**The Use of Satellite Fields in 0-1 Hour Convective Initiation
Forecasts**

**A Benchmark Report for Aviation Applications**

Version 5.0

31 October 2006

John R. Mecikalski^†^, Kristopher M. Bedka\* and David B. Johnson^Ø^

^†^University of Alabama, Huntsville

\*Cooperative Institute for Meteorological Satellite Studies, University
of Wisconsin--Madison

^Ø^NCAR, Research Applications Laboratory, Boulder, Colorado

**1. Motivation**

This report summarizes the current status of efforts conducted as part
of the Advanced Satellite Aviation-Weather Products (ASAP) Program via
the National Aeronautic and Space Administration (NASA) and the Federal
Aviation Administration (FAA) Aviation Weather Research Program (AWRP)
to enhance and extend the use of satellite data sets for applications in
aviation weather. Convective weather, and specifically convective
initiation (CI), is currently one of the main focus areas of the ASAP
project, working in conjunction with the FAA's Convective Weather
Product Development Team (PDT).

The AWRP focuses on developing and enhancing aviation weather products
that respond to the needs of users and FAA priorities. All new weather
products developed by the AWRP's Product Development Teams (PDTs) go
through extensive testing and evaluation, culminating in a formal review
by a joint FAA and NWS Aviation Weather Technology Transfer Board (AWTT)
before they are declared operational and made available to the public.
The AWTT review process puts an equal emphasis on high accuracy and low
false alarm rates.

Thunderstorms account for most of the air traffic delays in the U.S. and
cost the aviation industry many millions of dollars annually in lost
time, fuel and efficiency through delayed, cancelled and rerouted
flights (Kaplan et al. 1999; Murray 2002). In response to this need, the
FAA's Convective Weather Product Development Team is working to develop
reliable 0-8 hour probability forecasts of convective weather and
automated 0-2 hour high-resolution deterministic forecasts over the
continental United States (Mecikalski et al. 2006).

Historically, much of the Convective Weather PDT's work has centered on
the use of weather radars to monitor convective storm development.
Radars, however, do not see the early stages of convective cloud
formation and growth. The ASAP team is currently working with the FAA
Convective Weather PDT to develop and transfer satellite-based
techniques for detecting, tracking, and monitoring the early development
of small cumulus in order to improve thunderstorm initiation forecasts.

Currently the AWRP is reexamining its priorities and approach to
thunderstorms and convective weather. Specifically, there is some
concern about the large number of different convective products that
have evolved over time in response to specific users' needs. A
reorganization and design review in now considering ways to consolidate
and unify the various products now being generated and ensure that all
FAA products are consistent and provide a common shared awareness among
all users. The ultimate result will be a Consolidated Storm Prediction
for Aviation (CoSPA) product based on gridded data sets and providing a
common core of observational analysis, forecaster input, and numerical
models that can be used to create individual products for specific
applications. This approach will benefit ASAP efforts by ensuring that
our contributions will be transferred to broad-scale use, rather than
restricted to a single product or application.

2.  **Background**

> **a) Satellite Contributions**

In the context of this discussion, convective initiation (CI) can be
defined as the first appearance of a radar echo ≥35 dBZ within a growing
cumulus (Roberts and Rutledge 2003; Mecikalski and Bedka 2006). The
Mecikalski and Bedka (2006) CI methodology offers a sophisticated,
satellite-based approach for real-time detection and monitoring of early
convection of scales ≥1 km over large geographical regions (e.g., the
CONUS portion of a GOES-12 scan, with possible extension to cover the
entire Gulf of Mexico). The ASAP project leverages nearly five years of
previously funded CI research, as well as the expanding archive of
half-hourly CI datasets that describe cumulus cloud growth via eight
infrared (IR) and visible (VIS) GOES and two MODIS IR fields. Results to
date show considerable skill (\~60‑70%) in the 30 min--1 hour
identification of first-time thunderstorm development using this CI
algorithm.

The Mecikalski and Bedka (2006) methodology makes use of a number of
separate, satellite-based "interest fields" that are combined into a
single, integrated, CI indicator. In this method, cumulus clouds are
identified and tracked, using satellite-based atmospheric motion vectors
designed to capture mesoscale flows (Bedka and Mecikalski 2005), while
the IR characteristics of the clouds are monitored \[e.g., the 10.7 μm
temperature (T~B~) and the 6.5--10.7 μm T~B~ differences\]. Clouds of
various types are identified using a statistical clustering methodology
(Nair et al. 1998), as well as a separate "cumulus cloud mask" (CCM),
which allows only cumulus to be monitored within the CI algorithm.
Ultimately, cumuli meeting several cloud-top cooling and growth criteria
are identified as being likely to produce a ≥35 dBZ radar echo over the
next 30-60 minutes. Figure 1 shows the eight interest fields currently
used from GOES, and the criteria that define a "score" on the 1-km pixel
scale. The Mecikalski and Bedka algorithm has been given the name:
SATellite Convection AnalySis and Tracking (SATCAST) System,
highlighting its unique dependence on high-resolution satellite
observations and analysis. As of Spring 2006, this SATCAST CI algorithm
is being run on a real-time basis over the Midwest U.S., the
Southeastern quarter of the U.S., and over all of Central America.

