Development of a 1D ensemble forecast System with variational assimilation Mathias D. Müller Institute of Meteorology, Climatology & Remote Sensing University.

Slides:

Advertisements

Similar presentations

Variational data assimilation and forecast error statistics

Advertisements

Integrated Profiling at the AMF

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

Radiometrics Corporation 1 Fog Detection & Forecasting Using the the Radiometrics TV/WVP-3000 Temperature, Humidity & Cloud Liquid Profiling Radiometer.

An optimal estimation based retrieval method adapted to SEVIRI infra-red measurements M. Stengel (1), R. Bennartz (2), J. Schulz (3), A. Walther (2,4),

Effects of model error on ensemble forecast using the EnKF Hiroshi Koyama 1 and Masahiro Watanabe 2 1 : Center for Climate System Research, University.

Predictability and Chaos EPS and Probability Forecasting.

Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss The Latent Heat Nudging Scheme of COSMO EWGLAM/SRNWP Meeting,

Experiments with Assimilation of Fine Aerosols using GSI and EnKF with WRF-Chem (on the need of assimilating satellite observations) Mariusz Pagowski Georg.

For the Lesson: Eta Characteristics, Biases, and Usage December 1998 ETA-32 MODEL CHARACTERISTICS.

Qualitative Verification of the Operational GFS and Ensemble Mean for 1200 UTC 13 March 2003 Mike Richards Metr 503.

Brian Ancell, Cliff Mass, Gregory J. Hakim University of Washington

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 1 Click to edit Master title style.

Ensemble Post-Processing and it’s Potential Benefits for the Operational Forecaster Michael Erickson and Brian A. Colle School of Marine and Atmospheric.

COSMO General Meeting Zurich, 2005 Institute of Meteorology and Water Management Warsaw, Poland- 1 - Verification of the LM at IMGW Katarzyna Starosta,

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.

ESA DA Projects Progress Meeting 2University of Reading Advanced Data Assimilation Methods WP2.1 Perform (ensemble) experiments to quantify model errors.

EnKF Overview and Theory

Advances in the use of observations in the ALADIN/HU 3D-Var system Roger RANDRIAMAMPIANINA, Regina SZOTÁK and Gabriella Csima Hungarian Meteorological.

Eidgenössisches Departement des Innern EDI Bundesamt für Meteorologie und Klimatologie MeteoSchweiz Statistical Characteristics of High- Resolution COSMO.

ISDA 2014, Feb 24 – 28, Munich 1 Impact of ensemble perturbations provided by convective-scale ensemble data assimilation in the COSMO-DE model Florian.

June, 2003EUMETSAT GRAS SAF 2nd User Workshop. 2 The EPS/METOP Satellite.

Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss High-resolution data assimilation in COSMO: Status and.

Potential benefits from data assimilation of carbon observations for modellers and observers - prerequisites and current state J. Segschneider, Max-Planck-Institute.

Assimilating satellite cloud information with an Ensemble Kalman Filter at the convective scale Annika Schomburg, Christoph Schraff EUMETSAT fellow day,

Development of an EnKF/Hybrid Data Assimilation System for Mesoscale Application with the Rapid Refresh Ming Hu 1,2, Yujie Pan 3, Kefeng Zhu 3, Xuguang.

Météo-France / CNRM – T. Bergot 1) Introduction 2) The methodology of the inter-comparison 3) Phase 1 : cases study Inter-comparison of numerical models.

Latest results in verification over Poland Katarzyna Starosta, Joanna Linkowska Institute of Meteorology and Water Management, Warsaw 9th COSMO General.

Fog prediction in a 3D model with parameterized microphysics Mathias D. Müller 1, Matthieu Masbou 2, Andreas Bott 2, Zavisa I. Janjic 3 1) Institute of.

11 Background Error Daryl T. Kleist* National Monsoon Mission Scoping Workshop IITM, Pune, India April 2011.

Data assimilation, short-term forecast, and forecasting error

Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Local Probabilistic Weather Predictions for Switzerland.

GPS GPS derived integrated water vapor in aLMo: impact study with COST 716 near real time data Jean-Marie Bettems, MeteoSwiss Guergana Guerova, IAP, University.

Deutscher Wetterdienst Vertical localization issues in LETKF Breogan Gomez, Andreas Rhodin, Hendrik Reich.

Ensemble Kalman Filter in a boundary layer 1D numerical model Samuel Rémy and Thierry Bergot (Météo-France) Workshop on ensemble methods in meteorology.

Lennart Bengtsson ESSC, Uni. Reading THORPEX Conference December 2004 Predictability and predictive skill of weather systems and atmospheric flow patterns.

Errors, Uncertainties in Data Assimilation François-Xavier LE DIMET Université Joseph Fourier+INRIA Projet IDOPT, Grenoble, France.

Quality of model and Error Analysis in Variational Data Assimilation François-Xavier LE DIMET Victor SHUTYAEV Université Joseph Fourier+INRIA Projet IDOPT,

Introduction of temperature observation of radio-sonde in place of geopotential height to the global three dimensional variational data assimilation system.

