ARL-TR-7362 ● JULY 2015

US Army Research Laboratory

Meteorological Sensor Array (MSA)–Phase I, 

Volume 3 (Pre-Field Campaign Sensor 

Calibration)

by Gail Vaucher and Robert Edmonds

Approved for public release; distribution is unlimited.
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ARL-TR-7362 ● JULY 2015

US Army Research Laboratory

Meteorological Sensor Array (MSA)–Phase I,

Volume 3 (Pre-Field Campaign Sensor 

Calibration)

by Gail Vaucher and Robert Edmonds

Computational and Information Sciences Directorate, ARL

Approved for public release; distribution is unlimited.
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Meteorological Sensor Array (MSA)–Phase I, Volume 3 (Pre-Field Campaign 

Sensor Calibration)

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Gail Vaucher and Robert Edmonds

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US Army Research Laboratory

Computational and Information Sciences Directorate

Battlefield Environment Division (ATTN: RDRL-CIE-D) 

White Sands Missile Range, NM 88002-5501

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14. ABSTRACT

Army decisions are strengthened by accurate input. The US Army Research Laboratory (ARL) is developing a Meteorological 

Sensor Array (MSA) to provide reliable and persistent atmospheric data resources, which allow modelers and sensor 

developers to validate model and sensor performance with atmospheric observations. Such models and sensor data become a 

foundation for future, environmentally dependent Army decision aids. As an MSA feasibility study, ARL conducted an MSAPhase I (“Proof of Concept”) field campaign in 2014, which consisted of 5 meteorological towers, more than 25 sensors, and a 

5.5-week dataset of 24 h/day–7 days/week (24/7) atmospheric measurements. Before this field campaign could be executed, 2 

side-by-side relative calibration exercises were conducted. The first exercise examined the MSA-Phase I dynamic sensors 

(ultrasonic anemometers); the second assessed the MSA-Phase I thermodynamic sensors (barometers, thermometers, 

hygrometers, and pyranometers). This report documents the results of a detailed calibration assessment. In short, the results 

showed that most sensors were within the manufacturer’s specifications, confirming the qualitative assessments executed just 

prior to the MSA-Phase I field campaign execution.

15. SUBJECT TERMS

meteorological sensor array, MSA, sensor calibration, ultrasonic, sonic, barometer, thermometer, hydrometer, pyranometer

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Gail Vaucher

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iii

Contents

List of Figures v

List of Tables vi

Acknowledgments vii

Executive Summary ix

1. Background 1

1.1 The Meteorological Sensor Array (MSA) Program 1

1.2 MSA-Phase I, Field Campaign Overview 2

1.2.1 Dynamic Sensors 4

1.2.2 Thermodynamic Sensors 6

2. Pre-Field Campaign Sensor Calibration 7

3. Dynamic Sensor Relative Calibration 8

3.1 Dynamic Relative Calibration Design 8

3.2 Dynamic Sensors Calibration Data Analysis 12

3.2.1 Root-mean-square error as a function of distance 12

3.2.2 Calibration Fits 14

4. Thermodynamic Sensor Relative Calibration 15

4.1 Thermodynamic Relative Calibration Configuration 15

4.2 Thermodynamic Sensors Data Analysis 18

4.2.1 Pressure Calibration Data Analysis 18

4.2.2 Thermometer Calibration Data Analysis 19

4.2.3 Relative Humidity Calibration Data Analysis 21

4.2.4 Solar Radiation Calibration Data Analysis 21

5. Discussion 22

6. Summary and Recommendations 23
iv

7. Notes 26

8. References 27

List of Symbols, Abbreviations, and Acronyms 28

Distribution List 29
v

List of Figures

Fig. 1 MSA-Phase I tower layout and data acquisition flow: “node” boxes 

represent tower locations; North Node includes the general tower 

configuration..........................................................................................3

Fig. 2 MSA-Phase I tower configuration .........................................................4

Fig. 3 RM Young 81000 ultrasonic anemometers quantified the dynamic 

atmospheric attributes............................................................................5

Fig. 4 Dynamic sensor side-by-side intercomparisons.....................................9

Fig. 5 Overview of the dynamic side-by-side sensor configuration ................9

Fig. 6 Group 1—Dynamic sensor relative-calibration configuration ............10

Fig. 7 Group 2—Dynamic sensor relative-calibration configuration ............11

Fig. 8 Group 3—Dynamic sensor relative-calibration configuration ............11

Fig. 9 RMSE as a function of group and distance from the standard sonic: 

Linear trend (“best fit”) lines for each group are shown as black lines; 

slope and intercept of each trend line are presented in the table (top 

left) within the plot...............................................................................13

Fig. 10 Sonic #637 versus Standard Sonic #1341 u-components: Each 

measurement comparison is a black dot; best-fit linear is a thin black 

line........................................................................................................14

Fig. 11 Thermodynamic sensors side-by-side intercomparisons.....................16

Fig. 12 Thermodynamic sensor side-by-side inter-comparisons layout ..........17

Fig. 13 Barometric differences compared against Logger 4649 during the 2014 

Julian Day (JD) 60 local nighttime hours; logger associated with each 

sensor is indicated in the legend ..........................................................19

Fig. 14 Temperature sensor differences: standard was the T107 of Logger 

4647; Rotronic sensors are indicated with red lines, T107 sensors are 

blue lines, and the PRT is indicated by a gray line..............................20

Fig. 15 RH percentage difference from Logger 4649 RH measurements during 

the local nighttime of 2014 JD 60 (02 Mar); the logger associated with 

each non-standard RH sensor is demonstrated in the legend...............21

Fig. 16 Pyranometers percent difference from Logger 4649 pyranometerstandard, during the local daytime hours of 2014 JD 61; logger 

associated with each sensor is indicated in the legend ........................22
vi

List of Tables

Table 1 MSA-Phase I “Proof of Concept” sensors.............................................3

Table 2 Thermodynamic sensor specifications...................................................6

Table 3 Phase I sonic calibration position assignments. Position #1 was the 

southern-most position—Position #8 was northern-most. Each number 

listed represents a specific sonic..........................................................10

Table 4 RMSE relative to standard (Sensor No. 4) anemometer umeasurements. U-measurements were filtered to include only 

horizontal winds from within 45° of due west.....................................13

Table 5 Percentage “Diff” of slope from perfect alignment with the sensor 

standard (i.e., 1); same-color bold values are same sensor..................15

Table 6 Line offset (b) in m/s; same-color bold values represent the same 

sensor ...................................................................................................15

Table 7 MSA-Phase I thermodynamic sensors calibration configuration (28 

Feb 2014): Tripod No. 1 was on the west edge (left); Tripod No. 5 was 

located on the east edge (right). ...........................................................17
vii

Acknowledgments

The authors wish to thank Robert Brice and Michael “Sean” D’Arcy for their 

contributions in setting up and executing the 2 relative calibration tasks. Thanks 

also to Manny Bustillos and Dr Teresa Foley for their graphics contributions, and 

Ed Creegan for his assistance with the spatial measurements. Special thanks also 

go to the Technical Publishing Branch at White Sands Missile Range, New Mexico,

for their consistently high standard of editing, specifically to Jim LeNoir and Martin 

Kufus.
viii

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ix

Executive Summary

Environmentally dependent Army decisions rely on accurate representation, which 

often include the atmosphere. The US Army Research Laboratory (ARL) is 

building an “Army-scale”, high-resolution Meteorological Sensor Array (MSA) to 

provide reliable and persistent meteorological data resources that, in turn, will allow 

atmospheric modelers and sensor developers to validate and compare model and 

sensor performance with atmospheric observations at and near the surface and in 

close proximity to terrain of varying complexity.

