# Presentation: 034853

## Convective and Lightning Initiation 0-2 hour

**Convective and Lightning Initiation 0-2 hour**
**Nowcasting over Mesoamerica: ***QPE*** **
- John R. Mecikalski1 (Assistant Professor)
- Kristopher M. Bedka2
- Todd A. Berendes1
- Simon J. Paech1
-  and Laci Gambill1
*1**Atmospheric Science Department*
*University of Alabama in Huntsville*
*2**Cooperative Institute for Meteorological Satellite Studies*
*University of Wisconsin-Madison*
**Supported by:**
- NASA New Investigator Program (2002)
- NASA ASAP Initiative
- The NASA SPoRT Initiative

## Outline

- Current capability: Overview
- 	Convective initiation (CI)
- Background
- 		Error assessments in Daytime CI 0-1 h Nowcasting
- 		Confidence analysis
- 		Incorporation of MODIS information
- 		Discriminant scoring for CI
- Current & new initiatives: 2006
- 		Impacts of microwave data toward 0-2 hour QPE forecasting
- 		Nighttime CI forecasting
- 		Lightning (event) & Lightning Initiation forecasting
- 		CI/LI Climatology as *f*(location, time of year)

## 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)**

- •

- •

## Methods: Convective Nowcasts/Diagnoses

**How is this done?**

- Determine the necessary data to evaluate convective and lightning 		initiation from satellite
- _Is satellite data the best??_
- If so, how can it be used? *Why might it be superior to radar?*
_**A**_: Satellites “see” cumulus before they become thunderstorms!
_**A**_: There are many available methods for diagnosing/monitoring cumulus motion/development in real-time (every 15-min). _See the published research on satellite data usage !!!_
- So, once well-read, all the pieces are in place to move the (science) forward in the CI nowcasting arena, _with satellite analysis as the centerpiece for 0-1 h CI nowcasting_.

## Where are we now ...

** ****Applying CI algorithm over U.S., Central America & Caribbean**
** ****Validation & Confidence analysis**
** ****Satellite CI climatologies/CI Index: 1-6 h**
** ****Work with new instruments**
** ****Data assimilation possibilities**

## Where are we now ...

## Input Datasets for Convective Nowcasts/Diagnoses

-  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

**Use McIDAS to **_**acquire**_** data, generally NOT for processing:**
** ****GOES-12 1 km visible and 4-8 km infrared imagery every 15 minutes**
** ****UW-CIMSS visible/IR “Mesoscale” *****Atmospheric Motion Vectors***** (AMVs)**
-  **WSR-88D base reflectivity mosaic used for real-time validation**
-  **NWP model temperature data for AMV assignment to cumulus cloud pixels ... ****	****	****based on relationship between NWP temp profile and cumulus 10.7 ********m ****T****B**
**Other non-McIDAS data:**
** ****UAH *****Convective Cloud Mask***** to identify locations of cumulus clouds**

***Input Datasets for Convective Nowcasts/Diagnoses***

## Convective Cloud Mask

***Convective Cloud Mask***

-  Foundation of the CI nowcast algorithm: Calculate IR fields only where cumulus are present (10-30% of a domain)
-  Utilizes a multispectral and textural region clustering technique for classifying all scene types (land, water, stratus/fog, cumulus, cirrus) in a GOES image
-  Identifies 5 types of convectively-induced clouds: low cumulus, mid-level cumulus, deep cumulus, thick cirrus ice cloud/cumulonimbus tops, thin cirrus anvil ice cloud

## “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

## CI Interest Fields for CI Nowcasting

| 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 |

***CI Interest Fields for CI Nowcasting***

- from *Roberts and Rutledge (2003)*

## 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. Central Kansas.
-  PODs ~45% at 1 km (FARs ~40%)
-  ***NEW Linear Discriminant Analysis methods provide ~65% POD scores for 1-hour convective initiation.***

***CI Nowcast Pixels***

- These are 1 hour forecasted CI locations!

