weather modification—a scenario for the future

51JANUARY 2004AMERICAN METEOROLOGICAL SOCIETY |

E VOLUTION AND ACHIEVEMENTS IN WEATHER MODIFICATION. Modern weathermodification (WM) started with Langmuir and Schaefer in 1948 (Schaefer 1953) who used dry ice pel-lets to produce holes in super-

cooled stratus by snow-out (Fig. 1).This experiment was a very convinc-ing, visual proof that seeding works;it encouraged new rain enhance-ment and hail prevention projects allover the world. The euphoria abouthumanity’s unlimited expectation tochange the weather is reflected in aspeech by President John F. Kennedygiven in 1961 to the United Nations(Kennedy 2003) in which he statedthe following in the section on theexploration of the universe and thepeaceful use of space: “We shall pro-pose further cooperative efforts be-tween all nations in weather predic-tion and eventually weather control.”Note that he did not say “weathermodification;” he foresaw the timewhen weather could be controlled,

WEATHER MODIFICATION—ASCENARIO FOR THE FUTURE

BY ROLAND LIST

Weather modification can be substantially improved by a better understanding of the

precipitation processes and by more refined methods and technologies.

AFFILIATION: LIST—Department of Physics, University of Toronto,Toronto, Ontario, CanadaCORRESPONDING AUTHOR: Prof. Roland List, Dept. of Physics,University of Toronto, Toronto, ON M5S 1A7, Canada

E-mail: [email protected]: 10.1175/BAMS-85-1-51

In final form 10 August 2003©2004 American Meteorological Society

FIG. 1. Racetrack pattern produced in solid overcast by 1.7 lb of dryice per mile, 24 min after seeding (Schaefer 1953).

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that is, pressing a button at two o’clock in the morn-ing and getting rain at five o’clock.

Why has weather modification gone downhillsince then and why has it received such a bad reputa-tion? The blunt answer is that it has not delivered asexpected.

What WM can and cannot do is summarized in theperiodically updated Statement of the Art of WeatherModification by the World Meteorological Organiza-tion (WMO 2001b). The American MeteorologicalSociety (AMS 1998a,b) has issued a similar assess-ment. WMO has also issued guidelines for planningweather modification experiments (WMO 2001a). Inthese statements success is listed for dissolution ofwarm and supercooled fog, supercooled stratus decks,and, to a limited extent, for cold and warm rain en-hancement. No successful randomized experimentshave been reported on snowpack augmentation, hailand tornado prevention, or the moderation of floodsand tropical cyclones (hurricanes and typhoons). Thereduction of winds in hurricanes was once reportedin the WMO statement as a result of silver iodide(AgI) seeding (e.g., WMO 1976). However, that claimwas later removed at the request of the United States.In summary, the modification possibilities are quitelimited for the economically most important cases:rain and severe storms.

A very useful recent report on WM was issued bythe National Research Council (BASC 2000). A mas-terful review paper on glaciogenic seeding was re-cently written by Silverman (2001a). The commentsby Dennis (2001) and Hobbs (2001) and the replies bySilverman (2001b,c) highlight some of the key issues.

Before going into details I should also explainwhere I stand relative to WM. As an experimentalphysicist I like to do “live” tests on clouds and pre-cipitation mechanisms, and see the reactions to theinterventions. This approach is much more effectivecompared to intrinsically passive observations.Further, my comments need to be viewed in the con-text of expected rain increases by seeding of 10%–20%.1 This involves extracting signals that are deeplyembedded in noise and requires measurement accu-racies not normally achieved in the atmosphericsciences.

THE WMO CRITERIA FOR RAIN ENHANCE-MENT EXPERIMENTS. The basic requirements. Inthe late 1960s the World Meteorological Organization

established rules for WM experiments to preventunreasonable claims of success. The main point wasthat the experiments had to be randomized. Therebythe need for climatological records was stressed toallow testing of feasibility and duration of plannedexperiments. In addition four criteria for rain en-hancement projects were formulated: 1) The experi-ments have to be randomized and evaluated by sta-tistical methods; 2) success has to be judged on thebasis of the rain obtained at the ground; 3) statisticalsuccess of an experiment has to be supported byphysical insights and understanding; and 4) successhas to be repeated in other areas of the world(transferability).

These criteria (see also List 2001) can easily be trans-ferred from rain enhancement to hail suppression.

Randomized experiments (the first WMO criterion).The effect of seeding of supercooled stratus clouds orwarm and supercooled fog by aircraft (or by groundgenerators for fog) is readily observed and does notrequire statistical evaluation. For stratus, seeded ar-eas can easily be compared with neighboringunseeded areas of the same cloud because the aircraftflying pattern is reflected in the dissolving part of thestratus; the unseeded stratus section does not producesnow. Nevertheless, it is interesting to note that theoval seeding track in Fig. 1 did not convince the U.S.Weather Service, so Langmuir and Schaefer did seed-ing in the pattern of an omega and suggested that theycould also imprint a General Electric (GE) logo on acloud (T. Henderson 2003, personal communication).

