Ensemble Methods for Meteorological Predictions
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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. -
Chapter 6: Ensemble Forecasting and Atmospheric Predictability
Chapter 6: Ensemble Forecasting and Atmospheric Predictability Introduction Deterministic Chaos (what!?) In 1951 Charney indicated that forecast skill would break down, but he attributed it to model errors and errors in the initial conditions… In the 1960’s the forecasts were skillful for only one day or so. Statistical prediction was equal or better than dynamical predictions, Like it was until now for ENSO predictions! Lorenz wanted to show that statistical prediction could not match prediction with a nonlinear model for the Tokyo (1960) NWP conference So, he tried to find a model that was not periodic (otherwise stats would win!) He programmed in machine language on a 4K memory, 60 ops/sec Royal McBee computer He developed a low-order model (12 d.o.f) and changed the parameters and eventually found a nonperiodic solution Printed results with 3 significant digits (plenty!) Tried to reproduce results, went for a coffee and OOPS! Lorenz (1963) discovered that even with a perfect model and almost perfect initial conditions the forecast loses all skill in a finite time interval: “A butterfly in Brazil can change the forecast in Texas after one or two weeks”. In the 1960’s this was only of academic interest: forecasts were useless in two days Now, we are getting closer to the 2 week limit of predictability, and we have to extract the maximum information Central theorem of chaos (Lorenz, 1960s): a) Unstable systems have finite predictability (chaos) b) Stable systems are infinitely predictable a) Unstable dynamical system b) Stable dynamical -
Model Error in Weather and Climate Forecasting £ Myles Allen £ , Jamie Kettleborough† and David Stainforth
Model error in weather and climate forecasting £ Myles Allen £ , Jamie Kettleborough† and David Stainforth Department£ of Physics, University of Oxford †Space Science and Technology Department, Rutherford Appleton Laboratory “As if someone were to buy several copies of the morning newspaper to assure himself that what it said was true” Ludwig Wittgenstein 1 Introduction We review various interpretations of the phrase “model error”, choosing to focus here on how to quantify and minimise the cumulative effect of model “imperfections” that either have not been eliminated because of incomplete observations/understanding or cannot be eliminated because they are intrinsic to the model’s structure. We will not provide a recipe for eliminating these imperfections, but rather some ideas on how to live with them because, no matter how clever model-developers, or fast super-computers, become, these imperfections will always be with us and represent the hardest source of uncertainty to quantify in a weather or climate forecast. We spend a little time reviewing how uncertainty in the current state of the atmosphere/ocean system is used in the initialisation of ensemble weather or seasonal forecasting, because it might appear to provide a reasonable starting point for the treatment of model error. This turns out not to be the case, because of the fundamental problem of the lack of an objective metric or norm for model error: we have no way of measuring the distance between two models or model-versions in terms of their input parameters or structure in all but a trivial and irrelevant subset of cases. Hence there is no way of allowing for model error by sampling the space of “all possible models” in a representative way, because distance within this space is undefinable. -
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 ............................................................................................................. -
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 -
Lecture 29. Introduction to Atmospheric Chemical Transport Models
Lecture 29. Introduction to atmospheric chemical transport models. Part 1. Objectives: 1. Model types. 2. Box models. 3. One-dimensional models. 4. Two-dimensional models. 5. Three-dimensional models. Readings: Graedel T. and P.Crutzen. “Atmospheric change: an earth system perspective”. Chapter 15.”Bulding environmental chemical models”, 1992. 1. Model types. Mathematical models provide the necessary framework for integration of our understanding of individual atmospheric processes and study of their interactions. Note, that atmosphere is a complex reactive system in which numerous physical and chemical processes occur simultaneously. • Atmospheric chemical transport models are defined according to their spatial scale: Model Typical domain scale Typical resolution Microscale 200x200x100 m 5 m Mesoscale(urban) 100x100x5 km 2 km Regional 1000x1000x10 km 20 km Synoptic(continental) 3000x3000x20 km 80 km Global 65000x65000x20km 50x50 1 Figure 29.1 Components of a chemical transport model (Seinfeld and Pandis, 1998). 2 • Domain of the atmospheric model is the area that is simulated. The computation domain consists of an array of computational cells, each having uniform chemical composition. The size of cells determines the spatial resolution of the model. • Atmospheric chemical transport models are also characterized by their dimensionality: zero-dimensional (box) model; one-dimensional (column) model; two-dimensional model; and three-dimensional model. • Model time scale depends on a specific application varying from hours (e.g., air quality model) to hundreds of years (e.g., climate models) 3 Two principal approaches to simulate changes in the chemical composition of a given air parcel: 1) Lagrangian approach: air parcel moves with the local wind so that there is no mass exchange that is allowed to enter the air parcel and its surroundings (except of species emissions). -
