The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Brian J. Soden - One of the best experts on this subject based on the ideXlab platform.
-
an analysis of satellite radiosonde and lidar observations of upper tropospheric water vapor from the atmospheric radiation measurement program
Journal of Geophysical Research, 2004Co-Authors: Brian J. Soden, Barry M Lesht, David D. Turner, Larry M. MiloshevichAbstract:[1] To improve our understanding of the distribution and radiative effects of water vapor, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program has conducted a series of coordinated water vapor Intensive Observation Periods (IOPs). This study uses observations collected from four ARM IOPs to accomplish two goals: First we compare radiosonde and Raman lidar observations of upper tropospheric water vapor with colocated geostationary satellite radiances at 6.7 μm. During all four IOPs we find excellent agreement between the satellite and Raman lidar observations of upper tropospheric humidity with systematic differences of ∼10%. In contrast, Radiosondes equipped with Vaisala sensors are shown to be systematically drier in the upper troposphere by ∼40% relative to both the lidar and satellite measurements. Second, we assess the performance of various “correction” strategies designed to rectify known deficiencies in the radiosonde measurements. It is shown that existing methods for correcting the radiosonde dry bias, while effective in the lower troposphere, offer little improvement in the upper troposphere. An alternative method based on variational assimilation of satellite radiances is presented and, when applied to the radiosonde measurements, is shown to significantly improve their agreement with coincident Raman lidar observations. It is suggested that a similar strategy could be used to improve the quality of the global historical record of radiosonde water vapor observations during the satellite era.
-
An Assessment of Satellite and Radiosonde Climatologies of Upper-Tropospheric Water Vapor
Journal of Climate, 1996Co-Authors: Brian J. Soden, John R. LanzanteAbstract:Abstract This study compares radiosonde and satellite climatologies of upper-tropospheric water vapor for the period 1979–1991. Comparison of the two climatologies reveals significant differences in the regional distribution of upper-tropospheric relative humidity. These discrepancies exhibit a distinct geopolitical dependence that is demonstrated to result from international differences in radiosonde instrumentation. Specifically, Radiosondes equipped with goldbeater's skin humidity sensors (found primarily in the former Soviet Union, China, and eastern Europe) report a systematically moister upper troposphere relative to the satellite observations, whereas Radiosondes equipped with capacitive or carbon hygristor sensors (found at most other locations) report a systematically drier upper troposphere. The bias between humidity sensors is roughly 15%–20% in terms of the relative humidity, being slightly greater during summer than during winter and greater in the upper troposphere than in the midtroposphere...
Larry M. Miloshevich - One of the best experts on this subject based on the ideXlab platform.
-
tropospheric comparisons of vaisala Radiosondes and balloon borne frost point and lyman α hygrometers during the lautlos wavvap experiment
Journal of Atmospheric and Oceanic Technology, 2008Co-Authors: T Suortti, Larry M. Miloshevich, A. Paukkunen, U. Leiterer, A Kats, Rigel Kivi, Niklaus Kampfer, Roland Neuber, P Ruppert, H VomelAbstract:Abstract The accuracy of all types of Vaisala Radiosondes and two types of Snow White chilled-mirror hygrosondes was assessed in an intensive in situ comparison with reference hygrometers. Fourteen nighttime reference comparisons were performed to determine a working reference for the radiosonde comparisons. These showed that the night version of the Snow White agreed best with the references [i.e., the NOAA frost-point hygrometer (FPH) and University of Colorado cryogenic frost-point hygrometer (CFH)], but that the daytime version had severe problems with contamination in the humid upper troposphere. Since the RS92 performance was superior to the other Radiosondes and to the day version of the Snow White, it was selected to be the working reference. According to the reference comparison, the RS92 has no bias in the mid- and lower troposphere, with deviations <±5% in relative humidity (RH). In the upper troposphere, the RS92 has a ∼5% RH wet bias, which is partly due to the RS92 time lag error and the ter...
-
relative humidity over antarctica from Radiosondes satellites and a general circulation model
Journal of Geophysical Research, 2006Co-Authors: Andrew Gettelman, Larry M. Miloshevich, Von P Walden, W L Roth, B HalterAbstract:[1] Radiosonde measurements are used to validate measurements of relative humidity (RH) over Antarctica from the Atmospheric Infrared Sounder (AIRS) satellite instrument. Radiosonde observations are corrected for most known biases but still have a solar heating dry bias of up to 8% relative to other instruments. AIRS reproduces the observations of temperature and relative humidity with good fidelity. There is a ∼20% moist bias to the data in the upper troposphere relative to radiosonde measurements, but it is within the standard deviation of the measurements. Probability distribution functions of RH from Radiosondes and AIRS are similar, suggesting that variability over Antarctica is well reproduced by the satellite. AIRS data are also compared to simulations from the Community Atmosphere Model version 3 (CAM3) and are found to be significantly moister than the model, although the model does not allow supersaturation with respect to ice or liquid water. A climatology from AIRS indicates that it has a repeatable annual cycle over Antarctica. Supersaturation with respect to ice is very common over the continent, particularly in winter, where it might occur almost half the time in the troposphere. This may affect the quantity and isotopic composition of ice over Antarctica.
