Accuracy of climate station electronic sensors – not the best

Given all of the discussions recently on issues with the surface network, I thought it would be a good idea to present this excellent paper by Lin and Hubbard and what they discovered about the accuracy, calibration and maintenance of the different sensors used in the climatic networks of the USA. Pay particular attention to the errors cited for the ASOS and AWOS aviation networks, which are heavily used by GHCN.

Sensor and Electronic Biases/Errors in Air Temperature Measurements in Common Weather Station Networks*

X. LIN AND K. G. HUBBARD

School of Natural Resource Sciences, University of Nebraska at Lincoln, Lincoln, Nebraska

ABSTRACT

The biases of four commonly used air temperature sensors are examined and detailed. Each temperature transducer consists of three components: temperature sensing elements, signal conditioning circuitry, and corresponding analog-to-digital conversion devices or dataloggers. An error analysis of these components was performed to determine the major sources of error in common climate networks. It was found that, regardless of microclimate effects, sensor and electronic errors in air temperature measurements can be larger than those given in the sensor manufacturer’s specifications. The root-sum-of-squares (RSS) error for the HMP35C sensor with CR10X datalogger was above 0.2°C, and rapidly increases for both lower (<-20°C) and higher temperatures (>30°C). Likewise, the largest errors for the maximum–minimum temperature system (MMTS) were at low temperatures (<-40°C). The temperature linearization error in the HO-1088 hygrothermometer produced the largest errors when the temperature was lower than -20°C. For the temperature sensor in the U.S. Climate Reference Networks (USCRN), the error was found to be 0.2° to 0.33°C over the range -25° to 50°C. The results presented here are applicable when data from these sensors are applied to climate studies and should be considered in determining air temperature data continuity and climate data adjustment models.

Introduction

A primary goal of air temperature measurement with weather station networks is to provide temperature data of high quality and fidelity that can be widely used for atmospheric and related sciences. Air temperature measurement is a process in which an air temperature sensor measures an equilibrium temperature of the sensor’s physical body, which is optimally achieved through complete coupling between the atmosphere and air temperature sensor.

The process accomplished in the air temperature radiation shield is somewhat dynamic, mainly due to the heat convection and heat conduction of a small sensor mass. Many studies have demonstrated that to reach a higher measurement accuracy both good radiation shielding and ventilation are necessary for air temperature measurements (Fuchs and Tanner 1965; Tanner 1990; Quayle et al. 1991; Guttman and Baker 1996; Lin et al. 2001a,b; Hubbard et al. 2001; Hubbard and Lin 2002). Most of these studies are strongly associated with the study of air temperature bias or errors caused by microclimate effects (e.g., airflow speed in-side the radiation shields radiative properties of sensor surface and radiation shields, and effectiveness of the radiation shields). Essentially, these studies have assumed the equation governing the air temperature to be absolutely accurate, and the investigations have focused on the measurement accuracy and its dependence on how well the sensor is brought into equilibrium with the atmospheric temperature. Such findings are indeed very important for understanding air temperature measurement errors in climate monitoring, but it is well known that all microclimate-induced biases or errors also include the electronic biases or errors embedded in their temperature sensors and their corresponding data acquisition system components.

Three temperature sensors are commonly used in the weather station networks: A thermistor in the Cooperative Observing Program (COOP) that was formally recognized as a nationwide federally supported system in

1980; a platinum resistance thermometer (PRT) in the Automated Surface Observing System (ASOS), a network that focuses on aviation needs; and a thermistor in the Automated Weather Station (AWS) networks operated

by states for monitoring evaporation and surface climate data.

Each of these sensors has been used to observe climate data over at least a ten year period in the U.S. climate monitoring networks. The U.S. Climate Reference Network (USCRN) was established in 2001 and gradually and nationally deployed for monitoring long-term and high quality surface climate data. In the USCRN system, a PRT sensor was selected for the air temperature measurements. All sensing elements in these four climate monitoring networks are temperature sensitive resistors, and the temperature sensors are referred to as the maximum–minimum temperature system (MMTS), sensor: HMP35C, HO-1088, and USCRN PRT sensors, respectively, in the COOP, AWS, ASOS, and USCRN networks (see Table 1).

The basic specifications of each sensor system including operating temperature range, static accuracy, and display/output resolution can be found in operation manuals. However, these specifications do not allow a detailed evaluation, and some users even doubt the stated specifications and make their own calibrations before deploying sensors in the network. In fact, during the operation of either the MMTS sensor in the COOP or HO-1088 hygrothermometer in the ASOS, both field and laboratory calibrations were made by a simple comparison using one or two fixed precision resistors (National Weather Service 1983; ASOS Program Office 1992).

This type of calibration is only effective under the assumption of temporal nonvariant sensors with a pure linear relation of resistance versus temperature. For the HMP35C, some AWS networks may regularly calibrate the sensors in the laboratory, but these calibrations are static (e.g., calibration at room temperature for the data acquisition system).

It is not generally possible to detect and remove temperature-dependent bias and sensor nonlinearity with static calibration. In the USCRN, the PRT sensor was strictly calibrated from -50° to +50°C each year in the laboratory. However, this calibration does not include its corresponding datalogger. To accurately trace air temperature trends over the past decades or in the future in the COOP, AWS, ASOS, and USCRN and to reduce the influence of time-variant biases in air temperature data, a better understanding of electronic bias in air temperature measurements is necessary.

