In a region where agriculture supports over 40% of the population and contributes significantly to GDP, climate unpredictability poses more than just an environmental risk—it threatens livelihoods, food security, and economic stability. For countries like the Philippines, where rice farming dominates and extreme weather events are intensifying due to climate change, the stakes are particularly high. Despite this, many farmers still rely on generic, regional forecasts that lack the granularity needed for precise, real-time decision-making on the ground.
Enter a new wave of climate technology: AI-driven, plot-level weather forecasting designed to empower farmers and governments with hyperlocal insights. This emerging field is gaining traction in Southeast Asia, where outdated forecasting systems and limited infrastructure have traditionally hampered rural communities’ ability to adapt to climate shocks. Unlike traditional systems that rely heavily on ground-based radar or large-area models, the latest solutions integrate satellite data, machine learning, and adaptive AI models tailored to the region’s unique topography and microclimates.
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The Philippines has become a proving ground for this technology. In collaboration with the National Irrigation Administration (NIA) and supported by the Department of Agriculture, Tomorrow.io—a U.S.-based climate intelligence company—is piloting a new generation of forecasting tools that offer real-time, plot-specific weather updates. This shift isn’t merely technological; it represents a policy and infrastructure leap forward, aimed at reducing crop losses, optimising irrigation, and improving harvest yields in the face of increasingly erratic weather conditions.
What makes this initiative particularly noteworthy is its scope and scalability. Rather than rolling out a one-size-fits-all platform, the pilot program is being tailored to provinces most vulnerable to climate disruptions, serving as both a risk mitigation tool and a strategic framework for broader regional adoption. The platform’s ability to integrate data from local agencies, global satellites, and AI models specifically trained on Southeast Asian weather patterns positions it as a potential blueprint for climate adaptation across ASEAN.
Beyond agriculture, the implications of hyperlocal weather intelligence extend to sectors like logistics, aviation, disaster response, and energy—areas where real-time climate insights can reduce downtime, increase safety, and boost operational efficiency. As Southeast Asia continues to grapple with the dual pressures of digital transformation and climate resilience, innovations like AI-powered weather forecasting are no longer optional—they’re essential infrastructure for future-proofing the region’s most critical systems. We speak to Rei Goffer, Co-Founder and Chief Strategy Officer at Tomorrow.io, about the growth of AI in the industry in Southeast Asia.
Can you walk us through how this partnership with the National Irrigation Administration came about? What was the initial challenge you were trying to solve?
Absolutely.
This partnership was born from a shared mission: to strengthen climate resilience for the Filipino agricultural community, which is especially vulnerable to extreme weather events like typhoons, droughts, and monsoon variability. The National Irrigation Administration (NIA) approached the market with a clear challenge: farmers across the Philippines lacked access to timely, location-specific weather forecasts, which severely impacted planting decisions, irrigation efficiency, and harvest outcomes.
Which specific regions or provinces in the Philippines will be the first to benefit from Tomorrow.io’s AI forecasting tools? Why were these areas selected?
Our initial rollout focuses on pilot regions identified in partnership with the Department of Agriculture and NIA, based on several criteria: vulnerability to extreme weather, reliance on rice farming, and historical irrigation challenges.
While we haven’t named every province publicly yet, these are key agricultural zones where climate variability directly threatens crop yield and food security. By starting here, we’re ensuring the greatest immediate impact while building a scalable model for broader national implementation.
What sets Tomorrow.io’s forecasting system apart from PAGASA or traditional satellite and radar networks used in Southeast Asia?
Great question. While PAGASA does essential national forecasting, Tomorrow.io complements and enhances these capabilities in several key ways:
- Satellite + AI hybrid: Unlike traditional radar-reliant systems, our platform is augmented by a constellation of proprietary satellites and AI models. This enables global, continuous, and high-frequency data collection, including over oceans and remote areas underserved by ground-based infrastructure.
- Plot-level precision: We deliver forecasts down to the individual farm plot, not just regional zones, which is crucial in a country with microclimates like the Philippines.
- Faster revisit times: Our space-based sensors refresh atmospheric data more frequently than traditional systems, allowing for rapid updates multiple times per hour, even during rapidly evolving storm systems.
- AI-powered customisation: Our models are dynamically tailored to local use cases — whether that’s predicting rainfall for irrigation planning or typhoon onset for emergency preparedness.
Can you explain what ‘plot-level forecasting’ means in practice? How precise is the system, and how frequently is it updated?
Plot-level forecasting refers to the ability to deliver hyperlocal weather insights at the scale of individual farms, often within a few square meters. For farmers, this means receiving precise guidance on when to irrigate, plant, or apply fertiliser based on real-time weather conditions specific to their exact location.
Our system updates several times per hour, thanks to a combination of low-earth-orbit satellites, AI assimilation models, and cloud computing infrastructure. This results in forecasting accuracy that adapts to hyperlocal terrain, vegetation, and climate patterns, a level of precision that traditional radar systems can’t deliver.
What kind of AI models are used in Tomorrow.io’s platform? Are they trained specifically on Southeast Asian climate patterns?
We use a suite of machine learning, AI, and physics-informed models, many of which are trained on region-specific atmospheric, hydrologic, and agronomic data. Our AI models ingest not only satellite observations but also historical weather trends, crop cycles, and ground-truth data, including inputs from local agencies.
In Southeast Asia, we’ve adapted our models to handle unique challenges like monsoonal patterns, rapid tropical storm formation, and topographic diversity. The result is a highly adaptive forecasting engine that learns and improves as more localised data becomes available.
Beyond agriculture, how do you see this weather intelligence benefiting other critical sectors like logistics, aviation, or disaster response in the Philippines?
Our weather intelligence has broad applications across logistics, aviation, energy, mining, insurance, and disaster management:
- Logistics & supply chain: Predict and reroute deliveries to avoid flooded roads or storm delays.
- Aviation: Real-time crosswind and runway risk forecasts for flight planning.
- Disaster response: Hyperlocal storm surge or flooding alerts that enable faster, more targeted evacuations and resource deployment.
- Energy: Forecasting solar irradiance, wind potential, or lightning risk to protect infrastructure.
We’re not just forecasting the weather; we’re forecasting operational impact to help organisations make smarter, safer, and faster decisions.
What role is the Department of Agriculture playing in supporting farmer education and adoption of this technology? Is there funding or training involved?
Yes, the Department of Agriculture is playing a crucial leadership role in farmer engagement and training. Together with NIA, they are:
- Facilitating training sessions to help farmers interpret and act on the weather insights we provide.
- Developing agronomic advisories that link forecasts directly to farming decisions, like when to irrigate or delay planting.
- Exploring funding mechanisms and public-private partnerships to ensure sustainable access to the platform for all farmers, including those in underserved communities.
The goal is not just to provide cutting-edge tools but to make sure they are accessible, understood, and trusted.
Looking ahead, what are Tomorrow.io’s plans for Southeast Asia? Are you exploring similar partnerships in Indonesia, Vietnam, or Thailand?
Absolutely. Southeast Asia is a climate-vulnerable region with fast-growing economies, and we see tremendous opportunity to scale impact across agriculture, infrastructure, and public safety.
We’re actively exploring similar partnerships in the region, particularly where governments and enterprises are looking to combine resilience goals with digital transformation. Our satellite-AI ecosystem is inherently scalable, so what we’re building in the Philippines is designed to serve as a blueprint for regional rollout.