Version Of System Research Articles

Evaluation of Inverse Planned and Forward Planned Intensity Modulated Radiotherapy Techniques in Breast Cancer

Introduction: The objective of this study is to compare the dosimetric parameters of radiation to the whole breast between the two intensity-modulated radiotherapy (IP IMRT) techniques, i.e. Inverse planned IMRT (IP IMRT) and Forward Planned IMRT (FP IMRT) with regard to target coverage (PTV) and irradiation of organs at risk (OAR). Material and Methods: Plain and Contrast enhanced computed tomography (CECT) datasets were created for 41 patients treated with whole breast radiation therapy. CT simulation and treatment is performed using deep inspiratory breath hold technique (DIBH). Radiotherapy treatment Planning is done using Eclipse Treatment Planning System (version 13.7) with a prescription dose of 40 Gy in 15#. The developed treatment plans were subjected to objective comparison of PTV and OARs using dose volume histograms (DVH). Results: IP IMRT plans provided better coverage (99.5% vs 97.6%), comparable though higher maximum dose (Dmax 45.0 VS 44.1 Gy), higher hot spot (PTV105% 49.2 vs 33), lower volumes receiving 20, 25, 30 Gy (V20, V25, V30) for heart, more homogeneous (homogeneity index 0.10 vs. 0.14) and conformal dose distribution (conformity Index 1.0 vs 0.98) compared to FP IMRT. Regarding OAR dosimetry it is observed that FP IMRT showed reduced mean dose to Coronary artery (LADCA), Contralateral Lung (CL), Contralateral breast (CB) along with reduction in low dose region (V5) to all OARs under study. It was also observed that Monitor units used and planning time were lower for FP IMRT. Conclusion: On weighing different dosimetric factors, both the techniques have displayed their own advantages and disadvantages. Choosing a planning technique needs to be customized taking into consideration various factors such as breast topography, size and volumes of breast, availability of expertise planning skills and resources.

Development of a Wave Model Component in the First Coupled Global Ensemble Forecast System at NOAA

Abstract We describe the development of the wave component in the first global-scale coupled operational forecast system using the Unified Forecasting System (UFS) at the National Oceanic and Atmospheric Administration (NOAA), part of the US National Weather Service (NWS) operational forecasting suite. The operational implementation of the atmosphere-wave coupled Global Ensemble Forecast System version 12 (GEFSv12) in September 2020 was a critical step in NOAA’s transition to the broader community-based UFS framework. GEFSv12 represents a significant advancement, extending forecast ranges and empowering the NWS to deliver advanced weather predictions with extended lead times for high-impact events. The integration of a coupled wave component with higher spatial and temporal resolution and optimized physics parameterizations notably enhanced forecast skill and predictability, particularly benefiting winter storm predictions of wave heights and peak wave periods. This successful endeavor encountered challenges that were addressed by the simultaneous development of new features that enhanced wave model forecast skill and product quality and facilitated by a multidisciplinary team collaborating with NOAA’s operational forecasting centers. The GEFSv12 upgrade marks a pivotal shift in NOAA’s global forecasting capabilities, setting a new standard in wave prediction. We also describe the coupled GEFSv12-Wave component impacts on NOAA operational forecasts, and ongoing experimental enhancements, which altogether represent a substantial contribution to NOAA’s transition to the fully-coupled UFS framework.

Agrochemical control of gene expression using evolved split RNA polymerase. II.

Agrochemical inducible gene expression system provides cost-effective and orthogonal control of energy and information flow in bacterial cells. However, the previous version of Mandipropamid inducible gene expression system (Mandi-T7) became constitutively active at room temperature. We moved the split site of the eRNAP from position LYS179 to position ILE109. This new eRNAP showed proximity dependence at 23 °C, but not at 37 °C. We built Mandi-T7-v2 system based on the new eRNAP and it worked in both Escherichia coli and Agrobacterium tumefaciens. We also induced GFP expression in Agrobacterium cells in a semi-in vivo system. The modified eRNAP when combined with the leucine zipper-based dimerization system, behaved as a cold inducible gene expression system. Our new system provides a means to broaden the application of agrochemicals for both research and agricultural application. Portions of this text were previously published as part of a preprint (https://www.biorxiv.org/content/10.1101/2024.04.02.587689v1).