A Brief Summary of Plans for the GMAO Core Priorities and Initiatives for the Next 5 Years
A Brief Summary of Plans for the GMAO Core Priorities and Initiatives for the Next 5 years Provided as information for ROSES 2012 A.13 – MAP Developments in the GMAO are focused on the next generation systems, GEOS-6, and an Integrated Earth System Analysis and the associated modeling system that supports that analysis. GEOS-6 and IESA (1) The GEOS-6 system will be built around the next generation, non-hydrostatic atmospheric model with aerosol-cloud microphysics (advances upon the Morrison-Gettelman cloud microphysics and the Modal Aerosol Model (MAM) aerosol microphysics module for the inclusion of aerosol indirect effects) and an accompanying hybrid (ensemble-variational) 4DVar atmospheric assimilation system. (2) IESA capabilities for other parts of the earth system, including atmospheric chemical constituents and aerosols, ocean circulation, land hydrology, and carbon budget will be built upon our existing separate assimilation capabilities. The GEOS Model Our modeling strategy is driven by the need to have a comprehensive global model valid for both weather and climate and for use in both simulation and assimilation. Our main task in atmospheric modeling during the next five years will be to make the transition to GEOS-6. This direction is driven by (i) the need to improve the representation of clouds and precipitation to enable use of cloud- and precipitation-contaminated satellite radiance observations in NWP, and (ii) the research goal of understanding and predicting weather- climate connections. Development will focus on 1km to 10km resolutions that will be needed for the data assimilation system (DAS). Climate resolutions (10-100km) will not be ignored, but developments for resolutions coarser than 50 km will have lower priority. -
A Machine Learning Based Ensemble Forecasting Optimization Algorithm for Preseason Prediction of Atlantic Hurricane Activity
atmosphere Article A Machine Learning Based Ensemble Forecasting Optimization Algorithm for Preseason Prediction of Atlantic Hurricane Activity Xia Sun 1 , Lian Xie 1,*, Shahil Umeshkumar Shah 2 and Xipeng Shen 2 1 Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Box 8208, Raleigh, NC 27695-8208, USA; [email protected] 2 Department of Computer Sciences, North Carolina State University, Raleigh, NC 27695-8206, USA; [email protected] (S.U.S.); [email protected] (X.S.) * Correspondence: [email protected] Abstract: In this study, nine different statistical models are constructed using different combinations of predictors, including models with and without projected predictors. Multiple machine learning (ML) techniques are employed to optimize the ensemble predictions by selecting the top performing ensemble members and determining the weights for each ensemble member. The ML-Optimized Ensemble (ML-OE) forecasts are evaluated against the Simple-Averaging Ensemble (SAE) forecasts. The results show that for the response variables that are predicted with significant skill by individual ensemble members and SAE, such as Atlantic tropical cyclone counts, the performance of SAE is comparable to the best ML-OE results. However, for response variables that are poorly modeled by Citation: Sun, X.; Xie, L.; Shah, S.U.; individual ensemble members, such as Atlantic and Gulf of Mexico major hurricane counts, ML-OE Shen, X. A Machine Learning Based predictions often show higher skill score than individual model forecasts and the SAE predictions. Ensemble Forecasting Optimization Algorithm for Preseason Prediction of However, neither SAE nor ML-OE was able to improve the forecasts of the response variables when Atlantic Hurricane Activity. -
DJ Karoly Et
Supporting Online Material “Detection of a human influence on North American climate”, D. J. Karoly et al. (2003) Materials and Methods Description of the climate models GFDL R30 is a spectral atmospheric model with rhomboidal truncation at wavenumber 30 equivalent to 3.75° longitude × 2.2° latitude (96 × 80) with 14 levels in the vertical. The atmospheric model is coupled to an 18 level gridpoint (192 × 80) ocean model where two ocean grid boxes underlie each atmospheric grid box exactly. Both models are described by Delworth et al. (S1) and Dixon et al. (S2). HadCM2 and HadCM3 use the same atmospheric horizontal resolution, 3.75°× 2.5° (96 × 72) finite difference model (T42/R30 equivalent) with 19 levels in the atmosphere and 20 levels in the ocean (S3, S4). For HadCM2, the ocean horizontal grid lies exactly under that of the atmospheric model. The ocean component of HadCM3 uses much higher resolution (1.25° × 1.25°) with six ocean grid boxes for every atmospheric grid box. In the context of results shown here, the main difference between the two models is that HadCM3 includes improved representations of physical processes in the atmosphere and the ocean (S5). For example, HadCM3 employs a radiation scheme that explicitly represents the radiative effects of minor greenhouse gases as well as CO2, water vapor and ozone (S6), as well as a simple parametrization of background aerosol (S7). ECHAM4/OPYC3 is an atmospheric T42 spectral model equivalent to 2.8° longitude × 2.8° latitude (128 × 64) with 19 vertical layers. The ocean model OPYC3 uses isopycnals as the vertical coordinate system. -
