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There has been controversy regarding the air pollutants emitted from sources closely related to the activities of daily life in China, such as cooking, setting off fireworks, sacrificial incense and joss paper burning, and barbecue (which have been named the five missing sources – FMSs), and the extent to which they impact the outdoor air quality. To date, due to the lack of both an activity dataset and emission factors, there have been no estimations of the emission of air pollutants from FMSs. In this work, we have attempted to combine questionnaire data, various statistical data, and data on points of interest to obtain a relatively complete set of activity data. The emission factors (EFs) of carbonaceous aerosols were tested in our lab. Emission inventories of carbonaceous aerosols with a high spatial–temporal resolution for FMSs were then established, and the spatial variation trend and driving forces were discussed. From 2000 to 2018, organic carbon (OC) emissions were in the range of 4268–4919 t. OC emissions from FMSs were between 1.5 ‰ and 2.2 ‰ of the total emissions in China. Black carbon (BC), elemental carbon (EC), and brown carbon (BrC) absorption cross-section (ACS BrC) emissions from FMSs were in the ranges of 22.6–43.9 t, 213–324 t, and 14.7–35.6 Gm 2 , respectively. Their emissions tended to be concentrated in certain periods and areas. The OC emission intensities in central urban areas were 3.85–50.5 times those in rural areas (due to the high density of human activity), while the ACS BrC emissions in rural regions accounted for 63.0–79.5 % of the total emissions (resulting from uncontrolled fireworks). A mass of fireworks led to much higher ACS BrC and EC emissions on Chinese New Year's Eve, with respective values that were 1444 and 262 times their corresponding yearly averages. Significant (p<0.01) correlations between the population's income and pollutant emissions were also found: these correlations were positive (r=0.94) and negative (r=-0.94) for urban and rural regions, respectively, indicating the necessity to regulate residents' lifestyles and increase residents' income in respective urban and rural regions. This study provides firsthand data to identify emissions, variation trends, and impacting factors from the FMSs. This is helpful for modeling works on air quality, the climate effect, and human health risks during specific periods or in specific regions as well as for modifying emission control policies. The data compiled in this work can found at 10.6084/m9.figshare.19999991.v2 (Cheng et al., 2022). [ABSTRACT FROM AUTHOR]

There has been controversy about the air pollutants emitted from sources closely related to people's daily life (such as cooking, fireworks burning, sacrificial incenses and joss paper burning, and barbecue, named as five missing sources, FMS) impacting the outdoor air quality to what extent. Till now, there is no emission estimation of air pollutants from FMS, as the missing of both activity dataset and emission factors. We attempted to combine the questionnaire data, various statistical data, and data of points of interest to obtain a relatively complete set of activity data. The emission factors (EFs) of carbonaceous aerosols were tested in our lab. Then, the emission inventories of carbonaceous aerosol with the high spatial-temporal resolution for FMS were established firstly, and the spatial-variation trend and driving forces were discussed. From 2000 to 2018, organic carbon (OC) increased from 4268 t to 4919 t. The OC emission from FMS was 1.48-2.18 - of its total emission in China. The emissions of black carbon, element carbon (EC), and brown carbon absorption cross-section (ACSBrC) emissions from FMS were in the ranges of 22.6-43.9 t, 213-324 t, and 14.7-35.6 Gm2, respectively. Their emissions tended to concentrate in special periods and areas. The OC emission intensities in central urban areas were 3.85-50.5 times that of rural areas due to the high density of human activities. While the ACSBrC emissions in rural regions accounted for 63.0-79.5% of the total emission result from uncontrolled fireworks burning. A mass of fireworks burning led to extremely higher ACSBrC and EC emissions on Chinese New Year's eve, as 1444 and 262 times their corresponding yearly average values. Interestingly, significant (p<0.01) correlations between human incomes and pollutant emissions were found, but they were positive (r = 0.94) and negative (r = -0.94) for urban and rural regions, indicating the necessity of regulating human lifestyle and increasing income for urban and rural peoples, respectively. This study provided the first-hand data for identifying the emissions, variation trends and impacting factors of FMS, which is helpful for modeling works on air quality, climate effect, and human health risks at specific periods or regions and for modifying their emission control policies. The data in this work could be found at https://doi.org/10.6084/m9.figshare.19999991.v1 (Cheng et al., 2022). [ABSTRACT FROM AUTHOR]

The objective of this study is to prepare a cellulose paper that was impregnated with silver nanoparticles (AgNPs) for the purpose of water purification (disinfection and filtration). AgNP papers were prepared by chemical reduction of silver nitrate (AgNO3) with various concentrations (0.005 M , 0.015 M , 0.03 M , and 0.05 M) using sodium borohydride (NaBH4) as a reducing agent. Two ratios for NaBH4/AgNO3 of 2:1 and 10:1 were used to show the effect of reduction on the formation and removal efficiencies of AgNPs. AgNP papers were characterized using scanning electron microscopy and transmission electron microscopy. An acid digestion using HCl acid followed by analyzing the samples in an atomic absorption spectrometer (ASS) was conducted to measure the silver concentration in AgNP papers. TEM images showed that the silver nanoparticle size in the papers varied from 1.3 to 75 nm. Water samples, after filtration through AgNP papers, were analyzed using ASS to measure the silver concentration in the effluent water. AgNP paper antibacterial efficiency ranged from 99 % to 100 % for both reduction ratios. The average silver content in the effluent water for the three replicates ranged from 0 to 0.082 mgL-1 , which meets the United States Environmental Protection Agency (US-EPA) guideline for drinking water of less than 0.1 mgL-1. Turbidity tests showed that these papers can be usefully used as point-of-use filters as the turbidity reduced to less than 1 NTU (Nephelometric Turbidity Units). [ABSTRACT FROM AUTHOR]

Direct measurement of the acidity (pH) of ambient aerosol particles/droplets has long been a challenge for atmospheric scientists. A novel and facile method was introduced recently by Craig et al. (2018), where the pH of size-resolved aerosol droplets was directly measured by two types of pH-indicator papers (pH ranges: 0–2.5 and 2.5–4.5) combined with RGB-based colorimetric analyses using a model of G-B (G minus B) vs. pH2. Given the wide pH range of ambient aerosols, we optimize the RGB-based colorimetric analysis on pH papers with a wider detection range (pH ∼ 0 to 6). Here, we propose a new model to establish the linear relationship between RGB values and pH: pHpredict = a⋅Rnormal+b⋅Gnormal+c⋅Bnormal. This model shows a wider applicability and higher accuracy than those in previous studies and is thus recommended in future RGB-based colorimetric analyses on pH papers. Moreover, we identify one type of pH paper (Hydrion® Brilliant pH dip stiks, lot no. 3110, Sigma-Aldrich) that is more applicable for ambient aerosols in terms of its wide pH detection range (0 to 6) and strong anti-interference capacity. Custom-made impactors are used to collect lab-generated aerosols on this type of pH paper. Preliminary tests show that, with a collected particle size range of ∼ 0.4–2.2 µm , the pH paper method can be used to predict aerosol pH with an overall uncertainty ≤ 0.5 units. Based on laboratory tests, a relatively short sampling time (∼ 1 to 4 h) is speculated for pH prediction of ambient aerosols. More importantly, our design of the impactors minimizes potential influences of changed environmental conditions during pH paper photographing processes on the predicted aerosol pH. We further show that the routinely adopted way of using pH color charts to predict aerosol pH may be biased by the mismatch between the standard colors on the color charts and the real colors of investigated samples. Thus, instead of using the producer-provided color charts, we suggest an in situ calibration of pH papers with standard pH buffers. [ABSTRACT FROM AUTHOR]

The objective of this study is to prepare a cellulose paper was impregnated with silver nanoparticles (AgNPs) for the purpose of water purification (Disinfection (removal of Escherichia Coli, Staphylococcus Aureus, Enterococcus Faecalis, Enterobacter Aerogenes, Klebsiella Pneumoniae, and Proteus mirabilis) and filtration). AgNPs papers were prepared by chemical reduction of silver nitrate (AgNO3) with various concentrations (0.005 M, 0.015 M, 0.03 M, and 0.05 M) using sodium borohydride (NaBH4) as a reducing agent. Two ratios of NaBH4 / AgNO3 of 2 : 1 and 10 : 1 were used to show the effect of reduction on the formation and removal efficiencies of AgNPs. AgNPs papers were characterized using Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). An acid digestion using HCL acid followed by analyzing the samples in Atomic Absorption Spectrometer (ASS) was conducted to measure the silver concentration in AgNPs papers. TEM images showed that the silver nanoparticles size in the papers varies from 1.3 to 75 nm. Water samples, after filtration through AgNPs papers, were analyzed using (ASS) to measure the silver concentration in the effluent water. AgNPs paper antibacterial efficiency ranged (99 % to 100 %) for both reduction ratios. The average silver content in the effluent water for the three replicates ranged from 0 to 0.082 mg/L which meets the United States- Environmental Protection Agency (US-EPA) guideline for drinking water of less than 0.1 mg/L Turbidity tests showed that these papers can be usefully used as a point of use filters as the turbidity reduced to less than 1 NTU. [ABSTRACT FROM AUTHOR]

