Search

This paper analyzes the change in the heliocentric orbital elements due to the implementation of aerogravity and powered aerogravity assist maneuvers. Three cost functions were selected to maximize the planetocentric latitude, longitude, and velocity at the end of the atmospheric flight. An optimal control problem was solved to guide a spaceplane passing above the three inner planets: Venus, Earth, and Mars. The planets were selected because they have been relevant for gravity assists, and previous studies suggest that taking advantage of their atmospheres could increase the effects of the close approach. The research aims to analyze the variation in the heliocentric orbital elements of the trajectories after performing optimal aerogravity-assists and powered aerogravity-assisted maneuvers at high atmospheric altitudes. The trajectories were calculated via nonlinear programming to find the history of the control variables: angle of attack, bank angle, and thrust. Low-fidelity gravity and atmosphere models were implemented to evaluate this proof of concept. The optimization converged for all cases, and results show that maximization of the longitude increases the duration of the atmospheric flight, increasing the turning angle by almost twice the value of a single gravity assist. Furthermore, it reduces the energy and semimajor axis in half for approach angles between 20 and 90°, saving more than 10 km/s on propulsion. Maximizing the velocity with propulsion increases the semimajor axis by more than 1.1 times the semimajor axis of the respective planet, and the optimal latitude presents a change in inclination of more than 1.0°. This maneuver could be applied to collect atmospheric samples or increase the coverage above the planet of interest. Those kinds of applications are described at the end of this paper, including a brief discussion on the maturity of this technology. • Optimal control in aerogravity assists maneuvers at high atmospheric altitudes. • The maneuvers increase the values of the heliocentric orbital elements. • Multiple advantages over the gravity assist are observed. • Spaceplanes are feasible for performing those maneuvers. • Atmospheric passages at high altitudes reduce the risks associated with low altitudes. [ABSTRACT FROM AUTHOR]

In recent years, the remarkable progress in exoplanet science has unveiled a stunning diversity of exoplanets. Technological advancements have significantly expanded our capacity to investigate planetary objects beyond our solar system, which exhibit diverse properties including their sizes, compositions, and orbital characteristics. Although understanding the complex chemical-climate responses occurring in planetary-body atmospheres plays a pivotal role in determining potential habitability, much work is still required in this field. To interpret forthcoming spectral measurements obtained from exoplanetary atmospheres, there is a concerted effort to model these worlds. However, model parameters are often inferred from limited data sets. Consequently, laboratory-based atmospheric simulations are necessary for understanding complex gas-phase interactions and to complement atmospheric models through experimental examination of input parameters. In this paper we introduce the new Berlin Atmospheric Simulation Experimental chamber (BASE), present its current capabilities and provide an overview of experimental scenarios with applications ranging from planetary objects in the solar system (e.g. Mars, Venus, Titan) to Earth-like exoplanets. • Exoplanetary science demands experimental validation of atmospheric processes. • Laboratory simulations are fine tuned to a specific set of atmospheric parameters. • UV photons and electrons initiate important chemical reactions in the gas phase. • BASE simulates pressure, composition and radiation levels of planetary atmospheres. • BASE will provide important laboratory data to improve current atmospheric models. [ABSTRACT FROM AUTHOR]

[Display omitted] Aerosols greatly influence precipitation characteristics, thereby impacting the regional climate and human life. As an indispensable factor for cloud formation and a critical radiation budget regulator, aerosols can affect precipitation intensity, frequency, geographical distribution, area, and time. However, discrepancies exist among current studies due to aerosol properties, precipitation types, the vertical location of aerosols and meteorological conditions. The development of technology has driven advances in current research, but understanding the aerosol effects on precipitation remain complex and challenging. This paper revolves around the following topics from the two perspectives of Aerosol-Radiation Interaction (ARI) and Aerosol-Cloud Interaction (ACI): (1) the influence of different vertical locations of absorbing/scattering aerosols on the atmospheric thermal structure; (2) the fundamental theories of ARI reducing surface wind speed, redistributing water vapour and energy, and then modulating precipitation intensity; (3) different aerosol types (absorbing versus scattering) and aerosol concentrations causing different precipitation diurnal and weekly variations; (4) microphysical processes (cloud water competition, invigoration effect, and evaporation cooling) and observational evidence of different effects of aerosols on precipitation intensity, including enhancing, inhibiting, and transitional effects from enhancement to suppression; and (5) how meteorology, water vapor and dynamics influencing the effect of ACI and ARI on precipitation. In addition, this review lists the existing issues and future research directions for attaining a more comprehensive understanding of aerosol effects on precipitation. Overall, this review advances our understanding of aerosol effects on precipitation and could guide the improvement of weather and climate models to predict complex aerosol-precipitation interactions more accurately. [ABSTRACT FROM AUTHOR]

In this paper, data-driven models for wind speed forecasting are established by incorporating atmospheric variables, such as temperature, humidity divergence and etc. as input, and using forecasted wind speed as the output. Input data, statistics in every 3-h interval, utilized in this study are from CERA-20C within a period from the year 2001–2010. In the models, a feature selection technique is proposed to identify the relative dependence of each input to the output. Through a comparison between results with and without the selection technique, it is observed that the technique helps to select the most pertinent inputs, and thus further improve the forecast performance, accuracy and efficiency. In addition, three data-driven models are further revealed to acquire the best forecast accuracy, by including temporal effect, spatial effect, or coupled temporal and spatial effects on atmospheric conditions, which are named as temporal-based, spatial-based and hybrid data-driven models, respectively. By comparing the forecast performance among different models, it is revealed that the newly proposed hybrid data-driven model with feature selection technique is more suitable in dealing with wind speed forecasting. Moreover, an additional case study further reinforces the feasibility of the newly proposed hybrid data-driven model for forecasting of wind direction. The proposed date-driven models in the paper provide a high-cost efficiency alternative for wind speed forecast using machine learning instead of physical models. This approach can assist engineers in making prompt and informed decisions even when resources are constrained during emergency conditions. • Cost-efficiency wind speed forecast methods are developed by incorporating atmospheric variables into data-driven models. • Data-driven models are generated by considering temporal and spatial effects of atmospheric conditions on wind fields. • The feature selection is utilized to select the appropriate inputs and to enhance the accuracy of wind speed forecasts. • The important atmospheric variables that significantly affect wind fields are identified. • The feasibility of the newly proposed hybrid data-driven model for wind direction forecasting is demonstrated. [ABSTRACT FROM AUTHOR]

Swell waves, characterized by the long wavelength components generated by distant weather systems or storms, exert a significant influence on various air–sea interaction processes, thereby impacting weather and climate systems. Over recent decades, substantial progress has been achieved in comprehending the dynamics of swell waves and their implications for air–sea interactions. This paper presents a comprehensive review of advancements and key findings concerning surface swell waves and their interactions with the atmosphere. It encompasses a range of topics, including wave growth theory, the effects of swell waves on air–sea momentum, heat, and mass fluxes, as well as their influence on atmospheric turbulence and mixed layer processes. The most important characteristics of the swell impact (where it differs from wind sea conditions) are the wave-induced upward component of the surface stress leading to alteration of total surface stress, generation of a low-level wind maxima or changed wind profile and change of scale and behaviour of turbulence properties (turbulence kinetic energy and integral length scale). Furthermore, the paper explores the modelling of swell dissipation, the integration of swell influences in weather and climate models, and the broader climatic implications of surface swell waves. Despite notable advances in understanding swell processes, persistent knowledge gaps remain, underscoring the need for further research efforts, which are outlined in the paper. • A comprehensive review of studies on swells and their role in air–sea interactions. • The implementation of swell waves into climate models is summarized. • Some knowledge gaps about swell impacts on air–sea interaction are identified. [ABSTRACT FROM AUTHOR]

