Pronóstico de Precipitación por IA para Paraguay
AI Precipitation Forecasting for Paraguay
Presentación para GGGI Paraguay — Global Green Growth Institute
Relevante para CTAF, agricultura climáticamente inteligente, y financiamiento verde
Arawave complementa el portafolio de GGGI en Paraguay
Arawave directly complements GGGI's Paraguay portfolio
CTAF — Proyecto Paraguay (2026): Paraguay fue seleccionado para un proyecto de demostración de IA + imágenes satelitales para mapas de riesgo climático (inundaciones/sequías). Nuestro pronóstico de precipitación por IA complementa directamente ese trabajo.
Paraguay selected for CTAF field demo: AI + satellite imagery for flood/drought risk maps. Our AI precipitation forecasting directly complements this.
Financiamiento para Agricultura Climáticamente Inteligente: GGGI + AFD estructuran líneas de crédito y un Bono de Resiliencia. Nuestros pronósticos de días secos informan decisiones de riesgo agrícola.
GGGI + AFD structuring credit lines + Resilience Bond for climate-smart agriculture. Our dry-day forecasts inform agricultural risk decisions.
Estrategia SLCP + Mercado de Carbono (MADES): GGGI ejecuta proyectos de GCF con MADES. La red de 60 estaciones ($400–700K) es un proyecto de adaptación financiable vía GCF.
GGGI executes GCF projects with MADES (SLCP strategy, carbon market). The 60-station network ($400–700K) is a fundable adaptation project via GCF.
Paraguay no tiene un sistema de pronóstico construido para sus condiciones
Paraguay has no forecasting system built for its conditions
Red de observación con brechas críticas: 97 estaciones EMA listadas, solo ~25 reportaron en 24h. 26 silentes >6 meses. El Chaco es la región menos servida.
97 EMA stations listed, only ~25 reported in 24h. 26 silent >6 months. The Chaco is the least served.
Ningún modelo global se valida para Paraguay. GFS, IFS, ERA5 — se ejecutan sin calibración. Nadie sabe dónde fallan o cómo mejorarlos.
No global model is validated for Paraguay. GFS, IFS, ERA5 — run without calibration.
Nosotros cambiamos eso. We changed that.
Ensamble de pronóstico por IA, calibrado para Paraguay, con incertidumbre en cada número
AI forecast ensemble, calibrated for Paraguay, with uncertainty on every number
| Component | Description |
|---|---|
| AI Models | FCN3 (NVIDIA) + GraphCast (DeepMind) + GFS, cloud GPUs |
| Calibration | EMOS-NGR → probability distribution per grid cell |
| Probability Product | P(precip > 25mm/24h) — calibrated extreme-rain probabilities |
| Validation | 60 DMH EMA stations, 662 station-days, out-of-sample 2026 |
| Monitoring | DMH EMA feed polled every 5 min since May 2026 |
Every number verified, red-teamed, reproducible
El Chaco: menos observaciones, mayor vulnerabilidad, prioridad de adaptación para GGGI y MADES
The Chaco: fewest observations, highest vulnerability, adaptation priority for GGGI and MADES
Y es exactamente donde el modelo funciona mejor.
And it's exactly where the model works best.
El modelo compensa por la escasez de datos. El siguiente paso es instalar estaciones. Esa es la justificación para infraestructura financiable.
The model compensates for sparse data. Next step: deploy stations. That's the case for fundable infrastructure.
All already built. Cost to GGGI: $0.
| Offer | Relevance to GGGI |
|---|---|
| Climate adaptation evidence | Network gap documentation + validated skill data. Evidence for GGGI's GCF-funded adaptation work with MADES. |
| Heavy-rain probability | AUC 0.884. For early warning and climate risk management — core adaptation work. |
| The Chaco story | Model performs best in Paraguay's most climate-vulnerable region. Narrative ready for donor reports. |
| Fundable infrastructure case | 60-station network ($400–700K) qualifies for GCF/GEF. Model demonstrates ROI. |
| Sovereign capability | Built in Paraguay, Paraguayan data, public methodology. "National ownership" every donor requires. |
| Defensible methodology | Every number has a CI. Reproducible. Survives technical review. |
Everything we offer costs $0 (already built). Everything GGGI offers is knowledge, access, and framing.
Not asking for money. Asking for direction and access. GGGI knows which finance window fits a $400–700K network. That knowledge is worth months of research.
The business case for adaptation finance
The problem: Paraguay needs ~60 automatic stations for WMO standards. No observations → no good forecasts → no effective adaptation.
ROI demonstration: AI model works best where stations are sparsest. Each new station improves forecasts via data assimilation. Demonstrable feedback loop.
| Component | Cost | Notes |
|---|---|---|
| AI model (already built) | ~$120 | 2 months, full validation |
| Real-time operational pipeline | ~$1,500–3,000/yr | When a pilot user commits |
| 60-station observation network | $400,000–700,000 | Adaptation infrastructure — fundable via GCF/GEF |
GGGI structures the finance. We provide the technical case.
Small, specific, time-boxed. One ask per conversation.
| # | Ask | Why |
|---|---|---|
| 1 | Climate finance direction | Which window (GCF/GEF/KOICA) fits a $400–700K observation network? Who's the right accredited entity? |
| 2 | Inclusion in GCF evidence | Our network gap data and forecast skill are direct evidence for GGGI's GCF-funded adaptation work. |
| 3 | Letter of support / partnership | A GGGI partnership opens doors with DMH, donors, and cooperatives we can't open alone. |
Not asking GGGI to fund anything. Asking for the right path. They are the experts in structuring climate finance for Paraguay.
Already in conversations with relevant institutions
One specific conversation: How can Arawave complement GGGI's Paraguay portfolio — climate tech, climate-smart agriculture, or green finance for observation infrastructure?
¿Cómo puede Arawave encajar en el portafolio de adaptación de GGGI?
arawave.autoworkz.org →