| 19 Sep 2025
Numerical Experiments of Cloud Seeding for Mitigating Localization of Heavy Rainfall: A Case Study of Mesoscale Convective System in Japan
Abstract. This study investigated the potential of cloud seeding to mitigate extreme rainfall localization (i.e., overseeding) associated with mesoscale convective systems in Japan. Using a numerical weather prediction model, we conducted cloud seeding experiments by artificially increasing ice nuclei concentrations in a double-moment microphysics scheme for the heavy rainfall event in Hiroshima Prefecture, Japan, in August 2014. We examined the sensitivity of rainfall changes to altitudes and areas of the seeding. The results showed that seeding in the mid–upper troposphere (7.2–8.6 km), where air temperature ranged from −22 °C to −12 °C, resulted in the most pronounced changes in rainfall amount. At these levels, high supercooled cloud water content and strong updrafts favored heterogeneous freezing, resulting in a depletion of moisture and suppression of graupel growth. The cloud seeding led to reduced rainfall within the heavy rainfall region and increased rainfall downstream, demonstrating the hypothesized dispersal mechanism of “overseeding”. Expanding the seeding to cover the upstream region of the heavy rainfall area had a greater impact than increasing vertical thickness of the seeding. The most effective seeding configuration (24 km × 24 km area at 7.2 km) achieved an 11.5 % decrease in area-averaged 3-hr accumulated rainfall and a 32 % decrease as the maximum reduction in 3-hr accumulated rainfall over the heavy rainfall region. Future work should consider more realistic representations of seeding substance (i.e., transport, dispersion, and interactions) and explore a wider range of rainfall events to generalize the applicability of this approach.
The authors present a numerical analysis of the potential for cloud seeding to mitigate the rainfall from an extreme historical MCS event in Japan. The paper is interesting and well-written and I think is suitable for publication with moderate revisions that would help strengthen the novelty of the study and to highlight an addition (in my opinion, important) limitation and direction for future work. I also have a few minor grammar suggestions. I doubt I caught all the grammar mistakes, which anyway are so small and infrequent that they do not detract from the quality of the work.
My concern regarding novelty stems from the fact that very little information is provided on numerical cloud seeding studies, particularly those aimed at rainfall mitigation and those based in Japan. This lack of information on previous work made it impossible for anyone who isn’t themselves familiar with the literature to understand how much was new about the present study. That said, the introduction section is already lengthy. Therefore, I recommend creating a new section entitled “Background” or something like that, following the introduction, that can provide further details on methods and findings of a few key previous studies, which will help frame the current work. Some points now provided in the introduction may perhaps be moved into that background section as well.
My other major concern is the lack of mention of what I see as the biggest limitation of the work and one of the biggest questions that needs to be answered if such approaches are ever to be operationalized. The authors examine the effects of cloud seeding directly into the storm area. In an operational context, this is akin to knowing exactly where the storm will take place. MCSs are notoriously difficult to forecast, particularly with respect to precise timing and location. Therefore, it seems unrealistic that such seeding could be accomplished in so localized a way. Instead, one would presumably need, with some multi-hour lead time, a prediction that a larger region is likely to develop an MCS somewhere within it, and seeding is done throughout the region. It is easy to imagine several things as a consequence of this: 1) the amount of AgI or other material needed could increase by orders of magnitude, and 2) the likelihood that some adverse outcome occurs due to rainfall being shifted spatially, rather than mitigated entirely, will increase, perhaps substantially. I suggest that the authors consider followup work that examines seeding, perhaps at lower concentrations, over larger areas, rather than just the storm location, to examine this effect. It may also be wise to perform ensemble simulations using some perturbation approach to better understand how stochastic the result may be.
Minor comments:
Throughout: “downwind” is likely better than “downstream” for atmospheric work
L17 and 18; delete “s” from “altitudes” and “areas”
L25: “… decrease as the maximum reduction…” is awkward wording
L66 and 69: delete uses of “the” except at the start of line 69
L70: delete “to date” as it is unnecessary
L88: change “in” to “on”
L89: the 100 mm/h is unclear. Is that a peak rate over some short time interval?
L97: delete “s” from convections
L103: “under a future climate”
L104: countermeasure is one word
Eqn 1: Does beta have a physical meaning? If so, describe it. Also, what is a more typical value in WRF?
L195 and Table 2: use “design” rather than “flow”
L195: change “was” to “is”
Figure 4: remind the reader what the thick black polygon indicates