-
an analysis of satellite radiosonde and lidar observations of upper tropospheric water vapor from the atmospheric radiation measurement program
Journal of Geophysical Research, 2004Co-Authors: Brian J. Soden, Barry M Lesht, David D. Turner, Larry M. MiloshevichAbstract:[1] To improve our understanding of the distribution and radiative effects of water vapor, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program has conducted a series of coordinated water vapor Intensive Observation Periods (IOPs). This study uses observations collected from four ARM IOPs to accomplish two goals: First we compare radiosonde and Raman lidar observations of upper tropospheric water vapor with colocated geostationary satellite radiances at 6.7 μm. During all four IOPs we find excellent agreement between the satellite and Raman lidar observations of upper tropospheric humidity with systematic differences of ∼10%. In contrast, Radiosondes equipped with Vaisala sensors are shown to be systematically drier in the upper troposphere by ∼40% relative to both the lidar and satellite measurements. Second, we assess the performance of various “correction” strategies designed to rectify known deficiencies in the radiosonde measurements. It is shown that existing methods for correcting the radiosonde dry bias, while effective in the lower troposphere, offer little improvement in the upper troposphere. An alternative method based on variational assimilation of satellite radiances is presented and, when applied to the radiosonde measurements, is shown to significantly improve their agreement with coincident Raman lidar observations. It is suggested that a similar strategy could be used to improve the quality of the global historical record of radiosonde water vapor observations during the satellite era.
-
Impact of Vaisala Radiosonde Humidity Corrections on ARM IOP Data
2002Co-Authors: Larry M. Miloshevich, A. Paukkunen, Holger Vömel, S. J. OltmansAbstract:Radiosonde humidity measurements are fundamentally important to a variety of applications, including radiative transfer calculations, validation of remote-sensor retrievals, parameterization of cloud processes, and initialization of (or assimilation into) numerical models. Vaisala Radiosondes, used by the Atmospheric Radiation Measurement (ARM) Program and extensively throughout the world, are known to have accuracy limitations that result from several identified sources of measurement error (Miloshevich et al. 2001a). A systematic dry bias in Vaisala radiosonde humidity measurements has been noted in comparison to satellite water vapor retrievals (Soden and Lanzante 1996) and Raman lidar measurements (Ferrare et al. 1995), and in underpredicting clouds and precipitation in a numerical weather prediction model (Lorenc et al. 1996). The concurrent observations that variability exists in the accuracy of the ARM radiosonde humidity measurements when Radiosondes from different calibration batches are used (Lesht 1999), and that unrealistically dry tropical boundary layers were frequently observed in the radiosonde data during the Tropical Ocean Global Atmosphere-Coupled Ocean Atmosphere Response Experiment (TOGA-COARE) (Zipser and Johnson 1998), led to a substantial effort by Vaisala and the National Center for Atmospheric Research (NCAR) to identify the sources of the measurement inaccuracy and develop corrections (Wang et al. 2002).
-
characterization and correction of relative humidity measurements from vaisala rs80 a Radiosondes at cold temperatures
Journal of Atmospheric and Oceanic Technology, 2001Co-Authors: Larry M. Miloshevich, A. Paukkunen, H Vomel, Andrew J Heymsfield, Samuel J OltmansAbstract: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...
Leopold Haimberger - One of the best experts on this subject based on the ideXlab platform.
-
new estimates of tropical mean temperature trend profiles from zonal mean historical radiosonde and pilot balloon wind shear observations
Journal of Geophysical Research, 2015Co-Authors: Lorenzo Ramella Pralungo, Leopold HaimbergerAbstract:Estimation of global scale trends and low-frequency variability directly from radiosonde temperature records is challenging particularly in the Tropics due to large biases in the earlier parts of the records that need to be adjusted. Zonal mean zonal winds and wind shear observations are less prone to time varying biases. Together with a zonal mean reference temperature trend profile in the extratropics, the wind data can be used to estimate zonal mean temperature trends in the tropics using the thermal wind relationship. Earlier estimates using this approach for the periods 1970–2005 and 1979–2005 showed strong trend amplification in the tropical upper troposphere but also very large uncertainties because the latitude for the reference profile was chosen very far north and due to a lack of data in the tropics. Both sources of uncertainties have been addressed by (i) using reference temperature profiles from homogenized radiosonde temperature data sets, (ii) using the new Global historical Radiosondes and Tracked Balloons Archive on standard pressure levels (GRASP) as data source that includes not only Radiosondes but also pilot balloons, and (iii) more accurate vertical discretization and sampling of wind data. The new trend estimates are extended back to 1958 and are considerably less noisy. Trend amplification factors between 1.3 and 2.6 were found in the intervals 1979–2005 and 1958–2010, respectively, when using wind shear information. The best estimates for the warming maxima in the upper tropical troposphere agree well with those directly from temperatures but tend to be located at higher altitudes.
