ARM/GCIP NESOB-96 NOAA/ATDD 30 Minute Surface Momentum Flux 1.0 General Description This 30 Minute Surface Momentum Flux dataset is one of two surface-layer flux data sets provided in the Atmospheric Radiation Measurement(ARM)/Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP) Near Surface Observation Data Set - 1996 (NESOB-96). This Surface Momentum Flux dataset was formed from one data source: the National Oceanic and Atmospheric Administration(NOAA)/Atmospheric Turbulence and Diffusion Division (ATDD) Little Washita Watershed site. The University Corporation for Atmospheric Research/Joint Office for Science Support (UCAR/JOSS) did not do any quality control on the data set. This dataset contains data for the ARM/GCIP NESOB-96 domain and time period (01 April 1996 through 30 September 1996). The ARM/GCIP NESOB-96 domain is approximately 100.5W to 94.5W longitude and 34N to 39N latitude. 2.0 Data Description This 30 Minute Surface Momentum Flux data set has the following 5 flux parameters: average wind vector speed, kinematic shear stress, streamwise velocity variance, crosswind velocity variance, and vertical velocity variance. The format of these parameters in the data set are detailed in section 2.1. Statistical measures are used to study and represent the turbulent exchanges taking place in a flow. The simplest measures of fluctuation levels are streamwise velocity variance, crosswind velocity variance, and vertical velocity variance. According to Arya (1988), even more important in turbulent flows are the covariances, which include kinematic shear stress. As a covariance, it is an average of products of two fluctuating variables and depends on the correlations between the variables involved. The eddy covariance sensors at the Little Washita site are located at 3 m AGL. The standard meteorological sensors (see table below) are sampled every 2 seconds with a datalogger and multiplexor (CR21x, Campbell Scientific, Inc.) and averages are computed every 30 minutes, coincident with the eddy covariance data. 2.0.1 Table: Meteorological variables measured at NOAA Energy Flux Monitoring Sites along with model number and manufacturer of instrumentation used. Meteorological Variable Manufacturer model number ____________________________________________________________________ Air Temperature and RH Vaisala 50Y Net Radiation Radiation and Q*7 Energy Balance Systems (REBS) Global Radiation LI-COR LI-200 SB Precipitation Texas Instrument Wetness ATDD - Soil Heat Flux REBS PAR LI-COR LI-190 SB Atmospheric Pressure Vaisala PTB101B Surface Temperature Everest 4000A Soil Temperature ATDD - Soil Moisture Vitel hydra ____________________________________________________________________ 2.0.2 NOAA/ATDD Algorithm Traditionally, the use of the eddy correlation method (Businger, 1987; Baldocchi et al., 1988) has been constrained to mainly short term intensive field campaigns. Improvements in instrument design, stability, and power requirements over the past decade now allow for nearly continuous measurements of sensible and latent energy fluxes using the eddy covariance technique. Using this technique, the average vertical turbulent eddy fluxes of sensible and latent heat (and other scalars) are determined as ____ w'X' = $(w-{w})(X-{X}) --------------- n where w is the vertical velocity component of the wind vector, and X is the scalar of interest (e.g. water vapor concentration). Here, the {bracketed} quantities denote an average or "mean" that is subtracted from the instantaneous values to obtain the fluctuating component. The $ represents the summation from i = 1 to n. Average vertical turbulent ____ fluxes (w'X') are computed in real time using a digital recursive filter (200 s time constant) for the determination of a running "mean" from which the instantaneous values are subtracted. An averaging period of 30 minutes (denoted by the overbar) is used and is considered large enough for statistical confidence in the covariance quantity but is short enough to resolve the structure of the diurnal cycle. Wind vector measurements made at experimental sites that are not perfectly flat can result in non-zero vertical wind velocities measured from the "vertical" coordinate system of the measurement platform. At the end of an average period, vertical turbulent fluxes perpendicular to the mean horizontal wind (which generally follows the contour of the land surface) are obtained by mathematically rotating the coordinate system of the measurement frame of reference (sonic anemometer) to obtain a zero mean _ _ vertical and transverse velocity (w=v=0). Details of this procedure described by Wesely (1969) are outlined by Businger (1986) and Baldocchi et al., (1988). The three components of the wind vector are determined with a sonic anemometer ( R2, Gill Instruments, Hampshire, England). The stable long-term operational characteristics of this instrument and its ability to continue measurements during cold weather and light rain events (Yellard et al., 1994), as well as its low power consumption, were important considerations in the selection of this anemometer. The symmetric head design of the R2 with its slender support structure produces little flow distortion (Grelle and Lindroth, 1994) and is well suited