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Long Range Atmosphere-Ocean Forecasting in Support of USW Operations in the Western North Pacific. LT Sarah Heidt, USN Naval Postgraduate School 10 SEPTEMBER 2009 Co-Advisors: Prof. Tom Murphree & CDR Rebecca Stone, USN. Overview. Motivation Background Prior Work Data and Methods

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long range atmosphere ocean forecasting in support of usw operations in the western north pacific

Long Range Atmosphere-Ocean Forecasting in Support of USW Operations in the Western North Pacific

LT Sarah Heidt, USN

Naval Postgraduate School

10 SEPTEMBER 2009

Co-Advisors: Prof. Tom Murphree &

CDR Rebecca Stone, USN

UNCLASSIFIED

slide2
Overview
  • Motivation
  • Background
  • Prior Work
  • Data and Methods
  • Results
  • Conclusions
  • Recommendations for Future Work

UNCLASSIFIED

slide3
Motivation
  • A majority of long lead support products for USW planning use LTM based climatology datasets (e.g. GDEM)
    • Less than optimal temporal resolution
    • Do not adequately depict intraseasonal and interannual climate variations
  • Prior work by Turek (2008) highlighted advantages of using advanced reanalysis datasets and methods
    • Greatly enhanced temporal resolution
    • Additional variables (e.g., SSH, ocean currents, heat flux)
    • Conditional climatology capability
  • Prior studies have shown skill in using advanced LRF methods to produce skillful long range forecasts
  • LTM products are NOT forecasts – often used as such
  • GOAL OF THIS STUDY:
  • Use advanced datasets and methods to develop viable long range climatological forecasts of acoustically relevant variables for planning USW operations in the WNP

UNCLASSIFIED

slide4
Background

How can the Navy improve its long lead support for USW planning?

UNCLASSIFIED

Slide adapted from Turek (2008) thesis brief,

“Smart Climatology Applications for ASW in the WESTPAC.”

slide5
Background

How can the Navy improve its long lead support for USW planning?

Level of METOC Effort vs. Level of Potential Impact on Operations

Operational PlanningTeam

Engagement

MissionPlanning Cell Engagement

OPLAN/CONPLAN Development

EnvironmentalReconnaissance

EnvironmentalReconstruction and Analysis

Level of Impact

METOC Level of Effort

Trade Space

Opportunity

COCOM Battlespace Prep Survey Program

TAS

TOS

Mission execution briefs

OPLAN/CONPLAN Studies

Years

Months

Weeks

Days

Hours

Strategic

Operational

Tactical

From ASW Coordination/CONOPs Conf 14 Mar 05;

CAPT Jeff Best, CNMOC Director for ASW;

CDR Van Gurley, CNMOC Deputy Director for ASW

UNCLASSIFIED

slide6
Background

How can the Navy improve its long lead support for USW planning?

Battle Space on Demand

Smart climatology based decision surfaces

Smart climatological performance surfaces

Smart climatological analyses & forecasts

Smart climatology datasets

UNCLASSIFIED

Modified from Naval Oceanography Brief, CAPT White, April 2009.

slide7
Background

Sonic Layer Depth

The level of near surface maximum sound speed

LTM Sonic Layer Depth (January)

Sound Speed

Sound Speed

Sonic Layer Depth

Near Surface Shadow Zone

Depth

Depth

Shadow Zone

Range

Range

Where is the acoustic high ground?

UNCLASSIFIED

Sound speed images from http://www.fas.org/man/dod-101/navy/docs/

slide8
Prior Work

Interannual Variability of Sonic Layer Depth

GDEM

SODA

5 Highest Wind Years

5 Lowest Wind Years

UNCLASSIFIED

Sonic Layer Depth plots altered from Turek (2008) thesis brief,

“Smart Climatology Applications for ASW in the WESTPAC.”

slide9
Prior Work

October

September

Global SST Correlated with OctoberMeridional Wind Speed in the ECS

August

July

Region of strong negative correlation between October MerWnd speed and SST

UNCLASSIFIED

Images created at http://www.cdc.noaa.gov/Correlation/.

slide10
Data
  • Atmospheric and Oceanic reanalysis data from the National Center for Environmental Prediction (NCEP) / National Center for Atmospheric Research (NCAR)
    • Global restrospective analysis from 1957 to present
    • Temporal resolution of 6 hours
    • Spatial resolution of 2.5°x 2.5° and 28 vertical levels
  • Simple Ocean Data Assimilation (SODA) reanalysis dataset
    • Global restrospective ocean analysis from 1958 to present
    • Upper-ocean temp, sal, and currents
    • Temporal resolution of 5 days
    • Spatial resolution of .5° x .5° and 40 vertical levels (from 5 to 5374 m)
  • This study focused on years from 1970 - 2006
    • Maximize postive impacts of post 1970 satellite era data
    • SODA version available up to 2006
  • These two reanalysis datasets have the ability to capture low frequency climate variations which are not captured by traditional LTM based climatology datasets (e.g. GDEM)

