Cataphoric Flooding in U.S. - History of Cloud Seeding and the Gateway to AI-Driven Weather Models for Cloud Seeding Operations

Jul 11, 2025

Cataphoric Flooding in U.S. - History of Cloud Seeding and the Gateway to AI-Driven Weather Models for Cloud Seeding Operations

The Kerrville flood of 2025 refers to a catastrophic flash flood event that struck Kerr County, Texas, particularly along the Guadalupe River, during the Fourth of July weekend. For the past week, there has been major flooding and devastation in several regions of the United States. Our thoughts and prayers go to ALL those that have been affected but these cataphoric events. Here are a few areas that have had cataphoric floods.

On July 4, 2025, heavy rainfall led to severe flash flooding in Central Texas, with the Guadalupe River rising approximately 26 feet in just 45 minutes. Kerrville and nearby areas like Ingram and Hunt were among the hardest hit. The flooding caused widespread destruction, sweeping away campers, vehicles, and structures, including RV parks and summer camps like Camp Mystic. There are at least 84 people died in Kerr County alone, including 56 adults and 28 children. Statewide, the death toll reached at least 111, with reports of up to 120 fatalities across multiple counties.

https://www.nytimes.com/live/2025/07/08/us/texas-floods

IN Ruidoso New Mexico On July 8th, the US National Weather Service issued a Flash Flood Warning for Ruidoso. Images coming into our newsroom show high levels of floodwaters rushing through town.

https://kvia.com/news/new-mexico/2025/07/08/heavy-flash-flooding-in-ruidoso/

North Carolina facing another flood threat just days after Chantal’s deadly flooding

Days after deadly flooding in North Carolina from Tropical Depression Chantal, the flooding threat has returned, with more heavy rain expected to fall over the Carolinas and up through the Interstate 95 Corridor. Leftover moisture from Chantal brought nearly a foot of rain across parts of the mid-Atlantic toward the end of the July Fourth weekend and into this week, including in North Carolina.

https://nypost.com/2025/07/09/us-news/north-carolina-facing-another-flood-threat-just-days-after-chantals-deadly-flooding/

Lisa McGee’s (VaxxCHOICE) Report on the Texas Flood 2025

History of Cloud Seeding and the Gateway to AI-Driven Weather Models for Cloud Seeding Operations

Cloud seeding is a weather modification technique that aims to enhance precipitation (rain or snow), reduce hail, or disperse fog by introducing substances into clouds to encourage water droplet or ice crystal formation.

Aircraft or ground-based devices disperse materials like silver iodide, dry ice (solid carbon dioxide), calcium chloride and LIQUID PROPANE into clouds. These substances act as nuclei around which water droplets or ice crystals form, potentially increasing rainfall or snowfall.

The questionable “validation “for such is that it is used to boost water supply in drought-prone areas, reduce hail damage, or increase snowpack for water resources. It can also be used to clear fog or mitigate severe weather in some cases.
Results vary on the effectiveness of the manipulation of weather; as well as the corrosive materials / chemical compunds used. Studies suggest cloud seeding can increase precipitation by 5-15% under optimal conditions, but success depends on cloud type, temperature, and atmospheric conditions. It's not a guaranteed rainmaker.

Developed in the 1940s, cloud seeding has been used globally, notably in the U.S., China, Australia, and the UAE, often for agricultural or water management purposes.

There are many controversies surrounding - critics raise concerns about environmental impacts (e.g., silver iodide, liquid propane toxicity), unintended weather changes, or ethical issues like "stealing" rain from neighboring areas.
Beyond cloud seeding, techniques include hail suppression (disrupting hailstone formation) or fog dispersal. Valid concerns is the exposure to controversial and harmful geoengineering experiments (e.g., solar radiation management).

Here’s a brief history of its development and global use:

1940s – The Beginning

1946: The first successful cloud seeding experiment was conducted by Vincent Schaefer and Irving Langmuir at General Electric in New York.

Schaefer dropped dry ice (solid CO₂) into a supercooled cloud, causing the formation of ice crystals and precipitation.

Shortly after, Bernard Vonnegut (Kurt Vonnegut’s brother) discovered that silver iodide (AgI) could also be used effectively due to its similar crystal structure to ice.

Global Expansion and Research

1950s–1970s – Commercial and Military Use

Cloud seeding became a focus of government and military programs:

Project Cirrus (USA): Early attempts to control hurricanes (ultimately unsuccessful and controversial).

Project Stormfury (1962–1983): U.S. Navy program to weaken hurricanes by seeding eyewalls with silver iodide—results were inconclusive and led to program termination.

Civilian uses spread to agriculture (for rainfall) and aviation (for fog dispersal at airports).

China, Russia, Australia, and Israel began major weather modification programs during this period, many of which continue today.

Modern Cloud Seeding Programs

1980s–2000s

Scientific scrutiny increased, with mixed reviews about effectiveness due to the variability of natural weather and challenges in measuring impact.

