Ismail Gultepe

The goal of this study is to investigate how different land surface models (LSMs) affect mountain cold‐fog simulations and to study the sensitivity of key land surface parameters and clarify the associated physical mechanisms. Observations were collected during January–February 2022 as part of the Cold Fog Amongst Complex Terrain (CFACT) project conducted in Heber Valley, Utah, USA. Sensitivity simulations are conducted using the Weather Research and Forecasting (WRF) model at 1‐km resolution to investigate the role of land surface processes in cold‐fog evolution. Three LSMs including the five‐layer thermal diffusion model (SLAB), Noah land surface model (Noah), and Noah land surface model with multiparameterization options (Noah‐MP) are employed to examine the impacts of snow depth ( H _d ), soil thermal conductivity (STC), and soil moisture content (SMC). Results indicate that H _d derived from the fifth‐generation ECMWF reanalysis (ERA5) introduces cold biases in default Noah and Noah‐MP simulations. Reducing or removing the initial H _d obtained from ERA5 significantly improves near‐surface simulations. Further sensitivity experiments show that STC primarily regulates nocturnal surface cooling, whereas SMC mainly modulates near‐surface moisture availability. Both SMC and STC, therefore, affect the cold‐fog life cycle. Specifically, adjusting STC and SMC resulted in good simulation performance at 2‐m height, with air temperature ( T _a ) showing a correlation coefficient ( R ² ) of 87%, and relative humidity (RH) with R ² of 74%. As a result, WRF more realistically represents time and space variability of cold fog. The simulated fog droplet number concentration ( N _d ) is found to be within a reasonable range of observations (up to 200 cm ⁻³ ); however, the liquid water content (LWC) peaks are relatively higher, ranging from 0.1 to 0.4 g·m ⁻³ based on various microphysics schemes. It is concluded that cold‐fog predictions are sensitive to surface conditions related to snow cover, soil properties and microphysics.

Remote sensing of clouds and precipitation is undergoing a significant transition from descriptive observations toward process-oriented diagnoses. This transition has not progressed linearly, but has gradually taken place alongside the rapidly developing multi-source observation capabilities over recent decades. In terms of clouds and precipitation observational platforms, satellites provide continuous global-scale monitoring, airborne platforms complement high-resolution sampling of key processes, and ground-based observations offer long-term vertical structure evolution, which form an integrated space–air–ground observation system. In terms of clouds and precipitation retrieval algorithms, active–passive combination remote sensing significantly improves the ability to retrieve macro- and microphysical characteristics and structures, and machine learning methods further expand parameter estimation capabilities in complex scenarios. Nevertheless, key bottlenecks still persist in retrieval non-uniqueness, sensor trade-offs, cross-platform calibration, and validation over oceans, mountains, and polar regions. Based on the above background, this paper provides a systematic review of recent progress in clouds and precipitation physics remote sensing, focusing on the development of multi-platform collaborative observations, the evolution of microphysical parameter retrieval methods, and the improvements in remote sensing characterization of cloud and precipitation formation mechanisms. It further points out that future development will increasingly rely on improved uncertainty quantification, incorporation of physical constraints into retrieval frameworks, and the establishment of standardized multi-source datasets.

This study utilizes simultaneous observations obtained at an island station (Heuksando, HSD) and from a moving research vessel during the FATIMA Yellow Sea field campaign to analyze differences in sea fog evolution across a tidally induced thermal front. The coldest sea‐surface temperature T _w persisted at HSD located inshore of the front, whereas the research vessel operated around the front in relatively warmer waters. This study focuses on a concurrent long‐lasting fog event observed at both platforms. The approach of the Changma front and the accompanying precipitation coincided with the transition to dense fog at HSD. Enhanced downward turbulent fluxes of heat and moisture together with increased lower‐atmospheric stability over cold T _w contributed to maintaining deep and persistent fog for approximately 48 hr. In contrast, the observations indicate that thermally driven turbulence limited fog formation in warmer regions of the front. European Centre for Medium‐Range Weather Forecasts Reanalysis v.5 data showed limitations in representing the observed sea fog evolution, which were mainly associated with warm T _w biases. Considering the importance of forecasting the frequent occurrence of sea fog over tidally induced surface cold‐water patches in the Yellow Sea, accurate reproduction of T _w is essential in high‐resolution coupled ocean–atmosphere models.

