GCIP LSA-E EAOP 1999 Hourly Surface Composite 1.0 General Description The GEWEX Continental-Scale International Project (GCIP) Large Scale Area-East (LSA-E) Enhanced Annual Observing Period (EAOP) 1999 Hourly Surface Composite is composed of data from several sources (i.e., Automated Surface Observing System (ASOS), National Climatic Data Center (NCDC) DATSAV3, Great Lakes Environmental Research Laboratory (GLERL), Illinois Climate Network (ICN), National Oceanic and Atmospheric Administration (NOAA) Atmospheric Turbulence and Diffusion Division (ATDD) Oak Ridge Meteorological site, NOAA/ATDD Bondville Meteorological site, Natural Resource Conservation Service Soil Moisture Soil Temperature (NRCS/SMST), USDA/ARS North Appalachian Experimental Watershed (Coshocton), W. K. Kellogg Biological Station (KBS) LTER, University of Kentucky Research Farm, North Carolina Agricultural Research Service (NCARS) Weather and Climate Network, National Oceanic and Atmospheric Administration (NOAA) Profiler Network (NPN) Surface Data, Purdue Automated Agricultural Weather Station (PAAWS) Network, and the Tennessee Valley Authority (TVA)) within the GCIP LSA-E EAOP-99 domain. Data from these sources (approximately 508 stations) were merged and quality controlled to form this Surface Composite. This Surface Composite contains data for the GCIP LSA-E EAOP-99 time period (01 October 1998 through 30 September 1999) and for the GCIP LSA-E EAOP-99 domain only. The GCIP LSA-E EAOP-99 domain is approximately 33N to 43N latitude and 76W to 89W longitude. 2.0 Detailed Data Description The GCIP LSA-E EAOP-99 Hourly Surface Composite is composed of data from several different sources which report data at hourly frequencies. 2.0.1 Automated Surface Observing System Algorithms The following are descriptions of the algorithms used by the Automated Surface Observing System (ASOS) to produce hourly surface data. Complete details may be found in the ASOS User's Guide (1992). The ASOS hourly values were produced from ASOS 5-minute data by extracting all parameters (except precipitation) from the 55-minute observation of the previous hour and assigning those values to the current hourly observation. The precipitation is the sum of the precipitation values from the 05-minute of the previous hour through the 00-minute of the current hour. The following are descriptions of the algorithms used by ASOS to produce five minute surface data. Complete details may be found in the ASOS User's Guide (1992). Temperature/Dewpoint ASOS takes 30-sec measurements and computes a 1-min average. A 5-min running average of these 1-min averages is computed. A minimum of four 1-min averages are required to compute a valid 5-min average. 5-min averages are rounded to the nearest degree F. ASOS will report the latest valid 5-min average during the previous 15-min period. If one is not available, the data are reported as "missing". If the 5-min average dewpoint is 1 or 2 degrees higher than the 5-min average temperature, then the dew point is reported equal to temperature. If the 5-min average dewpoint exceeds the 5- min average temperature by more than 2 degrees, the dewpoint is reported as "missing". Station Pressure and Derived Pressure Elements ASOS 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 in the computation of derived parameters (e.g., altimeter setting, sea level pressure, and pressure remarks such as tendency). Wind ASOS takes 5-sec measurements of wind speed and direction and computes a 2-min running average. Wind direction is rounded to the nearest degree and wind speed is rounded to the nearest knot. If the 2-min running average is 2 knots or less, the wind is reported as calm. The gust is computed using the highest 5-sec average wind speed during the past 10-min period. A gust is computed only when the 2-min running average exceeds 9 knots and the highest 5-sec measurement exceeds the 2-min running average by 5 knots (during the past minute). Precipitation ASOS takes 1-min accumulated measurements and computes total precipitation over 5-min, 15-min, hourly, 3-hr, 6-hr, and daily increments. Monthly totals are summed from daily totals. Present Weather There are currently two automated ASOS present weather sensors. They are the Precipitation Identification (PI) sensor which discriminates between rain and snow and the Freezing Rain (ZR) sensor. Although there is no ASOS "Obstruction To Vision" (OTV) sensor, ASOS algorithms evaluate data from multiple sensors (i.e., visibility, temperature, dewpoint temperature, and PI) and infer the presence of obstructions to vision (fog or haze). Once each minute the PI sensor output is stored in memory (up to 12 hours). The latest 10 minutes of data are examined. If 3 or more samples are missing, ASOS reports "missing" for that minute. If 2 or more samples indicate precipitation, and at least 8 one minute samples are available, the algorithm determines the type and intensity to report. In general, to report anything other than light precipitation (P-), two of the samples are required to be the same type. If there is a tie between two types of precipitation, snow is reported. The highest intensity obtained from two or more samples determines the present weather type and intensity that is reported. Once each minute the ZR sensor output is stored in memory (up to 12 hours). Data from the latest 15 minutes are used to compute the current minute freezing rain report. If 3 or more sensor outputs in the past 15- minutes are missing, the report is set to "missing". If at least one positive freezing rain report occurs in the past 15-minutes, freezing rain is reported for the current minute. If freezing rain is reported, the PI sensor report is examined