# Presentation: 941728

## Navy Operational AssimilativeGlobal Ocean Modeling

- Dr. Charlie Barron
- barron@nrlssc.navy.mil
- Naval Research Laboratory
- Stennis Space Center

## Navy Global Ocean Modeling

- The Naval Research Laboratory has developed and transitioned the global ocean modeling system now operational at the Naval Oceanographic Office:
- Present system
- Key products
- Evaluate performance
- Future plans

**Notes:**
Sound speed prediction to support ASW is changing.
What capabilities are being developed and transitioned?
What is the vision for using these capabilities?
What are present operational constraints that limit effective use of these new capabilities?
Can we identify the bits of information most important for submarines
 efficient for low-bandwidth communication
 maintain benefits of enhanced capabilities

## Operational Navy Assimilative Ocean Modeling

- Ocean Analysis
- NCODA, ISOP

## Global NCOM: configuration and references

- ~14 km average spacing
- 41 vertical σ–z levels stretched logarithmically to 5500 m
- 1 m upper layer rest thickness
- CICE 3.0 Arctic ice model
- NOGAPS wind stress, bulk heat flux
- NCODA assimilation of in situ observations

- Key references:	http://www7320.nrlssc.navy.mil/global_ncom/pubs.html
- 	Barron *et al.*, *JGR Oceans*, 2007.			drifter evaluation
- 	Barron *et al.*, *Ocean Modelling*, 2006.		model formulation
- 	Kara *et al.*, *Ocean Mod*elling, 2006.		general evaluation
- 	Barron *et al.*, *J. Atm. Oceanic Techn*., 2004.	SSH evaluation

## Key Products

- Key operational products supported by global Navy ocean models include:
- Sound speed for acoustic calculations in 			anti submarine warfare
- Boundary conditions for nested ocean models and 	coupled atmospheric models
- Currents for buoy drift, mine drift, search and recovery
- Ice products such as ice edge, thickness
- Model validation studies focus on these operational products.
- New transitions must in some way improve information for key operational products.

**Notes:**
Sound speed prediction to support ASW is changing.
What capabilities are being developed and transitioned?
What is the vision for using these capabilities?
What are present operational constraints that limit effective use of these new capabilities?
Can we identify the bits of information most important for submarines
 efficient for low-bandwidth communication
 maintain benefits of enhanced capabilities

## Impact of ocean models on sound speed prediction

**	****Max Range error:****	****1.3 km**
- Assimilating synthetics into global NCOM:
- Dynamics produce a relatively deep mixed layer
- Smaller errors in predicting transmission loss

**0600Hz   at 20m depth**

**	****RMS Range error:****	****1.7 km**
**	****Max Range error:****	****4.1 km**
- Present operational synthetic based on SSH, SST:
- Fails to represent surface sonic layers
- Significant errors in predicting transmission loss

**GNCOM**

**Observed**

**Synthetic**

## Validate model attributes that are relevant to key products

**Validate model attributes that are relevant to key products**

- Evaluate hydrographic proxies to indicate expected acoustic fidelity:

- _Mixed Layer Depth_ (MLD)
-    Kara et al. (2000) density
-    threshold equivalent to ΔT=0.8°C

- _Below-Layer Gradient_ (BLG)
-    Fit a line to sound speed points between SLD and SLD+100m
- BLG is the slope*100
-         with units ms-1/100m
- Negative BLG	↔ downward refraction
- Positive BLG	↔ upward refraction
- speed(z) ≈
-         speed(SLD) + BLG*(z-SLD)/100

- _Sonic Layer Depth_ (SLD)
-    Helber et al. (submitted) near
-    surface sound speed maximum
-    appropriate for frequency range

## Evaluate NCOM assimilating MLD-modified synthetic vs. synthetic 

- For MLD, where is MLD-modified GNCOM better than a standard synthetic (MODAS)?

- Evaluate NCOM assimilating MLD-modified synthetic vs. synthetic

-  **11 m** GNCOM median improvement
-  GNCOM better in **80%** of the regions. (shown in red and yellow)

- Statistics based on 43,474 T,S profiles from 2006.

## Evaluate NCOM assimilating MLD-modified synthetic vs. synthetic 

-  **10 m** GNCOM median improvement
-  GNCOM better in **83%** of the regions. (shown in red and yellow)

- For MLD, where is MLD-modified GNCOM better than a standard synthetic (MODAS)?

- Evaluate NCOM assimilating MLD-modified synthetic vs. synthetic

- Statistics based on 6,942 unassimilated T,S profiles from 2006.