Direct measurement of the acidity (pH) of ambient aerosol particles/droplets has long been a challenge for atmospheric scientists. A novel and facile method was introduced recently by Craig et al. (2018), where the pH of size-resolved aerosol droplets was directly measured by two types of pH-indicator papers (pH ranges: 0–2.5 and 2.5–4.5) combined with RGB-based colorimetric analyses using a model of G − B (G minus B) versus pH². Given the wide pH range of ambient aerosols, we optimize the RGB-based colorimetric analysis on pH papers with a wider detection range (pH ~ 0 to 6). Here, we propose a new model to establish the linear relationship between RGB values and pH: pHpredict = a × Rnormal + b × Gnormal + c × Bnormal. This model shows a wider applicability and higher accuracy than those in previous studies, and is thus recommended in future RGB-based colorimetric analyses on pH papers. Moreover, we identify one type of pH paper (Hydrion® Brilliant pH dip stiks, Lot Nr. 3110, Sigma-Aldrich) that is more applicable for ambient aerosols in terms of its wide pH detection range (0 to 6) and strong anti-interference capacity. The determined minimum sample mass (~ 180 μg) highlights its potential to predict aerosol pH with a high time resolution (e.g., ≤ 1 hour). We further show that the routinely adopted way of using pH color charts to predict aerosol pH may be biased by the mismatch between the standard colors on the color charts and the real colors of investigated samples. Thus, instead of using the producer-provided color chart, we suggest an in-situ calibration of pH papers with standard pH buffers. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with data pertinent to transportation networks, which model situations in which objects move along a graph-like structure. We assume that these networks are equipped with sensors that monitor the network and the objects moving along it. These sensors produce time-series data resulting in sensor networks. Examples are river-, road- and electricity networks. Geographical information systems are used to gather, store and analyse data, and we focus on these tasks in the context of data emerging from transportation networks equipped with sensors. While tailored solutions exist for many contexts, they are limited for sensor-equipped networks at this moment. We view time-series data as temporal properties of the network and approach the problem from the viewpoint of property graphs. In this paper, we adapt and extend the theory of the existing property graph databases to model spatial networks, where nodes and edges can contain temporal properties that are time-series data originating from the sensors. We propose a language for querying these property graphs with time series, in which time-series and measurement patterns may be combined with graph patterns to describe, retrieve and analyse real-life situations. We demonstrate the model and language in practice by implementing both in Neo4j and explore questions hydrology researchers pose in the context of the Internet of Water, including salinity analysis in the Yser river basin. [ABSTRACT FROM AUTHOR]

Atmospheric emissions from anthropogenic hotspots, i.e., cities, power plants and industrial facilities, can be determined from remote sensing images obtained from airborne and space-based imaging spectrometers. In this paper, we present a Python library for data-driven emission quantification (ddeq) that implements various computationally light methods such as the Gaussian plume inversion, cross-sectional flux method, integrated mass enhancement method and divergence method. The library provides a shared interface for data input and output and tools for pre- and post-processing of data. The shared interface makes it possible to easily compare and benchmark the different methods. The paper describes the theoretical basis of the different emission quantification methods and their implementation in the ddeq library. The application of the methods is demonstrated using Jupyter notebooks included in the library, for example, for NO 2 images from the Sentinel-5P/TROPOMI satellite and for synthetic CO 2 and NO 2 images from the Copernicus CO 2 Monitoring (CO2M) satellite constellation. The library can be easily extended for new datasets and methods, providing a powerful community tool for users and developers interested in emission monitoring using remote sensing images. [ABSTRACT FROM AUTHOR]

During the Water Vapor Lidar Network Assimilation (WaLiNeAs) campaign, 8 lidars specifically designed to measure water vapor mixing ratio (WVMR) profiles were deployed on the western Mediterranean coast. The main objectives were to investigate the water vapor content during case studies of heavy precipitation events in the coastal Western Mediterranean and assess the impact of high spatio-temporal WVMR data on numerical weather prediction forecasts by means of state–of–the–art assimilation techniques. Given the increasing occurrence of extreme events due to climate change, WaLiNeAs is the first program in Europe to provide network–like, simultaneous and continuous water vapor profile measurements. This paper focuses on the WVMR profiling datasets obtained from three of the lidars managed by the French component of the WaLiNeAs team. These lidars were deployed in the towns of Coursan, Grau du Roi and Cannes. This measurement setup enabled monitoring of the water vapor content within the low troposphere along a period of three months over autumn – winter 2022 and four months in summer 2023. The lidars measured the WVMR profiles from the surface up to approximately 6–10 km at night, and 1–2 km during daytime; with a vertical resolution of 100 m and a time sampling between 15 – 30 min, selected to meet the needs of weather forecasting with an uncertainty lower than 0.4 g kg-1. The paper presents details about the instruments, the experimental strategy, as well as the datasets given in NETcdf format. The final dataset is divided in two datasets, the first with a time resolution of 15 min, which contains a total of 26 423 WVMR vertical profiles and the second with a time resolution of 30 min to improve the signal to noise ratio and signal altitude range. [ABSTRACT FROM AUTHOR]

This paper describes the aerosol in-situ laboratory at CIAO (CNR IMAA Atmospheric Observatory) in South Italy, outlining its configuration and detailing each instrument and sampling lines. The CIAO observatory has been collecting observations of atmospheric components since 2000. Initially the activities revolved around aerosol lidar, later radiosounding and cloud remote sensing observations were added over the years and made CIAO a leading atmospheric observatory in the Mediterranean region. In 2018, a significant upgrade started for enhancing the observational capability by adding aerosol in-situ instruments, with the objective to push new research boundaries for aerosol characterization and multi-instrumental synergistic approaches. Here, we describe each technical implementation step for building up an extensive aerosol in-situ laboratory compliant with ACTRIS (Aerosol Clouds and Trace gases Research InfraStructure) standard operating procedures. Starting from scratch, the long path initiated in 2018, with the design of the laboratory in terms of instruments, container organization, inlets and sampling lines optimizations, that required time and interactions with experts in the field. Reporting here all the details about the final solutions implemented at CIAO, this paper will be, for new aerosol in-situ laboratory, a practical guide for the implementation of the aerosol in-situ observational site. [ABSTRACT FROM AUTHOR]

When comparing model output with historical radiosonde observations, it is usually assumed that a radiosonde has risen exactly above its starting point and has not been displaced by wind. This changed only relatively recently with the availability of Global Navigation Satellite System (GNSS) receivers aboard radiosondes in the late 1990s, but even then the balloon trajectory data were often not transmitted, although this information was the basis for estimating the wind in the first place. Depending on the conditions and time of year, radiosondes can sometimes drift a few hundred kilometres, particularly at the middle latitudes during the winter months. The position errors can lead to non-negligible representation errors when the corresponding observations are assimilated. This paper presents a methodology to compute changes in the balloon position during its vertical ascent, using only limited information, such as the vertical profile of wind contained in the historical observation reports. The sensitivity of the method to various parameters is investigated, such as the vertical resolution of the input data, the assumption about the vertical ascent speed of the balloon, and the departure of the surface of Earth from a sphere. The paper considers modern GNSS sonde data reports for validation, for which the full trajectory of the balloon is available, alongside the reported wind. Evaluation is also conducted by comparison with ERA5 and by conducting low-resolution data assimilation experiments. Overall, the results indicate that the trajectory of the radiosondes can be accurately reconstructed from original data of varying vertical resolutions and that the more accurate balloon position reduces representation errors and, in some cases, systematic errors. [ABSTRACT FROM AUTHOR]

The optimization of aircraft trajectories involves balancing operating costs and climate impact, which are often conflicting objectives. To achieve compromised optimal solutions, higher-level information such as preferences of decision-makers must be taken into account. This paper introduces the SolFinder 1.0 module, a decision-making tool designed to identify eco-efficient aircraft trajectories, which allow for the reduction of the flight's climate impact with limited cost penalties compared to cost-optimal solutions. SolFinder 1.0 offers flexible decision-making options that allow users to select trade-offs between different objective functions, including fuel use, flight time, NOx emissions, contrail distance, and climate impact. The module is included in the AirTraf 3.0 submodel, which optimizes trajectories under atmospheric conditions simulated by the ECHAM/MESSy Atmospheric Chemistry model. This paper focuses on the ability of the module to identify eco-efficient trajectories while solving a bi-objective optimization problem that minimizes climate impact and operating costs. SolFinder 1.0 enables users to explore trajectory properties at varying locations of the Pareto fronts without prior knowledge of the problem results and to identify solutions that limit the cost of reducing the climate impact of a single flight. [ABSTRACT FROM AUTHOR]

