Ensemble Forecasting and Data Assimilation: Two Problems with the Same Solution?
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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 -
Jule Charney's Influence on Meteorology'
Jule Charney's Influence Norman A. Phillips National Weather Service, NOAA on Meteorology' Washington, D.C. 20233 The opportunity to address the Society on the contributions of Jule Charney to our science is an honor of the highest rank, and I thank you for this invitation. I will try to capture for you a meaningful impression of the extent to which our common undertaking has been influenced by this man (Fig. 1). Let me begin by recalling three historical contexts. The first of these is January 1,1917. Jule is born on this day in San Francisco, to Stella and Ely Charney. Five thousand miles away in Bergen, Norway, Vilhelm Bjerknes and his collabor- ators are developing the concepts of fronts and air masses. Some distance south of Bergen, Lewis Richardson is trans- porting wounded soldiers with the Friends Ambulance Corps. In spare moments, he is working on his monumental formulation of what is now called numerical weather prediction. My second context is around 1940. Jule had entered the University of California at Los Angeles in the mid-thirties, and is now a graduate student there in mathematics. UCLA is expanding, and Jacob Bjerknes and Jrirgen Holmboe ar- rive about this time. (A few years earlier, Bjerknes had pub- lished an important paper on long waves. In 1939, while he was at M.I.T., Carl Rossby published his well known model FIG. 1. A picture of Jule Charney (left), with E. Lorenz, taken in of long waves. These events are unknown to Jule.) Jule 1976 during a visit by Chinese meteorologists to the Massachusetts knows nothing of meteorology until one day he hears a talk Institute of Technology. -
AN INTRODUCTION to DATA ASSIMILATION the Availability Of
AN INTRODUCTION TO DATA ASSIMILATION AMIT APTE Abstract. This talk will introduce the audience to the main features of the problem of data assimilation, give some of the mathematical formulations of this problem, and present a specific example of application of these ideas in the context of Burgers' equation. The availability of ever increasing amounts of observational data in most fields of sciences, in particular in earth sciences, and the exponentially increasing computing resources have together lead to completely new approaches to resolving many of the questions in these sciences, and indeed to formulation of new questions that could not be asked or answered without the use of these data or the computations. In the context of earth sciences, the temporal as well as spatial variability is an important and essential feature of data about the oceans and the atmosphere, capturing the inherent dynamical, multiscale, chaotic nature of the systems being observed. This has led to development of mathematical methods that blend such data with computational models of the atmospheric and oceanic dynamics - in a process called data assimilation - with the aim of providing accurate state estimates and uncertainties associated with these estimates. This expository talk (and this short article) aims to introduce the audience (and the reader) to the main ideas behind the problem of data assimilation, specifically in the context of earth sciences. I will begin by giving a brief, but not a complete or exhaustive, historical overview of the problem of numerical weather prediction, mainly to emphasize the necessity for data assimilation. This discussion will lead to a definition of this problem. -
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. -
Radar Data Assimilation
Radar Data Assimilation David Dowell Assimilation and Modeling Branch NOAA/ESRL/GSD, Boulder, CO Acknowledgment: Warn-on-Forecast project Radar Data Assimilation (for analysis and prediction of convective storms) David Dowell Assimilation and Modeling Branch NOAA/ESRL/GSD, Boulder, CO Acknowledgment: Warn-on-Forecast project Atmospheric Data Assimilation Definition: using all available information – observations and physical laws (numerical models) – to estimate as accurately as possible the state of the atmosphere (Talagrand 1997) Atmospheric Data Assimilation Definition: using all available information – observations and physical laws (numerical models) – to estimate as accurately as possible the state of the atmosphere (Talagrand 1997) Applications: 1. Initializing NWP models NOAA NCEP, NCAR RAL Atmospheric Data Assimilation Definition: using all available information – observations and physical laws (numerical models) – to estimate as accurately as possible the state of the atmosphere (Talagrand 1997) Applications: 1. Initializing NWP models NOAA NCEP, NCAR RAL 2. Diagnosing atmospheric processes (analysis) Schultz and Knox 2009 Assimilating a Radar Observation radar observation (Doppler velocity, reflectivity, …) gridded model fields (wind, temperature, What field(s) should the radar ob. should affect? pressure, humidity, By how much? And how far from the ob.? rain, snow, …) determined by background error covariances (b.e.c.) Various methods have been developed for estimating and using b.e.c.: 3DVar, 4DVar, EnKF, hybrid, … Most -
Prospects for Improving Forecasts of Weather and Short-Term Climate Variability on Subseasonal
