Processing of surface meteorological data collected on the R/V Revelle winter SeaSoar cruise (Revelle3)
Introduction
This report describes the data return, sensor comparison, and basic processing of the surface meteorological data collected on the R/V Revelle winter SeaSoar cruise (Revelle3). The final "best" basic 1-min time series represents a combination of data collected with the ship's IMET system, WHOI ASIMET short- and long-wave radiation recorders, and SDSU units that measured wind speed and direction, air temperature, relative humidity, and air pressure. The SDSU units recorded data every second while the IMET and ASIMET units used 1-min record intervals.
The
1-sec SDSU wind data were vectored averaged and then low-pass filtered
and subsampled to obtain a 1-min time series. When possible, comparisons
of data collected with different sensor systems have been used to help
determine which data should be used for scientific analysis.
The
"best" 1-min time series are described in revel3_1m.txtand
stored in the MATLAB 5 file revel3_1m.mat.
This 1-min data was then low-pass filtered with PL66TN (half amplitude
period = 12 min) and subsampled to produce final 5-min time series for
scientific analysis. The 5-min data were used to estimate surface fluxes
using the MATLAB Air-Sea Toolbox (Pawlowicz et al, 2000; Beardsley et al,
1998). The 5-min time series are described in
revel3_5m.txt
and stored in revel3_5m.mat.
These files are available via anonymous ftp.
Wind
Speed and Direction
The
IMET wind sensor mounted on the bow mast returned 1-min wind speed for
most of the cruise with a 4-day gap in the center of the cruise. The direction
sensor failed (returning only constant directions), so that the IMET vector
average winds were bad.
C.
Dorman mounted SDSU wind speed and direction sensor sets at two levels
on the bow mast and on the port and starboard wings of the main mast. These
sensors returned 1-sec data that were then vector-averaged into 1-min apparent
wind speed and direction for subsequent analysis. Comparison of these winds
suggested that the upwind sensor set on the main mast gave the best measurement
(with no obvious sheltering effects, unlike the bow mast sensors). Comparison
of the upwind main mast wind speed with the IMET wind speed when the ship
was steaming into the wind indicated good agreement, with no adjustment
needed for the difference in sensor height. Thus, the upwind main mast
SDSU 1-min wind speed and direction data were used with a sensor height
of 15 m in all analysis.
True
winds were computed using the SDSU measured relative wind speed and direction,
the ship's heading as given by the gyro, and the motion of the ship as
given by the P-code GPS.
Air
Temperature
The
IMET air temperature sensor on the bow mast returned good data for the
entire cruise. A SDSU air temperature sensor, mounted on the bow mast about
0.5 m below the IMET sensor, returned data for periods when the air temperature
was above about -4°C.
This data, recorded at 0.25 sec, was low-pass filtered and subsampled to
obtain 3687 temperature values coincident with the 5-min IMET series. The
two series were highly correlated (cc2 = 0.990), with
a mean IMET minus SDSU difference and standard deviation of 0.28 ±
0.25°C,
the IMET reading about 0.3 OC higher on average. Linear
regression gave the fit
ATSDSU = a + b ATIMET ,
with
a = -0.36 ±
0.02°C,
b = 1.040 ±
0.003, and a standard deviation of ±
0.23°C
about the fit (Figure 1). The IMET air temperature data were used in subsequent
analysis.
Figure 1. Regression of the IMET and SDSU 5-min air temperature data for
periods
when the air temperature was above -4°C.
Relative
Humidity
The
IMET relative humidity sensor on the bow mast returned good data for the
entire cruise. A SDSU relative humidity sensor, mounted on the bow mast
about 0.5 m below the IMET sensor, returned data that generally tracked
the IMET data when the air temperature was above -4°C.
Following the same procedure used to compare air temperature, a set of
SDSU 3687 relative humidity values coincident with the 5-min IMET series
was obtained. With the exception of several short periods when the two
series differed noticeable, the two series agreed well (cc2 =
0.909), with a mean IMET minus SDSU difference and standard deviation of
-1.1 ±
3.9%, the IMET reading about 1% lower on average. Linear regression gave
the fit
RHSDSU = a + b RHIMET,
with
a = -5.0 ±
0.3%, b = 1.091 ±
0.011, and a standard deviation of ±
3.9% about the fit (Figure 2). The IMET relative humidity data were used
in subsequent analysis.
Figure 2. Regression of the IMET and SDSU 5-min relative humidity data for
periods
when the air temperature was above -4°C.
