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

Open Access

Year : 2025 | Volume : 16 | Issue : 03 | Page : 16 38
    By

    Janvi Patel,

  • Nishtha Ahuja,

  • Charu Singh,

  1. Student, Dhirubhai Ambani Institute of Information & Communication Technology, Gandhinagar, Gujarat, India
  2. Student, Dhirubhai Ambani Institute of Information & Communication Technology, Gandhinagar, Gujarat, India
  3. Scientist/Engineer, SF Marine and Atmospheric Science Department, Indian Institute of Remote Sensing, ISRO, Govt. of India, 4, Kalidas Road, Uttarakhand, India

Abstract

Western 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.

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

[This article belongs to Journal of Remote Sensing & GIS ]

How to cite this article:
Janvi Patel, Nishtha Ahuja, Charu Singh. Study of a Western Disturbance of 2023 Using Satellite-Based Observation, Reanalysis Data, and Numerical Simulation. Journal of Remote Sensing & GIS. 2025; 16(03):16-38.
How to cite this URL:
Janvi Patel, Nishtha Ahuja, Charu Singh. Study of a Western Disturbance of 2023 Using Satellite-Based Observation, Reanalysis Data, and Numerical Simulation. Journal of Remote Sensing & GIS. 2025; 16(03):16-38. Available from: https://journals.stmjournals.com/jorsg/article=2025/view=222890


References

  1. 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. Geoscience Frontiers. 2024 May 1;15(3):101780.
  2. 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. Hydrology and Earth System Sciences. 2018 Feb 8;22(2):1095-117.
  3. Dimri AP, Chevuturi A. Western disturbances-an Indian meteorological perspective.
  4. Dimri AP, Niyogi D, Barros AP, Ridley J, Mohanty UC, Yasunari T, Sikka DR. Western disturbances: a review. Reviews of Geophysics. 2015 Jun;53(2):225-46.
  5. Patil R, Pradeep Kumar P. WRF model sensitivity for simulating intense western disturbances over North West India. Modeling Earth Systems and Environment. 2016 Jun;2(2):82
  6. Samantray P, Gouda KC. A review on the extreme rainfall studies in India. Natural Hazards Research. 2024 Sep 1;4(3):347-56.
  7. 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 Sensing Applications: Society and Environment. 2024 Aug 1;35:101201.
  8. Shekhar MS, Chand H, Kumar S, Srinivasan K, Ganju A. Climate-change studies in the western Himalaya. Annals of Glaciology. 2010 Jan;51(54):105-12.
  9. 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 Sensing Applications: Society and Environment. 2025 Jan 1;37:101490.
  10. Sudha Rani Nalakurthi NV, Behera MR, Bhaskaran PK. Land subsidence detection using sentinel1 interferometer and its relation with environmental drivers: a case study for coastal Mumbai city. Spatial Information Research. 2024 Dec;32(6):665-81.
  11. Gill KK, Kaur S, Sandhu SS, Bhatt K. Western disturbances: Occurrence and impact on wheat productivity in Punjab. Journal of Agrometeorology. 2024 Sep 1;26(3):339-43.
  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.
Regular Issue Open Access Original Research
Volume 16
Issue 03
Received 22/07/2025
Accepted 28/07/2025
Published 08/08/2025
Publication Time 17 Days
346

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