UNCLASSIFIED

slide11
Methods
  • Climate Analysis
    • Seasonal variability in SLD
    • Month of interest based on operational and tactical significance
    • Monthly LTM and standard deviation SLD analysis
    • Monthly SLD threshold probabilities
  • Predictand selection
  • ESRLteleconnection correlations & predictor selection
    • Identification of potentially predictable long lead relationship between SLD (predictand) and SST (predictor)
  • Predictor-predictand time series analysis
  • Tercile matching hindcasts & verification metrics
    • Deterministic approach for verifying long lead predictability potential of the predictor-predictand pair
  • Composite Analysis Forecast Method
    • Probabilistic long-range forecasting method based on the conditional probability of a certain event occurring
  • Conditional Composite Climatology
    • Conditional upper and lower tercile composite means
    • Conditional upper and lower tercile composite threshold probabilities

UNCLASSIFIED

slide12
Climate Analysis and LRF Methodology

Climate Analysis:

Seasonal Variability

Monthly LTM and STD

Threshold probabilities

SODA Ocean Reanalysis Dataset

Choose Predictand

NCEP

Reanalysis Data

Evaluate Correlations & Teleconnections

Choose Predictor

  • Develop Composite Analysis Forecast
  • Perform composite analysis
  • Conduct risk analysis and determine statistical significance
  • Are CAF results statistically significant (≥ 90 %)

Tercile Matching Hindcasts & Verification

Is predictor/predictand pair viable?

YES

Evaluate other potential predictor variables

NO

Base LRF on LTM and/or conditional means

YES

Base LRF on CAF

Provide tailored long lead forecast support products

Conditional Climatology:

Upper tercile conditional mean

Lower tercile conditional mean

Conditional threshold probabilities

Supplement CAF with conditional climatology support products

UNCLASSIFIED

slide13
Seasonal Sonic Layer Depth Long Term Mean

JANUARY

APRIL

JULY

OCTOBER

*** Note the difference in color bar scales between January and other months***

UNCLASSIFIED

slide14
Seasonal Sonic Layer Depth Standard Deviation

JANUARY

APRIL

JULY

OCTOBER

UNCLASSIFIED

slide15
October Sonic Layer Depth Threshold Probabilities

≤ 5m

≥ 5m & ≤ 25m

  • Probability of SLD threshold occurrence
  • Based on SODA depth values
  • 5-25 meters -- associated with periscope operating depths
  • >25 meters -- associated with deep ocean operations

≥ 70m

≥ 25m & ≤ 45m

≥ 45m & ≤ 70m

UNCLASSIFIED

slide16
Predictand Selection

October LTM Sonic Layer Depth & ECSPredictand Region

  • Predictand Selection based on:
  • Operational interests
  • Tactical interests
  • Scientific interests

ECSpredictand region consisting of 37 years of area averaged monthly SLD values for October

UNCLASSIFIED

slide17
Teleconnection Correlations & Predictor Selection

ECSSLDPredictand Index Correlations with Global SST

Oct ECSSLD correlations with October SST

Oct ECSSLD correlations with September SST

Oct ECSSLD correlations with August SST

Oct ECSSLD correlations with July SST

Correlations greater then 0.3 are significant at the 95% confidence interval (Wilks 2006)

→ potential SST predictor

UNCLASSIFIED

Images created at http://www.cdc.noaa.gov/Correlation/.

slide18
Predictand & Predictor Timeseries

October ECSPredictand vs. Zero and Three month lead SST Predictors

  • Verify positive correlations between predictor and predictand
  • Identify possible trends

UNCLASSIFIED

slide19
Tercile Matching Hindcasts & Verification Metrics
  • VERIFICATION METRICS:
  • Hindcasts and metrics are used to determine the viability of our predictor-predictand relationship
  • % Corr = ratio of correct hindcasts to all hindcasts
  • FA Rate = ratio of false alarms to all hindcasts of the event
  • POD = ratio of hits to the total number of occurrences of the even
  • HSS = ratio of correct hindcasts to proportion of correct hindcasts that could have been generated using random independent forecasts (i.e. random chance)

UNCLASSIFIED

slide20
Composite Analysis Results

Results outlined in black are statistically significant at the 95% confidence level

UNCLASSIFIED

slide21
Composite Analysis Forecast Equations

NOAA CAF Equations

  • NOAA equations require a probability of occurrence for the predictor
  • Our results us an analyzed value of the predictor at a specified lead time
  • The analyzed value of AN, NN, or BN SST is given a value of 1 otherwise 0

