{"requestHeader":{"request":"fuentes"},"responseHeader":{"responseTimestamp":"2026-05-15T08:02:12","request":"fuentes","queryUrl":"request=fuentes","creationTime":"2026-05-15T08:02:12-03:00"},"data":[{"id":2,"nombre":"cpc","data_table":"pp_cpc","tipo":"PA","intervalo_de_muestreo":"1 day","abstract":"Análisis de la precipitación a paso diario realizado sobre la base de la red internacional de estaciones meteorológicas"},{"id":3,"nombre":"wm","data_table":"etp_wm","tipo":"C","intervalo_de_muestreo":"1 day","abstract":null},{"id":4,"nombre":"eta_3h","data_table":"pp_eta_3h","tipo":"QPF","intervalo_de_muestreo":"03:00:00","abstract":null},{"id":5,"nombre":"gfs_diario","data_table":"gfs_diario","tipo":"QPF","intervalo_de_muestreo":"1 day","abstract":"Pronóstico de precipitación a paso diario del modelo GFS"},{"id":6,"nombre":"gpm","data_table":"pp_gpm","tipo":"QPE","intervalo_de_muestreo":"1 day","abstract":"Estimación satelital de la precipitación a paso diario de la misión GPM. Fuente NASA"},{"id":7,"nombre":"campo","data_table":"pp_emas","tipo":"PI","intervalo_de_muestreo":"1 day","abstract":"Precipitación acumulada a paso diario [mm] de la red de estaciones meteorológicas convencionales para la Cuenca del Plata. Diagrama de Voronoi"},{"id":8,"nombre":"smap","data_table":"smap","tipo":"SM","intervalo_de_muestreo":"1 day","abstract":null},{"id":9,"nombre":"amsr2_mag_4days","data_table":"amsr2_mag_4days","tipo":"FM","intervalo_de_muestreo":"1 day","abstract":"Magnitud de inundación de AMSR2, medida en desvíos estándar * 1000."},{"id":13,"nombre":"pp_gpm_3h","data_table":"pp_gpm_3h","tipo":"QPE","intervalo_de_muestreo":"03:00:00","abstract":"Estimación satelital de la precipitación a paso 3 horario de la misión GPM. Fuente NASA"},{"id":15,"nombre":"smops","data_table":"smops","tipo":"SM","intervalo_de_muestreo":"1 day","abstract":"Humedad del suelo volumétrica de fusión de productos satelitales SMOPS. Fuente NOAA"},{"id":16,"nombre":"etpd_santi","data_table":null,"tipo":"C","intervalo_de_muestreo":"1 day","abstract":null},{"id":17,"nombre":"MODIS NRT FLOOD MAPPING PRODUCT","data_table":"nrt_global_floodmap","tipo":"WE","intervalo_de_muestreo":"1 day","abstract":null},{"id":18,"nombre":"fraccion_anegada_modis_nrt_5km","data_table":"resample_nrt","tipo":"WE","intervalo_de_muestreo":"1 day","abstract":"Fracción de área inundada MODIS [0-1000]. Resolución 5 km"},{"id":31,"nombre":"etp_wm_view","data_table":"etp_wm_view","tipo":"ETP","intervalo_de_muestreo":"1 day","abstract":null},{"id":32,"nombre":"marea_riodelaplata","data_table":"marea_riodelaplata","tipo":null,"intervalo_de_muestreo":"01:00:00","abstract":"Altura geométrica sobre tabla de mareas. Fuente: SMN/SHN"},{"id":33,"nombre":"campo_splines","data_table":"pp_emas_spl","tipo":"PI","intervalo_de_muestreo":"1 day","abstract":"Precipitación acumulada a paso diario [mm] de la red de estaciones meteorológicas convencionales para la Cuenca del Plata. Interpolación por splines"},{"id":36,"nombre":"campo_3h","data_table":"pp_emas_3h","tipo":"PI","intervalo_de_muestreo":"03:00:00","abstract":"Precipitación acumulada a 3 horario [mm] combinada de las redes automáticas y convencionales de la Cuenca del Plata. Interpolación mediante método de Thiessen"},{"id":37,"nombre":"campo_3h_spl","data_table":"pp_emas_3h_spl","tipo":"PI","intervalo_de_muestreo":"03:00:00","abstract":"Precipitación acumulada a 3 horario [mm] combinada de las redes automáticas y convencionales de la Cuenca del Plata. Interpolación mediante método de Splines"},{"id":40,"nombre":"mod09a1_ndwi37c_agua_superficial","data_table":"modis_water_surface","tipo":"WE","intervalo_de_muestreo":"1 day","abstract":null},{"id":41,"nombre":"mod09a1_water_surface_view","data_table":"ndwi37_floodmap","tipo":"WE","intervalo_de_muestreo":"1 day","abstract":null},{"id":45,"nombre":"ecmwf_mensual","data_table":"ecmwf_mensual","tipo":"QPF","intervalo_de_muestreo":"1 mon","abstract":"pronóstico de precipitaciones mensuales del modelo ECMWF"},{"id":46,"nombre":"ecmwf_mensual_anom","data_table":"ecmwf_mensual_anom","tipo":"QPF","intervalo_de_muestreo":"1 mon","abstract":"anomalía de precipitación mensual según pronóstico del modelo ECMWF"},{"id":47,"nombre":"conae_api","data_table":"conae_api","tipo":"SM","intervalo_de_muestreo":"1 day","abstract":"índice de precipitación antecedente para el sur de Sudamérica - CONAE - Simulado a partir de campo de precipitación GPM - Valor medio de 7 días"},{"id":48,"nombre":"gefs_wave","data_table":"gefs_wave","tipo":null,"intervalo_de_muestreo":"00:00:00","abstract":"NCEP WAVE Model Forecasts"},{"id":49,"nombre":"conae_gc","data_table":null,"tipo":"QPF","intervalo_de_muestreo":"1 day","abstract":null},{"id":51,"nombre":"persiann","data_table":"pp_persiann","tipo":"PA","intervalo_de_muestreo":"1 day","abstract":"The current operational PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) system developed by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI) uses neural network function classification/approximation procedures to compute an estimate of rainfall rate at each 0.25° x 0.25° pixel of the infrared brightness temperature image provided by geostationary satellites. An adaptive training feature facilitates updating of the network parameters whenever independent estimates of rainfall are available. The PERSIANN system was based on geostationary infrared imagery and later extended to include the use of both infrared and daytime visible imagery. The PERSIANN algorithm used here is based on the geostationary longwave infrared imagery to generate global rainfall. Rainfall product covers 60°S to 60°N globally."}]}