Studying AMV Errors With The NWP SAF Monitoring Web Site

Slides:

Advertisements

Similar presentations

Recent Advances in the Processing, Targeting and Data Assimilation Applications of Satellite-Derived Atmospheric Motion Vectors (AMVs) Howard Berger 1,

Advertisements

Atmospheric Motion Vectors Derived via a New Nested Tracking Algorithm Developed for the GOES-R Advanced Baseline Imager Jaime Daniels 1, Wayne Bresky.

1 Introduction into the Absorption Channels Description of characteristics and content of the Ozone channel: Ch08: 9.7  Contact person: Veronika Zwatz-Meise.

APPLICATIONS OF METEOSAT SECOND GENERATION (Meteosat-8) AIRMASS RGB Jochen Kerkmann Satellite Meteorologist, Training Officer

Dust (and Smoke) Detection with MSG SEVIRI RGB Products Exercises Dr. Jochen Kerkmann Satellite Meteorologist, Training Officer

CORP Symposium Fort Collins, CO August 16, 2006 Session 3: NPOESS AND GOES-R Applications Tropical Cyclone Applications Ray Zehr, NESDIS / RAMM.

Joe Sienkiewicz 1, Michael Folmer 2 and Hugh Cobb 3 1 NOAA/NWS/NCEP/OPC 2 University of Maryland/ESSIC/CICS 3 NOAA/NWS/NCEP/NHC/ Tropical Analysis and.

Introduction and Methodology Daniel T. Lindsey*, NOAA/NESDIS/STAR/RAMMB Louie Grasso, Cooperative Institute for Research in the Atmosphere

WEATHER SYSTEMS WEATHER AND CLIMATE.

Assimilation of GOES Hourly and Meteosat winds in the NCEP Global Forecast System (GFS) Assimilation of GOES Hourly and Meteosat winds in the NCEP Global.

New and Updated Operational Tropical Cyclone Wind Products John A. Knaff – NESDIS/StAR - RAMMB, Fort Collins, CO Alison Krautkramer – NCEP/TPC - NHC, Miami,

Available products for Seasonal forecasting J.P. Céron – Direction de la Climatologie.

30 November December International Workshop on Advancement of Typhoon Track Forecast Technique 11 Observing system experiments using the operational.

Régis Borde Polar Winds EUMETRAIN Polar satellite week 2012 Régis Borde

Version 1.0, 30 November 2004 Slide: 1 METEOSAT SECOND GENERATION (MSG) DUST DETECTION Author:Jochen Kerkmann Contributors:H.-J. Lutz.

Status of improving the use of MODIS, AVHRR, and VIIRS polar winds in the GDAS/GFS David Santek, Brett Hoover, Sharon Nebuda, James Jung Cooperative Institute.

© Crown copyright Met Office Data Assimilation Developments at the Met Office Recent operational changes, and plans Andrew Lorenc, DAOS, Montreal, August.

MODIS Polar Winds in ECMWF’s Data Assimilation System: Long-term Performance and Recent Case Studies Lueder von Bremen, Niels Bormann and Jean-Noël Thépaut.

Evaluation of three-dimensional cloud structures in DYMECS Robin Hogan John Nicol Robert Plant Peter Clark Kirsty Hanley Carol Halliwell Humphrey Lean.

1 Developing Assimilation Techniques For Atmospheric Motion Vectors Derived via a New Nested Tracking Algorithm Derived for the GOES-R Advanced Baseline.

Real-time Generation of Winds and Sea Ice Motion from MODIS Jeff Key 1, Dave Santek 2, Chris Velden 2 1 Office of Research and Applications, NOAA/NESDIS,

Satellite based instability indices for very short range forecasting of convection Estelle de Coning South African Weather Service Contributions from Marianne.

METEOSAT SECOND GENERATION FROM FIRST TO SECOND GENERATION METEOSAT

AMVs Derived via a New Nested Tracking Algorithm Developed for the GOES-R ABI Jaime Daniels 1, Wayne Bresky 2, Steve Wanzong 3 and Chris Velden 3 NOAA/NESDIS,

ITSC-1227 February-5 March 2002 Use of advanced infrared sounders in cloudy conditions Nadia Fourrié and Florence Rabier Météo France Acknowledgement G.

Clouds Ice cloud detection using the 8.7  m channel: areas of ice clouds (in particular thin cirrus) are red (positive difference), clear ground and water.

MODIS Winds Assimilation Impact Study with the CMC Operational Forecast System Réal Sarrazin Data Assimilation and Quality Control Canadian Meteorological.

1 3D-Var assimilation of CHAMP measurements at the Met Office Sean Healy, Adrian Jupp and Christian Marquardt.

Detecting Lee and Barrage clouds using Meteosat 8 Contact person: Jarno Schipper Version August 2005.

Diagnosis and modelling of Tropical Cyclone Catarina (2004) Ron McTaggart-Cowan and Lance Bosart University at Albany, State University of New York.

Towards Assimilation of GOES Hourly winds in the NCEP Global Forecast System (GFS) Xiujuan Su, Jaime Daniels, John Derber, Yangrong Lin, Andy Bailey, Wayne.

Slide: 1 APPLICATIONS OF METEOSAT SECOND GENERATION (MSG) RGB COMPOSITES WITH CHANNELS AND THEIR INTERPRETATION Authors:J. Kerkmann, HP. Roesli,

Atmospheric Motion Vectors - CIMSS winds and products (

- Current status of COMS AMV in KMA/NMSC E.J. CHA, H.K. JEONG, E.H. SOHN, S.J. RYU Satellite Analysis Division National Meteorological Satellite Center.

Station lists and bias corrections Jemma Davie, Colin Parrett, Richard Renshaw, Peter Jermey © Crown Copyright 2012 Source: Met Office© Crown copyright.

