Presentación realizada en la 3rd European Nowcasting Conference, celebrada en la sede central de AEMET en Madrid del 24 al 26 de abril de 2019

Atencia, Aitor

Kann, Alexander

Wang, Yong

Wastl, Clemens

English

Agencia Estatal de Meteorología

Impact of Bayesian weighting in a probabilistic nowcasting from INCA and C-LAEFA. Atencia*, A. Kann, Y. Wang and C. WastlINCA and C-LAEFNowcasting system – INCA:• Extrapolation of latest analysis• Motion vectors by correlation and model correction• Intensity-dependent elevation effectINCA:• Spatial resolution:  1 x 1 km (701x401)• Temporal resolution:  15 m – 6 h lead-time / 15 mAROME-EPS – C-LAEF:• Convection permitting – Limited Area Ens. Forecasting• A Hybrid-stochastic physics perturbation scheme• IC perturbation EDA + ensemble JkAROME-EPS:• Spatial resolution:  2.5 x 2.5 km (594x492)• Temporal resolution:  1 h – 30 h lead-time / 24h RUNTest monthThis study is carried out for the whole month of July of 2016:11 to 14 July:Temporary heat wave with increasing thunderstorm activity. 12 July the storms organized intoseveral large front-like systems that persisted over many hours; it was the worst severe weatherday of the year, there were numerous extreme wind and hail damages, and forecast quality waspoor. On 13 July, thunderstorm activity continued on the south side of the Alps while it wasreplaced by prolonged rain elsewhere as a cold front entered Austria.Day of July 2016MotivationThe probabilistic information of an EPS system as AROME-EPS is crucial forvery-short range forecasting.MotivationThe probabilistic information of an EPS system as AROME-EPS is crutial for very-short range forecasting.Yet, AROME-EPS runs are less frequent than INCA because they arecomputationally more expensive.Integration/blending the frequent INCA forecast with the probabilistic AROME-EPSState of the artThe current state of the art for blending NWP with NWC is still adding:1. Linearly both fields (Golding, 1998),2. Probabilities of occurrence (Kober et al., 2012) depending on the synoptic forcing (Kober et al., 2014) 3. Cascades from a scale decomposition of the reflectivity fields (Bowler etal., 2006) from both sources the Lagrangian extrapolation and the NWPoutput(e.g. STEPS, Alan Seeds).Challenges of the current methodologiesMethodologies 1 and 3 are Blending in the intensity space. They involve thefollowing challenges which need to be tackled:+ =The thunderstom (>35 mm/h) modelled by the NWP is smoothed (~ 18 mm/h) in the blended forecast due to the lack of precipitation in the radar-based extrapolation. Challenges of the current methodologiesAlso, the intensity-based blending (1 and 3) reduces the heavy rainfall and thevariance :The empirical probability distribution of rainfall valuesof both INCA (radar extrapolation) and AROME-RUC(NWP) is compared to the epdf of the blendedforecast showing the lessening of the intense rainfalland also the reduction of variance.Challenges of the current methodologiesBesides, recent studies about the performance of NWP flood forecasts (Cloke etal. 2017) and Nowcasts (Berenguer et al, 2016) have shown that both forecastqualities vary locally. The MSE skill score* of AROME-RUC (NWP with dataassimilation) versus the INCA (extrapolation radar-based nowcasting) for differentlead-times is computed to demonstrate that local dependence of the forecastingsystems.(*)Main goals of the proposed methodologyThe present methodology is a probabilistic blending of a radar-basedextrapolation Nowcasting (INCA) with a Ensemble Prediction System (EPS)implemented in a Convective-permitting NWP (AROME):• Taking advantage of the latest observations by including INCA analyses• Taking advantage of the probabilistic information from the 16 members of the AROME-EPS• Keeping the lead-time dependence on the weightsW=W(LT)• Computing a location-dependent weight for the blending of the different ensemble membersW=W(LT, x)• Benefiting from the ensemble members “closer” to the latest observations. W=W(LT, x, Member)• Using a technique for obtaining an epdf of rainfall values without losing the extreme/high values and by keeping the varianceBayesian weightingUsing Bayes’ theorem, the posterior probability can be computed as:In order to avoid the weight collapse, Van Leeuwn (2003) proposed a Cauchydistribution to compute the likelihood of a observation (y) given a forecastmember (xk)Old/CommonBlendingTo avoid losing the heavy rainfall values and also the reduction of variance caused by theintensity-based blending methods, a matching method is applied to each of the subdomains.This method can keep the chosen empirical distribution of rainfall values.However, a method to blend both empirical distribution has to be developed as well. Takinginto account the non-Gaussian shape of the distribution, a resampling method from the sorteddistribution of both sources of rainfall values (INCA and AROME-EPS member) is applied.Resampling and Matching MethodINCA AROME-EPSParticlesFrom INCAParticles from AROME-EPSResampledINCA Ens mean old blend Ens mean new blendAROME #1 Old Blending #1 New Blending #1AROME #2 Old Blending #2 New Blending #2OBSVerification and comparison with previous methodologiesVerification and comparison with previous methodologiesThe proposed blended field is similar to the INCA forecast for the first hour and similiar to theAROME-EPS for the latests time step with realistic structures in between. Thevariance/values are similar to the observed one (no smoothing)Verification and comparison with previous methodologiesEffect of the localized/Bayesian weights observed in the 2ndh lead-timeVerification and comparison with previous methodologiesEffect of the resampling of the distributions in the 4th h lead-time.Verification and comparison with previous methodologiesThe resampling can be observed during the whole forecasting period showing thereduction of the variance for the intermediate lead times in the previousmethodologies.Verification and comparison with previous methodologiesThe probabilistic verification is carried out for each hour …The threshold of 0.1 mm has been selected because the previous methodology for blending showseven worse results for larger rainfall amounts. The introduction of observation improves theperformance of AROME-EPS during the different lead-times of the 24 hours forecast horizon. Theblending methodology not always improve the forecast (grey circle highlight an example).Verification and comparison with previous methodologies… and then the results are pooled for the whole month of JulyTo remove the episode to episode variability, the BrierSkill Score using AROME-EPS as a reference hasbeen computed and pooled for the whole month. Theresults shows the improvement of both blendingmethodologies and the benefits of the new one evenfor the first lead-times against INCA.The aggregated results of the Brier Score for thewhole month shows the benefits of the proposedmethodology over the existing methodology. Themedian is shown as a solid line, whereas theepisode to episode variability can be observedwith the shaded areas 25-75% and 5%-95%.Summary• The problems or challenges of the previous methodologies have been presented in order to face them in the proposed methodology.– Global weight: The quality of the forecast has a location depedency.– Intensity blending: Variance reduction and missing the high values.• The new methodology tries to solve these problems by combining:– The domain is divided into subdomains and weights are computed at each one.– A different weight can be given to each ensemble member (Bayesian approach)– Resampling of the distributions from both forecasts sources.• The results show that– the Bayesian weight introduce information from NWP from the first hour– the proposed methodology has a realistic distribution after resampling– the comparison with the previous methodology shows an improvement in theprobability information (Brier Score) present in the new approachThank you!For details: Aitor.Atencia@zamg.ac.at

Impact of Bayesian weighting in a probabilistic nowcasting from INCA and C-LAEF

https://repositorio.aemet.es/bitstream/20.500.11765/10589/3/SPDA3_Atencia_3ENC2019.pdf

Impact of Bayesian weighting in a probabilistic nowcasting from INCA and C-LAEF

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