AI assistant
Conversational queries with weather tooling: clothing, alerts, forecast, location and actionable answers.
Snowy: a weather platform with maps, real-time data and AI.
Snowy is a weather platform for forecasts, maps, stations, alerts, reservoirs, air quality, earthquakes and smart tools, in a fast experience aimed at real decisions.
The difference is bringing product, data, SEO, interactive maps and agents together on an architecture of my own: SSR frontend, backend as the source of truth, per-domain cache and dedicated services for radar, CMS and batch jobs. Over the last 3 months Snowy has passed 7.5 million impressions, 51k organic clicks and 1,200 registered users.
What Snowy is
A weather platform for Spain with multi-model forecasting, live stations, reservoirs, earthquakes, air quality, pollen, alerts, interactive maps, WikiMeteo and an AI assistant.
Multi-model forecasting
Comparison of weather models, forecasts by location and tools to make sense of uncertainty.
Weather map
Radar, stations, alerts, earthquakes, air quality and environmental layers in one interactive interface.
Live stations
A station network with current and historical data, plus an onboarding flow for users.
SEO content
Indexable pages for locations, phenomena, pollen, air quality, reservoirs and WikiMeteo.
AI assistant
Conversation, voice, specialised tools and answers grounded in weather data.
Derived products
Energy, embeddable widgets, eclipse 2026 and new verticals on the same technical base.
Modules
Different surfaces, one architecture.
Each module has different requirements: external data, cache, visualisation, SEO, real-time state and shared internal models.
Reservoirs
Official data, weekly evolution, maps and comparisons by region, province and river basin.
Historical climate
Processing of historical series to analyse trends, anomalies and temperature change by area.
Earthquakes
Real-time monitor with official sources, magnitude, location, event detail and community reports.
Stations
Station detail with current metrics, history, favourites, owner and live weather data.
Real traction, not just architecture.
SEO, performance and product usefulness already show up in usage: organic search, clicks and registered users on a platform of my own.
7.5M
impressions
last 3 months in organic search
51k
clicks
traffic from Google over 3 months
1,200+
registered users
an owned base for community and new features
16
models
ECMWF, GFS, ICON, ARPEGE, GEM and more
1,000+
stations
official network and Snowy community
370+
reservoirs
status and evolution across Spain
1,000+
terms
WikiMeteo in Spanish
20+
AI tools
assistant, voice and daily decisions
10+
map layers
radar, stations, risks and air
How it is built.
Snowy runs on a decoupled architecture: Next.js for SSR, SEO and UI; NestJS for business logic and data; Redis for cache; MySQL for persistence; and separate services where radar, CMS or jobs carry different loads.
Architecture
A multi-repo ecosystem with a Next.js front, NestJS engine, Vite CMS, Node.js radar, batch jobs and cross-cutting documentation.
Technical SEO
Indexable pages for cities, models, tools, stations, reservoirs, earthquakes, air quality, pollen, alerts, WikiMeteo and special content.
Data
Professional models, stations, reservoirs, earthquakes, air quality, pollen and official sources unified under one internal model.
Infrastructure
Production on VPS, Docker, Caddy, Cloudflare, GHCR, GitHub Actions, healthchecks, rollback and operational runbooks.
Radar
Interactive map with radar, stations, earthquakes, air quality, risk zones and environmental layers in real time.
AI
A weather assistant with natural language, voice mode and specialised tools that turn weather data into practical decisions.
B2B
Snowy Energy, embeddable widgets and sector verticals as a natural extension of the core weather product.
SEO, data and performance as architecture decisions.
Users and Google both need fast answers. That's why the project runs on SSR, per-domain cache, an internal data model, IndexNow, revalidation and provider abstraction.
Integrated sources
AEMETEuskalmetMeteoGaliciaMITECOUSGSIGNCAMS
The goal is to unify heterogeneous providers into one consistent model, precompute the expensive parts and answer the end user very fast.
Energy, widgets and sector verticals.
The same data, maps, forecasts and AI make derived products possible: energy forecasting, embeddable widgets and tools for specific cases.
Snowy Energy
Renewable forecasting, simulator and dashboard for solar energy as a B2B vertical inside the Snowy ecosystem.
B2B widgets
An embeddable SDK to bring weather data, maps, tools or the AI assistant into third-party sites.
Eclipse 2026
A content and planning product around the total solar eclipse of 12 August 2026.
Snowy has had public reach too.
The project was born out of LaRiojaMeteo and has appeared in press, radio and public portals. That's a signal of a real product, a community and continuity.
Interview on RNE
Radio appearance explaining Snowy and how the weather project has evolved.
Article in larioja.com
Regional press coverage of the Snowy launch out of LaRiojaMeteo.
datos.gob.es
Public listing
Snowy is listed on Spain's national open data portal.
El Confidencial
18 January 2026
La Rioja Meteo launches Snowy, an AI-powered site for everyday weather decisions.
larioja.com
22 January 2026
Snowy is born: a La Rioja assistant for umbrellas, laundry and daily recommendations.
eldiario.es
12 January 2026
La Rioja Meteo creates Snowy, a weather platform with practical recommendations.
nuevecuatrouno
12 January 2026
Snowy launches as La Rioja Meteo's new weather platform.
Diario de León
14 April 2026
Snowy as a tool for planning eclipse viewing in León.
Actualidad Rioja Baja
12 January 2026
La Rioja Meteo launches Snowy as an advanced, accessible weather platform.
nuevecuatrouno
11 April 2026