Exploring photoionization of gas-phase free radicals with a widely tunable VUV laser at moderate spectral resolution

The VUv Laser for Considering Astrophysical and Isolated Molecules (VULCAIMs) setup [Harper et al., Phys. Chem. Chem. Phys. 24, 2777 (2022)] integrates a narrow-bandwidth tunable vacuum ultraviolet (VUV) and extreme ultraviolet (XUV) nanosecond-pulsed laser system (6–16 eV) and a photoelectron spectrometer, designed for recording high-spectral-resolution (rotationally resolved) photoelectron spectra of gas-phase free radicals. This approach usually needs beforehand medium-resolution synchrotron data to guide the selection of specific spectral regions to be investigated at higher resolution with the VULCAIM setup. We present an upgraded version of the VUV laser system integrating an optical parametric oscillator for continuously scanned medium-resolution measurements (<3 cm−1) across the whole VUV and XUV spectral ranges. This innovation enables broader coverage without the need to access synchrotron facilities. Furthermore, rapid mode switching allows for maintaining optimized radical production conditions from mid-resolution to high-resolution operation mode, enhancing spectroscopy capabilities significantly. The new capabilities of the VULCAIM setup are illustrated on two showcases of photoionization studies: the nitric oxide (NO) stable molecular species and the benzyl (C6H5CH2) free radical produced by pyrolysis.

Influence of the Atlantic and Pacific Multidecadal Variability on Arctic Sea Ice in Pacemaker Simulations during 1920–2013

Abstract The Atlantic multidecadal variability (AMV) and Pacific multidecadal variability (PMV) can influence Arctic sea ice and modulate its trend, but to what extent the AMV and PMV can affect Arctic sea ice and which processes are dominant are not well understood. Here, we analyze the Community Earth System Model, version 1, idealized and time-varying pacemaker ensemble simulations to investigate these issues. These experiments show that the sea ice concentration varies mainly over the marginal Arctic Ocean, while the sea ice thickness variations occur over the entire Arctic Ocean. The internal components of AMV and PMV can enhance or weaken the decadal sea ice loss rates over the marginal Arctic Ocean by more than 50%. The AMV- or PMV-induced anomalous atmospheric energy transport and downward longwave radiation related to low clouds (thermodynamical processes) and sea ice motion (dynamical processes) contribute to the Arctic surface air temperature and sea ice concentration and thickness changes. Anomalous oceanic heat flux is mainly a response to rather than a cause of sea ice variations. The dynamic processes contribute to the winter Arctic sea ice variations as much as the thermodynamic processes, but they contribute less (more) to the summer Arctic sea ice variability than the thermodynamic processes over the marginal Arctic Ocean (parts of the central Arctic Ocean). Sea ice loss enhances air–sea heat fluxes, which cause oceanic heat convergence and warm near-surface air and the lower troposphere, which in turn melt more sea ice.

Adaptive backstepping sliding mode control for single-inductor double-output boost converter

To reduce the cross-regulation and improve the dynamic response performance of a single-inductor double-output Boost converter, an adaptive backstepping sliding mode control (ABSMC) strategy is proposed in this paper. The nonlinear mathematical model of the converter is established, an output function satisfying the exact feedback linearization (EFL) is constructed based on the inverse system theory, and the linearization and decoupling of the model are implemented. Meanwhile, the problem of EFL heavily relying on an exact model is solved by combining backstepping control with sliding mode control. Furthermore, an adaptive reaching law is proposed to adjust the gain of the switching function, and the chattering phenomenon of sliding mode control is reduced. The stability of the system is proven according to the Lyapunov stability theorem. Finally, compared with the existing control methods, both the simulation results and experimental results verify the effectiveness and superiority of the proposed ABSMC strategy.

Tropical Cyclones in the GEOS-S2S-2 Subseasonal Forecasts

Abstract This paper analyzes the climatology, prediction skill, and predictability of tropical cyclones (TCs) in NASA’s Goddard Earth Observing System Subseasonal to Seasonal (GEOS-S2S) forecast system version 2. GEOS reasonably simulates the number and spatial distribution of TCs compared to observations except in the Atlantic where the model simulates too few TCs due to low genesis rates in the Caribbean Sea and Gulf of Mexico. The environmental conditions, diagnosed through a genesis potential index, do not clearly explain model biases in the genesis rates, especially in the Atlantic. At the storm scale, GEOS reforecasts replicate several key aspects of the thermodynamic and dynamic structures of observed TCs, such as a warm core and the secondary circulation. The model, however, fails to simulate an off-center eyewall when evaluating vertical velocity, precipitation, and moisture. The analysis of the prediction skill of TC genesis and occurrence shows that GEOS has comparable skill to other global models in WMO S2S archive and that its skill could be further improved by increasing the ensemble size. After calibration, GEOS forecasts are skillful in the western North Pacific and southern Indian Ocean up to 20 days in advance. A model-based predictability analysis demonstrates the importance of the Madden–Julian oscillation (MJO) as a source of predictability of TC occurrence beyond the 14-day lead time. Forecasts initialized under strong MJO conditions show evidence of predictability beyond week 3. However, due to model biases in the forecast distribution, there are notable gaps between the MJO-related prediction skill and predictability, which require further study.

