A New Hybrid Framework for Error Correction and Uncertainty Analysis of Precipitation Forecasts with Combined Postprocessors

Abstract

With the rapid development of meteorological models, numerical weather prediction is increasingly used in flood forecasting and reservoir regulation, but its forecasting ability is limited by the large amount of uncertainty from meteorological systems. In this paper, a new, hybrid framework is developed to improve numerical precipitation forecasting by combining the multimodel ensemble and probabilistic postprocessing methods. The results show that the multimodel ensemble method used in this paper is an efficient way to reduce prediction errors, especially missing alarm errors. In a comparison of the probabilistic postprocessors based the generalized Bayesian model (GBM) and bivariate probabilistic model (BPM), the GBM shows better performance from the aspects of indicators and is more suitable for real-time applications. Meanwhile, the assessment of probabilistic results shows that the skill of probabilistic precipitation forecasts is related to the quality of their inputs. According to these results, a new hybrid framework is proposed by taking the results from multimodel ensemble as the input of probabilistic postprocessor. Compared to using the raw numerical in GBM, the hybrid framework improves the accuracy, sharpness, reliability, and resolution ability from different lead times by 2–13%, 1–22%, and 0–12% respectively, especially when the lead time is less than 4 d, the improvement can reach 9–13%, 10–22%, and 5–12% respectively. In conclusion, the hybrid two-step framework can provide a more skillful precipitation forecast, which can be useful for flood forecasting and reservoir regulation.

Performance Evaluation of a National 7-Day Ensemble Streamflow Forecast Service for Australia

“…The percentile range of forecast also steadily decreased as the forecast horizon increased, with Day 1 having the largest range. Similar trends in steadily declining performance skills have been observed by other researchers -in India [48], Canada [7], China [22,49] and USA [17], just to name a few. Furthermore, when assessing rainfall forecast performance using KGE and PCC, we found relative increases, but an overall decrease as the forecast horizon extended.…”

“…We found no clear relationships between different performance evaluation metrics for both rainfall and streamflow forecasts (Figure 14). This finding is in contract to other studies around the world [7,17,22,49]. Further investigation is necessary to better understand these different findings.…”

“…In South America, performance skills of medium range (up to 15 days) ensemble streamflow forecasts were investigated and found a positive relationship between forecast skills and catchment area [19]. Similar results were also found in China [49], but with varying relationships between geographical location and differences in climate.…”

Probabilistic Forecast and Risk Assessment of Flash Droughts Based on Numeric Weather Forecast: A Case Study in Zhejiang, China

“…Fortunately, the accuracy of the NWF has been continuously improved over the past few decades and it may be possible to generate a more accurate probabilistic flash drought forecast based on the NWF in the future. Furthermore, former studies have pointed out that a ensemble forecast is also an effective way to improve the performance of the NWF and generate a better probabilistic forecast, which may be a feasible solution to improve the accuracy of probabilistic flash drought forecasting [47,48].…”

Weather Forecasting Using Radial Basis Function Neural Network in Warangal, India

“…It is a critical activity that impacts many areas such as agriculture [1][2][3], irrigation [4], and the marine trade [5], and it has the potential to save many lives from unanticipated mishaps [6]. It is described as the examination of atmospheric factors such as temperature [7,8], irradiation [9][10][11][12][13][14], airflow, wind speed [15,16], wind direction, humidity [17], precipitation [18], and rainfall [19]. Forecast precision can be affected by a variety of circumstances.…”