1. Evaluation of Various Datasets for Tropical Storm Climatology Asuka Suzuki

2. Tropical Storm Climatology New Field – “Merging mesoscale phenomena and climate into one” Many Questions  TS ↔ climate interactions  TS variabilities and trends  “What are tropical storms anyways?” Many Challenges  Sparse observations  Bringing models with different scales together

3. Datasets for TS Climatology Studies Problems with existing datasets  Tracking datasets Only says date, location, wind speed, and minimum pressure Not uniform  Aircraft observations Sparse in temporal resolution Not available globally TS Climatology Studies call for:  High spatial/temporal resolution  Good correlation with tracking datasets  Excellent sensitivity to global/regional long-term variabilities Candidates NCEP/NCAR2.5 o x 2.5 o (~277km)Most commonly used ERA401 o x 1 o (~111km)Finest spatial resolution in reanalysis JRA252.5 o x 2.5 o TS max wind info integrated WRF-NRCM36km x 36kmStraight model output Reanalysis

4. Evaluation of Reanalysis Datasets (1) Get date and location of each storm at maximum intensity (by wind) from Best Track (2) Go in to reanalysis data, define 20x20 degrees box around the storm center (3) Is there distinctive SLP minima? (4) Determine maximum wind speed If Yes,

5. Results: Some TS don’t show up on SLP NCEP has the lowest detection ERA keeps high detection (due to high spatial resolution) JRA does good till 1991, but drops significantly afterwards

6. Results: Winds are just too weak MeanSTD Best37.717.3 NCEP14.43.74 ERA12.63.03 JRA13.33.25 Max Wind Detected None of the reanalysis datasets produce high winds

7. Results: The Winner is… Combine min SLP detectivity and max wind  Find TS that: Have distinctive SLP minima Have max wind ≥10m/s JRA does the best till ~1994, ERA is the best afterwards

8. W eather R esearch & F orecasting N ested R egional C limate M odel 36x36km resolution for Tropics (30S~45N) Jan 1, 1996 ~ Jan 1, 2001 (4 times daily) Input data: NCEP/NCAR Reanalysis, AMIP SST data How well did WRF-NRCM capture tropical storms? 1)How many? 2)Where and when? 3)How strong? It’s a straight model output  Don’t know where TS’s are  Need numerical definition of TS

9. TS detection methodology New storm detection 1)Find SLP local minima 2)|Max relative vorticity| > 5x10 -5 s -1 3)Max wind speed > 18m/s 4)General wind pattern 5)Warm core Pre-existing storm continuity 1)Distinctive SLP local minima within 20 o of last storm location 2)Max wind speed > 18m/s 3)Warm core Updates storm location A storm satisfies continuity requirement for at least 48 hours Tropical Storm Make sure there is no overlap by newly found storms and pre-existing storms Build a tracking data with date, time, location (lat, lon), max wind, and min SLP

10. TS detection methodology Max wind requirement Maximum wind speed @1000hPa within 2x2 degrees box surrounding the storm center exceeds 18m/s Warm core requirement Average T@500hPa of core area exceeds that of surrounding General wind circulation requirement Winds@1000hPa follows circulating pattern Tc u<0 v<0 u>0 v>0

11. Results: Right Places, but Too Many Boundary condition problem in Southern hemisphere N.AtlanticE.PacificNW.PacificSW.PacificSE.PacificN.IndiaS.Indian WRF-NRCM18163238121033 Best track152534144410 Total 161 106

12. Results: Nice Timing, Good Intensity Red: WRF-NRCM Blue: Best Track Pretty good job in terms of “seasonal timing” Maximum wind does not get too high (which is expected), but so much better than reanalysis

13. Conclusion & Discussion Reanalysis datasets can resolve TS somewhat, yet winds are just not high enough WRF-NRCM output showed some promising results  Evaluation/detection methods  No standard measure in evaluating datasets for TS climatology  No standard numerical definitions for TS  Unreliable tracking datasets?