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It explains why models are imperfect by covering measurement error, analysis error, error growth over integration time, model error, and grid limitations. The material defines deterministic models and ensemble members, introduces dynamical ensembles, control runs, and spread, then discusses ensemble applications such as skill-loss rate estimation, probabilistic event occurrence, sensitivity to data via Ensemble Kalman Filter and targeted observations, and compares ensemble mean performance with deterministic runs.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/ensemble-prediction-systems-question-1/657255/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/ensemble-prediction-systems-question-1/657255.png","ImageObject",300,407,{"name":92,"@type":93},"404brain","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-26",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What advantages do single-model ensembles provide?","Question",{"text":112,"@type":113},"Single-model ensembles help estimate uncertainty, show TC intensity model spread, and enable alternative TC-track solutions; therefore, the correct choice is “All of the above.”","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why aren’t models perfect in ensemble forecasting?",{"text":117,"@type":113},"Models cannot perfectly measure atmospheric variables, the initial state never exactly matches the real atmosphere, errors grow with integration time (especially at smaller scales), model equations cannot represent all atmospheric processes, and the model grid cannot resolve every feature explicitly.",{"name":119,"@type":110,"acceptedAnswer":120},"How do ensemble mean forecasts compare with deterministic runs?",{"text":121,"@type":113},"Deterministic runs (e.g., GFS) usually have more skill than individual ensemble members, while the ensemble mean typically has at least as much skill as an equal-resolution control run and can outperform a higher-resolution deterministic run beyond about 3 days.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},657255,1793013358,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},1099529839291,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Ensemble Prediction Systems  \nEric Blake  \nNational Hurricane Center  \n7 March 2017  \nAcknowledgements to Michael Brennan  \nQuestion 1  \nWhat are some current advantages of using single-model ensembles?  \nA. Estimates of uncertainty  \nB. TC intensity model spread  \nC. Alternative TC-track solutions  \nD. All of the above  \nE. A & C  \nWhy Aren’t Models Perfect?  \n• Atmospheric variables cannot be measured to an infinite degree of accuracy or precision  \n(measurement error)  \n• Models’ initial state never matches the real atmosphere (analysis error)  \n• Initial condition errors grow with model integration time, most rapidly at smaller scales (error growth)  \n• Model equations do not fully represent all of the processes in the atmosphere (model error)  \n• Model grid cannot explicitly resolve all features and processes in the atmosphere (model error)  \nOptions?  \n• Increase our understanding of physical processes and how models represent them (research)  \n• More accurate and numerous observations with greater coverage (expensive)  \n• Improved data assimilation methods (4-D Variational Data Assimilation, Ensemble Kalman Filter)  \n• Faster computers and more complex models (many programs competing for resources)  \n• Probabilistic forecasting with ensembles  \nDefinitions  \n• Deterministic Model-single forecast from one forecast model or method using a single set of initial conditions  \n– Examples: GFS, ECMWF, UKMET, GFDL, HWRF, BAMS  \n• Ensemble-collection of “member” forecasts verifying at the same time created from:  \n– Different but equally viable initial conditions  \n– Different forecasting methods and/or models that (ideally) statistically represent nearly all forecast possibilities  \nDefinitions  \n• Dynamical Model Ensemble –based on perturbation of initial conditions of a single model or different models to create“member” forecasts  \n– Examples: NCEP Global Ensemble Forecast System (GEFS), ECMWF Ensemble Prediction System  \n• Control Run – for dynamical model ensembles, the member of the ensemble run with the “best” initial analysis  \n– The analysis used by the control run is usually perturbed to produce initial conditions for the remaining ensemble members  \n• Spread – measure of the degree of disagreement (i.e., standard deviation) between ensemble members  \nEnsemble Use  \n• Originally used for mediumto long-range forecasting of the large-scale pattern  \n• Uses have grown to encompass all temporal and spatial scales down to convective storm scale  \n• Address uncertainty, particularly those leading to rapidly diverging solutions  \n– Initial conditions, model physics, resolution, model numerics  \nEnsemble Use  \n• Estimate rate of skill loss  \nwith time  \n– Spread of solutions  \ngenerally increases with  \ntime  \n• Compute probabilities of occurrence of a particular event or condition  \n– 25 mm of precipitation, winds > 34 kt  \n• Identify regions where the analysis and forecast are sensitive to additional data in the analysis  \n– Ensemble Kalman Filter,  \ntargeted observations  \nEnsemble Mean vs. Deterministic  \n• Deterministic runs (e.g., GFS) usually have more skill than any individual ensemble member due to superior resolution  \n• Ensemble mean usually has at least as much skill as an equal-resolution control run  \n• Ensemble mean can be more skillful than a higher-resolution deterministic run, especially beyond ~3 days  \nEnsemble Mean vs. Deterministic  \n500-mb height anomaly correlation die-off chart – 30 Jan-16 Mar 2015  \nGEFS mean provides about 1 day of additional skill compared to the GFS  \nCourtesy NCEP EMC  \n10","cbCaisM2VVRoCkAa","https://ap.wps.com/l/cbCaisM2VVRoCkAa","pdf",5760431,51,"English","# Question 1\n## Advantages of Single-Model Ensembles\n## Why Aren’t Models Perfect?\n## Definitions\n## Ensemble Use\n## Ensemble Mean vs. Deterministic","[{\"question\":\"What advantages do single-model ensembles provide?\",\"answer\":\"Single-model ensembles help estimate uncertainty, show TC intensity model spread, and enable alternative TC-track solutions; therefore, the correct choice is “All of the above.”\"},{\"question\":\"Why aren’t models perfect in ensemble forecasting?\",\"answer\":\"Models cannot perfectly measure atmospheric variables, the initial state never exactly matches the real atmosphere, errors grow with integration time (especially at smaller scales), model equations cannot represent all atmospheric processes, and the model grid cannot resolve every feature explicitly.\"},{\"question\":\"How do ensemble mean forecasts compare with deterministic runs?\",\"answer\":\"Deterministic runs (e.g., GFS) usually have more skill than individual ensemble members, while the ensemble mean typically has at least as much skill as an equal-resolution control run and can outperform a higher-resolution deterministic run beyond about 3 days.\"}]","Ensemble Prediction Systems - Question 1 | PDF",1792976357,129]