River ice is a common occurrence in cold climate hydrological systems. The annual cycle of river ice formation, growth, decay and clearance can include low flows and ice jams, as well as mid-winter and spring break-up events. Reports and associated data on river ice occurrence are often limited to site and season-specific studies. Within Canada, the National Hydrometric Program (NHP) operates a network of gauging stations with water level as the primary measured variable to derive discharge. In the late 1990s, the Water Science and Technology Directorate of Environment and Climate Change Canada initiated a long-term effort to compile, archive and extract river ice related information from NHP hydrometric records. This data article describes the original research data set produced by this near 20-year effort: the Canadian River Ice Database (CRID). The CRID holds almost 73,000 variables from a network of 196 NHP stations throughout Canada that were in operation within the period 1894 to 2015. Over 100,000 paper and digital files were reviewed representing 10,378 station-years of active operation. The task of compiling this database involved manual extraction and input of more than 460,000 data entries on water level, discharge, date, time and data quality rating. Guidelines on the data extraction, rating procedure and challenges are provided. At each location, a time series of up to 15 variables specific to the occurrence of freeze-up and winter-low events, mid-winter break-up, ice thickness, spring break-up and maximum open-water level were compiled. This database follows up on several earlier efforts to compile information on river ice, which are summarized herein, and expands the scope and detail for use in Canadian river ice research and applications. Following the Government of Canada Open Data initiative, this original river ice data set is available at: https://doi.org/10.18164/c21e1852-ba8e-44af-bc13-48eeedfcf2f4 (de Rham et al., 2020). [ABSTRACT FROM AUTHOR]

In this paper we describe a method for retrieving surface emissivity across the wavenumber range 400–1600 cm-1 using novel radiance measurements from the Far INfrarEd Spectrometer for Surface Emissivity (FINESSE) instrument. FINESSE is described in detail in part I of this paper. We apply the method to two sets of measurements of distilled water. The first set of emissivity retrievals is of distilled water heated above ambient temperature to enhance the signal to noise ratio. The second set of emissivity retrievals is of ambient temperate water at a range of viewing angles. In both cases the observations agree well with calculations based on compiled refractive indices across the mid and far-infrared. It is found that the reduced contrast between the up and downwelling radiation in the ambient temperature case degrades the performance of the retrieval. Therefore a filter is developed to target regions of high contrast which improves the agreement between the ambient temperature emissivity retrieval and the predicted emissivity. These retrievals are, to the best of our knowledge, the first published 10 retrievals of the emissivity of water that extend into the far-infrared and demonstrate a method that can be used for the in-situ retrieval of the emissivity of other surfaces in the field. [ABSTRACT FROM AUTHOR]

This paper introduces a machine-learning-driven approach for automated Nocturnal Low-Level Jet (NLLJ) identification using observations of wind profiles from a Radar Wind Profiler (RWP). The work discussed here is an effort to lay the groundwork for a systematic study of the Mid-Atlantic NLLJ's formation mechanisms and their influence on nocturnal and diurnal air quality in major urban regions by establishing a general framework of NLLJ features and characteristics with an identification algorithm. Leveraging a comprehensive wind profile dataset maintained by the Maryland Department of Environment's RWP network, our methodology employs supervised machine learning techniques to isolate the features of the south-westerly NLLJ. This methodology was developed to illuminate spatiotemporal patterns and nuanced characteristics of NLLJ events, unveiling their significant role in shaping the planetary boundary layer. This paper discusses the construction of this methodology, its performance against known NLLJs in the current literature, intended usage, and a preliminary statistical analysis. First light results from this analysis have identified a total of 90 south-westerly NLLJs from May–September of 2017–2021 as captured by the RWP stationed in Beltsville, MD (39.05°, -76.87°, 135 m ASL). A composite of these 90 jets is presented to better illustrate many of the bulk parameters, such as core height, duration, and maximum wind speed, associated with the onset and decay of the Mid-Atlantic NLLJ. We hope our study equips researchers and policymakers with further means to monitor, predict, and address these nocturnal dynamics phenomena that frequently influence boundary layer composition and air quality in the U.S. Mid-Atlantic and Northeastern regions. [ABSTRACT FROM AUTHOR]

The accurate calibration of parameters in atmospheric and Earth system models is crucial for improving their performance, but remains a challenge due to their inherent complexity, which is reflected in input-output relationships often characterized by multiple interactions between the parameters and thus hindering the use of simple sensitivity analysis methods. This paper introduces the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a new tool designed with the aim of providing a simple and flexible framework to estimate the sensitivity and importance of parameters in complex numerical weather prediction models. This tool leverages the strengths of multiple regression-based and probabilistic machine learning methods including LASSO, Support Vector Machine, Classification and Decision Trees, Random Forest, Extreme Gradient Boosting, Gaussian Process Regression, and Bayesian Ridge Regression. These regression algorithms are used to construct computationally inexpensive surrogate models to effectively predict model outputs from input parameters, thereby significantly reducing the computational burden of running high-fidelity models for sensitivity analysis. Moreover, the multi-method approach allows for a comparative analysis of the results. Through a detailed case study with the Weather Research and Forecasting (WRF) model coupled with the Noah-MP land surface model, ML-AMPSIT is demonstrated to efficiently predict the behavior of Noah-MP model parameters with a relatively small number of model runs, by simulating a sea breeze circulation over an idealized flat domain. This paper points out how ML-AMPSIT can be an efficient tool for performing sensitivity and importance analysis also for complex models, guiding the user through the different steps and allowing for a simplification and automatization of the process. [ABSTRACT FROM AUTHOR]

This paper describes an attempt to use data obtained from sodar (sound detection and ranging) for short-term forecasting of PM 10 concentration levels in Krakow. Krakow is one of the most polluted cities in central Europe (CE) in terms of PM 10 concentration. This is due to the high municipal emissions. Thanks to intensive corrective actions taken by the city authorities, these are being effectively eliminated, but the unfavourable topographic location of the city limits natural ventilation. The article describes all these conditions, focusing on presenting the method of short-term correction of air quality for the time needed to take quick corrective actions by the city authorities in the event of anticipated exceedances of the permissible values. Based on several years of measurements of the physical properties of the atmosphere with sodar, the authors of the paper suggest that sodar data could be considered for operational use to generate short-term predictions. The proposed method is based on the use of the spectrum, i.e. the set of amplitudes of signals returning to the sodar receiver from the reflection of a single-frequency sound transmission and its characteristic properties depending on the physical state of the atmosphere. Similar spectra were parameterised with a single numerical value using statistical methods. It was found that, in some cases preceding high concentrations of PM 10 , the spectral parameters had similar values. This made it possible to develop a forecasting method for such concentrations by using data mining to search for conditions in historical data closest to the state of the atmosphere at the time of forecasting. In this part of the study, data from 2017–2018 were used. In the next step, three methods of using the sodar data developed for PM 10 prediction were proposed, comparing them with the method without sodar use. The study results were tested on independent material using data concerning the episodes of high concentrations of pollutants from October 2021 to March 2022 in Krakow. The findings were considered to be encouraging, also taking into account the speed and low cost of preparing the forecast. [ABSTRACT FROM AUTHOR]

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO 2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature. [ABSTRACT FROM AUTHOR]

The paper reviews and evaluates a 30-year-long atmospheric CO2 data series measured at Hegyhátsál tall-tower greenhouse gas monitoring site, a member of WMO GAW, NOAA, and ICOS networks (id. code: HUN). The paper also gives the technical description of the monitoring system, and that of the physical environment of the station. This low elevation (248 m above m.s.l.), mid-continental Central European site shows a 3.90±0.83 µmol mol‑1 offset relative to the latitudinally representative marine boundary layer reference concentration presumably due to the European net anthropogenic emissions. The long-term trend (2.20 µmol mol‑1 year‑1) closely follows the global tendencies. In the concentration growth rate, the ENSO effect is clearly detectable with a 6–7 months lag-time. The summer diurnal concentration amplitude is slightly decreasing due to the faster-than-average increase of the nighttime concentrations, which is related to the warming climate. The warming climate also caused a 0.96±0.41 day year‑1 advance in the beginning of the summer CO2-deficit season in the first half of the measurement period, which did not continue later. [ABSTRACT FROM AUTHOR]

