Statistical Post-processing Method Research Articles

Statistical post-processing of numerical weather prediction data using distribution-based scaling for wind energy

The performance of numerical weather prediction models has improved dramatically recently. However, model biases remain a fundamental limitation prohibiting the direct implementation of model results. There are several ways to describe wind speed data. The Weibull and lognormal distributions are used to obtain the best-fit model for the wind speed data. This study aims to develop a statistical post-processing method based on the distribution-based scaling (DBS) approach, which scales NWP data to fit the distribution derived using recorded wind speed at that site location. The performance of the suggested method was evaluated using four different error measures. The optimal model is anticipated to have the lowest Mean Bias Error (MBE), Mean Absolute Error (MAE), Root Mean square Error (RMSE), and variance (s2) values. It was determined that employing a DBS strategy significantly improved the NWP by at least 75%.

Technical note: Accurate, reliable, and high-resolution air quality predictions by improving the Copernicus Atmosphere Monitoring Service using a novel statistical post-processing method

Abstract. Starting from the regional air quality forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS), we propose a novel post-processing approach to improve and downscale results on a finer scale. Our approach is based on the combination of ensemble model output statistics (EMOS) with a spatio-temporal interpolation process performed through the stochastic partial differential equation–integrated nested laplace approximation (SPDE-INLA). Our interpolation approach includes several spatial and spatio-temporal predictors, including meteorological variables. A use case is provided that scales down the CAMS forecasts on the Italian peninsula. The calibration is focused on the concentrations of several air quality pollutants (PM10, PM2.5, NO2, and O3) at a daily resolution from a set of 750 monitoring sites, distributed throughout the Italian country. Our results show the key role that conditioning variables play in improving the forecast capabilities of ensemble predictions, thus allowing for a net improvement in the calibration with respect to ordinary EMOS strategies. From a deterministic point of view, the performance of the predictive model shows a significant improvement in the performance of the raw ensemble forecast, with an almost-zero bias, significantly reduced root mean square errors, and correlations that are almost always higher than 0.9 for each pollutant; moreover, the post-processing approach is able to significantly improve the prediction of exceedances, even for very low thresholds, such as those recently recommended by the World Health Organisation. This is particularly significant if a forecasting approach is used to predict air quality conditions and plan adequate human health protection measures, even for low alert thresholds. From a probabilistic point of view, the quality of the forecast was verified in terms of reliability and credible intervals. After post-processing, the predictive probability density functions were sharp and much better calibrated than the raw ensemble forecast. Finally, we present some additional results based on a set of gridded (4 km × 4 km) maps covering the entire Italian country for the detection of areas where pollution peaks occur (exceedances of the current and/or proposed regulatory thresholds).

Improving categorical and continuous accuracy of precipitation forecasts by integrating Empirical Quantile Mapping and Bernoulli-Gamma-Gaussian distribution

Statistical post-processing is a pivotal approach in enhancing the statistical accuracy and applicability of precipitation forecasts from numerical weather prediction models. The existing methods primarily focus on correcting errors in either categorical or quantitative aspects individually, failing to address both simultaneously. Furthermore, the benefits of post-processing in extending the effective lead time (ELT) have not been quantitatively examined. In this study, an integrated statistical post-processing method by combining the Empirical Quantile Mapping (EQM) and the Bernoulli-Gamma-Gaussian model (BGG) (EQM-BGG for short) is constructed, and a comprehensive accuracy metric for evaluating the ELT is also proposed. Taking the Bengbu Basin located in the upper and middle reaches of the Huai River Basin as the study area, the statistical post-processing using the three methods (EQM, BGG, EQM-BGG) is performed on the multi-model weighted integration forecast precipitation (MWIFP), which are derived from three numerical models, namely ECMWF, NCEP and CMA. The results demonstrate that the integration of EQM-BGG capitalizes on the combined strengths of EQM and BGG, facilitating a dual correction of categorical and quantitative errors. The forecast accuracy (FA) and mean absolute error (MAE) of the post-processed basin-average forecasts are enhanced by more than 10%, and the ELTs for both the entire basin and sub-basins are extended by 12–78 h. At the grid scale, EQM-BGG significantly reduces the decay rate of forecast accuracy and the expansion speed of low-accuracy regions as the lead time increases, the improvement of FAs and MAEs surpasses 10% and 20%, respectively, across all grids within the lead time of 96–102 h. Furthermore, certain grids in the northwestern part show an extension of ELTs by 18 to 54 h. These findings highlight that EQM-BGG significantly enhances both categorical and quantitative accuracy simultaneously, resulting in precipitation forecasts that exhibit greater reliability over longer lead times. This advancement enriches the methodologies for hydro-meteorological forecasts.

