GOESR3 Periodic Reporting
Project Team: J. Mecikalski, S. Weygandt, A. Heidinger, T. Lorraine Smith, C. Jewett, W. Straka,
and S. Wanzong.
Reporting Period:
July 2013 - June 2014
Team Lead: John Mecikalski, University of Alabama in Huntsville
Team Members:
Stephen Weygandt, NOAA ESRL
Andrew Heidinger, NOAA STAR/CIMSS
Tracy Lorraine Smith, NOAA CIRA
Christopher Jewett, University of Alabama in Huntsville
William Straka, NOAA CIMSS
Steve Wanzong, NOAA CIMSS
Project Title: Convective Storm Forecasting 1-6 Hours Prior to Initiation in the GOES–R Era
Project Number: 105
Executive Summary
This report summarizes the research completed in Year 2 supported by the National Oceanic and
Atmospheric Administration (NOAA): “Convective Storm Forecasting 1–6 Hours Prior to Initiation in
the GOES–R Era” which has grant number NA11NES4400014 by PI Dr. John R. Mecikalski (University of Alabama in Huntsville–UAHuntsville) and Co-PI Dr. Stephen Weygandt (NOAA/ESRL/GSD
Assimilation and Modeling Branch, Boulder, CO).
The proxy GOES-R CI algorithm produces very short-term (0-2 hour) CI forecasts, however the
meteorological community desires accurate convective forecasts on the order of 1-6 hours. Numerical
weather prediction (NWP) models provide these longer-term forecasts, yet CI placement, timing, and
occurrence remains a significant problem, which if wrong, leads to deleterious downstream impacts on
the larger-scale forecast, sometimes lasting days within a given model simulation. Therefore, the
research hypotheses guiding this work are:
(1) Information and techniques learned from the creation of an improved proxy GOES-R CI
algorithm can be transferred into improved strategies for direct assimilation of current and future
satellite datasets (that describe the CI process), leading to increased accuracy in NWP of the
onset of new convective storms.
(2) The assimilation of the raw satellite-based observation fields into rapid-update NWP models
such as the Rapid Refresh (RAP) and High Resolution Rapid Refresh (HRRR) will provide the
models with a better description of initiating convective storms, leading to beneficial
improvements to short- (0-6 hrs) and subsequently longer-range mesoscale forecasts (6-18 hrs).
Milestones
The milestones for the previous year include:
UAHuntsville:
 Update GOES-R CI algorithm with NOAA daytime cloud properties.
 Improve CI training database for GOES-West
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
Publish paper on results, showing the improved skill using logistic regression with 15 RAP
model predictor fields, in conjunction with 10 GOES-based CI predictors.
 Demonstrate CI algorithm at Experimental Warning Program in Spring 2014.
 Obtained a request that the CI algorithm move to become a more official GOES-R product.
NOAA STAR:
 Provide real-time AWG Cloud Properties (cloud phase, cloud height) to UAHuntsville.
 Consult with UAHuntsville regarding provided product(s) interpretation.
 Provide nighttime version of cloud properties (“asha”) to UAHuntsville.
NOAA ESRL:
 Conduct retrospective evaluation of impacts from assimilation of enhanced GOES-R CI.
o Collect GOES-R CI data and all other needed fields (other observations, lateral boundary
conditions, etc.) for a period of interest.
o Run retrospective experiment (both control and with GOES-R CI data) for RAP.
o Begin evaluation of GOES-R CI data impact on retrospective RAP and HRRR runs.
Accomplishments & Plans
1. 0–1 hour Convective Initiation Algorithm
The GOES–R CI algorithm as presented in Walker et al. (2012) was developed as a first step toward a
geostationary satellite-based, probabilistic style of CI nowcasts, with an emphasis on “cloud object
tracking.” The Walker et al. (2012) approach used the same geostationary satellite infrared (IR) interest
fields as the prior binary “yes/no” algorithm, yet instead of summing the number of achieved static
critical thresholds on a per-cloud basis to determine whether CI will or will not occur as the previous
algorithm did, it utilized logistic regression (LR) to produce an index on a scale from 0 to 100,
indicating the “strength” of a satellite-derived pre-CI signal, based on indicators of cloud top height,
growth rates and glaciation, as well as updraft width. A necessary component when developing an LRbased forecast method is a sizeable database of the IR field indicators, for CI and non-CI events across
seasons and geographical locations, which establish truth data that may be used to train/form the LR
equations or another predictive model. Such a database was assembled, with an emphasis on collecting
cases over the GOES-West domain.
While a great deal of information about growing clouds can be provided by satellite data alone, there
has been no directly available knowledge within the GOES-R CI algorithm provided about the
atmospheric environment in which clouds will grow. Numerous studies have shown the fundamental
importance of vertical (0–6 km) wind shear, CAPE, convective inhibition, and low-level moisture (to
name a few) to the occurrence and organization of convective storms. With the inclusion of NWP data
for environmental characterization used in tandem with satellite-derived information about individual
clouds, a more accurate set of nowcasts can be produced. This additional NWP information would act to
appropriately reduce the CI nowcast probabilities in situations like the one mentioned above, despite
strong satellite-retrieved cloud growth signals, and to increase the CI nowcast probabilities when both
the satellite-based cloud growth signals and the environment are more conducive to mature convective
storm development.
