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Statewide Monitoring of the Mesoscale Environment: A Technical Update on the Oklahoma Mesonet

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Established as a multipurpose network, the Oklahoma Mesonet operates more than 110 surface observing stations that send data every 5 min to an operations center for data quality assurance, product generation, and dissemination. Quality-assured data are available within 5 min of the observation time. Since 1994, the Oklahoma Mesonet has collected 3.5 billion weather and soil observations and produced millions of decision-making products for its customers.
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Statewide Monitoring of the Mesoscale Environment: A Technical Update on the
Oklahoma Mesonet
RENEE A. MCPHERSON,* CHRISTOPHER A. FIEBRICH,* KENNETH C. CRAWFORD,* RONALD L. ELLIOTT,
JAMES R. KILBY,* DAVID L. GRIMSLEY,* JANET E. MARTINEZ,* JEFFREY B. BASARA,*
BRADLEY G. ILLSTON,* DALE A. MORRIS,* KEVIN A. KLOESEL,* STEPHEN J. STADLER,
ANDREA D. MELVIN,* ALBERT J. SUTHERLAND,#HIMANSHU SHRIVASTAVA,* J. D. CARLSON,
J. MICHAEL WOLFINBARGER,* JARED P. BOSTIC,* AND DAVID B. DEMKO*
*Oklahoma Climatological Survey, Norman, Oklahoma
Oklahoma State University, Stillwater, Oklahoma
#Oklahoma Cooperative Extension Service, Oklahoma State University, Norman, Oklahoma
(Manuscript received 11 January 2006, in final form 27 June 2006)
ABSTRACT
Established as a multipurpose network, the Oklahoma Mesonet operates more than 110 surface observing
stations that send data every 5 min to an operations center for data quality assurance, product generation,
and dissemination. Quality-assured data are available within 5 min of the observation time. Since 1994, the
Oklahoma Mesonet has collected 3.5 billion weather and soil observations and produced millions of deci-
sion-making products for its customers.
1. Introduction
The University of Oklahoma (OU) and Oklahoma
State University (OSU) operate more than 110 surface
observing stations comprising the Oklahoma Mesonet
(Brock et al. 1995). Remote stations send data every 5
min to an operations center, located at the Oklahoma
Climatological Survey (OCS), for data quality assur-
ance, product generation, and dissemination. The Okla-
homa Mesonet (see online at http://www.mesonet.org)
was established as a multipurpose network to provide
research-quality data in real time. The mission of its
personnel is to operate a world-class environmental
monitoring network; to deliver high-quality observa-
tions and timely value-added products to Oklahoma
citizens; to support state decision makers; to enhance
public safety and education; and to stimulate advances
in resource management, agriculture, industry, and re-
search. Since 1994, the Oklahoma Mesonet has col-
lected 3.5 billion weather and soil observations and pro-
duced millions of decision-making products for its cus-
tomers.
2. Overview of the Oklahoma Mesonet
Scientists and engineers at OSU and OU planted the
seeds of the Oklahoma Mesonet during the early to
mid-1980s and joined forces in 1987, beginning the part-
nership to design, implement, maintain, and fund the
Oklahoma Mesonet. In late 1990, with the endorsement
of Oklahoma’s governor, the U.S. Department of En-
ergy (DOE) awarded $2.0 million in oil-overcharge
funds to support the design and implementation of the
network. OSU and OU provided an additional $0.7 mil-
lion.
All governing powers of the Oklahoma Mesonet are
vested in and exercised by a six-person steering com-
mittee. Each university controls interest in three seats
on the committee. The committee is responsible for 1)
general supervision of the affairs, funds, and property
of the Oklahoma Mesonet; 2) establishment of organi-
zational policies; and 3) consultation whenever an ac-
tivity creates significant obligations on network staff
time or funds. As a result, the steering committee also
guides strategic planning, develops fundraising strate-
gies, verifies compliance with state and federal statutes,
Corresponding author address: Dr. Renee A. McPherson, Okla-
homa Climatological Survey, University of Oklahoma, 120 David
L. Boren Blvd., Norman, OK 73072-7305.
E-mail: renee@ou.edu
VOLUME 24 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY MARCH 2007
DOI:10.1175/JTECH1976.1
© 2007 American Meteorological Society 301
JTECH1976
monitors long-term risks, and assesses requests for sub-
stantial changes in operational and service activities.
In early 1991, under the direction of the steering
committee, more than 50 experts, representing a variety
of organizations and potential data uses, served on 11
mission-oriented subcommittees to study specific plan-
ning issues (e.g., data communications, station siting,
variables to be measured, equipment specifications,
etc.) and provide recommendations to the steering
committee. The first operational stations were installed
in December 1991, and the last of the original 107 sta-
tions was installed in July 1993. After testing and
troubleshooting, networkwide data collection and dis-
semination began on 1 January 1994.
Support for network operations during the mid-1990s
resulted from contributions by eight state cabinet agen-
cies, a growing base of user fees, and a series of re-
search grants designed to enhance network operations.
In 2000, an external review panel from the American
Association for the Advancement of Science recom-
mended that the steering committee seek permanent
state funding to sustain core network operations and
maintenance. Permanent state dollars of $1.6 million
per year, administered by the Oklahoma State Regents
for Higher Education (OSRHE), were made available
in July 2001.
3. Remote stations and sensors
a. Station siting
In 1991, experts evaluated technical standards for
station siting (Shafer et al. 2000). Their main objective
was to increase data usefulness by ensuring that the
physical characteristics of a site be as representative of
as large an area as possible. Because National Weather
Service (NWS) stations already monitored urban areas,
a secondary objective was to establish a rural network.
Using guidance from the World Meteorological Orga-
nization (WMO; WMO 1983), the site standards com-
mittee provided the following recommendations to the
steering committee:
1) Minimize influences of urban landscapes, irrigation,
forests, and large bodies of water;
2) Minimize obstructions that impede ventilation of
the site;
3) Minimize the slope of the site and neighboring land-
scape;
4) Select locations accessible by light truck or van dur-
ing all seasons; and
5) Minimize extremes of both bare soil and fast-
growing vegetation coverage by selecting sites with
uniform, low-growing vegetation.
The experts also recommended collocation of several
sites with those of other networks, such as the NWSs
Automated Surface Observing System and stations in
the National Oceanic and Atmospheric Administration
(NOAA) Cooperative Observer Network.
Another committee applied these recommendations
and suggested private and public landowners who
would support the network with a no-charge lease of
the land required. Oklahoma Mesonet personnel
scouted possible locations and selected appropriate
sites.
After 12 yr of operation, only one site was moved by
request of the landowner. Two sites were relocated to
improve marginal exposures, one was moved to im-
prove all-season access, two were repositioned to be-
come extensive research stations, and one was moved
because of repeated vandalism. The renamed, alternate
sites were located within a few kilometers of the origi-
nal sites to minimize impacts on scientific research.
b. Station layout
An Oklahoma Mesonet station occupies a 100-m
2
plot of land and comprises a datalogger, solar panel,
radio transceiver, lightning rod, and environmental sen-
sors located on or surrounding a 10-m tower (Fig. 1;
Elliott et al. 1994). Remote stations measure more than
20 environmental variables (Table 1). Installed at the
top of the tower, a lightning rod connects to a ground
rod buried to 2.5 m. On average, lightning strikes eight
stations each year.
The wind speed and direction sensor is mounted on
the top of the tower (Fig. 1). Below, air temperature (at
1.5 and 9 m), humidity (at 1.5 m), and wind (at 2 m)
sensors mount from booms projecting 1 m from the
tower. Near the base of the station, a metal enclosure
contains the datalogger, radio transceiver and modem,
barometer, and battery for the station. Heavy-duty,
welded-wire fencing encloses most stations to protect
the tower from livestock and the groundcover from un-
documented vegetation maintenance by landowners.
