Turbulent statistics in the vicinity of an SST front: A north wind case, FASINEX February 16, 1986

The technique of boxcar variances and covariances is used to examine NCAR Electra data from FASINEX (Frontal Air-Sea Interaction EXperiment). This technique was developed to examine changes in turbulent fluxes near a sea surface temperature (SST) front. The results demonstrate the influence of the SST front on the MABL (Marine Atmospheric Boundary Layer). Data shown are for February 16, 1986, when the winds blew from over cold water to warm. The front directly produced horizontal variability in the turbulence. The front also induced a secondary circulation which further modified the turbulence

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Predicting Residential Treatment Outcomes for Emotionally and Behaviorally Disordered Youth: The Role of Pretreatment Factors

This study examined outcomes with 170 children and youth admitted to residential treatment with complex mental health problems. Overall, outcomes at 2 years post-treatment was predicted by children and youth\u27s behavioral pretreatment status reflected in lower internalizing and externalizing behavior at admission. These findings recognize a cluster of variables upon admission that are differentially predictive of specific outcomes. Higher school participation/achievement and an absence of witnessing interparental abuse predicted educational status. Family status was predicted at admission by higher family functioning, being younger in the family, and children and youth who had poor community behavior. The results are discussed as they relate to pretreatment screening and the need to evaluate service outcomes