Performance Assessment of General Circulation Model in Simulating Daily Precipitation and Temperature Using Multiple Gridded Datasets
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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. -
Gridded Model Output Statistics: Improving and Expanding
6A.6 GRIDDED MODEL OUTPUT STATISTICS: IMPROVING AND EXPANDING Kathryn K. Gilbert*, Bob Glahn, Rebecca L. Cosgrove1, Kari L. Sheets, Geoffrey A. Wagner2 Meteorological Development Laboratory National Weather Service, NOAA Silver Spring, MD 1National Center for Environmental Prediction, National Weather Service, Camp Springs, MD 2Wyle Information Systems, McLean, VA 1. INTRODUCTION Since the original implementation, gridded MOS has undergone several adaptations to improve For years, National Weather Service (NWS) consistency as well as regional applicability. MDL forecasters have used Model Output Statistics developers have welcomed assessments of grid- (MOS) guidance (Glahn and Lowry 1972) as an ded MOS from local forecasters in order to make aid in producing text forecast products issued to adjustments to provide guidance which is both the user community. However, the methods fore- representative of the local area, and compatible casters use to generate products have changed with the process for generating forecasts for because of requirements to produce the official NDFD. Some of the improvements resulting from NWS 7-day forecasts on high-resolution grids. forecaster feedback include: removal of unrepre- These gridded, or digital, forecasts are put into a sentative forecasts from the analysis, expansion of National Digital Forecast Database (NDFD; Glahn the grid coverage, and modifications to the and Ruth 2003). NWS forecasters need support- land/water mask. ing guidance available on a grid, at a resolution comparable to the grid resolution used at the local In this paper, we discuss the current state of forecast office. One approach to the problem of the gridded MOS suite, as well as ongoing en- providing guidance on the required grid is to objec- hancements to the operational GFS-based MOS tively analyze forecasts at MOS stations. -
Verification of Model Output Statistics Forecasts Associated with the North
Allison Monarski, University of Maryland Masters Scholarly Paper, December 6, 2011 1 Department of Atmospheric and Oceanic Science Verification of Model Output Statistics forecasts associated with the North American Mesoscale model upgrade Results of work done during the Student Career Experience Program tour with the Statistical Modeling Branch (SMB) of the Meteorological Development Lab (MDL) of the National Weather Service (NWS), Silver Spring, MD, under the guidance of Mark Antolik & Kathy Gilbert, NWS/MDL/SMB I. Introduction and Motivation For the past four decades, the model output statistics (MOS) technique has been in operation and forecasters and meteorologists globally have used forecasts generated through MOS. MOS is a statistical approach to forecasting which involves determination of a relationship between a predictand and numerical weather prediction (NWP) model output for a variety of variables (predictors) at the same projection times (Glahn and Lowry 1972). Through the years, MOS has provided reliable forecasts, predicting quantities that the underlying NWP model is unable to predict. Specifically, the MOS technique can account for local effects on certain elements that the coarser resolution NWP model cannot detect (Antolik and Baker 2009). Usually, statistical forecasting systems, such as MOS, rely on many years of stable development data for optimal performance. However, due to frequent changes in the underlying structure, physics, and dynamics of the NWP models, this kind of data is difficult to obtain. Fortunately, developers at the National Weather Service’s (NWS) Meteorological Development Laboratory (MDL) have been able to show that reliable MOS forecasts can be produced using as little as just two years of data from models which are evolving (Antolik and Baker 2009). -
Results from the Implementation of the Elastic Viscous Plastic Sea Ice Rheology in Hadcm3 W
Results from the implementation of the Elastic Viscous Plastic sea ice rheology in HadCM3 W. M. Connolley, A. B. Keen, A. J. Mclaren To cite this version: W. M. Connolley, A. B. Keen, A. J. Mclaren. Results from the implementation of the Elastic Viscous Plastic sea ice rheology in HadCM3. Ocean Science, European Geosciences Union, 2006, 2 (2), pp.201- 211. hal-00298295 HAL Id: hal-00298295 https://hal.archives-ouvertes.fr/hal-00298295 Submitted on 23 Oct 2006 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Ocean Sci., 2, 201–211, 2006 www.ocean-sci.net/2/201/2006/ Ocean Science © Author(s) 2006. This work is licensed under a Creative Commons License. Results from the implementation of the Elastic Viscous Plastic sea ice rheology in HadCM3 W. M. Connolley1, A. B. Keen2, and A. J. McLaren2 1British Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 0ET, UK 2Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK Received: 13 June 2006 – Published in Ocean Sci. Discuss.: 10 July 2006 Revised: 21 September 2006 – Accepted: 16 October 2006 – Published: 23 October 2006 Abstract. We present results of an implementation of the a full dynamical model incorporating wind stresses and in- Elastic Viscous Plastic (EVP) sea ice dynamics scheme into ternal ice stresses leads to errors in the detailed representa- the Hadley Centre coupled ocean-atmosphere climate model tion of sea ice and limits our confidence in its future predic- HadCM3. -
