Comparison of Homogenisation methods. On behalf of COST Action ES0601 group
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1 Comparison of Homogenisation methods Everything you wanted to know about homogenisation but where afraid to ask Olivier Mestre Ecole Nationale de la Météorologie Institut de Mathématiques de Toulouse Victor Venema Bönn University On behalf of COST Action ES0601 group
2 COST Action ES0601 EU funded project COST funds NETWORKING Start : june 2007 end : october 2011 Intercomparison of homogenisation methods Synthesis of the best aspects of each method SOFTWARE Methods for Daily data Methods for Urban trends
3 Salzburg annual mean temperatures
4 Homogénéisation : pourquoi? Salzburg metadata (ZAMG) Section 5: to High School (Oberrealschule), z=419m Section 6: to Studiengebäude Lehrerbildungsanstalt, z=423m Section 7: to Airport station 1, z=434m Observation hours: to : 7,14, to : 7,14,19 Section 8: Since Airport station 2, z=437m
5 Positions of station
6 Instruments, shelters 1935 Since 1996
7 Comparing Salzburg with neighbours Package segclust (Picard) penalized likelihood criteria
8 DETECTION A STATISTICAL problem But information provided by METADATA must not be forgotten Change point detection : a constantly evolving field Signal processing Econometrics Biology (DNA segmentation) etc...
9 Change point models Simple linear regression Change point model (mean)
10 How many change points?
11 How many change points?
12 How many change points?
13 How many change points?
14 How many change points?
15 TESTING DETECTION Different procedures have different properties Some behave well in some situations Maybe some behave well in every situation? We are interested in those who behave well in our case Simulated benchmarks, synthetic noise Courtesy of Peter Domonkos (URVCAT), Emilie Lebarbier (AgroParisTech), Franck Picard (U. Lyon)
16 ROC scores
17 Conclusion On those experiments, Jong, Caussinus & Lyazrhi, Zhang & Siegmund are best criteria when combined with DP, MASH, MDL and Lebarbier behave well. Robustness to skewness : rather good for parametric methods: the use of non parametric procedures is not justified! Robustness to autocorrelation: C&L and Jong tend to over detect, MASH is robust, Lebarbier tend to under detect On same configurations, but with 10 times more data (n=1500), many procedures behave much better
18 TESTING THE WHOLE PROCESS COST Action ES0601 «HOME» : 25 contributions Realistic surrogate benchmark created by Victor Venema 20 temperature networks, 20 precipitations networks Realistic temporal and spatial correlations 6 15 series/network, varying density Multiple change points, seasonal behaviour, outliers, local trends Scores computed for every method, on Truth vs Corrected, to be compared to Truth vs Raw
19
20 TESTING THE WHOLE PROCESS A BLIND EXPERIMENT
21 Overall results : RMSE
22 Correction of trends (SNHT)
23 Correction of trends USHCNv2 52x
24 Correction of trends (PRODIGE)
25 Correction of trends (MASH)
26 Correction of trends (SNHT)
27 Correction of trends (USHCNv2 52x)
28 Correction of trends (PRODIGE)
29 Correction of trends (MASH)
30 First conclusions Relative methods are to be privileged versus absolute Methods not relying on «homogeneous references» behave better detection is improved by multiple change points dedicated methods For expertise, training is important Many manual methods are worse than the best automatic ones Automatic algorithms can achieve good results and are necessary for large networks Many more conclusions way of comparing series, corrections, references vs pairwise, etc...
31 Software Detection DP penalized likelihood criteria (PRODIGE) Implement joint detection on summer/winter series (ACMANT) Attribution of changes Improvement of USHCNv2 system using bayesian posteriors (Hannart) Joint segmentation using MLM (Lebarbier Picard) Correction ANOVA two factors model (PRODIGE) GLS Estimation (introduction of spatial covariances, MASH)
32 CONCLUSION Most homogenisation methods improve the data Some methods behave better than others Still room for improved methods Comparing methods : Do not under estimate the difficulty of the task!
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