A Spectral Stochastic Kinetic Energy Backscatter Scheme and Its Impact on Flow-Dependent Predictability in the ECMWF Ensemble Prediction System

2009 ◽  
Vol 66 (3) ◽  
pp. 603-626 ◽  
Author(s):  
J. Berner ◽  
G. J. Shutts ◽  
M. Leutbecher ◽  
T. N. Palmer
Keyword(s):  
Kinetic Energy ◽  
Prediction Models ◽  
Positive Impact ◽  
Weather Prediction ◽  
Coarse Grained ◽  
Ensemble Prediction ◽  
Spectral Space ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Integration Schemes

Abstract Understanding model error in state-of-the-art numerical weather prediction models and representing its impact on flow-dependent predictability remains a complex and mostly unsolved problem. Here, a spectral stochastic kinetic energy backscatter scheme is used to simulate upscale-propagating errors caused by unresolved subgrid-scale processes. For this purpose, stochastic streamfunction perturbations are generated by autoregressive processes in spectral space and injected into regions where numerical integration schemes and parameterizations in the model lead to excessive systematic kinetic energy loss. It is demonstrated how output from coarse-grained high-resolution models can be used to inform the parameters of such a scheme. The performance of the spectral backscatter scheme is evaluated in the ensemble prediction system of the European Centre for Medium-Range Weather Forecasts. Its implementation in conjunction with reduced initial perturbations results in a better spread–error relationship, more realistic kinetic-energy spectra, a better representation of forecast-error growth, improved flow-dependent predictability, improved rainfall forecasts, and better probabilistic skill. The improvement is most pronounced in the tropics and for large-anomaly events. It is found that whereas a simplified scheme assuming a constant dissipation rate already has some positive impact, the best results are obtained for flow-dependent formulations of the unresolved processes.

A Unified Scheme of Stochastic Physics and Bias Correction in an Ensemble Model to Reduce Both Random and Systematic Errors

2019 ◽  
Vol 34 (6) ◽  
pp. 1675-1691 ◽  
Author(s):  
Yu Xia ◽  
Jing Chen ◽  
Jun Du ◽  
Xiefei Zhi ◽  
Jingzhuo Wang ◽  
...  
Keyword(s):  
Bias Correction ◽  
Weather Prediction ◽  
Systematic Errors ◽  
Ensemble Prediction ◽  
Ensemble Model ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
Stochastic Physics

Abstract This study experimented with a unified scheme of stochastic physics and bias correction within a regional ensemble model [Global and Regional Assimilation and Prediction System–Regional Ensemble Prediction System (GRAPES-REPS)]. It is intended to improve ensemble prediction skill by reducing both random and systematic errors at the same time. Three experiments were performed on top of GRAPES-REPS. The first experiment adds only the stochastic physics. The second experiment adds only the bias correction scheme. The third experiment adds both the stochastic physics and bias correction. The experimental period is one month from 1 to 31 July 2015 over the China domain. Using 850-hPa temperature as an example, the study reveals the following: 1) the stochastic physics can effectively increase the ensemble spread, while the bias correction cannot. Therefore, ensemble averaging of the stochastic physics runs can reduce more random error than the bias correction runs. 2) Bias correction can significantly reduce systematic error, while the stochastic physics cannot. As a result, the bias correction greatly improved the quality of ensemble mean forecasts but the stochastic physics did not. 3) The unified scheme can greatly reduce both random and systematic errors at the same time and performed the best of the three experiments. These results were further confirmed by verification of the ensemble mean, spread, and probabilistic forecasts of many other atmospheric fields for both upper air and the surface, including precipitation. Based on this study, we recommend that operational numerical weather prediction centers adopt this unified scheme approach in ensemble models to achieve the best forecasts.

scholarly journals Test of a Poor Man’s Ensemble Prediction System for Short-Range Probability Forecasting

2005 ◽  
Vol 133 (7) ◽  
pp. 1825-1839 ◽  
Author(s):  
A. Arribas ◽  
K. B. Robertson ◽  
K. R. Mylne
Keyword(s):  
Short Range ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Small Subset ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Sea Level Pressure ◽  
Ensemble Prediction System ◽  
To Come ◽  
Prediction Systems

