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Measuring the variability of incoming neutrons locally would be usefull for the cosmic-ray neutron sensing (CRNS) method. As the measurement of high energy neutrons is not so easy, alternative particles can be considered for such purpose. Among them, muons are particles created from the same cascade of primary cosmic-ray fluxes that generate neutrons at the ground. In addition, they can be easily detected by small and relatively inexpensive detectors. For these reasons they could provide a suitable local alternative to incoming corrections based on remote neutron monitor data. The reported measurements demonstrated that muon detection system can detect incoming cosmic-ray variations locally. Furthermore the precision of this measurement technique is considered adequate for many CRNS applications.
Temperature is a key factor controlling plant growth and vitality in the temperate climates of the mid-latitudes like in vast parts of the European continent. Beyond the effect of average conditions, the timings and magnitudes of temperature extremes play a particularly crucial role, which needs to be better understood in the context of projected future rises in the frequency and/or intensity of such events. In this work, we employ event coincidence analysis (ECA) to quantify the likelihood of simultaneous occurrences of extremes in daytime land surface temperature anomalies (LSTAD) and the normalized difference vegetation index (NDVI). We perform this analysis for entire Europe based upon remote sensing data, differentiating between three periods corresponding to different stages of plant development during the growing season. In addition, we analyze the typical elevation and land cover type of the regions showing significantly large event coincidences rates to identify the most severely affected vegetation types. Our results reveal distinct spatio-temporal impact patterns in terms of extraordinarily large co-occurrence rates between several combinations of temperature and NDVI extremes. Croplands are among the most frequently affected land cover types, while elevation is found to have only a minor effect on the spatial distribution of corresponding extreme weather impacts. These findings provide important insights into the vulnerability of European terrestrial ecosystems to extreme temperature events and demonstrate how event-based statistics like ECA can provide a valuable perspective on environmental nexuses.