Evaluation of high-resolution WRF model forecasts and their use for cloud seeding decisions. (February 2022)

• Record Type: Journal Article
• Authors: Gayatri, K.; Sandeep, J.; Murugavel, P.; Chowdhuri, S.; Konwar, M.; Dinesh, G.; Todekar, Kiran; Bankar, Shivdas; Dixit, Shivsai Ajit; Malap, Neelam; Prabhakaran, T.
• Is Part Of: Journal of atmospheric and solar-terrestrial physics. Volume 228(2022)
• Publication Date: 2022-02
• Subjects: WRF model -- CAIPEEX -- Cloud-seeding -- Seed score; Geophysics -- Periodicals; Atmospheric physics -- Periodicals; Géophysique -- Périodiques; Météorologie physique -- Périodiques; Electronic journals; 551.51
• DOI: 10.1016/j.jastp.2022.105825
• Languages: English
• ISSNs: 1364-6826

Abstract

Abstract: The high-resolution (1 km) forecasts are evaluated during the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX). The rainfall observations from a network of rain gauge stations and at high spatial resolution are used for verification purposes. Observed variations in the daily rainfall were adequately reproduced by the model. Several verification indices are used to evaluate the skill of the model forecast. The high rainfall events are overestimated by the model during the first half of the monsoon season and vice versa for the second half. The validated model was used to develop cloud seeding scores for a decision support system during the experiment. The systematic comparison of the model-derived seed score and actual seeding locations from the experiment satisfying the seeding criteria are verified. It is demonstrated that the high-resolution model forecast (<1 km) could be very valuable in identifying possible target regions for cloud seeding. A few real time applications of seed score maps generated from these high resolution forecasts are illustrated in the study. Highlights: The high resolution (1 km) WRF forecasts are verified with CAIPEEX observations. WRF model reasonably simulates variation of thermodynamic variables. High rainfall events are overestimated, and low rainfall events underestimated. High-resolution forecast is utilized to identify target regions for cloud seeding.