However, operational programs continued worldwide, especially in:

Australia (Snowy Mountains)

Israel (rain enhancement in the Judean Hills)

China (weather control during the 2008 Olympics)

UAE and Saudi Arabia (water resource management in arid climates)

UAE Research Program for Rain Enhancement Science (2015–present)

Aims to invest in cutting-edge cloud seeding and weather modification technologies using nanotechnology and AI-based forecasting

Today’s Advances

Modern cloud seeding uses aircraft, drones, and rockets to deliver agents like:

Silver iodide, Potassium iodide, Calcium chloride, Liquid propane (for colder cloud tops)

AI-Driven Weather Models for Cloud Seeding Operations

***AI and data modeling (like NVIDIA Earth-2) are now used to optimize targeting and improve effectiveness forecasts.

AI-driven weather models enhance cloud seeding by improving weather forecasting, cloud identification, and operational efficiency. Below are key AI-driven approaches and models relevant to cloud seeding, followed by information on their use by the U.S. Department of Defense (DoD) and Department of Energy (DOE).

AI-Driven Weather Models for Cloud Seeding

1. Machine Learning for Cloud Identification and Forecasting:

Description: Machine learning (ML) models, such as neural networks and decision trees, analyze meteorological data (e.g., temperature, humidity, wind patterns) from satellites, radar, and ground sensors to identify clouds suitable for seeding. These models predict supercooled liquid water content and optimal seeding conditions (e.g., temperatures around -5°C).

For example, convolutional neural networks (CNNs) process satellite imagery to detect convective clouds with high precipitation potential.

In the UAE, Bayanat’s AI-powered Synthetic Aperture Radar (SAR) maps clouds in real-time, improving seeding precision by identifying clouds invisible to traditional satellites.

https://en.wikipedia.org/wiki/Cloud_seeding

Application: These models guide meteorologists in directing aircraft or drones to seed clouds with agents like silver iodide or hygroscopic materials.

2. Numerical Weather Prediction (NWP) Enhanced by AI:

Description: AI enhances traditional NWP models like the Weather Research and Forecasting (WRF) model by improving parameterization of cloud microphysics and atmospheric dynamics. Deep learning models, such as recurrent neural networks (RNNs), refine short-term forecasts (nowcasting) to predict cloud development and movement, critical for timing seeding operations.

Example: The WRF model, when integrated with AI, can simulate cloud responses to seeding agents, as seen in Texas programs where Doppler radar data is analyzed to assess seeded storm outcomes.

https://www.tdlr.texas.gov/weather/summary.htm

Application: AI-driven NWP models help meteorologists select target areas and optimize seeding schedules.

3. AI-Based Decision Support Systems:

Description: AI systems integrate data from multiple sources (radar, satellite, weather stations) to provide real-time recommendations for seeding operations. Reinforcement learning algorithms can optimize flight paths for seeding aircraft or drones, reducing costs and improving efficiency.

Example: In Texas, projects like the West Texas Weather Modification Association (WTWMA) use AI to process Doppler radar data, guiding pilots to seed clouds with high precipitation potential.

https://www.tdlr.texas.gov/weather/summary.htm

Application: These systems support meteorologists in operational decisions, such as choosing between static seeding (for ice particle formation) or dynamic seeding (to enhance convective cloud growth).

4. AI-Driven Drone and Autonomous Seeding:

Description: AI enables autonomous drones to deliver seeding agents (e.g., silver iodide, dry ice, or electric charges) with precision. Computer vision and ML guide drones to optimal cloud layers, while AI models predict real-time atmospheric changes.

Example: Pakistan’s 2023 cloud seeding experiment, supported by the UAE, used AI-guided drones to seed clouds in Lahore, achieving drizzle in 10 areas.

https://en.wikipedia.org/wiki/Cloud_seeding

Application: Drones reduce human risk and operational costs, with AI ensuring accurate delivery of seeding agents.

5. Physics-Informed Neural Networks (PINNs):

Description: PINNs combine physical laws of atmospheric science with neural networks to simulate cloud seeding outcomes. These models predict how seeding agents interact with cloud microphysics, estimating precipitation increases.

Example: While not explicitly cited in cloud seeding, PINNs are used in weather modeling (e.g., by NVIDIA’s Earth-2 platform) to simulate precipitation processes, which could be adapted for seeding studies.

