Radiosonde Dataset Research Articles

Assessment of FY-3E GNOS II Radio Occultation Data Using an Improved Three-Cornered Hat Method

The spatial–temporal sampling errors arising from the differences in geographical locations and measurement times between co-located Global Navigation Satellite System (GNSS) radio occultation (RO) and radiosonde (RS) data represent systematic errors in the three-cornered hat (3CH) method. In this study, we propose a novel spatial–temporal sampling correction method to mitigate the sampling errors associated with both RO–RS and RS–model pairs. We analyze the 3CH processing chain with this new correction method in comparison to traditional approaches, utilizing Fengyun-3E (FY-3E) GNSS Occultation Sounder II (GNOS II) RO data, atmospheric models, and RS datasets from the Hailar and Xisha stations. Overall, the results demonstrate that the improved 3CH method performs better in terms of spatial–temporal sampling errors and the variances of atmospheric parameters, including refractivity, temperature, and specific humidity. Subsequently, we assess the error variances of the FY-3E GNOS II RO, RS and model atmospheric parameters in China, in particular the northern China and southern China regions, based on large ensemble datasets using the improved 3CH data processing chain. The results indicate that the FY-3E GNOS II BeiDou navigation satellite system (BDS) RO and Global Positioning System (GPS) RO show good consistency, with the average error variances of refractivity, temperature, and specific humidity being less than 1.12%2, 0.13%2, and 700%2, respectively. A comparison of the datasets from northern and southern China reveals that the error variances for refractivity are smaller in northern China, while temperature and specific humidity exhibit smaller error variances in southern China, which is attributable to the differing climatic conditions.

A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS

Abstract. The planetary boundary layer (PBL) is the lowermost part of the troposphere that governs the exchange of momentum, mass and heat between surface and atmosphere. To date, the radiosonde measurements have been extensively used to estimate PBL height (PBLH); suffering from low spatial coverage and temporal resolution, the radiosonde data are incapable of providing a diurnal description of PBLH across the globe. To fill this data gap, this paper aims to produce a temporally continuous PBLH dataset during the course of a day over the global land by applying machine learning algorithms to integrate high-resolution radiosonde measurements, ERA5 reanalysis, and the Global Land Data Assimilation System (GLDAS) product. This dataset covers the period from 2011 to 2021 with a temporal resolution of 3 h and a horizontal resolution of 0.25∘×0.25∘. The radiosonde dataset contains around 180 million profiles over 370 stations across the globe. The machine learning model was established by taking 18 parameters derived from ERA5 reanalysis and GLDAS as input variables, while the PBLH biases between radiosonde observations and ERA5 reanalysis were used as the learning targets. The input variables were presumably representative regarding the land properties, near-surface meteorological conditions, terrain elevations, lower tropospheric stabilities, and solar cycles. Once a state-of-the-art model had been trained, the model was then used to predict the PBLH bias at other grids across the globe with parameters acquired or derived from ERA5 and GLDAS. Eventually, the merged PBLH can be taken as the sum of the predicted PBLH bias and the PBLH retrieved from ERA5 reanalysis. Overall, this merged high-resolution PBLH dataset was globally consistent with the PBLH retrieved from radiosonde observations in terms of both magnitude and spatiotemporal variation, with a mean bias of as low as −0.9 m. The dataset and related codes are publicly available at https://doi.org/10.5281/zenodo.6498004 (Guo et al., 2022), and are of significance for a multitude of scientific research endeavors and applications, including air quality, convection initiation, climate, and climate change, to name but a few.