On the other hand, the seeding of convective cloudsis difficult to assess because it is not known how theseeded cloud would have behaved if it had not beenseeded. Was precipitation induced by seeding orwould it have started anyway, or, as is now the stan-dard approach and expectation, was an already on-going rain process enhanced and to what degree? Or,could hail formation be suppressed? These questionsnecessitate randomized comparisons of the seededcloud with similar, nonseeded clouds (experimentsduration 5–10 yr for crossover design)2 with one ex-periment day as the experimental unit. P. Pioggiashowed that even a high rain correlation factor of 0.68between neighboring target and control areas did notguarantee sufficient meteorological similarity (Listet al. 1999). Demanding design criteria and the diffi-culty in finding well-correlated target/control areas are

1 Note that in this paper the word “seeding” will be used as a generic name for any type of WM, unless specified otherwise.2 Comparison involves two similar sites: one used alternatively as target, the other for control purposes.


reasons for the limited number of randomized WMexperiments.

Seeding with single clouds as the experimental unitmay be assessed within about 3–4 yr (Mather et al.1997). This method assumes that the cloud’s neigh-bors, if far enough away, are not affected by the seed-ing and can be used for comparison. However, seed-ing of one element of a cloud ensemble can affect itsneighbors by turbulent transport of seeding materi-als [seeding with dry ice or liquid nitrogen (Fukutaet al. 2000) would exclude such transport], while dy-namic interactions are also possible.

Basic statistics. The intricacies of statistics as appliedto weather modification can be found in a paper byGabriel (1999) in which the confidence levels for re-jecting the null hypothesis and the power factor areused as key indicators to assess success. This is themethod according to which most past experimentshave been evaluated. Recently, Gabriel (2002) pub-lished a new revolutionizing approach to assess ran-domized WM experiments in which an acceptable in-terval of confidence is the key indicator of success orfailure. In that paper he has also showed how two pastexperiments, Israel 1 (Gabriel 1967) and Israel 2(Gabriel and Rosenfeld 1990), would fare when ap-plying the new method.3

Professor Gabriel also addressed the pooling ofdatasets from different experiments (Gabriel 2002)to improve the power of the combined result. Thispooling may lead to international experimentswhereby a few countries would plan their designs andconduct their work in parallel in different nationallocations, thus reducing individual costs and sharingresults.

“Bad draws.” In hail suppression one single heavy hail-storm (a “bad draw” or outlier) falling into the seededcategory may completely skew the statistical result.Quantification of such effects is possible by a “leaveone out” method.

Statistics need to be refined by “leave one out” andsimilar “bootstrap” methods to formulate a statisticalmeasure of the contribution of the most disastrous(hail) or successful (rain) single experimental units orseeding events to the final result, thus leading to linksbetween physics and statistics, that is, invaluableprogress and unexpected physical understanding.

Such assessments can be applied to any number ofseeding experiments during the data accumulationphase or after. These methods could also be tested onpast experiments to see if specific clouds reacted par-ticularly well to seeding.

Rain at the ground (the second WMO criterion). Claimsfor rain increases have to be based on the (enhanced)rain received at the ground. (For hail suppression theanalog would be a reduction of hail damage.) Onlysuch a measure is of economic value, considering thaton average about 50% of rain falling out of clouds mayevaporate on the way to the ground and that much ofthe hail melts before reaching the ground (stone di-ameter < ~ 1 cm). While rain gauges are acceptableto measure widespread precipitation, their value islimited for rain from convective clouds because theyrepresent point measurements. The other instrumentused is radar, which provides area-coveringreflectivity data based on the sum of the diameter tothe sixth power of all the contributing raindrops. Thisis not the rain amount; it is the square of the rain vol-ume. To get the rain amount an assumption has to bemade about the raindrop size distribution, often in theform of a Marshall–Palmer distribution (Marshall andPalmer 1948). Such distributions can vary widelythroughout a cloud. Further, radar measures rain asit falls at heights above the ground that increase fromthe radar site and, thus, neglects evaporation. Thus forconvective situations, a solution may be found in thedevelopment of assimilation models for cloud andmesoscales that integrate radar and other remote sens-ing data, be they from satellites,4 airborne platforms,dropsondes and additional ground-based remote sys-tems (such as sounders), rain gauges, and mesoscalemeteorological and hydrological networks into modelsdescribing the evolution of particle spectra in clouds andcloud systems.

Physical understanding (the third WMO criterion). Allsevere weather events under consideration for modi-fication involve weather systems in which the dynam-ics and cloud physics interact in different fashions,controlled by the laws of thermodynamics. Depend-ing on system configuration and forcing we may endup with rain- or snow-producing clouds, severestorms, hail and tornadoes, monsoons or hurricanes,etc. The laws of cloud physics control the formation

3 The trend of believing that statistically formulated results after the experiments have been terminated are sufficient is not accept-able for confirmatory experiments.