With the rapid development of infrared technology, infrared cameras are widely used in the military, medical, and industrial fields. However, the thermal radiation information received by infrared cameras often undergoes degradation due to random factors such as scattering or reflection during transmission. Consequently, the final infrared image suffers from poor contrast and blurred vision. To address these problems, this paper proposes an infrared image contrast enhancement algorithm based on the atmospheric scattering model. Firstly, we modify the hypothesis of the dark channel prior theory, and the pseudo dark channel prior theory is deduced to meet the processing requirements of single-channel images. Secondly, we propose anti-target interference and filtering threshold method to accurately estimate the atmospheric illumination value and transmittance of the atmospheric scattering model. These methods effectively mitigate the influence of infrared targets on the atmospheric illumination value and eliminate the blocking artifact effect caused by pixel value jumps in the transmittance images. Furthermore, we employ adaptive histogram equalization with contrast limitation, highlighting the global contrast. This paper conducts experiments on the M3FD dataset and employs objective evaluations using Visible Edge (E), Figure Definition (FD), Peak Signal-to-Noise Ratio (PSNR), Information Entropy (IE), Structure Similarity Index Measure (SSIM), and Visual Information Fidelity (VIF). The results indicate that the proposed algorithm enhances the overall contrast of images while highlighting fine texture details, exhibiting good visual effects. Both subjective and objective evaluations outperform seven other contrast algorithms, demonstrating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Q-SAT, a small spherical satellite designed and developed by Tsinghua university, was successfully launched on Aug 6, 2020 at Jiuquan satellite launch center in China. The mission of Q-Sat is the joint measurement of long-wavelength Earth gravity field and upper atmosphere density, which is of great significance for improving spacecraft orbit prediction and promoting the development of satellite gravity measurement technology. The satellite is designed in spherical shape innovatively and deploys high dynamic dual-frequency GPS as the main payload. On-orbit data shows that the atmospheric density detection precision is in the order of 10−14kg/m3 and the gravity recovery precision achieves the level of 30 orders. All mission success criteria and objectives were achieved. This paper presents the design, space mission, key technologies, science capabilities and preliminary results of the satellite. • Q-SAT, a spherical satellite for the joint measurement of long-wavelength Earth gravity field and upper atmosphere density, was launched in 2020. • An electromagnetic separation system with point connection-release structure is designed for the Q-Sat. • As secondary payload a powerful GPU module is implemented to validate artificial intelligence computing capabilities in orbit. • Modifications for the Jacchia-Roberts atmospheric density model are proposed in the paper. [ABSTRACT FROM AUTHOR]

In this paper we carried out a numerical experiment using the Specified Dynamics mode of the Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension (SD-WACCM-X). One SD-WACCM-X run was with realistic Kp and F10.7 and the other with constant Kp and F10.7. By comparing the day-to-day variability of thermosphere mass density at 300 km (low earth orbit, LEO) and 120 km (reentry level) in these two runs, we find that the density variation at 300 km is mainly driven by geomagnetic and solar forcing while at 120 km it is exclusively controlled by the lower atmosphere. At LEO altitudes, during solar minimum and geomagnetic quiet days, the impact from the lower atmosphere is much smaller than the effect of solar and geomagnetic variations but is not negligible (5–10% vs 20%). [ABSTRACT FROM AUTHOR]

Spacecraft in low-Earth orbit are exposed to environmental threats which can lead to material degradation and component failures. The presence of atomic oxygen and collisions from orbital debris have detrimental effects on the structures, thus affecting their performance. Cranfield University has developed a family of drag augmentation systems (DAS), for end-of-life de-orbit of satellites, addressing the space debris challenge and ensuring that satellites operate responsibly and sustainably. De-orbit devices are stowed on-orbit for the duration of the mission lifetime and, once deployed, the devices must withstand the harsh low Earth environment until re-entry; a process which can take several years. The DAS' deployable aluminised Kapton sails are particularly susceptible to undercutting by atomic oxygen. In preparation for commercialising the DAS, Cranfield University are investigating the degradation process of the drag sail materials, with the end goal of qualifying the materials for the specific application of drag sails in low Earth orbit (LEO). This paper will outline the proposed research and the expected benefits from the projects. This paper will conclude in a summation of the different on-going research projects at Cranfield University related to commercialising the DAS family. This research will benefit the wider space community by expanding the understanding of the effects of long-term exposure on certain materials, as well as improving the validity of future low Earth atmospheric models. • Drag augmentation systems for end-of-life de-orbit of satellites. • Low-cost, simple solutions for future space debris mitigation. • Long-term degradation of aluminised Kapton in low Earth orbit. • Testing campaigns characterising material, investigating atomic oxygen erosion. • Assessment of the drag sail environmental durability, performance and scalability. [ABSTRACT FROM AUTHOR]

This paper presents the first detailed analysis of cloud-to-ground (CG) and intra-cloud (IC) strokes characteristics from the Lightning Detection Network (LINET) over Italy and the Central Mediterranean area, a lightning active area in south Europe. We study the strokes over a 13-year period from 2010 to 2022, aiming to understand how it varies with different temporal scales (hourly, monthly, seasonally, and yearly), surface types (sea and land), and ground levels (0–100 m ; 100–200 m ; 200–400 m ; 400–800 m ; 800–1200 m ; 1200–2000 m and above 2000 m). We found that the stroke's maximum activity was observed in August; specifically, July has the maximum activity over the land with a maximum diurnal peak in the afternoon, while in September, the convection shifts over the sea with a secondary daily maximum in the morning. The largest current intensities are observed in January, over sea and during nighttime. Moreover we found that stroke current intensities, polarity and IC height emissions are influenced by ground altitude level. Our paper provides new insights into the spatio-temporal patterns and characteristics of lightning over Italy and the Central Mediterranean area, which can be useful for improving weather forecasting, climate modeling, risk assessment, and damage mitigation strategies in this area. • Orography has strong impact on lightning density, polarity, intensity and discharge height. • Vigorous convection develops over the land in summer and over the sea in autumn. • Overall stroke density reaches its peak in August. • Atmospheric instability decreases the breakdown threshold increasing weaker strokes. [ABSTRACT FROM AUTHOR]

Effective selection of climate predictors is a fundamental aspect of climate modeling research. Predictor Screening (PS) plays a crucial role in identifying regional climate drivers, reducing noise, expediting convergence, and minimizing time consumption, ultimately leading to the development of robust models. This review delves into the complex landscape of PS techniques within the context of Numerical Climate Modeling (NCM), with a specific focus on their applicability across various Köppen climate classifications and PS model structures. The analysis revealed substantial variations in the performance of PS methods, shedding light on their ability to capture –and prioritize predictors related to precipitation and temperature within distinct climate contexts. Furthermore, the provided methods have been categorized into two subsections: Feature Selection (FS) and Feature Extraction (FE), with FS encompassing filter, wrapper, embedded, and ensemble/hybrid techniques, and FE covering Linear Feature Extraction (LFE), Time-Domain Analysis (TDA), deep learning, and clustering methods. The initial compilation of papers, acquired through a keyword search on Scopus, consisted of 3650 documents. Following a meticulous evaluation process, 206 papers were identified as fitting for inclusion in the literature review, covering the time frame from 1974 to November 3, 2023. In conclusion, the results provide a detailed understanding of the strengths and limitations of each approach, establishing a hierarchy of effectiveness contingent upon the specific climate context. Additionally, insights into promising avenues for future research in this field are offered. This review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standard as its foundation. [ABSTRACT FROM AUTHOR]

Ice clouds can strongly affect the radiation budget through the reflection (absorption) of solar shortwave (longwave) radiation in the Earth-atmosphere system. Unlike water clouds, ice clouds present a longer lifetime in the upper troposphere. Therefore, the vertical patterns of diurnal variation of ice clouds are of very importance to understand climate change in the globe. The present paper investigates the diurnal variation of ice cloud frequency (ICF) from March 2015 to October 2017 over the global scale (51 ° S and 51 ° N) by CATS (Cloud-Aerosol Transport System) LiDAR onboard the International Space Station (ISS) and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) LiDAR measurements. Our results suggest that: (1) ICF derived from the CATS and CALIPSO LiDAR presents the similar geographical variations. (2) The vertical distribution of ICF in land area over the North Hemisphere (NH) is found larger than in the South Hemisphere (SH) likely due to the more dust aerosols as ice nuclei to influence ice cloud formation over the NH. (3) ICF at nighttime (00:00–06:00 and 18:00–23:00) exhibits higher value than daytime (06:00–18:00), especially in high altitude (more than 12 km) ice clouds in the tropics. (4) The number of ice cloud samples is higher in spring and winter compared to summer and autumn, and the highest amount of ice cloud samples in spring(winter) occurs during nighttime(daytime) for most hours. These findings provide significant insights that may help improving the ice cloud's representation in climate models. • The present paper investigates the diurnal variation of ice cloud frequency (ICF) from March 2015 to October 2017 over the global scale (51 ° S and 51 ° N) by CATS and CALIPSO LiDAR measurements. • ICF derived from the CATS and CALIPSO LiDAR presents the similar results. The vertical distribution of ICF in land area over the North Hemisphere is found larger than in the South Hemisphere. • ICF at nighttime (00:00–06:00 and 18:00–23:00) exhibits higher value than daytime (06:00–18:00). The samples of ice clouds in spring and winter are greater than those in summer and autumn. [ABSTRACT FROM AUTHOR]

• The matrix representation of grey possibility function is provided. • The solution method of possibility function's turning points is given. • The "maximum criterion" decision paradox is solved. • A variable stage self-correcting grey clustering model is proposed. • The meteorological drought grade of 17 stations in china henan province is evaluated. The grey clustering model based on the possibility function is frequently used in system evaluation, but the setting of the possibility function is frequently subjective, which results in the weak credibility of the clustering results. In addition, the "maximum criterion" decision paradox of the model has fuzzy boundaries, making the clustering process impossible to program. Based on this, the possibility function is represented as a matrix in the paper, and the possibility function is calculated by an optimization model to guarantee the objectivity of the clustering results. A variable stage self-correcting grey clustering model is built by using the kernel weight transformation as the primary tool, "entropy subtraction" as the signal of the decision paradox, and "entropy non-subtraction" as the model's termination signal. The model's Matlab source code is provided to reduce the complexity of the model. The illustration and comparative analysis demonstrate the model's logic and efficacy. Using the model for the evaluation of the meteorological drought grade in Henan Province can offer a fresh perspective for meteorological departments to make choices and assessments. [ABSTRACT FROM AUTHOR]