Model Output Statistics Cascade to Improve Day Ahead Solar Irradiance Forecast
Available online at www.sciencedirect.com ScienceDirect Solar Energy 117 (2015) 99–113 www.elsevier.com/locate/solener Model output statistics cascade to improve day ahead solar irradiance forecast M. Pierro a, F. Bucci a, C. Cornaro a,⇑, E. Maggioni b, A. Perotto b, M. Pravettoni c, F. Spada b a Department of Enterprise Engineering, University of Rome Tor Vergata, Via del Politecnico, 1, 00133 Rome, Italy b IDEAM srl, Via Frova 34, Cinisello Balsamo, MI, Italy c Institute of Applied Sustainability to the Build Environment, University of Applied Sciences and Arts of Southern Switzerland, Canobbio CH-6952, Switzerland Received 29 September 2014; received in revised form 9 April 2015; accepted 26 April 2015 Communicated by: Associate Editor Christian A. Gueymard Abstract In this paper a new hybrid Model Output Statistics (MOS), named MOS cascade, is developed to refine the day-ahead forecast of the global horizontal irradiance provided by the Weather Research and Forecast (WRF) model. The proposed approach is based on a sequence of two different MOS. The first, called MOSRH, is a new physically based algorithm, built to correct the treatment of humidity in the WRF radiation schemes. The second, called MOSNN, is based on artificial intelligence techniques and aims to correct the main systematic and learnable errors of the Numerical Weather Prediction output. The 1-day and 2-day forecast accuracies are analyzed via direct comparison with irradiance data measured in two sites, Rome and Lugano. The paper shows that a considerable reduction in error was achieved using MOSRH model and MOS cascade. The differences between the two sites are discussed in details. -
Supporting Gridded Model Output Statistics Forecast Guidance System
Supporting Gridded Model Output Statistics Forecast Guidance System Kari L. Sheets ABSTRACT The Meteorological Development Laboratory (MDL) of NOAA’s National Weather Service is creating a National Digital Guidance Database at a fine resolution to comple- ment the existing National Digital Forecast Database. To help accomplish this goal, MDL is producing a gridded Model Output Statistics (MOS) forecast guidance system. Currently, gridded MOS populates a 5-km grid with elements needed for weather fore- cast grids covering the contiguous United States. This paper describes the use of Geographic Information System (GIS) to gener- ate and quality control geophysical variables, create a station dictionary including land/water designations for the observing stations, and analyze or troubleshoot problem areas in gridded MOS weather elements. Stations used in traditional MOS development are unevenly distributed, leaving developers searching for additional data to aid in ana- lyzing these values to the aforementioned grid. GIS is integral in supplying the data to move from station-based MOS to gridded MOS. 1. Introduction The Meteorological Development Laboratory (MDL) of NOAA’s National Weather Service (NWS) is developing a National Digital Guidance Database (NDGD) at fine reso- lutions to complement the existing National Digital Forecast Database (NDFD, Glahn and Ruth 2003). To help accomplish this goal, MDL is creating a gridded forecast guid- ance system. Current forecast guidance is produced for the United States and its terri- tories at approximately 1800 hourly observing sites and over 5000 cooperative observ- ing sites by using the Model Output Statistics (MOS) technique (Glahn and Lowry 1972). In the MOS approach, observed predictand data are statistically related to predictors such as forecasts from dynamical models, surface observations, and geoclimatic infor- mation. -
Statistical Post-Processing of Ensemble Forecasts of Temperature in Santiago De Chile
Statistical post-processing of ensemble forecasts of temperature in Santiago de Chile Mailiu D´ıaza, Orietta Nicolisa;b, Julio Cesar´ Mar´ınc and Sandor´ Barand aDepartment of Statistics, University of Valpara´ıso Gran Breta~na1111, Valpara´ıso,Chile bFaculty of Engineering, Andres Bello University Quillota 980, Vi~nadel Mar, Chile cDepartment of Meteorology, University of Valpara´ıso Gran Breta~na644, Valpara´ıso,Chile dFaculty of Informatics, University of Debrecen Kassai ´ut26, H-4028 Debrecen, Hungary Abstract Currently all major meteorological centres generate ensemble forecasts using their operational ensemble prediction systems; however, it is a general problem that the spread of the ensemble is too small, resulting in underdispersive forecasts, leading to a lack of calibration. In order to correct this problem, different statistical calibration techniques have been developed in the last two decades. In the present work different post-processing techniques are tested for calibrating