Climate Observing System Research Articles

Comparison of the Heights of Sporadic E Layers and Vertical Ion Convergence Parameters

Sporadic E (Es) layers are thin layers of enhanced electron density that commonly appear at altitudes of 90–130 km, often impacting radio communications and navigation systems. The wind shear theory posits that the vertical ion drift, influenced by atmospheric neutral winds and the magnetic field, serves as a significant dynamic driver for the formation and movement of Es layers. In current studies, both the heights of ion vertical velocity null (IVN) and the maximum vertical ion convergence (VICmax) have been proposed as the potential height of Es layer occurrence. In this study, utilizing the neutral atmospheric wind data derived from the WACCM-X (The Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension), we computed and compared these two parameters with the observed Es layer heights recorded by the FORMOSAT-3/COSMIC (FORMOsa SATellite-3/Constellation Observing System for Meteorology, Ionosphere, and Climate) radio occultation (RO) observations. The comparative analysis suggests that IVN is a more likely node for Es layer occurrence than VICmax. Subsequently, we examined the height–time distributions of IVN and Es layers, as well as their respective descent rates at different latitudes. These results demonstrated a notable agreement in height variations between IVN and Es layers. The collective results presented in this paper provide strong support that the ion vertical velocity null plays a crucial role in determining the height of Es layers.

Convective tropopause over the tropics: Climatology, seasonality, and inter-annual variability inferred from long-term FORMOSAT-3/COSMIC-1 RO data

The present study aims to provide a comprehensive analysis of the convective tropopause height (COTH) and temperature (COT-T) over the tropics using long-term radio occultation (RO) measurements from the Formosa Satellite Mission 3–Constellation Observing System for Meteorology, Ionosphere and Climate (FORMOSAT-3/COSMIC) from July 2006 to December 2018. The study reveals that the COT-H (COT-T) exhibits a significant longitudinal structure with a maximum (minimum) over the Western Pacific and two secondary maxima (minima) over the American and African regions. The seasonal cycles of COT-H and COT-T are also prominent, with higher and colder values over the Western Pacific in boreal winter and over the Indian region in boreal summer. This seasonality in COT-H and COT-T is consistent with the seasonal process of tropical convective activity and is explained by outgoing longwave radiation (OLR) data. The correlation analysis shows that COT-H (COT-T) is negatively (positively) correlated with OLR, with the highest correlation found in the Indian region. Additionally, the study investigates the relationship between COT-H (COT-T) and upper tropospheric water vapor (UTWV) over the various regions in the tropics. Over the Indian region, the COT-H (COT-T) exhibits a positive (negative) correlation with UTWV, while over the Western Pacific, American, and African regions, it displays a negative (positive) correlation. Finally, the spatial distribution of the convective tropopause in El Niño and La Niña winters was examined. Results show that the COT-H (COT-T) is higher (lower) over the central and eastern Pacific Ocean and lower (higher) over the maritime continent during El Niño compared with La Niña.

Improved Himawari-8 10-minute scale aerosol optical depth product using deep neural network over Japan

Aerosol Optical Depth (AOD) is critical to air quality research and has a major impact on the Earth's energy budget. Recently, satellite retrieval has been a common data source for large-scale AOD information, for which the new-generation geostationary Himawari-8 satellite, which monitors East Asia and the Pacific region every 10 min, provides an unprecedented opportunity to track its dynamics. However, in the previous evaluation research, the Himawari-8 L2 AOD product has a large deviation from the ground observation. In this study, we proposed a deep neural network (DNN) model to correct the existing Himawari-8 L2 AOD using the synchronous information recorded in the Himawari-8 spectral bands in Japan. The results show that the accuracy has been dramatically improved, as indicated by the random 10-fold cross-validation (CV) (R2: 0.76 vs. 0.24; and RMSE: 0.068 vs. 0.194). Furthermore, many more samples fall in the error lines of the Global Climate Observation System (improved from 24.7% to 52.0% in 10-fold CV). In addition, we also obtained better performance when evaluated using the leave-one-station-out CV (R2 = 0.49 and RMSE = 0.101). The Shapley Additive exPlanations values (SHAP) based feature importance result showed that the Day of Year (DOY) contributed the most to the AOD retrievals, followed by the Himawari-8 L2 AOD. The performance decreased when either DOY or Himawari-8 L2 AOD was removed from the model. Overall, the results of this study will provide a methodology to obtain the high temporal resolution AOD with improved accuracy and thus will help to quantify the dynamics of aerosols in a synchronous manner.

