The Quality Control and Monitoring System

QCMS Quality Control Requirements

The design of the Quality Control and Monitoring Systems (QCMS) is largely based on requirements specified for the quality control (QC) of incoming data to AWIPS systems running at modernized NWS Weather Forecast Offices. These requirements are provided by the NWS Techniques Specification Package (TSP) 88-21-R2 (1994). The techniques described in the TSP are meant to

"assure that watches, warnings, and general information disseminated to the public are based on accurate and current data by:

Two categories of QC checks, static and dynamic, are described in the TSP for a variety of observation types, including surface, buoy, ship, profiler, aircraft, and rawinsonde data. The static QC checks are single-station, single-time checks which, as such, are unaware of the previous and current meteorological or hydrologic situation described by other observations and grids. Checks falling into this category include: validity, climatological, internal consistency, and vertical consistency checks. Although useful for locating extreme outliers in the observational database, the static checks have difficulty with statistically reasonable, but invalid data. To address these difficulties, the TSP also describes dynamic checks which refine the QC information by taking advantage of other available hydrometeorological information. Examples of dynamic QC checks include: positional consistency, temporal consistency, spatial consistency, and model consistency checks.

The TSP also describes the requirement for a "data descriptor," a data structure intended to provide an overall opinion of the quality of an observation by combining the information from the various QC checks. Algorithms described to compute the data descriptor are a function of the types of QC checks applied to the observation, the sophistication of those checks, and the departure of the observation from the expected values provided by the QC checks.

The TSP further requires that the results of the QC procedures be stored, logged, and provided to both NWS forecasters at the WFO and the data providers in charge of station maintenance. Forecasters should have the capability to override the results of the QC checks.

The Current Version of the QCMS

The QC capabilities that are provided in the current version of the AWIPS QCMS are described in this section. Note that in the QCMS running in the ESRL/GSD Central Facility, all surface observations (i.e. METARs, SAOs, buoys, modernized COOP, and mesonet reports) are quality controlled with the full subhourly/hourly procedures described here for LDAD Mesonet observations.

Automated QC Checks

Table 1 lists the QC checks applied to the various surface observations in AWIPS.

  LDAD Mesonet           Subhourly   Hourly
  Sea-Level Pressure     IC,VC,TC      SC
  Air Temperature        IC,VC,TC      SC
  Dewpoint Temperature   IC,VC,TC      SC 
  Wind Direction         VC            SC
  Wind Speed             VC,TC         SC
  Relative Humidity      VC,TC
  Station Pressure       IC,VC,TC
  Pressure Change        IC,VC
  Altimeter Setting      VC,TC         SC *
  Visibility             VC
  Accumulated Precip     VC
  SBN Surface            Subhourly   Hourly
  Sea-Level Pressure                IC,VC,TC,SC
  Air Temperature                   IC,VC,TC,SC
  Dewpoint Temperature              IC,VC,TC,SC 
  Wind Direction                    VC,SC
  Wind Speed                        VC,TC,SC
  Altimeter Setting                 VC,TC,SC *

Table 1. Quality control checks implemented in AWIPS 
for surface observations input through the LDAD and SBN
communications networks.  The checks consist of internal
consistency (IC), validity (VC), temporal consistency
(TC), and spatial consistency (SC).  

* As of OB4 the hourly QC applied to Altimeter Setting has
  had the SC added to the list of employed checks for LDAD
  mesonet data, and the VC, TC, and SC have been applied
  to the SBN altimeter data as well.

The validity checks restrict each observation to falling within a TSP-specified set of tolerance limits, while the temporal consistency checks restrict the temporal rate of change of each observation to a set of (other) TSP-specified tolerance limits. In both cases, observations not falling within the limits are flagged as failing the respective QC check. Table 2 lists the tolerance limits.

