1. Data Set Overview 2. Investigator(s) 3. Theory of Measurements 4. Equipment 5. Site Description 6. Data Acquisition Methods 7. Observations 8. Data Description 9. Data Manipulations 10 Errors and Limitations 11. Software 12. References 13. Glossary of Terms and Acronyms 14. Document Information 1. Data Set Overview 1.1 Data Set Identification [This section is aimed toward the person searching for a data set. Descriptions should be succinct and clear, and acronyms should be spelled out.] Boreal Ecosystem Research and Monitoring Sites (BERMS) Tower Flux Meteorological Data From the Southern Study Area Old Jack Pine Site 1.2 Study Overview [A short text describing the study/experiment, and its objective] The BERMS (Boreal Ecosystem Research and Monitoring Sites) project was designed as a 5 year partial follow-up to the BOREAS (Boreal Ecosystem-Atmosphere Study) experiment. The data set documented here, includes the near-surface meteorological measurements at the Old Jack Pine site in support of carbon, water and energy flux measurements at the Old Jack Pine site in Saskatchewan. The climate monitoring program serves some of the following purposes: 1) to provide supporting measurements for flux monitoring, 2) to contribute to the development and validation of weather and climate models and 3) to provide information for interannual climate variability studies in the boreal forest. BERMS has been a participant in Fluxnet Canada since 2002. 1.3 Data Set Introduction [The nature of the data, including a summary of the key parameters/variables studied, and the primary instruments used. A full description will be given in section 7.] The main meteorological data set is in text format. Variables have been delimited by commas, to make it easy to import into most software programs. Files have been grouped by site, month and then by data type. For example, at Old Jack Pine in one month, there are three files: the first contains ANCILLARY data (extra data not normally required by users), the second contains the MAIN subset of measured variables and the third is includes SUMMARIZED and gapfilled data. These files have two header lines, the first for variable names in APL (Adjective_Parameter_Location) format, and the second for units. Here is a summary of the measured parameters (most are measured at 30min intervals): -Air temperature: measured as deg C at various heights in relation to the structure of the forest canopy. -Humidity: measured as relative humidity at various heights in relation to the structure of the forest canopy. -Precipitation: snow depth, precipitation accumulation, rain events measured in mm. -Wind direction: measured in compass degrees above the forest canopy. -Wind speed: measured in meters per s above and within the forest canopy. -Atmospheric pressure: surface pressure measured in millibars (or Pascals). -Soil temperature: measured in deg C at 1 or more locations and at various depths in relation to ground level. -Soil moisture: volumetric water content measured at 1 or 2 locations and at various depths in relation to ground level. -Radiation: measured in watts per m sq. Includes: net radiation, downwelling shortwave, upwelling shortwave, downwelling longwave, upwelling longwave, diffuse shortwave radiation, global solar radiation and photosynthetically active radiation. -Water table height: measured in mm (not installed yet, as of Mar/04). 1.4 Related Data Sets [Note any similar or related data collected by the investigator, other investigators, or other data centres. Something like five or six related data sets is a good number to provide.] 1) BOREAS SRC mesonet climate data available from 1993 to late 1996. 2) Erica Howard's soil data near OJP. 3) Flux data at OA, OBS and OJP. 4) CS615 Volumetric Water Content (soil moisture data) 5) Ken van Rees’s minirhizotron data at OBS, OJP, HJP94, and OA 6) Snowpack temperature data. 2. Investigator(s) 2.1 Principal Investigator(s) Name and Title [Identify the Principal Investigator(s) for this data set, including general affiliation if applicable] Alan Barr (PI) Climate Resarch Branch Environment Canada National Hydrology Research Centre 11 Innovation Boulevard Saskatoon, SK S7N 3H5 Canada Alan.Barr@ec.gc.ca (306)975-4324 2.2 Title of Investigation [Official name of group taken from the Fluxnet-Canada Experiment Plan.] ***To be written*** 2.3 Contact Information [Identify and give full coordinates of the person(s) most knowledgeable about the actual collection and processing of the data sets. In many cases this will be a person (or persons), other than the Principal Investigator.] Erin Thompson BERMS Secretariat Climate Resarch Branch Environment Canada National Hydrology Research Centre 11 Innovation Boulevard Saskatoon, SK S7N 3H5 Canada Erin.Thompson@ec.gc.ca (306)975-4215 Charmaine Hrynkiw Climate Resarch Branch Environment Canada National Hydrology Research Centre 11 Innovation Boulevard Saskatoon, SK S7N 3H5 Canada Charmaine.Hrynkiw@ec.gc.ca (306)975-5627 2.4 Field and/or laboratory staff: Bruce Cole, Dell Bayne, Joe Eley, Natasha Neumann, Craig Smith, Erin Thompson, Steve Enns. 2.5 Acknowledgements : Students: Jodi Axelson, Andrea Eccleston, Matt Regier, Jenny Hill, Courtney Campbell, Lisa Christmas, Kim Kovacs, Justin Beckers, Brett Reynolds. --------------------------------------------------------------------------- 3. Theory of Measurements [Theoretical basis for the way in which the measurements were made (e.g. special procedures, characteristics of the instrument, etc.).] Meteorological measurements were taken in order to understand the general climate of the Canadian boreal region and provide supporting measurements to compute carbon, water and energy fluxes. Since many different instruments were used to measure meteorological data, please refer to the user manuals referenced in the reference section for details on operation. See section on Equipment below for summarized information. --------------------------------------------------------------------------- 4. Equipment 4.1.1 Sensor/Instrument Description, Manufacturer, Calibration, Specs [This section provides a listing of the instrumentation and the characteristics of the instrumentation.] Automated stations were set up to log data from meteorological sensors. These data was either downloaded remotely via modem or stored on modules until they could be downloaded to a computer. Various types of instruments were used to measure the following variables A) temperature and humidity, B) precipitation, C) radiation, D) water table height, E) soil heat flux, F) air pressure, G) wind speed and direction, H) soil moisture and I) Campbell Scientific data loggers. A)TEMPERATURE and HUMIDITY ? HMP35CF Temp/Humidity Probe, HMP45C Temp/Humidity Probe, Chromel-Constantan Fine-wire Thermocouples, Copper- Constantan Thermocouples, 107 Temperature Probe, Platinum resistance thermometer (description, manufacturer, calibration, specs, frequency of calibration, other calibration information) i) HMP35CF Temperature/Humidity Probe - Description: ?The model HMP35CF probe contains a Vaisala capacity relative humidity sensor and the YSI 44002A thermistor. The probe is designed to be housed in the 41002-12 Plate Gill Radiation Shield or equivalent.? (Campbell Scientific, 1992). - Manufacturer: Campbell Scientific - Humidity Calibration: Humidity has been calibrated over a range of approximately 15%-85%. Humidity was generated in controlled lab conditions; the measurements were checked against a Hygro M3 Dew Point hygrometer. Corrections determined from these calibrations have not been applied to humidity so far. -Temperature Calibration: none. Temperature has been subject to tests, in which 3 or more instruments were compared to each other outdoors, installed on the same platform. - Thermistor Specs: Vaisala HMP35CF temperature and relative humidity probe, YSI 44002A thermistor. Resolution: 0.1 deg C. Accuracy: +/- 0.4 deg C over the range of ?53 deg C to +48 deg C (overall accuracy is better than +/-0.2 deg C). The bridge resistors are 0.1% tolerance with a 10ppm temperature coefficient. - RH Specs: Resolution: 1%. Accuracy: at 20 deg C against factory references is +/-1%, against field references +/-3%, temperature dependence is +/-0.04%RH/deg C. Long term stability is better than 1% per year. Response time is 15 seconds with membrane filter. Operating temperature is ?20 to +60 deg C. - Frequency of humidity calibration: once every 2-5 years. Also, comparisons between sensors installed outdoors on the same platform took place every 1-2 years. - Frequency of temperature calibration: n/a. Also, comparisons between sensors installed outdoors on the same platform took place every 1-2 years. - Other temperature/humidity calibration information: n/a. ii) HMP45C Temperature/Humidity Probe - Description: This probe contains a Platinum Resistance Temperature detector (PRT) and a Vaisala HUMICAP 180 capacitive relative humidity sensor. - Manufacturer: Campbell Scientific - Calibration: Factory calibrated. Tested by MSC Saskatoon prior to deployment in the field. - Specs (PRT): 1000 Ohms PRT, IEC 751 1/3 Class B; Temp Measurement Range: -40 to +50 deg C; Temp Output signal Range: 0.008 to 1V; Temp Accuracy: +/- 0.2 to 0.5 deg C - Specs (Humicap): RH Measurement Range: 0 to 100% non-condensing; RH Output Signal Range: 0.008 to 1 VDC; Accuracy at 20 deg C: +/-2% RH (0 to 90% RH) and +/-3% RH (90 to 100% RH); Temperature Dependence of Relative Humidity Measurement: +/-0.05% RH/degC; Typical Long Term Stability: Better than 1% RH per year; Response Time (at 20 deg C, 90% response): 15 seconds with membrance filter. - Frequency of Calibration: Factory calibrated prior to purchase only. iii) Chromel-Constantan Fine-wire Thermocouples: - Description: Chromel-Constantan thermocouples had 0.001 inch or 0.003 inch thick bare wires with welded junctions. The lead wires were soldered to 30 gauge insulated wire. The 30 gauge wire and metal shielded wire were joined via miniature connectors. - Manufacturer: Parts ordered from Omega, and instrument constructed at MSC Saskatoon. - Calibration: none - Specs: Chromel-Constantan thermocouple, 0.001? and 0.003? wire thickness. Resolution: 0.001 deg C. Accuracy: ? - Frequency of Calibration: n/a. - Other Calibration information: n/a. iv) Copper-Constantan Thermocouples: - Description: Copper-Constantan thermocouples were used for measuring soil temperature and air temperature. The soil thermocouples were 105T welded junction thermocouple probes. The air thermocouples were constructed by soldering Copper-Constantan thermocouple wire junctions, and insulating the junction with silicone. - Manufacturer (Soil thermocouples): Campbell Scientific. - Manufacturer (Air thermocouples): wire manufactured by Omega? 