Abstract Current operational ensemble prediction systems (EPSs) are designed specifically for medium-range forecasting, but there is also considerable interest in predictability in the short range, particularly for potential severe-weather developments. A possible option is to use a poor man’s ensemble prediction system (PEPS) comprising output from different numerical weather prediction (NWP) centers. By making use of a range of different models and independent analyses, a PEPS provides essentially a random sampling of both the initial condition and model evolution errors. In this paper the authors investigate the ability of a PEPS using up to 14 models from nine operational NWP centers. The ensemble forecasts are verified for a 101-day period and five variables: mean sea level pressure, 500-hPa geopotential height, temperature at 850 hPa, 2-m temperature, and 10-m wind speed. Results are compared with the operational ECMWF EPS, using the ECMWF analysis as the verifying “truth.” It is shown that, despite its smaller size, PEPS is an efficient way of producing ensemble forecasts and can provide competitive performance in the short range. The best relative performance is found to come from hybrid configurations combining output from a small subset of the ECMWF EPS with other different NWP models.

scholarly journals Assessing parametrization uncertainty associated with horizontal resolution in numerical weather prediction models

2014 ◽  
Vol 372 (2018) ◽  
pp. 20130284 ◽  
Author(s):  
Glenn Shutts ◽  
Alfons Callado Pallarès
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Model Error ◽  
Horizontal Resolution ◽  
Phase Changes ◽  
Ensemble Prediction ◽  
Condensation Temperature ◽  
Ensemble Prediction System ◽  
The Mean ◽  
Temperature Tendency

The need to represent uncertainty resulting from model error in ensemble weather prediction systems has spawned a variety of ad hoc stochastic algorithms based on plausible assumptions about sub-grid-scale variability. Currently, few studies have been carried out to prove the veracity of such schemes and it seems likely that some implementations of stochastic parametrization are misrepresentations of the true source of model uncertainty. This paper describes an attempt to quantify the uncertainty in physical parametrization tendencies in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System with respect to horizontal resolution deficiency. High-resolution truth forecasts are compared with matching target forecasts at much lower resolution after coarse-graining to a common spatial and temporal resolution. In this way, model error is defined and its probability distribution function is examined as a function of tendency magnitude. It is found that the temperature tendency error associated with convection parametrization and explicit water phase changes behaves like a Poisson process for which the variance grows in proportion to the mean, which suggests that the assumptions underpinning the Craig and Cohen statistical model of convection might also apply to parametrized convection. By contrast, radiation temperature tendency errors have a very different relationship to their mean value. These findings suggest that the ECMWF stochastic perturbed parametrization tendency scheme could be improved since it assumes that the standard deviation of the tendency error is proportional to the mean. Using our finding that the variance error is proportional to the mean, a prototype stochastic parametrization scheme is devised for convective and large-scale condensation temperature tendencies and tested within the ECMWF Ensemble Prediction System. Significant impact on forecast skill is shown, implying its potential for further development.

scholarly journals Coarse Graining the Vorticity Equation in the ECMWF Integrated Forecasting System: The Search for Kinetic Energy Backscatter

2013 ◽  
Vol 70 (4) ◽  
pp. 1233-1241 ◽  
Author(s):  
G. J. Shutts
Keyword(s):  
Kinetic Energy ◽  
Prediction Models ◽  
Weather Prediction ◽  
Vorticity Equation ◽  
Ensemble Prediction ◽  
Spectral Energy ◽  
Net Energy ◽  
Parameterization Schemes ◽  
Advection Schemes ◽  
Prediction Systems

Abstract Stochastic kinetic energy backscatter parameterization schemes are now widely used in ensemble prediction systems to account for random error associated with excessive dissipation and unrepresented energy backscatter in numerical weather prediction models. This dissipation arises from numerical advection schemes and explicit diffusion terms and is also implicit in some parameterization schemes. In the absence of a backscatter theory applicable to the convective scale and mesoscale, current parameterization methods are based on simple heuristic models designed to scale the energy input in proportion to a suitable measure of net energy dissipation rate. Free parameters in the formulation of backscatter tend to be tuned for the optimal performance of ensemble prediction systems, yet other forms of uncertainty represented in these forecasting systems make that task harder. Results are presented here from a study that aims to characterize the form and magnitude of kinetic energy backscatter within a global spectral framework. This is achieved by comparing a high-resolution “truth” model with a spectrally truncated version of the model for which the effect of the missing scales of motion is to be gauged. Energy exchange between these omitted scales and the resolved scales of the truncated representation is computed for the dominant terms in the vorticity equation. It is found that although there is a nonlocal spectral energy flux to low wavenumbers due to the purely rotational part of the flow, it is much smaller than the dissipative effect associated with terms involving the divergent part of the flow. Biharmonic horizontal diffusion is found to contribute significantly as an energy sink across the entire wavenumber spectrum.

