An Analysis of Large Track Error North Atlantic Tropical Cyclones. By Nicholas Leonardo Advisor: Brian Colle

Motivation Joaquin (2015) Isabel (2003) Would need more time to find/make a plot for Mathew (2016) instead... It however seemed more like a >5day issue when it was crossing over the DR region; otherwise I have yet to see if it is a “large error” case (eg. which forecast(s) fit the criteria I’ve set) Despite overall improvements in North Atlantic TC track forecasts, some cases still have errors much larger than average. The solution in these cases may even be outside the ensemble envelope. Are there any commonalities to these extreme cases? WRF-ARW 2003 Sept 15th 0000UTC.

Objectives Identify outlier ECMWF ensemble forecasts in terms of day 3-5 track errors, focusing on extremes in the along-track direction. Note the similarities and differences between cases with large positive and negative along-track biases. Assess the forward speed and intensity errors, and their association with the track errors. Analyze the synoptic fields of the cases, noting any common flow patterns and biases related to the track evolution. Use ensemble-based diagnostics to further analyze the negative along-track bias cases.

Data and Methods Verified forecast tracks of the ECMWF (51 members) ensemble (http://rda.ucar.edu/datasets/ds330.3/) against the NHC best-track data for the 2008-2015 North Atlantic seasons. Along-track errors (ATEs) and cross-track errors (CTEs) are defined relative to the motion of the best-track. Forecasts in which the verifying best-track (never) crosses 30N are considered “North” (“South”) cases. The gridded model fields (http://apps.ecmwf.int/datasets/data/tigge/), were verified with the CFSR (http://rda.ucar.edu/datasets/ds093.0/).

Data and Methods The ATE’s are significantly more negative for North cases than for South. The CTE’s are < 80km in magnitude and not significantly different from 0 at >72h for either South or North cases. The ATE’s of North cases will be the focus of this talk. The CTE’s will be investigated in future work.

Along-track Bias Cases Took all 72-120h ECMWF mean forecasts of North cases that had at least 20 members by 72h. Found the largest magnitude ATE from each forecast. Defined the top and bottom 20% of these peak ATEs as “Fast” and “Slow” cases, respectfully. All subsequent analyses are relative to the forecast hour of largest ATE (e.g. at “t-X hours”). N = 303 Best-Tracks of Fast Cases Best-Tracks of Slow Cases

Impact of Forward Speed and Intensity Errors The slow cases are associated with inherently faster-moving TC’s than the fast cases. The model underestimates the forward speed of the slow cases significantly more than for the fast. The difference in speed error is significant and >1 m/s at ~t-60h. The difference in ATE is also significant and >100km at ~t-60h, growing exponentially afterwards.

Impact of Forward Speed and Intensity Errors The observed TC’s of the slow cases are ~10hPa stronger than the fast cases at t-84h to t-60h before peak ATE. Both slow and fast cases tend to weaken approaching t-00h. However, the SLP errors of both cases are comparable throughout the forecast.

Analysis of synoptic patterns Fast Cases at t-84h Slow Cases at t-84h m Best-track-centered composites of ensemble mean 300mb height errors (shaded) for fast (left) and slow (right) cases. CFSR and ensemble mean 300mb heights are contoured black and grey, respectively. Regions where the mean error exceeds 0.5 times the sample standard deviation are contoured green. The best-track and composited ensemble mean positions are black and amber circles, respectively.

Analysis of synoptic patterns Fast Cases at t-60h Slow Cases at t-60h m Best-track-centered composites of ensemble mean 300mb height errors (shaded) for fast (left) and slow (right) cases. CFSR and ensemble mean 300mb heights are contoured black and grey, respectively. Regions where the mean error exceeds 0.5 times the sample standard deviation are contoured green. The best-track and composited ensemble mean positions are black and amber circles, respectively.

Analysis of synoptic patterns Fast Cases at t-48h Slow Cases at t-48h m Best-track-centered composites of ensemble mean 300mb height errors (shaded) for fast (left) and slow (right) cases. CFSR and ensemble mean 300mb heights are contoured black and grey, respectively. Regions where the mean error exceeds 0.5 times the sample standard deviation are contoured green. The best-track and composited ensemble mean positions are black and amber circles, respectively.