Hybrid Post-Processing on GEFSv12 Reforecast for Summer Maximum Temperature Ensemble Forecasts with an Extended-Range Time Scale over Taiwan

Taiwan is highly susceptible to global warming, experiencing a 1.4 °C increase in air temperature from 1911 to 2005, which is twice the average for the Northern Hemisphere. This has potentially led to higher rates of respiratory and cardiovascular mortality. Accurately predicting maximum temperatures during the summer season is crucial, but numerical weather models become less accurate and more uncertain beyond five days. To enhance the reliability of a forecast, post-processing techniques are essential for addressing systematic errors. In September 2020, the NOAA NCEP implemented the Global Ensemble Forecast System version 12 (GEFSv12) to help manage climate risks. This study developed a Hybrid statistical post-processing method that combines Artificial Neural Networks (ANN) and quantile mapping (QQ) approaches to predict daily maximum temperatures (Tmax) and their extremes in Taiwan during the summer season. The Hybrid technique, utilizing deep learning techniques, was applied to the GEFSv12 reforecast data and evaluated against ERA5 reanalysis. The Hybrid technique was the most effective among the three techniques tested. It had the lowest bias and RMSE and the highest correlation coefficient and Index of Agreement. It successfully reduced the warm bias and overestimation of Tmax extreme days. This led to improved prediction skills for all forecast lead times. Compared to ANN and QQ, the Hybrid method proved to be more effective in predicting daily Tmax, including extreme Tmax during summer, on extended-range time-scale deterministic and ensemble probabilistic forecasts over Taiwan.

Statistical Post-Processing Method for Evaluating Bioaccumulation in Fish Due to Dietary Exposure in Japan.

In 2018, the dietary exposure bioaccumulation fish test of the Organization for Economic Co-operation and Development Test Guideline No. 305 was introduced into Japan's Chemical Substances Control Law. The Japanese government has adopted a single definitive testing criterion for the absence of high bioaccumulation: the growth-corrected kinetic dietary magnification factor (BMFKg) must be less than 0.007. The aim of this study was to decrease regulatory restrictions in order to increase newly developed chemical substances and their subsequent approval of their manufacture and import, i.e., the present study was motivated by concerns over the criterion being too restrictive, rather than scientific concerns, such as uncertainty in criterion. We used statistical post-processing to assess the possibility of expanding the criteria for not being highly bioaccumulative. Based on our results, we proposed the criterion that the test substance should be considered not highly bioaccumulative if the following two conditions are met: (1) The ratio of the maximum to the minimum measured 5% lipid-standardized biomagnification factor at the end of the uptake phase (BMF5%, n = 5) for the test substance and reference substance should be less than 3.0, and (2) For the measured BMF5% of the test substance (n = 5), the probability that the next (the sixth) BMF5% is below 0.0334 should exceed 95% based on statistical post-processing. It is worth noting that the BMF5% values should only be applied for non-ionizable lipid soluble compounds. Application of our suggested approach to Japan implies that the criterion for chemical substances that are not highly bioaccumulative in the dietary exposure bioaccumulation fish test would be increased from 0.007 to 0.0149.

A low-cost post-processing technique improves weather forecasts around the world

Computer-generated weather forecasts divide the Earth’s surface into gridboxes, each currently spanning about 400 km2, and predict one value per gridbox. If weather varies markedly within a gridbox, forecasts for specific sites inevitably fail. Here we present a statistical post-processing method for ensemble forecasts that accounts for the degree of variation within each gridbox, bias on the gridbox scale, and the weather dependence of each. When applying this post-processing, skill improves substantially across the globe; for extreme rainfall, for example, useful forecasts extend 5 days ahead, compared to less than 1 day without post-processing. Skill improvements are attributed to creation of huge calibration datasets by aggregating, globally rather than locally, forecast-observation differences wherever and whenever the observed “weather type” was similar. A strong focus on meteorological understanding also contributes. We suggest that applications for our methodology include improved flash flood warnings, physics-related insights into model weaknesses and global pointwise re-analyses.