and a hierarchical scheme is used to compute the present weather report. This scheme follows the familiar reporting hierarchy of LIQUID- FREEZING-FROZEN in ascending order of priority. ASOS does not report mixed precipitation. The beginning and ending times of one minute freezing rain reports are used in the hourly SAO reports. Once freezing rain has been sensed and the ambient air temperature is 36 degrees F or below, it will be carried in subsequent SAO reports for 15-minutes after it is no longer sensed. The OTV algorithm continuously monitors the reported visibility once each minute. When visibility drops below 7 statute miles, the algorithm obtains the current Dew Point Depression (DD) to distinguish between fog and haze. If the DD is < or equal to 4 degrees F, then fog will be reported and appended to the present weather report. If DD is > 4 degrees F and no present weather is reported by the PI and ZR sensors, then haze is reported as present weather. When present weather is reported by the PI and ZR sensors, haze is not reported. In the event DD is missing, visibility is used to discriminate between haze and fog. If visibility is < 4 miles, fog will be reported. When present weather is also reported, fog will be appended to the report. If visibility is > or equal to 4 miles but < 7 miles and no present weather is reported, then haze is reported. 2.0.2 National Climatic Data Center (NCDC) DATSAV3 Algorithms University Corporation for Atmospheric Research/Joint Office for Science Support (UCAR/JOSS) was given data in the NCDC Surface Hourly Abbreviated Format, which is derived by NCDC from their DATSAV3 formatted data. This "Abbreviated" format data was used by UCAR/JOSS to produce hourly surface data. These data were also used to generate the surface GCIP LSA-E EAOP-99 "Specials" dataset. The following are descriptions of the algorithms used by NCDC to produce their NCDC DATSAV3 formatted data. The NCDC DATSAV3 Surface Database is composed of worldwide surface weather observations from about 10,000 currently active stations, collected and stored from several sources such as the Automated Weather Network (AWN) and the Global Telecommunications System (GTS). Most collected observations are decoded at the Air Force Global Weather Central (AFGWC) at Offutt Air Force Base (AFB), Nebraska, and then sent electronically to the United States Air Force (USAF) Combat Climatology Center (AFCCC), collocated with NCDC in the Federal Climate Complex in Asheville, North Carolina. AFCCC builds the final database through decode, validation, and quality control software. All data are stored in a single ASCII format. The database is used in climatological applications by numerous Department of Defense (DoD) and civilian customers. DATSAV3 refers to the digital tape format in which decoded weather observations are stored. The DATSAV3 format conforms to Federal Information Processing Standards (FIPS). The DATSAV3 database includes data originating from various codes such as synoptic, airways, Meteorological Aviation Routine Weather Reports (METAR), and Supplementary Marine Reporting Station (SMARS), as well as observations from automatic weather stations. The users handbook provides complete documentation for the database and its format. AFCCC sorts the observations into station-date-time order, validates each station number against the Air Weather Service Master Station Catalog (AWSMSC), runs several quality control programs, and then merges and sorts the data further into monthly and yearly station-ordered files. AFCCC then provides the data to the collocated National Climatic Data Center (NCDC). For more information about the DATSAV3 format and the quality control performed on this data see NCDC, 1999a. For more information on the Abbreviated Format see NCDC, 1999b. UCAR/JOSS converts the DATSAV3 altimeter reading to station pressure using the information from Smithsonian Meteorological Tables, 1949. 2.0.3 Great Lakes Environmental Research Laboratory (GLERL) Algorithms The algorithms used to produce the Great Lakes Environmental Research Laboratory (GLERL) hourly surface data are not currently available. 2.0.4 Illinois Climate Network Algorithms The Illinois Climate Network (ICN) consists of 19 automated weather stations in Illinois operated by the Illinois State Water Survey. These stations were installed between 1988 and 1991 and are located on the University of Illinois Agricultural Experiment Station Farms, the Southern Illinois University Agronomy Experiment Farms, and on various community college campuses around the state. Each station consists of a 10 meter tower equipped with weather instruments which are interrogated every 10 seconds by a datalogger which computes hourly averages and totals. These hourly values are then included by UCAR/JOSS in this GCIP LSA-E EAOP-99 Hourly Surface Composite. Information regarding the ICN site instrumentation is given below. For detailed ICN site descriptions, ICN instrument verification, ICN Quality Control and more, refer to Hollinger, 1994. Wind Speed and Direction Wind speed and direction are monitored with an R.M. Young 8003 anemometer fitted with a wide range molded polypropylene plastic four-blade propeller. An anemometer is mounted on each tower at a height of 10 m. The anemometer has a functional wind speed range of 0 to 50 meters per second (m/s), with a threshold speed of 0.2 to 0.4 m/s. The propeller weighs 31 grams and has a distance constant of 3.3m. The distance constant is the wind passage required for a 63 percent recovery from a step change in wind speed. With wind speeds greater than 1.3 m/s, the propeller makes one revolution per 30 cm of wind passage. Below wind speeds of 1.3 