Summary & Introduction

Summary

This document describes the calibrations and analyses performed to produce the temperature and humidity parameters in the FAAM core netCDF. We show that almost all of the sources of uncertainty in these measurements are not due to random measurement error, and so will not be reduced by averaging. In this version of the document, descriptions of the temperature measurement is restricted to platinum resistance thermometers. Future versions will include information about thermistors.

Introduction

Measuring temperature and humidity on an aircraft is not straightforward, and is unavoidably linked with pressure measurements. Air is slowed down and compressed as it enters a housing, meaning that the temperature indicated by a sensor in that housing is higher than the static air temperature outside. To correct for this increase in temperature, the Mach number, calculated from pressure measurements, and a recovery correction are required. The measurement of water vapour is challenging due to the speed of the aircraft, the range of temperature and humidity a hygrometer experiences, and the potential for exposure to saturated conditions. Detailed discussion of these measurement challenges can be found in chapter 2 of Airborne Measurements for Environmental Research [Bange et al., n.d.]

This document describes the calibration procedures and data processing involved in producing temperature and humidity data on the FAAM BAe-146 aircraft, as well as listing future planned work. Our goal is to provide an uncertainty parameter alongside each variable in the FAAM core file, calculated for each datapoint, and reported as a standard uncertainty which can be multiplied by a coverage factor k=2 by the user, if required, to provide a coverage probability of approximately 95%. For most parameters, the standard uncertainty will represent a combination of the uncertainties arising from all known sources. The method of combining uncertainties will vary according to the application and the extent to which we are able to know how they contribute to the measurement. As will be described in this document, for the meteorological parameters these uncertainties are almost completely due to systematic measurement errors. It is therefore important to note that they will not be reduced if multiple datapoints in the time series are averaged.

In this document we use the definitions of error and uncertainty given in the International Vocabulary of Metrology (VIM) [200:2012, 2012] and NPL Good Practice Guide No. 11 [Bell, 2001]: error is the difference between the measured value and the ‘true value’ of the thing being measured; uncertainty is a quantification of the doubt about the measurement result. Throughout, we refer to two versions of the FAAM post processing framework for data collected on the aircraft, the older v004 [FAAM, n.d.], and the newer v005 [FAAM, n.d.].