Sensitivity of Satellite Altimetry Data Assimilation on a Naval Anti-Submarine Warfare Weapon System
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1 Sensitivity of Satellite Altimetry Data Assimilation on a Naval Anti-Submarine Warfare Weapon System Steven Mancini LCDR, USN Advisor: Prof. Peter Chu Second Readers: Dr. Charlie Barron Eric Gottshall, CDR USN Past Thesis: Michael Perry ENS, USNR Collaborator: David Cwalina
2 Contents Purpose and Objective Past and Present Thesis Summaries MODAS Introduction & Field Comparison WAPP Introduction & Processing Analysis of Output Conclusions and Recommendations
3 Purpose To define Navy altimeter requirements as a minimum number of satellite altimeters necessary to ensure maximum weapon effectiveness To determine the point at which additional altimeter input no longer increases weapon effectiveness NUWC Collaboration elements NAVO NPS SPAWAR NRL
4 Objective Identify the sensitivity of satellite altimetry data assimilation on weapon presets To determine if further work should be done to assess the value of the altimetry data
5 Past Thesis Michael Perry, June 23 GDEM vs MODAS with 3 altimeters March 15, vs 1633 profiles N W Area coverage is an effective metric for comparing weapon presets
6 Future Projects Recommended by Past Thesis More Extensive Data Set Observe changes over time and for different locations Examine areas of strong thermal or salinity contrast Altimeter Investigation Vary the number of altimeters and observe the effect on area coverage Determine optimal number of altimeters required
7 Current Thesis Compared WAPP output using 2 MODAS fields Looked at 3 geographic areas at 2 different times of year Used relative difference in area coverage for quantifying the effect on weapon presets
8 Process Flowchart Satellite SST & SSH MODAS WAPP Presets Satellite SST only Presets Relative Difference
9 Modular Ocean Data Assimilation System (MODAS) Analysis tool only, predictive capability using NCOM Dynamic climatology uses optimum interpolation to ingest SSH and SST from satellites In situ measurements (XBTs, CTDs) Produces 3-D Temp grid up to 1/8 degree resolution using surface-subsurface regressions 3-D Sal grid using T-S regressions Derives density, sound speed, mixed layer depth, etc.
10 MODAS Flowchart Remote SST & SSH Obs First Guess Fields OI 2-D Gridded SST & SSH Surf-Sub Regression In Situ Temp Obs 3-D Temp Profiles OI Temp Analysis Temp-Sal Regression 3-D Sal Profiles In Situ Salinity Obs OI Salinity Analysis
11 MODAS Climatological Temp MODAS results AXBT Temp SSH + SST + Clim 6-Aug-1995 Cold core eddy MODAS Temperature at 2m
12 MODAS Fields 2 daily global MODAS fields June 3, 21 October 1, 21 2 versions one with assimilated data from 3 altimeters (TOPEX, GFO, and ERS-2) one without altimeter data assimilated
13 Areas of Investigation 3 geographic areas (5 X 5 degree boxes) Sea of Japan (SOJ) 35-4N, E East China Sea (ECS) 3-35N, E Kuroshio Current Area (KCA) 3-35N, E 6 cases (2 days X 3 areas) Resulting input data set Latitude (N) ECS SOJ KCA Longitude (E) 4,379 pairs of water column profiles for each day
14 Comparison of MODAS Fields Compared fields at each horizontal grid point and depth ( X i ) Computed volumetric Mean X i (bias) X i standard deviation RMSD = RMSD 1 n 2 X i n i = 1 = Created scatter plots, histograms and horizontally averaged bias and RMSD vertical profiles
15 MODAS Volume RMSDs RMSD ECS Jun 3 KCA Jun 3 SOJ Jun 3 ECS Oct 1 KCA Oct 1 SOJ Oct 1 Case Temp RMSD ( C) Salinity RMSD (.1psu) SSpd RMSD (m/s)
16 Weapon Acoustic Preset Program (WAPP) Naval Undersea Warfare Center, Division Newport Generates Mk 48 torpedo acoustic presets Visualize predicted torpedo performance
17 WAPP Composition Graphical user interface (GUI) Entering of environmental, tactical, target, and weapon data Computational engine Generating acoustic performance predictions Output A ranked listset Acoustic ray traces Signal excess maps
18 Environmental Data Entry Water column parameters T, S, Sound Spd, VSS Surface conditions WS, WH, Sea State Bottom conditions Depth, type (EDE) Window
19 Presetting Process Various input data Establish valid SD/SA combinations SA selection algorithm Ray trace and signal excess map Ranking based on LD, ER Recommendation SA selection algorithm Identifies optimal pitch angle for each search depth Ray trace Range-independent ray propagation model accounts for spreading, refraction, volume absorption, boundary interactions Fan of rays bound torpedo beam pattern Signal excess map Uses monostatic, active sonar equation (reverb limited): SL - 2TL + TS RL DT = SE
20 WAPP Ranked Listset Listset of search depth/pitch angle/laminar distance/effectiveness Listset ranked based on acoustic effectiveness (area coverage) and recommendation made accounting for cavitation and depth separation
21 Ray Trace Display
22 Signal Excess Map
23 Generated Output NUWC routine fed MODAS T,S fields into WAPP grid point by grid point (bypassing EDE) 5 tactical scenarios run for each case: ASW with low Doppler, deep target ASW with high Doppler, deep target ASW with low Doppler, shallow target ASUW with low Doppler target ASUW with high Doppler target 3 scenarios (6 cases X 5 tactics) Presetting process repeated for each grid point in each scenario over 43, listset pairs!