Soil temperature is measured at depths of 5 and 10
cm below both a bare plot and a native vegetation
cover. Where bedrock does not prohibit sensor instal-
lation, soil temperature is also monitored at 30 cm un-
der native vegetation. Soil moisture sensors are in-
stalled to the west of the tower at depths of 5, 25, 60,
and 75 cm, if possible. During station installation, soil
samples were extracted from the locations of the below-
ground sensors for soil textural analysis.
Northwest of the tower, a 121-cm-diameter Alter
shield (Alter 1937) surrounds the aboveground rain
gauge. The height of the shield does not exceed that of
302 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 24
the orifice of the gauge, and the screen is beveled to
minimize wind flow across the orifice.
c. Datalogger
In 1999, the Oklahoma Mesonet upgraded from the
Campbell Scientific, Inc. (CSI) CR10T to CR10X-TD
and CR23X-TD dataloggers because of enhanced sen-
sor measurement, sensor control, and on-site data pro-
cessing. Approximately three-quarters of the stations
have more than 25 sensors, requiring the increased sen-
sor-interface capacity of the CR23X-TD (i.e., up to 24
analog voltage signals). Most stations are equipped
with a CSI AM416 relay multiplexer, allowing 32 addi-
tional analog measurements. Four pulse-counting chan-
nels measure wind speed, and eight digital input/output
ports are available to communicate with digital sensors,
switch power to the relative humidity sensor, and detect
reed-switch closure for the unheated tipping-bucket
rain gauge. Several stations use the CR23X-TD and
CR10X in tandem, with the latter operating as a supple-
mental processor for computing flux variables and per-
forming high-frequency measurements with a three-
dimensional ultrasonic anemometer.
d. Station sensors
The sensor suite of the Oklahoma Mesonet includes
primary sensors that are located at every site and sec-
ondary sensors at 100 stations. In 2000, a National
Science Foundation (NSF)-funded project permitted
the installation of experimental sensors at 10 sites that
represented different climate regimes. As of 2006, two
sites remain operational. Table 1 lists the primary, sec-
ondary, and experimental sensors installed across the
Oklahoma Mesonet.
Prior to sensor deployment, personnel in the calibra-
FIG. 1. A schematic of an Oklahoma Mesonet tower with standard equipment and instrumentation.
MARCH 2007 M C P HERSON ET AL. 303
tion laboratory verify that the equipment meets the
Mesonet lab accuracy specifications,the accuracy
designated by Oklahoma Mesonet management as at-
tainable using available calibration equipment. Table 2
catalogs the manufacturers specified accuracy, the Me-
sonets laboratory accuracy specifications, and the ex-
pected field accuracy.
1) BAROMETER
Prior to 2001, the Oklahoma Mesonet recorded at-
mospheric pressure with the Vaisala PTB202 barom-
eter. Production of the PTB202 ceased in 1993. Since
then, technicians have upgraded some stations to the
PTB220 model. The two models have nearly identical
specifications except that the PTB220 has a slightly
lower measurement range. A short length of tubing ex-
tends from the barometers pressure port to outside of
the datalogger enclosure to expose the sensor to the
free atmosphere. The manufacturers specified accu-
racy of the barometer is 0.2 hPa across the range of
7001100 hPa.
2) RELATIVE HUMIDITY SENSOR
The Oklahoma Mesonet upgraded its measurement
of relative humidity from Vaisalas HMP35C sensor to
its HMP45C in 2005, when the vendor stopped produc-
ing the former sensor. The manufacturers specified ac-
curacy for both sensors is 2% between values of 0%
and 90%, and 3% between 90% and 100%. Because
of the sorption sensors inherent inaccuracy at satura-
tion (S. Alpert 2004, personal communication), the Me-
sonet processing system restricts all readings greater
than 100% to an upper bound of 100%.
3) AIR TEMPERATURE SENSORS
Prior to 2004, air temperature at 1.5 m was measured
by the combination thermistorsorption HMP35C
probe (Brock et al. 1995). The HMP35C probe utilized
a Fenwal Electronics UUT51J1 thermistor, added to a
Vaisala sorption sensor by CSI. Temperature data prior
to March 1997 exhibited a varying bias (0.5°to 1.0°C)
caused by sampling the relative humidity (RH) prior to
the air temperature (Fredrickson et al. 1998; Shafer et
al. 2000).
On 1 January 2004, the network transitioned to a
faster-responding, bare-bead thermistor assembly for
1.5-m air temperature measurements (Table 2). This
sensor uses a Thermometrics Unitherm Interchange-
able Thermistor (UIM) DC95 mounted in a stainless
TABLE 1. Variables measured and sensors installed across the Oklahoma Mesonet.
Variable measured Measurement height Primary sensor No. of stations
Relative humidity 1.5 m Vaisala HMP45C 116
Air temperature 1.5 m Thermometrics UIM DC95 116
Rainfall 0.6 m MetOne 380C 116
Pressure 0.75 m Vaisala PTB202/PTB220 116
Solar radiation 1.5 m LI-COR LI-200 116
Wind speed and direction 10 m R. M. Young 5103 116
Soil temperatures under both bare soil and native sod 10 cm BetaTHERM 10K3D410 116
Variable measured Measurement height Secondary sensor No. of stations
Air temperature 9.0 m Thermometrics UIM DC95 100
Wind speed 2.0 m R. M. Young 3101 116
Soil temperature under bare soil 5 cm BetaTHERM 10K3D410 111
Soil temperature under native sod 5 cm BetaTHERM 10K3D410 107
Soil temperature under native sod 30 cm BetaTHERM 10K3D410 106
Soil moisture 5 cm Campbell Scientific 229-L 103
Soil moisture 25 cm Campbell Scientific 229-L 101
Soil moisture 60 cm Campbell Scientific 229-L 76
Soil moisture 75 cm* Campbell Scientific 229-L 37
Variable measured Measurement height Experimental sensor No. of stations
Wind speed 9.0 m R. M. Young 3101 2
Wind speed 3.5 m R. M. Young 3101 2
Net radiation 1.5 m Kipp & Zonen NR LITE 74
Soil heat flux 5 cm REBS HFT 3.1 2
Integrated soil temperature 0 to 5 cm REBS STP 2
Skin temperature 1.5 m Apogee IRT-P 74
Four-component radiation 1.5 m Kipp & Zonen CNR1 2
Three-dimensional wind speed 4 m Campbell Scientific CSAT3 2
* The 75-cm soil moisture sensors are being decommissioned from each site when they fail.
304 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 24
steel housing that couples the thermistor with the at-
mosphere. The Oklahoma Mesonet used this thermis-
tor for its 9-m measurements since 1994. The Thermo-
metrics sensor has an operating range of 30°to 50°C.
R. M. Young multiplate radiation shields house both
air temperature sensors. Power constraints at the sta-
tion required a nonaspirated shield; hence, field inac-
curacy can be as high as 1°C in light winds and strong
radiation (Hubbard et al. 2004; Tanner et al. 1996).
Fortunately, these conditions are relatively infrequent
across Oklahoma.
4) RAIN GAUGE
Since inception, the Oklahoma Mesonet has used a
30.5-cm-diameter MetOne tipping-bucket rain gauge.
During the mid-1990s, network personnel modified the
gauge substantially to reduce unscheduled technician
visits to fix problems (Shafer et al. 2000). Changes in-
cluded raising the height of the funnel rim, using a more
robust tipping-bucket bearing, and changing to a mag-
netic switch. The gauge has a resolution of 0.25 mm.
Station power constraints prohibit the use of any heat-
ing device that would allow for measurement of frozen
precipitation. As a result, snow and ice are measured as
liquid equivalent after melting occurs.
5) PYRANOMETER
The Oklahoma Mesonet uses LI-CORs LI-200 sili-
con photodiode-type pyranometer to measure down-
TABLE 2. Manufacturers specified accuracy, laboratory accuracy designated by the Oklahoma Mesonet, and expected field accuracy
(if different from laboratory accuracy). (More information is available online at http://www.mesonet.org/instruments.)
