Aerosols, Clouds and Precipitation Studies

Evaluating the Potential for and Impact of Cloud Seeding

Earth is the only planet known in the solar system to support life. Life on Earth is critically dependent upon the cycling of water back and forth among the various reservoirs (ocean, land, and atmosphere) in the Earth system, which is referred to as the hydrologic (water) cycle. Both natural and human–induced climate variations manifest themselves in the global water cycle. In fact, the most obvious signals to humans of climate change in the Earth system will likely be changes in the hydrologic cycle, particularly regional precipitation regimes, and the exacerbation of extreme hydrological events, such as floods, droughts, and hurricanes. The hydrologic cycle also affects and interacts with other components of the climate system. Consequently, any significant perturbations to the hydrologic cycle, whether they are caused by aerosols or other factors, are of paramount concern.

Clouds are known to play a major role in climate through their direct interactions with solar radiation; they can either reflect (albedo) or absorb incoming solar radiation. Aerosols serving as cloud condensation nuclei (CCN) and/or ice nuclei (IN) influence cloud microphysics, including the formation of precipitating particles and subsequent cloud lifetime, as well as cloud radiative properties, particularly cloud albedo and emission. As a consequence, these properties influence the local radiation budget, atmospheric temperatures, land surface, and ocean temperatures. Aerosols can therefore affect regional cloud properties and may affect precipitation amounts.

In the hydrologic cycle, water molecules evaporate from the oceans, seas, rivers, soils, and plants, condense to form clouds, and return to earth mainly by precipitation. Since precipitation from clouds is the only mechanism that replenishes ground water and completes the hydrologic cycle, small changes in cloud and precipitation properties may result in a spatial and temporal redistribution of rainfall, which can have a dramatic effect on climate and society. Such changes in rainfall distribution could mean drought in some regions (especially in arid regions) or flooding in others.

There is growing evidence that human activities (e.g. anthropogenic aerosols) can alter atmospheric processes, such as the hydrologic cycle, on scales ranging from local precipitation patterns to global climate. Research and documentation of anthropogenic effects on precipitation processes strengthen the physical basis for deliberate attempts to alter clouds (i.e., weather modification via cloud seeding) with the goal of enhancing precipitation or mitigating severe weather. The potential for such man–made increases in precipitation or mitigation of severe weather is strongly dependent on the natural microphysics and dynamics of the clouds that are to be seeded. HAP scientists are involved with a variety of projects around the world related to aerosol–cloud interactions and weather modification (cloud seeding), including efforts in Australia, Saudi Arabia, West Africa, Turkey, India and the state of Wyoming. RAL's role has been to scientifically evaluate the potential for cloud seeding to enhance rainfall (or snowpack in the case of the Wyoming project), as well as to conduct basic research on the impact of ambient and seeding aerosols on cloud and precipitation processes through field measurements and modeling.

About Hygroscopic Cloud Seeding

Figure 1: Hygroscopic seeding conceptual model diagram.
Figure 1: Hygroscopic seeding conceptual model diagram.

When a cloud forms, moisture in the form of water vapor is carried into the cloud until it condenses on particles into liquid water, thus forming cloud droplets (see Figure 1, part 1). The cloud droplets are very small, however, and cannot readily fall out of the cloud, and therefore other processes must occur to grow the liquid cloud droplets large enough to fall as rain. One such process is called collision and coalescence, in which cloud droplets of a variety of sizes (which therefore fall at different speeds to one another) collide with each other and coalesce into a combined larger drop (Figure 1, part 2). This process continues until drops are large enough to overcome the updraft speed within the cloud and fall as rain. 

However, in addition to processes that help the droplets grow, there are factors that can deplete the liquid water from eventually becoming rain on the ground, such as evaporation of the droplets or the freezing of small droplets (that were carried to the sub–freezing cloud top by the updraft) into small ice crystals that are unable to fall as precipitation (this process is known as cloud glaciation). Thus, the amount of water vapor that enters a cloud never all falls to the ground as rain. The precipitation efficiency (or the percent of incoming cloud water vapor that falls as rain) varies from cloud to cloud, and cloud seeding technologies aim to convert more of the water vapor processed in the cloud to more rainfall at the ground thereby increasing the cloud's precipitation efficiency.

