Ground-based Data Research Articles

Validation of precipitation reanalysis products for rainfall-runoff modelling in Slovenia

Abstract. Observational data scarcity often limits the potential of rainfall-runoff modelling around the globe. In ungauged catchments, earth observations or reanalysis products could be used to replace missing ground-based station data. However, performance of different datasets needs to be thoroughly tested, especially at finer temporal resolutions such as hourly time steps. This study evaluates the performance of ERA5-Land and COSMO-REA6 precipitation reanalysis products (PRPs) using 16 meso-scale catchments (41–460 km2) located in Slovenia, Europe. These two PRPs are firstly compared with a gridded precipitation dataset that was constructed based on ground observational data. Secondly, a comparison of the temperature data of these reanalysis products with station-based air temperature data is conducted. Thirdly, several data combinations are defined and used as input for the rainfall-runoff modelling using the GR4H model. A special focus is on the application of an additional snow module. Both tested PRPs underestimate, for at least 20 %, extreme rainfall events that are the driving force of natural hazards such as floods. In terms of air temperature, both tested reanalysis products show similar deviations from the observational dataset. Additionally, air temperature deviations are smaller in winter compared to summer. In terms of rainfall-runoff modelling, the ERA5-Land yields slightly better performance than COSMO-REA6. If a recalibration with PRP has been carried out, the performance is similar compared to the simulations where station-based data were used as input. Model recalibration proves to be essential in providing relatively sufficient rainfall-runoff modelling results. Hence, tested PRPs could be used as an alternative to the station-based data in case precipitation or air temperature data are lacking, but model calibration using discharge data would be needed to improve the performance.

Prospects for meteotsunami detection in earth’s atmosphere using GNSS observations

We study, for the first time, the physical coupling and detectability of meteotsunamis in the earth’s atmosphere. We study the June 13, 2013 event off the US East Coast using Global Navigation Satellite System (GNSS) radio occultation (RO) measurements, Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) temperatures, and ground-based GNSS ionospheric total electron content (TEC) observations. Hypothesizing that meteotsunamis also generate gravity waves (GWs), similar to tsunamigenic earthquakes, we use linear GW theory to trace their dynamic coupling in the atmosphere by comparing theory with observations. We find that RO data exhibit distinct stratospheric GW activity at near-field that is captured by SABER data in the mesosphere with increased vertical wavelength. Ground-based GNSS-TEC data also detect a far-field ionospheric response 9 h later, as expected by GW theory. We conclude that RO measurements could increase understanding of meteotsunamis and how they couple with the earth’s atmosphere, augmenting ground-based GNSS TEC observations.

Long-term trends of aerosol and cloud fraction over Eastern China based on ground-based observations

We looked into the long-term variability of aerosols and clouds over Eastern China (EC) using MICAPS ground-based data from 1995 to 2019 and MODIS aerosol optical depth (AOD) retrieval from 2003 to 2019. The ground-based observed visibility (VIS) was used to characterize aerosol concentrations. Our study indicate that VIS shows a significant shift over EC in recent decades, as evidenced by falling (P1: 1995–2006)-flat (P2: 2007–2013)-rising (P3: 2014–2019) periods. Correspondingly, a change was observed in cloud fraction (CF) from a strong increase to a slow fluctuation during the three periods over the Beijing-Tianjin-Hebei (BTH), but no significant variation over the Yangtze River Delta (YRD) region. In addition, the CF-VIS shows a negative correlation during both P1 and P3 periods while this relationship weakened during P3 (35% weaker in BTH and 44% weaker in YRD). However, it is important to note that this weakening may be influenced by a few factors, such as data processing methods, the location of aerosols relative to the cloud, relative humidity (RH), and large-scale circulation etc. Further studies, including numerical simulations and analysis, are required to determine how much change of cloudiness is attributed to aerosols.