In this paper we present a new model for the simulation of lahars based on the depth-averaged code IMEX-SfloW2D with new governing and constitutive equations introduced to better describe the dynamics of lahars. A thorough sensitivity analysis is carried out to identify the critical processes (such as erosion and deposition) and parameters (both numerical and physical) controlling lahar runout using both synthetic and real case topographies. In particular, an application of the model to a syn-eruptive lahar from a reference size eruption from Somma–Vesuvius, affecting the Campanian Plain (southern Italy), described in Di Vito et al. (2024), is used in this work for the sensitivity analysis. Effects of erosion and deposition are investigated by comparing simulations with and without these processes. By comparing flow thickness and area covered by the flow and their evolution with time, we show that the modelling of both the processes is important to properly simulate the effects of the bulking and debulking as well as the associated changes in rheology. From a computational point of view, the comparisons of simulations obtained for different numerical grids (from 25 to 100 m), scheme order, and grain size discretization were useful to find a good compromise between resolution and computational speed. The companion paper by Sandri et al. (2024) shows an application of the presented model for probabilistic volcanic hazard assessment for lahars from Vesuvius deposits in the Neapolitan area. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a spectral surface reflectivity climatology based on observations made by TROPOMI on board the Sentinel-5P satellite. The database contains the directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface for 21 wavelength bands ranging from 328 to 2314 nm and for each calendar month. The spatial resolution of the database grid is 0.125° × 0.125°. A recently developed cloud shadow detection technique is implemented to avoid dark scenes due to cloud shadow. In the database, the anisotropy of the surface reflection is described using a third-order parameterisation of the viewing angle dependence. The viewing angle dependence of the DLER is analysed globally and for a selection of surface type regions. The dependence is found to agree with the viewing angle dependence found in the GOME-2 surface DLER database. Differences exist, related to the actual solar position. On average, the viewing angle dependence in TROPOMI DLER is weaker than for GOME-2 DLER, but still important. Validation of the new database was first performed by comparison of the non-directional TROPOMI surface LER with heritage LER databases based on GOME-1 , OMI, SCIAMACHY, and GOME-2 data. Agreement was found within 0.002–0.02 in the UV-VIS (below 500 nm), up to 0.003 in the NIR (670–772 nm), and below 0.001 in the short-wave infrared (SWIR) (2314 nm). These performance numbers are dominated by the performance over ocean, but they are in most cases also representative for land surfaces. For the validation of the directional TROPOMI surface DLER, we made use of comparison with the MODIS surface bi-directional reflectance distribution function (BRDF) for a selection of surface type regions. In all cases the DLER performed significantly better than the traditional LER, and we found good agreement with the MODIS surface BRDF. The TROPOMI surface DLER database is a clear improvement on previous surface albedo databases and can be used as input not only for satellite retrievals from TROPOMI observations, but also for retrievals from observations from other polar-orbiting satellite instruments provided that their equator crossing time is close to that of TROPOMI. The algorithm that is introduced in this paper can be used for the retrieval of surface reflectivity climatologies from other polar satellite missions as well, including Ocean and Land Colour Instrument (OLCI) on the Sentinel-3 satellites, Sentinel-5, and Multi-viewing Multi-channel Multi-polarisation imager (3MI) on the MetOp-SG-A1 satellite to be launched in 2025, as well as the future CO2M mission. [ABSTRACT FROM AUTHOR]

The complexity of aerosol particle properties and the diversity of characterizations make aerosol vertical transport flux measurements and analysis difficult. Although there are different methods, such as aerosol particle number concentration flux and aerosol mass flux based on the eddy covariance principle as well as aerosol mass flux measurements based on the light-propagated large-aperture scintillation principle, there is a lack of mutual validation among the different methods. In this paper, a comparison of aerosol mass flux measurements based on the eddy covariance principle and aerosol mass flux measurements based on the light-propagated large-aperture scintillation principle is carried out. The key idea of aerosol mass flux measurements based on the light-propagated large-aperture scintillation principle is the determination of the imaginary part of the atmospheric equivalent refractive index structure parameter (AERISP). In this paper, we first compare the AERISPs measured by two different methods and then compare the aerosol mass vertical transport fluxes obtained by different methods. The experiments were conducted on the campus of the University of Science and Technology of China (USTC). A light propagation experiment was carried out between two tall buildings to obtain the imaginary and real parts of the AERISPs for the whole path, which in turn can be used to obtain the aerosol vertical transport flux. An updated visibility meter is installed on the meteorological tower in the middle of the light path, which is utilized to obtain the single-point visibility, which is then converted to the imaginary part of the atmospheric equivalent refractive index (AERI). The imaginary parts of the AERISP were obtained via spectral analysis with visibility data. The results show that the imaginary parts of the AERISPs obtained by different methods are in good agreement. The imaginary part of the AERI measured by the visibility meter and the vertical wind speed obtained by the ultrasonic anemometer were used for covariance calculations to obtain the aerosol vertical transport flux. The trends in aerosol vertical transport fluxes obtained by the different methods are consistent, and there are differences in some details, which may be caused by the inhomogeneity in the vertical transport of aerosol fluxes. The experimental results also showed that urban green land is a sink area for aerosol particles. [ABSTRACT FROM AUTHOR]

The new-generation atmospheric environment monitoring satellite DQ-1, launched successfully in April 2022, carries the Aerosol and Carbon Detection Lidar (ACDL), which is capable of globally profiling aerosol and cloud optical properties with high accuracy. The ACDL/DQ-1 is a high-spectral-resolution lidar (HSRL) that separates molecular backscatter signals using an iodine filter and has 532 nm polarization detection and dual-wavelength detection at 532 and 1064 nm, which can be utilized to derive aerosol optical properties. The methods have been specifically developed for data processing and optical property retrieval according to the specific characteristics of the ACDL system and are introduced in detail in this paper. Considering the different signal characteristics and different background noise behaviors of each channel during daytime and nighttime, the procedures of data pre-processing, denoising process and quality control are applied to the original measurement signals. The aerosol and cloud optical property products of the ACDL/DQ-1, including the total depolarization ratio, backscatter coefficient, extinction coefficient, lidar ratio and color ratio, can be calculated by the retrieval algorithms presented in this paper. Two measurement cases with use of the ACDL/DQ-1 on 27 June 2022 and the global averaged aerosol optical depth (AOD) from 1 June to 4 August 2022 are provided and analyzed, demonstrating the measurement capability of the ACDL/DQ-1. [ABSTRACT FROM AUTHOR]

This paper gives an overview over all currently available data sets for the European Long-term Ecosystem Research (eLTER) monitoring site Gesäuse-Johnsbachtal. The site is part of the eLTSER platform Eisenwurzen in the Alps of the province of Styria, Austria. It contains both protected (National Park Gesäuse) and non-protected areas (Johnsbachtal). Although the main research focus of the eLTER monitoring site Gesäuse-Johnsbachtal is on inland surface running waters, forests and other wooded land, the eLTER whole system (WAILS) approach was followed in regard to the data selection, systematically screening all available data in regard to its suitability as eLTER's Standard Observations (SOs). Thus, data from all system strata was included, incorporating Geosphere, Atmosphere, Hydrosphere, Biosphere and Sociosphere. In the WAILS approach these SOs are key data for a whole system approach towards long term ecosystem research. Altogether, 54 data sets have been collected for the eLTER monitoring site Gesäuse-Johnsbachtal and included in the Dynamical Ecological Information Management System – Site and Data Registry (DEIMS-SDR), which is the eLTER data platform. The presented work provides all these data sets through dedicated data repositories for FAIR use. This paper gives an overview on all compiled data sets and their main properties. Additionally, the available data is evaluated in a concluding gap analysis with regard to the needed observation data according to WAILS, followed by an outlook on how to fill these gaps. [ABSTRACT FROM AUTHOR]

This paper introduces the new Ocean Derived Column Optical Depth (ODCOD) algorithm. ODCOD is now being used to retrieve column optical depths from the 532 nm measure-ments acquired by the Cloud-Aerosol Lidar with Orthogonal Polari-zation (CALIOP) onboard the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) spacecraft. ODCOD retrieves total column optical depths using the lidar backscatter signal return from the ocean surface, together with collocated wind speed estimates from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA‑2). An advantage of ODCOD retrievals is that the column optical depths include contributions from particulates throughout the entire column including regions with attenuated backscatter below the CALIOP layer detection thresholds. In contrast, the standard CALIOP processing only estimates optical depths for clouds and aerosols detected by the CALIOP layer detection scheme. In this paper we describe the ODCOD algorithm, develop uncertainty estimates, and characterize the ODCOD retrievals relative to existing datasets. The paper presents detailed comparisons of ODCOD retrievals to collocated measurements from Langley Research Center's airborne high spectral resolution lidars (HSRL), daytime estimates derived from Moderate Resolution Imaging Spectro-radio-meter (MODIS), and daytime and nighttime optical depths estimates from the Synergized Optical Depth of Aerosols (SODA) algorithm. ODCOD aerosol-only optical depth estimates are higher compared to airborne HSRL measurements by 0.009 ± 0.043 (median ± median absolute deviation) or 6 % ± 27 % relative difference, lower than MODIS by ‑0.009 ± 0.041 (8.0 % ± 34 % relative difference), higher in the daytime than SODA by 0.004 ± 0.035 (12 % ± 34 % relative difference), and higher in the nighttime by 0.027 ± 0.034 (20 % ± 33 % relative difference). In addition to being a new method of retrieving column optical depth, ODCOD's estimates are independent from the standard CALIOP optical depth retrieval algorithms and have potential for further advances in the CALIPSO data record both to validate CALIOP estimates and as a potential column constraint for future improvements to extinction retrievals. [ABSTRACT FROM AUTHOR]

