High frequency (30 Hz) two-dimensional particle image velocimetry (PIV) data recorded during a field experiment exploring fire spread from point ignition in hand-spread pine needles under calm ambient wind conditions are analysed in this study. As the flame spreads approximately radially away from the ignition point in the absence of ambient wind forcing, it entrains cooler ambient air into the warmer fire core, thereby experiencing a dynamic pressure resistance. The fire-front, comprising a flame that is tilted inward, is surrounded by a region of downdraft. Coherent structures describe the initial shape of the fire-front and its response to local wind shifts while also revealing possible fire-spread mechanisms. Vortex tubes originating outside the fire spiral inward and get stretched thinner at the fire-front leading to higher vorticity there. These tubes comprise circulation structures that induce a radially outward velocity close to the fuel bed, which pushes hot gases outward, thereby causing the fire to spread. Moreover, these circulation structures confirm the presence of counter-rotating vortex pairs that are known to be a key mechanism for fire spread. The axis of the vortex tubes changes its orientation alternately towards and away from the surface of the fuel bed, causing the vortex tubes to be kinked. The strong updraft observed at the location of the fire-front could potentially advect and tilt the kinked vortex tube vertically upward leading to fire-whirl formation. As the fire evolves, its perimeter disintegrates in response to flow instabilities to form smaller fire “pockets”. These pockets are confined to certain points in the flow field that remain relatively fixed for a while and resemble the behavior of a chaotic system in the vicinity of an attractor. Increased magnitudes of the turbulent fluxes of horizontal momentum, computed at certain such fixed points along the fire-front, are symptomatic of irregular fire bursts and help contextualize the fire spread. Most importantly, the time-varying transport terms of the turbulent kinetic energy (TKE) budget equation computed at adjacent fixed points indicate that local fires along the fire-front primarily interact via the horizontal turbulent transport term.

A new model for the shape of the magnetopause is presented using a closed-form analytic function known as a tractrix. This shape is derived from several physics-based underpinnings, eliminating the need for fitting ad-hoc functional forms that, while convenient, are not physically motivated. One feature of the magnetopause predicted by this model is that the magnetotail flares outward until it reaches a constant width, a feature that has significant observational evidence but is seldom represented in functional forms of the magnetopause shape. To optimize the parameters of this model, a dataset of over 13,000 magnetopause crossings from THEMIS/ARTEMIS, Cluster, Geotail, Interball, and several other spacecraft is utilized. Using a Bayesian approach combined with a Markov Chain Monte Carlo (MCMC) method to estimate the posterior probability distribution in parameter space, the maximum likelihood parameters for the model that optimize its performance on this dataset are determined. The modelâ\euro™s performance is compared to that of other popular models of the magnetopause with a focus on their relative performance, and is shown to outperform models that assume the tail flares outward to infinity at far distances. The optimized model more accurately predicts magnetopause position along the tail than other popular static analytic magnetopause models, while still being easy to implement for a variety of applications.

Key Points: • The concept of precipitation efficiency is broad, and can be related to many proposed cloud feedback mechanisms • Microphysical precipitation efficiency of tropical clouds likely increases with warming, but bulk precipitation efficiency and precipitation efficiency of midlatitude clouds could decrease • The impacts of precipitation efficiency on clouds and feedbacks deserve further study and require better evaluation against observations A number of studies have demonstrated strong relationships between precipitation efficiency, particularly its changes under warming, and climate sensitivity. In this chapter, we review the evidence for these relationships, including how they depend on the definition of precipitation efficiency. We identify six mechanisms by which changes in precipitation efficiency may affect Earth’s net climate feedback, and also discuss evidence for an inverse relationship between present-day precipitation efficiency and climate sensitivity based on several perturbed physics ensembles. This inverse relationship hints at the possibility of developing emergent constraints on climate sensitivity using precipitation efficiency, though it is put in doubt by studies varying convective entrainment rates, which have found the opposite relationship. More work is required to refine our understanding of the mechanisms linking changes in precipitation efficiency to climate sensitivity and more observational data is needed to validate model results. In particular, the precipitation efficiency of mid-latitude clouds has been relatively understudied, but deserves more attention in light of the importance of extratropical cloud feedbacks for the high climate sensitivities of CMIP6 models.

