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 Boreal Ecosystem Research and Monitoring Sites (BERMS) Tower Flux Meteorological Data From the Southern Study Area Fen site 1.2 Study Overview 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 “Southern” Fen site, which was operational during BOREAS, was re-established for year-round flux measurement in 2002. The data set documented here, includes the near-surface meteorological measurements at the Fen site in support of carbon, water and energy flux measurements at this site. 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 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 the Fen site 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 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. -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 (by Garth Van der Kamp’s group). 1.4 Related Data Sets 2. Investigator(s) 2.1 Principal Investigator(s) Name and Title 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 Saskatchewan BERMS FEN Meteorological data. 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 Research Division 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 Research Division 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: Saskatoon: Dell Bayne, Newell Hedstrom, Raoul Granger, Randy Schmidt, Bruce Cole, Joe Eley, Natasha Neumann, Steve Enns, Craig Smith, Erin Thompson, Werner Bauer. 2.5 Acknowledgements: Students: Jodi Axelson, Andrea Eccleston, Matt Regier, Jenny Hill, Courtney Campbell, Lisa Christmas, Kim Kovacs, Justin Beckers, Stephnie Watson, Brett Reynolds. --------------------------------------------------------------------------- 3. Theory of Measurements 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) soil heat flux, E) wind speed and direction F) Campbell Scientific data loggers. (description, manufacturer, calibration, specs, frequency of calibration, other calibration information) A)TEMPERATURE and HUMIDITY: i)HMP35CF Temp/Humidity Probe, ii) Copper-Constantan Thermocouples, iii)237/237F Wetness Sensing Grid i) 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. ii) 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? And 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. iii) 237/237F Wetness Sensing Grid -Description: The wetness sensor was used to determine the amount of moisture at the ground surface. It was intended to give information which would supplement the measurments taken by the radiation sensors becasue moisture affects the longwave radiation measurments. -Manufacturer: Campbell Scientific. -Calibration: Not Calibtrated -Specs: consists of a rigid epoxy circuit board with interlacing gold-plated copper fingers. This model is an artificial leaf type suitable for use with CR7, CR10,and 21X dataloggers. Range: two scales have been used to obtain readings; 0-5 mV, the sensor will read ~2.5mV when measurably damp and ~4.95mV when very damp. 0-100 Kohms, 100 being measurably damp and 1 meaning lots of moisture. -Frequency of Calibration: n/a -Other Calibration Information: The data is quantitatively defined iv) Apogee Instruments (exact model unknown) - Description: Infra-red thermocouple used to measure the temperature of vegetation. Manufacturer: Apogee Instruments Calibration: Calibration procedures and performance details are described in: Evaluation and Modification of Commercial Infra-Red Transducers for Leaf Temperature Measurement. Bruce Bugbee, Matt Droter, Oscar Monje and Bert Tanner. 