Cloud seeding is a science–based technology that aims to add particles to a cloud that will help precipitation develop more efficiently, thus hopefully yielding more rainfall. The type of particle added to the cloud depends on the characteristics of the clouds that will be seeded. Deep clouds that have portions growing in sub–freezing temperatures may be seeded with silver iodide (AgI), a material whose properties are very similar to an ice crystal and thus serve as good surfaces on which ice can form (also called ice nuclei). Supercooled liquid water (liquid water that has been carried into suba–freezing temperatures, but has yet to freeze) can freeze on these AgI particles and initiate and enhance ice–based precipitation formation processes at key levels in the cloud, and prevent loss of that water to glaciation aloft. Other clouds may be seeded with hygroscopic materials (particles that take on water easily, such as salts) in the updraft region of the cloud just below the cloud base (Figure 1, part 3). These hygroscopic particles are then carried into the cloud by the updraft and water vapor condenses on them to form additional liquid cloud droplets, whose size depends on the size of the hygroscopic particles introduced in the cloud. Adding additional particles of larger sizes may help enhance collision and coalescence processes that are responsible for rain formation and convert more of the cloud water to rainfall. In essence, a more efficient collision and coalescence rain formation process yields more rainfall at the ground (Figure 1, part 4).

Cloud Microphysics

Cloud microphysical schemes fall into two categories:

  1. Explicit or bin schemes that predict mass and number of many different sizes of water drops and ice particles
  2. Bulk microphysical parameterizations (BMP) that predict mass/number of total particles of all sizes

Due to the very high computational cost of applying explicit schemes, HAP developers focus most of their attention on the BMP schemes for most applications. So–called "single–moment" schemes predict only the mass mixing ratio of the hydrometeors and then diagnose the number concentration by making various assumptions. A "two–moment" scheme also predicts the number concentration and provides more degrees of freedom for representing a size distribution. A recent scheme by Thompson et al, 2008 takes a hybrid approach in order to be computationally efficient while reproducing measurements from various field experiments. The scheme was implemented into the Weather Research and Forecasting (WRF) model and is regularly tested and improved based on results from cloud and precipitation measurements from a variety of convective and stratiform precipitation events.

One of the biggest challenges that HAP scientists face when developing these schemes is the intricate and complex interaction between microphysics and other physical processes. For instance, the amount and size of cloud water and ice greatly influence the radiation, which subsequently alters the surface heating and cooling that potentially lead to newly–formed clouds. Likewise, rain falling from convective clouds creates downdrafts that can greatly alter location, strength, and amount of subsequent updrafts and thunderstorms due to assumptions of rain drop or hail size and amount.

Additionally, there are a host of uncertainties within the microphysical schemes in general, some of which are amplified when applied to areas with complex terrain.

  • Condensation and "warm" rain processes – Creation of the initial precipitation particles is heavily dependent on the cloud condensation nuclei, which are essentially unknown in day–to–day circumstances. "Autoconversion" schemes that define how droplets collide and coalesce into larger drops are complex and highly variable and often create rain too early or too late compared to observations
  • Ice initiation – The freezing of water drops to ice particles is complex and not well understood, partly because it is very difficult to observe and measure in everyday clouds
  • Particle size distribution – The water drops and various forms of ice in microphysical schemes assume a simplified mathematical form with regard to numbers of each size. For water, these assumptions dictate when rain begins to form, how rapidly rain descends, and how strong the evaporation rate is below cloud base. For ice, these assumptions greatly affect the resulting growth of ice by vapor deposition and quantity of water vapor and fraction of ice versus liquid
  • Particle type issues – There are many uncertainties associated with 1) mass and fall speed versus diameter of ice particles, 2) how to apportion the growth of rime ice between snow and graupel, and 3) the effects of riming on snow particle properties.

HAP numerical model developers are working on all of these challenging areas in an attempt to find the best optimal solution for each application of the model.

Quantitative Precipitation Estimates (QPE)

Timely and accurate Quantitative Precipitation Estimates (QPE) are essential for forecasting stream flow, flash floods and localized urban flooding. Radar and rain gauge based QPE are being used in this prediction system for determining: “How hard it is raining? How much rain has fallen in the intermediate past?” Quality-controlled QPE fields, produced from dual-polarization radar data, are used as input for precipitation nowcasts (QPN), as input into the hydrology model (WRF- Hydro), for evaluation of radar-based QPE compared to rain gauge measurements of precipitation, and for verification of precipitation forecasts (QPF).