Team:UGent Belgium/Clouds

Clouds | Vsycle

Introduction

To perform cloud seeding and test cloud seeding agents in dynamic chambers, a good understanding of cloud physics is necessary. To that end, we studied the precipitation processes in cold and warm clouds and the parameters like temperature and humidity that have to be taken into account before seeding a cloud.

Cumulus clouds

In temperate regions like Belgium, precipitation usually begins to fall as snow high in the clouds, even in summer. In which form the precipitation reaches the earth’s surface, depends on the vertical temperature distribution Stommel, 1947 If the temperature remains negative everywhere for example, snow will fall.

When clouds are formed, they consist of supercooled water droplets, ice crystals or a combination of both. However, precipitation is only formed when the particles become sufficiently heavy. For this to happen, the cloud particles have to grow considerably: one raindrop contains about five million cloud droplets Weston, 1980. There are two processes that can cause cloud particles to grow.

Coalescence is the process that explains the formation of rain in relatively warm clouds with temperatures ranging from -10 to -12 °C, consisting largely of supercooled water droplets. Some droplets are a bigger than the others and start to fall in the cloud. In doing so, they collide with other droplets which get absorbed by the bigger one. When the drops have grown large enough, they can reach the ground. In our temperate regions, there is little rainfall from these so-called water clouds. In the tropics however, where the atmosphere can contain much larger condensation cores due to the strong vertical movements, coalescence can cause heavy precipitation Warner, 1969.

The Wegener-Bergeron-Findeisen theory describes a second process in which cloud elements are transformed into precipitation. This process takes place in clouds in which supercooled water droplets and ice crystals occur together. These types of clouds are also called mixed clouds and have temperatures lower than -12 ° C Warner, 1969.

At negative temperatures, cold air surrounding the ice crystals is able to contain less water vapor than air surrounding the supercooled droplets. This means that the maximum vapor pressure in ice clouds is lower than in water clouds . The lower vapor pressure above ice ensures that the saturation limit is crossed faster, which causes the water vapor to condense and freeze on the ice crystal. This results in more rapid precipitation of water vapor on the ice, which is called ripening . Due to the reduction of the water vapor in the air, the water droplets will evaporate. Next, the water vapor will ripen on the ice. The ice crystals will grow bigger and heavier, until they fall down in the form of snow KMI, 2020.

For this process, the initial ice crystals in the cloud are of great importance. Supercooled water on its own doesn’t easily freeze: rather, it requires an ice nucleus: a particle acting as a starting point for ice crystal growth. In nature, dirt and pollen often fulfil this role. A more efficient ice nucleus that also occurs in nature is the ice nucleation protein (INP). This efficiency is derived from the protein’s structure, which attracts water molecules in such a way that they end up in an ideal position to form into an ice crystal. Silver iodide (AgI), a currently commonly used chemical for the cloud seeding of cold clouds, serves as a good ice nucleus for about the same reason: its structure is similar to that of an ice crystal, thereby facilitating ice crystal formation.

Ice nucleation proteins

INPs are a family of proteins that are expressed on the outer membrane of several species of plant pathogenic Gram-negative bacteria. The proteins make sure these bacteria can act as nuclei for the formation of ice crystals. This can happen at a higher temperature than normally possible when non-biological particles, such as dirt for example, act as nuclei. Therefore, a higher efficiency of ice nucleation is reached. These bacteria use this as a way to acquire nutrients from plant cells by damaging them through the formation of ice crystals. Furthermore, they also use their ice nucleation ability as a dispersal method to reach new hosts by inducing rain when they get in clouds Morris et al., 2004. Pseudomonas syringae, which makes InaZ INPs, is the most studied INP producing bacteria. InaZ has been proven to be the most efficient INP Han et al, 2017. For this reason, InaZ was chosen for this project.