scholarly journals Outcomes and challenges of Forecast Demonstration Project (FDP) on landfalling cyclones over the Bay of Bengal

MAUSAM ◽  
2022 ◽  
Vol 64 (1) ◽  
pp. 1-12
Author(s):  
M. MOHAPATRA ◽  
B.K. BANDYOPADHYAY ◽  
D.R. SIKKA ◽  
AJIT TYAGI
Keyword(s):  
Bay Of Bengal ◽  
Weather Prediction ◽  
North Indian Ocean ◽  
Demonstration Project ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Pilot Phase ◽  
Ensemble Prediction System ◽  
The North ◽  
Before And After

Cakxky dh [kkM+h esa m".kdfVca/kh; rwQkuksa ds ekxZ vkSj mudh rhozrk ds iwokZuqeku rduhd esa lq/kkj ykus ds fy, iwokZuqeku fun’kZu ifj;kstuk ¼,Q-Mh-ih-½ uked ,d dk;ZØe rS;kj fd;k x;k gSA ,Q-Mh-ih- dk;ZØe dk mÌs’;] ftu {ks=ksa ls vk¡dM+s vO;ofLFkr :i  ls izkIr gksrs gSa ogk¡ muds loaf/kZr izs{k.kksa ds lkFk gh lkFk mRrjh fgUn egklkxj esa pØokrksa ds mRiUu gksus] muds rhoz gksus vkSj mudh xfr dk vkdyu djus ds fy, fofHkUu l[;kRed ekSle iwokZuqeku ¼,u- MCY;w- ih-½ fun’kksZ dh {kerk dk izn’kZu djuk rFkk fo’ks"k :i  ls caxky dh [kkM+h ls lacaf/kr  ogha mlh LFkku ij fy, x, ekiksa ds vk/kkj ij fun’kksZ esa lq/kkj djuk gSA ,Q-Mh-ih- dk;ZØe rhu pj.kksa esa fu/kkfjr fd;k x;k gS uker% ¼i½ izh&ikbyV pj.k ¼15 vDrwcj ls 30 uoacj 2008] 2009½] ¼ii½ ikbyV pj.k ¼15 vDrwcj ls 30 uoacj 2010&2012½ rFkk ¼iii½ vafre pj.k ¼15 vDrwcj ls 30 uoacj 2013&2014½A Hkkjr] fdjk, ds gokbZ tgkt vkSj MªkWilkSansa iz;ksxksa ls 15 vDrwcj ls 30 uoacj 2013&2014 ds nkSjku caxky dh [kkM+h esa cuus okys pØokrksa dk gokbZ tgkt ds tfj, irk yxkus dh ;kstuk cuk jgk gSA bl mÌs’; ds iwfrZ ds fy, ¼i½ izs{k.kkRed mUu;u ¼ii½ pØokr fo’ys"k.k vkSj iwokZuqeku iz.kkyh dk vk/kqfudhdj.k ¼iii½ pØokr fo’ys"k.k vkSj iwokZuqeku izfØ;k ¼iv½ psrkouh mRiknksa dks rS;kj djuk] mudk izLrqrhdj.k rFkk izlj.k ¼v½ fo’oluh;rk mik; vkSj {kerk fuekZ.k ij izkFkfedrk ds vk/kkj ij dk;Z fd, x,A pØokr ds izs{k.k] fo’ys"k.k vkSj iwokZuqeku esa lq/kkj ykus ds fy, fofHkUu dk;Z iz.kkfy;k¡ viukbZ xbZaA o"kZ 2008&11 ds nkSjku ,Q-Mh-ih- vfHk;ku ds izh&ikbyV vkSj ikbyV pj.kksa esa la;qDr izs{k.kkRed] lapkjkRed vkSj ,u-MCY;w-ih- xfrfof/k;ksa esa vusd jk"Vªh; laLFkkuksa us Hkkx fy;kA ,Q-Mh-ih- ds igys vkSj mlds ckn dh izs{k.kkRed iz.kkfy;ksa dh rqyuk ls {ks= esa jsMkj] Lopkfyr ekSle dsUnz ¼,- MCY;w-,l-½] mPp iou xfr fjdkWMjksa esa egRoiw.kZ lq/kkj dk irk pyk gSA bl lq/kkj ls ekWuhVju vkSj iwokZuqeku esa gksus okyh =qfV;ksa esa deh vkbZ gSA th- ,Q- ,l- MCY;w vkj- ,Q] ,p- MCY;w- vkj- ,Q- vkSj vlsEcy iwokZuqeku iz.kkyh ¼bZ- ih- ,l-½  ds vkjaHk gksus ls ,u- MCY;w- ih- funsZ’kksa ds dk;Z fu"iknu esa o`f) gqbZ gSA bl 'kks/k