Designing an optimal flood forecasting chain using convective-scale ensembles: a sensitivity study.

2021 ◽  
Author(s):  
Céline Cattoën ◽  
Stuart Moore ◽  
Trevor Carey-Smith
Keyword(s):  
New Zealand ◽  
Flood Forecasting ◽  
Horizontal Resolution ◽  
Scale Model ◽  
Statistical Ensemble ◽  
Ensemble Prediction ◽  
Flood Events ◽  
Convective Scale ◽  
Nwp Model ◽  
The Impact

<p>Flooding is New Zealand’s most frequent natural disaster with an average annual cost of approximately NZ$51 million. Accurately forecasting convective and orographically enhanced precipitation for hydrometeorological ensemble prediction systems is challenging in Aotearoa New Zealand’s complex topographic regions with fast-responding and mostly ungauged catchments. Globally, designing convection-permitting ensemble flood forecasting chains is still a work in progress, with errors in the forecast rainfall amount and the location or timing of storm events a significant contributor to uncertainties in river flow forecasts. Given operational, computational and model representation constraints, compromises are often required on ensemble size, frequency of forecast issue times, NWP model resolution, domain size and data assimilation strategies. This research aims to design an optimal operational forecasting chain for convective-scale flood forecasting in New Zealand.  In doing so, our goal is to improve uncertainty representation in hydrometeorological forecasts during flood events by understanding the impact of convective-scale ensemble strategies.</p><p>The NWP model used is a local implementation of the UK Met Office-developed Unified Model.  The New Zealand Convective-Scale Model (NZCSM) is NIWA’s 1.5km high-resolution operational forecast model, configured such that convective processes develop explicitly. The New Zealand Ensemble (NZENS) is configured with similar convection-permitting model physics but operates with a 4.5km horizontal resolution and features up to 18 members.  Flood forecasts were produced by coupling several weather ensemble configurations with the semi-distributed hydrological model TopNet and its built-in statistical ensemble generation tool. TopNet is based on TOPMODEL concepts of runoff generation controlled by sub-surface water storage.</p><p>In this study, we evaluated three ensemble strategies for flood forecasting. The experiment design allowed for the effect of model horizontal resolution (and thus the representation of orography) to be investigated using ensemble forecasts from consecutive initialization times (a “lagged ensemble”), and from the same initialisation time (a “dynamical ensemble”). The third forecasting chain is a “statistical ensemble” generated by perturbing the deterministic 1.5km NWP model and hydrological states. For recent flood events across multiple case study catchments, we evaluated the impact of each approach on flood forecast performance. Flood forecasts were most sensitive to convective-scale forecasts with consecutive issue time initialisations (lagged ensemble) over other hydrometeorological ensemble configurations considered. Given dynamical ensembles are computationally expensive, the study suggests an optimal strategy might be to produce a small ensemble pool of dynamical forecasts at more frequent issue times combined with statistically post-processed ensembles rather than a larger ensemble pool generated less frequently.</p>

scholarly journals Ensemble forecasting with a stochastic convective parametrization based on equilibrium statistics

2011 ◽  
Vol 11 (11) ◽  
pp. 30457-30485 ◽  
Author(s):  
P. Groenemeijer ◽  
G. C. Craig
Keyword(s):  
Large Scale ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Deep Convection ◽  
Horizontal Resolution ◽  
Ensemble Forecasting ◽  
Total Variance ◽  
Ensemble Prediction ◽  
Ensemble Prediction System ◽  
The Impact

Abstract. The stochastic Plant-Craig scheme for deep convection was implemented in the COSMO mesoscale model and used for ensemble forecasting. Ensembles consisting of 100 48 h forecasts at 7 km horizontal resolution were generated for a 2000 × 2000 km domain covering central Europe. Forecasts were made for seven case studies and characterized by different large-scale meteorological environments. Each 100 member ensemble consisted of 10 groups of 10 members, with each group driven by boundary and initial conditions from a selected member from the global ECMWF Ensemble Prediction System. The precipitation variability within and among these groups of members was computed, and it was found that the relative contribution to the ensemble variance introduced by the stochastic convection scheme was substantial, amounting to as much as 76% of the total variance in the ensemble in one of the studied cases. The impact of the scheme was not confined to the grid scale, and typically contributed 25–50% of the total variance even after the precipitation fields had been smoothed to a resolution of 35 km. The variability of precipitation introduced by the scheme was approximately proportional to the total amount of convection that occurred, while the variability due to large-scale conditions changed from case to case, being highest in cases exhibiting strong mid-tropospheric flow and pronounced meso- to synoptic scale vorticity extrema. The stochastic scheme was thus found to be an important source of variability in precipitation cases of weak large-scale flow lacking strong vorticity extrema, but high convective activity.