4 Many more observational tools will become available with the newest satellites under construction.

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of droplets and ice particles upon condensation andice nuclei, respectively; the sometimes parallel evolu-tion of the liquid and solid particle hierarchies; theparticle aerodynamics and the particle heat and masstransfer; the nature of particle interactions and thebehavior of particle ensembles; their interactive be-havior with the dynamics and thermodynamics of adeveloping cloud or cloud system; and the modelingof clouds, embedded in a mesoscale and synoptic en-vironment. Insufficient insight into these cloud physi-cal aspects (beautiful theses topics for students! see theappendix) is another of the key reasons for the stag-nation of weather modification. This is highlighted bythe lack of understanding of “simple” rain formation.I have no problem in explaining rain formation inthree sentences. However, if I have to write an essayof 10 pages, then I get into hand waving. Thus, howare we to interfere successfully with the precipitationprocess?

While cloud physics is at the core of the forma-tion of precipitation, nearly all other branches ofatmospheric physics/meteorology have a stake in theformation of precipitation: for example, synoptic anddynamic meteorology, radiation, atmosphericchemistry, weather forecasting, numerical modeling,etc. All play a role at many scales; they all need tobe better understood. Observational technology,instrumentation, and observation platform designs;seeding agent characteristics; and delivery methodsround out the technical aspects, with statistics in thewings for experiment design and evaluation. Thesuccess of weather modification depends on the un-derstanding of these related disciplines. We are deal-ing with one of the most complex problems of atmo-spheric sciences.

The study of the precipitation processes in all theirforms is of greatest importance to the evolution ofweather modification. Note also that the insufficientlyknown precipitation processes are the main stumblingblocks of forecasting and climate modeling.

The study of the physics of atmospheric particles inthe field, the laboratory, and by theory needs to be ex-panded and strengthened.

The statistical evaluation assumes that seeded andnonseeded clouds or cloud systems are “average” and“homogenous” collections of samples. Specific char-acteristics can be dealt with by appropriate covariates,but are they sufficient to represent an even biggervariety of physical processes and clouds?

Knight (1988, see the cover of the May issue) oncewrote that no two snowflakes are similar. This samestatement can easily be extended to clouds becausethey are also all different from each other. For ex-

ample, a cloud-seeding experiment is often based ona set of similar conditions for seeding, such as mini-mum cloud-top height, minimum radar reflectivityfactors (often set at 30 dBZ), no ice multiplication,similar synoptic situations, etc. This still describeswide classes of clouds producing precipitation byquite different processes, at different time and spacescales, in different environmental conditions includ-ing aerosol characteristics, and with different precipi-tation amounts and efficiencies, etc. Who would dareto average these variables over space and time? Theconcept of an average cloud is absurd. We cannotunderstand an “average cloud,” although we may beable to understand a specific cloud and figure out withphysical arguments and models how a weather modi-fication technique may affect such a cloud.

There is a good example supporting my point inthe field of hail suppression where the World Meteo-rological Organization (WMO 1995) identified six (!)different scenarios for hail formation: 1) growth-lim-iting competition among hail embryos (beneficialcompetition), 2) early rainout (from a zone of hail em-bryos), 3) glaciation of cloud water, 4) trajectory low-ering, 5) promotion of coalescence in inefficient weakstorm cells, and 6) seeding for dynamic effects. Noneof these could be excluded. For rain formation an evenlarger range of variations may exist.

This point is supported by a hailstorm cross sec-tion reproduced from Thomson and List (1999) show-ing that clouds have unexpected small-scale featuresthat are completely invisible to standard radars witha 1° beamwidth (Fig. 2). Such details were measuredby the University of Toronto vertically pointing EECLARS1988 X-band Doppler radar during a passinghailstorm, with the hail hitting the radar 3 times dur-ing the period 0–100 s. The power spectra, indirectlyrepresenting particle spectra, as shown in Fig. 2 right,are seen to vary significantly over vertical and hori-zontal distances as low as ~ 100 m. Such individualfeatures may determine the specific hail formationprocess. Assuming that clouds have smooth updraftsand downdrafts is wishful thinking. This, to me,means that the third WMO criterion cannot be justi-fied. Physical understanding has to be restricted tospecific single clouds.

Transferability (the fourth WMO criterion). The assump-tion that success in one country should be repeatedin other areas of the world may have been necessaryin a time of Cold War politics and mutual distrustamong nations. In practice “transferability” turnedout to be very “elastic” and has been adapted to anypolitically affected situation. In reality, the concept


only applies to technology, methodology, somewhatsimilar clouds, and concepts, period. Similarity con-cepts as applied in physics and engineering, such asthe Reynolds number, are nonexistent in WM.

In summary, the WMO criteria for rain enhance-ment need to be overhauled and reformulated, andextended to hail.