30 AWG. - Manufacturer (Snow Temp profile thermocouples): Thermocouple junction constructed at Queen's University, Kingston, Ontario? - Calibration: n/a. - Specs: Campbell Scientific 105T welded junction thermocouple probe. Range: -78 deg C to 100 deg C. Accuracy 0.13 deg F. - Frequency of Calibration: n/a. - Other Calibration information: n/a. v) 107 Temperature Probe - Description: 107 Temperature probe contains a Fenwal Electonics UUT51J1 thermistor (Campbell Scientific, 1983). - Manufacturer: Campbell Scientific - Calibration: factory calibration prior to purchase? - Specs: 107 Temperature probe resolution: 0.001 deg C. Accuracy: +/-0.4 deg C over the range of ?33 to 48 deg C (overall accuracy is better than +/-0.2 deg C. The bridge resistors are 0.1% tolerance with a 10ppm temperature coefficient. (Campbell Scientific, 1983) - Frequency of Calibration: none since purchase? vi) Platinum resistance thermometer. - Description: - Manufacturer: - Calibration: - Specs: - Frequency of Calibration: B)PRECIPITATION - Belfort Model 5915, Belfort Model 3000, Tipping Bucket Rain Gauge, CS700-L Rain Gauge, Ultrasonic Depth Gauge Snow Depth Sensor, i) Belfort Model 5915 (Universal) Weighing Gauge - Description: Belfort precipitation gauge series 5915 are weighing-type gauges in which a weighing mechanism converts the weight of the rainfall caught by a bucket (through a circular, horizontal 8? (203mm) opening at the top of the gauge) into a resistance. (Belfort Instrument Company, 1986). The capacity of this gauge is approximately 135mm of precipitation. - Manufacturer: Belfort. - Calibration: Calibrated by MSC Saskatoon in controlled lab conditions. - Specs: Belfort 5915 accuracy: 1/3 to 1/2 of 1% F.S. Sensitivity: 0.01" (.254mm). Collector Diameter: 8" (203mm). - Frequency of Calibration: Calibrated prior to deployment in field, or when measurements were suspect. ii) Belfort Model 3000 Weighing Gauge - Description: Weighing-type gauges in which a weighing mechanism converts the weight of the rainfall caught by a bucket (through a circular, horizontal 8" (203mm) opening at the top of the gauge) into a resistance of the potentiometer? (Belfort Instrument Company, 1986). The capacity of this gauge is approximately 19.5" (495mm) of precipitation. - Manufacturer: Belfort - Calibration: Calibrated by MSC Saskatoon in controlled lab conditions. - Specs: Belfort 3000 accuracy: +/-0.15" (3.8mm) of rain span, +/-2.5% of net change for a 2" (50.8mm) to 4" (101.6mm) change, and +/-0.05% for a 2" (50.8mm) change. Range: 0"-19.5". Sensitivity: 0.025" (.6mm). - Frequency of Calibration: Calibrated prior to deployment in field, or when measurements were suspect. iii) Tipping Bucket Rain Gauge Model 525M - Description: Is a smaller adaptation of the standard Weather Bureau Tipping Bucket Rain Gauge. It measures rainfall at rates up to 2" per hour with an accuracy of +/- 1%. Output is a switch closure for each bucket tip. A tip occurs with each .1mm of rain (Campbell Scientific, 1996). - Manufacturer: Texas Electronics Inc. - Calibration: Factory Calibrated. - Specs: Range: infinite increments of .1mm; Accuracy: 1% at 2" per hour or less; Signal output: momentary switch closure activated by the tipping bucket mechanism. Switch closure is approximately 135ms. - Frequency of Calibration: Tested in controlled lab conditions prior to deployment in field or if measurements were suspect. Necessary adjustments applied to increase or decrease the amount of tips. Not officially re-calibrated. iv) Tipping Bucket Rain Gauge Model CS700-L - Description: Is a smaller adaptation of the standard Weather Bureau Tipping Bucket Rain Gauge. Its measurement range is from 0 to 500mm/hr with an accuracy better than + 2%(@ 100mm/hr). When 0.2mm of rainfall are collected the tipping bucket assembly tips and activates a reed switch. The switch closure is recorded by the datalogger pulse channel. (Campbell Scientific, 1996). - Manufacturer: Hydrological Services Pty. Ltd. (model TB-3) - Calibration: Factory Calibrated. - Specs: Range: infinite increments of 0.2mm; Accuracy: + 2% at 100mm per hour; Signal output: momentary switch closure activated by the tipping bucket mechanism. - Frequency of Calibration: Tested in controlled lab conditions prior to deployment in field or if measurements were suspect. Necessary adjustments applied to increase or decrease the amount of tips. Not officially re-calibrated. v) Ultrasonic Depth Gauge Snow Depth Sensor - Description: The UDG01 is used to find the distance from the sensor to a surface and is typically used to measure snow depth. The UDG01 operates by sending out an ultrasonic pulse and determines the time for the echo to return. - Manufacturer: Campbell Scientific - Calibration: Factory Calibrated. - Specs: Measurement Range: 0.6 to 10m; Accuracy: +/-1cm or 0.4% of distance to the Target (whichever is greatest); Resolution: 0.5mm; Beam Acceptance Angle: Approx. 20 deg; Operating Temperature: -25 to 50 degC Standard; Operating Humidity: 5 to 95% non-condensing; Max Cable Length: 1000 feet. - Frequency of Calibration: Factory calibrated once and tested at MSC Saskatoon prior to deployment in field. Distances from sensor to target occasionally checked in the field. vi) SR50 Snow Depth Sensor - This sensor measures the distance from the sensor to a target. The SR50 determines the distance to a target by sending out ultrasonic pulses and listening for the returning echoes that are reflected from the target. The time from transmissions to return of an echo is the basis for obtaining the distance measurement. Since the speed of sound in air varies with temperature, an independent temperature measurement is required to compensate the distance reading. A simple calculation is applied to initial reading simple calculation is applied to initial readings for this purpose. - Manufacturer: Campbell Scientific - Calibration: Factory calibrated. - Specs: Range: 0.5 to 10m; Accuracy: +/- 1cm or 0.4% of distance to target (whichever is greatest) requires external temperature compensation; Resolution: 0.1mm; Beam Acceptance Angle: Approx 22 deg; Operating Temperature: -30 to +50 deg C Standard; Dimensions: Length 31cm Diameter 7.5cm; Weight: 1.3kg. - Frequency of Calibration: Factory calibrated once and tested at MSC Saskatoon prior to deployment in the field. Distances from sensor to target occasionally checked in the field. C)RADIATION i) Middleton CNR-1 Net Radiometer - Description: The Middleton CNR-1 is a pyrradiometer for the measurement of net total radiation flux (solar, terrestrial, and atmospheric). It can be used for portable or stationary applications (Carter-Scott Design, 1995) - Manufacturer: Carter-Scott Design - Calibration: Factory Calibrated. - Specs: Sensitivity: 35 to 45 microvolts/Watts per m sq; Impedance: 70-80 Ohms; Response Time: 45s @ 95%; Non-linearity: <+/-1% at 500 W per m sq; Operating Temperature: -40 to +60 deg C; Cosine Response: 4% of ideal; Spectral Range: 0.3 to 60 micro m. - Frequency of Calibration: Factory calibrated once prior to deployment in field and then calibrated at NARC in 1996?. ii) Li-Cor LI190 PAR Sensor - Description: LI-COR quantum sensors measure photosynthetically active radiation (PAR) in the 400 to 700 nm waveband. The unit of measurement is micromoles per s per sq m. The quantum sensor is designed to measure PAR received on a plane surface. A silicon photodiode with a an enhanced response in the visible wavelengths is used as the sensor (LI-COR, 1991). - Manufacturer: LI-COR - Calibration: Factory Calibrated. - Specs: Absolute Calibration: +/-5% traceable to the U.S. National Institute of Standars and Technology; Sensitivity: 8 microA per 1000 micromol per s per m sq; Linearity: Max deviation of 1% up to 10,000 micromol per s per m sq; Stability: <+/-2% change over a 1yr period; Response Time: 10 micro s; Temperature Dependence: +/-0.15% per deg C maximum; Cosine Correction: Cosine corrected up to 80deg angle of incidence; Azimuth: <+/-1% error over 360deg at 45deg elevation; Tilt: No error induced from orientation. - Frequency of Calibration: Factory calibrated once prior to deployment in field. iii) Kipp & Zonen CM11 Pyranometer (Shortwave) - Description: The pyranometer CM 11 is designed for measuring the irradiance (radiant-flux, Watts per m sq) on a plane surface, which results from the direct solar radiation and from the diffuse radiation incident from the hemisphere above (Kipp & Zonen). - Manufacturer: Kipp & Zonen. - Calibration: Factory Calibrated. - Specs: Response Time: < 15 s; Non-stability % Change Reponsitivity Per Year: +/-0.5%; Non-linearity: +/-0.6%; Directional Response for Beam Radiation: +/- 10Wm2; Spectral Selectivity: +/-2%; Temperature Response: +/-1%; Tilt Response: +/-0.25%; Irradiance: 0 to 1400 W per m sq; Spectral Range: 335 to 2200nm (95% points); Sensitivity: between 4 and 6 micro V per W per m sq. - Frequency of Calibration: Factory Calibrated once prior to deployment in field. iv) Eppley PSP Pyranometer (Shortwave) - Description: - Manufacturer: EPLAB - Calibration: Factory Calibrated. - Specs: - Frequency of Calibration: Factory calibrated once prior to deployment in field. v) Eppley PIR Pyrgeometer (Longwave) - Description: The Precision Infrared Radiometer, pyrgeometer, is an instrument designed for the measurement of (unidirectional) global incoming or outgoing long-wave terrestrial radiation (EPLAB). - Manufacturer: EPLAB - Calibration: Factory Calibrated. - Specs: Sensitivity: 4 microvolts/watt per m sq approx.; Impedance: 700 Ohms approx.; Temperature Dependence: +/-2%, -20 to 40 degC (nominal); Linearity: +/-1%, 0 to 700 Watts per m sq; Response Time: 2s (i/e signal); Cosine Response: better than 5% from normalization, insignificant for a diffuse source; Orientation: No effect on instrument performance; Mechanical Vibration: Capable of withstanding up to 20g?s; Calibration: Blackbody reference. - Frequency of Calibration: Factory calibrated once prior to deployment in field. D)WATER TABLE HEIGHT i) Druck PTX Depth Pressure Transmitter - Description: - Manufacturer: Druck Incorporated - Calibration: Factory Calibrated. - Specs: Non-linearity and hysteresis: +/-0.25% BSL; Temperature error band: +/-0.3%; Compensated temp. range: -1 to 30 deg C. - Frequency of Calibration: Factory calibrated once prior to installation. E)SOIL HEAT FLUX i) Middleton CN3 Heat Flux Plate - Description: Heat transfer occurs by conduction, convection, or radiation. The CN3 Heat Flux Plate is designed to directly measure the conductive heat transmission in the medium in which the sensor is embedded. It is small and thin to offer minimal disturbance to the heat flow pattern. A temperature difference between the top and bottom faces of the CN3 generates a DC voltage from the sensor thermopile. The temperature difference is proportional to the heat flow in the medium, and the polarity of the output voltage changes in accordance with the direction of the heat flow (Carter-Scott Design). - Manufacturer: Carter-Scott Design - Calibration: Factory calibrated. - Specs: Sensitivity: 21 microV/W.m2 (typical) in fine dry sand; Impedance: 23 Ohms (typical); Temperature Range: -20 to +70 deg C; Thermal Conductivity: .4W/m.deg C; Temperature Error: .2%/deg C; Response Time: 30sec. To 95% (in air); Sensor Thermopile: copper-constantan (250 junctions). - Frequency of Calibration: Factory calibrated once prior to installation. F)AIR PRESSURE i) Setra - Description: The SBP270 Barometric Pressure Sensor is a high accuracy barometer designed for use with the CR7, 21X and CR10 dataloggers. The Setra includes a variable capacitance barometer, interface circuitry, 5 foot cable and a rain-tight enclosure. - Manufacturer: Setra, distributed by Campbell Scientific. - Calibration: Factory calibrated prior to deployment. - Specs: Pressure Range: 800-1100 mb, Accuracy: +/- 0.2mb, Long Term Stability: < +/-0.1% FS over 6mo 70 deg F. - Frequency of Calibration: once prior to deployment. G)WIND SPEED AND DIRECTION i) RMY Gill Cup Wheel Anemometer (Speed only) - Description: Utilizes a d.c. tachometer generator whose analog output voltage is directly proportional to wind speed. The cup wheel assembly has three hemisphere shaped cups formed of polypropylene which exhibit a distance constant of approximately 2.7m. - Manufacturer: R.M. Young. - Calibration: Calibration is easily checked by removing the cup wheel and driving the shaft at a known rpm by means of a synchronous motor calibrating unit or similar device. - Specs: Threshold is 