m/s, slippage increases (i.e., a greater wind passage is needed per revolution) down to the wind threshold. Wind direction is measured from 0 to 355 degrees. The 10K-ohm precision resistor that measures wind direction has an open section in the potentiometer element from 355 to 360 degrees. The open section of the potentiometer element is oriented to the north represented by a direction signal of zero. Rotation of the vane clockwise from north to east, south, and west causes the azimuth signal to increase in value until the vane reaches 355 degrees, where the signal falls to zero. The vane and propeller combination has a damping ratio of 0.34. Air Temperature and Dew Point Air temperature and relative humidity are monitored using a Vaisala temperature and humidity probe, model HMP112Y. The temperature- humidity probe is mounted inside a radiation shield attached to a leg of each weather tower at a height of approximately 2 m. The operating temperature is from -5 to +55 degrees Celsius (C). A 216 micrometer sintered filter protects a platinum thermistor (the temperature sensor) and a capacitance film (the humidity sensor) from dust particles. The temperature measurement range is -40 to +80 degrees C with an output uncertainty of +/-0.3 degrees C at 20 degrees C. The measurement range for relative humidity is 0 to 100 percent. In the 0 to 80 percent range, output uncertainty is +/- 2 percent at +20 degrees C. The output uncertainty in the 80 to 100 percent range is +/-3 percent at 20 degrees C. UCAR/JOSS uses the reported relative humidity to compute the dew point (Bolton, 1980). The dew point is reported in this GCIP LSA-E EAOP-99 Hourly Surface Composite. Precipitation A Belfort weighing bucket rain gage fitted with an 8-inch collector opening, evaporation funnel, and a potentiometer is used to measure precipitation at each site. Each rain gage is located outside the rain shadow area of the weather tower. The 203 mm (8-inch) collector allows each gage to accept up to 305 mm (12 inches) of precipitation. Although this capacity is expressed in inches, it is actually measured in terms of weight, with 25.4 mm (1 inch) of precipitation being equivalent to 902.6 g (29.02 ounces) of water at 17 degrees C (62.6 degrees F). The accuracy of this type of rain gage over the range 0 to 152.4 mm (0 to 6 inches) is 0.26 mm (0.03 inches; +/- 0.5 to 1 percent), and over the range 152.4 to 304.8 mm (6 to 12 inches) is 1.52 mm (0.06 inches; 1 percent). The evaporation funnel is removed from the rain gage collector during the winter and a one-quart charge of environmentally safe anti- freeze added to each rain gage bucket to help melt frozen precipitation and protect the bucket from damage due to the expansion of freezing water in the bucket. Precipitation is determined by ICN by subtracting the weight of water collected in a bucket at the end of an hour or day from the weight at the beginning of an hour or day. Negative hourly observations are assumed to be zero and are due to "noise" in the instrument caused by evaporation, wind eddies, pressure fluctuations, electrical noise, and mechanical backlash of the instrument. ICN is working to correct these problems. Barometric Pressure A Campbell Scientific SBP270 barometric pressure sensor with an accuracy of +/- 0.2 millibar (mb) over a pressure range of 800 to 1000mb is used to measure barometric pressure at each station. Operating temperature of the sensor is -18 to 79 degrees C. The barometer in the sensor is a Sentra Model 270 variable capacitance barometer. 2.0.5 NOAA ATDD Oak Ridge Meteorological Data Algorithms Climate data were collected at the NOAA ATDD Oak Ridge Meteorological site (Lat 36 00 N, Lon 84 15 W, Elevation 267 m) located at 456 S. Illinois Ave. in Oak Ridge, Tennessee. Temperature The average temperature recorded in degrees Celsius for the 60 minutes ending at the given hour. Instrument error is less than +/-0.4C. Dew Point The average dew point temperature recorded in degrees Celsius for the 60 minutes ending at the given hour. This variable is dependent on temperature, humidity, and pressure. The relative humidity instrument error is less than +/-2% from 0 to 90% and less than +/-3% from 90 to 100%. Pressure The average station pressure recorded in millibars for the 60 minutes ending at the given hour. Precip Total water equivalent precipitation in millimeters measured during the 60 minutes ending at the given hour. This value is the liquid amount or equivalent of rain, snow, and all other types of precipitation that occurred during the given 60 minutes. Note that when ice storms or other similar events occur, values may not be accurate due to delayed melting. Efforts have been made to accurately estimate values in these circumstances; however, a determination is not always possible. Wind Dir The vector sum of wind directions measured every 1 second during the 60 minutes ending at the given hour. This value is given in degrees (0 to 359). Wind Speed The average wind speed in meters per second calculated by averaging wind speeds measured every 1 second during the 60 minutes ending at the given hour. 2.0.6 NOAA ATDD Bondville Meteorological Data Algorithms During the summer of 1996, the Bondville meteorological system was installed just south of Champaign, Illinois on a farm owned and operated by John Reifsteck (World Wide Web site is http://w3.aces.uiuc.edu/InfoAg/CyberFarm/Reifsteck/index.htm). This farm (40 00.366' N, 88 17.512' W) is within the GEWEX/GCIP large scale area north central (NC), and just on the northeastern edge of the LSA East region. The site characteristics are typical of those found throughout the midwestern U.S., with most of the land in agricultural production. The