FIG. 2. (left) Vertically pointing Doppler radar scans through a passing hailstorm. The colored regionsrepresent the total vertical velocity. The thick black line shows the contour of the 30-dBZ data. Theletters A, B, and C mark the west–east locations of the high negative velocity streaks (hail) extendingup to 6.5 km in the vertical direction. These hatched areas represent aliased data that have been cor-rected by hand. (right) Power spectra can be viewed as a measure of particle concentration for differ-ent particle speeds. They are superimposed to show the high spatial variability of precipitation. Theaxes for each spectrum span from –16 to 16 m s-----1 horizontally and –30 to 40 dBZ (m s-----1)-----1 vertically.The origins of the power spectrum axes [0 m s-----1, –20 dBZ (m s-----1)-----1] are positioned with respect to thebackground velocity data at the locations where each of the spectra was measured. Noisy data above10 km are caused by low signal quality (see Thomson and List 1999).

TECHNICAL ASPECTS. Radar. From the previ-ous example of a hailstorm it is obvious that there is aneed to develop radars capable of finer resolutions intime and space because clouds and cloud systems candevelop very quickly. With present-day equipmentthe first and last scans of a volume scan of a convec-tive storm are 5 min apart. During this time interval

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particles could easily have moved distances of the or-der of 2 (for raindrops) to 9 km (hailstones), not ac-counting for downdrafts or updrafts. In other wordsthe volumetric data fields are not consistent.

The speed of radar volume scans needs to be in-creased fivefold in order to avoid having to wait for a5-min period to pass between the first and last fixedelevation scan (PPI). Too many changes happen dur-ing that time. Further, the 1° beamwidth of present-day radars does not resolve the finer scales of hail orrain evolution.

The radar situation would drastically improve byapplying electronic scanning radars with 1-min (orbetter) time resolution. Spatial resolutions of 100 m¥ 100 m ¥ 100 m are also necessary to follow the spa-tial and temporal variations of particle spectra, as hailseems to fall in tongues or sheets [see Fig. 2 inThomson and List (1999); and Fig. 3 adopted from ahail cloud model by Farley and Orville (1999)]. Highradial resolution of radars is without value so long asthe radar return signals are integrated over the widthof the bell-shaped beam signals over the cross section(at a distance of 100 km, a radar beam width of 1° cor-responds to 1.6 km).

Seeding criteria are often based on first radarreflectivity factors (often 30 dBZ) at a given tempera-ture level. At such reflectivity factors the precipitationprocess is already under way. There should be thepossibility of starting seeding before this point. Butthere are no criteria on which such action could bebased. High-sensitivity radars and better numerical

forecasts and cloud models are needed in WM fieldexperiments.

The remote sensing, in scanning mode, of amountsof liquid and solid precipitation is as yet not resolved,considering that polarization methods to assess par-ticle size distributions over volumes have been triedunsuccessfully for 40 yr. As explained before, the ra-dar return signal is proportional to the square of therain volume. This makes the current interpretation ofrain increases by radar reflectivity factors alone ahighly questionable procedure [see application in theThunderstorm Identification, Tracking, Analysis, andNowcasting (TITAN) software system]. The issue ofradar calibration is also not trivial.

For 30 yr polarized radar has been heralded as thetool for producing raindrop spectra, the crucial ele-ment for calibrating radar in terms of precipitation.It has not materialized. Are there scientific and/ortechnical alternatives for scanning platforms to re-solve particle size distributions?

The Marshall–Palmer relation. Marshall and Palmer(1948) established the first radar reflectivity–rain-raterelationship (Z–R) on the basis of averaging rain overmany different types of storms and many differentrain rates. For 50 yr of blindfolded physics we haveestablished Z–R relationships ad absurdum for everyimaginable rainfall. It is only recently that Rosenfeldand Ulbrich (2003) have found physical meaning inthe Z–R shape. But this step will not be enough be-cause of the high variability of size distributions acrossa cloud, not to speak of seeded clouds.

In WM, expected enhancements of rain and snoware on the order of 10%–20%. Yet our instruments,from gauges to radars to satellites; our laboratory ca-pabilities; and, last but not least, our predictions andmodels are far from providing the necessary sensitivi-ties and accuracies.

Seeding material delivery. A weather modification pro-gram can only be successful if there is a possibility ofdelivering the seeding materials to the right location,at the right time, in the right amounts, and for theright time interval. These aspects are difficult to as-sess and to carry out with single clouds. It is a daunt-ing task with fields of clouds. Drones, as are presentlybeing proposed in China, may become a useful, veryflexible delivery system. Their use would also be ad-vantageous in mountainous areas where safety con-cerns for low flying may be limiting.