Barometric
Pressure
The
IMET and SDSU barometers mounted on the bow mast returned good data for
the entire cruise. After removing some obvious spikes from the SDSU series,
the SDSU series were filtered and subsampled to obtain an air pressure
series coincident with the 5-min IMET series. The two series were highly
correlated (cc2 = 0.982), with a mean IMET minus SDSU
difference and standard deviation of -1.5
±
0.7 mb, the IMET reading about 1.5 mb lower on average. Linear regression
gave the fit
BPSDSU = a + b BPIMET,
with
a = -3.7 ±
4.7 mb, b = 1.005 ±
0.004, and a standard deviation of ±
0.7 mb about the fit (Figure 3). The IMET air pressure data were used in
subsequent analysis.
Figure 3. Regression of the IMET and SDSU 5-min air pressure data. The two
series track well with several short periods of apparent noise in the
SDSU
data.
Incident
Short-Wave Radiation
Two
IMET units and one ASIMET unit were deployed on Revelle3. The second
IMET unit was placed near the primary IMET unit. The ASIMET unit was placed
on the port side of the O1-deck, about 5 m back from the forward railing,
by J. Ware.
IMET
unit 1 returned good data for several days before stopping. IMET unit 2
returned only bad (flagged) data. ASIMET returned good data for entire
cruise.
After
removing the night-time bias from each record and a few points where the
IMET record was constant in time, the IMET and ASIMET were compared using
linear regression for the period yd = 16.15 - 19.22 (3.07 days). The resulting
fit was
SWIMET = a + b SWASIMET,
where
a = 1.3 ±
0.3, b = 1.035 ±
0.002, with a standard deviation of ~7 W/m2 about the
fit. The two series agreed closely, with a cc2 = 0.995.
The IMET read about 3.5% higher than the ASIMET, which is within the manufacturer's
calibration uncertainty. The ASIMET record was used in all analysis.
Incident
Long-Wave Radiation
Two
IMET units and one ASIMET unit were deployed on Revelle3. The second
IMET unit was placed near the primary IMET unit. The ASIMET unit was placed
on the port side of the O1-deck, about 5 m back from the forward railing,
by J. Ware.
All
three units returned data for the entire cruise (Figure 4). The IMET1 and
IMET2 records differed by ~50 W/m2, with IMET2 exhibiting
much more high frequency variability and an occasional period of constant
values. The ASIMET record appeared more similar to IMET2, without as much
high frequency variability. The three records are similar during the center
period yd 24-27.5.
To help determine which of these three records is more accurate, the following comparisons were made. First, IMET1 was found to be highly correlated with air temperature, with a cc2 = 0.98 and a standard deviation about the fit of only ~3 W/m2. Thus, IMET1 was essentially a proxy for air temperature and was not used. Why all three records tend to track during the period yd 24-27.5 is not clear.
Figure 4. Basic 5-min
incident long-wave radiation time series obtained with
the
IMET1 (blue), IMET2 (green), and ASIMET (red) units.
The
IMET2 and ASIMET records were more similar, with IMET2 having more high
frequency variability but slightly weaker lower frequency changes. Both
records exhibit similar timing, with two notable exceptions, the period
January 19.95-20.25, when IMET2 appeared stuck, and the period January
27.6-28.3, when the ASIMET record lags IMET2 by about 0.6 days (Figure
5). After removing these two periods, the remaining IMET2 and ASIMET records
were compared using linear analysis, which gave
LWIMET = a + b LWASIMET,
where
a = 51.3 ±
1.8 W/m2, b = 0.768 ±
0.012, with a standard deviation of ~11 W/m2 about the
fit, and a cc2 = 0.77. On average, the ASIMET was 8.7
±
14.0 W/m2 higher than IMET2. Figure 5 shows that the
mean difference between the two records tended to vary on both short and
longer time scales on several days, suggesting a sensor or system drift
in one or both of the units. This tendency is shown in the regression plot
(Figure 3) by the cluster of values below the regression line. Both series
appear to have a cutoff value near 200 W/m2.
Figure 5. Difference between ASIMET and IMET2 long-wave time series
during
Revelle3 cruise.
Figure 6. Regression between the ASIMET and IMET2 long-wave time series
from
the Revelle3 cruise.
Pre-
and post-cruise comparison tests were made at WHOI between the
Revelle3
ASIMET unit (LW205) and the ASIMET unit (LW204) deployed on the winter
Khromov cruise. The post-cruise comparison is split into period
A (corresponding to the units as returned from the cruises) and period
B (after both units were modified to have two-stage amplification). During
the three comparison periods, the two ASIMET records agreed quite closely
with the exception of a mean offset of about 23.7 +/- 2.7 W/m2
prior to the unit modification and 12.6 +/- 2.6 W/m2
after the modification (Table 1).
Table 1. Results of pre- and post-cruise comparison tests for ASIMET LW204
and LW205 units. The mean and standard deviation of the individual
series
and the difference series are given in W/m2.