The Fresnel integral is used in diffraction of wave phenomena. It is demonstrated that the ordinary Fresnel integral has additional yet unkown $\pm 1$ multivaluedness. This result gives new insight in diffraction.

The urban expansion-induced heat can deteriorate heat stress for urban dwellers, especially during heat waves. With a focus on the intra-urban variability of UHIs and thermal comfort, the urban parameterization within the CLM5 was modified to incorporate the LCZs framework to simulate urban climate during a HW event in the summer of 2013 in East China. The simulations were validated by observation data from a flux tower measurement site, conventional stations and automatic meteorological stations, which exhibits a reasonable agreement. The aim of this work was to investigate: 1) the variability of temperature and heat stress in relation to each urban LCZ, 2) the influence of intra-urban inhomogeneity on attributing factors of SUHII, 3) the response of CUHII and SUHII, urban thermal comfort and controlling factors of SUHII to HW. The results show that daytime and nighttime CUHII were highest in LCZ3 and LCZ1 areas, respectively. SUHII separately peaked in LCZ8 and LCZ1 areas during daytime and nighttime. Contrasts of CUHII and SUHII between urban classes could exceed 1.7℃ and 5.4℃ which varied with background climate and HW episode. Urban dwellers were exposed to the most serious heat stress in LCZ3 and LCZ1 areas over the north subtropical climate zone. The intra-urban heterogeneity resulted in the changes in dominant factors controlling SUHII that were modulated by local climate and HW intensity. Moreover, UHIs and thermal comfort were obviously affected by the occurrence of HW events such as the changes in CUHII for LCZ7 (1.0℃) and SUHII for LCZ8 (3.8℃).

The amount and quality of land, water and atmospheric information is critical to plan, monitor, predict and mitigate the impacts of climate change, urbanization and population growth. To get reasonably accurate regional information, Ethiopia launched its first Earth Observing satellite on the 20thof December 2019 in collaboration with the government of China. The 65-kg Ethiopian Remote Sensing Satellite (ETRSS-1) carries one earth observing multispectral camera to measure many aspects of land, water and biosphere. It was placed on sun-synchronous orbit at an altitude of 628.61km and collects images at a Ground Sampling Distance (GSD) of 13.75-m within a revisit period of 4 days. Currently, all the instruments are operating as intended and the early-stage images captured by ETRSS-1 are within sensible and acceptable range. This indicates that it can serve as a supplementary and alternative data source to operational and research services.

Solar Spectrum Prediction for Applications and Modeling (SPAM) – a new empirical model of solar X-Ray, EUV and FUV radiation flux at the top of the Earth’s atmosphere. The model is based on 14 years of daily averaged TIMED spacecraft measurements from 2002 to 2016 when the SEE sensors were regularly calibrated. We used a second-order parametrization of the irradiance spectrum by a single parameter – the F10.7 index, which is a reliable and consistently observed measure of solar activity. The SPAM model consists of two submodels for general and specific use. The first is the Solar-SPAM model of the photon energy flux in the first 190 1nm spectral bands, which can be used for a wide range of applications in different fields of research. The second model, Aero-SPAM, is designed specifically for aeronomic research and provides a photon flux for 37 specific wavelength intervals (20 wave bands and 16 separate spectral lines within the range of 5 –105 nm and an additional 121.5 nm Ly-alpha line), that play a major role in the photoionization of atmospheric gas particles. We provide the full set of parameterization coefficients that allows for immediate implementation of the model for research and applications. In addition, we used the Aero-SPAM model to build a ready-to-use numerical application for calculating the photoionization rates of the main atmospheric components N2, O2, O, N and NO with known absorption and ionization cross sections.

