# Presentation: 472909

## Monitoring Satellite Data for Day- and Night time Convective and Lightning Initiation

- John R. Mecikalski1, Kristopher M. Bedka2
- Simon J. Paech1, Todd A. Berendes1, Wayne M. Mackenzie1
*1**Atmospheric Science Department*
*University of Alabama in Huntsville*
*2**Cooperative Institute for Meteorological Satellite Studies*
*University of Wisconsin-Madison*
**Supported by:**
** ****NASA ASAP Initiative**
**NASA New Investigator Program (2002)**

## How this began...

- •

- •

## How this began...

-  Which cumulus will become a thunderstorm?
-  GEO satellite seems to be well-suited to address this question.
-  What methods are available?
-  What changes to current, globally-developed codes are needed?
** ****Who can benefit from this research?**
** ****What user groups are interested (e.g., 0-2 h**
**nowcasting)**

- •

- •

## Where we are today...

***Where we are today...***

- •

_**Cumulus-filled land:**_
*** ******0-1 h CI nowcasting***
*** ******CI climatology studies***
*** ******CI kinematic studies***
*** ******New research in 1-2 h Lightning nowcasting***
*** ******Testbed for short-term (0-6 h) prediction***
*** ******Satellite data assimilation research (6-24 h)***

**UAH**

## Background/Motivation

-  There are many cumulus, and only a few are capable of growing to thunderstorms.
-  Cumulus thus are identifiers of surface forcing for vertical motions (mass and moisture convergence), and the eventual formation of organized updrafts of “significant” scale [O(1-5 km)]
-  Especially in the Tropics, cumulus are the landscape, for which describing them allows one to say *something* about (e.g., quantify) the atmosphere in which they are in (see Riehl 1954).
-  Satellites (especially geostationary)
- are able to monitor cumulus over time.
-  Tracking cumulus, while
- monitoring their growth, should
- allow for some predictive
- skill of thunderstorm development.
-  **Limited to >1 km cumulus in clear-sky**
**conditions.**

## Background/Motivation

-  This is especially true over oceanic regions where poor initialization results in incorrect location/intensity forecasts for convective storms
-  Since little real-time satellite-derived data is available in airplane cockpits, coupled with NWP deficiencies, mid-flight convective storm initiation and growth represents a significant hazard for aviation interests
-  A major portion of the accidents from aircraft turbulence encounters are within close proximity to atmospheric convection (Kaplan et al, 1999)
-  The cost of diverted flight can be as high as $150,000 and a cancellation close to $40,000, depending on the size of the plane (Irrgang and McKinney, 1992)

***Background/Motivation***

## Evaluation of Pre-CI Satellite Signatures

** ****Build relationships between GOES-12 and WSR-88D imagery:**
-  Studied numerous real-time and archived convective events with diverse mesoscale forcing regimes and thermodynamic environments [continental (U.S. Great Plains) to sub-tropical (S. Florida)]
-  Identified GOES IR TB and multi-spectral technique thresholds and time trends present before convective storms begin to precipitate
-  Leveraged upon documented satellite studies of convection/cirrus clouds [*Ackerman (1996),* *Schmetz et al. (1997), ****Roberts and Rutledge (2003)***]
-  After pre-CI signatures are established, test on other independent cases to assess algorithm performance

**Notes:**
May have to delete this slide

## “Mesoscale” Atmospheric Motion Vector Algorithm

***“******Mesoscale” Atmospheric Motion Vector Algorithm***

- “Operational Settings”

- New Mesoscale AMVs
- (only 20% shown)

-  We can combine mesoscale AMV’s with sequences of 10.7 m TB imagery to identify growing convective clouds, which represent a hazard to the aviation community

## “Mesoscale” Atmospheric Motion Vector Algorithm

***“******Mesoscale” Atmospheric Motion Vector Algorithm***

- “Operational Settings”

- New Mesoscale AMVs
- (only 20% shown)

-  We can combine mesoscale AMV’s with sequences of 10.7 m TB imagery to identify growing convective clouds, which represent a hazard to the aviation community

## Cloud-Top Cooling Rates for CI Assessment

***Cloud-Top Cooling Rates for CI Assessment***

-  Study of co-located 10.7 m TB and radar reflectivity trends for stationary convection along the Colorado Front Range
-  Found that sub-freezing 10.7 m TB’s and –4°C/15mins (–8 °C/15mins) correspond to weak (vigorous) growth

**“****By monitoring, via satellite, both the cloud growth and the occurrence of sub-freezing cloud-top temperatures, the potential for up to 30 mins advance notice of convective storm initiation (> 35 dBz), over the use of radar alone, is possible.”**

*Roberts and Rutledge (2003), Wea. Forecasting*

**15 min ********T****B**

## CI Nowcast Algorithm: 4 May 2003

_**2000 UTC**_

***CI Nowcast Algorithm: 4 May 2003***

-  Satellite-based CI indicators provided *30-45 min advanced notice* of CI in E. and N. Cent. KS