Sensor
Manufacturers specified
accuracy
Mesonet laboratory
accuracy Expected field accuracy
Vaisala PTB202/PTB220 0.20 hPa 0.4 hPa Same as laboratory
accuracy*
Vaisala HMP45C 2% from 0% to 90%
RH; 3% from 90% to
100% RH
3% for RH from 10% to
98%
Same as laboratory
accuracy*
Thermometrics UIM DC95 0.4°C0.35°CUpto1°C in light winds
and strong radiation
(Hubbard et al. 2004)
MetOne 380C 1% for rain rates from
2.54 to 7.62 cm h
1
at
21°C
2% for static test; 5%
over the range from 0 to
5cmh
1
Up to 5% during high
winds (Duchon and
Essenberg 2001)
LI-COR LI-200 5% Exact at 450 W m
2
5% for solar radiation
greater than 400 W
m
2
*
R. M. Young 5103 0.3 m s
1
for speed; 3°
for direction
0.3 m s
1
for speed; 3°
for direction
Same as laboratory
accuracy
R. M. Young 3101 0.5 m s
1
0.5 m s
1
Same as laboratory
accuracy
BetaTHERM 10K3D410 0.5°C0.5°C Same as laboratory
accuracy
Campbell Scientific 229-L Not specified for
volumetric water
content**
Not specified for
volumetric water
content**
0.06 volumetric water
content**
Kipp & Zonen NR LITE Not specified Factory calibrated N/A
REBS HFT 3.1 5% Factory calibrated Same as manufacturers
accuracy
REBS STP Not specified 2% Same as laboratory
accuracy
Apogee IRT-P 0.5°C Factory calibrated Same as manufacturers
accuracy
Kipp & Zonen CNR1 10% for daily total Factory calibrated Same as manufacturers
accuracy
Campbell Scientific CSAT3 Horizontal wind: 0.04
ms
1
; vertical wind:
0.02 m s
1
Factory calibrated Same as manufacturers
accuracy
* Verified via field intercomparisons (see section 5b).
** The vendor provides accuracy information only for the thermocouple component of the sensor. This specification is not translated
easily into accuracy of volumetric water content because of the variables dependence on soil type. Field experiments of the Oklahoma
Mesonet have resulted in the listed field accuracy.
MARCH 2007 M C P HERSON ET AL. 305
welling, global solar radiation. Sensor-specific calibra-
tion coefficients are applied to the data. The pyranom-
eter originally was mounted on a separate tripod south
of the tower (Brock et al. 1995); in 1999, however, the
sensor was moved to a boom extending southward from
the tower to minimize lightning damage.
Measurements during morning and evening are sen-
sitive to obstructions to the east and west of the station.
(This sensitivity may be examined using panoramic site
photos available online at http://www.mesonet.org/sites.)
6) WIND SENSORS
WMO-standard wind observations are measured at
10 m with the R. M. Young 5103 wind monitor, a com-
bination propeller and vane anemometer. The starting-
threshold wind speed is 1.0 m s
1
for the propeller and
1.1 m s
1
for the vane. The sensor can record speeds
between 1.0 and 60.0 m s
1
with a specified accuracy of
0.3 m s
1
. The wind direction accuracy is 3°. From
this sensor, the datalogger computes a 5-min-average
scalar wind speed, 5-min-average vector wind speed
and direction, 5-min standard deviations of wind speed
and direction, and maximum 3-s wind speed during the
5-min period.
The R. M. Young 3101 cup anemometer measures
wind speed observations at 2 and 4 m (Brock et al.
1995) with a manufacturers specified accuracy of 0.5
ms
1
. The datalogger only reports a 5-min average
wind speed for this sensor.
7) SOIL TEMPERATURE SENSOR
Fenwal Electronics, Inc., manufactured the original
soil temperature sensor installed by the Oklahoma Me-
sonet. The Fenwal sensor used a chip thermistor,
housed in a sealed stainless steel tube, and had a speci-
fied accuracy of 0.4°C. Because of a supply problem
that the manufacturer could not overcome, the Okla-
homa Mesonet switched to a BetaTHERM model in
1997. The BetaTHERM is similar to the previous sen-
sor except that electronics potting material, rather than
air, fills the gap between the thermistor and the tubular
housing.
Because soil temperature gradients can be substan-
tial through the top 10 cm of soil, field technicians at-
tend to maintenance of accurate sensor depth, espe-
cially after heaving occurs during the winter (Fiebrich
et al. 2006). From late spring to early fall, direct sunlight
on the tower casts afternoon shadows upon the soil
temperature plots for up to 30 min. In extreme in-
stances, this shading results in an artificial, 1°–2°C de-
crease in 5-cm soil temperature.
8) SOIL MOISTURE SENSOR
In 1996, the Oklahoma Mesonet installed CSIs
229-L soil moisture sensors at approximately half of its
sites (Basara and Crawford 2000, 2002). Since then, ad-
ditional sites have received soil moisture sensors.
The 229-L is a heat dissipation sensor that utilizes a
thermocouple as a temperature sensor and a resistor as
a heating element, both housed within a hypodermic
needle embedded within a porous ceramic matrix. Dur-
ing operation, the thermocouple measures the ambient
temperature immediately before and after a 21-s heat-
ing of the sensor by an electric current. The difference
between the two measurements is large (small) in dry
(wet) soil. This difference measures heat dissipation,
related directly to the soil-water potential (Reece 1996;
Starks 1999; Basara and Crawford 2000). Volumetric
water content of the soil is calculated via an empirical
relationship that uses soil-water potential and the soil
characteristics (Arya and Paris 1981).
9) EXPERIMENTAL SENSORS
The Oklahoma Mesonet has deployed many experi-
mental sensors. For example, from 1994 to 1999, leaf
wetness was measured using printed circuit boards
(Fisher et al. 1992). In 1999, 89 stations were aug-
mented with instruments to measure components of the
surface energy and radiation budgets. Sensors included
a net radiometer (Kipp and Zonen NR LITE; Brotzge
and Duchon 2000), ground heat flux plates (REBS HF
3.1), 05-cm integrated soil temperatures (REBS STP),
and infrared temperature sensors (Apogee Instru-
ments, Inc.) measuring surface skin temperature (Fie-
brich et al. 2003).
At 10 of these upgraded stations, a sonic anemom-
eter (CSIs CSAT3) and krypton hygrometer (CSIs
KH20) were added to compute sensible and latent heat
fluxes (Brotzge 2000). Furthermore, a four-component
net radiometer (Kipp and Zonen CNR 1) at these sites
provided observations of both upwelling and down-
welling shortwave and longwave radiation. The krypton
hygrometers were not designed for continuous, long-
term use and were removed from the network in 2004.
Other sensors were removed in 2006 (see Table 1).
e. Sampling and averaging
Stations report data in 5-, 15-, or 30-min records,
depending on the variable measured. The 5-min record
contains averages for all aboveground sensors, heat
flux, battery voltage, and station maintenance status.
The sampling rate for aboveground sensors is 3 s, with
the exception of the barometer (12 s) and rain gauge
(event driven). The record also reports the maximum
306 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 24
wind speed (i.e., the highest 3-s sample during the 5-min
period). Liquid precipitation is recorded as the number
of bucket tips since 0000 UTC.
The 15-min record contains averages for soil tem-
peratures using a sample rate of 30 s. Soil moisture,
based on a single sample per depth measured, com-
poses the 30-min record. On managements request,
stations can report 1-min averages of most above-
ground sensors in a separate data record. Two stations
also send three additional 5-min data records for sen-
sors sampling at 1 Hz (four-component radiation) and 8
Hz (three-dimensional wind speed).
f. Power requirements
Solar energy powers all Oklahoma Mesonet stations.
Power requirements range from 1.34 W, depending on
the communications and sensor configuration. In 2004,
a networkwide communications system upgrade re-
quired an increase in the capacity of the power system.