i= esa bl ifj;kstuk dh miyfC/k;ksa ds egRoiw.kZ y{k.kksa lfgr leL;kvksa vkSj laHkkoukvksa dks izLrqr fd;k x;k gS rFkk mudh foospuk dh xbZ gSA pØokrksa dk gokbZ tgkt }kjk irk yxkus ds fy, ckj&ckj fd, x, iz;klksa ds ckotwn ;g dk;Z vHkh laHko ugha gks ldk gSA o"kZ 2013&14 ds nkSjku Hkkoh vfHk;ku ds le; ;g ,d eq[; pqukSrh gksxhA A programme has been evolved for improvement in prediction of track and intensity of tropical cyclones over the Bay of Bengal resulting in the Forecast Demonstration Project (FDP). FDP programme is aimed to demonstrate the ability of various Numerical Weather Prediction (NWP) models to assess the genesis, intensification and movement of cyclones over the north Indian ocean with enhanced observations over the data sparse region and to incorporate modification into the models which could be specific to the Bay of Bengal based on the in-situ measurements. FDP Programme is scheduled in three phases, viz., (i) Pre-pilot phase (15 Oct - 30 Nov 2008, 2009, (ii) Pilot phase (15 Oct - 30 Nov, 2010-2012) and (iii) Final phase (15 Oct - 30 Nov, 2013-14). India is planning to take up aircraft probing of cyclones over the Bay of Bengal during 15 Oct - 30 Nov, 2013-14 with hired aircraft and dropsonde experiments. To accomplish the above objective, the initiative was carried out with priorities on (i) observational upgradation, (ii) modernisation of cyclone analysis and prediction system, (iii) cyclone analysis and forecasting procedure, (iv) warning products generation, presentation & dissemination, (v) confidence building measures and capacity building. Various strategies were adopted for improvement of observation, analysis and prediction of cyclone. Several national institutions participated for joint observational, communicational & NWP activities during the pre-pilot and pilot phases of FDP campaign during 2008-11. The comparison of observational systems before and after FDP indicates a significant improvement in terms of Radar, Automatic Weather Station (AWS), high wind speed recorders over the region. It has resulted in reduction in monitoring and forecasting errors. The performance of NWP models have increased along with the introduction of NWP platforms like IMD GFS, WRF, HWRF and ensemble prediction system (EPS). Salient features of achievements along with the problems and prospects of this project are presented and discussed in this paper. With repeated attempts, the aircraft probing of cyclones could not be possible till now. It is a major challenge for the future campaign during 2013-14.

