Since its inception, CDIP has been committed to using a fully-automated,
realtime processing system for all of the data the program collects.
This emphasis allows us to provide the most up-to-date and relevant
coastal data to a wide range of users, from surfers and boaters to
the National Weather Service. But the data are not only timely;
the processing system includes a range of rigorous quality control
tests, ensuring that CDIP data are highly accurate and reliable as
The computers in the Lab, CDIP's central computing facility at SIO,
contact the active shore stations every half hour to
collect their latest data. When these data arrive back in the Lab,
they are sequentially passed, file by file, through CDIP's
automated processing and distribution system. This system performs
a wide range of analyses and data transformations, producing
everything from error reports and diagnostic e-mail to condensed
paramters and web tables.
When the data first arrive in the Lab, they are in the form of
rd - raw data - files. This is data directly as read from the sensors
in the field, without any significant modification or editing. The
data in rd files have not been decoded or calibrated; they are
effectively a byte-by-byte record of a sensor's output.
Turning this raw data into to all of the valuable products and
information that are found on the CDIP website is basically a two-step
process. First, after verifying the source and timing of the rd file,
it is calibrated and used to produce a df - diskfarm - file. The
df files constitute CDIP's core data set. An accurate record of the
readings made by each sensor at each point in time - this is the
essential foundation for all of the information that the program provides.
While the df files are accurate records of a sensor's readings, another
major question remains: how well is the sensor measuring what it's
supposed to? The second step in data processing is to address this
very question. A range of quality control checks are performed on the data,
to check if they are suitable for further processing. If so, a variety
of calculations and transformations are performed on the data, and
finally the results are distributed to all the appropriate products.
(For a more detailed version of the image below, please see the
At the heart of CDIP's processing system lie two FORTRAN programs,
rd_to_df and meta_proc. These programs process hundreds
of data files a day, supplying thousands of users with the information
RD_TO_DF: Raw data and the diskfarm
As noted above, rd_to_df performs several important operations
on the sensors' raw data. First, it checks that the source of the data are
correctly identified. In the header of each rd file, a number of basic
sensor charactersitics are noted: the sensor type, its location,
calibration factors, etc. All of this information is checked against
CDIP sensor archive once rd_to_df begins to process
The sensor archive is a database that holds complete descriptions of all
the sensors that have been deployed in the field to collect data for CDIP.
Serial numbers, deployment and recovery dates, water depths: they are
all detailed in the sensor archive. The rd_to_df code checks the
header information and data format in the rd file against the
description in the sensor archive. If any discrepancies are noted they
are recorded in the processing error logs.
Sample information from the sensor archive
Next rd_to_df checks that the rd file's time is correct and
and confirms that all the data in the file can be assigned a definite
time. Some sensors' data are recorded with regularly-spaced sync words
and time tags injected into the data stream; rd_to_df decodes the
tags to ensure that the timing is accurate. Other sensors, such as
Datawell buoys, include sync words and counters, which are similarly
examined. Minor timing problems are noted and corrected; major problems
may result in the immediate cessation of processing for that file.
Once the source and timing of the data have been definitively established,
rd_to_df is ready to create diskfarm (df) files. For stations
with more than one sensor in use, the rd file will contain
data from several different sensors. For instance, an rd file from
Scripps Pier (Station 073) may include wave energy data from an underwater
pressure sensor, wind data from an anemometer, and air temperature
readings from a temperature sensor. So the first step in creating
df files is separating out the data from the different sensors. Once
this is done, the values from each sensor can be calibrated. The
calibration factors recorded in the sensor archive are then applied to
the data as appropriate, and the resulting values are written
to single-sensor df files.
There are two general formats for diskfarm files. For directional buoys,
the df files are composed of Datawell 'vectors', 10-byte lines containing
error, spectral, displacment, and parity information. For all other
sensors, the df files are decoded time series; depending on the sensor
type, these time series values may be vertical displacements, water
column heights, temperatures, etc. In both of these forms, the df files
constitute CDIP's core data repository.
META_PROC: From the diskfarm to our users
While the diskfarm files form a very detailed and comprehensive database,
they are but the starting point for CDIP's analyses of wave climatology
and ocean conditions. The FORTRAN program meta_proc takes the df
files, checks the quality of their data, performs a range of complex
calculations - such as spectral and directional wave analyses - and then
produces a variety of output, such as spectral (sp) and parameter (pm) files.
As the program name somewhat pretentiously implies, meta_proc's
first task is to figure out how each df file should be processed. What
quality control checks should be applied to the file? Which other files
should it be grouped with? What calculations should be performed, and
what products should be generated?