We utilise SuperCam’s Mars microphone to provide information on wind speed and turbulence at high frequencies on Mars. This is achieved through a correlation analysis between the microphone and meteorological data which shows that the microphone signal power has a consistent relationship with wind speed and air temperature. A calibration function is constructed using Gaussian process regression (a machine learning technique) to use the microphone signal and air temperature to produce an estimate of the wind speed. This wind speed estimate is at a high rate for in situ measurements on Mars, with a sample every 0.01 s. As a result, we determine the fast fluctuations of the wind at Jezero crater which highlights the nature of wind gusts over the martian day. We evaluate the normalised wind standard deviation (gustiness) on the estimated wind speed to analyse the turbulent behaviour. Correlations are shown between the evaluated gustiness statistic and pressure drop rates, temperature, energy fluxes and optical opacity to characterise the behaviour of high frequency turbulent intensity at Jezero crater. This has implications for future atmospheric models on Mars, taking into account turbulence at the finest scales.

Wave-induced adiabatic mixing in the winter midlatitudes is one of the key processes impacting stratospheric transport. Understanding its strength and structure is vital to understanding the distribution of trace gases and their modulation under a changing climate. age-of-air is often used to understand stratospheric transport, and this study proposes refinements to the vertical age gradient theory of Linz et al. (2021). The theory assumes exchange of air between a well-mixed tropics and a well-mixed extratropics, separated by a transport barrier, quantifying the adiabatic mixing flux across the interface using age-based measures. These assumptions are re-evaluated and a refined framework that includes the effects of meridional tracer gradients is established to quantify the mixing flux. This is achieved, in part, by computing a circulation streamfunction in age-potential temperature coordinates to generate a complete distribution of parcel ages being mixed in the midlatitudes. The streamfunction quantifies the “true” age of parcels mixed between the tropics and the extratropics. Applying the revised theory to an idealized and a comprehensive climate model reveals that ignoring the meridional gradients in age leads to an underestimation of the wave-driven mixing flux. Stronger, and qualitatively similar fluxes are obtained in both models, especially in the lower-to-middle stratosphere. While the meridional span of adiabatic mixing in the two models exhibits some differences, they show that the deep tropical pipe, i.e. latitudes equatorward of 15$^{\circ}$ barely mix with older midlatitude air. The novel age-potential temperature circulation can be used to quantify additional aspects of stratospheric transport.

Volcanic activity occurring in tropical moist atmospheres can promote deep convection and trigger volcanic thunderstorms. Intense heating at ground surface and entrainment of moist air generates positive buoyancy, rapidly transporting volcanic gases and ash particles up to the tropopause and beyond. Volcanically-induced deep convection, however, is rarely observed to last continuously for more than a day and so insights into the dynamics, microphysics and electrification processes are limited. Here we present a multidisciplinary study on an extreme case, where this phenomenon lasted for six days. We show that this unprecedented event was triggered and sustained by phreatomagmatic activity at Anak Krakatau volcano, Indonesia from 22-28 December 2018. During this period, a deep convective plume formed over the volcano and acted as a ‘volcanic freezer’ producing ~3 × 10⁹ kg of ice on average with maxima reaching ~10¹⁰ kg. Our satellite analyses reveal that the convective anvil cloud, reaching 16-18 km above sea level, was ice-rich and ash-poor. Cloud-top temperatures hovered around -80 °C and ice particles produced in the anvil were notably small (effective radius from 20-30 μm). Our modelling suggests that ice particles began to form above 5 km and experienced vigorous updrafts (>30 m/s). These findings explain the impressive number of lightning strikes (~100,000) recorded near the volcano during this time. Our results, together with the unique dataset we have compiled, provide new insights into volcanic and meteorological thunderstorms alike.