1998 Adv. in Space Research 22: 1425-1434. Specs: Exact model unknown. Specifications for all models on manufacturer's website Range: dependent on exact model B)PRECIPITATION - i) Tipping Bucket Rain Gauge, ii) SR50 Snow Depth Sensor, iii)Belfort 5915 Universal WeighingPrecipitation gauge i) 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. ii) 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 the initial reading 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. iii) 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. C)RADIATION- i)Middleton CNR-1 Net Radiometer, ii) Li-Cor LI190 PAR Sensor 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. D)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 E)WIND SPEED AND DIRECTION i) 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. F) 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 a 16m single scaffold tower with internal stairs. This tower was equipped with AC power and had a heated hut nearby, also with AC power. Parameters measured on this tower included: air temperature, humidity, radiation, wetness and wind. - Precipitation gauges: SR50 Snow Depth Sensor was installed on a 1.5 m high pole (pole installed in the ground) on the west side of the hut. The TBRG gauge was placed on the ground just slightly South East of the SR50. - Soil temperature, Soil moisture and soil heat flux, were measured below ground level, near the tower. - Dataloggers were housed in the above mentioned heated hut, or in an enclosure mounted to the tower. 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 a 3.2 m tall single scaffold tower.The exceptions were near ground measurements, below 2m. These sensors were installed near the tower, on their own platforms. All sensors were installed to optimize measurement requirements. -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...] - Tower Type 50' single scaffold tower, internal stairs - Latitude/Longitude 53.80209° N, 104.61795° W (Newell Hedstrom - GPS) - Elevation 524.7 m (BOREAS) - Mean Annual Air Temp. 0.4° C (Waskesiu normals) - Mean Annual Total Precipitation 467.2 mm (Waskesiu normals) - Cover Type Minerotrophic, patterned fen surrounded by black spruce and jack pine forests - Date Operations Began BOREAS 1994, 1995, BERMS climate measurements began July 2002 and flux measurements in Dec. 2002 - Frequency of EC Measurements 20 Hz - Frequency of Other Met. Sensors Most sampled every 5 sec, output every 30 min, year round ------------------------------------------------------------------------------ 6. Data Acquisition Methods 6.1 Methods of data acquisition 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 or internet 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 For more details on exact location of instruments, see section on Data Description. 6.2.2 Spatial sampling See section on Data Description for more information. 6.2.3 Temporal coverage Data was continuously collected all year round. 6.2.4 Temporal sampling 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 n/a 7.2 Field Notes See FieldNotes.txt (Contact Erin Thompson) --------------------------------------------------------------------------- 8 . Data Description 8.1 Data Organization 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. Is not available yet (Mar/06). 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). 8.3 Numerical Data Characteristics 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 Not availabe yet (Mar/06) 2) Main 1 DataType="Met2" (n/a) Second subset of meteorological data corrections applied but gaps not filled. 2 Site="SK-FEN" (n/a) Saskatchewan Fen. 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_NetRad_AbvCnpy (W/m2) Derived by: (downwelling shortwave - upwelling shortwave)+(downwelling longwave - upwelling longwave). 8 CNR1_GlobalShortwaveRad_AbvCnpy_15m (W/m2) Global Shortwave Radiation; on an extended boom attached to the walk-up tower, facing south 15m above the ground. Kipp and Zonen CNR1 9 CNR1_UpShortwaveRad_AbvCnpy_15m (W/m2) Upwelling Shortwave Radiation; on an extended boom attached to the walk-up tower, facing south 15m above the ground. Kipp and Zonen CNR1. 