Abstract

Stratospheric water vapor concentrations and age of air are investigated in an ensemble of coupled chemistry-climate model simulations covering the period from 1960 to 2005. Observed greenhouse gas concentrations, halogen concentrations, aerosol amounts, and sea surface temperatures are all specified in the model as time-varying fields. The results are compared with two experiments (time-slice runs) with constant forcings for the years 1960 and 2000, in which the sea surface temperatures are set to the same climatological values, aerosol concentrations are fixed at background levels, while greenhouse gas and halogen concentrations are set to the values for the relevant years.

The time-slice runs indicate an increase in stratospheric water vapor from 1960 to 2000 due primarily to methane oxidation. The age of air is found to be significantly less in the year 2000 run than the 1960 run. The transient runs from 1960 to 2005 indicate broadly similar results: an increase in water vapor and a decrease in age of air. However, the results do not change gradually. The age of air decreases significantly only after about 1975, corresponding to the period of ozone reduction. The age of air is related to tropical upwelling, which determines the transport of methane into the stratosphere. Oxidation of increased methane from enhanced tropical upwelling results in higher water vapor amounts. In the model simulations, the rate of increase of stratospheric water vapor during the period of enhanced upwelling is up to twice the long-term mean. The concentration of stratospheric water vapor also increases following volcanic eruptions during the simulations.

Abstract

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 −20°C and 10% RH at −70°C to about ±2% 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.

Abstract

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 0° to −70°C. 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 −35°C, 1.6 at −50°C, 2.0 at −60°C, and 2.4 at −70°C. The fractional uncertainty in the mean of corrected measurements, when large datasets are considered statistically, increases from 0.06 at 0°C to 0.11 at −70°C. The fractional uncertainty for correcting an individual sounding is about ±0.2, 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 0° to −70°C, 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 −35°C, 1.4 at −50°C, 1.8 at −60°C, and 2.5 at −70°C. 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.

Abstract

The Vaisala RS92 radiosonde is the most widely used type of sonde in the current global radiosonde network. One of the largest biases in the RS92 humidity data is its daytime solar radiation dry bias (SRDB). An algorithm [referred to as NCAR radiation bias correction (NRBC)] was developed to correct the SRDB based on a more complicated algorithm developed by the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN). The NRBC to relative humidity (RH) is a function of the measured RH and temperature, and the temperature solar radiation correction. The latter varies with pressure, season, and time of the day. The RH correction has a mean magnitude of about 2%–4% and 6%–8% in the lower–midtroposphere and upper troposphere, respectively. The NRBC is evaluated against the GRUAN-corrected RS92 data and the ground-based GPS-estimated precipitable water (PW). The corrected RH agrees with the GRUAN data within ±0.5% on average, with standard deviations of about 1%–2% and 2%–6% in the lower–midtroposphere and upper troposphere, respectively. The NRBC leads to reduced mean biases, and better agreement with the GPS PW and its diurnal cycle. The NRBC has been applied to historical radiosonde data at 65 stations. The radiosonde humidity data, both with and without the NRBC, are homogenized using the method of . The NRBC results in consistently elevated RHs throughout the whole record in the homogenized data. This could have a significant impact on global reanalysis products when they are assimilated into the reanalysis models. However, the NRBC has insignificant effects on the long-term trends as the correction is primarily for mean biases.

Abstract

This study examines the DigiCORA and Global Climate Observing System Reference Upper-Air Network (GRUAN) humidity corrections of Vaisala RS92 radiosondes at three sites over the tropical Indian Ocean and surrounding areas during the Dynamics of the Madden–Julian Oscillation (DYNAMO) field campaign in 2011. The proprietary DigiCORA correction algorithm is built into the ground station software provided by Vaisala, whereas the GRUAN correction is an open source algorithm. Included in the GRUAN data product are uncertainty estimates for their corrections. This information is used to examine the statistical consistency of the various corrections.

In general, the algorithms produce a positive relative humidity (RH) correction that increases with altitude related primarily to a solar radiation dry bias adjustment. For example, in daytime soundings the relative RH correction increases from a few percent for temperatures >0°C to 20%–40% between 100 and 200 hPa. Comparison of corrected RH vertical profiles show only small differences (on the order of a few percent or less at any given level) between the DigiCORA and GRUAN algorithms, such that these corrections are considered to be statistically consistent at most levels.

In evaluating corrected humidity data with independent estimates of total precipitable water (TPW), good agreement was found at all sites between corrected sounding and ground-based microwave radiometer (MWR) estimates of TPW with mean differences ≤0.9 mm (or <2%), which is well within the uncertainty of these measurements. Overall, the correction algorithms examined herein perform well over a wide range of tropical moisture conditions.

Abstract

Although atmospheric observing systems were already an important part of meteorology before the American Meteorological Society was established in 1919, the past 100 years have seen a steady increase in their numbers and types. Examples of how observing systems were developed and how they have enabled major scientific discoveries are presented. These examples include observing systems associated with the boundary layer, the upper air, clouds and precipitation, and solar and terrestrial radiation. Widely used specialized observing systems such as radar, lidar, and research aircraft are discussed, and examples of applications to weather forecasting and climate are given. Examples drawn from specific types of chemical measurements, such as ozone and carbon dioxide, are included. Sources of information on observing systems, including other chapters of this monograph, are also discussed. The past 100 years has been characterized by synergism between societal needs for weather observations and the needs of fundamental meteorological research into atmospheric processes. In the latter half of the period, observing system improvements have been driven by the increasing demands for higher-resolution data for numerical models, the need for long-term measurements, and for more global coverage. This has resulted in a growing demand for data access and for integrating data from an increasingly wide variety of observing system types and networks. These trends will likely continue.

