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

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

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.

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

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.

This dataset provides the processed CFH water vapor radiosoundings performed for 5 consecutive nights at the Maïdo Observatory (21.08°S, 55.38°E) on Réunion Island during the period 20-25 January 2022 following the eruption of the Hunga Tonga volcano on 15 January. Réunion Island is in the Southwest Indian Ocean and holds one of the very few atmospheric observatories in the subtropical Southern Hemisphere.

The station is managed by the Observatoire de la Zone Critique de la Réunion (OZC-R) from Observatoire des Sciences de l'Univers de La Réunion (OSU-Réunion, Université de La Réunion). This is a forest station located at 1285m asl in the Reunion National Park. Rainwater is monthly collected (PALMEX rain collector) for δ18O and δ2H water isotopes analysis from 2016. Analyses are carried out at the Institut de Physique du Globe in Paris (PARI analytical platform) and within the IR-OZCAR network. The major ions rainfall composition is also studied. The station also measures several meteorological variables outside the canopy (precipitation, atmospheric pressure, temperature, relative humidity, global and photosynthetically active radiation) but also humidity linked to clouds and fog, as well as ground temperature.

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.

Meteo station Vantage Pro 2 Recorded parameters: - Wind speed and direction - Pluviometer - Temperature - Humidity (inside shelter) and outside - Barometric pressure Data transmission towards station of acquisition via WiFi. This station is connected on a PC Windows software.

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).