Figure 2 shows an early example of this method\'s predictive skill for
one case example (6 July 2004).

Recent research demonstrates via principal component analysis (PCA) that
all eight IR indicators (Figure 1-right) contain important information
for nowcasting CI using GOES. Use of linear discriminant analysis (LDA)
has led to both improved accuracy of the CI nowcasts (to \~65% POD), as
well as provides forecasts of the increase in rainfall intensity over
the next 1 hour. This later development extends the CI algorithm toward
quantitative precipitation estimate (QPE) nowcasting in the 0-1 hour
timeframe (Mecikalski et al. 2006).

![](media/image1.png){width="3.0027777777777778in"
height="3.276388888888889in"}

**Figure 1:** Tendencies and values 8 CI interest fields (bracketed
numbers) used for GOES and MODIS data within the Mecikalski and Bedka
(2006) algorithm. The Table (right) lists the critical values for
evaluating CI within IR data.

The UAH/CIMSS team is also making significant progress toward nighttime
CI forecasting as an extension of current capabilities, an important
prelude to nighttime CI forecasting. Understanding the mesoscale dynamic
forcing that maintains thunderstorm development when solar heating is
absent is critical. These include low-level jets, decoupled atmospheric
boundary layers, outflow boundaries, and moisture transport (e.g.,
Raymond 1978; Kessler 1987). Evolving the daytime CI satellite-based
algorithm to a *full 24-hour* analysis system is underway involving one
of the PI's M.S. students. Satellite analysis of a number of nighttime
CI events, coupled with NWP simulations (using the MM5) has been
performed, and is demonstrating the importance of the 3.9 μm information
(Mackenzie and Mecikalski 2005; 2006).

For nighttime CI and new lightning initiation (LI), research is
beginning to use of the 3.7-3.9 μm channels over land, as well as the
3.75/3.9--11/10.7 μm channel difference (Nelson and Ellrod 1996), on
GOES and MODIS (which suffers from surface emissivity and visible light
interference problems during daytime). For the nighttime and daytime CI,
several of the \~27 IR MODIS channels (1.2, 1.37, 1.6, 2.1, 3.7-4.1,
6.7, 7.32, 8.55 and 11-14.2 μm) will be considered, in conjunction with
those from GOES, to enhance the current IR indicators of convective
cloud development. MODIS IR data are spectrally much narrower that those
from GOES, and are available at 1 km resolution. For guiding LI
prediction, we are challenged to use MODIS to help isolate a \~1-2
km-thick portion of a cumulus cloud that occurs below --10º C based on
cloud-top microphysical indicators, e.g., cold cloud growth leading to
the formation of a 30-35 dBZ echo through the --10º level (cf. Petersen
et al. 1996). Estimating cumulus mass flux from GOES cloud-top cooling
rates is an important component that will be developed along with the
MODIS microphysical information toward inferred LI. Collaboration with
other UAH and CIMSS scientists well versed in satellite microphysical
retrievals will assist in this component of the project, as discussed
below (e.g., Greenwald and Christopher 2000).

![](media/image2.wmf)![](media/image3.jpeg){width="2.9305555555555554in"
height="3.0in"} ![](media/image3.jpeg){width="3.0in" height="3.0in"}

**Figure 2:** GOES visible imagery (a), cloud-motion tracking vectors
(b), GOES infrared cloud-top cooling rates \[∂(10.7 μm T~B~)/∂*t*\] (c),
and thunderstorm echoes (d) at 20:45 UTC. See Figures 2(e-h) for
additional discussion.

The main limitations to the GOES and MODIS CI methodology lie in (1)
accurate cumulus cloud tracking, and (2) the 1 km-resolution of the GOES
sensor (i.e. cumulus with scale \<1 km are not well tracked or observed
by GOES VIS and IR sensors).

As of summer 2006, the CI algorithm is being run every 15-30 min during
the day and night over the Upper Midwest, and over the Southern through
Southeastern U.S. and across Florida (at UW-CIMSS and UAH; see
*nsstc.uah.edu/johnm/ci_studies.html* and
*biscayne.ssec.wisc.edu/\~johnm/CI_home*/), and over Central America and
the Caribbean. In addition, we are collaborating with NOAA and the
National Weather Service (NWS)

![](media/image3.jpeg){width="3.0694444444444446in"
height="3.0in"}![](media/image3.jpeg){width="2.9166666666666665in"
height="3.0in"}

![](media/image3.jpeg){width="3.013888888888889in"
height="3.0in"}![](media/image3.jpeg){width="2.9305555555555554in"
height="2.861111111111111in"}

**Figure 2 (cont.)**: CI forecast made at 20:45 UTC valid at \~21:30 UTC
(e) and thunderstorm development (≥35 dBZ echoes) at the 21:32 UTC
validation time. Panels (g) and (h) show the corresponding 5 minute
integrated lightning source counts at 20:45 UTC and 21:30 UTC,
independent data sets showing the initial growth of small thunderstorms
in this time period.

by using this algorithm over the NOAA Storm Prediction Center's severe
weather risk regions
([*www.ssec.wisc.edu/\~krisb/ConvInit/ConvInit.html*](http://www.ssec.wisc.edu/~krisb/ConvInit/ConvInit.html)).
And displaying the CI fields on the NWS Advanced Weather Information
Processing System (AWIPS) system, of the National Weather Service (NWS)
in Huntsville, Alabama, which is co-located in the same building as the
Atmospheric Science Department of UAH (see Figure 3).

