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Guidelines on Quality Control Procedures for Data
from Automatic Weather Stations
Igor Zahumenský
Slovak Hydrometeorological Institute
SHMI, Jeséniova 17, 833 15 Bratislava, Slovakia
Tel./Fax. +421 46 541 36 24, Igor.Zahumensky@shmu.sk
Abstract
There are different quality control procedures for the various phases of the data collection process
but there is an absence of comprehensive QC at all levels. The proposed Guidelines try to
overcome this deficiency and presents a comprehensive system of the check procedures and
algorithms and quality control flags that should be implemented at all levels of data quality control.
The proposal addresses only real time QC of data from a single AWS platform, while spatial QC is
beyond a scope of the proposal. The same is also true for checks against analysed or predicted
fields as well as for QC of formatting, transmission and decoding of errors, due to a specific
character of these processes.
At the CBS and CIMO expert meetings it was agreed that ET AWS, jointly with CCI, JCOMM,
GCOS, and CIMO, would continue with this work in the development of the guidelines for AWS
quality control procedures for future publication in WMO Guide on Global Data Processing System
(WMO-No. 305), the CIMO Guide, and WMO/TD-No.111.
INTRODUCTION
Quality control (QC) of data is the best known component of quality management systems. It
consists of examination of data with the aim to detect errors. Data quality control has to be applied
as real time QC performed at the Automatic Weather Station (AWS) and at Data Processing
Centre (DPC). In addition, it has to be performed as near real time and non real time quality control
at DPC.
There are two levels of the real time quality control of AWS data:
• QC of raw data (signal measurements). It is basic QC, performed at an AWS site. This QC
level is relevant during acquisition of Level I data and should eliminate errors of technical
devices, including sensors, measurement errors (systematic or random), errors inherent in
measurement procedures and methods. QC at this stage includes a gross error check, basic
time checks, and basic internal consistency checks. Application of these procedures is
extremely important because some errors introduced during the measuring process cannot be
eliminated later.
• QC of processed data: It is extended QC, partly performed at an AWS site, but mainly at a
Data Processing Centre. This QC level is relevant during the reduction and conversion of Level
I data into Level II data and Level II data themselves. It deals with comprehensive checking of
temporal and internal consistency, evaluation of biases and long-term drifts of sensors and
modules, malfunction of sensors, etc.
The schema of quality control levels is as follows:
2
Basic Quality Control Procedures (AWS):
I. Automatic QC of raw data
a) Plausible value check (the gross error check on measured values)
b) Check on a plausible rate of change (the time consistency check on measured values)
II. Automatic QC of processed data
a) Plausible value check
b) Time consistency check:
• Check on a maximum allowed variability of an instantaneous value (a step test)
• Check on a minimum required variability of instantaneous values (a persistence test)
• Calculation of a standard deviation
c) Internal consistency check
d) Technical monitoring of all crucial parts of AWS
Extended Quality Control Procedures (DPC):
a) Plausible value check
b) Time consistency check:
• Check on a maximum allowed variability of an instantaneous value (a step test)
• Check on a minimum required variability of instantaneous values (a persistence test)
• Calculation of a standard deviation
c) Internal consistency check
In the process of applying QC procedures to AWS data, the data are validated and, if necessary,
deleted or corrected. A quality control system should include procedures for returning to the source
of data to verify them and to prevent recurrence of the errors.
Comprehensive documentation on QC procedures applied, including the specification of basic data
processing procedures for a calculation of instantaneous (i.e. one minute) data and sums should
be a part of AWS’ standard documentation.
The guidelines deal only with QC of data from a single AWS, therefore spatial QC is beyond the
scope of the document. The same is also true in case of checks against analyzed or predicted
fields. Furthermore, QC of formatting, transmission and decoding errors is beyond the scope of the
document due to a specific character of these processes, as they are dependent on the type of a
message used and a way of its transmission.