Perturbation of Parametrized Tendencies and Surface Parameters in the Lokal-Modell Susanne Theis Andreas Hense Ulrich Damrath Volker Renner.

Layered Water Vapor Quick Guide by NASA / SPoRT and CIRA Why is the Layered Water Vapor Product important? Water vapor is essential for creating clouds,

EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of.

COSMO General Meeting Zurich, 2005 Institute of Meteorology and Water Management Warsaw, Poland- 1 - Simple Kalman filter – a “smoking gun” of shortages.

NCAR April 1 st 2003 Mesoscale and Microscale Meteorology Data Assimilation in AMPS Dale Barker S. Rizvi, and M. Duda MMM Division, NCAR

MSG cloud mask initialisation in hydrostatic and non-hydrostatic NWP models Sibbo van der Veen KNMI De Bilt, The Netherlands EMS conference, September.

Rapid Update Cycle-RUC. RUC A major issue is how to assimilate and use the rapidly increasing array of offtime or continuous observations (not a 00.

One-dimensional assimilation method for the humidity estimation with the wind profiling radar data using the MSM forecast as the first guess Jun-ichi Furumoto,

1 Simulations of Rapid Intensification of Hurricane Guillermo with Data assimilation Using Ensemble Kalman Filter and Radar Data Jim Kao (X-2, LANL) Presentation.

Météo-France / CNRM – T. Bergot 1) Methodology 2) The assimilation procedures at local scale 3) Results for the winter season Improved Site-Specific.

Korea Institute of Atmospheric Prediction Systems (KIAPS) ( 재 ) 한국형수치예보모델개발사업단 Identical Twin Experiments for the Representer Method with a Spectral Element.

June 20, 2005Workshop on Chemical data assimilation and data needs Data Assimilation Methods Experience from operational meteorological assimilation John.

ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course Mar 2016.

Intercomparison between 1D Fog Forecasting Models

Rapid Update Cycle-RUC

Local Ensemble Transform Kalman Filter for ROMS in Indian Ocean

Data Assimilation Training

Weak constraint 4D-Var at ECMWF

EUMETSAT fellow day, 17 March 2014, Darmstadt

Hui Liu, Jeff Anderson, and Bill Kuo

Improving forecasts through rapid updating of temperature trajectories and statistical post-processing Nina Schuhen, Thordis L. Thorarinsdottir and Alex.

Rapid Update Cycle-RUC Rapid Refresh-RR High Resolution Rapid Refresh-HRRR RTMA.

The ECMWF weak constraint 4D-Var formulation

Sampling Distribution

Sampling Distribution

FSOI adapted for used with 4D-EnVar

Error statistics in data assimilation

AGREPS – ACCESS Global and Regional Ensemble Prediction System

Comparison of different combinations of ensemble-based and variational data assimilation approaches for deterministic NWP Mark Buehner Data Assimilation.

Project Team: Mark Buehner Cecilien Charette Bin He Peter Houtekamer

Presentation transcript:

Development of a 1D ensemble forecast System with variational assimilation Mathias D. Müller Institute of Meteorology, Climatology & Remote Sensing University of Basel, Switzerland COST-722 MAY 2005, LARNAKA. Variational Assimilation Examples Advection Problem

variational assimilation B-matrices COBEL-NOAH PAFOG (future) Obser - vations 3D-Model runs post-processing Fog forecast period NMM-4 NMM-2 NMM-22 aLMo 3D - Forecast time Ensemble prediction system

Variational assimilation Background is taken from 3D-models (not 1D fog model): NMM-22, NMM-4, NMM-2, (ETA-22, ETA-4) and aLMo Radiosonde (about 150 km away) Surface observations of temperature and humidity at Zürich airport MTP-5 microwave temperature profiler Virtual profile of temperature and humidity from surface observations on nearby mountains

Assimilation example 28 Nov 2004 Zürich Kloten Airport 21 hour forecast of NMM-2

Write in incremental Form Introduce T and U transform to eliminate B from the cost function (physical space) (Control variable space) Cost function for variational assimilation

Error covariance matrix NMC-Method (use 3D models):

NMC estimates of B (winter season) NMM UTC large model and time dependence

Fog case - Observations CASE 1CASE 2

Without assimilation – CASE 1 12: Nov 2004 observed fog

With assimilation – CASE 1 12: Nov 2004 observed fog

Without assimilation – CASE 1 15: Nov 2004 observed fog

With assimilation – CASE 1 15: Nov 2004 observed fog

With assimilation – CASE 1 15: Nov 2004 observed fog

Fog case - Observations CASE 1CASE 2

Without assimilation – CASE 2 12: Nov 2004

With assimilation – CASE 2 12:00

Without assimilation – CASE 2 15: Nov 2004

With assimilation – CASE 2 15:00

T-Advection (stdv) Height Time

Advection statistics 1 December 2004 – 30 April 2005, all forecasthours and levels Deviation often stronger than signal

Conclusions Ensemble is based on members derived from different 3D model forecasts using variational assimilation Little information about humidity (except 00 and 12 UTC initialization with radiosonde) Assimilated ensemble still has remarkable spread Advection derived from 3D model is unreliable. Might be treated as stochastic process ?

T-Advection (mean)