Sampling Error Correction Evaluated Using a Convective-Scale 1000-Member Ensemble

2019 ◽  
Vol 148 (3) ◽  
pp. 1229-1249 ◽  
Author(s):  
Tobias Necker ◽  
Martin Weissmann ◽  
Yvonne Ruckstuhl ◽  
Jeffrey Anderson ◽  
Takemasa Miyoshi
Keyword(s):  
Sensitivity Analysis ◽  
Data Assimilation ◽  
Error Correction ◽  
Degrees Of Freedom ◽  
Sampling Error ◽  
Lookup Table ◽  
Ensemble Prediction ◽  
Sampling Errors ◽  
Convective Scale ◽  
The Impact

Abstract State-of-the-art ensemble prediction systems usually provide ensembles with only 20–250 members for estimating the uncertainty of the forecast and its spatial and spatiotemporal covariance. Given that the degrees of freedom of atmospheric models are several magnitudes higher, the estimates are therefore substantially affected by sampling errors. For error covariances, spurious correlations lead to random sampling errors, but also a systematic overestimation of the correlation. A common approach to mitigate the impact of sampling errors for data assimilation is to localize correlations. However, this is a challenging task given that physical correlations in the atmosphere can extend over long distances. Besides data assimilation, sampling errors pose an issue for the investigation of spatiotemporal correlations using ensemble sensitivity analysis. Our study evaluates a statistical approach for correcting sampling errors. The applied sampling error correction is a lookup table–based approach and therefore computationally very efficient. We show that this approach substantially improves both the estimates of spatial correlations for data assimilation as well as spatiotemporal correlations for ensemble sensitivity analysis. The evaluation is performed using the first convective-scale 1000-member ensemble simulation for central Europe. Correlations of the 1000-member ensemble forecast serve as truth to assess the performance of the sampling error correction for smaller subsets of the full ensemble. The sampling error correction strongly reduced both random and systematic errors for all evaluated variables, ensemble sizes, and lead times.

scholarly journals Effects of Increasing Horizontal Resolution in a Convection-Permitting Model on Flood Forecasting: The 2011 Dramatic Events in Liguria, Italy

2015 ◽  
Vol 16 (4) ◽  
pp. 1843-1856 ◽  
Author(s):  
Silvio Davolio ◽  
Francesco Silvestro ◽  
Piero Malguzzi
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Flood Forecasting ◽  
Horizontal Resolution ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Flood Prediction ◽  
Meteorological Model ◽  
The Impact ◽  
Atmospheric Simulations

Abstract Coupling meteorological and hydrological models is a common and standard practice in the field of flood forecasting. In this study, a numerical weather prediction (NWP) chain based on the BOLogna Limited Area Model (BOLAM) and the MOdello LOCale in Hybrid coordinates (MOLOCH) was coupled with the operational hydrological forecasting chain of the Ligurian Hydro-Meteorological Functional Centre to simulate two major floods that occurred during autumn 2011 in northern Italy. Different atmospheric simulations were performed by varying the grid spacing (between 1.0 and 3.0 km) of the high-resolution meteorological model and the set of initial/boundary conditions driving the NWP chain. The aim was to investigate the impact of these parameters not only from a meteorological perspective, but also in terms of discharge predictions for the two flood events. The operational flood forecasting system was thus used as a tool to validate in a more pragmatic sense the quantitative precipitation forecast obtained from different configurations of the NWP system. The results showed an improvement in flood prediction when a high-resolution grid was employed for atmospheric simulations. In turn, a better description of the evolution of the precipitating convective systems was beneficial for the hydrological prediction. Although the simulations underestimated the severity of both floods, the higher-resolution model chain would have provided useful information to the decision-makers in charge of protecting citizens.

scholarly journals Application of the LEPS technique for Quantitative Precipitation Forecasting (QPF) in Southern Italy: a preliminary study

2006 ◽  
Vol 13 (1) ◽  
pp. 53-66 ◽  
Author(s):  
S. Federico ◽  
E. Avolio ◽  
C. Bellecci ◽  
M. Colacino ◽  
R. L. Walko
Keyword(s):  
Case Studies ◽  
Horizontal Resolution ◽  
Probabilistic Forecast ◽  
Computational Time ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Prediction System ◽  
Precipitation Forecasting ◽  
Regional Atmospheric Modelling System ◽  
The Impact

