Precipitation Enhancement:Winter Orographic Cloud Seeding Research

National Center for Atmospheric Research

Research Applications in Weather ModificationWater is becoming an increasingly scarce resource as population continues to grow and potential climate changes threaten water supplies. The primary source of fresh water is from the atmosphere in the form of precipitation. In some clouds, atmospheric water is not efficiently transformed into precipitation on the ground. As a result, scientists and engineers have been exploring the possibility of augmenting precipitation by means of cloud seeding.

A key aspect to consider when determining if cloud seeding is beneficial for a particular area is a research evaluation.

In mountainous regions that rely on winter snow pack for water resources, cloud seeding is a potential technology to enhance snowfall. Winter orographic snow enhancement experiments utilize ground-based silver iodide (AgI) generators and/or seeding aircraft and snow gauges in their design. Determining whether orographic cloud seeding enhances precipitation is challenging due to the small seeding signature compared to the natural weather variability. As a result, a key aspect to consider when determining if cloud seeding is beneficial for a particular area is a research evaluation.

NCAR’s Research Applications Laboratory (RAL) has extensive experience in conducting snow enhancement research programs, including evaluating wintertime orographic cloud seeding effects with randomized statistical experiments, physical measurements, and most recently with state-of-the-art numerical modeling capabilities.

Randomized Statistical EvaluationPrecipitation measurements from a randomized set of seeded and unseeded cases can be used to draw statistical relationships comparing the measured precipitation of the seeded population with the unseeded population. If a statistically significant increase in precipitation is revealed in the seeded population, then the enhancement may be attributed to cloud seeding. Nonetheless, obtaining a large enough sample of cases to obtain statistical significance is often a challenge of most cloud seeding research programs. The Wyoming Weather Modification Pilot Project (WWMPP) is an example of a cloud seeding project for which RAL designed a sophisticated randomized statistical experiment.

Physical Evaluation StudiesEvaluating winter orographic cloud seeding with physical measurements is a key goal of precipitation enhancement research in RAL. However, discriminating effects on precipitation due to cloud seeding versus that from natural variability is still a challenge. Combining randomized seeding experiments with physical evaluations is one way to approach this, as done in the WWMPP. Ground-based measurements of precipitation (from high-resolution precipitation gauges) along with remote sensing observations of liquid water (from radiometers) and clouds and precipitation (from radars) are extremely valuable to provide complete assessments of the physical chain of events in precipitation development. Using these observations, the goal is to better understand and quantify perturbations, both intentional and inadvertent, to the physical chain of events.

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WRF simulation of cold-season precipitation with horizontal grid spacing of 36 and 2 km, and observations of winter precipitation accumulation from the Snowpack Telemetry (SNOTEL) sites in the Colorado Headwaters region. The results demonstrate that a high-resolution model can accurately depict winter precipitation.

Precipitation gauges at a target site for snowfall evaluation.

The AgI Seeding Cloud Impact Investigation (ASCII) field campaign, in coordination with the WWMPP, is a recent example of an observational program designed to conduct a physical evaluation of the winter orographic cloud seeding process. In addition to ground-based instruments, they also collected airborne cloud radar and lidar remote sensing measurements. The physical measurements in seeded versus unseeded events (or portions of the orographic cloud system) are being compared for physical signs of cloud seeding effects.

Cloud Modeling ApplicationsIn recent years, cloud-resolving modeling has undergone substantial improvements, yielding forecasts with considerable accuracy compared to models of the past. For instance, the Colorado Headwaters high-resolution WRF model simulations show excellent comparison to SNOTEL observations when model resolution is less than 6 km. As part of these modeling advances, RAL recently developed an AgI cloud seeding parameterization within the Weather Research and Forecasting (WRF) model to simulate the physical chain of events of AgI seeding in winter orographic clouds. Given the challenges associated with statistical and physical experiments and with the advent of the cloud seeding parameterization in WRF, a wealth of new possible applications for cloud seeding modeling has emerged, such as retrospective model evaluation of seeding effects in specific cases, real-time forecasting for cloud seeding opportunities, and simulations aimed to design new cloud seeding programs.

MODEL EVALUATION STUDIESThe cloud seeding parameterization has been used in modeling studies for the WWMPP and for organizations that conduct operational winter orographic cloud seeding programs, such as Idaho Power Company. The model is used to simulate seeding effects in cases by comparing the precipitation in a control model run without seeding to that in a seeding model run with simulated AgI seeding. It has been run for retrospective cases from the WWMPP, ASCII, and over Idaho, and current research is underway to evaluate the

For More Information, Contact:Roy Rasmussen 303-497-8430 rasmus@ucar.edu National Center for Atmospheric Research (NCAR)PO Box 3000 Boulder CO 80307-3000www.ral.ucar.edu 303-497-8401 fax

model results compared to observations. Preliminary results are quite promising, however continual model validation with observations is needed in order to verify the simulated seeding results from the model.

REAL-TIME MODEL GUIDANCEReal-time cloud seeding forecasting guidance using the cloud seeding parameterization in the WRF model has been demonstrated with research conducted for Idaho Power Company. As part of that program, a real-time cloud seeding case calling decision algorithm was developed and tailored to the Snake River basin region of Idaho to automatically assess the microphysical and environmental conditions from a basic cloud-resolving model forecast to identify opportunities for cloud seeding. From that algorithm, cloud seeding was simulated in a separate forecast model run to estimate the potential seeding impacts in advance of the potential event. Cloud seeding forecasters utilize these forecasts as guidance to prepare for potential cloud seeding operations and identify optimal cloud seeding opportunities.

Cloud seeding forecasters utilize these forecasts as guidance to prepare for potential cloud seeding operations and identify optimal cloud seeding opportunities.

DESIGNING OPERATIONAL AND FIELD PROGRAMSBeyond using the cloud seeding model for retrospective evaluation and forecasting guidance, the model has been extremely useful in planning and designing potential cloud seeding programs for both operational and research purposes. The model has been used to simulate cloud seeding effects in different watersheds to assess the impact cloud seeding might have on precipitation yield and has been coupled with a hydrology model (i.e. WRF-Hydro) to evaluate the subsequent impact on streamflow. Moreover, model forecasts have been used to assess how frequently conditions in a given watershed are optimal for cloud seeding and to aid in the design of flight tracks for aircraft seeding or field campaign airborne observations.

WRF simulated seeding effect on accumulated precipitation (mm)

WRF simulation of clouds (white), ice (yellow) and AgI seeding plumes (blue). Grid resolution is 667 m.