10 CNR1_DownLongwaveRad_AbvCnpy_15m (W/m2) Downwelling Longwave Radiation; 15m above ground. Derived by: CNR1_DownLongwave_Thermopile_AbvCnpy_15m+((5.67*10^8)*(( CNR1_BodyPRT_Temp+273.15)^4)) 11 CNR1_UpLongwaveRad_AbvCnpy_15m (W/m2) Upwelling Longwave Radiation; 15m above ground. Derived by: CNR1_UpLongwave_Thermopile_AbvCnpy_15m+((5.67*10^-8)*(( CNR1_BodyPRT_Temp+273.15)^4)) 12 LI_DownPAR_AbvCnpy_15m (umol/m2/s) Downwelling PAR Radiation; top of tower, mounted on railing facing southward 15m above ground. 13 LI_UpPAR_AbvCnpy_15m (umol/m2/s) Upwelling PAR Radiation; facing southward 15m above ground. 14 HMP_AirTemp_AbvCnpy_15m (degC) Air Temperature; on walk-up tower railing, 15m above ground. Vaisala HMP in gill radiation shield. 15 HMP_AirTemp_AbvCnpy_8m (degC) Air Temperature; on walk-up tower railing, 8m above ground. Vaisala HMP in a gill radiation shield. 16 HMP_AirTemp_AbvCnpy_4m (degC) Air Temperature; on walk up tower railing, 4m above ground. Vaisala HMP in a gill radiation shield. 17 HMP_AirTemp_AbvCnpy_2m (degC) Air Temperature; on walk up tower railing, 2m above ground. Vaisala HMP in a gill radiation shield. 18 IR_VegSurfaceTemp (degc) Infrared surface temperature; located on NW corner of tower at 2m. Apogee sensor. 19 IR_InstBodyTemp (degC) Infrared surface temperature internal body temperature; located on NW corner of tower at 2m. Apogee sensor. 20 RelHum_AbvCnpy_15m (%) Relative Humidity; on walk-up tower railing, 15m above ground. Vaisala HMP in a gill radiation shield. 21 RelHum_AbvCnpy_8m (%) Relative Humidity; on walk-up tower railing, 8m above ground. Vaisala HMP in a gill radiation shield. 22 RelHum_AbvCnpy_4m (%) Relative Humidity; on walk-up tower railing, 4m above ground. Vaisala HMP in a gill radiation shield. 23 RelHum_AbvCnpy_2m (%) Relative Humidity; on walk-up tower railing, 2m above ground. Vaisala HMP in a gill radiation shield. 24 WindSpd_AbvCnpy_15m (m/s) Wind Speed Tower Top; top platform of the walkup tower, at 15m above ground. RM Young propeller anemometer. 25 WindDir_AbvCnpy_15m (deg) Wind Direction Tower Top; same as Wind Speed Tower Top. RM Young Propeller Anemometer. 26 StdDev_WindDir_AbvCnpy_15m (deg) Standard Deviation of Wind Direction at Tower Top; same as Wind Speed Tower Top. RM Young Propeller Anemometer. 27 WindSpd_AbvCnpy_8m (m/s) Wind Speed at 8m above ground. RM Young propeller anemometer. 28 WindDir_AbvCnpy_8m (deg) Wind Direction at 8m (same as Wind Speed above). 29 StdDev_WindDir_AbvCnpy_8m (deg) Standard Deviation of Wind Direction at 8m (same as Wind Speed above). 30 NRGWindSpd_AbvCnpy_8m (m/s) Wind Speed at 8m above the ground. NRG Systems cup anemometer. 31 WindSpd_AbvCnpy_4m (m/s) Wind Speed at 4m above the ground. RM Young propeller anemometer. 32 WindDir_AbvCnpy_4m (deg) Wind Direction at 4m (same as Wind Speed above). 33 StdDev_WindDir_AbvCnpy_4m (deg) Standard Deviation of Wind Direction at 4m (same as Wind Speed above). 34 WindSpd_AbvCnpy_2m (m/s) Wind Speed Tower at 2m above the ground. RM Young propeller anemometer. 35 WindDir_AbvCnpy_2m (deg) Wind Direction at 2m (same as Wind Speed above). 36 StdDev_WindDir_AbvCnpy_2m (deg) Standard Deviation of Wind Direction at 2m (same as Wind Speed above). 37 Belfort_CumPrec (mm) Annual accumulation of the manually corrected Belfort precipitation. Belfort 3000 accumulating precipitation gauge. 38 TBRG_Rain (mm) Tipping Bucket Rainfall over the 30min period. Texas Electronics. 39 SnowDepth (mm) Snow Depth, 1.95m agl. CS SR50. 40 CorrectedSnowDepth (mm) Corrected snow depth, 1.95m agl. CS SR50. 