NOAA Technical Memorandum OAR PMEL

Calibration Procedures and Instrumental Accuracy

Estimates of ATLAS Air Temperature and Relative

Humidity Measurements

Brian J. Lake1, Sonya M. Noor2, H. Paul Freitag1, and Michael J. McPhaden1

1Pacific Marine Environmental Laboratory

7600 Sand Point Way NE

Seattle, WA 98115-6349

2Joint Institute for the Study of the Atmosphere and Ocean (JISAO)

University of Washington

Box 354235

Seattle, WA 98195-4235

April 2003

Contribution 2569 from NOAA/Pacific Marine Environmental Laboratory
NOTICE

Mention of a commercial company or product does not constitute an endorsement by

NOAA/OAR. Use of information from this publication concerning proprietary products

or the tests of such products for publicity or advertising purposes is not authorized.

Contribution No. 2569 from NOAA/Pacific Marine Environmental Laboratory

Contribution No. 981 from Joint Institute for the Study of the Atmosphere and Ocean

For sale by the National Technical Information Service, 5285 Port Royal Road

Springfield, VA 22161

ii
Contents iii

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2. Instrumentation . . . . . . . . . . . . . . . . . . . . . . . . . . 2

3. Humidity Calibration Procedures ............... 4

4. Air Temperature Calibration Procedures ........... 6

5. I/O Board Calibration Procedures ............... 6

6. Relative Humidity Equilibration . . . . . . . . . . . . . . . . 7

7. Humidity Calibration Repeatability .............. 10

8. Individual Sensor and I/O Board Calibrations . . . . . . . 13

8.1 Relative Humidity Sensor . . . . . . . . . . . . . . . . . . . . 13

8.2 Relative Humidity I/O Board . . . . . . . . . . . . . . . . . . 15

8.3 Air Temperature Sensor . . . . . . . . . . . . . . . . . . . . . 16

8.4 Air Temperature I/O Board . . . . . . . . . . . . . . . . . . . 16

9. Sensor and I/O Board Drift . . . . . . . . . . . . . . . . . . . 16

9.1 Relative Humidity Sensor . . . . . . . . . . . . . . . . . . . . 17

9.2 Relative Humidity I/O Board . . . . . . . . . . . . . . . . . . 18

9.3 Air Temperature Sensor . . . . . . . . . . . . . . . . . . . . . 18

9.4 Air Temperature I/O Board . . . . . . . . . . . . . . . . . . . 18

10. Combined Instrument Error . . . . . . . . . . . . . . . . . . 19

11. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

12. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Appendix: Specifications and Calibration of the Humidity Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

List of Figures

1 Sample RH sensor output during pre-deployment and post-recovery

calibration at the 55 %RH calibration set point. ............ 8

2 Ensemble distributions from 183 calibrations (915 set points). .... 9

3 Residual error for five sensors calibrated nine times. . . . . . . . . . 11

4 Sensor output (in %RH) computed using individual calibration coefficients from each of nine calibrations. . . . . . . . . . . . . . . . . . 12

5 Standard deviation (in %RH) of calibrated sensor output computed

over nine calibrations for each of five sensors. . . . . . . . . . . . . . 14

6 Mean and RMS differences between pre-deployment and post-recovery

calibration of humidity sensors plotted versus the number of days

each sensor was deployed at sea. . . . . . . . . . . . . . . . . . . . . 18

List of Tables

1 Manufacturer’s specifications for Rotronic Instrument Corp. model

MP101A air temperature (AT) and relative humidity (RH) sensors

used on NextGeneration ATLAS moorings. .............. 3

2 Statistics for NextGeneration ATLAS air temperature (AT) and relative humidity (RH) sensor calibrations. . . . . . . . . . . . . . . . . 15

3 Statistics for NextGeneration ATLAS I/O board calibrations. . . . . 15

4 Differences between pre- and post-deployment sensor calibrations. . . 17

5 Differences between pre- and post-deployment I/O board calibrations. 17
iv Contents

6 Combined (sensor and I/O board) instrument error for NextGeneration ATLAS air temperature and relative humidity measurements

based on unnormalized data in tables 4 and 5. . . . . . . . . . . . . . 19

A1 Test equipment accuracy standards. . . . . . . . . . . . . . . . . . . 23
Calibration Procedures and Instrumental Accuracy Estimates of

ATLAS Air Temperature and Relative Humidity Measurements

Brian J. Lake1, Sonya M. Noor2, H. Paul Freitag1, and Michael J. McPhaden1

Abstract. Calibration procedures for sensors measuring air temperature and relative humidity

from NextGeneration Autonomous Temperature Line Acquisition System (ATLAS) moorings are

described. Sensor accuracy when first deployed and calibration drift are quantified. Modifications

to sensors and procedures since a previous report (Freitag et al., 1994) are documented. Instrumental

error for air temperature measurements is estimated to be 0.22◦C, nearly equal to that from the

previous report. Instrumental error for relative humidity (RH) measurements is estimated to be

2.73 %RH, smaller than the previously reported value of 4.1 %RH. The decrease in relative humidity

is due to improvements in instrumentation and calibration procedures.

1. Introduction

The Tropical Atmosphere Ocean/Triangle Trans-Ocean Buoy Network

(TAO/TRITON) array of moored buoys spans the tropical Pacific Ocean and

is a major in situ component of the El Ni ̃no/Southern Oscillation (ENSO)

Observing System, the Global Climate Observing System (GCOS) and the

Global Ocean Observing System (GOOS) (McPhaden et al., 1998). A similar, but smaller scale array, the Pilot Research Moored Array in the Tropical Atlantic (PIRATA) spans the tropical Atlantic (Servain et al., 1998).

The majority of TAO/TRITON and all PIRATA sites are occupied by Autonomous Temperature Line Acquisition System (ATLAS) moorings (Hayes

et al., 1991), which are designed, manufactured, and maintained by the

National Oceanic and Atmospheric Administration’s (NOAA’s) Pacific Marine Environmental Laboratory (PMEL). Standard measurements from all

ATLAS moorings include wind speed and direction (WSD), air temperature

(AT), relative humidity (RH), sea surface temperature (SST) and subsurface

temperatures (T) down to 500 m depth. Additional measurements at all PIRATA moorings and selected TAO/TRITON sites include rainfall, shortwave

radiation (SWR), and conductivity. In addition, ocean currents, longwave

radiation, and barometric pressure are measured at selected TAO/TRITON

sites.

Calibration procedures and instrumental accuracy of ATLAS WSD measurements were described by Freitag et al. (2001) and ATLAS AT, RH, SWR,

and T calibrations and accuracy were documented by Freitag et al. (1994).