ALMA and Keck analysis of Fomalhaut field sources: JWST’s Great Dust Cloud is a background object

ABSTRACT At 7.7 pc, the A-type star Fomalhaut hosts a bright debris disc with multiple radial components. The disc is eccentric and misaligned, strongly suggesting that it is sculpted by interaction with one or more planets. Compact sources are now being detected with JWST, suggesting that new planet detections may be imminent. However, to confirm such sources as companions, common proper motion with the star must be established, as with unprecedented sensitivity comes a high probability that planet candidates are actually background objects. Here, Atacama Large Millimetre/Submillimetre Array and Keck observations of Fomalhaut are found to show significant emission at the same sky location as multiple compact sources in JWST Mid-Infrared Instrument coronagraphic observations, one of which has been dubbed the ‘Great Dust Cloud’ because it lies within the outer belt. Since the ground-based data were obtained between 6 and 18 yr prior to the JWST observations, these compact sources are unlikely to be common proper motion companions to Fomalhaut. More generally, this work illustrates that images collected at a range of wavelengths can be valuable for rejecting planet candidates uncovered via direct imaging with JWST.

Implementing and Improving CBMZ-MAM3 Chemistry and Aerosol Modules in the Regional Climate Model WRF-CAM5: An Evaluation over the Western US and Eastern North Pacific

The representation of aerosols in climate–chemistry models is important for air quality and climate change research, but it can require significant computational resources. The objective of this study was to improve the representation of aerosols in climate–chemistry models, specifically in the carbon bond mechanism, version Z (CBMZ), and modal aerosol modules with three lognormal modes (MAM3) in the WRF-CAM5 model. The study aimed to enhance the model’s chemistry capabilities by incorporating biomass burning emissions, establishing a conversion mechanism between volatile organic compounds (VOCs) and secondary organic carbons (SOCs), and evaluating its performance against observational benchmarks. The results of the study demonstrated the effectiveness of the enhanced chemistry capabilities in the WRF-CAM5 model. Six simulations were conducted over the western U.S. and northeastern Pacific region, comparing the model’s performance with observational benchmarks such as reanalysis, ground-based, and satellite data. The findings revealed a significant reduction in root-mean-square errors (RMSE) for surface concentrations of black carbon (BC) and organic carbon (OC). Specifically, the model exhibited a 31% reduction in RMSE for BC concentrations and a 58% reduction in RMSE for OC concentrations. These outcomes underscored the importance of accurate aerosol representation in climate–chemistry models and emphasized the potential for improving simulation accuracy and reducing errors through the incorporation of enhanced chemistry modules in such models.

Pygwb: A Python-based Library for Gravitational-wave Background Searches

The collection of gravitational waves (GWs) that are either too weak or too numerous to be individually resolved is commonly referred to as the gravitational-wave background (GWB). A confident detection and model-driven characterization of such a signal will provide invaluable information about the evolution of the universe and the population of GW sources within it. We present a new, user-friendly, Python-based package for GW data analysis to search for an isotropic GWB in ground-based interferometer data. We employ cross-correlation spectra of GW detector pairs to construct an optimal estimator of the Gaussian and isotropic GWB, and Bayesian parameter estimation to constrain GWB models. The modularity and clarity of the code allow for both a shallow learning curve and flexibility in adjusting the analysis to one’s own needs. We describe the individual modules that make up pygwb, following the traditional steps of stochastic analyses carried out within the LIGO, Virgo, and KAGRA Collaboration. We then describe the built-in pipeline that combines the different modules and validate it with both mock data and real GW data from the O3 Advanced LIGO and Virgo observing run. We successfully recover all mock data injections and reproduce published results.

The JWST Early Release Science Program for Direct Observations of Exoplanetary Systems I: High-contrast Imaging of the Exoplanet HIP 65426 b from 2 to 16 μm

