The NCEP Climate Forecast System Reanalysis
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Climate Models and Their Evaluation
8 Climate Models and Their Evaluation Coordinating Lead Authors: David A. Randall (USA), Richard A. Wood (UK) Lead Authors: Sandrine Bony (France), Robert Colman (Australia), Thierry Fichefet (Belgium), John Fyfe (Canada), Vladimir Kattsov (Russian Federation), Andrew Pitman (Australia), Jagadish Shukla (USA), Jayaraman Srinivasan (India), Ronald J. Stouffer (USA), Akimasa Sumi (Japan), Karl E. Taylor (USA) Contributing Authors: K. AchutaRao (USA), R. Allan (UK), A. Berger (Belgium), H. Blatter (Switzerland), C. Bonfi ls (USA, France), A. Boone (France, USA), C. Bretherton (USA), A. Broccoli (USA), V. Brovkin (Germany, Russian Federation), W. Cai (Australia), M. Claussen (Germany), P. Dirmeyer (USA), C. Doutriaux (USA, France), H. Drange (Norway), J.-L. Dufresne (France), S. Emori (Japan), P. Forster (UK), A. Frei (USA), A. Ganopolski (Germany), P. Gent (USA), P. Gleckler (USA), H. Goosse (Belgium), R. Graham (UK), J.M. Gregory (UK), R. Gudgel (USA), A. Hall (USA), S. Hallegatte (USA, France), H. Hasumi (Japan), A. Henderson-Sellers (Switzerland), H. Hendon (Australia), K. Hodges (UK), M. Holland (USA), A.A.M. Holtslag (Netherlands), E. Hunke (USA), P. Huybrechts (Belgium), W. Ingram (UK), F. Joos (Switzerland), B. Kirtman (USA), S. Klein (USA), R. Koster (USA), P. Kushner (Canada), J. Lanzante (USA), M. Latif (Germany), N.-C. Lau (USA), M. Meinshausen (Germany), A. Monahan (Canada), J.M. Murphy (UK), T. Osborn (UK), T. Pavlova (Russian Federationi), V. Petoukhov (Germany), T. Phillips (USA), S. Power (Australia), S. Rahmstorf (Germany), S.C.B. Raper (UK), H. Renssen (Netherlands), D. Rind (USA), M. Roberts (UK), A. Rosati (USA), C. Schär (Switzerland), A. Schmittner (USA, Germany), J. Scinocca (Canada), D. Seidov (USA), A.G. -
Emulation of a Full Suite of Atmospheric Physics
Emulation of a Full Suite of Atmospheric Physics Parameterizations in NCEP GFS using a Neural Network Alexei Belochitski1;2 and Vladimir Krasnopolsky2 1IMSG, 2NOAA/NWS/NCEP/EMC email: [email protected] 1 Introduction is captured by periodic functions of day and month, and variability of solar energy output is reflected in the solar constant. In addition to the full atmospheric state, NN receives full land-surface model (LSM) state to Machine learning (ML) can be used in parameterization development at least in two different ways: 1) as an capture impact of surface boundary conditions. All NN outputs are increments of dynamical core's prognostic emulation technique for accelerating calculation of parameterizations developed previously, and 2) for devel- variables that the original atmospheric physics package modifies. opment of new \empirical" parameterizations based on reanalysis/observed data or data simulated by high resolution models. An example of the former is the paper by Krasnopolsky et al. (2012) who have developed highly efficient neural network (NN) emulations of both long{ and short-wave radiation parameterizations for 4 Hybrid Coupling of Full Atmospheric Physics NN to GFS a high resolution state-of-the-art short{ to medium-range weather forecasting model. More recently, Gentine Full atmospheric physics NN only et al. (2018) have used a neural network to emulate 2D cloud resolving models (CRMs) embedded into columns updates the state of the atmo- of a \super-parameterized" general circulation model (GCM) in an aqua-planet configuration with prescribed sphere and does not update the invariant zonally symmetric SST, full diurnal cycle, and no annual cycle. -
Information Summaries
