Analyse statistique des séries de mesures
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Transcript of Analyse statistique des séries de mesures
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Analyse statistique des sries de mesuresPhilippe KeckhutUniversit Paris 6Pierre et Marie CurieUniversit Versailles Saint-QuentinInstitut Pierre-Simon Laplace des Sciences de lEnvironnementLATMOS
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Analyse de rgression : tendance T2 = R2 * Fc / S (ti-tm)2 incertitude sur la tendance variablit n d temps
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Auto-correlation Longues sries temporelle (plusieurs dcenies) t Optimisation si la variabilit est limite la variabilit atmosphrique R Mesures frquentes -> moyennes mensuelles Dcroissance exponentielle Temps caractristique joursFc=(1+/(1-
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Echelle temporelle et signification statistiqueTemps ncessaire pour une dtection significative (2s)
ParametresrgionVariabilit rsiduelleTendance attendue(/dcennie)Temps ncessaire(annes)Temprature
Ozone
VentHaute stratoMsosphre
Basse stratoHaute strato3-8 K8-10 K
20 %7%
5-15 m/s1-2 K1-3 K
5-20%5-10%
< 1m/s8-1715-30
7-205-10
20-50
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Analyse multi-rgressionForages naturels (solaire, volcanique) et anthropiques
Ramaswamy et al, Rev. Geoph., p71, 2001
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Erreur MthodologiqueLerreur mthodologique dpend du bruitAprs 20 ans lerreur mthodologique est ngligeable
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DiscontinuitsPrincipaux facteurs de discontinuit :Amlioration de la prcision, sensibilit, et chantillonnage Amlioration instrumentaleChangement des algorithmesChangements des protocoles et modes opratoirs
Eviter les changements instrumentaux Date des changements (Metafile) Mesures redondantes
Srie alatoire dcale de 1 s en
LAZANTE John R., International journal of climatology,
vol. 16, 1197-1226, 1996
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Changements Instrumentaux des fusesEstimated from the time serie analysesEstimated from the aerothermic calculations
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Regression multi-paramtres
T(t) = m + St + ATrend + BSolar + CQBO + DENSO + EAO + F.Step(ti) + Nt les termes A, B, C, D, E, F reprsente lamplitude des tendances / les facteurs de variabilit et les rsidus et biais.Les rsidus (AR(1)) correspondent la variabilit non considre dans le modle.Les limites de ces analyses correspondent : Linadquation du modle La linarit de la rponse atmosphrique Les biais dues au la non indpendance des forages (mthodologie) Discontinuits
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Mares atmosphriquesLes mares sont dues labsorption du rayonnement solaire par lozone et la vapeur deauCe forage induit des oscillations en phase avec le cycle diurneCe forage induit de larges amplitudes dans la haute stratosphre et dans la msosphreCes signatures sobservent partir des lidars et les satellitesLidar La Runion GSMWMLS/UARS 1 hPa6KLes mares induisent des biais dans les comparaisons de donnes, les tandances et la validation satellitaire
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Interfrences dues aux maresTime of launchAveraged temperature 45-55 km2:0010:0015:00Kubicki et al., submitted toJASTP , 2004.
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Influences des mares sur la tendanceKeckhut et al., J. Geophys. Res., p447, 1999
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Tendances en fonction de la latitudeVolgogradOHP, _ _Wallops, ---Riory, .US tropicalUS tropicalWallopsOHPVolgogradRiorySummerWinterKubicki et al., submitted toJASTP, 2004.US tropical: 8S-34NWallops Island: 37,5NRyori, Japan: 39NOHP, France 44NVolgograd 49N
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Tendance lchelle globaleNCEP analyses at 1 hPa (50 km)Keckhut et al., J. Geophys. Res., p546, 2001
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Evolution des forages
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Tendance ou variabilit naturelle?~43-57 km
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Tendance ou variabilit naturelle?~43-57 km
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Tendance non-linaireLestimation de tendances non-linaires est possible Dans le domaine 2 et dpend de la longueur de la srie et du bruitDans le domaine 3 dpendant uniquement du bruit rsiduel
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Rsultats concernant la tempratureTendances linaires varient en fonction de la dure des sriesTerme non-linaire indique l'inflexion des tendances et stabilise la tendance gnrale
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Stratgie de surveillance lors dune variabilit importante et multipleDistribution des anomaliesCycle hydrologiqueTransport verticalTransporthorizontalSources
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Cas de la haute troposphre
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Evolution des pics dozoneThe stratospheric origin increasesThe long-term tropospheric transport was minimum in 1980sBL origin remains constant since 1970sColette, et al. Atmos. Env., 2005
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Evolution de la temprature 44N
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Dviation standard de la temprature
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EOF of T profiles Summer (April to Oct)
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EOF of T profiles Winter (Dec-Jan-Feb)
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Mode msosphriqueMesospheric inversion
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Mode Stratosphrique-msosphriqueStratospheric warming
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Rponse de latmosphre aux cycles de 27-joursWavelet analysesMgII solar fluxLidar temperatureInterferences avec les ondes plantaires
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Variabilit de lOzoneTOMS Total Ozone Wavelet analysisAnalyse par ondelettes Changements de la variabilit associs ltablissement dun vortex Mise en vidence de changements significatifs de la dure de lhiver et de lt Quantification de la variabilit
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Rsultats de lanalyse de la variabilit1. Ozone variability is associated to the occurrence of stratospheric warmings2. The winter variability sems to be asscociated with solar cysle
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Observations des cirrus par lidar
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Climatologie des cirrusMid-latitude (44N)(from Goldfarb et al., GRL, 2001)Southern tropic (21S)(from Cadet et al., GRL, 2002)
Graph1
54.285714285716
64.285714285712
6014
4010
57.89473684217
502
24.32432432430
46.15384615380
55.88235294124
48.2758620691
6011
57.142857142923
OHP
La Runion
Month
Occurence (%)
Feuil1
Jan54.2857142857161935
Feb64.2857142857122742
Mar60142440
Apr40101025
May57.894736842171119
Jun5021938
Jul24.32432432430937
Aug46.153846153801839
Sep55.882352941241934
Oct48.27586206911429
Nov60111525
Dec57.1428571429231628
Feuil1
OHP
La Runion
Month
Occurence (%)
Feuil2
Feuil3
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Cirrus lOHP 1997-1999 Les cirrus sub-visibles correspondent une paisseur optique t < 0.03 Distribution et frquence doccurrences indpendant de la saison 3 classes identifies, fonction de laltitude et paisseur des nuagesClasse dpaisseur optique de A F
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Type de cirrus
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Distribution des anomalies
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Classes de cirrusKeckhut et al., J. Appl. Meteo., 2005 (in press)
ClassIII IIIOccurrence (%)362735Height (km)8.6 0.99.8 0.711.5 0.9Altitude relative to tropopause-78-0.513+716Thickness (km)0.9 0.63.2 0.90.9 0.6Temperature (C)-41 6-50 6-58 6
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ConclusionsAnalyse critique des donnesMetadonnesSources de biaisStationnarit des sriesAnalyse statistique des donnesDistribution des donnesIndpendance des donnesApport de la simulationAnalyse mathmatique interprtable physiquementAnalyse de la variabilitSources multiplesSparation des sourcesAdaptation des mthodesTF et OndelettesClassificationComposantes principales
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Le Lidar rtrodiffusionOccurrence des nuages + diffusion molculaire
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Lidar Rayleigh : Profil de tempratureSapplique pour de la diffusion molculaireDensit et la pression sont des mesures relativesLa temprature est dduite partir de la lois des gaz parfait est une estimation absolue