Currently, the system includes a 30-W photovoltaic
module, charging regulator, and 75-AH sealed lead-
acid battery. Two of the stations require an additional
100-AH battery and 30-W photovoltaic module to
power experimental sensors (Table 1). All stations have
a minimum power reserve of 18 days.
4. Communications, operations, and monitoring
a. Communications
The Oklahoma Law Enforcement Telecommunica-
tions System (OLETS) forms the backbone of the
Oklahoma Mesonet communications infrastructure at
no additional cost to state taxpayers. OLETS supports
a high-speed digital communications network con-
nected to 250 city, county, state, federal, and military
law enforcement and criminal justice agencies across
Oklahoma.
The Oklahoma Mesonet system polls each observa-
tion station every 5 min. Data are sent from the station
to a nearby OLETS connection via VHF radio (at 4800
bps) using one of two NOAA hydrologic frequencies
(Fig. 2). At each OLETS-connected agency, the data
are streamed via TCP/IP on a secure, encrypted Wide
Area Network to the OLETS Network Switching Cen-
ter and finally to OCS for ingest into the Mesonet
processing system (Fig. 3). The minimum speed for
OLETSs connections to its agencies is 56 Kbps, suffi-
cient for transmission of Oklahoma Mesonet data (at its
daily average bandwidth of 60 bps).
The Oklahoma Mesonet also employs two small net-
works of 900-MHz spread-spectrum communications
(Fig. 2). One network collects data in a region where
the Oklahoma Mesonet has experienced transmission
difficulties. These data also arrive in Norman, Oklaho-
ma, via OLETS linkages. The other network supports
data transfer directly to the operations center from sta-
tions in line of site to a 15-story building on the OU
campus.
All communications pathways from the station to the
Oklahoma Mesonets central computers are two way,
enabling operators at OCS to set clocks, download
FIG. 2. Radio communications links, including intermediary repeaters, from Oklahoma Mesonet stations to either law enforcement
offices (via VHF radio) or the Oklahoma Climatological Survey (via 900 MHz). Map valid December 2005.
MARCH 2007 M C P HERSON ET AL. 307
Fig 2 live 4/C
datalogger programs, and request missed or corrupted
observations to be resent. In the event of communica-
tions failure, 100 stations store data for more than 40
days and the remaining stations store data for 13 days.
b. Computer ingest, processing, and
dissemination system
Oklahoma Mesonet data are collected using mul-
tiple, redundant network connections to CSIs Logger-
Net software. The LoggerNet system uses four inexpen-
sive x86 servers running Windows XP Professional. Ob-
servation records are sent, via TCP/IP sockets, from
LoggerNet to multiple instances of ingest and archival
software running on four x86 Linux servers, where they
are stored as both ASCII and NetCDF files. Real-time
and historical data ingest, processing, quality assurance
(QA), and product generation are distributed across
the four Linux machines. Because generic computers
are used, any computer outage can be resolved by re-
placing the broken unit with an off-the-shelf spare.
XServe RAIDs archive data utilizing RAID-5 sets and
hot spare disks for redundancy and availability.
Data arrive at the central computing facility 33.5
min after the variables are measured. Within 2 min,
data are quality assured and products are disseminated
from three load-balanced Web and FTP servers. Users
downloaded more than 525 GB of Oklahoma Mesonet
data files and an additional terabyte of related data and
products (e.g., agricultural model output, radar data)
during 2004.
c. Database
To manage both data and metadata, the Oklahoma
Mesonet uses a MySQL relational database. The data-
base has four interrelated components: 1) a user mod-
ule, 2) a network site module, 3) an equipment module,
and 4) a quality assurance module. The database inte-
grates network data ingest, processing, and quality as-
surance.
The databases user module stores and retrieves in-
formation about each internal user type, including sys-
tem administrators, laboratory and field personnel, and
quality assurance meteorologists. The module manages
permissions to check instruments to or from the inven-
tory, to insert or edit calibration coefficients, or to view
metadata. The network site module stores information
regarding the overall network (e.g., commissioning
date), details about each site (e.g., latitude, distance to
nearest city), and both the variables measured and their
attributes (e.g., units of measurement).
FIG. 3. A schematic of the communications system of the Oklahoma Mesonet. Key features include the transfer of data through
OLETS and polling by CSI LoggerNet software.
308 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 24
Information about individual sensors and other
equipment is managed by the databases equipment
module. This module maintains the date received, serial
number, cost, manufacturer, dates commissioned and
retired, calibration history (e.g., date, resultant coeffi-
cients), installation date and location, and problem his-
tory of each item, as appropriate. When anyone handles
equipment, the location and handler are recorded in
the database.
The value of the previous modules increases signifi-
cantly when they integrate with the QA module. As
detailed in sections 5c and 5d, should a data problem be
detected, a meteorologist makes the final decision re-
garding how to flagthe data. The meteorologist is-
sues a trouble ticketthat records the problem, the
required deadline for repair, and the quality flag as-
signed to associated observations until the problems
resolution. The trouble ticket is sent automatically to
the field technicians. Repair details include the date
and time fixed, what error source was diagnosed, and
how the issue was resolved (e.g., sensor cleaning, equip-
ment replaced). The information submitted by the tech-
nician becomes available to the QA meteorologist for
inclusion in the database.
d. Real-time operations and monitoring
The Oklahoma Mesonet supports an operations and
monitoring center that combines both automated and
manual oversight of real-time data ingest, processing,
and dissemination. Operators monitor data ingest, sup-
port technicians in the field, check every product daily,
collect any data that missed their scheduled collection
time, provide customer support, serve on-call in case of
emergencies, and issue network status reports. Web
pages display information from database modules, al-
lowing these student operators to aid remote field tech-
nicians efficiently.
Automated monitoring programs issue pages if a sys-
tem fails, communications are disrupted, or products
are not created. These programs decrease the adminis-
trative pressure to staff the operations center continu-
ously. The operator on call can access necessary soft-
ware utilities via the Internet or modem connections
within minutes of an automated system page, or staff
can arrive at the operations center within 15 min if
network connections have been disrupted.
5. Data QA
The primary focus of network operations and main-
tenance is to obtain research-quality observations in
real time. From the receipt of a sensor from a vendor to
the dissemination of real-time and archived products,
the Oklahoma Mesonet follows a systematic, rigorous,
and continually maturing protocol to verify the quality
of all measurements (Fig. 4).
a. Sensor calibration
Oklahoma Mesonet personnel test or calibrate every
new sensor before it is deployed to the field (i.e., pre-
field calibration) and every previously installed sensor
after its return from the field for repair or rotation (i.e.,
postfield calibration). The postfield calibration occurs
prior to sensor cleaning and adjustment, thus docu-
menting the sensors performance at the time of re-
moval from the remote station. If the sensor can be
cleaned, repaired (if needed), and rotated back into the
field, then it is calibrated again, prior to redeployment.
Experience gained through Oklahoma Mesonet op-
erations since 1994 has led to preferred rotation inter-
vals for some sensor types (Table 3; Fiebrich et al.
2006). A technician replaces a stations sensor if it has
resided in the field beyond the residence time desig-
nated for its sensor type, even if no problems with the
sensors data are evident.
1) BAROMETER
Laboratory personnel calibrate barometers using a
two-reservoir pressure system and a commercial tem-
perature chamber. A technician connects each barom-
eter to the pressure system via tubing, and then places
a set of barometers into the chamber. The system uses
a Paroscientific barometer, certified by the National
Institute of Standards and Technology (NIST), as a ref-
erence. It also uses a Vaisala PTB220 as a lab stan-
dard(i.e., a barometer from the Mesonets general
sensor inventory that is designated to remain in the
calibration system during every run to ensure the sta-
bility and continuity of all tests).