The reason for the high efficiency of InaZ and other INPs can be traced back to its structure and amino acid sequence. Although there still exists a lot of uncertainty regarding the exact structure, it is thought that the protein forms a beta helix structure. Moreover, a large part of its sequence is made out of a series of tandem repeats. These repeats consist of two fixed motifs each containing three amino acids. The helix is positioned in such a way that on one side of the helix represents the first motif while the second motif is represented at the opposite site. The amino acids of both motifs have two hydrophilic and two hydrophobic groups. The hydrophilic groups attract water molecules and the hydrophobic groups entrap them in between the helix loops. The resulting distances between the water molecules in the helix are very similar to those of water molecules in an ice lattice, making INP a perfect starting point to form one Garnham et al., 2011. A visual representation is shown in Figure 1.

Visual representation of the helix structure of INP and how water molecules are entrapped in the helix loops. Hydrophilic functional groups are represented by blue dots, hydrophobic functional groups by red dots and water molecules by orange dots. The resulting distances between the entrapped water molecules are similar to those in an ice lattice (figure on the right); Adapted from (Garnham et al., 2011; Davies et al., 2002)

Figure 1: Visual representation of the helix structure of INP and how water molecules are entrapped in the helix loops. Hydrophilic functional groups are represented by blue dots, hydrophobic functional groups by red dots and water molecules by orange dots. The resulting distances between the entrapped water molecules are similar to those in an ice lattice (figure on the right); Adapted from (Garnham et al., 2011; Davies et al., 2002)

Since pure H20 only starts to freeze below -40 °C, an ice nucleus is needed to form ice crystals at higher temperatures. In this case the INP provides a structure that favors water in its solid form because of the structure of the protein and the way water interacts with it. This nucleus than goes on to grow bigger and bigger until it precipitates. The reason why the Vsycle team is so confident that the biological cloud seeding agent will be just as efficient, or even more efficient than AgI, is that the same process is present in cloud seeding techniques involving AgI. AgI crystals also have a structure that closely resembles a an ice lattice, in which liquid water molecules can settle and form solid H2O more easily and at higher temperatures than pure water can.

Cloud seeding with INPs in warm clouds

As mentioned above, coalescence is the main process that drives the formation of precipitation in warm clouds. Hygroscopic particles, which attract water, can speed this up. This is why salts are commonly used for the cloud seeding of warm clouds. As INPs don’t have strong hygroscopic properties, it is unlikely they will have much use for warm clouds.

What type of clouds can be seeded?

As cloud seeding consists of artificially aiding (or, in the context of flood and storm prevention, inhibiting ) the natural precipitation processes, it makes sense that clouds with the potential to form lots of precipitation are the main targets. Nimbostratus and cumulonimbus clouds are the typical rain clouds, with the latter often producing much more extreme weather like storms.

Testing and seeding efficiency

Once a biological cloud seeding agent is produced and the preliminary tests like efficiency tests are preformed, the next step will be to test it in a real cloud environment on a small scale. Dynamic climate chambers are ideal for this purpose. Dynamic climate chambers are closed environments in which multiple parameters such as temperature and relative humidity can be programmed Tajiri et al., 2013. These dynamic climate chambers can be used to study many aspects of climatology including clouds Ward & DeMott, 1989. The parameters in a dynamic chamber can be set to specific values to mimic the conditions in a given cloud. This can then be used to see how different cloud seeding techniques might affect the cloud. Specifically, dynamic climate chambers can be used to measure the influence the cloud seeding agent has on condensation and freezing DeMott, 1988. Cloud Seeding Technologies, a company based in Germany with whom we have exchanged information, has these dynamic climate chambers. There would have been an opportunity to test the Vsycle cloud seeding agent in these dynamic chambers and with pneumatic flares, but due to COVID-19 we were unable to produce enough of the biological cloud seeding agent for it to be properly tested.

