Stochastic Nature of Physical Parameterizations in Ensemble Prediction: A Stochastic Convection Approach

Abstract In this paper it is argued that ensemble prediction systems can be devised in such a way that physical parameterizations of subgrid-scale motions are utilized in a stochastic manner, rather than in a deterministic way as is typically done. This can be achieved within the context of current physical parameterization schemes in weather and climate prediction models. Parameterizations are typically used to predict the evolution of grid-mean quantities because of unresolved subgrid-scale processes. However, parameterizations can also provide estimates of higher moments that could be used to constrain the random determination of the future state of a certain variable. The general equations used to estimate the variance of a generic variable are briefly discussed, and a simplified algorithm for a stochastic moist convection parameterization is proposed as a preliminary attempt. Results from the implementation of this stochastic convection scheme in the Navy Operational Global Atmospheric Prediction System (NOGAPS) ensemble are presented. It is shown that this method is able to generate substantial tropical perturbations that grow and "migrate" to the midlatitudes as forecast time progresses while moving from the small scales where the perturbations are forced to the larger synoptic scales. This stochastic convection method is able to produce substantial ensemble spread in the Tropics when compared with results from ensembles created from initial-condition perturbations. Although smaller, there is still a sizeable impact of the stochastic convection method in terms of ensemble spread in the extratropics. Preliminary simulations with initial-condition and stochastic convection perturbations together in the same ensemble system show a promising increase in ensemble spread and a decrease in the number of outliers in the Tropics

Impact of Stochastic Convection on the Ensemble Transform

Abstract The impact of stochastic convection on ensembles produced using the ensemble transform (ET) initial perturbation scheme is examined. This note compares the behavior of ensemble forecasts based only on initial ET perturbations with the behavior of ensemble forecasts based on the ET initial perturbations and forecasts that include stochastic convection. It is illustrated that despite the fact that stochastic convection occurs only after the forecast integrations have started, it induces changes in the initial perturbations as well. This is because the ET is a "cycling" scheme, in which previous short-term forecasts are used to produce the initial perturbations for the current forecast. The stochastic convection scheme induces rapid perturbation growth in regions where convection is active, primarily in the tropics. When combined with the ET scheme, this results in larger initial perturbation variance in the tropics, and, because of a global constraint on total initial perturbation variance, smaller initial perturbation variance in the extratropics. Thus, the inclusion of stochastic convection helps to mitigate a problem found in the practical implementation of the ET, namely, that of too little initial variance in the tropics and too much in the extratropics. Various skill scores show that stochastic convection improves ensemble performance in the tropics, with little impact to modest improvement in the extratropics. Experiments performed using the initial perturbations from the control ensemble run but forecast integrations using the stochastic convection scheme indicate that the improved performance of the stochastic convection ensemble at early forecast times is due to both "indirect" changes in the initial perturbations and "direct" changes in the forecast. At later forecast times, it appears that most of the improvement can be gained through stochastic convection alone

A relational analysis of an invisible illness: A meta-ethnography of people with chronic fatigue syndrome/myalgic encephalomyelitis and their support needs

Chronic fatigue syndrome (CFS)/myalgic encephalomyelitis (ME) is indicated by prolonged, medically unexplained fatigue (amongst other symptoms), not alleviated by rest, and causing substantial disability. There are limited treatments on offer, which may not be effective and/or acceptable for all people, and treatment views are polarised. We, thus, aimed to take a step back from this debate, to explore more broadly preferences for formal and informal support among people with CFS/ME. We used a meta-ethnography approach to examine the substantial qualitative literature available. Using the process outlined by Noblit and Hare, and guided by patient involvement throughout, 47 studies were analysed. Our synthesis suggested that to understand people with CFS/ME (such as their invisibility, loss of self, and fraught clinical encounters), it was useful to shift focus to a ‘relational goods’ framework. Emotions and tensions encountered in CFS/ME care and support only emerge via ‘sui generis’ real life interactions, influenced by how social networks and health consultations unfold, and structures like disability support. This relational paradigm reveals the hidden forces at work producing the specific problems of CFS/ME, and offers a ‘no blame’ framework going forward. Chronic fatigue syndrome; myalgic encephalomyelitis; meta-ethnography: qualitative; relational goods; social support; Users' Experience

