Many present-day statistical schemes for postprocessing weather forecasts, in particular precipitation forecasts, rely on calibration using prescribed statistical models to relate forecast statistics to distributional parameters. The efficacy of such schemes is often constrained not only by prescribed predictor-predictand relation, but also by arbitrary choices of temporal window and lead time range for training. To address this limitation, we propose an end-to-end, computationally efficient hybrid postprocessing scheme capable of producing full predictive distributions of precipitation accumulation without explicit stratification of forecast-observation pairs by forecast lead time and season. The proposed framework uses the censored, shifted gamma distribution (CSGD) as the predictive distribution but uses an artificial neural network (ANN) to estimate the distributional parameters of CSGD through a unified approach. This approach, referred to as ANN-CSGD, allows for simultaneous estimation of distributional parameters over multiple lead times and seasons in a single model by incorporating the latter variables as predictors to the ANN. We test our proposed ANN-CSGD model for postprocessing of ensemble mean forecasts of 24-h precipitation totals over selected river basins in California, at one- to seven-day lead times, from the Global Ensemble Forecast System (GEFS). The probabilistic quantitative precipitation forecasts (PQPFs) from the ANN-CSGD, are more skillful overall than those from the benchmark CSGD and the Mixed-type meta-Gaussian distribution (MMGD) models. The ANN-CSGD PQPFs highly improve the performance of those from CSGD in predicting the probability of precipitation (PoP) and are also much sharper and reliable at higher precipitation thresholds. We demonstrate how the hybrid approach, by using the entire available training data and its modified formulation, efficiently represents interactions between GEFS forecasts and season/lead times, thus leading to enhanced predictive performance.

当前用于后处理天气预报（尤其是降水预报）的许多统计方案都依赖于使用规定的统计模型进行的校准，以将预报统计信息与分布参数相关联。这样的方案的有效性通常不仅受到规定的预测变量与预测变量的关系的约束，而且还受到训练的时间窗和前置时间范围的任意选择的约束。为了解决这一局限性，我们提出了一种端到端，计算有效的混合后处理方案，该方案能够产生降水累积的完整预测分布，而无需根据预测提前期和季节对预测观测对进行明确的分层。拟议的框架使用了经过审查的 偏移伽马分布（CSGD）作为预测分布，但使用人工神经网络（ANN）通过统一方法估算CSGD的分布参数。这种方法称为ANN-CSGD，通过将后继变量作为ANN的预测变量，可以在单个模型中同时估算多个提前期和季节的分布参数。我们测试了我们提出的ANN-CSGD模型，用于对来自全球整体预报系统（GEFS）的加利福尼亚州部分选定流域24小时降水总量的集合平均预报进行后处理，交付周期为1至7天。ANN-CSGD的概率定量降水预报（PQPF）总体上比基准CSGD和混合型超高斯分布（MMGD）模型的概率更高。ANN-CSGD PQPF大大提高了CSGD的PQPF在预测降水概率（PoP）方面的性能，并且在更高的降水阈值下也更加清晰和可靠。我们演示了混合方法如何通过使用全部可用的训练数据及其修改后的公式有效地表示GEFS预测与季节/提前期之间的相互作用，从而提高预测性能。