Bow Echo and Mesoscale Convective Vortex Experiment (BAMEX) 2003 One Minute Surface Composite

1.0 General Description

This dataset contains one-minute resolution surface meteorological data in University Corporation for Atmospheric Research/Joint Office for Science Support (UCAR/JOSS) Quality Control (QC) format from stations within the following networks:

Data for the Bow Echo and Mesoscale Convective Vortex Experiment (BAMEX) 2003 domain (30N to 48N latitude and 80W to 104W longitude) and time period (20 May 2003 through 6 July 2003) are contained within this dataset. This dataset contains data from 450 stations.

Section 2.0 contains a detailed description of the instrumentation, siting, and algorithms used by the source network to collect the data. Much of this information is taken from the individual network webpages listed in each subsection below. Section 2.1 contains a detailed description of the format of the composite dataset. See Section 2.2 for information on data processing, and Section 3.0 for the quality control processing performed by UCAR/JOSS on this dataset. Section 4.0 contains references.

2.0 Detailed Data Description

2.0.1 Automated Surface Observing System (ASOS) Algorithms

The Automated Surface Observing System (ASOS) is an automated observing system that is sponsored by the Federal Aviation Administration, National Weather Service (NWS) and the Department of Defense (DOD). ASOS provides weather observations which include: temperature, dew point, wind, altimeter setting, visibility, sky condition, and precipitation. ASOS stations are installed at airports throughout the country. All 309 stations that are in the BAMEX area of interest are included in this BAMEX 2003 One Minute Surface Composite.

The Automated Surface Observing Systems are designed to provide airport weather observations in real time. The observing systems work nonstop, updating observations every minute, 24 hours a day, every day of the year. Aviation weather parameters are measured in the runway touchdown zone on the airport.

The ASOS data were acquired by UCAR/JOSS for BAMEX via two methods. Stations were downloaded from an NCDC FTP website once monthly during the BAMEX TOI. Stations not available on the FTP site were acquired using a modem and a dial-in telephone system. UCAR/JOSS typically called each station every 8 hours. Note that there is a data gap in the dialed data in early June, 2003. The data gap is the result of an emergency change by NCDC in the passwords for dialing data, which caused the downloads to fail until NOAA provided UCAR/JOSS the new passwords to access the data.

The ASOS uses a chilled mirror to determine the dew point temperature. Once per day the mirror is heated to recalibrate the reference reflection expected from a dry mirror (since a clean mirror needs relatively less indirect light to determine when dew has formed than a dry mirror). This procedure compensates for a possible dirty or contaminated mirror and redefines adaptive criterion value used to determine when dew or frost has occurred. This once per day recalibration nominally takes about 15 min ( ASOS Users Guide, 1998 [ PDF]). The occurrence time of this recalibration varies from station to station. Some stations also vary the occurrence time of the recalibration on about weekly intervals. In an effort to remove the most extreme effects of this recalibration, for any occurrence of dew point temperatures 10 degrees F or more higher than the one in the preceding minute, and when the dewpoint is also 1 degree F or more than the temperature at the present time, the dewpoint flag for the preceding minute, the dewpoint flag for the present minute, and the dewpoint flags for the next 6 minutes (clock time) were set to Bad by JOSS. Note that this test applies to consecutive minutes, not obs. To test the first dewpoint value after this spike, it was compared to the previous value even though the previous one would have been set to Bad. If the dewpoint spike test failed, then the dewpoint flags were set to Bad when the dewpoint was 1 degree F or more than the temperature. The dewpoint values were not changed. This recalibration and associated flagging of dewpoint spikes affects all ASOS stations.

There are three station pressures reported at most station for each time period. All stations have at least two pressure sensors and the same methodology is used for stations with two sensors as we use for stations with three sensors. The lowest sensor pressure value obtained, whose pressure difference from either of the other sensors is 0.04 inches or less, is the designated ASOS pressure to be reported at the end of the minute. If no two sensors are within 0.04 inches of each other, then the station pressure is set to missing for that time period. ( ASOS Users Guide, 1998 Section 3.3.2 [ PDF]) Pressures are reported in inches of mercury. UCAR/JOSS converts the designated ASOS pressure to millibars and reports it in this 1-minute surface composite.

All Automated Surface Observing System sites included in this composite are commissioned.

For more information on ASOS data and instrumentation, see the National Weather Service Automated Surface Observing System web site (NWS, 2003), Federal Aviation Administration Automated Surface Observing System web site (FAA, 2004), and the ASOS Users Guide, 1998 [ PDF]. Also see the ASOS Users Guide appendices, 1998 [ PDF] which includes instrument specifications and frequencies.

For information on the calculation of parameters derived by UCAR/JOSS from the raw parameters available, see Section 2.2.

2.0.2 DOE ARM SMOS Surface Meteorological Data (ARMSFC) Algorithms

The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Surface Meteorological Observation System (SMOS) [ARMSFC] stations are located at many of the ARM SGP Extended Facilities in southern Kansas and northern Oklahoma. There are 15 SMOS station included within this BAMEX 2003 One Minute Surface Composite.

Instrumentation

The SMOS mostly uses conventional in situ sensors to obtain one-minute averages of surface wind speed, wind direction, air temperature, relative humidity, barometric pressure, and precipitation at the central facility and many of the extended facilities of the SGP Clouds and Radiation Testbed (CART) site. SMOSes have not been installed at extended facilities located within about 10 km of existing surface meteorological stations such as those of the Oklahoma MESONET.

