Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Research Articles

Early Access

Validation of medium range rainfall forecast accuracy in Jagtial district of Telangana

DOI
https://doi.org/10.14719/pst.7460
Submitted
27 January 2025
Published
10-04-2025
Versions

Abstract

Rainfall is found to vary to a greater extent in its amount, intensity and distribution than any other weather parameter. Variation in rainfall during the crop season, including delay in monsoon arrival, heavy downpours and prolonged dry spells, can have a more significant impact on crop growth and development. If farmers have timely access to a medium-range rainfall forecast, they can take full advantage of unfavourable weather situations in scheduling their agricultural operations. The present study was taken by Agromet Field Unit (AMFU), Regional Agricultural Research Station, Jagtial, to analyze and verify the accuracy of medium-range rainfall forecast issued by the India Meteorological Department using a location-specific Multi-Model Ensemble (MME) technique for the Jagtial district from 2017 to 2023. The methodology included adopting quantitative and qualitative methods with the Ratio score, Hanssen and Kuipers (H.K) Score, RMSE, usability analysis and correlation of validation for verifying the accuracy of rainfall forecast for the monsoon. The results of the study revealed that rain forecast accuracy for past 7 years is excellent for post monsoon (October - December) in Jagtial district that has high skill score (RS range 77.2 to 93.5 %) with lower Root Mean Square Error (RMSE) of rainfall (3.17 to 14.2) in contrast to poor to moderate skill score (RS range 47.5 to 60.7 %) of accuracy for south-west monsoon with higher RMSE (14 to 29.5). The percent usability of forecast was also found to be higher for the post-monsoon (88.5 to 100 %) than the southwest monsoon (39.3 to 84.9 %). Among the essential predictions, rainfall is the most crucial one, which affects the crop output over a given region and, finally, the farmer's economics. Hence, with access to enhanced accuracy of rain forecast, farmers of Jagtial can limit the damages caused directly or indirectly by adverse weather situations by executing timely need-based farm operations.

References

  1. Kumar V, Khan S. Importance of weather prediction for sustainable agriculture in Bihar, India. Arch Agric Environ Sci. 2017;2(2):105–8.
  2. Geneti TZ. Review on the effect of moisture or rain fall on crop production. Civ Environ Res. 2019;11(2):1–7.
  3. Chuku CA, Okoye C. Increasing resilience reducing vulnerability in sub- Saharan African agriculture: strategies for risk coping and management. Afr J Agric Res. 2009;4(13):1524–35.
  4. Arulmathi C, Arul Prasad S, Jayakumar J, Natarajan K, Vengateswari M. Seasonal Rainfall Forecast Verification Analysis for Vridhachalam, Tamil Nadu, India. Int J Environ Clim Chang. 2023;13(9):3282–87. https://doi.org/10.9734/ijecc/2023/v13i92579
  5. Cabrera VE, Solís D, Baigorria GA, Letson D. Managing climate variability in agricultural analysis. Ocean Circulation and El Niño: New Research. Nova Science Publishers, Inc. Hauppauge, NY. 2009:163–79.
  6. Ziervogel G, Opere A. Integrating meteorological and indigenous knowledge-based seasonal climate forecasts for the agricultural sector: lessons from participatory action research in sub-Saharan Africa. CCAA learning paper.2010:1–19.
  7. Hansen JW, Mason SJ, Sun L, Tall A. Review of seasonal climate forecasting for agriculture in Sub-Saharan Africa. Exp Agric. 2011;47(2):205–40. https://doi.org/10.1017/S0014479710000876
  8. Vaughan C, Dessai S. Climate services for society: Origins, institutional arrangements and design elements for an evaluation framework. Wiley Interdiscip Rev Clim Change. 2014;5(5):587–603. https://doi.org/10.1002/wcc.290
  9. Sneha Babu R, Uma G. Analyzing the Impact of Rainfall Patternson Agriculture, Economy and Tourism in India: A Statistical Approach. Int J Environ Clim Chang. 2023;13(11):4626–37. https://doi.org/10.9734/ijecc/2023/v13i113642
  10. Nyadzi E, Werners SE, Biesbroek R, Ludwig F. Techniques and skills of indigenous weather and seasonal climate forecast in Northern Ghana. Clim Dev. 2020;13(6):551–62. https://doi.org/10.1080/17565529.2020.1831429
  11. Paparrizos S, Dogbey RK, Sutanto SJ, Gbangou T, Kranjac Berisavljevic G, Gandaa BZ, et al. Hydro-climate information services for smallholder farmers: Farmer support app principles, implementation and evaluation. Climate Services. 2023;30:100387. https://doi.org/10.1016/j.cliser.2023.100387
  12. Rathore LS, Parvinder Maini. Economic impact assessment of agro-meteorological advisory service of NCMRWF 2008. Report No. NMRF/PR/01/2008, 104pp, Published by NCMRWF, Ministry of Earth Sciences, Government of India. https://www.ncmrwf.gov.in/NCMRWF_REPORT.pdf
  13. Jaseena KU, Kovoor BC. Deterministic weather forecasting models based on intelligent predictors: A survey. J King Saud Univ Comput Inf Sci. 2022;34(6):3393–412. https://doi.org/10.1016/j.jksuci.2020.09.009
  14. Paparrizos Spyridon, Emmanuel MNAN Attoh, Samuel J Sutanto, Nina Snoeren, Fulco Ludwig. Local rainfall forecast knowledge across the globe used for agricultural decision-making. Sci Total Environ. 2023;899:165539. https://doi.org/10.1016/j.scitotenv.2023.165539
  15. Lunagariya MM, Mishra SK, Pandey V. Verification and usability of medium range weather forecast for Anand region. J Agrometeorology. 2009;11:228–33.
  16. Damrath U, Doms G, Fruhwald D, Heise E, Richter B, Steppeler J. Operational quantitative precipitation forecasting at the German Weather Service. J Hydrol. 2000;239:260–85. https://doi.org/10.1016/S0022-1694(00)00353-X
  17. Singh Priyanka, Mall Rajesh, Singh KK, Das Ananda, IMD Mausam. Verification of WRF model forecasts and their use for agriculture decision support in Bihar, India. Mausam. 2024; 75(1):197–204. https://doi.org/10.54302/mausam.v75i1.6037
  18. Brian Collins, Yunru Lai, Uwe Grewer, Steve Attard, Justin Sexton, Keith G Pembleton. Evaluating the impact of weather forecasts on productivity and environmental footprint of irrigated maize production systems. Sci Total Environ. 2024;954:176368. Collins). https://doi.org/10.1016/j.scitotenv.2024.176368
  19. Sankar T, Arul Prasad, Sudarmanian NS, Dheebakaran Ganesan. Weather Forecasting in Agriculture. 2019;16:57–81. https://doi.org/10.22271/ed.book.390
  20. Jayawardena ISP, Punyawardena BVR, Karunarathne MDRK. Importance of integration of sub seasonal predictions to improve climate services in Sri Lanka case study: Southwest monsoon 2019. Climate Services. 2022;26:100–296. https://doi.org/10.1016/j.cliser.2022.100296
  21. Dheebakaran GA, Arulprasad S, Kokilavani S, Panneerselvam S. Astrometeorology: An option to improve the accuracy of numerical daily rainfall forecast of Tamil Nadu. Editorial Board. 2017;19:205–11.

Downloads

Download data is not yet available.