Vaisala RS92 Radiosonde Research Articles

Comparison of GRUAN data products for Meisei iMS-100 and Vaisala RS92 radiosondes at Tateno, Japan

Abstract. A total of 99 dual soundings with Meisei iMS-100 radiosonde and Vaisala RS92 radiosondes were carried out at the Aerological Observatory of the Japan Meteorological Agency, known as Tateno (36.06∘ N, 140.13∘ E, 25.2 m; the World Meteorological Organization, WMO, station number 47646), from September 2017 to January 2020. Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) data products (GDPs) from both sets of radiosonde data for 59 flights were subsequently created using a documented processing programme along with the provision of optimal estimates for measurement uncertainty. Differences in radiosonde performance were then quantified using these GDPs. For daytime observations, the iMS-100 temperature is around 0.5 K cooler than RS92-GDP in the stratosphere, with significant differences in the upper troposphere and lower stratosphere in consideration of combined uncertainties. For nighttime observations, the difference is around −0.1 K, and data are mostly in agreement. For relative humidity (RH), iMS-100 is around 1 % RH–2 % RH higher in the troposphere and 1 % RH smaller in the stratosphere than RS92, but both GDPs are in agreement for most of the profile. The mean pressure difference is ≤0.1 hPa, the wind speed difference is from −0.04 to +0.14 m s−1, the wind direction difference is ≤6.4∘, and the root mean square vector difference (RMSVD) for wind is ≤1.04 m s−1.

The SPARC Water Vapor Assessment II: assessment of satellite measurements of upper tropospheric humidity

Abstract. Nineteen limb-viewing data sets (occultation, passive thermal, and UV scattering) and two nadir upper tropospheric humidity (UTH) data sets are intercompared and also compared to frost-point hygrometer balloon sondes. The upper troposphere considered here covers the pressure range from 300–100 hPa. UTH is a challenging measurement, because concentrations vary between 2–1000 ppmv (parts per million by volume), with sharp changes in vertical gradients near the tropopause. Cloudiness in this region also makes the measurement challenging. The atmospheric temperature is also highly variable ranging from 180–250 K. The assessment of satellite-measured UTH is based on coincident comparisons with balloon frost-point hygrometer sondes, multi-month mapped comparisons, zonal mean time series comparisons, and coincident satellite-to-satellite comparisons. While the satellite fields show similar features in maps and time series, quantitatively they can differ by a factor of 2 in concentration, with strong dependencies on the amount of UTH. Additionally, time-lag response-corrected Vaisala RS92 radiosondes are compared to satellites and the frost-point hygrometer measurements. In summary, most satellite data sets reviewed here show on average ∼30 % agreement amongst themselves and frost-point data but with an additional ∼30 % variability about the mean bias. The Vaisala RS92 sonde, even with a time-lag correction, shows poor behavior for pressures less than 200 hPa.

Intercomparison of Vaisala RS92 and RS41 Radiosonde Temperature Sensors under Controlled Laboratory Conditions

Radiosoundings are essential for weather and climate applications, as well as for calibration and validation of remote sensing observations. Vaisala RS92 radiosondes have been widely used on a global scale until 2016; although in the fall of 2013, Vaisala introduced the RS41 model to progressively replace the RS92. To ensure the highest quality and homogeneity of measurements following the transition from RS92 to RS41, intercomparisons of the two radiosonde models are needed. A methodology was introduced to simultaneously test and compare the two radiosonde models inside climatic chambers, in terms of noise, calibration accuracy, and bias in temperature measurements. A pair of RS41 and RS92 radiosondes has been tested at ambient pressure under very different temperature and humidity conditions, reproducing the atmospheric conditions that a radiosonde can meet at the ground before launch. The radiosondes have also been tested before and after fast (within ≈ 10 s) temperature changes of about ±20 °C, simulating a scenario similar to steep thermal changes that radiosondes can meet when passing from indoor to outdoor environment during the pre-launch phase. The results show that the temperature sensor of RS41 is less affected by noise and more accurate than that of RS92, with noise values less than 0.06 °C for RS41 and less than 0.1 °C for RS92. The deviation from the reference value, referred to as calibration error, is within ±0.1 °C for RS41 and the related uncertainty (hereafter with coverage factor k = 1) is less than 0.06 °C, while RS92 is affected by a cold bias in the calibration, which ranges from 0.1 °C up to a few tenths of a degree, with a calibration uncertainty less than 0.1 °C. The temperature bias between RS41 and RS92 is within ±0.1 °C, while its uncertainty is less than 0.1 °C. The fast and steep thermal changes that radiosondes can meet during the pre-launch phase might lead to a noise increase in temperature sensors during radiosoundings, up to 0.1 °C for RS41 and up to 0.3 °C for RS92, with a similar increase in their calibration uncertainty, as well as an increase in the uncertainty of their bias up to 0.3 °C.

