|
|||||||
|
|
|
|
|
|
|
|
| |
 |
|
Data quality control | |
|
|
|||||
 |
 |
||||
|
TAO DATA quality control TAO
data undergo extensive quality control analysis to ensure that they meet
stringent accuracy standards. This page summarizes the various procedures
for real-time ATLAS data, delayed
mode ATLAS data, and acoustic Doppler current profiler (ADCP)
data. Quality indices which apply to these data
are also described.
Real-time
ATLAS data quality control is routinely performed on a daily, weekly,
and monthly basis. In
addition to the error checking program, daily comparisons are made between
TAO data that are processed at PMEL and TAO data that are transmitted
via the GTS. Any discrepancies between the data sets are immediately investigated
and corrected.
Weekly
real-time quality control Weekly
means of most variables are also compiled at PMEL
and compared to COADS climatology. Conditions which generate error alerts
are listed below.
Anomalies are investigated by trained personnel and flagged only if there
is a high probability that the data are bad.
Monthly
real-time quality control General Next, time series plots, spectral plots, and histograms are generated for all data. Plots of differences between adjacent subsurface temperature measurements are also generated. Statistics, including the mean, median, standard deviation, variance, minimum and maximum are calculated for each time series. Individual time series and statistical summaries are examined by trained analysts. Data that have passed gross error checks but which are unusual relative to neighboring data in the time series, and/or which are statistical outliers, are examined on a case-by-case basis. Mooring deployment and recovery logs are searched for corroborating information such as problems with battery failures, vandalism, damaged sensors, or incorrect clocks. Consistency with other variables is also checked. Data points that are ultimately judged to be erroneous are then flagged. For some variables,
additional postprocessing after recovery is required to ensure maximum
quality. These variable-specific procedures are described below. Rain Rate Rainfall data are collected using a RM Young rain gauge, and recorded internally at a 1-min sample rate. The RM Young rain gauge consists of a 500 ml catchment cylinder which, when full, empties automatically via a siphon tube. Data from a 3-min period centered near siphon events are ignored. Occasional random spikes, which typically occur during periods of rapid rain accumulation, or immediately preceding or following siphon events, are eliminated manually. Rain
rates computed from first differences of 1-min accumulations are often
noisy because of the sensitivity of rate calculations to noise in accumulations
over short time scales. To reduce this noise, 1-min accumulations are
filtered with a 16-point Hanning filter, and rates are computed at 10-min
intervals. Residual noise in the filtered time series may include occasional
spurious negative rain rates, but these rarely exceed a few mm hr Subsurface Pressure (and other measurements) The
majority of ATLAS moorings are taut-line moorings. Therefore, vertical
excursions of the mooring line are small in most situations, and subsurface
instruments do not deviate far from their nominal measurement depths.
Vertical excursions of the mooring line are detected by pressure sensors
typically placed at depths of 300 m and 500 m where the largest line variations
typically occur (McCarty et al (1997) [2]). Large, short-duration, upward
spikes in subsurface pressure data are occasionally observed. These spikes
usually indicate either purposeful or accidental interaction between fishermen
and the moorings. Each spike, and its effects on the subsurface data,
is individually evaluated. Data from all subsurface sensors are flagged
when pressure excursions exceed the range expected for normal variability.
Salinity
A thirteen point Hanning filter is applied to the high-resolution (ten minute interval) conductivity and temperature data. A filtered value is calculated at any point for which seven of the thirteen input points are available. The missing points are handled by dropping their weights from the calculation, rather than by adjusting the length of the filter. Salinity values are recalculated from the filtered data and subsampled to hourly intervals. The drift-corrected salinities are checked for continuity across deployments. In addition, for those deployments which had multiple depths instrumented with conductivity sensors, the records are compared to one another and checked for unusual density inversions indicating uncorrected drift of one or more instruments. If uncorrected drift is found, an attempt is made to identify the sensor at fault and adjust its data based on differences with data from adjacent depths during unstratified conditions. The procedures used to identify and adjust problematic data are similar to those described in Freitag et al (1999) [4] and used to correct Seacat salinity data. Delayed mode
daily salinity and density values are calculated by taking the mean of
the available hourly values for the day. If there are fewer than 12 hourly
values available, a daily mean value is not computed. [1] Serra, Y.L., P.A'Hearn, H.P. Freitag, and M.J. McPhaden, 2001: ATLAS self-siphoning rain gauge error estimates. J. Atmos. Ocean. Tech., in press. [2] McCarty, M.E., L.J. Mangum, and M.J. McPhaden, 1997: Temperature errors in TAO data induced by mooring motion. NOAA Tech. Memo. ERL PMEL-108, Pacific Marine Environmental Laboratory, Seattle, WA, 68 pp. [3] Fofonoff,
P., and R. C. Millard Jr., Algorithms for computation of fundamental properties
of seawater, Tech. Pap. Mar. Sci., 44, 53 pp., Unesco, Paris, 1983. Velocity profiles are obtained from upward looking Acoustic Doppler Current Profilers (ADCPs) deployed on subsurface moorings at nominal depths of 250 m to 300 m below the sea surface. The narrowband RD Instruments ADCPs have a 20 degree transducer orientation and are set to collect data with 8.68 m nominal bin and pulse lengths. The instruments collect data at a 3 second sample rate and form averages over 15 minutes beginning at the top of the hour.