In the latter, the method of RH calibration (described below under “Humidity Calibration Procedures”) was characterized as time consuming, requiring

significant manual interaction and subjective interpretation of sensor stability by laboratory technicians. Moreover, RH sensor drift was estimated to

be 4 %RH over the sensor deployment duration (nominally 1 year), which

was significantly larger than that specified by the manufacturer (2% accu1NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle,

WA 98115 2Joint Institute for the Study of the Atmosphere and Ocean (JISAO), University of

Washington, Box 354235, Seattle, WA 98195-4235
2 B.J. Lake et al.

racy and 1% drift per year, for the Rotronic MP100 model deployed at the

time). It was speculated that some of the difference between expected and

estimated sensor drift could be the result of inaccuracy in the calibration

procedure. As a result, PMEL has developed an automated, computer controlled RH calibration procedure, to improve upon RH sensor calibration

efficiency and accuracy.

This memorandum documents the new RH calibration procedure and

provides a revised estimate of instrumental accuracy for ATLAS RH measurements that reflect improvements in the calibration method at PMEL and

improvements in the sensors by the manufacturer. Calibration and accuracy

of AT, which is measured by the same sensor as RH, are also revisited. ATLAS mooring electronics were updated over the period 1996 to 2001, during

which an evolution toward use of the NextGeneration ATLAS system occurred in the Pacific. PIRATA moorings have been NextGeneration ATLAS

systems since the inception of the array in 1997. Differences in circuitry and

sampling from the earlier Standard ATLAS system are described.

2. Instrumentation

Relative humidity and air temperature are presently measured on ATLAS

moorings by model MP101A combined humidity and air temperature probes

manufactured by Rotronic Instrument Corporation of Huntington, NY. Within the probe, relative humidity is measured by a Rotronic Hygromer C94 thin

film capacitive sensor that is protected beneath a Teflon foam filter cap, while

temperature is measured by a Pt100 RTD (Resistance Temperature Detector). Manufacturer specifications regarding the accuracy of the MP101A are

listed in Table 1. Sensor output is an analog DC voltage that is a linear

function of either RH or AT expressed in engineering units. RH sensor output in the range 0 V to 1 V represents values of 0 %RH to 100 %RH. AT

sensor voltage in the range from −0.4 V to +0.6 V represents temperature

from −40◦C to 60◦C. The nominal calibration equations for the sensors are

thus

RH(%) = BrhVrh

AT(◦C) = BatVat

where Vrh and Vat are the output in volts for the RH and AT sensors, respectively, and Brh and Bat are calibration coefficients with nominal values

of 100 each.

ATLAS RH and AT measurements described in Freitag et al. (1994),

were made with a Rotronic model MP100 sensor. The manufacturer updated this sensor to the MP101A, which included modifications to the circuitry, the humidity sensor, and its protective filter. Modifications listed

below are based upon comparison of the manufacturer’s published specifications and/or personal communications with representatives of the company.

Surface-mount technology (the method of attaching components without

leads in feed through holes) was applied to the circuitry. The electronicsimposed operating temperature limits were increased from [−20 – +55◦C] to
ATLAS Calibration Procedures and Instrumental Accuracy Estimates 3

Table 1: Manufacturer’s specifications for Rotronic Instrument Corp. model MP101A air temperature

(AT) and relative humidity (RH) sensors used on

NextGeneration ATLAS moorings.

Sensor Specifications

AT ±0.2◦C accuracy

0.2◦C drift/year

±0.1◦C repeatability

RH ±1.0 %RH accuracy*

<1.0 %RH drift/year

±0.3 %RH repeatability

*RH accuracy when calibrated with a high quality

standard.

[−40−60◦C]. The Pt100 RTD thermistor’s linearity was improved and the

temperature accuracy improved from 0.5◦C to 0.2◦C. The humidity sensor

was upgraded from a C80 to a C94, which improved the nominal accuracy

from 2 %RH to 1.5 %RH and halved the %RH repeatability specifications

from 0.6 %RH to 0.3 %RH. The specified nominal accuracy is applicable

to factory or field calibrations. When calibrated with a high quality standard, such as the Thunder Scientific chamber, Rotronic specifies accuracy of

the MP101A as 1 %RH. Modifications to the electronics improved the linearity and temperature compensation of the sensor. The humidity sensor’s

electrodes were also improved to better resist contaminants. PMEL found

that some cases enclosing the MP100 and MP101A sensors leaked in the

ocean environment. Sensors purchased by PMEL now use watertight enclosures which are designed and provided by PMEL to Rotronic for instrument

assembly. One of the most substantial modifications by the manufacturer

was an improvement in the current drain, a specification vital to PMEL’s

battery-operated, low-power moorings. The current drain was reduced during several successive versions of the MP101A model. The current drains of

MP100 and early MP101A versions exceeded 10 mA, while the newer versions of MP101A are typically 4 mA. All NextGeneration ATLAS moorings

use the newer, lower-current MP101A sensors.

Digitization of the analog voltages is performed by electronic circuitry

designed and built at PMEL. The present NextGeneration ATLAS system

employs a 12-bit analog-to-digital (A/D) converter with RH resolution of

0.024 %RH over a range of 0 %RH to 100 %RH and AT resolution of 0.01◦C

over a range of 0◦C to 40◦C. For real-time telemetry and during calibration,

RH data are reduced to 8 bits or 0.39 %RH resolution, for compatibility

with telemetry buffers and data processing of the older ATLAS systems.

After mooring recovery, the full 12-bit resolution is obtained from internal

memory. The earlier Standard ATLAS systems used 10-bit A/D converters,

which gave 0.04◦C resolution for AT. RH data in the older system were

truncated to 8 bits (0.39 %RH resolution) for both real-time and delayed

modes.
4 B.J. Lake et al.

In the course of this investigation it was found that the reduction of RH

data from 12 to 8 bits during calibration was done via truncation, rather

than the more accurate method of rounding, which is used during real-time

telemetry of data. Assuming an even distribution of data, truncation would

bias the data by about 1/2 the reduced resolution or about 0.2 %RH. This

bias is not reflected in drift estimates presented below as it is present in

both pre-deployment and post-recovery calibrations. Modifications to the

next version of NextGeneration ATLAS firmware will eliminate this small

bias.

Temporal resolution has also increased in the present NextGeneration

ATLAS system. AT and RH sensors are sampled at 2 Hz for 2 min every 10

min. Two-minute means are computed at each 10-min interval and stored

in internal memory. Daily means and the 2-min mean at the most recent

hour are telemetered in real time. The earlier Standard ATLAS systems

computed and stored hourly average data, based on spot samples measured

at 10-min intervals.

