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Institute of Marine and Coastal Sciences

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Presentation on theme: "Institute of Marine and Coastal Sciences"— Presentation transcript:

1 Institute of Marine and Coastal Sciences
Coupled Atmospheric-Ocean Forecast Experiments on the New Jersey Shelf by Hernan G. Arango Institute of Marine and Coastal Sciences Rutgers University New Brunswick, NJ FNMOC, December 3, 2001

2 Collaborators Naval Research Laboratory, Monterey
IMCS, Rutgers University Louis A. Bowers Rob Cermak Scott M. Glenn Dale B. Haidvogel Sage Lichtenwalner Oscar Schofield John Wilkin Naval Research Laboratory, Monterey James D. Doyle Shouping Wang

3 Outline Rutgers Long-Term Ecosystem Observatory (LEO) Observations
Atmosphere-Ocean Models Data assimilation Sensitivity to Initialization and Forcing Ocean-Atmosphere Nowcasting/Forecasting Cycles Case Studies: Sea Breeze, Cycle 2, July 2001 Downwelling Event, Cycle 3, July 2001 Nor’easter, Cycle 6, July 2001 Metrics Conclusions

4 The Observations

5 LEO-15 LEO NJSOS RUTGERS 3km x 3km 1996-Present 30km x 30km 1998-2001
THE STATE UNIVERSITY OF NEW JERSEY RUTGERS Field Station Longterm Ecosystem Observatory 30km x 30km New Jersey Shelf Observing System Satellites, Aircraft, Surface RADAR, Glider AUVs 300km x 300km Beginning 2001 3km x 3km 1996-Present

6 Observational Network
CODAR Observational Network Towed CTD CTD, Glider, REMUS, Thermistor strings

7 Best Observation Example
LEO LEO NJSOS THE STATE UNIVERSITY OF NEW JERSEY RUTGERS Best Observation Example Surface Velocity (cm/s)

8 The Models

9 Navy NOAA, Rutgers, Products & FERI Global Atmospheric Forecasts Local
NOGAPS NCEP Large Scale Atmospheric Forcing Large Scale Atmospheric Forcing Local Atmospheric Forecasts COAMPS 6 km 30 min RAMS 4 km 30 min Atmospheric Forcing Atmospheric Forcing Atmospheric Forcing Wave & Ocean Models WAM or WaveWatch3 5 km 30 min ROMS 1 km 30 min Waves Currents Large Scale Ocean Ocean & Biological Models MODAS EcoSim

10 COAMPS Coupled Ocean Atmosphere Prediction System
Operational - MEL/FNMOC Experimental - NRL-MRY 27 km Spatial Resolution 6 hr Temporal Resolution 48 hr forecast issued twice daily Year Round 6 km Spatial Resolution 30 min Temporal Resolution 72 hr forecast issued twice weekly Summer 2001

11 Experimental COAMPS: Grids
Regional Model Horizontal Resolution: Triple-nested: km x 18 km x 6 km Vertical resolution: approximately 30 Sigma levels 54 KM Wind time-series at LEO from 6 km Experimental COAMPS Forecast 18 KM 6 KM

12 COAMPS AT LEO Pressure and Wind Fields: July 27, 2001 00Z
Operational Experimental Data provided via FTP from MEL (Master Environmental Laboratory) 27 km spatial resolution 6 hour temporal resolution Regional Synoptic/Mesoscale Model Data provided via FTP from NRL-MRY 6 km spatial resolution 30 min temporal resolution Regional Mesoscale Model

13 meters LEO ROMS Bathymetry

14 meters LEO ROMS Bathymetry

15 meters Node A ROMS Bathymetry

16 1. ABL 2. SBL 3. BBL 4. WCBL Boundary Layer Schematic L o n g w a v e
Shortwave E p O H 1. ABL 2. SBL 3. BBL 4. WCBL

17 Data Assimilation

18 Main ESSE Components ESSE Smoothing via Statistical Approximation
Field Nonlinear Forecast (Estimate of nature) Field and Error Initialization Minimum Error Variance Within Error Subspace (Data-Forecast Melding) Main ESSE Components Error Eigendecomposition Nonlinear Forecast (via Ensemble/Monte-Carlo Forecasts)