TIROS 8 12/21/63 Delta-22 TIROS-H (A-53) 17B S National Aeronautics and TIROS 9 1/22/65 Delta-28 TIROS-I (A-54) 17A S Space Administration TIROS Operational 2TIROS 10 7/1/65 Delta-32 OT-1 17B S John F. Kennedy Space Center 2ESSA 1 2/3/66 Delta-36 OT-3 (TOS) 17A S Information Summaries 2 2 ESSA 2 2/28/66 Delta-37 OT-2 (TOS) 17B S 2ESSA 3 10/2/66 2Delta-41 TOS-A 1SLC-2E S PMS 031 (KSC) OSO (Orbiting Solar Observatories) Lunar and Planetary 2ESSA 4 1/26/67 2Delta-45 TOS-B 1SLC-2E S June 1999 OSO 1 3/7/62 Delta-8 OSO-A (S-16) 17A S 2ESSA 5 4/20/67 2Delta-48 TOS-C 1SLC-2E S OSO 2 2/3/65 Delta-29 OSO-B2 (S-17) 17B S Mission Launch Launch Payload Launch 2ESSA 6 11/10/67 2Delta-54 TOS-D 1SLC-2E S OSO 8/25/65 Delta-33 OSO-C 17B U Name Date Vehicle Code Pad Results 2ESSA 7 8/16/68 2Delta-58 TOS-E 1SLC-2E S OSO 3 3/8/67 Delta-46 OSO-E1 17A S 2ESSA 8 12/15/68 2Delta-62 TOS-F 1SLC-2E S OSO 4 10/18/67 Delta-53 OSO-D 17B S PIONEER (Lunar) 2ESSA 9 2/26/69 2Delta-67 TOS-G 17B S OSO 5 1/22/69 Delta-64 OSO-F 17B S Pioneer 1 10/11/58 Thor-Able-1 –– 17A U Major NASA 2 1 OSO 6/PAC 8/9/69 Delta-72 OSO-G/PAC 17A S Pioneer 2 11/8/58 Thor-Able-2 –– 17A U IMPROVED TIROS OPERATIONAL 2 1 OSO 7/TETR 3 9/29/71 Delta-85 OSO-H/TETR-D 17A S Pioneer 3 12/6/58 Juno II AM-11 –– 5 U 3ITOS 1/OSCAR 5 1/23/70 2Delta-76 1TIROS-M/OSCAR 1SLC-2W S 2 OSO 8 6/21/75 Delta-112 OSO-1 17B S Pioneer 4 3/3/59 Juno II AM-14 –– 5 S 3NOAA 1 12/11/70 2Delta-81 ITOS-A 1SLC-2W S Launches Pioneer 11/26/59 Atlas-Able-1 –– 14 U 3ITOS 10/21/71 2Delta-86 ITOS-B 1SLC-2E U OGO (Orbiting Geophysical -
Documentation and Software User’S Manual, Version 4.1
The Canadian Seasonal to Interannual Prediction System version 2 (CanSIPSv2) Canadian Meteorological Centre Technical Note H. Lin1, W. J. Merryfield2, R. Muncaster1, G. Smith1, M. Markovic3, A. Erfani3, S. Kharin2, W.-S. Lee2, M. Charron1 1-Meteorological Research Division 2-Canadian Centre for Climate Modelling and Analysis (CCCma) 3-Canadian Meteorological Centre (CMC) 7 May 2019 i Revisions Version Date Authors Remarks 1.0 2019/04/22 Hai Lin First draft 1.1 2019/04/26 Hai Lin Corrected the bias figures. Comments from Ryan Muncaster, Bill Merryfield 1.2 2019/05/01 Hai Lin Figures of CanSIPSv2 uses CanCM4i plus GEM-NEMO 1.3 2019/05/03 Bill Merrifield Added CanCM4i information, sea ice Hai Lin verification, 6.6 and 9 1.4 2019/05/06 Hai Lin All figures of CanSIPSv2 with CanCM4i and GEM-NEMO, made available by Slava Kharin ii © Environment and Climate Change Canada, 2019 Table of Contents 1 Introduction ............................................................................................................................. 4 2 Modifications to models .......................................................................................................... 6 2.1 CanCM4i .......................................................................................................................... 6 2.2 GEM-NEMO .................................................................................................................... 6 3 Forecast initialization ............................................................................................................. -
Ocean Circulation Models and Modeling
2512-5 Fundamentals of Ocean Climate Modelling at Global and Regional Scales (Hyderabad - India) 5 - 14 August 2013 Ocean Circulation Models and Modeling GRIFFIES Stephen Princeton University U.S. Department of Commerce N.O.A.A., Geophysical Fluid Dynamics Laboratory, 201 Forrestal Road, Forrestal Campus P.O. Box 308, 08542-6649 Princeton NJ U.S.A. 5.1: Ocean Circulation Models and Modeling Stephen.Griffi[email protected] NOAA/Geophysical Fluid Dynamics Laboratory Princeton, USA [email protected] Laboratoire de Physique des Oceans,´ LPO Brest, France Draft from May 24, 2013 1 5.1.1. Scope of this chapter 2 We focus in this chapter on numerical models used to understand and predict large- 3 scale ocean circulation, such as the circulation comprising basin and global scales. It 4 is organized according to two themes, which we consider the “pillars” of numerical 5 oceanography. The first addresses physical and numerical topics forming a foundation 6 for ocean models. We focus here on the science of ocean models, in which we ask 7 questions about fundamental processes and develop the mathematical equations for 8 ocean thermo-hydrodynamics. We also touch upon various methods used to represent 9 the continuum ocean fluid with a discrete computer model, raising such topics as the 10 finite volume formulation of the ocean equations; the choice for vertical coordinate; 11 the complementary issues related to horizontal gridding; and the pervasive questions 12 of subgrid scale parameterizations. The second theme of this chapter concerns the 13 applications of ocean models, in particular how to design an experiment and how to 14 analyze results. -
