Does Indonesia's dry ice cloud-seeding reduce haze, and how does it work?

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1. 1

   Precipitation enhancement in stratocumulus clouds through airborne seeding: sensitivity analysis by UCLALES-SALSA

   Atmospheric chemistry and physics (Tonttila et al.)Published Jan 26, 2021Checked Oct 19, 2026

   “We focus our investigation on marine stratocumulus clouds and evaluate our model results by comparing them with recently published results from field observations. Creating multiple realizations of a single cloud event with the model provides a robust method to detect and attribute the seeding effects, which reinforces the analysis based on experimental data. Owing to the detailed representation of aerosol-cloud interactions, our model successfully reproduces the microphysical signatures attributed to the seeding, that were also seen in the observations. Moreover, the model simulations show up to a 2–3 fold increase in the precipitation flux due to the seeding, depending on the seeding rate and injection strategy. However, our simulations suggest that a relatively high seeding particle emission rate is needed for a substantial increase in the precipitation yield, as compared with the estimated seeding concentrations from the field campaign. In practical applications, the seeding aerosol is often produced by flare burning. It is speculated, that the required amount of large seeding particles suggested by our results could pose a technical challenge to the flare-based approach.”

2. 2

   Summary Of Studies That Document The Effectiveness Of Cloud Seeding For Snowfall Augmentation

   The Journal of Weather Modification (Huggins)Published Apr 1, 2009Checked Oct 19, 2026

   “The important research areas covered include studies on: (1) the availability of supercooled liquid water, including its spatial and temporal distribution, (2) verifying the transport and dispersion of seeding material to clouds over target areas, (3) the microphysical changes caused by seeding with silver iodide and liquid propane, and (4) the precipitation increases that have been verified in physical studies and randomized experiments. The summary of results from past research details the specifics of the “strong suggestions of positive seeding effects” referred to in the 2003 report to the National Academies of Science entitled Critical Issues in Weather Modification Research. Although plentiful evidence regarding the effectiveness of cloud seeding is presented, it is also noted that further research on wintertime cloud seeding could provide answers to uncertainties that still exist, and also potentially benefit ongoing operational cloud seeding projects. Some recommendations for future research that take advantage of recent technological developments are presented.”

3. 3

   Advances in the Evaluation of Cloud Seeding: Statistical Evidence for the Enhancement of Precipitation

   Earth and Space Science (Wu et al.)Published Sep 1, 2018Checked Oct 19, 2026

   “We describe important issues, aiming to reduce systematic errors and uncertainties in statistical tests as well as increase the level of quantitative sounding and evaluation. First, a regularized set of design and operation in the steps should be established to optimize techniques of cloud seeding. Second, the latest achievements in atmospheric physics, physical sounding, and statistics need to be introduced to help improve the correctness and scientificity. Third, middle‐ and long‐term special research projects are expected to investigate the influence of ideal hypotheses of seeding schemes, statistical test plans, and statistical methods. These demands can update our knowledge and technology of weather modification and increase the cooperation of multidiscipline, such as logical integration of statistical tests, physical analysis, and numerical modeling.”

4. 4

   Structured dataset of reported cloud seeding activities in the United States (2000-2025) using an LLM.

   Scientific data (Donohue & Lamb)Published Dec 11, 2025Checked Oct 19, 2026

   “Cloud seeding, a weather modification technique used to increase precipitation, has been practiced in the western United States since the 1940s. However, comprehensive datasets are not currently available to analyze these efforts. To address this gap, we present a structured dataset of reported cloud seeding activities in the U.S. from 2000-2025, including the project name, year, season, state, operator, seeding agent, apparatus used for deployment, stated purpose, target area, control area, start date, and end date. Combining our multi-stage PDF-to-text extraction pipeline with OpenAI's o3 large language model (LLM), we processed 832 historical reports from the National Oceanic and Atmospheric Administration (NOAA). The resulting dataset demonstrates 98.38% estimated accuracy, based on manual review of 200 randomly sampled records, and is publicly available on Zenodo. This dataset addresses the gap in cloud seeding data and demonstrates the potential for LLMs to extract structured information from historical environmental documents. More broadly, this work provides a scalable framework for unlocking historical data from scanned documents across scientific domains.”

5. 5

   Studying the effectiveness of cloud seeding on enhancing precipitation

   Scientific Reports (Ashi et al.)Published Aug 20, 2026Checked Oct 19, 2026

   “The assumption on which the model is based is to introduce a conducive mixture of aerosol chemicals into the environment at a rate proportional to the water vapor formation density rather than being introduced continuously into the atmosphere. The constant induction of the aerosol chemical, when the atmosphere lacks sufficient vapor densities, wastes the chemical, which adds more expenses to the process and pollutes the environment. Hence, our assumption takes into consideration avoiding polluting the environment with the constant induction of the aerosol chemical, avoiding wasting the conducive mixture when the atmosphere lacks vapor density, and reducing the expenses of the process. The theoretical analysis of the model reveals the existence of one critical point. On the other hand, the linear stability analysis, with the aid of Descartes’ rule of signs, confirmed the local asymptotic stability of this point. Global stability is also discussed via Lyapunov’s stability theory, and sufficient conditions were obtained. Finally, the numerical simulations conducted supported the analytical findings.”