Mesoscale convective systems (MCSs) play an important role in modulating the global hydrological cycle, general circulation, and radiative energy budget. In this study, we evaluate MCS simulations in the second version of U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv2). E3SMv2 atmosphere model (EAMv2) is run at the uniform 0.25° horizontal resolution. We track MCSs consistently in the model and observations using the PyFLEXTRKR algorithm, which defines MCS based on both cloud-top brightness temperature (Tb) and surface precipitation. Results from using Tb only to define MCS, commonly used in previous studies, are also discussed. Furthermore, sensitivity experiments are performed to examine the impact of new cloud and convection parameterizations developed for EAMv3 on simulated MCSs. Our results show that EAMv2 simulated MCS precipitation is largely underestimated in the tropics and contiguous United States. This is mainly attributed to the underestimated precipitation intensity in EAMv2. In contrast, the simulated MCS frequency becomes more comparable to observations if MCSs are defined only based on cloud-top Tb. The Tb-based MCS tracking method, however, includes many cloud systems with very weak precipitation which conflicts with the MCS definition. This result illustrates the importance of accounting for precipitation in evaluating simulated MCSs. We also find that the new physics parameterizations help increase the relative contribution of convective precipitation to total precipitation in the tropics, but the simulated MCS properties are overall not significantly improved. This suggests that simulating MCSs will remain a challenge for the next version of E3SM.

Recent research has described a ‘moist margin’ in the tropics, defined through a total column water vapor (TCWV) value of 48 kg m-2, that encloses most of the rainfall over the tropical oceans. Diagnosing the moist margin in the ERA5 reanalysis reveals that it varies particularly on synoptic time scales, which this study aims to quantify. We define ‘wet and dry perturbation’ objects based on the margin’s movement relative to its seasonal climatology. These perturbations are associated with a variety of features, such as tropical cyclones and lows, tropical waves, and extrusions of moisture towards the extratropics. Wet (dry) perturbations produce substantially more (less) rainfall compared to the seasonal average, confirming the clear link between moisture and precipitation. On synoptic scales we suggest that mid-tropospheric humidity plays a key role in creating these perturbations, while sea surface temperatures (SSTs) are relatively unimportant.

This study utilizes radar, sounding observations, and convective-permitting simulations with a non-hydrostatic mesoscale model to investigate the effects of gravity waves originating from the southwest mountain on the intensification of the extreme precipitation event occurred in Henan Province, Central China, in July 2021 (referred to as the “21.7” event). The gravity waves have wave speeds of approximately 11.5 m s-1 and wavelengths ranging from 60 to 90 km. These gravity waves are generated by the interaction between a northwest-southeast direction mountain (Funiu Mountain, FNM) and a southwesterly flow originated from the mesoscale convective vortex (MCV) developing from an inverted trough southwest of the rainfall center. Then, these waves propagate northeastward through a wave duct featuring a stable layer between 5 and 9 km altitude, capped by a low-stability reflecting layer with a critical level. As they propagate, these waves trigger banded convective cells along their path. Upon the arrival of gravity wave peaks at the rainfall center, they induce the downward energy flux of gravity waves from high troposphere levels (~7 km). The downward wave energy dynamically interacts with the upward wave energy from gravity waves excited by latent heating at the lower tropospheric level (~1 km). This synergistic effect intensifies the ascending motion and results in a precipitation increase of over 20% at the rainfall center. This study highlights the significance of orographic gravity waves in shaping extreme precipitation events.

As oceanic moisture evaporates, it leaves a signature on sea surface salinity. Roughly 10% of the moisture that evaporates over the ocean is transported over land, allowing the salinity fields to be a predictor of terrestrial precipitation. This research is among the first in published literature to assess the role of sea surface salinity for improved predictions on low-skill summertime subseasonal timescales for terrestrial precipitation predictions. Neural networks are trained with the CESM2 Large Ensemble using North Atlantic salinity anomalies to quantify predictability of U.S. Midwest summertime heavy rainfall events at 0 to 56-day leads. Using explainable artificial intelligence, salinity anomalies in the Caribbean Sea and Gulf of Mexico are found to provide skill for subseasonal forecasts of opportunity, e.g. confident and correct predictions. Further, a moisture-tracking algorithm applied to reanalysis data demonstrates that the regions of evaporation identified by neural networks directly provide moisture that precipitates in the Midwest.

