Probabilistic Precipitation-Type Forecasting Based on GEFS Ensemble Forecasts of Vertical Temperature Profiles

Scheuerer, Michael; Gregory, S.; Hamill, Thomas M.; Shafer, Phillip E.

doi:10.1175/mwr-d-16-0321.1

Cited by 8 publications

(9 citation statements)

References 13 publications

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“…The application of vertical temperature profiles for estimating precipitation type is tackled in various ways. For example, Reeves et al (2016) or Scheuerer et al (2017) solely concentrated on the vertical profiles of T w and proved the applicability of such a data source. In contrast to this, Schuur et al (2014) combined the temperature profiles with results of a hydrometeor classification based on weather radar data.…”

Section: The Extrapolation Methodsmentioning

confidence: 93%

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Hymec: Surface Precipitation Type Estimation at the German Weather Service

Steinert

Tracksdorf

Heizenreder

2021

Weather and Forecasting

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The analysis and forecast of precipitation characteristics is a key task for national meteorological services for providing high quality weather forecasts and warnings. Beside the precipitation amount, the precipitation type is essential to describe and evaluate the recent, ongoing and future weather situations.This paper introduces a new surface-based hybrid hydrometeor classification algorithm. The presented method combines polarimetric radar observations at radar beam height from the C-band dual-polarization weather radar network of the German Weather Service (DWD, Deutscher Wetterdienst) with corrected thermodynamical profiles of numerical weather prediction (NWP) model output and extrapolates the hydrometeor classes at radar beam height to a height of 2 m above ground level (AGL). The implemented technique parametrizes the microphysical processes in the lower troposphere based on the appropriate thermodynamical profile the hydrometeors have to pass along their way from the radar beam height to the surface. Due to errors in NWP output, the NWP vertical profiles of temperature and humidity are adjusted by using several types of surface stations with high spatial and temporal resolution.Verification results show considerable improvements in the hydrometeor classification near the ground compared to the radar sweep height. After an additional positive in-house evaluation, the presented method is integrated into DWD’s operational environment. The topic of this paper is to describe the processing steps for the computation of the near-surface precipitation type. In addition, example cases and a verification study complement the explanations.

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Section: The Extrapolation Methodsmentioning

confidence: 93%

“…with T w (K) and is executed for heights h between 2 m AGL and h max 5 500 m AGL. This kind of adaptation is comparable to the method in Scheuerer et al (2017) with differences in the applied ground truth and in the value of h max . Scheuerer et al…”

Section: Adaptation Of Nwp With Ground Measurementsmentioning

confidence: 93%

Hymec: Surface Precipitation Type Estimation at the German Weather Service

Steinert

Tracksdorf

Heizenreder

2021

Weather and Forecasting

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“…However, although remarkable progress has been made in this area in recent decades, numerical weather prediction (NWP) models still often fail to produce accurate precipitation patterns, especially for heavy precipitation events (Fritsch et al, 1998;Gourley and Vieux, 2005). Ensemble prediction systems (EPSs) promote the transition from deterministic to probabilistic forecasts by adding certain perturbations to the initial conditions, which enables the generation of a greater number of possible simulations of precipitation and hence improves forecasting ability (Majumdar and Torn, 2014;Scheuerer et al, 2017). However, because they are limited by imperfect model configurations and the chaotic nature of the atmosphere, even optimal EPSs suffer from their own systemic biases, and appropriate post-processing steps are thus required.…”

Section: Introductionmentioning

confidence: 99%

“…The parameters of EMOS are further estimated as regression coefficients of a multiple regression between the forecasts and their corresponding observations. Particularly for probabilistic precipitation forecasting, the censored generalized extreme value (GEV) (Scheuerer and Möller, 2015) and the censored and shifted gamma (CSG) (Baran and Nemoda, 2016;Scheuerer et al, 2017) distribution EMOS modeling techniques have been proposed. In the GEV EMOS framework, three parameters are optimized that represent location, ratio, and shape.…”

Section: Introductionmentioning

confidence: 99%

Deep-learning-based post-processing for probabilistic precipitation forecasting

Zhi

et al. 2022

Front. Earth Sci.

