Observation usage in Météo-France data assimilation systems

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Observation usage in Météo-France data assimilation systems
Observation usage in Météo-France
   data assimilation systems :

Current status
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            Jean-François Mahfouf
         On behalf of GMAP/OBS team
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   N. Boullot, P. Moll, C. Payan, E. Wattrelot,
             C. Augros, O. Caumont

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Observation usage in Météo-France data assimilation systems
Outline

     Operational NWP models at Météo-France

     Recent changes regarding the use of observations for
      assimilation (CY38T1 – July 2013)

     Planned evolutions for 2015 (CY40 – February 2015) :
      high resolution «e-suite »

     Ongoing activities for an increased usage of
      observations

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Observation usage in Météo-France data assimilation systems
Operational models at Météo-France (1)
 Global model ARPEGE
 Horizontal resolution: between 10 and 60 km

                     10 km

            70 vertical levels
                                               Limited area model
                                               AROME
                                               2.5 km horizontal mesh
                                               60 vertical levels

                                    6-hour     3-hour
hPa                                                            hPa
                                    4D-Var     3D-Var

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Observation usage in Météo-France data assimilation systems
Operational models at Météo-France (2)
    – Spectral limited area models : ALADIN « overseas »
    – 70 levels from 17m to 0.05 hPa, horizontal resolution 7.5 km
    – 3D-Var assimilation (6h window) :
         Same data as ARPEGE plus bogusing for tropical cyclones
    – Current operational domains :

                                           Aladin France discontinued
                                              since February 2012

                                Antilles-Guyana
                                  operational

             French Polynesia
                                                              Aladin La Réunion   New-Caledonia
               operational
                                                                  since 2006       operational

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Observation usage in Météo-France data assimilation systems
Main features of CY38T1 (OBS) – oper

     Last operational suite on the NEC SX9 (2 July 2013) – migrated to
      the new Météo-France HPC BULL B710 DLC (14 January 2014)
     Instruments from new satellites : NASA/Suomi-NPP,
      ISRO/Oceansat-2, EUMETSAT/MetOp-B
     Increased usage of existing instruments :
       –   AIRS (over land + additional upper tropospheric channels)
       –   IASI (WV channels)
       –   GNSS-RO (reduced vertical thinning)
       –   Clear sky radiances (1 WV channel) from GOES-E and W
       –   Channels 4 and 5 from MHS/NOAA19

       => Increase of data by a factor of 2 (in assimilation cycles)

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Observation usage in Météo-France data assimilation systems
New satellites

                                      Suomi-NPP (launched 28/10/2011)
                                      -Advanced Technology Microwave
                                      Sounder (ATMS)
                                      -Cross track Infrared Sounder (CrIS)

    OCEANSAT-2 (launched 09/2009)
    -OSCAT(Ku-band scatterometer)
6   Discontinued – 21/02/2014
Observation usage in Météo-France data assimilation systems
Evolution of the number of data in ARPEGE

        Actual numbers                        ---   Satellite data available
                                              __    Satellite data used

                                              ---   Conventional data available
                                              __    Conventional data used

    __ Satellite / All available
    __ Satellite used / Satellite available

    ---- Conv used / Conv available
    __ Satellite / All used                                              Percentages

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Observation usage in Météo-France data assimilation systems
Current usage of observations in ARPEGE

                AIRS                                     AMSU-A
                                      CY38T1
            AIRS

                    AMSU-A
        IASI                                               Aircrafts
                                                                RO
                                                                -
                                                            GPS
         IASI         Aircra
                   CriS      fts

                                                     Temp

                             CY37T1                             CY37T1
    Number of observations                     Fractional DFS

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Observation usage in Météo-France data assimilation systems
FSO : CY37T1 vs. CY38T1

 Number of          Forecat Error
observations
                    Contribution
  per type

               Forecast error
               Contribution in
               percentage           CY37T1

                                    CY38T1
Observation usage in Météo-France data assimilation systems
FSO : CY37T1 vs. CY38T1
CY37T1                                            CY38T1

  ++      +       -       --

                        Comp-U+V                      Comp-U+V

  Surface ocean winds                  Improvement
                                        Improvement

  CY37T1 : ASCAT-A
  CY38T1 : ASCAT-A + ASCAT-B + OSCAT   Degradation
                                       Degradation
Monitoring vs FSO (1)