0.35-0.45 m/s. - Frequency of Calibration: Once prior to deployment. ii) RMY Propeller Anemometer Model 05103 (Speed and Dir) - Description: Is a 4-blade helicoid propeller. Propeller rotation produces an AC sine wave voltage signal with frequency directly proportional to wind speed. The wind direction sensor is a lightweight vane with a sufficiently low aspect ratio. - Manufacturer: R.M. Young - Calibration: Factory Calibrated - Specs: Range: 0-60 m/s and 360 deg, Accuracy: +/-0.3m/s and +/-3deg - Frequency of Calibration: once prior to deployment. H)SOIL MOISTURE i) Campbell Scientific CS615 soil moisture probes - Description: Consists of 2 stainless steel rods connected to a printed circuit board. A shielded four-conductor cable is connected to the circuit board to supply power, enable the probe, and monitor the pulse output. - Manufacturer: Campbell Scientific - Calibration: Lab calibrated in Saskatoon prior to deployment. - Specs: +/-2% when using calibration for a specific soil. The accuracy depends on soil texture and mineral composition. - Frequency of Calibration: once prior to deployment. ii) ESI Environmental Time Domain Reflectometry Probes - Description: measures the propagation time of a signal as it travels through a transmission line (a probe) embedded in the soil. The longer the propagation time, the higher the moisture content. Propagation times, together with the probe length, soil and probe coefficients, can be converted directly into soil % moisture content. - Manufacturer: E.S.I. Environmental Sensors, Inc. - Calibration: n/a - Specs: n/a - Frequency of Calibration: n/a I) CAMPBELL SCIENTIFIC DATA LOGGERS - Description: provides the means to log information from sensors whose leads are connected to I/O cards. Control module functions include real-time task initiation, measurement processing, data storage, telecommunications and keyboard/display interaction. - Manufacturer: Campbell Scientific - Calibration: n/a - Specs: n/a - Frequency of Calibration: 4.1.2 Manufacturer of Sensor/Instrument [Name, address, and telephone number of the company that produced the instrument. If the measuring device was built by the investigator, or specially customized, please specify.] See section above. 4.1.3 Principles of Operation [Fundamental scientific basis for the way the instrument operates. This is a summary; where a full development is required, it should be placed in section 3.] See section above. 4.1.4 Source/Platform [What the instrument(s) is(are) mounted on, e.g. tower, hand held, aircraft.] - Most of the meteorological instruments were installed on double scaffold towers with stairs within. These towers were equipped with AC power and had heated huts nearby, also with AC power. Parameters measured on these towers included: air temperature, humidity, radiation and wind. - Precipitation sensors were accompanied by wind and air temperature instruments installed on a wooden platform approximately 2-3m high, 1m wide and 2m long. - Soil temperature, soil moisture, soil heat flux, water table height were measured below ground level, near the tower/hut. - Dataloggers were housed in the above mentioned heated huts or in datalogger enclosures. For more details see section on Data Description 4.1.5 Sensor/Instrument Measurement Geometry [Describe the sensor location(s), orientation, and any other parameters that affect the collection or analysis of data, e.g. field of view, optical characteristics, height, etc.] - Most above ground instruments, such as air temperature, wind and radiation sensors were installed on walk-up towers, ranging in height from 4 to 38m agl. The exceptions were near ground measurements, below 2m. These sensors were installed near the towers, on their own platforms. All sensors were installed to optimize measurement requirements. -Precipitation gauges and wind and temperature sensors associated with these gauges were installed on a platforms separate from the walk-up towers, at about 2 to 4m agl. This platform was located in a clearing approximately 50m wide -Atmospheric pressure sensors were installed in the Huts, at approximately 2m agl. -Soil moisture, ground heat flux and soil temperature sensors were installed below the ground, beneath organic layers of leaf litter. See the section on Data Description for more information. 4.1.6 Collection Environment [Under what environmental conditions were the data collected and the instrumentation operated. This includes descriptions of the types of sites visited and factors that may effect the measurements such as temperature range experienced during data collection.] Temperatures ranged from a maximum of 35 deg C in the summer to a minimum of -45 deg C in the winter. On average, the region has a frost-free season of between 80 to 100 days (Phillips, 1990). Precipitation events in the summer ranged from trace amounts to approximately 35mm. Summer storms consisted of wind gusts up to 15 m per s, with rain, lightning and/or hail. In the winter it was common to have periods of snow, ice and frost. The average maximum snow depth for the region is between 50 and 70cm (Gray, 1981). Most of the instrumentation was exposed to these elements, with the exception of dataloggers and the instrumentation installed below the ground. 4.2 Calibration [Describe how the measurements made by the device(s) are calibrated with known standards. Specific details should be given in the subsections below.] See section on Equipment above. 4.2.1 Specifications [Record any specifications that affect the calibration of the device, its operations, or the analysis of the data collected with it.] See section on Sensor/Instrument Description above. 4.2.1.1 Tolerance [Describe the acceptable range of inputs and the precision of the output values.] See section on Sensor/Instrument Description above. 4.2.2 Frequency of Calibration [Indicate how often the instrument is measured against a standard. Also indicate any other routine procedures required to maintain calibration or detect miscalibrations. Describe also the actual practice with this device.] See section on Sensor/Instrument Description above. 4.2.3 Other Calibration Information [Give factory calibration coefficients, information about independent calibrations, history of modifications, etc.] See section on Sensor/Instrument Description above. --------------------------------------------------------------------------- 5 Site description [Standard site description, should include site location in a well referenced coordinate system, site elevation, vegetation type, etc...] - Location: Near Narrow Hills Provincial Park (5km in from route 106, and 2.9km north of Harding Road and route 106 intersection). Lat: 53.91634° N, Long: -104.69203° W (BOREAS coordinates), elev: 579.27m - Topography: undulating. - Predominant vegetation: 12-15m tall jack pine trees with alder, bearberry, moss and lichen as ground cover. - Soil properties: Sandy soil with very good drainage. The organic layer is 10-15cm deep. 6. Data Acquisition Methods [Describe the procedures for acquiring this data in sufficient detail so that someone else with similar equipment could duplicate your measurements. Should be sufficient to include in the Methods section of a paper] 6.1 Methods of data acquisition [Describe the procedures for acquiring this data in sufficient detail so that someone else with similar equipment could duplicate your measurements. Should be sufficient to include in the Methods section of a paper] Instrumentation connected to Campbell Scientific data loggers were sampled and stored on the loggers. Most of the data were downloaded daily from dataloggers through dial-up modems using Campbell Scientific software. In cases where a datalogger was not connected to a phone line, data was downloaded either by a lap-top computer or storage module. 6.2 Sampling 6.2.1 Spatial Coverage/Geographic Location [Give enough information to locate the measurement site with suitable precision. This may be a list of sites visited, or a geographic range in the case of aircraft measurements or satellite imagery, or plot coordinates in UTM, including a clear reference to the coordinate system.] Instrumentation was installed within 50m of the main tower location. - Location: Near Narrow Hills Provincial Park (5km in from route 106, and 2.9km north of Harding Road and route 106 intersection). Lat: 53.916 deg N, Long: -104.69 deg W, elev: 579.27m For more details on exact location of instruments, see section on Data Description. 6.2.2 Spatial sampling [Includes a description of spatial sampling: how many sites/samples; how were they selected, the coordinates (e.g. UTM) of the plots, how many replicates over space, etc...] See section on Data Description for more information. 6.2.3 Temporal coverage [The period(s) of time during which data was collected more or less continuously.] Data was continuously collected all year round. 6.2.4 Temporal sampling [Includes a description of the temporal sampling scheme: when was the sampling carried out (time of day), at what frequency were the measurements taken, how long did the measurements take, etc...] Most meteorological variables were sampled every 5seconds and averaged over 30min intervals. The exception to this would be snow depth and cumulative precipitation. --------------------------------------------------------------------------- 7 . Observations 7.1 Procedural Notes [Use this section to record observations made during actual data collection, which could bear on the analysis of the data, e.g. condition of site, peculiar procedures or operations, the presence of U.F.O.'s or bears, oddities in equipment function, etc.] n/a 7.2 Field Notes [If a large amount field notes exist, a reference to a separate file will be adequate.] See FieldNotes.txt --------------------------------------------------------------------------- 8 . Data Description [This section describes the data in the data set: what the data are, units, format, data characteristics.] 8.1 Data Organization [Describe how your data is organized, e.g.: by site and/or month.] Data are organized by site and month. Three types of files are available: i)Ancillary: a subset of extra Meteorological variables that were used to derive and quality assure some variables in the Main directory. These variables would not normally be used by most people, but are valuable enough to keep in the archive. ii)Main: contains the most complete sub set of meteorological variables where most have been corrected and some have been derived, but none have been gap-filled. iii)Summarized: contains data that have been summarized from the Main subset. Variables have been aggregated (eg. 4 tower top temperature measurements have become one) and gaps have been filled. Most variables are not available yet (as of Mar/04). 8.2 Image andData Format [Specify the format that the image or the data is (are) provided in.] Data are comma delimited ASCII format. 8.3 Numerical Data Characteristics [Describe the types of data submitted. On separate lines, indicate each column number followed by its header, the variable description, the unit of measurement or format of presentation, the data source or sensor, and the variable range.] Note that there are three types of Meteorology files available: 1) Ancillary 2) Main and 3) Summarized (see Data Organization section above). All three are included below. This information may also be found in the "readme.txt" files found under each file type's directory. 1) Ancillary Variable List: ------------- 1 DataType="Met1" (n/a) Includes "raw" variables used to compute some other variables (corrections applied but gaps not filled.) 