Notes:
Recommendations provided in guidelines have to be used in conjunction with the relevant WMO
documentation dealing with data QC:
(1) Basic characteristics of the quality control and general principles to be followed within the
framework of the GOS are very briefly described in the Manual of GOS, WMO-No. 544. QC
levels, aspects, stages and methods are described in the Guide on GOS, WMO-No. 488.
(2) Basic steps of QC of AWS data are given in the Guide to Meteorological Instruments and
Methods of Observation, WMO-No. 8, especially in Part II, Chapter 1.
(3) Details of QC procedures and methods that have to be applied to meteorological data intended
for international exchange are described in Guide on GDPS, WMO-No. 305, Chapter 6.
(4) GDPS minimum standards for QC of data are defined in the Manual on GDPS, WMO-No. 485,
Vol. I).
3
CHAPTER I DEFINITIONS AND ABBREVIATIONS
Quality control, quality assurance
Quality control: The operational techniques and activities that are used to fulfil requirements for
quality.
The primary purpose of quality control of observational data is missing data detection, error
detection and possible error corrections in order to ensure the highest possible reasonable
standard of accuracy for the optimum use of these data by all possible users.
To ensure this purpose (the quality of AWS data), a well-designed quality control system is vital.
Effort shall be made to correct all erroneous data and validate suspicious data detected by QC
procedures. The quality of AWS data shall be known.
Quality assurance: All the planned and systematic activities implemented within the quality
system, and demonstrated as needed, to provide adequate confidence that an entity will fulfil
requirements for quality.
The primary objective of the quality assurance system is to ensure that data are consistent, meet
the data quality objectives and are supported by comprehensive description of methodology.
Note: Quality assurance and quality control are two terms that have many interpretations because
of the multiple definitions for the words "assurance" and "control."
Types of error
There are several types of errors that can occur in case of measured data and shall to be detected
by implemented quality control procedures. They are as follows:
Random errors are distributed more or less symmetrically around zero and do not depend on the
measured value. Random errors sometimes result in overestimation and sometimes in
underestimation of the actual value. On average, the errors cancel each other out.
Systematic errors on the other hand, are distributed asymmetrically around zero. On average
these errors tend to bias the measured value either above or below the actual value. One reason
of random errors is a long-term drift of sensors.
Large (rough) errors are caused by malfunctioning of measurement devices or by mistakes made
during data processing; errors are easily detected by checks.
Micrometeorological (representativeness) errors are the result of small-scale perturbations or
weather systems affecting a weather observation. These systems are not completely observable
by the observing system due to the temporal or spatial resolution of the observing system.
Nevertheless when such a phenomenon occurs during a routine observation, the results may look
strange compared to surrounding observations taking place at the same time.
4
Abbreviations
AWS Automatic Weather Station
B-QC Basic Quality Control
BUFR Binary Universal Form of the Representation
DPC Data Processing Centre
E-QC Extended Quality Control
GDPS Global Data-Processing System
QA Quality assurance
QC Quality control
CHAPTER II BASIC QUALITY CONTROL PROCEDURES
Automatic data validity checking (basic quality control procedures) shall be applied at an AWS to
monitor the quality of sensors’ data prior to their use in computation of weather parameter values.
This basic QC is designed to remove erroneous sensor information while retaining valid sensor
data. In modern automatic data acquisition systems, the high sampling rate of measurements and
the possible generation of noise necessitate checking of data at the level of samples as well as at
the level of instantaneous data (generally one-minute data). B-QC procedures shall be applied
(performed) at each stage of the conversion of raw sensor outputs into meteorological parameters.
The range of B-QC strongly depends on the capacity of AWS’ processing unit. The outputs of B-
QC would be included inside every AWS message.
The types of B-QC procedures are as follows:
• Automatic QC of raw data (sensor samples) intended primarily to indicate any sensor
malfunction, instability, interference in order to reduce potential corruption of processed data; the
values that fail this QC level are not used in further data processing.
• Automatic QC of processed data intended to identify erroneous or anomalous data. The
range of this control depends on the sensors used.