The SMOS stations directly measure:

Wind speed at 10 m, Precision: 0.01 m/s; Uncertainty: +/-1% for 2.5 to 30 m/s (see Assessment of System Uncertainties for Primary Quantities Measured for wind speeds below 2.5 m/s) Wind speed and direction sensor: Propellor anemometer and wind vane, R. M. Young Model 05103 Wind Monitor

Wind direction at 10 m, Precision: 0.1 deg; Uncertainty: +/-5 deg Wind speed and direction sensor: Propellor anemometer and wind vane, R. M. Young Model 05103 Wind Monitor

Air temperature at 2 m, Precision: 0.01 C; Uncertainty: a function of wind speed (see Assessment of System Uncertainties for Primary Quantities Measured) Temperature and relative humidity sensor: Thermistor and Vaisala RH, Campbell Scientific Model HMP35C Temperature and Relative Humidity Probe

Relative humidity at 2 m, Precision: 0.1% RH; Uncertainty: +/-2.06% RH (0% to 90% RH), +/-3.04% RH (90% to 100% RH) Temperature and relative humidity sensor: Thermistor and Vaisala RH, Campbell Scientific Model HMP35C Temperature and Relative Humidity Probe

Barometric pressure at 1 m, Precision: 0.01 kPa; Uncertainty: +/-0.035 kPa Barometric pressure sensor: Digital barometer, Vaisala Model PTB201A

Precipitation, Precision: 0.254 mm; Uncertainty: +/-0.254 mm (unknown during strong winds and for snow) Precipitation: Electrically heated, tipping bucket precipitation gauge, Novalynx Model 260-2500E-12 Rain/Snow Gage

The data logger is a Campbell Scientific Model CR10 Measurement & Control Module and Model SM716 Storage Module, Precision: A function of input type and range, Uncertainty: 0.2% of Full Scale Range for Analog Inputs

Assessment of System Uncertainties for Primary Quantities Measured

Uncertainty is the range of probable maximum deviation of a measured value from the true value within 95% confidence limits. It is defined as the square root of the sum of all random errors squared plus the square root of the sum of all bias errors squared. The errors are assumed to be statistically independent.

All SMOS uncertainty analyses are based on manufacturer's specifications. Manufacturers specify accuracies in several ways. Some give absolute range of error, some give uncertainties as defined above, while others give rms errors. In this analysis, rms errors are multiplied by 2. This results in confidence limits of approximately 95%.

Data Acquisition Errors

The Campbell Scientific CR10 A/D converter accuracy is +/-0.2 % of full scale range. The time base accuracy is +/-1 minute per month, or about 23 ppm. The Site Data System checks the time-of-day clock once per day and corrects the SMOS clock if it is off by more than a minute.

Wind Speed

The NIST calibration uncertainty is specified as +/-1% for wind speeds from the sensor threshold to 30 m/s. The conversion error is negligible. The schedule of routine maintenance and sensor verification is designed to eliminate any long-term stability error.

The sensor threshold is specified as 1 m/s. The following estimates of the range of underestimation caused by the threshold assume a normal distribution of wind speeds about the mean. When the true wind speed is 1.0 m/s, the winds will be below the threshold 50% of the time. This will result in an underestimate of 0.5 m/s. When the true wind speed is 1.5 m/s, assuming the standard deviation will be between 0.25 and 1.00 m/s, the winds will be below the threshold between 2 and 31% of the time. This will result in an underestimate between 0.02 and 0.23 m/s. When the true wind speed is 2.0 m/s with a range of standard deviations between 0.25 and 1.00 m/s, the winds will be below the threshold between 0 and 16% of the time. This will result in an underestimate between 0 and 0.12 m/s.

If the reported wind speed is 0.5 m/s, an underestimate of 0.5 is probable. This would bias the measurement by -0.5. If the reported wind speed is 1.0 m/s, an underestimate of 0.19 to 0.30 m/s is possible. If the reported wind speed is 1.5 m/s, an underestimate of 0.02 to 0.20 m/s is possible. If the reported wind speed is 2.0 m/s, an underestimate of 0 to 0.10 m/s is possible.

The uncertainty range with 95% confidence is approximately:

+/- 1%for a reported wind speed from 2.5 to 30.0 m/s
-0.12 to +0.02 m/sfor a reported wind speed of 2.0 m/s
-0.22 to +0.00 m/sfor a reported wind speed of 1.5 m/s
-0.31 to -0.20 m/sfor a reported wind speed of 1.0 m/s
-0.51 to -0.49 m/sfor a reported wind speed of 0.5 m/s

Wind Direction

The sensor accuracy is specified as +/-3 deg. The A/D conversion accuracy is equivalent to 0.7 deg over a temperature range of 0 to 40 deg C for a period of one year. I have estimated sensor alignment to true north to be accurate within +/-3 deg. The uncertainty with 95% confidence is, therefore, approximately +/-5 deg.

Temperature

The accuracy of the temperature measurement is specified as +/-0.4 C. Included in this accuracy are sensor interchangeability, bridge resistor precision, and polynomial curve fitting errors. The long-term stability is not known. The radiation error of the naturally aspirated multi-plate radiation shield used for all stations, except for the central facilities SMOS, is specified as +/-0.4 C rms at 3 m/s, +/-0.7 C rms at 2 m/s, and +/-1.5 C rms at 1 m/s.

The uncertainty with 95% confidence of temperature sensors in naturally aspirated radiation shields is approximately:

+/-0.45 Cwhen the wind speed is 6 m/s or greater
+/-0.89 Cwhen the wind speed is 3 m/s
+/-1.46 Cwhen the wind speed is 2 m/s
+/-3.07 C when the wind speed is 1 m/s

The radiation error of the aspirated radiation shield used at the Central Facility is specified as +/- 0.2 C rms. The uncertainty with 95% confidence of temperature sensors in this radiation shield is, therefore, +/- 0.57 C.

Relative Humidity

The accuracy of the sensor is specified as +/-2% RH for 0 to 90% RH, and +/-3% RH for 90 to 100% RH. Errors considered in this accuracy are calibration uncertainty, repeatability, hysteresis, temperature dependence, and long-term stability over a period of one year. The A/D conversion accuracy is equivalent to +/-0.5% RH.

The uncertainty with at least 95% confidence is, therefore,

+/-2.06 % RH, 0 to 90 % RH
+/-3.04 % RH, 90 to 100 % RH

The UNCERTAINTY of +/-2.06% RH (0% to 90% RH) or +/-3.04% RH (90% to 100% RH) is for a calibrated probe. The RH values reported by the probe normally drift slowly upward over time. Whenever a probe falls outside the range of uncertainty for a SIX-MONTH SENSOR VERIFICATION or reports values exceeding 104% RH, the probe is replaced by one that has been recently calibrated. Occasionally, a sensor will report values that are suspiciously low. A work order is then issued to perform a verification check and replacement if needed. A data quality report is issued for known erroneous data.

Barometric Pressure

The manufacturer's technical data contains an uncertainty analysis. Errors included in their analysis are linearity, hysteresis, calibration uncertainty, repeatability, temperature dependence, and long-term stability over a period of one year. Because the sensor has a digital output, no conversion error occurs in the Campbell data logger.