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

The Deep Propagating Gravity Wave Experiment (DEEPWAVE): An airborne and ground-based exploration of gravity wave propagation and effects from their sources throughout the lower and middle atmosphere

The Deep Propagating Gravity Wave Experiment (DEEPWAVE) was designed to quantify gravity wave (GW) dynamics and effects from orographic and other sources to regions of dissipation at high altitudes. The core DEEPWAVE field phase took place from May through July 2014 using a comprehensive suite of airborne and ground-based instruments providing measurements from Earth’s surface to ∼100 km. Austral winter was chosen to observe deep GW propagation to high altitudes. DEEPWAVE was based on South Island, New Zealand, to provide access to the New Zealand and Tasmanian “hotspots” of GW activity and additional GW sources over the Southern Ocean and Tasman Sea. To observe GWs up to ∼100 km, DEEPWAVE utilized three new instruments built specifically for the National Science Foundation (NSF)/National Center for Atmospheric Research (NCAR) Gulfstream V (GV): a Rayleigh lidar, a sodium resonance lidar, and an advanced mesosphere temperature mapper. These measurements were supplemented by in situ probes, dropsondes, and a microwave temperature profiler on the GV and by in situ probes and a Doppler lidar aboard the German DLR Falcon. Extensive ground-based instrumentation and radiosondes were deployed on South Island, Tasmania, and Southern Ocean islands. Deep orographic GWs were a primary target but multiple flights also observed deep GWs arising from deep convection, jet streams, and frontal systems. Highlights include the following: 1) strong orographic GW forcing accompanying strong cross-mountain flows, 2) strong high-altitude responses even when orographic forcing was weak, 3) large-scale GWs at high altitudes arising from jet stream sources, and 4) significant flight-level energy fluxes and often very large momentum fluxes at high altitudes

Autism Spectrum Disorder Symptoms Among Children Enrolled in the Study to Explore Early Development (SEED)

This study examined the phenotypic profiles of children aged 30–68 months in the Study to Explore Early Development (SEED). Children classified as autism spectrum disorder (ASD), developmental delay (DD) with ASD symptoms, DD without ASD symptoms, and population comparison (POP) differed significantly from each other on cognitive, adaptive, behavioral, and social functioning and the presence of parent-reported conditions. Children with ASD and DD with ASD symptoms had mild to severe ASD risk on several measures compared to children with other DD and POP who had little ASD risk across measures. We conclude that children in SEED have varying degrees of ASD impairment and associated deficits. SEED thus provides a valuable sample to explore ASD phenotypes and inform risk factor analyses

The North Atlantic Waveguide and Downstream Impact Experiment

The North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX) explored the impact of diabatic processes on disturbances of the jet stream and their influence on downstream high-impact weather through the deployment of four research aircraft, each with a sophisticated set of remote sensing and in situ instruments, and coordinated with a suite of ground-based measurements. A total of 49 research flights were performed, including, for the first time, coordinated flights of the four aircraft: the German High Altitude and Long Range Research Aircraft (HALO), the Deutsches Zentrum für Luft- und Raumfahrt (DLR) Dassault Falcon 20, the French Service des Avions Français Instrumentés pour la Recherche en Environnement (SAFIRE) Falcon 20, and the British Facility for Airborne Atmospheric Measurements (FAAM) BAe 146. The observation period from 17 September to 22 October 2016 with frequently occurring extratropical and tropical cyclones was ideal for investigating midlatitude weather over the North Atlantic. NAWDEX featured three sequences of upstream triggers of waveguide disturbances, as well as their dynamic interaction with the jet stream, subsequent development, and eventual downstream weather impact on Europe. Examples are presented to highlight the wealth of phenomena that were sampled, the comprehensive coverage, and the multifaceted nature of the measurements. This unique dataset forms the basis for future case studies and detailed evaluations of weather and climate predictions to improve our understanding of diabatic influences on Rossby waves and the downstream impacts of weather systems affecting Europe