© ECMWF ERA5: Collaboration between EUMETSAT and ECMWF (and TUWien) to use of reprocessed Scatterometer soil moisture data record for 1991-present in ERA5.

Slides:

Advertisements

Similar presentations

Rossana Dragani Using and evaluating PROMOTE services at ECMWF PROMOTE User Meeting Nice, 16 March 2009.

Advertisements

Update on the Drought of 2011 TWCA Mid Year Conference June 16, 2011 Bob Rose, Meteorologist LCRA.

Polly Smith, Alison Fowler, Amos Lawless School of Mathematical and Physical Sciences, University of Reading Exploring coupled data assimilation using.

© NASA Scatterometer Wind Climate Data Records Anton Verhoef Jos de Kloe Jeroen Verspeek Jur Vogelzang.

Update to COPC: Global Model Performance Dropouts Dr. Jordan Alpert NOAA Environmental Modeling Center contributions from Dr. Brad Ballish, Dr. Da Na Carlis,

Data assimilation schemes in numerical weather forecasting and their link with ensemble forecasting Gérald Desroziers Météo-France, Toulouse, France.

Developing Operational Soil Moisture Processing Chains: The Experience with European Scatterometers and Synthetic Aperture Radars Wolfgang Wagner

Recent activities on utilization of microwave imager data in the JMA NWP system - Preparation for AMSR2 data assimilation - Masahiro Kazumori Japan Meteorological.

Texas Drought Update TWCA Mid-Year Conference June 18, 2009 Bob Rose, Meteorologist, Lower Colorado River Authority TWCA Mid-Year Conference.

Operational Global Model Plans John Derber. Timeline July 25, 2013: Completion of phase 1 WCOSS transition August 20, 2013: GDAS/GFS model/analysis upgrade.

GRAS SAF Workshop, 12 June 2003 Assimilation of satellite data at ECMWF Prospects for use of radio-occultation measurements Jean-Noël Thépaut ECMWF thanks.

1 AMDAR Quality Assurance Bradley Ballish NOAA/NWS/NCEP/NCO/PMB SSMC2/Silver Spring 23 March, 2009.

Slide 1 EUMETSAT Fellow Day, 9 March 2015 Observation Errors for AMSU-A and a first look at the FY-3C MWHS-2 instrument Heather Lawrence, second-year EUMETSAT.

Page 1 Validation by Model Assimilation and/or Satellite Intercomparison - ESRIN 9–13 December 2002 Monitoring of near-real-time SCIAMACHY, MIPAS, and.

Reanalysis: When observations meet models

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

11 GRAS SAF Climate Products Hans Gleisner & Kent B. Lauritsen Danish Meteorological Institute Contents -GRAS SAF offline profiles and climate gridded.

The New Geophysical Model Function for QuikSCAT: Implementation and Validation Outline: GMF methodology GMF methodology New QSCAT wind speed and direction.

EWGLAM Oct Some recent developments in the ECMWF model Mariano Hortal ECMWF Thanks to: A. Beljars (physics), E. Holm (humidity analysis)

WP 3: DATA ASSIMILATION SMHI/FMI Status report 3rd CARPE DIEM meeting, University of Essex, Colchester, 9-10 January 2003 Structure SMHI/FMI plans for.

Potential for medium range global flood prediction Nathalie Voisin 1, Andrew W. Wood 1, Dennis P. Lettenmaier 1 1 Department of Civil and Environmental.

Scatterometer-derived sea surface wind variability associated with convective systems: a valuable source of air-sea interaction information Marcos Portabella.

ECMWF reanalysis using GPS RO data Sean Healy Shinya Kobayashi, Saki Uppala, Mark Ringer and Mike Rennie.

LANCE AMSR-E Status Helen Conover, UAHuntsville, Kathryn Regner, UAHuntsville,

Page 1 Developments in regional DA Oct 2007 © Crown copyright 2007 Mark Naylor, Bruce Macpherson, Richard Renshaw, Gareth Dow Data Assimilation and Ensembles,

ECMWF WMO Data Impact Workshop Geneva 2008 slide 1 Towards an adaptive observation network: monitoring the observations impact in ECMWF forecast Carla.

EMS - Reading - 10/9/2013 1(of 11) The added value of scatterometer ocean surface wind for the limited area model HARMONIE in extreme weather events Gert-Jan.

What can we learn about LSM’s from the ELDAS increments? Bart vd Hurk, Janneke Ettema & Pedro Viterbo.