Mesoscale-to-microscale coupling is an important tool to conduct turbulence-resolving multiscale simulations of realistic atmospheric flows, which are crucial for applications ranging from wind energy to wildfire spread studies. Different techniques are used to facilitate the development of realistic turbulence in the large-eddy simulation (LES) domain while minimizing computational cost. Here, we explore the impact of a simple and computationally efficient Stochastic Cell Perturbation method using momentum perturbation (SCPM-M) to accelerate turbulence generation in boundary-coupled LES simulations using the Weather Research and Forecasting (WRF) model. We simulate a convective boundary layer (CBL) to characterize the production and dissipation of turbulent kinetic energy (TKE) and the variation of TKE budget terms. Furthermore, we evaluate the impact of applying momentum perturbations of three magnitudes below, up to, and above the CBL on the TKE budget terms. Momentum perturbations greatly reduce the fetch associated with turbulence generation. When applied to half the vertical extent of the boundary layer, momentum 1 perturbations produce an adequate amount of turbulence. However, when applied above the CBL, additional structures are generated at the top of the CBL, near the inversion layer. The magnitudes of the TKE budgets produced by SCPM-M when applied at varying heights and with different perturbation amplitudes are always higher near the surface and inversion layer than those produced by No-SCPM, as are their contributions to the TKE. This study provides a better understanding of how SCPM-M reduces computational costs and how different budget terms contribute to TKE in a boundary-coupled LES simulation.

This study presents a novel analysis of a hailstone collected near Cordoba, Argentina, quantifying the composition, size distribution, and potential sources of non-soluble particles contained within. The hailstone contained diverse particles, with sizes ranging from 1.9 to 150.3 µm, primarily carbonaceous, including in the center, suggesting a possible biological and geological influence on hail formation. Silicate particles were distributed throughout the hailstone, likely from eroded soil and agricultural activities. Finally, salts were detected in the outer layers of the hailstone and may have originated from the nearby salt lake. This study highlights the regional influence of various land use types on hail formation and growth and points to the potential impacts of natural and anthropogenic factors on hailstone composition.

Research shows that complex terrain can affect the spatial distribution of solar radiation and atmospheric physical processes. Based on the high-resolution topographic data, there are already several parameterization schemes available to couple the terrain effects on solar radiation with atmospheric models. However, to reduce the amount of calculation, some methods that can lead to errors are used in the sub-grid parameterization schemes for clear-sky direct solar radiation (SPS-CSDSR). In addition, the common finite difference slope algorithms and the assumption of consistent sub-grid atmospheric transparency can also result in errors. This renders existing SPS-CSDSRs unsuitable for complex terrain in middle and high latitudes and in turbid weather. In this study, these three problems have been effectively solved. The most accurate geometric algorithms for direct solar radiation so far, a high-precision and fast terrain occlusion algorithm and the triangulated sub-grid algorithm, are proposed. On Taiwan Island, the accuracy of the two methods is verified in the virtual vacuum atmosphere. Based on the fact that atmospheric transparency actually increases with altitude, a correction term based on sub-grid anomaly altitude is proposed for converting the sub-grid terrain effect factors into the atmospheric model. Overall improvements constitute a high-precision SPS-CSDSR in complex terrain. Eleven reduced calculation methods and common finite difference slope algorithms can no longer be used. In further study, atmospheric models need improvement in coupling the terrain effects on solar radiation to accurately describe vertical distributions. In this case, the high-precision scheme proposed in this study can play a key role.