Abstract. This paper reports preliminary results for a Limited area model Ensemble Prediction System (LEPS), based on RAMS (Regional Atmospheric Modelling System), for eight case studies of moderate-intense precipitation over Calabria, the southernmost tip of the Italian peninsula. LEPS aims to transfer the benefits of a probabilistic forecast from global to regional scales in countries where local orographic forcing is a key factor to force convection. To accomplish this task and to limit computational time in an operational implementation of LEPS, we perform a cluster analysis of ECMWF-EPS runs. Starting from the 51 members that form the ECMWF-EPS we generate five clusters. For each cluster a representative member is selected and used to provide initial and dynamic boundary conditions to RAMS, whose integrations generate LEPS. RAMS runs have 12-km horizontal resolution. To analyze the impact of enhanced horizontal resolution on quantitative precipitation forecasts, LEPS forecasts are compared to a full Brute Force (BF) ensemble. This ensemble is based on RAMS, has 36 km horizontal resolution and is generated by 51 members, nested in each ECMWF-EPS member. LEPS and BF results are compared subjectively and by objective scores. Subjective analysis is based on precipitation and probability maps of case studies whereas objective analysis is made by deterministic and probabilistic scores. Scores and maps are calculated by comparing ensemble precipitation forecasts against reports from the Calabria regional raingauge network. Results show that LEPS provided better rainfall predictions than BF for all case studies selected. This strongly suggests the importance of the enhanced horizontal resolution, compared to ensemble population, for Calabria for these cases. To further explore the impact of local physiographic features on QPF (Quantitative Precipitation Forecasting), LEPS results are also compared with a 6-km horizontal resolution deterministic forecast. Due to local and mesoscale forcing, the high resolution forecast (Hi-Res) has better performance compared to the ensemble mean for rainfall thresholds larger than 10mm but it tends to overestimate precipitation for lower amounts. This yields larger false alarms that have a detrimental effect on objective scores for lower thresholds. To exploit the advantages of a probabilistic forecast compared to a deterministic one, the relation between the ECMWF-EPS 700 hPa geopotential height spread and LEPS performance is analyzed. Results are promising even if additional studies are required.

A state-of-the-art review on wind power deterministic prediction

2020 ◽  
pp. 0309524X2094120 ◽  
Author(s):  
Zhongda Tian
Keyword(s):  
Wind Power ◽  
Prediction Method ◽  
Scale Model ◽  
Ensemble Prediction ◽  
Prediction Methods ◽  
Power Prediction ◽  
Continuous Growth ◽  
Advantages And Disadvantages ◽  
Wind Power Prediction ◽  
The Impact

With the continuous growth of wind power access capacity, the impact of intermittent and volatile wind power generation on the grid is becoming more and more obvious, so the research of wind power prediction method has been widely concerned. Accurate wind power prediction can provide necessary support for the power grid dispatching, combined operation of generating units, operation, and maintenance of wind farms. According to the existing wind power prediction methods, the wind power prediction methods are systematically classified according to the time scale, model object, and model principle of prediction. The physical methods, statistical methods include single and ensemble prediction methods related to wind power prediction are introduced in detail. The error evaluation indicator of the prediction method is analyzed, and the advantages and disadvantages of each prediction method and its applicable occasions are given. At the same time, in view of the existing problems in the wind power prediction method, the corresponding improvement plan is put forward. Finally, this article points out that the research is needed for wind power prediction in the future.

scholarly journals Cloud-Resolving Ensemble Simulations of Mediterranean Heavy Precipitating Events: Uncertainty on Initial Conditions and Lateral Boundary Conditions

2011 ◽  
Vol 139 (2) ◽  
pp. 403-423 ◽  
Author(s):  
Benoît Vié ◽  
Olivier Nuissier ◽  
Véronique Ducrocq
Keyword(s):  
Boundary Conditions ◽  
Case Studies ◽  
Short Range ◽  
Initial Conditions ◽  
Ensemble Prediction ◽  
Lateral Boundary ◽  
Synoptic Scale ◽  
Lateral Boundary Conditions ◽  
Convective Scale ◽  
The Impact

Abstract This study assesses the impact of uncertainty on convective-scale initial conditions (ICs) and the uncertainty on lateral boundary conditions (LBCs) in cloud-resolving simulations with the Application of Research to Operations at Mesoscale (AROME) model. Special attention is paid to Mediterranean heavy precipitating events (HPEs). The goal is achieved by comparing high-resolution ensembles generated by different methods. First, an ensemble data assimilation technique has been used for assimilation of perturbed observations to generate different convective-scale ICs. Second, three ensembles used LBCs prescribed by the members of a global short-range ensemble prediction system (EPS). All ensembles obtained were then evaluated over 31- and/or 18-day periods, and on 2 specific case studies of HPEs. The ensembles are underdispersive, but both the probabilistic evaluation of their overall performance and the two case studies confirm that they can provide useful probabilistic information for the HPEs considered. The uncertainty on convective-scale ICs is shown to have an impact at short range (under 12 h), and it is strongly dependent on the synoptic-scale context. Specifically, given a marked circulation near the area of interest, the imposed LBCs rapidly overwhelm the initial differences, greatly reducing the spread of the ensemble. The uncertainty on LBCs shows an impact at longer range, as the spread in the coupling global ensemble increases, but it also depends on the synoptic-scale conditions and their predictability.

