Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/2008. 1 MTG-IRS: An Observing System Simulation Experiment (OSSE) on regional scales Xiang-Yu Huang, Hongli.

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Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ MTG-IRS: An Observing System Simulation Experiment (OSSE) on regional scales Xiang-Yu Huang, Hongli Wang, Yongsheng Chen National Center for Atmospheric Research, Boulder, Colorado, U.S.A. Xin Zhang University of Hawaii, Honolulu. Hawaii, U.S.A. Stephen A. Tjemkes, Rolf Stuhlmann EUMETSAT, Darmstadt, Germany

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Contents Background The nature run (MM5) Calibration experiments (WRF) MTG-IRS retrievals Data assimilation and forecast results (WRF) Summary Future work Identical twin experiments

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Background IRS sounding Mission on MTG will provide high-resolution data which includes temperature and water vapor information. Realistic mesoscale details in moisture are important for forecasting convective events (e.g., Koch et al. 1997; Parsons et al. 2000; Weckwerth 2000, 2004). Objective: To document the added value of water vapor observations derived from a hyperspectral infrared sounding instrument on a geostationary satellite for regional forecasting.

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ OSSE setup 2 models; Degraded resolution and LBCs

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ The nature run should be a long, uninterrupted forecast. Here, NR is a 5-day “free” run. 2.The nature run should exhibit the same statistical behavior as the real atmosphere but be completely independent of it. We need ideas on how to do this properly. We just made a comparison between NR and real obs. 3.The assimilation period runs sufficiently long that the statistics comparing control and experimental forecasts are stable. Data assimilation experiments are run over a 5-day period. 4.The lateral boundary conditions should vary with the experiment being performed in the inner domain. We cannot run global experiment with new data. More coordinated effort is needed. We tried to make the LBC in DA different to that in NR: Use ETA for the nature run and GFS for the assimilation run. We also add perturbations to lateral boundaries. 5.All major operational observing systems should be simulated. We have ADP data, but could be better. 6.Errors are added to the hypothetical observations extracted from the nature run. We add "realistic" errors to the truth. 7.True OSSEs are calibrated. We made calibration runs. Modern variational assimilation systems deal with radiances directly because the error characteristics are easier to track. It is still difficult to use radiance data over land. Data thinning is used to account for the correlated observation errors. This is also a pilot (small) project. (Tom Schlatter:) In a true OSSE,

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Nature Run: IHOP Case (11-16 June 2002) There are three convection cases in the selected period: 11 June: Dryline and Storm 12 June: Dryline and Storm 15 June: Severe MCS Map illustrating the operational instrumentation within the IHOP_2002 domain. (From Weckwerth et al )

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Nature Run Design 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 ~ 220 minutes with 256 CPUs model domain

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ observed 6-h rainfall simulated 6-h rainfall The observation is on Grid. The observation is on Polar Stereographic Projection Grid. The simulated rainfall is on Grid. The simulated rainfall is on Lambert Projection Grid. The color scales are different UTC 12 Jun Case A: 11 June Case

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ observed 6h-rainfall simulated 6h-rainfall 0600 UTC 13 Jun Case B: 12 June Case

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Case C: 15 June Case observed 6h-rainfall simulated 6h-rainfall 0000 UTC 16 Jun

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Calibration runs Generate pseudo (ADP) 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 (ADP) observations.

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Simulated Dataset WRF-Var is employed to produce simulated conventional observations (NCEP ADP Upper Air sounding and Surface Observation ) –Simulated conventional observations use the actual locations and times –Add realistic observation errors

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Simulated Dataset NCEP ADP Upper Air sounding NCEP ADP Surface Observation 572 Surface 17 SOUND Example of Simulated Data distribution within the time window: 1700 UTC to 1900 UTC 12 June 2002

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Observation errors in the simulated dataset

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ MOP: Modeled Observation Profiles; OP: (real) Observation Profiles Difference in T (K), 4 km results, averaged over 1800 UTC 11 to 1200 UTC 15 June 2002 At analysis time At 18 h FCST

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Difference in q (g/kg), 4 km results, averaged over 1800 UTC 11 to 1200 UTC 15 June 2002 At analysis timeAt 18 h FCST

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Difference in u (m/s), 4 km results, averaged over 1800 UTC 11 to 1200 UTC 15 June 2002 At analysis timeAt 18 h FCST

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ MTG-IRS Retrieval (I) Forward calculations Profile information for the forward calculations are combination of climatology (above 50 hPa) and MM5 results (below 50 hPa), Ozone information is extracted from climatology. For each hour for five days 505 x 505 profiles (= one “data cube”). RTM adopted is same code as used for HES/GIFTS trade-off studies by SSEC, which is a statistical model. Only clear sky calculations, accuracy is not known. CPU: To generate R(toa) for one “data cube” takes about 20 hours CPU.

