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

This ressource is part of the action 1 of the ESPOIRS Project. Multiple GNSS Stations have been installed or updated in the SWIO and the data are available here. "TO ADD : Different datasets, list of stations, etc ...."

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 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.

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).

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.

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.

This dataset provides the processed ECC ozonesonde 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 ressource describes the dataset obtained by deploying the GAMIC GMWR-25-DP RADAR in the South of Reunion Island, in Saint Joseph.