Characterizing the tropospheric water vapor spatial variation and trend using 2007–2018 COSMIC radio occultation and ECMWF reanalysis data

Abstract. Atmospheric water vapor plays a crucial role in the global energy balance, hydrological cycle, and climate system. High-quality and consistent water vapor data from different sources are vital for weather prediction and climate research. This study assesses the consistency between the Formosa Satellite Mission 3–Constellation Observing System for Meteorology, Ionosphere, and Climate (FORMOSAT-3/COSMIC) radio occultation (RO) and European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Model 5 (ERA5) water vapor datasets. Comparisons are made across different atmospheric pressure levels (300, 500, and 850 hPa) from 2007 to 2018. Generally, the two datasets show good spatial and temporal agreement. COSMIC's global water vapor retrieval is slightly lower than ERA5's at 500 and 850 hPa, with distinct latitudinal differences between hemispheres. COSMIC exhibits global water vapor increasing trends of 3.47 ± 1.77 % per decade, 3.25 ± 1.25 % per decade, and 2.03 ± 0.65 % per decade at 300, 500, and 850 hPa, respectively. Significant regional variability in water vapor trends, encompassing notable increasing and decreasing patterns, is observable in tropical and subtropical regions. At 500 and 850 hPa, strong water vapor increasing trends are noted in the equatorial Pacific Ocean and the Laccadive Sea, while decreasing trends are evident in the Indo-Pacific Ocean region and the Arabian Sea. Over land, substantial increasing trends at 850 hPa are observed in the southern United States, contrasting with decreasing trends in southern Africa and Australia. The differences between the water vapor trends of COSMIC and ERA5 are primarily negative in the tropical regions at 850 hPa. However, the water vapor increasing trends at 850 hPa estimated from COSMIC are significantly higher than the ones derived from ERA5 data for two low-height stratocumulus-cloud-rich ocean regions west of Africa and South America. These regions with notable water vapor trend differences are located in the Intertropical Convergence Zone (ITCZ) area with frequent occurrences of convection, such as deep clouds. The difference in characterizing water vapor distribution between RO and ERA5 in deep cloud regions may cause such trend differences. The assessment of spatiotemporal variability in RO-derived water vapor and reanalysis of atmospheric water vapor data helps ensure the quality of these datasets for climate studies.

Estimation and evaluation of hourly Meteorological Operational (MetOp) satellites' GPS receiver differential code biases (DCBs) with two different methods

