[1] A detailed assessment of radiosonde water vapor measurement accuracy throughout the tropospheric column is needed for assessing the impact of observational error on applications that use the radiosonde data as input, such as forecast modeling, radiative transfer calculations, remote sensor retrieval validation, climate trend studies, and development of climatologies and cloud and radiation parameterizations. Six operational radiosonde types were flown together in various combinations with a reference-quality hygrometer during the Atmospheric Infrared Sounder (AIRS) Water Vapor ExperimentGround (AWEX-G), while simultaneous measurements were acquired from Raman lidar and microwave radiometers. This study determines the mean accuracy and variability of the radiosonde water vapor measurements relative to simultaneous measurements from the University of Colorado (CU) Cryogenic Frostpoint Hygrometer (CFH), a referencequality standard of known absolute accuracy. The accuracy and performance characteristics of the following radiosonde types are evaluated: Vaisala RS80-H, RS90, and RS92; Sippican Mark IIa; Modem GL98; and the Meteolabor Snow White hygrometer. A validated correction for sensor time lag error is found to improve the accuracy and reduce the variability of upper tropospheric water vapor measurements from the Vaisala radiosondes. The AWEX data set is also used to derive and validate a new empirical correction that improves the mean calibration accuracy of Vaisala measurements by an amount that depends on the temperature, relative humidity, and sensor type. Fully corrected Vaisala radiosonde measurements are found to be suitably accurate for AIRS validation throughout the troposphere, whereas the other radiosonde types are suitably accurate under only a subset of tropospheric conditions. Although this study focuses on the accuracy of nighttime radiosonde measurements, comparison of Vaisala RS90 measurements to water vapor retrievals from a microwave radiometer reveals a 6-8% dry bias in daytime RS90 measurements that is caused by solar heating of the sensor. An AWEX-like data set of daytime measurements is highly desirable to complete the accuracy assessment, ideally from a tropical location where the full range of tropospheric temperatures can be sampled.

[1] Relative humidity (RH) measurements from Vaisala RS92 radiosondes are widely used in research and operational applications, but their accuracy is not well characterized as a function of height, RH, and time of day (or solar altitude angle). This study compares RS92 RH measurements to simultaneous water vapor measurements from three reference instruments of known accuracy. Cryogenic frost point hygrometer measurements are used to characterize the RS92 accuracy above the 700-mbar level, microwave radiometer measurements characterize the RS92 accuracy averaged over essentially the lower troposphere, and the RS92 accuracy at the surface is characterized by a system of 6 RH probes with National Institute of Standards and Technology-traceable calibrations. The three RS92 accuracy assessments are combined to yield a detailed estimate of RS92 accuracy for all RH conditions from the surface to the lowermost stratosphere. An empirical correction is derived to remove the mean bias error, yielding corrected RS92 measurements whose bias uncertainty is independent of height or RH and is estimated to be ±4% of the measured RH value for nighttime soundings and ±5% for daytime soundings, plus an RH offset uncertainty of ±0.5% RH that is significant for dry conditions. The accuracy of an individual RS92 sounding is further characterized by the 1-s ''random production variability,'' estimated to be ±1.5% of the measured RH value. The daytime bias correction must be used with caution, as it is only accurate for clear-sky or near-clear conditions owing to the complicated effect of clouds on the solar radiation error.

This study presents a method of improving the accuracy of relative humidity (RH) measurements from Vaisala RS80 and RS90 radiosondes by applying sensor-based corrections for well-understood sources of measurement error. Laboratory measurements of the sensor time constant as a function of temperature are used to develop a correction for a time-lag error that results from slow sensor response at low temperatures. The time-lag correction is a numerical inversion algorithm that calculates the ambient (''true'') humidity profile from the measured humidity and temperature profiles, based on the sensor time constant. Existing corrections for two sources of dry bias error in RS80 humidity measurements are also included in the correction procedure: inaccuracy in the sensor calibration at low temperatures, and chemical contamination of sensors manufactured before June 2000 by nonwater molecules from the radiosonde packaging material. The correction procedure was evaluated by comparing corrected RS80-H measurements with simultaneous measurements from the reference-quality NOAA/Climate Modeling and Diagnostics Laboratory balloon-borne cryogenic hygrometer. The time-lag correction is shown to recover vertical structure in the humidity profile that had been ''smoothed'' by the slow sensor response, especially in the upper troposphere and lower stratosphere, revealing a much sharper troposphere-stratosphere transition than is apparent in the original measurements. The corrections reduced the mean dry bias in the radiosonde measurements relative to the hygrometer from 4% RH at Ϫ20ЊC and 10% RH at Ϫ70ЊC to about Ϯ2% RH at all temperatures, and the variability at low temperatures is substantially reduced. A shortcoming of the existing contamination correction is also uncovered, and a modification is suggested. The impact of the corrections on several radiosonde datasets is shown.

The comparison of simultaneous humidity measurements by the Vaisala RS92 radiosonde and by the Cryogenic Frostpoint Hygrometer (CFH) launched at Alajuela, Costa Rica, during July 2005 reveals a large solar radiation dry bias of the Vaisala RS92 humidity sensor and a minor temperature-dependent calibration error. For soundings launched at solar zenith angles between 10°and 30°, the average dry bias is on the order of 9% at the surface and increases to 50% at 15 km. A simple pressure-and temperature-dependent correction based on the comparison with the CFH can reduce this error to less than 7% at all altitudes up to 15.2 km, which is 700 m below the tropical tropopause. The correction does not depend on relative humidity, but is able to reproduce the relative humidity distribution observed by the CFH.

Radiosonde relative humidity (RH) measurements are known to be unreliable at cold temperatures. This study characterizes radiosonde RH measurements from Vaisala RS80-A thin-film capacitive sensors in the temperature range 0Њ to Ϫ70ЊC. Sources of measurement error are identified, and two approaches for correcting the errors are presented. The corrections given in this paper apply only to the Vaisala RS80-A sensor, although the RS80-H sensor is briefly discussed for comparison. A temperature-dependent correction factor is derived from statistical analysis of simultaneous RH measurements from RS80-A radiosondes and the NOAA cryogenic frostpoint hygrometer. The mean RS80-A measurement error is shown to be a dry bias that increases with decreasing temperature, and the multiplicative correction factor is about 1.3 at Ϫ35ЊC, 1.6 at Ϫ50ЊC, 2.0 at Ϫ60ЊC, and 2.4 at Ϫ70ЊC. The fractional uncertainty in the mean of corrected measurements, when large datasets are considered statistically, increases from 0.06 at 0ЊC to 0.11 at Ϫ70ЊC. The fractional uncertainty for correcting an individual sounding is about Ϯ0.2, which is larger because this statistical approach considers only the mean value of measurement errors that are not purely temperature dependent. The correction must not be used outside the temperature range 0Њ to Ϫ70ЊC, because it is a meaningless extrapolation of a polynomial curve fit. Laboratory measurements of sensor response conducted at Vaisala are used to characterize some of the individual sources of RS80-A measurement error. A correction factor is derived for the dominant RS80-A measurement error at cold temperatures: an inaccurate approximation for the sensor's temperature dependence in the data processing algorithm. The correction factor for temperature-dependence error is about 1.1 at Ϫ35ЊC, 1.4 at Ϫ50ЊC, 1.8 at Ϫ60ЊC, and 2.5 at Ϫ70ЊC. Dependences and typical magnitudes are given for measurement errors that result from the temperature dependence of the sensor's time constant, and from several smaller bias errors and random uncertainties.