soil (silt loam) has a bulk density of 1.5 g/cm3 with sand, silt, and clay fractions of 5%, 70%, and 25%, respectively. The farm has been in continuous no-till since 1986, alternating each year between corn and soybeans. In 1996, the field consisted of soybeans, corn in 1997 and again soybeans in 1998. Table 2.0.6-1 below contains a list of the standard meteorological variables collected at this site. The instrumentation and solar panels are mounted on a scaffolding tower. The standard meteorological sensors (see Table 2.0.6-2) are sampled every 2 s with a datalogger and multiplexor (CR21x, Campbell Scientific, Inc.) and averages are computed every 30 minutes to produce the 30-minute raw data which is supplied to UCAR/JOSS. Except for the precipitation parameter, UCAR/JOSS provides the value collected on the hour in this GCIP LSA-E EAOP-99 Hourly Surface Composite. The two 30-minute precipitation values are summed for each hour to create the total hourly precipitation. Table 2.0.6-1 Meteorological variables measured at NOAA/ATDD Bondville Site with description. Variable Description -------- ----------- w_speed propeller anemometer (10 meters, Bondville ISIS) w_dir wind direction (10 meters, Bondville ISIS) Ta air temperature (C), at 3 m RH relative humidity at 3 m Pres surface pressure in mb rain total rain for half hour (inches) Table 2.0.6-2. Meteorological variables measured at NOAA/ATDD Bondville Site with model number and manufacturer of instrumentation used. Meteorological Variable Manufacturer model number ------------------------ ------------ ----------- Air Temperature and RH Vaisala 50Y Precipitation Texas Instrument Atmospheric Pressure Vaisala PTB101B Surface Temperature Everest 4000A Data Acquisition A laptop computer is configured in a multitasking mode to simultaneously perform three operations. For the first and foremost task, measurements of three components of the wind vector along with the speed of sound (from which the virtual temperature can be derived) are digitally sent from the sonic anemometer (which includes the digitized H2O and CO2 signals from the IRGA) to the laptop computer, which is housed in a small environmental enclosure. In the second task, the computer retrieves the standard meteorological data from the CR21X datalogger every 30 minutes and appends the data to an existing file. After midnight, the covariance data and standard meteorological data are copied to separate files with a name, year and calendar day header. The computer is equipped with a modem and cellular phone in order to retrieve the data and conduct occasional system checks. On average, data are retrieved from the laptop computers about once every two days. 2.0.7 NCRS/SMST Algorithms The following algorithms apply to the USDA National Resources Conservation Service (NRCS), NATIONAL WATER and CLIMATE CENTER, Soil Climate Analysis Network (SCAN), Soil Moisture/Soil Temperature (SMST) Surface Data. Note that these DATA ARE PROVISIONAL AND SUBJECT TO REVISION. The following channels were converted to JOSS QCF format when they existed: PC11 Current accumulated precipitation value beginning October 1 through September 30 was converted to incremental precip. BPMN Current barometric pressure. ATC1 or ATC5 Current air temperature. If both existed, then they were only converted if they reported the same value. WR1X1 Maximum wind speed for the previous hour RHC1 Current relative humidity was converted to dew pt. No other channels were converted. The algorithms used to produce NCRS/SMST hourly surface data are not currently available. Information about these stations can be found at the scan website at http://www.wcc.nrcs.usda.gov/scan. 2.0.8 USDA/ARS North Appalachian Experimental Watershed Data (Coshocton) Barometric Pressure Barometric Pressure is collected every 10 seconds using a Climatromics model 100099 sensor. Hourly values are calculated by averaging the 10 second measurements collected during the previous hour. Air Temperature Air Temperature is measured every 10 seconds using a Rotronics model MP-100 sensor. Hourly values are calculated by averaging the 10 second measurements collected during the previous hour. Dew Point Dew point is collected every 10 seconds using a EG&G model 200 sensor. Hourly values are calculated by averaging the 10 second measurements collected during the previous hour. Wind Speed Wind Speed is measured every 1 second using a MRI model 1022S sensor located 10 meters above the ground. Hourly values are calculated by averaging the 1 second measurements collected during the previous hour. Wind direction Wind direction is collected every 10 seconds using a MRI model 1022D sensor. Hourly values are calculated by averaging the 10 second measurements collected during the previous hour. Precipitation Precipitation is measured using a Belfort FW1 weighing bucket. The sensor is not heated. Precip on/off is detected every 10 seconds using a Wong precipitation sensor. 2.0.9 W. K. Kellogg Biological Station (KBS) LTER Data These data were provided by the W.K. Kellogg Biological Stations (KBS). The KBS LTER Home Page is at http://lter.kbs.msu.edu. The stations are run by Michigan State University and are located in Hickory Corners, MI. The algorithms used to produce the KBS LTER hourly surface data are not currently available. There are two stations in this network: LTER and Pond Lab. KBS provided the following information about these stations. LTER data: Relative humidity has been downloaded by KBS directly from calibrated field instruments without post-collection quality checks. Use with caution. The tipping bucket used at the LTER weather station is a NovaLynx(Sierra Misco) 2500 model 8" funnel heated in winter. There were three pairs of wind values given in the LTER raw data. UCAR/JOSS has only reported the first pair in our QCF formatted data. Pond Lab data: These data have not been verified by KBS; according to KBS, there are known errors. The Pond Lab weather station tipping bucket is a NovaLynx(Sierra Misco)2500E12 model 12" diameter funnel heated in the winter. 