INTEGRATED FORECAST and MANAGEMENT in NORTHERN CALIFORNIA – INFORM a Demonstration Project
CPASW March 23, 2006 INTEGRATED FORECAST AND MANAGEMENT IN NORTHERN CALIFORNIA – INFORM A Demonstration Project Present: Eylon Shamir HYDROLOGIC RESEARCH CENTER GEORGIA WATER RESOURCES INSTITUTE Eylon Shamir: [email protected] www.hrc-web.org INFORM: Integrated Forecast and Management in Northern California Hydrologic Research Center & Georgia Water Resources Institute Sponsors: CALFED Bay Delta Authority California Energy Commission National Oceanic and Atmospheric Administration Collaborators: California Department of Water Resources California-Nevada River Forecast Center Sacramento Area Flood Control Agency U.S. Army Corps of Engineers U.S. Bureau of Reclamation Eylon Shamir: [email protected] www.hrc-web.org Vision Statement Increase efficiency of water use in Northern California using climate, hydrologic and decision science Eylon Shamir: [email protected] www.hrc-web.org Goal and Objectives Demonstrate the utility of climate and hydrologic forecasts for water resources management in Northern California Implement integrated forecast-management systems for the Northern California reservoirs using real-time data Perform tests with actual data and with management input Eylon Shamir: [email protected] www.hrc-web.org Major Resevoirs in Nothern California Application Area 41.5 Sacramen to River Capacity of Major Reservoirs 41 (million acre-feet): Pit River Trinit y Trinity - 2.4 Trinity Shasta 40.5 Trinity River Shasta Shasta - 4.5 Oroville - 3.5 Feather River 40 Folsom - 1 39.5 Degrees North Latitude Oroville Oroville N. -
Atmospheric Dispersion of Radioactive Material in Radiological Risk Assessment and Emergency Response
Progress in NUCLEAR SCIENCE and TECHNOLOGY, Vol. 1, p.7-13 (2011) REVIEW Atmospheric Dispersion of Radioactive Material in Radiological Risk Assessment and Emergency Response YAO Rentai * China Institute for Radiation Protection, P.O.Box 120, Taiyuan, Shanxi 030006, China The purpose of a consequence assessment system is to assess the consequences of specific hazards on people and the environment. In this paper, the studies on technique and method of atmospheric dispersion modeling of radioactive material in radiological risk assessment and emergency response are reviewed in brief. Some current statuses of nuclear accident consequences assessment in China were introduced. In the future, extending the dispersion modeling scales such as urban building scale, establishing high quality experiment dataset and method of model evaluation, improved methods of real-time modeling using limited inputs, and so on, should be promoted with high priority of doing much more work. KEY WORDS: atmospheric model, risk assessment, emergency response, nuclear accident 11) I. Introduction from U.S. NOAA, and SPEEDI/WSPEEDI from The studies and developments of techniques and methods Japan/JAERI. However, the needs of emergency of atmospheric dispersion modeling of radioactive material management may not be well satisfied by existing models in radiological risk assessment and emergency response which are not well designed and confronted with difficulty have evolved over the past 50-60 years. The three marked in detailed constructions of local wind and turbulence -
5Th International Conference on Reanalysis (ICR5)
5th International Conference on Reanalysis (ICR5) 13–17 November 2017, Rome IMPLEMENTED BY Contents ECMWF | 5th International Conference on Reanalysis (ICR5) 2017 2 Introduction It is our pleasure to welcome the Climate research has benefited over the • Status and plans for future reanalyses • Evaluation of reanalyses reanalysis community at the 5th years from the continuing development Global and regional production, inclusive Comparisons with observations, International Conference on Reanalysis of reanalysis. As reanalysis datasets of all WCRP thematic areas: atmosphere, other types of analysis and models, (ICR5). We are delighted that we are become more diverse (atmosphere, land, ocean and cryosphere – Session