Responsible AI · Extreme Weather

In the face of extreme weather hazards,
we need AI built for people, not for profit.

Using AI responsibly and transparently to improve communication and outcomes of extreme weather hazards.

A nonprofit at the intersection of AI and extreme weather

The Challenge

Despite transformative advances in AI, challenges still exist in protecting life and property. Losses are still present, and in many cases, increasing.

Total Affected

Total Damages

Source: EM-DAT, CRED / UCLouvain ↗

Critical

Extreme weather forecasts often fail the communities who need them most

Warning

AI models used in emergency decisions lack transparency and explainability

Advisory

Critical research is siloed, slowing life-saving response coordination

Elevated

Growing hazard frequency outpaces the tools available to forecasters and responders

Our Mission

A convergent framework for equitable & sustainable impact in extreme weather AI.

01

Data Sovereignty

Communities and organizations retain control and privacy over the data used to train, validate, and deploy AI systems for extreme weather outcomes.

02

AI/ML Model Access/Building

Building and democratizing access to AI and ML tools for extreme hazard forecasting.

03

Community Driven Solutions

The most durable solutions emerge from collaboration. designed alongside the communities and organizations who need it, opposed to being delivered by the top-down.

Scope of Impact

195+

Countries facing extreme weather hazards

1B+

People in high-risk weather zones

500K+

Potential beneficiaries of improved AI guidance

Who We Are

A mission-driven team of scientists, engineers, and strategists united by a belief that AI for extreme weather must be responsible, open, and built for the people it serves.

Dr. Amy McGovern

Amy is a pioneer in AI for weather and climate hazards research. Leads efforts to ensure AI tools used in high-stakes weather decisions are explainable and trustworthy.

Mel Wilson Reyes

Mel's expertise is in product management and computer science. Her background includes lifecycle management in AI2ES and getting a myriad of grants and funding over the line.

Taylor Mandelbaum

Taylor is an atmospheric scientist, data scientist, and software engineer with expertise in synoptic meteorology and communicating uncertainty. He previously worked in the energy industry within various sized companies at the intersection of product, data science, and data stewardship.

Maria Madsen

Maria is a research scientist specializing in climate dynamics, subseasonal-to-seasonal predictability of extremes, machine learning, and science communication. Her postdoctoral experience at AI2ES and industry experience at Salient working with forecast end users have deepened her motivation to develop forecasts that are relevant, trustworthy, and understandable to the people who depend on them most.

Help us bring AI for extreme weather hazards to communities and organizations that need it most.

www.extremeearth.org