The specified uncertainty with 95% confidence is +/-0.035 kPa.

Precipitation

The tipping-bucket rain gauge produces a pulse output. The data logger counts the pulses for the period of integration. The uncertainty is, therefore, a minimum of one full bucket or 0.254 mm. For rain rates less than 75 mm per hour with light to moderate winds, the collection efficiency of the gauge is 99 to 100%. During heavy rain or strong, gusty winds, the collection efficiency is reduced. Manufacturers have not attempted to specify accuracies for these conditions.

Although Alter shields are used to increase the efficiency of snow collection, the efficiency of collection is variable and usually well below 100%. Furthermore, the heater does not melt snow at temperatures below -10 deg C. Thus the data user should use the water-equivalent estimates for snowfall with a great deal of skepticism. At best, the readings are only a rough indicator that snow occurred, for temperatures above -10 C. If snow occurred at -10 C or below and the temperature increased to above -10 C hours later, then some melting would occur and an incorrect time of precipitation would be reported.

Site maps are available at: http://www.arm.gov/sites/sgp/maps.stm (ARM, 2004d).

Algorithms

Description of System Configuration and Measurement Methods of all SMOS stations except at E21, Okmulgee, OK

The SMOS sensors are mounted on a 10 meter, triangular tower, except for the rain gauge.

The wind monitor propeller anemometer produces a magnetically controlled AC output whose frequency is proportional to the wind speed. The Wind Monitor direction vane drives a potentiometer, which is part of a resistance bridge. The Wind Monitor is mounted on a cross-arm at a height of 10 m.

The T-RH probe thermistor is part of a resistance bridge. The Vaisala RH circuitry produces a voltage that is proportional to the capacitance of a water vapor absorbing, thin polymer film. For all SMOSes except, the one at the central facility, the T-RH probe is mounted in a naturally aspirated R. M. Young Model 41002 Gill Multi-plate Radiation Shield. The central facilities (E13) T-RH probe is mounted in an R. M. Young Model 43408 Gill Aspirated Radiation Shield. The Radiation Shields are mounted at a height of 2 m on the southwestern leg of the tower.

The barometric pressure sensor uses a silicon capacitive pressure sensor and is housed in a weatherproof enclosure along with a data logger, a storage module, and serial communications equipment, all mounted on the tower at a height of 1 m.

The rain-snow gauge has a 12-inch orifice and is located near the tower. A thermostatically controlled heater melts frozen precipitation. The water is funneled to a tipping bucket, which triggers a magnetic reed switch. An Alter Shield is used to increase the reliability of rain collection in high winds and of snow collection.

The data logger measures each input once per second except for barometric pressure, which is measured once per minute. The data logger produces one-minute averages of wind speed, vector-averaged wind direction, air temperature, and relative humidity. The one-minute output includes the barometric pressure reading and total precipitation during the minute.

Description of SMOS at E21, Okmulgee, OK

The same sensors that are used on the other SMOS stations are used on the E21 SMOS. Since this SMOS is in a forested site, the sensors are mounted on a 20 m tower which extends above the top of the forest canopy. During the summer of 1999 the canopy height was estimated to be 47 feet or 14.3 m. The air temperature and relative humidity probe is mounted at 17.0 m or approximately 2.7 m above the average canopy height facing North. The wind speed and direction sensor is mounted at 18 m or approximately 3.7 m above the average canopy height on a boom 10ft out from the tower facing North. The barometric pressure sensor is mounted at 19 m or approximately 4.7 m above the average canopy height. New booms for sensor mounting were installed in July 2002. The two sensors affected are the wind speed and direction sensor and the T/RH probe. The height of the wind speed and direction sensor did not change, it is still 18 m or approximately 3.7 m above the average canopy height. The orientation of the wind speed and direction sensor did change and it is now facing West on a boom 15ft out from the tower. The date of the wind speed and direction sensor change was July 16, 2002 at 18:44 GMT. Both the orientation and the height of the T/RH probe changed. It is now 19.25 m above the surface or 4.95 m above the average canopy height and is facing Northeast. The date of the T/RH probe change was July 15, 2002 at 22:36 GMT.

Precipitation Measurements Questionable

Since the height of the tower at E21 extends above the forest canopy it has become a favorite roosting area for turkey vultures. The birds roosting on the tower has caused considerable amounts of bird droppings on the equipment, tower & sensors. The precipitation gage is frequently clogged with bird droppings during the time period that the vultures are in the area. The turkey vultures are migratory but are generally in the area from early April through sometime in November. Precipitation data during these times should be investigated closely to determine if the precipitation gage data is consistent with other nearby sites. Typically when the raingage is clogged a stairstep pattern in the data can be noticed.

Theory of Operations

Each of the primary measurements of wind speed, wind direction, air temperature, relative humidity, barometric pressure, and rainfall are intended to represent self-standing data streams that can be used independently or in combinations. The theory of operation of each of these sensors is similar to that for sensors typically used in other conventional surface meteorological stations. Some details can be found under Algorithms - Description of System Configuration and Measurement Methods but further, greatly detailed description of theory of operation is not considered necessary for effective use of the data for these rather common types of measurements. The SMOS instrument mentor or the manufacturer can be contacted for further information. Contact information can be found on the SMOS web page given at the bottom of this section 2.0.2.

Instrument Mentor Quality Control Checks

Data quality control procedures for this system is mature.

Graphical displays are generated at ANL and inspected on a weekly basis for the following parameters: relative humidity, temperature, wind speed, wind direction and barometric pressure. Any one of these parameters acquired on any one day for up to 4 SMOS stations are viewed on a single display to compare data from relatively close extended facilities. This procedure does not verify accuracy, but does help identify suspected drifts in the sensors. When any of the graphed data are suspect, a work request for investigation and/or sensor verification by SGP site operations personnel is issued. Every six months the aforementioned SMOS sensors are compared to secondary references. Given adequate funding, every two years the wind monitors are replaced and the removed sensors are returned to the manufacturer for preventative maintenance and, if necessary, re-calibration. Summary reports are sent weekly to the SGP site scientist team. Precipitation data are not visually inspected due to the site-specific nature of these parameters. It is left to the user to determine the validity and accuracy of these values.

