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dataset

131 record(s)
 
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  • WW3 model reanalysis on SWIO (south-western indian ocean) area at 0.5 degree of resolution

  • Le projet a pour objectif d'améliorer le signal de la pluie détectée par les géophones en comparant les données météorologiques d'un disdromètre, d'un pluviomètre et de 3 géophones afin d'extraire des données des sismographes pour mieux comprendre le transport sédimentaire issu du réseau sismologique installé dans la rivière des pluies et la rivière du Mat. Les objectifs sont : 1) déterminer les caractéristiques sismiques de la pluie sur le site de mesures 2) A terme, comprendre le déclenchement des éboulements et glissements liés aux pluies A court terme, ce projet devrait aussi permettre de : 1) comprendre pour un même type de pluie l'influence de sols de rugosités différentes sur les signaux enregistrés par les sismomètres 2) intégrer/contraindre pour un même type de sol l'influence de types de pluies différentes sur les enregistrements des sismomètres 3) déterminuer l'influence des tailles des gouttes et du nombre de gouttes (indications données par le disdromètre) sur le signal sismique

  • Le radar BASTA est un radar nuage (95GHz) dédié à l’étude des nuages et du brouillard. Le radar mesure l’énergie rétrodiffusée par les hydrométéores, cette énergie peut donc être reliée à la quantité d’eau contenue dans le nuage (liquide et glace). Il fonctionne en routine quotidiennement sur le site de l’observatoire du Maïdo, sur l'Ile de La Réunion. Le radar BASTA Réunion a été calibré au LATMOS avant son installation à la Réunion. Ce jeu de données est au format niveau L0. Paramètre principal: Profil vertical de réflectivité radar, mesure du décalage Doppler. Contexte de la mesure: observation routine.

  • Elevational Transect on the West side of the Piton des Neiges. Implementation of small permanents plots every 200 m of height between 750 and 2350 m. Differents biotics and abiotics parameters are observed on plots: - Climatic variables (T, RH) - Physico-chemical analyses of grounds - Inventories of vegetation, arthropods

  • The overall objective of the ESPOIRS project is to obtain a better understanding of the variability, statistical properties and formation mechanisms of intense tropical precipitation at regional and local scales. ESPOIRS is thus interested in the entire life cycle of precipitation at several space-time scales. * Through the analysis of the distribution of the large-scale humidity field which drives the formation of precipitation at the regional scale using a GNSS network. * Through the characterization of internal (dynamics, microphysics) and external (interactions with the relief) processes, which drive the formation and life cycle of extreme weather events at the local scale => transportable Polarized Doppler X-band precipitation radar.

  • The ressource describes the dataset obtained by deploying the GAMIC GMWR-25-DP RADAR in the South of Reunion Island, in Saint Joseph.

  • Since 2012, 3 lidars from the Atmospheric Physics Observatory of La Réunion (OPAR) have been performing aerosol profile measurements at the Maïdo observatory site, located at 2160 meters to the west of the island of La Réunion. These profiles are obtained at several wavelengths, 355nm and 532nm, and there are also depolarized channels at 532nm. The data from these 3 lidars are processed in two stages: initially, the data are manually cleaned of disturbed profiles, either by atmospheric effects, such as the passage of clouds, or by electronic effects like noise. They are then summed over the night. This is the L1b level, and the data are available in the Matlab format (.mat). Subsequently, the data are processed to convert from a profile of received photon number to a profile of aerosol extinction and scattering. The methodology used is based on the Klett calculation at one wavelength. This is the L2b level, and the data are available in the NetCDF format (.nc) with the NDACC convention in the choice of variable names. Therefore, the data are distributed across 6 directories, 2 levels of processing for each lidar. The raw data from the instrument (called L0) are in a proprietary format, the Licel format, and are not accessible in open access, only via FTP with restricted access.