![](media/image4.png){width="6.020833333333333in"
height="5.044444444444444in"}

**Figure 3**: GOES CI field within the AWIPS: 30-min cloud-top cooling
rates (ºC), overlaid with GOES 1‑km resolution brightness. These fields
are routinely provided to the NWS in Huntsville, Alabama.

**b) AutoNowcaster Overview**

The NCAR Auto-Nowcast System (ANC) is a software system that produces
time- and space-specific 0-2 hour nowcasts of convective storm location
and intensity (Mueller, et al., 2003). ANC combines meteorological
observations, a numerical boundary layer model, forecaster input
("forecaster in the loop"), and feature detection algorithms to provide
routine nowcasts of thunderstorm position.

In addition to routine demonstrations in the U.S. for the FAA and the
National Weather Service, the NCAR Auto-Nowcast System has also been
demonstrated at the Sydney 2000 Summer Olympic Games and is in the
process of being transferred to the Bureau of Meteorology in China for
use during the 2008 Olympics in Beijing.

Early nowcasting techniques relied on the simple extrapolation of storm
positions. Even for periods of less than 60 minutes, extrapolation
nowcasts are often not useful because they fail to take into account
storm initiation, growth and dissipation. The ANC is designed to address
these needs.

A primary component of ANC is the ability to identify and characterize
boundary lkayer convergence lines. Feature detection algorithms
algorithms and variational Doppler radar analysis techniques are used to
monitor and nowcast boundary layer structures. The importance of the
boundary layer in thunderstorm development was first shown by the
Thunderstorm Project (Byers and Braham, 1949). They found that boundary
layer mesoscale convergence was a common precursor to convective storm
development. While some attention was paid to monitoring cloud lines
from satellite data, most of the subsequent development work for
thunderstorm nowcasting was focused on radar-detected clear-air
"boundaries" (Wilson and Carbone, 1984). In this context, "boundaries"
are defined as narrow zones of boundary layer convergence associated
with weather phenomena such as gust fronts, sea-breeze circulations,
terrain-induced circulations, horizontal convective rolls, and
synoptic-scale fronts.

The ANC attempts to mimic much of what is normally done by the
experienced forecaster. It uses a data fusion system to assimilate a
variety of datasets, including forecaster input, to create nowcasts that
are updated at regular intervals, typically every 5 minutes. The ANC
uses a fuzzy logic system that provided an efficient approach to the
combination of datasets through the use of conceptual models. Datasets
are not just thrown into the system, but rather require at least a
qualitative understanding of the specific role of the importance of the
observation. Observational datasets most frequently used in the ANC
system include full resolution radar (generally WSR-88G), satellite,
surface stations, lightning, profilers, numerical models, and
radiosondes. These data are input into analysis algorithms to calculate
predictor fields. The relevant analysis algorithms are specifically
developed for each dataset and include data quality control routines and
feature detection algorithms as well as additional observation-specific
procedures. The fuzzy logic approach uses membership function to map the
predictor fields to the likelihood of storms. Fourteen or more
predictors are used in the ANC, including some that are based on
satellite observations. ASAP collaboration with the NCAR ANC development
team has been focused on upgrading and improving these satellite
predictor fields.

**3. Results: SATCAST (Satellite) Interest Fields within the
Auto-Nowcast System**

In 2004, ASAP investigators at the University of Wisconsin, CIMSS, began
collaboration with the NCAR members of the FAA's Convective Weather
Product Development Team, beginning to incorporate and test advanced
satellite data sets into the Auto-Nowcast System. The promise was to
begin evaluating the value of the new satellite fields within the ANC
and other FAA-supported nowcasting systems. This initial work was
motivated by the NASA ASAP Summary document, Mecikalski et al. (2003).

Collaboration with the Convective Weather Development Team began in Fall
2004, and continued throughout 2005. At the 2005 ASAP Science Meeting
held in Boulder, Huaqing Cai and Cindy Mueller showed some initial
results of their explorations with ASAP satellite-based CI fields in the
Auto-Nowcast System. The key issues in this initial collaborative work
were to identify how best to incorporate the new satellite-based
convective observations into the existing structure of the ANC. The
integrated ASAP CI product fields, for example, were difficult to
interpret in terms of quality control and feature identification and the
NCAR team decided to work more with the component fields that are used
to generate the ASAP CI product. This follows their general approach of
using individual observational fields instead of integrated summaries,
letting the final fuzzy logic integration do the combination of all
interest fields in a single step.