Because the accuracy of the silicon-capacitive abso-
lute-pressure sensor used in the digital barometers is
dependent on temperature, the barometers are cali-
brated across ranges of pressure (6501100 hPa) and
temperature (25°to 50°C). As the calibration pro-
gresses, electronic valves precisely release air from the
high to the low pressure reservoir, resulting in a series
of pressure steps. Meanwhile, the temperature chamber
sinusoidally varies the environmental air temperature.
Upon completion of the run, customized LabVIEW
software computes and downloads a set of eight coef-
ficients to bring the error of each barometer within the
laboratory accuracy of 0.4 hPa.
MARCH 2007 M C P HERSON ET AL. 309
2) RELATIVE HUMIDITY SENSOR
The Oklahoma Mesonet uses a Thunder Scientific
Model 2500 Benchtop Humidity Generator, a NIST-
certified reference instrument, to calibrate its relative
humidity sensors. Two different models of humidity
sensors remain in the chamber for every calibration run
and serve as laboratory-standard instruments. The
Thunder Scientific chamber uses a two-pressure
method to produce known humidity values. The cali-
bration system checks the accuracy of humidity mea-
surements at 10% intervals from 10% to 98%. If a sen-
TABLE 3. Rotation schedule for the instruments used in the Oklahoma Mesonet (adapted from Fiebrich et al. 2006).
Sensor Variable measured Rotation interval (months)
Campbell Scientific HMP45C Relative humidity 24
LI-COR LI-200 Solar radiation 36
MetOne 380C Rainfall Upon failing field drip test
R. M. Young 5103 Wind direction at 10 m 60
R. M. Young 5103 Wind speed at 10 m 48
R. M. Young 3101 Wind speed at 2 and 4 m 24
Thermometrics UIM DC95 Air temperature at 1.5 and 9 m 60
Vaisala PTB202/220 barometer Pressure 48
FIG. 4. Schematic of the data QA system of the Oklahoma Mesonet. Arrows display the transfer of information or equipment. The
diagram demonstrates the critical role of manual QA to the integrity of the observations.
310 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 24
Fig 4 live 4/C
sor is not accurate to 3% throughout this range, a
technician will adjust it appropriately using potentio-
meters within the sensors circuitry.
3) AIR TEMPERATURE SENSORS
Technicians calibrate air temperature sensors using a
Tenney temperature chamber and two NIST-certified
reference temperature probes by Hart Scientific. To
ensure that probes are in equilibrium, a technician
places the reference and test probes in an aspirated
inner chamber within the main temperature chamber.
The sensors are tested across the range from 30°to
50°C using 10°C steps. Because correction coeffi-
cients are not applied to temperature probes, every sen-
sor must meet the 0.35°C error specification or it is
not deployed.
4) RAIN GAUGE
Rain gauges are calibrated with a two-step process.
First, a laboratory technician performs a static calibra-
tion to ensure that the tipping-bucket mechanism re-
sults in a tip when 18.53 mL of water enters a bucket.
The bucket mechanism is adjusted so that a tip occurs
within 2% of the proper water volume.
Upon successful completion of the static calibration,
the technician performs a dynamic calibration similar to
that described in Brock et al. (1995). A digital scale
weighs water as it drains from a 5-L cylindrical reser-
voir into the rain gauge. Simulated rain rates are output
from measurements taken by both the scale and the
gauge. Software computes correction coefficients based
on rain rate, and the resultant calibration record de-
scribes the gauge performance at various rates. This
calibration accounts for the mechanics of the gauge that
prevent the buckets from tipping instantaneously (Du-
chon and Essenberg 2001). A higher rain rate results in
a larger underestimation of rainfall.
5) PYRANOMETER
Prior to 2005, the Oklahoma Mesonet tested its sili-
con-cell pyranometers against a calibrated Eppley PSP.
These sensors were mounted side by side outdoors, and
data were collected for 12 weeks during days with
relatively high solar-elevation angles (e.g., May
September). This method required a technician to de-
cide subjectively which observations should be used to
generate calibration coefficients (e.g., all observations
or those only during clear days).
In late 2004, the Oklahoma Mesonet purchased a
Kipp and Zonen Calibration Facility for indoor calibra-
tion. This system exposes both reference and test pyra-
nometers to a stable metal-halide lamp that radiates at
450Wm
2
. The reference sensor, a LI-COR silicon-
cell pyranometer, has been calibrated by the Solar Ra-
diation Research Laboratory at the National Renew-
able Energy Laboratory. Based on observed readings
from the two sensors, LabVIEW software calculates a
calibration coefficient. This method takes only minutes
to perform, provides an objective calculation of calibra-
tion coefficients, and allows for calibrations at any time
of the year.
6) WIND SENSORS
The Oklahoma Mesonet uses an outdoor facility to
check its propeller- and cup-type wind sensors. Techni-
cians install sensors on 3-m steel posts in a 9-m
2
grid for
714 days, after which they compare wind speeds from
the test sensors and a NIST-certified propeller-vane
reference sensor. Test sensors must have a mean error
of 0.2 m s
1
to pass. Mesonet personnel manually
check and adjust the direction portion of the propeller-
vane sensor using a vane-angle bench stand to align the
vane through the range of 0°–359°within a 3°error
specification.
7) SOIL TEMPERATURE SENSOR
Laboratory technicians use a bath of 50% water and
50% antifreeze to calibrate soil temperature sensors.
The probes are placed in a 4-L beaker with both a
NIST-certified Hart Scientific reference thermometer
and a laboratory standard sensor. A sensor mount
holds the probes at the same depth in the bath. A
freezer cools the beaker to 25°C. The beaker is placed
on a stirring hot plate and its temperature is increased
slowly until the liquid reaches 60°C. Every sensor must
meet a 0.5°C error specification before it can be de-
ployed to a station.
8) SOIL MOISTURE SENSOR
Soil moisture sensors receive both laboratory and
field calibrations. The laboratory calibration is a two-
point test that uses the driest and wettest observations
that can be attained in the laboratory environment. To
obtain the dry point, probes are sealed in a plastic bag
with desiccant; for the wet point, probes are immersed
in distilled water. A datalogger records the sensor read-
ings in each environment for 5 days, and calibration
coefficients are generated via linear regression. After
the sensors are deployed in the field, their individual
coefficients are updated if the soil creates drier or wet-
ter situations than observed in the laboratory.
MARCH 2007 M C P HERSON ET AL. 311
b. Site passes and field intercomparison
During each of three, scheduled annual maintenance
passes, Oklahoma Mesonet technicians perform stan-
dardized tasks, including cleaning and inspecting sen-
sors, verifying depth of subsurface sensors, cutting and
removing vegetation, taking photographs, and conduct-
ing on-site sensor calibrations (Fiebrich et al. 2006). In
addition, technicians may upgrade equipment, rotate
sensors, and perform communications testing and
maintenance. The technician opens the datalogger en-
closure door upon arrival and closes it on departure,
signaling the automated QA system to mark all data as
erroneous (Shafer et al. 2000). Digital photographs
(available online at http://www.mesonet.org/sitepass)
are taken of the 100-m
2
site; net radiometer footprint;
vegetation height (both upon arrival and departure);
and plots where soil temperature, soil moisture, and soil
heat flux are measured.
In 1999, Oklahoma Mesonet management required
that vegetation height be cut to match the height of
surrounding vegetation, to a maximum height of 45 cm.
To protect equipment from wildfire, the technician also
cuts a firebreak (maximum height of 5 cm) in a swath
that extends from the tower base to the rain gauge
(Fig. 5).