Oxygen-isotope proxies for air temperature in Greenland ice cores, with time resolutions of years to decades, document 25 periods from 120,000 to 14,000 BP when air temperatures warmed 5 to 16 oC within decades and cooled slowly, incrementally, over millennia back down into ice-age conditions. These clearly-observed Dansgaard--Oeschger events averaged 4000 years in length but were highly erratic in time of onset, intensity, and duration. They were typically associated with volcanic sulfate deposits and floods of fresh water into the North Atlantic. They appear to be caused primarily by sub-glacial basaltic eruptions in Iceland, the most intense of which lasted from 12,000 to 9500 BP, long enough to warm the oceans out of the last ice age. Similar sequences of current and warmer temperatures are observed in fine-layered sediments in the Eocene Green River Formation where erratic sequences averaged 5000 years. The most rapid and intense changes in sedimentation and fossils in the geologic time scale are contemporaneous with massive basaltic lava flows covering millions of square kilometers of continental rifts at the end of the Paleozoic, Carnian, Triassic, Pliensbachian, Albian, Mesozoic, Paleocene, Eocene, etc. Large, explosive, subduction-related volcanic eruptions form aerosols in the lower stratosphere cooling the globe 0.5 oC for a few years. Modelling shows that such short-term cooling of the whole ocean surface affects ocean temperatures for as long as a century. In this way, several major explosive eruptions per century over millennia cause slow, incremental cooling down into ice-age conditions as clearly resolved in deep ocean cores. It is very hard to explain these well-observed footprints of climate change using greenhouse gases. While Pinatubo erupted as much as 234 megatons of CO2 in 1991, concentrations at Mauna Loa slowed their rise due to cooling of the ocean surface. A set of 16 short videos, numerous papers, a book, and dozens of web pages all referenced at WhyClimateChanges.com document evidence for major effusive basaltic lava flows being the primary cause of fast global warming and sequences of major explosive volcanic eruptions being the major cause of slow incremental global cooling. Furthermore, they explain why greenhouse-warming theory is not only mistaken, it is Physically-Impossible.com.

Stratospheric volcanic aerosol can have major impacts on global climate. Despite a consensus among studies on an El Niño–like response in the first or second post-eruption year, the mechanisms that trigger a change in the state of El Niño-Southern Oscillation (ENSO) following volcanic eruptions are still debated. Here, we shed light on the processes that govern the ENSO response to tropical volcanic eruptions through a series of sensitivity experiments with an Earth System Model where a uniform stratospheric volcanic aerosol loading is imposed over different parts of the tropics. Three tropical mechanisms are tested: the “ocean dynamical thermostat” (ODT); the cooling of the Maritime Continent; and the cooling of tropical northern Africa (NAFR). We find that the NAFR mechanism plays the largest role, while the ODT mechanism is absent in our simulations as La Niña-like rather than El-Niño-like conditions develop following a uniform radiative forcing over the equatorial Pacific.

The preliminary information about the muon tracking by means of the GEANT4 simulations is summarized in accordance with the physics reference manual of GEANT4 10.7.

While soybeans are among the most consumed crops in the world, the majority of its production lies in hotspot regions in the US, Brazil and Argentina. The concentration of soybean growing regions in the Americas render the supply chain vulnerable to regional disruptions. In the year of 2012 anomalous hot and dry conditions occurring simultaneously in these regions led to low soybean yields, which drove global soybean prices to all-time records. Climate change has already negatively impacted agricultural systems, and this trend is expected to continue in the future. In this study we explore climate change impacts on simultaneous extreme crop failures as the one from 2012. We develop a hybrid model, coupling a process-based crop model with a machine learning model, to improve the simulation of soybean production. We assess the frequency and magnitude of events with similar or higher impacts than 2012 under different future scenarios, evaluating anomalies both with respect to present day and future conditions to disentangle the impacts of (changing) climate variability from the long-term mean trends. We find the long-term trends of mean climate increase the occurrence and magnitude of 2012 analogue crop yield losses. Conversely, anomalies like the 2012 event due to changes in climate variability show an increase in frequency in each country individually, but not simultaneously across the Americas. We deduce that adaptation of the crop production practice to the long-term mean trends of climate change may considerably reduce the future risk of simultaneous soybean losses across the Americas.

Clustering of tropical thunderstorms constitutes an important climate feedback because it influences the radiative balance. Convective self-aggregation (CSA) is a profound modeling paradigm for explaining the clustering of tropical oceanic thunderstorms. However, CSA is hampered in the realistic limit of fine model resolution when cold pools—dense air masses beneath thunderstorm clouds—are well-resolved. Studies on CSA usually assume the surface temperature to be constant, despite realistic surface temperatures varying significantly between night and day. Here we mimic the diurnal cycle in cloud-resolving numerical experiments by prescribing a surface temperature oscillation. Our simulations show that the diurnal cycle enables CSA at fine resolutions, and that the process is even accelerated by finer resolutions. We attribute these findings to vigorous combined cold pools emerging in symbiosis with mesoscale convective systems. Such cold pools suppress buoyancy in extended regions (~100 km) and enable the formation of persistent dry patches. Our findings help clarify how the tropical cloud field forms sustained clusters under realistic conditions and may have implications for the origin of extreme thunderstorm rainfall and tropical cyclones.

