Data assimilation, Real-time forecasting and high-resolution
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1 Data assimilation, Real-time forecasting and high-resolution simulation during the 2011 CINDY/DYNAMO field campaign Tim Li and X. Fu (UH), Kunio Yoneyama and Tomoe Nasuno (JAMSTEC) Real time observational support Real-time MJO prediction using U. Hawaii hybrid coupled GCM Hindcast experiments during have been conducted (Fu et al. 2009, 2010) A new initialization strategy WRF 3DVar/4DVar data assimilation during CINDY/DYNAMO campaign period Strategy: Combine in-situ observations with remotely sensing satellite measurements to generate regular grid (~10km) data A similar attempt during TCS-08 campaign was conducted using WRF 3DVar/4DVar (Li et al. 2009) NICAM global cloud-resolving (3.5km ~ 7 km) simulations JII: Initial condition and validation from the assimilation products Process-oriented study to reveal MJO initiation mechanisms (extratropical forcing vs. encircunavigation mode vs. local processes)
2 ISO Intensity in 14 IPCC AR-4 Climate Models OBS ECHAM CNRM Lin et al. (2006) East-West Direction 29HURRICANES, Tucson, May 10-14, 2010
3 NP-ISO is well Simulated by UH_HCM HCM UH_HCM OBS ECHAM-4 AGCM + UH_IOM Fu, Wang, Li, and McCreary, 2003, J. Climate
4 Experimental ISO Forecasts with UH_HCM Target Period: May-October Forecast Interval: Every 10 days, totally 16 forecasts 10 Ensembles: Perturbations are 10% of daily differences Integration Length: 60 days Initial Conditions: R1/R2; ERA-Interim and modified reanalyses Skill Measure: Anomaly Correlation Coefficient over Southeast Asia and global tropics. 29HURRICANES, Tucson, May 10-14, 2010
5 ISO Forecast Skills in 2004 Summer (1 July June 1993) Westward Eastward Observed OLR ISO intensity in the reanalysis is 2-3 factors smaller! Reanalysis OLR Shinoda et al. (1999)
6 ISO Forecast Skills in 2004 Summer (May-October) ACC of Filtered U850 ACC of Filtered Rainfall Fu et al. (2009, GRL) Challenge in real-time application: no time filtering! 29HURRICANES, Tucson, May 10-14, 2010
7 filtered (shad) vs. non-filtered (cont) DJF MJO_U Pattern corr. = 0.81 RMSE = 1.6 m/s
8 WRF 3DVar Data Assimilation during TCS-08 Field Campaign Case: Super-typhoon Sinlaku, Sept. 7-21, 2008 (minimum pressure: 929hPa) Date: In-situ data: USAF C-130 dropsonde data(including T, Td, RH, U-wind, V-wind) NRL P-3 dropsonde data (including T, Td, RH, U-wind, V-wind) DLR Falcon Doppler Wind Lidar data (including U-wind, V-wind) DLR Falcon flight level data (including T, Td, RH, U-wind, V-wind) Satellite data: AIRS temperature and humidity QSCAT surface wind derived from Seawinds instrument AMSU temperature TRMM rainfall rate Other data: Synthetic observations final analysis (1 degree by 1 degree)
9 NOGAPS The Profile of Pmin across TC center at 0906 TCDI/3DVar NOGAPS TCDI/3DVar The Profile of Speed across TC center at The Profile of Pmin across TC center at NOGAPS TCDI/3DVar The Profile of Speed across TC center at NOGAPS 5 TCDI/3DVar The Profile of Pmin across TC center at 1112 The Profile of Speed across TC center at NOGAPS 35 TCDI/3DVar NOGAPS 10 TCDI/3DVar
10 (A) The Speed Profile at South-East Quadrant at OBS 900 NOGAPS TCDI/3DVar (B) TheSpeedProfileatNorth-West Quadrant at OBS 900 NOGAPS TCDI/3DVar Top: 850hPa total wind speed 750 (C) 800 The Speed Profile at South-West Quadrant at 1006 Right: comparison of wind speed profile at OBS 900 NOGAPS TCDI/3DVar
11 (A) The Speed Profile at South-East Quadrant at OBS 900 NOGAPS TCDI/3DVar TheSpeedProfileatNorth-West Quadrant at (B) OBS 900 NOGAPS TCDI/3DVar Top: 850hPa total wind speed 800 The Speed Profile at South_West Quadrant at (C) Right: comparison of wind speed 900 profile at OBS 900 NOGAPS TCDI/3DVar
