Grand ensemble forecasts verification based on two high resolution (∼12 km) global ensemble prediction systems

• Published in: Atmospheric research Vol. 309; p. 107585
• Main Authors: Mamgain, Ashu, Sarkar, Abhijit, Dube, Anumeha, Abhijith, V., George, John P., Prasad, V.S.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.10.2024
• Subjects: Forecast verification/skill; Grand ensemble; High resolution ensemble prediction system; Predictability; Probability forecast; Super ensemble; High resolution ensemble prediction system; Forecast verification/skill; Predictability; Super ensemble; Probability forecast; Grand ensemble
• ISSN: 0169-8095
• DOI: 10.1016/j.atmosres.2024.107585

The primary objective of this study is to quantitatively assess the skill of grand-ensemble forecasts generated from two high-resolution global ensemble prediction systems (EPSs). We have used the National Centre for Medium Range Weather Forecasting (NCMRWF), India global EPS, denoted as NEPS, and India Meteorological Department (IMD) EPS, known as GEFS. Both of these operational EPSs operate at ∼12 km horizontal grid size and produce ensemble forecast outputs. In this study, these two high-resolution EPSs based grand-ensemble products have been evaluated. Statistical analysis is conducted using seven-day forecasts from NEPS's 22 perturbed members and GEFS's 20 perturbed members. The performance of the global grand ensemble, consisting of 42 members, is evaluated for summer monsoon season, 1st July to 30th September 2019 in comparison to the individual EPS forecasts. The verification metrics are prepared using variables such as Zonal Wind and Temperature at 850 hPa, Geopotential at 500 hPa and Precipitation. These metrics are applied across both the Northern Hemisphere (20°N-90°N) and over the domain located in the South Asia encompassing India and its neighbouring areas (0–40 oN & 50–100°E). The verification metrics employed include ensemble spread, root-mean-square error (RMSE) of ensemble mean, Brier score, relative operating characteristic, area skill score, reliability, rank histogram, ranked probability score, continuous ranked probability score, and relative economic value. We noted that the grand ensemble forecasts in this study carries a higher skill compared to the two constituent models for lead time >3 days and for higher forecast probability for the longer lead times. Lower RMSE in the grand ensemble for longer lead-time forecasts (day 4–7) indicates a better representation when compared to forecasts from individual EPSs. However, it is worth noting that shorter lead-time forecasts (day 1–3) in the grand ensemble could potentially result in over-dispersion. Brier scores further indicate an enhancement in predictability of nearly one day in grand ensemble compared to the individual EPSs for T850, U850 and Z500. RPS of rainfall in a smaller domain centring on India indicates a better score for grand ensemble compared to both the EPS. Grand ensembles demonstrate its ability to provide better reliability and discrimination quality. The economic value of the forecasts based on grand ensemble, is higher with a wider range of cost/loss ratio compared to those in the individual EPSs. •Global grand ensemble (MGE) products based on two ∼12 km grid prediction systems are discussed for N. Hemisphere and India.•The MGE shows higher skill than the constituent models for lead times over 3 days and for higher forecast probabilities.•Brier score, CRPS and RPS indicate that the MGE has the potential to enhance predictability.•The MGE offers benefits in terms of forecast value across a wider range of cost/loss ratios.