The ASAP satellite interest fields that were most directly compatible
with the existing ANC internal structures were satellite cloud type
(cloud mask), including a separate field for cloud free regions, and the
cloud-top cooling rate. The ASAP products showed more detail than the
lower resolution satellite fields previously used with the ANC and
promised more detailed identification of early cloud development. Figure
4 shows the 15-minute ASAP cloud-top IR cooling observation, the
corresponding ANC "interest field" and the older interest field based on
the pre-ASAP satellite data sets previously available to the ANC
development team. The new, higher resolution fields were found to provid
more detailed and smoother features in the interest field.

![](media/image5.png){width="3.182638888888889in"
height="2.5340277777777778in"} ![](media/image6.png){width="3.19375in"
height="2.5409722222222224in"}

![](media/image7.png){width="3.18125in" height="2.5319444444444446in"}

In addition to examining the cloud-top cooling rates, NCAR conducted a
series of comparison tests examining the features and performance of the
UAH/ASAP cloud mask field and the cloud mask that was developed by the
Navy Research Laboratory satellite group in Monterey, California. The
Navy mask included a more detailed breakdown of cloud type, and seemed
to perform better using its cumulus congestus classification as a
predictor of subsequent development than the more general cumulus
category of the ASAP product (see Figure 5). The FAA Convective Weather
Team has extended the use of convective cloud typing within the ANC and
other systems.

On the basis of this comparison, in part, the ASAP CI development group
has reexamined their cloud mask, and has formulated a clustering-based
approach (Figure 6). The Standard Deviation Limited Adaptive Clustering
(SDLAC) technique is an iterative, statistically based method that
combines aspects of k-means ISODATA and agglomerative hierarchical
clustering (Berendes et al. 2006). However, unlike most clustering
methods, SDLAC does not require initial cluster estimates. The SDLAC
method has been applied successfully for detection and classification
purposes. A convective cloud mask has been created using GOES data.
SDLAC is extremely flexible, and has been used on MODIS to detect
volcanic ash plumes for example.

Figures 7 through 10 show an example of the contribution of improved
early cloud satellite observations can make in concentrating and
condensing the ANC predictions based on boundary analysis. In these
figures the early cloud observations are used to reduce the estimated
probability of convective initiation in areas that aren't showing early
cloud development, and to increase the probability of convective growth
in areas that show clear signs of early development.

![](media/image8.png){width="4.18125in" height="3.3340277777777776in"}

![](media/image9.png){width="6.0in" height="4.419444444444444in"}

**Figure 6**: Subsequently developed and improved ASAP clustering-based
cloud typing algorithm, as being evaluated in CoSPA.

Figure 7 shows the initial cloud field (visible image), with the ANC
predicted boundary for preferential development shown in purple. The
region highlighted is over the state of Indiana. The outlines of the
subsequent areas of significant radar echo 60 minutes later are outlined
in black. Figure 8 shows the corresponding radar reflectivity field
showing little of no initial development. Figure 9 shows the IR
cloud-top cooling field, which clearly suggests preferential development
along the eastern end of the anticipated boundary feature.

The resultant, combined ANC prediction shown in Figure 10 successfully
identifies the location of the convective build up (indicated by the
purple, pink, and maroon contours), as compared with the subsequent
radar echo position boundaries (now show in white).

In summary: the results look very promising, and suggest that
incorporation of improved satellite observations may be able to enhance
ANC performance by providing a detailed analysis of the locations and
early growth of non-precipitating convective clouds.

![](media/image10.png){width="4.972916666666666in"
height="4.023611111111111in"}

**Figure 7**: Visible (GOES) satellite data for 2045 UTC 18 July 2006.

![](media/image11.png){width="5.018055555555556in"
height="4.060416666666667in"}

**Figure 8**: Radar (composite NEXRAD) data for 2042 UTC 18 July 2006.

![](media/image12.png){width="4.995138888888889in"
height="4.042361111111111in"}

**Figure 9**: GOES IR cloud-top cooling field for 2042 UTC 18 July 2006.

![](media/image13.png){width="5.018055555555556in"
height="4.0784722222222225in"}

**Figure 10**: ANC-produced interest field for CI for 2042 UTC 18 July
2006, based on integrating the ASAP cloud observations with the
traditional ANC data fields.

Outward from this initial test has been the need to extensively validate
the CI IR interest fields, cloud mask and winds. This will be expanded
on below. Because of the critical nature of cumulus cloud tracking in
the ANC (as well as in SATCAST, which provides fields for the ANC),
mesoscale atmospheric motion vectors (AMVs) have been enhanced by the
ASAP team since 2005. Figure 11 presents some accuracy statistics for
these AMV fields.

**4. Current Status, Lessons Learned and Recent Progress**

Recent research done to incorporate and utilize the SATCAST GOES VIS and
IR interest fields within the ANC and subsequent work demonstrating
SATCAST products for NOAA and the NWS has lead to considerable new
developments. This section summarizes the highlights of this work since
2005.