12 Data Assimilation Strategy: Combine in-situ ship/island/flight observations and satellite observations Cover the entire campaign period Provider regular-grid grid (~10 km) 3-hourly reanalysis products for analysis, model initialization, and validation
13 Idealized AGCM experiments: What initiates MJO in IO? sensitivity experiments: No_restore: control exp Restore_nsbound Restore_TATL Restore_TAL_nsbound Bottom: Eastwardpropagating MJO energy spectrum
14 Thanks Diamond Head
15 Reanalysis-1 underestimates ISO (2004 Summer) TRMM 3B42 GPCP Reanalysis Rainfall _R1 2-3 factors smaller _R1 Eastward Propagation Northward Propagation 29HURRICANES, Tucson, May 10-14, 2010
16 Forecast Sensitivity Experiments EXP. _R_f Initial Conditions AC 1 + Filtered 2 _R_unf AC + Filtered + High-frequency 2* _ R _ f AC + 2*Filtered ed 3*_R_f 3*_R_unf AC + 3*Filtered AC + 3*Filtered + High-frequency 1 AC (Annual Cycle) => mean + first three harmonics 2 Filtered => day variability 3 High-frequency => Total - AC - Filtered 29HURRICANES, Tucson, May 10-14, 2010
17 Daily Rainfall in 2004 Summer along the Equator R1 (10 o S-10 o N) 29HURRICANES, Tucson, May 10-14, 2010
18 Initialized on May 31, 2004 with Enhanced ISO in R1 29HURRICANES, Tucson, May 10-14, 2010
19 (A) The Speed Profile at East Quadrant at OBS 900 NOGAPS TCDI/3DVar (B) The Speed Profile at North Quadrant at OBS 900 NOGAPS TCDI/3DVar Top: 850hPa total t wind speed (C) The Speed Profile at West Quadrant at 0906 Right: comparison of wind speed profile at OBS 900 NOGAPS TCDI/3DVar
20 DJF MJO OLR forecast skill MJO pattern corr (DJF_OLR) Year 90 indicates mac m30d m30a5 forecast period from Dec 1989 to Feb MJO rmse (DJF_OLR) mac m30d m30a avg avg mac: remove annual cycle (minus 90d low-pass filrering) i m30d: remove interannual component ( minus previous 30d average) m30a5: remove synoptic component (previous 5d average) Process of removing interannual components shows significant impact on the forecast skills in El Nino years (1991/1992 and 1997/1998).
21 DJF MJO U850 forecast skill MJO pattern corr (DJF_U850) mac m30d m30a MJO rmse (DJF_U850) av vg mac m30d m30a5 p MJO U850 forecast shows similar il results to OLR. Removing interannual components are important for El Nino years avg
22 JJA MJO OLR forecast skill MJO pattern corr (JJA_OLR) mac m30d m30a For the MJO forecast in boreal summer, the 8effects of removing interannual components MJO rmse (JJA_OLR) is not as large as it in mac m30d m30a5 wintertime av vg avg
23 JJA MJO U850 forecast skill MJO pattern corr (JJA_U850) mac m30d m30a av vg MJO rmse (JJA_U850) MJO U850 forecast shows similar il results to OLR. During summertime, removing interannual components for the MJO forecast is not such important as it in winter. p mac m30d m30a avg
24 rmse Test of averaged periods for interannual and synoptic components MJO forecast skill (DJF_OLR) m30a5 m60a5 m30a10 m60a10 pattern corr rmse MJO forecast skill (DJF_U850) m30a5 m60a5 m30a10 m60a pattern corr rmse MJO forecast skill (JJA_OLR) m30a5 m60a5 m30a10 m60a10 pattern corr rmse MJO forecast skill (JJA_U850) m30a5 m60a5 m30a10 m60a pattern corr Optimal periods for interannual and synoptic components are previous 30day and previous 5day averages, respectively.
25 filtered (shad) and non-filtered (cont) DJF MJO_OLR Pattern corr. = 0.76
26 filtered (shad) and unfiltered (cont) JJA ISO_OLR Pattern corr. = 0.73 RMSE = W/m2
27 filtered (shad) and unfiltered (cont) JJA ISO_U850 Pattern corr. = 0.76 RMSE = 1.39 m/s
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