The characteristics of atmospheric boundary layer height over the Arctic Ocean during MOSAiC

Abstract. The important roles that the atmospheric boundary layer (ABL) plays in the central Arctic climate system have been recognized, but the atmospheric boundary layer height (ABLH), defined as the layer of continuous turbulence adjacent to the surface, has rarely been investigated. Using a year-round radiosonde dataset during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we improve a Richardson-number-based algorithm that takes cloud effects into consideration and subsequently analyze the characteristics and variability of the ABLH over the Arctic Ocean. The results reveal that the annual cycle is clearly characterized by a distinct peak in May and two respective minima in January and July. This annual variation in the ABLH is primarily controlled by the evolution of the ABL thermal structure. Temperature inversions in the winter and summer are intensified by seasonal radiative cooling and warm-air advection with the surface temperature constrained by melting, respectively, leading to the low ABLH at these times. Meteorological and turbulence variables also play a significant role in ABLH variation, including the near-surface potential temperature gradient, friction velocity, and turbulent kinetic energy (TKE) dissipation rate. In addition, the MOSAiC ABLH is more suppressed than the ABLH during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment in the summer, which indicates that there is large variability in the Arctic ABL structure during the summer melting season.

Characteristics and Formation Mechanism of the Cloud Vertical Structure Over the Southeastern Tibetan Plateau in Summer

AbstractCloud vertical structure (CVS) has important influence on cloud radiative effect and atmospheric circulation, especially for the third pole of the world–the Tibetan Plateau (TP). However, the characteristics and formation mechanism of the CVS over the TP are not well understood. This study revealed the characteristics and formation mechanism of the CVS over the southeastern Tibetan Plateau (SETP) in summer using the radiosonde datasets and the ERA5 data sets during June–August from 2015 to 2021. Results show that SETP is cloudy for 71% of the summertime with 73% of the clouds single–layered and 27% multi–layered. Multi–layer cloud occurs more frequently over the eastern SETP. With the number of cloud layers increases, the vertical range of cloud increases but the layer thickness and the inter–layer distance decrease. Water vapor, ascent, circulation and vertical wind shear are important factors for CVS formation. Single–layer cloud tends to form when the strengthening of the water vapor and the ascent is most significant at 400 hPa. In comparison, a stronger South Asian High (SAH) brings more water vapor and stronger ascent to the upper level, helping form the upper cloud and favoring the two–layer cloud structure. Three–layer cloud is likely to form when the upper–level vertical wind shear strengthens and the ascent between the middle and low cloud layer weakens. This study deepens the scientific understanding of the characteristics and formation mechanism of the CVS over the TP, and provides scientific reference for modeling the cloud–climate feedback process over the plateau.

How adequately are elevated moist layers represented in reanalysis and satellite observations?

Abstract. We assess the representation of elevated moist layers (EMLs) in ERA5 reanalysis, the Infrared Atmospheric Sounding Interferometer (IASI) L2 retrieval Climate Data Record (CDR) and the Atmospheric Infrared Sounder (AIRS)-based Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS)-Aqua L2 retrieval. EMLs are free-tropospheric moisture anomalies that typically occur in the vicinity of deep convection in the tropics. EMLs significantly affect the spatial structure of radiative heating, which is considered a key driver for meso-scale dynamics, in particular convective aggregation. To our knowledge, the representation of EMLs in the mentioned data products has not been explicitly studied – a gap we start to address in this work. We assess the different datasets' capability of capturing EMLs by collocating them with 2146 radiosondes launched from Manus Island within the western Pacific warm pool, a region where EMLs occur particularly often. We identify and characterise moisture anomalies in the collocated datasets in terms of moisture anomaly strength, vertical thickness and altitude. By comparing the distributions of these characteristics, we deduce what specific EML characteristics the datasets are capturing well and what they are missing. Distributions of ERA5 moisture anomaly characteristics match those of the radiosonde dataset quite well, and remaining biases can be removed by applying a 1 km moving average to the radiosonde profiles. We conclude that ERA5 is a suitable reference dataset for investigating EMLs. We find that the IASI L2 CDR is subject to stronger smoothing than ERA5, with moisture anomalies being on average 13 % weaker and 28 % thicker than collocated ERA5 anomalies. The CLIMCAPS L2 product shows significant biases in its mean vertical humidity structure compared to the other investigated datasets. These biases manifest as an underestimation of mean moist layer height of about 1.3 km compared to the three other datasets that yields a general mid-tropospheric moist bias and an upper-tropospheric dry bias. Aside from these biases, the CLIMCAPS L2 product shows a similar, if not better, capability of capturing EMLs compared to the IASI L2 CDR. More nuanced evaluations of CLIMCAPS' capabilities may be possible once the underlying cause for the identified biases has been found and fixed. Biases found in the all-sky scenes do not change significantly when limiting the analysis to clear-sky scenes. We calculate radiatively driven vertical velocities ωrad derived from longwave heating rates to estimate the dynamical effect of the moist layers. Moist-layer-associated ωrad values derived from Global Climate Observing System Reference Upper-Air Network (GRUAN) soundings range between 2 and 3 hPa h−1, while mean meso-scale pressure velocities from the EUREC4A (Elucidating the Role of Clouds-Circulation Coupling in Climate) field campaign range between 1 and 2 hPa h−1, highlighting the dynamical significance of EMLs. Subtle differences in the representation of moisture and temperature structures in ERA5 and the satellite datasets create large relative errors in ωrad on the order of 40 % to 80 % with reference to GRUAN, indicating limited usefulness of these datasets to assess the dynamical impact of EMLs.