Additional checks on the temperature and relative humidity probes, and rain gauges are accomplished every two weeks during routine maintenance. The temperature and relative humidity probes are compared to secondary standards (hand-held meters). The secondary standards are calibrated in a humidity generator chamber at the SGP facility every 7 to 10 days. The rain gauges are checked for proper operation, the screens are cleaned if necessary, and tip tests are done. The rain gauges are also inspected for being level and at the proper height for the Alter wind-screen. Adjustments are made at the time of inspection.

Data acquisition and processing is disabled whenever sensors are being tested.

One problem currently persists. The rain gauges on the SMOS and on the SWATS are tested biweekly. Data acquisition and processing can be disabled on the SMOS stations but not on the SWATS. Whether data acquisition and processing is disabled or false counts appear in the data, a Data Quality Report (DQR) should be issued. A method to automatically search the MDS and issue DQR's when rain gauge testing is performed is being considered by SMOS.

Calibration and Maintenance

Calibration Theory

The SMOSs are not calibrated as systems. The sensors and the data logger (which includes the analog-to-digital converter) are calibrated separately. All systems are installed using components that have a current calibration. The sensor calibrations are checked every six months in the field by SGP site operations personnel by comparison to calibrated references. Any sensor that fails a field check is returned to the manufacturer for recalibration. The Wind Monitors are returned to the manufacturer for recalibration after two years of use per manufacturer suggestion and given adequate funding. Therefore, it is possible that in some years the wind monitors are not sent back to the manufacturer for the 2 year recalibration and preventative maintenance. Overall, this should not lead to a problem, as the sensors rarely go out of calibration and are checked every 6 months.

Wind speed calibration is checked by rotating the propellor shaft at a series of fixed rpm's using an R. M. Young Model 18810 Anemometer Drive. The reported wind speeds are compared to a table of expected values and tolerances. If the reported wind speeds are outside the tolerances for any rate of rotation, the sensor is replaced by one with a current calibration.

Wind direction calibration is checked by using a vane angle fixture, R. M. Young Model 18212, to position the vane at a series of angles. The reported wind directions are compared to the expected values. If any direction is in error by more than 5 degrees, the sensor is replaced by one with a current calibration.

Air temperature and relative humidity calibrations are checked by comparison with a reference Vaisala Model HMI31 Digital Relative Humidity and Temperature Meter and HMP35 Probe. If the reported temperature and relative humidity vary by more than the sensor uncertainty from the reference, the probe is replaced by one with a current calibration.

Barometric pressure calibration is checked by comparison with a reference Vaisala PA-11 Barometer. If the reported pressure varies by more than the sensor uncertainty from the reference, the sensor is replaced by one with a current calibration.

Precipitation calibration is checked by allowing 500 ml of water to slowly pass through the sensor. If the reported number of tips varies by more than one from the expected value, the rain gauge is replaced by one with a current calibration.

The ARM SMOS web site contains a complete calibration History, as well as information on instrumentation, data collection and processing ( ARM, 2005). General information on ARM data quality (ARM, 2003b) can be found as well. Or see the ARM Program homepage (ARM, 2003a).

For information on the calculation of parameters derived by UCAR/JOSS from the raw parameters available, see Section 2.2.

2.0.3 DOE ABLE AWS Algorithms

These data contain one-minute resolution surface meteorological data from the Atmospheric Boundary Layer Experiments (ABLE) operated by the Argonne National Laboratory in the Walnut River Watershed in Butler County Kansas (east of Wichita). ABLE is a research initiative devoted to atmospheric research. This location is within the existing boundaries of DOE's Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Clouds and Radiation Testbed (CART) site. The ABLE Automated Weather Station (AWS) Network consists of five stations.

Instrumentation

The AWS stations directly measure:

Wind speed at 10 m, Precision: 0.01 m/s; Uncertainty: +/-1% for 2.5 to 30 m/s, increases to +/-0.5 m/s when 0.5 m/s is reported. Propellor anemometer and wind vane, R. M. Young Model 05103 Wind Monitor

Wind direction at 10 m, Precision: 0.1 deg; Uncertainty: +/-5 deg

Air temperature at 2 m, Precision: 0.01 C; Uncertainty: +/-0.7 C (This will eventually improve when an aspirated reference is obtained.) Thermistor and Vaisala RH, Campbell Scientific Model HMP35C Temperature and Relative Humidity Probe

Relative humidity at 2 m, Precision: 0.1% RH; Uncertainty: +/-2.06% RH (0% to 90% RH), +/-3.04% RH (90% to 100% RH) Thermistor and Vaisala RH, Campbell Scientific Model HMP35C Temperature and Relative Humidity Probe

Barometric pressure at 1 m, Precision: 0.01 kPa; Uncertainty: +/-0.035 kPa Digital barometer, Vaisala Model PTB201A

Precipitation, Precision: 0.254 mm; Uncertainty: +/-0.254 mm (unknown during strong winds and for snow) Electrically heated, tipping bucket precipitation gauge, MetOne Model 385 Rain/Snow Gage

The data logger is a Campbell Scientific Model CR10X-1M Measurement & Control Module with 1 MByte memory; Precision: A function of input type and range, Uncertainty: 0.2% of Full Scale Range for Analog Inputs

Topo maps and aerial photos are available at: http://gonzalo.er.anl.gov/ABLE/sitelatlon.html (ANL, 2004b).

Data Collection and Processing

The AWS sensors are mounted on a 10 meter, triangular tower, except for the rain gauge.

The wind monitor propeller anemometer produces a magnetically controlled AC output whose frequency is proportional to the wind speed. The Wind Monitor direction vane drives a potentiometer, which is part of a resistance bridge. The Wind Monitor is mounted on a cross-arm at a height of 10 m.

The T-RH probe temperature sensor, a thermistor, is connected into a resistance bridge. The Vaisala RH circuitry produces a voltage that is proportional to the capacitance of a water vapor absorbing, thin polymer film. The T-RH probe is mounted in an R. M. Young Mode l43408 Gill Aspirated Radiation Shield. The Radiation Shield is mounted at a height of 2 m on the southern face of the tower.

The barometric pressure sensor uses a silicon capacitive pressure sensor and is housed in a weatherproof enclosure along with a data logger, a storage module, and serial communications equipment, all mounted on the tower at a height of 1.5 m.