                       OSCAT

FSO(comp-U+V)                  RMS WV (obs-bg)
Monitoring vs FSO (2)

                       ASCAT-B

FSO(comp-U+V)                    RMS WV (obs-bg)
ARPEGE forecast scores

                                             CY38T1
                                             CY38T1

     Z 500 hPa at day-3 over Northern Hemisphere
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Use of observations in AROME (CY38T1)

      Same additional observations as in ARPEGE

      Assimilation of additional SEVIRI channels over land (Ts
       retrieval + use of LandSAF emissivity atlas)

      AMSU-A : channels 9 and 10 + thinning at 80 km

      Doppler wind from X band radar (Mt Maurel from
       RHYTMME)

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Use of observations in AROME (CY38T1)

                                      DFS
                                      Rain

                                     DFS
                                     No rain
     IASI
     RADAR radial wind
     RADAR humidity (reflectivity)
     SYNOP
     AIRCRAFTS
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Revision to our forecasting systems

        Planned for Feb. 2015 with CY40 (e-suite in Jul. 2014) :
        ARPEGE Model : T1198c2.2 (∆x = 7.5 km) L105
        AROME Model : ∆x = 1.3 km L90
        ARPEGE 4D-Var : Two inner loops (40 + 40 iterations)
        ARPEGE Ensemble 4D-Var : 25 members
        Planned usage of observations for ARPEGE :
         –   Revised tunning of observation errors
         –   Assimilation of SAPHIR (~ AMSU-B/MHS) from MEGHA-TROPIQUES
         –   Assimilation of sounding channels from SSMI/S F17 and F18
         –   Use of RTTOV-10 with internal interpolation
         –   Assimilation of GEORAD from METEOSAT-7 and MTSAT-2
         –   Assimilation of GPS ZTD from NOAA stations

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Ongoing developments on AROME

      Revisions of the satellite variational bias correction (new set of
       predictors) and to the channel selection with a model top at 10 hPa
      Improvement to the GPS bias correction (VarBC scheme) + revised
       ZTD observation operator
      Increased radar density : thinning on individual radars (more data in
       overlapping regions) + tests with an horizontal thinning at 8 km
      Diagnostics on observation error correlations (radiances + radars)
      Preliminary activities towards the assimilation of OPERA radar
       reflectivities
      Preparation of future instruments : polarimetric radar data, ground
       based radiometers, IR hyperspectral sounder MTG-IRS

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New GPS pre-processing

      Phase 1 : monitoring (1 month) to compute statistics for every
       couple (station/analysis centre)
         – Bias, Std(obs-guess), Nb gross errors, Nb data available
         ⇒ Extended white list (quality and regularity of data) + sigma_o
      Phase 2 : Pre-processing (Bator)
         – 1h superobing for ARPEGE, selection of closest data to the center of
           the time-slot (AROME, ALADIN)
      Phase 3 : Screening
         – Background check, dynamic selection of the best (station/analysis
           centre) at every location, no selected data = passive
      Phase 4 : Assimilation and VarBC
         – New observation operator to evaluate ZTD above model top
         – Two predictors for VarBC : constant + TCWV

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Current status of ODC for data assimilation

      About 130 european radars are received in real time : reflectivity +
       Doppler wind (for some countries : Croatia, Denmark, Estonia,
       Finland, France, Ireland, Netherlands, Poland, Slovakia, Sweden) in
       common format ODIM BUFR or ODIM HDF5 – polar coordinate
       (1°x1km)

      Recent evolution of radar data from France (18/03/2014) for ODC :
         – Identification of ground clutter with « no data » flag instead of
           « undetect »
         – No reflectivity threshold at 0 dBZ
         – No filtering of isolated pixels and groups of isolated pixels
         – Ground clutter filtering modified when SNR is small
         – Radial wind velocity added to the raw reflectivity

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Use of OPERA data : current strategy
Adaptation of CONRAD tool developed by Hirlam Consortia

CONversion of RADar data in MF-BUFR format:
• format conversion
• Use of Zraw, Zfiltered to add metadata to create ODB base for AROME
                                Format converter
Raw Radar data
                             Local reader libs     CONRAD CORE libs
                               CONRAD              BUFR write routines
                 HDF5
                 BUFR
                                                             Fortran
                                                             libemos

                                                   MF-BUFR AROME

                                                      BATOR       AROME /
                                                                  HARMONIE
                                                       ODB
Use of OPERA data : current strategy
Adaptation of CONRAD tool developed by Hirlam Consortia