Prediction Performance of Ocean Temperature and Salinity in Global Seasonal Forecast System Version 5 (GloSea5) on ARGO Float Data

Prediction Performance of Ocean Temperature and Salinity in Global Seasonal Forecast System Version 5 (GloSea5) on ARGO Float Data

Optimizing Prostate Imaging Practices in Saudi Arabian Hospitals: A Comprehensive Analysis of PI-RADS Compliance in Multiparametric MRI.

Prostate cancer, a significant contributor to male cancer mortality globally, demands improved diagnostic strategies. In Saudi Arabia, where the incidence is expected to double, this study assessed the compliance of multiparametric MRI (mpMRI) practices with Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) guidelines across diverse healthcare institutions. A survey was distributed to the radiology departments of all tertiary referral hospitals in Saudi Arabia (n=60) to assess their compliance with the technical specifications outlined in PI-RADS v2. Statistical analysis included chi-square, Fisher exact, ANOVA, and Student t-tests to examine the collected data. The study revealed an overall commendable compliance rate of 95.23%. However, significant variations were observed in technical parameters, particularly between 1.5 Tesla and 3 Tesla scanners and tertiary versus non-tertiary hospitals. Notable adherence in certain sequences contrasted with discrepancies in T2-weighted and diffusion-weighted imaging parameters. These findings underscore the need for nuanced approaches to optimize prostate imaging protocols, considering field strength and institutional differences. The study contributes to the ongoing refinement of standardized mpMRI practices, aiming to enhance diagnostic accuracy and improve clinical outcomes in prostate cancer.

Water-Coal Ratio Control Strategy of Ultra Supercritical Unit Based on Neural Network Inverse Model

Since the boiler water-coal ratio control system is a complex system with the characteristics of non-linearity and strong coupling, water-coal ratio control is one of the most difficult problems in the coal-fired power generation process control engineering, whose control strategy is of great importance. While, in order to achieve the control of water-coal ratio effectively during the coal-fired power generation process, the neural network inverse system scheme is proposed for the control of the water-coal ratio of ultra-supercritical units. Firstly, the model for the water-coal ratio system of an ultra-supercritical unit is presented in allusion to the characteristics of the water-coal ratio control system. Then the concept of the neural network based inverse system, the principle and method of the design of the neural network inverse controller are discussed. Finally, the control scheme is verified by establishing neural network inverse system on MATLAB toolbox. The experimental results show that the neural network based inverse system models has better control effect in terms of anti-interference ability, stability time than that of PID control system.

Antimicrobial Resistance in Sepsis Cases Due to Escherichia coli and Klebsiella pneumoniae: Pre-Pandemic Insights from a Single Center in Southwestern Romania.

To determine the resistance profile of Escherichia coli (E. coli) and Klebsiella pneumoniae (K. pneumoniae) strains isolated in sepsis cases diagnosed at the Infectious Diseases Clinic in Craiova, Romania. The bacteria responsible for sepsis cases were identified using the Vitek 2 Systems version 06.01, which was then employed to assess their antimicrobial susceptibility (Global CLSI and Phenotypic 2017). We have identified 989 patients diagnosed with bacterial sepsis. Among these, 953 cases were caused by Gram-negative rods, with 415 attributed to E. coli and 278 to K. pneumoniae. High levels of resistance to ampicillin were recorded for E. coli strains isolated in sepsis cases (64.6%); adding sulbactam lowers the level of resistance to 41.8%. Resistance to 3rd generation cephalosporins varied between 7.47 and 14.6% and another 3.41 to 11.1% are dose-dependent susceptibility strains. Resistance to carbapenems (i.e., ertapenem, meropenem) is low-2.18-2.42%. More than 95% of the tested K. pneumoniae strains were resistant to ampicillin and adding sulbactam as a β-lactamase inhibitor only halves that level. Resistance to 3rd generation cephalosporins varied between 20.7% and 22.5%; resistance levels for K. pneumoniae were notably higher than those for E. coli. Over 95% of K. pneumoniae strains showed resistance to ampicillin, and resistance to 3rd generation cephalosporins varied between 20.7% and 22.5%. Additionally, K. pneumoniae exhibited higher resistance to carbapenems (13.7-19.5%) compared to E. coli (2.18-2.42%). Antimicrobial resistance levels are generally lower than continental and national data, except for ampicillin and carbapenems (meropenem and ertapenem). K. pneumoniae strains are significantly more resistant than E. coli strains.