Climate Reanalysis
from Newsletter Number 139 – Spring 2014 METEOROLOGY Climate reanalysis doi:10.21957/qnreh9t5 Dick Dee interviewed by Bob Riddaway Climate reanalysis This article appeared in the Meteorology section of ECMWF Newsletter No. 139 – Spring 2014, pp. 15–21. Climate reanalysis Dick Dee interviewed by Bob Riddaway Dick Dee is the Head of the Reanalysis Section at ECMWF. He is responsible for leading a group of scientists producing state-of-the art global climate datasets. This has involved coordinating two international research projects: ERA-CLIM and ERA-CLIM2. The ERA-CLIM2 project has recently started – what is its purpose? This exciting new project extends and expands the work begun in the ERA-CLIM project that ended in December 2013. The aim is to produce physically consistent reanalyses of the global atmosphere, ocean, land-surface, cryosphere and the carbon cycle – see Box A for more information about what is involved in reanalysis. The project is at the heart of a concerted effort in Europe to build the information infrastructure needed to support climate monitoring, climate research and climate services, based on the best available science and observations. The ERA-CLIM2 project will rely on ECMWF’s expertise in modelling and data assimilation to develop a first th coupled ocean-atmosphere reanalysis spanning the 20 century. It includes activities aimed at improving the available observational record (e.g. through data rescue and reprocessing), the assimilating model (primarily by coupling the atmosphere with the ocean) and the data assimilation techniques (e.g. related to data assimilation in coupled models). The project will also develop improved data services in order to prepare for the need to support new classes of users, including providers of climate services and European policy makers. -
Atmospheric Dispersion Modeling of the February 2014 Waste Isolation
L L N L ‐ XAtmospheric Dispersion X XModeling of the February 2014 X ‐ Waste Isolation Pilot Plant X XRelease X X John Nasstrom, Tom Piggott, X Matthew Simpson, Megan Lobaugh, Lydia Tai, Brenda Pobanz, Kristen Yu July 22, 2015 LLNL-TR-666379 Lawrence Livermore National Laboratory is operated by Lawrence Livermore National Security, LLC, for the U.S. Department of Energy, National Nuclear Security Administration under Contract DE‐AC52‐07NA27344. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. This document was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor Lawrence Livermore National Security, LLC, nor any of their employees makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or Lawrence Livermore National Security, LLC. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC, and shall not -
The New Hadley Centre Climate Model (Hadgem1): Evaluation of Coupled Simulations
1APRIL 2006 J OHNS ET AL. 1327 The New Hadley Centre Climate Model (HadGEM1): Evaluation of Coupled Simulations T. C. JOHNS,* C. F. DURMAN,* H. T. BANKS,* M. J. ROBERTS,* A. J. MCLAREN,* J. K. RIDLEY,* ϩ C. A. SENIOR,* K. D. WILLIAMS,* A. JONES,* G. J. RICKARD, S. CUSACK,* W. J. INGRAM,# M. CRUCIFIX,* D. M. H. SEXTON,* M. M. JOSHI,* B.-W. DONG,* H. SPENCER,@ R. S. R. HILL,* J. M. GREGORY,& A. B. KEEN,* A. K. PARDAENS,* J. A. LOWE,* A. BODAS-SALCEDO,* S. STARK,* AND Y. SEARL* *Met Office, Exeter, United Kingdom ϩNIWA, Wellington, New Zealand #Met Office, Exeter, and Department of Physics, University of Oxford, Oxford, United Kingdom @CGAM, University of Reading, Reading, United Kingdom &Met Office, Exeter, and CGAM, University of Reading, Reading, United Kingdom (Manuscript received 30 May 2005, in final form 9 December 2005) ABSTRACT A new coupled general circulation climate model developed at the Met Office’s Hadley Centre is pre- sented, and aspects of its performance in climate simulations run for the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) documented with reference to previous models. The Hadley Centre Global Environmental Model version 1 (HadGEM1) is built around a new atmospheric dynamical core; uses higher resolution than the previous Hadley Centre model, HadCM3; and contains several improvements in its formulation including interactive atmospheric aerosols (sulphate, black carbon, biomass burning, and sea salt) plus their direct and indirect effects. The ocean component also has higher resolution and incorporates a sea ice component more advanced than HadCM3 in terms of both dynamics and thermodynamics.