To examine sensor accuracy in the field, an instru-
mentation meteorologist designed and manufactured a
portable system to compare field observations of air
temperature, relative humidity, solar radiation, and
barometric pressure to calibrated reference sensors
(Table 4; Fiebrich et al. 2006). This portable system
aspirates both the reference and station air temperature
and relative humidity sensors. A personal data assistant
interfaces with the system to collect comparison obser-
vations, to display data for on-site evaluation by the
technician, and to generate a detailed report for analy-
sis by QA meteorologists.
c. Automated QA software
The automated QA software of the Oklahoma Me-
sonet is designed to detect significant errors in the real-
time data stream and to incorporate manual QA flags
that capture subtle errors in the archived dataset. Ob-
servations are never altered; each datum is flagged as
good,”“suspect,”“warning,or failure.Automated
flags are set using a three-step process: 1) QA filter, 2)
QA independent algorithms, and 3) QA decision
maker. The QA filter immediately flags data coincident
with a technician visit, those failing the variables range
test, and those known to be bad (as determined by a
QA meteorologist). The QA independent algorithms
differ according to the variable observed, but include
step, persistence, and spatial tests. In addition, like-
instrument and variable-specific tests are applied when
appropriate. The QA decision maker compiles the re-
sults of the independent tests and uses logic to deter-
mine the final automated QA flag assigned to the ob-
servation. Only good and suspect data are delivered in
real time to users.
Written in C⫹⫹, the automated QA software uses a
single program for real-time, daily, and historical use
through XML-based configuration files. For real-time
data, up to eight QA tests are run per observation,
operating on the past6hofdata. Once per day, up to
13 QA tests are run on each variable, operating on the
past 30 days of data. As a result, more than 111 million
calculations are completed daily. Currently, real-time
QA completes within approximately 1 min.
d. Manual QA
Human intervention can override any automated QA
flag. The QA meteorologist determines whether auto-
mated flags mark real events (e.g., heatburst, gust front;
Fiebrich and Crawford 2001) by analyzing the results of
a Web-based, daily QA report. The report lists stations
with suspected data problems, what variable the auto-
mated QA software has flagged, and the number of
observations flagged by the individual tests (Martinez
et al. 2004; Shafer et al. 2000; Fiebrich and Crawford
2001). The meteorologist can obtain detailed output
from the automated tests, a graph of the variable from
the site in question and neighboring sites, a graph of
both the variable in question and other relevant vari-
ables at the station, and a tabular output of the original
observations. Should data be deemed erroneous, the
start date/time of the problem is analyzed and observa-
FIG. 5. An example of the firebreak vegetation cut at the
Broken Bow station of the Oklahoma Mesonet.
312 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 24
Fig 5 live 4/C
tions are flagged manually in the QA database. The
QA meteorologist then issues a trouble ticket to the
appropriate field technician. All sensor problems are
documented with a trouble ticket.
To detect subtle problems in the rainfall dataset, a
meteorologist compares a stations accumulated rainfall
to that of nearby sites using double mass analysis (Mar-
tinez et al. 2004). To aid in the analysis, the QA me-
teorologist uses complementary datasets, including soil
moisture observations, rain gauge data from the U.S.
Geological Survey, NWS surface observations, radar
data, and terrain maps. For example, OCS software
calculates storm-total rainfall from Oklahoma Mesonet
data for the time interval corresponding to the NWS
storm-total precipitation product. A resultant map is
generated (Fig. 6), and the QA meteorologist compares
the values to find any malfunctioning gauges.
Examination of monthly data supplements daily and
event-driven manual QA to detect slight biases or drift
in sensors. Monthly statistics for each variable (e.g.,
averages, differences, accumulations) are computed,
plotted, and analyzed both spatially and temporally
(Martinez et al. 2004). Data from similar instruments at
different heights or depths allow the diagnosis of sensor
biases, small-amplitude noise in the data, and anemom-
eter starting-threshold problems. After analyses are
complete, the meteorologist prepares a monthly QA
report that documents any problems as well as repairs
and activities performed by field technicians that may
have affected data quality during the previous month.
As a result of continual development of automated
and manual techniques, the QA process of the Okla-
homa Mesonet continues to improve. As improvements
are implemented, all data in the period of record are
reprocessed through the QA system to obtain the most
consistent, highest-quality dataset for climate analysis
and scientific research.
6. Applications of data
The Oklahoma Mesonet provides its data and value-
added products to citizens, businesses, state agencies,
growers, educators, first responders, and other Oklaho-
ma decision makers. High-quality datasets have sup-
ported a host of scientific investigations and attracted
multidisciplinary research projects to the state. Instruc-
tional programs help nonmeteorologists apply Oklaho-
ma Mesonet data and products properly to their daily
activities. Specific audiences targeted by these outreach
programs include education (both K12 and collegiate),
public safety, and agriculture. These customers, in turn,
provide grassroots support via vocal and positive feed-
back to the state legislature and OSRHE, the sources of
network funding. More importantly, local decision
makers have used the data to mitigate negative societal
impacts from weather events and natural or man-made
disasters (e.g., Morris et al. 2002).
a. Outreach programs
From 1992, the Oklahoma Mesonet made education-
al outreach a priority. The motivation was twofold: 1)
to foster multipurpose uses of its data, and 2) to ad-
vance political support statewide. Two years prior to
network commissioning, OCS launched a K12 pro-
gram known as EarthStorm with intensive workshops
for teachers (McPherson and Crawford 1996). Materi-
als evolved from hard copy lessons and a dial-up bul-
letin board system to a comprehensive Web site (see
online at http://earthstorm.ocs.ou.edu). Teacher work-
shops focused on authentic learning modules (Killion
1999), core scientific knowledge, and development of
curricula and science fair projects. Between 1993 and
2005, scientists, engineers, and mathematicians judged
885 Oklahoma Mesonet-related projects, representing
1215 K12 students, in statewide science fairs.
Based upon EarthStorm experiences, OCS began a
program for emergency management, fire service, and
law enforcement agencies in 1996. Called OK-First, this
initiative provides access to real-time weather data via
a Web-based decision-support system for multiple haz-
ards (see online at http://okfirst.ocs.ou.edu), a manda-
tory instructional regimen, and customer support (Mor-
ris et al. 2001). Consequently, OK-First participants
proactively have evacuated citizens, closed roads and
bridges before flooding, notified fire crews of impend-
ing wind shifts, provided weather support for local civic
and athletic events, and improved responses to natural
disasters.
EarthStorm and OK-First pioneered the dissemina-
tion of Oklahoma Mesonet products, radar data,
TABLE 4. Station and reference sensors used in field comparisons by the Oklahoma Mesonet.
Variable measured Station sensor Reference sensors (calibrated accuracy)
Slow-response air temperature Vaisala HMP45C Rotronics Pt100 RTD (0.1°C); Thermometrics UIM DC95 (0.2°C)
Fast-response air temperature Thermometrics UIM DC95 Rotronics Pt100 RTD(0.1°C); Thermometrics UIM DC95 (0.2°C)
Relative humidity Vaisala HMP45C Rotronics MP 100H (1% RH); Vaisala HMP35C (3% RH)
Barometric pressure Vaisala PTB 202/220 Vaisala PTB220 (0.1 hPa)
Solar radiation LI-COR LI-200 Kipp and Zonen SP LITE (5%)
MARCH 2007 M C P HERSON ET AL. 313
and instructional materials to rural users. In 1996, OSU
initiated outreach efforts for the agricultural and natu-
ral resource communities by creating an AgWeather
Web site (see online at http://agweather.mesonet.org).
AgWeather implements a variety of weather-based ag-
ricultural models and decision-support tools that inte-
grate Oklahoma Mesonet data and value-added prod-
ucts with market summaries from the U.S. Department
of Agriculture (USDA), programs and publications of
the Oklahoma Cooperative Extension Service, and in-
formation from grower associations.
b. Products using real-time data
The Oklahoma Mesonet Web site (see online at
http://www.mesonet.org) is the primary conduit for
both information about the network and observations
from the network. Real-time products range from
single- and multiple-variable plots, contour maps, and
meteograms for all customers to models and forecasts
generated for specific user communities. The suite of
static images and interactive displays (Table 5) refresh
every 5 min, allowing NWS forecasters and 180 Okla-
homa public safety officials to maintain situational
awareness 24 h day
1
.
