Performance evaluation of sub‐daily ensemble precipitation forecasts

Abstract

Nowadays, major advances have been made in meteorological forecasts. For instance, ensemble forecast systems have been developed to quantify prediction uncertainty. In this research, sub‐daily ensemble precipitation forecasts of five THORPEX Interactive Grand Global Ensemble (TIGGE) models from 2014 to 2018 were evaluated in 10 major basins located in north and west Iran. Furthermore, Bayesian model averaging (BMA) was used to combine five prediction models and construct a grand ensemble. The results indicate that the models had the best performance in the Karun and Western Border basins to the southwest and west, with average performance in the Sefidrood basin in the north. In terms of the prediction of precipitation depth, the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the UK Met Office (UKMO) models, and in terms of prediction of precipitation occurrence and non‐occurrence, the National Centers for Environmental Prediction (NCEP) model, performed best. Overall, the Japan Meteorological Agency (JMA) and the China Meteorological Administration (CMA) models acquired medium scores. The BMA technique greatly improved the probability forecasts, reducing uncertainties in the numerical models. Moreover, the models’ forecasts were weaker with a 6 hr lead time compared with those with 24 hr, which may be attributable to the inaccurate detection of the initiation time of precipitation by the models. In addition, the performance of the UKMO (ECMWF) models with increasing basin elevation increased (decreased), while all models better forecasted precipitation in wet years/seasons than they did in dry years/seasons. Overall, the evaluations showed that the ECMWF, UKMO and NCEP models performed well in the majority of the northern and western basins of Iran.

Was the extreme rainfall that caused the August 2022 flood in Pakistan predictable?

“…The THORPEX Interactive Grand Global Ensemble (TIGGE) datasets have been widely used for precipitation forecasts, which was established through The Observing System Research and Predictability Experiment (THORPEX) project. Several studies have assessed the effectiveness of TIGGE forecasts for meteorological and hydrological applications (Zhao et al 2011, Tao et al 2014, Louvet et al 2016, Liu et al 2019, Saedi et al 2020, Shu et al 2021. For instance, Zhao et al (2011) compared forecasts from different models including the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Center for Environmental Prediction (NCEP), and the China Meteorological Administration (CMA).…”

“…They found that ECMWF exhibited better performance than the other models; however, the forecast skills of all models decreased significantly after five days of lead time. Many previous studies highlighted the favorable performance of the UK Meteorological Office (UKMO) and ECMWF models compared to other models (Saedi et al 2020). Bhomia et al (2017) reported that forecasts from UKMO, NCEP, and ECMWF demonstrated satisfactory performance for rainfall predictions and tropical cyclones in India.…”

“…Specifically, we focused on the models from the TIGGE dataset, which included ECMWF, GEFS, UKMO, and KMA. These models were chosen based on their established reputation and track record of performance in previous research studies (Saedi et al 2020, Endris et al 2021. Additionally, we included GEFS as an Indian model to gain valuable insights that could have relevance and applicability to the neighboring country Pakistan.…”

How reliable are TIGGE daily deterministic precipitation forecasts over different climate and topographic conditions of Iran?

“…The development and enhancement of numerical weather prediction (NWP) models have been increased considerably during the last decade. Therefore, the meteorological forecasts provide new opportunities for rainfall–runoff modelling and early warning issues (Cloke & Pappenberger, 2009; Gevorgyan, 2013; Saedi et al, 2020). One of the widely used data sets for extracting precipitation forecasts is the THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE), which is established through THORPEX project.…”

“…In India, Bhomia et al (2017) evaluated seven NWP models within the TIGGE for studying the tropical cyclones and found that the forecasts of UKMO, NCEP and ECMWF were closer to the ground‐gauge observations. Saedi et al (2020) assessed five NWP models' sub‐daily forecasts in 10 river basins located in the north and west of Iran. Findings depicted that in terms of rainfall prediction, the UKMO and ECMWF are the best ones, whereas in detecting rainy or non‐rainy days, the NCEP model outperformed other models.…”