Application: PINNs could improve the understanding of seeding’s impact on complex thunderstorm clusters, as seen in Texas where seeded storms yielded 24% more rain on average.

https://www.tdlr.texas.gov/weather/summary.htm

Use by Department of Defense (DoD) and Department of Energy (DOE)

1. Department of Defense (DoD):

Historical Context: The DoD has a history of exploring weather modification, notably through **Operation Popeye** (1967–1972), where cloud seeding was used to prolong monsoons in Vietnam as a military tactic. This led to the 1977 Environmental Modification Convention (ENMOD) banning weather modification for hostile purposes.

https://en.wikipedia.org/wiki/Weather_modification

Current Involvement: There is no public evidence that the DoD currently uses AI-driven weather models for cloud seeding operations. Post-ENMOD, DoD’s focus has shifted to weather forecasting for military operations rather than active weather modification. Programs like the Air Force’s 54th Weather Reconnaissance Squadron have historically supported weather modification research (e.g., Project STORMFURY in the 1960s), but these efforts targeted hurricanes, not routine cloud seeding, and lacked conclusive AI integration. More on this included on the following report.

https://en.wikipedia.org/wiki/Cloud_seeding

AI Weather Models: The DoD uses AI-driven weather models for forecasting, such as those developed by the Air Force or DARPA, to support mission planning (e.g., predicting visibility or wind for aircraft operations). These models, like those based on NWP, could theoretically support cloud seeding but are not publicly linked to such activities today.

Speculation and Constraints: Conspiracy theories suggest ongoing DoD weather manipulation, but no credible evidence supports active cloud seeding programs. The DoD’s focus remains on defense-related weather forecasting, not precipitation enhancement.

https://en.wikipedia.org/wiki/Cloud_seeding

Department of Energy (DOE):

Current Involvement: The DOE does not directly engage in cloud seeding operations. Its role in weather-related research focuses on climate modeling, atmospheric science, and environmental impacts, often through national laboratories like Argonne or Pacific Northwest National Laboratory (PNNL).

AI Weather Models: The DOE funds AI-driven climate and weather models, such as those using machine learning to improve global climate simulations (e.g., Energy Exascale Earth System Model, E3SM). These models study atmospheric processes, including cloud formation, but are not tailored for operational cloud seeding. For example, PNNL’s AI research on aerosol-cloud interactions could inform seeding science.

https://www.gao.gov/products/gao-25-107328

Potential Relevance: DOE research on cloud microphysics and precipitation could indirectly support cloud seeding by improving AI models of cloud behavior. However, no DOE programs explicitly fund or conduct cloud seeding operations.

Challenges: DOE’s focus on long-term climate impacts contrasts with the short-term, localized nature of cloud seeding, limiting its involvement. Any application of DOE’s AI models to seeding would likely occur through partnerships with agencies like NOAA or state programs.

Key Notes

Effectiveness: AI-driven models improve cloud seeding precision, but effectiveness remains limited by natural cloud availability. Studies estimate 0–20% precipitation increases, with challenges in isolating seeding effects from natural variability.

https://www.gao.gov/products/gao-25-107328

NVIDIA’s Earth2 is a cuttingedge “digital twin” of our planet—an AI-enhanced, GPU-accelerated platform designed to simulate and visualize weather and climate at kilometerscale resolution.

NVIDIA Earth2 is a breakthrough in climate and weather simulation. By combining generative AI, GPU acceleration, cloud services, and visualization, it's making hyper-local, ultra-fast, and energy-efficient modeling possible—and being adopted by governments, enterprises, and research institutions globally.

Ultra-high-resolution insights: from global patterns to city-level weather phenomena, including urban wind downwash, cyclones, floods.

Speed + efficiency: enables rapid simulation, iterative scenario testing, ensemble forecasting—critical during extreme events.

Accessibility: cloud APIs and Python SDK democratize powerful weather modeling tools for a wide range of users.

Crosssector potential: valuable for disaster prep, agriculture, urban planning, energy, insurance, and policy analyses.

Core Highlights

Generative super-resolution (CorrDiff):

CorrDiff uses diffusion-based AI to convert coarse (~25 km) weather data into ultra-high-resolution forecasts (~2 km), operating 500× faster and 10 000× more energy-efficiently than CPU-driven models, and delivering synthesized variables like radar reflectivity.

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Global forecasting models (FourCastNet, cBottle):

Earth2 incorporates AI models like FourCastNet for extended forecasts (up to 21 days with 1000member ensembles 1000× faster)

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The new cBottle model further enables kilometer-scale climate simulations, compressing massive data volumes while improving resolution and speed.

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Omniverse visualization & APIs:

Using NVIDIA Omniverse, Earth2 offers interactive demos—viewable down to city street level and layered with simulations from ICON, WRF, or PALM—via cloud APIs and DGX Cloud-powered workflows.

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Python SDK & Microservices (NIMs):

Tools like Earth2Studio, CorrDiff NIM, FourCastNet NIM, and PhysicsNeMo let researchers and developers prototype in Python using public datasets (ERA5, GFS) and pretrained models

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Early adopters include:

Government agencies: e.g. NOAA, Taiwan Central Weather Admin, The Weather Company

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Climate-tech partners: Spire, G42, OroraTech integrate satellite and thermal-fire data; JBA Risk Management uses it for disaster risk modeling

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Research institutions: Max Planck Institute, Alan Turing Institute, MPI-M, Berkeley Lab and others leverage Earth2 for climate science.

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Resource links:

WEATHER AND CLIMATE MODIFICATION - National Science Foundation

https://nsf-gov-resources.nsf.gov/nsb/publications/1965/nsb1265.pdf

Weather Modification - Who and Where