We present JWST Early Release Science coronagraphic observations of the super-Jupiter exoplanet, HIP 65426b, with the Near-Infrared Camera (NIRCam) from 2 to 5 μm, and with the Mid-Infrared Instrument (MIRI) from 11 to 16 μm. At a separation of ∼0.″82 (87 au), HIP 65426b is clearly detected in all seven of our observational filters, representing the first images of an exoplanet to be obtained by JWST, and the first-ever direct detection of an exoplanet beyond 5 μm. These observations demonstrate that JWST is exceeding its nominal predicted performance by up to a factor of 10, depending on separation and subtraction method, with measured 5σ contrast limits of ∼1 × 10−5 and ∼2 × 10−4 at 1″ for NIRCam at 4.4 μm and MIRI at 11.3 μm, respectively. These contrast limits provide sensitivity to sub-Jupiter companions with masses as low as 0.3M Jup beyond separations of ∼100 au. Together with existing ground-based near-infrared data, the JWST photometry are fit well by a BT-SETTL atmospheric model from 1 to 16 μm, and they span ∼97% of HIP 65426b's luminous range. Independent of the choice of model atmosphere, we measure an empirical bolometric luminosity that is tightly constrained between = −4.31 and −4.14, which in turn provides a robust mass constraint of 7.1 ± 1.2 M Jup. In totality, these observations confirm that JWST presents a powerful and exciting opportunity to characterize the population of exoplanets amenable to high-contrast imaging in greater detail.

Analysis of MLP Structures for Geomagnetic Storm Recognition Based on Ground-Based Cosmic Ray Data

Analysis of MLP Structures for Geomagnetic Storm Recognition Based on Ground-Based Cosmic Ray Data

Ozone Monitoring Instrument (OMI) UV aerosol index data analysis over the Arctic region for future data assimilation and climate forcing applications

Abstract. Due to a lack of high-latitude ground-based and satellite-based data from traditional passive- and active-based measurements, the impact of aerosol particles on the Arctic region is one of the least understood factors contributing to recent Arctic sea ice changes. In this study, we investigated the feasibility of using the ultraviolet (UV) aerosol index (AI) parameter from the Ozone Monitoring Instrument (OMI), a semi-quantitative aerosol parameter, for quantifying spatiotemporal changes in UV-absorbing aerosols over the Arctic region. We found that OMI AI data are affected by an additional row anomaly that is unflagged by the OMI quality control flag and are systematically biased as functions of observing conditions, such as azimuth angle, and certain surface types over the Arctic region, resulting in an anomalous “ring” of climatologically high AI centered at about 70∘ N, surrounding an area of low AI over the pole. Two methods were developed in this study for quality-assuring the Arctic AI data. Using quality-controlled OMI AI data from 2005 through 2020, we found decreases in UV-absorbing aerosols in the spring months (April and May) over much of the Arctic region and increases in UV-absorbing aerosols in the summer months (June, July, and August) over northern Russia and northern Canada. Additionally, we found significant increases in the frequency and size of UV-absorbing aerosol events across the Arctic and high-Arctic (north of 80∘ N) regions for the latter half of the study period (2014–2020), driven primarily by a significant increase in boreal biomass-burning plume coverage.

The 14 Her Planetary System: Companion Masses and Architecture from Radial Velocities and Astrometry

We combine Hubble Space Telescope (HST) Fine Guidance Sensor, Hipparcos, and Gaia DR3 astrometric observations of the K0 V star 14 Her with the results of an analysis of extensive ground-based radial velocity data to determine perturbation orbits and masses for two previously known companions, 14 Her b and c. Radial velocities obtained with the Hobby–Eberly Telescope and from the literature now span over 25 years. With these data we obtain improved radial velocity (RV) orbital elements for both the inner companion, 14 Her b and the long-period outer companion, 14 Her c. We also find evidence of an additional RV signal with P ∼ 3789d. We then model astrometry from Hipparcos, HST, and Gaia with RV results to obtain system parallax and proper motion, perturbation periods, inclinations, and sizes due to 14 Her b and c. We find P b = 1767.6 ± 0.2 days, perturbation semimajor axis α b = 1.3 ± 0.1 mas, and inclination i b = 36° ± 3°, P c = 52160 ± 1028 days, perturbation semimajor axis α c = 10.3 ± 0.7 mas, and inclination i c = 82° ± 14°. In agreement with a past investigation, the 14 Her b, c orbits exhibit significant mutual inclination. Assuming a primary mass M * = 0.98 ± 0.04M ☉, we obtain companion masses and .