24 Analysis of Output Used a statistical software package to compare listsets Used relative difference (RD) in area coverage Area coverage (AC) Fraction of the search region with signal excess greater db (i.e., better than a 5% p(d)) RD = AC1-AC2 AC1 Only considered different SD/SA combinations chosen by WAPP Generated a histogram for each scenario Shows the number of combinations that fall into set RD ranges Calculated mean RDs, Prob(RD>.1) and Prob(RD>.2)
25 WAPP Output General Stats Mean RDs RD Probabilities ECS Jun HD Deep ASW ECS Jun LD Deep ASW ECS Jun LD Shallow ASW ECS Jun HD ASUW ECS Jun LD ASUW ECS Jun KCA Jun HD Deep ASW KCA Jun LD Deep ASW KCA Jun LD Shallow ASW KCA Jun HD ASUW KCA Jun LD ASUW KCA Jun SOJ Jun HD Deep ASW SOJ Jun LD Deep ASW SOJ Jun LD Shallow ASW SOJ Jun HD ASUW SOJ Jun LD ASUW SOJ Jun ECS Oct HD Deep ASW ECS Oct LD Deep ASW ECS Oct LD Shallow ASW ECS Oct HD ASUW ECS Oct LD ASUW ECS Oct KCA Oct HD Deep ASW KCA Oct LD Deep ASW KCA Oct LD Shallow ASW KCA Oct HD ASUW KCA Oct LD ASUW KCA Oct SOJ Oct HD Deep ASW SOJ Oct LD Deep ASW SOJ Oct LD Shallow ASW SOJ Oct HD ASUW SOJ Oct LD ASUW SOJ Oct ECS Jun HD Deep ASW ECS Jun LD Deep ASW ECS Jun LD Shallow ASW ECS Jun HD ASUW ECS Jun LD ASUW ECS Jun KCA Jun HD Deep ASW KCA Jun LD Deep ASW KCA Jun LD Shallow ASW KCA Jun HD ASUW KCA Jun LD ASUW KCA Jun SOJ Jun HD Deep ASW SOJ Jun LD Deep ASW SOJ Jun LD Shallow ASW SOJ Jun HD ASUW SOJ Jun LD ASUW SOJ Jun ECS Oct HD Deep ASW ECS Oct LD Deep ASW ECS Oct LD Shallow ASW ECS Oct HD ASUW ECS Oct LD ASUW ECS Oct KCA Oct HD Deep ASW KCA Oct LD Deep ASW KCA Oct LD Shallow ASW KCA Oct HD ASUW KCA Oct LD ASUW KCA Oct SOJ Oct HD Deep ASW SOJ Oct LD Deep ASW SOJ Oct LD Shallow ASW SOJ Oct HD ASUW SOJ Oct LD ASUW SOJ Oct Largest ASW values Largest ASUW values Prob (RD >.1) Prob (RD >.2)
26 ASW Histograms for KCA Jun Number of Different SD/SA Combinations Histogram for KCA on June 3 (LD Deep ASW) Prob (RD >.1) = 37.5% Prob (RD >.2) = 6.44% Prob (RD >.5) = % Mean RD =.925 RD SD = Relative Difference Number of Different SD/SA Combinations Histogram for KCA on June 3 (HD Deep ASW) Prob (RD >.1) = 39.3% Prob (RD >.2) = 6.63% Prob (RD >.5) = % Mean RD =.924 RD SD = Relative Difference Number of Different SD/SA Combinations Histogram for KCA on June 3 (LD Shallow ASW) Prob (RD >.1) = 46.8% Prob (RD >.2) = 8.46% Prob (RD >.5) = % Mean RD =.12 RD SD = Relative Difference
27 Temperature Statistics for KCA Jun 3 5 Histogram 45 Number of Occurrences Mean =.254 SD = 1.56 RMSD = Bias Temperature Difference (C) Root Mean Square Difference (RMSD) Depth (m) Depth (m) Temperature Bias (C) Temperature RMSD (C)
28 Comparison of Temperature at 4 m on Jun 3, 21 MODAS Temperature WITH Altimeters MODAS Temperature WITHOUT Altimeters 4 4 Latitude (N) 35 Latitude (N) Longitude (E) Longitude (E) Temperature (C) Temperature (C)
29 Sound Speed Statistics for KCA Jun 3 6 Histogram Number of Occurrences Mean =.24 SD = 1.6 RMSD = Bias Sound Speed Difference (m/s) Root Mean Square Difference (RMSD) Depth (m) Depth (m) Sound Speed Bias (m/s) Sound Speed RMSD (m/s)
30 Sound Speed Profiles for KCA Jun 3 Position: 33.75N, 135E Position: 33.75N, 137.5E Position: 33.75N, 14E Position: 32N, 135E Position: 32N, 137.5E Position: 32N, 14E Depth (m) Position: 3N, 135E Position: 3N, 137.5E Position: 3N, 14E Sound Speed (m/s) WITH altimeters WITHOUT altimeters
