Tropical Cyclone Data Impact Studies: Influence of Model Bias and Synthetic Observations

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1 Tropical Cyclone Data Impact Studies: Influence of Model Bias and Synthetic Observations C. Reynolds, R. Langland and P. Pauley, Naval Research Laboratory, Marine Meteorology Division, Monterey, CA C. Velden, CIMSS, University of Wisconsin Madison, Madison, WI Motivation OUTLINE Experiment description Track Errors Model Biases Synthetic Observation Errors Summary and Future Work NASA MODIS Image of Super-typhoon Jangmi, 27 September 2008

2 DROPSONDE and DRIFTSONDE OBS: September 2008 T-PARC/TCS-08: Observe TCs and their environment from genesis to extratropical transition. Aug-Oct 2008; 9 nations; 4 aircraft (lidar, Eldora radar, dropsondes), driftsondes, rapid-scan satellite obs, off-time radiosondes, buoys. TPARC/TCS08 Observations Falcon DOTSTAR Driftsondes C130, P3 NOAA Hurricane Observations Evaluate impact of additional observations, including Atmospheric Motion Vectors (AMVs), and Dropsondes. Source: Fleet Numerical Meteorology and Oceanography Center.

3 Previous Research Larger (positive) impact from T-PARC/TCS-08 dropsondes for 3DVAR systems then for 4-D systems (Weissmann et al. 2010, Chou et al. 2011). Synoptic surveillance dropsondes lead to improvements on average, but significant degradations in some cases (Aberson et al. 2008). Partial sampling of sensitive regions may lead to degradations (Aberson et al. 2011). Adding dropsondes to forecasts that already include enhanced CIMSS AMVs in NOGAPS results in slight degradation (Berger et al. 2011).

4 Why the mixed signal from additional data? Model Bias? Synoptic features such as subtropical high have large impact on forecast track (Chen et al. 2009, Brennan and Majumdar 2011). If forecast error is dominated by model error, this may mask or preclude track improvements from initial state improvements. Examine forecasts for evidence of persistent model bias. Synthetic Observations? Previous studies (Berger et al. 2011, Weismann et al. 2011) suggest the synthetic observations may lessen impact of additional data near TC. Increase model error assigned to synthetic observations such that analysis will draw more closely to observations.

5 T-PARC/TCS-08 Data Denial Experiments NOGAPS T239L42 with NAVDAS-AR (4DVAR) DA NAME CIMSS hourly Dropsondes Assigned (enhanced) AMVs synthetic observation error NO_ADD Not assimilated Not assimilated Control value CAMV Assimilated Not assimilated Control value DROP Not assimilated Assimilated Control value CAMV_DROP Assimilated Assimilated Control value SYNTH Assimilated Assimilated Increased value These data-assimilation and forecast experiments were completed for August-September 2008, with 120-h integrations every 12 hours.

6 Track Errors for Nuri and Jangmi for Data Denial Experiments NO_ADD CAMV_DROP CAMV_DROP NO_ADD Jangmi: More data improves forecasts Nuri: More data does not improve forecasts Mixed impact from enhanced AMVs (CAMVs) and dropsondes. Provide improvement for Jangmi, but not for Nuri.

7 Observed and Forecast Tracks for Nuri and Jangmi Jangmi: left-oftrack bias w/out CAMVs or DROPS Nuri: Erroneous recurvature w/out CAMVs or DROPS Jangmi: CAMVs and DROPS help reduce track bias Nuri: CAMVs and DROPS do not mitigate erroneous recurvature Mixed impact from enhanced AMVs (CAMVs) and dropsondes. Provide improvement for Jangmi, but not for Nuri.

8 Nuri Forecasts: 500-hPa Z and 850-hPa Vorticity ANALYSES 8/20 48 h Forecast valid 8/20 Nuri over Philippines, south of anticyclone Nuri in CAMV_DROP (black) and NO_ADD (red) show slow bias. Anticyclone much weaker. Nuri makes landfall in China ANALYSES 8/23 120h Forecast valid 8/23 Nuri starts to recurve ahead of mid-latitude trough Subtropical high too weak in forecasts (not helped by additional data). Forecast shows slow bias then erroneous recurvature. For Jangmi (not shown), bias in subtropical high slightly reduced with additional data.

9 Time-Longitude Diagram of the 500- hpa 48-h Forecast Error CAMV_DROP lowheight bias in western subtropical Pacific is persistent. Biases for NO_DROP are similar. Kammuri Forecast error for Nuri dominated by model bias, not mitigated by additional observations.

10 Assigned Synthetic Observation Error Experiments Nuri: SYNTH reduces (increases) error by more than 5% in 11 (7) cases. Small improvement on average for all cases for all basins. Jangmi: SYNTH reduces (increases) error by more than 5% in 29 (2) cases. Increasing assigned synthetic observation error reduces track error in most cases for Jangmi and Nuri. Also results in small average improvement for all basin storms.

11 T-PARC/TCS-08 Data Denial Experiments Summary Impact of enhanced AMVs and dropsondes strongly storm dependent. Persistent bias of weak sub-tropical high influences track error, may limit impact of additional observations. Synthetic observations may limit impact of other observations. Increasing assigned synthetic observation error improves forecast tracks on average. Future Work Increasing synthetic observations even further degrades forecasts, so synthetics are providing some benefit. Revisit synthetic observation formulation as resolution becomes finer and observations increase. Work to reduce biases in new model (NAVGEM).

12 Jangmi Forecasts: 500-hPa Z and 850-hPa Vorticity ANALYSES 9/27 48 h Forecast valid 9/27 Jangmi east of Philippines, recurving in northward flow west of anticyclone Jangmi in CAMV_DROP (black) and NO_ADD (red) south of observed location. Anticyclone much weaker. Jangmi recurves before making landfall ANALYSES 9/30 120h Forecast valid 9/30 Jangmi makes landfall near Hong Kong. 500-hPa heights (shaded) indicate subtropical high to east of Jangmi is too weak in forecast. Forecasts miss recurvature west of anticyclone.

13 Streamlines of deep layer mean wind indicates considerable weakening of anticyclone and steering flow both Nuri and Jangmi. Flow to north of Nuri is westward. Deep Layer Mean Wind ANALYSES 8/20 48 h Forecast valid 8/20 Flow to north of Nuri is weak. Break in subtropical high. Strong northward flow to east of Jangmi. ANALYSES 9/27 48 h Forecast valid 9/27 Flow in vicinity of Jangmi is much weaker.

14 Track Errors for Nuri and Jangmi for Data Denial Experiments CAMV_DROP error larger than NO_ADD NO_ADD error larger than CAMV_DROP Nuri: More data does not improve forecasts Jangmi: More data improves forecasts Thought not enough cases to be statistically significant, the signal is fairly uniform for both storms.

15 850-hPa Vorticity Analysis Differences Impact of DROPS and CAMVs Impact of DROPS Impact of CAMVs Impact of synthetic ob error increase Impact of DROPS more localized than impact of CAMVs. Impact of synthetic ob error increase of similar magnitude to impact of DROPS.