-
an assessment of differences in lower stratospheric temperature records from a msu Radiosondes and gps radio occultation
Atmospheric Measurement Techniques, 2011Co-Authors: Florian Ladstadter, Leopold Haimberger, Christina Tavolato, Andrea K Steiner, Ulrich Foelsche, Gottfried KirchengastAbstract:Abstract. Uncertainties for upper-air trend patterns are still substantial. Observations from the radio occultation (RO) technique offer new opportunities to assess the existing observational records there. Long-term time series are available from Radiosondes and from the (Advanced) Microwave Sounding Unit (A)MSU. None of them were originally intended to deliver data for climate applications. Demanding intercalibration and homogenization procedures are required to account for changes in instrumentation and observation techniques. In this comparative study three (A)MSU anomaly time series and two homogenized radiosonde records are compared to RO data from the CHAMP, SAC-C, GRACE-A and F3C missions for September 2001 to December 2010. Differences of monthly anomalies are examined to assess the differences in the datasets due to structural uncertainties. The difference of anomalies of the (A)MSU datasets relative to RO shows a statistically significant trend within about (−0.2±0.1) K/10 yr (95% confidence interval) at all latitudes. This signals a systematic deviation of the two datasets over time. The radiosonde network has known deficiencies in its global coverage, with sparse representation of most of the southern hemisphere, the tropics and the oceans. In this study the error that results from sparse sampling is estimated and accounted for by subtracting it from radiosonde and RO datasets. Surprisingly the sampling error correction is also important in the Northern Hemisphere (NH), where the radiosonde network is dense over the continents but does not capture large atmospheric variations in NH winter. Considering the sampling error, the consistency of radiosonde and RO anomalies is improving substantially; the trend in the anomaly differences is generally very small. Regarding (A)MSU, its poor vertical resolution poses another problem by missing important features of the vertical atmospheric structure. This points to the advantage of homogeneously distributed measurements with high vertical resolution.
-
Assessing Bias and Uncertainty in the HadAT-Adjusted Radiosonde Climate Record
Journal of Climate, 2008Co-Authors: Mark Mccarthy, Leopold Haimberger, Holly A. Titchner, Peter Thorne, Simon F. B. Tett, David E. ParkerAbstract:Uncertainties in observed records of atmospheric temperature aloft remain poorly quantified. This has resulted in considerable controversy regarding signals of climate change over recent decades from temperature records of Radiosondes and satellites. This work revisits the problems associated with the removal of inhomogeneities from the historical radiosonde temperature records, and provides a method for quantifying uncertainty in an adjusted radiosonde climate record due to the subjective choices made during the data homogenization. This paper presents an automated homogenization method designed to replicate the decisions made by manual judgment in the generation of an earlier radiosonde dataset [i.e., the Hadley Centre radiosonde temperature dataset (HadAT)]. A number of validation experiments have been conducted to test the system performance and impact on linear trends. Using climate model data to simulate biased radiosonde data, the authors show that limitations in the homogenization method are sufficiently large to explain much of the tropical trend discrepancy between HadAT and estimates from satellite platforms and climate models. This situation arises from the combination of systematic (unknown magnitude) and random uncertainties (of order 0.05 K decade 1 )i n the radiosonde data. Previous assessment of trends and uncertainty in HadAT is likely to have underestimated the systematic bias in tropical mean temperature trends. This objective assessment of radiosonde homogenization supports the conclusions of the synthesis report of the U.S. Climate Change Science Program (CCSP), and associated research, regarding potential bias in tropospheric temperature records from Radiosondes.