The rain-snow gauge has a 12-inch orifice and is located near the tower. A thermostatically controlled heater melts frozen precipitation. The water is funneled to a tipping bucket, which triggers a magnetic reed switch. An Alter Shield is used to increase the reliability of rain collection in high winds and of snow collection.

The data logger measures each input once per second except for barometric pressure, which is measured once per minute. The data logger produces one- and 30-minute averages of wind speed, vector-averaged wind direction, air temperature, and relative humidity. The one-minute output includes the barometric pressure reading and total precipitation during the minute. The 30-min output includes 30-min-total precipitation, average air temperature, average relative humidity, and barometric pressure.

More information on ABLE AWS can be found on the ABLE Home Page ( ANL, 2004a).

For information on the calculation of parameters derived by UCAR/JOSS from the raw parameters available, see Section 2.2.

2.0.4 Iowa Environmental Mesonet (IEM) Automated Weather Observing System (AWOS) Algorithms

The Iowa Environmental Mesonet (IEM) Automated Weather Observing System (AWOS) is a suite of sensors, which measure, collect and disseminate weather data to help meteorologists, pilots and flight dispatchers prepare and monitor weather forecasts, plan flight routes, and provide necessary information for correct takeoffs and landings. The sensors measure weather parameters such as wind speed and direction, temperature and dew point, visibility, cloud heights and types, precipitation, and barometric pressure. AWOSs are categorized as either Federal or NonFederal. Federal AWOSs were purchased and are currently maintained by the FAA. NonFederal AWOSs are purchased and maintainted by state, local, and private organizations. The AWOS stations in this composite are operated by the Iowa Department of Transportation and distributed by the IEM. There are 35 AWOS stations in this BAMEX 2003 1-minute Surface Composite.

All data contained in this composite dataset were collected on stations manufactured by Artais of Columbus, OH, a division of Vaisala. The following are descriptions of the AWOS station data. Further details can be found in the AWOS Operations Manual (USDOT, 1988).

Temperature/Dewpoint

AWOS measure Temperature and Dewpoint at 1-min intervals. When the dew point sensor is recalibrated, a large jump in dew point can occur. See the report about Iowa AWOS calibration issues (Herzmann, 2004) for a complete description.

Station Pressure and Derived Pressure Elements

AWOS takes 10-sec measurements from at least two independent pressure sensors and computes respective 1-min averages. A minimum of 5 measurements are required to compute a 1-min average. The 1-min averages from each sensor are compared to verify that differences do not exceed 0.04" Hg. If the sensors are in agreement, the lowest pressure reading from all sensors is reported. If the sensor differences exceed 0.04" Hg, the data are reported as "missing". The reported pressure is then used by AWOS in the computation of derived parameters (e.g., altimeter reading). UCAR/JOSS takes the altimeter reading and converts it back to station pressure using the algorithms found in the Smithsonian Meteorological Tables. Sea Level Pressure is then calculated from station pressure using standard GEMPAK algorithms (Unidata, 2003).

Wind

AWOS measures wind speed and direction at 1-sec intervals. Algorithms to produce 1-minute data are not currently available. Gust Speed was set to missing by UCAR/JOSS if the value was 0.

Precipitation

AWOS measures precipitation and 1-min intervals and reports cumulative precipitation each minute. The cumulative precipitation value is reset at 55 minutes after each hour. JOSS computes 1-minute precipitation values by taking the difference of two consecutive cumulative 1-min precipitation values. If this difference is negative at any point during the hour, then that minute is treated as a reset as well. The precipitation reported at each minute by UCAR/JOSS is the precipitation that fell during the previous minute.

Cloud cover

Iowa AWOS ceiling/cloud codes were mapped to the QCF ceiling height and cloud codes. The web page the contains the Iowa AWOS code definitions is: http://mesonet.agron.iastate.edu/AWOS/skyc.phtml.

IA Code	Description		QCF Ceiling Code    QCF Cloud Code
0	No report			15		15
1	Scattered (10-50% Coverage)	 4		11
2	Broken (60-90% Coverage)	 4		12
4	Overcast (90+% Coverage)	 4		 8
8	Full Obscuration		 5		 9
17	Partial Obscuration (scattered)	 4		10
18	Partial Obscuration (broken)	 4		10
20	Partial Obscuration (overcast)	 4		10
32	Indefinite			 5		15
64	Clear (No clouds under 12000 ft) 2		 0
128	Few (<10% coverage)		 4		 1
255	Missing				15		15
*	Unknown Code			error		error

The USDOT FAA AWOS Data Acquisition System (ADAS)/AWOS Interface Control Document (USDOT, 1998) contains detailed technical documentation. For more information see the Iowa Environmental Mesonet AWOS Network website ( IEM, 2004a) or the . FAA Automated Weather Observing System Information website (FAA, 2004).

2.0.5 Iowa Environmental Mesonet (IEM) School Network Algorithms

As the name implies, these automated weather stations are located at schools throughout the state. Currently, KCCI-TV (Des Moines, IA) and KELO-TV (Sioux Falls, SD) have graciously provided the IEM with the ability to process data from their observing networks. IEM is pursuing other TV stations, for their networks as well. There are 65 Iowa School Network stations included in this BAMEX 2003 1-minute surface composite.

Many of the school net stations are not located in good meteorological locations. While the stations may be accurate, their data may not be representative of the area in general. Often, they are placed on top of buildings and may have obstructions which could skew wind and temperature readings. The stations are placed at schools for educational purposes and to get students interested in the weather.

Descriptions of the instrumentation, siting, and algorithms used by the school networks to collect this data are not currently available.

Pressure, dewpoint and calculated sea level pressure were calculated by UCAR/JOSS. For information on the calculation of parameters derived by UCAR/JOSS from the raw parameters available, see Section 2.2. For the purpose of these calculations, relative humidity values between 0 and 10 were considered bad, temperatures >= 130 were ignored, and altimeter values > 1000 were ignored. Station pressures that did not fall between 300 and 1200 mb, and calculated sea level pressures that did not fall between 800 and 1200 mb were flagged bad.

The IEM school network measures precipitation at 1-minute intervals and reports cumulative precipitation each minute. The cumulative precipitation value is reset at the beginning of every month. 1-minute precipitation values were determined by taking the difference between consecutive records. Precipitation reported at each minute is the precipitation that fell during the previous minute.