19 Statistical Approximation
+ ESSE Flow Diagram DY0/N ^ ESSE Smoothing via Statistical Approximation DE0/N + + DP0/N - - Performance/ Analysis Modules Field Initialization Y0 Central Forecast Ycf(-) ^ Most Probable Forecast + Ymp(-) ^ Shooting Synoptic Obs Measurement Model A Posteriori Residules dr (+) Historical, Synoptic, Future in Situ/Remote Field/Error Observations d0R0 Sample Probability Density + - Select Best Forecast - Measurement Model Data Residuals Measurement Error Covariance Mean OA via ESSE ^ Ensemble Mean d-CY(-) + Options/ Assumptions ^ + eq{Yj(-)} Minimum Error Variance Within Error Subspace (Sequential processing of Observations) Gridded Residules ^ Y(-) + - j=1 ^ ^ Y(+) Y(+) Y1 Yj Yq Scalable Parallel Ensemble Forecast Y1 Yj Yq ^ - + + - Perturbations + E(-) P(-) Error Subspace Initialization ^ SVDp - + + + - E0 P0 +/- ^ j=q Normalization uj(o,Ip) with physical constraints Continuous Time Model Errors Q(t) Adaptive Error Subspace Learning Key Convergence Criterion Continue/Stop Iteration Breeding Peripherals Analysis Modules E(+) P(+) Ea(+) Pa(+) Field Operation Assumption

20 CODAR Normalized Dominant Error Covariance
74: : W :48 39:42N 39:30N 39:18N U component 4 3 2 1 -1 -2 -3 -4 74: : W :48 39:42N 39:30N 39:18N V component CODAR Normalized Dominant Error Covariance x10-2 Eigenvector Numbers

21 Initialization and Forcing
Sensitivity to Initialization and Forcing

22 July Tuckerton Winds

23 July 14, 1999 - 15:00 GMT Temperature Cross Sections
5 10 15 20 25 25 23 21 19 17 15 13 11 Temp (oC) Depth (m) Observations Distance (km) 5 10 15 20 25 25 23 21 19 17 15 13 11 5 10 15 20 25 25 23 21 19 17 15 13 11 Depth (m) Temp (oC) Depth (m) Temp (oC) COAMPS Forcing RAMS Forcing Distance (km) Distance (km)

24 Surface Currents and Temperature (oC)
COAMPS Forcing July 28, 1999 21:00 GMT 28 26 24 22 20 18 16 14 12 10 AVHRR SST July 28, 1999 08:00 GMT 39:42N 39:40N 39:30N 39:30N 39:20N 39:18N 39:10N 74:24W :12W :00W :48W 74:20W :10W :00W :50W Surface Currents and Temperature (oC)

25 Surface Currents and Temperature (oC)
MODAS/RAMS Forcing July 28, 1999 21:00 GMT 28 26 24 22 20 18 16 14 12 10 AVHRR SST July 28, 1999 08:00 GMT 39:42N 39:40N 39:30N 39:30N 39:20N 39:18N 39:10N 74:24W :12W :00W :48W 74:20W :10W :00W :50W Surface Currents and Temperature (oC)

26 Surface Currents and Temperature (oC)
RAMS Forcing July 28, 1999 21:00 GMT 28 26 24 22 20 18 16 14 12 10 AVHRR SST July 28, 1999 08:00 GMT 39:40N 39:40N 39:30N 39:30N 39:20N 39:20N 39:10N 39:10N 74:20W :10W :00W :50W 74:20W :10W :00W :50W Surface Currents and Temperature (oC)

27 Real-time Thermistors
ROMS/COAMPS ROMS/RAMS MODAS COAMPS

28 Ocean-Atmosphere Forecasting Cycles

29

30 Sea Breeze Cycle 2 July 17-18, 2001 00:00 GMT

31 The New Jersey Sea Breeze
Develops late afternoon during weak synoptic flow Sea Breeze front moves inland till just after sunset Front observed by NWS Doppler Radar Offshore Winds Sea Breeze Front Onshore Winds

32 Cycle 2 Sea Breeze Mesoscale Event –
July 17 00:00 GMT, 48 hour forecast 42 41 40 39 38 37 36 Seabreeze Front EXP 48 hr forecast 1030 1025 1020 1015 1010 1005 1000 Seabreeze Front 42 41 40 39 38 37 36 NO Seabreeze OP 48hr forecast NWS Radar 07/17 22Z

33 Downwelling Cycle 3 July 18-20, 2001 00:00 GMT

34 18 Jul 2001, 12:00 GMT NOGAPS COAMPS LR HR 90 85 80 75 70 65 60 55 50
50 45 40 35 30 25 20 18 Jul 2001, 12:00 GMT NOGAPS COAMPS 1 3 2 5 42 41 40 39 38 37 36 LR 42 41 40 39 38 37 36 HR