All AWS data should be flagged using appropriate QC flags. At B-QC five data QC categories are
enough:
• good (accurate; data with errors less than or equal to a specified value);
• inconsistent (one or more parameters are inconsistent);
• doubtful (suspect);
• erroneous (wrong; data with errors exceeding a specified value);
• missing data.
It is essential that data quality is known and demonstrable; data must pass all checks in the
framework of B-QC. In case of inconsistent, doubtful and erroneous data, additional information
should be transmitted; in case of missing data the reason of missing should be transmitted. In case
of BUFR messages for AWS data, BUFR descriptor 0 33 005 (Quality Information AWS data) and
0 33 020 (Quality control indication of following value) can be used.
I. Automatic QC of raw data
a) Plausible value check (the gross error check on measured values)
The aim of the check is to verify if the values are within the acceptable range limits. Each sample
shall be examined if its value lies within the measurement range of a pertinent sensor. If the value
fails the check it is rejected and not used in further computation of a relevant parameter.
5
b) Check on a plausible rate of change (the time consistency check on measured values)
The aim of the check is to verify the rate of change (unrealistic jumps in values). The check is best
applicable to data of high temporal resolution (a high sampling rate) as the correlation between the
adjacent samples increases with the sampling rate.
After each signal measurement the current sample shall be compared to the preceding one. If the
difference of these two samples is more than the specified limit then the current sample is
identified as suspect and not used for the computation of an average. However, it is still used for
checking the temporal consistency of samples. It means that the new sample is still checked with
the suspect one. The result of this procedure is that in case of large noise, one or two successive
samples are not used for the computation of the average. In case of sampling frequency five - ten
samples per minute (the sampling intervals 6 - 12 seconds), the limits of time variance of the
samples implemented at AWS can be as follows:
• Air temperature: 2 °C;
• Dew-point temperature: 2 °C;
• Ground and soil temperature: 2 °C;
• Relative humidity: 5 %;
• Atmospheric pressure: 0.3 hPa;
• Wind speed: 20 ms-1;
• Solar radiation (irradiance) : 800 Wm-2.
There should be at least 66% (2/3) of the samples available to compute an instantaneous (one-
minute) value; in case of the wind direction and speed at least 75 % of the samples to compute a
2- or 10-minute average. If less than 66% of the samples are available in one minute, the current
value fails the QC criterion and is not used in further computation of a relevant parameter; the
value should be flagged as missing.
II. Automatic QC of processed data
a) Plausible value check
The aim of the check is to verify if the values of instantaneous data (one-minute average or sum; in
case of wind 2- and 10-minute averages) are within acceptable range limits. Limits of different
meteorological parameters depend on the climatic conditions of AWS’ site and on a season. At this
stage of QC they can be independent of them and they can be set as broad and general. Possible
fixed-limit values implemented at an AWS can be as follows:
• Air temperature: -80 °C – +60 °C;
• Dew point temperature: -80 °C – 35 °C;
• Ground temperature: -80 °C – +80 °C;
• Soil temperature: -50 °C – +50 °C;
• Relative humidity: 0 – 100 %;
• Atmospheric pressure at the station level: 500 – 1100 hPa;
• Wind direction: 0 – 360 degrees;
• Wind speed: 0 – 75 ms-1 (2-minute, 10-minute average);
• Solar radiation (irradiance): 0 – 1600 Wm-2;
• Precipitation amount (1 minute interval): 0 – 40 mm.
•
Note: Of course there is a possibility to adjust the fixed-limit values listed above to reflect climatic
conditions of the region more preciously, if necessary.
If the value is outside the acceptable range limit it should be flagged as erroneous.
6
b) Time consistency check
The aim of the check is to verify the rate of change of instantaneous data (detection of unrealistic
jumps in values or dead band caused by blocked sensors).
• Check on a maximum allowed variability of an instantaneous value (a step test): if the
current instantaneous value differs from the prior one by more than a specific limit (step), then
the current instantaneous value fails the check and it should be flagged as doubtful (suspect).
