Cloud Seeding & Delayed Effects

Counterpoint #11

Public distrust, fuelled by ‘blue rain’ misinformation, suggests there is a need for the so-called experts to be more transparent, and in the first instance to acknowledge that there were two different forms of cloud seeding carried out immediately before the recent horrific flooding in Texas.

One of these forms of cloud seeding, targeting what are known as warm clouds with sodium chloride, could indeed have worked to supercharge the existing storm system.

On July 2, 2025, Rainmaker’s cloud seeding operation in Karnes County, Texas, deployed two distinct chemicals—silver iodide (AgI) and sodium chloride (NaCl)—targeting different cloud types to enhance rainfall. These chemicals, combined with a subsequent storm, may have amplified the devastating July 4 flood, with Danish physicist, Henrik Svensmark’s theory of cloud formation known as cosmoclimatology, offering a critical lens on the delayed effects.

Silver iodide (AgI, 70g) was used for cold clouds (below 0°C), triggering the Bergeron process. AgI mimics ice, promoting ice crystal growth that falls as drizzle. Its effects, seen in projects like Stormfury (1962–1971), are immediate but short-lived, dissipating within 20 hours.

Sodium chloride (NaCl, 500g) targeted warm clouds (above 0°C), dominant in Tropical Storm Barry. NaCl, a hygroscopic agent, absorbs water vapour to form droplets, driving condensation and coalescence for rainfall. Unlike AgI, NaCl’s effects can persist, reshaping cloud dynamics over days.

NaCl’s role extends beyond immediate rain. By forming cloud condensation nuclei (CCN), it enhances droplet formation in warm clouds. These CCN can linger for up to 4–7 days, as detailed in Svensmark’s cosmoclimatology research.

Rainmaker’s 500g NaCl, seeded on July 2, likely increased CCN availability, priming the atmosphere for heavier rainfall when Tropical Storm Barry arrived 36–48 hours later.

That is my hypothesis that I am sharing.

I am sharing this, because there is absolutely a need for more discussion, for a better understanding of precipitation processes by meteorologists and the public more generally. There is also a need for better tools to be used for rainfall forecasting.

My publications, many in the best atmospheric research journals, and concerning rainfall forecasting* have been repeatedly dismissed by the gatekeepers within the bureaucracies that control weather and climate research in The West because these men insist that everything accord with their catastrophic human-caused global warming paradigm that is irrelevant and they are mostly wedded to outdated tools for rainfall forecasting.

The recent flooding along the Guadalupe, with 12–15 inches of rain and 109 deaths, followed a turbulent storm that hit after seeding ceased. Svensmark’s theory suggests that lingering CCN, like those from NaCl, can amplify rainfall when a storm’s turbulence acts as a ‘spark’.

The 36–48-hour gap aligns with this 4–7-day delay, indicating that NaCl’s CCN, combined with the storm’s dynamics, likely intensified condensation and coalescence, driving the flood’s extreme rainfall.

There is nothing straight forward about cloud seeding, and we know from all the work by the US government during the period 1962 to 1971 that there can be unintended consequences.

Just because this river system has experienced terrible flooding historically, doesn’t mean that the newfound enthusiasm for weather modification didn’t play a part in the recent flooding.

A proper understanding of weather, including the different physical mechanisms that cause clouds to rain, is important. The continued reference to carbon dioxide as relevant to rainfall and flooding is nonsense.

Svensmark’s framework underscores the need for precise timing and monitoring to avoid exacerbating natural storms. This requires some understanding of cosmoclimatology, specifically that seeding with NaCl/sodium chloride needs to be evaluated in terms of the time it will take for CCN/cloud condensation nuclei to grow, and how their impact can be amplified by electrostatic affects within storm cells. Advances in artificial neural networks (ANN) for rainfall forecasting, work that I pioneered with John Abbot, would likely provide a better mechanism for forecasting these impacts.

