**18.13** AUTOMATED VALIDATION FOR SUMMARY OF THE DAY PRECIPITATION DATA

Michael L. Urzen, Stephen A. Del Greco, and Steve Ansari\*

NOAA/National Climatic Data Center

Asheville, NC 28801

\*and STG, Inc.

**I. INTRODUCTION**

In recent years, the value of weather and climate observations has
become ever more broadly recognized. Given that weather observations are
susceptible to a number of potential data errors, their value is
partially dependent on the degree to which errors can be identified and
appropriately addressed. While many data problems are characteristic of
an observing system or instrument type, no system is immune to at least
occasional instrument malfunction or human error. At NOAA's National
Climatic Data Center (NCDC), which is responsible for the acquisition,
quality assurance and archival of U.S. weather and climate information,
a variety of quality assurance techniques have been used to identify *in
situ* temperature and precipitation observation errors during data
ingest and review. Error detection approaches have ranged from
subjective review by human \"validators\" to more objective and
automated quality assurance algorithms.

From the standpoint of efficiency and reproducibility, fully automated
and objective data reviews clearly are desirable. However, the
establishment of threshold checks for data validation that work well
under most circumstances is a constant challenge. Frequently, subjective
interventions are necessary in automated data review procedures. Many
quality assurance systems, including those in place at NCDC for NWS
Cooperative Observer (Coop) Network observations, are at best nearly
automated. Whatever the approach used, it is important to note that
observations are not considered hidden

*Corresponding author's address:*

Michael L. Urzen, NOAA/National Climatic Data Center, Asheville, NC
28801

e-mail: Michael.Urzen@noaa.gov

to be \"error-free\" when they pass the scrutiny of a review system, but
rather to be reasonable or plausible as defined within the framework of
the testing algorithms or review by data experts.

For convenience, observational errors are often classified into two
basic types: random and systematic. Some random errors may be identified
by logical checks designed to expose internal consistency problems. In
the Coop Network, internal inconsistencies include, for example, cases
where a minimum temperature value for a given day is listed as greater
than the corresponding maximum temperature or when frozen precipitation
is recorded at temperatures well in excess of freezing. Random errors
that pass logical checks may be identified by comparing a target
observation to values from nearby stations. In this case, attempts are
made to establish the plausibility of an observation given the
characteristics of a local field of values from surrounding locations.
This type of validation rests on the assumption that the correlation
between observations is large at least within the distances defined by
the spacing between stations. While this assumption may be generally
true in temperature fields, in the case of daily precipitation totals,
the expected nature of the field of observations can be difficult to
establish under many (e.g., convective) circumstances. Consequently, the
use of station-to-station difference thresholds to determine the limits
of plausibility is problematic. In addition, daily precipitation totals
in the Coop Network are recorded by volunteers according to a variety of
observation schedules. Precipitation events in an area may therefore be
differentially apportioned across more than one calendar day at various
neighboring stations as an artifact of differences in the gauge reading
schedule. The result is an additional dimension of complexity to the
local precipitation field that further complicates data validation.

A potential way to overcome some of the problems associated with the
identification of random errors in precipitation totals is to compare
gauge totals to independent assessments. PrecipVal (*Precip*itation
*Val*idation) is the first systematic attempt at NCDC to compare in situ
precipitation totals to independent or quasi-independent estimates for
the purpose of data validation. Here we describe a system that can be
used to evaluate the potential of this approach to data validation.