When deployed on a buoy the MP101A sensor is shielded from direct

sunlight by a naturally aspirated, multi-plate radiation shield (supplied by

the R.M. Young Co., Traverse City, Michigan), the effectiveness of which

increases with wind speed. The manufacturer specifies that at a radiation

level of 1080 W m−2, temperature bias is 0.4◦C at 3 m s−1 wind speed, 0.7◦C

at 2 m s−1, and 1.5◦C at 1 m s−1. The present study focuses on instrumental

calibration errors and does not address errors associated with factors such

as radiant heating. Typical (mode) wind speeds measured within the TAO

Array are about 6 m s−1 and values below 3 m s−1 compose about 12% of

the data. Wind speed statistics are similar in the PIRATA Array. Thus bias

due to radiant heating of air temperature measured in TAO or PIRATA is

presumably limited to a small portion of the data.

3. Humidity Calibration Procedures

Rotronic recommends that RH calibration be verified on a 6- to 12-month

basis for maximum accuracy. ATLAS moorings are typically deployed for 1

year. Calibrations of sensors are performed before and after every mooring

deployment, thus the calibration frequency recommended by the manufacturer is roughly met.

The manufacturer-specified calibration procedure involves connecting

each probe to a calibration receptacle containing a humidity standard (nonaqueous salt solution). The method includes measuring sensor output after

the recommended 1-hour equilibrium period, followed by adjustment to the

correct value by use of a potentiometer in the sensor circuitry. Rotronic

recommends this procedure be performed at 35 %RH and 80 %RH, plus

either 0 %RH or 10 %RH. After this procedure, the RH sensor is assumed

to follow the nominal calibration equation given above, within the nominal

accuracy. The low-humidity calibration point is not performed at PMEL,

since tropical ocean humidity is rarely less than 50 %RH. This procedure to
ATLAS Calibration Procedures and Instrumental Accuracy Estimates 5

bring the sensors into nominal performance is performed routinely by PMEL

technicians before sensors are deployed in the field.

In an additional procedure developed at PMEL, the sensor linearity and

calibration accuracy is confirmed, and individual sensor calibration coefficients are computed by measuring the sensor output at additional levels, as

described below. Individual sensor coefficients are computed from a linear

least square fit to the equation

RH(%RH) = Arh + BrhVrh

which allows for individual bias (Arh) and gain (Brh) calibration coefficients

for each sensor. This process is performed and coefficients computed before

deployment and again after recovery in an “as is” state. Before subsequent

deployment, the sensor filter cap is cleaned or replaced, the sensor brought

back into nominal accuracy (using the procedure recommended by Rotronic

described above) and a new calibration performed and coefficients computed.

Thus for each deployment a sensor has unique pre-deployment and postrecovery calibration coefficients.

As described in Freitag et al. (1994), the previous method of checking

sensor output for accuracy and linearity employed standard salt solutions

between 50 %RH and 95 %RH at 15 %RH intervals. This procedure was

relatively labor intensive and time consuming. In addition, our experience

was that the 1-hour equilibration time recommended by the manufacturer

was often not sufficient, and determining when the sensor had equilibrated

was subjective and thus could vary between calibrations. Therefore, a more

automated and less subjective procedure was designed at PMEL to improve

both the efficiency and accuracy of calibrations. In the new calibration

procedure a Model 2500 Benchtop Two-Pressure Humidity Generator (manufactured by Thunder Scientific of Albuquerque, New Mexico) provides a

humidity source while under the direction of a PC computer program. Up

to 10 Rotronic probes are connected via a multimeter and monitored simultaneously by the computer. Temperature stability, vital to the accuracy of

calibrations, is maintained in the chamber to within 0.1◦C. The humidity

generator itself is calibrated on a yearly basis, according to manufacturer recommendations. Specifications of the humidity generator and its calibration

are given in the Appendix.

Sensor output is recorded at set points of 55 %RH, 65 %RH, 75 %RH,

85 %RH, and 95 %RH, with measurements taken after allowing for sensors

to come to equilibrium within the chamber. The length of the equilibrium

period is a function of the humidity set point. Based upon past experience,

the following equilibrium periods (Teq) are used for all calibrations: 1.5 hours

at 55 %RH, 65 %RH, and 75 %RH levels, 2.5 hours at 85 %RH, and 3.5 hours

at 95 %RH. Subsequent analysis of calibration data (described below) has

confirmed that these times are sufficient for equilibration in most cases.

In a typical calibration, 6 to 10 sensors are placed into the humidity

generator’s chamber, taking care to avoid blocking the chamber’s ventilation. From that point, the calibration is completely under computer control.

Temperature in the chamber is held at 21◦C and the humidity initially set at
6 B.J. Lake et al.

45 %RH. After a half-hour period for temperature equilibration, the chamber is brought to the first set point (55 %RH). The sensor output (volts) is

monitored during the equilibrium period at 1-min increments. After equilibrium a mean of the next ten 1-minute samples is computed and archived

for use in the computation of calibration coefficients. The humidity level

is then increased to the 65 %RH set point, and the process continued until

finished with the 95 %RH set point. The computer program then computes

the calibration coefficients (Brh and Arh) using a linear least-square fit to

the five mean sensor voltages and chamber humidities. Calibration residuals (differences between humidity computed from the calibration coefficients

and the chamber reading) are computed and displayed. For pre-deployment

calibrations, if any residual exceeds 1.0 %RH, sensors are set aside for adjustment or repair and recalibration. The maximum acceptable residual was

initially set at 2 %RH, based on the manufacturer’s original accuracy estimate for the MP100 sensor. Experience with the improved MP101 sensors

and automated calibration procedure indicated that this criterion could be

lowered to 1 %RH with only a small number of sensors failing predeployment

calibration.

4. Air Temperature Calibration Procedures

The ATLAS air temperature (AT) sensor calibration procedure as described

in Freitag et al. (1994), has remained unchanged. Sensors are calibrated

before deployment and again after recovery. Adjustment or repair of the

temperature circuitry is rarely necessary, thus in most cases a post-recovery

calibration for one deployment serves as the pre-deployment calibration for

the next. Calibrations are performed in a computer controlled water bath,

with a model SBE 03 temperature sensor from Sea-Bird Electronics, Inc.

(calibrated on a yearly basis by the manufacturer) used as a standard. The

Rotronic sensors are placed in protective rubber gloves before immersion in

the bath. The output of the Rotronic sensors and the calibration standard

are measured at set points ranging from 14◦C to 32◦C at 3◦C intervals. An

equilibration period of 1.5 hours is used for all set points, which has been

confirmed to be adequate for sensor and bath equilibrium. After the equilibration period, the sensors and standard are sampled for ten minutes, with

one reading per minute, and a mean value for each computed. Calibration

coefficients are produced from a linear least-square fit between the Sea-Bird

standard temperature and Rotronic voltages

AT(◦C) = Aat + BatVat,

where Aat and Bat are unique bias and gain calibration coefficients, respectively, for each sensor.

