A Metaheuristic approach to select Representative Measuring Points for Temperature Field Reconstruction Sancho Salcedo-Sanz

Trabajo presentado en el EMS Annual Meeting: European Conference for Applied Meteorology and Climatology, celebrado en Budapest (Hungría), del 3 al 7 de 2018The assessment of past climate relies on natural and documentary proxies. These may be combined in different forms to characterize the average time evolution of meteorological variables for a given region or even for the globe. These procedures have several problems since the distribution of proxies is irregular, their time resolution varies and their availability decreases back in time. In particular, as natural proxies may be clustered around a given ecosystem (i.e. tree rings), discerning their representativeness may be problematic. Here we show how soft computing techniques can be applied to identify the most representative points from a given dataset. This has been formulated as an optimization problem which can be solved through biologically-inspired operators. Specifically, the Coral Reef Optimization (CRO) algorithm has provided efficient solutions to the Representative Selection problem, outperforming other well-known methodologies. Here, we combine the CRO algorithm with the widely-used Analog Method in order to identify the most representative set of monthly average temperature time series for Europe from gridded (ERA Interim) and un-gridded (ECA) datasets. The best solutions obtained with this methodology are climatologically consistent, and include points from Scandinavia, Central and Southern Europe, Eastern Europe and the Black Sea. Interestingly, once the number of selected locations reaches a certain threshold, the improvement in the temperature reconstruction is achieved by adding points near the previously-identified zones and not by reshaping the selected sub-regions. Therefore, a reduced subset of points over key areas is enough to capture the climate variability of the entire region. Furthermore, our method excels by reducing the reconstruction misfit up to 28% when compared with other metaheuristic procedures, such as the greedy algorithm, based on individual selections. These results provide a clear guide to identify the best set of proxy locations for field reconstructionsPeer reviewe

Selection of Representative Measuring Points for Temperature Field Reconstruction using a Metaheuristic approach

Trabajo presentado en la European Geosciences Union General Assembly, celebrada en Viena (Austria), del 8 al 13 de abril de 2018Currently the atmosphere is scrutinized using a number of systems ranging from conventional meteorological stations, to geostationary and polar orbiting satellites. These observations are then assimilated in models for meteorological forecasting or climate reanalysis. The increasing availability of these products should not hide the need of keeping and improving the operation of weather and climate stations, which can be compromised due to constraints in the budget of the operating agencies. In this context, it is highly relevant to identify which are the stations producing the most relevant data for a given variable or process. This can be formulated as an optimization problem, in the aim of finding the ideal subset of points that best reproduces the characteristics of a certain region. Recent developments in Evolutionary Algorithms have validated the performance of biologically-inspired operators to solve an extensive collection of optimization problems. Specifically, the Coral Reef Optimization (CRO) algorithm has provided efficient solutions to the Representative Selection problem, outperforming other well-known methodologies. Here, we combine the CRO algorithm with the widely-used Analog Method in order to identify the most representative set of monthly average temperature time series for Europe from gridded (ERA Interim) and un-gridded (ECA) datasets. Our approach exhibits good performance and shows similar results in both cases. Moreover, the best solutions obtained with this methodology are climatologically consistent, and include points from Scandinavia, Central and Southern Europe, Eastern Europe and the Black Sea. Interestingly, once the number of selected locations reaches a certain threshold, the improvement in the temperature reconstruction is achieved by adding points nearby the previously-identified zones and not by reshaping the number of sub-regions. Therefore, a reduced subset of original points over key areas is enough to capture the climate variability of the entire region. Furthermore, our method excels by reducing the reconstruction misfit up to 28% when compared with other metaheuristic procedures, such as the greedy algorithm, based on individual selections. These results are just an example of the multiple applications of this methodology, which range from the identification of best located climate proxies to the design of operational meteorological networks. Besides, they also stress the need for further studies where evolutionary algorithms can shed light on the dynamics of several climate variables by providing the minimum number of sampling locations to obtain spatially-resolved climate fields.Peer reviewe

Near-optimal selection of representative measuring points for robust temperature field reconstruction with the CRO-SL and analogue methods

In this paper we tackle a problem of representative measuring points selection for temperature field reconstruction. This problem is a version of the more general Representative Selection (RS) problem, well-known in computer and data science. In this particular case, the objective is to select the best set of N measuring points (i.e. N representative points), in such a way that a reconstruction error is minimized when reconstructing the monthly average temperature field. We use a novel meta-heuristic algorithm, the Coral Reefs Optimization with Substrate Layer (CRO-SL), which is an evolutionary-type method able to combine several different search procedures within a single population. The CRO-SL is combined with the Analogue Method (AM) to identify the most representative points. This approach exhibits strong performance from experiments with gridded and un-gridded temperature field datasets (European Climate Assessment & Dataset (ECA) and ERA-Interim reanalysis (ERA)). Different aspects such as the error assessment and the comparison with alternative approaches, are discussed in the experimental analysis of this article. We show that the algorithm performs better than a greedy approach, i.e. the best solution for N points is different from the N best individual predictors. The solutions obtained with the proposed methodology are climatologically consistent and include points from Scandinavia, Central and Southern Europe, the Black Sea and Central and South Western Asia as the more representative in the case of the ECA dataset; similar areas are selected for ERA. We have found out that once the number of stations/points goes over a threshold, the improvement in the model is obtained by increasing the density of data in the given zones, instead of adding data from different zones to the algorithm. The method proposed may have direct application in Palaeoclimalogy, where there are a large amount of distributed proxies with scarce information, so the proposed approach could be useful to select the most important ones to reconstruct a desired field.This work has been partially supported by the projects TIN2014-54583-C2-2-R, TIN2017-85887-C2-2-P and PALEOSTRAT (CGL2015-69699-R) of the Spanish Ministerial Commission of Science and Technology (MICYT), and by the Comunidad Autónoma de Madrid, under project number S2013ICE-2933_02.Peer reviewe