The well recognized constraint of non-linear and non-Gaussian distribution of rainfall observation limits its assimilation in the high-dimensional numerical weather prediction (NWP) model. In this study, rain-gauges’ observed rainfall from Indian Meteorological Department (IMD) over Indian landmass is assimilated in the Weather Research and Forecasting (WRF) model using particle filter. In the framework of imperfect weather model, particles (or ensembles) for rainfall predictions are created with various combinations of model physics (viz. cumulus parameterization, micro-physics, planetary boundary layer schemes). With the help of IMD observed rainfall, weights are provided to various particles using multiple hypotheses, and this is the step in which IMD rainfall observations are used for assimilation. Further, a resampling step is performed to generate new particle from high weight particle using stochastic kinetic-energy backscatter scheme (SKEBS) method in which dynamical variables are perturbed into the model physics. Results based on rainfall verification scores suggest that assimilation of the rain-gauges observed rainfall using particle filter improved prediction of rainfall over CNT runs (unweighted particle; without assimilation). Moreover, surface and vertical profile of temperature, water vapour mixing ratio (WVMR) and wind speed are also improved in 24 h forecasts.

This study quantifies the air volume injected into the stratosphere by overshooting convection detected by GOES-16/17 geostationary infrared imagery and NOAA NEXRAD precipitation echo top during the 2021 and 2022 Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) missions. This analysis seeks to address a key DCOTSS science question, namely “How much tropospheric air and water is irreversibly injected into the stratosphere by convection?” A novel method for defining individual storms or a cluster of adjacent storms as objects and tracking them throughout their lifetime facilitates the analysis. Overshooting convection injected 2,178,154 - 5,360,162 km3 of air into the stratosphere in 2021 and 6,017,486 – 10,642,008 km3 in 2022 over the North American study domain with GOES being higher than GridRad during both years. GOES overshooting detections were more uncertain due to difficulty differentiating updrafts from adjacent broad areas of cold outflow. Overshooting volume from the top 10 storm objects each year contributed 37 to 52% of the total domain wide volume. Total object-lifetime volume from these top 10 events ranged from ~90,000-790,000 km3 for GOES and ~49,000-560,000 km3 for GridRad. It was found that overshooting seldomly exceeded 5% of the total anvil area, demonstrating that very small regions within convection are responsible for impacting stratosphere composition. Despite differences in overshooting characteristics by the two sensors, airmasses initiated from GOES and NEXRAD overshooting and advected forward in time had similar spatial and vertical distributions, indicating that geostationary satellite data could be used to study the long-range transport of overshooting airmasses.

Measurements of riverine dissolved inorganic carbon (DIC), total alkalinity (AT), pH, and the partial pressure of carbon dioxide (pCO2) can provide insights into the biogeochemical function of rivers including the processes that control biological production, chemical speciation, and air-water CO2 fluxes. The complexity created by these combined processes dictates that studies of inorganic carbon be made over broad spatial and temporal scales. Time-series data like these are relatively rare, however, because sampling and measurements are labor intensive and, for some variables, good measurement quality is difficult to achieve (e.g., pH). In this study, spectrophotometric pH and total alkalinity (AT) were quantified with high precision and accuracy at biweekly to monthly intervals over a four year period (2018-2021) along 216 km of the Upper Clark Fork River (UCFR) in the northern Rocky Mountains, USA. We use these and other time-series data to provide insights into the processes that control river inorganic carbon, with a focus on pCO2 and air-water CO2 fluxes. We found that seasonal snowmelt runoff increased pCO2 and that expected increase and decrease of pCO2due to seasonal heating and cooling were likely offset by an increase and loss of algal biomass, respectively. Overall, the UCFR was a small net source (0.08 ± 0.14 mol m-2 d-1) of CO2 to the atmosphere for all four years of our study with highly variable annual averages. The highly dynamic, seasonally correlated, offsetting mechanisms highlight the challenges in predicting pCO2 and air-water CO2 fluxes in rivers.

It remains uncertain whether an earthquake ”knows” its final magnitude part-way through its rupture. Several studies have found that early features of rupture can constrain final earthquake magnitude, but many of these have considered datasets with fewer than 100 earthquakes in order to reduce extraneous sources of variability. We consider a larger, but less carefully curated dataset. We calculate four parameters of the early portions of seismograms using $>$4000 earthquakes from around the world; we simply accept the scatter inherent in such a range of events. Despite this scatter, we find that on average larger earthquakes have distinct features in their early stages: they have longer predominant and average periods, release more energy, and reach higher maximum moment rates. The differences between small and large earthquakes persist even when we consider short measurement windows less than 1/3 of the earthquake duration. These results further evidence that there are physical differences between the early stages of large and small earthquakes. These, and further observations, could help us better understand how local stresses and frictional weakening drive earthquake rupture.

