Weather Quantity Research Articles

Clustering-Based Spatial Interpolation of Parametric Postprocessing Models

Abstract Since the start of the operational use of ensemble prediction systems, ensemble-based probabilistic forecasting has become the most advanced approach in weather prediction. However, despite the persistent development of the last three decades, ensemble forecasts still often suffer from the lack of calibration and might exhibit systematic bias, which calls for some form of statistical postprocessing. Nowadays, one can choose from a large variety of postprocessing approaches, where parametric methods provide full predictive distributions of the investigated weather quantity. Parameter estimation in these models is based on training data consisting of past forecast-observation pairs; thus, postprocessed forecasts are usually available only at those locations where training data are accessible. We propose a general clustering-based interpolation technique of extending calibrated predictive distributions from observation stations to any location in the ensemble domain where there are ensemble forecasts at hand. Focusing on the ensemble model output statistics (EMOS) postprocessing technique, in a case study based on 10-m wind speed ensemble forecasts of the European Centre for Medium-Range Weather Forecasts, we demonstrate the predictive performance of various versions of the suggested method and show its superiority over the regionally estimated and interpolated EMOS models and the raw ensemble forecasts as well.

Parametric model for post-processing visibility ensemble forecasts

Abstract. Although, by now, ensemble-based probabilistic forecasting is the most advanced approach to weather prediction, ensemble forecasts still suffer from a lack of calibration and/or display systematic bias, thus requiring some post-processing to improve their forecast skill. Here, we focus on visibility, a weather quantity that plays a crucial role in, for example, aviation and road safety or ship navigation, and we propose a parametric model where the predictive distribution is a mixture of a gamma and a truncated normal distribution, both right censored at the maximal reported visibility value. The new model is evaluated in two case studies based on visibility ensemble forecasts of the European Centre for Medium-Range Weather Forecasts covering two distinct domains in central and western Europe and two different time periods. The results of the case studies indicate that post-processed forecasts are substantially superior to raw ensembles; moreover, the proposed mixture model consistently outperforms the Bayesian model averaging approach used as a reference post-processing technique.

Statistical post‐processing of visibility ensemble forecasts

AbstractTo be able to produce accurate and reliable predictions of visibility has crucial importance in aviation meteorology, as well as in water‐ and road transportation. Nowadays, several meteorological services provide ensemble forecasts of visibility; however, the skill and reliability of visibility predictions are far reduced compared with other variables, such as temperature or wind speed. Hence, some form of calibration is strongly advised, which usually means estimation of the predictive distribution of the weather quantity at hand either by parametric or nonparametric approaches, including machine learning‐based techniques. As visibility observations—according to the suggestion of the World Meteorological Organization—are usually reported in discrete values, the predictive distribution for this particular variable is a discrete probability law, hence calibration can be reduced to a classification problem. Based on visibility ensemble forecasts of the European Centre for Medium‐Range Weather Forecasts covering two slightly overlapping domains in Central and Western Europe and two different time periods, we investigate the predictive performance of locally, semi‐locally and regionally trained proportional odds logistic regression (POLR) and multilayer perceptron (MLP) neural network classifiers. We show that while climatological forecasts outperform the raw ensemble by a wide margin, post‐processing results in further substantial improvement in forecast skill, and in general, POLR models are superior to their MLP counterparts.

The Relative Importance of Herbage Nutritive Value and Climate in Determining Daily Performance per Cow in a Pasture-Based Dairy Farm

The objective of this study was to assess the relative importance of herbage nutritive value (NV), herbage quantity and climate-related factors in determining daily performance per cow in a pasture-based dairy farm. Data on milk production, live weight, body condition score, weather, herbage NV and herbage quantity were regularly collected from August 2016 to April 2017 and from July 2017 to April 2018 at Dairy 1, Massey University, Palmerston North, New Zealand. Data were analyzed using multiple linear regression. Results indicated herbage NV was of higher relative importance in explaining the variation in performance per cow than herbage quantity and climate factors. The relative importance of the interaction between herbage metabolizable energy (ME) and crude protein (CP) on explaining variation in yields of milk, fat and protein was high (0.11 ≤ R2 ≤ 0.15). Herbage ME was of high relative importance in determining milk urea and body condition score, while neutral detergent fiber was a key driver of milk urea and liveweight (0.12 ≤ R2 ≤ 0.16). The quantity of herbage supplied at Dairy 1 might have been high enough to not limit cow performance. Developing feeding strategies aimed at improving the efficiency of cow feeding by exploiting the daily variation in herbage NV to better match supply and demand of nutrients may be useful to improve the overall performance per cow of pasture-based dairy farms.