In 2014, an MSA “Proof of Concept” (MSA-Phase I) was executed to test the 

feasibility for such a significant ARL investment. A representative Phase I field 

campaign was designed. A detailed description of the MSA-Phase I and its 

preliminary results is documented in ARL publications cited within this report. 

Before the field campaign could be confidently executed, all sensors needed to be 

calibrated. Time and fiscal limitations prohibited the option of submitting all 

instruments to a certified National Institute of Standards and Technology (NIST) 

calibration laboratory. Consequently, a side-by-side sensor intercomparison 

(relative calibration) was designed and conducted. The relative calibration task was 

subdivided into 2 parts: dynamic and thermodynamic sensor-calibration segments. 

The dynamic sensors were calibrated first, to optimize the steady, high-wind events 

of the February–March “windy season” in New Mexico. Aligning the sensors 

perpendicular to the prevailing wind on top of a “flat” roof, the strong, steady 

airflow equated that of a calibration wind tunnel. The thermodynamic sensor

calibration followed, sampling data from a more protected location. The calibration 

acquisition period ran from 10 Feb through 06 Mar 2014. 

A preliminary, qualitative calibration review showed that the majority of sensors 

were worthy of the MSA-Phase I field campaign. Those instruments that did not 

meet the standard were either retested and/or replaced. With a persistent deadline,

to complete the MSA-Phase I measurement portion by mid-fiscal year, the sensors 

passing the calibration assessment were integrated into the field campaign design, 

which promptly followed the calibration task. A detailed calibration analysis was

postponed due to the uncompromising schedule and lack of available personnel. 

Fortunately, after the successful “Proof of Concept” was completed and MSAPhase II was underway, a more detailed relative-calibration data analysis has now 

been executed, with results documented in this report. 

The calibration “standard” sensors selected for the analyses were either new or 

instruments calibrated within the last year. The dynamic calibration configuration 

and data analyses are described in Section 3. The 20 ultrasonic anemometers 
x

(“Sonics”) calibrated were subdivided into 3 groups of 8 side-by-side Sonics

sampling sessions. The “standard” sensor (Sonic #1341) was used in all 3 

calibration sessions. Table 3 shows the sonic placements and data-acquisition dates 

for each group. 

Section 4 describes the 24 thermodynamic sensor relative calibration layout, 

methodology, and analysis. To help identify these sensors, each instrument was 

correlated with its data logger, which stored the sampled data. Table 7 summarizes 

the thermodynamic sensor configuration details.

The detailed analyses confirmed the qualitative assessments made prior to the 

MSA-Phase I field campaign execution. That is, most sensors were found to be 

within the manufacturer’s specifications. Summarizing the calibration results by 

sensor type:

1) Sonic Anemometers: Based on the intercomparison root-mean-square error

(RMSE) and curve-fitting analyses, there were no significant calibration issues 

identified. The instrument calibration for each anemometer was good to 

approximately 0.1 m/s, which was close to the manufacturer-stated accuracy of 

±0.05 m/s. Since the results fell within the manufacturer-stated accuracy of ±1% 

root mean square (rms), no correction curve was suggested. 

2) Barometer: Barometer 4607 reported erratic data during the initial 

calibration assessment and was consequently removed from consideration prior 

to the field campaign. Based on the more detailed post-campaign review, the 

remaining barometers were within manufacturer’s specifications. Barometer 

4649, however, showed a potential need for an offset correction of about 

–0.5 mb when used with the other fielded barometers.

3) Temperature sensors: The thermometers were found to be within the general 

manufacturer specifications.

a) The Rotronic sensors reported values about 0.1 °C higher than the 

T107 sensors. Note: During the MSA-Phase I field campaign, the Rotronic 

sensors were mounted at 2 m above ground level (AGL) and the T107s at 

10 m AGL. The analyzed results imply that uncorrected MSA-Phase I data

may report overly unstable, near-surface, vertical temperature profiles.

Since an absolute calibration was not available, the direction of the 0.1 °C 

correction is debatable. For studies concerned with relative temperature 

differences only, it is suggested to either correct the T107 measurements by 

an offset of +0.1 °C or, the Rotronic data by –0.1 °C. 

b) The Platinum Resistance Thermometer (PRT) sensor reported 

values about 0.4 °C colder than the standard. This sensor was ultimately 
xi

replaced with a new T107, during the MSA-Phase I field campaign. (Note: 

The process of purchasing a new sensor began during the pre-campaign 

calibration. The PRT was used as a “placeholder” until the new instrument 

arrived and could be installed on Tower No. 3, at the 10-m AGL location. 

The new T107 started data acquisition on 15 Apr 2014 at 0942 Mountain 

daylight time (MDT).

4) Hygrometers: Relative Humidity (RH) measurements for Loggers 4653, 

4607, 4647, and 3405 reported values within 1% of each other. Logger 4649 

measurements were on average approximately 1% lower than the other 4

recorded logger values. The sensor variations (excluding Logger 4649 RH 

sensor) between the 4 RH sensors were within the manufacturer-stated 

calibration of ±0.8%. 

5) Pyranometers: Solar Radiation measurements from Loggers 4607, 4647,

and 3405 reported measurements within 1% of each other. Loggers 4649 and 

4653 reported 3–9% departures from the 3 sensors. A percentage departure drift 

was observed over the course of a day. Logger 4649 solar radiation 

measurements were noted as being potentially too high during the MSA-Phase 

I field campaign.

This calibration exercise has demonstrated the strengths and weakness of a relative 

calibration. For the dynamic sensors, where the “standard” instrument was closely 

aligned to a NIST-standard quality (such as, Sonic #1341), confidence in the 

measured data was re-enforced. For the thermodynamic sensors, even though the 

instruments were technically new or calibrated within the last year, the comparison 

against the standards exposed data limitations. Fortunately, with this analysis, the 

results will help scientiststo use the data in an informed manner. Recommendations 

gleaned from this project include the following: 

1) A sensor with NIST paperwork does not guarantee that the sensor has not

been dropped or accidentally hit in a manner that negates the calibration; and 

2) When a field project’s sensor calibration is postponed or deleted, this 

sacrifice can reduce the quality and usefulness of the final measurements. 

In an ideal world, all meteorological sensors would be calibrated against NIST 

standards prior to their field installation, rechecked in a side-by-side assessment 

just before fielding, and then put on a routine recalibration schedule; thus, ensuring 

the best quality measurements for the data users.
xii

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1

1. Background

Successful Army decisions can be reached only when the information on which the 

decisions are made accurately represents the given scenario. Environmental 

decisions involving the atmosphere rely heavily on detailed meteorological models. 

“Army-scale” atmospheric models include high-resolution (<1-km) models. 

Locating meteorological observations to validate these high-resolution atmospheric 

models is very difficult. The National Research Council (NRC) recognized this 

technological gap in 2009 after reviewing the US Weather Research and 

Researcher-to-Operations progress and priorities (National Research Council

2010). Their assessment prompted numerous NRC conclusions and 

recommendations. 