Calibration of a water vapour Raman lidar using GRUAN-certified radiosondes and a new trajectory method

Abstract. Raman lidars have been designated as potential candidates for trend studies by the Network for the Detection of Atmospheric Composition Change (NDACC) and GCOS (Global Climate Observing System) Reference Upper Air Network (GRUAN); however, for such studies improved calibration techniques are needed as well as careful consideration of the calibration uncertainties. Trend determinations require frequent, accurate, and well-characterized measurements. However, water vapour Raman lidars produce a relative measurement and require calibration in order to transform the measurement into a mixing ratio, a conserved quantity when no sources or sinks for water vapour are present. Typically, the calibration is done using a reference instrument such as a radiosonde. We present an improved trajectory technique to calibrate water vapour Raman lidars based on the previous work of Whiteman et al. (2006), Leblanc and Mcdermid (2008), Adam et al. (2010), and Herold et al. (2011), who used radiosondes as an external calibration source and matched the lidar measurements to the corresponding radiosonde measurement. However, they did not consider the movement of the radiosonde relative to the air mass and fronts. Our trajectory method is a general technique which may be used for any lidar and only requires that the radiosonde report wind speed and direction. As calibrations can be affected by a lack of co-location with the reference instrument, we have attempted to improve their technique by tracking the air parcels measured by the radiosonde relative to the field of view of the lidar. This study uses GRUAN Vaisala RS92 radiosonde measurements and lidar measurements taken by the MeteoSwiss RAman Lidar for Meteorological Observation (RALMO), located in Payerne, Switzerland, from 2011 to 2016 to demonstrate this improved calibration technique. We compare this technique to the traditional radiosonde–lidar calibration technique which does not involve tracking the radiosonde and uses the same integration time for all altitudes. Both traditional and our trajectory methods produce similar profiles when the water vapour field is homogeneous over the 30 min calibration period. We show that the trajectory method reduces differences between the radiosonde and lidar by an average of 10 % when the water vapour field is not homogeneous over a 30 min calibration period. We also calculate a calibration uncertainty budget that can be performed on a nightly basis. The calibration uncertainty budget includes the uncertainties due to phototube paralysis, aerosol extinctions, the assumption of the Ångström exponent, and the radiosonde. The study showed that the radiosonde was the major source of uncertainty in the calibration at 4 % of the calibration value. This trajectory method showed small improvements for RALMO's calibration but would be more useful for stations in different climatological regions or when non-co-located radiosondes are the only available calibration source.