  Validity Checks
  Sea-Level Pressure         846 - 1100  mb
  Air Temperature            -60 -  130   F
  Dewpoint Temperature       -90 -   90   F
  Wind Direction               0 -  360  deg
  Wind Speed                   0 -  250  kts
  Relative Humidity            0 -  100  %
  Station Pressure           568 - 1100  mb
  Pressure Change              0 - 30.5  mb
  Altimeter Setting          568 - 1100  mb
  Visibility                   0 -  100 miles
  Accumulated Precip           0 -   44  in
  Temporal Consistency Checks
  Sea-Level Pressure               15  mb/hour
  Air Temperature                  35  F/hour
  Dewpoint Temperature             35  F/hour
  Wind Speed                       20 kts/hour

Table 2. Tolerance limits for the validity and temporal
consistency checks implemented for AWIPS.  Observations
not falling between these limits are flagged as bad.

QCMS internal consistency checks enforce reasonable, meteorological relationships among observations measured at a single station. For example, a dewpoint temperature observation must not exceed the temperature observation made at the same station. If it does, both the dewpoint and temperature observation are flagged as failing their internal consistency check. Pressure internal consistency checks include a comparison of pressure change observations at each station with the difference of the current station pressure and the station pressure three hours previous, and a comparison of the reported sea-level pressure with a sea-level pressure estimated from the station pressure and the 12 hour mean surface temperature. In the former check, if the reported 3h pressure change observation does not match the calculated ob, then only the reported observation is flagged as bad. In the latter check, however, if the reported sea-level pressure does not match the calculated ob, then both the sea-level and station pressure obs are flagged as failing.

The spatial consistency (or "buddy") check is performed using an Optimal Interpolation (OI) technique developed by Belousov et al. (1968). At each observation location, the difference between the measured value and the value analyzed by OI is computed. If the magnitude of the difference is small, the observation agrees with its neighbors and is considered correct. If, however, the difference is large, either the observation being checked or one of the observations used in the analysis is bad. To determine which is the case, a reanalysis to the observation location is performed by eliminating one neighboring observation at a time. If successively eliminating each neighbor does not produce an analysis that agrees with the target observation (the observation being checked), the observation is flagged as bad. If eliminating one of the neighboring observations produces an analysis that agrees with the target observation, then the target observation is flagged as "good" and the neighbor is flagged as "suspect." Suspect observations are not used in subsequent OI analyses. Figure 1 illustrates the reanalysis procedure.

Figure 1. Graphic illustration of reanalysis procedure used in the spatial consistency check to determine if the target observation is bad or if one of the observations used in the QC analysis is bad. The reanalysis procedure is implemented only if the difference between the target observation and the analysis is greater than an error.

To improve the performance of the OI, MSAS analysis fields from the previous hour are used as background grids. The analyses provide an accurate 1-h persistence forecast and allow the incorporation of previous surface observations, thus improving temporal continuity near stations that report less frequently than hourly. The differences between the observations and the background are calculated and then interpolated to each observation point before the OI analysis is performed. In addition, uniform distribution of the neighboring observations used in the spatial consistency check is guaranteed (whenever possible) by a search algorithm which locates the nearest observation in each of eight directional sectors distributed around the target observation.

Temperature observations are converted to potential temperature before application of the spatial consistency check. Potential temperature varies more smoothly over mountainous terrain when the boundary layer is relatively deep and well mixed, a marked advantage during daytime hours. For example, potential temperature gradients associated with fronts tend to be well defined during the day even in mountainous terrain (Sanders and Doswell 1995). Unfortunately, this advantage often disappears at night when cool air pools in valleys. To improve the efficacy of the spatial consistency check in these circumstances, elevation differences are incorporated to help model the horizontal correlation between mountain stations. (Miller and Benjamin 1992). The error threshold (to which the absolute value of the difference between analyzed and observed values is compared) is a function of the forecast error, the observational measurement error, and the expected analysis error (Belousov et al. 1968, pg. 128).