In particular, satellite-derived CI interest fields and radar
reflectivity data (as a verification field to evaluate satellite-based
predictions) from several CI events were used to (a) estimate the
relative importance of each IR interest field as well as the confidence
we have that a given interest field is useful to nowcasting CI, (b)
evaluate the skill in the SATCAST algorithm, and (c) serve as a training
data set for new approaches for integrating the eight CI interest
fields, such as linear discrimenant analysis (LDA) and other non-linear
models.

![](media/image14.png){width="2.551388888888889in"
height="2.0118055555555556in"}![](media/image15.png){width="2.6194444444444445in"
height="2.060416666666667in"}![](media/image16.png){width="2.5965277777777778in"
height="2.0388888888888888in"}![](media/image16.png){width="2.5965277777777778in"
height="2.0409722222222224in"}

**Figure 11:** A comparison of OPER AMV wind speed (left), and direction
(middle-left), and MESO AMV wind speed (middle-right), and direction
(right) to NOAA Wind Profiler observations over the one-year study
period. Regions of higher scatterpoint density are colored by lighter
greyscale tones

> **a) Principal component analysis (PCA)**

Principal component analysis (PCA) can be used to discern the relative
amount of information within the IR data of a GOES pixel (i.e. within
each of the eight CI interest fields). Also known as empirical
orthogonal function (EOF) analysis, PCA is a common method used to
reduce a data set from a large number of variables to one in which only
a few new variable are required to describe the necessary and important
information (Wilks 2006). Because of the commonness of use of PCA within
atmospheric analysis, we refer the reader to Wilks (2006), as well as
more classical treatments by Jolliffe (1986) and Preisendorfer (1988),
for an overview of this technique.

The primary focus of this analysis is to determine which of the eight
interest fields contain a relative amount of important information in
comparison to others, or, which combination of indicators is important;
it is likely that not all of the eight fields contain the same level of
information on future CI, with some likely containing redundant
information. Table 1 presents the 8 IR fields, while Table 2 presents
the PCA results.

**Table 1:** The information the GOES-12 satellite can provide to the CI
nowcast algorithm, and their individual contributions in terms of
uniqueness for assessing CI. For GOES satellites prior to GOES-12,
similar statements can be made when comparing 6.7 versus 6.5 µm, and
12.0 versus 13.3 µm channels.

Assessment of the principal components and explained variance tables
provides and interesting conclusion: (1) Principal component 1 alone
describes 67.64% of the variance for forecasting CI from these data,
with component 2 adding another 20.83% (totaling 88.47%) and component 3
adding another 7.26% (totaling 95.73%). (2) If principal components 1-3
are considered valuable together, then analysis of the explained
variances between all *interest fields* shows that *all of the seven
fields contribute some level of useful information to CI nowcasting*.
This implies that use of fewer that seven *interest fields* will not
provide more accurate or optimal CI predictions and lends credence to
the scoring approach used in Mecikalski and Bedka (2006). This scoring
approach, however, suffers some in terms of POD accuracy as compared to
an LDA-based method for using the GOES IR data, as shown below.

+-----------+-------+-------+-------+-------+-------+-------+-------+
| # Cor     | #     | #     | #     | #     | #     | #     | #     |
| relations | # IF1 | # IF2 | # IF3 | # IF5 | # IF6 | # IF7 | # IF8 |
+-----------+-------+-------+-------+-------+-------+-------+-------+
| *IF1*     | 1.000 | ---   | ---   | ---   | ---   | ---   | ---   |
+-----------+-------+-------+-------+-------+-------+-------+-------+
| *IF2*     | 0     | 1.000 | ---   | ---   | ---   | ---   | ---   |
|           | .8581 |       |       |       |       |       |       |
+-----------+-------+-------+-------+-------+-------+-------+-------+
| *IF3*     | -0    | -0    | 1.000 | ---   | ---   | ---   | ---   |
|           | .9388 | .8690 |       |       |       |       |       |
+-----------+-------+-------+-------+-------+-------+-------+-------+
| *IF5*     | -0    | -0    | 0     | 1.000 | ---   | ---   | ---   |
|           | .2411 | .3304 | .2818 |       |       |       |       |
+-----------+-------+-------+-------+-------+-------+-------+-------+
| *IF6*     | -0    | -0    | 0     | 0     | 1.000 | ---   | ---   |
|           | .4403 | .4989 | .5143 | .5976 |       |       |       |
+-----------+-------+-------+-------+-------+-------+-------+-------+
| *IF7*     | 0     | 0     | -0    | -0    | -0    | 1.000 | ---   |
|           | .1405 | .2246 | .1358 | .9323 | .5029 |       |       |
+-----------+-------+-------+-------+-------+-------+-------+-------+
| *IF8*     | 0     | 0     | -0    | -0    | -0    | 0     | 1.000 |
|           | .0914 | .3405 | .0892 | .7743 | .3654 | .7826 |       |
+-----------+-------+-------+-------+-------+-------+-------+-------+
| **ExVar** |       |       |       |       |       |       |       |
+-----------+-------+-------+-------+-------+-------+-------+-------+
| *PC#1*    | 2     | 1     | -3    | -     | -1    | 4.048 | 2.986 |
|           | 3.745 | 2.318 | 3.025 | 7.552 | 6.327 |       |       |
+-----------+-------+-------+-------+-------+-------+-------+-------+
| *PC#2*    | 1     | 1.758 | -1    | 2     | 2     | -1    | -1    |
|           | 0.425 |       | 1.616 | 2.017 | 2.970 | 7.277 | 3.936 |
+-----------+-------+-------+-------+-------+-------+-------+-------+
| *PC#3*    | 7.712 | 5.807 | -     | -1    | 3     | 1     | 1     |
|           |       |       | 1.729 | 7.353 | 4.192 | 5.330 | 7.877 |
+-----------+-------+-------+-------+-------+-------+-------+-------+
| *PC#4*    | -     | -2    | -1    | -1    | 5.090 | 1     | -2    |
|           | 1.057 | 8.687 | 1.201 | 7.045 |       | 0.407 | 6.514 |
+-----------+-------+-------+-------+-------+-------+-------+-------+
| *PC#5*    | -3    | 6.427 | -2    | -     | 4.812 | -     | 6.700 |
|           | 9.710 |       | 8.009 | 4.775 |       | 9.567 |       |
+-----------+-------+-------+-------+-------+-------+-------+-------+
| *PC#6*    | -     | 3     | 7.016 | -     | 2.323 | 1     | -2    |
|           | 6.859 | 4.038 |       | 5.932 |       | 4.112 | 9.721 |
+-----------+-------+-------+-------+-------+-------+-------+-------+
| *PC#7*    | -     | -     | -     | 3     | -     | 3     | 4.465 |
|           | 7.123 | 5.950 | 9.345 | 3.245 | 0.824 | 9.048 |       |
+-----------+-------+-------+-------+-------+-------+-------+-------+

