Severe Weather Project Summary for Week 34
Summary of Week 3–4 Severe Weather Project
Hui Wang, Alima Diwara, Arun Kumar, David DeWitt
12 September 2017
Acknowledgment:
Brad Pugh, Daniel Harnos, Melissa Ou
Jon Gottschalck, Stephen Baxter, Matthew Rosencrans
1
Goals
To expand development and perform additional
evaluation of Week-2 severe weather potential
model guidance
To explore the potential and develop experimental
forecast tools for severe weather at Week 3 – 4
time range
Year 1
Week-2 forecast (hybrid model)
o
Model predicted environmental condition (SCP)
o
Empirical relationship between model SCP and OBS SW
To support Week-2 U.S. Hazard Outlook
2
SCP
(Supercell Composite Parameter)
SCP
= (
CAPE
/1000 J kg
1
)×(
SRH
/50 m
2
s
2
)×(
BWD
/20 m s
1
)
CAPE:
convective available potential energy
BWD:
bulk wind difference
SRH:
storm-relative helicity
Data
Model data:
GEFS hindcast/forecast
OBS data:
CFSR
SW: LSR (Local Storm Report)
Hail
Tornado
Damaging wind
LSR regrided --- 0.5
o
×0.5
o
3
Storm Prediction Center
Example of
Severe Weather Outlook
o
Probabilistic forecast
o
Tornado
o
Hail
o
Damaging wind
Tornado
Wind
Hail
Day 1 Outlook
Valid: 10/2000Z–11/1200Z
4
Characteristics of SCP in OBS
SCP in CFSR is the proxy for
OBS.
o
Monthly climatology
o
Spatial pattern
o
Seasonality
5
Characteristics of LSR in OBS
Hail
o
Monthly climatology
o
Spatial pattern
o
Seasonality
6
Characteristics of LSR in OBS
Tornado
Wind
7
LSR3
Tornado
Hail
Wind
Relationship between
Observed SCP and LSR
o
How does LSR vary with SCP?
o
Are there thresholds of SCP
for LSR?
8
GEFS Prediction of SCP
o
GEFS hindcast
o
5 members
o
1996 – 2012
o
4 days apart
o
Day 1 to 16 forecasts
o
Monthly climatology
o
Spatial pattern
o
Seasonality
Day-1 Forecast
9
GEFS Prediction of SCP
Lead Time Dependence
Forecast Skill for SCP
May
Daily
data
10
GEFS Prediction of SCP
Skill for Weeks 1 and 2
Anomaly Correlation (AC)
11
Relationship between GEFS predicted SCP
and observed LSR based on hindcasts
o
Basis of dynamical-statistical prediction
o
3-month shift windows
o
LSR3: hail + tornado + wind
o
Week 1
o
Relatively strong relationship in spring
and weak in late fall/early winter
12
Relationship between GEFS predicted SCP
and observed LSR based on hindcasts
o
Basis of dynamical-statistical prediction
o
3-month shift windows
o
LSR3: hail + tornado + wind
o
Week 2
o
Weak relationship
13
Linear regression model for forecasting LSR3
o
Using GEFS predicted SCP as a predictor
o
Week-1 and week-2 forecasts for MAM
o
Forecast skill assessed based cross-validation
Anomaly correlation (AC)
Hit rate (3 categories, 33% each)
14
15
Significantly increased correlation
OBS
16
Week 1 Week 2
0.5
o
×0.5
o
3-month Window
17
Week 1 Week 2
5
o
×5
o
3-month Window
18
5
o
×5
o
0.5
o
×0.5
o
19
Summary
1.
Following Carbin et al. (2016), SCP was selected
as a variable to represent the large-scale
environment and link the model forecast to
severe weather.
2.
The hybrid model forecasts suggest a low skill
for week-2 severe weather.
3.
The forecast skill can be improved by using
5
o
×5
o
area-averaged anomalies.
20
21
Future Work
1.
To extend the analysis for Weeks 3 – 4 using
the CFSv2 45-day hindcast.
2.
To explore potential predictors for Week 3 – 4
severe weather, such as SST.
