Clustering and selection of boundary conditions for limited‐area ensemble prediction

Bouttier, François; Raynaud, Laure

doi:10.1002/qj.3304

Cited by 17 publications

(12 citation statements)

References 39 publications

(110 reference statements)

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“…AROME-EPS is a 16-member ensemble (since summer 2019) with a horizontal grid spacing of 2.5 km and 90 vertical levels. It is perturbed with four different sources of uncertainties: lateral boundary conditions (Bouttier and Raynaud 2018), surface conditions (Bouttier et al 2016), initial conditions (Montmerle et al 2018;Raynaud and Bouttier 2017), and model errors (Bouttier et al 2012). AROME-EPS is initialized 4 times a day at 0300, 0900, 1500, and 2100 UTC with lead times up to 51 h. AROME-EPS has been developed to improve the prediction of high-impact phenomena such as convective systems.…”

Section: A Arome and Arome-epsmentioning

confidence: 99%

Detection of Bow Echoes in Kilometer-Scale Forecasts Using a Convolutional Neural Network

Mounier

Raynaud

Rottner

et al. 2022

Artificial Intelligence for the Earth Systems

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Bow echoes (BEs) are bow-shaped lines of convective cells that are often associated with swaths of damaging straight-line winds and small tornadoes. This paper describes a convolutional neural network (CNN) able to detect BEs directly from French kilometer-scale model outputs in order to facilitate and accelerate the operational forecasting of BEs. The detections are only based on the maximum pseudoreflectivity field predictor (“pseudo” because it is expressed in mm h−1 and not in dBZ). A preprocessing of the training database is carried out in order to reduce imbalance issues between the two classes (inside or outside bow echoes). A CNN sensitivity analysis against a set of hyperparameters is done. The selected CNN configuration has a hit rate of 86% and a false alarm rate of 39%. The strengths and weaknesses of this CNN are then emphasized with an object-oriented evaluation. The BE largest pseudoreflectivities are correctly detected by the CNN, which tends to underestimate the size of BEs. Detected BE objects have wind gusts similar to the hand-labeled BE. Most of the time, false alarm objects and missed objects are rather small (e.g., <1500 km2). Based on a cooperation with forecasters, synthesis plots are proposed that summarize the BE detections in French kilometer-scale models. A subjective evaluation of the CNN performances is also reported. The overall positive feedback from forecasters is in good agreement with the object-oriented evaluation. Forecasters perceive these products as relevant and potentially useful to handle the large amount of available data from numerical weather prediction models.

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Section: A Arome and Arome-epsmentioning

confidence: 99%

Detection of Bow Echoes in Kilometer-Scale Forecasts Using a Convolutional Neural Network

Mounier

Raynaud

Rottner

et al. 2022

Artificial Intelligence for the Earth Systems

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“…Their lateral conditions are provided by a selection of members of PEARP. Their initial conditions come from an ensemble of assimilations of 25 members at 3.5 km centered around the 3DVAR operational analysis of the deterministic model AROME (Bouttier and Raynaud 2018).…”

Section: ) the Ensemble Version Of The Model Arpege Known Asmentioning

confidence: 99%

Neighborhood-Based Ensemble Evaluation Using the CRPS

Stein

Stoop

2022

Monthly Weather Review

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The neighborhood-based ensemble evaluation using the Continuous Ranked Probability Score is based on the pooling of the Cumulative Density Function (CDF) for all the points inside a neighborhood. This methodology can be applied to the forecast CDF for measuring the predictive input of neighboring points in the center of the neighborhood. It can also be applied at the same time to forecast CDF and observed CDF so as to quantify the quality of the pooled ensemble forecast at the scale of the neighborhood. Fair versions of these two neighborhood scores are also defined in order to reduce their dependencies on the size of ensemble forecasts. The borderline case of deterministic forecasts is also explored so as to be able to compare them with ensemble forecasts. The information of these new scores is analyzed on idealized and real cases of rain accumulated during three hours and of 2-m temperature forecast by four deterministic and probabilistic forecasting systems operational at Météo-France.

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“…Both Schwartz et al (2017) and Raynaud and Bouttier (2017) found that decreasing the grid spacing is beneficial for short-range forecasts, whereas increasing the number of members has positive impacts at longer lead times. It is also relevant to question the perturbations of the lateral boundary conditions (Leoncini et al 2010;Romine et al 2014;Bouttier and Raynaud 2018). In fact, it has been proven that such perturbations control the ensemble spread from 12 hours of integration onward (Hohenegger et al 2008;Peralta et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Reforecasting two heavy-precipitation events with three convection-permitting ensembles

Capecchi¹

2021

Preprint

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We investigate the potential added value of running three limited-area ensemble systems (with the WRF, Meso-NH and MOLOCH models and a grid spacing of approximately 2.5 km) for two heavy-precipitation events in Italy. Such high-resolution ensembles include an explicit treatment of convective processes and dynamically downscale the ECMWF global data, which have a grid spacing of approximately 18 km. The predictions are verified against rain-gauge data and their accuracy is evaluated over that of the driving coarser-resolution ensemble system. Furthermore, we compare the simulation speed (defined as the ratio of simulation length to wall-clock time) of the three limited-area models to estimate the computational effort for operational convection-permitting ensemble forecasting. We also study how the simulation wall-clock time scales with increasing numbers of computing elements (from 36 to 1152 cores). Objective verification methods generally show that convection-permitting forecasts outperform global forecasts for both events, although precipitation peaks remain largely underestimated for one of the two events. Comparing simulation speeds, the MOLOCH model is the fastest and the Meso-NH is the slowest one. The WRF model attains efficient scalability, whereas it is limited for the Meso-NH and MOLOCH models when using more than 288 cores. We finally demonstrate how the model simulation speed has the largest impact on a joint evaluation with the model performance because the accuracy of the three limited-area ensembles, amplifying the forecasting capability of the global predictions, does not differ substantially.Significance statement. We reforecasted two heavyprecipitation events that struck Italy in autumn 2011 by using recent versions of three limited-area weather models, which use a more accurate description of convective processes than the driving global model. By reforecasting past events, the aim of this work is to assess the information content carried by current numerical forecasting systems in the event of similar high-impact events happening again. In view of the potential use for operational forecasting, this study evaluates how the time needed to deliver the forecasts decreases as the computer speed increases. It is shown that such a scaling capability depends not only on the computing elements but also on the geometry of the model domain.

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Clustering and selection of boundary conditions for limited‐area ensemble prediction

Cited by 17 publications

References 39 publications

Detection of Bow Echoes in Kilometer-Scale Forecasts Using a Convolutional Neural Network

Detection of Bow Echoes in Kilometer-Scale Forecasts Using a Convolutional Neural Network

Neighborhood-Based Ensemble Evaluation Using the CRPS

Reforecasting two heavy-precipitation events with three convection-permitting ensembles

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