A stochastic physics scheme is tested in the Application of Research to Operations at Mesoscale (AROME) short-range convection-permitting ensemble prediction system. It an adaptation ECMWF’s perturbation tendencies (SPPT) scheme. The probabilistic performance AROME model found be significantly improved, when verified against observations over two 2-week periods. main improvement lies reliability and spread–skill consistency. Probabilistic scores for several weather parameters are improved....

The AROME‐EPS convection‐permitting ensemble prediction system has been evaluated over the HyMeX‐SOP1 period. Objective verification scores are computed using dense observing networks prepared for HyMeX experiment. In probabilistic terms, performs better than AROME‐France deterministic system, and a state‐of‐the‐art at lower resolution. strengths weaknesses of discussed. Here, impact experiments used to study perturbation schemes initial conditions model surface. Both have significant effect...

Convective‐scale ensemble prediction systems (EPSs) are often initialized with downscaled initial condition perturbations (ICPs) from a global coarser EPS. Although ICPs have been shown to positive impact at short ranges, they cannot represent the uncertainty small scales. Hence, there is spin‐up of around 9–12 h until forecast develop realistic small‐scale structures. On other hand, data assimilation (EDA) common approach obtain all scales resolved by numerical model. However, high...

The relative benefits of ensemble size and model resolution are investigated within the AROME–France convective‐scale prediction system (EPS), which operationally runs 12 perturbed members at 2.5 km horizontal resolution. This baseline configuration is compared with two auxiliary experiments, run resolutions 1.3 km, 34 respectively. In addition, post‐processing techniques including neighbourhood approaches time‐lagging examined as potential alternatives to increase sample a lower...

Abstract Ensemble of data assimilations (EDA) methods have been shown to be able provide flow‐dependent estimates analysis and background error statistics. For this reason, they potentially present a way overcome one the main limitations current variational assimilation systems. However, limited number ensemble members which can realistically run in an operational context stochastic nature EDA approach lead high levels sampling noise relevant To answer problem, ‘objective filtering’...

Abstract Flow‐dependent background‐error variances can be estimated by means of an ensemble assimilations. However, the finite size implies a sampling noise, which is detrimental for variance estimation. This article presents filtering procedure ensemble‐estimated fields, relies on estimate spectral signal/noise ratios. It first demonstrated that noise covariance expressed analytically as simple function covariance. The resulting formula shows in particular spatial structure closely related...

Abstract Since July 2008, a variational ensemble data assimilation system has been used operationally at Météo‐France to provide background error variances ‘of the day’ operational 4D‐Var of global Arpège model. The current is run in perfect model framework and estimated are inflated ‘offline’ (i.e. after completed) account for errors. inflation coefficient tuned according posteriori diagnostics relative minimum cost function. In this study, variance replaced by an ‘online’ multiplicative 6...

Abstract A variational ensemble assimilation has been developed at Météo‐France to provide errors ‘of the day’ operational 4D‐Var of Arpège model. In reference system (operational until April 2010), ensemble‐based variances are used for vorticity and associated balanced parts mass divergence, while unbalanced remain static horizontally homogeneous, humidity calculated with an empirical flow‐dependent formula. This article presents some diagnostic impact studies examine effects extending...

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).Corresponding author: Massimo Bonavita, massimo.bonavita@ecmwf.int

In case of natural and / or technological disaster, decision making relies on predictions based available information, monitoring data model-based forecasts. Uncertainties are particularly high in emergency situations, with scarce information strong time constraints [1].Uncertainty quantification propagation methods well established used numerous applications such as meteorological forecasting risk evaluation various domains (seismic hazard, flooding, environmental consequences radioactive...

Abstract The main approach to account for uncertainty in numerical weather prediction (NWP) is the use of Ensemble Prediction Systems (EPS), which run several simulations parallel. However, huge cost these systems, especially high-resolution modeling, severely limits their implementation, and particular number ensemble members. This size constraint can have significant impact on accuracy robustness distributions sampling, as well utility EPS low-predictability situations. study introduces an...