5. I/O Board Calibration Procedures

In the NextGeneration ATLAS system electronics, AT and RH sensor outputs first pass through individual signal-conditioning hardware interface cir-
ATLAS Calibration Procedures and Instrumental Accuracy Estimates 7

cuitry. Digitization of humidity and air temperature sensor outputs is then

performed by a common 12-bit A/D converter. The Input/Output (I/O)

board calibration procedure is nearly unchanged from that described by

Freitag et al. (1994). Each board is calibrated twice, once over the range of

voltage output by the humidity sensor, then again over the range of voltage for temperature. For humidity the voltage range is 0.4 V to 0.99 V, at

roughly 0.1 V increments. For temperature the voltage range is 0.1 V to

0.4 V, at 0.05 V increments. Previously, the calibration range for air temperature I/O boards was 0.0 V to 0.4 V and for relative humidity boards it

was 0.0 V to 0.9 V. It was noted in the 1994 report that maximum residuals

occurred predominantly at the 0.0 V set point, presumably due to error in

the voltage source at this level, and/or a zero bias in the I/O circuitry. Because tropical air temperatures and relative humidity never reach these levels

(0◦C, 0 %RH) the 0.0 V calibration point was dropped from the calibration

procedure.

I/O board calibration coefficients are produced from a linear least square

fit between voltages and output counts

Vrh = Crh + DrhNrh

Vat = Cat + DatNat

where N is the I/O board output in counts and C and D are the calibration

coefficients.

6. Relative Humidity Equilibration

Freitag et al. (1994) noted that an equilibrium period of 1-hour during humidity calibrations (as suggested by the manufacturer) did not appear to be

sufficient to insure stability of the sensor output. It was proposed that a

lack of stability could be responsible for a portion of the measured residual

error and/or sensor drift. The automated humidity calibration procedure

described above presented an opportunity to determine appropriate equilibration periods.

To quantify equilibrium times, 183 sensor calibrations (both pre-deployment and post-recovery) performed in 2001 were analyzed. For each of five

calibration set points, sensor output voltage (Vdata) measured at 1-minute

intervals throughout the equilibration period (Teq) was fit to the exponential

equation

Vt = Veq + ∆V e−kt

Where Vt is the predicted voltage at time t, Veq the predicted voltage at

equilibrium, ∆V the difference between Veq and Vt=0, and k−1 the sensor

time constant. An example of the exponential fit for a calibration set point

is shown in the upper part of Fig. 1. In this and most other cases, the

exponential fit was reasonably representative of the data. For the 915 calibration set points, 97% of the maximum residuals from the fits (Vdata − Vt)

fell within ±0.005 V, or 0.5 %RH, the accuracy of the humidity chamber
8 B.J. Lake et al.

0.53

0.54

0 25 50 75 100

0.41

0.42

0.43

0.44

0.45

0.46

0.47

0.48

0.49

0.5

0.51

0.52

Time (minutes)

Volts

Vcal = 0.5324

Veq = 0.5332 

Vcal = 0.4806 

Veq = 0.4839 

Post-recovery 

Pre-deployment 

Figure 1: Sample RH sensor output (dots) during pre-deployment (upper) and

post-recovery (lower) calibration at the 55 %RH calibration set point. Solid lines

are exponential fits to the data. Units are volts. Vcal is the mean of the last 10

points in the time series. Veq is the equilibrium value predicted by the exponential

fit.

(Fig. 2a). With the exception of a limited number of outliers, computed

time constants ranged from 5 to 180 minutes, with typical (median) values of about 30 minutes for the three lowest set points, 60 minutes for the

85 %RH set point, and 90 minutes for the 95 %RH set point. A scaled

equilibrium time, Teq/(k−1), was calculated for each fit, which provided a

comparison of the chosen equilibrium period to the actual sensor equilibrium

characteristics. The median scaled equilibrium time was greater than 3 for

the 55 %RH, 65 %RH, and 85 %RH set points, 2.7 for the 75 %RH set point,

and 2.5 for the 95 %RH set point. Thus, 2.5 or more sensor time constants

were encompassed by the chosen equilibration times for typical calibrations.
ATLAS Calibration Procedures and Instrumental Accuracy Estimates 9

–2 –1 0 1 2 3 4 5

0

5

10

15

20

25

30

35

Veq – Vcal (V * 100)

Percentage of Total

–1 –0.5 0 0.5 1 1.5 2 2.5 3

0

5

10

15

20

25

Rate of Change (%RH/hr)

Percentage of Total

–2 –1.5 –1 –0.5 0 0.5 1

0

5

10

15

20

25

Percentage of Total

Vt – Vdata (V * 100)

(a) 

(b) 

(c) 

Figure 2: Ensemble distributions from 183 calibrations (915 set points). (a) Maximum difference between

measured calibration data and exponential fit (as in Fig. 1). Units are nominally %RH (Volts*100). (b)

Difference between the sensor output at equilibrium as predicted by the exponential fit, Veq, and the mean of

the last 10 points in the time series, Vcal. Units are nominally %RH (Volts*100). (c) Rate of change of sensor

output computed from a linear least-squares fit to last 10 points of the time series. Units are nominally %RH

(Volts*100) per hour.
10 B.J. Lake et al.

In 87% of cases, the 10-minute mean sensor outputs used for computing

the calibration coefficients, Vcal, fell within 0.5 %RH (the specified accuracy

of the humidity chamber) of the equilibrium value, Veq, predicted by the

exponential fit (Fig. 2b). As another measure of the degree to which the

sensors equilibrate during calibration, the rate of change over the 10-minute

averaging period (estimated by the trend of a linear least-squares fit) was

less than 0.5 %RH hr−1 in 71% of cases (Fig. 2c). Thus, changes in sensor

output would generally be small compared to the accuracy of the humidity

generator, even if equilibrium times were lengthened by as much as an hour.

The analysis above was for the combined set of 535 pre-deployment and

380 post-recovery calibration set points. When the analysis was separated

by calibration type, there was strong indication that some post-deployment

sensors took longer to equilibrate. The data shown in the upper part of

Fig. 1 was from a pre-deployment calibration. When the same sensor was

calibrated after recovery, the rate of change of the sensor output after the

equilibration period was an order of magnitude larger than that for the

pre-deployment calibration (Fig. 1, lower part). In addition, the sensor output during post-recovery calibration did not fit the exponential equation as

well. Most significantly, the mean of the final 10 samples (Vcal) was 0.05 V

(5 %RH) lower for the post-deployment calibration. Presumably some sensor

drift occurred between calibrations, but the estimated drift would have been

smaller had the sensor equilibrated during the post-deployment calibration.