Abstract. Differential code bias (DCB) is one of the Global Positioning System (GPS) errors, which typically affects the calculation of total electron content (TEC) and ionospheric modeling. In the past, DCB was normally estimated as a constant in 1 d, while DCB of a low Earth orbit (LEO) satellite GPS receiver may have large variations within 1 d due to complex space environments and highly dynamic orbit conditions. In this study, daily and hourly DCBs of Meteorological Operational (MetOp) satellites' GPS receivers are calculated and evaluated using the spherical harmonic function (SHF) and the local spherical symmetry (LSS) assumption. The results demonstrated that both approaches could obtain accurate and consistent DCB values. The estimated daily DCB standard deviation (SD) is within 0.1 ns in accordance with the LSS assumption, and it is numerically less than the standard deviation of the reference value provided by the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) Data Analysis and Archive Center (CDAAC). The average error's absolute value is within 0.2 ns with respect to the provided DCB reference value. As for the SHF method, the DCB's standard deviation is within 0.1 ns, which is also less than the standard deviation of the CDAAC reference value. The average error of the absolute value is within 0.2 ns. The estimated hourly DCB with LSS assumptions suggested that calculated results of MetOpA, MetOpB, and MetOpC are, respectively, 0.5 to 3.1 ns, −1.1 to 1.5 ns, and −1.3 to 0.7 ns. The root mean square error (RMSE) is less than 1.2 ns, and the SD is under 0.6 ns. According to the SHF method, the results of MetOpA, MetOpB, and MetOpC are 1 to 2.7 ns, −1 to 1 ns, and −1.3 to 0.6 ns, respectively. The RMSE is under 1.3 ns and the SD is less than 0.5 ns. The SD for solar active days is less than 0.43, 0.49, and 0.44 ns, respectively, with the LSS assumption, and the appropriate fluctuation ranges are 2.0, 2.2, and 2.2 ns. The variation ranges for the SHF method are 1.5, 1.2, and 1.2 ns, respectively, while the SD is under 0.28, 0.35, and 0.29 ns.

Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020

Abstract. Leaf area index (LAI) with an explicit biophysical meaning is a critical variable to characterize terrestrial ecosystems. Long-term global datasets of LAI have served as fundamental data support for monitoring vegetation dynamics and exploring its interactions with other Earth components. However, current LAI products face several limitations associated with spatiotemporal consistency. In this study, we employed the back propagation neural network (BPNN) and a data consolidation method to generate a new version of the half-month 1/12∘ Global Inventory Modeling and Mapping Studies (GIMMS) LAI product, i.e., GIMMS LAI4g, for the period 1982–2020. The significance of the GIMMS LAI4g was the use of the latest PKU GIMMS normalized difference vegetation index (NDVI) product and 3.6 million high-quality global Landsat LAI samples to remove the effects of satellite orbital drift and sensor degradation and to develop spatiotemporally consistent BPNN models. The results showed that the GIMMS LAI4g exhibited overall higher accuracy and lower underestimation than its predecessor (GIMMS LAI3g) and two mainstream LAI products (Global LAnd Surface Satellite (GLASS) LAI and Long-term Global Mapping (GLOBMAP) LAI) using field LAI measurements and Landsat LAI samples. Its validation against Landsat LAI samples revealed an R2 of 0.96, root mean square error of 0.32 m2 m−2, mean absolute error of 0.16 m2 m−2, and mean absolute percentage error of 13.6 % which meets the accuracy target proposed by the Global Climate Observation System. It outperformed other LAI products for most vegetation biomes in a majority area of the land. It efficiently eliminated the effects of satellite orbital drift and sensor degradation and presented a better temporal consistency before and after the year 2000. The consolidation with the reprocessed MODIS LAI allows the GIMMS LAI4g to extend the temporal coverage from 2015 to a recent period (2020), producing the LAI trend that maintains high consistency before and after 2000 and aligns with the reprocessed MODIS LAI trend during the MODIS era. The GIMMS LAI4g product could potentially facilitate mitigating the disagreements between studies of the long-term global vegetation changes and could also benefit the model development in earth and environmental sciences. The GIMMS LAI4g product is open access and available under Attribution 4.0 International at https://doi.org/10.5281/zenodo.7649107 (Cao et al., 2023).

Structure and variations of global planetary boundary layer top from 2008-2022 multiple GNSS RO observations

