RSAS employs two types of quality control (QC) checks: static and dynamic. The static 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 are the validity and internal 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, RSAS also utilizes dynamic checks which refine the QC information by taking advantage of other available hydrometeorological information. The dynamic QC checks consist of temporal consistency and spatial consistency checks. Table 1 lists the QC checks applied to the various surface observations in RSAS.
--------------------------------------------- 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 RSAS for surface observations. The checks consist of internal consistency (IC), validity (VC), temporal consistency (TC), and spatial consistency (SC).
The validity checks restrict each observation to falling within a specified set of tolerance limits, while the temporal consistency checks restrict the temporal rate of change of each observation to a set of (other) 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 Altimeter Setting 568 - 1100 mb --------------------------------------------- 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 in RSAS. Observations not falling between these limits are flagged as bad.
RSAS 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 consist of 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. If the reported sea-level pressure does not match the calculated ob, then the observation is 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 a threshold.
To improve the performance of the OI, RSAS 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.
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).
Reference
Belousov, S.L., L.S. Gandin, and S.A. Mashkovich, 1968: Computer Processing of Current Meteorological Data. Ed. V. Bugaev. Meteorological Translation No. 18, 1972, Atmospheric Environment Service, Downsview, Ontario, Canada, 227 pp.