At a time when water resources are becoming ever more strained, especially in regions in the western United States and in the face of climate change, cloud-seeding research and technologies are being increasingly pursued. Yet, the scientific understanding of the effectiveness of cloud seeding, including associated microphysical processes, has long held a high degree of uncertainty. A National Research Council (2003, p. 3) report concluded, “there still is no convincing scientific proof of the efficacy of intentional weather modification efforts.” Despite this, several weather modification operational and research programs have continued and the results of several high-profile research programs were presented at the 20th Conference on Planned and Inadvertent Weather Modification held in Phoenix, Arizona, on 6–8 January 2015.

The meeting came on the heels of the highly anticipated release of the results from the Wyoming Weather Modification Pilot Project (WWMPP). The WWMPP was a multiyear project sponsored by the state of Wyoming that included a randomized seeding experiment to statistically evaluate the effectiveness of glaciogenic orographic cloud seeding with silver iodide (AgI) to enhance the snowpack in the state. In addition to the statistical experiment, both physical and numerical modeling studies were conducted using transformative approaches. The main physical process study was the National Science Foundation–funded research program called the AgI Seeding Cloud Impact Investigation (ASCII), which piggybacked on the WWMPP and provided first-of-its-kind airborne cloud radar and lidar measurements of seeded and unseeded clouds. Concurrent with the WWMPP, two back-to-back randomized seeding programs were conducted in the Snowy Mountains of Australia, and the preliminary results of the second of those two programs were also completed in the past year. At least one full day of the presentations at this meeting focused on the results from these various innovative winter orographic cloud-seeding research programs.

At the conclusion of the daylong presentations on winter orographic cloud seeding, a panel was convened consisting of key scientists and stakeholders involved in winter orographic cloud-seeding research and operational programs:

• • Bruce Boe, vice president of meteorology at Weather Modification, Inc., and leader of the cloud-seeding operations for the WWMPP

• • Dr. Bart Geerts, professor at the University of Wyoming and principal investigator (PI) of ASCII

• • Dr. Michael Manton, professor emeritus at Monash University and lead author of the statistical analysis studies from projects in the Snowy Mountains of Australia

• • Dr. Shaun Parkinson, water resources leader at Idaho Power Company, who leads its operational cloud-seeding program

• • Dr. Roy Rasmussen, senior scientist at the National Center for Atmospheric Research (NCAR) and PI of the scientific evaluation for the WWMPP

The panelists shared their vision on the future challenges and opportunities for winter orographic cloud seeding in light of the recent innovations and results presented at the meeting. This summary describes the key sentiments and outcomes from the discussion.

EXCITING ADVANCES.

Federal funding for weather modification research rapidly declined in the late 1980s. In the past decade, however, a number of key advances in technology related to studying winter orographic cloud seeding have emerged. While cloud-seeding research programs have been going on for over 60 years, the panelists all agreed that the science has come a long way and that has in part happened thanks to recent innovations in technology, such as the use of airborne W-band cloud radar and high-resolution cloud modeling in cloud-seeding research. Cloud modeling research in particular has been aided by greatly increased computational capabilities, as well as the development of a silver iodide cloud-seeding parameterization in the Weather Research and Forecasting (WRF) model (Xue et al. 2013). In addition, the capability of high-resolution models to simulate the physical processes involved in winter orographic clouds has now been demonstrated (e.g., Rasmussen et al. 2014). Moreover, modeling has the potential to rapidly simulate a random sequence of historical events, and thereby reduce the time required to establish statistically robust estimates of the impact of seeding over a watershed-scale area, over several years of operation. The advent of rapid update and high-resolution satellite data, including the next generation of geostationary satellites [i.e., Geostationary Operational Environmental Satellite-R (GOES-R), Himawari 8] and the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud liquid water path product, is also an exciting new frontier that could be useful to cloud-seeding operations and research.

From the operational perspective, the new modeling capabilities are an exciting new advance as well, given that the improved and more explicit cloud-seeding models have been used to help plan, conduct, and optimize cloud-seeding operations, as well as to evaluate the benefits from operational programs. Another key advance, also partly due to enhanced modeling capabilities and observational technologies, is in the ability to better define windows of seeding opportunity and to detect a seeding response, as recently demonstrated in the Snowy Mountains studies. Moreover, the use of model reanalysis datasets or high-resolution regional climate model output to study the climatology of seeding conditions over mountain barriers where observations are limited—or not available at all—has been instrumental in evaluating the fraction of winter storms that are “seedable.”