This paper identifies an incorrect drag formulation that exists in numerous papers concerning solution to the Goddard problem. Two constants of this formulation are erroneous. The first one is used to model the atmospheric density decay and its incorrect value has led to an overestimation of the atmospheric drag. The second constant was driven by a rocket's mass-to-cross section area ratio, and its value would only be possible for rockets with extremely low densities, not achievable in reality. The paper explains why these constants are not correct and proposes new values to be used in future formulations of the Goddard problem. • Constants in a dimension-free frag formulation in the Goddard problem used in numerous research papers are erroneous. • Evidence for erroneous nature of these constants is shown. • New, correct constants in a dimension-free drag formulation in the Goddard problem are proposed. [ABSTRACT FROM AUTHOR]

In the face of anthropogenic climate change, forest and tree populations must adapt, migrate, or face extirpation. Assisted migration (AM) has emerged as a promising forestry strategy to mitigate the impacts of climate change, yet its role in the evolutionary and ecological processes remains unclear. Various models, including climate niche models and transfer and response functions, have been employed to guide AM efforts. Each of these models has advantages and limitations, necessitating a comprehensive understanding of their rationale and constraints for further development and improvement. In our review, we aimed to define the role of forestry AM and elucidate the scenarios in which AM becomes a critical necessity in the context of climate change. Furthermore, we provided a comprehensive overview of the rationale and development of commonly used models and functions in the realm of forestry AM. Finally, we analyzed 214 published papers related to forestry AM, examining how previous evidence either supports or opposes the implementation of AM, and the underlying reasons for the agreement or disagreement. Our findings reveal that nearly 60% of the reviewed papers express a positive stance towards AM. More than one-fifth of the papers emphasize AM's potential to enhance adaptation. Another significant aspect supporting AM is its capacity to increase production and reduce extirpation risks. However, AM can also introduce invasive risks, lead to maladaptation, and even raise moral concerns. In conclusion, we advocate for a holistic approach to AM, incorporating genetic knowledge, models accounting for biological interactions and edaphic adaptation, and the formulation of comprehensive policies. This integrated strategy will be pivotal in guiding AM practices aimed at mitigating the impacts of anthropogenic climate change. ● Explores AM's ecological and evolutionary role under climate change. ● AM enhances species adaptation and productivity. ● AM risks: invasive species and maladaptation. ● Advantages and limits of forestry AM models. ● Calls for integrated policy in AM strategy. [ABSTRACT FROM AUTHOR]

• The uncertainties of atmospheric density, position and velocity are combined to jointly participate in the orbital error propagation. • MC Dropout neural networks are introduced to correct the density model and to quantify the model uncertainty and input uncertainty. • GMM-UT method is used to approximate the orbital error propagation considering atmospheric density uncertainty. Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty). [ABSTRACT FROM AUTHOR]

The Pliocene Model Intercomparison Project (PlioMIP) was initiated in 2008. Over two phases PlioMIP has helped co-ordinate the experimental design and publication strategy of the community, which has included an increasing number of climate models and modelling groups from around the world. It has engaged with palaeoenvironmental scientists to foster new data synthesis supporting the construction of new model boundary conditions, as well as to facilitate new data-model comparisons. The work has advanced our understanding of Pliocene climates and environments, enhanced our knowledge regarding the ability of complex climate and Earth System models to accurately simulate climate change, and helped to refine our estimates of how sensitive the climate system is to forcing conditions. In this community protocol paper, we outline the scientific plan for PlioMIP Phase 3 (PlioMIP3). This plan provides the required guidance to participating modelling groups from around the world to successfully set up and perform PlioMIP3 climate model experiments. The project is open to new participants from the scientific community (both from the climate modelling and geosciences communities). In PlioMIP3, we retain the PlioMIP2 Core experiments (Eoi 400 , E 280 ) and extend the Core requirements to include either an experiment focussed on the Early Pliocene or an alternative Late Pliocene simulation (or both). These additions (a) allow a comparison of Early and Late Pliocene warm intervals and help build research connections and synergy with the MioMIP (Miocene Model Intercomparison Project - also known as DeepMIP-Miocene) and PlioMioVAR projects (Pliocene-Miocene Variability Working Group), and (b) create an alternative time slice simulation for 3.205 Ma (MIS KM5c) through removal of some of the largest palaeogeographic differences introduced between PlioMIP1 and 2 resulting in minimal land-sea mask variations from the modern. In addition, we present ten optional experiments designed to enhance our assessment of climate sensitivity and to explore the uncertainty in greenhouse gas-related forcing. For the first time, we introduce orbital sensitivity experiments into the science plan, as well as simulations incorporating dynamic vegetation-climate feedbacks and an experiment designed to examine the potential significance of East Antarctic Ice Sheet boundary condition uncertainty. These changes enhance palaeo-to-future scientific connections and enable an exploration of the significance of palaeogeographic uncertainties on climate simulations. • This paper presents the community agreed science plan for the 3rd phase of the Pliocene Model Intercomparison Project (PlioMIP3). • Experiments are prioritised/designed to enhance our knowledge of Pliocene and its use in understanding future climate change. • Guidance on model set-up, timescales, format of the model outputs and where these should be stored is outlined. • Information for modelling groups to access the required boundary conditions underpinning model experiments is provided. • Linkages between the climate modelling effort and ongoing co-ordinated geological proxy synthesis is described. [ABSTRACT FROM AUTHOR]

• Imaginaries influence climate knowledge and action and fix or unsettle visions of the future. • The imbrication of discourse and materiality is key to understanding the concept of imaginaries. • The medium through which imaginaries are produced and circulated matter. • Different mediums offer different affordances and constraints in shaping climate imaginaries. • Attending to the role of mediums in climate imaginaries informs environmental governance debates. Imaginaries are increasingly recognised as influential in shaping climate knowledge and action and in fixing or unsettling particular visions of the future. Whilst the imbrication of discourse and materiality is key to the distinctiveness of the concept of imaginaries, attention has tended to focus on their symbolic and discursive aspects, overlooking the role of the material dimension. In this paper we seek to bring back the material into thinking about imaginaries by critically examining the relationship between climate imaginarie s and the mediums through which they are produced and circulated. We argue that the medium is an active constituent of the productive work that imaginaries do. To construct this argument, we first distinguish the imaginary from associated debates around images and imagination, highlighting its theoretical utility in rebutting modernist dualism between the material and the ideal. Second, we propose the concept of 'medium' as a way of foregrounding the material dimensions of the imaginary without re-inscribing the binary between the virtual and the material or imputing an overly agentic or predetermined role for the latter. Deploying a co-productionist approach and foregrounding process of mediation, we signal the affordances and constraints of mediums in shaping climate imaginaries. Third, we provide a comparison between climate poetry and computerised climate scenario models to examine how each offers different potentials and limitations to the imaginaries that they co-produce, and how the medium matters to the production and circulation of climate change imaginaries. We conclude by outlining an emerging research agenda and highlighting some of the implications of attending to the role of the medium in climate change imaginaries for contemporary environmental governance debates. [ABSTRACT FROM AUTHOR]

Radiological assessments on zones to take protective actions in case of a nuclear or radiological emergency involve a series of real-time forecasts of radiological impact on the public at various distances from the release point, using actual weather or forecast data, information on the source term or facility status, and primary radiation monitoring data. This practice is implemented during the operation of emergency centers around the world in order to promptly report the occurrence and possible consequences of radiological accidents in the country and abroad in the event of a possible transboundary impact. Since the Chornobyl disaster, a lot of emergency exercises, research programs and projects, in particular, benchmarking, have served as international platforms for improving modeling capacity in atmospheric dispersion. This activity is carried out both on the basis of past severe accidents with significant atmospheric releases and corresponding radiological consequences, and on the basis of specific conditional (hypothetical) events that are developed in accordance with the purpose of the study. The paper is focused on the comparison results performed under the international project "Benchmarking on Assessment of Radiological COnsequences" (BARCO) conducted in 2020–2021 between five technical support organisations – members of the European Technical Safety Organisations Network (ETSON). The work contains a short overview of relevant international activity conducted in the past, a description of the BARCO project and its objectives, a list of participants, project tasks, initial data (source term, meteorology, list of benchmarking quantities, approach to data exchange, codes used). The study presents some of comparative analysis results obtained via two techniques such as code-to-code analysis (CTCA) and matched-pair analysis (MPA). The results discussion concentrates on the overall recommendations for code users. Conclusions provide the main outputs of the project. • Dose projection results can significantly deviate depending on selected software. • Emergency exercises do not allow user to understand reasons for these deviations. • Degree of deviation is not constant over entire distance from the release point. • MPA has proven to be a reliable benchmarking method to study the causes. [ABSTRACT FROM AUTHOR]