With the increasing deployment of autonomous and semi-autonomous road vehicles, Advanced Driver Assistance Systems (ADASs) rely heavily on multi-modal sensing technologies to ensure safe and reliable operation. Among these sensors, Light Detection and Ranging (LiDAR) provides high-resolution three-dimensional environmental perception but is particularly vulnerable to adverse weather conditions such as snowfall. Snowfall can degrade LiDAR performance through signal attenuation, backscattering, false detections, and sensor surface contamination, ultimately reducing visibility and detection reliability. In this study, an experimental investigation was conducted in a climatic chamber to systematically assess LiDAR performance degradation under controlled snowfall conditions. Key parameters influencing sensor behavior, including chamber air temperature, precipitation intensity, and sensor orientation, were isolated and examined. Chamber temperature was varied to generate snow characteristics representative of dry and wet snow, while precipitation intensity was controlled by adjusting snow gun flow rates. Sensor orientation was modified to evaluate its effect on perceived precipitation and snow accumulation. The experimental results confirm the initial hypothesis that snowfall intensity, snow physical properties, and sensor orientation exert a significant influence on LiDAR performance degradation. Increasing precipitation intensity significantly accelerates both 3D target detection loss and 2D visibility reduction, with polynomial regression revealing a non-linear degradation response. Inclined sensor orientations exhibited more rapid performance deterioration compared to a horizontal configuration. These findings provide valuable insights into LiDAR vulnerability in snowy environments and support the development of mitigation strategies to improve ADAS and autonomous vehicle operation in cold climates.

The main objective of this work is to characterize Sudden Stratospheric Warming (SSW) conditions and their impact on local weather forecasting and climate change, using SSW definition criteria. The SSWs strongly affect Arctic vortex structure and midlatitude weather conditions. This work evaluates the frequency, amplitude, and dynamical–thermal characteristics of SSWs under historical and Representative Concentration Pathway (RCP) 4.5 scenarios, focusing on stratospheric air temperature (T_s) and zonal wind speed (U_h) at the 10° N and 60° N latitudes. The fifth-generation ECMWF atmospheric reanalysis (ERA5) is employed as the reference dataset. Simulations of five Coupled Model Intercomparison Project Phase 5 (CMIP5) models, represented by M1 to M5, are analyzed. The primary group of models included 1) the Australian Community Climate and Earth-System Simulator, version 1.3 (ACCESS1-3, M1), 2) the Hadley Center Global Environmental Model, version 2—Carbon Cycle (HadGEM2-CC, M2), and 3) the Max Planck Institute Earth System Model—Medium Resolution (MPI-ESM-MR, M3). The analysis period covers SSW events related to the Quasi-Biennial Oscillation (QBO) in the Northern Hemisphere (NH) from 1980 to 2100. The key findings indicate that while M1, M2, and M3 simulate SSW occurrence correctly for the 21st century, they exhibit significant systematic deficiencies in capturing the structural dynamics of SSW events. Specifically, the M1, M2, and M3 models underestimate the polar stratospheric temperature amplitude (T_amp) by approximately 75–80% and zonal wind amplitude (U_amp) by more than 60% compared to the ERA5 analysis. Furthermore, ERA5 exhibits a strong negative correlation (R ≈ −0.8) between U_h and T_s that is not estimated accurately using the present models. The importance of the horizontal resolution of the models and wave–mean flow interactions in determining SSW intensity and occurrence is also found to be a critical metric. Results suggest that SSW definition criteria affect Arctic and midlatitude weather system prediction at a rate of 61–82%. It is concluded that the primary configurations of CMIP5 models for accurately capturing the dynamical structure and evolution of QBO–SSW interactions are needed, and that they affect future projections of SSW events.

The objective of this study is to analyze microphysical parameters affecting visibility parameterizations of a freezing fog case that occurred on 19 February 2022, during the Cold Fog Amongst Complex Terrain (CFACT) project conducted in a high-elevation alpine valley in Utah, USA. Observations are collected using visibility, droplet spectra, ice crystal spectra, and aerosol spectral instruments, as well as in-situ meteorological instruments. Particle phase is determined from relative humidity with respect to water (RH_w) as well as ground cloud imaging probe (GCIP), ceilometer (CL61) depolarization ratio, and icing accumulation on the platforms. Results showed that freezing droplet density can affect visibility (Vis) up to 100 m during Vis less than 1 km. In addition, increasing volume can lead to up to a 2 μm increase in droplet radius due to a change in the chemical composition of aerosols from Sodium Chloride (NaCl) to Ammonium Nitrate (NH₄NO₃). Overall, comparisons suggested that Vis parameterizations are highly variable, and freezing fog conditions resulted in lower Vis values compared to warm fog microphysical parameterizations. Furthermore, riming of freezing fog conditions can lead to more than 50% uncertainty in Vis. It is concluded that changing aerosol composition and freezing fog droplet density and riming can play a major role in Vis simulations.

