Charles M. Pearcy II, 1Lt, USAF MR Sep 04

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Presentation transcript:

Charles M. Pearcy II, 1Lt, USAF MR3570 17 Sep 04 A Cursory Verification of the Target Acquisition Weapons Software 3.1.3 (TAWS) Water Background Algorithms. Charles M. Pearcy II, 1Lt, USAF MR3570 17 Sep 04

Overview Importance of TAWS Data TAWS Water Algorithms Density Differences Water Clarity Conclusion

Importance of TAWS How soon will you see the target? How soon will the enemy see you? Change the question Why did the munition miss? To Which munition should I use?

Data Used UDAS, Rawinsondes, sfc obs for TAWS weather files. Used the spar pyroheliometer for light attenuation measurements Used 4 different CTD sites upcast of the top 5m. For variables in density calculations For light attenuation measurements

TAWS Water Algorithms Three forms of water background. Soil Moisture (vegetation & bare soil routines) Swamp/Marsh routines Water only routines Water only routines are the focus Three inputs have an impact Weather SST Water clarity

TAWS Water Algorithms Create 15 Layer water column .6m-5m Layer 15 properties based on residual energy Mid-layers based mainly on clarity & layer exchanges Properties .6m layer based mainly on weather inputs Initialize layer density based on average SST Updated surface temp is background temp Mixing based on penetrative convection and heat differences

TAWS Water Algorithms Solar Forcing .6m 5m Residual energy Difference in temperature between background and target determine it’s detection range. .6m Turbid Clear New Background Temperature Determined Heat absorption and transmittance in each layer Convective mixing 5m Residual energy

Initialize layer density based on average SST Density Differences Initialize layer density based on average SST TAWS assumes rv to be constant across all layers (top 5m of water) 2 of 4 cases of CTD data this was exactly true 2 of 4 cases accurate to within .5kg/m^3 TAWS estimates the density of water at all levels based on a 30hr SST average. rv = 1000 – 0.019549*(ABS(AveSST-4)^1.68) Note density is always less than 1000 kg/m^3, this is a fresh water calculation.

Density Differences 2.5% error in Density calculations Site CTD Density kg/m^3 TAWS Density kg/m^3 Difference kg/m^3 Percent Error B037 1024.3 998.6 25.7 2.509 B039 1024.2 25.6 2.4995 B052 1024 998.4 2.500 B054 1023.5 25.1 2.4524 2.5% error in Density calculations rv is used in 45 calculations and variables affected by it in 433 calculations.

based mainly on clarity & layer exchanges Water Clarity Mid-layers based mainly on clarity & layer exchanges TAWS uses 2 user determined settings for water clarity. Clear—valued at .05 Turbid—valued at 1.00 TAWS uses clarity exclusively to approximate heat gain due to solar energy exchange processes for the sub layers (.91m-4.69m).

Water Clarity Solar Forcing Residual energy Clear Turbid Heat absorption and transmittance in each layer Residual energy

Water Clarity Clear setting attenuation of .05 Turbid setting has attenuation of 1.00 Took light values (einsteins/sec/m^2) from CTD on upcast and SPAR CTD/SPAR to get actual light attenuation.

Water Clarity Average light attenuation for all cases and all layers .81 The ocean was more turbid than clear. Testing TAWS runs for all 4 CTD sites showed almost no difference in detection range due to changing between turbid and clear water.

Conclusions Keeping in mind the limited sample size. Assuming Density is constant for the top 5m of the ocean is a good approximation. Assuming fresh water in place of sea water may be a problem. Direct manipulation of the code would be necessary. Turbid vs. clear water only made negligible differences in these cases.

Recomendations Working with TAWS source code is difficult, performance of the overall model on a specific output would be better than nuts and bolts of source code. For example, determining whether a water background or a swamp/marsh background is more accurate over a kelp forest and/or plankton blooms.

References TAWS Source Code, provided by Dr. Andreas Goroch, NRL SEAWATER: A Library of MATLAB Computational Routines for the Properties of Sea Water. CSIRO Marine Laboratories, provided by Tarry Rago