Comparisons of ensemble statistics for pre-deployment vs. post-recovery calibrations indicate that a significant portion of post-recovery sensors exhibited slower time response. The percentage of cases in which Veq − Vcal was

within ±0.5 %RH was 95% for pre-deployment calibrations, but 76% for

post-recovery calibrations; and the rate of change over the 10-min averaging

period was less than 0.5 %RH hr−1 in 91% of pre-deployment calibrations,

but this statistic was only 43% for post-recovery calibrations.

7. Humidity Calibration Repeatability

To quantify the repeatability of PMEL RH calibrations, five sensors were

calibrated nine times over the course of a month and the resultant calibration coefficients compared. Prior to the study, the multimeter had been

calibrated according to manufacturer specifications and all sensors had been

pre-calibrated and adjusted. The sensors’ locations within the chamber remained unchanged, and normal calibration procedures were used consistently

throughout the study. Calibration residuals (%RHchamber − %RHcalculated)

were typically in the range ±0.3 %RH (Fig. 3). Residuals tended to be

negative at the 55 %RH, 65 %RH and 95 %RH set points, and positive at

the 75 %RH and 85 %RH set points, indicating a small (0.1 to 0.2 %RH)

non-linearity in the sensor output.

Resultant calibration coefficients were applied at nominal set point voltages (i.e., .55 V, .65 V, .75 V, .85 V, .95 V) to calculate sensor %RH values for each calibration (Fig. 4). Computed in such a manner, differences

in %RH values reflect the combined stability of sensor output and calibra-
ATLAS Calibration Procedures and Instrumental Accuracy Estimates 11

50 60 70 80 90 100

–0.3

–0.2

–0.1

0

0.1

0.2

0.3

0.4

Humidity Chamber (%RH)

Residuals (%RH)

Figure 3: Residual error (in %RH) for five sensors calibrated nine times. Each color represents one of the

nine calibrations. The mean (diamond) and standard deviation (vertical lines) of the residuals computed

over the five sensors is shown vs. the humidity chamber setting.
12 B.J. Lake et al.

1 2 3 4 5 6 7 8 9

51

53

55

57

%RH

1 2 3 4 5 6 7 8 9

61

63

65

67

%RH

1 2 3 4 5 6 7 8 9

71

73

75

77

%RH

1 2 3 4 5 6 7 8 9

81

83

85

87

%RH

1 2 3 4 5 6 7 8 9

92

94

96

98

%RH

Calibration Number

Figure 4: Sensor output (in %RH) computed using individual calibration coefficients from each of nine

calibrations. Each color represents one of five sensors. Computations were made at each of the five calibration

set points from 55 %RH to 95 %RH.
ATLAS Calibration Procedures and Instrumental Accuracy Estimates 13

tion uncertainty, while accounting for small (∼0.03 %RH) humidity chamber

variability during the nine calibrations. The maximum deviation in relative

humidity was about ±1 %RH for a given sensor and calibration set point.

The standard deviation of calibrated sensor output computed over the

nine calibrations for a given sensor ranged from 0.25 %RH to 0.75 %RH

(Fig. 5). Variations in the deviations reflected both differences between sensors (deviations for two sensors were larger than for the other three sensors)

and general stability characteristics of the sensors and/or calibration system

(deviations were generally lower at the 65 %RH and 75 %RH set points relative to the other set points). The RMS of standard deviations computed over

all set points and sensors was 0.42 %RH. These deviations were a measure

of both the repeatability of the sensors, and of the calibration system itself,

including the humidity chamber and associated instrumentation. As such,

the RMS value of 0.42 %RH compares favorably with the manufacturer’s

specified sensor repeatability of 0.3 %RH.

8. Individual Sensor and I/O Board Calibrations

A large number of calibrations (1111 RH sensors, 848 AT sensors, and

394 I/O boards) were analyzed to determine the instrumental accuracy of

NextGeneration air temperature and relative humidity measurements. Previous ATLAS analysis (Freitag et al., 1994) considered a much smaller ensemble (200 or fewer sensor and I/O board calibrations). As in the previous

study, the root-mean-square (RMS) of the maximum residuals (the largest

difference between the calibration standard and the sensor or I/O board output, as calculated using the calibration coefficients) was used as an estimate

of sensor or board accuracy at the time of calibration.

8.1 Relative Humidity Sensor

A total of 1111 RH sensors were calibrated during the study period (1996–

2002). The RMS maximum residual (expressed in humidity units) of

0.56 %RH (Table 2) is roughly half the manufacturer’s specified accuracy

of 1 %RH (Table 1). Freitag et al. (1994) reported a RMS maximum residual of 2.6 %RH for ATLAS RH sensors. Thus the new, automated RH sensor

calibration procedure (along with the RH sensor’s improved electronics) has

led to improved calibration results. Sensor gain (coefficient B in Table 2)

remained about the same as in the previous study at 6% above the nominal

value of 100. The difference in nominal vs. measured gain is compensated

by the measured offset (A = −3.8) and is due in part to the fact that PMEL

calibrations are limited to the range 55 %RH to 95 %RH.

The RH sensor and filter can become fouled during deployment, which

can affect sensor performance. Therefore, the RH sensors are calibrated in

both pre-deployment (pre-cal) and post-recovery (post-cal) conditions. The

RMS maximum residual for 693 pre-deployment calibrations was 0.37 %RH

(Table 2). For 418 post-recovery calibrations, the value was roughly double

(0.78 %RH), but remained less than the manufacturer’s specified accuracy.

Pre-deployment calibration sensor gain was 4% higher than the nominal
14 B.J. Lake et al.

1 2 3 4 5

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

Sensor Number

Standard Deviation (%RH)

Figure 5: Standard deviation (in %RH) of calibrated sensor output computed over nine calibrations for

each of five sensors. Colors represent chamber set points from 55 %RH to 95 %RH.
ATLAS Calibration Procedures and Instrumental Accuracy Estimates 15

Table 2: Statistics for NextGeneration ATLAS air temperature (AT) and relative

humidity (RH) sensor calibrations. M is the number of sensors calibrated.

RMS Max Coef. A Coef. B

Sensor M Residual Mean Std. Dev. Mean Std. Dev.

AT (all) 848 0.022◦C 0.024 0.482 100.12 1.34

RH (all) 1111 0.56 %RH −3.803 9.70 105.65 7.24

RH (pre-cal) 693 0.37 %RH −2.110 3.02 103.51 4.28

RH (post-cal) 418 0.78 %RH −6.608 14.94 109.20 9.42

Table 3: Statistics for NextGeneration ATLAS I/O board calibrations. M is the

number of sensors calibrated. Residuals have been scaled to the same units as

sensors, by application of the nominal sensor gain of 100. The resolution listed for

RH is for internally recorded data. Data telemetered in real time and calibration

data have a resolution of 0.39 %RH.

