CLIMORAPLATFORMS, DATA & HARDWARE
Ready-made solutions to integrate climate data, reports and field monitoring — from cloud to sensor.
Weather Brasil is a climate data and analytics platform focused on Brazil. We combine information from the INMET network, historical series since the 2000s and AI tools to fill gaps, validate data and support forecasts. We offer API, dashboard with maps and charts, regional and period analysis, reports, alerts and multi-horizon forecasts — aiming to support agriculture, risk management, economics and research. We transform climate data into smarter decisions.
A fixed measurement point that regularly records what happens in the air and soil around the sensor: wind, temperature, humidity, rain and, depending on the model, radiation and pressure. No wires during deployment, no reliance on local Wi-Fi and no field trips to collect records — transmission and storage run autonomously, with 100% independent daily operation. This data supports alerts, historical series and analysis for agriculture, construction, events and risk management.
Series from the 2000s, based on the INMET network, at three levels of detail — national, regional and local — plus an overview of the evolution and coverage of data and stations.
Weather Brasil's historical reports translate decades of meteorological records into clear reading for planning and decision-making: long-term trends, extremes, accumulated rainfall, temperature and wind, with visualizations and comparisons according to the chosen scope. The evolution and coverage module shows how the station network and record quality evolve over time (data without AI completion, for methodological transparency).
Integration and harmonization of surface observations, hydrological series and reanalysis fields — a single pipeline with metadata and traceability.
We combine INMET network data, ANA series and stations and ERA5 reanalysis products into a coherent view: same units, grids and periods where applicable, with explicit fusion and validation rules. The goal is to reduce silos between meteorology and hydrology, accelerate cross-referencing for models and reports and support decisions with a single query interface — from station to reanalysis field, without manual copies between systems.