AgI and bacterial ghost comparison

In order to compare AgI and the bacterial ghosts (BGs), one important factor is their size. An AgI particle in an aerosol for cloud seeding has a diameter of approximately 25 nm Kalnina, 2015. The BG has about the same dimensions as an E. coli cell: a cylinder of 1.0 to 2.0 µm in length, with a radius of 0.05 µm Riley, 1999. However, the exact size isn’t known since it will have its cell material removed and get lyophilized in order to be in an optimal form for the pneumatic dispersion method of the company Cloud Seeding Technologies. Lyophilisation is known to for aggregates, which can in turn also change the size of the particles.

Parameters affecting seeding efficiency

There are different parameters that can affect seeding efficiency with the first one being the presence of aerosols. Aerosols can help with the seeding of clouds. Most of the time this is done unintended by anthropogenic pollution. Humans are known to seed the clouds with polluting aerosols in a negative way on a much grander scale than we do positively with ice nuclei and giant cloud condensation nuclei. Being the two sides of the same coin Rosenfeld, 2007. For example, the presence of pollution aerosols helps with the coalescence in the cloud, thus adding to the cloud seeding process. The problem here is that this actually does as much harm as cloud seeding with AgI because these pollutants will also drop down and intoxicate the soil.

Secondly, temperature, wind speed and air pressure are important parameters, that are all interconnected. There is an optimal window for these variables that implies the best seeding conditions. Clouds are formed on average around 3000 meters above sea-level. At this height there is an average air pressure of 70 kPa. R. W. Shaffer observed that the wind speed at this air pressure should be lower than 10 m/s for ideal seeding. In a cloud there is obviously a temperature gradient. Shaffer has recorded the temperatures at the top of each cloud he investigated, with a rawinsonde (radar-wind sonde) and concluded that this top temperature should be greater than -29°C. In the cloud, the altitude that corresponds with a temperature of -5°C, should also be lower than 3000m. When staying in this window of optimal seeding, he measured a seeding/no-seeding ratio of 2.14 which is measured by the amount of rainfall in mm over a period of three hours.

Finally, because of the previous parameters we can also deduct the influence of the cloud size. The horizontal dimensions of the cloud shouldn’t make that much difference, other than needing a lot of seeding agent to cover this area if the whole cloud is to be seeded. The vertical dimensions are more important. Optimally, the temperature at the top of the cloud should be higher than -29°C so if the cloud reaches higher than the height corresponding with this air temperature, it would affect the seeding efficiency negatively.

An estimation of the number of BGs needed for cloud seeding

In order to know how many BGs are needed for cloud seeding, we made an estimation based on the results of the 2016 iGEM team at UGhent called Dewpal. To be clear, this estimation based on results of the 2016 iGEM team at UGhent don’t reflect the minimal amount of lysate needed to seed a cloud, because the minimum amount of lysate needed to freeze a volume of water was not tested IGEM UGent, 2016. Only the whether or not the given amount of lysate can indeed freeze the given amount of water. Because of these reasons, the estimated amount will most likely be an overestimation of the actual amount of lysate needed to seed an average cumulus cloud.

Table 1: A rough estimation of the amount of BGs required for cloud seeding an average cumulus cloud
AttributeValueUnit
Average density0.3g/m3
Diameter3000m
Volume1.41 * 1010m3
Total water mass4.239 * 106kg
Density liquid water1000kg/m3
Total volume supercooled water4239m3
Volume lysate needed to freeze 1L0.01l
Volume lysate needed to seed an average cumulus cloud42390l

References

  1. Davies, P. L., Baardsnes, J., Kuiper, M. J., and Walker, V. K. (2002).

    Structure and function of antifreeze proteins.

    Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 357(1423):927–935.

    CrossRefGoogle ScholarBack to text
  2. DeMott, P. J. (1988).

    The microstructure of cumulus cloud. Part II. The effect on droplet size distribution of the cloud nucleus spectrum and updraft velocity.

    The Journal of Weather Modification. 20(1), 44-50

    Google ScholarBack to text
  3. Garnham, C. P., Campbell, R. L., Walker, V. K., & Davies, P. L. (2011).