The structural changes at the planetary boundary layer (PBL) top are very complex and closely related to climate and environmental changes. With the development of Global Navigation Satellite System Radio Occultation (GNSS RO), it provides a good opportunity to estimate and study PBL variations. In this paper, long-term variations and structures of planetary boundary layer height (PBLH) from 2008 to 2022 are investigated by the Wavelet Covariance Transform (WCT) method based on refractivity profiles from Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) and Korea Multi-purpose Satellite-5 (KOMPSAT-5). Furthermore, the temperature and pressure of PBL top are detected using the temperature and pressure profiles from COSMIC and KOMPSAT-5 occultation data. The results demonstrate the latitudinal distribution of the PBL top height, temperature, and pressure, with the more apparent temperature's latitudinal characteristics. The pressure is more strongly correlated with PBL height than temperature. The PBL height in most land and oceans is high in summer and low in winter, and the PBL pressure is low in summer and high in winter, according to seasonal variation characteristics. The global PBL temperature is high in summer and low in winter, which is more obvious in high latitude region. A positive correlation between latitude and the magnitude of seasonal variation in PBL temperature is observed worldwide. Seasonal variation is less pronounced in the Southern Hemisphere (SH) than in the Northern Hemisphere (NH) due to sea-land differences. The annual average value of the global PBL height, temperature, and pressure do not change significantly over the long term, and also there is no a discernible upward or downward trend.

The Novel Copernicus Global Dataset of Atmospheric Total Water Vapour Content with Related Uncertainties from GNSS Observations

A novel algorithm has been designed and implemented in the Climate Data Store (CDS) frame of the Copernicus Climate Change Service (C3S) with the main goal of providing high-quality GNSS-based integrated water vapour (IWV) datasets for climate research and applications. For this purpose, the related CDS GNSS datasets were primarily obtained from GNSS reprocessing campaigns, given their highest quality in adjusting systematic effects due to changes in instrumentation and data processing. The algorithm is currently applied to the International GNSS Service (IGS) tropospheric products, which are consistently extended in near real-time and date back to 2000, and to the results of a reprocessing campaign conducted by the EUREF Permanent GNSS Network (EPN repro2), covering the period from 1996 to 2014. The GNSS IWV retrieval employs ancillary meteorological data sourced from ERA5. Moreover, IWV estimates are provided with associated uncertainty, using an approach similar to that used for the Global Climate Observing System Reference Upper-Air Network (GRUAN) GNSS data product. To assess the quality of the newly introduced GNSS IWV datasets, a comparison is made against the radiosonde data from GRUAN and the Radiosounding HARMonization (RHARM) dataset as well as with the IGS repro3, which will be the next GNSS-based extension of IWV time series at CDS. The comparison indicates that the average difference in IWV among the reprocessed GNSS datasets is less than 0.1 mm. Compared to RHARM and GRUAN IWV values, a small dry bias of less than 1 mm for the GNSS IWV is detected. Additionally, the study compares GNSS IWV trends with the corresponding values derived from RHARM at selected radiosonde sites with more than ten years of data. The trends are mostly statistically significant and in good agreement.

Ionospheric irregularity reconstruction using multisource data fusion via deep learning

Abstract. Ionospheric sporadic E layers (Es) are intense plasma irregularities between 80 and 130 km in altitude and are generally unpredictable. Reconstructing the morphology of sporadic E layers is not only essential for understanding the nature of ionospheric irregularities and many other atmospheric coupling systems, but is also useful for solving a broad range of demands for reliable radio communication of many sectors reliant on ionosphere-dependent decision-making. Despite the efforts of many empirical and theoretical models, a predictive algorithm with both high accuracy and high efficiency is still lacking. Here we introduce a new approach for Sporadic E Layer Forecast using Artificial Neural Networks (SELF-ANN). The prediction engine is trained by fusing observational data from multiple sources, including a high-resolution ERA5 reanalysis dataset, Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) measurements, and integrated data from OMNIWeb. The results show that the model can effectively reconstruct the morphology of the ionospheric E layer with intraseasonal variability by learning complex patterns. The model obtains good performance and generalization capability by applying multiple evaluation criteria. The random forest algorithm used for preliminary processing shows that local time, altitude, longitude, and latitude are significantly essential for forecasting the E-layer region. Extensive evaluations based on ground-based observations demonstrate the superior utility of the model in dealing with unknown information. The presented framework will help us better understand the nature of the ionospheric irregularities, which is a fundamental challenge in upper-atmospheric and ionospheric physics. Moreover, the proposed SELF-ANN can make a significant contribution to the development of the prediction of ionospheric irregularities in the E layer, particularly when the formation mechanisms and evolution processes of the Es layer are not well understood.