On the Accuracy of Vaisala RS41 versus RS92 Upper-Air Temperature Observations

AbstractThe accuracy of Vaisala RS92 versus RS41 global radiosonde soundings, emphasizing stratospheric temperature, is assessed from January 2015 to June 2017 using ~311 500 RS92 and ~65 800 RS41 profiles and three different reference data sources. First, numerical weather prediction (NWP) model outputs are used as a transfer medium to produce relative RS92 and RS41 comparisons by analyzing observation minus NWP model background (OB–BG) and observation minus analysis (OB–AN) differences using the NOAA Climate Forecast System Reanalysis (CFSR; both comparisons) and the operational European Centre for Medium-Range Weather Forecasts (ECMWF) model (OB–AN comparison only). Second, GPS radio occultation (GPSRO) dry temperature profiles are directly compared with radiosondes, using GPSRO data from the University Corporation for Atmospheric Research (UCAR) Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) and EUMETSAT Radio Occultation Meteorology (ROM) Satellite Application Facility (SAF). Third, dual launches (RS92 and RS41 suspended from the same balloon) at five sites allow direct assessments. Comparisons of RS92 versus RS41 from all reference data sources are basically consistent. These two sondes agree well with global average temperature differences <0.1–0.2 K in the lower stratosphere from 51.5 to 26.1 hPa based on global stations and the dual launches. RS41 appears to be less sensitive than RS92 to changes in solar elevation angle. This study indicates that nighttime RS92 and RS41 radiosonde temperature biases are negligible, but infers a stratospheric cold bias (<0.5 K) in the CFSR and ECMWF model data.

Evaluation of MUSICA IASI tropospheric water vapour profiles using theoretical error assessments and comparisons to GRUAN Vaisala RS92 measurements

Abstract. Volume mixing ratio water vapour profiles have been retrieved from IASI (Infrared Atmospheric Sounding Interferometer) spectra using the MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) processor. The retrievals are done for IASI observations that coincide with Vaisala RS92 radiosonde measurements performed in the framework of the GCOS (Global Climate Observing System) Reference Upper-Air Network (GRUAN) in three different climate zones: the tropics (Manus Island, 2° S), mid-latitudes (Lindenberg, 52° N), and polar regions (Sodankylä, 67° N). The retrievals show good sensitivity with respect to the vertical H2O distribution between 1 km above ground and the upper troposphere. Typical DOFS (degrees of freedom for signal) values are about 5.6 for the tropics, 5.1 for summertime mid-latitudes, 3.8 for wintertime mid-latitudes, and 4.4 for summertime polar regions. The errors of the MUSICA IASI water vapour profiles have been theoretically estimated considering the contribution of many different uncertainty sources. For all three climate regions, unrecognized cirrus clouds and uncertainties in atmospheric temperature have been identified as the most important error sources and they can reach about 25 %. The MUSICA IASI water vapour profiles have been compared to 100 individual coincident GRUAN water vapour profiles. The systematic difference between the data is within 11 % below 12 km altitude; however, at higher altitudes the MUSICA IASI data show a dry bias with respect to the GRUAN data of up to 21 %. The scatter is largest close to the surface (30 %), but never exceeds 21 % above 1 km altitude. The comparison study documents that the MUSICA IASI retrieval processor provides H2O profiles that capture the large variations in H2O volume mixing ratio profiles well from 1 km above ground up to altitudes close to the tropopause. Above 5 km the observed scatter with respect to GRUAN data is in reasonable agreement with the combined MUSICA IASI and GRUAN random errors. The increased scatter at lower altitudes might be explained by surface emissivity uncertainties at the summertime continental sites of Lindenberg and Sodankylä, and the upper tropospheric dry bias might suggest deficits in correctly modelling the spectroscopic line shapes of water vapour.

Zenith: A Radiosonde Detector for Rapid-Response Ionizing Atmospheric Radiation Measurements During Solar Particle Events