Multi-Layer Networks for Ensemble Precipitation Forecasts Postprocessing

The postprocessing method of ensemble forecasts is usually used to find a more precise estimate of future precipitation, because dynamic meteorology models have limitations in fitting fine-grained atmospheric processes and precipitation is driven more often by smaller-scale processes, while ensemble forecasts can hit this precipitation at times. However, the pattern of these hits cannot be easily summarized. The existing objective postprocessing methods tend to extend the rain area or false alarm the precipitation intensity categories. In this work, we introduce a multi-layer structure to simultaneously reduce the bias in forecast ensembles output by meteorology models and merge them to a quality deterministic (single-valued) forecast using cross-grid information, which differs quite dramatically from the previous statistical postprocessing method. The multi-layer network is designed to model the spatial distribution of future precipitation of different intensity categories(IC-MLNet). We provide a comparison of IC-MLNet to simple average as well as another two state-of-the-art ensemble quantitative precipitation forecasts (QPFs) postprocessing approaches over both single-model and multi-model ensemble forecasts datasets from TIGGE. The experimental results indicate that our model achieves superior performance over the compared baselines in precipitation amount prediction as well as precipitation intensities categories prediction.

Configuration of Bayesian Model Averaging Training Window to Improve Seasonal Rainfall Ensemble Prediction

Bayesian Model Averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensemble prediction in the form of predictive Probability Density Function (PDF). It is known that BMA is able to improve the reliability of the probabilistic forecast of short-and medium-range rainfall forecasts. This study aims to develop the application of BMA to calibrate long-range forecast in order to improve the quality of the seasonal forecast in Indonesia. The seasonal forecast that has been used is monthly rainfall from the output of the ensemble prediction European Center for Medium-Range Weather Forecasts (ECMWF) system 4 model (ECS4). This model was calibrated against observational data at 26 stations of Agency for Meteorology, Climatology and Geophysics of Republic of Indonesia (BMKG) over Java Island in 1981-2018. BMA predictive PDF was generated with a Gamma distribution which was obtained based on Training Window Sequential (TWS) and Conditional (TWC) training windows. Output of BMA-TWC was slightly better than BMA-TWS. Nevertheless, both of them were superior to raw model ECS4. BMA-TWC or BMA-TWS output was varying depend on spatial and temporal, but in general, the best result was in the dry season and during the El Nino phase. BMA was able to improve the distribution characteristics of ensemble prediction. BMA also increased the skill, resolution, and reliability of probability forecast of Below Normal (BN) and Above Normal (AN). Furthermore, the reliability of BN and AN of BMA output were also have the categories of “still very useful” and “perfect” compared to raw model ECS4 that were in the “dangerous” and “not useful” categories. The reliability “still very useful” and “perfect” show that the probabilistic forecast of BN and AN event can be used for making decisions related to seasonal forecast especially over Java island.

Vertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theory

Abstract. Many applications require wind gust estimates at very different atmospheric height levels. For example, the renewable energy sector is interested in wind and gust predictions at the hub height of a wind power plant. However, numerical weather prediction models typically only derive estimates for wind gusts at the standard measurement height of 10 m above the land surface. Here, we present a statistical post-processing method to derive a conditional distribution for hourly peak wind speed as a function of height. The conditioning variables are taken from the COSMO-REA6 regional reanalysis. The post-processing method was trained using peak wind speed observations at five vertical levels between 10 and 250 m from the Hamburg Weather Mast. The statistical post-processing method is based on a censored generalized extreme value (cGEV) distribution with non-homogeneous parameters. We use a least absolute shrinkage and selection operator to select the most informative variables. Vertical variations of the cGEV parameters are approximated using Legendre polynomials, such that predictions may be derived at any desired vertical height. Further, the Pickands dependence function is used to assess dependencies between gusts at different heights. The most important predictors are the 10 m gust diagnostic, the barotropic and the baroclinic mode of absolute horizontal wind speed, the mean absolute horizontal wind at 700 hPa, the surface pressure tendency, and the lifted index. Proper scores show improvements of up to 60 % with respect to climatology, especially at higher vertical levels. The post-processing model with a Legendre approximation is able to provide reliable predictions of gusts' statistics at non-observed intermediate levels. The strength of dependency between gusts at different levels is non-homogeneous and strongly modulated by the vertical stability of the atmosphere.