scholarly journals The AEMET-<i>γ</i>SREPS over the Antarctic Peninsula and the impact of kilometric-resolution EPS on logistic activities on the continent

2020 ◽  
Vol 17 ◽  
pp. 209-217
Author(s):  
Sergi Gonzalez ◽  
Alfons Callado ◽  
Mauricia Martínez ◽  
Benito Elvira
Keyword(s):  
Boundary Conditions ◽  
Antarctic Peninsula ◽  
Positive Impact ◽  
Austral Summer ◽  
Added Value ◽  
Ensemble Prediction ◽  
Prediction Systems ◽  
Nwp Model ◽  
The Antarctic ◽  
The Impact

Abstract. Kilometric-resolution Ensemble Prediction Systems (EPSs) will be the new state-of-the-art forecasting tools for short-range prediction in the following decade. Their value will be even greater in Antarctica due to the increasingly demanding weather forecasts for logistic services. During the 2018–2019 austral summer (1 December–31 March), coinciding with the Southern Hemisphere Special Observation Period of the Year of Polar Prediction (YOPP), the 2.5 km AEMET-γSREPS was operationally integrated over the Antarctic Peninsula. In particular, the Antarctic version of γSREPS comes up with crossing four non-hydrostatic convection-permitting NWP models at 2.5 km with three global NWP driving models as boundary conditions. The γSREPS forecasting system has been validated in comparison with ECMWF EPS. It is concluded that γSREPS has an added value to ECMWF EPS due to both its higher resolution and its multi-boundary conditions and multi-NWP model approach. γSREPS performance has a positive impact on logistic activities at research stations and its design may contribute to polar prediction research.

scholarly journals Data-based stochastic subgrid-scale parametrization: an approach using cluster-weighted modelling

2012 ◽  
Vol 370 (1962) ◽  
pp. 1061-1086 ◽  
Author(s):  
Frank Kwasniok
Keyword(s):  
Clustering Algorithm ◽  
Prediction Models ◽  
Statistical Modelling ◽  
Scale Model ◽  
Ensemble Prediction ◽  
Subgrid Scale ◽  
New Approach ◽  
Lorenz 96 ◽  
The Impact

A new approach for data-based stochastic parametrization of unresolved scales and processes in numerical weather and climate prediction models is introduced. The subgrid-scale model is conditional on the state of the resolved scales, consisting of a collection of local models. A clustering algorithm in the space of the resolved variables is combined with statistical modelling of the impact of the unresolved variables. The clusters and the parameters of the associated subgrid models are estimated simultaneously from data. The method is implemented and explored in the framework of the Lorenz '96 model using discrete Markov processes as local statistical models. Performance of the cluster-weighted Markov chain scheme is investigated for long-term simulations as well as ensemble prediction. It clearly outperforms simple parametrization schemes and compares favourably with another recently proposed subgrid modelling scheme also based on conditional Markov chains.

scholarly journals The HARMONIE–AROME Model Configuration in the ALADIN–HIRLAM NWP System

2017 ◽  
Vol 145 (5) ◽  
pp. 1919-1935 ◽  
Author(s):  
Lisa Bengtsson ◽  
Ulf Andrae ◽  
Trygve Aspelien ◽  
Yurii Batrak ◽  
Javier Calvo ◽  
...  
Keyword(s):  
Short Range ◽  
Weather Prediction ◽  
Joint Modeling ◽  
Horizontal Resolution ◽  
Ensemble Prediction ◽  
Modeling Framework ◽  
Time Step ◽  
Ensemble Prediction System ◽  
Nwp Model ◽  
Physical Parameterizations

Abstract The aim of this article is to describe the reference configuration of the convection-permitting numerical weather prediction (NWP) model HARMONIE-AROME, which is used for operational short-range weather forecasts in Denmark, Estonia, Finland, Iceland, Ireland, Lithuania, the Netherlands, Norway, Spain, and Sweden. It is developed, maintained, and validated as part of the shared ALADIN–HIRLAM system by a collaboration of 26 countries in Europe and northern Africa on short-range mesoscale NWP. HARMONIE–AROME is based on the model AROME developed within the ALADIN consortium. Along with the joint modeling framework, AROME was implemented and utilized in both northern and southern European conditions by the above listed countries, and this activity has led to extensive updates to the model’s physical parameterizations. In this paper the authors present the differences in model dynamics and physical parameterizations compared with AROME, as well as important configuration choices of the reference, such as lateral boundary conditions, model levels, horizontal resolution, model time step, as well as topography, physiography, and aerosol databases used. Separate documentation will be provided for the atmospheric and surface data-assimilation algorithms and observation types used, as well as a separate description of the ensemble prediction system based on HARMONIE–AROME, which is called HarmonEPS.

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