2.0.10 University of Kentucky Research Farm Data The University of Kentucky Research Farm Data was provided by the University of Kentucky Agricultural Weather Center (Phone 606/257-5850) (World Wide Web URL: http://wwwagwx.ca.uky.edu/). The algorithms used to produce the Kentucky Research Farm Data are not currently available. 2.0.11 North Carolina Agricultural Research Service (NCARS) Weather and Climate Network There are 11 stations in the North Carolina Agricultural Research Service Weather and Climate (NCARS) network: castle_hayne, clayton, clinton, fletcher, jackson_springs, kinston, oxford, raleigh_turf, rocky_mount, salisbury, and waynesville. Station castle_hayne (NCDC/NOAA ID number 9467) is located at the Horticultural Crops Research Station, 3800 Castle Hayne Road, Castle Hayne, NC 28429-6519 in New Hanover County. Station clayton (NCDC/NOAA ID number 1820) is located at the Central Crops Research Station, 13223 U.S. 70 West, Clayton, NC 27520- 2127 in Johnston County. Station clinton (NCDC/NOAA ID number 1881) is located at the Horticultural Crops Research Station, 2450 Faison Highway, Clinton, NC 28328-9501 in Sampson County. Station fletcher (NCDC/NOAA ID number 3106) is located at the Mountain Horticultural Crops Research Station, 2000 Fanning Bridge Road, Fletcher, NC 28732-9629 in Henderson County. Station jackson_springs (NCDC/NOAA ID number 4464) is located at Sandhills Research Station, 2664 Windblow Road, Jackson Springs, NC 27281-9505 in Montgomery County. Station kinston (NCDC/NOAA ID number 4689) is located at Cunningham Research Station, 200 Cunningham Road, Kinston, NC 28501 in Lenoir County. Station oxford (NCDC/NOAA ID number 6510) is located at Oxford Tobacco Research Station, 300 Providence Road, Oxford, NC 27565-1555 in Granville County. Station raleigh_turf (no NCDC/NOAA ID) is located at Turfgrass Field Laboratory, 4200 Hillsborough Street, Raleigh, NC 27695-7643 in Wake County. Station rocky_mount (NCDC/NOAA ID number 7400) is located at Upper Coastal Plain Research Station, Route 2, Box 400, Rocky Mount, NC 27801-9276 in Edgecombe County. Station salisbury (NCDC/NOAA ID number 7618) is located at Piedmont Research Station, 8350 Sherrills Ford Road, Salisbury, NC 28147-7579 in Rowan County. Station waynesville (NCDC/NOAA ID number 9147) is located at Mountain Research Station, 516 Test Farm Road, Waynesville, NC 28786-4016 in Haywood County. Maps of the station locations can be viewed through the NCARS web page at http://www.nc-climate.ncsu.edu/agnet/index.html. NCARS Data Acquisition System Stations clayton, fletcher, jackson_springs, kinston, oxford, rocky_mount, salisbury, raleigh_turf, and waynesville use the Campbell CSI (Campbell Scientific Inc.) CR-10 Data Acquisition System. Stations castle_hayne and clinton use the Campbell CSI CR-10X Data Acquisition System. Barometric Pressure Stations clayton, kinston, oxford, rocky_mount, and salisbury measure barometric pressure using a Yellow Springs Model 2014 Pressure Transducer located inside the office building. Station waynesville measures barometric pressure using a CSI CS105 Pressure Transducer located inside the office building. Stations castle_hayne, clinton, fletcher, and jackson_springs measure barometric pressure using a Setra Model 270 Pressure Transducer located inside the office building. Station raleigh_turf does not measure barometric pressure. Temperature and Relative Humidity Stations castle_hayne, clinton, jackson_springs, kinston, oxford, salisbury, and waynesville measure air temperature and relative humidity using a CSI HMP35C Vaisala Probe covered by a gill shield and located 5 feet above the ground. Stations clayton, fletcher, raleigh_turf, and rocky_mount measure air temperature and relative humidity using a CSI CS500 Vaisala Hummeter Probe covered by a gill shield and located 5 feet above the ground. Precipitation Stations castle_hayne, clayton, clinton, fletcher, jackson_springs, kinston, oxford, rocky_mount, salisbury, and waynesville measure precipitation using a Texas Electronics TE525 - Tipping Bucket Rain Gauge located 4 feet above the ground. Station raleigh_turf measures precipitation using a Sierro Misco Tipping Bucket Rain Gage located 4 feet above the ground. Wind Speed and Direction Stations castle_hayne, clayton, clinton, fletcher, jackson_springs, kinston, oxford, rocky_mount, salisbury, and waynesville measure wind speed and direction using an RM Young 05103 Wind Monitor (with a Gill Propeller for wind speed) located 30 feet above the ground on a pole mounted on the roof top. Station raleigh_turf measures wind speed using a Cup Anemometer located 10 feet above the ground on a tripod, and measures wind direction with a wind vane located 10 feet above the ground on a tripod. 2.0.12 National Oceanic and Atmospheric Administration (NOAA) Profiler Network (NPN) The algorithms used to produce the NOAA NPN hourly surface data are not currently available. 