1-bit RMS Max Coef. C Coef. D

Board Resolution M Residual Mean Std. Dev. Mean Std. Dev.

AT 0.01◦C 394 0.082◦C 0.006 0.057 0.0230 0.012

RH 0.024%RH 394 0.18%RH 0.00206 0.00198 1.003 0.006

value of 100 whereas the post-cal gain was 9% higher than the nominal

value. Differences between pre- and post-cal statistics are presumably due

to fouling of the filter, sensor drift, and electronic drift during deployment.

Sensor and electronic drift are examined in more detail below.

8.2 Relative Humidity I/O Board

As noted above, RH calibration data are reduced to 8-bit resolution, to

produce coefficients for use with real-time telemetered data which also have

8-bit resolution. This procedure decreases the precision of RH I/O board

calibrations and induces a small (∼0.2 %RH) positive bias, but does not have

a significant effect on overall accuracy as the truncation bias and measured

drift of the I/O boards are an order of magnitude smaller than the RH sensor

calibration drift (see below). Field data stored in internal memory have the

full 12-bit resolution.

The RMS maximum residual for 394 RH I/O board calibrations was

0.18 %RH, about half the resolution of RH calibration data (0.39 %RH,

Table 3). This value is also about half that for pre-cal RH sensors (Table 2).

It is somewhat less than the value reported previously (Freitag et al., 1994)

of 0.26 %RH for ATLAS RH I/O boards. The improvement may be due to

the reduction in calibration range (now 40 %RH to 99 %RH compared to

0 %RH to 95 %RH previously). RH board gain values were consistent with

the previous study.
16 B.J. Lake et al.

8.3 Air Temperature Sensor

Statistics from the calibration of 848 air temperature sensors (Table 2) were

similar to those reported in Freitag et al. (1994). The RMS of the maximum

residual was 0.022◦C, an order of magnitude smaller than the manufacturer’s

stated accuracy of 0.2◦C. Sensor gain, 100.12, and offset, 0.024, were nearly

identical to the manufacturer’s nominal values of 100 and 0, respectively.

8.4 Air Temperature I/O Board

The RMS maximum residual for 394 air temperature I/O boards was 0.082◦C

(Table 3), about half that reported previously (Freitag et al., 1994). The

improvement is presumably due to the reduction in calibration range (now

10◦C to 40◦C compared to 0◦C to 40◦C previously).

9. Sensor and I/O Board Drift

Instrumentation recovered in working condition is returned to PMEL for

post-recovery calibration before being reused on future deployments. After

post-recovery calibrations are completed, the resultant coefficients are compared to the pre-deployment coefficients. First, a set of output values is

computed by application of the calibration equation using pre-deployment

coefficients to a set of nominal input values. Input values are chosen so

that the output values span the standard calibration range. A second set of

output values is generated by application of the calibration equation using

post-recovery coefficients to the same set of input values. Sensor drift is

calculated by subtracting the first set of output values from the second set

of output values. This method of calculating sensor drift is identical to that

employed by Freitag et al. (1994). Mean and RMS differences for sensor

calibration pairs are given in Table 4. Mean and RMS differences for I/O

board pairs are given in Table 5.

ATLAS moorings are nominally deployed for 1 year, but due to the replacement of failed, damaged, or questionable sensors the actual mean deployment length was about 9 months. The mean time between calibrations

was about 16 months. RMS drift normalized to 1 year (by the ratio of

365 over the mean number of days between calibrations), appear in parentheses in Tables 4 and 5. Normalization in this manner assumes that the

drift occurred linearly throughout the time between calibrations and decreases unnormalized drift estimates. Normalization by the mean time between mooring deployment and recovery (assuming that sensor drift occurs

primarily while in the field) would increase drift estimates. Freitag et al.

(1994) suggested a correlation between the magnitude of calibration drift

and the length of the deployment at sea. The data used in this study indicate very little correlation between the size of calibration drift and either

the time between calibrations or the time deployed at sea (Fig. 6). Other

factors, such as environmental conditions while deployed, or handling and

conditions before or after deployment, may be important in some cases. Because the factors affecting drift rates are not well known, we have opted to
ATLAS Calibration Procedures and Instrumental Accuracy Estimates 17

Table 4: Differences between pre- and post-deployment sensor calibrations. RMS differences in parentheses

are normalized to 1 year based on days between calibrations assuming linear drift with time.

Number of Mean Mean

Calibration Days Between Deployment Mean RMS

Sensor Pairs Calib. Days Range Difference Difference

AT 225 506 272 14–32◦C 0.025◦C 0.157◦C(0.113)

RH 201 476 262 55–95 %RH 1.04 %RH 2.72 %RH(2.09)

Table 5: Differences between pre- and post-deployment I/O board calibrations. RMS differences in parentheses are normalized to 1 year based on days between calibrations assuming linear drift with time.

Number of Mean Mean

Calibration Days Between Deployment Mean RMS

Board Pairs Calib. Days Range Difference Difference

AT 211 461 274 5–35◦C −0.057◦C 0.146◦C(0.115)

RH 223 462 272 40–100 %RH −0.02 %RH 0.21 %RH(0.16)

use non-normalized drift values as our primary estimate of sensor and I/O

board accuracy.

9.1 Relative Humidity Sensor

In computing the sensor drift estimate, 21 calibration pairs (9 percent of the

total) with RMS drifts ranging from 6 %RH to 13 %RH were rejected as

outliers. Two criteria were considered in rejecting these data. In some cases

the sensor did not reach equilibrium during the post-deployment calibration,

but would have reached values closer to those of the pre-deployment calibration if the equilibration time were increased substantially (Fig. 1, lower

part). The longer response time of these sensors was probably due to fouling

of the sensor or filter cap while at sea. If these sensors exhibited similar performance while at sea, then their data would not necessarily be biased, but

would lag fluctuations by periods of up to several hours. A second criteria

used to reject some large drift estimates was to apply post-recovery calibrations to the data at the time of recovery and compare these to the data

from the next sensor deployed at that site. In many cases the application

of the post-deployment calibrations resulted in large discontinuities between

deployments, or values that were unreasonably high or low. Under these

circumstances it was concluded that the post-recovery calibrations did not

accurately reflect the condition of the sensors while deployed. The majority

of cases with RMS drift between 6 %RH to 13 %RH were rejected because

they met one or both of these criteria. A few cases with large drift did not

clearly meet these criteria and remain in the ensemble over which the drift

estimate was computed.