*Some of my publications on rainfall forecasting using Artificial intelligence include:

Abbot, J. & Marohasy, J. 2017. Forecasting extreme monthly rainfall events in regions of Queensland, Australia, using artificial neural networks. International Journal of Sustainable Development & Planning, Volume 12, Pages 1117-1131.DOI 10.2495/SDP-V12-N7-1117-1131. (Open access.)

Abbot, J. & Marohasy, J. 2017. Application of artificial neural networks to forecasting monthly rainfall one year in advance for locations within the Murray Darling Basin, Australia, International Journal of Sustainable Development & Planning. Volume 12, Pages 1282-1298. DOI 10.2495/SDP-V12-N8-1282-1298.

Abbot, J. & Marohasy, J. 2016. Forecasting monthly rainfall in the Bowen Basin of Queensland, Australia, using neural networks with Nino indices. In AI 2016: Advances in Artificial Intelligence, Eds. B.H. Kand & Q. Bai. DOI: 10.1007/978-3-319-50127-7_7.

Abbot, J. & Marohasy, J. 2016. Forecasting monthly rainfall in the Western Australian wheat-belt up to 18-months in advance using artificial neural networks. In AI 2016: Advances in Artificial Intelligence, Eds. B.H. Kand & Q. Bai. DOI: 10.1007/978-3-319-50127-7_6.

Marohasy, J. & Abbot J. 2016. Southeast Australian Maximum Temperature Trends, 1887–2013: An Evidence-Based Reappraisal. In Evidence-Based Climate Science (Second Edition), Ed. D. Easterbrook. Pages 83-99. http://dx.doi.org/10.1016/B978-0-12-804588-6.00005-7

Marohasy, J. & Abbot, J. 2015. Assessing the quality of eight different maximum temperature time series as inputs when using artificial neural networks to forecast monthly rainfall at Cape Otway, Australia, Atmospheric Research, Volume 166, Pages 141-149. doi: 10.1016/j.atmosres.2015.06.025.

Abbot J. & Marohasy J. 2015. Using artificial intelligence to forecast monthly rainfall under present and future climates for the Bowen Basin, Queensland, Australia, International Journal of Sustainable Development and Planning, Volume 10, Issue 1, Pages 66 – 75. DOI: 10.2495/SDP-V10-N1-66-75

Abbot J. & Marohasy J. 2015. Using lagged and forecast climate indices with artificial intelligence to predict monthly rainfall in the Brisbane Catchment, Queensland, Australia, International Journal of Sustainable Development and Planning. Volume 10, Issue 1, Pages 29-41.

Abbot J. & Marohasy J., 2015. Improving monthly rainfall forecasts using artificial neural networks and single-month optimisation in the Brisbane Catchment, Queensland, Australia. WIT Transactions on Ecology and the Environment, 196: 3-13.

Abbot J. & Marohasy J., 2015. Forecasting of monthly rainfall in the Murray Darling Basin, Australia: Miles as a case study. WIT Transactions on Ecology and the Environment, 197: 149-159.

Abbot J. & Marohasy J. 2014. Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks. Atmospheric Research, Volume 138, Pages 166-178.

Abbot J. & Marohasy J. 2013. The application of artificial intelligence for monthly rainfall forecasting in the Brisbane Catchment, Queensland, Australia. River Basin Management VII. WIT Press. Editor C.A. Brebbia. Pages 125-135.

Abbot J. & Marohasy J. 2013. The potential benefits of using artificial intelligence for monthly rainfall forecasting for the Bowen Basin, Queensland, Australia. Water Resources Management VII. WIT Press. Editor C.A. Brebbia. Pages 287-297.

Abbot J., & J. Marohasy, 2012. Application of artificial neural networks to rainfall forecasting in Queensland, Australia. Advances in Atmospheric Sciences, Volume 29, Number 4, Pages 717-730. doi: 10.1007/s00376-012-1259-9 .