A Comparison of Two Versions of the CRISPR-Sirius System for the Live-Cell Visualization of the Borders of Topologically Associating Domains.

In recent years, various technologies have emerged for the imaging of chromatin loci in living cells via catalytically inactive Cas9 (dCas9). These technologies facilitate a deeper understanding of the mechanisms behind the chromatin dynamics and provide valuable kinetic data that could not have previously been obtained via FISH applied to fixed cells. However, such technologies are relatively complicated, as they involve the expression of several chimeric proteins as well as sgRNAs targeting the visualized loci, a process that entails many technical subtleties. Therefore, the effectiveness in visualizing a specific target locus may be quite low. In this study, we directly compared two versions of a previously published CRISPR-Sirius method based on the use of sgRNAs containing eight MS2 or PP7 stem loops and the expression of MCP or PCP fused to fluorescent proteins. We assessed the visualization efficiency for several unique genomic loci by comparing the two approaches in delivering sgRNA genes (transient transfection and lentiviral transduction), as well as two CRISPR-Sirius versions (with PCP and with MCP). The efficiency of visualization varied among the loci, and not all loci could be visualized. However, the MCP-sfGFP version provided more efficient visualization in terms of the number of cells with signals than PCP-sfGFP for all tested loci. We also showed that lentiviral transduction was more efficient in locus imaging than transient transfection for both CRISPR-Sirius systems. Most of the target loci in our study were located at the borders of topologically associating domains, and we defined a set of TAD borders that could be effectively visualized using the MCP-sfGFP version of the CRISPR-Sirius system. Altogether, our study validates the use of the CRISPR-Sirius technology for live-cell visualization and highlights various technical details that should be considered when using this method.

Deep residual fully connected network for GNSS-R wind speed retrieval and its interpretation

Global navigation satellite system reflectometry (GNSS-R) has emerged as a new technique to provide L-band bistatic measurements for ocean wind speed retrieval, in which traditional geophysical model functions (GMFs) or shallow neural networks (NNs) are normally used. However, it is still challenging to identify and consider all relevant parameters in the GMF. Meanwhile, NN models face limitations due to the degradation problem, which restricts their depth and consequently their performance. Furthermore, the interpretation of NN models for GNSS-R wind retrieval is another issue. To this end, we propose a residual fully connected network (RFCN) fusing auxiliary information such as geometry, receiver gain, significant wave height, and current speed with track-wise corrected σ0. Referred to the European Centre for Medium-Range Weather Forecast (ECMWF) ERA5 wind product, the root mean square error (RMSE) and bias of RFCN winds are 1.031 m/s and -0.0003 m/s, respectively, with a 6% improvement in RMSE compared to debiased NOAA Cyclone Global Navigation Satellite System (CYGNSS) Version 1.2 (V1.2) wind speed retrieval. Moreover, in an intertropical convergence zone (ITCZ) area with large current speeds, the RMSE and bias are 1.006 m/s and -0.022 m/s: an improvement of 11.6% and 87.9% compared to debiased NOAA CYGNSS V1.2 winds. The bias ‘strips’ in these areas are nearly eliminated. Daily averaged error analyses also demonstrate that RFCN winds are more robust and consistent with ECMWF winds. For wind speeds larger than 20 m/s, referred to Soil Moisture Active Passive (SMAP) Level 3 final wind products, the RMSE and bias of fine-tuning RFCN (FT_RFCN) winds are reduced by 25.7% and 91.5% compared to NOAA winds. Finally, the RMSE and bias of retrievals in tropical cyclones, measured by Stepped Frequency Microwave Radiometer (SFMR) during 2021-2022, reveal an improvement of 3.5% and 21.2% compared to NOAA winds. Through SHapley Additive exPlanations (SHAP) models developed for RFCN and FT_RFCN, the contribution of each feature is quantitatively evaluated, while providing insights into their interactions within the ‘black-box’ NN models with clear physical meanings.

