An attempt is made to find the main monthly modes of variation of precipitation over the Iberian Peninsula. The modes of variation of precipitation were derived from principal component analysis. The dataset used consists of records of monthly precipitation from 40 meteorological stations over 74 yr (1919–92). The stations are spatially representative of most of the Iberian Peninsula. To take into account the seasonality of precipitation over the Iberian Peninsula, one analysis was performed separately for each calendar month. The modes of variation resulting from the different analyses were compared and clustered in groups according to their loading patterns. Seven main patterns were found to be of importance during various months of the year. These seven patterns explained more than 75% of the variance of the precipitation field from December to April. However, less than 20% of the total variance is explained for July and August. It is concluded that, depending on the month or season of interes... Abstract An attempt is made to find the main monthly modes of variation of precipitation over the Iberian Peninsula. The modes of variation of precipitation were derived from principal component analysis. The dataset used consists of records of monthly precipitation from 40 meteorological stations over 74 yr (1919–92). The stations are spatially representative of most of the Iberian Peninsula. To take into account the seasonality of precipitation over the Iberian Peninsula, one analysis was performed separately for each calendar month. The modes of variation resulting from the different analyses were compared and clustered in groups according to their loading patterns. Seven main patterns were found to be of importance during various months of the year. These seven patterns explained more than 75% of the variance of the precipitation field from December to April. However, less than 20% of the total variance is explained for July and August. It is concluded that, depending on the month or season of interes...

The interest in the use of ceilometers for optical aerosol characterization has increased in the last few years. They operate continuously almost unattended and are also much less expensive than lidars; hence, they can be distributed in dense networks over large areas. However, due to the low signal-to-noise ratio it is not always possible to obtain particle backscatter coefficient profiles, and the vast number of data generated require an automated and unsupervised method that ensures the quality of the profiles inversions. In this work we describe a method that uses aerosol optical depth (AOD) measurements from the AERONET network that it is applied for the calibration and automated quality assurance of inversion of ceilometer profiles. The method is compared with independent inversions obtained by co-located multiwavelength lidar measurements. A difference smaller than 15 % in backscatter is found between both instruments. This method is continuously and automatically applied to the Iberian Ceilometer Network (ICENET) and a case example during an unusually intense dust outbreak affecting the Iberian Peninsula between 20 and 24 February 2016 is shown. Results reveal that it is possible to obtain quantitative optical aerosol properties (particle backscatter coefficient) and discriminate the quality of these retrievals with ceilometers over large areas. This information has a great potential for alert systems and model assimilation and evaluation.

In this study trends in extreme rainfall over the Iberian Peninsula at a daily scale in the second half of the twentieth century have been detected and analyzed. For this goal 35 stations evenly distributed over the region of study covering the period 1958–97 have been studied. Two different approaches have been used. The first one consists of the nonparametric Mann–Kendall test and the Sen method. The second approach is based on the statistical theory of extreme values, involving time-dependent parameters in order to be able to reflect possible temporal changes in the frequency distribution. Results from both methods agree, confirming the reliability of the analysis. Negative trends are found for the west and southwest of the Iberian Peninsula in spring and winter. In autumn a spatial dipolar pattern appears, but trends are not so evident.

This article focuses on the comparison of the total ozone column data from the Ozone Monitoring Instrument (OMI) flying aboard the NASA EOS‐Aura satellite platform with ground‐based measurement recorded by Brewer spectroradiometers located at five Spanish remote sensing ground stations between January 2005 and December 2007. The satellite data are derived from two algorithms: OMI Total Ozone Mapping Spectrometer (OMI‐TOMS) and OMI Differential Optical Absorption Spectroscopy (OMI‐DOAS). The largest relative differences between these OMI total ozone column estimates reach 5% with a significant seasonal dependence. The agreement between OMI ozone data and Brewer measurements is excellent. Total ozone columns from OMI‐TOMS are on average a mere 2.0% lower than Brewer data. For OMI‐DOAS data the bias is a mere 1.4%. However, the relative difference between OMI‐TOMS and Brewer measurements shows a notably lower seasonal dependence and variability than the differences between OMI‐DOAS and ground‐based data. For both OMI ozone data products these relative differences show significant dependence on the satellite ground pixel solar zenith angle for cloud‐free cases as well as for cloudy conditions. However, the OMI ozone data products are shown to reveal opposite behavior with respect to the two antagonistic sky conditions. No significant dependency of the ground‐based to satellite‐based differences with respect to the satellite cross‐track position is seen for either OMI retrieval algorithm.

The UV Index was established more than 20 years ago as a tool for sun protection and health care. Shortly after its introduction, UV Index monitoring started in several countries either by newly acquired instruments or by converting measurements from existing instruments into the UV Index. The number of stations and networks has increased over the years. Currently, 160 stations in 25 European countries deliver online values to the public via the Internet. In this paper an overview of these UV Index monitoring sites in Europe is given. The overview includes instruments as well as quality assurance and quality control procedures. Furthermore, some examples are given about how UV Index values are presented to the public. Through these efforts, 57% of the European population is supplied with high quality information, enabling them to adapt behaviour. Although health care, including skin cancer prevention, is cost-effective, a proportion of the European population still doesn't have access to UV Index information.