This article introduces PyRTlib, a new standalone Python package for non-scattering line-by-line microwave radiative transfer simulations. PyRTlib is a flexible and user-friendly tool for computing down- and upwelling brightness temperatures and related quantities (e.g., atmospheric absorption, optical depth, opacity, mean radiating temperature) written in Python, a language commonly used nowadays for scientific software development, especially by students and early-career scientists. PyRTlib allows for simulating observations from ground-based, airborne, and satellite microwave sensors in clear-sky and in cloudy conditions (under non-scattering Rayleigh approximation). The intention for PyRTlib is not to be a competitor to state-of-the-art atmospheric radiative transfer codes that excel in speed and/or versatility (e.g., ARTS, Atmospheric Radiative Transfer Simulator; RTTOV, Radiative Transfer for TOVS (Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder)). The intention is to provide an educational tool, completely written in Python, to readily simulate atmospheric microwave radiative transfer from a variety of input profiles, including predefined climatologies, global radiosonde archives, and model reanalysis. The paper presents quick examples for the built-in modules to access popular open data archives. The paper also presents examples for computing the simulated brightness temperature for different platforms (ground-based, airborne, and satellite), using various input profiles, showing how to easily modify other relevant parameters, such as the observing angle (zenith, nadir, slant), surface emissivity, and gas absorption model. PyRTlib can be easily embedded in other Python codes needing atmospheric microwave radiative transfer (e.g., surface emissivity models and retrievals). Despite its simplicity, PyRTlib can be readily used to produce present-day scientific results, as demonstrated by two examples showing (i) an absorption model comparison and validation with ground-based radiometric observations and (ii) uncertainty propagation of spectroscopic parameters through the radiative transfer calculations following a rigorous approach. To our knowledge, the uncertainty estimate is not provided by any other currently available microwave radiative transfer code, making PyRTlib unique for this aspect in the atmospheric microwave radiative transfer code scenario. [ABSTRACT FROM AUTHOR]

Accurate modeling of ash clouds from volcanic eruptions requires knowledge about the eruption source parameters including eruption onset, duration, mass eruption rates, particle size distribution, and vertical-emission profiles. However, most of these parameters are unknown and must be estimated somehow. Some are estimated based on observed correlations and known volcano parameters. However, a more accurate estimate is often needed to bring the model into closer agreement with observations. This paper describes the inversion procedure implemented at the Norwegian Meteorological Institute for estimating ash emission rates from retrieved satellite ash column amounts and a priori knowledge. The overall procedure consists of five stages: (1) generate a priori emission estimates, (2) run forward simulations with a set of unit emission profiles, (3) collocate/match observations with emission simulations, (4) build system of linear equations, and (5) solve overdetermined systems. We go through the mathematical foundations for the inversion procedure, performance for synthetic cases, and performance for real-world cases. The novelties of this paper include a memory efficient formulation of the inversion problem, a detailed description and illustrations of the mathematical formulations, evaluation of the inversion method using synthetic known-truth data as well as real data, and inclusion of observations of ash cloud-top height. The source code used in this work is freely available under an open-source license and is able to be used for other similar applications. [ABSTRACT FROM AUTHOR]

This paper discusses strategies to improve the Gravity Recovery And Climate Experiment (GRACE) monthly solutions computed at the Astronomical Institute of the University of Bern (AIUB) which are contributing to the Horizon 2020 project G3P – Global Gravity-based Groundwater Product. To improve the AIUB-GRACE gravity field solutions, we updated the use of the Level-1B observations, adapted the background models, and improved the processing strategies in terms of instrument screening and parameterization. We used the Release 3 K-band product (KBR) and star camera data (L1B RL03), and we adopted Release 6 of the Atmospheric and Ocean De-aliasing (AOD1B RL06) product. For the accelerometer parameterization, we used arc-wise full scale factor matrix and arc-wise third-order polynomial biases. The new accelerometer parameterization is effective in reducing noise over the oceans in gravity field solutions, especially for the late years of the GRACE mission when the thermal control was switched off. In this paper, we show that the outliers in the KBR antenna offset correction (AOC) are projected into the range rate residuals; therefore, we used the KBR AOC as the main source for outlier detection and eliminated the AOC above a threshold for all data before the gravity field processing. The full time series of GRACE AIUB-G3P gravity field solutions (Darbeheshti et al., 2023b) is available at 10.5880/ICGEM.2023.001. [ABSTRACT FROM AUTHOR]

This paper reviews evidence on how the fast growth in renewable energy technologies can trigger social tipping dynamics that potentially accelerate a system-wide energy transition. It does so by reviewing a variety of literature across several disciplines addressing socio-technical dimensions of energy transitions. The tipping dynamics in wind and solar power create potential for cascading effects to energy demand sectors, including household energy demand. These most likely start with shift actions and adoption of household-scale batteries and heat pumps. Key enablers are strong regulations incentivising reductions in demand and setting minimum efficiency levels for buildings and appliances. While there is evidence of spillovers to more environmentally friendly behaviour, the extent of these and the key leverage points to bring them about present a knowledge gap. Moreover, these behavioural feedback loops require strong additional policy support to "make them stick". Understanding the economic and social tipping dynamics in a system can empower decision-makers, fostering realistic energy transition policies. This paper highlights energy communities as a promising niche for leveraging tipping dynamics. Ultimately, bridging the gap between these tipping dynamics and institutional reforms is crucial for unlocking the full potential of sustainable energy systems. [ABSTRACT FROM AUTHOR]

The carbon budget concept states that the global mean temperature (GMT) increase is roughly linearly dependent on cumulative emissions of CO 2. The proportionality is measured as the transient climate response to cumulative emissions of carbon dioxide (TCRE). In this paper, the deviations of the carbon budget from the strict linear relationship implied by the TCRE are examined through the lens of a temperature response to an emission pulse (i.e., pulse response) and its relationship with a nonlinear TCRE. Hereby, two sources of deviation are distinguished: emission scenario and climate state dependence. The former stems from the scenario choice, i.e., the specific emission pathway for a given level of cumulative emissions and the latter from the change in TCRE with changing climatic conditions. Previous literature argues for scenario independence using a stylized set of emission scenarios, and offers a way to fit a nonlinear carbon budget equation. This paper shows how the pulse response, viewed as a Green's function, gives a unifying perspective on both scenario and state dependence. Moreover, it provides an optimization program that tests the scenario independence under the full range of emission pathways for a given set of constraints. In a setup chosen in this paper, the deviations stemming from emission pathway choices are less than 10 % of the overall temperature increase and gradually diminish. Moreover, using the pulse response as a Green's function, the scenario-dependent effects of a reduced-complexity climate model were replicated to a high degree, confirming that the behavior of scenario-dependent deviations can be explained and predicted by the shape of the pulse response. Additionally, it is shown that the pulse response changes with climatic conditions, through which the carbon budget state dependency is explained. Using a pulse response as an approximation for a state-dependent TCRE, an alternative method to derive a nonlinear carbon budget equation is provided. Finally, it is shown how different calibrations of a model can lead to different degrees of carbon budget nonlinearities. The analysis is done using FaIRv2.0.0, a simple climate emulator model that includes climate feedback modifying the carbon cycle, along with a one-box model used for comparison purposes. The Green's function approach can be used to diagnose both models' carbon budget scenario dependency, paving the way for future investigations and applications with other and more complex models. [ABSTRACT FROM AUTHOR]

ATLID (“ATmospheric LIDar”) is the lidar to be flown on the multi-instrument Earth Clouds and Radiation Explorer (EarthCARE). EarthCARE is a joint ESA/JAXA mission now scheduled for launch in 2024. ATLID is a 3 channel linearly polarized High-Spectral Resolution (HSRL) system operating at 355 nm. Cloud and aerosol optical properties are key EarthCARE products. This paper will provide an overview of the ATLID L2a (i.e. single instrument) retrieval algorithms being developed and implemented in order to derive cloud and aerosol optical properties. The L2a lidar algorithms that retrieve the aerosol and cloud optical property profiles and classify the detected targets are grouped together in the so-called A-PRO (ATLID-profile) processor. The A-PRO processor produces the ATLID L2a Aerosol product (A-AER), the Extinction, Backscatter and Depolarization product (A-EBD), the ATLID L2a Target Classification (A-TC), and the ATLID L2a Ice microphysical estimation product (A-ICE). This paper provides and overview of the processor and its component algorithms. [ABSTRACT FROM AUTHOR]