Solving a bilevel strategic offering model for a generation company in a hydrothermal energy market: A new convexification approach

This paper proposes a bilevel strategic offering (SO) model suitable for a strategic company (SC) that operates in a hydro-dominated day-ahead energy market. The upper-level model of the proposed SO model maximizes the SC’s profits, while the lower-level model embodies a hydrothermal market clearing (MC). This MC incorporates detailed hydro and thermal constraints for units of both the SC and non-strategic companies (NSC), in addition to the network constraints. The proposed solution approach entails replacing the lower-level model with its corresponding primal and dual constraints, along with a strong duality equality constraint. This leads to a non-convex mathematical programming with equilibrium constraints (MPEC) model, which is then reformulated to feature only a single bilinear term in its structure. Finally, the recast MPEC model is convexified around an initial point and solved iteratively until a convergence criteria is achieved. The model and solution approach proposed are evaluated on an adapted version of the IEEE 24-bus system, exploring scenarios where the SC acts strategically or non-strategically (perfect competition).

Research on English Classroom Teaching Interactive Strategy for Higher Vocational Students in smart Classroom Environment

This study conducted experimental teaching of vocational college English listening and speaking courses in both smart classroom and simple multimedia classroom environments. Using an improved version of the Flipped Classroom Interaction Analysis System (SCIAS) and questionnaires, supplemented by S-T analysis as a complementary research method, the study conducted a comparative analysis of classroom records in terms of classroom structure, emotional atmosphere, interactive behaviors between teachers and students, interpersonal interactions, and interactions between teachers, students, and media technology. The results showed that compared to traditional classrooms, teaching in a smart classroom environment is more conducive to stimulating students' initiative, leading to deeper and more active classroom interactions.

Classification of Hydroxychloroquine Retinopathy: A Literature Review and Proposal for Revision.

Establishing universal standards for the nomenclature and classification of hydroxychloroquine retinopathy is essential. This review summarizes the classifications used for categorizing the patterns of hydroxychloroquine retinopathy and grading its severity in the literature, highlighting the limitations of these classifications based on recent findings. To overcome these limitations, I propose categorizing hydroxychloroquine retinopathy into four categories based on optical coherence tomography (OCT) findings: parafoveal (parafoveal damage only), pericentral (pericentral damage only), combined parafoveal and pericentral (both parafoveal and pericentral damage), and posterior polar (widespread damage over parafoveal, pericentral, and more peripheral areas), with or without foveal involvement. Alternatively, eyes can be categorized simply into parafoveal and pericentral retinopathy based on the most dominant area of damage, rather than the topographic distribution of overall retinal damage. Furthermore, I suggest a five-stage modified version of the current three-stage grading system of disease severity based on fundus autofluorescence (FAF) as follows: 0, no hyperautofluorescence (normal); 1, localized parafoveal or pericentral hyperautofluorescence on FAF; 2, hyperautofluorescence extending greater than 180° around the fovea; 3, combined retinal pigment epithelium (RPE) defects (hypoautofluorescence on FAF) without foveal involvement; and 4, fovea-involving hypoautofluorescence. These classification systems can better address the topographic characteristics of hydroxychloroquine retinopathy using disease patterns and assess the risk of vision-threatening retinopathy by stage, particularly with foveal involvement.

Ensemble‐based monthly to seasonal precipitation forecasting for Iran using a regional weather model

AbstractMonthly and seasonal precipitation forecasts can potentially assist disaster risk reduction and water resource management. The aim of this study is to assess the skill of an ensemble framework for monthly and seasonal precipitation forecasts over Iran by focusing on system design and model performance evaluation. The ensemble framework presented in this paper is based on a one‐way double‐nested model that uses Weather Research and Forecasting (WRF) modelling system to downscale the second version of the NCEP Climate Forecast System (CFSv2). The performance is evaluated for October–April period at 1‐, 2‐ and 3‐month lead time. Multiple initial conditions, model parameters and physics are used to construct ensemble members. Using quantile mapping (QM) method, the outputs of the model are bias corrected. This methodology is applied for two periods: (i) climatology from 2000 to 2019 to evaluate the model's ability to precipitation forecast on a monthly and seasonal time scale; (ii) the forecast for 2020 to evaluate the model's performance operationally. The model evaluation is performed using the continuous (e.g., RMSE, r, MBE, NSE) and categorical (e.g., POD, FAR, PC, Heidke skill score) assessment metrics. We conclude that model outputs were improved by the QM bias correction method. According to results, the proposed ensemble framework can accurately predict amount of monthly and seasonal precipitation in Iran with an accuracy of 58 to 45% for lead‐1 to 3. For all three lead times, the averaged NSE, CC, MBE, and RMSE were 0.4, 0.56, −15.5, and 41.6, indicating that the framework has reasonable performance. Our results suggest that precipitation forecast accuracy varies with lead time, so the accuracy for lead‐1 is higher than lead‐2 and lead‐3. Additionally, the model's accuracy differs in various regions of the country and decreases in the spring. Using the approach for an operational case, it was found that the spatial features of precipitation predicted by the framework were close to those observed.