Comparative Assessment of Spire and COSMIC-2 Radio Occultation Data Quality

In this study, we investigate the performances of a commercial Global Navigation Satellite System (GNSS) Radio Occultation (RO) mission and a new-generation RO constellation, i.e., Spire and Constellation Observing System for Meteorology, Ionosphere, and Climate 2 (COSMIC-2), respectively. In the statistical comparison between Spire and COSMIC-2, the results indicate that although the average signal-to-noise ratio (SNR) of Spire is far weaker than that of COSMIC-2, the penetration of Spire is comparable to, and occasionally even better than, that of COSMIC-2. In our analysis, we find that the penetration depth is contingent upon various factors including SNR, GNSS, RO modes, topography, and latitude. With the reanalysis of the European Centre for Medium-Range Weather Forecasts and Radiosonde as the reference data, the identical error characteristics of Spire and COSMIC-2 reveal that overall, the accuracy of Spire’s neutral-atmosphere data products was found to be comparable to that of COSMIC-2.

Validation of E‐Region Model Electron Density Profiles With AURIC Utilizing High‐Resolution Cross Sections

AbstractE‐region models have traditionally underestimated the ionospheric electron density. We believe that this deficiency can be remedied by using high‐resolution photoabsorption and photoionization cross sections in the models. Deep dips in the cross sections allow solar radiation to penetrate deeper into the E‐region producing additional ionization. To validate our concept, we perform a study of model electron density profiles (EDPs) calculated using the Atmospheric Ultraviolet Radiance Integrated Code (AURIC; D. Strickland et al., 1999, https://doi.org/10.1016/s0022-4073(98)00098-3) in the E‐region of the terrestrial ionosphere. We compare AURIC model outputs using new high‐resolution photoionization and photoabsorption cross sections, and solar spectral irradiances during low solar activity with incoherent scatter radar (ISR) measurements from the Arecibo and Millstone Hills observatories, Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC‐1) observations, and outputs from empirical models (IRI‐2016 and FIRI‐2018). AURIC results utilizing the new high‐resolution cross sections reveal a significant difference to model outputs calculated with the low‐resolution cross sections currently used. Analysis of AURIC EDPs using the new high‐resolution data indicate fair agreement with ISR measurements obtained at various times at Arecibo but very good agreement with Millstone Hills ISR observations from ∼96–140 km. However, discrepancies in the altitude of the E‐region peak persist. High‐resolution AURIC calculations are in agreement with COSMIC‐1 observations and IRI‐2016 model outputs between ∼105 and 140 km while FIRI‐2018 outputs underestimate the EDP in this region. Overall, AURIC modeling shows increased E‐region electron densities when utilizing high‐resolution cross sections and high‐resolution solar irradiances, and are likely to be the key to resolving the long standing data‐model discrepancies.

Recent Impact of COSMIC-2 with Improved Radio Occultation Data Assimilation Algorithms

Abstract A Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) follow-on constellation, COSMIC-2, was successfully launched into equatorial orbit on 24 June 2019. With an increased signal-to-noise ratio due to improved receivers and digital beam-steering antennas, COSMIC-2 is producing about 5000 high-quality radio occultation (RO) profiles daily over the tropics and subtropics. The initial evaluation of the impact of assimilating COSMIC-2 into NOAA’s Global Forecast System (GFS) showed mixed results, and adjustments to quality control procedures and observation error characteristics had to be made prior to the assimilation of this dataset in the operational configuration in May 2020. Additional changes in the GFS that followed this initial operational implementation resulted in a larger percentage of rejection (∼90%) of all RO observations, including COSMIC-2, in the mid- to lower troposphere. Since then, two software upgrades directly related to the assimilation of RO bending angle observations were developed. These improvements were aimed at optimizing the utilization of COSMIC-2 and other RO observations to improve global weather analyses and forecasts. The first upgrade was implemented operationally in September 2021 and the second one in November 2022. This study describes both RO software upgrades and evaluates the impact of COSMIC-2 with this most recently improved configuration. Specifically, we show that the assimilation of COSMIC-2 observations has a significant impact in improving temperature and winds in the tropics, though benefits also extend to the extratropical latitudes.