## An Example over the Tropics: CI

**An example of the CI nowcasting method over Central America:**
-  Real-time
-  Every 30 min during the day (*nighttime coming soon*)
-  GOES (*MODIS soon*)
-  **RED****/****GREEN**** pixels have highest CI probability**

## CI “Interest Fields”:  8-GOES  2-MODIS

| 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 |

** ****	****    **8.5-10.7 m (MODIS)**	****  ****	****    **

- > 0° C

- > 0° C

** ****	****    **3.7-10.7 m (MODIS)**	****  ****	****    **

## “Interest Field” Importance: POD/FAR

| 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 |

-  Instantaneous 13.3–10.7 um:	**Highest POD (84%)**
-  Time-trend 13.3–10.7 um:	**Lowest FAR (as low as 38%)**
** ****Important for CI & Lightning Initiation**

** ****	****    **8.5-10.7 m (MODIS)**	****  ****	****    **

- > 0° C

- > 0° C

** ****	****    **3.7-10.7 m (MODIS)**	****  ****	****    **

## Use of MODIS in Convective Initiation Forecasts

- MODIS 3.7-11.0 m

- MODIS 8.5-11.0 m

- Smaller ice
- particles/
- higher
- numbers

- 0

- 0

## Slide 16

##  The lower right shows the supercooled water in green. 

-  The lower right shows the supercooled water in green.
-  Proof of concept for determining the difference between supercooled water and glaciated towering cumulus clouds.
- _Super-cooled tops_:
- Lightning indicator as a function of convective environment

## New: CI/LI Validation & Discriminant Analysis

**Remap GOES data to 1 km gridded radar reflectivity data**
- Correct for parallax effect by obtaining cloud height through matching the 10.7 m TB to standard atmospheric T profile
**Identify radar/lightning pixels that have undergone CI/LI at t+30 mins **
- Advect pixels forward using low-level satellite wind field to find their approximate location 30 mins later
**Determine what has occurred between imagery at time t, t-15, & t-30 mins to force CI/LI to occur in the future (t+30 mins)**
**Collect database of IR interest fields (IFs) for these CI/LI pixels**
**Apply LDA: identify relative contribution of each IF toward an accurate nowcast**
- Test LDA equation on independent cases to assess skill of new method

**New:***** CI/LI Validation & Discriminant Analysis***

## New: CI/LI Linear Discriminant Analysis (LDA)

**New:***** CI/LI Linear Discriminant Analysis (LDA)***

- Some correlation between a
- confidence in CI [*f*(LDA score)] and
- 30-min dBZ (increase): _*QPE nowcast*_

-  Improved POD of** ~65% **and FAR of** ~34%**
** **A virtual-radar from satellite

## Outline

- Current capability: Overview
- 	Convective initiation (CI)
- Background
- 		Error assessments in Daytime CI 0-1 h Nowcasting
- 		Confidence analysis
- 		Incorporation of MODIS information
- 		Discriminant scoring for CI
- Current & new initiatives: 2006
- 		Impacts of microwave data toward 0-2 hour QPE forecasting
- 		Nighttime CI forecasting
- 		Lightning (event) & Lightning Initiation forecasting
- 		CI/LI Climatology as *f*(*location, time of year*)

## Detecting Convective Initiation at Night

- Detection of convective initiation at night must address several
- unique issues:
- Restricted to 4 km data (unless MODIS is relied upon)
- Visible data cannot be used to formulate cumulus mask
- Highly-dense, GOES visible winds are unavailable for tracking
- Forcing for convection often elevated and difficult to detect
- 	(e.g., low-level jets, bores, elevated boundaries)
- However, the advantages are:
- 	a)	Ability to use ~3.9 m channel (near-infrared) data (!!!)
- 	b)	More “interest fields” become available for assessing cumulus cloud
- 		development
- Therefore, new work is toward expanding CI detection for
- nocturnal conditions, and/or *where lower resolution may be*
*preferred* (i.e. over large oceanic regions).