The US Army Research Laboratory (ARL) has answered the national and military 

concern by proposing an observational data resource specifically designed to 

address the “Army-scale”, high-resolution atmospheric model validation and 

verification issues. This solution is called the “Meteorological Sensor Array 

(MSA)”. The MSA is intended to provide reliable and persistent data resources that, 

in turn, allow atmospheric modelers and sensor developers to validate and compare 

model and sensor performance with atmospheric observations at and near the 

surface and in close proximity to terrain of varying complexity.

1.1 The Meteorological Sensor Array (MSA) Program 

Before the MSA endeavor could be approved as a multi-phased program, a “Proof 

of Concept” was organized and executed to test the feasibility of success for such 

a significant ARL investment. The “Proof of Concept” became known as the 

“MSA-Phase I”. Phase I began with a survey of all potential ARL data users, 

inquiring what they envisioned gleaning from the MSA data and capability. These 

multiple, diverse concepts were assimilated and reduced into a handful of practical 

objectives that could be demonstrated within the 8 months allotted for the Phase I 

task. A representative field campaign was quickly designed, along with an 

expandable data-management strategy that would accommodate the initial field 

requirements and provide a foundation for follow-on MSA phases. A detailed 

description of the MSA-Phase I and its preliminary results is found in MSA, 

Volumes 1 (Vaucher et al. 2014) and 2 (Harrison and Vaucher 2014). 

Based on the timely Phase I success, the MSA Project advanced into a full MSA 

Program within the same fiscal year. Phase II was initially designed to be a simple 

expansion of the Phase I meteorological tower array which incorporated the Phase 

I lessons learned. However, with added user input from outside ARL, Phase II 
2

managers elected to diversify the tower configurations and locations, thus 

facilitating a larger number of scientific and technical applications for the future 

phases. 

1.2 MSA-Phase I, Field Campaign Overview

The MSA-Phase I field campaign was originally designed as a 5.5-week (17 Mar–

25 Apr 2014), 24 h/day–7 days/week (24/7), meteorological data-acquisition event. 

The intended campaign function was to provide a tangible platform from which 

high resolution data for multiple scientific and technical objectives could be 

completed. Specifically, the Phase I objectives included

1) Setting up and testing a model/observation verification process. 

2) Acquiring data for wind assessment of the White Sands Missile Range 

(WSMR) solar photovoltaic (PV) Farm. 

3) Acquiring data to assess turbulence impact of the WSMR solar array. 

4) Designing, developing, testing, and evaluating integrated Data Acquisition 

System (DAS) hardware and software. 

The field campaign was located at WSMR, New Mexico. A series of relative 

calibration tests preceded the campaign execution. Preliminary calibration results 

were included in MSA-Phase I, Volume 1 (Vaucher et al. 2014). A more detailed 

data analysis of the Phase I relative calibration task is given in this report. The 

successfully calibrated sensors were used and/or designated as “backup sensors”

for the field portion of Phase I.

The Phase I field campaign design consisted of 5 equally separated, 10-m

meteorological towers positioned around a large solar PV farm. Three towers were 

situated west of the solar PV farm, aligned along a north–south axis, with a 100-m 

separation between towers. Two towers stood east of the solar PV farm, oriented 

along an east–west axis and separated by 100 m (see Fig. 1).
North 

Node 

Sensor 

Interface I Data logger 

Wire less 

Transceiver 

[/ 

( 

MSA 

Data Acquisition Flow 

t.!ffi0!30!00K'!&l 

~]:- · l----- ~=-----, • ·- -

• 

(( I/ 

( 

1"-

• 

• 

" ••••l· ... •< ... ~••W• 

" ·-...--··'='"• 

0 · ..... - .• ~ 

( ( ( (( ( 

W$Mf\ Sol~r Arr ~ 

To MSA Houdqu arun Bldg 

]Last Rev: 131126, «Vgv, be-ad] 

Fig. 1 MSA-Phase I towe1· layout. and dat.a acquisition flow: "node" boxes represent t.owe1· 

locations; Nort.h Node includes t.he general tower configuration 

The meteorological tower configuration included 2 data-acquisition systems, based 

on the 2 data acquisition rates. Dynamic sensors sampled and archived 20-Hz data, 

after which these data were reduced to 1-min averages for sensor quality-control 

assessment purposes. The1modynamic sensors acquired and archived 1-min 

average samples. Table 1 describes the variables and sensors used for the "Proof of 

Concept" field campaign. 

Table 1 MSA-Phase I "Proof of Concept." sensors 

Va1·iablt> St>nSOl" Manufactm·t>r Modt>l Units 

Pressure Barometer Vaisala PTB-101B/PTB110 Millibars 

Temperature Thermometer Campbell Tl07 Celsius 

Temperature/ Thennometer/ Rotronic HC2S3 

Celsius/ 

!Relative Hurnidit) Hygrometer Percent 

Solar Radiation Pyranometer Kipp/Zonen CM3/CMP3 W/m2 

Micro-logger ALL Campbell CR23X Scientific ' . ' 

Wind Speed and Ultrasonic RMYoung 81000 m/s and degrees Wind Direction Anemometer 

3 
4

The dynamic sensors (wind) were mounted at 10 m and 2 m above ground level 

(AGL). The thermodynamic sensors (pressure, temperature, relative humidity, and 

solar radiation) were distributed as follows:

• Pressure: about 1 m AGL

• Temperature: 2 m and 10 m AGL

• Relative Humidity: 2 m AGL

• Solar Radiation: 2 m AGL

Figure 2 shows a schematic of the composite MSA Phase I sensor layout on the 

UT30 flat-based (10 m) mounting towers.

Fig. 2 MSA-Phase I tower configuration

1.2.1 Dynamic Sensors

The dynamic sensors consisted of RM Young Ultrasonic 81000 anemometers (see 

Fig. 3). This ultrasonic anemometer (sonic) model had no moving parts and 

sampled wind, sonic temperature, and speed of sound. The wind variable was 

quantified into u-component, v-component, w-component, wind speed, and wind 

direction. 
5

Fig. 3 RM Young 81000 ultrasonic anemometers quantified the dynamic atmospheric 

attributes

The sonic wind velocity magnitudes were based on the time it takes a sound wave, 

or sonic pulse, to travel through the atmosphere between a fixed pair of transducers. 

The measurements worked on the principle that sound-wave propagation in a 

moving medium is equal to the sound-wave velocity, with respect to the medium, 

plus the medium velocity. Wind velocity vector was determined by a measure of 

the time required for the sound wave to travel from its origin to 3 points on space. 

This method presumed that the speed of sound was known for the medium, which 

can be determined from the medium temperature or from an additional travel-time 

measurement (Huschke 1970).

With 6 transducers, the sonic anemometer was able to quantify a 3-component 

wind. The sensor casing to support the transducers was known to impact the 

sampling, which is why manufacturers include a correction factor in their datasampling routines. Another known sonic shortcoming was the reduced accuracy 

during precipitation events. Raindrops can interfere with the speed of sound; thus, 

distorting the processed sonic wind data results. Since the ultrasonic sampling rate 

can be 25 Hz (MSA-Phase I used 20 Hz), these sensors are often used to 

characterize atmospheric turbulence. Sonic anemometers have been used to sample 

temperature. However, the technology was still being perfected at the time of the 

MSA-Phase I field campaign, so this capability was not emphasized in the 

calibration/operational data analyses.