On the Kalman Smoother Interpolation Error Distribution in Collocation Comparison of Atmospheric Profiles

The intercomparison between different atmospheric monitoring systems is key for instrument calibration and validation. Common cases involve satellites, radiosonde and atmospheric model outputs. Since instruments and/or measures are not perfectly collocated, miss-collocation uncertainty must be considered in related intercomparison uncertainty budgets. This paper is motivated by the comparison of GNSS-RO, the Global Navigation Satellite System Radio Occultation, with ERA5, the version 5 Reanalysis of the European Centre for Medium-range Weather Forecasts. We consider temperature interpolation observed at GNSS-RO pressure levels to the ERA5 levels. We assess the interpolation uncertainty using as ‘truth’ high-resolution reference data obtained by GRUAN, the Reference Upper-Air Network of the Global Climate Observing System. In this paper, we propose a mathematical representation of the interpolation problem based on the well-known State-space model and the related Kalman filter and smoother. We show that it performs the same (sometimes better) than linear interpolation and, in addition, provides an estimate of the interpolation uncertainty. Moreover, with both techniques, the interpolation error is not Gaussian distributed, and a scaled Student’s t distribution with about 4.3 degrees of freedom is an appropriate approximation for various altitudes, latitudes, seasons and times of day. With our data, interpolation uncertainty results larger at the equator, the Mean Absolute Error being MAE≅0.32 K, and smaller at a high latitude, MAE≅0.21 K at −80° latitude. At lower altitudes, it is close to the measurement uncertainty, with MAE<0.2 K below the tropopause. Around 300 hPa, it starts increasing and reaches about 0.8 K above 100 hPa, except at the equator, where we observed MAE about 1 K.

Evaluation of COSMIC-2 performance in detecting atmospheric boundary layer height and analysis of its variation

The atmospheric boundary layer (ABL) height (ABLH) is an essential index for climate and weather studies in the troposphere. Obtaining abundant and precise observations is crucial for modeling the top of the ABL. The radio occultation can provide observation data of Earth’s atmosphere with high vertical resolution and comprehensive spatial coverage, benefiting from its measurement mode. Thanks to the improved quality of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC)-2 lower atmosphere observations, its substantial amount and higher-quality atmospheric profile data are well suited for ABLH detection. Therefore, 1.4 million COSMIC-2 refractivity profiles from October 2019 to September 2020 were employed to determine the ABLH, and its retrieval performance is assessed using the official COSMIC-2 product (zbalmax) and radiosonde data. The retrieval results show that most of the effectively detected ABLH occultation events occur over the ocean. Comparing COSMIC-2 ABLH and zbalmax, consistent accuracy is found with a standard deviation of 0.22 km. Similar biases are observed each month. And the inversion accuracy in the Northern Hemisphere is higher than in the Southern Hemisphere. The ABLH derived from radiosonde data and COSMIC-2 has a high correlation coefficient of no less than 0.75. The better accuracy happened in the DJF (i.e. from December to February) for both comparisons with two datasets. Additionally, the distribution and seasonal variations were revealed by modeling verified COSMIC-2 ABLH. The fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5) surface temperature and self-calibrated Palmer Drought Severity Index are used to analyze the impact of temperature and drought on ABLH. The results indicate that higher surface temperatures lead to a smaller ABLH, whereas drought at the surface leads to a larger ABLH. Therefore, the higher precision ABLH can be derived from COSMIC-2 after verification, and we can reveal its seasonal variation and influencing factors on this basis.

