Mtg irs osse on regional scales
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MTG-IRS OSSE on regional scales. Xiang-Yu Huang, Hongli Wang, Yongsheng Chen and Xin Zhang National Center for Atmospheric Research, Boulder, Colorado, U.S.A. Stephen A. Tjemkes, Rolf Stuhlmann EUMETSAT, Darmstadt, Germany. Contents. The OSSE configuration The nature run

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MTG-IRS OSSE on regional scales

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Mtg irs osse on regional scales

MTG-IRS OSSE on regional scales

Xiang-Yu Huang, Hongli Wang, Yongsheng Chen and Xin Zhang

National Center for Atmospheric Research, Boulder, Colorado, U.S.A.

Stephen A. Tjemkes, Rolf Stuhlmann

EUMETSAT, Darmstadt, Germany

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 1


Contents

Contents

The OSSE configuration

The nature run

Simulated (conventional and MTG) observations

Calibration experiments

Data assimilation and forecast results

4D-Var results

Summary

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 2


The osse configuration

The OSSE Configuration

  • Nature run model: MM5

  • Assimilation model: WRF

  • Data assimilation scheme: WRF 3D-Var (4D-Var)

  • Selected cases:

    • International H2O Project (IHOP, 13 May - 25 June 2002)

    • Southern Great Plains of US

http://catalog.eol.ucar.edu/ihop/catalog/missions.html

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 3


Osse setup

OSSE setup

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 4


Ihop case

IHOP Case

Three convection cases are

selected from 11 June 2002

to 16 June 2002:

  • 11 June: Dryline and Storm

  • 12 June: Dryline and Storm

  • 15 June: Severe MCS

Map illustrating the operational instrumentation within the IHOP_2002 domain (Weckwerth et al. 2004)

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 5


Nature run configuration

Nature run configuration

  • Nature model: MM5

  • Grid points: 505X505X35

  • Horizontal resolution: 4Km

  • Time step: 20s

  • Physics parameterizations:

    • Reisner 2 microphysics

    • No cumulus parameterization

    • MRF boundary layer

  • Initial and Lateral boundary condition:

    • 6-hourly ETA model 40-km analyses (phase I: GFS analyses)

  • ~ 220 minutes with 256 CPUs

model domain

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 6


Case a 11 june case

Case A: 11 June Case

observed 6-h rainfall

simulated 6-h rainfall

0600 UTC 12 Jun

0600 UTC 12 Jun

The observation is on Polar Stereographic Projection Grid.

The simulated rainfall is on Lambert Projection Grid.

The color scales are different.

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 7


Case b 12 june case

Case B: 12 June Case

0600 UTC 13 Jun

0600 UTC 13 Jun

observed 6h-rainfall

simulated 6h-rainfall

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 8


Case c 15 june case

Case C: 15 June Case

0000 UTC 16 Jun

0000 UTC 16 Jun

observed 6h-rainfall

simulated 6h-rainfall

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 9


Simulated dataset

Simulated Dataset

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 10

WRF-Var is employed to produce simulated conventional observations

(NCEP ADP Upper Air sounding, Surface Observation, AIRCAR/AIRCFT and Satellite wind data )

  • Simulated observations use the actual locations and times

  • Use realistic observation errors


Simulated dataset1

Simulated Dataset

NCEP ADP Upper Air sounding

NCEP ADP Surface Observation

Example of Simulated Data distribution

within the time window: 2300 UTC 11 Juneto0100 UTC 12 June 2002

33 Sounding

501 Surface

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 11


Simulated dataset2

Simulated Dataset

NCEP ADP AIRCAR/CRAFT

NCEP ADP Satellite wind

Example of Simulated Data distribution

within the time window: 2300 UTC 11 June to 0100 UTC 12 June 2002

748 ACAR/CFT

717 SATOB

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 12


Mtg irs retrieved profiles

MTG-IRS retrieved profiles

Example of Simulated Data distribution

within the time window: 2300 UTC 11 June to 0100 UTC 12 June 2002

11679 MTG-IRS RP

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 13


Error statistics for mtg irs

Error statistics for MTG-IRS

q

T

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 14


Calibration runs

Calibration runs

  • Generate pseudo radiosonde observations from the nature run.