A popular product for Oklahomas broadcast media
is the Mesonet Top-10(Fig. 7), a current summary of
temperature, precipitation, and solar radiation ex-
tremes since midnight local time. The media, public
safety, and water resources communities use maps of
rainfall accumulation for periods ranging from 1 to 72 h.
FIG. 6. An example of tools used by a meteorologist to ensure the quality of Oklahoma Mesonet data. In this case, a malfunctioning
rain gauge at the Tahlequah (TAHL) site was diagnosed by overlaying storm-total precipitation from the Fort Smith, AR, radar (at 1933
UTC 5 Jun 2005) with (top right) Oklahoma Mesonet rainfall observations (in.), (bottom left) radar-estimated rainfall (in.), and
(bottom right) the difference between the gauge observations and radar estimates (in.). The TAHL gauge recorded no rain during the
event from 2307 UTC 4 Jun 2005 to 1933 UTC 5 Jun 2005. Nearby Oklahoma Mesonet gauges, however, received up to 25 mm (1.0
in. on map) of rain. The quality assurance meteorologist confirmed that the gauge at TAHL had malfunctioned, issued a trouble ticket,
and manually flagged the rainfall data as erroneous.
314 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 24
Fig 6 live 4/C
In addition, a decision-support tool compares current
rainfall accumulations with corresponding county-by-
county values of flash flood guidance from the NWS
(Fig. 8). To help officials document significant wind
events, the Oklahoma Mesonet produces maps and
tables of the current and previous days maximum wind
gusts. First responders also receive pages automatically
when gusts exceed severe criteria (25.7 m s
1
).
To serve fire managers, the Oklahoma Mesonet cre-
ated the Oklahoma Fire Danger Model (Carlson et al.
2002), adapting the National Fire Danger Rating Sys-
tem to incorporate real-time county-scale data. This
model generates tables and 1-km-resolution maps of
fire danger parameters, including burning index, spread
component, ignition component, and the Keetch
Byram Drought Index.
Tools designed to support agricultural operations in-
clude a dispersion model; evapotranspiration models
(for more than a dozen specific crops); degree-day cal-
culators for Oklahoma crops; and pest-management
models for alfalfa weevils, peanut leafspot, pecan scab,
watermelon anthracnose, pecan casebearer, and spin-
ach white rust. Pest models feature interactive site-
specific spraying recommendation forms (Fig. 9). Ini-
tially developed to mitigate odors downstream from
livestock operations, the Oklahoma Dispersion Model
also has applications for smoke management, pesticide
application, and hazardous material spills.
c. Products using archived data
Climatological, agricultural, and hydrological prod-
ucts are generated from archived Oklahoma Mesonet
data to assist Oklahoma decision makers. Statewide
and county-scale climatologies of temperature, precipi-
tation, and heating/cooling degree-days have applica-
tions to energy, agriculture, and water resources man-
agement.
Some agricultural tools blend Mesonet archives with
daily data. For example, OSUs Spinach White Rust
Model incorporates 10-yr averages of specific variables
and compares them graphically to the current years
values and projected values of white rust infection
hours.
Climate-division and county-level precipitation sta-
TABLE 5. Suite of products produced by the Oklahoma Mesonet
that update every observation period on its Web site (see online
at http://www.mesonet.org).
Meteorological station model plot
Fire-weather station model plot
Heat index or wind chill
Hours below freezing
High and low temperatures*
Max wind gusts*
Rainfall accumulations for 1, 3, 6, 12, 24, 48, and 72 h since
midnight local time*
3- and 24-h change in temperature and dewpoint
3-h pressure change
Meteograms (24-h time series of air temperature, dewpoint, wind
speed and direction, pressure, rainfall, and solar radiation)
Flash flood guidance product
Dispersion conditions
Inversion conditions
Contour and vector (gridded) plots of standard meteorological
variables
Graphs of individual (or grouped) variables for past 6, 12, 24, 36,
and 48 h
Graphs of soil moisture for past 7, 15, 30, 60, or 90 days
Maps of average soil temperature for past 1, 3, and 7 days
Maps of fractional water index, moisture category, and matric
potential
* This product includes values for both current and previous days.
FIG. 7. Oklahoma Mesonet top-10product developed for the
broadcast media. The product denotes daily extreme values ob-
served across the network.
MARCH 2007 M C P HERSON ET AL. 315
Fig 7 live 4/C
tistics, such as total rainfall, departure from and percent
of normal rainfall, driest and wettest periods, and sta-
tistical rankings for various time periods (e.g., year, sea-
son, water year), help water managers determine cur-
rent water supplies and demands. Soil moisture obser-
vations at four different depths (Fig. 10) are used to
derive the fractional water index, representing the rela-
tive dryness of the soil (Schneider et al. 2003). These
products and other historical data compose the Okla-
homa Drought Monitor (see online at http://
climate.ocs.ou.edu/drought/), designed for the Oklaho-
ma Water Resources Board and other hydrologic agen-
cies.
d. Visualization software
To aid customers in viewing weather information,
software engineers of the Oklahoma Mesonet have de-
veloped easy-to-use visualization software. The most
recent display and analysis tool, called WeatherScope
(see online at http://sdg.ocs.ou.edu), is based on C⫹⫹
and OpenGL and displays weather and geographical
information from sources both within and outside
Oklahoma (Fig. 11). WeatherScope is a stand-alone,
HTTP-based tool that operates on Microsoft Windows
2000/XP and Apple Macintosh OS X. It displays maps
and animations as data plots, wind vectors, color con-
tours, or line contours. Radar and other geospatial data
can be displayed on the same map with Oklahoma Me-
sonet observations.
The software downloads quality-assured data to the
users computer and generates images via a customiz-
able XML file. The XML file contains information
about the data (e.g., source, date, and time), geographic
overlays, map projection, font type and size, line or
symbol size, translucency of the map layer, and refresh
rate.
FIG. 8. Comparison product between Oklahoma Mesonet rainfall accumulation and corresponding values of flash flood guidance
(FFG) issued by the NWS River Forecast Center. Counties are colored according to the ratio of rainfall accumulation (point value) and
FFG (countywide value). For example, counties highlighted in yellow indicate that current rainfall accumulation is 50%75% of FFG.
Maps display 1-, 3-, and 6-h values as well as the worst case,incorporating the highest ratio for a given county.
316 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 24
Fig 8 live 4/C
FIG. 10. An example of a soil moisture product from the Oklahoma Mesonet. The Fractional Water Index is plotted for 5 (red), 25
(orange), 60 (green), and 75 cm (blue) over a 60-day period at the Perkins station.
FIG. 9. Example output from the Site-Specific Spraying Recommendation for the Peanut Leafspot model on the Oklahoma Ag-
Weather Web site (see online at http://agweather.mesonet.org). In this case, the grower specified a station (Hinton, OK), the planting
date (15 May 2005), and the date of the most recent fungicide application (1 Aug 2005).
MARCH 2007 M C P HERSON ET AL. 317
Fig 9 and 10 live 4/C
e. Research uses of data and products
Because of the quality, quantity, and types of obser-
vations collected, the Oklahoma Mesonet has provided
scientists with data to study sensor deployment, data
quality assurance, mesoscale processes and phenom-
ena, improvements in forecast models, and validation
of remote sensing technologies. In addition, the net-
work aids field experiments in Oklahoma through pro-
vision of real-time and archived data, collaborations
with its research team, dissemination of daily weather
forecasts, and community support via appropriate pub-
lic relations activities.
Studies have used the observations to gain knowl-
edge of physical processes that occur across various
spatial and temporal scales. For example, McPherson
et al. (2004), McPherson and Stensrud (2005), and
Haugland and Crawford (2005) studied the impact of
Oklahomas winter wheat belt on the overlying atmo-
sphere. Illston et al. (2004) quantified soil moisture
FIG. 11. An example of a map generated by the Oklahoma Mesonets WeatherScope software from data files and an XML-based
configuration file. Data are displayed for the 8 May 2003 tornadic storm near Oklahoma City, OK. Air temperature (light pink text),
dewpoint temperature (light green text), and winds (white barbs) from the Oklahoma Mesonet are plotted for 5:05 P.M. CDT.
