GOESR3 Periodic Reporting
Project Team: CIRA, UAH, CIMSS, NSSL, CREST
Reporting Period:
February 2013 - June 2014
Team Lead: Dan Lindsey (STAR/RAMMB)
Team Members: John Mecikalski (UAH), Chris Velden (CIMSS), Bob Rabin (NSSL), Brian VantHull (CREST)
Project Title: Convective Storm Forecasting 1-6 Hours Prior to Initiation
Project Number: 30
Executive Summary
This reporting period represents the final funding cycle of the project. Carryover funds from FY12 into
FY13 totalled just over $63 K, and this was split among the 5 institutes involved in the project. The
grand majority of these funds over the final year was used to draft and submit a paper to the Journal of
Applied Meteorology and Climatology, and that paper has subsequently been accepted for publication
and is currently in press. The primary conclusion from the project is that some of the ideas investigated
to aid in 1-6-hour storm forecasting were shown to be both viable and potentially very helpful after the
launch of GOES-R. The product development itself will be very straightforward and can wait until the
real GOES-R data begins flowing.
Milestones
The following 2 milestones for the project were submitted in December 2012 for FY13

Complete the analysis of the CI predictors saved from the summer of 2012
Analysis of the CI predictors from the summer 2012 data was completed. The original idea was to attempt to
combine all the predictors into a single CI-prediction product, but this was determined to be unfeasible
because each of the predictors only work under certain conditions and certain parts of the country (e.g., in
cloud free conditions for one of them). However, all of the candidate CI predictors were shown to be
individually useful (under those certain conditions).

Use the results of the statistical analysis to make a recommendation for a GOES-R-based pre-CI
product
As noted above, a single product using all of the CI predictors simultaneously was shown to be unfeasible.
Heating gradients and mesoscale Atmospheric Motion Vectors (AMVs) may be used individually to gain prestorm environment information (or to feed into numerical models, in the case of the AMVs). The 10.35 –
12.3 µm split window difference was shown to provide information about where low-level water vapor is
locally deeper in cloud-free environments, and this information will help forecasters in pre-storm mesoscale
analysis after GOES-R is launched.
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GOES-R3 Status Report Template
NESDIS STAR GOES-R
Accomplishments
- The heating index portion of the project (UAH) was completed in calendar year 2013, so the final
carryover FY13 funds were used on a related GOES-R project. CI events over the western U.S. (within
the GOES-West view) were collected and verified against WSR-88D data. The goal was to evaluate
and improve the accuracy of the GOES-R CI algorithm in convective regimes common to the dry
western U.S. The CI statistics (GOES infrared fields, RAP model environmental fields) for both CI and
non-CI cases were tallied, and then used within a logistic regression model to produce probabilistic 0–1
hour CI nowcasts. The results of this work helped produce a refined GOES-R CI algorithm as
demonstrated at the Spring 2014 Hazardous Weather Testbed.
- For the mesoscale AMV and radar-derived winds portion of the project (CIMSS/NSSL), the final task
was to combine radar and satellite wind analyses using LAPS or NSSL assimilation scheme, and
consider what diagnostics from this output can be used as a CI predictor. Both options of 1) using
LAPS and 2) a 3D-VAR analysis scheme developed at NSSL were considered. Since the use of the
LAPS model required assistance from scientists at ESRL, it was not feasible to complete an analysis
using that model before the completion data of the project. Nevertheless, future work is planned to
assimilate AMV's from 1-minute GOES-14 datasets in LAPS and investigate the impact on forecasting
convective initiation. The 3D-VAR scheme developed at the NSSL has been modified to include AMV's
in addition to WSR-88D radial velocities and surface mesonet winds in the Oklahoma area. Currently,
water vapor (upper-level) winds are being assimilated into the analysis in near real-time. It appears that
the upper-level winds from satellite compliments low-level wind information from radar in diagnosing
areas of low-level convergence and upper-level divergence, a factor in convective initiation. In addition
to the real-time analysis, the evolution of winds and divergence preceding the severe weather are being
evaluated from 20 May 2013 severe storms in Oklahoma (Moore tornado supercell).