Truncated generalized extreme value distribution‐based ensemble model output statistics model for calibration of wind speed ensemble forecasts

AbstractIn recent years, ensemble weather forecasting has become a routine at all major weather prediction centers. These forecasts are obtained from multiple runs of numerical weather prediction models with different initial conditions or model parametrizations. However, ensemble forecasts can often be underdispersive and also biased, so some kind of postprocessing is needed to account for these deficiencies. One of the most popular state of the art statistical postprocessing techniques is the ensemble model output statistics (EMOS), which provides a full predictive distribution of the studied weather quantity. We propose a novel EMOS model for calibrating wind speed ensemble forecasts, where the predictive distribution is a generalized extreme value (GEV) distribution left truncated at zero (TGEV). The truncation corrects the disadvantage of the GEV distribution‐based EMOS models of occasionally predicting negative wind speed values, without affecting its favorable properties. The new model is tested on four datasets of wind speed ensemble forecasts provided by three different ensemble prediction systems, covering various geographical domains and time periods. The forecast skill of the TGEV EMOS model is compared with the predictive performance of the truncated normal, log‐normal and GEV methods and the raw and climatological forecasts as well. The results verify the advantageous properties of the novel TGEV EMOS approach.

Probabilistic temperature forecasting based on an ensemble autoregressive modification

To address the uncertainty in outputs of numerical weather prediction (NWP) models, ensembles of forecasts are used. To obtain such an ensemble of forecasts, the NWP model is run multiple times, each time with variations in the mathematical representations of the model and/or initial or boundary conditions. To correct for possible biases and dispersion errors in the ensemble, statistical postprocessing models are frequently employed. These statistical models yield full predictive probability distributions for a weather quantity of interest and thus allow for a more accurate representation of forecast uncertainty. This article proposes to combine the state‐of‐the‐art Ensemble Model Output Statistics (EMOS) with an ensemble that is adjusted by an autoregressive process fitted to the respective error series by a spread‐adjusted linear pool in the case of temperature forecasts. The basic ensemble modification technique we introduce may be used to simply adjust the ensemble itself as well as to obtain a full predictive distribution for the weather quantity. As demonstrated for temperature forecasts from the European Centre for Medium‐Range Weather Forecasts ensemble, the proposed procedure gives rise to improved results over the basic (local) EMOS method.

A Similarity-Based Implementation of the Schaake Shuffle

Contemporary weather forecasts are typically based on ensemble prediction systems, which consist of multiple runs of numerical weather prediction models that vary with respect to the initial conditions and/or the parameterization of the atmosphere. Ensemble forecasts are frequently biased and show dispersion errors and thus need to be statistically postprocessed. However, current postprocessing approaches are often univariate and apply to a single weather quantity at a single location and for a single prediction horizon only, thereby failing to account for potentially crucial dependence structures. Nonparametric multivariate postprocessing methods based on empirical copulas, such as ensemble copula coupling or the Schaake shuffle, can address this shortcoming. A specific implementation of the Schaake shuffle, called the SimSchaake approach, is introduced. The SimSchaake method aggregates univariately postprocessed ensemble forecasts using dependence patterns from past observations. Specifically, the observations are taken from historical dates at which the ensemble forecasts resembled the current ensemble prediction with respect to a specific similarity criterion. The SimSchaake ensemble outperforms all reference ensembles in an application to ensemble forecasts for 2-m temperature from the European Centre for Medium-Range Weather Forecasts.

Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components

Bayesian model averaging (BMA) is a statistical method for post-processing forecast ensembles of atmospheric variables, obtained from multiple runs of numerical weather prediction models, in order to create calibrated predictive probability density functions (PDFs). The BMA predictive PDF of the future weather quantity is the mixture of the individual PDFs corresponding to the ensemble members and the weights and model parameters are estimated using forecast ensembles and validating observations from a given training period. A BMA model for calibrating wind speed forecasts is introduced using truncated normal distributions as conditional PDFs and the method is applied to the ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service and to the University of Washington Mesoscale Ensemble. Three parameter estimation methods are proposed and each of the corresponding models outperforms the traditional gamma BMA model both in calibration and in accuracy of predictions.

Multi‐objective calibration of forecast ensembles using Bayesian model averaging

Bayesian Model Averaging (BMA) has recently been proposed as a method for statistical postprocessing of forecast ensembles from numerical weather prediction models. The BMA predictive probability density function (PDF) of any weather quantity of interest is a weighted average of PDFs centered on the bias‐corrected forecasts from a set of different models. However, current applications of BMA calibrate the forecast specific PDFs by optimizing a single measure of predictive skill. Here we propose a multi‐criteria formulation for postprocessing of forecast ensembles. Our multi‐criteria framework implements different diagnostic measures to reflect different but complementary metrics of forecast skill, and uses a numerical algorithm to solve for the Pareto set of parameters that have consistently good performance across multiple performance metrics. Two illustrative case studies using 48‐hour ensemble data of surface temperature and sea level pressure, and multi‐model seasonal forecasts of temperature, show that a multi‐criteria formulation provides a more appealing basis for selecting the appropriate BMA model.