Science and engineering. A scientist is often very con-cerned when unproven theories and concepts are

FIG. 3. (a) The cloud outline and airflow (dashed contours)for the main hail cell at 63 min and (b) the source regionfor 1-cm (dotted–dashed line) and 2-cm (solid black re-gions) hailstones at the surface for particles initially 6 mmin diameter, as calculated with a two-dimensional, time-dependent hail model (according to Farley and Orville1999). Note the tonguelike shape of the hail region.


applied and tested before proof is established. It is thenature of an engineer to find solutions that work—even if they are not fully understood. Weather modi-fication will also turn out to be possible long beforewe understand all the details. The contributions of theengineers and operators to the science of WM alsoneed to be acknowledged. They apply advancingknowledge of precipitation processes to their opera-tions with new continuously improved equipment,methods, and materials (fast-acting AgI!) and alsoprovide new scientific insight into weather phenom-ena of concern—often far more than in science-controlled randomized studies. Engineers, like the sci-entists, need to be on a learning curve; they have tobe involved now, so that when we approach the nextlevel of sophisticated WM experiments they knowhow it is done.

LARGE-SCALE EFFECTS OF SEEDING.Weather modification is not just addressing the seed-ing of single clouds and storms; it looks at the effectsof seeding on surrounding clouds, and at seeding oflarger entities such as hurricanes. First, let us addressthe extra-area effect.

Robbing Peter to pay Paul. Having all the componentsfor a WM experiment ready (statistical design, mea-surement techniques, physical understanding, andtested models) is not enough to start an experiment.We have to deal with the perceptions of society andbe fully aware of legal and environmental boundaryconditions and take preventive action, if necessary.One of the scientifically most intriguing aspects is theperception that producing rain in one area will takeit away from another, the “robbing Peter to pay Paul”principle. This produces societal and political rever-berations. It is also a realistic concern consideringthat Cho and List (1980) showed that producingstronger convection by seeding may lead to a greatermoisture convergence, thus reducing moisture avail-able at other locations of a synoptic field. A study ofthe extra-area effect is indicated with numericalmesoscale and synoptic-scale models involving de-tailed cloud microphysics. This situation has led tothe requirement that seeding experiments need tomeasure the precipitation in the areas surroundinga WM experiment, which allows quantification of theeffect.

Hurricanes and typhoons. In the 1970s, when theUnited States and the Philippines considered a ty-phoon moderation program, the Chinese governmentwas very concerned about the project. The Chinese

were convinced that seeding over the Ho Chi-MinhTrail during the Vietnam War had been successful inmuddying up the trail with artificial rain and, thus,making if impassable. Thus, they assumed that modi-fying typhoons might also work. While any reductionin damage would have been welcome, any accompa-nying, probable loss of precipitation was not accept-able. This is also an important aspect in the Caribbeanwhere > 25% of the total precipitation can be assignedto hurricane systems. Hence, seeding of tropical cy-clones (and monsoons) should only be consideredafter clarification of their overall effects. This also ap-plies to seeding before hurricane landfall to preventexcessive damage. [When I chaired the WMO Ty-phoon Modification Conference (WMO 1974), a gen-eral warning was expressed and WMO was asked “toarrange the acceptance of a ‘24-h limit’ for typhooninterference; that is, typhoons should be seeded on anexperimental basis only if they are not expected toreach land within 24 h.”]

U.S. weather systems. It is amazing how little we knowabout the rain systems in the United States. It wasCarbone et al. (2002) who found that radar echo com-plexes from convective systems are moving wavelikefrom west to east across the country. Obviously, themoisture supply for these cloud systems has to comefrom the south. It is only now that basic models deal-ing with this surprising aspect are being developed.For WM there is a message in these findings: do nottry to increase rain “out of phase” with natural con-ditions that favor rain. [Similar rain systems seem tomove across Indonesia, as Tropical Rainfall Measur-ing Mission (TRMM) data show.]

Increase in precipitation cycles. Rain enhancement isnot only achievable by making clouds to producemore precipitation. It is also possible to recycle thewater more often and to add full or partial cycles ofrain. Precipitation over continents shows this cyclicpattern of evaporation of rainwater at the ground,formation of new clouds and new storms, followedby rain, etc. There is negligible water in biomass; add-ing to the aquifers is no loss because the water canbe pumped out and used in irrigation. The only po-tential loss is through runoff into the river systems.This might be kept under control such that the neteffect is a more frequent use of the same water in thesky, a true recycling.

Climate scenarios. Considering a time horizon of 20–40 yr, there is another reason for advancing WM now.In a climate change scenario the shift of precipitation

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zones to unproductive agriculture regions may occur.For example, if the rain zones over the midwest andthe high plains of the United States, which efficientlysupport dry farming, would migrate into an area withless productive soil, in addition to soil improvements,the agricultural production apparatus would have tobe rebuilt or moved at costs of trillions of dollars.Could such a process be softened by WM?

NUMERICAL MODELING. Numerical modelingis the topic overarching everything in WM fromphysical understanding to testing of hypotheses, toprocessing effects, to data assimilation and parameter-ization of processes at all scales. Models are based onthe physics of the processes as we understand them.Without physical relations there are no worthwhilemodels, except for empirical ones, which describeevents without scientific explanations.

