Satellite-Based Assessment of Orographic Cloud Seeding Responses in Semi-Arid Nevada

Water scarcity in semi-arid and arid regions has increased interest in cloud seeding as a potential approach for enhancing precipitation and supporting regional water resources. Six seeding events were selected from the 288‑event operational record to ensure consistent atmospheric and observational conditions. This study evaluates wintertime glaciogenic cloud seeding events in Nevada’s Ruby Mountains and Santa Rosa Range using multispectral observations from the Advanced Baseline Imager aboard the GOES-R satellite series. Visible, near-infrared, infrared-window, and water-vapor bands were examined to characterize cloud phase, cloud-top brightness temperature, optical thickness, particle development, and atmospheric moisture conditions. Satellite interpretation used standard multispectral indicators of cloud microphysical change, including 0.86–1.61 µm reflectance differences for droplet‑to‑ice conversion, cloud-top cooling from 10.3 and 11.2 µm brightness temperatures, and optical depth variations from 0.64 µm reflectance. Water vapor bands (6.2–7.3 µm) characterized upper-, mid-, and lower‑level moisture to define the thermodynamic environment.

The analysis revealed clear differences between the two mountainous regions. The Ruby Mountains showed mixed responses, with some events displaying pronounced cloud microphysical changes, including spectral patterns consistent with droplet-to-ice conversion, cloud-top cooling, and increased precipitation potential, while other events exhibited only marginal changes. In contrast, Santa Rosa events generally showed weaker responses, characterized by warmer cloud tops, thinner cloud optical depth, limited cloud development, and insufficient supercooled liquid water. Meteorological observations indicated broader variability in temperature, relative humidity, wind speed, and wind direction across both regions, helping explain the event-to-event differences in cloud response.

These findings demonstrate the value of geostationary multispectral satellite observations for evaluating cloud seeding responses in remote, data-sparse mountainous environments. They also emphasize that cloud seeding effectiveness in semi-arid regions depends strongly on local thermodynamic, moisture, and wind conditions. Operational programs should therefore use site-specific atmospheric criteria to improve the timing, targeting, and evaluation of seeding activities.