ECMWF Status Report

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ECMWF Status Report. Stephen English ECMWF. Brief overview of ECMWF systems and activities Overview of operational satellite data usage / monitoring Research topics related to satellite observations. Overview:. Main areas of activity at ECMWF. Numerical Weather Prediction (NWP). - PowerPoint PPT Presentation

Text of ECMWF Status Report

PowerPoint PresentationECMWF Status Report
Research topics related to satellite observations
Overview:
ECMWF
Numerical
Weather
Environmental
ECMWF
Numerical
Weather
Environmental
Re-analysis
Operational NWP
The forecast model
NAEDEX 2012 – ECMWF Status Report – Stephen Engilsh
Grid structure of the Global operational NWP forecast model: (time step = 10mins)
91 levels in the vertical
(surface to 0.01hPa)
T1279 spectral resolution
(surface to 0.01hPa)
~210km
~125km
~63km
~39km
~25km
~16km
ECMWF
The Assimilation System
Operational 4D-Var Algorithm
Operational 4D-Var Algorithm
Weak constraint term to correct model bias
Flow dependent errors from EDA system
ECMWF
Overview of data usage
Ground based / launched observations
Overview of data usage
BUOYS:
Dropsondes released from aircrafts (NOAA, Met Office, tropical cyclones, experimental field campaigns, e.g., FASTEX, NORPEX)
PROFILERS:
Aircraft:
Now within VarBC!
Satellite Observations
Overview of data usage
Overview of data usage
Level-1 radiances
Level-2 products
Level-1.5 products
AMSU-A, ATMS, MHS, MWTS, MWHS, HIRS, IASI, AIRS, CrIS, SEVIRI, MTSAT, GOES, SSMIS, TMI, SMOS, WindSat
GRAS, GRACE, CNOFS, COSMIC, TERRASAR-X, ASCAT, OSCAT
Level-2* products
Radiances
AMSU-A on NOAA-15/18/19, AQUA, Metop-A+B, ATMS on Suomi-NPP (from Sept 2012)
AMSU-B/MHS on NOAA-19, Metop-A+B
SSMIS on F-17, TMI on TRMM, Windsat on Coriolis
HIRS on NOAA-17/19, Metop-A
SMOS imager
Bending angles
Ozone
Atmospheric Motion Vectors
Sea surface parameters
Near-surface wind speed from ASCAT on Metop-A, OSCAT on OceanSat
Significant wave height from Jason altimeters
Overview of data usage
Automated Satellite Alert and Monitoring
http://www.ecmwf.int/products/forecasts/d/charts/monitoring/satellite/
ECMWF
Automated Satellite Alert and Monitoring
http://www.ecmwf.int/products/forecasts/d/charts/monitoring/satellite/
ECMWF
Automated Satellite Alert and Monitoring
ECMWF
NAEDEX 2012 – ECMWF Status Report – Stephen Engilsh
Out of threshold anomalies trigger alerts on web site and launch emails to
key personnel prompting action
*
New instruments: FY3 and Suomi-NPP
CrIS 14µm
ATMS Ch.9
MWHS Ch.4
MWTS Ch.3
CrIS 14µm
R+D activities relevant to NAEDEX
ECMWF
*
ECMWF
*
Improved microwave surface emissivity
New sensors: ATMS, FY3
*
ECMWF
AMV error model and understanding
AMV QC
ECMWF
*
Surface pressure
EDA analysis spread for
temperature at 100 hPa
Ops, 2010 - 2011
Ops, 2011 - 2012
(Dragani)
+SBUV21
This slide shows the impact of the ENVISAT ozone products (particularly MIPAS) on the ozone analyses as the ozone observing system evolved. The animation should help follow that evolution. Note that top 2 panels were presented by Tony at the last OD/RD meeting to show the impact of both MIPAS and IR/O3 (ozone sensitive radiances from AIRS, IASI, and HIRS) on the ozone analyses.
Each panel shows the temporal mean (see below for the periods) of the zonal mean differences between the off-line MLS ozone profiles (available down to 215hPa) and their co-located ozone analyses from various experiments, as following:
- Top left: operations for DJF 2010-2011 before MIPAS and IR/O3 assimilation started
- Top right: operations for DJF 2011-2012 with MIPAS and IR/O3 assimilation
- Bottom right: research experiment (T511) for 1 Dec 2011 to 10 Feb 2012 in which neither SCIA nor MIPAS were assimilated (no other modification was made to the system)
- Bottom left: research experiment (T511) for 1 Dec 2011 to 10 Feb 2012 in which neither SCIA nor MIPAS were assimilated, but this time the SBUV/2 ozone data were used on their original 21 levels instead of the 6 layer we produce in house (basically I’m investigating whether these data could provide some of the information about the vertical distribution of ozone that was given before by MIPAS) . Note that in the assimilation of the SBUV on 21 layers I didn’t account for the vertical correlations nor the vertical averaging kernels.
ECMWF
7. Radar and lidar
Validating model clouds and physical parameterisations
Choices of observation error, QC and bias correction
ECMWF
Summary
Early CrIS and ATMS results are encouraging
Loss of ENVISAT was a major event, especially for ocean and ozone analysis
Principal component assimilation has been demonstrated for cloud-free scenes
FY3 characterisation led to very valuable re-analysis of errors in MSU and AMSU-A data record
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