*Wayne Mackenzie, MS student*

## Detecting Convective Initiation at Night

- Nighttime CI: Southeast
- 	Oklahoma

- Enhanced 10.7 m

- SHV: 3:44 UTC

## Detecting Convective Initiation at Night

**Use of 3.9 μm channel:**

- 10.7-3.9 m channel difference (Ellrod “fog” product)

- Evaluation is being done in light of the forcing for the convection (e.g., low-level jets, QG).

## Satellite-Lightning Relationships

***Satellite-Lightning Relationships***

-  *Current Work:* Develop relationships between IR TB/TB trends and lightning source counts/flash densities toward nowcasting (0-2 hr) future lightning occurrence
*Supported by the NASA New Investigator Program Award #:NAG5-12536*
* ***Incorporate in NWS Lightning Hazard/Prediction Tool**

**2047 UTC**

**2147 UTC**

_**Northern Alabama LMA Lightning Source Counts**_

**2040-2050 UTC**

**2140-2150 UTC**

## Lightning Initiation

**2040-2050 UTC**

**2140-2150 UTC**

_**Northern Alabama LMA Lightning Source Counts**_

***Lightning Initiation***
***Potential***

## CI Climatology Research

***CI Climatology Research***

**Simply put...**
- If all the CI locations
- as obtained from
- the GOES satellite
- are integrated, clear
- signals of how the
- land surface, weather
- regime, convective
- regime/environment
- influences CI should
- become apparent.

## CI Climatology Research

**GOES CI Interest Fields: 21 July 2005 (afternoon)**

***CI Climatology Research***

- _Details:_
- 	-topography
- 	-main updrafts

**...****for LI**

**1 km Resolution**

## Hyperspectral: GOES-R & GLM

- LEFT
- 8.508-10.98 m Band Difference: Red (’s >0) = Ice

- Comparison between the 10.98 m (right) and 11.00 m (far right) bands:
- 22 UTC 6.12.2002

**Small wavenumber change**
**results in significant changes**
**in view:**
-  Low-level water vapor
-  Surface temperature
-  Subtle cloud growth
- & microphysical changes

## Plans for Mesoamerica

- Heavy Rainfall, Flood and related Hazard Forecasting.
-  Improved understanding of Convective Processes and Characterization in the
- Tropics; the Development of Satellite Climatological Data Sets.
-  Basic Research utilizing the NSSTC/UAH/NASA STORMnet Testbed.

- Mapping of convective storm initiation, both in a 0-2 h prediction mode,
- and from a climatological perspective;
- Mapping of the frequency of occurrence of CI across Mesoamerica;
- Storm path and motion climatologies;
- Classification of convective storm intensity (in terms of lightning,
- 	rainfall potential, or other derived index); Convective initiation index.

***Plans for Mesoamerica***

**Four Main Themes**

## Contact Information/Publications

***Contact Info:***
- Prof. John Mecikalski: *johnm@nsstc.uah.edu*
- Kristopher Bedka: *krisb@ssec.wisc.edu*
***Web Pages:***
- nsstc.uah.edu/johnm/ci_home (biscayne.ssec.wisc.edu/~johnm/CI_home/)
- http://www.ssec.wisc.edu/asap
***Publications:***
- Mecikalski, J. R. and K. M. Bedka, 2006: Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery. *Mon. Wea. Rev.* (IHOP Special Issue, January 2006), **134**, 49-68.
- Bedka, K. M. and J. R. Mecikalski, 2005: Applications of satellite-derived atmospheric motion vectors for estimating mesoscale flows. *J. Appl. Meteor*. **44**, 1761-1772.
- Mecikalski, J. R., K. M. Bedka, and S. J. Paech, 2006: A statistical evaluation of GOES cloud-top properties for predicting convective initiation. In preparation. *Mon. Wea. Rev.*
- Mecikalski, J. R., S. J. Paech, and K. M. Bedka, 2006: Lightning initiation forecasts within the 2-hour timeframe. In preparation. *Geophys. Res. Letters.*