1 / 12

Gatlin and Goodman (2010) To develop an algorithm t o provide early warning a bout impending

Gatlin and Goodman (2010) To develop an algorithm t o provide early warning a bout impending s evere weather Based on time rate of change o f lightning flash rate --termed lightning jumps Lightning jumps identified 90% o f the severe events However, 37% of lightning jumps

bottoms
Télécharger la présentation

Gatlin and Goodman (2010) To develop an algorithm t o provide early warning a bout impending

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Gatlin and Goodman (2010) To develop an algorithm to provide early warning about impending severe weather Based on time rate of change of lightning flash rate --termed lightning jumps Lightning jumps identified 90% of the severe events However, 37% of lightning jumps were not associated with severe weather, increasing FAR rate

  2. Main statistical tests The lightning jump occurs when the FR exceeds the running average FR (6-10 min period) plus twice the standard deviation of FR. Running average computed from 6-10 min period of 1 min FR data or 12-20 min when using 2 min FR data

  3. Basic premise is that increases in lightning flash rate is tied to updraft intensification… Why should this be the case? What about supercellular vs. non-supercellular storms in terms of lightning jump timing and occurrence of tornadoes (landspouts)?

  4. GLM was developed to help with “nowcasting” severe weather… Is GLM up to the task???

  5. LMA sources are concentrated near 6 km throughout the duration of the analysis period; we identify this as a “anomalous” storm, with enhanced low level positive charge • Greeley anomalous storm is very intense, displaying multiple regions of 65 dBZ extending to 8 km agl

  6. Greeley Anomalous Polarity Storm • GLM produces fewer flashes with little variance in flash rate compared to LMA, the latter showing a factor of 2 variation in FR • GLM/LMA ratio (~detection efficiency) averages near 0.1 • Largest DE occurs when the LMA FR is a minimum • GLM events and groups are roughly the same magnitude as LMA flash rates • GLM events and groups show a gradual increase in minute by minute counts, but GLM flashes do not

  7. median flash area; median flash altitude • Relative maxima in DE coincide with peaks in LMA-derived median flash size • The DE “tracks” with flash area (flash size); DE decreases as median flash area decreases even though the median flash height increases; however these median flash heights are still fairly low, characteristic of anomalous storms (we will see later that flash heights are substantially greater in normal polarity storms which appears to impact DE)

  8. 8 May CO inverted case

  9. Kind of the crux of the matter…ice water path, flash height and flash size all matter to impact detection efficiency

  10. LIS_LMA comparison

More Related