Full waveform inversion (FWI), a state-of-the-art seismic inversion algorithm, comprises an iterative data-fitting process to recover high-resolution Earth’s properties (e.g., velocity). At the heart of this process lies the numerical wave equation solver, which necessitates discretization. To perform efficient discretization-free FWI for large-scale problems, we introduce physics-informed neural networks (PINNs) as surrogates for conventional numerical solvers. Unfortunately, the original PINN implementation requires additional training for the new velocity model when used in the forward simulation. To make PINNs more suitable for such scenarios, we introduce latent representation learning to PINNs. We first append the input with the encoded velocity vectors, which are the latent representation of the velocity models using an autoencoder model. Unlike the original implementation, the trained PINN model can instantly produce different wavefield solutions without retraining with this additional information. To further improve the FWI efficiency, instead of computing the FWI updates on the original velocity dimension, we resort to updating in its latent representation dimension. Specifically, only the latent representation vectors get updated while the weights of the autoencoder and the PINN model are kept fixed during FWI. Through a series of numerical tests, the proposed framework shows a significant increase in accuracy and computational efficiency compared to the conventional FWI. The improved performance of our framework can be attributed to implicit regularization introduced by the velocity encoding and physics-informed training procedures. The proposed framework presents a significant step forward in utilizing a discretization-free wave equation solver for a more efficient and accurate FWI application.

We present a methodology that uses pilot and anchor points with probability distributions for saturated hydraulic conductivity in a groundwater contaminant transport model. This approach directly links locations with calibration target data (e.g., water levels and drawdown at monitoring wells) to the most relevant physical parameter(s) that drive behavior, in a way that promotes model parsimony. Distributions for hydraulic conductivity are developed for monitoring well locations with pumping tests in order to reflect the state of uncertainty in the local estimates; these locations are called anchor points. Pilot points are placed between monitoring wells, and because they have more uncertainty these are generally assigned wider distributions that reflect plausible hydraulic conductivity values for the geologic material in which they are located. Scaling issues are considered in the development of these distributions. Pilot points are not randomly or uniformly distributed in the domain; rather they are considered connectors between locations with data (anchor points) and placed strategically between them. For a given model realization, hydraulic conductivity values at both pilot and anchor points are sampled from their respective distributions and all remaining locations are derived using an interpolation scheme (e.g., kriging). This approach to hydraulic conductivity assignment honors location-specific data, geologic heterogeneity, and spatial patterns. Given that inverse analysis of high-dimensional models tends to be ill-posed and thus sensitive to initialization of parameters, the distribution development process plays a critical role in driving the outcome of model calibration.

A mathematical model is considered for Rayleigh-Benard convection of mantle whose viscosity depends strongly both on temperature and pressure defined in an Arrhenius form. The model is solved numerically for extremely large viscosity variations across a unit aspect ratio cell, and steady solutions are obtained. To improve the efficiency of numerical computation, a modified viscosity law with a low temperature cut-off is used. The aim is to investigate the convection pattern with internal heating at a very high viscosity variation in the presence of high Rayleigh number. The study also investigates the relation between temperature-dependent parameter and pressure dependent parameter. The numerical simulation is done using the finite element method based PDE solver and the results are presented through figures, tables and graphs.

High-latitude frozen soils contain a vast store of organic matter, a potential source of greenhouse gases due to permafrost thaw. Understanding natural carbon cycle responses to climate change is crucial for emission reduction strategies. We use the Max Planck Institute Earth System Model, driven by the Adaptive Emission Reduction Approach (AERA) and accounting for the impact of frozen soil carbon (FSC), to assess emission pathways and remaining emissions budgets for limiting global warming to 2°C and 3°C relative to preindustrial levels. We found that thawing FSC adds 122 PgC under 2°C and 229 PgC under 3°C warming, available for decomposition in active layer, with about 75% reaching the atmosphere as carbon-dioxide by 2300. Emission pathways that include the release of FSC diverge from their respective reference simulations without permafrost by the middle and end of the current century. By 2300, remaining budgets are reduced by ~13% (115 PgC) for 2°C and ~11% (156 PgC) for 3°C stabilization levels. Annual permafrost emissions average ~0.7 PgC/yr for 3°C and ~0.3 PgC/yr for 2°C scenarios. However, temporary emission peaks reaching half of present-day annual fossil fuel emissions (~5 PgC) are possible. Surprisingly, while negative emissions are required for both reference simulations, only the simulation for the 3°C warming, accounting for FSC, requires negative fossil fuel emissions. This occurs because the FSC release causes an earlier initiation of emission reduction by AERA, resulting in a smoother emission curve. These findings underscore the importance of factoring in permafrost thaw in mitigation action.