Calibration Of Rainfall Ensemble Prediction Of ECMWF System 4 Using Bayesian Model Averaging

Bayesian model averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensembles in the form of predictive PDFs. It is known that BMA is able to improve the reliability of probabilistic forecast of short range and medium range rainfall forecast. This study aims to develop the application of BMA to calibrate seasonal forecast (long range) in order to improved quality of seasonal forecast in Indonesia. The seasonal forecast used is monthly rainfall from the output of the ensemble prediction system European Center for Medium-Range Weather Forecasts (ECMWF) system 4 model (ECS4) and it is calibrated against observational data at 26 stations of the Agency for Meteorology Climatology and Geophysics of Republic of Indonesia (BMKG) in Java Island in 1981 – 2018. BMA predictive PDFs is generated with Gamma distribution approach which is obtained based on sequential training windows (JTS) and conditionals training windows (JTC). BMA-JTS approach is done by selecting the width of the 30-month training window as the optimal training period while the BMA-JTC is carried out with a cross-validation scheme for each month. In general, Both of BMA-JTS and BMA-JTC better than RAW models. BMA-JTC calibration results are varying according to spatial and temporal, but in general the result is better in the dry season and during the El Nino phase. BMA is able to improve the distribution characteristics of the RAW model ECS4 prediction which is shown by: a smaller value of Continuous Rank Probability Score (CRPS), a larger value of the Continuous Rank Probability Skill Score (CRPSS) and more flat form of the Verification Rank Histogram (VRH) than the RAW model. BMA also increases the skill, esolution and reliability of prediction of probability Below Normal (BN) and Above Normal (AN), which is known from the increasing Brier Skill Score (BSS), and the increasing area under curve of Relative Operating Characteristics (ROC) compared to the RAW model. Furthermore, the reliability of BN and AN of BMA results also has the category of “still very useful” and “perfect” compared to RAW models that are in the “dangerous”, “not useful” and “marginally useful” categories. The reliability of BMA results with the category “still very useful” and “perfect” show that the probabilistic forecast of BN and AN events can be used in making decisions related to seasonal forecast.

Probabilistic wind forecasting up to three months ahead using ensemble predictions for geopotential height

We develop a method for forecasting the distribution of the daily surface wind speed at timescales from 15-days to 3-months in France. On such long-term timescales, ensemble predictions of the surface wind speed have poor performance, however, the wind speed distribution may be related to the large-scale circulation of the atmosphere, for which the ensemble forecasts have better skill. The information from the large-scale circulation, represented by the 500 hPa geopotential height, is summarized into a single index by first running a PCA and then a polynomial regression. We estimate, over 20 years of daily data, the conditional probability density of the wind speed at a specific location given the index. We then use the ECMWF seasonal forecast ensemble to predict the index for horizons from 15-days to 3-months. These predictions are plugged into the conditional density to obtain a distributional forecast of surface wind. These probabilistic forecasts remain sharper than the climatology up to 1-month forecast horizon. Using a statistical postprocessing method to recalibrate the ensemble leads to further improvement of our probabilistic forecast, which then remains calibrated and sharper than the climatology up to 3-months horizon, particularly in the north of France in winter and fall.

SPATIAL BAYESIAN MODEL AVERAGING TO CALIBRATE SHORT-RANGE WEATHER FORECAST IN JAKARTA, INDONESIA

Bayesian Model Averaging (BMA) is a statistical post-processing method to calibrate the ensemble forecasts and create more reliable predictive interval. However, BMA does not consider spatial correlation. Geostatistical Output Perturbation (GOP) considers spatial correlation among several locations altogether. It has spatial parameters that modifies the forecast output to capture spatial information. Spatial Bayesian Model Averaging (Spatial BMA) is a method which combines BMA and GOP. This method is applied to calibrate the temperature forecast at 8 stations in Indonesia that is previously predicted by Numerical Weather Prediction (NWP). Temperature forecasts of BMA are used to obtain simulated spatially correlated error that modify temperature forecasts. Spatial BMA is able to calibrate the temperature forecast better than raw ensemble whose coverage comes closer to the standard 50%. Based on Root Mean Square Error (RMSE) criteria, Spatial BMA is able to correct forecast bias NWP with RMSE value of 1.399° lower than NWP of 2.180°.