12 th International Winds Workshop Copenhagen, June 2014 Manuel Carranza Régis Borde Marie Doutriaux-Boucher Recent Changes in the Derivation of.

Slide 1 Investigations on alternative interpretations of AMVs Kirsti Salonen and Niels Bormann 12 th International Winds Workshop, 19 th June 2014.

ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course

National Meteorological Satellite Center

Chairs: James Cotton and Niels Bormann

Welcome to IWW12 What are AMVs?

Radio occultation (RO) and its use in NWP

Jianyu Liang (York U.) Yongsheng Chen (York U.) Zhiquan Liu (NCAR)

Validation of SAFNWC/MSG PGE09 High Resolution Winds

Warm front in radar images

GOES visible (or “sun-lit”) image

Winds in the Polar Regions from MODIS: Atmospheric Considerations

WG5-Report from Switzerland: Verification of aLMo in the year 2005

Hyperspectral Wind Retrievals Dave Santek Chris Velden CIMSS Madison, Wisconsin 5th Workshop on Hyperspectral Science 8 June 2005.

ATMOPHERIC MOTION VECTORS CLIMATE DATA RECORD PRODUCED AT EUMETSAT

Reprocessing of Atmospheric Motion Vector for JRA-3Q at JMA/MSC

Use of satellite winds at Deutscher Wetterdienst (DWD)

Investigating AMV and NWP model errors using the NWP SAF monitoring

Technical and scientific verifications of the EUMETSAT MTG-FCI AMV prototype Manuel Carranza Régis Borde.

A study of the AMV correlation surface

Assimilation of GOES-R Atmospheric Motion Vectors

Stéphane Laroche Judy St-James Iriola Mati Réal Sarrazin

Status of operational AMVs from FY-2 satellites since the 11th winds workshop I will talk about the Status of operational AMVs from FY-2 satellites since.

UPDATE ON SATELLITE-DERIVED amv RESEARCH AND DEVELOPMENTS

Status of Operational AMV products at EUMETSAT

A Variational Approach for Estimating AMV Reassigned Heights

James Cotton, Mary Forsythe IWW14, Jeju City, South Korea.

METEOSAT SECOND GENERATION (MSG)

GOES-16 AMV data evaluation and algorithm assessment

WEATHER SYSTEMS WEATHER AND CLIMATE.

Item Taking into account radiosonde position in verification

AMV impact studies at the Met Office

Validation for TPW (PGE06)

Impact of aircraft data in the MSC forecast systems

VALIDATION OF DUAL-MODE METOP AMVs

Presentation transcript:

Studying AMV Errors With The NWP SAF Monitoring Web Site Francis Warrick, James Cotton

NWP SAF AMV Monitoring nwpsaf.eu -> Monitoring -> Winds Quality Evaluation -> AMVs AMV usage by NWP centre Monthly monitoring plots versus Met Office and ECMWF models Analysis reports every two years One-off investigations

Speed Histograms Maps Maps with Wind Vectors Zonal Plots

Jet Region Negative Speed Bias High–Level (heights above 400 hPa) Feature 2.10

Heritage Algorithm (Un-edited) Heritage Algorithm (Un-edited) Nested Tracking Nested Tracking

O-B Speed Differences Heritage Algorithm (Un-edited) 9th March 2017 0545 UTC GOES-13 IR

O-B Speed Differences Nested Tracking 9th March 2017 0545 UTC GOES-13 IR

Heritage Algorithm (Un-edited) 9th March 2017 0545 UTC GOES-13 IR

Nested Tracking 9th March 2017 0545 UTC GOES-13 IR

Co-located within 10km, 10 minutes. 9th March 2017 06Z cycle

Positive Speed Bias in Tropics High–Level (heights above 400 hPa) Feature 2.13

OCA Infra-red 10.8 micron December 2016 Data over South Atlantic Operational Heights (CLA) OCA Infra-red 10.8 micron December 2016 Data over South Atlantic Heights above 400 hPa 24th-27th December

Meteosat-10 IR 10.8 26th December 2016 1500-2100 UTC (18Z cycle)

Operational Heights (CLA) Meteosat-10 infra-red 10.8 micron 1815 UTC 26/12/2016

OCA Meteosat-10 infra-red 10.8 micron 1815 UTC 26/12/2016

Operational Heights (CLA) Meteosat-10 infra-red 10.8 micron 1815 UTC 26/12/2016

OCA Meteosat-10 infra-red 10.8 micron 1815 UTC 26/12/2016

Operational Heights (CLA) OCA

MSG Positive Bias over North Africa Low–Level (heights below 700 hPa) Feature 2.6

Below 700 hPa Seen in MSG visible and infra-red over desert Worse in winter (position of jet) Linked with cirrus & semi-transparent cloud Meteosat-10 IR 10.8 Sahara December 2016

Why is this feature only present in the 18Z cycle, and not the 00, 06 and 12Z cycles? Faint cloud is moving north-east ward

Met Office Cloud Product OCA Met Office Cloud Product

Meteosat-8 (IODC) Positive Speed Difference in the Tropics Low–Level (heights below 700 hPa) Feature 8.1

Meteosat-8, visible 0.8 micron channel, August 2017 Feature Background: Peaks June-August AMV minus best-fit pressure averages -100 hPa, suggesting AMVs assigned too high

(also seen versus ECMWF model) AMV profile versus model Lack wind shear ~800-950 hPa (also seen versus ECMWF model)

AMV profile versus sondes Cocos Islands Saint Denis, Reunion AMV profile versus sondes Only 2 sites available in region of interest… Saint Denis: background profiles do not match Cocos Islands: lack of AMV wind shear 700-850 hPa

Other AMVs versus Met Office model

Any questions?