bonjour,
je suis en train de faire une petite station qui mesure (pour l'instant À L'INTERIEUR de mon appartement) la température et l'humidité, et j'aimerais pouvoir la comparer à l'éxterieur, dans un premier temps, via les relevés éxterieurs d'autres personnes, puis dans un second temps faire une station éxterieur et partager mes données, du coup pour ces deux cas de figures (partage/récupérations de données météo) je voulais savoir quels services vous recommandez de façon à faire ça simplement soit depuis linux, soit directement en embarqué (avec esp-idf par exemple)

(libre et gratuit obviously)

hésitez évidement pas à partager !

merci ! :)

#meteo #esp32 #linux #meteoStations #help #technology #opensource #weather

Long-term data collection + high resolution + alpine habitats + #OpenAccess = GREAT! 🥳

Our colleagues from #Eurac #AlpineEnvironment published a #DataPaper in #ScientificData with a bunch of climatic data along an elevational gradient.

The data come from an LT(S)ER area in #Vinschgau, #SouthTyrol, #italy

👉 https://www.nature.com/articles/s41597-024-03580-x

#LTER #LTSER #LTSERMatsch #LTERItaly #LTEREurope #LongTermData #MeteoStations #OpenData

Six years of high-resolution climatic data collected along an elevation gradient in the Italian Alps - Scientific Data

The complex meso- and microclimatic heterogeneity inherent to mountainous regions, driven by both topographic and biotic factors, and the lack of observations, poses significant challenges to using climate models to predict and understand impacts at various scales. We present here a six-year dataset (2017–2022) of continuous climatic measurements collected at five elevations from 983 m to 2705 m above sea level in the Val Mazia - Matschertal valley in the Italian Alps. The measurements include the air temperature, relative humidity, wind speed and direction, solar radiation, soil properties, precipitation, and snow height. Collected within the European Long-Term Ecological Research program (LTER), this dataset is freely available in an open access repository. The time series may be valuable for the validation of regional climate models, atmospheric exchange modelling, and providing support for hydrological models and remote sensing products in mountain environments. Additionally, our data may be useful for research on the influence of elevation on ecological processes such as vegetation growth, plant composition, and soil biology. Beyond its utility in advancing such fundamental research, meteorological monitoring data contribute to informed socio-political decisions on climate adaptation strategies, land management, and water resource planning, enhancing the safety and resilience of mountain communities and biodiversity.

Nature