Customer Oriented Warning Systems

Transkript

1 Customer Oriented Warning Systems Overview! Aim! Position of the problem! Financial input! Customer profile! Three examples!! Key message Jacques Ambühl, Meteo Swiss; ECAM Dienstag,. September

2 Customer Oriented Warning Systems Aim! Optimise meteorological warning systems, taking! into consideration:! - the performance of a warning system! - customer s exposures! and using methods emanating from finance management Dienstag,. September

3 Position Mitigating actions available Warnings Ordinary weather events Weather Derivatives Insurance Extreme weather events No mitigating actions Jacques Ambühl, Meteo Swiss; ECAM Dienstag,. September

4 Position Obs.- Systems Models Decision Success Datamining Forecasters Weather Products Decision Met. Service Customer Jacques Ambühl, Meteo Swiss; ECAM Dienstag,. September

5 Finance B_Simul_Ens_Exhib_..9.nb.. it Rate 7.. Ø Met. Threshold. ROC.... Capital asset pricing Motivation: aus der Finanzwelt! Return (%) Medium risk Medium return igh risk igh return False Alarm Ratio Ensemble Forecast Probability of occurrence ow risk ow return Inefficient portfolios & securities Portfolio of Shares Risk (volatility) Jacques Ambühl, Meteo Swiss; ECAM Dienstag,. September

6 son, M can be expressed in terms of fre (Ω), hit rate (r) and false alarm rate ( (Ω), hit rate (r) and fa Eidgenössisches Departement des Innern EDI Finance Following Richardson, however slightly extended M R (r, F ar) = [F ar Γ ( Ω M R (r, F ar) = Outcome Alarm issued Alarm not issued Event did not occur Event did occur The derivation of this expession is p The derivation of th Besides False Alarm theit cost-loss Cost /oss ratio Ratio Γ = C, th Costs C C + λ Besides the cost-loss r the parameter Λ = λ, baptized residua Expression Correct Miss (), thewhose parameter Residual Graph oss is Λ presented = λ rejection, b Ratio of equation (7), however formulated in te Expression (), whose G of equation (7), however Jacques Ambühl, Meteo Swiss; ECAM Dienstag,. September

7 D_Farr_..9.nb Eidgenössisches Departement des Innern EDI Customer Profile Continuous, depending on event intensity and resulting costs Climate distribution x Probability of disaster Climateolive, Eventsblue-green Prob of Occurrence Intensity oss multiplier Costs sensitivity l (q) = m (q) m (q) : costs multiplier Int. Exposure.... Climateolive, Eventsblue-green Mix Climate / Exposure.... Int Jacques Ambühl, Meteo Swiss; ECAM Dienstag,. September

8 B_Simul_Ens_Exhib_9..9.nb Synthesis Customer profile Impact: Impact of the warning system... Relationship between: avoided expenses and climate burden Both expressed in monetary units. Eidgenössisches Departement des Innern EDI it Rate Ø Met. Threshold Warning profile (ROC) False Alarm Ratio Optimal Warning Probability Customer exposure.... Jacques Ambühl, Meteo Swiss; ECAM Dienstag,. September

9 Synthesis: Customer s FindSigmoid..7.9.nb Risk Awareness Ø Met. thres. Addressee's Exposure Insurance and Weather derivatives. Exposure Group Meteorological decision Weather forecasts... Group Group Probability Jacques Ambühl, Meteo Swiss; ECAM Dienstag,. September

10 B_Simul_Ens_Exhib_..9.nb Example: Warnings Meteo Swiss Forecasters: Average of heavy Precip, Storm, Temperature Warning Threshold Prob. threshold - % ypothetical Customer Cost oss Ratio - %.... it Rate Unwetterwarnungen 7-. MG.. MC. MZ M False Alarm Ratio.... Jacques Ambühl, Meteo Swiss; ECAM Dienstag,. September

