Earth and Atmospheric Sciences EAS535 Atmospheric Measurements and Observations II EAS l Laboratory Exercise Weather Station Instrument Performance Characterization and Calibration Dr. J. Haase

Earth and Atmospheric Sciences EAS535 Class Objectives Gain experience and familiarity with surface meteorological equipment Understand components of an observation system Understand how to verify data quality Analyze data to check for random and systematic errors

Earth and Atmospheric Sciences EAS535 Vaisala MAWS weather station

Earth and Atmospheric Sciences EAS535 Davis Weather Monitor II Weather Station Inside temperature 32F to 140F Outside temperature from - 50F to 140F High and low temperature memory with time and date stamp and alarms Wind speed and direction in 1 or 10 degree increments with wind speed to 175mph, wind speed memory with date and time stamp and alarms Barometric pressure, with memory and alarms, and trend arrow. Pressure from 26 to 32 inches of mercury Humidity inside 10% to 90%, outside 0% to 100% Dew point from -99F to 140F with high and low memory and alarms

Earth and Atmospheric Sciences EAS535 Instrument model Temperature of air t 1 t 2 t 3 t 4 V 1 V 2 V 3 V 4 ΔT deg = c deg/volt ·ΔV volts

Earth and Atmospheric Sciences EAS535 Characterize errors Temperature error Relative humidity error Pressure error Use the MAWS sensor as the reference For example: T error_tm02 = T observed_tm02 -T MAWS

Earth and Atmospheric Sciences EAS535 Systematic Errors BIAS – a sensor measures a parameter with an average constant offset compared to a reference measurement. calibration DRIFT over time—e.g. the sensor measures a parameter more accurately at the beginning of the period than at the end of the period. CONTAMINATION by another environmental variable – in this case, a parameter error may be correlated with another measured value, for example contamination by heating by direct sunlight. NONLINEARITY – the linear relationship assumed in a calibration equation is not correct. This will typically be manifested as an error that is a smooth function of the reference or true value, and would be evident in a plot, for example, of dT verus T. TIME LAGGED RESPONSE – the error is due to the sensor not responding to the most rapid fluctuations in the actual parameter, so the measured parameter appears as a smoothed version of the reference or true parameter. This will usually be most obvious in the comparison of the raw time series with the true value or reference value.

Earth and Atmospheric Sciences EAS535 Interpretation Which parameters and which instruments, if any, seem to show RANDOM errors? –SYSTEMATIC BIAS errors? –SYSTEMATIC DRIFT errors? –SYSTEMATIC CONTAMINATION errors and what might be the source of the contamination? –SYSTEMATIC NONLINEARITY errors? –SYSTEMATIC TIME LAGGED RESPONSE errors? Which instruments would you tend to recommend as giving the most precise measurements? What are the possible sources of the major errors? When answering these questions, refer to the graphs that you have created in the previous parts of the exercise. In some cases, the answer might be none. Also refer to instrument specifications, lab notebooks, and notes on experimental setup

Earth and Atmospheric Sciences EAS535 Examples from last year’s data

Earth and Atmospheric Sciences EAS535 Time lagged response Plot parameters versus time –Time error due to clock offset? –Different response times for different instrument type

Earth and Atmospheric Sciences EAS535 Contamination Plot parameter versus time of day –Overlap several days of data

Earth and Atmospheric Sciences EAS535 Bias Plot residual (observation minus reference) versus time Average offset is bias – is it a calibration error? Diurnal contamination error?

Earth and Atmospheric Sciences EAS535 Nonlinearity Plot residual as a function of the reference parameter

Earth and Atmospheric Sciences EAS535 Correlation Plot one parameter versus the reference parameter