Parameterization. All numerical models are based onparameterizations of the smaller subgrid scales. Theapproach is applied to allow scientific insights with-out having to model the behavior of every single wa-ter molecule, aerosol particle, cloud droplet, precipi-tation particle group, or cloud, etc., as appropriate atthe different scales, up to the climate and earth sys-tem models. A good parameterization is to describethe essence of a phenomenon quite correctly, but witha broad brush. The most amazing example in cloudphysics is the “Kessler parameterization” of rain for-mation, based on a threshold liquid water content anddifferent evolution rates. Kessler (1969) floated thisidea and later quantified it. This piece of intuition hasnot been surpassed for more than 30 yr.

Modeling requires experimentalists and observersto parameterize their results for easy utilization inmodels of higher hierarchy.

This approach needs to be continued, and exist-ing parameterizations should be improved based onbetter measurements and concepts.

Forecast models. If forecasts were perfect—which isequivalent to saying that the physics of a given pro-cess in a given environment is fully understood anddetermined—then evaluation of a WM experimentwould be trivial. Because this is not the case, the ques-tion needs to be asked if forecasting will ever be use-ful to WM? It is obvious that forecasting will neverbe able to predict precipitation at a given location withaccuracy better than 10%–20%. Nevertheless, goodpredictions of weather situations and types of clouds,etc., are very helpful in the categorization of poten-tial seeding days or seeding units.

When W. R. Cotton (2001, personal communica-tion) and his group tried to simulate the Fort Collins,Colorado, flood some years ago on the basis ofweather network data, they had no problem in repro-ducing the disaster, but it was occurring in a differ-ent location. This leads to another question related toWM. To what degree can more finescale meteorologi-cal observations improve forecasts? To what degreewould it help in a flat experimental area to have amesoscale observational network in support of a WMexperiment? What about a mountainous region andthe weather in mountain valleys? Bernardet et al.(2000) addressed some of these questions, togetherwith the limits. Such pioneering work needs to be in-tensified.

Overall, there is limited skill in forecasting of pre-cipitation from convective systems; there is also no skillin precipitation projections of climate and climatechange models. The latter is not surprising consider-ing that the role of the weather-active aerosol, the nu-clei for droplets and ice crystals, has been neglected, notrealizing that their effect is two orders of magnitudegreater than the effect of all aerosol on radiation: Pre-cipitation determines climate.

WM-related modeling. Modeling has become a tool forthe experimentalist and observer for quality and trendassessment. However, there are areas where model-ing has to provide the solution by unifying data andobservations.

1) Assessment of seedability. Before any experimentis undertaken, it is strongly advisable to test seed-ing hypotheses and success by three-dimensional,time-dependent cloud models with detailed mi-crophysics. Z. Levin and his colleagues have suc-cessfully embarked on testing the sensitivity ofclouds to seeding type, seeding material, the seed-ing levels, the dosage, the time relative to the evo-lution of the cloud, the duration of seeding, etc.(Reisin et al. 1996; Yin et al. 1999).

2) The shift from seeding on a basis of day units tothe seeding of single clouds of an ensemble re-quires the study of the cloud-to-cloud dynamic in-teractions by mesoscale models. The results ofsuch studies would then establish the minimumdistances between seeded and unseeded controlclouds, thus allowing optimal multiple and par-allel seeding within a field of clouds. Packing moreseeded target and unseeded control clouds intoone cloud ensemble during 1 day could shortenthe total seeding period, without a loss of confi-dence in the results. However, satellite observa-


tions of outflows of precipitating clouds may showrestricting limits (J. Purdom 2003, personal com-munication).

3) Models could also test if seeding of single cloudsin a field of naturally interacting clouds would in-crease or decrease the total precipitation of thatcloud ensemble.

4) The modeling of the “robbing Peter to pay Paul”question needs to be addressed.

5) At the synoptic level, the precipitation increase ordecrease by seeding within the system should bestudied. This should be done in view of the pre-cipitation behavior of systems (Carbone et al.2002) and the limits imposed by them.

6) Considering that runoff can be as low as 10% inthe Canadian prairies, the question of increasedlarge-scale recycling of water should be addressed.

Assimilation models. The case has been made that as-similation models are required to extrapolate rain re-ceived by the ground by integrating data from raingauges, radar, and all other possible measuring sys-tems. This is a very major task.

Statement on computing: The existing computerpower is insufficient. It is true that the National Cen-ter for Atmospheric Research (NCAR) has increased itscomputer capacity over the past few years by a factorof more than 4. However, we need several such “factorof 4" increases in computer power over the next 10 yrto prepare ourselves for the modeling of precipitationin all its forms and at all scales up to climate models,including seeding scenarios and sensitivity studies.Computer intensive ensemble modeling in forecastingshould also be applied to cloud and mesoscale models.

