Methods

wind, pressure, temperature and precipitation sensors on buoy.

Methods

Raw data (in English units) were assembled by Douglas Clark - Wisconsin State Climatologist. Data were converted to metric units and adjusted for temporal biases by Dale M. Robertson. For adjustments applied to various parameters see Robertson, 1989 Ph.D. Thesis UW-Madison. Adjusted data represent the BEST estimated daily data and may be raw data. Data collected at Washburn observatory, 8-1-1883 to 9-30-1904. Data collected at North Hall, 10-1-1904 to 12-31-1947 Data collected at Truax Field (Admin BLDG), 1-1-1948 to 12-31-1959. Data collected at Truax Field (Center of Field), 1-1-1960 to Present. Much of the data after 1990 were obtained in digital form from Ed Hopkins, UW-Meteorology. Data starting in 2002-05 were obtained from Sullivan at <a href="http://www.weather.gov/climate/index.php?wfo=mkx%20">http://www.weather.gov/climate/index.php?wfo=mkx</a> ,then go to CF6 and download monthly data to Madison_sullivan_conversion. Since Robertson (1989) adjusted all historical data to that collected from 1884-1989; no adjustments were applied to the recent data except for (1) wind and (2) estimated vapor pressure:(1) Wind after January 1997, and only wind from the southwest after November 2007, was extended by Dale M. Robertson and Yvonne Hsieh.In 1996, a discontinuity in the wind record was caused by change in observational techniques and sensor locations (Mckee et al. 2000). To address the non-climatic changes in wind speed, data from MSN were carefully compared with those collected from the tower of the Atmospheric and Oceanic Science Building at the University of Wisconsin-Madison, see http://ginsea.aos.wisc.edu/labs/mendota/index.htm. Hourly data from both sites (UMSN,hourly and UAOS,hourly) during 2003&ndash;2010 were used to form a 4&times;12 (four components of wind direction &times; 12 months) matrix (K4,12) of wind correction factors, yielding UAOS,daily= Ki,j&times;UMSN,daily. The comparison results indicated that the MSN weather station reported a higher magnitude in winds out of the east by 5% and lower magnitude in winds out of the west and south by 30% and 10%. The adjusted wind data (=Ki,j&times;UMSN,daily) were therefore employed and used in the model simulation. After adjustments, there was a decrease in wind velocities starting shortly before 1996. Overall the adjusted wind data had a decline in wind velocities of 16% from 1988&ndash;93 to 1994&ndash;2009) compared to a 7% decline at a nearby weather station with no known observational changes (St. Charles, Illinois; 150 km southeast of Lake Mendota). (2) Estimated vapor pressure was updated (after April 2002) by using the equation from DYRESM for estimation of vapor pressure (a function of both air temperature and dew point temperature); where a=7.5, b=237.3, and c=.7858.

Methods

The instrumented raft on Sparkling Lake is equipped with a D-Opto dissolved oxygen sensor, a thermistor chain, and meteorological sensors that provide fundamental information on lake thermal structure, weather conditions, evaporation rates, and lake metabolism. Estimating the flux of solutes to and from lakes requires accurate water budgets. Evaporation rates are a critical component of the water budget of lakes. Data from the instrumented raft on Sparkling Lake includes micrometeorological parameters from which evaporation can be calculated. Raft measurements of relative humidity and air temperature (2 m height), wind velocity (2m) , and water temperatures (from thermistors placed throughout the water column at intervals varying from 0.5 to 3m) are combined with measurements of total long-wave and short-wave radiation data from a nearby shore station to determine evaporation by the energy budget technique. Comparable evaporation estimates from mass transfer techniques are calibrated against energy budget estimates to produce a lake-specific mass transfer coefficient for use in estimating evaporation rates. After correcting for flux to or from the atmosphere and vertical mixing within the water column, high frequency measurements of dissolved gases such as carbon dioxide and oxygen can be used to estimate gross primary productivity, respiration, and net ecosystem productivity, the basic components of whole lake metabolism. Other parameters measured include precipitation, wind direction (beginning in 2008), and barometric pressure (beginning in 2008). Sampling Frequency: one minute; averaged to hourly and daily values as well as higher resolution values such as 2 min and 10 min.Dissolved oxygen sensors: 2004-2006: Greenspan Technology series 1200; 2007-2016: Zebra-Tech Ltd. D-Opto; 2018+: OTT HydrolabCO2 sensors: 2018+: ProOceanos MiniCO2 for dissolved CO2; Eosense Inc. eosGP for atmospheric CO2

Methods

The instrumented buoy on Trout Lake is equipped with a dissolved oxygen sensor, a thermistor chain, and meteorological sensors that provide fundamental information on lake thermal structure, weather conditions, and lake metabolism. Data are usually collected every 10 minutes with occasional periods of 2 minute data for short periods to answer specific questions. The D-Opto dissolved oxygen sensor is 0.5m from the lake surface. Meteorological sensors measure wind speed, wind direction, relative humidity, air temperature, photosynthetically active radiation (PAR), and barometric pressure. Starting in 2005, thermistors were placed every 0.5-1m from the surface through 14m and every 2 to 4m from 14m to the bottom of the water column at 31m. In July 2006, a new thermistor chain was deployed with thermistors placed every meter from the surface through a depth of 19 meters. After correcting for flux to or from the atmosphere and vertical mixing within the water column, high frequency measurements of dissolved gases such as carbon dioxide and oxygen can be used to estimate gross primary productivity, respiration, and net ecosystem productivity, the basic components of whole lake metabolism. Data are averaged to daily values from one minute samples for years 2005 - 2006. Daily values are computed from high resolution data starting in year 2007. Data are averaged to hourly values from one minute samples for years 2005 - 2008, Hourly values are computed from high resolution data starting in year 2009. Hourly and daily values may not be current with high resolution data in the current year. Sampling Frequency: varies for instantaneous sample. averaged to hourly and daily values from one minute samples