The rupture behavior of large oceanic strike-slip earthquakes remains largely unresolved using seismic signals recorded thousands of kilometers away from the source area. Large submarine earthquakes, however, generate hydroacoustic T-waves propagating through the ocean over long distances. Here, we show that these T-waves recorded at regional distances on the Ascension hydrophone array of the International Monitoring System can provide critical information on the earthquake location and rupture behavior. We use recordings from 47 events in oceanic transform faults, ranging in magnitude from 5.6≤Mw ≤7.1, to investigate the rupture processes. We find that most strike-slip earthquakes show unilateral rupture behavior, while a few larger events were more complex. Furthermore, earthquakes in oceanic transforms have longer ruptures than events of the same magnitude in continental faults. We argue that differences in the scaling relation of oceanic and continental strike-slip earthquakes support a lower rigidity in the oceanic lithosphere caused by hydration.

A document by Fanyu Zhang. Click on the document to view its contents.

Sub-Antarctic Mode Waters (SAMWs) form to the north of the Antarctic Circumpolar Current (ACC) in the Indo-Pacific Ocean, whence they ventilate the ocean’s lower pycnocline and play an important role in the climate system. With a backward Lagrangian particle-tracking experiment in a data-assimilative model of the Southern Ocean (B-SOSE), we address the long-standing question of whether SAMWs originate from densification of southward-flowing subtropical waters, or lightening of northward-flowing Antarctic waters sourced by Circumpolar Deep Water (CDW) upwelling. Our analysis evidences the co-occurrence of both sources of SAMWs in all formation areas, and strong inter-basin contrasts in their relative contributions. Subtropical waters are the main precursor of Indian Ocean SAMWs (70-75% of particles) but contribute a smaller amount ($<$40%) to Pacific SAMWs, which are mainly sourced by CDW. By tracking property changes along particle trajectories, we show that SAMW formation from northern and southern sources involves contrasting drivers: subtropical source waters are cooled and densified by surface heat fluxes, and freshened by ocean mixing. Southern source waters are warmed and lightened by surface heat and freshwater fluxes, and they are made either saltier by mixing in the case of Indian SAMWs, or fresher by surface fluxes in the case of Pacific SAMWs. Our results underscore the distinct climatic impact of Indian and Pacific SAMWs, as net sources of atmospheric heat and net sinks of freshwater, respectively; a role that is conferred by the relative contributions of subtropical and Antarctic sources to their formation.

We present a new parametric autocovariance kernel function for characterizing properties of the mesoscale eddy field and the non-phase-locked internal tide from ocean time series records. We demonstrate that the model captures the important spectral properties, namely the spectral roll-off of the mesoscale continuum and the broad spectral “cusps” centered around the tidal forcing frequencies. The spectral cusp model has three main parameters that characterize the non-phase-locked internal tide: the amplitude, a decorrelation timescale and a shape parameter that captures the rate at which the cusp rolls away. Estimation of the third shape parameter is novel. We argue that an integral timescale is the most suitable characteristic timescale and show how it relates to the parametric decorrelation timescale. A key innovation of this work is that we estimate the parameters in the frequency domain using the debiased Whittle likelihood, thus avoiding the computational demands of estimating the autocovariance in the time domain. We apply our spectral parameter estimation technique to output from idealized and realistic numerical experiments of internal tides propagating through a mesoscale eddy field. We robustly demonstrate that both the non-phase-locked amplitude and integral timescale are influenced by the amplitude of the mesoscale flow field. Furthermore, we reveal that the integral timescale is set by global properties of the eddy field, whereas the shape of the spectral cusp is set by its local properties. The semi-diurnal integral timescale, calculated from a 12-month long, realistically forced ocean basin experiment, was 5–7 d and relatively constant in space.