In the atmosphere, aerosols can originate from numerous sources, leading to the mixing of different particle types. This paper introduces an approach to the partitioning of aerosol mixtures in terms of backscattering coefficients. The method utilizes data collected from the Mie-Raman-fluorescence lidar, with the primary input information being the aerosol backscattering coefficient, particle depolarization ratio (δ), and fluorescence capacity (GF). The fluorescence capacity is defined as the ratio of the fluorescence backscattering coefficient to the particle backscattering coefficient at the laser wavelength. By solving a system of equations that model these three properties (bF, δ and GF), it is possible to characterize a three-component aerosol mixture. Specifically, the paper assesses the contributions of smoke, urban, and dust aerosols to the overall backscattering coefficient at 532 nm. It is important to note that aerosol properties (δ and GF) may exhibit variations even within a specified aerosol type. To estimate the associated uncertainty, we employ the Monte Carlo technique, which assumes that GF and δ are random values uniformly distributed within predefined intervals. In each Monte Carlo run, a solution is obtained. Rather than relying on a singular solution, an average is computed across the whole set of solutions, and their dispersion serves as a metric for method uncertainty. This methodology was tested using observations conducted at the ATOLL observatory, Laboratoire d'Optique Atmosphérique, University of Lille, France. [ABSTRACT FROM AUTHOR]

Water dissolved in nominally anhydrous minerals (NAMs) can be transported to deep regions of the Earth through subducting slabs, thereby significantly influencing the physicochemical properties of deep-Earth materials and impacting dynamic processes in the deep Earth. Stishovite, a prominent mineral present in subducting slabs, remains stable at mantle pressures of 9–50 GPa and can incorporate various amounts of water (H + , OH - , and H 2 O) in its crystal structure. Consequently, stishovite can play a crucial role in transporting water into the deep Earth through subducting slabs. This paper provides a comprehensive review of the research process concerning water (hydrogen) solubility in stishovite. The key factors that govern water solubility in stishovite are summarized as temperature, pressure, water fugacity, and aluminum content. Combined with published results on the dependence of water solubility on the aforementioned parameters, this paper proposes a new equation to describe the solubility of water in Al-bearing stishovite. Calculation results based on this equation suggest that stishovite may effectively accommodate water released from processes such as hydrous mineral breakdown, which could ultimately contribute to the presence of a water-rich transition zone. [ABSTRACT FROM AUTHOR]

Borehole data are essential for conducting precise urban geological surveys and large-scale geological investigations. Traditionally, explicit modelling and implicit modelling have been the primary methods for visualizing borehole data and constructing 3D geological models. However, explicit modelling requires substantial manual labour, while implicit modelling faces problems related to uncertainty analysis. Recently, machine learning approaches have emerged as effective solutions for addressing these issues in 3D geological modelling. Nevertheless, the use of machine learning methods for constructing 3D geological models is often limited by insufficient training data. In this paper, we propose the semi-supervised deep learning using pseudo-labels (SDLP) algorithm to overcome the issue of insufficient training data. Specifically, we construct the pseudo-labels in the training dataset using the triangular irregular network (TIN) method. A 3D geological model is constructed using borehole data obtained from a real building engineering project in Shenyang, Liaoning Province, NE China. Then, we compare the results of the 3D geological model constructed based on SDLP with those constructed by a support vector machine (SVM) method and an implicit Hermite radial basis function (HRBF) modelling method. Compared to the 3D geological models constructed using the HRBF algorithm and the SVM algorithm, the 3D geological model constructed based on the SDLP algorithm better conforms to the sedimentation patterns of the region. The findings demonstrate that our proposed method effectively resolves the issues of insufficient training data when using machine learning methods and the inability to perform uncertainty analysis when using the implicit method. In conclusion, the semi-supervised deep learning method with pseudo-labelling proposed in this paper provides a solution for 3D geological modelling in engineering project areas with borehole data. [ABSTRACT FROM AUTHOR]

An earlier study evaluating dust life cycle in the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1) has revealed that the simulated global mean dust lifetime is substantially shorter when higher vertical resolution is used, primarily due to significant strengthening of dust dry removal in source regions. This paper demonstrates that the sequential splitting of aerosol emissions, dry removal, and turbulent mixing in the model's time integration loop, especially the calculation of dry removal after surface emissions and before turbulent mixing, is the primary reason for the vertical resolution sensitivity reported in that earlier study. Based on this reasoning, we propose a revised numerical process coupling scheme that requires the least amount of code changes, in which the surface emissions are applied before turbulent mixing instead of before dry removal. The revised scheme allows newly emitted particles to be transported aloft by turbulence before being removed from the atmosphere, and hence better resembles the dust life cycle in the real world. Sensitivity experiments show that the revised process coupling substantially weakens dry removal and strengthens vertical mixing in dust source regions. It also strengthens the large-scale transport from source to non-source regions, strengthens dry removal outside the source regions, and strengthens wet removal and activation globally. In transient simulations of the years 2000–2009 conducted using 1 ∘ horizontal grid spacing, 72 vertical layers, and unchanged tuning parameters of emission strength, the revised process coupling leads to a 40 % increase in the global total dust burden and an increase of dust lifetime from 1.8 to 2.5 d in terms of 10-year averages. Weakened dry removal and increased mixing ratios are also seen for other aerosol species that have substantial surface emissions, although the changes in mixing ratio are considerably smaller for the submicron species than for dust and sea salt. Numerical experiments confirm that the revised coupling scheme significantly reduces the strong and non-physical sensitivities of model results to vertical resolution in the original EAMv1. This provides a motivation for adopting the revised scheme in EAM as well as for further improvements on the simple revision presented in this paper. [ABSTRACT FROM AUTHOR]

The ocean-surface downward longwave radiation (Rl) is one of the most fundamental components of the radiative energy balance, and it has a remarkable influence on air–sea interactions. Because of various shortcomings and limits, a lot of empirical models were established for ocean-surface Rl estimation for practical applications. In this paper, based on comprehensive measurements collected from 65 moored buoys distributed across global seas from 1988 to 2019, a new model for estimating the all-sky ocean-surface Rl at both hourly and daily scales was built. The ocean-surface Rl was formulated as a nonlinear function of the screen-level air temperature, relative humidity, cloud fraction, total column cloud liquid, and ice water. A comprehensive evaluation of this new model relative to eight existing models was conducted under clear-sky and all-sky conditions at daytime/nighttime hourly and daily scales. The validation results showed that the accuracy of the newly constructed model is superior to other models, yielding overall RMSE values of 14.82 and 10.76 W/m2 under clear-sky conditions, and 15.95 and 10.27 W/m2 under all-sky conditions, at hourly and daily scales, respectively. Our analysis indicates that the effects of the total column cloud liquid and ice water on the ocean-surface Rl also need to be considered besides cloud cover. Overall, the newly developed model has strong potential to be widely used. [ABSTRACT FROM AUTHOR]

Temperature and wind observations from aircraft are regarded of major importance for aviation meteorology and numerical weather prediction (NWP). The European Meteorological Aircraft Derived Data Center (EMADDC) system processes aircraft surveillance data received from air traffic control (ATC) and converts it into observations of wind and temperature. Only so-called Mode-S Enhanced Surveillance data can be used, because this data contains the air vector and ground vector of the aircraft, from which a wind vector can be inferred. To acquire high quality observations, the data is processed in three steps: pre-processing, processing, and post-processing. The pre-processing is needed to obtain high-quality information and to calculate several correction values such as temperature corrections and heading corrections. Processing converts the aircraft data into meteorological information and finally post-processing guarantees that only high-quality information is made available. The quality of the observations produced is verified by comparing these observation to other upper air wind and temperature observations from radiosondes and comparing them with NWP data. This paper presents the EMADDC system, operational since 2019. [ABSTRACT FROM AUTHOR]