-
New evidence of a tropical mean 1 K stratospheric bias in radiosonde temperatures in the 1980s from an intercomparison of (un)adjusted radiosonde data, ERA-40 reanalyses and MSU retrievals
2007Co-Authors: Leopold Haimberger, Christina TavolatoAbstract:The discrepancy between global mean temperature series from homogenized Radiosondes and temperature series derived from Microwave Sounding Unit (MSU) radiances is a long standing problem in upper air climatology. This paper provides new evidence that the gradual cooling of tropical and global mean lower stratospheric radiosonde temperatures compared to satellite data since the early 1980s is the composite effect of shifts with size larger than 0.5K in individual radiosonde time series. The breaks have been detected with an automatic method (RAdiosonde OBservation COrrection using REanalyses RAOBCORE). The differences between satellite products and time series of ERA-40+ECMWF background forecasts (BG) are much smaller, on the order 0.2K, even on regional (10◦ × 10◦) scales. The warm bias of Radiosondes in the 1980s is estimated 0.6±0.2K in the global mean, and 1.0±0.3K in the tropical (20◦S-20◦N) mean for the MSU lower stratosphere (LS) equivalent layer. A similar comparison for MSU-3 (upper tropospheric) layer mean temperatures yielded radiosonde temperature biases of 0.35± 0.1K in the global mean and 0.5± 0.2K in the tropical mean in the late 1980s RAOBCORE uses the BG data as reference for homogenization. The consistency of the BG with MSU satellite products from 1987 onwards is quite high although it has been created with substantially different algorithms for assimilating the radiances. Only before 1987 some shifts in the global mean have been detected that must be removed before using the BG as reference for homogenization. Due to the better understanding of the background behaviour, the uncertainty estimates for the homogenized radiosonde dataset could be reduced compared to a recent paper on RAOBCORE. The resulting adjusted radiosonde time series are in much better agreement with satellite data than was the case in recent satellite-radiosonde intercomparisons. At high latitudes sizeable seasonally varying differences between individual Radiosondes and satellite data remain after homogenization. These are partly related to seasonal variations of the daytime radiation error, which are not adjusted by RAOBCORE. Still existing
John R. Lanzante - One of the best experts on this subject based on the ideXlab platform.
-
An Assessment of Satellite and Radiosonde Climatologies of Upper-Tropospheric Water Vapor
Journal of Climate, 1996Co-Authors: Brian J. Soden, John R. LanzanteAbstract:Abstract This study compares radiosonde and satellite climatologies of upper-tropospheric water vapor for the period 1979–1991. Comparison of the two climatologies reveals significant differences in the regional distribution of upper-tropospheric relative humidity. These discrepancies exhibit a distinct geopolitical dependence that is demonstrated to result from international differences in radiosonde instrumentation. Specifically, Radiosondes equipped with goldbeater's skin humidity sensors (found primarily in the former Soviet Union, China, and eastern Europe) report a systematically moister upper troposphere relative to the satellite observations, whereas Radiosondes equipped with capacitive or carbon hygristor sensors (found at most other locations) report a systematically drier upper troposphere. The bias between humidity sensors is roughly 15%–20% in terms of the relative humidity, being slightly greater during summer than during winter and greater in the upper troposphere than in the midtroposphere...
Steven C Sherwood - One of the best experts on this subject based on the ideXlab platform.
-
warming maximum in the tropical upper troposphere deduced from thermal winds
Nature Geoscience, 2008Co-Authors: Robert J Allen, Steven C SherwoodAbstract:Climate models and theoretical expectations have predicted that the upper troposphere should be warming faster than the surface. Surprisingly, direct temperature observations from radiosonde and satellite data have often not shown this expected trend. However, non-climatic biases have been found in such measurements. Here we apply the thermal-wind equation to wind measurements from radiosonde data, which seem to be more stable than the temperature data. We derive estimates of temperature trends for the upper tropospheretothelowerstratospheresince1970.Overtheperiodofobservations,wefindamaximumwarmingtrendof0.65±0.47K per decade near the 200hPa pressure level, below the tropical tropopause. Warming patterns are consistent with model predictions except for small discrepancies close to the tropopause. Our findings are inconsistent with the trends derived from radiosonde temperature datasets and from NCEP reanalyses of temperature and wind fields. The agreement with models increases confidence in current model-based predictions of future climate change. It has long been recognized that radiosonde temperature data are a ected by non-climatic artifacts due to station relocations, observation time changes and radiosonde type or design changes 1 . Several investigators have attempted to detect and adjust (that is homogenize) these artefacts using a variety of tools, including statistical procedures, station metadata, various indicators of natural variability (such as volcanic eruptions, vertical coherence) and forecasts from a climate data assimilation system 2‐6 . Despite these attempts, most analyses of Radiosondes continue to show less warming of the tropical troposphere since 1979 than reported at the surface 1 . At least one satellite dataset also implies this 7 . By contrast, theoretical and model expectations 7,8 indicate that the troposphere should warm somewhat faster than the surface. Recently, time-varying biases were shown to remain in the radiosonde temperature data, including a daytime cooling bias related to solar heating of the instrument (especially in the stratosphere) 9 . They were significantly larger than the average adjustments that had previously been made, and comparable to the above discrepancies, calling into question whether the adjustments had been adequate. A similar cooling bias was also found in night-time soundings 10,11 . Subsequent attempts to produce better homogenized records have yielded more warming than before 5,12,13 ,