11 E N Eidgenössisches Departement des Innern EDI W N Example: forecast of particulate PM with neural network N Classification of ECMWF EPS on torus () and T(), annual cycle substracted Friday, Z ( 7, ): +opr+ctr ( 7,): (, ): ( 7, ): (,): (,): ( 9, 9): ( 9,): + Saturday 7, Z ( 7, ): +opr+ctr (, ): (, ): ( 7, ): (, 7): (, 9): ( 9,): Tuesday August Z Neural Classifier for ECMWF EPS MOS based on Customer s statistics 9 ( 9, ): (,): 9 (,): ( 9, ): (, ): (, ): +ctr Sunday, Z ( 7, ): 7 (, 7): +opr+ctr (, ): 9 ( 9, ): (, 9): Collaboration (, ): with air protection authorities NE Switzerland ( 7, ): Monday 9, Z (, 7): +opr+ctr ( 7, ): ( 9, 7): ( 7, ): ( 9, ): (, ): (,): (, 7): Entropy + Tuesday, Z ( 9, 7): +opr (,): (, ): (, 7): (, 7): +ctr ( 9, ): (,): (, 9): ( 7, ): ( 7, ): + Wednesday, Z (, 7): (, 9): (, ): (,): (, 7): (, ): (, ): (, ): ( 9, 7): +opr ( 9, ): +ctr + Thursday, Z (, 9): 7 (,): (, 7): (, ): (, ): +opr (, 7): ( 7, 9): 7 9 Forecast days MeteoSwiss Jacques Ambühl, Meteo Swiss; ECAM opr ctr Jacques Ambühl, Daniel Cattani, Pierre Eckert Dienstag,. September mean actual FRI z CI W E +T, run No in unit ( 7, ), members +T, run No in unit (, ), members +T, run No in unit ( 7,), members+opr+ctr +T, run No 9 in unit (,), members

12 Example: forecast of particulate PM with neural network PM Neurons.nb Customer s profile Performance profile.. R Green: customer E=, Orange: Neural PM Impact / Exposure System under further development. FAR.... Collaboration with air protection authorities NE Switzerland Jacques Ambühl, Meteo Swiss; ECAM Dienstag,. September

13 $&'()$*+,'-,.,/+'),'$.+,*+'*,+7,9! Eidgenössisches Departement des Innern EDI Further Example: nowcasting of gales with genetic programming #!"#$%#&'()*+$)",)-$%$#&.)!/"/'&%)&%)#"/)'/%&%)+#$+)!") - generations individuals per generation ensembles with individuals $%&'()#*+#,-./)##'#())#7()(#9:%;#<.=>=;#?#@7:A));#BCCDE# :$,;<,=+').+,$.+7,+.+'*,'-,/>$+?$+,*$.,$*7$*,@>$+$'/A,BC!CD, System under further development!"#$#%&'#%#a)#7().%=##.#=)f#7a%>#(&(./#g#(.=>/g#.)(%=&#.#(.=>/g#7a)=#.(##.#, $.7, '-, +7$$, +.+'*, ))7)>#.()=#(&(./#9:%;#<.=>=;#?#@7:A));#BCCDE#I#7=%#%=#7A.=&%=&#A)#7=)=##.# &$)$*+.+'*E,F7$$,(..)$+$,.$,)$.$G,$%$,+$*,)*+$E, (.=>/G# ))7)># =>)# F%A#.=A)(# ))/)=# (/# A)#./)# (%/%%)# )# $%&'()# D# AF#.=# Uni Zurich, AI-ab: Roman )-./)##'#())#/'.%=# Voisard, Manuela Züger, Rudolf Füchslin.J&$, #$(+'*, K*+, ()*+&,"-#%&'#%#A)#7().%=##.#7A%>#(&(./#FA%7A#%#.=#)-.7#7G##.#))7)>#%=>%%>'.##A)# ()%'# &)=)(.%=# 9:%;# BCCDE# I=# JK:;# A)# (.%%G# # 7()(#.=># Jacques Ambühl, Meteo Swiss; ECAM R$+',9..&, ()(>'7%=#/'#.>>#'##!# &C!CMC,,, N*G,G$+'*O,+$*,)*+$,)$.*,, #$:$$, $+.C,,, =, ($$,.+, *+.*+.*$', %.&$, BC!CM!,, Dienstag, 9$.,'-,:+,@$'*GDO,).S)),. September T*'+,@*.+E)&$,(E7'D,, #

14 Further Example: nowcasting of gales with genetic programs Customer s profile Performance profile.. R Bachelorarbeit Manuela Züger: SZG, Grenchen S@9.9D d f b g ce S@.D h j. Out[7]= R i. Impact / Exposure. System under further development FAR FAR Uni Zurich, AI-ab: Roman Voisard, Manuela Züger, Rudolf Füchslin Jacques Ambühl, Meteo Swiss; ECAM Dienstag,. September

15 Key message Three fields of competence are being merged - Meteorology, (probabilistic) ensemble prediction systems Decisional techniques, possibly related to (bio-inspired) artificial intelligence Financial methods, (exposure, risk profiles, portfolio optimization,...), mathematically expressed. Customer s and issuer s roles being disentangled, services level agreements or contracts may be settled between them. Jacques Ambühl, Meteo Swiss; ECAM Dienstag,. September