NASA/TM_2002-104606, Vol. 23 Techmcal Report Series• on Global Modehn_,• _J and Data Assimilation Volume 23 Prospects for Improved Forecasts of Weather and Short-Term Climate Variability on Subseasonal (2-Week to 2-Month) Time Scales S. Schubert, R. Dole, H. van den DooL MI Suarez, and D. Waliser Ptvceedings flvm a _fbrkshop Sponsored hy the Earth Sciences Directorate at NASA's Goddard Space Flight Centez Co-sponsored by 2v_dSA Seasonal-to-bm_rannual Prediction Project and NAS_d Data Assimilation OJfice April 16-18, 2002 Nc_vember__ 2002 The NASA STI Program Office ... m Profile Since its founding, NASA has been dedicated to CONFERENCE PUBLICATION. Collected the advancement of aeronautics and space papers from scientific and technical science. The NASA Scientific and Technical conferences, symposia, seminars, or other hlf()rmation (STI) Program Office plays a key meetings sponsored or cosponsored by NASA. part in helping NASA maintain this important role. SPECIAL PUBLICATION. Scientific, techni- cal, or historical information from NASA The NASA STI Program Office is operated by programs, projects, and mission, often con- Langley Research Center, the lead center for cemed with subjects having substantial public NASA's scientific and technical information. interest. The NASA STI Program Office provides access to the NASA STI Database, the largest collection TECHNICAL TRANSLATION. of aeronautical and space science STI in the English-I angu age translations of foreign scien- world. The Program Office i s also NASA' s tific and technical material pertinent to NASA's institutional mechanism for disseminating the mission. results of its research and development activi- ties. These results are published by NASA in the Specialized services that complement the STI NASA STI Report Series, which includes the Program Office's diverse offerings include creat- following report types: ing custom thesauri, building customized data- bases, organizing and publishing research results.. -
5B.2 4-Dimensional Variational Data Assimilation for the Weather Research and Forecasting Model
5B.2 4-Dimensional Variational Data Assimilation for the Weather Research and Forecasting Model Xiang-Yu Huang*1, Qingnong Xiao1, Xin Zhang2, John Michalakes1, Wei Huang1, Dale M. Barker1, John Bray1, Zaizhong Ma1, Tom Henderson1, Jimy Dudhia1, Xiaoyan Zhang1, Duk-Jin Won3, Yongsheng Chen1, Yongrun Guo1, Hui-Chuan Lin1, Ying-Hwa Kuo1 1National Center for Atmospheric Research, Boulder, Colorado, USA 2University of Hawaii, Hawaii, USA 3Korean Meteorological Administration, Seoul, South Korea 1. Introduction The 4D-Var prototype was built in 2005 and has under continuous refinement since then. Many single observation experiments have been carried out to The 4-dimensional variational data assimilation validate the correctness of the 4D-Var formulation. A (4D-Var) (Le Dimet and Talagrand, 1986; Lewis and series of real data experiments have been conducted to Derber, 1985) has been pursued actively by research assess the performance of the 4D-Var (Huang et al. community and operational centers over the past two th 2006). Another year of fast development of 4D-Var has decades. The 5 generation Pennsylvania State led to the completion of a basic system, which will be University – National Center for Atmospheric Research described in section 3. mesoscale model (MM5) based 4D-Var (Zou et al. 1995; Ruggiero et al. 2006), for example, has been widely used for more than 10 years. There are also 2. The WRF 4D-Var Algorithm successful operational implementations of 4D-Var (e.g. Rabier et al. 2000). The WRF 4D-Var follows closely the incremental The 4D-Var technique has a number of advantages 4D-Var formulation of Courtier et al. -
Assimilation of GOES-16 Radiances and Retrievals Into the Warn-On-Forecast System
MAY 2020 J O N E S E T A L . 1829 Assimilation of GOES-16 Radiances and Retrievals into the Warn-on-Forecast System THOMAS A. JONES,PATRICK SKINNER, AND NUSRAT YUSSOUF Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and National Severe Storms Laboratory, and University of Oklahoma, Norman, Oklahoma Downloaded from http://journals.ametsoc.org/mwr/article-pdf/148/5/1829/4928277/mwrd190379.pdf by NOAA Central Library user on 11 August 2020 KENT KNOPFMEIER AND ANTHONY REINHART Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and National Severe Storms Laboratory, Norman, Oklahoma XUGUANG WANG University of Oklahoma, Norman, Oklahoma KRISTOPHER BEDKA AND WILLIAM SMITH JR. NASA Langley Research Center, Hampton, Virginia RABINDRA PALIKONDA Science Systems and Applications, Inc., Hampton, Virginia (Manuscript received 14 November 2019, in final form 28 January 2020) ABSTRACT The increasing maturity of the Warn-on-Forecast System (WoFS) coupled with the now operational GOES-16 satellite allows for the first time a comprehensive analysis of the relative impacts of assimilating GOES-16 all-sky 6.2-, 6.9-, and 7.3-mm channel radiances compared to other radar and satellite observations. The WoFS relies on cloud property retrievals such as cloud water path, which have been proven to increase forecast skill compared to only assimilating radar data and other conventional observations. The impacts of assimilating clear-sky radiances have also been explored and shown to provide useful information on midtropospheric moisture content in the near-storm environment. Assimilation of all-sky radiances adds a layer of complexity and is tested to determine its effectiveness across four events occurring in the spring and summer of 2019. -