Abstract

The “Snow White” hygrometer is a low-cost, chilled-mirror hygrometer for radiosonde applications provided by a Swiss company, Meteolabor AG. A total of 54 Snow White soundings were conducted at five tropical stations in different seasons in 2000–01. All soundings were made with Vaisala RS80 radiosondes equipped either with the A-Humicap (22 soundings) or H-Humicap (32) relative humidity (RH) sensor. Comparisons of the RH with respect to liquid water between the Snow White and the different RS80 Humicap sensors are made. The Snow White measurements show reasonable agreement with the H-Humicap measurements from the surface up to ∼12 km (above −50°C air temperature), the region where the H-Humicap sensor can be considered reliable. Above 12 km, the H-Humicap sensor tends to miss small vertical-scale structures in RH due to the time lag error, but on average both instruments show no significant difference up to 14 km (−65°C). The comparison between the Snow White and A-Humicap sensors shows the known A-Humicap dry bias error at low temperatures and second dry bias error in the wet lower troposphere. The latter error [(A-Humicap RH) ≃ 0.9 × (Snow White RH) above 50% RH] may be a common problem for the recent A-Humicap sensors. These intercomparisons confirm the validity of the Snow White measurements at least up to the tropical upper troposphere and above 3%–6% RH.

Abstract

A new remotely controlled Airborne Vertical Atmospheric Profiling System (AVAPS) dropsonde system has been developed for and deployed on the NASA Global Hawk (GH) unmanned aircraft. Design, fabrication, and operation of the system was led by the National Center for Atmospheric Research (NCAR) with support from the National Oceanic and Atmospheric Administration (NOAA) Unmanned Aircraft Systems (UAS) Program. The system has employed the NCAR Research Dropsonde 94 (NRD94) dropsonde, a smaller and lighter version of the standard RD94 dropsonde deployed from manned aircraft but with virtually identical sensors. The dropsondes provide in situ atmospheric profiles of temperature, pressure, and humidity at a 2-Hz data rate, and wind speed and direction at 4 Hz. The system is capable of carrying up to 90 dropsondes and can support eight simultaneous soundings. Operation from the GH means that the dropsondes can be deployed from altitudes up to 19.8 km during flights in excess of 24-h duration. The dropsonde launch is commanded directly by an operator on the ground in coordination with the aircraft commander. Over 2700 total dropsondes have been deployed from the GH during four major campaigns since 2011. Data are processed in near–real time and have been employed by forecasters, for assimilation in numerical weather prediction models, and in diverse research studies. Intercomparison studies suggest the performance of the GH NRD94 dropsondes is similar to those deployed from manned aircraft. This paper describes the components and operation of the system and illustrates its unique capabilities through highlights of data application to research on the Arctic atmosphere, atmospheric rivers, and tropical cyclones.

Abstract

Definition of the tropopause has remained a focus of atmospheric science since its discovery near the beginning of the twentieth century. Few universal definitions (those that can be reliably applied globally and to both common observations and numerical model output) exist and many definitions with unique limitations have been developed over the years. The most commonly used universal definition of the tropopause is the temperature lapse-rate definition established by the World Meteorological Organization (WMO) in 1957 (the LRT). Despite its widespread use, there are recurrent situations where the LRT definition fails to reliably identify the tropopause. Motivated by increased availability of coincident observations of stability and composition, this study seeks to reexamine the relationship between stability and composition change in the tropopause transition layer and identify areas for improvement in a stability-based definition of the tropopause. In particular, long-term (40+ years) balloon observations of temperature, ozone, and water vapor from six locations across the globe are used to identify covariability between several metrics of atmospheric stability and composition. We found that the vertical gradient of potential temperature is a superior stability metric to identify the greatest composition change in the tropopause transition layer, which we use to propose a new universally applicable potential temperature gradient tropopause (PTGT) definition. Application of the new definition to both observations and reanalysis output reveals that the PTGT largely agrees with the LRT, but more reliably identifies tropopause-level composition change when the two definitions differ greatly.

While the global upper-air observing network has provided useful observations for operational weather forecasting for decades, its measurements lack the accuracy and long-term continuity needed for understanding climate change. Consequently, the scientific community faces uncertainty on key climate issues, such as the nature of temperature trends in the troposphere and stratosphere; the climatology, radiative effects, and hydrological role of water vapor in the upper troposphere and stratosphere; and the vertical profile of changes in atmospheric ozone, aerosols, and other trace constituents. Radiosonde data provide adequate vertical resolution to address these issues, but they have questionable accuracy and time-varying biases due to changing instrumentation and techniques. Although satellite systems provide global coverage, their vertical resolution is sometimes inadequate and they require independent reference observations for sensor and data product validation, and for merging observations from different platforms into homogeneous climate records. To address these shortcomings, and to ensure that future climate records will be more useful than the records to date, the Global Climate Observing System (GCOS) program is initiating a GCOS Reference Upper-Air Network (GRUAN) to provide high-quality observations using specialized radiosondes and complementary remote sensing profiling instrumentation that can be used for validation. This paper outlines the scientific rationale for GRUAN, its role in the Global Earth Observation System of Systems, network requirements and likely instrumentation, management structure, current status, and future plans. It also illustrates the value of prototype reference upper-air observations in constructing climate records and their potential contribution to the Global Space-Based Inter-Calibration System. We invite constructive feedback on the GRUAN concept and the engagement of the scientific community.