2.0.13 Purdue Automated Agricultural Weather Station (PAAWS) network The PAAWS network is a system of remote automatic weather stations located at each of the eight regional Purdue Agricultural Research Centers (ARCs) throughout Indiana and the Purdue Agronomy Research Center. The purpose of the network is to continuously measure weather elements of special interest to Purdue agricultural researchers. The weather sensors currently installed with each datalogger were selected based on a survey of Purdue agricultural researchers. Some sensors commonly found at automatic weather stations, such as dewpoint, did not receive enough votes in the initial survey. The following measurements of the automatic weather station datalogger are included in this dataset: wind direction and speed at 10' air temperature at 4.5' precipitation Early in 1999 there are gaps in the database because faulty dataloggers and sensors were discovered during the installation process. These were returned to the factory for repair. For example, the Butlerville datalogger was found to be defective and was later replaced. The wind equipment at Oolitic malfunctioned until April 1999. Instrumentation Most weather sensors and the datalogger are mounted on a 10 foot high tower mast. The wind vane and cup anemometer are mounted on a crossarm at the top of the mast. A cell phone antenna is also mounted on the mast. Mounted lower on the mast is the air temperature sensor, a thermistor shielded by an enclosure to avoid exposure to sunlight. All weather sensor sampling, data storage, and data retrieval at the weather station is controlled by a datalogger, the Zeno 3200, manufactured by Coastal Environmental Systems of Seattle, Washington. The Zeno datalogger is mounted in an enclosure on the mast a few feet above the ground. All sensor leads run into the enclosure. The cell phone transceiver and modem are also inside the datalogger enclosure with a lead to the outside antenna. The raingage is located about 20 feet away from the tower mast, with its leads buried underground. The gage has a very low profile, with its top funnel opening just one foot above ground level. PAAWS Data Algorithms Wind Direction and Speed Wind direction is measured by a conventional balanced wind vane, sensitive to winds of at least 3 mph. The circular position of the wind vane is converted to an electrical signal by a conductive plastic potentiometer. A 15 volt signal is applied to the potentiometer. A percentage of this voltage is output by the potentiometer, directly related to the wind direction angle. A limitation of this sensor is that the potentiometer becomes worn over time, resulting in noisy or non-linear output. The only remedy is to replace the potentiometer. Wind speed is measured by a conventional rotating cup anemometer. As the cups rotate they produce an AC sine wave voltage signal with its frequency directly related to the wind speed. One complete sine wave corresponds to one rotation of the cup wheel. The AC sine wave is induced in a stationary coil by a two pole ring magnet mounted on the cup wheel shaft. A limitation of this sensor is that the precision ball bearings become worn rather quickly. As bearings wear they become noisy or the minimum detectable wind speed increases above an acceptable level. New bearings give the anemometer a starting speed of 2.5 mph, indicating it will start turning from a calm situation when wind speeds exceed 2.5 mph. Lower wind speeds are measureable if the winds were first above 2.5 mph. Bearings will be replaced at least yearly and probably twice a year. The measurement of wind gusts is highly sensitive to the number of samples included in a running average of wind speed. Our dataloggers at each Purdue ARC have been set to average the last 4 seconds of instantaneous samples when calculating wind gust. This is the factory recommended setting. A longer averaging period, such as the 5 seconds used by the National Weather Service in their automated ASOS system, will nearly always result in lower gust values. The National Weather Service will soon make a decision on whether to shorten the averaging time to 3 seconds, at the request of some of their field offices. Air Temperature Air sensors are sheltered by a 6-plate passive ventilation shield to avoid exposure to sunlight. Thermistors are sensitive to small changes in temperature. The sensors used in this project are precisely manufactured so that they are directly interchangable should sensors require replacement. All thermistors are calibrated at the factory at multiple temperatures to insure that the sensor resistance and slope meet the device's interchangability specification. Thermistors cannot be repaired. Faulty devices must be replaced in their entirety. Precipitation Precipitation is measured by a miniature version of the standard National Weather Service tipping bucket electronic raingage. The sensor has an aluminum collector funnel with a knife edge that directs incoming water into twin tiny buckets able to hold exactly .01 inch of water and counter-balanced on a central pivot. As one of the two chambers fills, it tips, spilling out to the bottom of the housing. A magnet is attached to a tipping bucket, which as the bucket tips, triggers a magnetic switch. A momentary switch closure occurs with each tip, which the datalogger senses and so increments an event counter. The alternate bucket is now exposed and begins to collect the next .01 inch of precipitation. After this bucket fills and tips the first bucket is returned to its original position and the whole cycle repeated. The total events counted at the end of a reporting period corresponds to precipitation accumulation in units of hundreths of inches. After the report the event counter is reset to zero. The tipping bucket has several limitations. First, the sensor must be kept clean. Any accumulation of bugs, dust, twigs, and other material can invalidate its measurements. Second, the tipping bucket has a maximum reliable flow rate of one inch of rain per hour. In more intense rain events the instrument will read low as a fixed amount of time is required for the