Station Monitoring

The QCMS also keeps statistics on the frequency and magnitude of the observational errors encountered for NWS sea-level pressure, potential temperature, dewpoint, and surface wind. At the completion of each hourly analysis, the system provides the total number of observations for each variable, the number of observations that failed the QC check, the station names for the failed observations, and the error and threshold values for each of the failed observations. The error is defined as the difference between the QC analysis value and the observed value, as computed in the spatial consistency check described above. Statistics are calculated for all stations in the 48 contiguous states and neighboring areas of Canada and Mexico. Stations from different networks are kept statistically separate. Specifically, the following stratifications are currently maintained: "SAO", "COOP", "ASOS", "OTHER-MTR" (non-ASOS METARs), and "MARITIME". Local mesonets are stratified by provider. For example, "CODOT," for the Colorado Department of Transportation. (Also note that SAO and COOP are not generally available on AWIPS systems.)

Current hourly, daily, weekly, and monthly QC messages generated at the ESRL/GSD Central Facility are available for various surface observing networks.

Subjective Intervention

Two text files, a "reject" and an "accept" list provide the capability to subjectively override the results of the automated QC checks provided by the Quality Control and Monitoring System. The reject list is a list of stations and associated input observations that will be labeled as bad, regardless of the outcome of the QC checks; the accept list is the corresponding list of stations that will be labeled as good, regardless of the outcome of the QC checks. Applications reading the lists (e.g. the MSAS analysis) will then reject or accept the stations specified. In both cases, observations associated with the stations in the lists can be individually flagged. For example, wind observations at a particular station may be added to the reject list, but not the temperature observations.

QC and station monitoring procedures are not affected by subjective intervention lists, with the sole exception that observations on the reject list will be labeled as "suspect" and not used to check the spatial consistency of neighboring observations. This will allow the WFO to continue to monitor the performance of the stations contained in the lists. For example, a Hydro-Meteorological Technician (HMT) may notice a station with wind observations that fail the QC checks a large percentage of the time, and choose to add that station to the reject list. However, once the observation failure rate at the station falls back to near zero (possibly due to an anemometer that has been repaired), the HMT will likely delete that station from the list.

QC Database

The QCMS also provides netCDF files (in AWIPS for LDAD obs only) which contain raw observations, and the following QC structures: a "QC applied" bit map indicating which QC checks were applied to each observation, a "QC results" bit map indicating the results of the various QC checks, and a "QC departures" array holding the estimated values calculated by the QC checks (e.g. the analysis-minus-observation value calculated by the spatial consistency check). Also included in the netCDF files are single-character "data descriptors," the data structures intended to define an overall opinion of the quality of each observation by combining the information from the various QC checks.

Table 3 provides a complete list of the netCDF data descriptors.

  Data Descriptor Definitions
  Preliminary  (Z)                 No QC Applied
  Coarse Pass  (C)                Passed stage 1
  Screened     (S)           Passed stages 1 & 2
  Verified     (V)       Passed stages 1, 2, & 3 
  Erroneous    (X)                Failed stage 1
  Questionable (Q)           Passed stage 1, but
                            failed stages 2 or 3
  Subjective Good (G)    Included in accept list
  Subjective Bad  (B)    Included in reject list

Table 3. NetCDF data descriptor definitions.  Stage 1 QC
consists of observation validity checks; stage 2,
temporal and internal consistency checks; and stage 3
spatial consistency checks.

In AWIPS Build 5.0, the QCMS also provides data descriptors to the SHEF encoder. SHEF descriptors relate to the netCDF descriptors as follows:

   netCDF                                     SHEF
  --------                                   ------

    Z - no QC                                   Z
    X - failed stage 1                          R *
    Q - passed stage 1, failed 2 or 3           Q
    C - passed stage 1                          S *
    S - passed stages 1 and 2                   V *
    V - passed stages 1, 2, and 3               P *
    G - subjective override - good              G 
    B - subjective override - bad               B 

QC Displays

In addition to the text QC messages, AWIPS contains the ability to display LDAD QC information along with the raw observations. The displays are part of the D2D (Display-2-Dimensional) component of AWIPS, an interactive display for two-dimensional data. The QC displays consist of color-coded stations plots. Stations with observations found bad by the QCMS are distinctly colored to indicate possible problems with their reported data. Pointing and clicking on any station invokes the display of a small QC table indicating which QC checks have been applied at the time of the display, which ones have been passed, and which ones have been failed. Plots are automatically updated as new data arrives and is quality controlled.

Last updated 24 February 2004.
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