***CI Nowcast Pixels***

## CI Nowcast Algorithm: 12 June IHOP

-  Since 5 min GOES-11 data was used, time trend thresholds are cut in half, resulting in noisy nowcasts for quasi-stationary convection in New Mexico
-  TX Panhandle/OK convective development captured well

**CI Nowcast Algorithm: 12 June IHOP**

## CI Nowcast Algorithm: 3 August 2003

**CI Nowcast Algorithm: 3 August 2003**

**1815 UTC**

-  Complex convective forcing from upper-level cold core cyclone, combined with lake breeze circulation
-  Although noisy at first glance, CI over central/western IL identified up to 1 hour in advance
-  Objective validation methodology very difficult to develop

## “Interest Field” Importance

| CI Interest Field | Critical Value |
| --- | --- |
| 10.7 μm TB (1 score) | < 0° C |
| 10.7 μm TB Time Trend (2 scores) | < -4° C/15 mins
∆TB/30 mins < ∆TB/15 mins |
| Timing of 10.7 μm TB drop below 0° C (1 score) | Within prior 30 mins |
| 6.5  - 10.7 μm difference (1 score) | -35° C to -10° C |
| 13.3 - 10.7 μm difference (1 score) | -25° C to -5° C |
| 6.5  - 10.7 μm Time Trend (1 score) | > 3° C/15 mins |
| 13.3 - 10.7 μm Time Trend (1 score) | > 3° C/15 mins |

***“******Interest Field” Importance***

-  All eight interest fields are NOT necessarily important for predicting future
- 	locations of a ≥35 dBZ radar echo. They carry different weights.
-  There is a strong “convective regime” dependence on what fields are most
- important.

## “Interest Field” Importance

| CI Interest Field | Critical Value |
| --- | --- |
| 10.7 μm TB (1 score) | < 0° C |
| 10.7 μm TB Time Trend (2 scores) | < -4° C/15 mins
∆TB/30 mins < ∆TB/15 mins |
| Timing of 10.7 μm TB drop below 0° C (1 score) | Within prior 30 mins |
| 6.5  - 10.7 μm difference (1 score) | -35° C to -10° C |
| 13.3 - 10.7 μm difference (1 score) | -25° C to -5° C |
| 6.5  - 10.7 μm Time Trend (1 score) | > 3° C/15 mins |
| 13.3 - 10.7 μm Time Trend (1 score) | > 3° C/15 mins |

-  Deep convection, dry upper troposphere.
-  Best for high CAPE environments, and strong updrafts.
-  Winter-time, Midlatitudes

## “Interest Field” Importance

| CI Interest Field | Critical Value |
| --- | --- |
| 10.7 μm TB (1 score) | < 0° C |
| 10.7 μm TB Time Trend (2 scores) | < -4° C/15 mins
∆TB/30 mins < ∆TB/15 mins |
| Timing of 10.7 μm TB drop below 0° C (1 score) | Within prior 30 mins |
| 6.5  - 10.7 μm difference (1 score) | -35° C to -10° C |
| 13.3 - 10.7 μm difference (1 score) | -25° C to -5° C |
| 6.5  - 10.7 μm Time Trend (1 score) | > 3° C/15 mins |
| 13.3 - 10.7 μm Time Trend (1 score) | > 3° C/15 mins |

-  Moist upper troposphere, “warm-top” convection. Shallow convection.
-  Low CAPE environments (tropical, cold-upper atmosphere). *f*(Physics)
-  Optimal in Tropics during summer.

## Limitations

-  If cirrus is present with in a pixel, it will be identified as cirrus and will not monitor that pixel.
-  Not available operationally at night at this time.
-  Has some difficulty monitoring previous convection for redevelopment (depends on convective cloud mask classification).

***Limitations***

## Advantages

-  Excellent for monitoring areas of growing or towering cumulus.
-  Cloud top cooling rates can provide some information into updraft strength.
-  Easy monitoring of fast moving cumulus for development.

***Advantages***

## CI Validation

- Conditional FAR

- Conditional POD

## Lightning Initiation

**Lightning (event) Initiation:**
-  Linear regression of lightning trends against GOES interest fields (once lightning and
- GOES are in same reference frame; parallax corrected) shows a ~95% significant correlation
- between cloud-top cooling rates on 1 km scales and the amount of “total” lightning observed
- (in-cloud + cloud-to-ground); 30-60 min nowcasted source density increases over 1 km pixels.

| CI Interest Field | Critical Value |
| --- | --- |
| 10.7 μm TB (1 score) | < 0° C |
| 10.7 μm TB Time Trend (2 scores) | < -4° C/15 mins
∆TB/30 mins < ∆TB/15 mins |
| Timing of 10.7 μm TB drop below 0° C (1 score) | Within prior 30 mins |
| 6.5  - 10.7 μm difference (1 score) | -35° C to -10° C |
| 13.3 - 10.7 μm difference (1 score) | -25° C to -5° C |
| 6.5  - 10.7 μm Time Trend (1 score) | > 3° C/15 mins |
| 13.3 - 10.7 μm Time Trend (1 score) | > 3° C/15 mins |

- We are currently looking at using 10.7-3.9 (reflected component removed) difference to determine cloud top phase which can aide in Lightning Initiation.