bucket to tip and position the alternate bucket in place. Intermediate rainfall will be lost as water flows into the buckets rather than drips. The third deficiency of the tipping bucket is it does not work well in frozen precipitation events. Thus precipitation reports printed by the Indiana Climate Page when the air temperature is below freezing should be discarded. The data are suspect. This winter limitation could be partly resolved by adding heat tape around the gage funnel. This is not a foolproof solution, however, and creates its own set of problems. We have chosen to not install heat tape in the tipping bucket gages at any of the ARCs. Cold season precipitation data should be ignored as being unreliable. Zeno Datalogger All weather sensor sampling, data storage, and data retrieval at the weather station is controlled by a datalogger, the Zeno 3200, manufactured by Coastal Environmental Systems of Seattle, Washington. Once each second the Zeno datalogger samples each weather sensor. The data are calibrated from electrical units (such as volts) into meteorological units (such as temperature degrees). Each weather sample may undergo further evaluation at this time depending on what type of sensor is involved and what extended processing the Zeno software manager has instructed the datalogger to do for that sensor. The results of all such Zeno sampling and processing are stored in its internal memory until the end of the sampling period is reached. The Zeno software manager can instruct the datalogger how long to continue sampling until a summary report is generated. The Zenos at the Purdue ARCs have been set to sampling periods of 30 minutes. At the end of each half hour, a summary table is generated which includes 30-minute averages of wind direction and speed, air temperature, soil temperatures, and solar radiation. The extreme wind gust for the 30-minute period, and the total precipitation are also calculated into the summary table. All 30-minute summary tables are held in Zeno memory indefinitely to the extent of memory available. Each Zeno datalogger in the PAAWS network is capable of storing about 5 months of 30-minute tables in memory. When this memory capacity is reached, the oldest table is shifted out of memory to make room for the newest table. UCAR/JOSS Algorithms This GCIP/EAOP99-E Surface: PAAWS Hourly Meteorological Composite data set contains the following parameters which are either taken directly or derived from the 60 minute observations in the PAAWS data: Dry Bulb Temperature Celsius Wind Speed m/s Wind Direction Degrees Precipitation mm Squall Gust m/s All other parameters in the JOSS QC formatted data are set to missing. The precipitation values in this data set are incremental. The value reported at any hourly observation represents data collected during the previous 60 minutes. Data Remarks At least twice monthly each Zeno will be called to synchronize its datalogger clock to universal time (UTC) as kept by the National Institute of Standards and Technology (NIST) in Boulder CO. Resetting the Zeno clock results in the loss of data for the current 30 minute sample period. Early in 1999 there are gaps in the PAAWS data because faulty dataloggers and sensors were discovered during the installation process. These were returned to the factory for repair. For example, the Butlerville datalogger was found to be defective and was later replaced. The wind equipment at Oolitic malfunctioned until April 1999. Wind gust values which were flagged by PAAWS as out of range, or stale, were given a Questionable ("D") QC flag. All precip flags for the period from Oct 1 through Apr 30 have been set to Questionable ("D") because this network has unheated gages. 2.0.14 Tennessee Valley Authority (TVA) The following describes the TVA instrumentation at meteorological towers located at nuclear plants. These TVA data meet NRC compliance standards for nuclear plants. All of the TVA data included in this GCIP LSA-E EAOP-99 Hourly Surface Composite came from nuclear plant towers. Wind Speed Horizontal wind speed is measured every 5 seconds using a Light-chopper 3 cup anemometer located 10 meters above the ground. The sensors range is 0-100mph, and the calibrated range is from the sensor starting threshold (of <1 mph) to 25 mph. Hourly values reported are the instantaneous value measured on the hour. Wind Direction Horizontal wind direction is measured every 5 seconds using a Vane-type sensor with voltage output located 10 meters above the ground. The sensor range is 0-540 degrees. Software is used to convert values in the range 360-540 degrees to 0-180 degrees. The calibrated range is 0-540 degrees. Hourly values reported are the instantaneous value measured on the hour. Air Temperature Air temperature is measured every one minute using a 100-ohm Platinum RTD, 4-wire ohms measurement with powered aspirators located 10 meters above the ground. The sensor range is -30 to 120 degrees Fahrenheit, and the calibrated range is -20 to 120 degrees F. Hourly values reported are the instantaneous one minute value measured on the hour. Dewpoint Dewpoint is measured every one minute using an optically-controlled cool mirror and platinum RTD sensor with voltage output. Sensor range is -40 to 167 degrees F. The calibrated range is unknown. Hourly values reported are the instantaneous one minute value measured on the hour. Rainfall Rainfall is reported hourly using a weighing bucket with 25k ohm potentiometer. The gage is heated, but snow is not a significant factor at TVA met tower sites because of low elevation along the river. Sensor range is 0 to 10 inches, and the calibrated range is 0 to 10 inches. Hourly values reported are the rainfall that fell during the previous hour. 2.1 Detailed Format Description The GCIP LSA-E EAOP-99 Hourly Surface Composite 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. Days begin at UTC hour 0100 and end at UTC hour 0000 the following day. The data parameters are valid for the reported times. Missing values are reported as 9's in the data field. The table below details the data parameters in each record. Several data parameters have an associated Quality Control (QC) Flag Code which is assigned during the Joint Office for Science Support (JOSS) quality control processing. 