Inertia-gravity wave energy and instability drive turbulence: evidence from a near-global high-resolution radiosonde dataset

Inertia-gravity wave energy and instability drive turbulence: evidence from a near-global high-resolution radiosonde dataset

Variations of Tropical Lapse Rates in Climate Models and their Implications for Upper Tropospheric Warming

AbstractThe vertical temperature structure in the tropics is primarily set by convection and therefore follows a moist adiabat to first order. However, tropical upper tropospheric temperatures differ among climate models and observations, as atmospheric convection remains poorly understood. Here, we quantify the variations in tropical lapse rates in CMIP6 models and explore reasons for these variations. We find that differences in surface temperatures weighted by the regions of strongest convection cannot explain these variations and therefore we hypothesise that the representation of convection itself and associated small scale processes are responsible. We reproduce these variations in perturbed physics experiments with the global atmospheric model ICON-A, in which we vary autoconversion and entrainment parameters. For smaller autoconversion values, additional freezing enthalpy from the cloud water that is not precipitated warms the upper troposphere. Smaller entrainment rates also lead to a warmer upper troposphere, as convection and thus latent heating reaches higher. Furthermore, we show that according to most radiosonde datasets all CMIP6 AMIP simulations overestimate recent upper tropospheric warming. Additionally, all radiosonde datasets agree that climate models on average overestimate the amount of upper tropospheric warming for a given lower tropospheric warming. We demonstrate that increased entrainment rates reduce this overestimation, likely because of the reduction of latent heat release in the upper troposphere. Our results suggest that imperfect convection parameterisations are responsible for a considerable part of the variations in tropical lapse rates and also part of the overestimation of warming compared to the observations.

Long-term variation of boundary layer height and possible contribution factors: A global analysis

Boundary layer height (BLH) plays an important role in regulating global weather/climate, as well as the dispersion and transportation of pollutants. Until now, however, the attribution and contributions of different controlling factors to BLH long-term variability and trends have not been quantified on a global scale. The long-term radiosonde dataset was used in this study to retrieve global BLH climatology; seasonal, diurnal, long-term variation and trends were analyzed over a 39-year period (1980–2018). Statistical results show that the global distribution of the BLH and its trend have apparent day-night differences. BLH during daytime is deeper during clear-sky conditions compared to cloudy sky conditions, indicating a significant effect of clouds; BLH during nighttime is deeper under cloudy conditions. BLH was also found to vary over different land types; dry and hot soil exhibits a deeper BLH than those of wet and cool soil. The long-term variation and trend of BLH are highly influenced by near-surface meteorological parameters. In particular, based on multiple linear stepwise regression models and the contribution calculation method, this investigation initiatively quantifies the influences of meteorological parameters on global BLH long-term variation and trend. Our results emphasized that a 10 m wind speed (WS) and low tropospheric stability (LTS) have significant contributions to long-term BLH variation; WS and LTS anomalies alternately dominated the contribution of the diurnal cycle of the BLH anomaly. Annual BLH recorded an average increasing trend (38.9–42.1 m/decade), and LTS is more dominant than WS from a contribution perspective, especially for increased BLH anomaly. Contributions from near-surface temperature (T) and relative humidity (RH) also play important roles. However, a decreasing WS trend dominated the decreased trends of BLH anomaly, accounting for nearly 40% of the total contribution.