inter-comparisons, diagnostics – all able to come together in Rome. ocean and land components), more organisers: Mike Bosilovich (NASA Session organisers: Franco Desiato This five-day international conference complete (moving towards Earth-system GMAO), Shinya Kobayashi (JMA), (ISPRA), Masatomo Fujiwara (Hokkaido is the worldwide leading event for the coupled reanalysis), more detailed, and Simona Masina (CMCC) University), Sonia Seneviratne (ETH), continuing development of reanalysis of longer timespan, community efforts Adrian Simmons (ECMWF) • Observations for reanalyses for climate research, which provides a to evaluate and intercompare them Preparation, organisation in large • Applications of reanalyses comprehensive numerical description become more important. archives, data rescue, reanalysis Generating time-series of Essential -
Reliability” Within the Qualitative Methodological Literature As a Proxy for the Concept of “Error” Within the Qualitative Research Domain
The “Error” in Psychology: An Analysis of Quantitative and Qualitative Approaches in the Pursuit of Accuracy by Donna Tafreshi MA, Simon Fraser University, 2014 BA, Simon Fraser University, 2011 Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in the Department of Psychology Faculty of Arts and Social Sciences © Donna Tafreshi 2018 SIMON FRASER UNIVERSITY Fall 2018 Copyright in this work rests with the author. Please ensure that any reproduction or re-use is done in accordance with the relevant national copyright legislation. Approval Name: Donna Tafreshi Degree: Doctor of Philosophy Title: The “Error” in Psychology: An Analysis of Quantitative and Qualitative Approaches in the Pursuit of Accuracy Examining Committee: Chair: Thomas Spalek Professor Kathleen Slaney Senior Supervisor Professor Timothy Racine Supervisor Professor Susan O’Neill Supervisor Professor Barbara Mitchell Internal Examiner Professor Departments of Sociology & Gerontology Christopher Green External Examiner Professor Department of Psychology York University Date Defended/Approved: October 12, 2018 ii Abstract The concept of “error” is central to the development and use of statistical tools in psychology. Yet, little work has focused on elucidating its conceptual meanings and the potential implications for research practice. I explore the emergence of uses of the “error” concept within the field of psychology through a historical mapping of its uses from early observational astronomy, to the study of social statistics, and subsequently to its adoption under 20th century psychometrics. In so doing, I consider the philosophical foundations on which the concepts “error” and “true score” are built and the relevance of these foundations for its usages in psychology. -
Evaluation of Cloud Properties in the NOAA/NCEP Global Forecast System Using Multiple Satellite Products
Clim Dyn DOI 10.1007/s00382-012-1430-0 Evaluation of cloud properties in the NOAA/NCEP global forecast system using multiple satellite products Hyelim Yoo • Zhanqing Li Received: 15 July 2011 / Accepted: 19 June 2012 Ó Springer-Verlag 2012 Abstract Knowledge of cloud properties and their vertical are similar to those from the model, latitudinal variations structure is important for meteorological studies due to their show discrepancies in terms of structure and pattern. The impact on both the Earth’s radiation budget and adiabatic modeled cloud optical depth over storm track region and heating within the atmosphere. The objective of this study is to subtropical region is less than that from the passive sensor and evaluate bulk cloud properties and vertical distribution sim- is overestimated for deep convective clouds. The distributions ulated by the US National Oceanic and Atmospheric of ice water path (IWP) agree better with satellite observations Administration National Centers for Environmental than do liquid water path (LWP) distributions. Discrepancies Prediction Global Forecast System (GFS) using three global in LWP/IWP distributions between observations and the satellite products. Cloud variables evaluated include the model are attributed to differences in cloud water mixing ratio occurrence and fraction of clouds in up to three layers, cloud and mean relative humidity fields, which are major control optical depth, liquid water path, and ice water path. Cloud variables determining the formation of clouds. vertical structure data are retrieved from both active (Cloud- Sat/CALIPSO) and passive sensors and are subsequently Keywords Cloud fraction Á NCEP global forecast system Á compared with GFS model results. -