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When
the Revelle3 IMET2-ASIMET and the WHOI ASIMET comparisons are considered
together, it seems clear that the Revelle ASIMET unit (LW205) was
biased high in comparison to both IMET2 and the
Khromov ASIMET unit
(LW204). While the calibration and system accuracy of the IMET2 unit is
not readily available, the calibration histories of the long-wave sensors
used in the two ASIMET units are known to be stable in time, so we chose
to use the ASIMET (LW205) record minus a positive bias
as the best measure of downward long-wave radiation during the
Revelle3
cruise. The exact value of
may not be determined from the data available, so we chose
=
12 W/m2, one-half the mean bias between LW205 and LW204.
(By the same argument, we will add
to
LW204 to obtain our best estimate of the downward long-wave radiation during
the winter Khromov cruise.) The inherent uncertainties in the WHOI
Eppley PIR sensor is roughly ±
1.5-1.8% (or 5 W/m2) and in
is of order ±
10-15 W/m2, giving a rough estimate of the total uncertainty
in the downward long-wave radiation of ±
20 W/m2.
Note:
An estimate of the net long-wave heat flux into the ocean was made using
bulk formula involving measured insolation, RH, and SST. This approach
uses the difference between measured insolation and clear-sky insolation
to estimate the cloud cover and cloud correction factor, which is then
used to estimate the amount of upward long-wave radiation that is absorbed
and re-radiated downward back to the ocean surface. While the bulk estimate
showed occasional similarity with the IMET 2 and ASIMET records, the bulk
estimate was not correlated (cc2 < 0.09) with either
measured series. This emphasizes the need for direct measurement of incident
long-wave radiation on ships and buoys for air-sea interaction studies.
Sea
Surface Temperature
The
IMET sea surface temperature sensor, mounted near the bow thruster, returned
good data. At first, the large negative temperature jumps were thought
to be in error, possibly due to air and ice being sucked into the intake.
A comparison between the IMET SST data and the temperature measured in
the top 4 m with the SeaSoar during the SeaSoar survey work showed excellent
agreement. The 649 comparison values were highly correlated (cc2=0.999),
with a mean difference between the IMET and SeaSoar SST of only -0.0001
OC. The standard deviation of the SST difference was only +/-
0.0832
OC, due in part because these samples were taken in the
surface mixed layer.
Table 2. Summary of meteorological instrumentation and estimated measurement
uncertainties
for Revelle3.
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WS | SDSU Anemometer |
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WD | SDSU Anemometer |
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AT | IMET - RM Young |
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RH | IMET - RM Young |
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BP | IMET - RM Young |
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SW | ASIMET - Eppley PSP |
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LW | ASIMET - Eppley PIR |
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SST | IMET - SeaBird |
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Notes: |
1.
SDSU anemometers (clive, can you describe?)
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2. The IMET AT and RH sensors were mounted at 15 m on the bow mast, and experienced winds above 5 (2.5) m/s 89 (97) % of the time. This coupled with the reduced solar heating due to generally cloudy skies and reduced insolation due to the time of year should minimize additional errors due to solar heating at very low wind speeds. |
3. This estimate includes possible dynamical effects due to the airflow over the ship. (Payne, personal communication) |
4. Eppley calibration uncertainty. |
5.
While the ASIMET Eppley PIR sensor is thought to be accurate to roughly
1.5-1.8%, the system uncertainty is larger due in part to the lack of a
system comparison with an accepted long-wave system standard (Fairall et
al., Payne.. ). This estimate of the uncertainty is based on the inherent
sensor uncertainty of 5 W/m2 and one/half the measured
bias difference between LW204 and LW205 of 24 W/m2, assuming
the true value lies in the middle (see Payne and Anderson, 1999).
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6.
This uncertainty is based on the standard deviation of the IMET-SeaSoar
SST difference series. The mean uncertainty is considerable less, probably
of order 0.01 OC.
7. Additional information concerning the ASIMET units is available at |
References
Beardsley,
R.C., E.P. Dever, S.J. Lentz, and J.P. Dean, 1998. Surface heat flux variability
over the northern California shelf. Journal of Geophysical Research,
103(C10), 21,553-21,586.
Fairall, C.W., P.O.G. Persson, E.F. Bradley, R.E. Payne, and S.P. Anderson,
1998.
A new look at calibration and use of Eppley precision infrared radiometers.
Part I: Theory and Application. Journal of Atmosphere and Oceanic
Technology, 15, 1229-1242.
Payne, R.E. and S.P. Anderson, 1999. A new look at calibration and use of Eppley precision infrared radiometers. Part II: Calibration and use of the Woods Hole Oceanographic Institution Improved Meteorology Precision Infrared Radiometer.
Journal
of Atmosphere and Oceanic Technology, 16, 739-751.
Pawlowicz, R., R. Beardsley, S. Lentz, E. Dever, and A. Anis, 2001. The Air-Sea Toolbox: Boundary-layer parameterization for everyone. EOS, (in press).