The objective of this paper is to carefully analyze the sensor and electronic biases/errors induced by the temperature sensing element, signal conditioning circuitry, and data acquisition system.

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This implies that the MMTS temperature observations are unable to discriminate  ±0.25°C changes

in the lower temperature ranges (Fig. 5 and Table 2). The interchangeability of the MMTS thermistors is from

60.2°C from temperature -40° to +40°C and ±0.45°C elsewhere (Fig. 4). Two fixed resistors (R2 and R3) with

a 0.02% tolerance produced larger temperature errors of measurement in low temperatures, but the error

caused by the fixed resistor R19 in Fig. 1 can be ignored. Therefore, the RSS errors in the MMTS are from 0.31°

to 0.62°C from temperature -40°C to -50°C (Fig. 5).

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The major errors in the HO-1088 (ASOS Temp/DP sensor) are interchangeability, linearization error, fixed resistor error, and self-heating error (Table 2 and Fig. 7). The linearization error in the HO-1088 is relatively serious because the analog signal (Fig. 3) is simply linearized from -50° to 50°C versus -2 to 2 V. The maximum magnitude of linearization error reached over 1°C (Fig. 7). There are four fixed precision resistors: R13, R14, R15, and R16 with a 0.1% tolerance. However, the error of temperature measurement caused by the R14, R15, and R16 can be eliminated by the adjustment of amplifier gain and offsets during onboard calibration operations in the HO-1088.

The error caused by the input fixed resistor R13 is illustrated in Fig. 7. Since this error was constantly varied from -0.2° to -0.3°C, it can be cancelled during the onboard calibration. It is obvious that a 5-mA current flowing through the PRT in the HO-1088 is not appropriate, especially because it has a small sensing element (20 mm in length and 2 mm in diameter). The self-heating factor for the PRT in the HO-1088 is 0.25°C mW21 at 1 m s21 airflow (Omega Engineering 1995), corresponding to the selfheating errors 0.5°C when the self-heating power is 2mW (Table 2 and Fig. 7). Compared to the linearization error and self-heating error, the interchangeability and LSB error in the HO-1088 sensor are relative small, ±0.1° and ±0.01°C, respectively (Table 2).

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Conclusions and discussion

This study provides a better understanding of temperature measurement errors caused by the sensor, analog signal conditioning, and data acquisition system. The MMTS sensor and the HO-1088 sensor use the ratiometric method to eliminate voltage reference errors. However, the RSS errors in the MMTS sensor can reach 0.3–0.6 under temperatures beyond -40° to +40°C. Only under yearly replacement of the MMTS thermistor with the calibrated MMTS readout can errors be constrained within ±0.2°C under the temperature range from -40° to +40°C. Because the MMTS is a calibration- free device (National Weather Service 1983), testing of one or a few fixed resistors for the MMTS is unable to guarantee the nonlinear temperature relations of the MMTS thermistor. For the HO-1088 sensor, the self-heating error is quite serious and can make temperature 0.5°C higher under 1 m/s airflow, which is slightly less than the actual normal ventilation rate in the ASOS shield (Lin et al. 2001a). The simple linear method for the PRT of the HO-1088 causes unacceptable errors that are more serious in the low temperature range. These findings are helpful for explaining the ASOS warm biases found by Kessler et al. (1993) in their climate data and Gall et al. (1992) in the climate data archives. For the dewpoint temperature measurements in the ASOS, such self-heating effects might be cancelled out by the chill mirror mechanism: heating or cooling the chill mirror body (conductively contains the dewpoint PRT inside) to reach an equilibrium thin dew layer–dewpoint temperature.

Thus, in this case, the selfheating error for dewpoint temperature measurements might not be as large as the air temperature after correct calibration adjustment. Likewise, the relative humidity data from the ASOS network, derived from air temperature and dewpoint temperature, is likely be contaminated by the biased air temperature.

Both resistance measurements in the HMP35C and USCRN PRT sensors are interrogated by the dataloggers.

The HMP35C is delivered from Campbell Scientific, Inc., with recommended measurement methods.

Even so, the HMP35C sensor in the AWS network can experience more than 0.28C errors in temperatures from

-30° to +30°C. Beyond this range, the RSS error increases from 0.4° to 1.0°C due to thermistor interchangeability, polynomial error, and CR10X datalogger inaccuracy. For the USCRN PRT sensor in the USCRN network, the RSS errors can reach 0.2°–0.34°C due to the inaccuracy of CR23X datalogger, which suggests that the configuration of USCRN PRT and measurement taken in the CR23X could be improved if higher accuracy is needed. Since the USCRN network is a new setup, the current configuration of the USCRN PRT temperature sensor could be reconstructed for better measurements.

This reconstruction should focus on the increase of signal sensitivity, the selection of fixed resistor(s) with smaller temperature coefficient of resistance, and the decrease of the self-heating power, so that it could be more compatible with the CR23X for longterm climate monitoring.

These findings are applicable\ to the future of temperature data generated from the USCRN network and possible modification of the PRT sensor for higher quality measurements in the reference climate network.

The complete Lin-Hubbard papert (PDF) is available here.