UNCLASSIFIED

slide22
Composite Analysis Forecast Equations

Modified NOAA CAF Equations

1

0

0

0

0

1

0

0

1

EXAMPLE: SST in our predictor region is AN

UNCLASSIFIED

slide23
Probablistic Composite Analysis Forecast

July 2009 SST ANOMALY

AN JULY 2009 SST PREDICTOR ANOMALY

UNCLASSIFIED

Image created at http://www.cdc.noaa.gov/Correlation/.

slide24
Conditional Climatology

October Lower TercileSLD

October Upper TercileSLD

Issued July 2009

Valid October 2009

17% probability that October 2009 SLD will look like this

58% probability that October 2009 SLD will look like this

UNCLASSIFIED

slide25
Conditional Climatology

AN October Sonic Layer Depth Threshold Probabilities

≤ 5m

≥ 5m & ≤ 25m

Issued July 2009

Valid October 2009

≥ 25m & ≤ 45m

≥ 45m & ≤ 70m

≥ 70m

UNCLASSIFIED

slide26
ANNUALEX 2009 LONG RANGE SUPPORT PRODUCTS

November LTM Sonic Layer Depth

November STD Sonic Layer Depth

  • Long lead support was requested for WNP exercise  ANNUALEX
  • Black box indicates approximate ANNUALEXOPAREA
  • Red box indicates region chosen for SLDpredictand index

UNCLASSIFIED

slide27
ANNUALEX 2009 LONG RANGE SUPPORT PRODUCTS

November Sonic Layer Depth Threshold Probabilities

≤ 5m

≥ 5m & ≤ 25m

≥ 25m & ≤ 45m

≥ 45m & ≤ 70m

≥ 70m & ≤ 110m

≥ 110m

UNCLASSIFIED

slide28
ANNUALEX 2009 LONG RANGE SUPPORT PRODUCTS

July 2009 SST ANOMALY

AN JULY 2009 SST PREDICTOR ANOMALY

UNCLASSIFIED

Image created at http://www.cdc.noaa.gov/Correlation/.

slide29
ANNUALEX 2009 LONG RANGE SUPPORT PRODUCTS

November Lower TercileSLD

November Upper TercileSLD

Issued July 2009

Valid November 2009

0% probability that November 2009 SLD will look like this

50% probability that November 2009 SLD will look like this

UNCLASSIFIED

slide30
ANNUALEX 2009 LONG RANGE SUPPORT PRODUCTS

AN November Sonic Layer Depth Threshold Probabilities

Issued July 2009Valid October 2009

≥ 5m & ≤ 25m

≥ 25m & ≤ 45m

≤ 5m

≥ 70m & ≤ 110m

≥ 110m

≥ 45m & ≤ 70m

UNCLASSIFIED

slide31
Conclusions
  • Navy LTM based climatology does not capture large-scale climate system variations that are necessary for long range predictability and operational planning
  • Civilian agencies have developed advanced datasets and methods to analyze and forecast the climate system
  • The U.S. Navy has adapted and used very little of civilian technology to advance their long range climate prediction capabilities
  • Viable teleconnections exist between SST predictors in the equatorial and south Pacific & SLD in the WNP
  • These teleconnections can be used to generate skillful long lead probabilistic forecasts of SLD in the WNP at lead times of 0-4 months
  • Using advanced climate datasets and methods provide a more complete and accurate forecasts than the current practice of using LTM based products for long range military planning
  • Better long range planning products have the potential for saving the military time and tax dollars

UNCLASSIFIED

slide32
Future Work
  • Provide the long lead planning products developed in this study to USWMETOC support staff and USW planners for experimental use in USW planning
  • Explore the use of other USW relevant predictands (e.g. BLG, ILG, COF)
  • Apply advanced data sets and methods used in this study to other tactically significant and strategically important regions in the world
  • Explore potential predictability of SST from multiple regions as well as other oceanic variables (e.g. subsurface ocean temperature)
  • Use the results presented in this study to develop tier 2 sonar performance predictions and tier 3 decision recommendation products for USW operations
  • This research primarily focuses on statistical links between acoustic derived USW variables of interest (SLD) and global climate variables (SST). Future work should focus on developing a deeper understanding the dynamics that cause the teleconnections identified in this study
  • Conduct verification of the CAF method used to develop long lead probabilistic predictions of WNPSLD (e.g. extensive hindcasts and real forecasts)
  • Pursue development of a web-based application for SODA

UNCLASSIFIED

slide33
QUESTIONS?Contact Information:LT Sarah Heidt, Student, [email protected] Tom Murphree, Meteorology Co-Advisor, [email protected] Rebecca Stone, Oceanography Co-Advisor, [email protected]

UNCLASSIFIED

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