Hourly probabilistic snow forecasts over complex terrain: a hybrid ensemble postprocessing approach

Abstract. Accurate and high-resolution snowfall and fresh snow forecasts are important for a range of economic sectors as well as for the safety of people and infrastructure, especially in mountainous regions. In this article a new hybrid statistical postprocessing method is proposed, which combines standardized anomaly model output statistics (SAMOS) with ensemble copula coupling (ECC) and a novel re-weighting scheme to produce spatially and temporally high-resolution probabilistic snow forecasts. Ensemble forecasts and hindcasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) serve as input for the statistical postprocessing method, while measurements from two different networks provide the required observations.This new approach is applied to a region with very complex topography in the eastern European Alps. The results demonstrate that the new hybrid method allows one not only to provide reliable high-resolution forecasts, but also to combine different data sources with different temporal resolutions to create hourly probabilistic and physically consistent predictions.

A Statistical Parameter Correction Technique for WRF Medium-Range Prediction of Near-Surface Temperature and Wind Speed Using Generalized Linear Model

A statistical post-processing method was developed to increase the accuracy of numerical weather prediction (NWP) and simulation by matching the daily distribution of predicted temperatures and wind speeds using the generalized linear model (GLM) and parameter correction, considering an increase in model bias when the range of the prediction time lengthens. The Land Atmosphere Modeling Package Weather Research and Forecasting model, which provides 12-day agrometeorological predictions for East Asia, was employed from May 2017 to April 2018. Training periods occurred one month prior to and after the test period (12 days). A probabilistic consideration accounts for the relatively short training period. Based on the total and monthly root mean square error values for each test site, the results show an improvement in the NWP accuracy after bias correction. The spatial distributions in July and January were compared in detail. It was also shown that the physical consistency between temperature and wind speed was retained in the correction procedure, and that the GLM exhibited better performance than the quantile matching method based on monthly Pearson correlation comparison. The characteristics of coastal and mountainous sites are different from inland automatic weather stations, indicating that supplements to cover these distinctive topographic locations are necessary.

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

ABSTRACTUltrasonic and digital dermatoscopy diagnostic methods are used in order to estimate the changes of structure, as well as to non-invasively measure the changes of parameters of lesions of human tissue. These days, it is very actual to perform the quantitative analysis of medical data, which allows to achieve the reliable early-stage diagnosis of lesions and help to save more lives. The proposed automatic statistical post-processing method based on integration of ultrasonic and digital dermatoscopy measurements is intended to estimate the parameters of malignant tumours, measure spatial dimensions (e.g. thickness) and shape, and perform faster diagnostics by increasing the accuracy of tumours differentiation. It leads to optimization of time-consuming analysis procedures of medical images and could be used as a reliable decision support tool in the field of dermatology.

A Postprocessing Method for Seasonal Forecasts Using Temporally and Spatially Smoothed Statistics

AbstractA statistical postprocessing method for seasonal forecasts based on temporally and spatially smoothed climate statistics is introduced. The method uses information available from seasonal hindcasts initialized at the beginning of 12 calendar months. The performance of the method is tested within both deterministic and probabilistic frameworks using output from the ensemble of seasonal hindcasts produced by the Canadian Seasonal to Interannual Prediction System for the 30-yr period 1981–2010. Forecast skill improvements are found to be greater when forecast adjustment parameters estimated for individual seasons and at individual grid points are temporally and spatially smoothed. The greatest skill improvements are typically achieved for seasonally invariant parameters while skill improvements due to additional spatial smoothing are modest.

A probabilistic approach to the estimation of regional photovoltaic power production

Forecasting the total photovoltaic (PV) power generated in the control areas of the transmission system operators (TSO) is an important step in the integration of the large amounts of PV energy into the German electricity supply system. A standard approach for evaluating the regional PV power generation from weather forecast consists in upscaling the forecast of a limited set of reference plants to the complete area. Previous studies shown that this method can lead to large errors when the set of reference plants has different characteristics or weather conditions than the set of unknown plants. In this paper, an alternative to the upscaling approach is proposed. In this method, called a probabilistic regional PV model, an average PV model with a very limited number of inputs (two module orientation angles) is used to calculate the power generation of the most frequent module orientation angles. The resulting power values are finally weighted according to their probability of occurrence to estimate the actual power generation. The implementation of this model thus only requires information on the location and peak capacity of the plant installed in a region and no PV plant measurement is necessary. The proposed method has been evaluated against the estimate of the total power generation provided by the German TSOs, which shows that an RMSE ranging from 4.2 to 4.9% can be obtained with this method using on IFS meteorological forecast. The regional power forecasted with the probabilistic approach was also compared to the day-ahead forecast disseminated by the TSO. This analysis shows that the forecast evaluated with the proposed approach has an RMSE less than 0.5% higher than the reference forecasts. This is considered a promising result given that the forecast evaluated with the probabilistic model is based on one single weather model and that – at the exception of the model calibration - no statistical post-processing method is used to optimize its performance.