Microwave Remote Sensing of Soil Moisture

Soil moisture is an important component of the global terrestrial ecosystem and has been recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS) [...]

Comparing three observation instruments of the coach's behaviour in grassroots football

Coach behaviour has long been known to influence the sporting experience of grassroots players; however, it is currently unclear how the instruments used to assess their behaviour are related. Therefore, the aim of this study is to evaluate the relationships between three observational coaching behaviour tools. Sixteen matches with teams led by four female football coaches were recorded and analysed using three observational instruments: (a) Coaching Behaviour Assessment System (CBAS); (b) Multidimensional Motivational Climate Observation System (MMCOS) and (c) Coach Leadership Assessment System (CLAS). The dimensions measured by each instrument are theorised as being adaptive or maladaptive. The specific objectives of this study are targeted to assess: (a) the commonalities between the three instruments and (b) what differentiated aspects each instrument provides. The results show that both the adaptive and the maladaptive dimensions of the three instruments are positively related among themselves but negatively related to the others. The results also show that the MMCOS is sensitive to behavioural changes during the matches, while the CBAS and the CLAS do not reflect these changes but assess the stability of the behaviour. In conclusion, this research provides empirical evidence supporting the conceptual relationship between the three instruments and of the specific information that each instrument offers. Furthermore, it highlights the need to observe the sport environment more closely in a more diversified way.

Decadal Stability and Trends in the Global Cloud Amount and Cloud Top Temperature in the Satellite-Based Climate Data Records

Forty years of cloud observations are available globally from satellites, allowing derivation of climate data records (CDRs) for climate change studies. The aim of this study is to investigate how stable these cloud CDRs are and whether they qualify stability requirements recommended by the WMO’s Global Climate Observing System (GCOS). We also investigate robust trends in global total cloud amount (CA) and cloud top temperature (CTT) that are significant and common across all CDRs. The latest versions of four global cloud CDRs, namely CLARA-A3, ESA Cloud CCI, PATMOS-x, and ISCCP-HGM are analysed. This assessment finds that all three AVHRR-based cloud CDRs (i.e., CLARA-A3, ESA Cloud CCI and PATMOS-x) satisfy even the strictest GCOS stability requirements for CA and CTT when averaged globally. While CLARA-A3 is most stable in global averages when tested against MODIS-Aqua, PATMOS-x offers the most stable CDR spatially. While we find these results highly encouraging, there remain, however, large spatial differences in the stability of and across the CDRs. All four CDRs continue to agree on the statistically significant decrease in global cloud amount over the last four decades, although this decrease is now weaker compared to the previous assessments. This decreasing trend has been stabilizing or even reversing in the last two decades; the latter is seen also in MODIS-Aqua and CALIPSO GEWEX datasets. Statistically significant trends in CTT are observed in global averages in the AVHRR-based CDRs, but the spatial agreement in the sign and the magnitude of the trends is weaker compared to those in CA. We also present maps of Common Stability Coverage and Common Trend Coverage that could provide a valuable metric to carry out an ensemble-based analysis of the CDRs.

Surface Displacement of Hurd Rock Glacier from 1956 to 2019 from Historical Aerial Frames and Satellite Imagery (Livingston Island, Antarctic Peninsula)