Efficient and accurate calculation of Mie scattering parameters for aerosol particles holds significant scientific value and practical implications across various fields such as climate change and environmental science. Traditional multilayer Mie scattering computations are challenged in effectively handling particles that exhibit radial refractive index gradients, leading to low computation speed and accuracy. This paper proposes a novel method driven by deep learning, named RIMie, to offer accuracy and efficient Mie parameters prediction, addressing major challenges in computational efficiency and accuracy. This study provides an efficient and accurate deep learning strategy for calculating Mie scattering parameters of complex aerosol particles, markedly outperforming existing methods. • Novel approach predicts complex particle Mie scattering. • LSTM network boosts accuracy and efficiency in scattering prediction. • Optimizes quantification method for aerosol particle scattering. • Offers significant implications for improving climate modeling accuracy. [ABSTRACT FROM AUTHOR]

The Holocene Temperature Conundrum refers to the mismatch between proxy-based temperature records and those based on climate model simulations. A possible reason for this mismatch is a putative proxy-based bias in reconstructed summer temperatures, and therefore, regional reconstructions of seasonal temperature are crucial for resolving the conundrum. In this paper, we reconstruct vegetation and climate changes over the last ∼10,000 years BP based on a high-resolution pollen record from Gushantun peatland, Changbai Mountains, Northeast China. Multiple quantitative reconstruction approaches were used and weighted averaging partial least squares regression (WAPLS) was found to be the most appropriate method for reconstructing Holocene temperature and precipitation. The reconstructed climate record shows that the Holocene Climate Optimum occurred between 8 ka and 6 ka and exhibited a cold month mean temperature that was 3 °C warmer than modern temperatures. Climate gradually cooled during late Holocene with a minimum cold month temperature of −19.6 °C. Four prominent cold events occurred around 8.7 ka BP, 7.8 ka BP, 5.7 ka BP, and 2.5 ka BP with an amplitude variation up to 3 °C. The synthesized seasonal temperature time series and a comparison with other proxies show that the decreasing trend in mean annual temperature is not a seasonal bias caused by summer temperature change. This study provides evidence of a Holocene seasonal temperature change at a regional scale and insights for further understanding of the Holocene Temperature Conundrum. • Reconstructed seasonal temperature changes based on a new high-resolution fossil pollen record in the Changbai Mountains • Synthesized climatic series in Northeast China to investigate the characteristics of regional temperature changes and their driving factors • Reconstructed winter temperature as evidence to address the alleged seasonal bias for the Holocene temperature conundrum [ABSTRACT FROM AUTHOR]

Stream-observing cameras have recently been deployed in stream systems to monitor water depth dynamics. However, most existing image-based water depth monitoring methods require additional gauging equipment, extensive manual annotations, or complex manual calibration. In this paper, we propose the hierarchical model, a novel multi-modal and multi-scale deep learning framework for monitoring water depth in headwater streams with only a field camera capable of night vision and no additional equipment. In particular, the hierarchical model integrates long-term dynamic patterns extracted from large-scale meteorological data with short-term dynamic patterns extracted from small-scale stream image data to jointly monitor water depth at a fine-level temporal resolution. In order to overcome the issue of limited availability of images, we introduce a transfer learning strategy and incorporate more accurate long-term patterns that enable the hierarchical model to perform competitively even with a small number of images. We evaluate our method on a real-world headwater stream monitoring dataset from the West Brook study area in western Massachusetts, United States. Our extensive experiments demonstrate that the hierarchical model outperforms several state-of-the-art methods for water depth monitoring, and that more accurate long-term patterns can better guide the monitoring of short-term patterns with excellent flexibility and less computational cost. The mean absolute error of our hierarchical model achieves a remarkable level of 4. 9 c m at the study site with 0. 89 m average water depths, and only 12. 5 c m at more drastically varied site with 3. 95 m average depths. • A multi-modal and multi-scale deep learning framework for water depth monitoring. • Integrating long and short-term dynamic patterns from two distinct modal sources. • Transfer learning strategy makes it competitive even with limited available images. • The component replaceability provides excellent flexibility to fit various streams. [ABSTRACT FROM AUTHOR]

Atmospheric density is one of the most important elements of the atmospheric environment. The accurate acquisition of atmospheric density in the near space plays a vital role in understanding atmospheric environment changes and aerospace operations. Spaceborne lidar, as an active observation method, has the advantages of high precision and all-time detection and is a unique payload for obtaining global atmospheric density. To promote the development of a spaceborne Rayleigh lidar payload, the key parameters of the payload need to be designed and optimized, and the detection performance needs to be simulated. In this paper, based on the atmospheric model, lidar equation, and load design parameters, the performance of spaceborne Rayleigh lidar for atmospheric density detection is simulated, and the atmospheric density of the global reference altitude obtained from ERA-5 reanalysis data to conduct simulation analysis of the atmospheric density of the global near space. The results show that the detection height ranges of night and day are 30–70 km and 30–40 km, respectively, and the detection accuracy is better than 10 %. Moreover, the distribution characteristics of the atmospheric density in the global near space with the change of altitude, season, latitude, and longitude are given. Finally, the feasibility of the proposed algorithm was verified by comparing it with the atmospheric density of TIMED/SABER and NRLMSIS-00 models. It provides a reference for the development of spaceborne Rayleigh lidar and global atmospheric density inversion. • The load parameters of lidar are designed and its detection performance is evaluated. • The spatial and temporal distribution characteristics of atmospheric density in near space are simulated. • The feasibility of spaceborne Rayleigh lidar algorithm for retrieving atmospheric density is verified. [ABSTRACT FROM AUTHOR]

• The VMD decomposition processing module can improve prediction accuracy. • VMD-CBiLSTM can improve the accuracy of drought prediction in arid and semi-arid regions. • The VMD-CBiLSTM prediction model has good stability. Drought can cause great harm and loss, so accurate and efficient drought prediction has certain research significance. In previous studies, a single machine learning model is often used to predict the factors related to drought, such as precipitation and temperature, to indirectly explain the influence degree of drought, but the overall prediction accuracy is low, which can not fully and effectively predict the nonlinear and non-stationary drought characteristic information. In this paper, the variational modal decomposition model (VMD) is used to decompose the meteorological drought time series, and the convolution neural network (CNN) and the improved bidirectional long short-term memory neural network (BiLSTM) are combined to construct the meteorological drought hybrid prediction model (VMD-CBiLSTM). The research results show that using VMD-CBiLSTM model to forecast the monthly daily evapotranspiration deficit index (DEDI) of three weather stations, compared with the results predicted by VMD-LSTM, VMD-BiLSTM and VMD-ARIMA models, the average prediction accuracy R2 is increased by 0.076, 0.034 and 0.328 respectively, and the average RMSE is decreased by 0.178, 0.094 and 1.373 respectively. Compared with single model, VMD-CBiLSTM can not only reduce the uncertainty of meteorological drought prediction caused by climate model heterogeneity, but also improve the accuracy of drought prediction in arid and semi-arid regions, which can provide reference for coping with drought occurrence and drought early warning in advance. [ABSTRACT FROM AUTHOR]

