Peak Performance Technical Environment FMI NWP Activities.

Slides:



Advertisements
Similar presentations
Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, september 2008.
Advertisements

HIRLAM Use of the Hirlam NWP Model at Met Éireann (Irish Meteorological Service) (James Hamilton -- Met Éireann)
A NUMERICAL PREDICTION OF LOCAL ATMOSPHERIC PROCESSES A.V.Starchenko Tomsk State University.
NWP in the Met Office © Crown copyright 2006.
Status and performance of HIRLAM 4D-Var Nils Gustafsson.
Lidar-Based Microphysical Retrievals During M-PACE Gijs de Boer Edwin Eloranta The University of Wisconsin - Madison ARM CPMWG Meeting, October 31, 2006.
Towards Rapid Update Cycling for Short Range NWP Forecasts in the HIRLAM Community WMO/WWRP Workshop on Use of NWP for Nowcasting UCAR Center Green Campus,
Meteorological Driver for CTM Freie Universität Berlin Institut für Meteorologie Eberhard Reimer.
10/05/041 Utilisation of satellite data in the verification of HIRLAM cloud forecasts Christoph Zingerle and Pertti Nurmi.
Rapid Update Cycle Model William Sachman and Steven Earle ESC452 - Spring 2006.
Geophysical Modelling: Climate Modelling How advection, diffusion, choice of grids, timesteps etc are defined in state of the art models.
Hector simulation We found simulation largely depending on: Model initialization scheme Lateral boundary conditions Physical processes represented in the.
© Crown copyright Met Office SRNWP Interoperability Workshop, ECMWF, January 2008 SRNWP Interoperability Terry Davies Met Office.
Institute of Oceanogphy Gdańsk University Jan Jędrasik The Hydrodynamic Model of the Southern Baltic Sea.
Numerical weather prediction: current state and perspectives M.A.Tolstykh Institute of Numerical Mathematics RAS, and Hydrometcentre of Russia.
Hongli Jiang, Yuanfu Xie, Steve Albers, Zoltan Toth
Francesca Marcucci, Lucio Torrisi with the contribution of Valeria Montesarchio, ISMAR-CNR CNMCA, National Meteorological Center,Italy First experiments.
Dr Mark Cresswell Model Assimilation 69EG6517 – Impacts & Models of Climate Change.
HIRLAM-6 plan and work MoU, Motivations, Targets Data assimilation, 3D-VAR and 4D-VAR Observation usage Parameterisation Dynamics System and embedding.
GLOBAL PATTERNS OF THE CLIMATIC ELEMENTS: (1) SOLAR ENERGY (Linked to solar insolation & R, net radiation)
Overview of the present HIRLAM surface assimilation Mainly taken from: HIRLAM Technical Report No. 58.
NUMERICAL WEATHER PREDICTION K. Lagouvardos-V. Kotroni Institute of Environmental Research National Observatory of Athens NUMERICAL WEATHER PREDICTION.
The National Environmental Agency of Georgia L. Megrelidze, N. Kutaladze, Kh. Kokosadze NWP Local Area Models’ Failure in Simulation of Eastern Invasion.
Forecasting ATS 113. Forecasts made by PEOPLE Folklore: –Groundhog Day –Fuzzy caterpillars –Walnut shells –Farmers Almanac.
Coupled Climate Models OCEAN-ATMOSPHEREINTERACTIONS.
Introducing the Lokal-Modell LME at the German Weather Service Jan-Peter Schulz Deutscher Wetterdienst 27 th EWGLAM and 12 th SRNWP Meeting 2005.
Collaborative Research: Toward reanalysis of the Arctic Climate System—sea ice and ocean reconstruction with data assimilation Synthesis of Arctic System.
Verification and Case Studies for Urban Effects in HIRLAM Numerical Weather Forecasting A. Baklanov, A. Mahura, C. Petersen, N.W. Nielsen, B. Amstrup Danish.
Non-hydrostatic Numerical Model Study on Tropical Mesoscale System During SCOUT DARWIN Campaign Wuhu Feng 1 and M.P. Chipperfield 1 IAS, School of Earth.
Status report from the Lead Centre for Surface Processes and Assimilation E. Rodríguez-Camino (INM) and S. Gollvik (SMHI)
Météo-France / CNRM – T. Bergot 1) Introduction 2) The methodology of the inter-comparison 3) Phase 1 : cases study Inter-comparison of numerical models.
INSTYTUT METEOROLOGII I GOSPODARKI WODNEJ INSTITUTE OF METEOROLOGY AND WATER MANAGEMENT TITLE : IMPLEMENTATION OF MOSAIC APPROACH IN COSMO AT IMWM AUTHORS:
The ALADIN-France Limited-Area Model Joël Stein Marc Tardy Hervé Bénichou.
Soil moisture generation at ECMWF Gisela Seuffert and Pedro Viterbo European Centre for Medium Range Weather Forecasts ELDAS Interim Data Co-ordination.
Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:
Ensemble data assimilation in an operational context: the experience at the Italian Weather Service Massimo Bonavita and Lucio Torrisi CNMCA-UGM, Rome.
Weather forecasting by computer Michael Revell NIWA
Development of a one-dimensional version of the Hirlam-model in Sweden Background: This model has been run operationally for about nine years now. Mainly.
Improved road weather forecasting by using high resolution satellite data Claus Petersen and Bent H. Sass Danish Meteorological Institute.
EWGLAM Oct Some recent developments in the ECMWF model Mariano Hortal ECMWF Thanks to: A. Beljars (physics), E. Holm (humidity analysis)
DATA ASSIMILATION M. Derkova, M. Bellus, M. Nestiak.
1 Rachel Capon 04/2004 © Crown copyright Met Office Unified Model NIMROD Nowcasting Rachel Capon Met Office JCMM.
NOAA’s Climate Prediction Center & *Environmental Modeling Center Camp Springs, MD Impact of High-Frequency Variability of Soil Moisture on Seasonal.
Comparison of HIRLAM data with Sodankylä soundings – tools and results Evgeny Atlaskin Russina State Hydrometeorological University Saint-Petersburg.
INTERCOMPARISON – HIRLAM vs. ARPA-SIM CARPE DIEM AREA 1 Per Kållberg Magnus Lindskog.
Bogdan Rosa 1, Marcin Kurowski 1 and Michał Ziemiański 1 1. Institute of Meteorology and Water Management (IMGW), Warsaw Podleśna, 61
CHANGSHENG CHEN, HEDONG LIU, And ROBERT C. BEARDSLEY
Mass Coordinate WRF Dynamical Core - Eulerian geometric height coordinate (z) core (in framework, parallel, tested in idealized, NWP applications) - Eulerian.
HIRLAM-6, development since last time Strategy - ALADIN - MF - collaboration Data assimilation, 3D/4D-VAR, surface Observation Usage Parameterisation.
1 INM’s contribution to ELDAS project E. Rodríguez and B. Navascués INM.
Vincent N. Sakwa RSMC, Nairobi
Simulations of MAP IOPs with Lokal Modell: impact of nudging on forecast precipitation Francesco Boccanera, Andrea Montani ARPA – Servizio Idro-Meteorologico.
Evaluation of cloudy convective boundary layer forecast by ARPEGE and IFS Comparisons with observations from Cabauw, Chilbolton, and Palaiseau  Comparisons.
Nansen Environmental and Remote Sensing Center Modifications of the MICOM version used in the Bergen Climate Model Mats Bentsen and Helge Drange Nansen.
Météo-France / CNRM – T. Bergot 1) Methodology 2) The assimilation procedures at local scale 3) Results for the winter season Improved Site-Specific.
OSEs with HIRLAM and HARMONIE for EUCOS Nils Gustafsson, SMHI Sigurdur Thorsteinsson, IMO John de Vries, KNMI Roger Randriamampianina, met.no.
Regional Re-analyses of Observations, Ensembles and Uncertainties of Climate information Per Undén Coordinator UERRA SMHI.
Station lists and bias corrections Jemma Davie, Colin Parrett, Richard Renshaw, Peter Jermey © Crown Copyright 2012 Source: Met Office© Crown copyright.
3. Modelling module 3.1 Basics of numerical atmospheric modelling M. Déqué – CNRM – Météo-France J.P. Céron – DClim – Météo-France.
Numerical simulations of the severe rainfall in Pula, Croatia, on 25 th September 2010 Antonio Stanešić, Stjepan Ivatek-Šahdan, Martina Tudor and Dunja.
Soil analysis scheme for AROME within SURFEX
Introducing the Lokal-Modell LME at the German Weather Service
Development of nonhydrostatic models at the JMA
SMHI operational HIRLAM EWGLAM October , OSLO Lars Meuller SMHI
Numerical Weather Prediction models at FMI
Representation of the Great Lakes in CRCM5 using 3D ocean model NEMO: impacts on simulated climate Huziy O and Sushama L.
Use of the Hirlam NWP Model
NWP Strategy of DWD after 2006 GF XY DWD Feb-19.
Poster Session: Numerical Weather Prediction at MeteoSwiss
Objective verification results for RK parallel runs
Presentation transcript:

Peak Performance

Technical Environment

FMI NWP Activities

Model Configuration Data assimilation Upper air analysis3-dimensional variational data assimilation VersionHIRVDA 6.2.1, FGAT option ParametersSurface pressure, temperature, wind components, humidity Surface analysisSeparate analysis, consistent with the mosaic approach of surface/soil processes, for: SST, fraction of ice snow depth screen level temperature and humidity soil temperature and humidity at two layers Grid length 0.2  in the horizontal Integration domain 438 x 336 gridpoints in rotated lat/lon grid Levels40 hybrid levels defined by A and B. Observation typesTEMP, PILOT, SYNOP, SHIP, BUOY, AIREP First guess3 h forecast, 3 h cycle InitializationDigital filter (IDFI) Cut-off time2 h for main cycles, 4 h 20 min for intermediate cycles Assimilation cycle3 h cycle Forecast model Limited area gridpoint model VersionHirlam Basic equationsPrimitive equations Independent variables,  (transformed lat-lon coordinates), η, t Dependent variablesT, u, v, q, p s, cloud water, turbulent kinetic energy DiscretizationArakawa C grid Grid length 0.2  in the horizontal Integration domain438 x 336 gridpoints in rotated lat/lon grid Levels40 hybrid levels defined by A and B Integration scheme Semi-Lagrangian, semi-implicit,  t=360 s. OrographyHirlam physiographic data base, filtered PhysicsSavijärvi radiation scheme Turbulence based on turbulent kinetic energy STRACO condensation scheme Surface fluxes using the drag formulation Surface and soil processes using mosaic tiles No gravity wave drag Horizontal diffusionImplicit fourth order Forecast length54 hours Output frequency1 hour Boundaries 0.2  “frame” boundaries from ECMWF received four times a day 3 h temporal resolution

Sodankylä Mast Comparison: HIRLAM vs. ARPEGE HIRLAM ARPEGE 32m Temperature