  • Exp 1. No-obs.

  • Exp 2. Pseudo-obs. Data assimilation experiment using the pseudo observations.

  • Exp 3. Real-obs. Data assimilation experiment using real (radiosonde) observations.

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 15


Difference in t k 12 km results averaged over 1800 utc 11 to 1200 utc 15 june 2002

Difference in T (K), 12 km results, averaged over 1800 UTC 11 to 1200 UTC 15 June 2002

At 12 h FCST

At analysis time

SOP: Simulated Observation Profiles; ROP: Real Observation Profiles

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 16


Difference in q g kg 12 km results averaged over 1800 utc 11 to 1200 utc 15 june 2002

Difference in q (g/kg), 12 km results, averaged over 1800 UTC 11 to 1200 UTC 15 June 2002

At analysis time

At 12 h FCST

SOP: Simulated Observation Profiles; ROP: Real Observation Profiles

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 17


Difference in u m s 12 km results averaged over 1800 utc 11 to 1200 utc 15 june 2002

Difference in u (m/s), 12 km results, averaged over 1800 UTC 11 to 1200 UTC 15 June 2002

At analysis time

At 12 h FCST

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 18


Experiments design 3d var

Experiments design (3D-Var)

Forecast model: WRF V3.0

Grid points: 169X169X35

Horizontal resolution: 12 Km

Time step: 60s

Physics parameterizations:

WSM6 microphysics

Grell cumulus paramerization

MRF boundary layer

Cases: 2002-06-11 12Z to 2002-06-15 12Z

Data: MTG-IRS retrieved profiles + SOP

Verification against truth

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 19


Lists of experiments

Lists of Experiments

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 20


Rmse 6hourly cycling results

RMSE: 6hourly cycling results

Analysis Time

12 h FCST

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 21


Rmse 6hourly 1hourly cycling results

RMSE: 6hourly & 1hourly cycling results

Analysis Time

12 h FCST

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 22


Averaged ets and bias at 18 h fcst

Averaged ETS and BIAS at 18 h FCST

ETS

BIAS

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 23


Experiments design 4d var

Forecast model: WRF

Data assimilation system: WRF 4D-Var

Grid points: 169X169X23

Horizontal resolution: 12Km

Time step: 60s

Assimilation window: 3 hours

Physics parameterizations:

WSM6 microphysics

New Grell cumulus paramerization

MRF boundary layer

Case A: 2002-06-11 18Z to 2002-06-12 12Z

Case B: 2002-06-12 18Z to 2002-06-13 12Z

Background: GFS analysis and forecasts

Data: Retrieved profiles + SOP or “Truth”

Verification against truth

Experiments design (4D-Var)

D1

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 24


Rms error of case a

RMS error of Case A

12 h FCST

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 25


Case a at 18h fcst

Case A at 18H FCST

ETS

BIAS

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 26


Rms error of case b

RMS error of Case B

12 h FCST

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 27


Case b at 18h fcst

Case B at 18H FCST

ETS

BIAS

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 28


Summary

Summary

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 29

Three storms are well reproduced in the 5 day nature run.

The calibration experiment shows that the real and simulated observations have the similar impacts on the analyses increments and forecasts differences.

The quality of the retrievals has been improved significantly.

The forecast skill of wind, temperature and moisture forecasts is improved when MTG-IRS T and/or q profiles are assimilated. Rainfall forecast skill is improved also.

In two short periods of experiments, assimilating MTG-IRS data using 4D-Var gives slightly better forecast skills over 3D-Var.


Current and planned work

Current and planned work

Continue the 4D-Var experiments

Other nature runs

Try other cases

Europe

Tropics

Over oceans

Assimilate wind data

Huang et al: MTG-IRS OSSEMMT, 17-18 June 2008. 30


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