The Ultrasonic 81000 sensor specifications published by RM Young Co. (2004) 

describe the sensors as having a 0–40 m/s (0–90 mph) range, a sensor resolution of
0.01 m/s, and a threshold of 0.01 rnls. The manufacturer-stated accuracy is ±1% 

root mean square (rms), ±0.05 rnls (0- 30 m/s) and ±3% nns (30-40 rnls). 

1.2.2 Thermodynamic Sensors 

The thermodynamic measurements utilized sensors linked by 5 Campbell 

Scientific, Inc., micro-loggers. As mentioned above, the thermodynamic variables 

sampled included pressure, temperature, relative humidity, and solar radiation. All 

thermodynamic data were saved in 1-min averaged samples. 

The pressure sensor was a Vaisala Barometer, PTB-1 01 B (also known by Campbell 

Scientific as "CS 1 05"). The sensor was housed in an anodized aluminum case fitted 

with an intake valve for pressure equilibration. The sensor utilized a silicon 

capacitive sensor to measure barometric pressure over a 600- 1,060 mb range. The 

general barometer specifications are listed in Table 2, along with all of the other 

sensor specifications (Campbell Scientific 2001a). 

Table 2 Thermodynamic sensor specifications 

St>nso1· Towt>l' Modt>l Range Accu•·acy Commt>nts AGL 

Barometer 1m P1B-101B, 600-1,060 mb .:!;.0.5mb @ +20 oc 

"CS105" :!:1.5mb @ 0 to +40 oc 

.:!;.2.0mb @ -20 to +45 oc 

+3.0mb @l -40 to +60 oc 

Thermometer 10m T107 - 35 octo +50 oc .:!;.0.4°C (- 24 to 48 oc ) 

+0.9°C (- 38 to 53 oc ) 

Thermometer 10m PRT -0.01 oc "max of0.02 °F at 

98 °F" 

Thermometer 2m HC2-S3 - 50 oc to +100 oc At 23 oc , +0.1 oc 

Hygromete1· 2m HC2-S3 0 to 100% At 23 oc , .:!:0.8% RH Accm·acy changes 

with Temperattu·e 

(Max + 1.3%) 

Pyr anometer 2m CM3/ 0-2,000 W/m2 .:!:10% Spectral range 

CMP3 (50% pts nm) 

305-2,800 lUll 

The temperature sensors designated for the 10-m AGL position were Campbell 

T107 probes and a Platinum Resistance Thermometer (PRT). The probes were 

therm istors encapsulated in an epoxy-filled aluminum housing. Temperature range 

was from - 35 oc to +50 oc; the T107 manufacturer-stated accuracy is ±0.4 oc 

between - 24 oc and 48 oc (Campbell Scientific 2001b) as shown in Table 2. The 

PRT accuracy was reported to be approximately 0.01 °C. 1 

The Rotronic HC2-S3 measured temperature and relative humidity (RH) at the 

MSA-Phase I Tower, 2m AGL. The temperature sensor used a 100-0hm PRT; the 

RH was measured with a capacitive sensor. The Rotronic manufacturer stated 

6 
7

temperature accuracy is ±0.1 °C at 23 °C; the RH accuracy is 0.8% at 23 °C

(Campbell Scientific 2012).

The thermometers and hygrometers were mounted in naturally aspirated radiation 

shields. Ten and 12 disk radiation shields were used during the calibration data 

collection. During MSA-Phase I operations, 10 and 12 disk radiation shields were 

used with the Rotronic sensors and 6 disk radiation shields were used with the 

T107s. The expected impact of the different radiation shields on the calibration 

study was minimal. 

The solar radiation was quantified by Kipp and Zonen CM3 and CMP3

pyranometers. The primary difference between the 2 models is that the CMP3 

includes a snap-on, sun shield around the circular instrument, to reduce sensor 

temperature. MSA-Phase I used four CM3 sensors and one CMP3. During the 

calibration exercise, the CMP3 was wired to Logger 4653. These instruments utilize

a thermopile sensor that is coated with a black absorbent coating. The radiation is 

absorbed by the paint and converted into heat. A resultant temperature difference 

is converted into voltage by a copper constantin thermopile, which has a 180° field 

of view (fov). The fov provides the angular characteristics needed for the cosine 

response requirements. The pyranometers are intended to have a flat spectral 

sensitivity from 305- to 2,800-nm wavelengths (Campbell Scientific 2002b). The 

worst-case error stated by the manufacturer for both pyranometers is ±10%, with 

the typical accuracy being ±5% (Campbell Scientific 2002b).

2. Pre-Field Campaign Sensor Calibration

The official start of the MSA-Phase I project was 1 Oct 2013. With less than a year 

to identify participants, design a multi-phased program, construct and execute a 

full-scaled “Proof of Concept” project that included a successfully accomplished 

field campaign justifying this major investment program, and solicit funding

resources, timely access to a certified National Institute of Standards and 

Technology (NIST) calibration laboratory was not an option for the MSA-Phase I. 

Consequently, a side-by-side sensor intercomparison (a relative calibration) was 

designed and conducted. The relative calibration task was subdivided into 2 parts:

dynamic and thermodynamic sensor-calibration segments. Utilizing the projected 

high winds of the February–March, New Mexico “windy season”, the dynamic 

sensors were calibrated first. Steady, high winds were sought to best equate the 

airflow of a calibration wind tunnel. The thermodynamic sensors followed, using a 

more protected location. The total acquisition period ran from 10 February to 06 

March 2014. 
8

A preliminary, qualitative review of the side-by-side calibration data (reported in 

Volume 1) showed the majority of sensors as being worthy of the MSA “Proof of 

Concept” field campaign. Those instruments that did not meet the standard were 

either re-tested and/or replaced. With a pressing deadline to complete the “Proof of 

Concept” measurement portion by mid-fiscal year, the sensors that passed the 

calibration assessment were integrated into the field campaign design, which 

promptly followed the calibration task. A detailed calibration analysis also had to 

be postponed due to the uncompromising schedule and lack of available personnel. 

Fortunately, with the successful “Proof of Concept” completed and MSA-Phase II 

underway, a more detailed analysis of relative calibration data has now been 

executed. The results are documented in this report. 

3. Dynamic Sensor Relative Calibration

In this section, the dynamic sensor relative-calibration configuration and data 

analyses are described. Results confirm the qualitative assessment executed prior 

to field campaign execution. That is, all sensors fell within the manufacturer-stated 

accuracy.

3.1 Dynamic Relative Calibration Design

The ultrasonic anemometer intercomparisons were conducted on the prevailingwindward side of a “flat” roof on a 2-story building. The anemometers were 

arranged in a single row, mounted on 4 tripods with 2 sensors per tripod (see Fig. 

4). The tripods were approximately 4.4 m from the roof’s west-facing edge and 

aligned in a south–north orientation to optimize the strong, prevailing westerly 

winds. The sensors were mounted at the ends of the tripod crossbar, with all 8 

sensors separated by an even spacing of 1.13 m (see Figs. 4–8).
9

Fig. 4 Dynamic sensor side-by-side intercomparisons

Fig. 5 Overview of the dynamic side-by-side sensor configuration

A total of 20 sonic anemometers were calibrated using 3 intercomparison groups

(see Table 3). The same configuration pattern was repeated for all 3 anemometer 

calibration periods (10–24 Feb 2014), keeping one sensor common to all 3 

acquisition periods. Figures 6–8 detail the layouts for all 3 groups.
Table 3 Phase I sonic calibration position assignments. Position #1 was the southern-most 

position-Position #8 was northern-most. Each number listed represents a specific sonic. 

