earth and atmospheric sciences eas535 atmospheric measurements and observations ii eas 535...

Earth and Atmospheric Sciences

EAS535

Atmospheric Measurements and Observations IIEAS 535

http://web.ics.purdue.edu/~jhaase/teaching/eas535/index.html

Laboratory ExerciseWeather Station Instrument Performance Characterization and Calibration

Dr. J. Haase


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


EAS535

Vaisala MAWS weather station


EAS535

Davis Weather Monitor II Weather StationInside temperature 32F to 140F Outside temperature from - 50F to 140FHigh 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 mercuryHumidity inside 10% to 90%, outside 0%

to 100% Dew point from -99F to 140F with high

and low memory and alarms


EAS535

Instrument model

Temperature of air

t1 t2 t3 t4

V1 V2 V3 V4

ΔT deg

= c deg/volt ·ΔV volts


EAS535

Characterize errors

• Temperature error

• Relative humidity error

• Pressure error

• Use the MAWS sensor as the reference

• For example:Terror_tm02 = Tobserved_tm02-TMAWS


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.


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


EAS535

Examples from last year’s data


EAS535

Time lagged response

• Plot parameters versus time– Time error due to clock offset?

– Different response times for different instrument type

MAWS Wind vs TMO2 &TMO3 Wind

0

1

2

3

4

5

6

7

1 36 71 106 141 176 211 246 281 316 351 386

measurement sample (proxy for time)

Win

d s

peed

(m

/s)

MAWS Wind

TMO2 Wind

TMO3 Wind


EAS535

Contamination

• Plot parameter versus time of day – Overlap several days of data

maws h-tmo2h

-6

-4

-2

0

2

4

6

8

12:00AM

4:48AM

9:36AM

2:24PM

7:12PM

12:00AM

4:48AM

Time of day

RH

dif

fere

nce

(%

)

maws h-tmo2h


EAS535

Bias• Plot residual (observation minus reference) versus

time• Average offset is bias – is it a calibration error?• Diurnal contamination error?

MAWS P - TMO2 P

-6

-5

-4

-3

-2

-1

0

1 26 51 76 101 126 151 176 201 226 251 276 301 326 351 376 401

Data Sample #

MA

WS

P -

TM

O2

P (

hP

a)


EAS535

Nonlinearity

• Plot residual as a function of the reference parameter

maws h-tmo2h vs humidity

-6

-4

-2

0

2

4

6

8

0 20 40 60 80 100

humidity

maw

s -

tmo

2


EAS535

Correlation

• Plot one parameter versus the reference parameter

Part III MAWS vs TMO3 Pressure

R2 = 0.946

999

1000

1001

1002

1003

1004

1005

1002 1003 1004 1005 1006 1007 1008 1009

TMO3 Pressure (hPa)

MA

WS

Pre

ssu

re (

hP

a)

earth and atmospheric sciences eas535 atmospheric measurements and observations ii eas 535...

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