Cirrus clouds play a crucial role in regulating the Earth's radiation budget, and this paper aims to contribute to a deeper understanding of their optical and geometrical properties over the Arabian Sea (AS) and Bay of Bengal (BoB) regions using CALIPSO data. Comprehensive statistics are derived, encompassing mean values of cirrus cloud top, base altitude, geometrical thickness, cloud optical depth, and temperature. Over the AS region, the mean values are 15.10 ± 1.50 km, 12.63 ± 1.75 km, 2.52 ± 1.37 km, 0.4 ± 0.58, and -62.30 ± 10.6 (°C), respectively. For the BoB region, the corresponding values are 15.43 ± 1.51 km, 12.72 ± 1.74 km, 2.71 ± 1.46 km, 0.49 ± 0.67, and -64.35 ± 10.7 (°C). A larger spread in optical depth and a higher frequency of occurrence for both cirrus and subvisible cirrus (SVC) clouds were observed over BoB compared to AS. Additionally, the study delves into SVC cloud characteristics, emphasizing their thinness and higher base altitudes compared to cirrus clouds. This comprehensive investigation contributes valuable insights into the distinctive properties of cirrus and SVC clouds in these regions, enhancing our knowledge of atmospheric processes and their implications for climate modelling and predictions. • Spatial variability of SVC and cirrus cloud over ISMR region using CALIPSO data • Detailed analysis of physical and optical properties of cirrus and SVC over AS and BoB • Higher optical depth range and occurrence frequency of cirrus and SVC over BoB. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to investigate atmospheric corrosion kinetics of carbon steel under multi-droplet conditions. A homemade climate chamber has been developed to accurately control and monitor environmental conditions, including temperature (T) and relative humidity (RH), during exposure. Carbon steel corrosion kinetics are monitored with a custom-designed Electrical Resistance (ER) sensor pair. Savitzky-Golay (S-G) based filtering technique has been used for the corrosion signal processing. In parallel, top-view droplet temporal evolution has been recorded by microscopic imaging and analyzed for both droplet size distribution and the solid-liquid contact angle. The droplet size distribution can typically be described with a power-law form curve. The curve shows a decrease in height and a concurrent expansion in width with progressive drying. The introduction of NaCl into the electrolyte and surface roughness variations have also been identified to substantially influence the carbon steel corrosion rate. A strong correlation between the corrosion rate derived from the ER monitoring method and the RH can be observed. This correlation is further analyzed to incorporate the impact of droplet-based electrolyte conditions. This study offers valuable insights into the development of mechanistic and kinetic prediction models for atmospheric corrosion. [Display omitted] • Atmospheric corrosion of carbon steel is studied under multi-droplet conditions. • Corrosion rate is monitored with a custom-designed electrical resistance track pair. • Droplet-based electrolyte temporal evolution and environmental conditions are monitored. • A strong correlation exists between corrosion rate evolution and relative humidity (RH). • Small variations between corrosion rate evolution and RH relate to droplet distribution. [ABSTRACT FROM AUTHOR]

The Full of Economic-Environment Linkages and Integration dX/dt (FeliX) model is a System Dynamics-based Integrated Assessment Model (IAM), explicitly incorporating human behaviors and their dynamic interactions among global systems. This paper presents FeliX 2.0, describing the detailed framework and key interactions among nine integrated modules. FeliX 2.0 refined its original version in population dynamics, food and land use systems, and socioeconomic settings for poverty analysis. Robust calibration is applied to key variables against their historical data since 1950. Future projections of multiple variables up to 2100 demonstrate coherences between FeliX 2.0 and the IAMs used in IPCC assessments. Both outputs (the robust calibration results and future projections) underscore the efficacy of FeliX 2.0 in capturing complex interdependencies within global systems. FeliX 2.0 stands as an informative tool and offers insights into interactions within the human-Earth system and the analysis of complex economic-environmental-social challenges in short- and long-term future. • A SD-based IAM model—FeliX 2.0—integrating human behavior for the human-Earth system simulation. • FeliX 2.0 refines FeliX 1.0 in population dynamics, food and land use, and poverty modeling. • Coherent projections up to 2100 for key variables in human-Earth systems by FeliX 2.0 • FeliX 2.0 stands as a pioneering tool for analyzing economic, environmental and social challenges. [ABSTRACT FROM AUTHOR]

The stratigraphic association of late Precambrian glacial deposits with shallow-marine carbonate successions, which are biased toward the warmer parts of the surface ocean, was known in Africa and elsewhere since the 1930's, but geologists under-appreciated it as a paradox in physical climatology. A physical explanation for global freeze-up emerged from one-dimensional energy-balance climate models in 1969, after which it was widely discussed and refined. In 1981, it was suggested that a global freeze-up could be self-terminating after millions of years through slow accumulation of atmospheric CO 2 , a greenhouse gas. The idea of a self-reversing global freeze-up, or 'snowball Earth,' was first applied geologically by Kirschvink (1992) based on paleomagnetic evidence in South Australia. He cited no paper in physical climatology and geologists remained unaware of developments in that science until after 2000. A self-reversing global freeze-up involves strong predictions in duration, synchroneity and termination state that have now been tested in the geological record. Inflections in growth rate and disciplinary diversity of peer-reviewed papers in 1997 implies that climate physics and geology were galvanized to test both the Siderian (ca 2430 Ma) and Cryogenian (717−635 Ma) snowball Earth hypotheses by new field research conducted in South Africa and northern Namibia, respectively. • A Siderian snowball Earth (SBE) was first inferred from African paleomagnetic data. • Tandem Cryogenian SBEs were most forcefully posed by African carbonate stratigraphy. • SBEs have been tested by geochronology, geochemistry, sedimentology & climate models. • SBEs followed supercontinent break-up and flood-basalt volcanism on the paleoequator. • Preexisting polar−alpine biota survived SBEs and were ancestral to later surface life. [ABSTRACT FROM AUTHOR]

Although there is a growing interest in the concept of climate justice, both in academia and society in general, there is still no consensus regarding the best models and data to represent it. Thus, the objective of this paper is to test and statistically validate the confirmatory factor model for climate justice. To do this, we applied Confirmatory Factor Analysis (CFA), which designs and tests statistically the significance of a multidimensional theoretical construct. The main results were: a) the individual freedoms derived from the capability approach and the climate actions of adaptation and mitigation were empirically confirmed as viable perspectives for assessing climate justice; b) the theoretical constructs of human development, climate actions and climate justice can be constructed based on a set of 25 indicators; and c) the three confirmatory models were successfully tested to validate a theoretical/measurement model for climate justice. We believe that the results observed will provide policymakers and national governments with a new perspective for the alignment of different types of human development programs with climate change actions, helping them on the establishment of possible links between actions taken at the national level and the definition of global goals. • Presence of synergies between the confirmatory model and the Sustainable Development Goals. • The proposition of definition of Climate Justice based on the Capability Approach. • The 25 selected indicators can guide nations in their transition to a fairer low-carbon economy. [ABSTRACT FROM AUTHOR]

The modeling of atmospheric clouds is one of the crucial elements in the natural phenomena visualization system. Over the years, a wide range of approaches has been proposed on this topic to deal with the challenging issues associated with visual realism and performance. However, the lack of recent review papers on the atmospheric cloud modeling methods available in computer graphics makes it difficult for researchers and practitioners to understand and choose the well-suited solutions for developing the atmospheric cloud visualization system. Hence, we conducted a comprehensive review to identify, analyze, classify, and summarize the existing atmospheric cloud modeling solutions. We selected 113 research studies from recognizable data sources and analyzed the research trends on this topic. We defined a taxonomy by categorizing the atmospheric cloud modeling methods based on the methods' similar characteristics and summarized each of the particular methods. Finally, we underlined several research issues and directions for potential future work. The review results provide an overview and general picture of the atmospheric cloud modeling methods that would be beneficial for researchers and practitioners. [ABSTRACT FROM AUTHOR]

Roads play a vital role in transportation systems, and their construction and maintenance expenses are high. Those reasons make crucial the examination of factors that accelerate road deterioration and those factors' negative impacts on societies. Climatic-related elements, especially temperature and precipitation, significantly affect the quality and lifespans of roads. The Intergovernmental Panel on Climate Change has reported that increasing greenhouse gas (GHG) emissions is causing changes in climate and that the rate of change has accelerated during the past several decades. To assess the impacts of changing climatic parameters on flexible-pavement performance, this study used 32 climate models to evaluate the performance of interstate, primary, and secondary roads across 11 diverse locations with varying traffic and climatic conditions. The study's findings reveal that changing climate exacerbates pavement distresses, leading to reduced pavement lifespans and increased numbers of reconstruction projects, which in turn raise demands for materials and equipment and contribute to higher GHG emissions. The effects of changing climate and changing climate's associated economic and environmental costs make the application of various engineering solutions to enhance pavement resilience essential, as described in this paper, in the absence of comprehensive emissions reduction strategies. [Display omitted] [ABSTRACT FROM AUTHOR]

• An LRFD framework for embankments considering climate change effects is established. • Additional partial factors are introduced to account for the climate change effects. • Non-stationary maximum flood depth is developed using a peak-over-threshold method. • Embankment reliability is evaluated against overtopping and slope failure. • The proposed framework can serve as a reference for future code design. Riverine floods have become increasingly prevalent on a global scale, posing significant risks to infrastructure systems and communities. The escalating impacts of climate change associated with the increase in rainfall intensities and frequencies necessitate the improvement of the existing design methodologies to account for the non-stationary climate change effects to ensure that the reliability is above the target level and mitigate future flood disasters. This paper presents a novel LRFD approach for river embankments subjected to extreme rainfall under non-stationary climate change effects. This approach introduces an additional partial factor to account for the effects of climate change. Precipitation and temperature projections are collected from various climate models considering several cases of emission scenarios. An integrated hydrological and hydraulic modeling of the analyzed river is carried out to estimate the associated time-variant river discharge and water surface elevation. The non-stationary extreme value associated with the maximum flood level is leveraged using the peak-over-threshold approach. The embankment reliability and the corresponding most probable points are evaluated using limit states associated with overtopping and slope failures. Based on the estimated and target reliability indexes, the design point for each random variable is assessed considering the cases with and without climate change effects. Finally, the partial factors associated with climate change effects are determined. As an illustrative example, the proposed framework is applied to the Ashida River in Fukuyama city of Japan. [ABSTRACT FROM AUTHOR]

