Study of a Western Disturbance of 2023 Using Satellite-Based Observation, Reanalysis Data, and Numerical Simulation

[{“box”:0,”content”:”n[if 992 equals=”Open Access”]n

n

n

n

Open Access

nn

n

n[/if 992]n[if 2704 equals=”Yes”]n

n

Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

n[/if 2704]n

n

Year : 2025 [if 2224 equals=””]25/09/2025 at 1:57 PM[/if 2224] | [if 1553 equals=””] Volume : 16 [else] Volume : 16[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 16 38

n

n

nn

n

n

n

    By

    n

    [foreach 286]n

    n

    Janvi Patel,

    n t

  • n

    n[/foreach]

    n

n[if 2099 not_equal=”Yes”]n

    [foreach 286] [if 1175 not_equal=””]n t

  1. Student, Dhirubhai Ambani Institute of Information & Communication Technology, Gandhinagar, Gujarat, India

    2. n[/if 1175][/foreach]

n[/if 2099][if 2099 equals=”Yes”][/if 2099]n

n

Abstract

n

n

nWestern Disturbances (WDs) are synoptic-scale, extratropical storm systems that influence winter precipitation across northwest India. This study focuses on a specific WD event that occurred from 24– 25 March 2023, affecting Jammu & Kashmir, Himachal Pradesh, Uttarakhand, and Punjab. The analysis integrates satellite observations, ERA-5 reanalysis data, and simulations from the Weather Research and Forecasting (WRF) model to evaluate the model’s performance. The novelty of this study lies in its event-specific analysis of a recent WD and in quantifying the model’s predictive accuracy over complex Himalayan terrain. The WRF model effectively captures snowfall (RMSE: 1.40 mm), U10 wind (RMSE: 0.63 m/s), and V10 wind (RMSE: 0.30 m/s), but underperforms significantly for rainfall (RMSE: 4.46 mm). Time-series analysis reveals inverse relationships between total cloud cover and outgoing longwave radiation (OLR), and between snowfall and wind speed, consistent with expected meteorological dynamics. These findings highlight the strengths and limitations of mesoscale numerical modeling for WD forecasting. The study underscores the need for improved rainfall simulation techniques in complex terrains and supports the integration of high-resolution models for better regional forecasting, agricultural planning, and disaster management.nn

n

n

n

Keywords: Western disturbance, Northwest India, WRF model, satellite observation, ERA-5, model validation

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Remote Sensing & GIS ]

n

[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Remote Sensing & GIS (jorsg)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

n

n

n

How to cite this article:
nJanvi Patel. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Study of a Western Disturbance of 2023 Using Satellite-Based Observation, Reanalysis Data, and Numerical Simulation[/if 2584]. Journal of Remote Sensing & GIS. 08/09/2025; 16(03):16-38.

n

How to cite this URL:
nJanvi Patel. [if 2584 equals=”][226 striphtml=1][else]Study of a Western Disturbance of 2023 Using Satellite-Based Observation, Reanalysis Data, and Numerical Simulation[/if 2584]. Journal of Remote Sensing & GIS. 08/09/2025; 16(03):16-38. Available from: https://journals.stmjournals.com/jorsg/article=08/09/2025/view=0

nn

n

n[if 992 equals=”Open Access”]Full Text PDF[/if 992]n

n

n[if 992 not_equal=”Open Access”]n

n

n[/if 992]n

nn

nnn

n[if 379 not_equal=””]nn

Browse Figures

n

n

n[foreach 379]

figures

[/foreach]n

n

n

n[/if 379]

n

n

n

n

n

References n

n[if 1104 equals=””]n

  1.  Dimri AP, Niyogi D, Barros AP, Ridley J, Mohanty UC, Yasunari T, Sikka DR. Western disturbances: a review. Rev Geophys. 2015 Jun; 53(2): 225–46.
  2. Dimri AP, Chevuturi A. Western disturbances-an Indian meteorological perspective. Cham: Springer; 2016.
  3. Shekhar MS, Chand H, Kumar S, Srinivasan K, Ganju A. Climate-change studies in the western Himalaya. Ann Glaciol. 2010 Jan; 51(54): 105–12.
  4. Chawla I, Osuri KK, Mujumdar PP, Niyogi D. Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin. Hydrol Earth Syst Sci. 2018 Feb 8; 22(2): 1095–117.
  5. Patil R, Pradeep Kumar P. WRF model sensitivity for simulating intense western disturbances over North West India. Model Earth Syst Environ. 2016 Jun; 2(2): 82.
  6. Samantray P, Gouda KC. A review on the extreme rainfall studies in India. Nat Hazards Res. 2024 Sep 1; 4(3): 347–56.
  7. Gill KK, Kaur S, Sandhu SS, Bhatt K. Western disturbances: Occurrence and impact on wheat productivity in Punjab. J Agrometeorol. 2024 Sep 1; 26(3): 339–43.
  8. Al-Ruzouq R, Shanableh A, Jena R, Gibril MB, Hammouri NA, Lamghari F. Flood susceptibility mapping using a novel integration of multi-temporal sentinel-1 data and eXtreme deep learning model. Geosci Front. 2024 May 1; 15(3): 101780.
  9. Sudha Rani Nalakurthi NV, Behera MR, Bhaskaran PK. Land subsidence detection using sentinel- 1 interferometer and its relation with environmental drivers: a case study for coastal Mumbai city. Spat Inf Res. 2024 Dec; 32(6): 665–81.
  10. Shamsuzzoha M, Shaw R, Ahamed T. Machine learning system to assess rice crop change detection from satellite-derived RGVI due to tropical cyclones using remote sensing dataset. Remote Sens Appl: Soc Environ. 2024 Aug 1; 35: 101201.
  11. Srivastava A, Thakur AK, Garg RD. An assessment of the spatiotemporal dynamics and seasonal trends in NO2 concentrations across India using advanced statistical analysis. Remote Sens Appl: Soc Environ. 2025 Jan 1; 37: 101490.
  12. Yáñez-Morroni G, Gironás J, Caneo M, Delgado R, Garreaud R. Using the Weather Research and Forecasting (WRF) model for precipitation forecasting in an Andean region with complex topography. Atmosphere. 2018 Aug 2; 9(8): 304.

nn[/if 1104][if 1104 not_equal=””]n

    [foreach 1102]n t

  1. [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]

    2. n[/foreach]

n[/if 1104]

n

nn[if 1114 equals=”Yes”]n

n[/if 1114]

n

n

[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Original Research

n

n

n

n

n

Journal of Remote Sensing & GIS

n

[if 344 not_equal=””]ISSN: 2230-7990[/if 344]

n

n

n

nn

n

[if 2146 equals=”Yes”][/if 2146][if 2146 not_equal=”Yes”][/if 2146]n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n[if 1748 not_equal=””]

[else]

[/if 1748]n

n[if 1746 equals=”Retracted”]n

n

n

n

[/if 1746]n[if 4734 not_equal=””]

n

n

n

[/if 4734]n

n

Volume 16
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 22/07/2025
Accepted 28/07/2025
Published 08/09/2025
Retracted
Publication Time 48 Days

n

n

88

nn

n

Login

n
My IP
n

PlumX Metrics

nn

n

n

n[if 1746 equals=”Retracted”]n

[/if 1746]nnn

nnn”}]