SUMMARY, COMMENTS, AND PROJEC-TIONS. Aim. The aim of the most advanced type ofweather modification, rain enhancement, is to pro-duce, detect, and understand rain increases of 10%–20%, and to substantiate such gains within 1 yr afterthe experiment’s end. Such a difficult feat would notbe tried in forecasting, climate modeling, or any otherfield of meteorology.

General status. The general status of weather modifi-cation, in a nutshell, is as follows: Fog and stratus dis-solution are in the realm of operations. Rain enhance-ment has been demonstrated to work. Hailsuppression has not been supported by randomizedexperiments. Interfering with tornadoes and hurri-canes is hazardous, should it work, and is encumberedby possible legal challenges. It should await better sci-entific understanding. Reducing torrential rains and

dealing with other disaster scenarios has not been at-tempted yet.

The science. The study of all precipitation processes isof the greatest importance to the evolution of weathermodification. Such studies will also lead to consider-able advances in forecasting and climate modeling.

Substantial upgrades are also needed in statistics,statistical design and evaluation methods, and engi-neering technology, be they through better and moreaccurate measurement technology, observational plat-forms, seeding material sciences, and/or seeding ma-terial delivery.

The National Precipitation Research and Weather Modi-fication Program.5 All the above issues point to the needfor an all-encompassing, coordinated national precipi-tation research and weather modification program.Such a program should cover all research aspects ofprecipitation from the laboratory, to computer ex-periments, field measurements, precipitation(weather) modification experiments, applications toforecast and climate models, and, last but not least,to environmental and legal issues. Such a programshould be planned and guided by all stakeholdersfrom academia to government agencies, the privatesector, and the public and political bodies.

All components of such a program can be identi-fied easily, and after an overall planning phase, shouldbe started simultaneously, considering that none ofthe subfields is fully developed.

The decision about the first type of weather modi-fication project to be tackled (rain, snowpack, hail,tornadoes, hurricanes, other disasters, etc.) should beleft to the guiding body. Starting with a project prom-ising success might be wise!

Weather modification experiments should bebased on the proven sequence of steps: developmentof a conceptual model, site selection process, explor-atory field studies, randomized experiment, followedby evaluation. Field studies should involve the bestavailable equipment and make use of all the know-how from all the sectors, including the private sectorwith its vast experience and competence. Seedingshould be incorporated already into the exploratoryprogram in order to test if the perceived understand-ing is supported by appropriate changes in the cloudsfollowing seeding (seeding signatures).

5 Some of the following statements on the setup of WM experi-ments have been voiced before. They are listed for completeness.

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Financial aspects. Weather modification by alteringand steering weather events with catalysts was at onetime considered very attractive because large atmo-spheric energies could be affected by little energy and,thus, involve only limited financial resources. Thismay still be true in a relative sense. However, it be-comes obvious that this does not mean that it will becheap. Atmosphere-related disasters in the UnitedStates alone are estimated to cost in the range of tensof billions of dollars per annum. Planning, develop-ing, building, and testing of WM experiment designs,numerical models, instruments, platforms, and ex-periments themselves for an eventually unified assaulton weather-caused disasters may easily amount to$1 billion over a 5-yr period, just as a start. Not leftto be unmentioned is the need for well-educated andtrained staff from all atmosphere-related specialties,imaginative scientists as leaders, and politicians withinsight. Such a prize tag is not exorbitant, consider-ing that the present annual expenditures for climatechange research in the United States is given as$1.7 billion (Lautenbacher 2002), with $5 billion pro-posed for the next budget.

Final comments. In the long run the future of WM isbright. There is growing evidence that the basic con-cepts are correct, that successful implementation isfeasible. Weather modification works beyond anydoubt. Nobody else but nature itself is doing weathermodification on a grand scale: clouds and cloud sys-tems, unable to yield rain over continents due to heavyloads of small-sized cloud condensation nuclei (CCN;many produced by biomass burning) are revitalized,while moving back over the oceans, by the ingestionof sea salt nuclei, and can yield rain again [Rosenfeld(2003), confirmed by R. Bruintjes (2003, personalcommunication)]. This is nothing but a rousing sup-port for weather modification and what it stands for!

ACKNOWLEDGMENTS. This paper is dedicated tothe “grand master” of weather modification statistics whodied so prematurely, my friend and mentor, ProfessorRuben Gabriel.

The idea for this paper came after the new AMS PolicyStatement on Planned and Inadvertent Weather Modifi-cation (AMS 1998a) and the corresponding scientific back-ground paper (AMS 1998b) had been issued and it becameclear that a vision had to be developed about where to go.A grant by the Canadian National Science and Engineer-ing Research Council of Canada made this study possible.The input by members of the AMS Committee on Plannedand Inadvertent Weather Modification is gratefully acknowl-edged. I am indebted to Dr. A. Thomson for the preparation

of Fig. 2 and wish to thank the reviewers for their thoughtfuladvice. I credit Dr. Eugene Bierly for referring me to thespeech by President J. F. Kennedy to the United Nations.The excerpt from that speech was taken from the Websiteof the J. F. Kennedy Library in Boston, Massachusetts.