To address the negative impacts of image degradation on vision measurement in hazy tunnel construction, this paper presents a unified enhancement method of color and grayscale images in tunnel haze environments. Firstly, a tunnel haze environment light scattering model is introduced utilizing geometric optics principles and the conventional atmospheric scattering model. Secondly, a model for perceiving image haze is established within the image feature space to estimate ambient light and transmission. The efficacy of the method is assessed by constructing a tunnel haze simulation chamber and a platform for measurement of tunnel boring machine (TBM) cutter wear. Comparative experiments demonstrate the method reduces the error in 3D point cloud reconstruction by 77% and improves the measurement precision of TBM cutter wear by 62%. The results demonstrate the potential application of this method in automated tunnel construction. However, further research is needed to enhance its efficacy in dense haze images. • Improved the atmospheric scattering model based on the tunnel environment and geometric optics. • Established a haze perception model based on image compression features. • Proposed a method for calculating atmospheric light and transmission. • Built a tunnel haze environment simulation chamber and a TBM cutter wear measurement platform. [ABSTRACT FROM AUTHOR]

This paper aims to assess the mean precipitation and precipitation extremes over Iran as simulated by the Regional Climate Model (RegCM4). A simulation spanning 20 years (1991–2010) at a horizontal resolution of 20 Km is driven by the NCEP / NCAR reanalysis. We evaluated the model by comparing simulated precipitation with observations using Bias, Root-Mean-Square Error, and Index of Agreement metrics. We examined the extreme precipitations based on a set of extreme indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) in three categories of intensity (Rx1day and SDII), duration (CDD and CWD), and frequency (R10mm and R20mm). The linear trends are calculated using the Theil–Sen estimator method, and the statistical significance (95% confidence level) is determined by using a modified Mann-Kendall (MMK) trend test. The RegCM4 model satisfactory captured the spatial distribution of precipitation and precipitation extremes, although high bias remained in small parts of Iran, including the northwest and southeast. The northwest bias is due to spring convectional precipitation and the southeast bias could be caused by precipitation from the Asian summer monsoon system, which both of them may not be well simulated by the applied Grell convective scheme. Results indicate that the model reasonably captures Rx1day, SDII, CWD, R10mm, and R20mm Indices over Iran. In good agreement with precipitation observations, the southern coast of the Caspian Sea represents the second-highest extreme precipitation, except for SDII, which is probably due to the high frequency of rainy days in this region. The highest CDD of more than 200 days is found in the arid and semi-arid regions of the southeast. In general, precipitation decreased in most regions of Iran, especially the western, southern, and interior regions. In addition, the results reveal that heavy (R10mm) and very heavy (R20mm) precipitation events have also decreased in the same regions. Results also emphasize an increase in consecutive dry days (CDD) in most parts, especially in the southeast, which deserves more attention in future research. The decreasing trend of precipitation and the increasing trend of CDD show that Iran has become drier in the 2000 s compared to the 1990 s • The Grell convective scheme applied in RegCM4 has been successful in simulating precipitation extremes in Iran. • The RegCM4 has been able to capture precipitation extremes well in Iran. • Consecutive dry days (CDD) are increasing in most parts of Iran. [ABSTRACT FROM AUTHOR]

[Display omitted] • Climate change affects the stormwater quality due to variation in climatic parameters. • Scientific interventions with strong database should be geared up in developing nations. • Emerging pollutants needs more attention by undertaking systematic research. • Groundwater recharge with stormwater may raise groundwater level and reduce pollution. The problem of water logging and deteriorating water quality of surface water bodies has been witnessed in and around the urban centers owing to rapid population growth, urbanization, industrialization and climate variability. The stormwater runoff management is the need of the hour, and with increasing public and political awareness and rapid economic growth, it is receiving due attention. In the climate-change scenario, it needs to be addressed with scientific interventions as changes in pattern of precipitation affects the runoff volume and movement of pollutants including nutrients. This paper discussed and critically reviewed the stormwater runoff characterization, modelling, relationship between runoff and water quality, first flush phenomena, runoff treatment in changing climate scenarios. Researchers have used regional climate models (RCMs) to project future precipitation and then incorporated in various hydrologic and hydraulic models such as SWMM, SWAT, HSPF, SUSTAIN and MUSIC etc to study the impact of climate change and its adaptation. The flooding and water quality problem can be combatted with adopting green infrastructure practices. Many works on stormwater management have been carried out in developed countries, but efforts need to be geared up in the developing countries, particularly for runoff characterization and effective management. Emerging pollutants need to be paid more attention by undertaking systematic research. Future studies should include measurements and quantification of ecosystem services and integration of stormwater management into environmental policy. This review suggests intriguing future research avenues at the nexus of stormwater management and ecological services. [ABSTRACT FROM AUTHOR]

• We propose a new method to quantify Climate Change Effects at hydrological scales. • The method allows to assimilate climatic projections in CDFs at rain gauge scale. • The initial (i.e., before any change scenario begins) skewness plays a crucial role. • A strong increase of outliers frequency is expected for series EV1 alike skewed. Quantifying the potential effects of Climate Change (CC) on hydrological scales is a topic with a more and more increasing interest for the scientific community, given the CC impact on agriculture, industry, economy, human health, ecosystems, among the others. In this context, the paper focuses on the sub-daily Annual Maxima (AM) of rainfall height time series, and specifically on the crucial role played by initial skewness, i.e. the skewness of the observed rainfall series used to evaluate potential parametric trends. Here a quick and user-friendly methodology is proposed, that is aimed at quantifying plausible future changes in terms of probability distributions assumed at rain gauge scale, from projections of any climatic model. In detail, the Generalized Extreme Value (GEV) and the Two-Component Extreme Value (TCEV) distributions are adopted as probability functions suitable for modelling observed rainfall AM series, which could increase their frequency and magnitude into future horizons under CC. EURO-CORDEX projections for Europe are considered, under the hypothesis that the values of the change factor, i.e. the ratio between the values of a specific quantile at two specific time horizons, are invariant when moving from an areally-averaged scale (typical for any climate model) to a point rain gauge scale, which induces that future changes are provided (in terms of frequency and magnitude of extreme events) without the need of any spatial downscaling from the assumed projections. The proposed methodology can contribute to hazard quantification associated to potential climate changes, and thus it can play a crucial role in the assessment of hydraulic structures resilience; the obtained results, specific for the study area of Italy, but easily extendable on a global scale, showed that larger increases in frequency of future heavy events are expected for time series with "EV1 alike" values of initial skewness. [ABSTRACT FROM AUTHOR]

This work introduces CyTRACK (Cyclone TRACKing framework), a new open-source, comprehensive and user-friendly Python toolbox for detecting and tracking cyclones in model and reanalysis datasets. The kernel of CyTRACK is based on detecting critical cyclone centres in the mean sea level pressure field at a single time slice, which are then filtered following several threshold parameters. This paper also compares ten years of CyTRACK outputs forced with the ERA5 reanalysis against best-track archives and available cyclones track datasets. The results reveal that CyTRACK can capture the inter-annual (year to year) and intra annual (seasonal cycle) variability of cyclone frequency, life cycle characteristics and spatial distribution of track densities. Largest differences were observed in the annual and seasonal frequency. In summary, CyTRACK provides a user-friendly framework for sensitivity analysis of several free parameters used to perform the tracking, and it is useful for case or climatological studies of cyclone features. • CyTRACK is an open-source and user-friendly toolbox for tracking cyclones. • It can capture cyclone frequency, lifetime and spatial distribution. • It is useful for case or climatological studies of cyclone features. [ABSTRACT FROM AUTHOR]

This paper presents projections of climate extremes over China under global warming of 1.5, 2, and 3 °C above pre-industrial (1861–1900), based on the latest Coupled Model Intercomparison Project phase 6 (CMIP6) simulations. Results are compared with what produced by the precedent phase of the project, CMIP5. Model evaluation for the reference period (1985–2005) indicates that CMIP6 models outperform their predecessors in CMIP5, especially in simulating precipitation extremes. Areal averages for changes of most indices are found larger in CMIP6 than in CMIP5. The emblematic annual mean temperature, when averaged over the whole of China in CMIP6, increases by 1.49, 2.21, and 3.53 °C (relative to 1985–2005) for 1.5, 2, and 3 °C above-preindustrial global warming levels, while the counterpart in CMIP5 is 1.20, 1.93 and 3.39 °C respectively. Similarly, total precipitation increases by 5.3%, 8.6%, and 16.3% in CMIP6 and by 4.4%, 7.0% and 12.8% in CMIP5, respectively. The spatial distribution of changes for extreme indices is generally consistent in both CMIP5 and CMIP6, but with significantly higher increases in CMIP6 over Northeast and Northwest China for the hottest day temperature, and South China for the coldest night temperature. In the south bank of the Yangtze River, and most regions around 40°N, CMIP6 shows higher increases for both total precipitation and heavy precipitation. The projected difference between CMIP6 and CMIP5 is mainly attributable to the physical upgrading of climate models and largely independent from their emission scenarios. [ABSTRACT FROM AUTHOR]