    Novel dimeric β-helical model of an ice nucleation protein with bridged active sites.

    BMC structural biology 11(1), 1-12.

    CrossRefGoogle ScholarBack to text
  4. Han, Y. J., Song, H., Lee, C. W., Ly, N. H., Joo, S.-W., Lee, J. H., Kim, S.-J., and Park, S. (2017).

    Biophysical characterization of soluble pseudomonas syringae ice nucleation protein inaz fragments.

    International journal of biological macromolecules 94:634–641.

    CrossRefGoogle ScholarBack to text
  5. IGEM UGent, (2016).

    SOP.

    Retrieved on September 22, 2020. from http://2016.igem.org/Team:UGent_Belgium/SOP

    Back to text
  6. IGEM UGent, (2016).

    Measurement.

    Retrieved on September 22, 2020. from http://2016.igem.org/Team:UGent_Belgium/Measurement

    Back to text
  7. IGEM UGent, (2016).

    Biofunction.

    Retrieved on September 22, 2020. from http://2016.igem.org/Team:UGent_Belgium/Biofunction

    Back to text
  8. Kalnina, D., Gross, K. A., Medvids, A., & Onufrijevs, P. (2015).

    Formation of negatively charged AgI colloid nanoparticles by condensation.

    Advanced Materials Research

    CrossRefGoogle ScholarBack to text
  9. KMI, (2020).

    Neerslag (vorming)

    Retrieved on September 22, 2020. from https://www.meteo.be/nl/info/weerwoorden/neerslag-vorming

    Back to text
  10. Morris, C. E., Georgakopoulos, D. G., & Sands, D. C. (2004)

    Ice nucleation active bacteria and their potential role in precipitation

    Journal de Physique IV (Proceedings) Vol. 121, pp. 87-103

    CrossRefGoogle ScholarBack to text
  11. NASA

    Estimating the Mass of a Cloud!

    Retrieved on September 22, 2020. from https://spacemath.gsfc.nasa.gov/earth/79Clouds3.pdf

    Back to text
  12. Riley, M. (1999, October).

    Correlates of smallest sizes for microorganisms.

    Size Limits of Very Small Microorganisms Proceedings of a Workshop

    Google ScholarBack to text
  13. Rosenfeld, D. (2007).

    New insights to cloud seeding for enhancing precipitation and for hail suppression. The Journal of Weather Modification, 39(1), 61-69.

    Retrieved on September 15, 2020. from http://www.journalofweathermodification.org/index.php/JWM/article/view/203

    Back to text
  14. Stommel, H. (1947).

    ENTRAINMENT OF AIR INTO A CUMULUS CLOUD.

    Journal of Meteorology.

    CrossRefGoogle ScholarBack to text
  15. Tajiri, T., Yamashita, K., Murakami, M., Saito, A., Kusunoki, K., Orikasa, N., & Lilie, L. (2013).

    A novel adiabatic-expansion-type cloud simulation chamber.

    Journal of the Meteorological Society of Japan.

    CrossRefGoogle ScholarBack to text
  16. Ward, P. J., & DeMott, P. J. (1989).

    Preliminary experimental evaluation of snomax (TM) snow inducer, nucleus Pseudomonas syringae, as an artificial ice for weather modification.

    The Journal of Weather Modification. 21(1), 9-13

    Google ScholarBack to text
  17. Warner, J. (1969).

    The microstructure of cumulus cloud. Part I. General features of the droplet spectrum.

    Journal of the Atmospheric Sciences.

    CrossRefGoogle ScholarBack to text
  18. Warner, J. (1969).

    The microstructure of cumulus cloud. Part II. The effect on droplet size distribution of the cloud nucleus spectrum and updraft velocity.

    Journal of the Atmospheric Sciences.

    CrossRefGoogle ScholarBack to text
  19. Weston, K. J. (1980).

    An observational study of convective cloud streets.

    Tellus.

    CrossRefGoogle ScholarBack to text