Therefore, within this timeframe, the Walker et al. (2012) has been expanded to include 15 NWP
model fields, in this case, from the Rapid Refresh (RAP) model, which are combined with 10 GOES
fields. The use of a much larger training database has significantly improved the skill in the GOES-R CI
method. Many of these new developments (i.e. the use of a logistic regression model and the associated
skill improvements to GOES-R CI) are described in Mecikalski et al. (2014).
The GOES-R daytime cloud properties have also been incorporated into GOES-R CI, providing
much improved detection of CI beneath thin higher clouds.
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Plans:
 More fully exploit the Cloud Property dataset from NOAA CIMSS to enhance the baseline GOES-R
CI Algorithm (e.g., enhanced CI detection beneath cirrus, nighttime CI detection, perform estimates
of CI “intensity”).
 Obtain GOES-R DMV dataset for incorporation into GOES-R CI.
 Further develop new understanding and further tests of 1-min rapid scan operations (RSO) datasets,
for eventual use within AWG CI.
 Maintain and grow a CI database in the “RAP model era” as a means of improving GOES-R CI
performance across CONUS/OCONUS domains.
 Develop Cloud Property data relationships in context of GOES-R CI framework.
 Further enhance nighttime cloud mask for improved CI detection.
 Develop new fields of information within the GOES-R CI algorithm for display to forecasters within
the AWIPS-II environment.
 Demonstrate GOES-R CI at HWT, AWC, OPC Proving Grounds; respond to forecaster feedback.
2. RAP and HRRR Model Assimilation
For this project, GOES cloud top cooling rate data provided by UAHuntsville are being assimilated into
an experimental Rapid Refresh (RAP) version at the Global Systems Division of the Earth Systems
Research Laboratory. Within this RAP modeling framework, the cloud-top cooling rate data are mapped
to latent heating profiles and are applied as prescribed heating during the diabatic forward model
integration part of the RAP digital filter initialization (DFI). For the HRRR, the satellite-derived latent
heating profile is prescribed in a one-hour pre-forecast spin-up cycle with no application of the digital
filter. For both the RAP and the HRRR, the GOES-satellite-based cloud-top cooling-rate information is
blended with data from radar reflectivity and lightning flash density to create a unified convective
heating rate field. In the current HRRR configuration, four 15-min cycles of latent heating are applied
during a pre-forecast hour of integration. This is followed by a final application of GSI at 3-km to fit the
latest conventional observation data.
Previous work on this project has demonstrated that these cloud-top cooling rates can help with the
location and intensity of storms in the RAP system. A new retrospective period of June 15-22, 2014 has
been chosen to continue investigation of the use of cloud top cooling rates in partnership with other
satellite derived convective initiation indicators in the RAP forecasts. This period was quite active with
severe storms, especially in the northern Plains states, with numerous tornadoes and large hail reports
over the period. In addition to the RAP model, we are also investigating the impact of the satellite
derived cloud-top cooling rates in the HRRR model that uses the RAP for boundary conditions. Other
parameters to be evaluated are the CI probability information provided by UAHuntsville, and the impact
in variation in the vertical structure of the assumed heating profile using information on the cumulus
clouds as derived from GOES.
Plans:
 Collaborate with ESRL (via site visits and through email and teleconferencing) on two-tiered
methods for assimilating GOES-R CI fields within the RAP/HRRR models using the GSI.
 Develop improved functional relationship relating GOES-R CI fields and logistic regression CI
probabilities to enhance the structure of latent heating profiles in R.
 Implement and further test analysis procedure toward reducing false-positive, low-probability CI
objects, which will help improve performance skills in the RAP and HRRR assimilation
procedures.
 Perform additional retrospective runs to test the CI assimilation procedures, and further develop
these procedures into the real-time HRRR model.
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Additional Information
1. Interaction with operational partners – Experimental Warning Program: Chris Jewett and John
Mecikalski travelled to Norman, OK to demonstrate the GOES-R CI product to National Weather
Service Forecasters, for two weeks in May 2014.
Additionally, the UAHuntsville team is continually interacting with Chris White of the NWS Huntsville
Forecast Office, and NASA SPoRT, for transition and evaluation of the CI algorithm.
2. Conference/workshop participation –
 Mecikalski, J. R., T. Smith, S. Weygandt, C. P. Jewett, and W. Straka, 2013: Assimilation of GOES
infrared fields for improving convective initiation within the NOAA Rapid Refresh model. 2013
EUMETSAT Meteorological Satellite Conference and 19th American Meteorological Society (AMS)
Satellite Meteorology, Oceanography, and Climatology Conference, 16-20 September 2013, Vienna,
Austria.
