Impact of Cloud Seeding

1. Evaluation Approaches

There are 3 basic approaches to evaluate a cloud seeding project. Each has advantages and disadvantages.

1. Randomization of seeding

Seeding is conducted on a randomized basis to develop two populations of storms: seeded and not seeded.
Ideally, randomization should be blind to those conducting the seeding and also to those involved in the subsequent analysis (double blind).

2. Target vs. control

All storms in the target area are seeded.
Measurements are compared with non-seeded storms in a nearby unseeded, crosswind (not downwind) control area.

3. Historical relationship of target and control areas

The longer-term (climatological) relationship between precipitation in the target and in a nearby upwind unseeded (control) area is examined to establish the relationship when seeding does not occur.
The relationship is reexamined for situations when seeding occurred.
The two relationships are compared.

2. Measurement and Analysis Techniques

2.1 "Best-match" Relationship in Radar Analysis

Refers to the Ze-S relationship that minimized the difference between radar-based snowfall estimates and gauge observations

Key Points:

In the SNOWIE project, differences less than 0.05 mm between radar estimates and gauge measurements
Helps in accurately quantifying seeding-induced precipitation

2.2 Acre-feet as a Measurement Unit

Used to measure large amounts of water produced by cloud seeding

Unit	Equivalent
1 acre-foot	Volume of water covering one acre of land to a depth of one foot About 325,851 gallons 1,233.5 cubic meters

Example from SNOWIE project:

Date	Seeding Lines	Production	Duration
January 19, 2017	2	123,220 m³ (100 acre-feet)	67 minutes
January 20, 2017	8	241,260 m³ (196 acre-feet)	160 minutes
January 31, 2017	2	339,540 m³ (275 acre-feet)	25 minutes

Source for acre-feet values: Friedrich et al., 2020, “Quantifying snowfall from orographic cloud seeding,” PNAS

2.3 Wind Effects on Particle Dispersion

Wind speed and direction significantly affect transport and dispersion of seeding agents
Stronger winds led to faster propagation of seeding lines through the observational domain
Wind direction influenced where precipitation fell relative to the seeding location

Example from SNOWIE:

On January 31, high winds (about 30 m/s at flight level) resulted in seeding lines remaining in the radar observational domain for only about 25 minutes, with some precipitation falling outside the domain

3. Challenges in Attribution

3.1 Isolating Seeding Effects from Natural Variability

One of the primary challenges in cloud seeding research

Difficulties:

Natural variability in precipitation can mask or mimic seeding effects
Requires sophisticated measurement techniques and statistical analysis

Recent Advances:

Use of tracer materials to track seeding agents
Advanced radar and satellite observations
Improved statistical methods for isolating seeding signals

3.2 Statistical vs. Physically-based Approaches

Approach	Description	Challenges
Statistical	Comparing seeded and unseeded events Target/control area comparisons	Natural variability Limited sample sizes
Physically-based	Directly observing and measuring physical changes induced by seeding E.g., radar observations of ice crystal formation and growth	May be limited in spatial/temporal coverage

3.3 Uncertainty in Measurement and Modeling

Sources of Uncertainty:

Variability in cloud microphysics and dynamics
Limitations in measurement techniques
Assumptions in numerical models

SNOWIE Project Approach:

Used multiple measurement techniques (radar and gauges)
Developed ensembles of Ze-S relationships
Focused on cases with light or no natural precipitation to more clearly isolate seeding effects

Uncertainty in total accumulation estimates ranged from 20% to 47% in the SNOWIE study

4. Applications of Cloud Seeding

4.1 Water Resource Management

Cloud seeding is used as a tool for enhancing water resources in various regions: 1. Drought mitigation 2. Snowpack enhancement for water supply 3. Hydroelectric power generation 4. Agricultural water supply augmentation

4.2 Snowpack Enhancement

Widely used in mountainous regions to increase winter snowpack

Benefits:

Increased spring/summer runoff for water supply
Enhanced ski conditions for tourism

Example: Cloud seeding operations in the Sierra Nevada mountains of California

4.3 Agricultural Benefits

Used in some agricultural regions to supplement rainfall

Potential Benefits	Challenges
Increased crop yields Reduced irrigation needs	Ensuring timely and targeted seeding operations Balancing benefits across different agricultural sectors

4.4 Hail Suppression

Some cloud seeding programs aim to reduce hail damage
Technique involves introducing more ice nuclei to create smaller, less damaging hailstones
Effectiveness is still debated in the scientific community

4.5 Fog Dispersion

Cloud seeding techniques have been applied to disperse fog at airports
Aims to improve visibility and reduce flight delays
Typically uses hygroscopic seeding materials

5.1 Cost-Benefit Analysis

Factors Considered:

Cost of seeding operations
Estimated value of additional water produced
Potential economic benefits (e.g., increased agricultural output, hydropower generation)

Challenges in quantifying long-term benefits due to natural variability

Public perceptions of cloud seeding vary widely.

Factors Influencing Acceptance:

Understanding of the technology
Trust in implementing organizations
Cultural and religious beliefs about weather modification

Importance of public education and transparent communication

5.3 Policy and Legal Considerations

Development of regulations and guidelines for cloud seeding operations:

Licensing and certification of operators
Environmental impact assessments
Liability for potential negative impacts

Variability in regulations across different countries and regions

5.4 International Implications

Potential for cross-border impacts of cloud seeding
Need for international cooperation and guidelines
Considerations for shared water resources and atmospheric effects