arXiv:1809.04042v1 [stat.AP] 11 Sep 2018 9 member ensemble forecast of temperature for Santiago de Chile, obtained by the Weather Research and Forecasting (WRF) model using different planetary boundary layer and land surface model parametrization. In particular, the ensemble model out- put statistics (EMOS) and Bayesian model averaging techniques are implemented and since the observations are characterized by large altitude differences, the estimation of model parameters is adapted to the actual conditions at hand. Compared to the raw ensemble all tested post-processing approaches significantly improve the calibration of probabilistic and the accuracy of point forecasts. The EMOS method using parameter estimation based on expert clustering of stations (according to their altitudes) shows the best forecast skill. -
Coordination of Atmospheric Dispersion Activities for the Real-Time Decision Support System RODOS
RODOS R-2-1997 RIS0-R-93O (EN) DK9700116 Coordination of Atmospheric Dispersion Activities for the Real-Time Decision Support System RODOS DECISION SUPPORT FOR NUCLEAR EMERGENCIES RODOS R-2-1997 RIS0-R-93O (EN) Coordination of Atmospheric Dispersion Activities for the Real-Time Decision Support System RODOS Torben Mikkelsen RIS0 National Laboratory Denmark July 1997 Secretariat of the RODOS Project: Forschungszentrum Karlsruhe Institut fur Neutronenphysik und Reaktortechnik P.O. Box 3640, 76021 Karlsruhe, Germany Phone: +49 7247 82 5507, Fax: +49 7247 82 5508 EMail: [email protected], Internet: http://rodos.fzk.de This work has been performed with the support of the European Commission Radiation Protection Research Action (DGXII-F-6) contract FI3P-CT92-0044 This report has been published as Report RIS0-R-93O (EN) (ISSN 0106-2840) (ISBN 87-550-2230-8) in May 1997 by RIS0 National Laboratory P.O. Box 49 DK-4000 Roskilde, Denmark Management Summary 1.1 Global Objectives: This projects task has been to coordinate activities among the RODOS Atmospheric Dispersion sub-group A participants (1) - (8), with the overall objective of developing and integrating an atmospheric transport and dispersion module for the joint European Real-time On- line DecisiOn Support system RODOS headed by FZK (formerly KfK), Germany. The projects final goal is the establishment of a fully operational, system-integrated atmospheric transport module for the RODOS system by year 2000, capable of consistent now- and forecasting of radioactive airborne spread over all types of terrain and on all scales of interest, including in particular complex terrain and the different scales of operation, such as the local, the national and the European scale. -
Dispersion Modeling of Air Pollutants in the Atmosphere: a Review
Cent. Eur. J. Geosci. • 6(3) • 2014 • 257-278 DOI: 10.2478/s13533-012-0188-6 Central European Journal of Geosciences Dispersion modeling of air pollutants in the atmosphere: a review Research Article Ádám Leelossy˝ 1, Ferenc Molnár Jr.2, Ferenc Izsák3, Ágnes Havasi3, István Lagzi4, Róbert Mészáros1∗ 1 Department of Meteorology, Eötvös Loránd University, Budapest, Hungary 2 Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, Troy, New York, USA 3 Department of Applied Analysis and Computational Mathematics, Eötvös Loránd University, Budapest, Hungary 4 Department of Physics, Budapest University of Technology and Economics, Budapest, Hungary Received 22 October 2013; accepted 15 May 2014 Abstract: Modeling of dispersion of air pollutants in the atmosphere is one of the most important and challenging scientific problems. There are several natural and anthropogenic events where passive or chemically active compounds are emitted into the atmosphere. The effect of these chemical species can have serious impacts on our environment and human health. Modeling the dispersion of air pollutants can predict this effect. Therefore, development of various model strategies is a key element for the governmental and scientific communities. We provide here a brief review on the mathematical modeling of the dispersion of air pollutants in the atmosphere. We discuss the advantages and drawbacks of several model tools and strategies, namely Gaussian, Lagrangian, Eulerian and CFD models. We especially focus on several recent advances in this multidisciplinary research field, like parallel computing using graphical processing units, or adaptive mesh refinement. Keywords: air pollution modeling • Lagrangian model • Eulerian model • CFD; accidental release • parallel computing © Versita sp. -
Statistical Postprocessing of Ensemble Forecasts for Severe Weather at Deutscher Wetterdienst