Possible limits of a maximum variability can be as follows:
Parameter Limit for suspect Limit for erroneous
Air temperature: 3 °C
Dew point temperature: 2 - 3°C; 4 - 5°C 1 4°C
Ground temperature: 5 °C 10°C
Soil temperature 5 cm: 0.5°C 1°C
Soil temperature 10 cm: 0.5°C 1°C
Soil temperature 20 cm: 0.5°C 1°C
Soil temperature 50 cm: 0.3°C 0.5°C
Soil temperature 100 cm: 0.1°C 0.2°C
Relative humidity: 10 % 15%
Atmospheric pressure: 0.5 hPa 2 hPa
Wind speed (2-minute average) 10 ms-1 20 ms-1
Solar radiation (irradiance): 800 Wm-2 1000 Wm-2
In case of extreme meteorological conditions, an unusual variability of the parameter(s) may occur.
In such circumstances, data may be flagged as suspect, though being correct. They are not
rejected and are further validated during extended quality control implemented at Data Processing
Centre whether they are good or wrong.
• Check on a minimum required variability of instantaneous values during a certain period
(a persistence test), once the measurement of the parameter has been done for at least 60
minutes. If the one-minute values do not vary over the past at least 60 minutes by more than
the specified limit (a threshold value) then the current one-minute value fails the check.
Possible limits of minimum required variability can be as follows:
• Air temperature: 0.1°C over the past 60 minutes;
• Dew point temperature: 0.1°C over the past 60 minutes;
• Ground temperature: 0.1°C over the past 60 minutes2;
1 If dew point temperature is directly measured by a sensor, the lower limit is to be used. If dew point is
calculated from measurements of air temperature and relative humidity, a larger limit is recommended
(taking into account the influence of the screen protecting the thermometer and hygrometer). A screen
usually has different ‘system response time’ for air temperature and water vapour, and the combination of
these two parameters may generate fast variations of dew point temperature, which are not representative of
a sensor default, but are representative of the influence of the screen during fast variations of air temperature
and relative humidity.
2 For ground temperature outside the interval [-0.2 °C +0.2 °C]. Melting snow can generate isothermy, during
which the limit should be 0 °C (to take into account the measurement uncertainty).
7
• Soil temperature may be very stable, so there is no minimum required variability.
• Relative humidity: 1% over the past 60 minutes3;
• Atmospheric pressure: 0.1 hPa over the past 60 minutes;
• Wind direction: 10 degrees over the past 60 minutes4;
• Wind speed: 0.5 ms-1 over the past 60 minutes5.
If the value fails the time consistency checks it should be flagged as doubtful (suspect).
A calculation of a standard deviation of basic variables such as temperature, pressure, humidity,
wind at least for the last one-hour period is highly recommended. If the standard deviation of the
parameter is below an acceptable minimum, all data from the period should be flagged as suspect.
In combination with the persistence test, the standard deviation is a very good tool for detection of
a blocked sensor as well as a long-term sensor drift.
c) Internal consistency check
The basic algorithms used for checking internal consistency of data are based on the relation
between two parameters (the following conditions shall be true):
• dew point temperature ≤ air temperature;
• wind speed = 00 and wind direction = 00;
• wind speed ≠ 00 and wind direction ≠ 00;
• wind gust (speed) ≥ wind speed;
• both elements are suspect* if total cloud cover = 0 and amount of precipitation > 06;
• both elements are suspect* if total cloud cover = 0 and precipitation duration > 07;
• both elements are suspect* if total cloud cover = 8 and sunshine duration > 0;
• both elements are suspect* if sunshine duration > 0 and solar radiation = 0;
• both elements are suspect* if solar radiation > 500 Wm-2 and sunshine duration = 0;
• both elements are suspect* if amount of precipitation > 0 and precipitation duration = 0;
• both elements are suspect* if precipitation duration > 0 and weather phenomenon is
different from precipitation type;
(*: possible used only for data from a period not longer than 10-15 minutes).
If the value fails the internal consistency checks it should be flagged as inconsistent.