We present a statistical investigation of the seasonal effect on hemispheric asymmetry in the auroral currents during low (Kp $<$ 2) and high (Kp $\geq$ 2) geomagnetic activity. Five years of magnetic data from the Swarm satellites has been analysed by applying the spherical elementary current system (SECS) method. Bootstrap resampling has been used to remove the seasonal differences between the hemispheres in the dataset. In general, the currents are larger in the Northern Hemisphere (NH) than in the Southern Hemisphere (SH). Asymmetry is larger during low than high Kp, and during winter and autumn than summer and spring. The NH/SH ratio for FACs in winter, autumn, spring and summer are 1.17 $\pm$ 0.05, 1.14 $\pm$ 0.05, 1.07 $\pm$ 0.04 and 1.02 $\pm$ 0.04, respectively. The largest asymmetry is observed during low Kp winter, when the excess in the NH currents is 21$\pm$5\% in FAC, 14 $\pm$ 3\% in curl-free (CF), and 10$\pm$3\% in divergence-free (DF) current. We also find that evening sector (13-24 MLT) contributes more to the high NH/SH ratio than the morning (01-12 MLT) sector. The physical mechanisms producing the hemispheric asymmetry are not presently understood. We calculated the background ionospheric conductances during low Kp conditions from the IRI, NRLMSISE and CHAOS models. The results indicate that only a small part of the hemispheric asymmetry can be explained by variations in the solar induced conductances.

We use airborne observations to extend a previous analysis by Lang and Rutledge (2008) of remotely sensed radar and lightning mapping array observations of the 11 June 2000 asymmetric mesoscale convective system (MCS) that moved through the primary observation region of the Severe Thunderstorm Electrification/Precipitation Study in northeastern Colorado and northwestern Kansas. We analyze in detail aircraft observations, radar, and remotely-mapped lightning discharges from a portion of the MCS that was starting to produce a bow echo during the time of the aircraft mission. The observations are interpreted to indicate the presence of a rearward and downward-sloping positive charge layer detraining from a mature cell in the leading convective region. In the convective cell the positive charge region was at an altitude of 10 km MSL. It then descended and crossed the 6 km MSL altitude plane 40 km to the rear of the leading convective region. A pattern of rearward and downward propagating lightning discharges from the upper convective region to trailing stratiform region was associated with this layer. The pattern persisted over a period of at least 8 minutes within which time 3 major lightning discharges initiated in the convective region and propagated rearward into the trailing stratiform region through the positive charge layer. Lightning initiation was not observed in the trailing stratiform region during the hour the aircraft was sampling it. The lack of lightning initiation in the trailing stratiform region is attributed to relatively weak electric fields there.

A prediction algorithm of tropical cyclone (TC) intensity change based on deep learning is proposed by exploring the distribution characteristics of atmospheric and oceanic elements. we adopted three dimensional convolutional neural network (3D-CNN), which is part of a most advanced approach, to learn the implicit correlation between the spatial distribution characteristics of three dimensional environmental variables and TC intensity change. Image processing technology is also used to enhance the data of a small number of TC samples to generate the train set. On the basis of TC instantaneous three dimensional state and the influence of sea surface temperature, we extract the spatial hybrid features from TC image patterns to predict 24 h intensity change. Experimental results show that the Mean Absolute Error (MAE) of TC intensity change prediction and the accuracy of strengthening and weakening classification are both have a significant improvement