Zebra II as A Novel System to Record Electrophysiological Signals in Zebrafish.

Zebrafish and their mutant lines have been extensively used in biomedical investigations, cardiovascular studies, and drug screening. In the current study, the commercial version of the novel system, Zebra II, is presented. The protocol demonstrates electrocardiogram (ECG) acquisition and analysis from multiple zebrafish within controllable working environments. The device is composed of an external and independent perfusion system, a 4-point electrode, temperature sensors, and an embedded electronic system. In previous studies, the device prototype underwent validation against the established iWORX system through several tests, demonstrating similar data quality and ECG response to drug interventions. Following this, the study delved into examining the impact of anesthetic drugs and temperature fluctuations on zebrafish ECG, necessitating instant data evaluation. Thanks to the apparatus's capacity for consistent delivery of anesthetics and drugs, it was possible to extend ECG data collection up to 1 h, markedly longer than the 5 min duration supported by current systems. This paper introduces a pioneering, cloud-based, automated analysis utilizing data from four zebrafish, offering an efficient method for conducting combination experiments and significantly reducing time and effort. The system proved effective in capturing and analyzing ECG, especially in detecting drug-induced arrhythmias in wild-type zebrafish. Additionally, the capability to gather data across multiple channels facilitated the execution of randomized controlled trials with zebrafish models. The developed ECG system overcomes existing limitations, showing the potential to greatly expedite drug discovery and cardiovascular research involving zebrafish.

Generation of state‐dependent ensemble perturbations based on time‐varying seawater density for GloSea5 initialization

AbstractIn this study, we developed a flow‐dependent oceanic initialization system for initializing the oceanic temperature and salinity in the Global Seasonal forecast system version 5 (GloSea5). Our algorithm overcomes the limitation of stationary perturbations for Ensemble Optimal Interpolation (EnOI) by spreading observed information along isopycnal lines to create three‐dimensional snapshot density states. The proposed algorithm, which we call state‐dependent ensemble‐based EnOI (SD‐EnOI), takes into account changes in the background error covariance over time without relying on ensemble model simulations. To evaluate the quality of the oceanic initial conditions (ICs) produced by SD‐EnOI, we compared them with those generated by the Global Ocean Data Assimilation and Prediction System version 1 (GODAPS1) operated by the Korea Meteorological Agency (KMA) throughout January 2017 to December 2017. Our findings show that the thermal construction of the SD‐EnOI ICs is more realistic than that of GODAPS1, particularly in the tropical Pacific region. The strong warm bias in sea surface temperature (SST) and the shallow mixed‐layer depth bias observed in the GODAPS1 ICs are not shown in SD‐EnOI. Due to the more realistic oceanic thermal structure present in the SD‐EnOI ICs, their use in retrospective forecast experiments resulted in a systematic reduction in climatological SST drift in the central‐eastern Pacific for forecasts up to four lead months compared to using GODAPS1 ICs. This demonstrates the significant impact of the initialization process on the quality of dynamical seasonal forecasts.

Modernizing the US National Fire Danger Rating System (version 4): Simplified fuel models and improved live and dead fuel moisture calculations

The US National Fire Danger Rating System (USNFDRS) supports wildfire management decisions nationwide, but it has not been updated since 1988. Here we implement new fuel moisture models, and we simplify the fuel models while maintaining the overall USNFDRS structure. Modeled and measured live fuel moisture content values were highly correlated (r2=0.629 with defaults and r2=0.693 when species and location optimized). We also consolidated fuel models to five fuel types that eliminated significant index cross-correlation. Index seasonality compared between old (V2) and new USNFDRS models (v4) across six US National Forests was very similar (ρ= 0.97). V4 was as good or better than V2 at predicting fire days in 92% of the cases tested and V4 effectively predicted wildfire days and large fire ignition days (AUCs 0.647 to 0.915). USNFDRS V4 can adequately depict spatial and temporal wildland fire potential and it can be adapted for worldwide use.