Formulation of the cosmic ray–driven electron-induced reaction mechanism for quantitative understanding of global ozone depletion

This paper formulates the cosmic ray-driven electron-induced reaction as a universal mechanism to provide a quantitative understanding of global ozone depletion. Based on a proposed electrostatic bonding mechanism for charge-induced adsorption of molecules on surfaces and on the measured dissociative electron transfer (DET) cross sections of ozone-depleting substances (ODSs) adsorbed on ice, an analytical equation is derived to give atmospheric chlorine atom concentration: [Formula: see text] where Φe is the prehydrated electron (epre-) flux produced by cosmic ray ionization on atmospheric particle surfaces, [Formula: see text] is the surface coverage of an ODS, and ki is the ODS's effective DET coefficient that is the product of the DET cross section, the lifetimes of surface-trapped epre- and Cl-, and the particle surface area density. With concentrations of ODSs as the sole variable, our calculated results of time-series ozone depletion rates in global regions in the 1960s, 1980s, and 2000s show generally good agreement with observations, particularly with ground-based ozonesonde data and satellite-measured data over Antarctica and with satellite data in a narrow altitude band at 13 to 20 km of the tropics. Good agreements with satellite data in the Arctic and midlatitudes are also found. A previously unreported effect of denitrification on ozone loss is found and expressed quantitatively. But this equation overestimates tropospheric ozone loss at northern midlatitudes and the Arctic, likely due to increased ozone production by the halogen chemistry in polluted regions. The results render confidence in applying the equation to achieve a quantitative understanding of global ozone depletion.

Global evaluation of simulated surface shortwave radiation in CMIP6 models

Surface solar radiation (SSR) is the main energy source of Earth system, and has direct influence on climate change, the hydrological cycle, and the development of renewable energy technologies. In this study, we compared simulated monthly SSR profiles generated from 62 Coupled Model Intercomparison Project Phase 6 (CMIP6) models against natural SSR observational data collected at 226 GEBA sites during 1990–2014. The accuracy with which CMIP6 models simulated spatial features of SSR was further assessed using ISCCP-ITP satellite data. Our results show that most CMIP6 models overestimate the monthly SSR averaged over all GEBA sites, with an averaged mean bias error (MBE) of approximately 7.8 W/m2. The root mean square error (RMSE) for individual models ranged from 26 to 38 W/m2, and the RMSE for their ensemble mean decreased by an average of 8.1 W/m2. Averaging of the multi-model ensemble led to a significant improvement in the correlation coefficient, which increased from 0.90 to 0.96. Ground-based data showed that the accuracy and annual trend for the CMIP6 multi-model mean (MMM) simulations were more accurate than any individual model. The spatial distribution of CMIP6 MMM multi-year average SSR values agrees well with ISCCP-ITP satellite data retrievals; however, the overall model performance is overestimated in most locations on Earth (e.g., the southern Sahara Desert, the land along the Mediterranean Sea, and the Yangtze River Delta plain), but underestimated in some others (e.g., the Rocky Mountains, the Andes Mountains, Indonesia). The results of this study will allow more accurate predictions to be made of future SSR data produced by CMIP6 models.

Assessment of HCHO in Beijing during 2009 to 2020 using satellite observation and numerical model: Spatial characteristic and impact factor

Formaldehyde (HCHO) is an air pollutant that has a detrimental effect on human health and atmospheric environment. Until now, satellite observation has been increasingly a valuable source for monitoring the unconventional atmospheric pollutants due to the limited availability of ground-based HCHO data. Here, we used Ozone Monitoring Instrument (OMI) and the weather research and forecasting with chemistry (WRF–Chem) model to synergistically analyze the spatiotemporal variations of tropospheric HCHO in Beijing during 2009–2020, and the response of O3 to HCHO and NO2 in hotspots. We also discuss the multiple factors influencing the variation of HCHO and identify potential source area. The results indicated that HCHO column concentration is higher in eastern Beijing, and peaking in 2018 (16.68 × 1015 mol/cm2). O3 shows a good response to HCHO, with higher HCHO and NO2 photolysis leading to O3 increase in summer. In winter, decreasing HCHO and increasing NO2 inhibits the formation of O3. Transportation emissions contributed the most to HCHO, followed by the industrial sector, while residential sources have long-term effects. Isoprene produced by plants is one of the main sources of HCHO, whereas meteorological conditions can affect production efficiency. Biomass burning contributes less. Moreover, HCHO in Beijing is affected by the combined effects of local emission and external transport, and Hebei is the potential source area. This study reveals HCHO has a great accumulation potential in cities and highlights the dominant role of anthropogenic emissions, but also need to consider the influence of natural factors and regional transport.

