The relationship between Science and DSS in the NWS – iss

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2017-07-11 39K 39 0 0

The relationship between Science and DSS in the NWS – iss - Description

Mike Evans. WFO Binghamton, NY. Some quotes on the increasing emphasis on decision support services in the National Weather Service:. Ten years ago – “If we are not careful and don’t maintain the importance of science in the NWS, forecasters will turn into nothing more than communicators”.. ID: 569045 Download Presentation

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The relationship between Science and DSS in the NWS – iss




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Presentations text content in The relationship between Science and DSS in the NWS – iss

Slide1

The relationship between Science and DSS in the NWS – issues and discussion

Mike Evans

WFO Binghamton, NY

Slide2

Some quotes on the increasing emphasis on decision support services in the National Weather Service:

Ten years ago – “If we are not careful and don’t maintain the importance of science in the NWS, forecasters will turn into nothing more than communicators”.

Earlier this year – “Science is dead”. (joking?)

So… what is the role of science in the NWS with the increased focus on DSS and communication?

Slide3

From the Great Lakes conference this year:

“Before you can communicate risk, you have to identify risk”. – Dick

Wagenmaker

– MIC DTX.

Implication is that the science program in the NWS needs to focus (more than ever) on hazardous weather identification (increased situational awareness, diagnosis of significant weather events).

Slide4

Research and training topics directly related to improved DSS

Optimal and efficient use of data sets (we’re being hit by a firehose of information; NY-

meso

-net, GOES-R, models / ensembles).

Convection (factors that promote severe weather outbreaks for situational awareness, application of high-resolution (convection-allowing) modelling and ensembles, radar diagnostic tools and techniques).

Flooding (QPF forecasting, ensembles, probabilistic forecasting, QPE).

Winter weather (QPF forecasting, ensembles, probabilistic forecasting).

Aviation (IFR forecasting, other aviation hazards).

Probabilistic forecasting techniques (ensembles, anomalies, analogs).

QPF forecasting (role of the human, models, ensembles and model QPF blending).

Slide5

Today’s talks from our university partners:

Environmental factors that affect lake effect snow

(situational awareness, diagnosis of hazardous weather).

Examination of structures in lake effect snow bands (diagnosis of hazardous weather).

NY State

meso

-net (diagnosis / situational awareness).

Environmental factors that affect convection (situational awareness / diagnosis).

Tornado warnings / dual pol radar (diagnosis of hazardous weather).

Ensembles and QPF / heavy rainfall (situational awareness).

Slide6

SOO meeting science program vision

Transition to DSS focus, driven by groundbreaking improvements in modeling and remote sensing.

Transition to information-centric mindset and operating concept (forecast production to managing the forecast process and communicating).

More involvement in research to operations.

Development and delivery

of training

Slide7

Some issues for the (near) future:

Automation of routine forecasting tasks will increase – more time for

training, science, communication of probabilistic information?

Again – QPF forecasting. Where are we going? Model blending vs. can humans improve on the models?

F

orecaster

personality types – how can we integrate detail

oriented people

into a process requiring increasing communication skills?

Universities – should this paradigm shift change how new forecasters are being trained? (communication and social science vs. traditional forecasting contests).


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