Equivalent potential temperature is colored as a gradient. Geographic overlays include county borders (light yellow lines) and major
roads (black lines). The map also displays NEXRAD level-II reflectivity data from 5:06 P.M. CDT and the level-III storm attribute
product, including storm motion, from 5:05 P.M. CDT.
318 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 24
Fig 11 live 4/C
variability across Oklahoma at a variety of temporal
scales, and Brotzge and Richardson (2003) investigated
the temporal correlation of atmospheric and soil vari-
ables. Basara and Crawford (2002) used Oklahoma Me-
sonet data to quantify the relationship between soil
moisture and atmospheric variables throughout the
planetary boundary layer, and Brotzge (2004) studied
the differences in the energy and water budgets of sites
across Oklahoma. In addition, Illston and Basara
(2003) examined the relationship between short-term
droughts and soil moisture conditions in Oklahoma.
The unique suite of observations collected by the
Oklahoma Mesonet has been applied to develop or en-
hance new technologies and products. These data have
helped validate and improve land surface models used
in numerical weather prediction (e.g., Sridhar et al.
2002; Marshall et al. 2003; Robock et al. 2003; Nemu-
naitis et al. 2004), satellite technologies and products
(e.g., Czajkowski et al. 2000; Anderson et al. 2004; Sun
et al. 2004), and radar-derived products (e.g., Lu et al.
1996; Pereira Fo. et al. 1998; Young et al. 2000). In
addition, studies conducted with Oklahoma Mesonet
data have improved fire prediction capabilities (Carl-
son and Burgan 2003) and the characterization of
downwelling longwave radiation (Sridhar and Elliott
2002).
Oklahoma Mesonet data provide a consistent, long-
term dataset to enhance field experiments. Limited-
term experiments have included the Fall Water Vapor
Intensive Observation Period, sponsored by the DOE
(Richardson et al. 2000); the Southern Great Plains
(SGP) experiments of 1997 and 1999, sponsored by the
National Aeronautics and Space Administration
(NASA; Jackson et al. 1999); the International H
2
O
Project, sponsored by the NSF (Weckwerth et al. 2004);
the Soil Moisture Experiment of 2003, sponsored by
NASA (Cosh et al. 2003); and the Joint Urban 2003,
sponsored by the Departments of Defense and Home-
land Security (Allwine et al. 2004). The entire archive
of Oklahoma Mesonet data serves research programs
of the DOE and USDA.
7. Future of the Oklahoma Mesonet
The vision of the Oklahoma Mesonet is to pioneer
state-of-the-science collection, dissemination, and ap-
plication of surface weather observations to provide ex-
traordinary dividends for Oklahomans and to be a pro-
totype for future monitoring networks and applications.
Because the Oklahoma Mesonet was established as a
multipurpose network that focused on research-quality
data available in real time, enhancements to its suite of
sensors and data services are limited only by personnel
time for research and development while maintaining a
24/7 operational environment. Current targets for en-
hancement to and development of the Oklahoma Me-
sonet include an upgrade to the networks wind and
pressure sensors, integration with road weather moni-
toring needs of the Oklahoma Department of Trans-
portation, environmental monitoring within urban are-
as, and decision-support tools for transportation engi-
neers, economic development agencies, and urban gar-
deners.
Acknowledgments. This manuscript is dedicated to
the Oklahoma Mesonet employees who have devoted
countless hours to ensure excellence in network opera-
tions and outreach. Oklahomas taxpayers fund the
Oklahoma Mesonet through the Oklahoma State Re-
gents for Higher Education. Soil moisture observations
resulted from funding by NSF and NOAA.
We appreciate the continued support of the OU and
OSU administrations in embracing the Oklahoma Me-
sonet partnership. Dr. Fred Brock, Timothy Hughes,
Dr. Scott Richardson, and Christopher Fiebrich have
served as managers of the network. Stdrovia Black-
burn, John Humphrey, and Ryan Davis designed
graphics for this manuscript.
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The EARTHSTORM Project is a K-12 teacher enhancement program for Oklahoma science and mathematics teachers who desire to incorporate real-time environmental data into their classroom activities. EARTHSTORM provides a model for the professional development of teachers, daily staff support for participants, scientific mentorship of students, development of data analysis tools for students and teachers, and use of real-time data in the K-12 classroom.
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A statistical objective analysis (SOA) scheme is used to reanalyze the stage III estimate of rainfall, an hourly mosaic of digital precipitation arrays produced by a network of WSR-88Ds. The technique also uses rainfall measurements from the Oklahoma Mesonetwork that are taken over the Lake Altus area in southwest Oklahoma. The Lake Altus area is monitored by four WSR-88D radars: Frederick, Oklahoma; Twin Lakes, Oklahoma; Amarillo, Texas; and Lubbock, Texas. A total of 185 hourly maps of precipitation accumulation between June 1995 and July 1996 are used in the reanalysis. The results indicate that the stage III analysis underestimates total rainfall accumulations by as much as 40% when compared to the SOA reanalysis. Furthermore, the largest discrepancies between the stage III analysis and the SOA reanalysis coincide with overlapping areas of coverage between WSR-88D umbrellas. Some stage III precipitation fields used in this study clearly show fictitious high gradients of rainfall that exactly coincide with the maximum range ring of adjacent WSR-88Ds. Currently, stage II precipitation fields are mosaicked by averaging nonzero rainfall accumulations, regardless of their respective distance from a WSR-88D, to generate the stage III analysis. It is shown that wherever WSR-88D surveillance areas overlap, analysis errors, introduced solely by the radar-range effect, will adversely affect the accuracy of the stage III estimate. An error-weighted averaging method is proposed to eliminate this problem.
Article
On 31 January 1996, the National Centers for Environmental Prediction/Environmental Modeling Center (NCEP/EMC) implemented a state-of-the-art land surface parameterization in the operational Eta Model. The purpose of this study is to evaluate and test its performance and demonstrate its impacts on the diurnal cycle of the modeled planetary boundary layer (PBL). Operational Eta Model output from summer 1997 are evaluated against the unique observations of near-surface and subsurface fields provided by the Oklahoma Mesonet. The evaluation is partially extended to July 1998 to examine the effects of significant changes that were made to the operational model configuration during the intervening time. Results indicate a severe positive bias in top-layer soil moisture, which was significantly reduced in 1998 by a change in the initialization technique. Net radiation was overestimated, largely because of a positive bias in the downward shortwave component. Also, the ground heat flux was severely underestimated. Given energy balance constraints, the combination of these two factors resulted in too much available energy for the turbulent fluxes of sensible and latent heat. Comparison of model and observed vertical thermodynamic profiles demonstrates that these errors had a marked impact on the model PBL throughout its entire depth. Evidence also is presented that suggests a systematic underestimation of the downward entrainment of relatively warmer, drier air at the top of the PBL during daylight hours. Analyses of the monthly mean bias of 2-m temperature and specific humidity revealed a cool, moist bias over western Oklahoma, and a warm, dry bias over the eastern portion of the state. A very sharp transition existed across central Oklahoma between these two regimes. The sharp spatial gradient in both the air temperature and humidity bias fields is strikingly correlated with a sharp west-east gradient in the model vegetation greenness database. This result suggests too much (too little) latent heat flux over less (more) vegetated areas of the model domain. A series of sensitivity tests are presented that were designed to explore the reasons for the documented error in the simulated surface fluxes. These tests have been used as supporting evidence for changes in the operational model. Specifically, an alternative specification for the soil thermal conductivity yields a more realistic ground heat flux. Also, the alternative thermal conductivity, when combined with a slight adjustment to the thermal roughness length, yields much better internal consistency among the simulated skin temperature and surface fluxes, and better agreement with observations.