Applying Multi-Model Superensemble Methods to Global Ocean Operational Systems

Transcription

1 Applying Multi-Model Superensemble Methods to Global Ocean Operational Systems Todd Spindler 1, Avichal Mehra 2, Deanna Spindler 1 1 IMSG at NWS/NCEP/EMC 2 NWS/NCEP/EMC We wish to acknowledge the data and services provided by the following organizations GODAE OceanView IV/TT Workshop, Montreal, Canada Sep 20-22,

2 Outline o The Ensemble Components o K-Means Clustering o Ensemble Work Flow o First Ensemble: Global SST, SSS, U, V o Nowcast and 6 days of forecasts o Training verification o GODAE Class-4 validation o Second Ensemble: North Atlantic o Potential Temperature to 500m, nowcast o Preliminary results o Conclusions and Future Work 2

3 The Ensemble Components Member Models: o UK Met Office Global Seasonal forecast system (GloSea5, 1/4 ) o Operational Mercator-Ocean Global Ocean forecasting system (1/12 ) o US Navy HYCOM + NCODA Global Analysis (1/12 ) o NCEP Operational Global Real Time Ocean Forecasting System (1/12 ) Reference Data (Daily Observations): o SST, SSS and the North Atlantic Regional Ensemble: FNMOC High Resolution Ocean Analysis for GODAE (1/6 ) o U, V: SSALTO/DUACS MADT Near Real Time Absolute Geostrophic velocities from multi-satellite observations (1/4 ) Climatologies: o SST, SSS: World Ocean Atlas 2013 V2 o U, V: GLORYS2V3 22-year reanalysis bootstrapped as a proxy 3

4 K-Means Clustering K-Means Clustering is an unsupervised machine learning method of partitioning data into groups based on a minimization of phase-space metrics (euclidean distance, typically) In this case, we want to partition the model fields (Time,Lon,Lat) or (Time,Depth,Lon,Lat) along the time axis. 4

5 Ensemble Workflow Acquire Member Data Interpolate to reference grid Nowcasts/Forecasts obtained via OpenDAP from HYCOM.org, MOTU Python Client from Copernicus.eu, and local access o KD-Tree Nearest Neighbor for high-resolution models o KD-Tree 8 Nearest-Neighbors w/ Gaussian Weights for UKMET o 3D Bi-linear for North Atlantic Regional (pre-processed) Models References 5

6 Ensemble Workflow K-Means Cluster Analysis Acquire Member Data Interpolate to reference grid n_clusters=7 Calculate Clusters 30 days training for each daily run 6

7 Acquire Member Data Interpolate to reference grid Calculate Clusters Calculate weights Ensemble Workflow Inverse weighted averages using anomaly RMSE within each cluster o FNMOC 1 or MADT 2 as reference field for calculating weights o WOA 2013 V2 or GLORYS2V3 as climatology o 1/12 resolution o 4 Members Combine all members o 7 Clusters 1 FNMOC High Resolution Analysis for GODAE (SST, SSS) 2 SSALTO/DUACS MADT Near Real Time Geostrophic Surface Currents 7

8 Training Verification: SST Nowcasts & Forecasts show about a 30% improvement in RMSE Bias is mid-range Cross-correlation to the reference field increased by about 1-3% 8

9 Training Verification: SSS Simple Average ensemble shows no improvement. The other methods show ~18% improvement. Bias is still midrange Simple Average Cross-Corr shows no improvement. The others show 3-4% improvement. 9

10 Training Verification: U Nowcasts & Forecasts show about a 20-26% improvement in RMSE Bias is mid-range Cross-correlation to the reference field increased by about 20-25% 10

11 Training Verification: V Nowcasts & Forecasts show about a 25-32% improvement in RMSE Bias is mid-range Cross-correlation to the reference field increased by about 25% 11

12 GODAE Class 4 SST Validation Forecast loop 26 days of data All points normalized by the standard deviation of the observations 21 September 2016 GODAE OceanView IV/TT Workshop, Montreal, Canada 12

13 GODAE Class 4 SSS Validation Forecast loop 26 days of data All points normalized by the standard deviation of the observations 21 September 2016 GODAE OceanView IV/TT Workshop, Montreal, Canada 13

14 GODAE Class 4 U Validation Forecast loop 26 days of data All points normalized by the standard deviation of the observations 21 September 2016 GODAE OceanView IV/TT Workshop, Montreal, Canada 14

15 GODAE Class 4 V Validation Forecast loop 26 days of data All points normalized by the standard deviation of the observations 21 September 2016 GODAE OceanView IV/TT Workshop, Montreal, Canada 15

16 North Atlantic Regional Ensemble o 1/12 resolution o 14 layers (0-500m) o 4 Members: o Nowcasts only o 7 Clusters * The Fleet Numerical Meteorology and Oceanography Center, Monterey, CA o FNMOC* High Resolution Ocean Analysis for GODAE as reference for calculating weights 16

17 Gulf Stream Location Front location defined as the intersection of the 12 C isotherm and the 400m isobath (Halkin and Rossby, 1985) Gulf Stream north and south walls from the Naval Eastern Ocean Center 17

18 GODAE Class-4 ARGO Profiles The ensemble method captures the sharp gradients in the upper layers 18

19 GODAE Class-4 ARGO Profiles The nowcast aggregate RMSE shows modest improvement, and the bias is again mid-range 19

20 Conclusions o K-Means Cluster trained global ensembles provide nowcasts and forecasts of surface fields with improved statistics over the individual members. o The quality of the ensemble is influenced by the resolution of the reference fields. o The training method gives an estimate of the spread of the members forecast skills. o Depth-dependent regional ensemble modeling results show promise. 20

21 Future Work o o o Improve the data acquisition step to reduce the chance of interruptions in the data flow. Use the member forecast cluster weights to help design guidance products. Expand the Regional ensemble to include forecasts, go to full depth, and explore the use of clustering methods on depth as well as time. 21

22 Any Questions? Thank You 22

23 GODAE Class 4 SST Validation 21 September 2016 GODAE OceanView IV/TT Workshop, Montreal, Canada 23

24 GODAE Class 4 SST Validation 21 September 2016 GODAE OceanView IV/TT Workshop, Montreal, Canada 24

25 October 2015 n000 25

26 October 2015 f024 26

27 October 2015 f048 27

28 October 2015 f072 28

29 October 2015 f096 29

30 October 2015 f120 30

31 October 2015 f144 31