for measurements in the the relatively flat and open locations of the Little Washita Watershed and Champaign, Illinois sites Fast response water vapor and CO2 concentration measurement are made with an open-path, fast response infrared gas analyzer (Auble and Meyers, 1992). This sensor was used extensively for flux measurements during recent ARM (Doran et al., 1992) and BOREAS (Baldocchi et al., 1997) experiments. In a recent evaluation of open and closed path sensors for water vapor and CO2 concentrations, Leuning and Judd (1996) found that for the measurement of CO2, this sensor displayed minimal cross sensitivity to water vapor (see Leuning and Moncrieff, 1990). (Meyers, 1999) 2.1 Detailed Format Description The GCIP/ARM NESOB-96 Surface Momentum Flux dataset contains 8 metadata parameters and 5 data parameters. The metadata parameters describe the date/time, network, station, and location at which the data were collected. The 5 data parameters repeat once for each 30 minute period from UTC 0000 through UTC 2330. Data reported for a designated 30 minute time represents data collected during the previous 30 minute period. All times are reported in UTC, and flux data values are reported in meters squared per seconds squared (m2/s2), except Average Wind Vector Speed which is meters per second (m/s). Each data value is followed by a Quality Control flag, but UCAR/JOSS does not Quality Control the data at the present time. The Quality Control flag is set to "U" for "Unchecked", unless the datum is missing, in which case the flag is set to "M". The table below details each parameter in the data set. Parameters Units ---------------------------- --------------------------------- Date of Observation UTC Time of Observation UTC Network Identifier Abbreviation of platform name Station Identifier Network dependent Latitude Decimal degrees, South is negative Longitude Decimal degrees, West is negative Station Occurrence Unitless Station Elevation Meters above sea level Average Wind Vector Speed m/s QC flag U or M Kinematic Shear Stress m2/s2 QC flag U or M Streamwise Velocity Variance m2/s2 QC flag U or M Crosswind Velocity variance m2/s2 QC flag U or M Vertical Velocity Variance m2/s2 QC flag U or M 2.2 Data Remarks The first long-term flux monitoring site was established within the Little Washita Watershed, near Chickasha, Oklahoma, which is within the LSA-SW region. The tower (34 58' N, 97 57' W) was placed about one quarter mile north of State Road 19 within a grazed pasture owned by Earl Morris. Pasture surrounds the 3 meter tower in all sectors providing a minimum fetch of 200 meters over gently rolling terrain. The soil at this site (upper 60 cm) is classified as clay loam with sand, silt, and clay fractions of 25%, 45%, and 30%, respectively. The bulk density at this site is 1.6 g/cm3 +/- 0.1. The grasses and vegetation surrounding the tower are occasionally grazed by the farmers cattle. 2.2.1 Data Acquisition A laptop computer is configured in a mulitasking mode to simultaneously perform three operations. For the first and foremost task, measurements of the three components of the wind vector along with the speed of sound (from which the virtual temperature can be derived) are digitally sent from the sonic anemometer (which includes the digitized H2O and CO2 signals from the IRGA) to the laptop computer, which is housed in a small environmental enclosure. In the second task, the computer retrieves the standard meteorological data from the CR21X datalogger every 30 minutes and appends the data to an existing file. After midnight, the covariance data and standard meteorological data are copied to separate files with a name, year and calendar day header. The computer is equipped with a modem and cellular phone in order to retrieve the data and conduct occasional system checks. On average, data are retrieved from the laptop computers about once every two days. 2.2.2 Power Operation and Management To avoid the constraints of using standard line power, the entire lux/meteorological system is designed to operate on 12 volts DC making it truly remote and portable. The entire flux system, including all the instruments and data logging devices are powered by nine deep-cycle 12 volts DC batteries that are charged daily with solar panels ( M75, Siemans, Inc.). Each solar panel is capable of producing 3 amperes at 12 volts in full sunlight. The batteries are enclosed in an insulated container that is 2/3 submerged into the ground near the base of the tower. Ten solar panels are required at the Illinois site while eight are used at the Little Washita location. Each charging system is controlled with a 30 ampere regulator. The regulator is equipped with a low voltage disconnect option that disconnects electronic devices with the largest power consumption when the battery voltage falls below 11.5 VDC. Table (2) lists the major instruments and corresponding power requirements. The entire system continuously draws about 3 amperes at 12 VDC. The 21X datalogger and cellular phone are connected directly to the batteries and operate continuously since they have low power demands. When the regulator disconnects the computer because of low voltage (i.e. long periods of cloudy weather), only standard meteorological data are logged. After the batteries are charged to 12.5 VDC, the computer, IRGA and sonic are reconnected and logging of the flux data resumes. As will be discussed later, this happens infrequently and only during the winter months when cloudy conditions persist during the relatively short daylight hours. 