+-----------------+-------------------------+-------------------------+
| ### PC          | ### Eigenvalue          | **% Variance**          |
+-----------------+-------------------------+-------------------------+
| *1*             | 396.87                  | 67.64                   |
+-----------------+-------------------------+-------------------------+
| *2*             | 122.21                  | 20.83                   |
+-----------------+-------------------------+-------------------------+
| *3*             | 42.57                   | 7.26                    |
+-----------------+-------------------------+-------------------------+
| *4*             | 11.59                   | 1.98                    |
+-----------------+-------------------------+-------------------------+
| *5*             | 8.93                    | 1.52                    |
+-----------------+-------------------------+-------------------------+
| *6*             | 2.66                    | 0.454                   |
+-----------------+-------------------------+-------------------------+
| *7*             | 1.89                    | 0.322                   |
+-----------------+-------------------------+-------------------------+

**Table 2**: Principal component analysis (PCA) results for eight CI
interest fields. Shown (top) is the correlation matrix as a
lower-triangular matrix. For this analysis, because IF4 was a binary
(yes/no) condition, we could not use it within PCA. Based on the
analysis (bottom table), PC 1-3 are considered to contain useful
information, and more importantly, non of the seven CI interest fields
contain a significant amount of redundant information on forecasting the
occurrence of CI using GOES IR data. Here, "ExVar" is explained
variance.

**b) Skill Assessment: POD and FAR Statistics**

A simple statistical analysis was performed as a means of assessing the
accuracy of te CI nowcasts from SATCAST, as a function of IR interest
field. Here we determine measures of accuracy, false alarm rates,
probability of detection and uncertainty in the GOES CI methodology.
This analysis consisted of determining for all CI pixels the maximum
probability of detection (POD) and minimum false alarm rate (FAR) as a
function of ≤ 8 interest fields. For this project, POD is defined as in
Wilks (2006), namely POD=*a*/(*a*+*c*); FAR is defined similarly as
FAR=*b*/(*a*+*b*). In these, POD is the fraction of those occasions when
the forecast event (CI in this case) occurred when it was also forecast
to occur (*a*) compared to all forecasts (*a*+*c*). FAR is the fraction
of 'yes' forecasts that turned out to be incorrect (*b*) compared to all
non-events (*b*+*c*). Here, *c* is the number of event that where not
forecasted but that were observed.

  ------------ --------- --------- --------- --------- --------- --------- --------- ---------- ------------
  **Interest   **IF1**   **IF2**   **IF3**   **IF4**   **IF5**   **IF6**   **IF7**   **IF8**    **Mean**
  Field**                                                                                       

  **IF1**      0.683     0.627     0.421     0.451     0.444     0.537     0.454     0.371      49.83%

  **IF2**      0.627     0.843     0.409     0.507     0.484     0.656     0.500     0.382      55.09%

  **IF3**      0.421     0.409     0.428     0.380     0.289     0.345     0.296     0.235      35.03%

  **IF4**      0.451     0.507     0.380     0.655     0.450     0.560     0.432     0.339      47.18%

  **IF5**      0.444     0.484     0.289     0.450     0.595     0.445     0.552     0.429      46.10%

  **IF6**      0.537     0.656     0.345     0.560     0.445     0.793     0.446     0.345      51.58%

  **IF7**      0.454     0.500     0.296     0.432     0.552     0.446     0.598     0.428      46.33%

  **IF8**      0.371     0.382     0.235     0.339     0.429     0.345     0.428     0.471      37.51%

                                                                                     Minimum:   **35.03%**
  ------------ --------- --------- --------- --------- --------- --------- --------- ---------- ------------

**Table 3:** Preliminary, conditional POD scores as a function of
interest field usage. For example, if the top line is considered,
interest field "1" (IF1 from Table 1) fell within its critical range
68.3% when CI occurred. When IF1 was within range, IF2 was within its
critical range 62.7% of the time, etc. The sum on the far right denotes
the mean of all percentages per row. The shaded diagonal elements
provide the single-field POD scores (e.g., IF2 used alone gives an 84.3%
POD for CI, which falls to 55.09% when all subsequent fields are
considered).