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ MTG-IRS Retrieval (II) Inverse Calculations Results are based on EOF retrievals Four datasets: S t : Training dataset: T t (p), q t (p) and R t (toa), S o : Synthetic observational dataset: R o (toa) S r : Retrieval dataset: T r (p), q r (p) S n : Nature (here taken from MM5): T n (p), q n (p) Objective of retrieval is to generate a S r from S o, which is equal to S n Flowchart of EOF retrieval: Step 1: Truncate R t (toa) through an EOF decomposition Step 2: Correlate the truncated R t (toa) with T t (p), q t (p) to generate regression coefficients Step 3: Project R o (toa) onto EOF space of R t (toa) Step 4: Generate T r (p), q r (p) using regression coefficients from 3) and EOF from 2)

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Two EOF Training methods Two different training methods applied: Global Training: generated a “global dataset” by random selection of profiles from a number data cubes covering dynamical range of the diurnal cycle. About profiles, a single training dataset As this global dataset had different properties than an individual data-cube; assimilation generated not satisfactory results (mainly because of bias) “Bias free Training”: For each datacube a separate training dataset consisting of 10% of the data in the particular datacube.

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Simulated Dataset NCEP ADP Upper Air sounding NCEP ADP Surface Observation MTG-IRS retrieved profiles 572 Surface17 SOUND10706 MTG-IRS RP Example of Simulated Data distribution within the time window: 1700 UTC to 1900 UTC 12 June 2002

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Old physical retrieval profiles New EOF retrieval profiles Temperature error statistics for RP

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Humidity error statistics Old physical retrieval profiles New EOF retrieval profiles

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Temperature error correlation 200km 100km

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Humidity error correlation 200km 100km

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Vertical temperature error correlation at 18Z 11 June

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Vertical humidity error correlation at 18Z 11 June

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Experiments design Forecast model: WRF Data assimilation system: WRF 3D-Var Grid points: 169X169X35 Horizontal resolution: 12Km Time step: 60s Physics parameterizations: Lin microphysics Grell cumulus parameterization MRF boundary layer Cases: Z to Z Data: MOP EOF retrieved profiles (18 levels with 100 km resolution) Verification against truth

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Lists of Experiments Experiment name Cycling period Initial condition and assimilated data Control MOP MOP-RPtq-6hc No 6 h GFS analysis + perturbed lateral boundary conditions Background (BG)+ Modeled Observation Profiles Background (BG)+ MOP +Retrieved Profiles(T,q)

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Averaged RMS error profiles at analysis time

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Averaged RMS error profiles at 12h FCST

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Averaged ETS 12h FCST 18h FCST 24h FCST

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ (64,64) 4Dvar Forecast model: WRF Data assimilation system: WRF-4Dvar Grid points: 85X85X35 Horizontal resolution: 12Km Time step: 60s Physics parameterizations: Lin microphysics Grell cumulus paramerization MRF boundary layer Cases: Z to Z Background: Extracted from 18 h Control FCST at D1 (EC) Data: MOP (simulated conventional data) EOF retrieved profiles (18 levels, 100 km) Verification against truth 4D-Var experiments design D1

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ RMS error profiles at analysis time

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ RMS error profiles at 12 h FCST

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Summary 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 is improved when MTG-IRS T and q retrieved profiles are assimilated.

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Future work WRF-4DVAR experiments –Cycling assimilation and forecast experiments –Time and computer source permitted Reduction of error correlations in MTG-IRS T(p) and q(p) Assimilating modeled wind observations from other platforms (such as wind profilers or radars) OSSE for European cases. –Two nature runs have been carried out

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Identical Twin Experiments Experiment name Observation data Initial condition and assimilated data Control MM5MOP-6hc WRFMOP-6hc MM5-NR WRF-NR GFS analysis + perturbed lateral boundary conditions Background (BG)+ Modeled Observation Profiles from MM5 nature run Background (BG)+ Modeled Observation Profiles from WRF nature run

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Averaged RMS error profiles at analysis time T q

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Averaged RMS error profiles at analysis time uv

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Averaged RMS error profiles at 12 h FCST Tq

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Averaged RMS error profiles at 12 h FCST uv

Huang et al:MTG-IRS OSSE. EMC seminar, 1/11/ Compared with “non-identical” twin experiments The control experiment has smaller errors. The observation impact is larger. Identical-twin experiments are not too bad … may still be useful?!