This paper exposes the risk of generalization of wind conditions from a single met-mast measurement to be representative of the actual flow field in a wind farm situated in complex terrain. As a case study, Large-Eddy Simulation (LES) of the neutral Atmospheric Boundary Layer (ABL) flow for a mid-western Sweden wind farm is performed. The site-specific complex topography and the forest properties like the Plant Area Density and the tree heights are extracted from the Airborne Laser Scanning (ALS) 3D data, thus the forest is heterogeneous. To emphasize the impact of the local topography and surface roughness on the wind field, the wind turbines are not included in the numerical simulations. The predicted wind speeds using LES are compared to wind speed from the nacelle-mounted anemometers taken from the wind farm's turbine SCADA data, focusing on the wake-free turbines. A sufficient degree of match is observed, supporting the accuracy of the numerical simulations. The results show that inflow variables i.e., mean wind speed, shear exponent and turbulence intensity vary at each wind turbine location justifying the need for turbine-specific assessment of the wind resource in a wind farm located in forested complex terrain. The inter-turbine (between turbines in the wind farm) differences in wind resource is quantified in terms of the difference in turbine-specific structural and mechanical loads by running wind turbine mechanical simulations using the extracting the wind fields predicted by the LES. The results show that not only inter-turbine loads varying significantly in the wind farm, but the turbine loads also differ significantly if a homogeneous assumption is made for the forest. Most importantly, it was found that the homogeneous forest assumption predicted a higher turbulence intensity compared to a heterogeneous forest resulting. • CFD modelling of atmospheric flow through heterogeneous forest in complex terrain. • Using Airborne Laser Scans data to extract terrain and heterogeneous forest property. • Poor wind prediction based on a single met-mast measurement in a hilly wind farm. • Impact of forest modeling assumptions on fatigue life of a wind turbine components. [ABSTRACT FROM AUTHOR]

This paper examines the local and non-local climatic effects of wetter arid areas of Northwestern China (ANWC) in summer using a fully coupled climate model. In response to soil moisture increase, the local air temperature significantly decreases, and precipitation increases in summer. The wetter ANWC results in a warmer and rainless climate over the Central part of North China Plain (CNCP). While the influence of increasing soil moisture on the Tibetan Plateau (TP) precipitation is negligible due to the high altitude. A cooler climate over ANWC leads to an abnormal descending motion occurring. Meanwhile, an anomalous high pressure and an abnormal anticyclonic circulation occurs in 500 hPa over the west of ANWC. Correspondingly, a huge anomalous cyclone circulation occurs over the northeastern part of China and Mongolia, which can hinder the northward movement of the East Asian summer Monsoon (EASM) and reduce the water vapor transport into Central China Plain. As a result, the weakened EASM thus leads to rainless over CNCP. As soil moisture increases over ANWC is stronger, the magnitudes of the influence on the atmospheric field become larger. This study highlights the wetter ANWC effect on local climate and provides more information about the mechanism of wetter ANWC remote effect on non-local climate. • This paper examines the local and non-local climatic effects of wetter arid areas of Northwestern China (ANWC) in summer using a fully coupled climate model. • It finds that the wetter ANWC can result in a warmer and rainless climate over the Central part of North China Plain (CNCP) through a complex atmospheric circulation response. • A cooler climate leads to an abnormal descending motion occurring over ANWC. • A huge anomalous cyclone circulation over the northeastern part of China and Mongolia can block the East Asian summer Monsoon northward and decrease the water vapor transport into Central China Plain. • This study highlights the wetter ANWC effect on local climate and provides more information about the mechanism of wetter ANWC remote effect on non-local climate. [ABSTRACT FROM AUTHOR]

In this paper, an innovative framework is introduced for assessing the impact of climate change on coastal structures, with a primary emphasis on modeling future climate conditions. The framework encompasses several key components: the development of a new climate emulator to facilitate multivariate extreme value analysis; a hybrid statistical–numerical wave propagation strategy; and the integration of a novel compound modeling approach specific to coastal structure sites. These advancements are designed to effectively manage multiple climate scenarios and models within the context of high uncertainty. The methodology is applied and validated for a coastal structure located on the Mediterranean coast in Spain. Focusing on compound wave and sea-level events, the projections indicate an increase in the frequency and severity of extreme conditions, exacerbating coastal impacts such as wave overtopping. For instance, the present-day 500-year coastal flooding level would be associated with a return period of 50 years by mid-century under a stabilization scenario and would be further reduced to 25 years under a high-emission global warming scenario, resulting in important consequences for structure performance if no adaptation is implemented. • Develops a framework for assessing the impact of climate change on coastal structures. • It develops a new climate emulator to facilitate multivariate extreme value analysis. • It encompasses a hybrid statistical–numerical wave propagation strategy. • It integrates a novel compound modeling approach specific to coastal structures sites. • It has been applied and validated for a coastal structure in Spain. [ABSTRACT FROM AUTHOR]

Crop models are essential tools for assessing the impact of climate change on national or regional agricultural production. Starting from meteorology, soil and crop management, fertilization and irrigation practices, they predict the yield of specific crop varieties. For long term assessments, climate models are the source of primary information. To make climate model results usable in a specific time frame context, bias adjustment (BA) is required. In fact, climate models tend to deviate from day-to-day values of the physical parameters while conserving the climate variability signal. BA brings the climatic signal to the actual values observed in a specific location and period, and to be representative of a specific period in absolute terms. BA techniques come in different flavours. The broadest categorization is univariate and multivariate methods. Multivariate methods adjust the variables considering possible cross-correlations while univariate methods treat the variables one by one without accounting for possible dependence on one another. The hypothesis tested in this paper is that since crop models require as input climate variables that are in most of the cases cross-correlated, the multi-variate bias adjustment of the latter is likely to improve performance compared to univariate bias adjusted climate model results. To verify this hypothesis, 14 BA methods were applied to 9 variables from 8 climate models at 21 locations across Europe and Northern Africa for a period of 5 years. Twelve crop models, from the AgMIP Wheat community, were run using the climate model results. All crop models, except one, were restarted at every growing season. The crop models were also run using the AgMERRA re-analysis. The latter were used as reference to compare the results when using the other climate models treated with the various sets of bias-adjustment methods. The results show that multivariate BA treatment should be preferred to univariate ones. The error obtained by comparing crop simulation obtained with AgMERRA with those obtained with multivariate bias-adjusted climate prediction is systematically lower. The error reduction varies as a function of the variable, the location, the crop model, and the climate model though the tendency is for smaller errors when multivariate methods are used to treat the latter. The results are attributed to the nature of crop models and the fact that multivariate methods consider more adequately the correlation existing between the meteorological variables. The study shows the importance of considering the nature of a model and the selection of input data that best suited to the former. In this case the improvements produced when using multivariate data appears to be significant especially in the light of the variety of crop models used and the similar response obtained and it is therefore recommended. [Display omitted] • The impact of climate data multivariate bias-adjustment methods versus univariate on crop model results was estimated. • Crop model results improved when input data was treated using multivariate methods compared to univariate methods. • Multivariate methods maintain the variables correlation as required by crop models. • This result is attributed to the parameterized nature of the participating crop-model formulations. • Climate, bias adjustment and the crop modelling communities joined efforts in this unprecedented collaboration. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient and fast low-light image enhancement method using an atmospheric scattering model based on an inverted low-light image. The transmission map is derived as a function of two saturations of the original image in the two color spaces. Due to the difficulty in estimating the saturation of the original image, the transmission map is converted into a function of the average and maximum values of the original image. These two values are estimated from a given low-light image using the gamma correction prior. In addition, a pixel-adaptive gamma value determination algorithm is proposed to prevent under- or over-enhancement. The proposed algorithm is fast because it does not require the training or refinement process. The simulation results show that the proposed low-light enhancement scheme outperforms state-of-the-art approaches regarding both computational simplicity and enhancement efficiency. The code is available on https://github.com/TripleJ2543. [ABSTRACT FROM AUTHOR]