35 18 Jul 2001, 12:00 GMT NOGAPS COAMPS ROMS LR 90 85 80 75 70 65 60 55
50 45 40 35 30 25 20 18 Jul 2001, 12:00 GMT NOGAPS COAMPS 1 3 2 5 ROMS 39.45 39.10 MY2.5 KPP 42 41 40 39 38 37 36 LR

36 18 Jul 2001, 12:00 GMT NOGAPS COAMPS ROMS HR 90 85 80 75 70 65 60 55
50 45 40 35 30 25 20 18 Jul 2001, 12:00 GMT NOGAPS COAMPS 1 3 2 5 ROMS 39.45 39.10 MY2.5 KPP 42 41 40 39 38 37 36 HR

37

38 Nor’easter Cycle 6 July 31, 2001 00:00 GMT

39 Cycle 6 Nor’easter Synoptic-Scale Event
Biggest Storm of the Experiment 30 knot ENE Winds 10-12 ft Seas Heavy Rains EXP 48 hr forecast OP 48 hr forecast 42 41 40 39 38 37 36 42 41 40 39 38 37 36 1030 1025 1020 1015 1010 1005 1000 OP Analysis 42 41 40 39 38 37 36 31 Jul :00 31 Jul :00 31 Jul :00

40 The Metrics

41 Atmospheric Model Validation
Time Series Comparisons - Observations - Experimental COAMPS (EXP) - Operational COAMPS (OP) - Operational NOGAPS (NGP) - RUC Analyses Variables - Wind Speed and Direction - Barometric Pressure - Relative Humidity - Temperature Locations Over water -NOAA Buoys Over Land - NWS ASOS At the coast - Tuckerton Products - Plots - RMS Difference - Average Error CYCLE 3: July 18 - July 21, 2001 Wind Vectors : Tuckerton | | BLM | ACY | WWD Temperature: Tuckerton | BLM | ACY | PHL | EWR

42 FAIR Seabreeze Nor’easter Seabreeze FAIR Nor’easter Heat Wave Heat Wave

43 FAIR Seabreeze Nor’easter Seabreeze FAIR Nor’easter Heat Wave Heat Wave

44 FAIR Seabreeze Nor’easter Seabreeze FAIR Nor’easter Heat Wave Heat Wave

45 FAIR Seabreeze Nor’easter Seabreeze FAIR Nor’easter Heat Wave Heat Wave

46 Tuckerton, NJ All Forecast Cycles (1-8) RMS Errors

47 Ocean Model Metrics Thermistor strings Two-layer system Temperature:
Surface Top average Bottom average Thermocline Slope RMS=0.6865 RMS=0.7972 RMS=1.4863 RMS=3.7447 Observations Model Estimate RMS=1.2795

48 Cycle 3, Temperature Time Series: COOL2
4 6 8 10 12 Depth (m) July, 2001 18 19 20 21 HR COAMPS / ROMS KPP Thermistor 26 24 22 20 18 16 14 12 10 8 July, 2001 19 21 2 4 6 Depth (m) 2 4 6 8 10 12 Depth (m) July, 2001 18 19 20 21 MY2.5

49 Cycle 3, Temperature Time Series: COOL2
LR COAMPS / ROMS KPP 2 4 6 8 10 12 Depth (m) July, 2001 18 19 20 21 Thermistor 26 24 22 20 18 16 14 12 10 8 July, 2001 19 21 2 4 6 Depth (m) 2 4 6 8 10 12 Depth (m) July, 2001 18 19 20 21 MY2.5

50 HR COAMPS / ROMS LR COAMPS / ROMS KPP MY2.5

51 Conclusions High spatial and temporal resolution atmospheric forcing
produced better ocean forecasts than coarser atmospheric forcing. There is a need for a fully coupled, high-resolution ocean- atmosphere system. In shallow coastal regions, the success of ocean data assimilation is affected by the accuracy of atmospheric forecasts, specially during high wind events. Is very difficult to evaluate a forecast with a single number.

52 Future Work Multiple levels of nesting.
Better vertical mixing parameterization? K-epsilon? Ensemble forecasting via optimal perturbations using tangent linear and adjoint techniques. 4D variational and IOMS data assimilation.

53 R.U. C.O.O.L.

54 R.U. C.O.O.L.

55 For more information contact: Rutgers University, New Brunswick, NJ
Hernan G. Arango Rutgers University, New Brunswick, NJ x266

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