Influence of Assimilating Satellite- Derived High-resolution data on Analyses and Forecasts of Tropical Cyclone Track and Structure: A case study of Sinlaku (2008) Ting-Chi Wu 1, Hui Liu 2, Sharanya J. Majumdar 1, Christopher S. Velden 3, Jun Li 3 and Jeffrey Anderson 2 University of Miami, RSMAS 1 National Center for Atmospheric Research 2 University of Wisconsin, CIMSS 3 IR image of Sinlaku Forecast error covariance of TC and its environment is highly flow-dependent and multivariate. Use multiple and integrated satellite data sets at their highest resolution to build up an advanced analysis/forecast system for tropical cyclones and their environments. Seek an optimal assimilation strategy for integrated satellite data, under WRF-DART EnKF framework. Dynamic variables: Atmospheric Motion Vectors (AMVs), ASCAT surface wind Thermodynamic variables: Temperature and moisture sounding, Total Precipitable Water (TPW) Challenges and overall goals The satellite observation types in TC case Satellite-derived observation types TPW ASCAT winds Work of Cloudy sky IR soundings is underway. IR Temperature and moisture sounding (clear-sky so far) AMV H WRF (v3.1.1) - DART (EAKF) with 84 members 9km moving nest grid with feedback to 27km grid when TC is present in forecast. Assimilation cycle started 1 September (one week before genesis) Analysis variables: U, V, W, PH, T, MU, T2, Q2, TH2, Psfc, U10, V10, Qvapor, Qcloud, Qrain, Qice, Qsnow, WRF-DART Cycles CIMSS: Cooperative Institute for Meteorological Satellite Studies CaseConventional data Satellite data Cycling interval CTLRadiosondes > 200km (U,V,T,Q), aircraft (U,V,T), surface pressure from NCEP/GFS and JTWC advisory TC positions, 6-hourly analyses NCEP bufr AMVs (origin: JMA)6h AIRS-TQAIRS clear-sky Single FOV TQ-profile6h TPWAMSR-E Total Precipitable Water6h CIMSS(h)CIMSS Hourly AMVs3h CIMSS(h+RS)CIMSS Hourly + Rapid-Scan AMVs (Rapid-Scan is available after 12UTC September 10, 2008) 3h ASCATASCAT surface wind3h ALLAll above satellite data3h Satellite data: Dynamic Contour every 200km mb mb mb mb Wind vectors from ASCAT are only at sea surface Satellite data: Thermodynamic The coverage of AIRS T/Q soundings are quite remote from Sinlaku at most of the times. AMSR-E derived TPW has better data coverage through the lifetime of Sinlaku. Analysis track and intensity Against Independent Obs (I) QuikSCAT-UCF CTL CIMSS(h) CIMSS(h+RS) ASCAT CIMSS(h+RS) is available after 2008/09/10:12Z CIMSS-bt: Modified JTWC best track from CIMSS-Wisconsin. As seen in previous slide, assimilating AMVs helps to resolve reasonable TC sizes especially in the early stage of Sinlaku. Against Independent Obs (II) Size Analysis Structure: Azimuthal mean (I) Shading: Vr Contour: Vt Grey line: RMW Shading: W Contour: Divergence CTL ASCAT CIMSS(h) 2008/09/09:12Z Analysis Structure: Azimuthal mean (II) CTL TPW AIRS-TQ Shading: Relative Vorticity; Contour: Potential temperature Analysis Increment (Vwind) EW CTL CIMSS(h) Cyclonic increments! EW 2008/09/09:12Z It is until 00Z 10 Sep, CTL starts to show cyclonic increment, half day later than CIMSS(h). For ASCAT, it is around 18Z 10 Sep, 6 hours later than CIMSS(h). ASCAT EW Analysis Structure Spread CTL CIMSS(h) CIMSS(h+RS) Analysis mean and spread of RMW as function of height in 12 hourly intervals. 72h forecast from 16 members Initialized at 00Z September 11, 2008 ALL combines all satellite-derived observations mentioned above. ALL is dominated by CIMSS(h+RS). CTL and TPW erroneously made early landfall in central/south part of Taiwan. Ensemble Forecast from Analyses 24H 48H 72H Shading: 500 mb geopotential height difference CIMSS(h+RS) CTL Contour: CTL, CIMSS(h+RS) Forecast Track differences (I) CIMSS(h+RS) has the best ensemble mean track forecast. Trough to the NE of and ridge to the E of Sinlaku. Summary Assimilation of the various satellite data types, particularly AMVs and TPW, using ensemble DA shows potential to improve analyses and forecasts of the hurricane track and intensity. Assimilating CIMSS hourly AMV exhibits best track and intensity analysis in early stage of Sinlaku. However, the inclusion of Rapid-Scan AMVs has problem with large uncertainty in the later time of Sinlaku that needs further investigation. The ALL cycles seem to be dominated by CIMSS(h+RS). However, this is preliminary results. More investigation of impacts from individual satellite data type is needed before conducting the ALL cycles. Current and future work Investigate the impacts of each satellite- derived data experiment in more depth. Data denial experiments on AMVs to clarify which layer/region of AMVs has the most impacts on intensity/track. (or ensemble based observation impact first) Look for advanced diagnostic metrics? Investigate the linkage between model covariance structure and storm evolution. Data Denial Experiments By height By distance