Nonlin. Processes Geophys., 27, 473–487, 2020 https://doi.org/10.5194/npg-27-473-2020 © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Statistical postprocessing of ensemble forecasts for severe weather at Deutscher Wetterdienst Reinhold Hess Deutscher Wetterdienst, Offenbach, Germany Correspondence: Reinhold Hess ([email protected]) Received: 7 January 2020 – Discussion started: 20 January 2020 Revised: 13 August 2020 – Accepted: 19 August 2020 – Published: 6 October 2020 Abstract. This paper gives an overview of Deutscher Wet- 1 Introduction terdienst’s (DWD’s) postprocessing system called Ensemble- MOS together with its motivation and the design con- Ensemble forecasting rose with the understanding of the lim- sequences for probabilistic forecasts of extreme events ited predictability of weather. This limitation is caused by based on ensemble data. Forecasts of the ensemble systems sparse and imperfect observations, approximating numerical COSMO-D2-EPS and ECMWF-ENS are statistically opti- data assimilation and modelling, and by the chaotic phys- mised and calibrated by Ensemble-MOS with a focus on se- ical nature of the atmosphere. The basic idea of ensemble vere weather in order to support the warning decision man- forecasting is to vary observations, initial and boundary con- agement at DWD. ditions, and physical parameterisations within their assumed Ensemble mean and spread are used as predictors for lin- scale of uncertainty and rerun the forecast model with these ear and logistic multiple regressions to correct for conditional changes. biases. The predictands are derived from synoptic observa- The obtained ensemble of forecasts expresses the distribu- tions and include temperature, precipitation amounts, wind tion of possible weather scenarios to be expected. -
Influence of Ozone Recovery and Greenhouse Gas Increases on Southern Hemisphere Circulation Alexey Y
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, D22117, doi:10.1029/2010JD014423, 2010 Influence of ozone recovery and greenhouse gas increases on Southern Hemisphere circulation Alexey Y. Karpechko,1,2 Nathan P. Gillett,3 Lesley J. Gray,4 and Mauro Dall’Amico5,6 Received 27 April 2010; revised 6 September 2010; accepted 13 September 2010; published 24 November 2010. [1] Stratospheric ozone depletion has significantly influenced the tropospheric circulation and climate of the Southern Hemisphere (SH) over recent decades, the largest trends being detected in summer. These circulation changes include acceleration of the extratropical tropospheric westerly jet on its poleward side and lowered Antarctic sea level pressure. It is therefore expected that ozone changes will continue to influence climate during the 21st century when ozone recovery is expected. Here we use two contrasting future ozone projections from two chemistry‐climate models (CCMs) to force 21st century simulations of the HadGEM1 coupled atmosphere‐ocean model, along with A1B greenhouse gas (GHG) concentrations, and study the simulated response in the SH circulation. According to several studies, HadGEM1 simulates present tropospheric climate better than the majority of other available models. When forced by the larger ozone recovery trends, HadGEM1 simulates significant deceleration of the tropospheric jet on its poleward side in the upper troposphere in summer, but the trends in the lower troposphere are not significant. In the simulations with the smaller ozone recovery trends the zonal mean zonal wind trends are not significant throughout the troposphere. The response of the SH circulation to GHG concentration increases in HadGEM1 includes an increase in poleward eddy heat flux in the stratosphere and positive sea level pressure trends in southeastern Pacific. -
REFORECASTS an Important Dataset for Improving Weather Predictions
REFORECASTS An Important Dataset for Improving Weather Predictions BY THOMAS M. HAMILL, JEFFREY S. WHITAKER, AND STEVEN L. MULLEN Statistical correction of today’s numerical forecast using a long set of reforecasts (hindcasts) dramatically improves its forecast skill. eanalyses such as the National Centers for reanalysis datasets have facilitated a wide range of Environmental Prediction (NCEP)–National research; for example, the Kalnay et al. (1996) article R Center for Atmospheric Research (NCAR) re- above has been cited more than 3200 times. analysis (Kalnay et al. 1996) and the European Centre In this article, we explore the value of a companion for Medium-Range Weather Forecasts (ECMWF) dataset to reanalyses, which we shall call “reforecasts.” 40-year reanalysis (ERA-40; Uppala et al. 2005) have These are retrospective weather forecasts generated become heavily used products for geophysical science with a fixed numerical model. Model developers research. These reanalyses run a practical, consistent could use them for diagnosing model bias, thereby data assimilation and short-range forecast system facilitating the development of new, improved ver- over a long period of time. While the observation sions of the model. Others could use them as data type and quality may change somewhat, the forecast for statistically correcting weather forecasts, thereby model and assimilation system are typically fixed. developing improved, user-specific products [e.g., This facilitates the generation of a reanalysis dataset model output statistics (MOS) Glahn and Lowry that is fairly consistent in quality over time. These 1972; Carter et al. 1989]. Others may use them for studies of atmospheric predictability. Unfortunately, extensive sets of reforecasts are not commonly pro- AFFILIATIONS: HAMILL AND WHITAKER—NOAA Earth System duced utilizing the same model version as is run Research Lab, Physical Sciences Division, Boulder, Colorado; operationally.