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 When not present in the raw data, the dewpoint is computed using the formula from Bolton (1980). Calculated Sea Level pressure is computed from station pressure, temperature, dewpoint, and station elevation using the formula of Wallace and Hobbs (1977). This composite contains only the nominal records for the ASOS and DATSAV3 datasets. The special records for the ASOS and DATSAV3 datasets are located in the GCIP LSA-E EAOP-99 "Specials" Dataset. At least twice monthly each PAAWS Zeno will be called to synchronize its datalogger clock to universal time (UTC) as kept by the National Institute of Standards and Technology (NIST) in Boulder CO. Resetting the Zeno clock results in the loss of data for the current 30 minute sample period. Early in 1999 there are gaps in the PAAWS data because faulty dataloggers and sensors were discovered during the installation process. These were returned to the factory for repair. For example, the Butlerville datalogger was found to be defective and was later replaced. The wind equipment at Oolitic malfunctioned until April 1999. Wind gust values which were flagged by PAAWS as out of range, or stale, were given a Questionable ("D") QC flag. All PAAWS precip flags for the period from Oct 1 through Apr 30 have been set to Questionable ("D") because this network has unheated gages. 3.0 Quality Control Processing The GCIP LSA-E EAOP-99 Hourly Surface Composite was formed from several datasets (i.e., Automated Surface Observing System (ASOS), National Climatic Data Center (NCDC) DATSAV3, Great Lakes Environmental Research Laboratory (GLERL), Illinois Climate Network (ICN), National Oceanic and Atmospheric Administration (NOAA) Atmospheric Turbulence and Diffusion Division (ATDD) Oak Ridge Meteorological site, NOAA/ATDD Bondville Meteorological site, Natural Resource Conservation Service Soil Moisture Soil Temperature (NRCS/SMST), USDA/ARS North Appalachian Experimental Watershed (Coshocton), W. K. Kellogg Biological Station (KBS) LTER, University of Kentucky Research Farm, North Carolina Agricultural Research Service (NCARS) Weather and Climate Network, National Oceanic and Atmospheric Administration (NOAA) Profiler Network (NPN) Surface Data, Purdue Automated Agricultural Weather Station (PAAWS) Network, and the Tennessee Valley Authority (TVA)). These datasets were collected over the GCIP LSA-E EAOP-99 domain (i.e., 33N to 43N and 76W to 89W) and were combined to form a surface composite. The composite was quality controlled to form the final GCIP LSA-E EAOP-99 Hourly 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 300 km Radius Of Influence (ROI) centered about the station being quality controlled (the station being quality controlled was excluded); i.e.; theta_e = / 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 GCIP LSA-E EAOP-99 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 GCIP LSA-E EAOP-99 data fell within these ranges. For example, 95% of the observed station pressure values that were flagged as "good" were within 1.7 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, sea level 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. Table 3-1 Normalizing factors used for GCIP LSA-E EAOP-99 Hourly Surface Composite Parameter Good Questionable Unlikely --------- ---- ------------ -------- Station Pressure 0.2 0.2 0.5 Sea Level Pressure (SLP) 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 GCIP LSA-E EAOP-99 Hourly Surface Composite Parameter Good Questionable Unlikely --------- ---- ------------ -------- Station Pressure (mb) < 1.7 [0.5-4.4] > 1.1 Sea Level Pressure (mb) < 1.8 [0.5-4.5] > 1.1 Calculated SLP (mb) < 3.7 [0.9-9.3] > 2.2 Dry Bulb Temperature (deg.C) < 3.6 [1.0-7.3] > 2.0 Dew Point Temperature (deg.C) < 4.0 [0.9-7.9] > 1.7 Wind Speed (m/s) < 7.1 [1.6-12.7] > 2.8 Wind Direction(degrees) < 168.2 [79.6-180.0] >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 --------- ---- ------------ -------- Hourly Precipitation < 20.0 mm >= 20.0 mm >= 50.0 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 ASOS User's Guide, ASOS Project Office, NOAA, National Weather Service, Washington D.C., June 1992. 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. Hollinger, Steven E., Reinke, Beth C., and Peppler, Randy A. Illinois Climate Network: Site Descriptions, Instrumentation, and Data Management. Illinois State Water Survey, Champaign, IL., Circular 178. 1994. National Climatic Data Center, 1999a: Data documentation for DATSAV3 surface, TD-9956, April 20, 1999, National Climatic Data Center, 151 Patton Ave, Asheville, NC 28801-5001 USA. National Climatic Data Center, 1999b: Surface Hourly Abbreviated Format, 09/02/99 National Climatic Data Center, 151 Patton Ave, Asheville, NC 28801-5001 USA. Purdue Automated Agricultural Weather Station Network, 1999: PAAWS Frequently Asked Questions information supplied with raw data by Ken Scheeringa, Acting State Climatologist for Indiana Smithsonian Meteorological Tables, Table No. 65, p.269. Smithsonian Institution Press, Washington, D.C., September, 1949. 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.