Governing Collective Action in the Face of Observational Error
Governing Collective Action in the Face of Observational Error Thomas Markussen, Louis Putterman and Liangjun Wang* August 2017 Abstract: We present results from a repeated public goods experiment where subjects choose by vote one of two sanctioning schemes: peer-to-peer (informal) or centralized (formal). We introduce, in some treatments, a moderate amount of noise (a 10 percent probability that a contribution is reported incorrectly) affecting either one or both sanctioning environments. We find that the institution with more accurate information is always by far the most popular, but noisy information undermines the popularity of peer-to-peer sanctions more strongly than that of centralized sanctions. This may contribute to explaining the greater reliance on centralized sanctioning institutions in complex environments. JEL codes: H41, C92, D02 Keywords: Public goods, sanctions, information, institution, voting * University of Copenhagen, Brown University, and Zhejiang University of Technology, respectively. We would like to thank The National Natural Science Foundation of China (Grant No.71473225) and The Danish Council for Independent Research (DFF|FSE) for funding. We thank Xinyi Zhang for assistance in checking translation of instructions, and Vicky Zhang and Kristin Petersmann for help in checking of proofs. We are grateful to seminar participants at University of Copenhagen, at the Seventh Biennial Social Dilemmas Conference held at University of Massachusetts, Amherst in June 2017, and to Attila Ambrus, Laura Gee and James Walker for helpful comments. 1 Introduction When and why do groups establish centralized, formal authorities to solve collective action problems? This fundamental question was central to classical social contract theory (Hobbes, Locke) and has recently been the subject of a group of experimental studies (Okada, Kosfeld and Riedl 2009, Traulsen et al. -
The Role of Observational Errors in Optimum Interpolation Analysis :I0
U.S. DEPARTMENT OF COMMERCE NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION NATIONAL WEATHER SERVICE NATIONAL METEOROLOGICAL CENTER OFFICE NOTE 175 The Role of Observational Errors in Optimum Interpolation Analysis :I0. K. H. Bergman Development Division APRIL 1978 This is an unreviewed manuscript, primarily intended for informal exchange of information among NMC staff members. k Iil The Role of Observational Errors in Optimum Interpolation Analysis 1. Introduction With the advent of new observing systems and the approach of the First GARP Global Experiment (FGGE), it appears desirable for data providers to have some knowledge of how their product is to be used in the statistical "optimum interpolation" analysis schemes which will operate at most of the Level III centers during FGGE. It is the hope of the author that this paper will serve as a source of information on the way observational data is handled by optimum interpolation analysis schemes. The theory of optimum interpolation analysis, especially with regard to the role of observational errors, is reviewed in Section 2. In Section 3, some comments about determining observational error characteristics are given along with examples of same. Section 4 discusses error-checking procedures which are used to identify suspect observations. These latter procedures are an integral part of any analysis scheme. 2. Optimum Interpolation Analysis The job of the analyst is to take an irregular distribution of obser- vations of variable quality and obtain the best possible estimate of a meteorological field at a regular network of grid points. The optimum interpolation analysis scheme attempts to accomplish this goal by mini- mizing the mean square interpolation error for a large ensemble of analysis situations.