More information on this network can be found on the IEM School Network webpage (IEM, 2004b).

2.0.6 Louisiana Agriclimatic Information System (LAIS) Algorithms

The Louisiana Agriclimatic Information System (LAIS) is a network of 24 automated weather stations operated by the LSU AgCenter (LSU, 2004). The network is managed by the Department of Biological and Agricultural Engineering (BAE, 2004). Stations are located primarily at farms of the Louisiana Agricultural Experiment Station, the research arm of the Louisiana State University Agricultural Center. These automated stations collect air temperature, soil temperature, humidity, rainfall, wind and solar radiation observations. These data are regularly transmitted to a centralized computer and are subsequently made available, via the internet, to the public. Air temperature, rainfall and wind, as well as dew point calculated by UCAR/JOSS from the LAIS humidity, are included in this composite. There are 21 LAIS stations included in this BAMEX 2003 1-minute surface composite.

Mission

The Louisiana Agriclimatic Information System (LAIS) collects, processes and distributes detailed climatic data relevant to agricultural production, natural resource management, environmental protection and public safety.

Equipment Descriptions

Dataloggers: Each LAIS station is equipped with a model CR23X datalogger manufactured by Campbell Scientific. A datalogger is a specialized computer which accepts electronic signals from various instruments, performs mathematical functions on the data, and records summaries in internal memory at designated intervals.

Communication: Most stations transmit data to a centralized computer every five minutes by using a combination of RAD brand short-haul modems, buried communications cable, and a Lantronix UDS-10 network interface. Some stations have radios substituted in place of the buried cable, and some still use telephone modems. Where the network interface and the weather station are not within the same local calling area, data is generally transmitted only once per day.

Power: The dataloggers operate on 12 volts direct current. In most cases, this is provided by an internal battery that is kept fully charged by a 30-watt solar panel. In some cases, the internal battery is kept charged by an adapter plugged into a regular 110-volt AC outlet. Some stations substitute a larger gelcell battery for the internal battery.

Air Temperature and Relative Humidity: A dual sensor measures temperature and relative humidity. Each station has a Vaisala HMP35A, which has a platinum temperature sensing element, and a Humicap relative humidity sensor.

Backup Air Temperature:All stations have a second temperature sensor to help in judging the quality of the primary temperature sensor's data. If both temperatures agree, it is unlikely that they are far off from actual air temperature. In many cases, the backup sensor is identical to that used in National Weather Service electronic Maximum Minimum Temperature Systems. All stations will soon have the Campbell Scientific 107 thermistor as the backup, along with a matching sensor at a height of 9 meters. The backup temperature is not included in this composite.

Wind Speed and Direction: All stations measure both wind speed and direction with an RM Young Wind Monitor, configured for use with Campbell Scientific dataloggers, at a height of 10 meters.

Precipitation: All stations have Handar 444A tipping bucket rain gauge or a similar unit manufactured by Hydrological Services. Either brand transmits a signal to the datalogger each time .01 inches of rainfall accumulates. Many of the stations also have an official manual precipitation gauge in the same location. The tipping bucket rain gauge reports accumulated rainfall in inches since midnight CST. UCAR/JOSS calculates 1-minute precipitation in UTC from the accumulated precipitation. Precipitation reported in this composite is the precipitation that fell during the previous minute.

Barometric Pressure: All stations in the LAIS network also observe barometric pressure.

Each station consists of a datalogger that observes several electronic instruments on a 3-second interval. Output is generated by the datalogger every 3 seconds, although this data is normally not archived, every minute, hourly, daily at midnight, and daily at 7am. Only the 1-minute data is included in this composite.

Dewpoint and calculated sea level pressure were calculated by UCAR/JOSS. For information on the calculation of parameters derived by UCAR/JOSS from the raw parameters available, see Section 2.2.

During the BAMEX time period, the LAIS instrumentation was not routinely calibrated or maintained so some stations have problems with certain parameters (particularly pressure and dew point). After the BAMEX period the LAIS stations have started to be routinely calibrated and maintained.

More information on this network can be found on the LAIS webpage (LAIS, 2004).

2.1 Detailed Format Description

The BAMEX 2003 One Minute Surface Composite observation data contains ten metadata parameters and 38 data parameters and flags. The metadata parameters describe the station location and time at which the data were collected. The time of observation is reported both in Universal Time Coordinated (UTC) Nominal and UTC actual time. For this one minute surface composite, reported nominal time and actual time are the same. Days begin at UTC 0001 and end at UTC 0000 the following day. The table below details the data parameters in each record. Several data parameters have an associated Quality Control (QC) Flag Code which are assigned by the Joint Office for Science Support (JOSS). For a list of possible QC Flag values see the Quality Control Section 3.0.

     
     Parameters                              Units 
     ----------                              -----
     Date of Observation                     UTC Nominal 
     Time of Observation                     UTC Nominal 
     Date of Observation                     UTC Actual
     Time of Observation                     UTC Actual
     Network Identifier                      Abbreviation of platform name 
     Station Identifier                      Network Dependent 
     Latitude                                Decimal degrees, South is negative
     Longitude                               Decimal degrees, West is negative
     Station Occurrence                      Unitless
     Station Elevation                       Meters 
     Station Pressure, QC flag               Hectopascals (mb) 
     Reported Sea Level Pressure, QC flag    Hectopascals (mb) 
     Computed Sea Level Pressure, QC flag    Hectopascals (mb) 
     Dry Bulb Temperature, QC flag           Celsius 
     Dew Point, QC flag                      Celsius 
     Wind Speed, QC flag                     m/s
     Wind Direction, QC flag                 Degrees 
     Total Precipitation, QC flag            mm
     Squall/Gust Indicator                   Code Value
     Squall/Gust Value, QC flag              m/s 
     Present Weather, QC flag                Code Value 
     Visibility, QC flag                     Meters 
     Ceiling Height (first layer)            Hundreds of feet 
     Ceiling Flag (first layer), QC flag     Code Value 
     Cloud Amount (first layer), QC flag     Code Value
     Ceiling Height (second layer)           Hundreds of feet 
     Ceiling Flag (second layer), QC flag    Code Value
     Cloud Amount (second layer), QC flag    Code Value
     Ceiling Height (third layer)            Hundreds of feet 
     Ceiling Flag (third layer), QC flag     Code Value
     Cloud Amount (third layer), QC flag     Code Value
     
     The list of code values for the Present Weather is too large to reproduce
     in this document. Refer to WMO, 1988 for a complete
     list of Present Weather codes.
     