Solar energetic particle events create radiation risks for aircraft, notably single event effects (SEEs) in microelectronics along with increased dose to crew and passengers. In response to this, some airlines modify their flight routes after automatic alerts are issued. At present these alerts are based on proton flux measurements from instruments on-board satellites, so it is important that contemporary atmospheric radiation measurements are made and compared. This paper presents the development of a rapid-response system built around the use of radiosondes equipped with a radiation detector, Zenith, which can be launched from a Met Office weather station after significant solar proton level alerts are issued. Zenith is a compact, battery-powered solid-state radiation monitor designed to be connected to a Vaisala RS-92 radiosonde which transmits all data to a ground station as it ascends to an altitude of ~33 km. Zenith can also be operated as a stand-alone detector when connected to a laptop, providing real-time count rates. It can also be adapted for use on unmanned aerial vehicles. Zenith has been flown on the Met Office Civil Contingency Aircraft (MOCCA), taken to the CERN-EU high energy Reference Field (CERF) facility for calibration and launched on a meteorological balloon at the Met Office's weather station in Camborne, Cornwall, UK. During this sounding, Zenith measured the Pfotzer-Regener maximum to be at an altitude of 18 - 20 km where the count rate was measured to be 1.15 counts s-1 cm-2 compared to 0.02 counts s-1 cm-2 at ground level.

Near real-time tropospheric water vapour profiling using a ground-based GPS receiver

ABSTRACTThis article presents a remote-sensing tool employing an artificial neural networks algorithm for near real-time determination of the relative humidity (RH) profile above a site using global positioning system (GPS) data recorded by a ground-based GPS receiver. GPS data were processed to obtain Integrated Water Vapour. The integrated water vapour in conjunction with ground level information for temperature, pressure, and RH was fed as inputs to the developed neural network which in turn generated the instantaneous RH profile, at six standard pressure levels of 100, 150, 200, 300, 500, and 700 hPa, as output. GPS and radiosonde data for the years 2009 and 2010 were used to train the system while the same data for 2011 were used to validate the system. The relative humidity profile results for 2011 generated using GPS data and the neural network, upon comparison with recorded in situ radiosonde RH profile measurements for the same days and times in the year 2011, had root mean square error of less than 4%, which falls within the margin of error of the Vaisala RS92 Radiosonde’s humidity measurement regime.

Evidence of horizontal and vertical transport of water in the Southern Hemisphere tropical tropopause layer (TTL) from high-resolution balloon observations

Abstract. High-resolution in situ balloon measurements of water vapour, aerosol, methane and temperature in the upper tropical tropopause layer (TTL) and lower stratosphere are used to evaluate the processes affecting the stratospheric water budget: horizontal transport (in-mixing) and hydration by cross-tropopause overshooting updrafts. The obtained in situ evidence of these phenomena are analysed using satellite observations by Aura MLS (Microwave Limb Sounder) and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) together with trajectory and transport modelling performed using CLaMS (Chemical Lagrangian Model of the Stratosphere) and HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model. Balloon soundings were conducted during March 2012 in Bauru, Brazil (22.3° S) in the frame of the TRO-Pico campaign for studying the impact of convective overshooting on the stratospheric water budget. The balloon payloads included two stratospheric hygrometers: FLASH-B (Fluorescence Lyman-Alpha Stratospheric Hygrometer for Balloon) and Pico-SDLA instrument as well as COBALD (Compact Optical Backscatter Aerosol Detector) sondes, complemented by Vaisala RS92 radiosondes. Water vapour vertical profiles obtained independently by the two stratospheric hygrometers are in excellent agreement, ensuring credibility of the vertical structures observed. A signature of in-mixing is inferred from a series of vertical profiles, showing coincident enhancements in water vapour (of up to 0.5 ppmv) and aerosol at the 425 K (18.5 km) level. Trajectory analysis unambiguously links these features to intrusions from the Southern Hemisphere extratropical stratosphere, containing more water and aerosol, as demonstrated by MLS and CALIPSO global observations. The in-mixing is successfully reproduced by CLaMS simulations, showing a relatively moist filament extending to 20° S. A signature of local cross-tropopause transport of water is observed in a particular sounding, performed on a convective day and revealing water vapour enhancements of up to 0.6 ppmv as high as the 404 K (17.8 km) level. These are shown to originate from convective overshoots upwind detected by an S-band weather radar operating locally in Bauru. The accurate in situ observations uncover two independent moisture pathways into the tropical lower stratosphere, which are hardly detectable by space-borne sounders. We argue that the moistening by horizontal transport is limited by the weak meridional gradients of water, whereas the fast convective cross-tropopause transport, largely missed by global models, can have a substantial effect, at least at a regional scale.

Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

Abstract. Solar heating of the relative humidity (RH) probe on Vaisala RS92 radiosondes results in a large dry bias in the upper troposphere. Two different algorithms (Miloshevich et al., 2009, MILO hereafter; and Wang et al., 2013, WANG hereafter) have been designed to account for this solar radiative dry bias (SRDB). These corrections are markedly different with MILO adding up to 40 % more moisture to the original radiosonde profile than WANG; however, the impact of the two algorithms varies with height. The accuracy of these two algorithms is evaluated using three different approaches: a comparison of precipitable water vapor (PWV), downwelling radiative closure with a surface-based microwave radiometer at a high-altitude site (5.3 km m.s.l.), and upwelling radiative closure with the space-based Atmospheric Infrared Sounder (AIRS). The PWV computed from the uncorrected and corrected RH data is compared against PWV retrieved from ground-based microwave radiometers at tropical, midlatitude, and arctic sites. Although MILO generally adds more moisture to the original radiosonde profile in the upper troposphere compared to WANG, both corrections yield similar changes to the PWV, and the corrected data agree well with the ground-based retrievals. The two closure activities – done for clear-sky scenes – use the radiative transfer models MonoRTM and LBLRTM to compute radiance from the radiosonde profiles to compare against spectral observations. Both WANG- and MILO-corrected RHs are statistically better than original RH in all cases except for the driest 30 % of cases in the downwelling experiment, where both algorithms add too much water vapor to the original profile. In the upwelling experiment, the RH correction applied by the WANG vs. MILO algorithm is statistically different above 10 km for the driest 30 % of cases and above 8 km for the moistest 30 % of cases, suggesting that the MILO correction performs better than the WANG in clear-sky scenes. The cause of this statistical significance is likely explained by the fact the WANG correction also accounts for cloud cover – a condition not accounted for in the radiance closure experiments.

Water vapor observations up to the lower stratosphere through the Raman lidar during the Maïdo Lidar Calibration Campaign

Abstract. A new lidar system devoted to tropospheric and lower stratospheric water vapor measurements has been installed at the Maïdo altitude station facility of Réunion island, in the southern subtropics. To evaluate the performances and the capabilities of the new system with a particular focus on UTLS (Upper Troposphere Lower Stratosphere) measurements, the Maïdo Lidar Calibration Campaign (MALICCA) was performed in April 2013. Varying the characteristics of the transmitter and the receiver components, different system configuration scenarios were tested and possible parasite signals (fluorescent contamination, rejection) were investigated. A hybrid calibration methodology has been set up and validated to insure optimal lidar calibration stability with time. In particular, the receiver transmittance is monitored through the calibration lamp method that, at the moment, can detect transmittance variations greater than 10–15%. Calibration coefficients are then calculated through the hourly values of IWV (Integrated Water Vapor) provided by the co-located GPS. The comparison between the constants derived by GPS and Vaisala RS92 radiosondes launched at Maïdo during MALICCA, points out an acceptable agreement in terms of accuracy of the mean calibration value (with a difference of approximately 2–3%), but a significant difference in terms of variability (14% vs. 7–9%, for GPS and RS92 calibration procedures, respectively). We obtained a relatively good agreement between the lidar measurements and 15 co-located and simultaneous RS92 radiosondes. A relative difference below 10% is measured in the low and middle troposphere (2–10 km). The upper troposphere (up to 15 km) is characterized by a larger spread (approximately 20%), because of the increasing distance between the two sensors. To measure water vapor in the UTLS region, nighttime and monthly water vapor profiles are presented and compared. The good agreement between the lidar monthly profile and the mean WVMR profile measured by satellite MLS (Microwave Limb Sounder) has been used as a quality control procedure of the lidar product, attesting the absence of significant wet biases and validating the calibration procedure. Due to its performance and location, the MAIDO H2O lidar will become a reference instrument in the southern subtropics, insuring the long-term survey of the vertical distribution of water vapor. Furthermore, this system allows the investigation of several scientific themes, such as stratosphere–troposphere exchange, tropospheric dynamics in the subtropics, and links between cirrus clouds and water vapor.