A technical monitoring of all crucial parts of AWS including all sensors is an inseparable part of
the QA system. It provides information on quality of data through the technical status of the
instrument and information on the internal measurement status. Corresponding information should
be exchanged together with measured data; in case of BUFR messages for AWS data it can be
done by using BUFR descriptor 0 33 006 – Internal measurement status (AWS).
3 For relative humidity < 95% (to take into account the measurement uncertainty).
4 For 10-minute average wind speed during the period > 0.1 ms-1.
5 For 10-minute average wind speed during the period > 0.1 ms-1.
6 Or greater than the minimum resolution of the rain gauge, to take into account the deposition of water by
dew, etc.
7with the exception of snow pellets, which can occur with cloud cover = 0
8
CHAPTER III EXTENDED QUALITY CONTROL PROCEDURES
Extended Quality Control procedures should be applied at the national Data Processing Centre.
The checks that had already been performed at the AWS site have to be repeated at DPC but in
more elaborate form. This should include comprehensive checks against physical and
climatological limits, time consistency checks for a longer measurement period, checks on logical
relations among a number of variables (internal consistency of data), statistical methods to analyze
data, etc.
Suggested limit values (gross-error limit checks) for surface wind speed, air temperature, dew point
temperature, and station pressure are presented in the Guide on GDPS, WMO-No. 305. The limits
can be adjusted on the basis of improved climatological statistics and experience. Besides that, the
Guide on GDPS also presents internal consistency checks for surface data, where different
parameters in a SYNOP report are checked against each other. In case of another type of report
for AWS data, such a BUFR, the relevant checking algorithms have to be redefined; in case of
BUFR corresponding BUFR descriptors and code/flag tables.
Internal consistency checks of data
[0]An internal consistency check of data can cause that both corresponding values are flagged as
inconsistent, doubtful or erroneous when only one of them is really suspect or wrong. Therefore
further checking by other means should be performed so that only the suspect / wrong value is
correspondingly flagged and the other value is flagged as good.
In comparison with B-QC performed at AWS more QC categories should be used, e.g.:
• data verified (at B-QC: data flagged as suspect, wrong or inconsistent; at E-QC validated as
good using other checking procedures);
• data corrected (at B-QC: data flagged as wrong or suspect data; at E-QC corrected using
appropriate procedures).
The different parameters in the AWS N-minute data report (N ≤ 10-15 minutes) are checked
against each other. In the description below, the suggested checking algorithms have been divided
into areas where the physical parameters are closely connected. The symbolic names of
parameters with the corresponding BUFR descriptors used in the algorithms are explained in the
table below.
(a) Wind direction and wind speed
The wind information is considered to be erroneous in the following cases:
• wind direction = 00 and wind speed ≠ 00;
• wind direction ≠ 00 and wind speed = 00;
• wind gust (speed) ≤ wind speed;
(b) Air temperature and dew point temperature
The temperature information is considered to be erroneous in the following case:
• dew point temperature > air temperature;
• air temperature - dew point temperature > 5°C and obscuration is from {1, 2, 3};
(c) Air temperature and present weather
Both elements are considered suspect when:
• air temperature > +5°C and precipitation type is from {6, …, 12};
• air temperature < -2°C and precipitation type is from {2};
9
• air temperature > +3°C and precipitation type is from {3};
• air temperature < -10°C and precipitation type is from {3};
• air temperature > +3°C and obscuration is from {2} or
(obscuration is from {1} and character of obscuration is from {4});
(d) Visibility and present weather
The values for visibility and weather are considered suspect when:
• obscuration is from {1, 2, 3} and visibility > 1 000 m;
• obscuration is from {7, 8, 9, 11, 12, 13} and visibility > 10 000 m;