31 ASUW Histograms for SOJ Oct 1 Number of Different SD/SA Combinations Histogram for SOJ on October 1 (HD ASUW) Prob (RD >.1) = 91.5% Prob (RD >.2) = 84.1% Prob (RD >.5) = 1.82% Mean RD =.33 RD SD =.12 Number of Different SD/SA Combinations Histogram for SOJ on October 1 (LD ASUW) Prob (RD >.1) = 81.8% Prob (RD >.2) = 62.3% Prob (RD >.5) = 1.1% Mean RD =.241 RD SD = Relative Difference Relative Difference
32 Temperature Statistics for SOJ Oct 1 16 Number of Occurrences Histogram Mean =.867 SD = 1.53 RMSD = Temperature Difference (C) Bias Root Mean Square Difference (RMSD) Depth (m) Depth (m) Temperature Bias (C) Temperature RMSD (C)
33 Comparison of Temperature at 1 m on Oct 1, 21 MODAS Temperature WITH Altimeters MODAS Temperature WITHOUT Altimeters 4 4 Latitude (N) 35 Latitude (N) Longitude (E) Longitude (E) Temperature (C) Temperature (C)
34 Sound Speed Statistics for SOJ Oct 1 16 Number of Occurrences Histogram Mean =.813 SD = 1.46 RMSD = Sound Speed Difference (m/s) Bias Root Mean Square Difference (RMSD) Depth (m) Depth (m) Sound Speed Bias (m/s) Sound Speed RMSD (m/s)
35 Sound Speed Profiles for SOJ Oct 1 Position: 4N, 13E Position: 4N, 132.5E Position: 4N, 135E Position: 38N, 13E Position: 38N, 132.5E Position: 38N, 135E Depth (m) Position: 36N, 13E Position: 36N, 132.5E Position: 36N, 135E Sound Speed (m/s) WITH altimeters WITHOUT altimeters
36 SOJ Oct HD Deep ASW SOJ Oct LD Deep ASW SOJ Oct LD Shallow ASW SOJ Oct HD ASUW SOJ Oct LD ASUW Sensitivity Table Scenario Prob(RD>.1) Prob(RD>.2) P T C ECS Jun HD Deep ASW ECS Jun LD Deep ASW ECS Jun LD Shallow ASW ECS Jun HD ASUW ECS Jun LD ASUW KCA Jun HD Deep ASW KCA Jun LD Deep ASW KCA Jun LD Shallow ASW KCA Jun HD ASUW KCA Jun LD ASUW SOJ Jun HD Deep ASW SOJ Jun LD Deep ASW SOJ Jun LD Shallow ASW SOJ Jun HD ASUW SOJ Jun LD ASUW ECS Oct HD Deep ASW ECS Oct LD Deep ASW ECS Oct LD Shallow ASW ECS Oct HD ASUW ECS Oct LD ASUW KCA Oct HD Deep ASW KCA Oct LD Deep ASW KCA Oct LD Shallow ASW KCA Oct HD ASUW KCA Oct LD ASUW
37 Conclusions WAPP output is sensitive to satellite altimetry data assimilation Especially when MODAS fields differ significantly in the depth zone of interest (due to better depiction of mesoscale features by the field with altimetry) Satellite altimeter data contributed as much as an 8-9% chance of having a different engagement outcome Assuming RD of.1-.2 in AC is enough
38 Recommendations Proceed with the next step Value is related to positive affect on outcome (hit versus miss) Perform a study that compares WAPP output using MODAS fields and in situ measurements Ultimate MODAS verification To correlate satellite data value to predicted real world performance Perform hardware-in-the-loop simulations To compare hit-miss ratios using presets generated in above experiment for MODAS fields and reality Vary the number of altimeters assimilated To answer the how many are required question
39 Recommended Future Work PART 1 PART 2 Satellite SST & SSH MODAS WAPP Presets Hardwarein-the-loop simulations Hitmiss ratio Satellite SST only Presets Hitmiss ratio In situ obs Presets Hitmiss ratio
40 Questions?
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