41 IR_SurfaceTemp (degC) Infra-Red Ground surface temperature 42 IR_InstBodyTemp (degC) Infra-Red instrument body temperature 43 SurfaceTemp_N_10cm (degC) Surface temperature of ground 25m N of the tower. Instrument 10cm above ground. Queen's Univ-made thermocouple mounted on a rod. 44 SurfaceTemp_N_2cm (degC) Surface temperature of ground 25m N of the tower. Instrument 2cm above ground. Queen's Univ-made thermocouple mounted on a rod. 45 SoilTemp_N_5cm (degC) Soil Temperature; 5cm below ground surface in a pit 25m N of tower. Queen’s Univ-made thermocouple mounted on a rod. 46 SoilTemp_N_10cm (degC) Soil Temperature; 10cm below ground surface in a pit 25m N of tower. Queen’s Univ-made thermocouple mounted on a rod. 47 SoilTemp_N_20cm (degC) Soil Temperature; 20cm below ground surface in a pit 25m N of tower. Queen’s Univ-made thermocouple mounted on a rod. 48 SoilTemp_N_50cm (degC) Soil Temperature; 50cm below ground surface in a pit 25m N of tower. Queen’s Univ-made thermocouple mounted on a rod. 49 SoilTemp_N_100cm (degC) Soil Temperature; 100cm below ground surface in a pit 25m N of tower. Queen’s Univ-made thermocouple mounted on a rod. 50 SurfaceTemp_W_10cm (degC) Surface temperature of ground 75m W of the tower. Instrument 10cm above ground. 51 SurfaceTemp_W_2cm (degC) Surface temperature of ground 75m W of the tower. Instrument 2cm above ground. 52 SoilTemp_W_2cm (degC) Soil Temperature; 2cm below ground surface in a pit 75m W of tower. Queen’s Univ-made thermocouple mounted on a rod. 53 SoilTemp_W_5cm (degC) Soil Temperature; 5cm below ground surface in a pit 75m W of tower. Queen’s Univ-made thermocouple mounted on a rod. 54 SoilTemp_W_10cm (degC) Soil Temperature; 10cm below ground surface in a pit 75m W of tower. Queen’s Univ-made thermocouple mounted on a rod. 55 SoilTemp_W_20cm (degC) Soil Temperature; 20cm below ground surface in a pit 75m W of tower. Queen’s Univ-made thermocouple mounted on a rod. 56 SoilTemp_W_50cm (degC) Soil Temperature; 50cm below ground surface in a pit 75m W of tower. Queen’s Univ-made thermocouple mounted on a rod. 57 SoilTemp_W_100cm (degC) Soil Temperature; 100cm below ground surface in a pit 75m W of tower. Queen’s Univ-made thermocouple mounted on a rod. 58 CertificationCode (n/a) Quality control measure. PRE is preliminary, CPI is cerfitied by principal invesigator. 59 RevisionDate (dymoyear) Date data last revised by PI. 3) Summarized 1 DataType="Met3" (n/a) Third subset of meteorological data. Gaps filled. 2 Site="SK-FEN" (n/a) Saskatchewan Fen Site. 3 SubSite="FlxTwr" (n/a) Flux Tower sub-site 4 Year (UTC) 4 digit year in UTC. 5 Day (UTC) Day of Year in UTC 6 End_Time (UTC) End of 30min time period, in hours and minutes UTC. 7 FourWay_NetRad_AbvCnpy (W/m2) Derived by: (downwelling shortwave - upwelling shortwave)+(downwelling longwave - upwelling longwave). 8 CNR1_GlobalShortwaveRad_AbvCnpy_15m (W/m2) Global Shortwave Radiation; on an extended boom attached to the walk-up tower, facing south 15m above the ground. Kipp and Zonen CNR1. 9 LI_DownPAR_AbvCnpy_15m (umol/m2/s) Downwelling PAR Radiation; top of tower, mounted on railing facing southward 15m above ground. 10 AirTemp_AbvCnpy_15m (degC) Air Temperature; on walk-up tower railing, 15m above ground. Vaisala HMP in gill radiation shield. 11 RelHum_AbvCnpy_15m (%) Relative Humidity; on walk-up tower railing, 15m above ground. Vaisala HMP in a gill radiation shield. 12 SpecificHum_AbvCnpy (g/kg) n/a 13 WindSpd_AbvCnpy_15m (m/s) Wind Speed Tower Top; top platform of the walkup tower, at 15m above ground. RM Young propeller anemometer. 14 SoilTemp_2cm (degC) n/a 15 SoilTemp_5cm (degC) n/a 16 SoilTemp_10cm (degC) n/a 17 SoilTemp_20cm (degC) n/a 18 SoilTemp_50cm (degC) n/a 19 SoilTemp_100cm (degC) n/a 20 Belfort_CumPrec (mm) Annual accumulation of the manually corrected Belfort precipitation. Belfort 3000 accumulating precipitation gauge. 21 EventPrec (mm) Tipping Bucket Rainfall over the 30min period. Texas Electronics. 22 CertificationCode (n/a) Quality control measure. PRE is preliminary, CPI is cerfitied by principal invesigator. 