- As noted above, the majority of the final year of funding at CIRA was used to do some further analysis
on the split window difference work and prepare a manuscript for publication. The Community
Radiative Transfer Model (CRTM) was used to perform a series of sensitivity tests to examine how the
low-level temperature and dewpoint profiles affect the GOES-R 10.35 – 12.3 µm Split Window
Difference (SWD). Figure 1 below shows the results. As the low-level temperature lapse rate becomes
steeper (i.e., more unstable), the SWD increases. And as the low-level water vapor becomes deeper
(for a fixed amount of surface moisture), the SWD also increases. These two effects were shown to be
the primary drivers in the GOES-R-observed SWD.
- Figure 2 shows simulated imagery from 20 May 2011, including the 10.35 µm band (left column), and
the SWD (right column). Note that a local maximum in SWD can be seen across central Texas 2-3
hours before any convective clouds form there. Clouds begin to form along this dryline by 17 UTC and
storms have initiated by 18 UTC. The maximum in SWD was due to locally deeper water there, owing
to surface convergence along a dryline.
- The horizontal black line in Fig. 2b shows the location of a vertical cross-section presented in Figure
3. Plotted are the SWD (white line), temperature lapse rate (black line), and specific humidity (colors).
Note that although the lapse rate is very steep in west Texas, the low-level moisture is lacking there, so
the SWD is small. In east Texas, there’s lots of near-surface moisture and the lapse rates are more
smaller. But along the dryline in central Texas, the lapse rates are slightly steeper, but most
importantly the moisture is locally deepest here. The result is a local maximum in the SWD. It’s this
signal that will help forecasters pinpoint where convective clouds and possibly storms will form using
data from the GOES-R ABI.
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GOES-R3 Status Report Template
NESDIS STAR GOES-R
Additional Information
1. Interaction with operational partners – Output from the NSSL WRF model is being used in real time
to generate the 10.35-12.3 µm simulated imagery, and this data is being sent out via the LDM. The
SPC/HWT has viewed the output during past spring experiments, and currently WPC is pulling in the
data in real time and making it available on their NAWIPS system
2. Conference/workshop participation – A presentation on this topic was given by D. Lindsey in July
2013 as part of the inaugural GOES-R Science Series
3. Outside project publicity – This work has been well-publicized via the Hazardous Weather Testbed at
the Storm Prediction Center in Norman
4. Plans for operational transition – Operational transition of any product that results from this work will
have to wait until the real GOES-R data is flowing in approximately 2 years.
5. Journal articles – Lindsey, D. T., L. Grasso, J. F. Dostalek, and J. Kerkmann, 2014: Use of the
GOES-R split window difference to diagnose deepening low-level water vapor. J. Appl. Meteor. Climat.,
in press. Early Online Release: http://journals.ametsoc.org/doi/pdf/10.1175/JAMC-D-14-0010.1
Key Graphics
Figure 1. a) Six different temperature profiles and 5 different dewpoint profiles used in the sensitivity experiments
described in the text, shown on a skew-T log P diagram. The total precipitable water (TPW) in cm is listed next to
each of the dewpoint profiles. b) CRTM-generated split window difference value using dewpoint profile 1 with the
6 different temperature profiles. c) Same as b), except using temperature profile 1 along with the 5 different
dewpoint profiles.
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GOES-R3 Status Report Template
NESDIS STAR GOES-R
Figure 2. Simulated 10.35 µm (a, c, e, and g) and SWD (BT(10.35 µm ) - BT(12.3 µm), b, d, f, and h) imagery
based on the 18-hour (a and b), 19-hour (c and d), 20-hour (e and f), and 21-hour (g and h) forecast of the NSSL
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GOES-R3 Status Report Template
NESDIS STAR GOES-R
WRF initialized at 00 UTC on 20 May 2013, valid at 18-21 UTC on 20 May 2013. The brightness temperature and
SWD units are °C. The horizontal black line in b) is the location of the vertical cross section shown in Fig. 3.
Figure 3. West-east vertical cross section through the line in Fig. 2b showing the specific humidity (colors, in kg of
water vapor per kg of air), the split window difference (°C), and Lapse Rate (in °C per km), from the 18-hour
forecast of the NSSL WRF valid at 1800 UTC on 20 May 2013. The specific humidity is plotted as a function of
pressure on the left axis and longitude on the bottom, and the SWD and LR are functions of longitude, with their
scales on the right axis.
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GOES-R3 Status Report Template
NESDIS STAR GOES-R