Using a deterministic time-lagged ensemble forecast with a probabilistic threshold for improving 6–15day summer precipitation prediction in China

Abstract

A Deterministic Time-lagged Ensemble Forecast using a Probabilistic Threshold (DEFPT) method is suggested for improving summer 6-15 day categorical precipitation prediction in China from the Beijing Climate Center Atmospheric General Circulation Model version 2.1 (BCC_AGCM2.1). It is based on a time-lagged ensemble system that consists of 13 ensemble members separated sequentially at 6 hour intervals lagging the last three days. The DEFPT is not intended to predict the probability of rainfall, but rather to forecast rainfall (yes/no) occurrence for different categories of precipitation at any model grid box. A given categorical precipitation is forecasted to occur at one gridbox only when the ensemble probability for that categorical precipitation exceeds a certain threshold. This method is useful for providing an estimate of whether precipitation events will occur to decision-makers based on probabilistic forecasts during days 6-15. A large number of hindcast experiments for 1996-2005 summers reveal that this threshold can be best (and empirically) set as 5/13 and 4/13 respectively for the 6-15 day prediction of 1+ mm (i.e., above 1 mm per day) and 5+ mm rainfall events, using the Relative Operating Characteristic (ROC) curve, the Equitable Threat Score (ETS), the Hanssen and Kuipers (HK) score, and frequency bias (BIA) to achieve best prediction performance. With this set of thresholds, the DEFPT shows skill improvement over the corresponding single deterministic forecast using one initial value and the Time-Lagged Average Forecast (LAF) ensemble method. Similar improvements by the DEFPT are also found for the prediction of several other categories of precipitation between 1+ mm and 10+ mm per day. Application of DEFPT to larger ensemble size and BCC_AGCM version 2.2 with a higher horizontal resolution also demonstrates the effectiveness of the DEFPT for 6-15 day categorical precipitation forecasts.

Simulations of the Asian summer monsoon in the sub‐seasonal to seasonal prediction project (S2S) database

“…El Niño/Southern Oscillation (ENSO)), and show enormous social and economic impacts all over the world, especially in Asia (e.g. Wang et al, 2008Wang et al, , 2014Liu et al, 2013a;Jie et al, 2014Jie et al, , 2015. However, monsoons are difficult to simulate because of the complexity of these events over the Asian regions (Liu et al, 2013b).…”

Improving Quantitative Rainfall Prediction Using Ensemble Analogues in the Tropics: Case Study of Uganda

“…The spatial and temporal variability of rainfall makes its quantitative prediction a challenge [8,9]. However, according to He et al [5] and Jie et al [10], rainfall can be predicted quantitatively up to 7 days using models but the prediction accuracy degrades with increasing lead time [11].…”

“…The output from the ensemble members are then statistically post-processed to obtain a skillful probabilistic forecast [1,17] which addresses the uncertainty inherent in the initial conditions and associated model imperfections faced by a deterministic NWP model [18,19]. This is because the ensemble spread gives the measure of the uncertainty of the prediction [10,20]. However, quantifying absolute uncertainty presents additional challenge due to the bias inherent in the models used [20].…”

“…The ensemble members can be obtained in many ways, such as running NWP models with varying initial conditions [22], perturbing the physical parameterization schemes of the model [14,26], initializing the models at the different times (time-lagged) [10], combining output from different NWP models (multi-model ensemble) [10,27]. However, it is important to integrate multiple methods of generating ensemble members because, for example, perturbing initial conditions or model physics has been found to be under-dispersive [22,25].…”

Improving Quantitative Rainfall Prediction Using Ensemble Analogues in the Tropics: Case study of Uganda

“…The spatial and temporal variability of rainfall makes its quantitative prediction a challenge [8,9]. However, according to He et al [5] and Jie et al [10], rainfall can be predicted quantitatively up to 7 days.…”