  ------------ --------- --------- --------- --------- --------- --------- --------- ---------- ------------
  **Interest   **IF1**   **IF2**   **IF3**   **IF4**   **IF5**   **IF6**   **IF7**   **IF8**    **Mean**
  Field**                                                                                       

  **IF1**      0.676     0.619     0.403     0.428     0.365     0.497     0.366     0.283      45.48%

  **IF2**      0.619     0.804     0.395     0.481     0.392     0.596     0.399     0.288      49.68%

  **IF3**      0.403     0.395     0.411     0.322     0.219     0.295     0.219     0.172      30.46%

  **IF4**      0.428     0.481     0.322     0.66      0.405     0.565     0.384     0.298      44.30%

  **IF5**      0.365     0.392     0.219     0.405     0.512     0.385     0.468     0.352      38.73%

  **IF6**      0.497     0.596     0.295     0.565     0.385     0.770     0.379     0.289      47.20%

  **IF7**      0.366     0.399     0.219     0.384     0.468     0.379     0.505     0.346      38.34%

  **IF8**      0.283     0.288     0.172     0.298     0.352     0.289     0.346     0.392      30.25%

                                                                                     Maximum:   **49.68%**
  ------------ --------- --------- --------- --------- --------- --------- --------- ---------- ------------

**Table 4**: Preliminary, conditional FAR scores as a function of
interest field usage. Table 4 description is the same as for Table 3,
except that the shaded diagonal elements provide the single-field FAR
scores.

Table 3 presents these results, and shows that use of the instantaneous
13.3-10.7 µm channel difference provides the highest POD at 84.3%.
Stated another way, when this channel difference is within the --25 to
--5 K range, there is an 84.3% probability that CI (as we've defined it)
will occur within the following 30 minutes for the moving cumulus as
observed by GOES. The POD falls to 74.9% when the instantaneous
13.3--10.7 µm channel difference is used together with the pixel's 30
minute 10.7 µm (cloud top) T~B~ time trend. Continuing, we see that use
of interest fields 2, 6, 1, 4 and 7 together (see Table 3) limits the
POD for CI to 62.7%. In contrast, Table 4 shows that FAR scores are
minimized at 39.2% when interest field 8 is used alone (the time trend
of the 13.3--10.7 µm T~B~ difference at a pixel). When interest field 8
is used in together with fields 5, 7, 4 and 6, the FAR decreases
slightly to 33.5%.

The relationship of these results to physical cumulus cloud behavior
observed by GOES suggests the following: (1) Incorporation of the 8 km
resolution 13.3 µm channel (as interpolated to the 1 km radar data
resolution for this application) provides high value in detecting and
observing cloud growth of the larger cumulus clouds, i.e., the larger
updrafts. In other words, when a cumulus cloud produces a strong signal
in the 13.3 µm channel it implies an updraft of considerable
size---between 4 and 8 km in width. We surmise then that as updraft
widths increase there is increased likelihood of CI over the next hour
(larger updrafts are more likely to persist for longer time periods).
(2) Cloud-top cooling rates (15- and 30-min 10.7 µm IR T~B~ trends) are
important for monitoring in-cloud updrafts toward the occurrence of CI.
This was anticipated given the results of Roberts and Rutledge (2003).
(3) The 6.5--10.7 µm IR channel difference seems to provide redundant
information to the 13.3--10.7 µm T~B~ difference and 10.7 µm time trend
information given that it appears as the third most important field for
high POD and low FAR. (4) The interest field denoting the transition
from above to below 273° K, and especially the actual cloud-top T~B~
field appears to add limited and nearly no information, respectively, to
the 0-1 hour CI nowcasting capability from GOES-12.

The POD and FAR results suggest then the following hierarchy of interest
field value for nowcasting the occurrence of a \>35 dBZ radar echo given
information from the GOES-12 instruments: (1) 13.3--10.7 µm TB and its
time trend, (2) cloud-top cooling rate measured through the 10.7 µm
channel values, (3) 6.5--10.7 µm T~B~ and time trend, (4) the transition
from above to below 273 K as measured by the 10.7 µm channel, and (5)
the instantaneous 10.7 µm T~B~. These analyses do show the need however
to incorporate multiple interest fields toward maximizing POD and
minimizing FAR scores, with all but one (the instantaneous 10.7 µm T~B~)
providing some value. There is no indication that using interest fields
that have little value negatively affects the results, they just do not
help. This is demonstrated in the LDA analysis to follow in which the
best answers (LDA-determined CI versus actual CI for 10,820 pixels) are
obtained when all eight-interest fields are combined.

