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To improve the representation of all relevant scales in initial conditions for large-domain convection-allowing models, a new multiscale ensemble Kalman filter (MEnKF) algorithm is developed and implemented within the Gridpoint Statistical Interpolation analysis system (GSI) data assimilation framework coupled with the Finite-Volume Cubed-Sphere Dynamical Core (FV3) limited-area model. The algorithm utilizes ensemble background error covariances filtered to match the observations assimilated. This is realized in a sequential manner. 1) When assimilating coarse-resolution observations such as radiosondes, ensemble background perturbations are filtered to remove scales smaller than those the observations can represent, along with relatively large horizontal localization radii to ensure low-noise and balanced analysis increments. 2) The resulting ensemble analyses from the first step then serve as the background to assimilate denser observations such as radar data with smaller localization radii. Several passes can be taken to assimilate all observations. In this paper, vertically increasing horizontal filter scales are used when assimilating rawinsonde and surface observations together, while radar data are assimilated in the second step. The algorithm is evaluated through six convective storm cases during May 2021, with cycled assimilation of either conventional data only or with additional radar reflectivity followed by 24-h ensemble forecasts. Overall, positive impacts of the MEnKF on forecasts are obtained regardless of reflectivity data; its advantage over the single-scale EnKF is most significant in surface humidity and temperature forecasts up to at least 12 h. More accurate hourly precipitation forecasts with MEnKF can last up to 24 h for light rain. Furthermore, MEnKF forecasts higher ensemble probabilities for the observed hazardous events. [ABSTRACT FROM AUTHOR]

Aims and Objectives: To identify the most accurate postoperative delirium screening tools for detecting postoperative delirium among patients who underwent general anaesthesia surgery in general wards. Background: The lack of detection of postoperative delirium can negatively affect the patient's condition, along with their postoperative treatment and rehabilitation, and it can prolong their hospitalisation, persists cognitive dysfunction and increases mortality. Screening for postoperative delirium in hospitalised patients as nursing assessment is routine clinical practice for early detection. Design: A systematic review and meta‐analysis. Methods: MEDLINE, Embase, CINAHL, KoreaMed and Cochrane electronic databases were searched using the key words delirium, postoperative, assessment or screening, and adult for articles published up to April 2020, with no limit on the year of publishing. Only prospective cohort studies reporting sensitivity and specificity values were included. We followed the recommendations of the Cochrane Handbook of Diagnostic Test Accuracy Reviews and the PRISMA checklist. The Quality Assessment of the Diagnostic Accuracy Studies‐2 tool was used for data extraction and quality assessment, while a bivariate random‐effects meta‐analysis model was used for pooling and comparing diagnostic accuracy and providing a summary of evidence. Results: Six delirium assessment tools were evaluated from nine papers including 3088 patients. Due to the limited number of papers, the meta‐analysis included the Confusion Assessment Method (CAM) and its variants, Delirium Detection Score (DDS) and Nurses' Delirium Screening Checklist (NuDESC). Overall, NuDESC demonstrated higher sensitivity than CAM or DDS, while all showed high specificity (0.90 or greater). Conclusion: This review suggested that NuDESC can be employed as an accurate screening tool with high specificity for assessing postoperative delirium during routine checkups. However, it is necessary to consider suitable cut‐off values, which is the reference point, in accordance with the clinical setting and the patients' condition. Relevance to clinical practice: NuDESC reported the best evidence of diagnostic accuracy, and we recommend clinical nurses to employ this easy‐to‐use and validated tool for daily screening of postoperative delirium in general wards to facilitate its early detection and the accurate estimation of its prevalence. [ABSTRACT FROM AUTHOR]

A new dynamical core, known as the Finite Volume Cubed-Sphere (FV3) and developed at both NASA and NOAA, is used in NOAA's Global Forecast System (GFS) and in limited area models (LAMs) for regional weather and air quality applications. NOAA has also upgraded the operational FV3GFS to version 16 (GFSv16), and includes a number of significant developmental advances to the model configuration, data assimilation, and underlying model physics, particularly for atmospheric composition to weather feedback. Concurrent with the GFSv16 upgrade, we couple the GFSv16 with the Community Multiscale Air Quality (CMAQ) model to form an advanced version of the National Air Quality Forecast Capability (NAQFC) that will continue to protect human and ecosystem health in the U.S. Here we describe the development of the FV3GFSv16 coupling with a "state-of-the-science" CMAQ model version 5.3.1. The GFS-CMAQ coupling is made possible by the seminal version of the NOAA-ARL Atmosphere-Chemistry Coupler (NACC), which became the next operational NAQFC system (i.e., NACC-CMAQ) on July 20, 2021. NACC-CMAQ has a number of scientific advancements that include satellite- based data acquisition technology to improve land cover and soil characteristics, and inline wildfire smoke and dust predictions that are vital to predictions of fine particulate matter (PM2.5) concentrations during hazardous events affecting society, ecosystems, and human health. The GFS-driven NACC-CMAQ has significantly different meteorological and chemical predictions than the previous operational NAQFC, where evaluation of NACC-CMAQ shows generally improved near-surface ozone and PM2.5 predictions and diurnal patterns, both of which are extended to a 72-hour (3-day) forecast with this system. [ABSTRACT FROM AUTHOR]

In this study, a hybrid En3DVar data assimilation (DA) scheme is compared with 3DVar, EnKF, and pure En3DVar for the assimilation of radar data in a real tornadic storm case. Results using hydrometeor mixing ratios (CVq) or logarithmic mixing ratios (CVlogq) as the control variables are compared in the variational DA framework. To address the lack of radial velocity impact issues when using CVq, a procedure that assimilates reflectivity and radial velocity data in two separate analysis passes is adopted. Comparisons are made in terms of the root-mean-square innovations (RMSIs) as well as the intensity and structure of the analyzed and forecast storms. For pure En3DVar that uses 100% ensemble covariance, CVlogq and CVq have similar RMSIs in the velocity analyses, but errors grow faster during forecasts when using CVlogq. Introducing static background error covariance B at 5% in hybrid En3DVar (with CVlogq) significantly reduces the forecast error growth. Pure En3DVar produces more intense reflectivity analyses than EnKF that more closely match the observations. Hybrid En3DVar with 50% B outperforms other weights in terms of the RMSIs and forecasts of updraft helicity and is thus used in the final comparison with 3DVar and EnKF. The hybrid En3DVar is found to outperform EnKF in better capturing the intensity and structure of the analyzed and forecast storms and outperform 3DVAR in better capturing the intensity and evolution of the rotating updraft. [ABSTRACT FROM AUTHOR]