In this paper, the new dynamic geophysical and geochemical MUltiparametric DAtabase (MUDA) is presented. MUDA is a new infrastructure of the National Institute of Geophysics and Volcanology (INGV), published on-line in December 2023, with the aim of archiving and disseminating multiparametric data collected by multidisciplinary monitoring networks. MUDA is a MySQL relational database with a web interface developed in php, aimed at investigating in quasi real time possible correlations between seismic phenomena and variations in endogenous and environmental parameters. At present, MUDA collects data from different types of sensors such as hydrogeochemical probes for physical-chemical parameters in waters, meteorological stations, detectors of air Radon concentration, diffusive flux of carbon dioxide (CO2) and seismometers belonging both to the National Seismic Network of INGV and to temporary networks installed in the framework of multidisciplinary research projects. MUDA daily publishes data updated to the previous day and offers the chance to view and download multiparametric time series selected for different time periods. The resultant dataset provides broad perspectives in the framework of future high frequency and continuous multiparametric monitorings as a starting point to identify possible seismic precursors for short-term earthquake forecasting. MUDA is now quoted with the Digital Object Identifier https://doi.org/10.13127/muda (Massa et al., 2023). [ABSTRACT FROM AUTHOR]

This study describes the digitization and calibration of historically significant solar absorption spectra recorded at the Jungfraujoch International Scientific Station in the 1950s. Using a homemade Pfund-type grating spectrometer, these spectra were recorded on paper rolls to study the solar spectrum which was then used to compile a solar atlas between 2.8 and 23.7 microns (~421 to 3571 cm −1) that later contributed to the development of the HITRAN database. We now digitized these old spectra to make them available for atmospheric studies. Our approach involves image processing techniques, including colour masking for digitization and peak detection for accurate wavenumber calibration against a synthetic spectrum. We also developed a validation method by re-digitizing degraded FTIR spectra to the same resolution as the old spectra to evaluate the digitization accuracy. Furthermore, we studied the influence of line thickness on the digitization error. The number of spectra transformed into a machine-readable format is 108 (freely available for download), with an average digitization error of 1.55 % and a wavenumber shift standard diviation of 0.075 cm −1. These digitized and calibrated spectra now offer a valuable resource for atmospheric studies, providing essential historical data for atmospheric research. This work not only helps to preserve scientific heritage but also enhances the utility of historical data in contemporary research. [ABSTRACT FROM AUTHOR]

Earth – satellite microwave links such as TV-SAT can help for rainfall monitoring and could be a complement or an alternative to ground-based weather radars, rain gauges or satellites dedicated to Earth observation. Rain induced attenuation which is harmful for telecommunication is exploited here as an opportunistic way to estimate rain rate along the path link. This technique makes it possible to obtain rain measurements at a fine temporal resolution (a few tens of seconds) and with a spatial resolution of few kilometers, which is a good compromise for human activities such as civil security (watershed monitoring, flash flood), agriculture or transport. Among the advantages of this technique, one can note the low cost of the hardware used (which can be commercial) as well as that of its maintenance on site. However, measured attenuation does not directly provide rain intensity and some parameters have to be estimated. Among these, it is necessary to take into account the contribution of the natural radiation of the atmosphere. In this paper, we detail a theoretical framework allowing to estimate rainfall from the measurements of a low-cost sensor operating simultaneously over two parts of the Ku-frenquency band. Then this framework is assessed in a densely instrumented area in south of France, where it is shown that very good results are obtained when compared to rain gauges measurements, both in terms of overall rain accumulation and in terms of rainfall rate distribution. Then we apply this dual channel method in Ivory Coast, in the metropolitan area of Abidjan, where such an approach is very promising. It is shown that this technique when compared to rain gauge measurements give results far better than a single-channel naive approach neglecting natural radiation of atmosphere, but that there are still significant errors in the rain assessment, leading to a persistent underestimation of rain accumulation. Finally we discuss various effects that could lead to this remaining underestimation, opening the door to further studies. [ABSTRACT FROM AUTHOR]

Radiative transfer models of vegetation play a crucial role in the development of remote sensing methods by providing a theoretical framework to explain how electromagnetic radiation interacts with vegetation in different spectral regions. A limiting factor in model development has been the lack of sufficiently detailed ground reference data on both structural and spectral characteristics of forests needed for testing and validating the models. In this data description paper, we present a dataset on the structural and spectral properties of 58 stands in temperate, hemiboreal and boreal European forests. It is specifically designed for the development and validation of radiative transfer models for forests but can also be utilized in other remote sensing studies. It comprises detailed data on forest structure based on forest inventory measurements, terrestrial and airborne laser scanning, and digital hemispherical photography. Furthermore, the data include spectral properties of the same forests at multiple scales: reflectance spectra of tree leaves and needles (based on laboratory measurements), forest floor (based on in situ measurements) and entire stands (based on airborne measurements), as well as transmittance spectra of tree leaves and needles and entire tree canopies (based on laboratory and in situ measurements, respectively). We anticipate that these data will have wide use in testing and validating radiative transfer models for forests and in the development of remote sensing methods for vegetation. The data can be accessed at: Hovi et al. 2024a, https://doi.org/10.23729/9a8d90cd-73e2-438d-9230-94e10e61adc9 (for laboratory and field data) and Hovi et al. 2024b, https://doi.org/10.23729/c6da63dd-f527-4ec9-8401-57c14f77d19f (for airborne data). [ABSTRACT FROM AUTHOR]

The productivity of the world's natural resources is critically dependent on a variety of highly uncertain factors, which obscure individual investors and governments that seek to make long-term, sometimes irreversible, investments in their exploration and utilization. These dynamic considerations are poorly represented in disaggregated resource models, as incorporating uncertainty into large-dimensional problems presents a challenging computational task. In this paper, we apply the SCEQ algorithm to solve a large-scale dynamic stochastic global land resource use problem with stochastic crop yields due to adverse climate impacts and limits on further technological progress. For the same model parameters and bounded shocks, the range of land conversion is considerably smaller for the dynamic stochastic model than for deterministic scenario analysis. [ABSTRACT FROM AUTHOR]

Mixed-phase clouds are not well represented in climate and weather forecasting models, due to a lack of the key processes controlling their life cycle. Developing methods to study these clouds is therefore essential, despite the complexity of mixed-phase cloud processes and the difficulty of observing two cloud phases simultaneously. We propose in this paper a new method to retrieve the microphysical properties of mixed-phase clouds, ice clouds and supercooled water clouds using airborne or satellite radar and lidar measurements, called VarPy-mix. This new approach extends an existing variational method developed for ice cloud retrieval using lidar, radar and passive radiometers. We assume that the lidar attenuated backscatter β at 532 nm is more sensitive to particle concentration and is consequently mainly sensitive to the presence of supercooled water. In addition, radar reflectivity Z at 95 GHz is sensitive to the size of hydrometeors and hence more sensitive to the presence of ice particles. Consequently, in the mixed phase the supercooled droplets are retrieved with the lidar signal and the ice particles with the radar signal, meaning that the retrievals rely strongly on a priori and error values. This method retrieves simultaneously the visible extinction for ice αice and liquid αliq particles, the ice and liquid water contents IWC and LWC, the effective radius of ice re,ice and liquid re,liq particles, and the ice and liquid number concentrations Nice and Nliq. Moreover, total extinction αtot , total water content (TWC) and total number concentration Ntot can also be estimated. As the retrieval of ice and liquid is different, it is necessary to correctly identify each phase of the cloud. To this end, a cloud-phase classification is used as input to the algorithm and has been adapted for mixed-phase retrieval. The data used in this study are from DARDAR-MASK v2.23 products, based on the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) observations from the CALIPSO and CloudSat satellites, respectively, belonging to the A-Train constellation launched in 2006. Airborne in situ measurements performed on 7 April 2007 during the Arctic Study of Tropospheric Aerosol, Clouds and Radiation (ASTAR) campaign and collected under the track of CloudSat–CALIPSO are compared with the retrievals of the new algorithm to validate its performance. Visible extinctions, water contents, effective radii and number concentrations derived from in situ measurements and the retrievals showed similar trends and are globally in good agreement. The mean percent error between the retrievals and in situ measurements is 39 % for αliq , 398 % for αice , 49 % for LWC and 75 % for IWC. It is also important to note that temporal and spatial collocations are not perfect, with a maximum spatial shift of 1.68 km and a maximum temporal shift of about 10 min between the two platforms. In addition, the sensitivity of remote sensing and that of in situ measurements is not the same, and in situ measurement uncertainties are between 25 % and 60 %. [ABSTRACT FROM AUTHOR]

MATS (Mesospheric Airglow/Aerosol Tomography and Spectroscopy) is a Swedish satellite designed to investigate atmospheric dynamics in the mesosphere and lower thermosphere (MLT). By observing structures in noctilucent clouds over polar regions and oxygen atmospheric-band (A-band) emissions globally, MATS will provide the research community with properties of the MLT atmospheric wave field. Individual A-band images taken by MATS's main instrument, a six-channel limb imager, are transformed through tomography and spectroscopy into three-dimensional temperature fields, within which the wave structures are embedded. To identify wave properties, particularly the gravity wave momentum flux, from the temperature field, smaller-scale perturbations (associated with the targeted waves) must be separated from large-scale background variations using a method of scale separation. This paper investigates the possibilities of employing a simple method based on smoothing polynomials to separate the smaller and larger scales. Using using synthetic tomography data based on the HIAMCM (HIgh Altitude Mechanistic general Circulation Model), we demonstrate that smoothing polynomials can be applied to MLT temperatures to obtain fields corresponding to global-scale separation at zonal wavenumber 18. The simplicity of the method makes it a promising candidate for studying wave dynamics in MATS temperature fields. [ABSTRACT FROM AUTHOR]