Remote-sensing and radiosonde datasets collected in the San Luis Valley during the LAPSE-RATE campaign

Abstract. In July 2018, the International Society for Atmospheric Research using Remotely piloted Aircraft (ISARRA) hosted a flight week to showcase the role remotely piloted aircraft systems (RPASs) can have in filling the atmospheric data gap. This campaign was called Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). In support of this campaign, ground-based remote and in situ systems were also deployed for the campaign. The University of Oklahoma deployed the Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS), the University of Colorado deployed two Doppler wind lidars, and the National Severe Storms Laboratory deployed a mobile mesonet with the ability to launch radiosondes. This paper focuses on the data products from these instruments that result in profiles of the atmospheric state. The data are publicly available in the Zenodo LAPSE-RATE community portal (https://zenodo.org/communities/lapse-rate/, 19 January 2021). The profile data discussed are available at https://doi.org/10.5281/zenodo.3780623 (Bell and Klein, 2020), https://doi.org/10.5281/zenodo.3780593 (Bell et al., 2020b), https://doi.org/10.5281/zenodo.3727224 (Bell et al., 2020a), https://doi.org/10.5281/zenodo.3738175 (Waugh, 2020b), https://doi.org/10.5281/zenodo.3720444 (Waugh, 2020a), and https://doi.org/10.5281/zenodo.3698228 (Lundquist et al., 2020).

Ship- and island-based atmospheric soundings from the 2020 EUREC<sup>4</sup>A field campaign

Abstract. To advance the understanding of the interplay among clouds, convection, and circulation, and its role in climate change, the Elucidating the role of clouds–circulation coupling in climate campaign (EUREC4A) and Atlantic Tradewind Ocean–Atmosphere Mesoscale Interaction Campaign (ATOMIC) collected measurements in the western tropical Atlantic during January and February 2020. Upper-air radiosondes were launched regularly (usually 4-hourly) from a network consisting of the Barbados Cloud Observatory (BCO) and four ships within 6–16∘ N, 51–60∘ W. From 8 January to 19 February, a total of 811 radiosondes measured wind, temperature, and relative humidity. In addition to the ascent, the descent was recorded for 82 % of the soundings. The soundings sampled changes in atmospheric pressure, winds, lifting condensation level, boundary layer depth, and vertical distribution of moisture associated with different ocean surface conditions, synoptic variability, and mesoscale convective organization. Raw (Level 0), quality-controlled 1 s (Level 1), and vertically gridded (Level 2) data in NetCDF format (Stephan et al., 2020) are available to the public at AERIS (https://doi.org/10.25326/137). The methods of data collection and post-processing for the radiosonde data set are described here.

Bias Adjustment and Analysis of Chinese Daily Historical Radiosonde Temperature Data

The discontinuities in historical Chinese radiosonde datasets are attributed to artificial errors. In order to reflect more realistically basic conditions of the atmosphere over China and provide more reasonable radiosonde data as input to climate change analysis and to atmospheric reanalysis data assimilation systems, this paper proposes a scheme to identify breakpoints and adjust biases in daily radiosonde observations. The ongoing ECMWF ReAnalysis-Interim (ERA-Interim) 12-h forecasts are used as reference series in the scheme, complemented by the ECMWF Twentieth Century Reanalysis (ERA-20C). A series of breakpoint identification schemes are developed and combined with metadata to detect breakpoints. The Quantile-Matching (QM) method is applied to test and adjust daily radiosonde data on 12 mandatory pressure levels collected at 80 sounding stations during 1979–2013. The adjusted temperatures on mandatory levels are interpolated to significant levels for temperature adjustment on these levels. The adjustment scheme not only solves the data discontinuity problem caused by changes in observational instruments and bias correction methods, but also solves the discontinuity problem in the 1200 minus 0000 UTC temperature time series on mandatory levels at individual sounding stations. Before the adjustment, obvious discontinuities can be found in the deviation field between the raw radiosonde data and ERA-Interim reanalysis with relatively large deviations before 2001. The deviation discontinuity is mainly attributed to the nationwide upgrade of the radiosonde system in China around 2001. After the adjustment, the time series of deviations becomes more continuous. In addition, compared with the adjusted temperature data on mandatory levels over 80 radiosonde stations in China contained in the Radiosonde Observation Correction Using Reanalyses (RAOBCORE) 1.5, the dataset adjusted by the method proposed in the present study exhibits higher quality than RAOBCORE 1.5, while discontinuities still exist in the time series of temperature at 0000, 1200, and 1200 minus 0000 UTC in RAOBCORE 1.5.