Sonic Calibration G.-oup I Group II 

Sampling Positions (2014 Feb 10-13) (2014 Feb 13- 18) 

I (southem-most position) #1343 #626 

2 #1355 #633 

3 #1356 #634 

4 #1341 #1341 

5 #1357 #637 

6 #1359 #638 

7 #1361 #646 

8 (northem-most position) #1370 #1354 

MSA: Vertical View of Calibration Array 

Group I (2014 Feb 10-13) 

1.13 m 1.13 m 

1.16 m 1.1 5 m 

Group !, Vertical Height = ground to middle of sonic sensor. 

@ 

·] 1.13 m 

@ 

1.12 m 

Group III 

(2014 Feb 18- 24) 

E 

0 

0 

N 

#498 

#499 

#633 

#1341 

#646 

#650 

#712 

#726 

1.1 3 m 

® 

E 

0 

0 

N 

(1d0218, IVgvisd} 

Fig. 6 Group 1-Dynamic senso1· relative-calibration configuration 

10 
11

Fig. 7 Group 2—Dynamic sensor relative-calibration configuration

Fig. 8 Group 3—Dynamic sensor relative-calibration configuration
12

The sonic measurements were acquired on a central data-collection computer, using

an 8-port RS232 adapter, and Labview software to collect and time-stamp each 

reading.

The orientation of the building’s western edge was used to minimize flow-variation 

influences by the local and surrounding building structures. Some upward vertical 

velocity was noted; however, later analysis confirmed that the u-component of the 

velocity was on average fairly uniform. 

Within each group, toward the spatial center of each group (Position #4), one sonic 

anemometer was repeatedly used. This sensor was selected based on earlier field 

study performance, and was defined as the calibration “standard”. Two sonic 

anemometers (#633 and #646) were sampled in 2 group calibration sessions 

(Groups II and III), due to questionable performance in their initial sampling 

session. Statistical data analyses and data fitting were used to assess the sensorcalibration quality and to determine if data corrections were required.

3.2 Dynamic Sensors Calibration Data Analysis

The sonic relative calibration data were initially examined by calculating the 

standard deviation of each sonic anemometer against the Standard. Also examined 

was whether any linear corrections could be confidently applied to improve 

instrument calibration of the sonic anemometers. The analysis for each instrument 

group used all available data where the horizontal wind from the standard sonic was 

coming from within 45° of due west. To compare measurements taken at similar 

times, measurements were interpolated to a fixed time grid using 25-Hz temporal 

grid spacing and nearest-neighbor interpolation, and then time-averaged to a 1-min 

increment to aid in making the analysis easier to process. 

3.2.1 Root-mean-square error as a function of distance

Within each group, ultrasonic anemometer #1341 was utilized as the “standard”

sonic anemometer for comparison. This originally NIST-calibrated sonic was 

selected based on its historical usage as an un-fielded, carefully managed sensor 

preserved for relative-calibration purposes. Consequently, the sensor was placed 

toward the center of each group of 8 sonic anemometers (Position #4).

The root-mean-square error (RMSE) or difference of each sonic anemometer 

1-min, averaged u-component, measured wind value from the standard sonic 

anemometer measured value is presented in Table 4. Apparent within Table 4 is

that the RMSE generally increases within each group depending on the distance of 

the anemometer relative to the standard. The RMSE of each anemometer plotted as 

a function of distance from the standard highlights this issue, and the growth in the 
RMSE appears linearly conelated with distance (see Fig. 9). Also noted was that 

the RMSE was well con elated with the standard deviation of the measurements 

from the sonic measurements, meaning the RMSE was not due to offset/mean enor 

differences. This effect ofRMSE growing with distance was likely due to turbulent 

fluctuations, and other spatial/temporal deviations in the wind flow (see Fig. 9). If 

an anemometer significantly deviated from this linear trend, the deviation would 

suggest that an added source of enor was significant, in addition to the turbulent 

fluctuations potentially from calibration issues. Since no anemometer shows a large 

deviation from each group's linear trend of the RMSE growing with distance, this 

suggests no anemometer has a significant calibration issue; and that the primary 

source of the discrepancy between the measurements is due to turbulent 

fluctuations/differences in the wind between stations. Backing out this increase in 

noise/standard deviation, assuming the linear trend, suggests that the instrument 

calibration for each sensor is good to about 0.1 rnls. This is close to the 

manufacturer-stated accuracy of ±0.05 mls. 

Table 4 RMSE relative to standard (Sensot· No. 4) anemometer u-measurements. 

U-measurements were filtered to include only hot·izontal winds from within 45° of due west. 

Sensor 

Position 

Group I 

Group 2 

Group 3 

I 

(m/s) 

0.25 

0.41 

0.51 

0.5 

2 3 4 5 

(m/s) (m/s) (m/s) (m/s) 

0.19 0.12 Standard 0.13 

0.27 0.18 Standard 0.18 

0.37 0.17 Standard 0.19 

RMSE of Measurements 

vs. Distance 

Linear F jt lnformatjon 

UlO'I' I ~ ~ce~S Gcmp2 0089 0073mfs 

0)0.4 ... 0.U'-V3 0.100 0 .0181'1"1/'S 

E 

;:;J'o.3 

(f) 

~ 

a: 0.2 

0.1 

0.0 

0 2 3 4 

Distance (m) 

6 7 8 

(m/s) (m/s) (m/s) 

0.20 0.28 0.35 

0.27 0.40 0.45 

0.31 0.43 0.51 

• Group 1 

• Group2 

• Group 3 

5 

Fig. 9 RMSE as a function of group and distance from the standard sonic: Linear trend 

(" best fit") lines fot· each group are shown as black lines; slope and intercept of each trend line 

are presented in the table (top left) within the plot 

13 
14

3.2.2 Calibration Fits

While satisfied that sensors were within specifications, the sonic-calibration data 

were also analyzed to see whether any linear corrections should be applied to 

improve instrument calibration of the sonic anemometers. In this investigation, the 

u-component of each sonic was plotted against the standard u-component and a

linear “best fit” (from a least squares standpoint) was determined using the 

following equation: 

usonic = mm × ustandard sonic + bb, (1)

where usonic is the u-measurement being compared against the standard sonic 

measurement, ustandard sonic; m is the dimensionless slope of the best-fit line; and b is 

the offset of the measurement, when ustandard sonic is zero. For a perfect fit between 

the sonic and the standard, the offset/intercept would be zero and the slope of the 

fit would be unity. 

The linear fits were conducted with the Matlab polyfit function. Figure 10 shows

an example of this fit using Sonic #637 against Standard Sonic #1341. Note the 

measurement spread in the y direction is well correlated with the RMSE values 

presented in Table 4 and Fig. 9. 

Fig. 10 Sonic #637 versus Standard Sonic #1341 u-components: Each measurement 

comparison is a black dot; best-fit linear is a thin black line
The resulting sensor fits are tabulated in Tables 5 and 6. Table 5 shows the 

percentage difference ("Diff ') of the slope from the Standard. A perfect alignment 

would equal l. Table 6 tabulates the line offset (or "b" from equation 1), for each 

fit. 