CONversion of RADar data in MF-BUFR format:
• format conversion
• Use of Zraw, Zfiltered to add metadata to create ODB base for AROME
                                         Format converter
Raw Radar data
                                      Local reader libs            CONRAD CORE libs
                                        CONRAD                     BUFR write routines
                     HDF5
                    BUFR               Odim structure
                                       Written in C (from OPERA)             Fortran
                    ODIM BUFR                                                libemos
                                       radar_data_t structure…

                                                                   MF-BUFR AROME
  ODIM BUFR: not a standard/native BUFR format: need
  for a specific data decompression structure:                        BATOR       AROME /
  my_decompress …
                                                                                  HARMONIE
                                                                       ODB
Use of OPERA data : current strategy
Adaptation of CONRAD tool developed by Hirlam Consortia

CONversion of RADar data in MF-BUFR format:
• format conversion
• Use of Zraw, Zfiltered to add metadata to create ODB base for AROME
                                          Format converter
Raw Radar data
                                       Local reader libs            CONRAD CORE libs
                                         CONRAD                     BUFR write routines
                     HDF5
                     BUFR               Odim structure
                                        Written in C (from OPERA)            Fortran
                    ODIM BUFR                                                libemos
                                        radar_data_t structure…

                                                                  Ok for French and
                                                                  German radar data
  ODIM BUFR: not a standard/native BUFR format: need
  for a specific data decompression structure:
  my_decompress …                                                 Not yet for all other
  ODIM HDF5: development is underway, still not                   countries
  satisfactory
The MF-BUFR radar product for AROME
                                                 0 - Dark blue: low         8 - Red: Rain
                                                 Signal-Noise-ratio
  Trappes OPER
     Z                                    Vr                           QFlag

0 : Noise                                        8 : rain
1 : static clutter                               9 : large dropplets        1- 2 - 3: clutter
2 : dynamic clutter (sigma)                      10 : rain/hail
3 : close to clutter < 1 km                      11 : fine hail        4 – 5 : insects,
4 : clear sky (insects, birds…)                  12 : hail             birds, sea clutter,
5 : military decoy, sea clutter                  13 : dry snow
                                                                       military decoy
6 : sea clutter by vertical gradient and texture 14 : wet snow
7 : drizzle                                      15: crystals
Comparison between MF-BUFR and ODC products

Trappes MF-BUFR

                           2- Less detected clutter
Trappes Zfiltered          8- wrong rain
ODC
Issues with filtering of ground clutter in ODC (1)

Toulouse OPER

Toulouse Zfiltered            8 – wrong small rain
ODC –10 dBZ
Issues with filtering of ground clutter in ODC (2)
Toulouse –10 dBZ

                            0 – wrong rain
Toulouse+10 dBZ             becomes low SNR
Wrong rain becomes low SNR: to adapt treatment in AROME

Threshold : -10 dBZ

                                             Threshold : +10 dBZ !
                                             1. Ok: loss of good
                                                detected rain!
                                             2. But, to be careful
                                                to not wrongly
                                                dry!
Polarimetric forward operator
    Inputs: model prognostic variables (T°, qv, qr,
            qs, qg, qc, qi …) on a 3D grid

Caumont et al., 2006                                    Parameters fixed by the
                                                       microphysical scheme ICE3 :
                                                       PSD, snow/graupel/ice
                                                       density
                                                        « Free parameters » :
                                                       Hydrometeor shape,
                                                       orientation (oscillation),
                                                       dielectric constant
                                                       => Defined after a
                                                       sensitivity study

   Outputs : radar variables (VR, ZH, ZDR, ρHV, ΦDP,
       KDP, …) interpolated onto radar PPIs

     Simulates beam bending, beam propagation (attenuation, differential
    attenuation, differential phase) and backscattering
     Simulates Signal-to-Noise Ratio (SNR)
Radar/model comparisons
       Montclar radar (C band, 24/09/2012)
Rain                        Snow

                                             Distribution
                                              of Zdr =
                                               f(Zhh)

                                             Distribution
                                              of Kdp =
                                               f(Zhh)
Radar/model comparisons                                  30

                       Montclar radar (C band, 24/09/2012)

Distribution of Zhh, Zdr and Kdp as a function of temperature in convective areas

  Radar

   Model

          Zhh                       Zdr                          Kdp
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