Evaluation of Humidity Correction Methods for Vaisala RS92 Tropical Sounding Data

AbstractThis 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.

Reference quality upper-air measurements: GRUAN data processing for the Vaisala RS92 radiosonde

Abstract. The GCOS (Global Climate Observing System) Reference Upper-Air Network (GRUAN) data processing for the Vaisala RS92 radiosonde was developed to meet the criteria for reference measurements. These criteria stipulate the collection of metadata, the use of well-documented correction algorithms, and estimates of the measurement uncertainty. An important and novel aspect of the GRUAN processing is that the uncertainty estimates are vertically resolved. This paper describes the algorithms that are applied in version 2 of the GRUAN processing to correct for systematic errors in radiosonde measurements of pressure, temperature, humidity, and wind, as well as how the uncertainties related to these error sources are derived. Currently, the RS92 is launched on a regular basis at 13 out of 15 GRUAN sites. An additional GRUAN requirement for performing reference measurements with the RS92 is that the manufacturer-prescribed procedure for the radiosonde's preparation, i.e. heated reconditioning of the sensors and recalibration during ground check, is followed. In the GRUAN processing however, the recalibration of the humidity sensors that is applied during ground check is removed. For the dominant error source, solar radiation, laboratory experiments were performed to investigate and model its effect on the RS92's temperature and humidity measurements. GRUAN uncertainty estimates are 0.15 K for night-time temperature measurements and approximately 0.6 K at 25 km during daytime. The other uncertainty estimates are up to 6% relative humidity for humidity, 10–50 m for geopotential height, 0.6 hPa for pressure, 0.4–1 m s−1 for wind speed, and 1° for wind direction. Daytime temperature profiles for GRUAN and Vaisala processing are comparable and consistent within the estimated uncertainty. GRUAN daytime humidity profiles are up to 15% moister than Vaisala processed profiles, of which two-thirds is due to the radiation dry bias correction and one-third is due to an additional calibration correction. Redundant measurements with frost point hygrometers (CFH and NOAA FPH) show that GRUAN-processed RS92 humidity profiles and frost point data agree within 15% in the troposphere. No systematic biases occur, apart from a 5% dry bias for GRUAN data around −40 °C at night.

Radiation Dry Bias Correction of Vaisala RS92 Humidity Data and Its Impacts on Historical Radiosonde Data

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 Dai et al. (2011). 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.

Correction technique for Raman water vapor lidar signal-dependent bias and suitability for water vapor trend monitoring in the upper troposphere

Abstract. The MOHAVE-2009 campaign brought together diverse instrumentation for measuring atmospheric water vapor. We report on the participation of the ALVICE (Atmospheric Laboratory for Validation, Interagency Collaboration and Education) mobile laboratory in the MOHAVE-2009 campaign. In appendices we also report on the performance of the corrected Vaisala RS92 radiosonde measurements during the campaign, on a new radiosonde based calibration algorithm that reduces the influence of atmospheric variability on the derived calibration constant, and on other results of the ALVICE deployment. The MOHAVE-2009 campaign permitted the Raman lidar systems participating to discover and address measurement biases in the upper troposphere and lower stratosphere. The ALVICE lidar system was found to possess a wet bias which was attributed to fluorescence of insect material that was deposited on the telescope early in the mission. Other sources of wet biases are discussed and data from other Raman lidar systems are investigated, revealing that wet biases in upper tropospheric (UT) and lower stratospheric (LS) water vapor measurements appear to be quite common in Raman lidar systems. Lower stratospheric climatology of water vapor is investigated both as a means to check for the existence of these wet biases in Raman lidar data and as a source of correction for the bias. A correction technique is derived and applied to the ALVICE lidar water vapor profiles. Good agreement is found between corrected ALVICE lidar measurments and those of RS92, frost point hygrometer and total column water. The correction is offered as a general method to both quality control Raman water vapor lidar data and to correct those data that have signal-dependent bias. The influence of the correction is shown to be small at regions in the upper troposphere where recent work indicates detection of trends in atmospheric water vapor may be most robust. The correction shown here holds promise for permitting useful upper tropospheric water vapor profiles to be consistently measured by Raman lidar within NDACC (Network for the Detection of Atmospheric Composition Change) and elsewhere, despite the prevalence of instrumental and atmospheric effects that can contaminate the very low signal to noise measurements in the UT.