Table 5 Percentage "Diff" of slope from perfect. alignment with the sensot· standard (i.e., 

1); same-color bold values are same sensot· 

Sl'nsot• 1 2 3 4 5 6 7 8 

Position (% Diff) (% Diff) (% Diff) (% Diff) (% Diff) (% Diff) (% Diff) 

Group 1 0.32 - 0.49 -0.15 Standard - 3.22 -0.95 -4.04 -6.76 

Group2 2.01 -1.40 1.76 Standard 0.06 -0.83 -5.24 - 3.28 

Group3 5.61 4.12 -0.44 Standard -2.09 - 2.98 -4.99 - 5.13 

Table 6 Line offset (b) in m/s; same-colot· bold values represent the same sensor 

Sl'nsot• 1 2 3 4 5 6 7 8 

Position (m/s) (m/s) (m/s) (m/s) (m/s) (m/s) (m/s) 

Group 1 0.007 -0.006 -0.015 Standard -0.020 -0.024 -0.041 -0.051 

Group 2 -0.019 -0.031 0.011 Standard 0.032 -0.046 -0.112 -0.090 

Group 3 0.025 0.020 0.006 Standard - 0.021 -0.045 -0.065 -0.070 

If the difference between the sensor and standard was purely a ftmction of the 

instrument, then the fit should be similar when the sensor is used in different 

groups. Sonics #633 and #646 were included in both Groups 2 and 3. Sonic #633 

was placed in Group 2, Position 2, and Group 3, Position 3. Sonic #646 was located 

in Position 7 for Group 2 and Position 5 in Group 3. The reason for the 2-group 

sampling was due to the need for a physical con ection to the sensor alignment. 

Comparing the results from each group nm implies that the source of the numerical 

differences is not purely a ftmction of the sensor. The results do fall within the 

manufacturer-stated accuracy of ±1% 1ms, so no conection curve was suggested. 

4. Thermodynamic Sensor Relative Calibration 

This section describes the the1modynamic sensors relative calibration layout, 

methodology, and analysis. The results confi1m the original MSA-Phase I field 

campaign's qualitative assessment. 

4.1 Thermodynamic Relative Calibration Configuration 

The the1modynamic sensor calibration began once the dynamic calibration was 

completed; and ran for 10 days, 25 Fel:H)6 Mar 201 5. The side-by-side 

intercomparison method was siinilar to the dynamics calibration study. However, 

all of the thennodynainics sensors were tested at the same time and the 

15 
16

measurement location was situated on the south side of a 1-story office building 

(see Fig. 11).

Fig. 11 Thermodynamic sensors side-by-side intercomparisons

The sensors were grouped into 5 stations and mounted on 5 independent tripods. 

These tripods were aligned in a west–east orientation. 

Each station sampled pressure, 2 temperatures, relative humidity, and solar 

radiation. Campbell Scientific, Inc., micro-loggers (CR23X) recorded the data in 

1-min samples. The loggers were assigned a number, which was then associated 

with the sensors reporting to that logger. (See Fig. 12 and Table 7.) The center 

tripod (Tripod No. 3) had sensors that were new or had been calibrated within the 

last year. The sensors mounted on the tripods to either side of the recently calibrated 

“standard”, had the “unknowns” or instruments for testing. All sensor positions on 

the individual tripods (height above ground, distance from the tripod or boom) were 

carefully aligned to within 2 cm of each other. For reference, the wall of the 

building, which was north of the tripods, was almost exactly aligned on a true west–

east line. Tripod No. 1 was positioned on the west (Fig. 11, left side); Tripod No. 5 

was on the eastern edge (Fig. 11, right side).

The MSA-Phase I “Proof of Concept” used hardware components from previous

field tests. This resource insured that the components had a proven durability and 

that system development costs would be kept very low. The deviation of 

measurements from the predetermined “standard” sensor will be examined in the 

next section. 
Large 

Backup 

Generator 

MSA-Phase I Thermodynamic Sensor Calibration Site 

E 

<li 

0 

Existing Building South Side 

4653_#1 4647_#2 4649_#3 4607_#4 

Side-by-side Thermodynamic Sensor Inter-comparison 

Building South Wall 

Dirt Ground on South Side of Building 

3504_#5 

(1-'0228 &140303. GVIRB) 

Fig. 12 Thet·modynamic sensor side-by-side inter-comparisons layout 

Table 7 MSA-Phase I thermodynamic sensors calibration configuration (28 Feb 2014): 

Tt·ipod No. 1 was on the west edge Oeft); Tripod No. 5 was located on the east edge (t·ight). 

Logger Pressure Temp Temp RH Solar Rad Sol at· Sonic 

1m) (10m) (2m) (2m) (2m) Batte ties (10m) 

4653 X1720009 4653 61081155 61081155 092102 Fow- 1354 I 

Tripod #1 Vaisala/CSI CSI Ron·onic Ron·onic Kipp& 6v 499 

(MSA -PI, (PTBlOl b, (Tl 07) Zonen Batteries RM 

Tower 4) CSI05) (CMP3) Young 

4647 X1720010 4647 61081276 61081276 025277 Fow- 1359 

Tripod# 2 Vaisala/CSI CSI Ron·onic Ron·onic Kipp& 6v RM 

(MSA -PI, (PTBlOl b, (Tl 07) Zonen Batteries Young 

Tower #2) CSI05) (CM3) 

4649 F1220011 4649 61085447 61085447 014907 Fow- 13701 

Tripod#3 Vaisala/CSI CSI Ron·onic Ron·onic Kipp& 6v 650163 

(MSA -PI, (PTBlOl b, (Tl 07) Zonen Batteries 7 

Tower # I) CSI05) (CM3) RM 

Young 

4607 X1630015 4607 61053263 61053263 014906 Two 726 

Tripod#4 Vaisala/CSI CSI Ron·onic Ron·onic Kipp& 6v RM 

(MSA -PI, Bad Sensor (Tl 07) Zonen Batteries Young 

Tower #5) (not fielded) (CM3) 

3405 No sensor 3405 61085415 61085415 014912 Fow- 1356 

Tripod#5 PRT* Ron·onic Ron·onic Kipp& 6v RM 

(MSA -PI, Zonen Batteries Young 

Tower #3) (CM3) 

Sonic 

(2m) 

1343 

RM 

Young 

1357 

RM 

Young 

1361 

RM 

Young 

712 

RM 

Young 

1355 I 

638 

RM 

Young 

" ,. . *PRT generated questiOnable data during the relattve cahbratton. Consequently, the sensor was used as a placeholder mstrument until a new 

Tl07 was purchased and installed in Tower No. 3, at 10-mAGL, onl 5 Apr2015. 

17 
18

4.2 Thermodynamic Sensors Data Analysis

The calibration data analyses for the barometers, thermometers, and hygrometer 

sensors focused on the time period: 2014 Julian Decimal Days (JDDs) 61.1–JDD 

61.3 Universal Time Coordinated (UTC). The equivalent local time was during the 

nighttime hours of 2014 JDD 60 (Mar 01), 19.4 hours Mountain Standard Time 

(MST) through JDD 61 (Mar 02), 0.2 hours MST. The night hours were selected 

based on an observed, ambient environmental consistency over that time period.

The pyranometer calibration data analysis was centered on JDD 61.7–61.9 UTC

(local time: 2014 Mar 02, 0.2–14.6 hours MST), which was during daylight hours.

This period was selected based on the persistent atmospheric attributes generated 

by the cloudless day. Wind measurements were not acquired during the 

thermodynamic calibration due to limited resources. Consequently, no filtering 

based on airflow attributes could be utilized. To compare measurements, sampling 

data were interpolated to a fixed time grid, using 1-min temporal grid spacing and 

nearest-neighbor interpolation. 