RMS difference between 201 RH sensor calibration pairs (Table 4) was

2.72 %RH, about seven times greater than the RMS of sensor pre-cal residuals. Thus, significant sensor drift had occurred between calibrations. Mean
18 B.J. Lake et al.

Figure 6: Mean (left panel) and RMS (right panel) differences between pre-deployment and post-recovery

calibration of humidity sensors plotted versus the number of days each sensor was deployed at sea. The

dashed lines represent a linear, least-squares fit to the data. R is the correlation coefficient.

differences were about 1/3 of RMS differences and positive, indicating that

on average the sensors were reading about 1.0 %RH low when recovered.

Based on a much smaller number of calibrations, Freitag et al. (1994) estimated relative humidity sensor RMS drift to be 4 %RH. Modifications

to the sensors combined with improved sensor calibration techniques have

decreased the error estimate by more than 1 %RH.

9.2 Relative Humidity I/O Board

The mean difference between 223 RH I/O calibration pairs was −0.02 %RH

and the RMS difference was 0.21 %RH (Table 5). The mean difference being

near zero indicated that there was no preferred direction of drift. The RMS

was about one half the resolution of the calibration data. This indicates that

there was little or no measurable drift in RH boards between calibrations.

9.3 Air Temperature Sensor

RMS difference between 225 AT sensor calibration pairs was 0.157◦C (Table 4), nearly an order of magnitude larger than the RMS maximum residual

for AT sensors (Table 2), indicating that measurable drift had taken place.

The observed RMS difference is comparable to the manufacturer’s nominal

drift rate of 0.2◦C (Table 1). Mean AT difference was 0.025◦C, indicating

little or no preferred direction to the drift.

9.4 Air Temperature I/O Board

In computing drift statistics, a small number of calibration pairs (10 or about

5% of the total) were omitted as outliers. The pre-deployment calibrations
ATLAS Calibration Procedures and Instrumental Accuracy Estimates 19

Table 6: Combined (sensor and I/O board) instrument error for NextGeneration ATLAS air

temperature and relative humidity measurements

based on unnormalized data in tables 4 and 5.

Sensor Instrument Error

AT 0.22◦C

RH 2.73 %RH

for all these cases were performed between January 1998 and January 1999,

and had drifts between 0.5◦C and 1.1◦C, larger than any observed before or

after. The majority of boards with pre-deployment calibrations during 1998

had drifts comparable to the overall ensemble. Beginning in 1998 and continuing throughout 1999, NextGeneration ATLAS electronics were upgraded

by replacement of boards which included the sensor IO A/D components.

It was intended that post-deployment calibrations would be performed before board replacement, but in several of the outlier cases it was found that

the calibration pairs were not performed on identical electronics. In other

cases the date of electronic upgrades was not documented. It was concluded

that the large drifts in these cases were not representative of the true electronic performance and were not included in the computation of ensemble

statistics.

RMS difference between 211 AT I/O board calibration pairs was 0.146◦C

(Table 5), less than twice the RMS maximum residual for individual AT I/O

boards (Table 3), indicating that the drift in AT boards between calibrations

was measureable and comparable to the sensor drift. The mean drift was

–0.057◦C, indicating little or no preferred direction to the drift.

10. Combined Instrument Error

The combined effect of I/O board and sensor drifts (i.e., instrument error)

was computed as (RMS2

board drift + RMS2sensor drift)1/2, which assumes that

sensor and I/O board drifts were independent of one another. Using unnormalized data, instrumental error estimates for the RH sensor improved from

4.07 %RH in 1994 to 2.73 %RH in the present investigation (Table 6). We

conclude that the lower instrument error estimate is due to the improved

RH calibration procedure at PMEL and modifications to the sensor by the

manufacturer.

AT instrument error estimates remained virtually unchanged from the

previous study—0.20◦C in 1994 compared to 0.22◦C for the present study.

11. Summary

TAO air temperature and relative humidity sensor calibrations have been

analyzed to estimate both their instrumental accuracy when first deployed
20 B.J. Lake et al.

and the calibration drift over time. Both air temperature and relative humidity are measured by the same sensor package, presently the model MP101A

from Rotronic Instrument Corp. This study updates Freitag et al. (1994),

which was repeated because the previous work was based on a fairly limited

number of calibrations. In addition, since the publication of Freitag et al.,

the sensors were upgraded by the manufacturer, the electronics were modified by PMEL, the calibration method for relative humidity was automated,

and a new calibration standard was used.

The manufacturer’s modifications to the air temperature sensor included

improved nominal accuracy (from 0.5◦C to 0.2◦C) and lower power consumption. The PMEL circuitry was modified to increase resolution from 0.04◦C to

0.01◦C. The present PMEL estimate of combined (sensor and circuitry) instrumental error for TAO air temperature measurement is 0.22◦C, virtually

unchanged from the previous value. The fact that the previous PMEL estimate (0.20◦C) was better than the manufacturer’s nominal sensor accuracy

at that time was because the manufacturer’s estimate was based on nominal

calibration coefficients and applicable to a wider temperature range, whereas

PMEL sensors are individually calibrated over a reduced temperature range.

The changes to the relative humidity portion of the sensor included improved linearity and temperature compensation which lead to an improved

nominal accuracy of 1% (when individually calibrated with a high accuracy

standard). The PMEL circuitry was modified to increase resolution from

0.39 %RH to 0.02 %RH (for internally recorded data). A major modification to the PMEL calibration method was the replacement of saturated salt

solutions for the calibration standard by a humidity chamber, plus automation of the procedure which standardized and increased sensor equilibration

times. The combined (sensor and circuitry) error estimate for TAO relative humidity measurements is 2.73 %RH, which is an improvement of over

1 %RH from the 1994 estimate.

This PMEL error estimate is larger than the manufacturer’s nominal

value because the latter does not reflect environmental issues such as contamination of the sensor or its filter by salt. Analysis of calibration data

has confirmed that after deployment at sea the time constant of these sensors can increase significantly, which results in overestimation of calibration

drift estimates. Moreover, in some cases the post-recovery calibrations were

found to not accurately reflect the sensor condition when recovered. This

could be due to changes between recovery and calibration, e.g., drying and

precipitation of salts on the sensor or filter.

A small (about 0.2 %RH) error due to the PMEL circuitry calibration

firmware was discovered during the course of this study. This error will be

eliminated in future versions of the firmware.