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. -
Recent Results of Observation Data Denial Experiments
Recent results of observation data denial experiments Weather Science Technical Report 641 24th February 2021 Brett Candy, James Cotton and John Eyre www.metoffice.gov.uk © Crown Copyright 2021, Met Office Contents Contents ............................................................................................................................... 1 1 Introduction .................................................................................................................... 2 2 Operational NWP configuration ...................................................................................... 3 3 Data Denial Experiments ............................................................................................... 6 3.1 Introduction ......................................................................................................... 6 3.2 Results ................................................................................................................ 8 3.3 Continued Impact of POES............................................................................... 12 3.4 Verification of Tropical Cyclone Tracks .............................................................. 15 3.5 A Data Denial Experiment including withdrawal from the ensemble ................... 16 4 FSOI Results ............................................................................................................... 18 5 Conclusions ................................................................................................................. 21 Acknowledgements -
Computer Models, Climate Data, and the Politics of Global Warming (Cambridge: MIT Press, 2010)
Complete bibliography of all items cited in A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming (Cambridge: MIT Press, 2010) Paul N. Edwards Caveat: this bibliography contains occasional typographical errors and incomplete citations. Abbate, Janet. Inventing the Internet. Inside Technology. Cambridge: MIT Press, 1999. Abbe, Cleveland. “The Weather Map on the Polar Projection.” Monthly Weather Review 42, no. 1 (1914): 36-38. Abelson, P. H. “Scientific Communication.” Science 209, no. 4452 (1980): 60-62. Aber, John D. “Terrestrial Ecosystems.” In Climate System Modeling, edited by Kevin E. Trenberth, 173- 200. Cambridge: Cambridge University Press, 1992. Ad Hoc Study Group on Carbon Dioxide and Climate. “Carbon Dioxide and Climate: A Scientific Assessment.” (1979): Air Force Data Control Unit. Machine Methods of Weather Statistics. New Orleans: Air Weather Service, 1948. Air Force Data Control Unit. Machine Methods of Weather Statistics. New Orleans: Air Weather Service, 1949. Alaka, MA, and RC Elvander. “Optimum Interpolation From Observations of Mixed Quality.” Monthly Weather Review 100, no. 8 (1972): 612-24. Edwards, A Vast Machine Bibliography 1 Alder, Ken. The Measure of All Things: The Seven-Year Odyssey and Hidden Error That Transformed the World. New York: Free Press, 2002. Allen, MR, and DJ Frame. “Call Off the Quest.” Science 318, no. 5850 (2007): 582. Alvarez, LW, W Alvarez, F Asaro, and HV Michel. “Extraterrestrial Cause for the Cretaceous-Tertiary Extinction.” Science 208, no. 4448 (1980): 1095-108. American Meteorological Society. 2000. Glossary of Meteorology. http://amsglossary.allenpress.com/glossary/ Anderson, E. C., and W. F. Libby. “World-Wide Distribution of Natural Radiocarbon.” Physical Review 81, no. -
Global Weat Her Prediction and High-End Computing at NASA
Global Weat her Prediction and High-End Computing at NASA Shian-Jiann Lin, Robert Atlas, and Kao-San Yeh* NASA Goddard Space Flight Center *Corresponding author address: Dr. Kao-San Yeh Code 900.3, NASA Goddard Space Flight Center, Greenbelt, MD 20771 E-mail: [email protected] August 18th, 2003 Abstract We demonstrate current capabilities of the NASA finite-volume General Circulation Model an high-resolution global weather prediction, and discuss its development path in the foreseeable future. This model can be regarded as a prototype of a future NASA Earth modeling system intended to unify development activities cutting across various disciplines within the NASA Earth Science Enterprise. 1 1. Introduction NASA’s goal for an Earth modeling system is to unify the model development activities that cut across various disciplines within the Earth Science Enterprise. Applications of the Earth modeling system include, but are not limited to, weather and chemistry-climate change predictions, and atmospheric and oceanic data assimilation. Among these applications, high-resolution global weather prediction requires the highest temporal and spatial resolution, and hence demands the most capability of a high-end computing system. In the continuing quest to improve and perhaps push to the limit of the predictability of the weather (see the related side bar), we are adopting more physically based algorithms with much higher resolution than those in earlier models. We are also including additional physical and chemical components that have not been coupled to the modeling system previously. As a comprehensive high-resolution Earth modeling system will require enormous computing power, it is important to design all component models efficiently for modern parallel computers with distributed-memory platforms.