The “standard” sensor measurements used for the relative calibration analysis were

recorded by Logger 4649 on Tripod No. 3. The instruments mounted on this 

centrally located tripod were either new or calibrated within the last year, as 

explained earlier. 

4.2.1 Pressure Calibration Data Analysis

Three barometer outputs were compared in the pressure relative-calibration 

assessment process. A fourth barometer, wired to Logger 4607, showed erratic data 

time series during the preliminary calibration review. Consequently, that sensor 

was not included in this analysis. Fiscal and temporal limitations prevented the 

acquisition of a replacement pressure sensor. 

Figure 13 shows the pressure-measurement differences with respect to Logger 4649 

during the 2014 JDD 61.1–61.3 time frame. Pressure measurements from the 3 

loggers were within 0.8 mb of each other. In general, there was little drift 

(approximately 0.1 mb) in the typical offset between each sensor. Since there is 

technically no solid basis for an increased confidence in one sensor over another, 

only the spread in the measurements can be reported, with no absolute correction 

to any particular sensor advised. The deviation between sensor measurements is 

only slightly larger than the sensor manufacturer-stated calibration of 0.3 mb at 

20 °C and 0.6 mb at 0–40 °C. While an absolute calibration cannot be determined, 

for a relative calibration correction an offset correction of about –0.5 mb for the 

Logger 4649 barometer would be recommended when comparing fielded sensors.
19

Fig. 13 Barometric differences compared against Logger 4649 during the 2014 Julian Day 

(JD) 60 local nighttime hours; logger associated with each sensor is indicated in the legend

4.2.2 Thermometer Calibration Data Analysis

Calibration measurements from 9 temperature sensors (3 T107s, 5 Rotronics, and 

1 PRT) were examined. The temperature differences were calculated using the 

Logger 4647–T107 as the standard, over the 2014 JDD 61.1–61.3 UTC time frame. 

As mentioned above, the equivalent local time was during the night of 01 Mar 2014, 

19.4 hours MST through 02 Mar, 0.2 MST. (Results are displayed in Fig. 14.) Most 

temperature measurements were within about 0.2 °C of each other. An exception 

was the PRT sensor, which reported values about 0.4 °C colder than the standard. 

This significant deviation from the expected accuracy of the thermometer was noted 

during the initial qualitative-calibration assessment. Consequently, this sensor was 

used as a quasi-placeholder in the MSA-Phase I field campaign until a “new” T107 

replacement could be purchased and installed. The replacement sensor was 

installed on Tower No. 3 at 10 m AGL on 15 Apr 2014. Data ingest for the new 

sensor began around 0942 MDT (Vaucher and D’Arcy 2014). The need for 

applying a post-campaign PRT-correction term to the “placeholder” data was noted 

in the preliminary calibration assessment for the MSA-Phase I field campaign. 
20

Fig. 14 Temperature sensor differences: standard was the T107 of Logger 4647; Rotronic 

sensors are indicated with red lines, T107 sensors are blue lines, and the PRT is indicated by 

a gray line

On average, the Rotronic sensors reported values about 0.1 °C higher than the T107

sensors. 

Note: During the MSA-Phase I campaign, Rotronics were used at 2 m AGL and the 

T107 sensors were used at 10 m AGL. The impact of this observed trend means 

that during the daytime, if the Rotronics represented accurate readings, then the 

actual upper-level temperature should be warmer than what was measured by the 

T107. Likewise, if the T107 sensors were reporting “truth”, the actual 2-m surface 

values should be cooler than what the Rotronic sensors reported. 

The net result of this observed trend would imply that the uncorrected MSA-Phase

I campaign data may report overly unstable vertical profiles in the near-surface 

temperature gradients. Since an absolute calibration was not available to 

confidently distinguish one sensor over another, the direction of the 0.1 °C 

correction is debatable. For studies concerned with relative temperature differences

where absolute calibration is not necessary, it may be appropriate to either correct 

the T107 measurements by an offset of +0.1 °C or the Rotronic by –0.1 °C. 

The calculated sensor differences appear to be within the manufacturer-stated error 

of 0.4 °C (at –24 to 48 °C) for the T107 sensors and 0.1 °C for the Rotronics.
21

4.2.3 Relative Humidity Calibration Data Analysis

Five RH sensors were subjected to a relative calibration analysis. The percentage 

difference of each RH sensor from the Logger 4649 RH-sensor standard were 

calculated during the JDD 61.1–61.3 time frame (and are presented in Fig. 15). The

RH measurements for Loggers 4653, 4607, 4647, and 3405 reported values within 

1% of each other. Logger 4649 measurements were on average approximately 1% 

lower than the average of the other 4 logger-recorded values. The sensor variations

(excluding the Logger 4649 RH sensor) between the 4 RH sensors were within the 

manufacturer-stated calibration of ±0.8%. 

Fig. 15 RH percentage difference from Logger 4649 RH measurements during the local 

nighttime of 2014 JD 60 (02 Mar); the logger associated with each non-standard RH sensor is 

demonstrated in the legend

4.2.4 Solar Radiation Calibration Data Analysis

Solar radiation measurements were taken from 5 pyranometers during the daylight 

hours of JDD 61.7–61.9 UTC (local time: 02 Mar 2014, 0.2–14.6 hours MST). The 

percent difference between each pyranometer and the Logger 4649–pyranometer

standard are shown in Fig. 16. Solar radiation measurements from Loggers 4607, 

4647, and 3405 were within 1% of each other. The other 2 instruments reported 

significant departures (3–9%) from this group of 3 sensors. Drift in the percentage 

departure was observed over the course of a day. (During the field campaign, 
22

Logger 4649 solar radiation measurements were noted as being potentially too high,

considering the location/latitude of where the measurements were obtained. Despite 

the departure of several percent, the results were still within the expected accuracy 

determined by the manufacturer for daily sums of ±10%.)

Fig. 16 Pyranometers percent difference from Logger 4649 pyranometer-standard, during 

the local daytime hours of 2014 JD 61; logger associated with each sensor is indicated in the 

legend

5. Discussion

Relative calibration assessments work well if the “standard” by which the 

comparisons are made is NIST approved. This relative calibration assessment 

demonstrated both the strength and weakness of a relative comparison. For the 

dynamic sensors, whose “standard” was closely aligned to a NIST standard, 

confidence in the measured data was re-enforced. For the thermodynamic sensors, 

the comparison against the standard did not entirely enhance the data confidence, 

even though the instruments were technically new or calibrated within the last year. 

Fortunately, the thermodynamic-sensor intercomparison results have preserved the 

data value by defining how to use these measurements in an informed manner. 

When time and funding run short, sensor calibration tends to become a “low” 

priority. Unfortunately, this sacrifice can reduce the quality and usefulness of the 
23

final measurements. In an ideal world, all sensors would be calibrated against NIST 

standards prior to their field installation, rechecked in a side-by-side relative 

calibration before fielding, and then put on a routine recalibration schedule.

This study showed that 1) a sensor’s NIST paperwork does not guarantee that the 

sensor has not been dropped or accidentally hit in a manner that negates the 

calibration; and 2) all sensors need some calibration before being fielded, so that 

the data user can employ the sensor output in an informed manner.

