'Model errors' presentation slideshows

683 views • 51 slides

Yan Xue Climate Prediction Center, NCEP

The NCEP GODAS Ocean Analysis of the Tropical Pacific Mixed Layer Heat Budget on Seasonal to Interannual Time Scales. Yan Xue Climate Prediction Center, NCEP. Acknowledgements: Boyin Huang, David Behringer, Arun Kumar (CPC) Dongxiao Zhang, Michael J. McPhaden (PMEL).

690 views • 50 slides

Chapter 5 Transformations and Weighting to Correct Model Inadequacies

Chapter 5 Transformations and Weighting to Correct Model Inadequacies. Ray-Bing Chen Institute of Statistics National University of Kaohsiung. 5.1 Introduction. Recall several implicit assumptions: The model errors have mean zero and constant variance and are uncorrelated.

1.2k views • 48 slides

Frank Selten, Xueli Wang Wilco Hazeleger, Rein Haarsma, Henk van den Brink,Pier Siebesma,

ECEARTH. Experiments and sensitivity runs. Frank Selten, Xueli Wang Wilco Hazeleger, Rein Haarsma, Henk van den Brink,Pier Siebesma, Geert Lenderink, Peter van Velthoven, Alwin Haklander, Camiel Severijns. 10-year integrations at ECMWF. Hindcasts with observed SSTs. Climatological SSTs

485 views • 36 slides

Proposal for a WWRP High Impact Weather Project

Proposal for a WWRP High Impact Weather Project. Sarah Jones, Brian Golding Philippe Arbogast, Ana Barros, Aida Diongue, Beth Ebert, Grant Elliott, Pat Harr, Tim Hewson, Julia Keller, Stefan Klink, Sharan Majumdar, Rebecca Morss, Pierre Pellerin,

397 views • 29 slides

Some OSSEs on Assimilation of Radar Data with a Hybrid 3DVAR/EnKF Method

Some OSSEs on Assimilation of Radar Data with a Hybrid 3DVAR/EnKF Method. Jidong Gao and David Stensrud. Goal : To test if the hybrid 3DVAR/EnKF scheme is good for convective scale DA and WoF strategy. OUTLINE Why hybrid 3(4)DVAR/EnKF ?

397 views • 20 slides

AOML South Atlantic MOC related Observations and Plans

AOML South Atlantic MOC related Observations and Plans. Silvia L. Garzoli. Molly O. Baringer (AOML) Christopher S. Meinen (AOML) Carlisle Thacker (AOML) Shenfu Dong (CIMAS) Zulema Garraffo (RSMAS) George Halliwell (RSMAS) Ricardo Matano (OSU) Alberto Piola and Ariel Troisi (SHN)

354 views • 19 slides

Research activities

Research activities. Rational Thermodynamics. Physical chemistry is the ancestor of chemical engineering thermodynamics: => Inheritance of definitions originally introduced to please the experimentator.

277 views • 19 slides

MODEL-INDEPENDENT ESTIMATION OF SYSTEMATIC ERRORS IN SMOS BRIGHTNESS TEMPERATURE IMAGES

279 views • 18 slides

Representing Model Error in Ensemble DA

Representing Model Error in Ensemble DA. Chris Snyder (NCAR) NCAR is supported by the National Science Foundation. Representing Model Error in Ensemble DA. Results from: Soyoung Ha (NCAR) Judith Berner (NCAR) Josh Hacker (NPS). Introduction.

316 views • 18 slides

A unified linear Model Output Statistics scheme for both deterministic and ensemble forecasts

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V Gorin and M Tsyrulnikov Can model error be perceived from routine observations?

V Gorin and M Tsyrulnikov Can model error be perceived from routine observations?. Motivation : all existing approaches in model-error modeling are largely ad-hoc . Any objective knowledge on real model errors is very welcome. Model error: definition. The forecast equation:.

251 views • 15 slides

Evaluation of AMPS Forecasts Using Self-Organizing Maps (SOMs)

Evaluation of AMPS Forecasts Using Self-Organizing Maps (SOMs). John J. Cassano Cooperative Institute for Research in Environmental Science and Program in Atmospheric and Oceanic Sciences University of Colorado at Boulder. Beardmore Glacier, Jan 2004. Outline. What are SOMs?

285 views • 15 slides

NASA GEOS-3/TRMM Re-Analysis:

NASA GEOS-3/TRMM Re-Analysis: Capturing Observed Rainfall Variability in Global Analysis Arthur Hou NASA Goddard Space Flight Center. 2 nd IPWG Workshop, Naval Research Laboratory Monterey, CA, 25-28 October 2004. Scope of talk.

298 views • 14 slides

Chemical Source Inversion Using Assimilated Constituent Observations

223 views • 13 slides

Flavour break-up July9th 2008

170 views • 11 slides

Abstract

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Snow dominated

A Subbasin-based Framework to Represent Land Surface Processes in an Earth System Model. Right: Stream network in major US river basins. Bottom: Comparison of skill metric based on Nash-Sutcliffe Efficiency (NSE) of subbasin (SCLM) vs. grid (CLM) based approaches for simulating streamflow.

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P1.8 QUANTIFYING AND REDUCING UNCERTAINTY BY EMPLOYING MODEL ERROR ESTIMATION METHODS

P1.8 QUANTIFYING AND REDUCING UNCERTAINTY BY EMPLOYING MODEL ERROR ESTIMATION METHODS Dusanka Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University, Fort Collins, CO. AMS Ed Lorenz Symposium. Introduction

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Harvesting Climate Model Uncertainty

Harvesting Climate Model Uncertainty. Results : A climate model is run for 100 years with (EXPT) and 100 years without (CONTROL) the multi-parameter methodology. Figures 1 and 2 are obtained from the analysis of these model results:. a) b) c).

139 views • 1 slides