Seasonality in the Vertical Structure of Long-Term Temperature Trends Over North America

ABSTRACT The surface warming of northern continents during the twentieth century is not uniform across seasons. Surface warming is particularly pronounced over northwestern Canada, where winter trends are much larger than summer ones. The upper-air temperature trends over the region are analyzed in three radiosonde datasets from 1958 to 2012 to assess their seasonal structure. The seasonal variation of upper-air trends can provide insights into the dynamical and thermodynamical processes generating these trends, including warming at the surface. The focus is not on the canonical structure of secular (i.e., long-term) trends—tropospheric warming and stratospheric cooling—but its seasonal variation. We find the boreal winter-minus-summer difference in trends over northwestern Canada to be positive and large in the lower troposphere (p ≳ 500 hPa) and lower stratosphere (50 hPa ≲ p ≲ 150 hPa); it is largest at the surface and smallest at the tropopause. The decreasing seasonality of the tropospheric trend with height supports the attribution of the notable seasonality of surface warming in this region to both land–surface–hydroclimate interactions and changes in winter circulation. In the lower stratosphere, a cooling trend is evident in all seasons, not unexpectedly, but a pronounced seasonality is again apparent, with the strongest cooling in summer. The near-zero trend tropopause region is a rare point of confluence for seasonal trends.

Validation of the WRF Model for Estimating Precipitable Water Vapor at the Ali Observatory on the Tibetan Plateau

In this study, we assess the validity of the Weather Research and Forecasting (WRF) numerical model for estimating precipitable water vapor (PWV) at the Ali observatory, located in the southwestern part of the Tibetan Plateau. We have run WRF in three nested domains with different horizontal resolutions, centered at the Ali observatory, and the ability of the WRF model on estimating PWV conditions at an astronomical observatory on the Tibetan Plateau with complex terrain has been discussed. In the validation, we make use of a verification sample of 1 yr of radiosonde data set obtained directly at the Ali radiosonde station in 2016, which is about 25 km north of the observatory. The results from model with the highest horizontal resolution of 1 km and the temporal resolution of 0.5 hr in domain 03 are presented in this article. On the Tibetan Plateau, it should be noted that the WRF model slightly overestimates the PWV, while there are high correlation coefficients between the PWV from model and that from observation. In summary, the WRF model shows an excellent performance in estimating PWV at the observatory, and a good agreement between model simulation and radiosonde observation has been found, including for the extremely low value of the PWV (≤1 mm), thus allowing it to complement the PWV estimation above the Tibetan Plateau.

Intercomparison of Sonde, WRF/CAMx and Satellite Sounder Profile Data for the Paso Del Norte Region

The Paso Del Norte (PdN) region comprises the city of El Paso, TX, Ciudad Juarez, Mexico, and some neighboring cities in the state of New Mexico. Developing a regional weather model for this specific region has always been challenging due to its complex terrain. To obtain more accurate weather and pollution forecasting for the PdN region, the results of the downscaled WRF (Weather Research and Forecast) model were intercompared with meteorological satellite data, with ground and radiosonde dataset. In addition, it is critical to analyze the distributions of ozone concentrations to better understand atmospheric aerosol concentrations and predict them both more accurately. Hence, in this study the ozone results of CAMx (Comprehensive Air Quality Model with Extensions) were extensively intercompared with ozonesonde data. The radiosonde/ozonesonde data were obtained throughout a campaign conducted during the summer of 2017 in the PdN region. Different meteorological variables such as temperature, pressure, relative humidity, wind speed, and ozone concentrations were used for comparison at several locations in the PdN region. The TCEQ (Texas Commission of Environment Quality) data from different CAMS (Continuous Ambient Monitoring Stations) were used for ground data intercomparison with the WRF results. The meteorological satellite sounding data were retrieved using an in-house satellite antenna receiver. The results of this research paper will not only provide better pollution forecasting capability for the PdN region but also for other regions with similar topography and terrain.