CHALLENGES REMAIN.

While there are certainly exciting and important aspects related to the future of winter orographic cloud seeding, challenges remain. The panelists provided a wide range of issues that still need to be addressed and/or that present challenges to the evaluation of cloud seeding, from measurement and modeling limitations to logistical and public relations challenges. With regard to measurement and modeling limitations, small-scale features, such as embedded convection and generating cells where natural ice initiation may be occurring, and the intricacies of storm systems over complex terrain were identified as challenging areas to model and verify with observations. On the logistical and public relations front, funding and improving public perception of cloud seeding were identified as continual challenges. Moreover, there needs to be continuity in the management of these projects in order to maintain consistent, well-designed operations, but this aim is challenging, partly due to funding limitations and the human desire for change.

It has long been recognized that the scarcity of ice nuclei is the key to the timing of natural wintertime precipitation and to the physics of AgI cloud seeding. Thus, one might expect that there would have been systematic studies of the global distribution and properties of ice nuclei; however, measurement technologies have been a limiting factor in doing this. Yet, the current interest in observing and modeling cloud condensation nuclei for climate purposes could well be balanced by a modest, but focused, effort in studying ice nuclei.

LOOKING FORWARD.

Numerical modeling is proving to be an efficient means to simulate both natural precipitation events and the impacts of seeding on clouds. However, modeling does have uncertainties, and these uncertainties need to be further understood and quantified. Some key uncertainties in weather and climate modeling, as well as general scientific understanding, pertain to natural ice initiation and multiplication processes, the partitioning of cloud liquid and ice, and the droplet size distribution, which affect snow growth mechanisms. Weather modification research can, in fact, be viewed as a rather controlled experiment to better understand the microphysics of clouds and precipitation. Moreover, the chaotic nature of weather is clearly apparent at the space and time scales where cloud seeding is conducted; that is, even with high-resolution data assimilation, it will be difficult to simulate the actual distribution of quantities like supercooled liquid water or precipitation on scales of a few tens of kilometers and a few hours. Thus, it will be necessary to carry out sequences of simulations to quantify the expected long-term impact of seeding at a specific location based on modeling. This process needs to be preceded by model evaluation studies to confirm the basic capability of the model to capture the key features of both the natural precipitation and the effects of seeding on the local cloud physics.

The quantification of uncertainties is necessary for both the research and operational communities. While the operational perspectives on the panel seemed to be fairly content with the status of the science, the scientific credibility of cloud seeding in the research community will not be established until the basic physics are understood and quantified much more closely than at present, and until the real uncertainties in the impacts of seeding are also understood and quantified.

For that reason, the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE) field program has been proposed as a collaboration among several of the panelists to the National Science Foundation in order to collect cutting-edge in situ and remote sensing measurements within winter orographic clouds seeded from the air and from the ground. This dataset would not only provide the best set of observations of the microphysical chain of events related to natural and seeded winter orographic precipitation production to compare with and evaluate the models, but would also be used as additional physical measurements to improve our understanding of the seeding conceptual model. A key observational advance for SNOWIE is a focus on airborne seeding using in situ measurements, as well as airborne cloud radar and lidar and ground-based mobile radars.

Given that the rationale for cloud seeding is driven by the need for more water resources (streamflow for municipal and agricultural consumption, hydropower generation, etc.), the ultimate impact from cloud seeding really needs to be measured and demonstrated on a watershed scale over a season. The focus on streamflow impacts has largely been ignored in many cloud-seeding research programs to date. The WWMPP is one exception, in which streamflow changes due to cloud seeding were estimated utilizing measurements from a small watershed to drive the hydrological model and the general estimates of precipitation enhancement from the randomized experiment (WWDC 2014). Nonetheless, spatially distributed estimates of cloud-seeding impacts on precipitation can now be simulated with the cloud-seeding model and then could be used to force process-based hydrological models, such as WRF-Hydro (Gochis et al. 2013), to provide more detailed estimates on streamflow impacts. While models can help in this evaluation, measurements of precipitation and streamflow are still required to constrain and verify the models. A sustained experiment is really needed for this to happen, and therefore the panelists suggested the need for a well-instrumented (ideally unregulated) watershed as a test bed that would be maintained for many years. Such a long-term, continuous investment that follows the cloud-seeding impact on the precipitation process all the way to the ground and into the streams would provide the “boots on the ground” evidence that matters most to stakeholders and is the end result that needs to be achieved from future research in this field.