RESUMO Este trabalho tem por objetivo pesquisar as principais contribuições da entrevista inicial psicológica no contexto da prática clínica na identificação de sintomas depressivos. Portanto, propôs-se também a identificar os principais aspectos e sintomas do transtorno depressivo, bem como a análise dos sintomas a partir do relato do paciente. Foi realizado uma pesquisa bibliográfica nas bases de dados BVS, MedLine e PsycoInfo de artigos indexados e literaturas impressas que discorriam sobre a temática. Utilizou-se das palavras-chave: entrevista inicial, avaliação psicológica, sintomas depressivos. Com isso, concluiu que as principais contribuições encontradas na literatura condizem com a apresentação de comportamentos e emoções disfuncionais que podem indicar a presença do transtorno depressivo ou evoluir para o mesmo, bem como com as contribuições frente aos cuidados e análise criteriosa desses sintomas identificados. Ainda, diante dos resultados, conclui-se que a entrevista inicial enquanto prática, exerce importantes contribuições nas mais variadas aplicabilidades clínicas. Todavia, a literatura chama a atenção para os aspectos da aplicação adequada por parte do profissional. Palavras-chave: Entrevista inicial. Avaliação psicológica. Depressão. Sintomas. Psicologia. ABSTRACT This work aims to research the main contributions of the initial psychological interview in the context of clinical practice in the identification of depressive symptoms. Therefore, it was also proposed to identify the main aspects and symptoms of depressive disorder, as well as the analysis of symptoms based on the patient's report. A bibliographic search was carried out in the VHL, MedLine and PsycoInfo databases of indexed articles and printed literature that discussed the theme. The following keywords were used: initial interview, psychological assessment, depressive symptoms. Thus, he concluded that the main contributions found in the literature are consistent with the presentation of dysfunctional behaviors and emotions that may indicate the presence of the depressive disorder or progress to it, as well as with the contributions towards the care and careful analysis of these identified symptoms. Still, in view of the results, it is concluded that the initial interview as a practice, makes important contributions in the most varied clinical applications. However, the literature draws attention to aspects of proper application by the professional.

A document by Richard Hester. Click on the document to view its contents.

Previous tornado climatology studies often rely on tornado reports, which can suffer from underreporting bias due to heavy reliance on human reporting. This study aims to develop a new tornado climatology based on storm object data built from the Multi-Radar/Multi-Sensor reanalysis and Rapid Refresh modelsimulated near storm environment (NSE) variables. The dataset includes millions of storm clusters from 2005 to 2011, each characterized by over 1,000 radar and NSE variables and derivatives, making it ideal for developing machine learning models to classify tornadic storms. By using storm cluster data collocated with tornado reports, ML models are built using two methods: Random Forest (RF) and TabNet, a deep learning method. Three sub-models for each method, with different tornado damage-rating predictions are developed to understand the effects of sample size and model setup on performance. Both methods produce similar results, with overall Critical Success Index scores ranging from 0.720 to 0.826. TabNet predictions emphasize radar-related predictors, whereas RF predictions rely more on environmental variables with physical relationships to rotating thunderstorms. The RF model slightly outperforms TabNet and is therefore used to assign tornado probabilities to all remaining clusters. The new tornado climatology based on this RF model aligns well with observed data in terms of the magnitude of weak and strong tornadoes occurring each year. The spatial distribution of tornado days also resembles observations, providing confidence in the model's use for future applications.

A document by Amber Holdsworth. Click on the document to view its contents.

A series of processes involving sediment delivery from channels, its dispersal onto the floodplain, and storage at their channel margins, often generates natural levees. When levees breach, they release water and sediment onto the floodplain, leading to crevasse splays or avulsions. Despite their importance in constructing floodplains and influencing channel mobility, levee growth is poorly understood. Presently, no model fully explains dynamic channel-levee evolution. A common assumption is that levee and channel bed aggradation rates are coupled. If they coevolve, levees should progressively accumulate along any aggrading channel belt, yet observations indicate otherwise. Using a one-dimensional numerical model, we investigate levee growth decoupled from channel bed aggradation across two flood scenarios wherein the flooded level: 1) exceeds the levee crest height (i.e., front loading); 2) is lower than the levee crest causing partial inundation of distal levee deposits (i.e., back loading). Initially, rapid levee aggradation confines the channel and increases bankfull depth, which mitigates flooding. As confinement slows levee growth, channel bed aggrades until bankfull depth is sufficiently reduced to trigger overflows. This releasing process increases flood likelihood and enhances overbank accumulation, promoting front loading and re-confining the channel. Our findings suggest aggradational channels may experience confined-release phases, characterized by episodic levee growth and fluctuations in bankfull depth. Rapid in-channel aggradation increases flood frequency and variability with more confined-release cycles. These results imply that river avulsions and associated floods might preferentially occur when the channel bed aggrades faster than adjacent levees, whereby the channel becomes shallower and destabilized.

A document by Kedar Dabhadkar. Click on the document to view its contents.