DOWNSCALING GLOBAL MEDIUM RANGE METEOROLOGICAL PREDICTIONS FOR FLOOD PREDICTION Nathalie Voisin, Andy W. Wood, Dennis P. Lettenmaier University of Washington,

The SCM Experiments at ECMWF Gisela Seuffert and Pedro Viterbo European Centre for Medium Range Weather Forecasts ELDAS Progress Meeting 12./

Satellite Derived Ocean Surface Vector Winds Joe Sienkiewicz, NOAA/NWS Ocean Prediction Center Zorana Jelenak, UCAR/NOAA NESDIS.

Status on Cloudy Radiance Data Assimilation in NCEP GSI 1 Min-Jeong Kim JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim 2.

Slide 1 SRNWP, Land surfaces, 28 Sept Oct Recent advances on land surface modelling & data assimilation at ECMWF in preparation for SMOS mission.

CGMS-43-NOAA-WP-25 Coordination Group for Meteorological Satellites - CGMS NOAA Use of Soil Moisture Products Presented to CGMS-43 Working Group 2 session,

Slide 1© ECMWF Extended climate reanalysis ERA-Interim replacement Coupled with ocean REPROCESSED AMVs FOR THE NEXT ECMWF GLOBAL REANALYSES C.Peubey, N.

Snow data assimilation at ECMWF

Land Surface Data Assimilation – Part 2

Variational Quality Control

ASCAT soil moisture data assimilation with SURFEX

EUMETSAT Report Jörg Schulz.

Chairs: James Cotton and Niels Bormann

European Geosciences Union General Assembly 2015

ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course

Snow Depth Reports Availability on the GTS

Data Assimilation Training

MO – Design & Plans UERRA GA 2016 Peter Jermey

EUMETSAT FUTURE PRODUCTS RELEVANTS FOR LAND SAF ACTIVITIES

Weak constraint 4D-Var at ECMWF

Reduced Costs Service & Support Innovative Technology Performance Quality.

Reduced Costs Service & Support Innovative Technology Performance Quality.

Recent work using Satellite winds at the Deutscher Wetterdienst (DWD)

continued on next slide

continued on next slide

ECV Requirements Wolfgang Wagner.

عمل الطالبة : هايدى محمد عبد المنعم حسين

آشنايی با اصول و پايه های يک آزمايش

Impact Studies Of Ascat Winds in the ECMWF 4D-var Assimilation System

Seasonal Climate Forecast For NAME 2004 J. E. Schemm

Calibration, Validation and Status of OSI SAF ScatSat-1 products

The ECMWF weak constraint 4D-Var formulation

As needed for progress monitoring

ચાલો માટી વિશે જાણીએ All About Soil.

Monitoring and assimilation of SCIAMACHY data at ECMWF

Изразеното в настоящата презентация мнение обвързва единствено автора и не представлява официално становище на Комисията за финансов надзор Данил Джоргов.

New DA techniques and applications for stratospheric data sets

Project collaborators’ names

February 2007 Note: Source:.

Metop-C AMSU & MHS Radiometric Quality Status Update

continued on next slide

Types of Errors And Error Analysis.

Presentation transcript:

© ECMWF ERA5: Collaboration between EUMETSAT and ECMWF (and TUWien) to use of reprocessed Scatterometer soil moisture data record for 1991-present in ERA5 ERA5 production started in June for the NRT stream  Monitoring Statistics from 01 June to 15 September 2014: ASCAT-A FG Departure StDev in m 3 m -3 Nb Obs per 1x1 degree FG departure (Mean, m 3 m -3 ) FG departure (StDev, m 3 m -3 ) ASCAT-A ASCAT-B

© ECMWF Old slides from 2015 on implementation and test in rd expt and ERA-SCOUT experiments

© ECMWF Two pre-ERA5 analysis experiments in FMA 2010 using: - the operational (CTRL) - Re-processed ASCAT data (REPROC) Number of Obs 2010 CTRL REPROC  More observations after QC in the reprocessed data set than with the 2010 operational ASCAT data Tests to use of ASCAT soil moisture data for ERA5 (April 2015)

© ECMWF CTRL REPROC Reduced background departure standard deviation Tests to use ASCAT soil moisture data for ERA5 soil moisture

© ECMWF CTRL REPROC  Reduce background departure errors (mean and Stdev) with the reprocessed data Tests to use ASCAT soil moisture data for ERA5 soil moisture

© ECMWF Nb Obs per 1x1 degree area FG departure (Mean, m 3 m - 3 ) FG departure (StDev, m 3 m - 3 ) CTRL g87r REPROC g8qv Tests to use ASCAT soil moisture data for ERA5 soil moisture  Reprocessed ASCAT soil moisture: -More observation pass the QC -Reduced background departure statistics both in mean and Stdev

© ECMWF ERA-SCOUT 2203 ERS soil moisture DA (Oct 2015) SEKF Gain Layer 1 SEKF Gain Layer 2 SCAT Innovation

© ECMWF Soil Moisture increments due to ERS-SCAT data layer 1