• visibility < 1 000 m and obscuration is not from {1, 2, 3, 8, 9, 10, 11, 12, 13}
and precipitation type is not from {1, … , 14};
• obscuration = 7 and visibility < 1 000 m;
• visibility > 10 000 m and precipitation type is missing and obscuration is missing
and weather phenomenon is missing;
(e) Present weather and cloud information
Clouds and weather are considered suspect when:
• total cloud cover = 0 and precipitation type is from {1, …, 11, 13, 14}
or weather phenomenon is from {2, 5, … , 10};
(f) Present weather and duration of precipitation
Present weather and duration of precipitation are considered suspect when:
• precipitation type is from {1, … , 10, 13, 14} and precipitation duration = 0;
• precipitation type is not from {1, … , 10, 13, 14} and precipitation duration > 0;
•
(g) Cloud information and precipitation information
Clouds and precipitation are considered suspect when:
• total cloud cover = 0 and amount of precipitation > 08;
•
(h) Cloud information and duration of precipitation
Clouds and duration of precipitation are considered suspect when:
• total cloud cover = 0 and precipitation duration > 0;
(i) Duration of precipitation and other precipitation information
Precipitation data are considered suspect when:
• amount of precipitation > 0 and precipitation duration = 0;
(j) Cloud information and sunshine duration
Clouds and sunshine duration are considered suspect when:
• total cloud cover = 100% and sunshine duration > 0;
For each check, if the checked values fail the internal consistency check, they should be flagged
as erroneous or suspect (depending on the type of the check) and inconsistent. Further checking
8 Or greater than the minimum resolution of the rain gauge, to take into account the deposition of water by
dew, etc.
10
by other means should be performed so that only the suspect / wrong value is correspondingly
flagged and the other value is flagged as good.
The symbolic name and the corresponding BUFR descriptor (as reference) used in QC algorithms
(a) – (j) are as follows:
Symbolic name BUFR Descriptor
Wind direction 0 11 001
Wind speed 0 11 002
Wind gust (speed) 0 11 041
Air temperature 0 12 101
Dew point temperature 0 12 103
Total cloud cover 0 20 010
Visibility 0 20 001
Precipitation type 0 20 021
Precipitation character 0 20 022
Precipitation duration 0 26 020
Weather phenomenon 0 20 023
For further treatment of data it is necessary to keep the results of the E-QC data quality control
together with the information on how suspect or wrong data were treated (using sophisticated
system of flags). The output of the quality control system should include QC flags that indicate
whether the measurement passed or failed, as well as a set of summary statements about the
sensors.
Every effort has to be made to fill data gaps, correct all erroneous values and validate doubtful
data detected by QC procedures at the Data Processing Centre choosing appropriate procedures.
REFERENCES
1. International Organization for Standardization, Second Edition, Quality Management and Quality
Assurance, Vocabulary, ISO 8402
2. World Meteorological Organization, 1996, Guide to Meteorological Instruments and Methods of
Observation, WMO-No. 8
3. World Meteorological Organization, 1993, Guide on GDPS, WMO-No. 305
4. World Meteorological Organization, 2001, Manual on Codes, WMO-No. 306, Volumes I.2
5. World Meteorological Organization, 1992, Manual on GDPS, WMO-No. 485, Volume I.
6. World Meteorological Organization, 1989, Guide on GOS, WMO-No. 488
7. World Meteorological Organization, 2003, Manual on GOS, WMO-No. 544, Volume I.
8. Automated Surface Observing System (ASOS) User’s Guide
www.nws.noaa.gov/asos/aum-toc.pdf
9. The Impact of Unique Meteorological Phenomena Detected by the Oklahoma Mesonet and ARS Micronet
on Automated Quality Control, Fiebrich, C.A., Crawford, K.C., 2001, Bulletin of the American
Meteorological Society, Vol. 82, No. 10.
http://hprcc.unl.edu/aws/publications.htm
10. Quality Control of Meteorological Observations, Automatic Methods Used in the Nordic Countries, Report
8/2002, Flemming Vejen (ed), Caje Jacobsson, Ulf Fredriksson, Margareth Moe, Lars Andresen, Eino
Hellsten, Pauli Rissanen, Ţóranna Pálsdóttir, Ţordur Arason
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