Diurnal Cycle in Surface Incident Solar Radiation Characterized by CERES Satellite Retrieval

Surface incident solar radiation (Rs) plays an important role in climate change on Earth. Recently, the use of satellite-retrieved datasets to obtain global-scale Rs with high spatial and temporal resolutions has become an indispensable tool for research in related fields. Many studies were carried out for Rs evaluation based on the monthly satellite retrievals; however, few evaluations have been performed on their diurnal variation in Rs. This study used independently widely distributed ground-based data from the Baseline Surface Radiation Network (BSRN) to evaluate hourly Rs from the Clouds and the Earth’s Radiant Energy System Synoptic (CERES) SYN1deg–1Hour product through a detrended standardization process. Furthermore, we explored the influence of cloud cover and aerosols on the diurnal variation in Rs. We found that CERES-retrieved Rs performs better at midday than at 7:00–9:00 and 15:00–17:00. For spatial distribution, CERES-retrieved Rs performs better over the continent than over the island/coast and polar regions. The Bias, MAB and RMSE in CERES-retrieved Rs under clear-sky conditions are rather small, although the correlation coefficients are slightly lower than those under overcast-sky conditions from 9:00 to 15:00. In addition, the range in Rs bias caused by cloud cover is 1.97–5.38%, which is significantly larger than 0.31–2.52% by AOD.

Satellite Sensed Data-Dose Response Functions: A Totally New Approach for Estimating Materials’ Deterioration from Space

When construction materials are exposed to the atmospheric environment, they are subject to deterioration, which varies according to the time period of exposure and the location. A tool named Dose–Response Functions (DRFs) has been developed to estimate this deterioration. DRFs use specific air pollutants and climatic parameters as input data. Existing DRFs in the literature use only ground-based measurements as input data. This fact constitutes a limitation for the application of this tool because it is too expensive to establish and maintain such a large network of ground-based stations for pollution monitoring. In this study, we present the development of new DRFs using only satellite data as an input named Satellite Sensed Data Dose-Response Functions (SSD-DRFs). Due to the global coverage provided by satellites, this new tool for monitoring the corrosion/soiling of materials overcomes the previous limitation because it can be applied to any area of interest. To develop SSD-DRFs, we used measurements from MODIS (Moderate Resolution Imaging Spectroradiometer) and AIRS (Atmospheric Infrared Sounder) on board Aqua and OMI (Ozone Monitoring Instrument) on Aura. According to the obtained results, SSD-DRFs were developed for the case of carbon steel, zinc, limestone and modern glass materials. SSD-DRFs are shown to produce more reliable corrosion/soiling estimates than “traditional” DRFs using ground-based data. Furthermore, research into the development of the SSD-DRFs revealed that the different corrosion mechanisms taking place on the surface of a material do not act additively with each other but rather synergistically.

Evaluating the effects of columnar NO2on the accuracy of aerosol optical properties retrievals