APPENDIX: THE STATE OF CLOUD MI-CROPHYSICS. The impressive state of cloud mi-crophysics is well described by Pruppacher and Klett(1997) and other texts. However, many processes arestill unknown or not known to sufficient accuracy forbeneficial use in weather modification; neither are theinstruments and models adequate. Following are ma-jor areas of my concern in the microphysics.

Droplet and ice particle nucleation. The developmentof cloud condensation, CCN, and ice nucleus (IN),counters needs to be continued in order to bettersimulate nature’s scenarios of nucleus activation. Thepresent working definition of a CCN is that it is acti-vated at supersaturations of 10%. This value is muchtoo high for the cloud formation phase where su-persaturations of 0.10% may activate all CCNs that areto form droplets. The definition of a CCN is also inad-equate because the speed of cloud formation determineswhich aerosol particles of a group will be activated(Neiburger and Chen 1960). We need new and bettercounters that work with lower supersaturations and atmuch greater accuracy. In the end, observations of thedroplet spectra above cloud base may give the mostaccurate information. However, this information shouldbe related to the aerosol properties below cloud base.

The ice nuclei counting is also not satisfactory be-cause it does not simulate the different ice nucleationprocesses and the related time scales.

Droplet collisions and coalescence. 1) Hocking’s assumption (Davies and Sartor 1967)

that collisions occur in Stokes flow is incorrect(Stuart 1975). A theoretical solution based on twocoupled Navier–Stokes equations would substan-tially improve our knowledge on droplet (anddrop!) interactions.

2) A lot of effort is spent on the theoretical study ofdroplet collisions in turbulent air. However, theturbulence level in clouds at a scale < 10 m is un-known. List and Hand (1971) established thatcloud droplets in wakes of freely falling raindropsdid not behave erratically; they basically followedthe local flow of the air. Turbulence measure-ments in clouds by hot-wire, sound anemometersor small temperature sensors are really needed atscales of 1 mm.


3) To include shear in the concept of collision effi-ciency is no problem; however, turbulence has notbeen included, nor are there corresponding labo-ratory measurements.

Raindrop growth by collision, coalescence, and breakup.Interactions between 10 drop pairs have been mea-sured in the laboratory at laboratory pressure (Lowand List 1982a), while 5 pairs have been studied at50 kPa (Fung 1984). The standard pressure data havebeen parameterized to cover all diameter combina-tions (Low and List 1982b) and are being applied incloud models with detailed drop evolutions. However,the experimental data pool is too limited and the it-erative parameterization is unsuitable for exploringthe sensitivities of the positions, heights, and widthsof the assumed peaks.

The hierarchy of atmospheric ice particles. The weatherobservers use a WMO classification of ice particles.The basic version is close to useless because it classi-fies all the rarely seen pristine crystal shapes (normallyforming in a quiet fog or stratus) and has no space forsnow, the different types of snow, and the complex-ity of the larger ice particles.

Ice crystals. There are experiments by Nakaya et al.(1958) that show how droplets impact single crystalsof ice at a slanting angle, roll over the ice surface, andevaporate at a very fast rate (high water vapor gradi-ents)! Thus, growth is by accretion of droplets, but theresult looks like growth by vapor deposition. Is thathow large single crystalline, saltlike structures growon hailstones, as first described by Abich (1869)?

Snow. What are the different types of weather condi-tions leading to the different types of snow? For ex-ample, how and when is granular snow formed (com-pact conglomerates of frozen cloud droplets) andwhen is it mixed into other types of snow crystals?

Graupel. Graupel formation needs to be revisited sincethe work by Arenberg (1941), Sasyo (1971), andCober and List (1993).

Hailstones.1) There is still no understanding of how the differ-

ent growth stages of hailstones evolve and howthis change is connected to changing shapes andaerodynamics as proposed by List (1961).

2) There is only negligible understanding of thesingle crystal arrangement and c-axes directionsin hailstones, as they grow under different envi-

ronment and aerodynamic regimes, and there is stillthe unanswered old dream of deducing hailstonegrowth conditions from the structural character-istics, as attempted so valiantly by Macklin (1961).

Change separation. Experiments were carried out for50 yr with “graupel” mounted on a swirling arm in abox filled with supercooled droplets and ice crystals(Takahashi 1978; Saunders 1994). The recent changeto an icing tunnel environment revealed the hithertounknown controlling variable: the relative humidityin the cloud. The change from an environment of iceto water saturation affected charging more than theliquid water content or temperature (Berdeklis 1998;Pereyra et al. 2000; Berdeklis and List 2001). Such ex-periments need to be expanded to lead, after arduouswork, to a physical understanding and a charge sepa-ration theory describing thunderstorm charge genera-tion. Measurements by storm-penetrating aircraft arealso highly desirable to establish the relative humid-ity in situ by tunable diode lasers.

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weather modification—a scenario for the future

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