     The code values for the Squall/Gust Indicator are:
     
     Code      Definition
     ----      ----------
     blank     No Squall or Gust
     S         Squall
     G         Gust
     
     The code values for the ceiling flag Indicator are:
     
     Code      Definition
     ----      ----------
     0         None
     1         Thin
     2         Clear below 12,000 feet
     3         Estimated
     4         Measured
     5         Indefinite
     6         Balloon
     7         Aircraft
     8         Measured/Variable
     9         Clear below 6,000 feet (AUTOB)
     10        Estimated / Variable
     11        Indefinite / Variable
     12        12-14 reserved
     15        Missing
     
     The code values for the Cloud Amount Indicator are:
     
     Code      Definition
     ----      ----------
     0         0 ( or clear)
     1         1 okta or less, but not zero or 1/10 or less, but not zero
     2         2 oktas or 2/10-3/10 
     3         3 oktas or 4/10
     4         4 oktas or 5/10
     5         5 oktas or 6/10
     6         6 oktas or 7/10-8/10
     7         7 oktas or more, but no 8 oktas or 9/10 or more, but not 10/10
     8         8 oktas or 10/10 (or overcast)
     9         Sky obscured by fog and/or other meteorological phenomena
     10        Sky partially obscured by fog and/or other meteorological 
                phenomena
     11        Scattered
     12        Broken
     13        13-14 Reserved
     15        Cloud cover is indiscernible for reasons other than fog or
               other meteorological phenomena, or observation is not made.

2.2 Data Remarks

This dataset contains only one minute observations for the BAMEX 2003 domain and time period. The component datasets from which this dataset was compiled are available on-line in native format via the BAMEX 2003 Master Table of Datasets (UCAR/JOSS, 2003)

When not present in the raw data, station pressure is computed from altimeter using the algorithms found in the Smithsonian Meteorological Tables (1949).

Calculated Sea Level pressure is computed from station pressure, temperature, dewpoint, and station elevation using the formula of Wallace and Hobbs (1977).

When not present in the raw data, the dewpoint temperature was computed by UCAR/JOSS from station pressure, temperature, and relative humidity (RH) using the formula from Bolton (1980) . For the purpose of this calculation, RH <= 104.0 was considered valid. For RH > 104.0, the dew point was set to missing. Dew point was calculated for the following networks: ARM SMOS, ABLE AWS, Iowa School Net, and LAIS.

3.0 Quality Control Processing

The BAMEX 2003 One Minute Surface Composite was formed from several datasets:

These BAMEX 2003 One Minute Surface Composite datasets were collected over the BAMEX 2003 domain (i.e., 30N to 48N latitude and 80W to 104W longitude) and time period (20 May 2003 through 6 July 2003) and were combined to form a surface composite. The composite was quality controlled to form the final BAMEX 2003 One Minute Surface Composite.

During the JOSS Horizontal Quality Control (JOSS HQC) processing, station observations of pressure, temperature, dew point, wind speed and wind direction were compared to "expected values" computed using an objective analysis method adapted from that developed by Cressman (1959) and Barnes (1964). The JOSS HQC method allowed for short term (>/= 30 day) variations by using 30 day standard deviations computed for each parameter when determining the acceptable limits for "good", "questionable", or "unlikely" flags. "Expected values" were computed from inverse distance weighted station observations within a 200 km Radius Of Influence (ROI) centered about the station being quality controlled (the station being quality controlled was excluded); i.e.;

theta_e = < theta(i)/w(i) > / < w(i) >

Where theta_e is the "expected value" of the parameter at the site in question, theta(i) is the observed value of the parameter at site i, w(i) is the weighting factor for site i (here the inverse of the distance between site i and the station being quality controlled), and <...> is the sum over all stations "i" in the current ROI that have valid observations of the parameter at the time in question. Data were always compared at like solar times.

To determine an observation's HQC flag setting, the difference between the actual observation and its "expected value" was compared to that parameter's normalized standard deviation. Normalizing factors (also called the sensitivity coefficients) were chosen to control the "good", "questionable", and "unlikely" flag limits for each parameter. See Table 3-1 for BAMEX 2003 normalizing factors. Table 3-2 contains the HQC flag limit ranges derived from the normalizing factors given in Table 3-1 and estimated standard deviations for each parameter so that 95% of the QC limits applied to the BAMEX 2003 data fell within these ranges. For example, 95% of the observed station pressure values that were flagged as "good" were within 1.1 mb of the expected value. The significant overlap of the ranges seen in Table 3-2 was partially due to seasonal and station differences in standard deviations. The actual HQC limits applied at any particular time depended upon the dynamic nature of the particular station's parameter values over time.

Data were never changed, only flagged.

HQC was only applied to station pressure, calculated sea level pressure, temperature, dew point, wind speed and wind direction. If the calculated sea level pressure quality control information was available, its flag was applied to the station and sea level pressures. If the calculated sea level pressure could not be quality controlled, the sea level pressure quality control flag was applied to the station pressure. If the sea level pressure could not be quality controlled, the station pressure quality control flag was not overridden. Note that there were no sea level pressures in this composite.

Table 3-1 Normalizing factors used for BAMEX 2002 One Minute Surface Composite

     
     Parameter                  Good      Questionable   Unlikely
     ---------                  ----      ------------   --------
     Station Pressure           0.2           0.2          0.5
     Calculated SLP             0.4           0.4          1.0
     Dry Bulb Temperature       0.5           0.5          1.0
     Dew Point Temperature      0.5           0.5          1.0
     Wind Speed                 2.25         2.25          4.0
     Wind Direction             1.22         1.22          2.2
Table 3-2 Ranges of HQC flag limit values for BAMEX 2003 One Minute Surface Composite
    
     Parameter                      Good      Questionable   Unlikely
     ---------                      ----      ------------   --------
     Station Pressure (mb)         < 1.1       [0.5-2.8]      > 1.1
     Calculated SLP (mb)           < 2.5       [0.9-6.2]      > 2.2
     Dry Bulb Temperature (deg.C)  < 2.6       [0.9-5.2]      > 1.7
     Dew Point Temperature (deg.C) < 2.7       [0.7-5.4]      > 1.4
     Wind Speed (m/s)              < 6.1       [1.6-10.8]     > 2.8
     Wind Direction(degrees)       < 156.7     [79.5-180.0]   >143.4

The squall/gust wind speed data were not quality controlled.

