IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v527y2019ics0378437119307915.html
   My bibliography  Save this article

Stochastic stability of aerosols-stimulated rainfall model

Author

Listed:
  • Misra, A.K.
  • Tripathi, Amita

Abstract

With increasingly erratic nature of monsoon, especially in Indian Ocean region clamors for artificial rain have gathering pace recently. For artificial rain, aerosols are used which act as cloud condensation nuclei (CCN) in the formation of cloud droplets from water vapors. In this paper, a nonlinear mathematical model is proposed and analyzed to increase the intensity of rain in the rain deficient regions using aerosols. To stimulate rainfall two kinds of aerosols are used. We have considered three different stages of water involved in the process of precipitation namely, (i) the density of water vapors, (ii) the density of cloud droplets, and (iii) the density of raindrops. Previously proposed models consist of a set of ordinary differential equations (ODE), whereas the use of stochastic differential equations (SDE) depicts more realistic situation as they include environmental disturbances. First, we propose a deterministic model and then convert it into a stochastic model by introducing white noise terms. We have found that the deterministic model is globally asymptotically stable without any condition, while the stability of stochastic model solely depends on the intensity of white noise.

Suggested Citation

  • Misra, A.K. & Tripathi, Amita, 2019. "Stochastic stability of aerosols-stimulated rainfall model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119307915
    DOI: 10.1016/j.physa.2019.121337
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119307915
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.121337?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Das, Parthasakha & Das, Samhita & Das, Pritha & Rihan, Fathalla A. & Uzuntarla, Muhammet & Ghosh, Dibakar, 2021. "Optimal control strategy for cancer remission using combinatorial therapy: A mathematical model-based approach," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119307915. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.