Machine learning (ML) is used to build a bulk microphysical parameterization including ice processes. Simulations of the Lagrangian super-particle model McSnow are used as training data. The machine learning performs a coarse-graining of the particle-resolved microphysics to multi-category two-moment bulk equations. Besides mass and number, prognostic particle properties (P3) like melt water, rime mass, and rime volume are predicted by the ML-based bulk model. The ML-based scheme is tested with simulations of increasing complexity. As a box model, the ML-based bulk scheme can reproduce the simulations of McSnow quite accurately. In 3d idealized squall line simulations, the ML-based P3-like scheme provides a more realistic extended stratiform region when compared to the standard two-moment bulk scheme in ICON. In a realistic case study, the ML-based scheme runs stably, but can not significantly improve the results. This shows that machine learning can be used to coarse-grain super-particle simulations to a bulk scheme of arbitrary complexity.

Stable water isotopes in inland Antarctic ice cores are powerful paleoclimate proxies; however, their relationship with dynamical atmospheric circulations remains controversial. Using a water isotope climate model (MIROC5-iso), we assessed the influence of the Last Glacial Maximum (LGM; ~21,000 years ago) sea surface temperatures (SST) and sea ice (SIC) on Antarctic precipitation isotopes (δ18Op) through atmospheric circulation. The results revealed that the synoptic circulation mostly maintained southward moisture transport, reaching inland Antarctica. The steepened meridional SST gradient in the mid-latitudes increased δ18Op in inland Antarctica by enhancing the baroclinic instability and synoptic moisture transport. In contrast, enhanced SIC reduced the atmospheric humidity around Antarctica and lowered δ18Op through extensive surface cooling and transport from the ocean. These findings elucidate the isotopic proxies and enable us to constrain the southern hemisphere atmospheric circulation, including the westerlies, using ice cores during past climates, including the LGM.

Convective Quasi-Equilibrium (CQE) is often adopted as a useful closure assumption to summarize the effects of unresolved convection on large-scale thermodynamics, while existing efforts to observationally validate CQE largely rely on specific spatial domains or sites rather than the source of CQE constraints—deep convection. This study employs a Lagrangian framework to investigate leading temperature perturbation patterns near deep convection, of which the centers are located by use of an ensemble of satellite measurements. Temperature perturbations near deep convection with high peak precipitation are rapidly adjusted towards the CQE structure within the two hours centered on peak precipitation. The top 1% precipitating deep convection constrains the neighboring free-tropospheric leading perturbations up to 8 degrees. Notable CQE validity beyond a 1-degree radius is observed when peak precipitation exceeds the 95th percentile. These findings suggest that only a small fraction of deep convection with extreme precipitation shapes tropical free-tropospheric temperature patterns dominantly.

Accurate fog prediction in densely urbanized cities poses a challenge due to the complex influence of urban morphology on meteorological conditions in the urban roughness sublayer. This study implemented a coupled WRF-Urban Asymmetric Convective Model (WRF-UACM) for Delhi, India, integrating explicit urban physics with Sentinel-updated USGS land-use and urban morphological parameters derived from the UT-GLOBUS dataset. When evaluated against the baseline Asymmetric Convective Model (WRF-BACM) using Winter Fog Experiment (WiFEX) data, WRF-UACM significantly improved urban meteorological variables like diurnal variation of 10-meter wind speed, 2-meter air temperature (T2), and 2-meter relative humidity (RH2) on a fog day. UACM also demonstrates improved accuracy in simulating temperature and a significant reduction in biases for RH2 and wind speed under clear sky conditions. UACM reproduced the nighttime urban heat island effect within the city, showing realistic diurnal heating and cooling patterns that are important for accurate fog onset and duration. UACM effectively predicts the onset, evolution, and dissipation of fog, aligning well with observed data and satellite imagery. Compared to WRF-BACM, WRF-UACM reduces the cold bias soon after the sunset, thus improving the fog onset error by ~4 hours. This study underscores the UACM’s potential in enhancing fog prediction, urging further exploration of various fog types and its application in operational settings, thus offering invaluable insights for preventive measures and mitigating disruptions in urban regions.