On the Importance of Vaisala RS92 Radiosonde Humidity Corrections for a Better Agreement between Measured and Modeled Satellite Radiances

Abstract A study has been carried out to assess the importance of radiosonde corrections in improving the agreement between satellite and radiosonde measurements of upper-tropospheric humidity. Infrared [High Resolution Infrared Radiation Sounder (HIRS)-12] and microwave [Advanced Microwave Sounding Unit (AMSU)-18] measurements from the NOAA-17 satellite were used for this purpose. The agreement was assessed by comparing the satellite measurements against simulated measurements using collocated radiosonde profiles of the Atmospheric Radiation Measurement (ARM) Program undertaken at tropical and midlatitude sites. The Atmospheric Radiative Transfer Simulator (ARTS) was used to simulate the satellite radiances. The comparisons have been done under clear-sky conditions, separately for daytime and nighttime soundings. Only Vaisala RS92 radiosonde sensors were used and an empirical correction (EC) was applied to the radiosonde measurements. The EC includes correction for mean calibration bias and for solar radiation error, and it removes radiosonde bias relative to three instruments of known accuracy. For the nighttime dataset, the EC significantly reduces the bias from 0.63 to −0.10 K in AMSU-18 and from 1.26 to 0.35 K in HIRS-12. The EC has an even greater impact on the daytime dataset with a bias reduction from 2.38 to 0.28 K in AMSU-18 and from 2.51 to 0.59 K in HIRS-12. The present study promises a more accurate approach in future radiosonde-based studies in the upper troposphere.

H<sub>2</sub>O and δD profiles remotely-sensed from ground in different spectral infrared regions

Abstract. We present ground-based FTIR (Fourier Transform Infrared) water vapour analyses performed in four different spectral regions: 790–880, 1090–1330, 2650–3180, and 4560–4710 cm−1. All four regions allow the retrieval of lower, middle, and upper tropospheric water vapour amounts with a vertical resolution of about 3, 6, and 10 km, respectively. In addition the analyses at 1090–1330 and 2650–3180 cm−1 allow the retrieval of lower and middle/upper tropospheric δD values with vertical resolutions of 3 and 10 km, respectively. A theoretical and empirical error assessment – taking coincident Vaisala RS92 radiosonde measurements as a reference – suggests that the H2O data retrieved at high wavenumbers are slightly more precise than those retrieved at low wavenumbers. We deduce an H2O profile precision and accuracy of generally better than 20% except for the low wavenumber retrieval at 790–880 cm−1, where the assessed upper precision limit of middle/upper tropospheric H2O is 35%. The scatter between the H2O profiles produced by the four different retrievals is generally below 20% and the bias below 10%, except for the boundary layer, where it can reach 24%. These values well confirm the theoretical and empirical error assessment and are rather small compared to the huge tropospheric H2O variability of about one order of magnitude thereby demonstrating the large consistency between the different H2O profile retrievals. By comparing the two δD profile versions we deduce a precision of about 8 and 17‰ for the lower and middle/upper troposphere, respectively. However, at the same time we observe a systematic difference between the two retrievals of up to 40‰ in the middle/upper troposphere which is a large value compared to the typical tropospheric δD variability of only 80‰.