2 Site="SK-OJP" (n/a) Saskatchewan Old Jack Pine site 3 SubSite="FlxTwr" (n/a) Flux Tower sub-site. 4 Year (UTC) 4 digit year. 5 Day (UTC) Day of Year. 6 End_Time (UTC) End of 30min time period, in hours and minutes UTC. 7 CompDownLong (W/m2) "Compensated" downwelling longwave radiation waveRad_Abv on top of walk-up tower, mounted on railing Cnpy_28m facing southward at 28m agl. Eppley PIR. 8 DownLongwave (W/m2) Raw thermopile output from the downwelling _Thermopile longwave sensor above. 9 DownLongwave (degC) Instrument body temperature of the downwelling _BodyTemp longwave sensor above. 10 DownLongwave (degC) Instrument dome temperature of the downwelling _DomeTemp longwave sensor above. 11 CompUpLong (W/m2) "Compensated" upwelling longwave radiation waveRad_Abv on an extended boom attached to the walk-up Cnpy_23m tower, facing southward at 23m agl. Eppley PIR. 12 UpLongwave_ (W/m2) Raw thermopile output from the upwelling Thermopile longwave sensor above. 13 UpLongwave_ (degC) Instrument body temperature of the upwelling BodyTemp longwave sensor above. 14 UpLongwave_ (degC) Instrument dome temperature of the upwelling DomeTemp longwave sensor above. 15 WindSpd_At (m/s) Wind speed at Belfort weighing precip gauge. Belfort_5m RM Young propeller anemometer. 16 AirTemp_At (degC) Reference air temperature used to compute SnowD_Clrg snow depth, near snow depth sensor in clearing. CSI 107 sensor. 17 AirTemp_At (degC) Reference air temperature used to compute SnowD_Cnpy snow depth, near within canopy snow depth sensor. CSI 107 sensor. 18 MetLogger (degC) Internal Main Meteorological data logger Temp_Card1 temperature on Card1. CSI CR7 logger. 19 PcpLogger (degC) Internal Soil Properties' data logger Temp temperature. 20 SnowRef_ (degC) Reference temp of the Multiplexer, used to Temp_Mplexer determine snow temperatures. AM25T. 21 MetLogger (Volts) Main Meteorological data logger battery Battery voltage. CSI CR7 logger. 22 PcpLogger (Volts) Precipitation data logger battery voltage. Battery CSI CR10 logger. 23 SnowLogger (Volts) Snow Temperature logger battery voltage. Battery CSI CR10 logger. 24 Certification (n/a) CPI: checked by PI; PRE: preliminary. Code 25 RevisionDate (dymo Date data last revised by PI. year) 2) Main 1 DataType="Met2" (n/a) Second subset of meteorological data corrections applied but gaps not filled. 2 Site="SK-OJP" (n/a) Saskatchewan Old Jack Pine site 3 SubSite="FlxTwr" (n/a) Flux Tower sub-site. 4 Year (UTC) 4 digit year UTC. 5 Day (UTC) Day of Year UTC. 6 End_Time (UTC) End of 30min time period, in hours and minutes UTC. 7 FourWay_Net (W/m2) Derived by: (downwelling shortwave Rad_AbvCnpy - upwelling shortwave)+(downwelling longwave - upwelling longwave). 8 Middleton_ (W/m2) Net radiation; on an extended boom NetRad_Abv attached to the walk-up tower, Cnpy_23m facing south at 23m agl. Adjusted for short and longwave sensitivities. Middleton CNR-1. 9 GlobalShort (W/m2) Global Shortwave Radiation; on top waveRad_Abv of walk-up tower, mounted on a Cnpy_28m railing facing southward at 28m agl. Kipp & Zonen CM11. 9 GlobalShort (W/m2) Global Shortwave Radiation; on top waveRad_Abv of walk-up tower, facing south at Cnpy_28m 28m agl. Kipp & Zonen CM11. 10 UpShortwave (W/m2) Upwelling Shortwave Radiation; on an Rad_AbvCnpy extended boom attached to the walk-up _23m tower, facing southward at 23m agl. Kipp & Zonen CM11. 11 DownLongwave (W/m2) Derived from thermopile, body and dome Rad_AbvCnpy temperatures measured by the down- _28m welling longwave sensor mounted on top of walk-up tower, facing south at 28m agl. Eppley PIR. 12 UpLongwaveRad (W/m2) Derived from thermopile, body and dome _AbvCnpy_23m temperatures measured by the upwelling longwave sensor mounted extended boom attached to the walk-up tower, facing southward at 23m agl. Eppley PIR. 13 LI_DownPAR (umol/ Downwelling Photosynthetically Active _AbvCnpy_28m /m2/s) Radiation on walk-up tower, facing southward at 28m agl. Li-Cor LI 190SB. 14 LI_UpPAR_ (umol/ Upwelling PAR Radiation; on extended AbvCnpy_23m /m2/s) boom attached to walk-up tower facing southward at 23m agl. Li-Cor LI 190SB. 15 LI_DownPAR_ (umol/ Downwelling PAR Radiation; below canopy BlwCnpy_1m /m2/s) W of Hut at 1m agl. Li-Cor LI 190SB. 16 BF3_DownPAR (umol/ Not installed yet. _AbvCnpy_28m /m2/s) 17 BF3_Diffuse (umol/ Not installed yet. PAR_AbvCnpy /m2/s) _28m 18 Wetness_ (n/a) Not installed yet. AbvCnpy_28m 19 Tc_AirTemp_ (degC) Thermocouple Air Temperature; top of AbvCnpy_28m walk-up tower at 28m agl. Unshielded 0.003" thick Chromel-Constantan thermocouple, made in-house. 20 MetOne_Air (degC) Ventilated HMP Relative Humidity; Temp_AbvCnpy installed at 27m agl. Vaisala HMP _27m housed in a Met-One ventilator. Sensor removed Apr 24, 2003. 21 MetOneTc_ (degC) Ventialted Thermocouple Air Temp; AirTemp_Abv installed at 27m agl. Copper- Cnpy_27 Constantan thermocouple in a Met-One ventilator. 22 MetOnePRT_ (degC) Ventilated Platinum Resistance Air AirTemp_Abv Temperature; installed at 27m agl in Cnpy_27m at Met-One ventilator. 23 AirTemp_Abv (degC) Air Temperature; top of walk-up tower, Cnpy_28m on railing at 28m agl. Vaisala HMP in a gill radiation shield. 24 AirTemp_Abv (degC) Air Temperature; on walk-up tower, at Cnpy_16m 16m agl. Vaisala HMP in a gill rad- iation shield. 25 AirTemp_ (degC) Air Temperature; on walk-up tower in Cnpy_10m canopy at 10m agl. Vaisala HMP in a gill radiation shield. 26 AirTemp_Lwr (degC) Air Temp; on walk-up tower in lower Cnpy_5m canopy at 5m agl. Vaisala HMP in a gill radiation shield. 27 AirTemp_ (degC) Air Temp; on tree W of Hut below AbvGnd_1m canopy at 1m agl. Vaisala HMP in a gill radiation shield. 28 MetOne_Rel (%) Relative Humidity; installed at 27m Hum_AbvCnpy agl. Vaisala HMP housed in a Met-One _27m ventilator. Sensor removed Apr 24, 2003. 29 RelHum_Abv (%) Relative Humidity; top of walk-up tower Cnpy_28m on railing at 28m agl. Vaisala HMP in a gill radiation shield. 30 RelHum_Abv (%) Relative Humidity; on walk-up tower at Cnpy_16m 16m agl. Vaisala HMP in a gill rad- iation shield. 31 RelHum_ (%) Relative Humidity; on walk-up tower in Cnpy_10m canopy at 10m agl. Vaisala HMP in a gill radiation shield. 32 RelHum_Lwr (%) Relative Humidity; on walk-up tower in Cnpy_5m lower canopy at 5m agl. Vaisala HMP in a gill radiation shield. 33 RelHum_Abv (%) Relative Humidity; on tree W of Hut Gnd_1m below canopy at 1m agl. Vaisala HMP in a gill radiation shield. 34 WindSpd_Abv (m/s) Wind Speed; mounted on a pipe extend- Cnpy_29m ing above the top platform of the walk up tower, at 29m agl. RM Young pro- peller anemometer. 35 WindDir_Abv (deg) Wind Direction; same as wind speed Cnpy_29m above. 36 StdDev_Wind (deg) Standard Deviation of Wind Direction Dir_AbvCnpy above. _29m 37 SurfPress (kPa) Barometric Pressure; in Hut 2m agl. Setra. 38 Belfort (mm) Manually quality controlled annual _CumPrec accumulated precipitation from weighing gauge. Belfort 3000 or other. 39 TBRG_Rain (mm) Total Tipping Bucket Rainfall over the 30min period. On precip stand in 10m wide natural clearing 200m ESE of the Hut at 5m agl. TBRG 525M. 40 SnowDepth (cm) Snow Depth; on a boom extending from _Clrng precip stand in 10m wide natural clearing 200m ESE of the Hut. 41 SnowDepth (cm) Snow Depth; under canopy North of _Cnpy walk-up tower or NW of Hut. 42 WaterTable (mm) Not installed yet. Depth 43 SoilTemp (degC) Soil Temperature; 2cm below ground _SW_2cm surface in a pit SW of Hut (down hill from Hut). Queen's Univ-made thermo- couple rod. 44 SoilTemp (degC) Soil Temperature; 5cm below ground _SW_5cm surface in a pit SW of Hut (down hill from Hut). Queen's Univ-made thermo- couple rod. 45 SoilTemp (degC) Soil Temperature; 10cm below ground _SW_10cm surface in a pit SW of Hut (down hill from Hut). Queen's Univ-made thermo- couple rod. 46 SoilTemp (degC) Soil Temperature; 20cm below ground _SW_20cm surface in a pit SW of Hut (down hill from Hut). Queen's Univ-made thermo- couple rod. 47 SoilTemp (degC) Soil Temperature; 50cm below ground _SW_50cm surface in a pit SW of Hut (down hill from Hut). Queen's Univ-made thermo- couple rod. 48 SoilTemp (degC) Soil Temperature; 100cm below ground _SW_100cm surface in a pit SW of Hut (down hill from Hut). Queen's Univ-made thermo- couple rod. 49 SoilTemp (degC) Soil Temperature; 2cm below ground _NW_2cm surface in a pit NW of Hut (on rise beyond tower). Campbell Scientific Copper-Constantan 105T thermocouple. 50 SoilTemp (degC) Soil Temperature; 5cm below ground _NW_5cm surface in a pit NW of Hut (on rise beyond tower). Campbell Scientific Copper-Constantan 105T thermocouple. 51 SoilTemp_ (degC) Soil Temperature; 10cm below ground _NW_10cm surface in a pit NW of Hut (on rise beyond tower). Campbell Scientific Copper-Constantan 105T thermocouple. 52 SoilTemp (degC) Soil Temperature; 20cm below ground _NW_20cm surface in a pit NW of Hut (on rise beyond tower). Campbell Scientific Copper-Constantan 105T thermocouple. 53 SoilTemp (degC) Soil Temperature; 50cm below ground _NW_50cm surface in a pit NW of Hut (on rise beyond tower). Campbell Scientific Copper-Constantan 105T thermocouple. 54 SoilTemp (degC) Soil Temperature; 100cm below ground _NW_100cm surface in a pit NW of Hut (on rise beyond tower). Campbell Scientific Copper-Constantan 105T thermocouple. 55 SnowTemp (degC) Snow Temp; 1cm above ground on rod _SW_1cm installed SW of Hut. Cu-Co thermo- couple. 56 SnowTemp (degC) Snow Temp; 2cm above ground on rod _SW_2cm installed SW of Hut. Cu-Co thermo- couple. 57 SnowTemp (degC) Snow Temp; 5cm above ground on rod _SW_5cm installed SW of Hut. Cu-Co thermo- couple. 58 SnowTemp (degC) Snow Temp; 10cm above ground on rod _SW_10cm installed SW of Hut. Cu-Co thermo- couple. 59 SnowTemp (degC) Snow Temp; 20cm above ground on rod _SW_20cm installed SW of Hut. Cu-Co thermo- couple. 60 SnowTemp (degC) Snow Temp; 30cm above ground on rod _SW_30cm installed SW of Hut. Cu-Co thermo- couple. 61 SnowTemp (degC) Snow Temp; 30cm above ground on rod _SW_30cm installed SW of Hut. Cu-Co thermo- couple. 62 SnowTemp (degC) Snow Temp; 50cm above ground on rod _SW_50cm installed SW of Hut. Cu-Co thermo- couple. 63 SnowTemp (degC) Snow Temp; 1cm above ground on rod _NW_1cm installed NW of Hut. Cu-Co thermo- couple. 64 SnowTemp (degC) Snow Temp; 2cm above ground on rod _NW_2cm installed NW of Hut. Cu-Co thermo- couple. 65 SnowTemp (degC) Snow Temp; 5cm above ground on rod _NW_5cm installed NW of Hut. Cu-Co thermo- couple. 66 SnowTemp (degC) Snow Temp; 10cm above ground on rod _NW_10cm installed NW of Hut. Cu-Co thermo- couple. 67 SnowTemp (degC) Snow Temp; 20cm above ground on rod _NW_20cm installed NW of Hut. Cu-Co thermo- couple. 68 SnowTemp (degC) Snow Temp; 30cm above ground on rod _NW_30cm installed NW of Hut. Cu-Co thermo- couple. 69 SnowTemp (degC) Snow Temp; 40cm above ground on rod _NW_40cm installed NW of Hut. Cu-Co thermo- couple. 70 SnowTemp (degC) Snow Temp; 50cm above ground on rod _NW_50cm installed NW of Hut. Cu-Co thermo- couple. 