Synthetic downscaling of tropical cyclones (TCs) is critically important to estimate the long-term hazard of rare high-impact storm events. Existing downscaling approaches rely on statistical or statistical-deterministic models that are capable of generating large samples of synthetic storms with characteristics similar to observed storms. However, these models do not capture the complex two-way interactions between a storm and its environment. In addition, these approaches either necessitate a separate TC size model to simulate storm size or involve post-processing to introduce asymmetries in the simulated surface wind. In this study, we present an innovative data-driven approach for TC synthetic downscaling. Using a machine learning-based high-resolution global weather model (ML-GWM), our approach is able to simulate the full life cycle of a storm with asymmetric surface wind that accounts for the two-way interactions between the storm and its environment. This approach consists of multiple components: a data-driven model for generating synthetic TC seeds, a blending method that seamlessly integrate storm seeds into the surrounding while maintain the seed structure, and a recurrent neural network-based model for correcting the biases in maximum wind speed. Compared to observations and synthetic storms simulated using existing statistical-deterministic and statistical downscaling approaches, our method shows the ability to effectively capture many aspects of TC statistics, including track density, landfall frequency, landfall intensity, and outermost wind extent. Taking advantage of the computational efficiency of ML-GWM, our approach shows substantial potential for TC regional hazard and risk assessment.

A document by Cassandra D W Rogers. Click on the document to view its contents.

Accurate wildfire forecasting can inform regional management and mitigation strategies in advance of fire occurrence. Existing systems typically use fire danger indices to predict landscape flammability, based on meteorological forecasts alone, often using little or no direct information on land surface or vegetation state. Here, we use a vegetation characteristic model, weather forecasts and a data-driven machine learning approach to construct a global daily ~9 km resolution Probability of Fire (PoF) model operating at multiple lead times. The PoF model outperforms existing indices, providing accurate forecasts of fire activity up to 10 days in advance, and in some cases up to 30 days. The model can also be used to investigate historical shifts in regional fire patterns. Furthermore, the underlying data driven approach allows PoF to be used for fire attribution, isolating key variables for specific fire events or for looking at the relationships between variables and fire occurrence.

The Regional Climate Modeling system (RegCM) has undergone a significant evolution over the years, leading for example to the widely used versions RegCM4 and RegCM4-NH. In response to the demand for higher resolution, a new version of the system has been developed, RegCM5, incorporating the non-hydrostatic dynamical core of the MOLOCH weather prediction model. In this paper we assess the RegCM5’s performance for 5 CORDEX-CORE domains, including a pan-European domain at convection-permitting resolution. We find temperature biases generally in the range of -2 to 2 degrees Celsius, higher in the northernmost regions of North America and Asia during winter, linked to cloud water overestimation. Central Asia and the Tibetan Plateau show cold biases, possibly due to sparse station coverage. The model exhibits a prevailing cold bias in maximum temperature and warm bias in minimum temperature, associated with a systematic overestimation of lower-level cloud fraction, especially in winter. Taylor diagrams indicate a high spatial temperature pattern correlation with ERA5 and CRU data, except in South America and the Caribbean region. The precipitation evaluation shows an overestimation in South America, East Asia, and Africa. RegCM5 improves the daily precipitation distribution compared to RegCM4, particularly at high intensities. The analysis of wind fields confirms the model’s ability to simulate monsoon circulations. The assessment of tropical cyclone tracks highlights a strong sensitivity to the tracking algorithms, thus necessitating a careful model interpretation. Over the European region, the convection permitting simulations especially improve the diurnal cycle of precipitation and the hourly precipitation intensities.

Understanding the vertical structure of atmospheric aerosol is important for solar radiometric study across all spectra. This work pertains to three relevant Southern Hemispheric regions of interest: Southeast Atlantic (SEA), Amazonia (AMZ), and Southeast Pacific (SEP), where seasonal biomass burning events produce smoke plumes of climatic interest. We make use of our previously validated aerosol typology based in AERONET retrieved optical properties, to identify each individual measurement classified as biomass burning within the geographic region of interest. The data is trimmed to select only those classifications measured within the recognized fire-dominant season of each geographic region. We employ The European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis of Copernicus Atmosphere Monitoring Service (CAMS) Carbon Monoxide (CO) data to construct canonical vertical CO profiles (characteristic "shape" curves) from records in the historic burn season window, and within the geographic rectangular boundaries of interest. These canonical CO curves then proxy for AOD curves, constrained to be distributed vertically such that their integrated sum matches to specific Bulk Columnar AOD (BCA) values as determined in the corresponding AERONET record. The layer CO values are normalized to fractional coefficients of the columnar CO total for each canonical profile. These coefficients then are distributed as AOD layer coefficients of the bulk columnar AOD; thus, preserving the canonical profile shapes. This results in vertically resolved AOD profiles for specific geo-region which can be fed into a Radiative Transfer model to result in Total Layered Heating Rates (TLHR) and Aerosol Layered Heating Rates (ALHR) expressed in K/day. We found for example: smoke aerosol plumes in the SEA during the August to October season tend to bi-modally develop between a characteristically higher plume or a lower plume, separated by approximately 1 km vertically. Layer Heating rates develop accordingly. We present methodology, developments, and some cases of these studies specifically for the Southeast Atlantic (SEA) region dominated by seasonal wildfire in Sub-Sahel Africa.