3.0 Quality Control Processing This data set was not Quality Controlled by UCAR/JOSS. 4.0 References Arya, Pal S, 1988, Introduction to Micrometeorology, Academic Press, San Diego, CA Auble, D. L, T. P. Meyers, 1992. An open path, fast response infrared absorption gas analyzer for H2O and CO2, Boundary-Layer Meteorology, 59, 243-256. Atlas, R., N. Wolfson and J. Terry, 1993. The effect of SST and soil moisture anomalies on GLA model simulations of the 1988 U.S. summer drought, J. of Climate, 2034-2048. Baldocchi, D. D. and T. P. Meyers, 1991. Trace gas exchange at the floor of a deciduous forest: I Evaporation and CO2 efflux, Journal of Geophysical Research, Atmospheres, 96, 7271-7285. Baldocchi, D. D., B. B. Hicks and T. P. Meyers, 1988: Measuring biosphere-atmosphere exchanges of biologically related gases with micrometeorological methods. Ecology 69:1331-1340. Chen. T. H., A. Henderson-Sellers, and A. J. Pitman, 1994. Recent progress in the Project for Intercomparison of Land Surface Parameterization Schemes (PLIPS), GEWEX News, 4, 8-9. Dolske, D. A. and D. F. Gatz, 1985. A field intercomparison of methods for the measurement of article and gas dry deposition, J. Geophysical Research, 90, 2076-2084. Doran, J. C., F. J. Barnes, R. L. Coulter, T. L. Crawford, D. D. Baldocchi, L Ballick, D. R. Cook, D. Cooper, R. J. Dobosy, W. A. Dugas, L. Fritschen, R. L. Hart, L Hipps, J. M. Hubbe, W. Gao, R. Hicks, R. R. Kirkham, K. E. Kunkel, T. J. Martin, T. P. Meyers, W. Porch, J. D. Shannon, W. J. Shaw, E. Swiatek, and C. D. Whiteman, 1992. The Boardman Regional Flux Experiment, Bulletin of the American Meteorological Society, 73, 1785-1795. Garratt, J. R., 1993. Sensitivity of climate simulations to land-surface and atmospheric boundary layer treatments - a review, J. of Climate, 6, 419-449. Grelle, A. and A. Lindroth, 1994. Flow distortion by a Solent Sonic anemometer: wind tunnel calibration and its assessment for flux measurements over forest and field, Journal of Atmospheric and Oceanic Technology, 11, 1529-1542. Henderson-Sellers, A., 1993. A factorial assessment of the sensitivity of the BATS land-surface parameterization scheme, J. of Climate, 6, 227-247. Henderson-Sellers, A. and R. E. Dickinson, 1992. Intercomparison of land surface parameterization launched, EOS, 73, 195-196. Jacquemin, B., J. Noilhan, 1990. Sensitivity study and validation of a land surface parameterization using the HAPEX-MOBILHY data set, Boundary-Layer Meteorology, 52, 93-134. Leuning, R. and J. Moncrieff, 1990. Eddy covariance CO2 flux measurements using openpath and closed-path CO2 analyzers-corrections for analyzer water vapor sensitivity and damping of fluctuations in air sampling tubes, Boundary-Layer Meteorology, 53, 63-76. Leuning, R. and M. J. Judd, 1996. The relative merits of open- and closed-path analyzers for the measurement of eddy fluxes, Global Change Biology, 2, 241-253. Meehl, G. A. and W, M. Washington, 1988. A comparison of soil-moisture sensitivity in two global climate models, J. Atmospheric Sciences, 45, 1476-1492. Meyers, Tilden, 1999, GCIP/EOP NOAA/ATDD Little Washita Watershed Long Term Flux Site, information supplied with data Meyers, T. P. and D. D. Baldocchi, 1993. Trace gas exchange at the floor of a deciduous forest: II O3 and SO2 deposition rates, Journal of Geophysical Research, Atmospheres,98,2519-2528. Meyers, T. P. and D. D. Baldocchi, 1988, A comparison of models for deriving dry deposition fluxes of O3 and SO2 to a forest canopy. Tellus 40B:270-284. Pan, H. L., and L. Mahrt, 1987. Interaction between soil hydrology and boundary-layer development, Boundary-Layer Meteorology, 38, 185-202. Sato, N., P. J. Sellers, D. A. Randall, E. K. Schneider,, J. Shukla, J. L. Kinter III, Y. T. Hou, and E. Albertazzi, 1989. Effects of implementing the simple biosphere model in a general circulation model, J. Atmospheric Science, 46, 2757-2782. Sellers, P. J., and J. L. Dorman, 1987. Testing the simple biosphere (SiB) using point micrometeorological and biophysical data, J. Climate Applied Meteorology, 26, 622-651. Sellers, P. J., Y. Mintz, Y. C. Sud, and A. Dalcher, 1986. A simple biosphere model (SiB) for use within general circulation models, J. Atmospheric Science, 43, 505-531. Troen, I., and L. Mahrt, 1986. A simple model of the atmospheric boundary layer; sensitivity to surface evaporation, Boundary-Layer Meteorology, 37, 129-148. Vogel, C. A., D. D. Baldocchi, A. K. Luhar, K. S. Rao, 1994. A comparison of a hierarchy of models for determining energy balance components over vegetation canopies (submitted). Yellard, M. J., P. K. Taylor, I. E. Consterdine, and M. H. Smith, 1994. The use of the inertial dissipation technique for shipboard wind stress determination, J. Oceanic and Atmospheric Technology, 11,1093-1108. Zeller, K. F., 1993. Eddy diffusivities for sensible heat, ozone, and momentum from eddy correlation and gradient measurements, USDA Forest Service Research Paper RM-313.