71 Certification (n/a) CPI: checked by PI; PRE: preliminary. Code 72 RevisionDate (dymo Date data last revised by PI. year) 3) Summarized 4) Gap Filled Meteorology 1 DataType (n/a) 2 Site (n/a) 3 SubSite (n/a) 4 Year (UTC) 5 Day (UTC) 6 End_Time (UTC) 7 GapfilledPIPref_FourWay_NetRad_AbvCnpy (W/m2) 8 GapfilledPIPref_Middleton_NetRad_AbvCnpy_23m (W/m2) 9 GapfilledPIPref_GlobalShortwaveRad_AbvCnpy_28m (W/m2) 10 GapfilledPIPref_UpShortwaveRad_AbvCnpy_23m (W/m2) 11 GapfilledPIPref_DownLongwaveRad_AbvCnpy_28m (W/m2) 12 GapfilledPIPref_UpLongwaveRad_AbvCnpy_23m (W/m2) 13 GapfilledPIPref_LI_DownPAR_AbvCnpy_28m (umol/m2/s) 14 GapfilledPIPref_LI_UpPAR_AbvCnpy_23m (umol/m2/s) 15 GapfilledPIPref_LI_DownPAR_BlwCnpy_1m (umol/m2/s) 16 GapfilledPIPref_Tc_AirTemp_AbvCnpy_28m (degC) 17 GapfilledPIPref_MetOne_AirTemp_AbvCnpy_27m (degC) 18 GapfilledPIPref_MetOneTc_AirTemp_AbvCnpy_27m (degC) 19 GapfilledPIPref_MetOnePRT_AirTemp_AbvCnpy_27m (degC) 20 GapfilledPIPref_AirTemp_AbvCnpy_28m (degC) 21 GapfilledPIPref_AirTemp_AbvCnpy_16m (degC) 22 GapfilledPIPref_AirTemp_Cnpy_10m (degC) 23 GapfilledPIPref_AirTemp_LwrCnpy_5m (degC) 24 GapfilledPIPref_AirTemp_AbvGnd_1m (degC) 25 GapfilledPIPref_MetOne_RelHum_AbvCnpy_27m (%) 26 GapfilledPIPref_RelHum_AbvCnpy_28m (%) 27 GapfilledPIPref_RelHum_AbvCnpy_16m (%) 28 GapfilledPIPref_RelHum_Cnpy_10m (%) 29 GapfilledPIPref_RelHum_LwrCnpy_5m (%) 30 GapfilledPIPref_RelHum_AbvGnd_1m (%) 31 GapfilledPIPref_WindSpd_AbvCnpy_29m (m/s) 32 GapfilledPIPref_WindDir_AbvCnpy_29m (deg) 33 GapfilledPIPref_SurfPress (kPa) 34 GapfilledPIPref_Belfort_CumPrec (mm) 35 GapfilledPIPref_SnowDepth_Clrng (mm) 36 GapfilledPIPref_SnowDepth_Cnpy (mm) 37 GapfilledPIPref_WaterTableDepth (mm) 38 GapfilledPIPref_SoilTemp_SW_2cm (degC) 39 GapfilledPIPref_SoilTemp_SW_5cm (degC) 40 GapfilledPIPref_SoilTemp_SW_10cm (degC) 41 GapfilledPIPref_SoilTemp_SW_20cm (degC) 42 GapfilledPIPref_SoilTemp_SW_50cm (degC) 43 GapfilledPIPref_SoilTemp_SW_100cm (degC) 44 GapfilledPIPref_SoilTemp_NW_2cm (degC) 45 GapfilledPIPref_SoilTemp_NW_5cm (degC) 46 GapfilledPIPref_SoilTemp_NW_10cm (degC) 47 GapfilledPIPref_SoilTemp_NW_20cm (degC) 48 GapfilledPIPref_SoilTemp_NW_50cm (degC) 49 GapfilledPIPref_SoilTemp_NW_100cm (degC) 50 CertificationCode (n/a) 51 RevisionDate (dymoyear) 8.3.1.6 Sample Data Record DataType,Site,SubSite,Year,Day,End_Time,FourWay_NetRad_AbvCnpy,Middleton_NetRad_AbvCnpy_23m,GlobalShortwaveRad_AbvCnpy_28m,UpShortwaveRad_AbvCnpy_23m,DownLongwaveRad_AbvCnpy_28m,UpLongwaveRad_AbvCnpy_23m,LI_DownPAR_AbvCnpy_28m,LI_UpPAR_AbvCnpy_23m,LI_DownPAR_BlwCnpy_1m,BF3_DownPAR_AbvCnpy_28m,BF3_DiffusePAR_AbvCnpy_28m,Wetness_AbvCnpy_28m,Tc_AirTemp_AbvCnpy_28m,MetOne_AirTemp_AbvCnpy_27m,MetOneTc_AirTemp_AbvCnpy_27m,MetOnePRT_AirTemp_AbvCnpy_27m,AirTemp_AbvCnpy_28m,AirTemp_AbvCnpy_16m,AirTemp_Cnpy_10m,AirTemp_LwrCnpy_5m,AirTemp_AbvGnd_1m,MetOne_RelHum_AbvCnpy_27m,RelHum_AbvCnpy_28m,RelHum_AbvCnpy_16m,RelHum_Cnpy_10m,RelHum_LwrCnpy_5m,RelHum_AbvGnd_1m,WindSpd_AbvCnpy_29m,WindDir_AbvCnpy_29m,StdDev_WindDir_AbvCnpy_29m,SurfPress,Belfort_CumPrec,TBRG_Rain,SnowDepth_Clrng,SnowDepth_Cnpy,WaterTableDepth,SoilTemp_SW_2cm,SoilTemp_SW_5cm,SoilTemp_SW_10cm,SoilTemp_SW_20cm,SoilTemp_SW_50cm,SoilTemp_SW_100cm,SoilTemp_NW_2cm,SoilTemp_NW_5cm,SoilTemp_NW_10cm,SoilTemp_NW_20cm,SoilTemp_NW_50cm,SoilTemp_NW_100cm,SnowTemp_SW_1cm,SnowTemp_SW_2 cm,SnowTemp_SW_5cm,SnowTemp_SW_10cm,SnowTemp_SW_20cm,SnowTemp_SW_30cm,SnowTemp_SW_40cm,SnowTemp_SW_50cm,SnowTemp_NW_1cm,SnowTemp_NW_2cm,SnowTemp_NW_5cm,SnowTemp_NW_10cm,SnowTemp_NW_20cm,SnowTemp_NW_30cm,SnowTemp_NW_40cm,SnowTemp_NW_50cm,CertificationCode,RevisionDate (n/a),(n/a),(n/a),(UTC),(UTC),(UTC),(W/m2),(W/m2),(W/m2),(W/m2),(W/m2),(W/m2),(umol/m2/s),(umol/m2/s),(umol/m2/s),(umol/m2/s),(umol/m2/s),(n/a),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(%),(%),(%),(%),(%),(%),(m/s),(deg),(deg),(kPa),(mm),(mm),(cm),(cm),(mm),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(n/a),(dymoyear) Met2,SK-OJP,FlxTwr,2003,244,30,25.52,10.99,139.87,16.35,320.14,-999,274.98,11.75,51.62,-999,-999,-999,20.433,-999,20.394,20.334,20.498,21.14,20.828,21.1,20.749,-999,39.949,40.434,39.401,39.981,39.275,3.522,150.19,19.13,95.664,-999,-999,-999,999.36,-999,14.765,14.348,13.828,13.324,12.86,12.287,14.84,14.167,13.711,12.979,12.633,12.19,20.128,20.105,20.253,20.315,20.404,20.449,20.461,20.505,20.165,20.182,20.223,20.333,20.424,20.462,20.5,20.544,PRE,01122003 Met2,SK-OJP,FlxTwr,2003,244,100,-23.11,-27.4,78.83,9.78,320.64,-999,151.79,6.41,35.11,-999,-999,-999,19.776,-999,19.764,19.704,19.866,20.4,20.004,20.27,19.936,-999,42.014,42.696,41.973,42.389,41.714,2.819,141.99,14.83,95.65,-999,-999,-999,1000.6,-999,14.703,14.336,13.858,13.364,12.858,12.289,14.58,14.138,13.735,13.016,12.629,12.187,18.92,18.917,19.147,19.266,19.402,19.47,19.504,19.559,18.961,19.009,19.093,19.221,19.338,19.412,19.459,19.524,PRE,01122003 8.4 Image Data [Describe the data submitted, with subsections 7.4.1 through 7.4.13 (below) being represented as columns in a tableExample: Identifier:OBS02031HH.PIX Date of Acquisition (UTC):31 January 2002 Time of Acquisition (UTC):16:13 Sensor / Mode:RADARSAT-1 SAR Standard Beam S1 Wavelength (nm) / Frequency (GHz):Standard Platform Altitude (magl):N/A Spatial Ground Resolution (m):30 Incidence Angle - Average:N/A Incidence Angle - Minimum:20.0 Incidence Angle - Maximum:27.4 Polarization:HH Gain Control:Automatic Flight Azimuth:Ascending Scene Centre:53.80206 N 104.61797 W 8.4.1 1 Image Identifier [A unique image file name that the image will be archived as, e.g. OBS02031HH.PIX.] 8.4.2 2 Date of Acquisition [As UTC.] 8.4.3 Time of Acquisition [Time as UTC; to allow later users to reproduce such things as sun angle.] 8.4.4 Sensor [Identify the imaging sensor and mode used.] 8.4.5 Wavelength [The wavelength range or frequency used. If settings are fixed, the descriptor "standard" can be used.] 8.4.6 Platform Altitude [The height of the sensor above the ground surface (m). If the altitude is fixed, such as for satellite platforms, N/A may be used.] 8.4.7 Ground Spatial Resolution [The smallest resolvable unit on the ground (m).] 8.4.8 .7 Incidence Angle - Average [The average angle from vertical.] 8.4.9 .8 Incidence Angle - Minimum [The minimum angle from vertical.] 8.4.10 Incidence Angle - Maximum [The maximum angle from vertical.] 8.4.11 Polarization [The polarization set on the sensor.] 8.4.12 Gain Control [Automatic or manual gain control.] 8.4.13 Flight Azimuth [Identify the direction of travel of the platform. For satellite-based platforms, Ascending or Descending is sufficient.] 8.4.14 Scene Centre [Give the scene centre in lat/long format.] --------------------------------------------------------------------------- 9 . Data Manipulations [This section describes the steps by which the data were processed to their final form.] 9.1 Post Processing and Calculated Variables [Specify all post-treatment of data, including data processing steps and calculations. Include relevant equations with definitions of terms and units.] Initially, data were recorded on a variety of loggers in various formats. Data from these loggers were brought into a common format and were organized by site, month and data type. Once data were organized into a common format, the first level of quality control was applied. This first stage of quality control included: - one-time "hard wired" fixes to resolve mistakes in data logger programming. - range and limit checking - application of calibration coefficients, and fixing of problems like incorrect wiring and conversion of units where required - manual exclusion of bad data where they could not be detected by automated range checking procedures - merging of manually quality controlled elements (including Atmospheric Pressure, Snow Depth, and Manual Accumulated Precipitation. - and computation of derived or adjusted elements (including, Top-of-the-Atmosphere Shortwave Radiation, Downwelling and Upwelling Longwave Radiation Above Canopy, Net Radiation Above Canopy, and Four Way Net Radiation Above Canopy. This first stage of quality control was conducted automatically as part of the automated daily data retrieval. Once a week, the data were also plotted and inspected visually, using a graphical user interface that automated the plotting and data inspection processes. After the 1st stage of quality control was completed, some additional adjustments were applied to these climate data in a second stage of QC, which included - correction of RH for a maximum value that exceeds 100% - correction of Shortwave_Rad and PAR for nighttime zero offsets - gap filling of some parameters where the estimates were judged to be highly accurate, e.g., the Longwave (PIR) data were analyzed to sort out the relationship between compensated and derived outputs, and then this relationship was used to fill gaps in the derived output. Following the 2nd stage of QC above, data gaps in some key variables were filled in a third stage of processing. Most data gaps were filled using statistical relationships with related data from the same site or the other sites. These data files are not available on the FC DIS, but are available upon request (PI or data contact). 9.2 Special Corrections/Adjustments [List any 'special' corrections/adjustments made to portions but not all of the data to make it compatible with the data set as a whole.] 9.2.1 The following are details on quality control procedures, stage 1.(applicable to BERMS Met2 data). a) Limit checking and Range Checking This procedure sets out-of-limit data to missing. Two types of checks are performed, these include: - Rate-of-change checking; a maximum rate of change per time increment is set (i.e. air temperature must not exceed a rate of change per 30min period of 30 deg C, otherwise it will be set to missing). These values were purposely set high, so as not to exclude anything that might be real. - Limit Checking: each variable was assigned an absolute maximum and minimum value that recorded data was required to fall in to. Example: air temperature had to fall in between 45 and -50 deg C, otherwise it would be set to missing. These limits were also set high so that anything remotely real would not be excluded. Limit checking was used to flag questionable data but the data were not automatically excluded. b) Application of Calibration Coefficients and fixing of problems like incorrect wiring as well as conversion of units. Known corrections were listed in a separate file that was queried by the 1st level QC program. See "e) Derivation of new parameters" below for more information on these corrections. c) Manual exclusion of bad data Bad data that could not be detected by automated range checking procedures were excluded manually, in a semi-automated program. Outliers that could be recognized by the human eye were manually selected using a data visualization program that enabled the user to point and click on "bad" points. Once these points were selected, a record of the "bad" element and the time at which these data occurred were automatically written to a data exclusion file. The 1st stage QC program then automatically set these manually rejected data to "missing". d) Merging of manually quality controlled elements Some elements were quality controlled manually, including snow depth, accumulated precipitation and atmospheric pressure (one time only). These elements were created in separate files that were merged into the main meteorological files at the first level of QC. Manual quality control of procedures are explained below: Atmospheric Pressure -------------------- - two pressure sensors of the same type were installed at each site, one in conjunction with the flux measurement program and the other with the meteorological program. - when the met pressure data were missing, data from the flux pressure sensor were filled in where possible. Snow Depth (non-gapfilled found in “Main” data files) ---------- The procedure for quality controlling snow depth in the Met2 files was partly manual and partly automated: - Snow depth in its raw form is recorded as the sensor height above ground, in mm. - First, the amount to subtract from the raw snow depth value to get actual snow depth was determined by manually browsing for a stable reference period of about one week before and after the snow season, where the instrument was “seeing” bare ground. - If this period was not manually