Abstract In order to contribute ongoing efforts on tropical cyclone (TC) forecasting, a new, convection‐permitting, limited‐area coupled model called AROME‐Indian Ocean (AROME‐IO) was deployed in the Southwest Indian basin (SWIO) April 2016. The skill of this numerical weather predicting system for TC prediction is evaluated against its coupling (European Center Medium Range Weather Forecasting‐Integrated Forecasting System [ECMWF‐IFS]) using 120‐hr reforecasts 11 major storms that developed...

Abstract It is common to compute background‐error variances from an ensemble of forecasts, in order calculate either climatological or flow‐dependent estimates. However, the finite size induces a sampling noise, which degrades accuracy variance estimation. An idealized 1D framework firstly considered, show that spatial structure noise relatively small‐scale, and closely related correlations. This motivates investigations on local averaging, here applied ensemble‐based fields this context....

Abstract Detection and tracking of tropical cyclones (TCs) in numerical weather prediction model outputs is essential for many applications, such as forecast guidance real-time monitoring events. While this task has been automated the 1990s with heuristic models, relying on a set empirical rules thresholds, recent success machine learning methods to detect objects images opens new perspectives. This paper introduces evaluates capacity convolutional neural network based U-Net architecture TC...

Limited‐area ensemble predictions can be sensitive to the specification of lateral boundary conditions, which are often built by subsampling larger ensembles. Using operational PEARP and AROME‐EPS ensembles, we compare several methods, including random selection, representative members, a new selection method. The tests show that algorithms used for clustering member have significant impact on resulting Clustering‐based methods shown outperform subsampling, mostly (but not only) because they...

Abstract Ensemble prediction systems at the convective scale are often under‐dispersive. In order to alleviate this problem, a time‐lagged ensemble can be created from forecasts initialized different production times. While an equal‐weight combination of lagged generally provides competitive results, article introduces and discusses efficiency objective weighting. The proposed approach is based on nonlinear Bayesian filtering, weights determined online for each member according observation...

Abstract. The weather-regime-dependent predictability of precipitation in the convection-permitting kilometric-scale AROME-EPS is examined for entire HyMeX-SOP1 employing convective adjustment timescale. This diagnostic quantifies variations synoptic forcing on and associated with different characteristics, forecast skill predictability. During strong control, which dominates weather 80 % days 2-month period, domain-integrated assessed normalized ensemble standard deviation above average,...

Abstract Bow echoes (BEs) are bow-shaped lines of convective cells that often associated with swaths 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 facilitate accelerate the operational forecasting BEs. The detections only based on maximum pseudoreflectivity field predictor (“pseudo” because it is expressed mm h −1 not dB Z ). A preprocessing training...

Abstract This paper aims to explore machine learning techniques for post‐processing high‐resolution Numerical Weather Prediction (NWP) products the early detection of convection. Data from Arome Ensemble System and satellite observations Rapidly Developing Thunderstorm (RDT) product by Météo‐France are used train a recurrent neural network model predict areas total convection moderate The task is formulated as binary classification problem using long short‐term memory (LSTM) architecture....

Traditional pointwise verification scores are not always appropriate for the evaluation of high‐resolution precipitation forecasts because double‐penalty problems. An alternative approach, based on identification homogeneous rainfall areas called “precipitating objects”, allows forecast at a larger and thus more predictable scale, specific information about nature errors (e.g. location, size, intensity) can be obtained. A novel object detection method is first introduced object‐based from...

In order to cope with small‐scale unpredictable details of mesoscale structures in cloud‐resolving models, it is suggested that model outputs are processed following a fuzzy object‐oriented approach extract and track precipitating features (which associated higher predictability than the direct outputs). The present uses particle filter method recognize patterns based on predefined texture or spatial variability output. This provides an ensemble objects, which then propagated time using...

Abstract A methodology for estimating model error statistics is proposed. Its application to the global operational ARPEGE of Météo‐France provides valuable insights into spatio‐temporal dynamics variances. In particular larger errors are found in midlatitude storm tracks (high cyclonic activity) dynamical variables such as 500 hPa geopotential height and 850 wind speed. The average over both hemispheres show a linear growth until they reach saturation. Model also shown grow more rapidly...