Satellite-observed microwave radiances provide information on both surface and atmosphere. For operational weather forecasting, information on atmospheric temperature, humidity, cloud and precipitation is directly inferred using all-sky radiance data assimilation. In contrast, information on the surface state, such as sea surface temperature (SST) and sea ice fraction, is typically provided through third-party retrieval products. Scientifically, this is a sub-optimal use of the observations, and practically it has disadvantages such as time delays of more than 48 hours. A better solution is to jointly estimate the surface and atmospheric state from the radiance observations. This has not been possible until now due to incomplete knowledge of the surface state and the radiative transfer that links this to the observed radiances. A new approach based on an empirical state and empirical sea ice surface emissivity model is used here to add sea ice state estimation, including sea ice concentration (SIC), to the European Centre for Medium-range Weather Forecasts atmospheric data assimilation system. The sea ice state is estimated using augmented control variables at the observation locations. The resulting SIC estimates are of good quality and they highlight apparent defects in the existing OCEAN5 sea ice analysis. The SIC estimates can also be used to track giant icebergs, which may provide a novel maritime application for passive microwave radiances. Further, the SIC estimates should be suitable for onward use in coupled ocean-atmosphere data assimilation. There is also increased coverage of microwave observations in the proximity of sea ice, leading to improved atmospheric forecasts out to day 4 in the Southern Ocean.

It has been found that wintertime mixed-phase cloud properties can present significant differences based on the degree of interaction with air masses coming from locations with reduced sea ice concentration or high presence of sea ice leads. When these air masses are represented by the water vapor transport (WVT) which can interact with the clouds, the properties of the clouds show contrasting differences with respect to cases where the WVT is not interacting with the cloud, i.e. it is not coupled to the cloud. These findings have been reported first for the analysis of the MOSAiC expedition dataset from 2019 to 2020 in the central Arctic \cite{Shupe_2022,Saavedra_Garfias_2023}. In the present contribution, we expand that analysis to long-term measurements (2012-2022) at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) at the North Slope Alaska (NSA) site in Utqiaǵvik, Alaska. Based on those 10 years of characterized cloud and  sea ice properties, statistically more robust analysis is performed to support or contradict the MOSAiC results. Furthermore, the statistically richer data set from NSA allows to narrow down cases where the properties or coupled clouds to WVT are substantially dissimilar to decoupled cases. Among those are the increase of liquid water path correlated to a decrease of sea ice concentration and ice water paths which are not exhibiting an influence by sea ice concentration. The thermodynamic phase of the clouds also exposes differences based on the state of coupling among the cloud--WVT--sea ice system. These results are put into consideration for the modeling community since sea ice leads are not explicitly resolved in such models, thus the sea ice leads or polynyas effects to processes responsible for mixed-phase cloud formation/dissipation and thermodynamic phase balance are of considerable interest for the parametrization of energy exchange between the surface and the atmosphere in the Arctic.AGU 2023 Session Selection: A093. Microphysical and Macrophysical Properties and Processes of Ice and Mixed-Phase Clouds: Linking in Situ and Remote Sensing Observations and Multiscale Models.