found, then an automated procedure was in place do this, although typically it would not do as good a job as the human eye. o So far, it’s likely that this hasn’t happened yet. - In most cases, where the reference period did not change significantly over the snow season, one manual reference was used (usually a Fall measurement). - The snow depth was then calculated by subtracting the sensor measurement from the manual reference. - Example, the height of the sensor on Oct 31, 1998 was 2000mm and on April 15, 1999, it was 2000mm, therefore snow depth was computed by: (2000 - Distance from Sensor to Snow) = Actual Snow Depth - Ground truths, such as snow surveys and manual measurements of the height of the sensor above ground were also used to confirm/refute that the sensor was/was not working properly. Snow Depth (gapfilled – found in “MainGapfilled” data files) ---------- The procedure for quality controlling and gap filling snow depth in the Gap Filled files was as follows: - Any erratic summer data was cleaned up. - Some smoothing applied to noisy data o A filter called SGOLAY (in Matlab) was used to remove high frequency noise. The goal was to remove daily noise, which was usually the result of problems with the reference temperature required by the instrument to make its measurement. This filter works almost like a running mean. - Gaps were filled using a 5 day moving window linear regression. - If the height of the sensor changed significantly from what it was at the start of the season to what it was at the end of the season, a linear correction was applied. This correction was determined by computing the slope and intercept between the start and end times of the period in which the correction was to be applied. For example, if the height of the sensor on Oct 31 was 2000mm, but at the end of April, it was 1970mm, this type of correction was warranted. Manual snow depth measurements were also used to confirm that these linear corrections were justified. - Example: RawDepthPreSnow = Height of Sensor Above Ground on Last Snow Free Day = 1963mm RawDepthPostSnow = Height of Sensor Above Ground on 1st Snow Free Day = 2002mm TimePreSnow = Date of Last Snow Free Day = Nov 5, 2000 TimePostSnow = Date of 1st Snow Free Day = Apr 22, 2001 Slope = (TimePreSnow:RawDepthPreSnow,TimePostSnow:RawDepthPostSnow) Intercept = (TimePreSnow:RawDepthPreSnow,TimePostSnow:RawDepthPostSnow) Adjusted Snow Depth =(Slope*Time+Intercept-RawDepth) Manual Cumulative Precipitation ------------------------------- The procedure for accumulating Belfort Precipitation was a manual one. The procedure was quite simple (although a bit labour intensive): 1. If data was from a Belfort Universal (150mm) gauge, the 30 minute values were rounded off to the nearest 1/10th mm 2. If data was from a Belfort 3000 (500mm) gauge, the 30 minute values were rounded off to the nearest mm (note that the above steps may have already been done by the logger or an automated qc procedure) 3. Using the difference between 30 minute observations, a 30 minute accumulation was calculated for each 30 minute period. 4. The data was manually screened for gauge servicing (the dates of those were cross referenced with field notes when possible). 5. As a check, the difference between the last total weight before servicing and the last accumulated weight after the previous servicing was calculated. It was known that the Belfort gauges were usually accurate when it came to measuring the total weight in the bucket...this is what they do well. 6. The total of the 30 minute accumulations should have matched the difference in bucket contents as calculated above. However, it never did! 7. The observations were manually screened, one at a time, in order to distinguish between real and fictitious measurements. The most common error was rounding error computed by the datalogger. ie. A small change in the signal from the gauge may have alternated between a value of 100.64 mm (which = 100.6 mm) and 100.65 mm (which = 100.7 mm), resulting in 0.1 mm of false precip. These were systematically removed from the totals. For the most part, by eliminating these, the weights would match between servicings. Evaporation was another problem that made the weights mismatched. Eliminating this factor involved some judgment as to when evaporation occurred. It then had to be determined if the next positive weight increase was a result of the rounding problem or actual precipitation. The TBRG data helped in the summer but it was a judgment call in the winter. There were also instances where someone dumped fluid into the gauge. These false values also had to be removed. e) Derivation of new parameters. The following new elements were computed at the first stage of QC: - Top-of-the-Atmosphere Shortwave Radiation - Derived Downwelling Longwave Radiation - Derived Upwelling Longwave Radiation - Adjusted Net Radiation above canopy - Four Way Net Radiation The DerivedDownwelling and DerivedUpwelling LongwaveRadAboveCanopy from the Eppley PIR longwave radiometer were computed from the instrument?s thermopile output, body temperature and dome temperature as follows. First, for both Down- and Upwelling Longwave radiometers, BodyTemp (TK) and DomeTemp (TK) were computed from the logged thermistor resistances (kOhm): c1=1.0295e-3; c2=2.391e-4; c3=1.568e-7; TK=1/(c1+c2*ln(1e3*kOhm)+c3*(ln(1e3*kOhm)); The value for TK was refiled in place of kOhm and the units were changed from (kOhm) to (K). Second, the DerivedDownwelling and DerivedUpwelling LongwaveRadAboveCanopy were estimated from the respective PIR?s Thermopile output, BodyTemp, and DomeTemp. sigma=5.6705e-8; k=3.2; DerivedDownLongwaveRadAboveCanopy = ThermopileOutput + sigma*BodyTemp^4 - k*sigma*(DomeTemp^4-BodyTemp^4); The Middleton CN1-R Net Radiation above canopy was adjusted for differences in the instrument?s short- and longwave sensitivities, using three ?NetRad? adjustment parameters: c, cl, and cs, where c is the multiplier used in the data logger program, cl is the correct long-wave calibration factor, cs is the correct short-wave calibration factor and Rn on the RHS of the equation is the logged value for Rn. Rn = Rn*cl/c if Rn<=0; Rn = Rn*cs/c if Rn>0; FourWayNetRadAboveCanopy was computed as: GlobalShortwaveRadAboveCanopy - UpShortwaveRadAboveCanopy + DerivedDownLongwaveRadAboveCanopy - DerivedUpLongwaveRadAboveCanopy. 9.2.2 The following are details on quality control procedures, stage 2 (applicable to BERMS OA, OBS and OJP Met2 Data only). Also see "Equations" document for formatted version of the equations in this section. a) Correction of RH, as measured by Vaisala HMP35 and 45C, for maximum values exceeding 100%. Each sensor has a unique maximum range which exceeds 100%. This maximum RH is independent of temperature for temperatures above 0.0 degC and appears to be stable over time. We use this value of maximum RH for each sensor as a one point calibration to adjust RH to bring the maximum RH back down to 100% as: RHadj = RH*100/RHx (1) Note that the same value for RHx is used above and below freezing although RHx(T) falls off at temperatures below 0 degC. Vaisala has designed the HMP so that the vapour pressure is calculated as es(Ta)*RH/100% using a saturated vapour pressure es(Ta) that is with respect to water at all temperatures. There is no need to estimate es(Ta) with respect to ice at sub-zero temperature because the low temperature effect is dealt with in the change of RHx(T) with temperature at sub-zero temperatures. The fCalcRHgt100 option estimates and files the value of RHx for the entire history of HMP deployment at each site and HMP level. RHx is computed as follows. - For each HMP level and site, we first input the history of sensor deployment. - For each sensor deployment, we input the data and stratify it by temperature using 5 degC increments. - For each 5 degC increment, with more than 500 data points between 0 and 20 degC, we estimate RHx as the 99.8% percentile of the data. This percentile excludes one outlier for every 500 data points. This procedure produces up to four estimates of RHx, for 0-5, 5-10, 10-15 and 15-20 degC. - If any of the individual estimates of RHx is more than 1.5% from the mean value, it is excluded. - If two or more acceptable values remain, they are averaged to estimate RHx. b) Correction of Shortwave Radiation and PAR for nighttime zero offsets. The adjustments are usually near zero for UpShortwave_Rad. For UpShortwave_Rad, they are near zero when the ventilator fan is unplugged, +2-3 W m-2 when the ventilator fan is on and operating properly, and +5-15 W m-2 when the ventilator fan is on but stalled. The zero offset adjustments are computed from nighttime data when Shortwave_Rad is zero, assuming that the day and nighttime offsets are the same. A smooth curve is fit through the nighttime data (the zero offset) by: - calculating the mean for each night - rejecting nighttime means with too few observations, a high standard deviation, or a large mean change from one night to the next - interpolating the nightly mean offsets to each 30-min period. When the nighttime offsets are unstable (i.e., the standard deviation is high or the mean changes from one night to the next).The data from that night and the adjacent days are rejected. When the nighttime offsets are stable, the interpolated 30-min offsets are applied as adjustments both day and night. The adjustment brings the nighttime means to zero and adjusts the daytime values upwards accordingly. 9.2.3 The following are details on quality control procedures, stage 3 (applicable to BERMS OA, OBS and OJP Met3 Data). Also see "Equations" document for formatted version of the equations in this section. a) Gap filling in the “Summarized” (Met3) files is given below. This methodology will slowly be phased out in favor of that used to produce the “MainGapFilled” data files (See section below for information on the “MainGapFilled” data files). Non-statistical methods There are programs in place to fill gaps in variables that are used to derive Down- and UpLongwave_Rad from the Eppley PIR. The best estimates of Down- and UpLongwave_Rad are derived (DerivedDown- and DerivedUpLongwave_Rad) based on three logged variables: the raw PIR thermopile output (mVpile, logged as Down- and UpLongwave_Thermopile), body temperature (Tb (K)) and dome temperature (Td (K)) as: DerivedLongwave_Rad = mVpile + S* Tb^4 – c*S(Td^4-Tb^4) (2) Where mVpile is the raw longwave sensor (Eppley PIR) thermopile output in mV, S is the Stephan-Boltzmann constant, Tb is the instrument’s body temperature in K and Td is the instrument’s dome temperature in K, c is an empirical correction factor (set to 3.2), A second estimate of Longwave_Rad can be made from the output of the Eppley PIR’s compensation circuit, but is not recommended for use because of two important differences from (2): - The compensation circuit adds bias. Ideally, the compensated output (mVcompensated) should equal (mVpile+ S* Tb^4) from (2). In practice, however, the compensation circuit is not ideal; it requires an internal battery, which can and does fail, and its performance is sensitive to temperature and varies among instruments. The result is an instrument- and temperature-dependent Bias between the compensated output mVcompensated and (mVpile+ S* Tb^4): Bias = mVcompensated – (mVpile +S*Tb^4) (3) The Bias (3) is a well-behaved function of body temperature, and the Bias=f(Tb) relationship is unique for each instrument-battery pair. - The compensated output does not include the third (c*S(Td^4-Tb^)) term from (2). This fills gaps in Down- and UpLongwave_Thermopile and Tb based on the output