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

Collaborators Naval Research Laboratory, Monterey IMCS, Rutgers University Louis A. Bowers (bowers@arctic.rutgers.edu) Rob Cermak (cermak@sfos.uaf.edu) Scott M. Glenn (glenn@imcs.rutgers.edu) Dale B. Haidvogel (dale@imcs.rutgers.edu) Sage Lichtenwalner (sage@arctic.rutgers.edu) Oscar Schofield (oscar@imcs.rutgers.edu) John Wilkin (wilkin@imcs.rutgers.edu) Naval Research Laboratory, Monterey James D. Doyle (doyle@nrlmry.mil) Shouping Wang (wang@nrlmry.mil)

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

The Observations

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 1998-2001 New Jersey Shelf Observing System Satellites, Aircraft, Surface RADAR, Glider AUVs 300km x 300km Beginning 2001 3km x 3km 1996-Present

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

Best Observation Example LEO-15 LEO NJSOS THE STATE UNIVERSITY OF NEW JERSEY RUTGERS Best Observation Example Surface Velocity (cm/s) 0 10 20 30 40 50

The Models

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

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

Experimental COAMPS: Grids Regional Model Horizontal Resolution: Triple-nested: 54 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

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

meters LEO ROMS Bathymetry

meters LEO ROMS Bathymetry

meters Node A ROMS Bathymetry

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

Data Assimilation

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)

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

CODAR Normalized Dominant Error Covariance 74:24 74:12 74W 73:48 39:42N 39:30N 39:18N U component 4 3 2 1 -1 -2 -3 -4 74:24 74:12 74W 73:48 39:42N 39:30N 39:18N V component CODAR Normalized Dominant Error Covariance x10-2 0 5 10 15 20 25 Eigenvector Numbers 0 5 10 15 20 25

Initialization and Forcing Sensitivity to Initialization and Forcing

July 1999 - Tuckerton Winds

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 0 5 10 15 20 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 0 5 10 15 20 0 5 10 15 20 Distance (km) Distance (km)

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 74:12W 74:00W 73:48W 74:20W 74:10W 74:00W 73:50W Surface Currents and Temperature (oC)

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 74:12W 74:00W 73:48W 74:20W 74:10W 74:00W 73:50W Surface Currents and Temperature (oC)

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 74:10W 74:00W 73:50W 74:20W 74:10W 74:00W 73:50W Surface Currents and Temperature (oC)

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

Ocean-Atmosphere Forecasting Cycles

11 12 13 14 15 16 17

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

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

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 76 75 74 73 72 71 70 42 41 40 39 38 37 36 NO Seabreeze OP 48hr forecast NWS Radar 07/17 22Z

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

18 Jul 2001, 12:00 GMT NOGAPS COAMPS LR HR 90 85 80 75 70 65 60 55 50 90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 18 Jul 2001, 12:00 GMT NOGAPS 75 74 73 72 71 70 COAMPS 1 3 2 5 42 41 40 39 38 37 36 LR 42 41 40 39 38 37 36 HR

18 Jul 2001, 12:00 GMT NOGAPS COAMPS ROMS LR 90 85 80 75 70 65 60 55 90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 18 Jul 2001, 12:00 GMT NOGAPS 75 74 73 72 71 70 COAMPS 1 3 2 5 ROMS 74.25 73.45 39.45 39.10 MY2.5 KPP 42 41 40 39 38 37 36 LR

18 Jul 2001, 12:00 GMT NOGAPS COAMPS ROMS HR 90 85 80 75 70 65 60 55 90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 18 Jul 2001, 12:00 GMT NOGAPS 75 74 73 72 71 70 COAMPS 1 3 2 5 ROMS 74.25 73.45 39.45 39.10 MY2.5 KPP 42 41 40 39 38 37 36 HR

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

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 76 75 74 73 72 71 70 42 41 40 39 38 37 36 76 75 74 73 72 71 70 42 41 40 39 38 37 36 1030 1025 1020 1015 1010 1005 1000 OP Analysis 76 75 74 73 72 71 70 42 41 40 39 38 37 36 31 Jul 2001 00:00 31 Jul 2001 00:00 31 Jul 2001 00:00

The Metrics

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 http://marine.rutgers.edu/cool/hycode2/forecast.html CYCLE 3: July 18 - July 21, 2001 Wind Vectors : Tuckerton | 44009 | BLM | ACY | WWD Temperature: Tuckerton | BLM | ACY | PHL | EWR

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

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

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

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

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

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

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

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

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

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.

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.

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

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

For more information contact: Rutgers University, New Brunswick, NJ Hernan G. Arango Rutgers University, New Brunswick, NJ 732-932-6555 x266 arango@imcs.rutgers.edu http://marine.rutgers.edu/cool http://marine.rutgers.edu/po/models/roms/index.html