6. Summary and Recommendations

Successful Army decisions rely on accurate representation. Environmental 

decisions involving the atmosphere rely heavily on detailed meteorological models. 

The ARL is building an “Army-scale”, high-resolution MSA to provide reliable 

and persistent data resources that, in turn, allow atmospheric modelers and sensor 

developers to validate and compare model and sensor performance with 

atmospheric observations at and near the surface and in close proximity to terrain 

of varying complexity.

In 2014, the MSA “Proof of Concept” (MSA-Phase I) was organized and executed 

to test the feasibility of success for such a significant ARL investment. With limited 

time and funds, a representative Phase I field campaign was designed along with 

an expandable data-management strategy that would accommodate the initial field 

requirements and provide a foundation for follow-on MSA phases. (A detailed 

description of the MSA-Phase I and its preliminary results is found in publications 

mentioned in Section 1.)

Before the field campaign could be executed all sensors needed to be calibrated. 

Since timely access to a NIST-certified calibration laboratory was not an option for 

the MSA-Phase I, a side-by-side sensor intercomparison (a relative calibration) was 

designed and conducted. The relative calibration task was subdivided into 2 parts: 

dynamic and thermodynamic sensor-calibration segments. Utilizing the projected 

high winds of the New Mexico, February–March “windy season”, the dynamic 

sensors were set up first for calibration. Steady, high winds were sought to best 

equate the airflow of a calibration wind tunnel. The thermodynamic-sensor

calibration followed using a more protected location. The acquisition period ran 

10 Feb–06 Mar 2014. 

A preliminary, qualitative review of the calibration data showed that the majority 

of sensors were worthy of the MSA “Proof of Concept” field campaign. Those 

instruments that did not meet the standard were either re-tested and/or replaced. 

With a pressing deadline to complete the MSA-Phase I measurement portion by 
24

mid-fiscal year, the sensors passing the calibration assessment were integrated into 

the field campaign design, which promptly followed the calibration task. A detailed 

calibration analysis also had to be postponed due to the uncompromising schedule 

and lack of available personnel. Fortunately, with the successful “Proof of Concept”

completed and Phase II underway, a more detailed relative-calibration data analysis 

has now been executed, with results documented in this report. The standard 

sensors selected for the relative-calibration analyses were either new or had been

calibrated within the last year. 

The dynamic sensor relative calibration configuration and data analyses were 

described in detail in Section 3. Section 4 described the thermodynamic sensors 

relative-calibration layout, methodology, and analysis. In short, the results 

confirmed the qualitative assessments executed prior to the MSA-Phase I field 

campaign execution. That is, the more detailed analysis showed that most sensors 

were found to be within the manufacturer specifications. 

Looking at each sensor group individually:

1) Sonic Anemometers: Based on the intercomparison RMSE and curvefitting analyses, there were no significant calibration issues identified. The 

instrument calibration for each anemometer was good to about 0.1 m/s, which 

was close to the manufacturer-stated accuracy of ±0.05 m/s. Since the results 

fell within the manufacturer-stated accuracy of ±1% rms, no correction curve 

was suggested. 

2) Barometer: Barometer 4607 reported erratic data during the initial 

calibration assessment and was consequently removed from consideration prior 

to the field campaign. Based on this more detailed post-campaign review, the 

remaining barometers were within manufacturer specifications. Barometer 

4649, however, showed a potential need for an offset correction of about 

–0.5 mb when used with the other fielded barometers.

3) Temperature sensors: Thermometers were found to be within the 

manufacturer-general specifications.

a) The Rotronic sensors reported values about 0.1 °C higher than the 

T107 sensors. Note: During the MSA-Phase I field campaign, the Rotronic 

sensors were mounted at 2 m AGL and the T107 at 10 m AGL. The 

analyzed results imply that uncorrected MSA-Phase I data may report 

overly unstable, near-surface, vertical temperature profiles. Since an 

absolute calibration was not available, the direction of the 0.1 °C correction 

is debatable. For studies concerned with relative temperature differences 
25

only, it is suggested to either correct the T107 measurements by an offset 

of +0.1 °C or, the Rotronic data by –0.1 °C. 

b) The PRT sensor reported values about 0.4 °C colder than the 

standard. This sensor was replaced with a new T107 during the MSA-Phase

I field campaign. The new T107’s data acquisition started on 15 Apr 2014

at 0942 MDT.

4) Hygrometers: RH measurements for Loggers 4653, 4607, 4647, and 3405 

reported values within 1% of each other. Logger 4649 measurements were on 

average approximately 1% lower than the average of the other 4 recorded logger 

values. The sensor variations (excluding the Logger 4649 RH sensor) between 

the 4 RH sensors were within the manufacturer-stated calibration of ±0.8%. 

5) Pyranometers: Solar radiation measurements from Loggers 4607, 4647, and 

3405 reported measurements within 1% of each other. Loggers 4649 and 4653 

reported significant departures (3–9%) from the 3 sensors. Drift in the 

percentage departure was observed over the course of a day. Logger 4649 solar 

radiation measurements were noted as being potentially too high during the 

MSA-Phase I field campaign.

This relative calibration exercise has provided insights into the quality of the MSAPhase I field campaign data, as well as ideas for future data sets. For the dynamic 

sensors, whose “standard” was closely aligned to a NIST-standard quality, 

confidence in the measured data was re-enforced. For the thermodynamic sensors, 

even though the instruments were technically new or calibrated within the last year, 

the comparison against the standards exposed the data limitations. Fortunately, 

these analyses provide results that will help scientists use the data in an informed 

manner. As part of the recommendations, the following observations were noted:

1) A sensor’s NIST paperwork does not mean that the sensor has not been 

dropped or accidentally hit in a manner that negates the calibration; and 

2) When a field project’s sensor calibration is postponed or deleted, this 

sacrifice can reduce the quality and usefulness of the final measurements. 

In an ideal world, all meteorological sensors would be calibrated against NIST 

standards prior to their field installation, rechecked in a side-by-side relative 

calibration before fielding, and then put on a routine recalibration schedule; thus, 

ensuring the best quality measurements for the data users.
26

7. Notes

Personal communication between Tim Chavez of BAE Systems and Robert 

Brice of ARL, 2015 Jan 26.
27

8. References

Campbell Scientific, Inc. Barometric pressure sensor, model CS105. Logan, (UT): 

Campbell Scientific, Inc.; 2001a.

Campbell Scientific, Inc. Temperature probes, models 107 and 108. Logan, (UT): 

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instruction manual. Rev. 2012 Oct. Logan, (UT): Campbell Scientific, Inc.; 

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(data management tool: “proof of concept”). White Sands Missile Range 

(NM): Army Research Laboratory (US); 2014 Oct. Report No.: 

ARL-TR-7133. Also available at

http://www.arl.army.mil/www/default.cfm?technical_report=7251

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28

List of Symbols, Abbreviations, and Acronyms

24/7 24 h/day–7 days/week

AGL above ground level

ARL US Army Research Laboratory

DAS Data Acquisition System 

fov field of view

JD Julian Day

JDD Julian Decimal Day

MDT Mountain Daylight Time

MSA Meteorological Sensor Array

MST Mountain Standard Time

NIST National Institute of Standards and Technology

NRC National Research Council

PRT Platinum Resistance Thermometer

PV photovoltaic

RH relative humidity

rms root mean square 

RMSE root-mean-square error

UTC universal time coordinated

WSMR White Sands Missile Range
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