Comparison of Aircraft Observations

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Comparison of Aircraft Observations With Surface
Observations from R/V POINT SUR
Dominick Vincent OC 3570 18 March 2003
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Operational Motivation Naval Oceanography
Program Operational Concept - Guiding Principles
(2002) We will employ a doctrinal Rapid
Environmental Assessment (REA) process, using
both dynamic and static data to characterize the
battlespace environment and reflect it in the 4D
Cube as part of deliberate and contingency
military operations. - Page 6
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Rapid Environmental Assessment
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Question for Investigation Can Aircraft
provide comparable or superior observational data
for environmental assessment?
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Method of Investigation Compare aircraft and
ship data as used in the following -
Determining surface fluxes based on a bulk
method calculation, and - Determining the
spatial variability of wind field over a
given area.
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Data and Methods of Collection Data were
collected during the 8 day OC 3570 Operational
Oceanography cruise from 27 Jan 2003 to 03 Feb
2003 aboard the R/V POINT SUR. Additionally, the
Center for Interdisciplinary Remotely-Piloted
Aircraft Studies (CIRPAS) provided aircraft data
collection support for 27 Jan and 1 Feb.
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Area of Collection Area of investigation
for the R/V POINT SUR covered a box off the coast
of Central California and was bounded by
California Cooperative Oceanic Fisheries
Investigations (CalCOFI) line 67 to station
67-70, alongshore to station 77-70, and inshore
to Port San Luis along line 77. Additional
CalCOFI Hydrography lines were covered within the
outer box by both the R/V POINT SUR and the
CIRPAS Aircraft.
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CalCOFI Line 67
CalCOFI Line 77
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Collection Platforms R/V POINT SUR Data
Collected every 54 seconds (SAIL) Position
Time (UTC) Wind Direction Wind Speed Air
Temperature Relative Humidity Pressure Sea
Surface Temperature
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Collection Platforms UV-18A Twin
Otter Data Collected every second Position
Time (UTC) Wind Direction Wind
Speed Air Temperature Relative
Humidity Pressure Sea Surface
Temperature Altitude
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Comparison of Bulk Method-derived Surface
Fluxes Calculated using a Matlab program,
sfcfluxoc3570, by Guest (1997) using a bulk
method formulation based on Smith (1990) and
requiring the following inputs - Wind speed -
Air temperature - Relative humidity - Sea
Surface Temperature - Pressure
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Comparison of Bulk Method-derived Surface
Fluxes Measurements needed to be spatially and
temporally coincident to achieve a valid
comparison. One window, approximately 15
minutes in length, was found for each day.
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Ship
Plane
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Comparison of Bulk Method-derived Surface
Fluxes Ship Data - Averaged over the 15
minute window. Aircraft Data - Input
parameters taken at lowest altitude, - Winds
averaged over lowest 10 meters in window.
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Data Comparison - 27 January 2003
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Data Comparison - 27 January 2003
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Data Comparison - 1 February 2003
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Data Comparison - 1 February 2003
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Results of Comparison of Bulk
Method-derived Surface Fluxes 27 January
2003 Differing total heat flux values
likely due to difference in in relative humidity
and wind speed measurements. Differing
wind stress values likely due to 10-meter wind
speed difference. t cDu102
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Results of Comparison of Bulk
Method-derived Surface Fluxes 1 February
2003 Valid comparison impossible due to
height of aircraft data.
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Comparison of Measured Wind Fields Ship
Data - Plotted every 15 minutes Aircraft
Data - Plotted every minute
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Results of Comparison of Measured Wind Fields
- Offshore winds show clockwise turning with
increasing elevation consistent with Ekman Theory
as described in Holton (1992). - Nearshore
winds show terrain effects. - Ship data likely
a combination of spatial and temporal variability.
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Recommendations for Future Studies -
Specifically design an experiment to collect a
significant amount of temporally and spatially
coincident data sufficient to quantitatively
describe biases. - Ensure careful calibration
of sensors before experiment. - Ensure
aircraft flies at a consistently low altitude to
ensure collection of data within the surface
layer.
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Conclusions - Aircraft data are a powerful
tool for REA but must have some ground truth. -
Biases must be carefully studied for data to
have any validity for data assimilation
purposes.
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References Guest, P., sfcfluxoc3570.m, 1997.
Matlab program.   Guest, P., windvector.m,
2003. Matlab program.   Holton, J.
R., An Introduction to Dynamic Meteorology.
Academic Press, San Diego, 1992.  
Naval Oceanography Program Operational Concept,
March 2002, 48 pp.   Smith, S.D. 1988
Coefficients of sea surface wind stress, heat
flux, and wind profiles as a function of wind
speed and temperature. J. Geophys. Res, 93,
15,467-15,472.