In times of growing concern about the impacts of air pollution across the globe, lower-cost sensor technology is giving the first steps in helping to enhance our understanding and ability to manage air quality issues, particularly in regions without established monitoring networks. While the benefits of greater spatial coverage and real-time measurements that these systems offer are evident, challenges still need to be addressed regarding sensor reliability and data quality. Given the limitations imposed by intellectual property, commercial implementations are often "black boxes", which represents an extra challenge as it limits end users' understanding of the data production process. In this paper we present an overview of the QUANT (Quantification of Utility of Atmospheric Network Technologies) study, a comprehensive 3-year assessment across a range of urban environments in the United Kingdom, evaluating 43 sensor devices, including 119 gas sensors and 118 particulate matter (PM) sensors, from multiple companies. QUANT stands out as one of the most comprehensive studies of commercial air quality sensor systems carried out to date, encompassing a wide variety of companies in a single evaluation and including two generations of sensor technologies. Integrated into an extensive dataset open to the public, it was designed to provide a long-term evaluation of the precision, accuracy and stability of commercially available sensor systems. To attain a nuanced understanding of sensor performance, we have complemented commonly used single-value metrics (e.g. coefficient of determination, R2 ; root mean square error, RMSE; mean absolute error, MAE) with visual tools. These include regression plots, relative expanded uncertainty (REU) plots and target plots, enhancing our analysis beyond traditional metrics. This overview discusses the assessment methodology and key findings showcasing the significance of the study. While more comprehensive analyses are reserved for future detailed publications, the results shown here highlight the significant variation between systems, the incidence of corrections made by manufacturers, the effects of relocation to different environments and the long-term behaviour of the systems. Additionally, the importance of accounting for uncertainties associated with reference instruments in sensor evaluations is emphasised. Practical considerations in the application of these sensors in real-world scenarios are also discussed, and potential solutions to end-user data challenges are presented. Offering key information about the sensor systems' capabilities, the QUANT study will serve as a valuable resource for those seeking to implement commercial solutions as complementary tools to tackle air pollution. [ABSTRACT FROM AUTHOR]

Accurate cloud quantification is essential in climate change research. In this work, we construct an automated computer vision framework by synergistically incorporating deep neural networks and finite-sector clustering to achieve robust whole-sky image-based cloud classification, adaptive segmentation and recognition under intricate illumination dynamics. A bespoke YOLOv8 (You Only Look Once 8) architecture attains over 95 % categorical precision across four archetypal cloud varieties curated from extensive annual observations (2020) at a Tibetan highland station. Tailor-made segmentation strategies adapted to distinct cloud configurations, allied with illumination-invariant image enhancement algorithms, effectively eliminate solar interference and substantially boost quantitative performance even in illumination-adverse analysis scenarios. Compared with the traditional threshold analysis method, the cloud quantification accuracy calculated within the framework of this paper is significantly improved. Collectively, the methodological innovations provide an advanced solution to markedly escalate cloud quantification precision levels imperative for climate change research while offering a paradigm for cloud analytics transferable to various meteorological stations. [ABSTRACT FROM AUTHOR]

This work presents the first step in the development of the VISION toolkit, a set of python tools that allows for easy, efficient and more meaningful comparison between global atmospheric models and observational data. Whilst observational data and modelling capabilities are expanding in parallel, there are still barriers preventing these two data sources to be used in synergy. This arises from differences in spatial and temporal sampling between models and observational platforms: observational data from a research aircraft, for example, is sampled on specified flight trajectories at very high temporal resolution. Proper comparison with model data requires generating, storing and handling a large amount of highly temporally resolved model files, resulting in a process which is data, labour, and time intensive. In this paper we focus on comparison between model data and in-situ observations (from aircrafts, ships, buoys, sondes etc.). A stand-alone code, In-Situ Observation simulator, or ISO_simulator in short, is described here: this software reads modelled variables and observational data files and outputs model data interpolated in space and time to match observations. This model data is then written to NetCDF files that can be efficiently archived, due to their small sizes, and directly compared to observations. This method achieves a large reduction in the size of model data being produced for comparison with flight and other in-situ data. By interpolating global, gridded, hourly files onto observations locations, we reduce data output for a typical climate resolution run, from ~3 Gb per model variable per month to ~15 Mb per model variable per month (a 200 times reduction in data volume). The VISION toolkit is fast and easy to use, therefore enabling the exploitation of large observational datasets spanning decades, to be used for large scale model evaluation. Although this code has been initially tested within the Unified Model (UM) framework, which is shared by the UK Earth System Model (UKESM), it was written as a flexible tool and it can be extended to work with other models. [ABSTRACT FROM AUTHOR]

High-resolution bathymetry provides critical information to marine geoscientists. Bathymetric big data help characterise the seafloor and its benthic habitats, understand sedimentary records, and support the development of offshore engineering infrastructures. From September 27th to October 20th, 2022, the new CNR Research Vessel GAIA BLU explored the seafloor of the Naples and Pozzuoli Gulfs, and the Amalfi coastal area (Tyrrhenian Sea, Italy) from 50 to more than 2000 m water depth, acquiring about 5000 km2 of multi beam echosounder data. This area is particularly vulnerable to abrupt changes driven by the dynamics of several volcanic complexes, active in the area, and by human-induced impacts reflecting the proximity to the highly populated and touristic coastal area of Naples and nearby famous islands. For these reasons, the seafloor of the area needs to be known and constantly monitored. The digital bathymetric data previously available are restricted to the shallow highly dynamic area of the Gulf of Naples and appear fragmented as they were acquired in successive years, with different goals thereby using a variety of devices, with markedly different spatial resolutions. In this paper, we present bathymetric maps of the Gulf of Naples and adjacent slope basins at unprecedented resolution using three state-of-the-art multi beam echosounders. These high-resolution data highlight the technological advances of geophysical surveys achieved over the last 20 years and contribute to assessing the most dynamic areas where changes in the seafloor over time can be quantified. The new digital multi-resolution bathymetric products are openly accessible via Marine Geosciences Data System MGDS (refer to section Data Availability, Table 8, for datasets and products DOIs), perfectly matching the FAIR (Findable, Accessible, Interoperable and Reusable) and Open Science Principles. [ABSTRACT FROM AUTHOR]

Exceptional precision and excellent resolution reconstruction of sea surface currents are beneficial for exploring complex oceanic dynamic processes. Normally, this required physical inversion models for global or regional oceans are constructed to reconstruct oceanic currents. These models are based on the analysis of sea surface geostrophic and Ekman currents derived from satellite observations of sea level and wind stress fields. Nevertheless, the presence of various typical dynamic processes in marine environments, such as mesoscale eddies and small-scale waves, continues to pose challenges in accurately reconstructing oceanic currents. Meanwhile, any product of surface current that neglects the contribution of wave motion would, at best, be incomplete. Therefore, in this paper, we introduce an accurate sea surface current product at a depth of 15 m, named GEST (Geostrophic-Ekman-Stokes-Tide). This product is produced by a multi-scale dynamics-informed neural network that learns the intricate representation of concealed characteristics in Ekman, geostrophic currents, wave-induced Stokes drift, and TPXO9 tidal currents. Its structure design is predicated upon the intricate coupling relationships between various ocean surface components and the veritable currents discerned by the deployment of drift buoys, with each ocean surface component correlating to discrete physical processes. Compared with the prevailing product, the GEST confers an elevation in precision by approximately 9.2 cm/s over the traditional multinomial fitting method, 10.4 cm/s beyond the OSCAR, and 8.81 cm/s surpassing GlobCurrent. [ABSTRACT FROM AUTHOR]

In this paper, an Eulerian two-phase-flow model sedFoam is extended to include an air phase together with the water and sediment phases. The numerical model called sedInterFoam is implemented using the open source library OpenFOAM. SedInterFoam includes the previous features of sedFoam for sediment transport modeling and also solves the air/water interface using the volume of fluid method coupled with the waves2Foam toolbox for free surface wave generation and absorption. Using sedInterFoam, four test cases are successfully reproduced to validate the free-surface evolution algorithm implementation, mass conservation of sediment and fluid phases, predictive capabilities and demonstrate its potential in modelling a broader range of coastal applications with sediment transport dominated by surface waves. [ABSTRACT FROM AUTHOR]