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ECMWF Status Report

This report provides an overview of ECMWF's systems and activities, with a focus on operational satellite data usage and monitoring. It covers research topics related to satellite observations, CrIS/ATMS preparations, evaluation of sounder data, assimilation of PC data, numerical weather prediction, environmental monitoring and modeling, historical reanalysis, trend analysis of climate parameters, improved climatology, and cleansed historical observation datasets.

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ECMWF Status Report

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  1. ECMWF Status Report Tony McNally ECMWF

  2. Brief overview of ECMWF systems and activities Overview of operational satellite data usage / monitoring Research topics related to satellite observations CrIS / ATMS preparations Evaluation of Sounder data from FY3A Assimilation of PC data Overview:

  3. Main areas of activity at ECMWF Numerical Weather Prediction (NWP) Environmental monitoring and modelling Historical reanalysis for climate research

  4. Numerical Weather Prediction (NWP) Deterministic Monthly Seasonal

  5. Environmental monitoring and modelling Estimating greenhouse gas concentration and flux inversion Monitoring and forecasting trajectory of dust events Monitoring and forecasting trajectory of volcanic events

  6. Re-analysis for climate research Trend analysis of climate parameters Improved climatology for process studies Cleansed historical observation data sets

  7. Operational NWP

  8. The forecast model

  9. Grid structure of the operational NWP forecast model: (time step = 10mins) 91 levels in the vertical (surface to 0.01hPa) Global domain T1279 spectral resolution (typical 16km grid point spacing)

  10. Grid structure of the forecast model:(time step = 10mins) 91 levels in the vertical (surface to 0.01hPa) Global domain T1279 spectral resolution (typical 16km grid point spacing) 6,300,000,000,000,000 floating point operations for a single 10 day forecast

  11. The Assimilation System

  12. Operational 4D-Var Algorithm

  13. Operational 4D-Var Algorithm Flow dependent errors from EDA system VARBC term to correct observation bias Weak constraint term to correct model bias

  14. Overview of data usage

  15. Ground based / launched observations

  16. Overview of data usage SYNOP/SHIP/METAR: Meteorological/aeronautical land surface weather stations (2m-temperature, dew-point temperature, 10m-wind) BUOYS: Moored buoys (TAO, PIRATA) Drifters TEMP/TEMPSHIP/DROPSONDES: Radiosondes ASAPs (commercial ships replacing stationary weather ships) Dropsondes released from aircrafts (NOAA, Met Office, tropical cyclones, experimental field campaigns, e.g., FASTEX, NORPEX) PROFILERS: UHF/VHF Doppler radars (Europe, US, Japan) Aircraft: AIREPS (manual reports from pilots) AMDARs, ACARs, etc. (automated readings)

  17. Satellite Observations

  18. Overview of data usage

  19. Overview of data usage ~ 150,000,000 observations processed every 12 hours ~ 11,000,000 observations used every 12 hours

  20. Overview of data usage Level-1 radiances Level-1.5 products Level-2 products SBUV, OMI, GOME MLS, SCIA, SEVIRI GOES, MTSAT, FYX, MERIS GRAS, COSMIC, ASCAT, ERS, AMSU, HIRS, MHS, IASI , AIRS, SEVIRI, MTSAT, GOES, SSMI, SSMIS, TMI,, AMSRE Level-2* products SST / SNOW / ICE

  21. Overview of data usage Radiances AMSU-A on NOAA-15/18/19, AQUA, Metop AMSU-B/MHS on NOAA-19, Metop SSMIS on F-17, AMSR-E on Aqua, TMI on TRMM HIRS on NOAA-17/19, Metop AIRS on AQUA, IASI on Metop MVIRI on Meteosat-7, SEVIRI on Meteosat-9, GOES-11/13, MTSAT-2 imagers Bending angles COSMIC (6 satellites), GRAS on Metop, GRACE-A, Terrasar-X Ozone SBUV on NOAA-17/18, OMI on Aura, SCIAMACHY on Envisat, AIRS, IASI, HIRS (soon) Atmospheric Motion Vectors Meteosat-7/9, GOES-11/13, MTSAT-2, MODIS on Terra/Aqua / FY2 (monitored only) Sea surface parameters Near-surface wind speed from ERS-2 scatterometer, ASCAT on Metop Significant wave height from RA-2/ASAR on Envisat, Jason altimeters

  22. Automated Satellite Alert and Monitoring http://www.ecmwf.int/products/forecasts/d/charts/monitoring/satellite/

  23. Automated Satellite Alert and Monitoring http://www.ecmwf.int/products/forecasts/d/charts/monitoring/satellite/

  24. Automated Satellite Alert and Monitoring

  25. Automated Satellite Alert and Monitoring Time series of area averaged statistics showing loss of NOAA data in February 2011