Preliminary Study of Land–Sea Microphysics Associated with the East Asian Summer Monsoon Rainband and Its Application to GPM DPR

AbstractRaindrop size distribution (DSD) characteristics during the East Asian summer monsoon (EASM) were studied, using measurements from three OTT Particle Size Velocity (Parsivel) disdrometers in Nanjing, Chuzhou, and the northwestern Pacific (NWP), respectively. Western and eastern parts of the monsoon rainband were separated for a comparative study of the DSD variability. Along with disdrometer data, GPM Dual-Frequency Precipitation Radar (DPR), Fengyun-2E (FY-2E), MODIS, GPCP, ERA-Interim, and in situ radiosonde datasets are combined to illustrate the possible microphysical mechanisms for the significant DSD variability in two parts, in terms of convective intensity, cloud structure, and aerosol effects. The DSD characteristics of six rain-rate classes and two rainfall categories (convective and stratiform) were studied. The western part has larger mass-weighted mean diameter Dm while smaller normalized intercept log10(Nw) than the eastern part, and the convective clusters of the western part (land) could be identified more maritime-like than continental-like due to moisture transport from the tropical ocean, while that of the eastern part (sea) is between maritime-like and continental-like. Cross validation of GPM rainfall products are implemented based on surface disdrometer observations. DPR products manifest better performance over sea than land areas of the EASM rainband. Empirical Dm–Ze and Nw–Dm relations were also derived preliminarily to improve the GPM rain-retrieval algorithms in the EASM season.

Cloud-top temperature inversion derived from long-term radiosonde measurements at the ARM TWP and NSA sites

A temperature inversion layer often occurs above a cloud layer due to longwave cooling in the upper cloud levels; however, few previous studies have been conducted on the climatologic statistics of cloud-top temperature inversion features. By using a high-quality continuous radiosonde dataset from 2002/05 to 2014/05 at the Atmospheric Radiation Measurement (ARM) Tropical Western Pacific (TWP) site at the islands of Manus and North Slope of Alaska (NSA) site near Barrow, Alaska, this study investigated the long-term characteristics of cloud-top temperature inversion over the single- and double-layer clouds (SLC and DLC, respectively) in these two different geographic locations and climate regimes. Cloud tops extended much higher vertically at the TWP than at the NSA due to their different local atmospheric environments. Because of the radiative energy interactions between the two layers of clouds, a thinner temperature inversion depth was presented by the DLC lower clouds than by the DLC higher clouds at the TWP site. However, much weaker temperature inversions were detected above the DLC higher clouds than the DLC lower clouds which were often bounded by a ubiquitous surface radiative cooling-induced inversion at the NSA site. A close temperature inversion was observed between the morning and evening at both sites. A thinner temperature inversion depth was displayed in the summer and autumn melt seasons when weak surface radiative cooling occurred at the NSA, whereas a smooth seasonal temperature inversion occurred at the TWP. The climatologic results of this study are expected to provide a better understanding of the complex interactions between clouds and temperature inversion, especially their unique climatic features in the tropics and Arctic.