scholarly journals Impact of Surface Parameter Uncertainties within the Canadian Regional Ensemble Prediction System

2013 ◽  
Vol 141 (5) ◽  
pp. 1506-1526 ◽  
Author(s):  
Christophe Lavaysse ◽  
Marco Carrera ◽  
Stéphane Bélair ◽  
Normand Gagnon ◽  
Ronald Frenette ◽  
...  
Keyword(s):  
Wind Speed ◽  
Weather Prediction ◽  
Initial Time ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Surface Parameter ◽  
Ensemble Prediction System ◽  
Surface Parameters ◽  
Screen Level ◽  
The Impact

Abstract The aim of this study is to assess the impact of uncertainties in surface parameter and initial conditions on numerical prediction with the Canadian Regional Ensemble Prediction System (REPS). As part of this study, the Canadian version of the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface scheme has been coupled to Environment Canada’s numerical weather prediction model within the REPS. For 20 summer periods in 2009, stochastic perturbations of surface parameters have been generated in several experiments. Each experiment corresponds to 20 simulations differing by the perturbations at the initial time of one or several surface parameters or prognostic variables. The sensitivity to these perturbations is quantified especially for 2-m temperature, 10-m wind speed, cloud fraction, and precipitation up to 48-h lead time. Spatial variability of these sensitivities over the North American continent shows that soil moisture, albedo, leaf area index, and SST have the largest impacts on the screen-level variables. The temporal evolution of these sensitivities appears to be closely linked to the diurnal cycle of the boundary layer. The surface perturbations are shown to increase the ensemble spread of the REPS for all screen-level variables especially for 2-m temperature and 10-m wind speed during daytime. A preliminary study of the impact on the ensemble forecast has shown that the inclusion of the surface perturbations tends to significantly increase the 2-m temperature and 10-m wind speed skill.

scholarly journals Benefit of microwave radiometer and cloud radar observations for data assimilation and fog process studies during the SOFOG3D experiment

2021 ◽  
Author(s):  
Pauline Martinet ◽  
Frédéric Burnet ◽  
Alistair Bell ◽  
Arthur Kremer ◽  
Matthias Letillois ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Initial State ◽  
Ensemble Prediction System ◽  
Radar Observations ◽  
Stratus Clouds ◽  
Cloud Radar ◽  
Physical Parameterizations

<p>Fog forecasts still remain quite inaccurate due to the complexity, non linearities and fine scale of the main physical processes driving the fog lifecycle. Additionally to the complex modelling of fog processes, current numerical weather prediction models are known to suffer from a lack of operational observations in the atmospheric boundary layer and more generally during cloudy-sky conditions. Continuous observations of both thermodynamics and microphysics during the fog lifecycle are thus essential to develop future operational networks with the aim of validating current physical parameterizations and improving the model initial state through data assimilation techniques. In this context, an international network of 8 ground-based microwave radiometers (MWRs) has been deployed at a regional-scale on a 300 x 300 km domain during the SOFOG3D (SOuth FOGs 3D experiment for fog processes study) that has been conducted from October 2019 to April 2020. The MWR network has been extended with ceilometers at all MWR sites and additional microphysical observations from the 95 GHz cloud radar BASTA at two major sites as well as wind measurements from a Doppler lidar deployed at the super-site. After an overview of the SOFOG3D objectives and experimental set-up, preliminary results exploiting mainly the MWR network and cloud radar observations will be presented. Firstly, the capability of MWRs to provide temperature and humidity retrievals within fog and stratus clouds will be evaluated and discussed against radiosoundings launched during intensive observation periods (IOPs). Secondly, first retrievals of liquid water content profiles within fog and stratus clouds derived from the synergy between MWRs and the BASTA cloud radar will be presented. To that end, a one dimensional variational approach (1D-Var) directly assimilating MWR brightness temperatures and cloud-radar reflectivities has been developed. 1D-Var retrievals will be validated through a dataset of simulated observations and real fog cases of the SOFOG3D experiment. The capability of MWR and cloud radar observations to improve the initial state of the AROME model during fog conditions will be discussed with a focus on selected case studies. Finally, the usefulness of ground-based remote sensing networks to improve our understanding of fog processes and to validate physical parameterizations will be illustrated using the operational AROME model and the AROME Ensemble Prediction System</p>

scholarly journals Prediction of Lake-Effect Snow Using Convection-Allowing Ensemble Forecasts and Regional Data Assimilation

2017 ◽  
Vol 32 (5) ◽  
pp. 1727-1744 ◽  
Author(s):  
Seth Saslo ◽  
Steven J. Greybush
Keyword(s):  
Data Assimilation ◽  
Great Lakes ◽  
Forecast Error ◽  
Weather Prediction ◽  
The United States ◽  
Ensemble Prediction ◽  
Great Lakes Region ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Lake Effect

