The Operational Program on the Exchange of Weather Radar Information (OPERA) has co-ordinated radar co-operation among national weather services in Europe for more than 20 years. It has introduced its own, manufacturer-independent data model, runs its own data center, and produces Pan-European radar composites. The applications using this data vary from data assimilation to flood warnings and the monitoring of animal migration. It has used several approaches to provide a homogeneous combination of disparate raw data and to indicate the reliability of its products. In particular, if a pixel shows no precipitation, it is important to know if that pixel is dry or if the measurement was missing.

The goal of the present study was to exploit the volumetric data from the Wideumont weather radar to estimate the occurrence and severity of hail over a period of 10 years (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). The radar is located in the southeastern part of Belgium and its domain covers Belgium, Luxembourg and some parts of Germany, France and The Netherlands. Two hail detection algorithms were used for detecting hail falls in the volumetric radar data. The algorithms provide an empirical estimation of the probability of hail (POH) and the probability of severe hail (POSH). The study shows that post-processing of probabilities by means of the advection correction significantly influences the statistical results about hail occurrence. The advection correction is very effective in reducing the 'fishbone effect' due to a temporal sampling of the radar data that is too low, which has an impact on the geographical distribution of the hail fall frequencies over the study domain. The post-processed POH and POSH datasets are verified against hail reports at the ground. The statistics obtained show that the diurnal cycle of hail falls has a pronounced peak in the 1500-1600 UTC (local solar time + 1 h) time interval with 28% of all hail events occurring in July and 30% of severe hail events occurring in May. Nevertheless, severe hail events have a low occurrence in absolute terms and longer time series of observations are required to obtain a more reliable severe hail climatology.

Large parts of the continents are continuously scanned by terrestrial weather radars to monitor precipitation and wind conditions. These systems also monitor the mass movements of bird, bat, and insect migration, but it is still unknown how many of these systems perform with regard to detection and quantification of migration intensities of the different groups. In this study that was undertaken within five regions across Europe and the Middle East we examined to what extent bird migration intensities derived from different weather radars are comparable between each other and relate to intensities measured by local small‐scaled radars, some of them specifically developed to monitor birds. Good correspondence was found for the relative day‐to‐day pattern in migration intensities among most radar systems that were compared. Absolute intensities varied between different systems and regions. The findings of this study can be used to infer about absolute bird migration intensities measured by different radar systems and consequently help resolving methodological issues regarding the estimation of migrant numbers in the Western‐Palearctic region. It further depicts a scientific basis for the future monitoring of migratory bird populations across a large spatio‐temporal scale, predicting their movements and studying its consequences on ecological systems and human lives.

Abstract. Correct, timely and meaningful interpretation of polarimetric weather radar observations requires an accurate understanding of hydrometeors and their associated microphysical processes along with well-developed techniques that automatize their recognition in both the spatial and temporal dimensions of the data. This study presents a novel technique for identifying different types of hydrometeors from quasi-vertical profiles (QVPs). In this new technique, the hydrometeor types are identified as clusters belonging to a hierarchical structure. The number of different hydrometeor types in the data is not predefined, and the method obtains the optimal number of clusters through a recursive process. The optimal clustering is then used to label the original data. Initial results using observations from the National Centre for Atmospheric Science (NCAS) X-band dual-polarization Doppler weather radar (NXPol) show that the technique provides stable and consistent results. Comparison with available airborne in situ measurements also indicates the value of this novel method for providing a physical delineation of radar observations. Although this demonstration uses NXPol data, the technique is generally applicable to similar multivariate data from other radar observations.

Observations of the real-time state of the atmosphere are required in order to initialize numerical weather prediction (NWP) models. As NWP resolution improves, more observations are needed, to better capture regional variations in atmospheric conditions. In particular, surface observations are necessary to reflect conditions experienced on the surface. One proposed opportunity to increase the number of surface observations available for assimilation into NWP is to crowdsource the data from home weather stations. This study investigates the outdoor air temperature measurements made by Netatmo home weather stations, through validation against a calibrated laboratory chamber and by evaluating quality control schemes that are applied to a UK-wide network of Netatmo stations. In a series of controlled lab experiments, it was found that the Netatmo temperature sensor was accurate to 0.3 C. The response to fluctuations in temperature is lagged, with τ (the time taken for 63% of the change to be measured) calculated as 12.7 min for a nearinstantaneous decrease in temperature. Netatmo temperature observations were compared with Met Office MIDAS hourly weather observations. A warm bias in excess of 1 C was present in the Netatmo temperature observations, which was lessened by the three quality control schemes tested, but still in excess of 0.5 C. Hence, Netatmo temperature measurements have potential to be assimilated in NWP in the United Kingdom, but work is required to find a suitable agreed quality control scheme to filter out anomalous observations in the United Kingdom.

Contemporary analyses of insect population trends are based, for the most part, on a large body of heterogeneous and short-term datasets of diurnal species that are representative of limited spatial domains. This makes monitoring changes in insect biomass and biodiversity difficult. What is needed is a method for monitoring that provides a consistent, high-resolution picture of insect populations through time over large areas during day and night. Here, we explore the use of X-band weather surveillance radar (WSR) for the study of local insect populations using a high-quality, multi-week time series of nocturnal moth light trapping data. Specifically, we test the hypotheses that (i) unsupervised data-driven classification algorithms can differentiate meteorological and biological phenomena, (ii) the diversity of the classes of bioscatterers are quantitatively related to the diversity of insects as measured on the ground and (iii) insect abundance measured at ground level can be predicted quantitatively based on dual-polarization Doppler WSR variables. Adapting the quasi-vertical profile analysis method and data clustering techniques developed for the analysis of hydrometeors, we demonstrate that our bioscatterer classification algorithm successfully differentiates bioscatterers from hydrometeors over a large spatial scale and at high temporal resolutions. Furthermore, our results also show a clear relationship between biological and meteorological scatterers and a link between the abundance and diversity of radar-based bioscatterer clusters and that of nocturnal aerial insects. Thus, we demonstrate the potential utility of this approach for landscape scale monitoring of biodiversity.

Observations of the precipitation rate/depth, drop-size distribution, drop-velocity distribution and precipitation type are compared from six in-situ precipitation sensor designs over 12 months to assess their performance and provide a benchmark for future design and deployment. The designs considered are: tipping-bucket (TBR), drop-counting (RAL), acoustic (JWD), optical (LPM), single-angle visiometer with capacitor (PWD21) and dual-angle visiometer (PWS100). Precipitation rates are compared for multiple time resolutions over the study period, while drop size and velocity distributions are compared with cases at stable precipitation rates. To examine precipitation type a new index and a logic algorithm to amalgamate consecutive precipitation type observations consistently is introduced and applied. Overall the choice of instrument for deployment depends on the usage. For fast response (less than 15 minutes), the PWD21 and TBR should not be used. As precipitation rate or the duration of a sample increases, the correlation of the TBR with the majority of other instruments increases. However, the PWD21 consistently underestimates precipitation. The RAL,PWS100 andJWDare within ± 15% for precipitation depth over 12 months. All instruments are inconsistent in their ability to observe drop size and velocity distributions for differing precipitation rates. There is low agreement between the instruments for precipitation type estimation. The PWD21 and PWS100 rarely report some precipitation types, but the LPM reports more broadly. Meteorological stations should use several instrument designs for redundancy and to more accurately capture precipitation characteristics.

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