Overview%20of%20some%20operational%20issues%20related%20to%20EPS%20data

1
Overview of some operational issues related to
EPS data

• Richard H. Grumm
• National Weather Service Office
• State College, PA 16803
• Contributions from
• Larry Struble, Pittsburgh
• Josh Korotky, Pittsburgh
• Chris Mellow Cleveland
• Justin Arnott Binghamton
• David Bright SPC

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OVERVIEW

• Training Issues, experiences and initiatives
• NWS
• Eastern Region
• And beyond
• The Future?
• new data and forecasting
• NAEFS/GEFS in GFE
• SREF GFE
• Real world ensemble applications
• Recent winter of 2008
• Real cases affecting real forecasters

3
Training Issues

• COMAP-2008
• Training on ensembles for new SOOs
• 2 full days from chaos to spaghetti
• State College-Pittsburgh
• Sub-regional workshop in State College
• 5 offices attended locally BUF-BGM-PHI-WBC-CTP
• Remote offices Go-to-Meeting ER/SSD-BTV-OKX-LWX
  and CWSU Leesburg

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Local Workshop 5-6 May 2008

• Requested by CTP operations team
• Learn how to use the data better
• Become more knowledgeable in these data
• Topics
• Chaos and uncertainty from Mary Baxter (Central
  Michigan), Jim Hansen (Monterey), and Josh
  Korotky (WFO-PIT)
• Products and forecasting David Bright (SPC)
• Basic products and statistics Richard Grumm
  (CTP)
• Local modeling and ensemble efforts- Justin
  Arnott and Mike Evans (Binghamton)

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Personal ExperiencesTraining and use of EPS data

• Effort Growing world wide
• China, Africa, South America
• Involvement in these activities provides cases
  and contacts (the high touch side of it all).
• Local WFOs and Universities
• Could be short window of opportunity
• Exploit these data? now
• Training in Universities is slowly evolving as I
  learned visit St Louis University April 2008

6
Future and DataNWS perspective

• Importing NAEF/GEFS data into GFE (CTP)
• 2m temperatures and winds
• Add value to the forecast
• Skill comparable to GMOS.
• Data provided by NCEP (Yuejian Zhu)
• SREF data into GFE (WFO-PIT)
• Assists in complicated forecast problems
• NAEFS/GEFS in GFE
• SREF GFE

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NAEFS Data5km downscale EPS data aid in
forecasting winds and temperatures
8
SREF Probability of Rain is copied into Potential
grid (PotRainShowers grid)
9
3 hr PoP grid from SREF

• PoP input to a local PoP grid can be from
  forecaster created PoP or SREF PoP grids
• This figure shows the SREF PoP grid

10
Winter Precipitation Type Grids

• Procedure is especially useful when creating
  complex winter weather grids
• SREF PoP and SREF precipitation types can be
  easily merged into resultant weather grid
• Weather grid created from SREF precipitation type
  and PoP are coherent in space and time

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SREF Smart Init Probability Snow
SPC Probability Snow
12
SPC Probability ZR
Probability ZR
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Real world ensemble applications

• Proving the value of EPS data to users
• Around the world
• China cold outbreak in January 2008
• Korean floods August 2007
• Within the NWS
• Cases for impacted offices
• Recent winter of 2008 ? provided many good cases
  to facilitate training, cases studies, and
  applications.

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Severe Weather 5-6 Feb 2008
Figure Storm Prediction Center (SPC) storm report
by type for the 24 hour periods ending 0800 CDT
on 6 and 7 February 2008. Reports are color coded
by severe weather event type. http//www.spc.noaa.
gov/climo/reports/ Courtesy of the SPC.
15
Snow fall Image
16
Surface and QPF
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SREF 04/0300 UTC has the big rain too
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Korean Flood August 2007

• Ideal application of ensembles
• Precipitation amounts (Probabilities) and timing
  provided excellent guidance of a significant
  event.
• The probability of extreme amounts was high a
  clear sign to be cautious and aware of a big if
  not record event.
• Meteorological setting
• Intensity of key features associated with heavy
  rainfall.
• Key features associated with heavy rains events
• Climate anomalies of key features adding
  confidence to the forecasts and put the event
  into a meteorological context thus closing the
  loop.

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Estimate Rainfall valid 0000 UTC 10-14 August
2007North Korean Flood GEFS forecast up to 14
inches of rainfall!
20
PROB 100mm 48 hrs
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Pattern Change in China

• Xu Xuan Jia Shenyang Central Meteorological
  Observatory, 11001
• Examination of cold and snow in China January
  2008?
• Warm pattern turned very cold
• Well forecast by Global Ensembles.
• Good demonstration Case studies

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Comparative Forecasts
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Midwest Floods 18-19 March 2008

• Big rainfall event
• Most of it fell over 36 hours
• Over the central Mississippi and Ohio Valleys
• Was particularly well forecast
• Begs the question why?
• Some extreme rain amounts for a cold season
  event.
• Some large anomaly signals in the pattern too.

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Rainfall with the event
Figure 1. Observed Precipitation (mm) from the
unified precipitation data set showing a) storm
total precipitation from 1200 UTC 17 March
through 1200 UTC 19 March 2008 and b) 24 hour
precipitation for the period ending at 1200 UTC
19 March 2008.
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Short range QPF? for 3 inches
Figure 14. As in Figure 12 except GEFS
initialized at (left) 1200 UTC 17 March and
(right) 1800 UTC 17 March 2008.
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The Pattern as forecast
Figure 1. As in Figure 9 except showing PW
forecasts. Upper panels show each members 25,
12.5 and 6.75 mm contour and the spread about the
mean. Lower panels show the ensemble mean and the
standardized anomalies of the ensemble mean
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Case Studies

• Bring ensembles impact to users
• Prove they can work any where then they can work
  here
• Local cases get local offices interested
• Louisville is doing a case Study on the Heavy
  rain events
• LaCrosse on the Snow events
• Western Region on the Big Jan 2008 winter storm
  (Salt Lake and Reno).
• Case studies?
• Great form of training and learning
• Help users see the value and increase interest in
  using these data
• And you get to interact with fun people
• David Bright offers to help anyone do severe
  cases at the end of all of his talks!

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Review

• Training
• There is a lot going on and it is getting better
• Interest is growing
• Case studies are a great training tool and
  incentive.
• The Future?
• NWS? getting data into GFE to help with forecast
  problems
• NAEFS/GEFS in GFE
• SREF GFE ? winter weather problem is but one
• Will evolve more probabilistic forecast outcomes
  and products it is inevitable and it will be
  unstoppable.
• Real world ensemble applications
• Case studies of big and significant events sparks
  users interest and will grow the use of ensembles

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Acknowledgements

• Mentors Yuejian Zhu and Jun Du
• Input and ideas
• ER/SSD
• All those listed in the beginning who made
  contributions Larry Struble, Pittsburgh, Josh
  Korotky, Pittsburgh, Chris Mellow Cleveland,
  Justin Arnott Binghamton, David Bright SPC
• Others for great support and encouragement Zoltan
  Toth, Louis Uccellini, Robert Hart, and several
  individuals in the WMO and my local MIC Bruce
  Budd.