from the PIR’s compensation circuit. Gaps are filled by fitting a third-order polynomial to the Bias=f(Tb) relationship (3) for each instrument-battery pair, and then calculating Longwave_Thermopile or Tb from (2). Note that Vcompensated is more commonly missing than Vpile, so that there are many instances when gaps in Longwave_Thermopile cannot be filled from the compensated Longwave_Rad. Statistical methods *** This section is being written and is VERY incomplete. Gap filling using statistical methods is done in many ways, depending on the type of missing data and the type of data that are available for gap filling. One-off Regression method Gaps in Tb and Td are only filled when only one of the two is missing. The Td - Tb difference is modeled by multiple linear regression with GlobalShortwave_Rad and Longwave_Thermopile as the independent variables. Missing temperature are estimated from non-missing temperatures and the modeled temperature difference. The analysis is slightly more complicated for the tower-top PIR that measures DownLongwave_Rad; this PIR is ventilated and the regression relationship changes when the ventilator fan stalls. It is thus necessary to do separate analyses for periods when the fan is stalled and not stalled. Difference interpolation methods Regression methods. (Or Statistical) Gap filling is implemented using MatLab. The gap filling options include: -Small gaps in temperature and humidity. Where data gaps are for one or two periods only and there is a proximate measurement of the same variable, missing values from A are estimated using data from nearby sensor B, as follows. The difference between the two sensors (B-A) is calculated for all periods when both are present. The difference (B-A) is then linearally interpolated to periods when A is missing, and missing values of A are estimated as B-(B-A) where (B-A) are the interpolated values. -Statistical relationships (linear regression and linear regression forced through the origin) with nearby, associated variables. Gap filling HMP data when data is missing From one sensor only and for short periods only (<=3 periods). Missing values from sensor A are estimated using data from nearby sensor B, as follows. The difference between the two sensors (B-A) is calculated for all periods when both are present. The difference (B-A) is then linearally interpolated to periods when A is missing. Lastly, missing values of A are estimated as B-(B-A) where (B-A) are the interpolated values. -This effectively fixes the HMP dropout problem during phone calls. For HMP data, gaps are filled for longer periods and where all data are missing, based on linear regression with the most proximate measurement. Done from q. 9.3 Gap filling (applicable to files found in the “MainGapfilled” directory). A summary of gap filling procedures is given here. A more detailed document is available upon request from the PI. Operationally, gaps were filled one year at a time considering all possible related and available time series, however, only the variable that was most closely related to the missing variable was used to gap fill. Three different methods were used depending on the size of the gap and what variable needed to be filled. Here are the methods used: - Interpolated Difference o For this method, the difference between two similar variables was interpolated linearly. The difference between y and x were calculated for two points on either side of the missing section of data (when both were not missing). The difference was then estimated by linear interpolation for periods when y was missing and x was not. Gaps were then filled for y by adding the difference to x. - 5-Day Moving Window Linear Regression o This method used a linear regression to determine the relationship between the missing and non-missing variables. A flexible window of 240 (5d) non-missing data points, or 120 points, on either side of the missing section of data was used to determine this relationship and hence predict the missing section of data. - Moving Window Linear Regression, Same Time of Day o This method used a moving window, as in the 5-Day linear regression method above, except that the data were stratified by time of day (into 48 periods). A regression was done separately within each stratum, using a flexible window of 12 non-missing data pairs or 6 points (6 days) on either side of the section of missing data. Example: if data were missing at 1200 UTC, then a regression was calculated using data from 1200 UTC only, over a period of 6 days on either side of the missing section of data. Given below are the methods and variables used to gap fill, organized by variable type: - Global Shortwave Above Canopy o Moving Window Linear Regression Same Time of Day (forced through origin) for all gap sizes o Variables used to fill are either Global Shortwave or Downwelling PAR Above Canopy, however, if the gap couldn’t be filled by these two, then it’s possible that Upwelling Shortwave and Upwelling PAR were used. - Upwelling Shortwave Above Canopy o Moving Window Linear Regression Same Time of Day (forced through origin) for all gap sizes o Variables used to fill include Upwelling or Downwelling Shortwave or Upwelling or Downwelling PAR Above Canopy - Downwelling PAR (Above Canopy) o Moving Window Linear Regression Same Time of Day (forced through origin) o Variables used to fill are either Global Shortwave or Downwelling PAR Above Canopy, if the gap wasn’t filled by these two, then it’s possible that upwelling Shortwave and PAR were used. - Downwelling PAR (Below Canopy) o Moving Window Linear Regression Same Time of Day (forced through origin) o Any PAR variable could have been used - Upwelling PAR (Above Canopy) o Moving Window Linear Regression Same Time of Day (forced through origin) o Variables used: Downwelling or Upwelling PAR and Global Shortwave and Upwelling Shortwave (Above Canopy only for all). - Derived Downwelling Longwave Above Canopy o Interpolated Difference for small gaps (<24 periods, or 12h) and 5-Day Moving Window Linear Regression for large gaps (24 periods or more). o Variables used: Derived Downwelling Longwave, Derived Upwelling Longwave, Down or Upwelling Shortwave Above Canopy, Down or Upwelling PAR Above Canopy. - Derived Upwelling Longwave Above Canopy o Interpolated Difference for small gaps (<24 periods, or 12h) and 5-Day Moving Window Linear Regression for large gaps (24 periods or more). o Variables used: Derived Downwelling Longwave, Derived Upwelling Longwave, Down or Upwelling Shortwave Above Canopy, Down or Upwelling PAR Above Canopy. - 4-Way Computed Net Radiation Above Canopy. If any gaps existed in the gap filled data, it would have been due to one of the 4 components being missing. First, gaps were filled in the components that were missing and then the 4-Way was re-computed. - Net Radiation Above Canopy o Moving Window Linear Regression Same Time of Day o Variables Used: Net Radiation Above Canopy (also included the 4-Way Computed Net Radiation) and Downwelling or Upwelling Shortwave Above Canopy. - Air Temperature (any height, any type of measurement) o Interpolated Difference for small gaps and Moving Window Linear Regression Same Time of Day for large gaps. o Variables Used: Air Temperatures, Shallow (<=20cm depth) Soil Temperatures and Tree Temperatures (any level or location). - Shallow Soil Temperature (<=20cm depth) o Interpolated Difference for small gaps and Moving Window Linear Regression Same Time of Day for large gaps. o Variables Used: Air Temperatures, Soil Temperatures and Tree Temperatures (any level or location). - Tree Temperatures (any location or tree depth) o Interpolated Difference for small gaps and Moving Window Linear Regression Same Time of Day for large gaps. o Variables Used: Air Temperatures, Shallow (<=20cm depth) Soil Temperatures and Tree Temperatures (any level or location). - Deep Soil Temperature (50cm and 100cm depth) o Interpolated Difference for all gap sizes. o Variables Used: All Soil Temperatures (any depth). - Relative Humidity (any height) o Interpolated Difference for small gaps and Moving Window Linear Regression Same Time of Day for large gaps o Only RH used to fill gaps. - Soil Heat Flux (any location or depth) o Interpolated Difference for small gaps and Moving Window Linear Regression Same Time of Day for large gaps o Only Soil Heat Flux used to fill gaps. - Wind Speed (any location or height) o Interpolated Difference used for small gaps and 5-Day Moving Window Linear Regression for large gaps. o Only Wind Speed used to fill gaps. - Atmospheric Pressure o Interpolated Difference used for small gaps and 5-Day Moving Window Linear Regression for large gaps. o Only Atmospheric Pressure used to fill gaps. --------------------------------------------------------------------------- 10 . Errors and Limitations [This section describes an error analysis for the data.] 10.1 Sources of Error [Describe what factors of the instrument or environment may introduce errors in the observations.] See section on "Known Problems with the Data" for more information. 10.2 Quality Assessment 10.2.1 Data Validation by Source [Describe all efforts to validate the data by the submitter, e.g. comparisons with data from other investigators.] Data were quality assured by comparing simiar variables at the same site and also by comparing variables at different sites within the BERMS area. This was done weekly, in near real time, to ensure that problems were flagged and fixed in a timely manner. 10.2.2 Confidence Level/Accuracy Judgment [Subjective discussion of data quality.] The data submitted are of good quality with minimal amount of errors. However, any measurement is not perfect, and the user must be aware of the limitations of the instrumentation. 10.2.3 Measurement Error for Parameters [Quantitative error estimates.] See section on Equipment for more instrument specs. 10.2.4 Additional Quality Assessments [May include visual review of plots, etc.] 10.3 . Limitations and Representativeness [Provide warnings on the use of the data, e.g. data were collected under drought conditions relations between variables may be different when things are wet, as well as known problems. Discuss how representative your data is, eg: of the landscape, climate, footprint, etc.] See section on Known Problems below for more information. 