  26. Automated Satellite Alert and Monitoring Sudden stratospheric warmings (SSW) are very evident in the monitoring of stratospheric radiance observations over polar regions in winter. SSW over the N.Pole are common, but over the S.Pole they are very rare (last one 2002)

  27. Automated Satellite Alert and Monitoring The radiance monitoring detected a very rare and dramatic SSW over Antarctica during the last winter – corresponding to a complete disruption of the upper level vortex . 5hPa Temperature winter 2009 5hPa Temperature winter 2010

  28. Automated Satellite Alert and Monitoring Out of threshold anomalies trigger alerts on web site and launch emails to key personnel prompting action

  29. R+D activities relevant to NAEDEX

  30. Preparations for CrIS / ATMS data

  31. Preparations for CrIS / ATMS • Ready to handle real CrIS / ATMS data • when available: • BUFR proxy data, from NESDIS, • archived at ECMWF since Feb 2011 • RTTOV Fast RT model coefficients • available ( P.Rayer (UKMO) based on • rectangular band shapes) • Code to handle CrIS / ATMS lodged in • ECMWF IFS CY37R3 • Preliminary results generated • from simulated data, as technical • check-out of code. (Thanks to Haibing !) • Aim to provide feedback on data quality within • days during : • early orbit check-out • subsequent commissioning phase • IF data is available in BUFR format & • data streams in place Status: April 2011 CrIS data archived, RTTOV RT coefficients available (from Meteo-France) & code under development. On-schedule for launch date.

  32. Preparations for CrIS / ATMS Spatial averaging required for ATMS noise reduction to achieve AMSUA performance Statistics of simulated data display a few minor anomalies, but are generally as expected

  33. Evaluation and Preparations for FY3 Sounder data

  34. The Evaluation of FY-3A Microwave Temperature Sounder (MWTS) Data at ECMWF

  35. Evaluation of MWTS - Noise SPIKES MWTS-3 MWTS-3 FG DEPARTURES STD (FG_DEP) -5K 31K

  36. Status of FY3A data at ECMWF • MWTS / MWHS monitored since 9th Nov 2010. • Problems with data stability and spikes continue (but recent steps to ground processing are an improvement) • We will activate the data in ECMWF operations when the data is reliable and stable MEAN FG_DEP STD FG_DEP See : http://www.ecmwf.int/products/forecasts/d/charts/monitoring/satellite/FY/mwts/ 17th Feb 2011 – 8th April 2011

  37. Direct Assimilation of Principal Components (PC) (AIRS / IASI / CrIS)

  38. These provide an extremely efficient mechanism to exploit a high proportion of the full spectral information that could not be achieved in radiance space. There is potential to tune the eigenvector representation to filter out undesirable spectral features (e.g. noise or gas contamination) We may be forced to use PC data, as future pressure on communications bandwidth may result in only PC data being disseminated to users. Assimilation of PC data Why assimilate Principal Components of advanced IR spectra ?

  39. Assimilation of PC data observed IASI spectrum Model background XB (T,Q,O3) System design: cloud screening PCRTTOV Project observed spectrum on PC basis 4DVAR (PCRTTOV-AD) (PCRTTOV-TL) YPCB YPCOBS XA

  40. Assimilation of PC data Temperature and humidity increments from assimilating PC and radiances Red line = increments from 850 IASI B3 radiances Blue line= increments from 50 IASI B3 PCs Temperature increment at 500hPa from assimilating 50 PC T T-inc

  41. Assimilation of PC data Spectrally correlated contaminating gas species can have a significant impact on the accuracy of PC simulated from the NWP model. For this reason channels are carefully selected to avoid parts of the spectrum prone to trace gas absorption. Full spectrum (LW + SW) SW only Selected SW

  42. Assimilation of PC data Spectrally correlated contaminating gas species can have a significant impact on the accuracy of PC simulated from the NWP model. For this reason channels are carefully selected to avoid parts of the spectrum prone to trace gas absorption. Full spectrum (LW + SW) Optimal channel selection for PC is very dependent on the desired application so it is unlikely one set of disseminated PC will suit all users! SW only Selected SW

  43. Assimilation of PC data Next Steps: • Establish optimal configuration and consider operational assimilation (land / sea / day / night) • Extend to assimilation of partly cloudy scenes via cloud detection in PC space and cloud analysis (cloudy PC-RTTOV) • Blending radiance assimilation and PC assimilation • Monitor and provide feedback on EUMETSAT generated disseminated PCs

  44. Summary • Data volumes / instruments continue to increase • Monitoring and alert systems have been automated • Simulated data has allowed us to be ready for CrIS / ATMS • FY3A sounder data is potentially very high quality but serious issues with processing reliability remain • Principal component assimilation is being pursued as a priority to handle high spectral resolution IR sounder data

  45. Spare Slides

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