Abstract. We aim to evaluate the NO2 absorption effect in aerosol columnar properties, namely the aerosol optical depth (AOD), Ångström exponent (AE), and single scattering albedo (SSA), derived from sun–sky radiometers in addition to the possible retrieval algorithm improvements by using more accurate characterization of NO2 optical depth from co-located or satellite-based real-time measurements. For this purpose, we employ multiannual (2017–2022) records of AOD, AE, and SSA collected by sun photometers at an urban and a suburban site in the Rome area (Italy) in the framework of both the Aerosol Robotic Network (AERONET) and SKYNET networks. The uncertainties introduced in the aerosol retrievals by the NO2 absorption are investigated using high-frequency observations of total NO2 derived from co-located Pandora spectroradiometer systems in addition to spaceborne NO2 products from the Tropospheric Monitoring Instrument (TROPOMI). For both AERONET and SKYNET, the standard network products were found to systematically overestimate AOD and AE. The average AOD bias found for Rome is relatively low for AERONET (∼ 0.002 at 440 nm and ∼ 0.003 at 380 nm) compared to the retrieval uncertainties but quite a bit higher for SKYNET (∼ 0.007). On average, an AE bias of ∼ 0.02 and ∼ 0.05 was estimated for AERONET and SKYNET, respectively. In general, the correction seems to be low for areas with low columnar NO2 concentrations, but it is still useful for low AODs (< 0.3), where the majority of observations are found, especially under high NO2 pollution events. For the cases of relatively high NO2 levels (> 0.7 DU), the mean AOD bias was found within the range 0.009–0.012 for AERONET, depending on wavelength and location, and about 0.018 for SKYNET. The analysis does not reveal any significant impact of the NO2 correction on the derived aerosol temporal trends for the very limited data sets used in this study. However, the effect is expected to become more evident for trends derived from larger data sets and in the case of an important NO2 trend. In addition, the comparisons of the NO2-modified ground-based AOD data with satellite retrievals from the Deep Blue (DB) algorithm of the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) resulted in a slight improvement in the agreement of about 0.003 and 0.006 for AERONET and SKYNET, respectively. Finally, the uncertainty in assumptions on NO2 seems to have a non-negligible impact on the retrieved values of SSA at 440 nm leading to an average positive bias of about 0.02 (2 %) in both locations for high NO2 loadings (> 0.7 DU).

A Case Study on the Effect of Atmospheric Density Calibration on Orbit Predictions with Sparse Angular Data

Accurately modeling the density of atmospheric mass is critical for orbit determination and prediction of space objects. Existing atmospheric mass density models (ADMs) have an accuracy of about 15%. Developing high-precision ADMs is a long-term goal that requires a better understanding of atmospheric density characteristics, more accurate modeling methods, and improved spatiotemporal data. This study proposes a method for calibrating ADMs using sparse angular data of space objects in low-Earth orbit over a certain period of time. Applying the corrected ADM not only improves the accuracy of orbit determination, but also enhances the accuracy of orbit prediction beyond the correction period. The study compares the impact of two calibration methods: atmospheric mass density model coefficient (ADMC) calibration and high precision satellite drag model (HASDM) calibration on the accuracy of orbit prediction of space objects. One month of ground-based telescope array angular data is used to validate the results. Space objects are classified as calibration objects, participating in ADM calibration, and verification objects, inside and outside the calibration orbit region, respectively. The results show that applying the calibrated ADM can significantly increase the accuracy of orbit prediction. For objects within the calibration orbit region, the calibration object’s orbit prediction error was reduced by about 55%, while that of verification objects was reduced by about 45%. The reduction in orbit prediction error outside this region was about 30%. This proposed method contributes significantly to the development of more reliable ADMs for orbit prediction of space objects with sparse angular data and can provide significant academic value in the field of space situational awareness.

Reanalysis of neutron-capture elements in the benchmark r-rich star CS 31082−001

ABSTRACT We revisit the abundances of neutron-capture elements in the metal-poor ([Fe/H] = −2.9) r-process-rich halo star CS 31082−001. Partly motivated by the development of the new near-ultraviolet Cassegrain U-band Efficient Spectrograph for the Very Large Telescope, we compiled an expanded line list for heavy elements over the range 3000–4000 Å, including hyperfine structure for several elements. Combining archival near-ultraviolet spectra of CS 31082−001 from the Hubble Space Telescope and the Very Large Telescope, we investigate the abundances and nucleosynthesis of 35 heavy elements (Ge, Sr, Y, Zr, Nb, Mo, Ru, Rh, Pd, Ag, Cd, Sn, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Os, Ir, Pt, Pb, Bi, Th, and U). Our analysis includes the first abundance estimates for tin, holmium, and ytterbium from these data, and the first for lutetium from ground-based data, enabling a more complete view of the abundance pattern of this important reference star. In general, the r-process-dominated elements are as enhanced as those in the Sun, particularly for elements with Z ≥ 56 (Ba and heavier). However, the abundances for the lighter elements in our sample, from Ge to Sn (31 ≤ Z ≤ 50), do not scale with the solar abundance pattern. Moreover, the Ge abundance is deficient relative to solar, indicating that it is dominantly an iron-peak rather than neutron-capture element. Our results (or upper limits) on Sn, Pt, Au, Pb, and Bi all pose further questions, prompting further study on the origin and evolution of the known r-rich and actinide-rich, metal-poor stars.