Application of Bayesian Model Averaging in the Reconstruction of Past Climate Change Using PMIP3/CMIP5 Multimodel Ensemble Simulations

Abstract Climate change simulations based on climate models are inevitably uncertain. This uncertainty typically stems from parametric and structural uncertainties in climate models as well as climate forcings. However, combining model simulations with instrumental observations using appropriate statistical methods is an effective approach for describing this uncertainty. In this study, the authors applied Bayesian model averaging (BMA), a statistical postprocessing method, to an ensemble of climate model simulations from the Paleoclimate Modelling Intercomparison Project phase 3 (PMIP3) and phase 5 of the Coupled Model Intercomparison Project (CMIP5). Uncertainties, weights, and variances of individual model simulations were estimated from a training period using the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset. The results presented here demonstrate that the BMA method is successful and attains a positive performance in this study. These results show that the selected proxy-based reconstructions and simulations are consistent with BMA estimates regarding climate variability in the past 1000 years, though differences can be found for some periods. The authors conclude that BMA is an effective tool for describing uncertainties associated with individual model simulations, as it accounts for the diverse capabilities of different models and generates a more credible range of past climate change over a relatively long-term period based on multimodel ensemble simulations and training data.

Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks

There has been a growing demand on energy sector for short-term predictions of energy resources to support the planning and management of electricity generation and distribution systems. The purpose of this work is establishing a methodology to produce solar irradiation forecasts for the Brazilian Northeastern region by using Weather Research and Forecasting Model (WRF) combined with a statistical post-processing method. The 24 h solar irradiance forecasts were obtained using the WRF model. In order to reduce uncertainties, a cluster analysis technique was employed to select areas presenting similar climate features. Comparison analysis between WRF model outputs and observational data were performed to evaluate the model skill in forecasting surface solar irradiance. Next, model-derived short-term solar irradiance forecasts from the WRF outputs were refined by using an artificial neural networks (ANNs) technique. The output variables of the WRF model representing the forecasted atmospheric conditions were used as predictors by ANNs, adjusted to calculate the solar radiation incident for the entire Brazilian Northeastern (NEB) (which was divided into four homogeneous regions, defined by the Ward method). The data used in this study was from rainy and dry seasons between 2009 and 2011. Several predictors were tested to adjust and simulate the ANNs. We found the best ANN architecture and a group of 10 predictors, in which a deeper analyzes were carried out, including performance evaluation for Fall and Spring of 2011 (rainy and dry season in NEB, mainly in the northern section). There was a significant improvement of the WRF model forecasts when adjusted by the ANNs, yielding lower bias and RMSE, and an increase in the correlation coefficient.

Statistical Postprocessing of Ensemble Precipitation Forecasts by Fitting Censored, Shifted Gamma Distributions*

Abstract A parametric statistical postprocessing method is presented that transforms raw (and frequently biased) ensemble forecasts from the Global Ensemble Forecast System (GEFS) into reliable predictive probability distributions for precipitation accumulations. Exploratory analysis based on 12 years of reforecast data and ⅛° climatology-calibrated precipitation analyses shows that censored, shifted gamma distributions can well approximate the conditional distribution of observed precipitation accumulations given the ensemble forecasts. A nonhomogeneous regression model is set up to link the parameters of this distribution to ensemble statistics that summarize the mean and spread of predicted precipitation amounts within a certain neighborhood of the location of interest, and in addition the predicted mean of precipitable water. The proposed method is demonstrated with precipitation reforecasts over the conterminous United States using common metrics such as Brier skill scores and reliability diagrams. It yields probabilistic forecasts that are reliable, highly skillful, and sharper than the previously demonstrated analog procedure. In situations with limited predictability, increasing the size of the neighborhood within which ensemble forecasts are considered as predictors can further improve forecast skill. It is found, however, that even a parametric postprocessing approach crucially relies on the availability of a sufficiently large training dataset.