Based on wintertime observations during the MOSAiC expedition in 2019-2020 \cite{Shupe_2022}, it has been found that Arctic cloud properties show significant differences when clouds are coupled to the fluxes of water vapor transport (WVT) coming from upwind regions of sea ice leads \cite{Saavedra_Garfias_2023,saavedragarfias2023}. Mixed-phase clouds (MPC) were characterized by the Cloudnet algorithm using observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) mobile facility and the Leibniz Institute for Tropospheric Research (TROPOS) OCEANet facility, both on board the RV Polarstern . A coupling mechanism to entangle the upwind sea ice leads via the water vapor transport entraintment to the cloud layer has been proposed to successfully identify differences of MPC properties under and without the influence of WVT. For MPC below 3 km liquid water path was found to be increasingly influenced by sea ice lead fraction whereas ice water path was not significantly different in the presence of sea ice leads. However, the ice water fraction, defined as the fraction of ice water path to the total water path, was exhibiting distinguishable asymmetries for cases of MPC coupled to WVT versus decoupled cases. Mainly, the ice water fractions of MPC coupled to WVT were monotonically increasing with decreasing cloud top temperature, while the decoupled cases show increases and decreases in ice water fraction at some specific temperature ranges. The dissimilar behavior of ice water fraction suggests that WVT could importantly influence the processes responsible for heterogeneous ice formation and solid precipitation, therefore coupled MPC and the ice water fraction was also analyzed as a function of snowfall rates at ground. These characteristics are presented based on case studies where WVT back trajectories are available to have a deeper understanding of the interaction processes with sea ice leads that drives the cloud coupled/decoupled differences. Moreover the statistics of our findings based on the whole MOSAiC wintertime period will be put into  consideration.\cite{von_Albedyll_2023}\cite{Shupe_2022}\cite{saavedragarfias2022}AGU 2023 Session Selection: C014. Coupled-system Processes of the Central Arctic Atmosphere-Sea Ice-Ocean System: Harnessing Field Observations and Advancing Models.

Data assimilation (DA) plays a pivotal role in diverse applications, ranging from climate predictions and weather forecasts to trajectory planning for autonomous vehicles. A prime example is the widely used ensemble Kalman filter (EnKF), which relies on linear updates to minimize variance among the ensemble of forecast states. Recent advancements have seen the emergence of deep learning approaches in this domain, primarily within a supervised learning framework. However, the adaptability of such models to untrained scenarios remains a challenge. In this study, we introduce a novel DA strategy that utilizes reinforcement learning (RL) to apply state corrections using full or partial observations of the state variables. Our investigation focuses on demonstrating this approach to the chaotic Lorenz ’63 system, where the agent’s objective is to minimize the root-mean-squared error between the observations and corresponding forecast states. Consequently, the agent develops a correction strategy, enhancing model forecasts based on available system state observations. Our strategy employs a stochastic action policy, enabling a Monte Carlo-based DA framework that relies on randomly sampling the policy to generate an ensemble of assimilated realizations. Results demonstrate that the developed RL algorithm performs favorably when compared to the EnKF. Additionally, we illustrate the agent’s capability to assimilate non-Gaussian data, addressing a significant limitation of the EnKF.

  This study presents a novel methodology for extracting latent variables from high-dimensional sparse data, particularly emphasizing spatial distributions such as precipitation distribution. This approach utilizes multidimensional scaling with a distance matrix derived from a new similarity metric, the Unbalanced Optimal Transport Score (UOTS). UOTS effectively captures discrepancies in spatial distributions while preserving physical units. This is similar to mean absolute error, however it considers location errors, providing a more robust measure crucial for understanding differences between observations, forecasts, and ensembles. Probability distribution estimation of these latent variables enhances the analytical utility, quantifying ensemble characteristics. The adaptability of the method to spatiotemporal data and its ability to handle errors suggest its potential as a promising tool for diverse research applications.