Significant errors can arise if the adjacency effect (i.e., the contribution of neighbouring pixels to the radiance of a pixel) is neglected in the interpretation of remote sensing images. For example, adjacency radiances can account for >30% of the signal at the top of the atmosphere (TOA) of a white sand-lined coastline (Bulgarelli and Zibordi, 2018). This paper shows that 3D radiative transfer (RT) modelling can quantify this phenomenon. It presents a new 3D Monte Carlo surface-atmosphere RT modelling in the DART RT model, and a resulting virtual 3D Earth-Atmosphere laboratory for accurate simulation of atmospheric RT, including the adjacency effect. It was first validated with the atmosphere model SMART-G for 2D scenes: relative difference is 0.20% in TOA directional reflectance of infinite black surface for solar zenith equal to 60° , and 0.03% in TOA nadir reflectance of a black disc inside infinite white Lambertian surface, for solar zenith equal to 0° and 30°. Then, the adjacency effect on the TOA radiance and TOA albedo of a 3D scene was studied for a circular city (radius 2 km) surrounded by a forest (dimension 10 km), for four Sentinel-2A bands (blue, green, red, near infrared). Its contribution to TOA nadir radiance reaches ∼20% in the near infrared band, and increases with viewing zenith angle (VZA): ∼60% if VZA = 80°. The 3D scene TOA albedo was calculated after adapting the DART Bidirectional Reflectance Factor (BRF) camera to simulate TOA radiance for all upward directions at any angular resolution. For Sentinel-2A's four bands, the adjacency effect influences the TOA albedo of the circular city by up to 10% for the green band and up to 27% for the near infrared band, well above the maximum uncertainty 5% usually required in land surface applications. The adjacency effect of the city neighbourhood 3D structure was studied by replacing it by a Lambertian surface with its albedo. The city nadir TOA radiance changed by up to 1.3% with very small change in TOA albedo (<0.5%). This new modelling greatly improves DART potential for accurate simulation of atmospheric effects. It is in the DART version freely available for research and education (https://dart.omp.eu). • New 3D Monte Carlo surface-atmosphere RT modelling in DART model. • A virtual Earth-Atmosphere laboratory for simulating the adjacency effect. • Study of the impact of the surface 3D structure on the adjacency effect. • Study of the adjacency effect contribution on radiance and albedo at TOC and TOA. [ABSTRACT FROM AUTHOR]

Machine learning (ML) methods have become an important tool for modelling and forecasting complex high-dimensional spatiotemporal datasets such as those found in environmental and climate modelling applications. ML approaches can offer a fast, low-cost alternative to short-term forecasting than expensive numerical simulation while addressing a significant outstanding limitation of numerical modelling by being able to robustly and dynamically quantify predictive uncertainty. Low-cost and near-instantaneous forecasting of high-level climate variables has clear applications in early warning systems, nowcasting, and parameterising small-scale locally relevant simulations. This paper presents a novel approach for multi-task spatiotemporal regression by combining data-driven autoencoders with Gaussian Processes (GP) to produce a probabilistic tensor-based regression model. The proposed method is demonstrated for forecasting one-step-ahead temperature and pressure on a global scale simultaneously. By conducting probabilistic regression in the learned latent space, samples can be propagated back to the original feature space to produce uncertainty estimates at a vastly reduced computational cost. The composite GP-autoencoder model was able to simultaneously forecast global temperature and pressure values with average errors of 3.82 °C and 638 hPa, respectively. Further, on average the true values were within the proposed posterior distribution 95.6% of the time illustrating that the model produces a well-calibrated predictive posterior distribution. [Display omitted] • Gaussian Processes effectively learn latent space temporal dynamics. • Autoencoders extend the capabilities of reduced order models. • Uncertainty can be propagated into the feature space through sampling. • Probabilistic approaches produce more interpretable and confidence-adjusted forecasts. • Through efficient encoding one model can produce probabilistic multi-task forecasts. [ABSTRACT FROM AUTHOR]

Tree fall onto railway lines puts passengers at risk and causes large economic losses due to disruption of train services and damage to infrastructure. Railway lines in Germany are vulnerable to tree fall because of the large number of trackside trees that exist in that country with approximately 70% of all railway lines being tree-lined. In this paper we first tested whether a hybrid-mechanistic tree wind damage model, ForestGALES, could identify the sections of the railway network affected by tree fall in two federal states of Germany, Northrhine-Westphalia (NRW) and Thuringia (TH). We secondly tested whether the model, in combination with meteorological forecast models, could predict where tree fall occurred during a damaging windstorm. We used information on tree characteristics derived from LiDAR and aerial photography along the railway line network in NRW and TH to calculate the critical wind speed (CWS) at which damage is expected to happen for every individual tree as a function of its size and species, and the underlying soil. The railway network was then divided into 500 m sections and the statistics of the CWS , tree height, and species composition (broadleaf/conifer mix) within each section were calculated. Analysis of past tree fall events recorded by Deutsche Bahn AG (DB) showed that there was a significantly lower minimum CWS and significantly greater maximum tree height in sections that had recorded damage. In a second step we compared the calculated CWS values for all trees against downscaled wind speed assessments across the two federal states during Storm Friederike (named Storm David internationally) on 18 January 2018 and tested the ability of the model to discriminate sections with recorded damage during the storm. Excellent model discrimination was found with an AUC value of 0.82 and an overall model accuracy of 74.2%. The first test showed that the ForestGALES model with precise individual tree information can identify the sections of a railway network most vulnerable to tree fall. The second analysis showed, for the one storm tested, that the ForestGALES model when combined with predicted storm wind speeds can identify the most probable sections of the railway network to experience tree fall during an approaching damaging storm. Such information could be of value in firstly planning remedial work along railway lines, and secondly preparing the railway network ahead of a major storm. • Tree fall is a major hazard to the railway network in Germany. • A mechanistic model could identify sections of a railway most at risk of tree fall. • The model identified sections likely to suffer tree fall prior to a storm. • Sections with tree fall had trees with significantly greater maximum heights. • The model can be used to identify sections requiring remedial action. [ABSTRACT FROM AUTHOR]

The occurrence and development of cloud and precipitation are the products of the combination of atmospheric dynamic, thermal processes and cloud microphysical processes. In order to understand the interaction between these influencing factors in more detail, in this paper we study a moisture model with multi-phase for warm clouds, which consists of the primitive equations and a set of humidity equations where water is present in the form of water vapor, rain water and cloud condensates. This model has been considered by Cao et al. in (2018), where the velocity field is a given function in L ∞ ((0 , t 1) × M). In this paper, the velocity field is governed by the atmosphere dynamic equation, which is more in line with the actual situation. In order to overcome the difficulty caused by the physical range requirement for the humidity, we introduce a new penalized function which is different from that introduced in Cao et al. (2018). Then we obtain the global existence of both quasi-strong solutions and strong solutions. • A multi-phase moisture atmosphere model is given. • The velocity field is governed by the atmosphere dynamic equation. • The saturation of water vapor in the air is considered. • The global well-posedness of quasi-strong and strong solution is proved. • A new penalized function which is different from the literature is introduced. [ABSTRACT FROM AUTHOR]

Reliable and efficient power modeling from meteorological wind data is vital for optimal implementation and monitoring of wind energy, and it is important for understanding turbine control, farm operational optimization, and grid load balance. Based on the idea that similar wind conditions lead to similar wind powers; this paper constructs a modeling scheme that orderly integrates three types of ensemble learning algorithms—bagging, boosting, and stacking—and clustering approaches to achieve wind power modeling from multiple wind-based meteorological factors in a wind farm. The paper also investigates the applications of different clustering algorithms and methodologies to determine cluster numbers in the modeling. The results reveal that all ensemble models with clustering exploit the intrinsic information in wind data and thus outperform models without clustering by approximately 15% on average in modeling wind power. The model with the best-performing Farthest First clustering is computationally rapid and with an improvement of around 30% compared with the baselines. Given the diversity introduced by clustering algorithms, the power modeling performance is further boosted by about 5% by introducing stacking that fuses ensembles with varying clusters. The proposed modeling framework thus demonstrates promise by delivering efficient and robust performance on the targeted problem. • Systematic demonstration of wind power modeling from weather data. • Construction and comparison of various clustering methods for classifying wind data. • Combination of bagging, boosting, and stacking for modeling. • Multiple wind meteorological characteristics are considered in the modeling. [ABSTRACT FROM AUTHOR]

Climate variability occurs on a multitude of temporal and spatial scales and the associated phenomena are studied using observations and a hierarchy of climate models. The aim of this paper is to describe the role that relatively simple models, usually referred to as conceptual climate models, have in the understanding of climate variability phenomena. To illustrate the importance of these conceptual models, we focus here exclusively on the tipping of the Atlantic Meridional Overturning Circulation (AMOC) under a changing surface freshwater forcing. In this case, results from the full hierarchy of models are available. Conceptual climate models and their mathematical analysis have generated basic physical concepts of AMOC tipping. In addition, these results have served as an important interpretation framework for more detailed models up to state-of-the-art global climate models. • A review is provided of the tipping of the Atlantic Meridional Overturning Circulation (AMOC) using a hierarchy of models. • Results of conceptual climate models have greatly contributed to understanding the collapse and recovery of the AMOC, specifically the feedbacks involved. • The AMOC case serves as a illustrating example of how research in the emerging field of Mathematics and Climate can be successful. [ABSTRACT FROM AUTHOR]