23 RevisionDate (dymoyear) Date data last revised by PI. 4) Gap Filled Meteorology (Variable list is similar to “Main” above – see section on gap filling in section 9 for more information on gap filling methodology) 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_GlobalShortwaveRad_AbvCnpy_15m (W/m2) 9 GapfilledPIPref_UpShortwaveRad_AbvCnpy_15m (W/m2) 10 GapfilledPIPref_DownLongwaveRad_AbvCnpy_15m (W/m2) 11 GapfilledPIPref_UpLongwaveRad_AbvCnpy_15m (W/m2) 12 GapfilledPIPref_DownPAR_AbvCnpy_15m (umol/m2/s) 13 GapfilledPIPref_UpPAR_AbvCnpy_15m (umol/m2/s) 14 GapfilledPIPref_HMP_AirTemp_AbvCnpy_15m (degC) 15 GapfilledPIPref_HMP_AirTemp_AbvCnpy_8m (degC) 16 GapfilledPIPref_HMP_AirTemp_AbvCnpy_4m (degC) 17 GapfilledPIPref_HMP_AirTemp_AbvCnpy_2m (degC) 18 GapfilledPIPref_IR_VegSurfaceTemp (degC) 19 GapfilledPIPref_IR_InstBodyTemp (degC) 20 GapfilledPIPref_RelHum_AbvCnpy_15m (%) 21 GapfilledPIPref_RelHum_AbvCnpy_8m (%) 22 GapfilledPIPref_RelHum_AbvCnpy_4m (%) 23 GapfilledPIPref_RelHum_AbvCnpy_2m (%) 24 GapfilledPIPref_WindSpd_AbvCnpy_15m (m/s) 25 GapfilledPIPref_WindDir_AbvCnpy_15m (deg) 26 GapfilledPIPref_WindSpd_AbvCnpy_8m (m/s) 27 GapfilledPIPref_WindDir_AbvCnpy_8m (deg) 28 GapfilledPIPref_NRGWindSpd_AbvCnpy_8m (m/s) 29 GapfilledPIPref_WindSpd_AbvCnpy_4m (m/s) 30 GapfilledPIPref_WindDir_AbvCnpy_4m (deg) 31 GapfilledPIPref_WindSpd_AbvCnpy_2m (m/s) 32 GapfilledPIPref_WindDir_AbvCnpy_2m (deg) 33 GapfilledPIPref_Belfort_CumPrec (mm) 34 GapfilledPIPref_SnowDepth (mm) 35 GapfilledPIPref_IR_SurfaceTemp (degC) 36 GapfilledPIPref_SurfaceTemp_N_10cm (degC) 37 GapfilledPIPref_SurfaceTemp_N_2cm (degC) 38 GapfilledPIPref_SoilTemp_N_5cm (degC) 39 GapfilledPIPref_SoilTemp_N_10cm (degC) 40 GapfilledPIPref_SoilTemp_N_20cm (degC) 41 GapfilledPIPref_SoilTemp_N_50cm (degC) 42 GapfilledPIPref_SoilTemp_N_100cm (degC) 43 GapfilledPIPref_SurfaceTemp_W_10cm (degC) 44 GapfilledPIPref_SurfaceTemp_W_2cm (degC) 45 GapfilledPIPref_SoilTemp_W_2cm (degC) 46 GapfilledPIPref_SoilTemp_W_5cm (degC) 47 GapfilledPIPref_SoilTemp_W_10cm (degC) 48 GapfilledPIPref_SoilTemp_W_20cm (degC) 49 GapfilledPIPref_SoilTemp_W_50cm (degC) 50 GapfilledPIPref_SoilTemp_W_100cm (degC) 51 CertificationCode (n/a) 52 RevisionDate (dymoyear) 8.3.1.6 Sample Data Record DataType,Site,SubSite,Year,Day,End_Time,FourWay_NetRad_AbvCnpy,CNR1_GlobalShortwaveRad_AbvCnpy_15m,CNR1_UpShortwaveRad_AbvCnpy_15m,CNR1_DownLongwaveRad_AbvCnpy_15m,CNR1_UpLongwaveRad_AbvCnpy_12m,LI_DownPAR_AbvCnpy_15m,LI_UpPAR_AbvCnpy_15m,HMP_AirTemp_AbvCnpy_15m,HMP_AirTemp_AbvCnpy_8m,HMP_AirTemp_AbvCnpy_4m,HMP_AirTemp_AbvCnpy_2m,IR_VegSurfaceTemp,IR_InstBodyTemp,RelHum_AbvCnpy_15m,RelHum_Cnpy_8m,RelHum_AbvGnd_4m,RelHum_AbvGnd_2m,WindSpd_AbvCnpy_15m,WindDir_AbvCnpy_15m,StdDev_WindDir_AbvCnpy_15m,WindSpd_AbvCnpy_8m,WindDir_AbvCnpy_8m,StdDev_WindDir_AbvCnpy_8m,NRGWindSpd_AbvCnpy_8m,WindSpd_AbvCnpy_4m,WindDir_AbvCnpy_4m,StdDev_WindDir_AbvCnpy_4m,WindSpd_AbvCnpy_2m,WindDir_AbvCnpy_2m,StdDev_WindDir_AbvCnpy_2m,Belfort_Precip,TBRG_Rain,SnowDepth,CorrectedSnowDepth,IR_SurfaceTemp,IR_InstBodyTemp,SurfaceTemp_N_10cm,SurfaceTemp_N_2cm,SoilTemp_N_5cm,SoilTemp_N_10cm,SoilTemp_N_20cm,SoilTemp_N_50cm,SoilTemp_N_100cm,SurfaceTemp_W_10cm,SurfaceTemp_W_2cm,SoilTemp_W_2cm,SoilTemp_W_5cm,SoilTemp_W_10cm,SoilTemp_W_20cm,SoilTemp_W_50cm,SoilTemp_W_100cm,CertificationCode,RevisionDate (n/a),(n/a),(n/a),(UTC),(UTC),(UTC),(W/m2),(W/m2),(W/m2),(W/m2),(W/m2),(umol/m2/s),(umol/m2/s),(degC),(degC),(degC),(degC),(degC),(degC),(%),(%),(%),(%),(m/s),(deg),(deg),(m/s),(deg),(deg),(m/s),(m/s),(deg),(deg),(m/s),(deg),(deg),(mm),(mm),(mm),(mm),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(degC),(n/a),(dymoyear) Met2,SK-FEN,FlxTwr,2003,121,30,-999,243.028,39.023,250.374,-999,519.913,45.211,11.114,11.698,11.865,11.817,35.66,29.634,27.2624,26.512,27.399,28.253,4.3404,23.973,20.482,3.9755,25.227,25.099,-999,3.4717,60.436,14.495,2.7656,62.027,17.162,137.9,0,-999,-999,35.66,29.634,13.102,8.2604,5.0183,2.1016,-0.2211,-0.14495,1.0419,8.7652,2.111,1.7131,1.6463,0.59455,0.37147,0.53395,1.589,PRE,21022006 