Temperature and tropopause characteristics from reanalyses data in the tropical tropopause layer

Abstract. The tropical tropopause layer (TTL) is the transition region between the well-mixed convective troposphere and the radiatively controlled stratosphere with air masses showing chemical and dynamical properties of both regions. The representation of the TTL in meteorological reanalysis data sets is important for studying the complex interactions of circulation, convection, trace gases, clouds, and radiation. In this paper, we present the evaluation of climatological and long-term TTL temperature and tropopause characteristics in the reanalysis data sets ERA-Interim, ERA5, JRA-25, JRA-55, MERRA, MERRA-2, NCEP-NCAR (R1), and CFSR. The evaluation has been performed as part of the SPARC (Stratosphere–troposphere Processes and their Role in Climate) Reanalysis Intercomparison Project (S-RIP). The most recent atmospheric reanalysis data sets (ERA-Interim, ERA5, JRA-55, MERRA-2, and CFSR) all provide realistic representations of the major characteristics of the temperature structure within the TTL. There is good agreement between reanalysis estimates of tropical mean temperatures and radio occultation data, with relatively small cold biases for most data sets. Temperatures at the cold point and lapse rate tropopause levels, on the other hand, show warm biases in reanalyses when compared to observations. This tropopause-level warm bias is related to the vertical resolution of the reanalysis data, with the smallest bias found for data sets with the highest vertical resolution around the tropopause. Differences in the cold point temperature maximize over equatorial Africa, related to Kelvin wave activity and associated disturbances in TTL temperatures. Interannual variability in reanalysis temperatures is best constrained in the upper TTL, with larger differences at levels below the cold point. The reanalyses reproduce the temperature responses to major dynamical and radiative signals such as volcanic eruptions and the quasi-biennial oscillation (QBO). Long-term reanalysis trends in temperature in the upper TTL show good agreement with trends derived from adjusted radiosonde data sets indicating significant stratospheric cooling of around −0.5 to −1 K per decade. At 100 hPa and the cold point, most of the reanalyses suggest small but significant cooling trends of −0.3 to −0.6 K per decade that are statistically consistent with trends based on the adjusted radiosonde data sets. Advances of the reanalysis and observational systems over the last decades have led to a clear improvement in the TTL reanalysis products over time. Biases of the temperature profiles and differences in interannual variability clearly decreased in 2006, when densely sampled radio occultation data started being assimilated by the reanalyses. While there is an overall good agreement, different reanalyses offer different advantages in the TTL such as realistic profile and cold point temperature, continuous time series, or a realistic representation of signals of interannual variability. Their use in model simulations and in comparisons with climate model output should be tailored to their specific strengths and weaknesses.

A 17-year climatology of temperature inversions above clouds over the ARM SGP site: The roles of cloud radiative effects

Abstract Atmospheric temperature inversions, i.e., temperatures increasing with altitude, modulate both radiative and buoyancy fluxes in the atmosphere. A temperature inversion layer often occurs immediately above a cloud layer that cools radiatively and thereby strengthens the capping temperature inversion. This study aims to investigate the characteristics of temperature inversions above clouds and their relationships with cloud-top radiative cooling. Using a 17-year (January 2001 to December 2017) high-quality and continuous radiosonde dataset collected at the Atmospheric Radiation Measurement Southern Great Plains Central Facility site, key temperature inversion parameters, namely, the occurrence frequency (dp), depth (dz), temperature difference (dT), and gradient (dT/dz), are derived for single- and double-layer clouds (SLC and DLC, respectively). The occurrence frequency of temperature inversions above single-layer clouds decreases dramatically as cloud tops rise from low to high altitudes. When an overlying higher cloud layer is present, the inversion becomes less frequent, shallower, and weaker than without it. This may be because higher clouds weaken the cloud-top radiative cooling of the underneath clouds by enhancing downwelling infrared radiation. This is supported by radiative transfer simulations. There are distinctive seasonal cycles of cloud-top radiative cooling for high clouds that are primarily driven by variations in shortwave heating. Distinctive seasonal cycles of temperature inversions also occurred regardless of the cloud regime (SLC or DLC) and altitude (low or high clouds). They appear to be driven by the seasonal cycle of cloud coverage (i.e., a greater amount of clouds undergoes stronger area-mean radiative cooling) although the shortwave heating seasonal cycle also plays a role for high clouds. Cloud radiative cooling cannot explain the diurnal cycle of temperature inversions.