  • Le radar mini-BASTA est un radar nuage (95GHz) dédié à l’étude des nuages et du brouillard. Le radar mesure l’énergie rétrodiffusée par les hydrométéores, cette énergie peut donc être reliée à la quantité d’eau contenue dans le nuage (liquide et glace). Il fonctionne en routine quotidiennement sur le site de l’observatoire du Maïdo, sur l'Ile de La Réunion. Ce jeu de données est au niveau L0, et les données sont non calibrées. Paramètre principal: Profil vertical de réflectivité radar, mesure du décalage Doppler. Contexte de la mesure: observation routine.

  • Categories  

    The dataset of the first observatory from the French network of critical zone observatories (OZCAR) located in an insular tropical and volcanic context, integrating a “Tropical Montane Cloud Forest”: The ERORUN-STAFOR observatory. This collaborative observatory is located in the northern part of La Réunion island (Indian Ocean) within the watershed of Rivière des Pluies (45.0 km²) which hosts the TMCF of Plaines des Fougères, one of the best preserved natural habitats in La Réunion Island. Since 2015, the ERORUN-STAFOR monitoring in collaboration with local partners collected a multidisciplinary dataset with a constant improvement of the instrumentation over time. At the watershed scale and in its vicinity, the ERORUN-STAFOR observatory includes 10 measurement stations covering the upstream, midstream and downstream part of the watershed. The stations record a total of 48 different variables through continuous (sensors) or periodic (sampling) monitoring. The dataset consists of continuous time series variables related to (i) meteorology, including precipitation, air temperature, relative humidity, wind speed and direction, net radiation, atmospheric pressure, cloud water flux, irradiance, leaf wetness and soil temperature (ii) hydrology, including water level and temperature, discharge and electrical conductivity (EC) of stream, (iii) hydrogeology, including groundwater level, water temperature and EC in two piezometers and one groundwater gallery completed by soil moisture measurements under the canopy. The dataset is completed by periodic time series variables related to (iv) hydrogeochemistry, including field parameters and water analysis results. The periodic sampling survey provides chemical and isotopic compositions of rainfall, groundwater, and stream water at different locations of this watershed. The ERORUN-STAFOR monitoring dataset extends from 2014 to 2022 with an acquisition frequency from 10 min to hourly for the sensor variables and from weekly to monthly frequency for the sampling. Despite the frequent maintenance of the monitoring sites, several data gaps exist due to the remote location of some sites and instrument destruction by cyclones. This observatory is a unique research site in an insular volcanic tropical environment offering three windows of observation for the study of critical zone processes through upstream-midstream-downstream measurements sites. This high-resolution dataset is valuable to assess the response of volcanic tropical watersheds and aquifers at both event and long-term scales (i.e. global change). It will also allow various progress in understanding the significant role of the TMCFs in the recharge processes, the hydrogeological conceptual model of volcanic islands, the watershed hydrosedimentary responses to extreme climatic events and their respective evolution under changing climatic conditions.

  • The resource provides two land cover maps of Réunion Island for the years 1950 and 2022 derived from the analysis of ortho-photographs at the island scale (Source IGN). The produced typology uses five cover classes: forest, low vegetation, agriculture, urban, and shadow (related to topography). The method used is based on encoding the two aligned rasters, converted into a single band of grayscale for 2022, using a vision-transformer deep learning model. From the features calculated for each pixel, a random forest classification model is trained separately for each year using a set of ROIs (Regions Of Interest), target polygons delineated within each of the selected classes through photo-interpretation of the original images. Model validation is performed on independent sets of polygons also defined by photo-interpretation. The maps provided in the resource are derived from the prediction of cover classes for both years using the trained and validated models. These are raw predictions, meaning that no post-processing has been applied to reduce potential noise due to classification errors. The shared resource is part of the results from the FRAG'ILE research and development program (FRAGmentation en milieu InsuLairRE, UR / CBNM/ IRD, funded by OFB, https://fragile.frama.io).