**II. PRECIPITATION ESTIMATES**

The PrecipVal system currently has the capability of comparing daily
precipitation totals from the Coop Network to three different
precipitation estimates. These include other (non Coop) in situ
measurements, radar and gauge blended estimates, and a merged estimate
from multiple sources. The in situ estimate is obtained by inverse
distance-squared weighted averaging of observations from the nearest
three non-Coop stations within 25 mi. (40 km) of the target Coop site.
The radar/gauge estimate is the National Centers for Environmental
Prediction's (NCEP) Stage IV product, which is a blend of radar data and
sub-daily in situ measurements. The third value is derived by averaging
multiple data sources that include satellite, radar, forecast model and
gauge-based data (Fig. 1). More specifically, these sources consist of
six satellite-based estimates, the WSR-88D Level III radar Digital
Precipitation Array, Stage IV values, estimates from the Rapid Update
Cycle (RUC) forecast model and gauge totals from other observation
networks. Fig. 2 provides examples of daily precipitation estimates from
two of these sources. The non-gauge-based data sources used in the
merged estimate are screened for gross errors using various
inter-comparison tests. At any particular location and time, the
screening eliminates those sources considered to be of limited utility.
The remaining values are used in the calculation of an average and
various other summary statistics at each nominal 4.7 kilometer grid
cell.

**III. PRECIPITATION VALIDATION**

The PrecipVal system was established to carry out a series of comparison
tests between Coop precipitation totals and the various estimates. The
first test attempts to identify multi-day accumulations of precipitation
that can occur when the observer fails to read the gauge each
observational day. When a gap is encountered in the Coop data, the daily
values of each of the three estimates are accumulated throughout the
period of missing Coop data to create three alternative accumulated
precipitation estimates for the day on which Coop observations resume.
If the first non-missing Coop value following the data gap more closely
matches one of the three accumulated values than the corresponding daily
estimate, the Coop value is considered to be a likely accumulation. The
Coop accumulated value is then distributed over the missing period based
on percentages derived from the daily estimates whose accumulation best
matches the Coop value.

The second comparison is designed to identify cases where the
precipitation total is attributed to the previous calendar day rather
than to the day on which the gauge was read. In an effort to detect the
error, a lagged comparison between the Coop total and a selected
estimate is performed. The Coop values for the current day and the
following day are compared to the estimate shifted forward by one day.
If the lagged values agree better than the concurrent values and the
Coop values are increasing as estimated values are decreasing, or vice
versa, then the Coop value on the current day is flagged as a shifter.
Conversely, the \"reverse shifter\" check identifies cases in which the
precipitation total of a particular day is mistakenly reported for the
subsequent day. The estimate used is based on availability.

When either the Coop value or the estimate closest in magnitude to the
observed value exceeds one inch, then the observation is further tested
for a shift in the decimal point. Such decimal point displacement is
deemed to be present when multiplication of the observation by a factor
of 0.01, 0.1, 10, or 100 results in the closer match with the estimate.

Following these three checks, all observations except those identified
as accumulations are subjected to a same day comparison test in which
each Coop observation is compared to the estimate closest in magnitude.
An observation is considered valid if the estimate falls within 50% or
within 0.25 in. (6 mm) of the observed amount. If at least two of the
estimates are available, and none falls within the validation threshold,
the observation is considered "suspect". When only one estimate is
present, and this estimate falls outside of the validation range, the
observed value is neither validated nor flagged as suspect. When zero
precipitation is reported at the Coop station and the estimate is
greater than zero, but less than or equal to 0.25 in. (6 mm), it is
noted that precipitation may have been present. In the case in which an
observation is missing, estimates are available.

**IV. CONCLUSION**

In addition to random error, systematic errors should be considered when
comparing observed rainfall totals to estimates. Nevertheless, PrecipVal
provides a convenient mechanism for testing the feasibility of
validating precipitation gauge totals with independent data sources. The
statistics produced by the approach when applied to several months of
Coop observations serve as a starting point for evaluating its
robustness, the value of each individual data source, and the utility of
using estimates generated by spatial interpolation of independent
station observations. A principal benefit of using independent data
sources is that they provide hourly estimates and thus aid in
establishing the timing of precipitation events. Further, the system
lends itself to the addition of other sources as they become available.

Acknowledgements:

The authors thank Imke Durre and Matthew Menne of NCDC for guidance and
review of this paper.

![](media/image1.png){width="6.75in" height="4.917361111111111in"}

**Figure 1. Example of multiple data source precipitation estimates.**

![](media/image2.jpeg){width="6.0in"
height="4.636111111111111in"}**Figure 2. Comparison of daily
precipitation estimates from two data sources.**