Sea ice mediates the exchange of momentum, heat, and moisture between the atmosphere and the ocean. Cyclones produce strong gradients in the wind field, imparting stress into the ice and causing the ice to deform. In turn, increased sea ice drift speeds and rapid changes in drift direction during the passage of a cyclone may result in enhanced momentum flux into the upper ocean.  During the year-long MOSAiC expedition, an array of drifting buoys was deployed surrounding the R/V Polarstern, enabling the characterization of sea ice motion and deformation across a range of spatial scales. In addition, autonomous sensors at a subset of sites measured the atmospheric and oceanic structure and vertical fluxes. Here, we examine a strong cyclone that impacted the MOSAiC site during January and February, 2020, while the MOSAiC site was near the North Pole. The cyclone track intersected the MOSAiC buoy array, providing an opportunity to examine spatial variability in sea ice motion during the storm in unprecedented detail. A key feature of the storm was the formation of a low-level jet (LLJ), first in the warm sector of the storm, then growing to eventually encircle the central low. The highest rates of ice motion and deformation coincide with effects of LLJ transitions. Analysis of deformation using the Green’s theorem approach indicates divergence and cyclonic vorticity as the LLJ enters the region, and convergence and anticyclonic vorticity as the LLJ leaves; maximum shear strain rate is enhanced throughout the LLJ’s passage. While the vorticity signal is particularly clear, floe structure and internal ice stresses result in high spatial variability in the magnitude of divergence and shear strain rates, especially at smaller scales. Increased current speed and shear in the upper layer of the ocean during the passage of the LLJ resulted from ice drag forcing the ocean mixed layer current. The results suggest an important role for cyclone-forced ocean mixing in pack ice during the Arctic winter.

The International Arctic Buoy Programme (IABP) maintains fundamental in situ components of the Arctic Observing Network. Automated Drifting Stations (ADS) consisting of sea ice, meteorological, and oceanographic buoys are collectively deployed at many sites with webcams to help understand the intricate and complex interactions between sea ice, the atmosphere, and the ocean.While passive microwave satellites provide substantial information about the Arctic, remote sensing still has resolution limitations despite broad spatial coverage. Climate modeling and atmospheric reanalysis help surmount these limitations, but traditional observational methods of in situ data collection still have many advantages. Buoys and webcams can monitor Arctic sea ice changes above and below, allowing for more direct observations of localized ice floes when deployed in close proximity.Using data from webcams in the Arctic, we have stitched together images into time-lapse animations that provide insight into physical sea ice processes. Coupled with buoy data, we compare physical measurements (like temperature) with webcam observations (like cloud cover) to explain trends and anomalies. For example, isothermal periods in the buoy temperature data match time-lapse images with cloudy skies, while the opposite is also true: high variability correlates with sunny skies. Hence, these instruments allow for the verification of Arctic observations both visually and statistically.Although significant challenges like camera lifetimes and temporal resolution still persist, we argue that buoys and time-lapse videos can help validate satellite data and offer cheaper solutions to collecting vital information that increases our understanding of geophysical processes. We’ve compiled these datasets and present case studies showing the use of time-lapse videos to help monitor and understand the interplay and processes of the Arctic environment.

Accurate fog prediction in densely urbanized cities poses a challenge due to the complex influence of urban morphology on meteorological conditions in the urban roughness sublayer. This study implemented a coupled WRF-Urban Asymmetric Convective Model (WRF-UACM) for Delhi, India, integrating explicit urban physics with Sentinel-updated USGS land-use and urban morphological parameters derived from the UT-GLOBUS dataset. When evaluated against the baseline Asymmetric Convective Model (WRF-BACM) using Winter Fog Experiment (WiFEX) data, WRF-UACM significantly improved urban meteorological variables like diurnal variation of 10-meter wind speed, 2-meter air temperature (T2), and 2-meter relative humidity (RH2) on a fog day. UACM also demonstrates improved accuracy in simulating temperature and a significant reduction in biases for RH2 and wind speed under clear sky conditions. UACM reproduced the nighttime urban heat island effect within the city, showing realistic diurnal heating and cooling patterns that are important for accurate fog onset and duration. UACM effectively predicts the onset, evolution, and dissipation of fog, aligning well with observed data and satellite imagery. Compared to WRF-BACM, WRF-UACM reduces the cold bias soon after the sunset, thus improving the fog onset error by ~4 hours. This study underscores the UACM’s potential in enhancing fog prediction, urging further exploration of various fog types and its application in operational settings, thus offering invaluable insights for preventive measures and mitigating disruptions in urban regions.