For meta_proc, the key to answering these questions is CDIP's
processing archive. Like the sensor archive, the processing archive is
a database containing a number of time frames for each station. These
time frames give complete processing instructions for a station at any
point in its history. For example, for an array of pressure sensors,
the processing archive will state which of the sensors should undergo
"Gauge comparisons", a rigorous set of quality control checks designed
to confirm that the data from closely-associated pressure sensors are
in agreement. The processing archive will also state which sensors should
be used for directional processing. For a station like Harvest Platform
(063), with eight pressure sensors in close proximity, the sensors used
for directional processing may vary considerably over the life of the
Sample information from the sensor archive
The processing archive also spells out which sensors should be used
to produce all of the plots, tables, and other products that are generated
from the diskfarm and made accessible on the web. By setting up a
number of "parameter streams" in the processing archive, different
products can be generated for a single station and presented to web
users as separate data sets.
Once meta_proc has consulted the processing archive and assembled
all the instructions for handling a station's data, it proceeds through
three main stages of processing. First comes quality control and editing.
The data are subjected to a range of tests - checks for extreme values,
spikes, abnormal distributions, etc. (For a more detailed description of
the tests used, please see the following section, on
When problems are found in the data, the values may be edited - perhaps
removing a spike from a time series - or they may be rejected as unfit
for further processing.
If the data pass the QC tests, processing continues to the next stage:
decoding and transformation. This is where FORTRAN's number-crunching
facilities are used to great advantage, as the bulk of the calculations
occur at this point. In wave analysis, for instance, the time series from
pressure sensors undergo spectral and directional analyses, and a range of
algorithms transform the data into spectral coefficients, condensed
parameters (Hs, Tp, etc.), and the like.
At this stage of the processing - as at most others - Datawell directional
buoys are treated somewhat differently than other sensors. Datawell buoys
perrform spectral and directional analyses internally, and the buoys
output these spectral data along with the buoy's displacement time series.
For this reason, CDIP does not actually do any number-crunching for the
Datawell buoys. Instead, meta_proc simply decodes the spectral
information produced by the buoy itself.
Once all of the calculations have been completed, further QC is performed:
are the results reasonable? If so, there is one remaining stage to the
processing: distributing the results to all of the relevant products. Since
a single station's sensors may be used to generate a number of different
data sets at any point in time, meta_proc carefully follows the
processing archive's instructions to ensure that its results are added
to all of the appropriate files and databases. Once this last stage is
complete, all of CDIP's standard products are easily accessible to web
users, the National Weather Service, and a number of research
CDIP needs to provide its users with data which are not only timely,
but accurate as well; this is a responsibility that is taken very
seriously. Rigorous quality controls are implemented at several stages
in the processing, and catch the vast majority of problematic files.
As described above, the first quality control checks ensure that each
data file is properly attributed, with its full provenience - both time
and place - accounted for. Then, as the data are processed by
meta_proc, CDIP's full suite of QC algorithms and analyses is
deployed. For time series data, a wide range of analyses are used,
with different tests applied to different data types. For water column
and vertical displacement time series - i.e. wave measurements - the
checks include: extreme values test, spike test, mean shift test, flat
episodes test, mean crossing test, equal peaks test, acceleration test,
and period distribution test. Some of the tests edit the time series,
cleaning up the data where possible; others simply flag it bad. Where
multiple sensors are deployed in close proximity, the above tests
are followed by a battery of comparison tests, to ensure that the
sensors are in agreement. For a full description of the tests used
and the data types to which they are applied, please refer to our
Time series from a buoy with a bad hippy
Sample time series
from directional buoys
Once the time series have been processed, the resulting values -
condensed parameters and spectral information - also undergo QC checks.
For Datawell buoys these checks are quite extensive, since the buoys
perform their own time-series handling and spectral processing internally.
For example, the time series above shows a problem - spikes
in a large, long-period waveform - that CDIP's time series tests could
easily identify. But since these data are from a Datawell buoy, the time
series was processed internally, so no CDIP editing was applied.
Nonetheless, CDIP's post-processing checks correctly identify the
problem with this file, since the resulting spectral distribution and
parameter values (in this case Tp) are skewed. Here the long-period
spikes result in a spectral shift to lower frequencies, and in an
unnaturally high Tp value (approximately 28 seconds).
Two spectral plots. The upper plot shows the
shifted distribution of the time series data above; the lower plot
shows a more typical spectral distribution.
In addition to the checks outlined above, there is one more full stage
of automated QC applied to CDIP data. While all the previous analyses are
applied to a single file's data, the final QC tests address a station's
data over longer time periods, dozens of files. Once per day, all of
a station's recently acquired condensed parameters are compiled and
compared. Once again, a range of tests check for spikes, unusual values,
and the like, notifying CDIP staff via e-mail if anything seems amiss.
(Please read our
post-processing documentation for more details.)
Only data which pass all of these QC tests are publicly released. Data
which are flagged as suspect or bad at any point along the way are
archived at CDIP but not publicly disseminated except when specifically
requested. All of these automated measures, combined with periodic
visual inspections, are very effective in preventing the distribution
of erroneous data.
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