In the second half of the 20th century, the western Antarctic Peninsula recorded the highest mean annual air temperature rise in the Antarctic. The South Shetland Islands are located about 100 km northwest of the Antarctic Peninsula. The mean annual air temperature at sea level in this Maritime Antarctic region is close to −2 °C and, therefore, very sensitive to permafrost degradation following atmospheric warming. Among geomorphological indicators of permafrost are rock glaciers found below steep slopes as a consequence of permafrost creep, but with surficial movement also generated by solifluction and shallow landslides of rock debris and finer sediments. Rock glacier surface velocity is a new essential climate variable parameter by the Global Climate Observing System, and its historical analysis allows insight into past permafrost behavior. Recovery of 1950s aerial image stereo-pairs and structure-from-motion processing, together with the analysis of QuickBird 2007 and Pleiades 2019 high-resolution satellite imagery, allowed inferring displacements of the Hurd rock glacier using compression ridge-and-furrow morphology analysis over 60 years. Displacements measured on the rock glacier surface from 1956 until 2019 were from 7.5 m to 22.5 m and surface velocity of 12 cm/year to 36 cm/year, measured on orthographic images, with combined deviation root-mean-square of 2.5 m and 2.4 m in easting and northing. The inferred surface velocity also provides a baseline reference to assess today’s displacements. The results show patterns of the Hurd rock glacier displacement velocity, which are analogous to those reported within the last decade, without being possible to assess any displacement acceleration.

Detection and localization of F-layer ionospheric irregularities with the back-propagation method along the radio occultation ray path

Abstract. The back-propagation (BP) method consists of diffractive integrals computed over a trajectory path, projecting a signal to different planes. It unwinds the diffraction and multipath, resulting in minimum disturbance to the BP amplitude when the auxiliary plane coincides with the region causing the diffraction. The method has been previously applied in global navigation satellite system (GNSS) radio occultation (RO) measurements to estimate the location of ionospheric irregularities but without complementary data to validate the estimation. In this study, the BP method is applied to a Communications/Navigation Outage Forecasting System (C/NOFS) occultation event containing scintillation signatures caused by an equatorial plasma bubble (EPB), which was parameterized with the aid of collocated data and reproduced in a wave optics propagator (WOP) simulation. In addition, a few more test cases were designed to assess the BP method with regard to the size, intensity, and placement of single- and multiple-irregularity regions. The results show a location estimate accuracy following the resolution at which the method is implemented (single bubble, reference case), whereas a bias is observed in multiple-bubble scenarios. The minimum detectable disturbance level and the estimation accuracy depend on the receiver noise level and, in the case of several bubbles, on the distance between them. These remarks provide insight into the BP results for two Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) occultation events.

Evaluation of tropospheric water vapour and temperature profiles retrieved from MetOp-A by the Infrared and Microwave Sounding scheme

Abstract. Since 2007, the Meteorological Operational satellite (MetOp) series of platforms operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) has provided valuable observations of the Earth's surface and atmosphere for meteorological and climate applications. With 15 years of data already collected, the next generation of MetOp satellites will see this measurement record extend to and beyond 2045. Although a primary role is in operational meteorology, tropospheric temperature and water vapour profiles will be key data products produced using infrared and microwave sounding instruments on board. Considering the MetOp data record that will span 40 years, these profiles will form an essential climate data record (CDR) for studying long-term atmospheric changes. Therefore, the performance of these products must be characterized to support the robustness of any current or future analysis. In this study, we validate 9.5 years of profile data produced using the Infrared and Microwave Sounding (IMS) scheme with the European Space Agency (ESA) Water Vapour Climate Change Initiative (WV_cci) project against radiosondes from two different archives. The Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) and Analyzed RadioSoundings Archive (ARSA) data records were chosen for the validation exercise to provide the contrast between global observations (ARSA) with sparser characterized climate measurements (GRUAN). Results from this study show that IMS temperature and water vapour profile biases are within 0.5 K and 10 % of the reference for “global” scales. We further demonstrate the difference between diurnal sampling and cloud amount match-ups on observed biases and discuss the implications that sampling also plays on attributing these effects. Finally, we present the first look at the profile bias stability from the IMS product, where we observe global stabilities ranging from −0.32 ± 0.18 to 0.1 ± 0.27 K per decade and −1.76 ± 0.19 to 0.79 ± 0.83 % ppmv (parts per million by volume) per decade for temperature and water vapour profiles, respectively. We further break down the profile stability into diurnal and latitudinal values and relate all observed results to required climate performance. Overall, we find the results from this study demonstrate the real potential for tropospheric water vapour and temperature profile CDRs from the MetOp series of platforms.