General consistency checks were also applied to the dry bulb temperature, wind direction, and the relationship between precipitation and cloud amount/cloud cover. If the dew point temperature was greater than the dry bulb temperature both values were coded "questionable". Also, wind direction for observed "calm" winds was given the same QC code as the wind speed. If precipitation was reported, but the cloud amount was "none" or "clear", then both the cloud amount and precipitation values were coded "questionable".

Several impossible values were also checked. Negative wind speeds were coded "unlikely". Negative squall/gust wind speeds were coded "unlikely". Wind directions of less than 0 degrees or greater than 360 degrees were coded "unlikely". If these consistency checks would have upgraded the quality control flags previously set by HQC or gross limit checks, then they were not applied. However, if these consistency checks would have degraded the previously set QC flags, they were applied.

The JOSS HQC scheme relied on spatial and temporal continuity to flag the data. It has been shown that this method works very well for temperature, dew point, pressure, and wind speed, but is not a very good scheme for the wind direction. The flags appear to be overly lax and perhaps could be tightened.

Gross limit checks were also used to determine the quality of the precipitation values. The gross limits are shown in Table 3-3. Certain "questionable" and "unlikely" data values were also manually inspected. After inspection, the quality control flag may have been manually modified to better reflect the physical reasonableness of the data. Data were never modified, only flagged. Negative precipitation was also coded "unlikely". See Table 3-4 for a list of the possible quality control flags and their meanings.

Table 3-3 - Precipitation Gross Limit Values

     Parameter              Good      Questionable     Unlikely
     ---------              ----      ------------     --------
     1 Minute Precip       < 3.0 mm   >= 3.0 mm      >= 6.0 mm
Table 3-4 - Quality Control Flags
     
     QC Code   Description
     -------   -----------
     U         Unchecked
     G         Good
     M         Normally recorded but missing.
     D         Questionable
     B         Unlikely
     N         Not available or Not observed
     X         Glitch                        
     E         Estimated
     C         Reported value exceeds output format field size or
               was negative precipitation.
     T         Trace precipitation amount recorded
     I         Derived parameter can not be computed due to
               insufficient data.

4.0 References

Argonne National Laboratory, cited 2004a: ABLE Automatic Weather Station (AWS) [Available online from http://www.atmos.anl.gov/ABLE/aws.html]

Argonne National Laboratory, cited 2004b: ABLE Site Latitude/Longitude [Available online from: http://gonzalo.er.anl.gov/ABLE/sitelatlon.html]

ARM, cited 2003a: Atmospheric Radiation Measurement Program [Available online from http://www.arm.gov/]

ARM, cited 2003b: Data Quality HandS Explorer [Available online from http://dq.arm.gov/cgi-bin/dqmenu.pl]

ARM, cited 2005: Surface Meteorological Observation System Instruments for SGP (SMOS)[Available online from http://www.arm.gov/instruments/instrument.php?id=36]

ARM, cited 2004d: SGP Overview Map [Available online from http://www.arm.gov/sites/sgp/maps.stm]

ASOS User's Guide, 1998, ASOS Project Office, NOAA, National Weather Service, Washington D.C., June 1998. [Available online from http://www.nws.noaa.gov/asos/aum-toc.pdf]

ASOS User's Guide Appendices, 1998, ASOS Project Office, NOAA, National Weather Service, Washington D.C., June 1998. [Available online from http://www.nws.noaa.gov/asos/appen.pdf]

BAE, cited 2004: Department of Biological and Agricultural Engineering [Available online from http://www.bae.lsu.edu]

Barnes, S. L., 1964: A technique for maximizing details in numerical weather map analysis. J. Appl. Meteor., 3, 396-409.

Bolton, D., 1980: The computation of equivalent potential temperature., Mon. Wea. Rev., 108, pp 1046-1053.

Cressman, G. P., 1959: An operational objective analysis system. Mon. Wea. Rev., 87, 367-374.

FAA, cited 2004: Automated Surface Observing System [Available online from http://www.faa.gov/asos/asosinfo.htm]

FAA, cited 2004b: Automated Weather Observing System [Available online from http://www.faa.gov/asos/awosinfo.htm]

Herzmann, 2004: About the Iowa AWOS calibration issues, 25 January 2004 [Available online from http://mesonet.agron.iastate.edu/AWOS/reports/awos040125.pdf]

IEM, cited 2004a: Iowa Environmental Mesonet AWOS Network [Available online from http://mesonet.agron.iastate.edu/AWOS]

IEM, cited 2004b: Iowa Environmental Mesonet School Network [Available online from http://mesonet.agron.iastate.edu/schoolnet]

LAIS, cited 2004: Louisiana Agriclimatic Information [Available online from http://www.agctr.lsu.edu/weather/]

LSU, cited 2004: LSU AgCenter [Available online from http://www.lsuagcenter.com]

NWS, cited 2003: Automated Surface Observing System [Available online from http://www.nws.noaa.gov/asos]

Smithsonian Meteorological Tables, Table No. 65, p.269. Smithsonian Institution Press, Washington, D.C., September, 1949.

UCAR/JOSS, cited 2003: BAMEX Master Table of Datasets [ Available online from http://www.joss.ucar.edu/bamex/dm/archive/index.html]

Unidata, cited 2003: GEMPAK/N-AWIPS [Available online from http://www.unidata.ucar.edu/packages/gempak]

United States Department of Transportation (USDOT), 1988. AWOS Operations Manual, Federal Aviation Administration.

USDOT, 1998: U.S. Department of Transportation, Federal Aviation Administration Interface Control Document between AWOS Data Acquisition System (ADAS) and Automated Weather Observing System (AWOS), April 14, 1998 [Available online from http://mesonet.agron.iastate.edu/AWOS/manual/]

Wallace, J.M., P.V. Hobbs, 1977: Atmospheric Science, Academic Press, 467 pp.

World Meteorological Organization (WMO), 1988: Manual on Codes Volume I, Part B - Binary Codes. WMO, Geneva, Switzerland.