Asian water tower evinced in total column water vapor: a comparison among multiple satellite and reanalysis data sets

The total column water vapor (TCWV) over the Tibetan Plateau (TP) is one important indicator of the Asian water tower, and the changes in the TCWV are vital to the climate and ecosystem in downstream regions. However, the observational data is insufficient to understand the changes in the TCWV due to the high elevation of the TP. Satellite and reanalysis data can be used as substitutes, but their quality needs to be evaluated. In this study, based on a homogenized radiosonde data set, a comprehensive evaluation of the TCWV over the TP derived from two satellite data sets (AIRS-only and AIRS/AMSU) and seven existing reanalysis data sets (MERRA, MERRA2, NCEP1, NCEP2, CFSR, ERA-I, JRA55) is performed in the context of the climatology, annual cycle and interannual variability. Both satellite data sets reasonably reproduce the characteristics of the TCWV over the TP. All reanalysis data sets perform well in reproducing the annual mean climatology of the TCWV over the TP (R = 0.99), except for NCEP1 (R = 0.96) and NCEP2 (R = 0.92). ERA-I is more reliable in capturing the spatial pattern of the annual cycle (R = 0.94), while NCEP1 shows the lowest skill (R = 0.72). JRA55 performs best in capturing the features of the interannual coherent variation (EOF1, R = 0.97). The skill-weighted ensemble mean of the reanalysis data performs better than the unweighted ensemble mean and most of the single reanalysis data sets. The evaluation provides essential information on both the strengths and weaknesses of the major satellite and reanalysis data sets in measuring the total column water vapor over the TP.

Comparison of ground-based and satellite measurements of water vapour vertical profiles over Ellesmere Island, Nunavut

Abstract. Improving measurements of water vapour in the upper troposphere and lower stratosphere (UTLS) is a priority for the atmospheric science community. In this work, UTLS water vapour profiles derived from Atmospheric Chemistry Experiment (ACE) satellite measurements are assessed with coincident ground-based measurements taken at a high Arctic observatory at Eureka, Nunavut, Canada. Additional comparisons to satellite measurements taken by the Atmospheric Infrared Sounder (AIRS), Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), Microwave Limb Sounder (MLS), Scanning Imaging Absorption Spectrometer for Atmospheric CHartography (SCIAMACHY), and Tropospheric Emission Spectrometer (TES) are included to put the ACE Fourier transform spectrometer (ACE-FTS) and ACE Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation (ACE-MAESTRO) results in context. Measurements of water vapour profiles at Eureka are made using a Bruker 125HR solar absorption Fourier transform infrared spectrometer at the Polar Environment Atmospheric Research Laboratory (PEARL) and radiosondes launched from the Eureka Weather Station. Radiosonde measurements used in this study were processed with software developed by the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) to account for known biases and calculate uncertainties in a well-documented and consistent manner. ACE-FTS measurements were within 11 ppmv (parts per million by volume; 13 %) of 125HR measurements between 6 and 14 km. Between 8 and 14 km ACE-FTS profiles showed a small wet bias of approximately 8 % relative to the 125HR. ACE-FTS water vapour profiles had mean differences of 13 ppmv (32 %) or better when compared to coincident radiosonde profiles at altitudes between 6 and 14 km; mean differences were within 6 ppmv (12 %) between 7 and 11 km. ACE-MAESTRO profiles showed a small dry bias relative to the 125HR of approximately 7 % between 6 and 9 km and 10 % between 10 and 14 km. ACE-MAESTRO profiles agreed within 30 ppmv (36 %) of the radiosondes between 7 and 14 km. ACE-FTS and ACE-MAESTRO comparison results show closer agreement with the radiosondes and PEARL 125HR overall than other satellite datasets – except for AIRS. Close agreement was observed between AIRS and the 125HR and radiosonde measurements, with mean differences within 5 % and correlation coefficients above 0.83 in the troposphere between 1 and 7 km. Comparisons to MLS at altitudes around 10 km showed a dry bias, e.g. mean differences between MLS and radiosondes were −25.6 %. SCIAMACHY comparisons were very limited due to minimal overlap between the vertical extent of the measurements. TES had no temporal overlap with the radiosonde dataset used in this study. Comparisons between TES and the 125HR showed a wet bias of approximately 25 % in the UTLS and mean differences within 14 % below 5 km.