 Mecikalski, J. R., T. Smith, S. Weygandt, W. Straka, C. Jewett, and A. Heidinger, 2013:
Assimilation of GOES infrared fields for improving convective initiation within the NOAA Rapid
Refresh model. 2013 EUMETSAT Satellite Conference and 19th AMS Conference on Satellite,
Oceanography and Climatology. Vienna, Austria 16–20 September 2013.
 Mecikalski, J., C. Jewett, S. Weygandt, T. Smith, A. Heidinger, W. Straka, and S. Benjamin, 2014:
Convective Initiation and 0-6 hr Storm Nowcasting for GOES-R. 26th Conference on Weather
Analysis and Forecasting / 22nd Conference on Numerical Weather Prediction. Atlanta, GA, 2-6
February.
 Smith, T. L., S. S. Weygandt, C. Alexander, M. Hu, H. Lin, and J. R. Mecikalski, 2014: Assimilation
of GOES satellite based convective initiation data into the Rapid Refresh and HRRR systems to
improve aviation forecast guidance. Fourth Aviation, Range, and Aerospace Special Symposium.
Atlanta, GA, 2-6 February 2014.
3. Funding concerns – It has been challenging to obtain Year 2 funding through NOAA CICS-Maryland,
which is partially caused by UAHuntsville becoming a new member of CICS only in June 2014. Hence,
as of this report, Year 2 funding is not in place at UAHuntsville, NOAA ESRL or NOAA CIMSS.
Despite this, we have operated on this project per our agreed to plans.
4. Outside project publicity – The National Weather Service Operational Advisory Team (NOAT)
approved the GOES-R CI Algorithm as a priority algorithm for GOES-R in July 2014.
5. Journal articles – This project partially supported development of the paper:
 Mecikalski, J. R., J. K. Williams, C. P. Jewett, D. Ahijevych, A. LeRoy, and J. R. Walker, 2014:
Probabilistic 0–1 hour convective initiation nowcasts that combine geostationary satellite
observations and numerical weather prediction model data. J. Climat. Appl. Meteorol., Submitted.
Plans for the next Reporting Period:
(1) Develop the use of the nighttime GOES-R cloud property datasets as obtained from NOAA CIMSS:
Use these fields to improve on the nighttime detection of CI, and to improve the performance of the
daytime detections in challenging situations (e.g. in layered cloud situations).
(2) RAP Real-time parallel experiments and HRRR experiments: Work has been completed to set a realtime parallel RAP run assimilating the CTCR data within one of the parallel RAP cycles run by GSD
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and is awaiting a test slot. This has involved porting the CTCR assimilation code, including the data
ingest to the latest SVN trunk version of the RAP. Work is also nearly complete to test assimilation of
the CTCR data within the 3-km HRRR. Modifications to the assimilation procedure have been made and
will initially include just a single assimilation.
(3) Work to implement a quadratic discriminant analysis (QDA) procedure: QDA helps reduce the CI
false alarms, and specifically the CI objects with <70% probability of developing into a new convective
storm in the coming 45 min to 1 hour. Also, assess how well QDA can work to isolate which CI events
will become strong/severe storms (using validation against SPC reports).
(4) Move to use the GOES-R derived motion vectors (DMVs): The DMV method will serve as a
replacement for the current atmospheric motion vectors (AMVs) used in the GOES-R CI algorithm.
(5) Obtain feedback from the user community: Attend the HWT (Spring 2015) and AWT (August 2014)
to obtain forecaster feedback, and also interact with NASA SPoRT as needed on training material.
Publish a paper in the Bulletin of the American Meteorological Society (AMS) on how the GOES-R CI
algorithms are used in the operational environment.
Key Graphics
Detecting CI Beneath Thin Cirrus
Determine locations of “thin” cirrus from cloud mask and optical depth properties
Thin cirrus from Berendes et al. (2008) mask
Methodology
Ø Run a 20 x 20 kernel over each pixel meeting
the criteria to determine if it is anomalous to
the surrounding pixels.
Cirrus with optical depth <30 (cyan), >30 (red)
Trackable cirrus objects
Ø Use results of Smith et al. (2008), Mecikalski
et al. (2011, 2013) and other studies to set
pixel optical depth (τ) criteria.
Ø If τ > 2σsurrounding then it is deemed a trackable
object, and the GOES-R CI algorithm
continues (Walker et al. 2012).
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UAH/Mecikalski
Figure 1: An example of improved CI detection beneath cirrus clouds in the GOES-R CI algorithm, through
the use of real-time NOAA daytime cloud property datasets.
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3h forecast 20 dBz from 15z, valid
18z, 8 July 12
–3 CTCR
HIT
MISS
FA
–5 CTCR
33
Figure 2: An example of retrospective RAP model runs using the GOES-R cloud-top cooling rate fields.
Figure 3: POD-Y and bias scores for the three experiments (CNTL, CTCR –3 K, and CTCR –5 K) as a
function of lead-time for 8 forecasts 8 July 2012.
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