Met2,SK-FEN,FlxTwr,2003,121,100,-999,172.943,34.1469,246.338,-999,363.681,33.826,10.289,10.822,10.955,10.85,34.417,27.592,27.9277,27.2576,28.203,29.05,5.1289,2.997,10.734,4.6324,3.1982,10.922,-999,4.0005,52.9,13.489,3.2314,55.412,16.426,137.92,0,-999,-999,34.417,27.592,11.23,7.7661,5.1193,2.2256,-0.17125,-0.10055,1.0779,7.3102,1.9858,1.6583,1.6293,0.63081,0.40336,0.57974,1.6188,PRE,21022006 --------------------------------------------------------------------------- 9 . Data Manipulations 9.1 Post Processing and Calculated Variables 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; Snow Depth and Cumulative Belfort Precip.) - and computation of derived or adjusted elements (including Four Way Net Radiation Above Canopy.) 9.2 Special Corrections/Adjustments 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) 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. d) Merging of manually quality controlled elements 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. d) Derivation of new parameters. CNR1 FourWayNetRadAboveCanopy was computed as: CNR1_GlobalShortwave_Rad_AbvCnpy_6m- CNR1_UpShortwave_Rad_AbvCnpy_6m+ CNR1_DownLongwave_Thermopile_AbvCnpy_6m- CNR1_UpLongwave_Thermopile_AbvCnpy_6m 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 10.1 Sources of Error See section on "Known Problems with the Data" for more information. 10.2 Quality Assessment 10.2.1 Data Validation by Source Data were quality assured by comparing similar 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 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 See section on Equipment for more instrument specs. 10.2.4 Additional Quality Assessments 10.3 . Limitations and Representativeness 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: - 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. - Relative humidity measured by Vaisala HMPs often drifted above 100%, adjustments were made to correct this problem. Data problems at Fen: - Problems with SoilHeatFlux_W_10cm throughout 2005. Data is questionable as they are largely negative do not correspond to other heat flux plates at the same locations Problem with NetRad_15m intermittently spiking in excess of -100W/m2 thought out 2005. - Because there has been so much standing water at the fen over the years since about 2004, Snow Depth is not accurate. - Soil temperatures don't seem to go below freezing after early 2004, which is suspicious but probably okay. This probably is related to do with where the freezing front is located. When the sensors were originally installed they were at the soil surface and extended into the peat layer. Then the fen flooded midway through 2004 and when this water layer froze it effectively became the soil surface. So a combination of having a thick insolating layer of ice and snow over the sensors in the winter, as well as energy that is being released as the peat decomposes results in the most shallow sensors only hovering around and very occasionally falling below 0 degrees. - Maximum downwelling PAR appeared to be dropping over the years, which may be suspect. - The low threshold for Wind Speed at 2m and 4m is variable between Dec 2002 and Jul 2004. Cause is unknown. --------------------------------------------------------------------------- 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. 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 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] FCDIS - Fluxnet-Canada Data Information System 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 14.2 Document Author Charmaine Hrynkiw, Justin Beckers 14.3 Keywords Meteorology, climate, southern boreal forest, BERMS, BOREAS, FEN