The ASAP CI nowcasting method involves a simple summation of the number
of interest fields satisfied per pixel. This differs from the
conditional methodology described above where a selected interest field
must meet the CI criteria before other interest fields are evaluated.
Thus, we assume that the POD and FAR for the MB06 method corresponds to
the smallest POD and largest FAR values from Tables 3 and 4. Thus, the
POD for the MB06 method is 35 % and the FAR is 49.7%.

Beyond these simple statistics, it is of greater importance to provide
the users of the satellite CI indicators measures of the uncertainty and
confidence.

**c) New Approaches**

The primary motivation for the LDA is to recognize and isolate
satellite-observed cloud-top diagnostics and time trends associated with
cumulus clouds that lead to an enhancement in radar-observed
precipitation at a future time. Thus, the location of a cumulus cloud
pixel must be identified within imagery at times *t*--15 and *t*--30
minutes such that cloud-top cooling and channel differencing trends can
be computed, and at time *t+*30 minutes to identify how the
precipitation intensity has changed in response to this
satellite-observed cloud trends.

At this point in the process, the satellite CI interest fields and
current/future radar reflectivity information are known, assuming
satellite/radar data were properly tracking both backward and forward in
time. When these techniques are applied to the six cases mentioned
above, a total of 30,567 1 km^2^ GOES pixels co-located with WSR-88D
level II radar data were compiled, with each pixel possessing all eight
CI interest fields, as well as current and future radar reflectivity
information.

A total of 10,820 of these 30,567 pixels are defined as "CI pixels" in
the 6-event training data set given that the observed reflectivity
values increased from less than to greater than 35 dBZ in a 30 minute
period. Including the remaining 19,747 "non-CI pixels" was obviously
necessary if we are to evaluate the skill of the LDA-based nowcasting
method in capturing null events. This 10,820-pixel data set represents
then the number of CI pixels that our methods (i.e. the original ASAP
"scoring" versus new LDA-based methods) were challenged to provide a
correct CI nowcast.

For this effort, LDA provides an ability to: (a) translate all eight
interest fields per pixel into one number *L* (via a regression-like
equation), that will range between a minimum (lowest CI potential) to a
maximum (highest CI potential), and (b) use this *L* score then to
delineate the likelihood of CI per pixel, and in effect, have the
ability to estimate the confidence in this prediction (described below).
The LDA method in-turn provides us with a more robust measure of
per-pixel CI (a score and confidence) compared to the original MB06
CI-scoring system, and as will be shown, results in significantly lower
FAR and higher POD scores within the 0-1 h CI nowcasts.

Confidence in a given pixel's objective discrimination can then be
subsequently measured as the numerical distance from the mid-point of
all CI scores provided by Eq. (1), or from some average of all "yes" or
"no" events provided *a priori* knowledge. Figure 1 illustrates the
means for determining confidence in a "yes" LDA event prediction, in
this example normalized between 0 and 1, as the difference between 5 and
7.5 (the *a priori* determined optimal score for "yes" events).
Likewise, confidence in a "no" LDA event prediction can be determined in
a similar manner, using 2.5 in this example.

For the LDA training analysis described above, a N-pixel "training" data
set has been developed. This consists of GOES-12 pixels for which 8 of
the eight CI interest fields are indicative of CI (based on Table 1) and
the radar-observed dBZ values indeed increase from below to above 35 dBZ
within the 1-hour period of actual CI occurrence. Outward of this
analysis, a set of coefficients (*c*'s and *F*'s) is formed that
populates Eq. (1) for delineating a *L~yes~* and an *L~no~* equation.
These coefficients are listed in and are used to predict CI from the
GOES data.

An additional training set consisting of N "yes" and "no" 1 km GOES
pixels for using LDA to nowcast the increase in dBZ echoes over the
coming 1 hour. This "yes" pixel set consists of GOES pixels for which
the IR data measured cumulus that subsequently produced rainfall that
increased their echo intensity (*e*) from *e*~1~≥10 dBZ to
*e*~2~=*e*~1~+35 dBZ, where *e*~2~ is 30-minute later than that at
*e*~1~. We will use this LDA equation to evaluate the rainfall intensity
nowcast potential from GOES over the next 30-60 minutes.

**5. Conclusions**

This ASAP Convective Benchmark Report outlines the initial efforts,
during 2005 and 2006, to evaluate satellite-derived convective
initiation interest fields within existing nowcasting systems under
development by the FAA's Convective Weather Development Team.

Results show promise in the continued use and improvement of satellite
IR fields for nowcasting CI in the 0-1 h time frame. Work with the CoSPA
in 2007 and 2008 has been initiated.

**6. References**

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Mecikalski, J. R., K. M. Bedka, and S. J. Paech, 2005: Correlating
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initiation and development. *Bull. Amer. Meteor Soc*., NOWCAST:
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Mecikalski, J. R., and K. M. Bedka, 2006: Forecasting convective
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