Week 34 of the Severe Weather Project focused on expanding development and evaluation of severe weather potential model guidance. The project aimed to develop experimental forecast tools for severe weather at Week 3-4 time range, utilizing a hybrid model and SCP (Supercell Composite Parameter). The study involved examining the relationship between model SCP and observed severe weather, supporting U.S. Hazard Outlooks, and analyzing characteristics of SCP and LSR in observational data.
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Presentation Transcript
Summary of Week 34 Severe Weather Project Hui Wang, Alima Diwara, Arun Kumar, David DeWitt Acknowledgment: Brad Pugh, Daniel Harnos, Melissa Ou Jon Gottschalck, Stephen Baxter, Matthew Rosencrans 12 September 2017 1
Goals To expand development and perform additional evaluation of Week-2 severe weather potential model guidance To explore the potential and develop experimental forecast tools for severe weather at Week 3 4 time range Year 1 Week-2 forecast (hybrid model) o Model predicted environmental condition (SCP) o Empirical relationship between model SCP and OBS SW To support Week-2 U.S. Hazard Outlook 2
SCP (Supercell Composite Parameter) SCP = (CAPE/1000 J kg 1) (SRH/50 m 2 s 2) (BWD/20 m s 1) CAPE: convective available potential energy BWD: bulk wind difference SRH: storm-relative helicity Data Model data: GEFS hindcast/forecast OBS data: CFSR SW: LSR (Local Storm Report) Hail Tornado Damaging wind LSR regrided --- 0.5o 0.5o 3
Storm Prediction Center Example of Severe Weather Outlook o Probabilistic forecast o Tornado o Hail o Damaging wind Tornado Day 1 Outlook Valid: 10/2000Z 11/1200Z Hail Wind 4
Characteristics of SCP in OBS SCP in CFSR is the proxy for OBS. o Monthly climatology o Spatial pattern o Seasonality 5
Characteristics of LSR in OBS Hail o Monthly climatology o Spatial pattern o Seasonality 6
Characteristics of LSR in OBS Tornado Wind 7
Relationship between Observed SCP and LSR o How does LSR vary with SCP? o Are there thresholds of SCP for LSR? LSR3 Hail Wind Tornado 8
GEFS Prediction of SCP o GEFS hindcast o 5 members o 1996 2012 o 4 days apart o Day 1 to 16 forecasts o Monthly climatology o Spatial pattern o Seasonality Day-1 Forecast 9
GEFS Prediction of SCP Daily data Lead Time Dependence May Forecast Skill for SCP 10
GEFS Prediction of SCP Skill for Weeks 1 and 2 Anomaly Correlation (AC) 11
Relationship between GEFS predicted SCP and observed LSR based on hindcasts o Basis of dynamical-statistical prediction o 3-month shift windows o LSR3: hail + tornado + wind o Week 1 o Relatively strong relationship in spring and weak in late fall/early winter 12
Relationship between GEFS predicted SCP and observed LSR based on hindcasts o Basis of dynamical-statistical prediction o 3-month shift windows o LSR3: hail + tornado + wind o Week 2 o Weak relationship 13
Linear regression model for forecasting LSR3 o Using GEFS predicted SCP as a predictor o Week-1 and week-2 forecasts for MAM o Forecast skill assessed based cross-validation Anomaly correlation (AC) Hit rate (3 categories, 33% each) 14
OBS Significantly increased correlation 16
0.5o0.5o Week 1 Week 2 3-month Window 17
5o5o Week 1 Week 2 3-month Window 18
5o5o 0.5o 0.5o 19
Summary 1. Following Carbin et al. (2016), SCP was selected as a variable to represent the large-scale environment and link the model forecast to severe weather. 2. The hybrid model forecasts suggest a low skill for week-2 severe weather. 3. The forecast skill can be improved by using 5o 5o area-averaged anomalies. 20
Future Work 1. To extend the analysis for Weeks 3 4 using the CFSv2 45-day hindcast. 2. To explore potential predictors for Week 3 4 severe weather, such as SST. 21