Validation of the Cloud_CCI (Cloud Climate Change Initiative) cloud products in the Arctic

Abstract. The role of clouds in the Arctic radiation budget is not well understood. Ground-based and airborne measurements provide valuable data to test and improve our understanding. However, the ground-based measurements are intrinsically sparse, and the airborne observations are snapshots in time and space. Passive remote sensing measurements from satellite sensors offer high spatial coverage and an evolving time series, having lengths potentially of decades. However, detecting clouds by passive satellite remote sensing sensors is challenging over the Arctic because of the brightness of snow and ice in the ultraviolet and visible spectral regions and because of the small brightness temperature contrast to the surface. Consequently, the quality of the resulting cloud data products needs to be assessed quantitatively. In this study, we validate the cloud data products retrieved from the Advanced Very High Resolution Radiometer (AVHRR) post meridiem (PM) data from the polar-orbiting NOAA-19 satellite and compare them with those derived from the ground-based instruments during the sunlit months. The AVHRR cloud data products by the European Space Agency (ESA) Cloud Climate Change Initiative (Cloud_CCI) project uses the observations in the visible and IR bands to determine cloud properties. The ground-based measurements from four high-latitude sites have been selected for this investigation: Hyytiälä (61.84∘ N, 24.29∘ E), North Slope of Alaska (NSA; 71.32∘ N, 156.61∘ W), Ny-Ålesund (Ny-Å; 78.92∘ N, 11.93∘ E), and Summit (72.59∘ N, 38.42∘ W). The liquid water path (LWP) ground-based data are retrieved from microwave radiometers, while the cloud top height (CTH) has been determined from the integrated lidar–radar measurements. The quality of the satellite products, cloud mask and cloud optical depth (COD), has been assessed using data from NSA, whereas LWP and CTH have been investigated over Hyytiälä, NSA, Ny-Å, and Summit. The Cloud_CCI COD results for liquid water clouds are in better agreement with the NSA radiometer data than those for ice clouds. For liquid water clouds, the Cloud_CCI COD is underestimated roughly by 3 optical depth (OD) units. When ice clouds are included, the underestimation increases to about 5 OD units. The Cloud_CCI LWP is overestimated over Hyytiälä by ≈7 g m−2, over NSA by ≈16 g m−2, and over Ny-Å by ≈24 g m−2. Over Summit, CCI LWP is overestimated for values ≤20 g m−2 and underestimated for values >20 g m−2. Overall the results of the CCI LWP retrievals are within the ground-based instrument uncertainties. To understand the effects of multi-layer clouds on the CTH retrievals, the statistics are compared between the single-layer clouds and all types (single-layer + multi-layer). For CTH retrievals, the Cloud_CCI product overestimates the CTH for single-layer clouds. When the multi-layer clouds are included (i.e., all types), the observed CTH overestimation becomes an underestimation of about 360–420 m. The CTH results over Summit station showed the highest biases compared to the other three sites. To understand the scale-dependent differences between the satellite and ground-based data, the Bland–Altman method is applied. This method does not identify any scale-dependent differences for all the selected cloud parameters except for the retrievals over the Summit station. In summary, the Cloud_CCI cloud data products investigated agree reasonably well with those retrieved from ground-based measurements made at the four high-latitude sites.

Editorial for Special Issue “Ground and Structural Deformations Monitoring Systems Integrating Remote Sensing and Ground-Based Data”

Ground deformations due to landslides [...]