Abstract Lake-effect snow (LES) is a cold-season mesoscale convective phenomenon that can lead to significant snowfall rates and accumulations in the Great Lakes region of the United States. While limited-area numerical weather prediction models have shown skill in prediction of warm-season convective storms, forecasting the sharp nature of LES precipitation timing, intensity, and location is difficult because of model error and initial and boundary condition uncertainties. Ensemble forecasting can incorporate and quantify some sources of forecast error, but ensemble design must be considered. This study examines the relative contributions of forecast uncertainties to LES forecast error using a regional convection-allowing data assimilation and ensemble prediction system. Ensembles are developed using various methods of perturbations to simulate a long-lived and high-precipitation LES event in December 2013, and forecast performance is evaluated using observations including those from the Ontario Winter Lake-Effect Systems (OWLeS) campaign. Model lateral boundary conditions corresponding to weather conditions beyond the Great Lakes region play an influential role in LES precipitation forecasts and their uncertainty, as evidenced by ensemble spread, particularly at lead times beyond one day. A strong forecast dependence on regional initial conditions was shown using data assimilation. This sensitivity impacts the timing and intensity of predicted precipitation, as well as band location and orientation assessed with an object-based verification approach, giving insight into the time scales of practical predictability of LES. Overall, an assimilation-cycling convection-allowing ensemble prediction system could improve future lake-effect snow precipitation forecasts and analyses and can help quantify and understand sources of forecast uncertainty.

scholarly journals Experiments with EOF-Based Perturbation Methods and Their Impact on the CPTEC/INPE Ensemble Prediction System

2009 ◽  
Vol 137 (4) ◽  
pp. 1438-1459 ◽  
Author(s):  
Antônio Marcos Mendonça ◽  
JoséPaulo Bonatti
Keyword(s):  
South America ◽  
Weather Prediction ◽  
Specific Humidity ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Simultaneous Application ◽  
Ensemble Prediction System ◽  
Perturbation Versus ◽  
The Impact

Abstract The impact of modifications of the perturbation method based on empirical orthogonal functions (EOFs) used operationally upon the ensemble prediction system (EPS) at the Center for Weather Prediction and Climate Studies/National Institute for Space Research (CPTEC/INPE) is evaluated. The main changes proposed in this study are to apply the EOF method to perturb the midlatitudes, apply additional perturbations to the surface pressure (P) and specific humidity (Q) fields, and compute regional perturbations over South America. The impact of these modifications in the characteristics of the initial perturbations and in the quality of the EPS forecasts is investigated. The EPS forecasts are evaluated through average statistical scores over the period 15 December 2004–15 February 2005. The statistical scores used in the evaluation are pattern anomaly correlation, root-mean-square error, ensemble spread, Brier skill score, and perturbation versus error correlation analysis (PECA). Results indicate that with the inclusion of perturbations on P and Q, EOF-based perturbations acquire a more baroclinic structure. It is also observed that the simultaneous application of additional perturbations both in the extratropics and to the P and Q fields improves the performance of CPTEC EPS and enhances the quality of forecast perturbations. Moreover, regional EOF-based perturbations computed over South America have positive impact on the ensemble forecasts over the target region.

scholarly journals Toward Real-Time Daily PQPF by an Analog Sorting Approach: Application to Flash-Flood Catchments

2012 ◽  
Vol 51 (3) ◽  
pp. 505-520 ◽  
Author(s):  
Renaud Marty ◽  
Isabella Zin ◽  
Charles Obled ◽  
Guillaume Bontron ◽  
Abdelatif Djerboua
Keyword(s):  
Heavy Rainfall ◽  
Prediction Models ◽  
Flash Flood ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Prediction System ◽  
Rainfall Events ◽  
Sorting Technique ◽  
Heavy Rainfall Events

AbstractHeavy-rainfall events are common in southern France and frequently result in devastating flash floods. Thus, an appropriate anticipation of future rainfall is required: for early flood warning, at least 12–24 h in advance; for alerting operational services, at least 2–3 days ahead. Precipitation forecasts are generally provided by numerical weather prediction models (NWP), and their associated uncertainty is generally estimated through an ensemble approach. Precipitation forecasts also have to be adapted to hydrological scales. This study describes an alternative approach to commonly used limited-area models. Probabilistic quantitative precipitation forecasts (PQPFs) are provided through an analog sorting technique, which directly links synoptic-scale NWP output to catchment-scale rainfall probability distributions. One issue concerns the latest developments in implementing a daily version of this technique into operational conditions. It is shown that the obtained PQPFs depend on the meteorological forecasts used for selecting analogous days and that the method has to be reoptimized when changing the source of synoptic forecasts, because of the NWP output uncertainties. Second, an evaluation of the PQPFs demonstrates that the analog technique performs well for early warning of heavy-rainfall events and provides useful information as potential input to a hydrological ensemble prediction system. It is shown that the obtained daily rainfall distributions can be unreliable. A statistical correction of the observed bias is proposed as a function of the no-rain frequency values, leading to a significant improvement in PQPF sharpness.

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