The Dark-target (DT) aerosol algorithm retrieves spectral Aerosol Optical Depth (AOD) and other aerosol properties from Moderate-resolution Imaging Spectrometer (MODIS) reflectance observations. Over the ocean, the DT algorithm is known to contain scattering-angle-dependent biases in its retrievals of AOD, Angstrom Exponent (AE) and Fine Mode Fraction (FMF) for dust aerosols. Following a two-step strategy to improve the DT retrieval of dust over ocean, for which the first step is to identify dusty pixels (reported in ‘Part I’), in this ‘Part II’, we report on construction of a new dust model lookup table (LUT) and the strategy for applying it within the existing DT algorithm. In particular, we evaluate different characterizations of dust optical properties from a variety of frameworks and databases, and compare them with the current DT retrieval assumptions. Substituting the standard operational LUT with a spheroid dust model with identified dusty pixels shows significant improvement when compared with collocated AERONET-identified dusty pixels. The application of the new dust model to dusty pixels reduces their AOD bias from 0.06 to 0.02 while improving the fraction of retrievals within expected error (EE) from 64% to 82%. At the same time, the overall bias in AE is reduced from 0.13 to 0.06, and the scattering-angle-dependent AE bias is largely eliminated. In testing with wo full months of data (April and July), the new retrieval reduces the monthly mean AOD by up to 0.1 and 0.2 in the north Atlantic and Arabian seas, respectively. The average AE and FMF are also reduced.

To prepare for implementation of a new aerosol retrieval specifically designed for dust aerosol over ocean in the operational Dark Target (DT) algorithms for the Moderate-resolution Imaging Spectrometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensors, we focus on the challenge of detecting dust. We first survey the literature on existing dust detection algorithms and then develop an innovative algorithm that combines near-UV (deep blue), visible, and thermal infrared (TIR) wavelength spectral tests. The new detection algorithm is applied to Terra and Aqua MODIS granules and compared with other dust detection possibilities from existing MODIS products. Quantitative evaluation of the new dust detection algorithm is conducted using both a collocated AERONET - MODIS dataset and collocated CALIPSO – MODIS dataset. From comparison with both AERONET and CALIOP measurements, we estimate the new dust detection algorithm detects about 30% of weakly dusty pixels and more than 80% of heavily dusty pixels, with false detections in the range of 1-2%. The very low false detection rate is particularly noteworthy in comparison with existing literature. Compared with the dust flag currently available as part of the MODIS cloud mask product (MOD35/MYD35), and dust classification based on commonly used thresholds with AOD and AE, the new dust detection algorithm finds more dusty pixels and fewer false detections.

In an effort to better represent aerosol transport in meso- and global-scale models, large eddy simulations (LES) from the NCAR Turbulence with Particles (NTLP) code are used to develop a Markov chain random walk model that predicts aerosol particle vertical profiles in a cloud-free marine atmospheric boundary layer (MABL). The evolution of vertical concentration profiles are simulated for a range of aerosol particle sizes and in a neutral and an unstable boundary layer. For the neutral boundary layer we find, based on the LES statistics, that there exist temporal correlation structures for particle positions, meaning that over short time intervals (T= 500 s, or T/Tneut= 0.25), particles near the bottom of the boundary are more likely to remain near the bottom of the boundary layer than being abruptly transported to the top, and vice versa. For the unstable boundary layer, a similar time interval of T= 500 s (T/Teddy= 0.39) exhibits weaker temporal correlation compared to the neutral case due to the strong non-local convective motions. In the limit of a large time interval, T= 2000 s (T/Teddy= 1.56), particles have been mixed throughout the MABL and virtually no correlation exists. We leverage this information to parameterize a Markov chain random walk model that accurately predicts the evolution of vertical concentration profiles for the range of particle size and stability tested in LES, even over short time intervals which exhibit substantial correlation. The new methodology has significant potential to be applied at the subgrid level for coarser-scale weather and climate models.