Velocity data are processed and quality controlled at PMEL after the mooring
is recovered and the data retrieved from the instrument's memory. The
adcp velocity measurements assume a constant sound speed of 1536 m s The actual depth of the ADCP transducer head is variable in time, as the mooring reacts to variations in ocean currents beneath the instrument. Therefore, velocity profiles need to be adjusted for head depth. The transducer head depth is computed using two independant methods. In the first, the hourly target strength for each beam and each depth bin is computed from the echo intensities. The sea surface appears as a maximum target strength for most (>80%) hourly profiles. A polynomial is fit to the target strengths of the three bins closest to the surface. The position of the maximum target strength with respect to the adcp transducer is then used as the depth of the instrument for each hourly profile. The second method of estimating the head depth is from pressure time series recorded by duplicate pressure sensors mounted near the adcp transducer. Estimates of head depth from the maximum target strength and the pressure sensors are typically within +/- 2m, less than half of the adcp bin width. The computed transducer head depth and the bin widths (nominal bin widths which have been adjusted for sound velocity) are used to compute the bin depths for the hourly adcp velocity data. Near surface velocity measurements may be in error due to strong reflections from the surface that overcome the sidelobe suppression of the transducer. Hourly data are flagged as bad if the bin depth (the center of the velocity bin) is closer to the surface than D*(1-cos(theta)) + bin width where D is the transducer depth, theta is the angle of the transducer beam relative to vertical, and the bin width has been adjusted for sound velocity. Velocities from the remaining depth bins are then interpolated to standard depths at 5 meter intervals. Velocity time series at the shallowest five standard depths are plotted to visually verify that no contamination from surface reflections appears in the data. The
ADCP velocities are also compared with coincident point velocity measurements
when available on nearby surface moorings. ADCP and point velocity measurements
generally agree to within 5 cm ADCP
data are carefully reviewed when no velocity data from other nearby instruments
are available for comparison. For equatorial sites, contour plots of zonal
and meridional velocities are checked to ensure that no obvious aliasing
of zonal flow appears in the meridional velocities, which could indicate
the existance of compass error. Direction comparisons are also made with
the preceeding and following ADCP deployments at the same location. A
depth range with minimal direction variance is selected. The average direction
for these depths is computed for four time periods, the first and last
two weeks of the deployment, the last two weeks of the preceeding deployment
and the first two weeks of the following deployment. The average direction
difference is calculated for both consecutive two week pairs and used
to adjust the deployment directions if necessary. Quality
indices and sensor drift 1
- Highest Quality. Pre/post-deployment
calibrations agree to within sensor specifications.
In most cases, only pre-deployment
calibrations have been applied. 2
- Default Quality.
Pre-deployment calibrations only or post-deployment calibrations
only applied. Default
value for sensors presently deployed and for sensors which were
not recovered or not calibratable when recovered, or for which pre-deployment calibrations have been determined to be invalid. 3
- Adjusted Data. Pre/post calibrations differ, or original data do not agree with other data sources (e.g., other in situ data or climatology), or
original data are noisy. Data have been adjusted in an attempt to reduce the error. 4 - Lower Quality. Pre/post calibrations differ, or data do not agree with other data sources (e.g., other in situ data or climatology), or data are noisy. Data could not be confidently adjusted to correct for error.
Nominal
drift criteria:
|
|
|
| Home | Project overview | Data display | Data delivery | El Niño & La Niña | Site map |
|
|
TAO
Project Office |
from previous day
. If
any SWR value (mean, standard deviation, maximum) reads 0, all are
set to missing for that day.