Analysis of synoptic patterns Fast Cases at t-36h Slow Cases at t-36h m Best-track-centered composites of ensemble mean 300mb height errors (shaded) for fast (left) and slow (right) cases. CFSR and ensemble mean 300mb heights are contoured black and grey, respectively. Regions where the mean error exceeds 0.5 times the sample standard deviation are contoured green. The best-track and composited ensemble mean positions are black and amber circles, respectively.

Analysis of synoptic patterns Slow Cases at t-48h m In the slow cases at ~t-48h, the upstream trough is under-amplified and too far west. There is also under-amplification of the downstream ridge. The combination of the two results in a gradient that’s weaker and displaced further upstream; failing to accelerate the TC northeastward. The underprediction of the ridge may be explained by the TC’s diabatic outflow.

Analysis of synoptic patterns Fast Cases at t-96h Slow Cases at t-96h m∙s-1 Vortex-centered composites of ensemble mean 200-300mb divergent wind speed errors (shaded) for fast (left) and slow (right) cases. CFSR and ensemble mean 300mb heights are contoured black and grey, respectively. The CFSR 200-300mb divergent winds are the vectors. Regions where the mean error exceeds 0.5 times the sample standard deviation are contoured green.

Analysis of synoptic patterns Fast Cases at t-72h Slow Cases at t-72h m∙s-1 Vortex-centered composites of ensemble mean 200-300mb divergent wind speed errors (shaded) for fast (left) and slow (right) cases. CFSR and ensemble mean 300mb heights are contoured black and grey, respectively. The CFSR 200-300mb divergent winds are the vectors. Regions where the mean error exceeds 0.5 times the sample standard deviation are contoured green.

Analysis of synoptic patterns Fast Cases at t-48h Slow Cases at t-48h m∙s-1 Vortex-centered composites of ensemble mean 200-300mb divergent wind speed errors (shaded) for fast (left) and slow (right) cases. CFSR and ensemble mean 300mb heights are contoured black and grey, respectively. The CFSR 200-300mb divergent winds are the vectors. Regions where the mean error exceeds 0.5 times the sample standard deviation are contoured green.

Analysis of synoptic patterns Slow Cases at t-48h m∙s-1 Throughout the forecast, the divergent circulation and its interaction with the upper-level environment are inherently stronger in slow cases compared to fast cases. This circulation is also significantly underestimated (by > 2m∙s-1 ) in slow cases.

Ensemble analysis of slow cases Can the same principles be shown within the ensemble variability of these slow cases? That is, is there consistency such that the slowest (least correct) ensemble members are associated with particular patterns different from the fastest members? On the next two slides are vortex-centered composites of standardized differences (10 slowest – 10 fastest members) for all slow cases. Differences are shaded. The mean 10 slowest and 10 fastest member heights are contoured in grey and black, respectively. The green, dark green, and gold dashed contours show the percentage of cases that had a statistically-significant difference at a point.

Ensemble analysis of slow cases

Ensemble analysis of slow cases

Ensemble analysis of slow cases Consistent with the mean errors, there is a significant difference in the mass field by t-72h, corresponding to the slower members tending to amplify the approaching trough/ridge system less than the faster. The divergent circulation of the faster/more correct members further amplified the downstream ridge more than the slower members.

Conclusions The negative along-track bias of the ECMWF is largely associated with inherently faster-moving, stronger TC’s. The synoptic patterns of the slow cases are more amplified. The model underpredicts the amplification of the upstream trough and downstream ridge at t-60h before the large track error. The under-amplified ridge may be driven by feedbacks of the TC’s divergent outflow with the synoptic environment. This interaction is significantly under-predicted in the slow cases. The variability of the ensemble members tends to agree with this concept: the faster/more correct members have stronger outflow feeding into the ridge.

Questions? Comments?

Extra Slides

Role of ET The cyclone phase space (CPS; Hart 2003). 925-700mb Thermal Wind (VTlo) 925-700mb Thermal Asymmetry (B) The cyclone phase space (CPS; Hart 2003). The slow cases undergo (and complete) ET faster than the fast cases, both in the ensemble and the observation. Fast cases better follow the observed TC’s transition. This is most apparent in terms of B, with the slow cases exceeding 10 (e.g. becoming asymmetric direct circulations) at ~t-36h. The difference is first significant at t-60h. 500-300mb Thermal Wind (VTup)

Extra Slides Bill 12z 19 Aug 2009 (t-36h) Danielle 12z 25 Aug 2010 (t-48h) Gustav 12z 31 Aug 2008 (t-36h) Igor 00z 19 Sep 2010 (t-48h) ESA: ATE to 300mb GH