Dynamical downscaling of Indian summer monsoon rainfall (ISMR) by using regional climate models (RCMs) portrays the inability of the RCMs in simulating the ISMR, and certain systematic biases appear in the seasonal monsoon rainfall climatology. The inconsistency in RCMs simulation of ISMR can be due to the improper representation of convection by convective and/or microphysical parameterization schemes in different RCMs. In this study, we conducted convection permitting simulations in WRFv3.8.1 and compared with parameterized simulations, to understand the difference of reproducibilities of time-space patterns in the ISMR. Our experimental set-up consists of two sets of simulations with parameterized and explicit convection on a grid resolution of 25 km. The simulations are conducted for three different monsoon seasons: flood, drought, and normal years, to ascertain robustness in the analysis of the model output. These simulations are forced by using ERA-Interim reanalysis as the lateral boundary and large-scale forcing input. The mean large-scale circulation, the spatial distribution of rainfall, seasonal northward propagation of rain bands, and magnitude-phase of the Indian summer monsoon rainfall are verified against the JRA55 reanalysis and India Meteorological Department gridded rainfall datasets. The results show that regional simulations with explicit convection have benefited in the simulation of ISMR features. Simulated seasonal mean rainfall in parameterized convection shows positive bias over Gangetic plains and the Western Ghats. The same bias reduced in explicit simulations and seasonal mean ISMR behaves realistically concerning IMD observations. The added value in the simulation of ISMR in explicit experiments is found to be consistent during the flood, drought, and normal monsoon seasons. Further evaluation of the results reveals that over Indian region, explicit convection simulations of Indian summer monsoon are more realistic than parameterized convection simulations. Therefore, the current study tried to show up the uncertainties in ISMR simulation associated with parameterizations, and explicit convection experiments highlight the reduction of these uncertainties.

Total electron content (TEC) observations can provide insights into electron density variations in the ionosphere. Such variations are associated with many aspects of ionospheric dynamics, including traveling ionospheric disturbances. In the present work we assimilate observations of TEC and horizontal TEC gradients into the SAMI3 (SAMI3 is Another Model of the Ionosphere 3D) ionosphere model. Assimilation into SAMI3 is accomplished using an ensemble Kalman filter implemented within LightDA, an extensible data assimilation library. Our TEC gradient observations are obtained from the Very Large Array Low-band Ionosphere and Transient Experiment (VLITE) and the TEC measurements are derived from GNSS receiver data. VLITE provides high precision and high spatial resolution TEC gradient observations over a small area, while GNSS observations supplement these with global coverage. By leveraging TEC observations in a physics-based model through data assimilation, we aim to improve our understanding of ionospheric processes and develop tools for improved ionospheric forecasting capabilities.

Louisiana is among the most vulnerable places on Earth to coastal flooding, for many reasons. Tropical-cyclone-induced storm surge, shoreline erosion accelerated by eustatic sea level rise, tidal influences, minimization of river sediment nourishment due to the presence of levees, and land subsidence caused by compaction of marsh lands and underground resource extraction all contribute to the flood hazard. In addition, increasing frequency and intensity of natural hazards under climate change scenarios are expected to exacerbate the coastal flood risk. Many studies focus on flood risk assessment and mitigation strategies both for the present and future, and other research has analyzed future flood risk considering climate change and sea level rise. Yet few studies consider all of these factors in concert. This research represents a comprehensive approach that considers coastal subsidence, eustatic sea level rise, and tropical cyclone storm surge variability under climate change scenarios, to evaluate future flood risk at the individual building level in Grand Isle, Louisiana. Results suggest that on average, the 100-year flood depth will increase by 37 cm at the individual building level in Grand Isle by 2050, with subsidence contributing over 80 percent of this increase. Subsidence is projected to increase structure and content losses by approximately 18 percent above modeled losses at present, while eustatic sea level rise may contribute approximately one percent of additional losses. A 100-year storm surge event amid a “low” scenario of environmental change would increase the structure and content losses at Grand Isle by 68–74 percent of today’s value in ten years, 141–149 percent in 25 years, and 346–359 percent in 50 years. Even more menacingly, “high” scenarios of environmental change are expected to increase the 100-year storm surge losses by approximately 85–91 percent of today’s value in ten years, 199–218 percent in 25 years, and 407–415 percent in 50 years. Outcomes from this study will fill the gap in the current literature by implementing a more realistic risk assessment model and will direct flood risk managers, property owners, and other stakeholders to build a comprehensive framework to minimize future flood risk in one of the most vulnerable sites in the USA to coastal flooding.