**Notes:**
Over 2001-2003, MLD determined from  13,060 T,S profiles in the western North Pacific is compared with MLD estimated by synthetic profiles and GNCOM nowcasts. Comparing median absolute error in 568 2°x2° bins,
GNCOM is more accurate than synthetics in 80% of the regions. (shown in red and yellow)
In the median, GNCOM MLD is 7 m closer to observed MLD than is MLD from synthetics.

## Validate model attributes that are relevant to key products

| Case | MLD bias (m) | SLD bias (m) | BLG bias
(ms-1/100m) |
| --- | --- | --- | --- |
| Climatology
(MODAS) | -26 | -27 | 2.1 |
| Synthetic
(MODAS) | -30 | -27 | 1.2 |
| Standard GNCOM | -13 | -12 | 0.7 |
| GNCOM assimilating MLD-modified synthetics | -5 | -7 | 0.3 |
| Median observed value (not bias) | MLD
41  | SLD
47  | BLG
-11.6 |

- Statistics based on 43,474 T,S profiles from 2006. Profiles are compared with nearest climatology, synthetic, and GNCOM nowcasts. None of the products here assimilate profiles.

**Validate model attributes that are relevant to key products**

## Validate model attributes that are relevant to key products

| Case | MLD bias (m) | SLD bias (m) | BLG bias
(ms-1/100m) |
| --- | --- | --- | --- |
| Climatology
(MODAS) | -28 | -24 | 2.1 |
| Synthetic
(MODAS) | -34 | -27 | 1.8 |
| Standard GNCOM | -11 | -9 | 1.0 |
| GNCOM assimilating MLD-modified synthetics | -1 | -4 | 0.3 |
| Median observed value (not bias) | MLD
 39 | SLD
44  | BLG
-11.6  |

- Statistics based on 6,942 T,S profiles from 2006. Profiles are compared with nearest climatology, synthetic, and GNCOM nowcasts. None of the products here assimilate profiles.

**Validate model attributes that are relevant to key products**

## Product example: currents for search and recovery

- Adam Air flight 574 crashed in the Java Sea on 1 January 2007.
- USNS Mary Sears assisted in search and recovery, locating black boxes on 21 January.

**initial crash debris**

**Pinger Locations**

**NTSB location**

- Reverse 10-day trajectories starting from debris field

- Forward 10-day trajectories starting 		around crash		site

- EAS NCOM results (nested within global NCOM) guided the search by predicting sources of debris that washed ashore ten days after the crash and evaluating debris distributions from potential crash sites.