Fog, rain, snow, and icing are the high-impact weather events often lead to the highway blockings, which in turn causes serious economic and human losses. At present, there is no clear calculation method for the severity of highway blocking which is related to highway load degree and economic losses. Therefore, there is an urgent need to propose a method for assessing the economic losses caused by high-impact weather events that lead to highway blockages, in order to facilitate the management and control of highways and the evaluation of economic losses. The goal of this work is to develop a method to be used to assess the high impact weather (HIW) effects on the highway blocking. Based on the K-means cluster analysis and the CRITIC (Criteria Importance through Intercriteria Correlation) weight assignment method, we analysed the highway blocking events occurred in Chinese provinces in 2020. Through cluster analysis, a new method of severity levels of highway blocking is developed to distinguish the severity into five levels. The severity levels of highway blocking due to high-impact weather are evaluated for all weather types. As a part of calculating the degree of highway blocking, the highway load in each province is evaluated. The economic losses caused by dense fog are specifically assessed for the entire country.

Fog, rain, snow, and ice are the high-impact weather events that often lead to highway blockings, which in turn causes serious economic and human losses. At present, there is no clear calculation method for the severity of highway blocking, which is related to highway load degree and economic losses. Therefore, there is an urgent need to propose a method for assessing the economic losses caused by high-impact weather events that lead to highway blockages, in order to facilitate the management and control of highways and the evaluation of economic losses. The goal of this work is to develop a method to be used to assess the high-impact weather (HIW) effects on highway blocking. Based on the K-means cluster analysis and the CRITIC (criteria importance through intercriteria correlation) weight assignment method, we analyzed the highway-blocking events that occurred in Chinese provinces in 2020. Through cluster analysis, a new method of severity levels of highway blocking is developed to distinguish the severity into five levels. The severity levels of highway blocking due to high-impact weather are evaluated for all weather types. As a part of calculating the degree of highway blocking, the highway load in each province is evaluated. The economic losses caused by dense fog are specifically assessed for the entire country.

The objective of this study is to characterize visibility and microphysics of cold-fog conditions during The Cold Fog Amongst Complex Terrain (CFACT) project, which was designed to investigate the life cycle of cold fog in the Heber Valley, Utah. The field campaign was conducted from 7 January to 23 February 2022 and was supported with observations and resources from the NSF Lower Atmospheric Observing Facilities (LAOF), managed by NCAR’s Earth Observing Laboratory (EOL), as well as the University of Utah and Ontario Technical University. Heber Valley is surrounded by canyons, mountains and irregular topography where the Provo River streams along the valley floor from Jordanelle Reservoir at the north to Deer Creek (DC) Reservoir at the southeastern end at 1652 m above sea level (ASL). The highest peaks surrounding the valley are at about 3500 m (ASL) to the west and southwest of the project area.The DC supersite had extensive ice and droplet microphysical as well as precipitation measurements obtained using a ground-based Gondola (composed of a Droplet Measurement Technologies (DMT) Cloud Droplet Probe - CDP and Back-scatter Cloud Probe - BCP), a DMT Fog Monitor (FM120), a Mesaphotonics Cloud Droplet Measurement System (CDMS), a DMT Ground-based Cloud Imaging Probe (GCIP), a Vaisala Present Weather Detector (PWD52), and an OTT Parsivel. During the project, aerosol measurements were performed using a GRIMM Aerosol Spectrometer, a T.S.I. Scanning Mobility Particle Sizer (SMPS), and a DMT Cloud Condensation Nuclei (CCN) counter, as well as filter samplers. These instruments covered a size range from 8 nm up to cm size range representing aerosols, fog particles, and precipitation. Measurements from a Halo Photonics doppler wind lidar, a Vaisala CL61 ceilometer, a tethered balloon system (TBS), and a 32-m turbulence tower were used to characterize vertical profiles of fog microphysics and aerosols, as well as the dynamic and thermodynamic structure. Eleven significant weather events occurred during the 47 days of the CFACT campaign that included snowfall, freezing fog, ice fog (IF), and light ice crystal precipitation when Vis