Acknowledgments. We thank Nuria Ruiz for her support in the design and

development of the TAO calibration data base, which was essential for the analysis of

these data. This work was supported by NOAA’s Office of Oceanic and Atmospheric

Research, and by the Joint Institute for the Study of the Atmosphere and Ocean

(JISAO) under NOAA Cooperative Agreement No. NA178RG1232.
ATLAS Calibration Procedures and Instrumental Accuracy Estimates 21

12. References

Freitag, H.P., Y. Feng, L.J. Mangum, M.P. McPhaden, J. Neander, and L.D. Stratton (1994): Calibration procedures and instrumental accuracy estimates of

TAO temperature, relative humidity and radiation measurements. NOAA Tech.

Memo. ERL PMEL-104, Pacific Marine Environmental Laboratory, Seattle,

Washington, 32 pp.

Freitag, H.P., M. O’Haleck, G.C. Thomas, and M.J. McPhaden (2001): Calibration

procedures and instrumental accuracies for ATLAS wind measurements. NOAA

Tech. Memo. OAR PMEL-119, Pacific Marine Environmental Laboratory, Seattle, Washington, 20 pp.

Hayes, S.P., L.J. Mangum, J. Picaut, A. Sumi, and K. Takeuchi (1991): TOGATAO: A moored array for real-time measurements in the tropical Pacific Ocean.

Bull. Am. Meteorol. Soc., 72, 339–347.

McPhaden, M.J., A.J. Busalacchi, R. Cheney, J.R. Donguy, K.S. Gage, D. Halpern,

M. Ji, P. Julian, G. Meyers, G.T. Mitchum, P.P. Niiler, J. Picaut, R.W.

Reynolds, N. Smith, and K. Takeuchi (1998): The Tropical Ocean-Global Atmosphere (TOGA) observing system: A decade of progress. J. Geophys. Res.,

103, 14,169–14,240.

Servain, J., A.J. Busalacchi, M.J. McPhaden, A.D. Moura, G. Reverdin, M. Vianna,

and S.E. Zebiak (1998): A Pilot Research Moored Array in the Tropical Atlantic

(PIRATA). Bull. Am. Meteorol. Soc., 79, 2019–2031.
22 B.J. Lake et al.

Appendix: Specifications and Calibration of the

Humidity Generator

The Thunder Scientific Humidity Generator uses a “two-pressure” method

to humidify its chamber. It first saturates an air stream with water vapor at a set pressure and temperature, and then isothermally expands the

stream into the chamber. The resulting chamber humidity is equivalent to

the ratio of final to initial pressures. Corrections are applied both for minor temperature differences between the air stream and chamber, and for

an “enhancement factor ratio” that takes into account the dependence of

the effective saturation vapor pressure upon the absolute pressure as well as

temperature (Thunder Scientific Humidity Generator manual).

The humidity generator is calibrated annually, alternately by the manufacturer or by PMEL. Calibrations at PMEL follow a procedure recommended by the manufacturer. The humidity generator’s thermistors are

calibrated by immersion in a water bath at three temperature set points

that encompass the range of operating temperatures used at PMEL during

RH sensor calibrations. Typical set points of 11◦C, 26◦C, and 40◦C are

controlled by a model 5010 Programmable Fluid Bath by Guildline Instruments, Ltd. A model SBE-03 temperature sensor from Sea-Bird Electronics,

Inc. (calibrated annually by the manufacturer) is used as a temperature

standard, and its frequency output is counted by an HP3457A Multimeter,

manufactured by Hewlett-Packard Company. Depending on the range of

operating pressure, calibration of the humidity generator’s pressure gauges

is performed by the application of either a high (0–150 psi) or low (0–50

psi) static pressure source of nitrogen gas. Three set points per source are

chosen which span the full range of operating pressures. Typical set points

are 15 psi, 30 psi, and 50 psi for the low-pressure source, and 30 psi, 100 psi,

and 150 psi for the high-pressure source. A 0 to 1000 psi, model 6010-1660C

Direct Reading Pressure Gauge and 6005 Pressure Controller, from Ruska

Instrument Corp., are used as the pressure standard and source controller.

A Digiquartz Barometer, model MET1-2, from Paroscientific, Inc. measures

atmospheric pressure, for conversion of the Ruska standard’s psi readings to

psia. The Ruska standard is calibrated bi-yearly by the manufacturer. The

accuracies of the temperature standard, the pressure standard/controller,

the temperature bath and other associated test equipment (tabulated below) are comparable to or exceed those required by Thunder Scientific for

humidity generator calibrations. For each transducer, the generator’s circuitry produces three raw counts and solves three quadratic equations, internally making any needed adjustments to the transducer and iterating the

process until the desired tolerances are attained. Following this calibration

procedure, the generator is specified to have an accuracy of ±0.5 %RH over

the operating range 10–95 %RH.
ATLAS Calibration Procedures and Instrumental Accuracy Estimates 23

Table A1: Test equipment accuracy standards.

Instrument: Specifications:

Thunder Scientific 2500 Two-Pressure RH Range = 10% to 95%

Humidity Generator RH Resolution = 0.02%

RH Accuracy = ±0.5% at the chamber pressure and temperature

Temperature Range = 0 to +70◦C

Temperature Resolution = ±0.02◦C

Temperature Uniformity = ±0.1◦C

Temperature Accuracy = ±0.06◦C

HP3457A Multimeter Voltage Accuracy = ±0.00254% of reading, from 0 to 1 V

Frequency Accuracy = ±0.01% of reading, from 400 Hz to 1.5 MHz

Frequency Resolution = 0.1 Hz, from 2000 HZ to 19000 Hz

Sea-Bird SBE-03 Thermometer Range = −5.0 to +35◦C

Accuracy = ±0.001◦C

Stability = 0.002◦C per year, typical

SBE-03 Thermometer & HP3457A Temperature Accuracy* = ±0.005◦C

Multimeter System Temperature Resolution* = 0.0005◦C

Guildline Model 5010 Programmable Range = −9.90 to +65.00◦C

Fluid Bath Resolution = 0.01◦C

Stability = ±0.002◦C

Ruska 6010-1660C Pressure Gauge and Range = 0 to 1000 psig

6005 Pressure Controller Resolution = 0.01 psia

Accuracy* = ±0.027 psia from 0 to 136 psi (14 to 150 psia)

Stability (control mode noise) = 0.01 psia

Repeatability ≤ ±0.02 psia

Paroscientific Digiquartz Barometer, Range = 11.5 to 16 psia

Model MET 1-2 Accuracy = ±0.01% of reading

Stability (drift) <0.00145 psia per year

Repeatability = ±0.003% of reading

*When combining specifications from two instruments, manufacturer specifications listed as % reading values were

converted to physical units using the scales and readings appropriate for the calibrations. The Ruska Pressure Gauge

specifications were calculated using the typical 11 ppm error in the standard that was used to calibrate the gauge in

year 2001.