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Tropical Cyclone-frontal interactions Lee and Eloise Richard H. Grumm National Weather Service State College PA 16803.

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Presentation on theme: "Tropical Cyclone-frontal interactions Lee and Eloise Richard H. Grumm National Weather Service State College PA 16803."— Presentation transcript:

1 Tropical Cyclone-frontal interactions Lee and Eloise Richard H. Grumm National Weather Service State College PA 16803

2 Motivation Tropical cyclone Lee brought flooding to northeastern US – Rain fell on heels of previous event – Enduring event frontal to synoptic-like rainfall event – Pattern & rainfall pattern relatively well predicted – Thus rainfall pattern evolution was well predicted Pattern was similar to that associated with Eloise 1975 We can learn from patterns and probabilities

3 The power of antecedent conditions Irene rainfall August 2011 Then a frontal event ahead of Lee – Not a true PRE – Transitioned with remnants of Lee

4 Frontal rain to TC Lee Plume Produces T-bone rainfall pattern (A1)

5 24-hour Rainfall 12Z 4-8 Sept 2011

6 T-bone rainfall Eloise September 1975 Frontal rains followed by southerly flow with remnant system

7 The tale of two T-bones 2011 & 1975

8 Eloise vs Lee Large scale Pattern

9 Eloise and Lee Pattern and Evolution

10 Moisture Plume evolution

11 TC Lee vs Eloise Salient points Similar large scale patterns – Atlantic ridge southern US trough Similar general storm track – And resulting moisture surge – Similar transition frontal to more synoptic rain pattern T-bone rain pattern due to transition – Both were significant flood events

12 Lee Forecast Issues The pattern was “well predicted” – We will show one example – 12Z 6-8 September frontal  synoptic 6hrs from 0000 UTC 7 September The rainfall was relatively well predicted Or was it…. – The devil is always in the details…and that is a hell of a problem! – Controversial too

13 Pattern Large scale to Mid-Atlantic end with QPF

14 GEFS QPF and Prob 75 mm

15 GFS Total QPF Forecast and errors

16 Shorter range GFS QPF

17 NAM Total QPF and Errors

18 QPF Pattern Looked good details not so good The GFS: – Over predicted the extent of heavy rainfall – And had position errors too far west – Saw this in Irene and the August NJ floods NAM – Too much QPF and clear location errors GEFS – See above so we likely have to over warn to protect those who might actually be affected

19 Decision to forecasts issues to ponder Uncertainty is not a reason not to act QPFs are not so good when verified over a small region  details are wanting For High Impact Weather we may have to accept that there are fewer wolves then we see, or are forced to see.

20 Summary Tropical cyclone Lee brought flooding to NE US – The pattern was well predicted – The event spanned days and antecedent conditions likely played role in impacts/flooding – The QPFs looked good but verified not so good The Pattern – was similar to that associated with Eloise 1975 – Similar rainfall T-bone – Similarly both were high impact events We can learn from patterns and probabilities – But may have to accept we know a big event but – We may not know the exact areas to be affect (will we ever?)

21 High resolution satellite image historic snow and how it was predicted!

22 Historic October East Coast Winter Storm October record snows Snow from Appalachians into New England Well predicted and nearly ideal pattern – For January! – Though October has February sun angles

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28 Great Pattern and Forecast Forecasting record events – That deviate from climate – Climate is our base state our reference anchor We regress toward the mean – How we decide? Rare events take confidence Pattern and Probabilities  ENSEMBLES – Allow us not to regress toward the mean – They provide the confidence we need.

29 High probability record event

30 Classic ECWS pattern and -3to -5C Confidence and probabilities  good decisions

31 Thinking Fast and Slow Kahneman, D.(2011) – Need good data and confidence to forecast rare events – Not easy to do intuition, experience and reliable data Rare event snow: – Good pattern with supporting anomalies – Ensembles for probabilities and confidence – Result in relatively well forecast historic event!


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