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Ensemble prediction systems (EPSs) serve as a popular technique to provide probabilistic precipitation prediction in short- and medium-range forecasting. However, numerical models still suffer from imperfect configurations associated with data assimilation and physical parameterization, which can lead to systemic bias. Even state-of-the-art models often fail to provide high-quality precipitation forecasting, especially for extreme events. In this study, two deep-learning-based models—a shallow neural network (NN) and a deep NN with convolutional layers (CNN)—were used as alternative post-processing approaches to further improve the probabilistic forecasting of precipitation over China with 1–7 lead days. A popular conventional method—the censored and shifted gamma distribution-based ensemble model output statistics (CSG EMOS)—was used as the baseline. Re-forecasts run using a frozen EPS—Global Ensemble Forecast System version 12—were collected as the raw ensembles spanning from 2000 to 2019. The re-forecast data were generated once per day and consisted of one control run and four perturbed members. We used the calendar year 2018 as the validation period and 2019 as the testing period, and the remaining 18 years of data were used for training. According to the results, in terms of the continuous ranked probability score (CRPS) and the Brier score, the CNN model significantly outperforms the shallow NN model, as well as the CSG EMOS approach and the raw ensemble, especially for heavy or extreme precipitation events (those exceeding 50 mm/day). A remarkable degradation was seen when reducing the size of training samples from 18 years of data to two years. The spatial distribution of the CRPS shows that the stations in central China were better calibrated than those in other regions. With a lead time of 1 day, the CNN model was found to be superior to the other models (in terms of the CRPS) at 74.5% of the study stations. These results indicate that deep NNs can serve as a promising approach to the statistical post-processing of probabilistic precipitation forecasting.

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“…Ensemble forecasting is a key technique used for conceptual transition from a single deterministic forecast to a probabilistic forecast [11][12][13]. Ensemble forecasting aims to quantitatively describe the uncertainty of the forecast and to provide a more reliable probability density function (PDF) rather than a better deterministic forecast [14,15].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Forecasting of the 500 hPa Geopotential Height over the Northern Hemisphere Using TIGGE Multi-model Ensemble Forecasts

Qi-xiang

et al. 2021

Atmosphere

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Bayesian model averaging (BMA) and ensemble model output statistics (EMOS) were used to improve the prediction skill of the 500 hPa geopotential height field over the northern hemisphere with lead times of 1–7 days based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and UK Met Office (UKMO) ensemble prediction systems. The performance of BMA and EMOS were compared with each other and with the raw ensembles and climatological forecasts from the perspective of both deterministic and probabilistic forecasting. The results show that the deterministic forecasts of the 500 hPa geopotential height distribution obtained from BMA and EMOS are more similar to the observed distribution than the raw ensembles, especially for the prediction of the western Pacific subtropical high. BMA and EMOS provide a better calibrated and sharper probability density function than the raw ensembles. They are also superior to the raw ensembles and climatological forecasts according to the Brier score and the Brier skill score. Comparisons between BMA and EMOS show that EMOS performs slightly better for lead times of 1–4 days, whereas BMA performs better for longer lead times. In general, BMA and EMOS both improve the prediction skill of the 500 hPa geopotential height field.

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Probabilistic Precipitation-Type Forecasting Based on GEFS Ensemble Forecasts of Vertical Temperature Profiles

Cited by 8 publications

References 13 publications

Hymec: Surface Precipitation Type Estimation at the German Weather Service

Hymec: Surface Precipitation Type Estimation at the German Weather Service

Deep-learning-based post-processing for probabilistic precipitation forecasting

Probabilistic Forecasting of the 500 hPa Geopotential Height over the Northern Hemisphere Using TIGGE Multi-model Ensemble Forecasts

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