An empirical study on the importance of a speed-dependent Voigt line shape model for tropospheric water vapor profile remote sensing

We use high quality ground-based solar absorption spectra measured in close coincidence with Vaisala RS92 radiosonde in situ water vapor profiles to demonstrate that a Voigt line shape model yields systematic errors in the remotely sensed tropospheric water vapor profiles. We analyse absorption signatures of 4 H 2 16O and 2 HD 16O bands situated between 790 and 4710 cm −1. We find that applying a speed-dependent Voigt line shape model instead of a Voigt line shape model significantly improves the agreement between the water vapor profiles obtained by the radiosondes and by infrared remote-sensing in the different bands. An optimal agreement is obtained for a Γ 2 (relaxation rate for speed-dependence) of 6–21% of Γ 0 (Voigt relaxation rate), which is consistent to the values derived from laboratory experiments. Our study suggests that further extensive laboratory investigations of line shape models are a key for improving the quality of modern water vapor remote sensing products.

Water Vapor Measurements by Howard University Raman Lidar during the WAVES 2006 Campaign

Abstract Water vapor mixing ratio retrieval using the Howard University Raman lidar is presented with emphasis on three aspects: (i) comparison of the lidar with collocated radiosondes and Raman lidar, (ii) investigation of the relationship between atmospheric state variables and the relative performance of the lidar and sonde (in particular, their poor agreement), and (iii) comparison with satellite-based measurements. The measurements were acquired during the Water Vapor Validation Experiment Sondes/Satellites 2006 campaign. Ensemble averaging of water vapor mixing ratio data from 10 nighttime comparisons with Vaisala RS92 radiosondes shows, on average, an agreement within ±10%, up to ∼8 km. A similar analysis of lidar-to-lidar data of over 700 profiles revealed an agreement to within 20% over the first 7 km (10% below 4 km). A grid analysis, defined in the temperature–relative humidity space, was developed to characterize the lidar–radiosonde agreement and quantitatively localizes regions of strong and weak correlations as a function of altitude, temperature, or relative humidity. Three main regions of weak correlation emerge: (i) regions of low relative humidity and low temperature, (ii) regions of moderate relative humidity at low temperatures, and (iii) regions of low relative humidity at moderate temperatures. Comparison of Atmospheric Infrared Sounder and Tropospheric Emission Sounder satellite retrievals of moisture with those of Howard University Raman lidar showed a general agreement in the trend, but the satellites miss details in atmospheric structure because of their low resolution. A relative difference of about ±20% is usually found between lidar and satellite measurements for the coincidences available.

Improved Daytime Column-Integrated Precipitable Water Vapor from Vaisala Radiosonde Humidity Sensors

Abstract Accurate water vapor profiles from radiosondes are essential for long-term climate prediction, weather prediction, validation of remote sensing retrievals, and other applications. The Vaisala RS80, RS90, and RS92 radiosondes are among the more commonly deployed radiosondes in the world. However, numerous investigators have shown that the daytime water vapor profiles measured by these instruments present a significant dry bias due to the solar heating of the humidity sensor. This bias in the column-integrated precipitable water vapor (PWV), along with variability due to calibration, can be removed by scaling the humidity profile to agree with the PWV retrieved from a microwave radiometer (MWR), as has been demonstrated by several previous studies. Infrared radiative closure analyses have shown that the MWR PWV does not present daytime versus nighttime differences; thus, scaling by the MWR is a possible approach for removing the daytime dry bias. However, MWR measurements are not routinely available at all radiosonde launch sites. Starting from a long-term series of sonde and MWR PWV measurements from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, the authors have developed a simple correction to the column-integrated sonde PWV, derived from an analysis of the ratio of the MWR and sonde measurements; this correction is a function of the atmospheric transmittance as determined by the solar zenith angle, and it effectively removes the daytime dry bias at all solar zenith angles. The correction was validated by successfully applying it to an independent dataset from the ARM tropical western Pacific (TWP) site.