10.4 Known Problems with the Data Problems to be aware of at all Saskatchewan BERMS sites: - Beware that there was an unusual amount of frost build-up, particularly on the radiation sensors, during the winter of 1997/98. Frost problems have occurred in other years, but not to the same extent. Frost can either cause a drop or an increase in radiation measurements depending on the sensor. Frost can also cause the propellers on wind instruments to seize up and not record wind speeds above 0 m/s. - Non-zero shortwave and PAR at night during some periods: some possible causes of this include poor wiring and/or stray voltages from the ventilation fan. - Specific problems have been listed in the file: QC_Notes.txt/xls. Searches on specific variables, study sites and time can be made in this file. To avoid most of these problems, use the Qcd version of the Met data (called *.AL1 files). - Relative humidity measured by Vaisala HMPs often drift above 100%. Nothing has been done to correct this problem at the first stage of QC. Higher level QCd files, which will be available at a later date will make adjustments to RH, particularly for values near and above 100%. - Some Vaisala HMP temperatures would often go missing for the one 30min period in which phone downloads took place. This problem remained unresolved. - Downwelling and Upwelling Longwave radiation as measured by the Eppley PIR sensor (variables: DownLongwave_Rad_AbvCnpy and UpLongwave_Rad_AbvCnpy) were OK as long as the battery in the sensor was fully charged. If this battery began to die, the measurement would slowly drop off the acceptable range. If these data are found to be missing, it was likely that the battery in the sensor was dying or dead. In this case, use DerivedDownLongwave_Rad_AbvCnpy_37m and DerivedUpLongwave_Rad_AbvCnpy_37m instead. - Some gaps in the thermocouple air temperature measurements exist due to the fine wires on the sensor breaking. Data problems at OJP: - As at OBS, there was the occasional non-zero downwelling shortwave data spike or small excursion from 0 at night from 1997 to July 1998, then again in late 1999. The cause of these problems was also unknown. - Temperature data at 1m, 10m and in the Met One Ventilator at 27m often went missing during data downloads via modem (mostly in 1999). - The tipping bucket sensor measured only ~7% of Belfort precipitation from May 7 to July 9, 1998 due to a programming error. No attempt was made to correct these data, instead, it has been set to missing in the QCd data files (*.AL1). - The cup wheels on the anemometer at the Belfort rain gauge sometimes froze up during the winter. This could be detected when the wind speed stayed at 0 for days at a time. - The wind speed at the Belfort was negative at the start, but has been fixed in the AL1 file. Other inconsistencies to note: - a calm wind speed was =.2 from the start until October 29, 1997, then it was =0 from that point until the program was changed on Feb 24, 1999. This has been fixed in the QCd data files (*.AL1) - Wind was averaged over 2min instead of 30min from the start until 1998 July 8. - All instruments were re-wired from 19 June, 2000 and 21 June, 2000. Some data gaps exist during this period. Following this, some sensors were not working properly for a while, these included: wind speed at Belfort 3000, air temperature at Snow Depth sensor and surface pressure. Note that bad surface pressure data were replaced with data from Univ. of BC's pressure sensor. - Problems with MetOneAir_Temp_AbvCnpy_27m data missing consistently at 1030 from day 10 to day 173 in 2000 - Surf_Press unstable from 20 June 0030 to 02 Aug 1800 2000. Data spikes, ie. Changes of 1-3mb per 30min period, mostly at 0730 and 1030, when calls for data take place. Also, pressure is high by ~10mb for this same period. Suspect a stray voltage on the common grounding block on the JP. Problem disappeared after the sensor was grounded directly to the logger on 02 Aug 1800. - Wind_Spd_AtBelfort3000_5m bad following reconnection on 21 June 2000. Peaks at ~4m/s (too high) at around 1900 UTC and then goes down to near 0 or slightly less at around 1130 UTC. Rewired by 19 July 2100, but wires reversed causing -ve windspeed from a starting point of .2. - Air_Temp_AtUDG missing or bad since re-installation on day 173, year 2000 at 1900. Then switched to logger temp mainly in order to troubleshoot the UDG on 19 July 2100, year 2000. This temperature is in deg K. New thermocouple installed by 22 Aug 1730 2000 - Time problem. Precip logger (CR10) julian day set 1d behind following rewire on June 22 1900. - Missing data. CR7 logger was not responding to calls for data from 24-29 June (day 176-181, 2000); problem unknown (possibly lightning?). Dwight re-set the logger on site by turning it off and on again - Since the logger was re-set by Dwight on June 29 (day 181, 2000), UpShortwave_Rad_AbvCnpy_23m was abnormally high at night (up to +14) between 29 June and 28 June. This was suspicious because this nighttime offset has been consistently around 0 for months now. It's possible that water in the wiring may be causing this problem. Ventilation fan switched off between 28 Sept and 21 Oct, 2000 and offsets dropped. When turned on again on 21 Oct, offsets returned again. Fan replaced by 9 Nov 2230, 2000. - GlobalShortwave_Rad_AbvCnpy_28m at night too low (~-5 to -19 Wm2) between 15 Aug 1000, 2000 until ventilation fan disconnected on 28 Sept 2030, 2000. - Data problems withSnow_Depth SR50 sensor from day 235, year 2000 to day 294, year 2000. Varies by up to 20mm daily since it was installed on 22 Aug 1730. It follows a distinct diurnal cycle, peaking at ~1630 and bottoming out at ~0100. This is likely due to a poor air temp measurement. Air_Temp_AtUDG changed from a thermocouple to a 107 probe by 20 Oct 2130. This seems to have reduced the amount of daily fluctuation ("noise") in the snow depth measurement by ~15mm - Tree_Temp_NW_14m becomes bad and erratic after 19 Apr 1830, year 2001 to end of 2001. - Numerous sensor problems on Day 340, 2001. - HMP_Temp_28m data bad or missing during numerous periods in 2002. - Shortwave downwelling radiation_28m missing from day 229 to day 317, 2003. - AirTemp_16m and 5m dropping out dailey between 23:30 and 00:30, from 2003 through to 2006 - 4way net radiation 23m bad or missing intermittently from day 112 to day 248, 2004. - Thermocouple_Air_Temp_28M Data Missing from day 105 to day 333, 2005. Thermocouple air temperature at 28m operational again when repaired by field staff. " - Precipitation Logger down from day 173 to day 179, 2005. Variables affected include SR50 reference temperature and snow depth, tipping bucket and Belfort precipitation, and wind speed at the Belfort gauge. Logger and instruments are currently functioning normally. - 2009 July 24 to 29: Downwelling shortwave missing intermittently due to a wiring problem. --------------------------------------------------------------------------- 11 . Software 11.1 Software Description [Describe all software that was used to process the data.] Various kinds of plotting and housekeeping software were used to view and process BERMS meteorology data. Some software was commercially available (like PC208 from Campbell Scientific), while others were programs written in C++ and Matlab. Here is a list of current programs and their functions: *** To be written *** 11.2 Software Access [Describe any software that may be available for use by someone who may want to perform further processing of the data. Also describe where a user can get it -- commercial source, Web site, FTP archive, e-mail to author, etc.] Please feel free to contact the following people to discuss software availability and usage: Steve Enns Phone: (306) 975-5683 Email: Steve.Enns@ec.gc.ca Alan Barr Phone: (306) 975-4324 Email: Alan.Barr@ec.gc.ca --------------------------------------------------------------------------- 12 . References 12.1 Platform/Sensor/Instrument/Data Processing Documentation [List any published documentation relevant to the data collected, such as manufacturer's instruction manuals, government technical manuals, user's guides, etc.] Belfort Instrument Company. 1986. Instruction Manual Catalog Number 5-780 Series Universal Recording Rain Gage, Instruction Manual number 8777. Campbell Scientific. 1983. Model 207 Temperature and Relative Humidity Probe Instruction Manual. Campbell Scientific. 1990. SBP270 Barometric Pressure Sensor Instruction Manual. Campbell Scientific Canada Corp. Campbell Scientific. 1992. UDG01 Ultrasonic Depth Gauge Operator?s Manual. Campbell Scientific Canada Corp. Campbell Scientific. 1992. Model HMP35CF Temperature and Relative Humidity Probe Instruction Manual. Campbell Scientific. 1993. CR7 Measurement and Control System Instruction Manual. Campbell Scientific Inc. Campbell Scientific. 1996. CS615 Water Content Reflectometer Instruction Manual. Campbell Scientific Inc. Campbell Scientific. May, 1998. Model HMP45C Temperature and Relative Humidity Probe Instruction Manual. Campbell Scientific Corp. Campbell Scientific. Sept, 1998. SR50 Sonic Ranging Sensor Operator's Manual. Campbell Scientific Corp. Carter-Scott Design. No Date. Middleton CN3 Heat Flux Plate Application Note. Carter-Scott Design. May 1995. Instruction Manual for Middleton CN1-R Net Pyrradiometer Edition: CN1R-v1.1. Druck Incorporated. No Date. PTX Depth Pressure Transmitter, Installation and Application Notes. EPLAB, The Eppley Laboratory, Inc. No Date. Instruction Sheet for the Eppley Precision Radiometer (Model PIR). ESI Environmental Sensors Inc. No Date. Moisture Point Instruction Manual. Gray, D.M. 1981. Handbook of Snow. Toronto, ON: Pergamon. Kipp and Zonen. No Date. Instruction Manual Pyranometer CM 11/14. LI-COR. 1991. LI-COR Radiation Sensors Instruction Manual. NASA. 1994. BOREAS Experimental Plan, Version 3. Phillips, D. 1990. The climates of Canada. Ottawa: Canadian Government Publishing Centre. R.M. Young Company. 1980. Instructions, Gill Microvane 3 Cup Anemometer. R.M. Young Co. R.M. Young Company. 1990. Wind Monitor High Resolution Wind Sensor information sheet. R.M. Young Co. Environment Canada. 1993. Canadian Climate Normals 1961-90. Wheaton, E. 1998. But It?s a Dry Cold!. Calgary: Fifth House Ltd 12.2 Journal Articles and Study Reports [List technical reports and scientific publications that concern the methods, instruments, or data described in this document. Publications by the Principal Investigator or investigating group that would help a reader understand or analyze the data are particularly important.] Arain, M.A., T.A. Black, A.G. Barr, P.G. Jarvis, J.M. Massheder, D.L. Verseghy, and Z Nesic. 2002. Effects of seasonal and interannual climate variability on net ecosystem productivity of boreal deciduous and conifer forests. Can. J. For. Res. 32: 878-891. [Abstract] Blanken, P.D., T.A. Black, H. H. Neumann, G. den Hartog, P. C. Yang, Z. Nesic and X. Lee. 2001. The seasonal water and energy exchange above and wthin a boreal aspen forest. Journal of Hydrology. 245(1-4): 118-136. [Abstract] Barr, Alan G., G. van der Kamp, R. Schmidt and T.A. Black. 2000. Monitoring the moisture balance of a boreal aspen forest using a deep groundwater piezometer, Agric. For. Meteorol. 102:13-24. [Abstract] Black, T.A., W.J. Chen, A.G. Barr, Z. Chen, M.A. Arain, Z. Nesic, E.H. Hogg, H.H. Neumann and P.C. Yang. 2000. Increased carbon sequestration by a boreal deciduous forest in years with a warm spring. Geophys. Res. Letters. 29(9): 1471-1274. Chen, W., Black, T.A., Yang, P., Barr, A.G., Neumann, H.H., Nesic, Z., Novak, M.D., Eley, J., Ketler, R., and Cuenca, C. 1999. Effects of Climatic Variability on the Annual Carbon Sequestration by a Boreal Aspen Forest. Global Change Biology, 5(1): 41-53. [Abstract] --------------------------------------------------------------------------- 13. Glossary of Terms and Acronyms [Define discipline-related jargon and the wealth of scientific notations/symbols that may be used in the text, as well all "local" acronyms. Items from the following list may be included. BERMS - Boreal Ecosystem Research and Monitoring Sites BOREAS - BOReal Ecosystem-Atmosphere Study PANP - Prince Albert National Park] MSC - Meteorological Service of Canada (a branch of Environment Canada) BERMS - Boreal Ecosystem Research and Monitoring Sites BOREAS - BOReal Ecosystem-Atmosphere Study BORIS - BOREAS Information System NHRC - National Hydrology Research Centre NWRI - National Water Research Institute SRC - Saskatchewan Research Council UTC - Universal Coordinated Time --------------------------------------------------------------------------- 14 . Document Information 14.1 Document Revision Date [Use yyyy-mm-dd-mmm format] 2006-03-20 2005-06-07 2004-03-22 2003-09-29 14.2 Document Author Charmaine Hrynkiw 14.3 Keywords [Include a list of appropriate key words to assist in searching for information.] Meteorology, climate, Jack Pine, southern boreal forest