## RMS separation (km) after one day between observed and simulated drifter trajectories for experiments in 2003.

| Region | Persist
(km) | V2.0
(km) | V2.5
(km) | V2.0a
(km) | V2.5a
(km) | Clim
(km) | V 
(cm s-1) | n pairs |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Celtic-Biscay-North Sea | 16.55 | 15.53 | 13.63 | 13.76 | 13.68 | 16.21 | 19 | 2819 |
| Iberia Region | 10.89 | 11.72 | 10.35 | 10.12 | 10.36 | 11.71 | 19 | 2843 |
| Humboldt Current | 12.60 | 11.85 | 11.29 | 10.73 | 11.20 | 11.53 | 21 | 6550 |
| Eastern North Pacific | 14.75 | 14.67 | 12.94 | 12.78 | 12.91 | 14.38 | 22 | 6420 |
| Brazil Malvinas | 17.81 | 16.74 | 15.31 | 15.06 | 15.28 | 16.77 | 23 | 13349 |
| Gulf of Alaska | 13.52 | 13.90 | 12.12 | 12.12 | 12.31 | 13.70 | 23 | 2371 |
| Benguela Current | 17.15 | 16.76 | 15.32 | 15.89 | 15.46 | 15.77 | 24 | 3292 |
| Black Sea | 17.00 | 16.96 | 16.02 | 16.15 | 16.01 | 16.03 | 24 | 1130 |
| Australia - New Zealand | 18.26 | 16.90 | 15.31 | 15.40 | 15.36 | 16.66 | 25 | 4459 |
| Central Pacific | 22.20 | 17.00 | 16.13 | 16.06 | 16.17 | 18.68 | 26 | 7855 |
| Gulf of Guinea | 18.90 | 17.40 | 16.65 | 17.90 | 17.75 | 17.24 | 28 | 5182 |
| Equatorial Atlantic | 16.42 | 15.14 | 14.30 | 14.67 | 14.78 | 14.65 | 29 | 15607 |
| North Atlantic | 17.92 | 17.71 | 15.91 | 15.90 | 16.06 | 17.30 | 29 | 41155 |
| Pacific Islands | 23.60 | 18.82 | 17.90 | 18.38 | 18.44 | 20.44 | 29 | 36512 |
| Japan/East Sea | 21.61 | 20.82 | 18.96 | 19.00 | 19.42 | 19.76 | 30 | 1646 |
| South China Sea | 33.63 | 24.64 | 22.40 | 23.79 | 23.24 | 26.95 | 30 | 2793 |
| Tehuantepec | 25.50 | 19.14 | 17.82 | 18.05 | 18.23 | 22.10 | 30 | 9537 |
| Equatorial Pacific | 24.45 | 19.18 | 18.50 | 18.82 | 19.22 | 20.57 | 32 | 44482 |
| Indian Ocean | 25.20 | 21.67 | 19.77 | 20.77 | 19.90 | 25.40 | 32 | 15661 |
| Java Sea | 26.75 | 26.31 | 24.87 | 26.75 | 25.92 | 24.64 | 32 | 1039 |
| Intra Americas Seas | 19.67 | 18.76 | 17.20 | 17.66 | 17.21 | 19.13 | 33 | 6250 |
| Agulhas | 29.27 | 24.77 | 22.30 | 22.70 | 22.30 | 26.95 | 34 | 5971 |
| East Asian Pacific | 32.12 | 24.20 | 22.10 | 23.03 | 22.43 | 25.71 | 34 | 5111 |
| Madagascar | 30.62 | 26.76 | 24.77 | 25.34 | 24.32 | 26.29 | 34 | 990 |
| Kuroshio | 24.67 | 22.55 | 20.41 | 20.17 | 20.13 | 22.44 | 35 | 5456 |
| Taiwan | 31.76 | 24.98 | 22.83 | 23.15 | 21.37 | 25.22 | 35 | 3201 |
| North Brazil Current | 19.74 | 17.68 | 17.49 | 17.17 | 17.80 | 18.09 | 36 | 2743 |
| Arabian Sea | 27.90 | 23.78 | 21.11 | 22.50 | 21.75 | 28.68 | 38 | 2978 |
| Gulf of Mexico | 25.53 | 22.88 | 21.32 | 22.15 | 21.75 | 23.58 | 38 | 546 |
| Gulf Stream | 23.49 | 22.66 | 20.22 | 20.76 | 20.31 | 21.81 | 38 | 3499 |
| Top | 0.00% | 0.00% | 60.00% | 26.67% | 16.67% | 3.33% |  |  |
| In top 2 | 0.00% | 0.00% | 86.67% | 43.33% | 60.00% | 10.00% |  |  |

- Regions sorted by increasing *V*.
- Best results are highlighted in green, second-place in grey.
- 200,000 + comparisons
- see Barron et al.,       *J. Geophys Res.,* 2007.

**Notes:**
RMS separation (km) after one day between observed and simulated drifter trajectories for model experiments in 2003. Regions are sorted by increasing V, square root of velocity variance. For each region, the most accurate simulation results are highlighted in black, while the second-place results are highlighted in grey. The last two rows summarize relative experiment performance, indicating the percentage of regions in which each experiment produces the best (green) or one of the two best (grey) sets of results.

## Assessment Summary

- RMS drifter separation after one day
- prior operational V2.0     and new V2.5 (1/16° to 1/32° NLOM, no mean correction)
- RMS error is linearly proportional to σ*V*.
- 8% reduction in prediction uncertainty
- 15% reduction in predicted search area

**Notes:**
Summary of RMS separation (km) after one day and seven days between observed and simulated drifter trajectories for model experiments in 2003. Each panel shows a linear fit to the data that estimates separation error to be proportional to V, the square root of regional velocity variance. Results fromV2.5, the experiment with the smallest errors overall, are included in each panel for reference.

## Future Plans

- Similar metrics are being applied to planned upgrades. Upgrades will transition to operations if they demonstrate improved performance.

- Global HYCOM

- Global HYCOM
- ~6.5 km avg. spacing
- 32 hybrid layers
- Cycling NCODA assimilation
- ESMF-coupled CICE

- ISOP
- New covariances, EOF models to estimate synthetic profiles

## Operational Navy Assimilative Ocean Modeling

- Summary
- Identify key products
- Focus on relevant metrics
- Establish performance of present system
- Demonstrate benefit of potential upgrades

## Backup slides

## Most Important Navy Product: Sound Speed

**Most Important Navy Product: Sound Speed**

**Objective: Develop next-generation methods to estimate ocean sound speed.**
**NAVOCEANO uses climatologies, observations and models ...**
**                                          ... ****to estimate**
**                                               ****sound speed ...**
**... ****used in acoustic tactical**
**decision aids by the fleet.**

**Notes:**
Greensheet # 7320-06-6345