Why Good Forecasts Go Bad PowerPoint Presentation

Why Good Forecasts Go Bad PowerPoint Presentation

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Dr. Jonathan Fairman. 21 April 2016. Presentation by Prof. Dave Schultz. Early meteorology was . not. a science. . “. Whatever may be the progress of sciences, NEVER will observers. who are trust-worthy, and careful of their reputation, venture to foretell the state of the weather.. ID: 641567

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Slide1

Why Good Forecasts Go Bad

Dr Jonathan Fairman21 April 2016

Presentation by Prof. Dave Schultz

Slide2

Early meteorology was

not

a science.

Whatever may be the progress of sciences, NEVER will observers

who are trust-worthy, and careful of their reputation, venture to foretell the state of the weather.

François Arago (1846)

Slide3

Director of Meteorology Department: 1854–1865

13 observing stations around Britain and 5 on the

Continent

Table

for the newspapers

Coastal signals for mariners

Daily 48-h forecasts of wind speed, direction, and coming weather for five regions in British Isles

Weather Book

(1862)

Robert Fitzroy

Slide4

Criticism of the Forecasts

Scientific

gentlemen

of the Royal Society belittled weather observers and Fitzroy

so inaccurate and haphazard a character, as not to be of any true scientific

value

Robert Fitzroy

Slide5

So, meteorologists should make

perfect

weather forecasts, right?

Numerical weather prediction as a process is fairly well understood

Slide6

However, occasionally things go wrong

Rodwell et al., 2013

Slide7

Reason #1: Imperfect Knowledge About Current State of Atmosphere

Data voids

Interesting weather in between observing locationsInstrument sensitivity and errorsNo observations of important quantities

Slide8

Slide9

Slide10

We do not have the observations

to know what

s going on inside

the cloud within each box!

Slide11

The State of the Problem

The atmosphere has a total mass of approximately 5.148 x 1021 g (Trenberth and Smith, 2005)The mass of 1 mol of dry air is 29.87 g

This leads to 1.78 x 1020 mol of airEach mol of air has Avogadro’s number of particles in it- 6.022 x 1023 atomsSo, there are 1.07 x 1044

particles of air in the atmosphere -> And we can’t exactly measure these directly anyways!

Slide12

Reason #2: Imperfect Computer Model

Need faster, more powerful computers for more grid points

Slide13

Reason #2: Imperfect Computer Model

Need faster, more powerful computers for more grid points

Need better understanding of physical processes

Slide14

Many processes in models are

parameterized

. Simplifies physical processes that we don’

t fully understand or don

t have measurements of

Calculates processes occurring on scales smaller than grid boxes

Slide15

Slide16

Slide17

Slide18

Slide19

No Man’

s Land

(Joe Klemp)

horizontal grid spacing

PBL = planetary boundary layer

LES = large-eddy simulation

Slide20

No Man’

s Land

(Joe Klemp)

What cannot be resolved must be parameterized!

Slide21

Philosophy of Forecasting

Peter Clark, UK Met OfficeWhat is the phenomenon producing the hazard?

Can our models directly represent it?Yes: Detection and presentationNo: Diagnostic parameterization

Slide22

Why parameterize?

Unresolved physical processes need to be included in NWP modelsParameterized quantities often reflect the sensible weather (clouds, precipitation, surface temperature, near-surface wind)

Parameterizations “distill only the essential aspects of the physical processes they represent”

(

Stensrud

2007, p. 9)

Slide23

Challenge

Find relationships between subgrid-scale processes and model-predicted (grid-scale) variables to “

close” the parameterizations. This process is reductionist — we assume we can explain the whole as a sum of its parts.

We separate boundary layer and land surface processes, separate boundary layer and shallow cumulus processes, and represent each one separately.

We apply the resulting schemes individually, and the summed outcome is assumed correct!

Stensrud

Slide24

Subgrid-scale physical processes act within the vertical column of each grid cell.

The vertical region affected by each scheme varies.

Stensrud

Slide25

What measurements are needed for NWP?

What observations are routinely available for the following?

soilsvegetationboundary layer

oceans and lakes

convection

Stensrud

Slide26

Measurements

soils

Temperature, moisture, soil type

vegetation

Leaf area (radiation), transpiration rate, moisture content, plant type

boundary layer

Turbulent kinetic energy; temperature, moisture, and wind profiles; aerosol content oceans and lakesSurface temperature, roughness, surface wind speed, salinityconvectionHydrometeors (type, size, phase, crystal habit), winds

Slide27

Observations

soils

Few and far between.vegetation

AVHRR, MODIS available but not often used

boundary layer

Boundary layer depth estimated by soundings, seen in 915-MHz profilers. No turbulence measurements.

oceans and lakesSatellite data, buoys, ships (little below surface)convectionRadar, satelliteStensrud

Slide28

Many of the quantities needed to initialize or verify parameterization schemes are not routinely available.

Soil moisture, soil temperature, surface fluxesTurbulent kinetic energy

Boundary layer depthAerosol concentrations, cloud water pathRadiation amounts for all componentsGround temperature, water temperature

Vegetation coverage and biomass

Microphysical particle size distributions

Convective heating profiles

Stensrud

Slide29

Field observations are usually required to develop new parameterization schemes.

Special observations are used to tease out relationships needed to close the schemes (relate them to model variables).Schemes are then applied to every grid point in the model domain.

Stensrud

Slide30

Parameterizations in NWP models

Land surface–atmosphereSurface energy budget, and sensible and latent fluxes

Soil–vegetation–atmo-sphereWater–atmospherePlanetary boundary layer and turbulenceConvectionCloud microphysics

Clear-sky radiation

Cloud-cover and cloudy-sky radiation

Orographic drag

Stensrud

radiation

boundary layer

convection and

microphysics

soil-vegetation

cloud cover

Slide31

Reason #3: Chaos

Butterfly effect”Sensitive dependence to initial conditions

Slide32

Prof. Ed Lorenz

time

(Verlaan and Heemink 2001)

Slide33

Prof. Ed Lorenz

time

(Verlaan and Heemink 2001)

Slide34

Prof. Ed Lorenz

time

(Verlaan and Heemink 2001)

bifurcation

Slide35

Prof. Ed Lorenz

time

(Verlaan and Heemink 2001)

bifurcation

Small differences in initial conditions

will lead to large differences later.

Slide36

Small differences in initial conditions

will lead to large differences later.

start

end

Prof. Ed Lorenz

bifurcation

Slide37

Prof. Ed Lorenz

Even with a

perfect model

starting

with

perfect initial conditions

Slide38

Prof. Ed Lorenz

Even with a

perfect model

starting

with

perfect initial conditions

weather forecasting is limited to two weeks

.

Slide39

True genius resides in the capacity for evaluation of uncertain, hazardous, and conflicting information

.”

Slide40

Given all these limitations,

we have no right to do so well in forecasting the weather!

What other discipline forecasts the future with as much success as meteorology?

Dave sez:

Slide41

Improved Computer Forecasts =

Increasing amount and better use of data

+

Improving numerical models

+

Ensemble prediction systems

+Increasing resolution

Slide42

Improved Computer Forecasts =

Increasing amount and better use of data

+

Improving numerical models

+

Ensemble prediction systems

+Increasing resolution

Slide43

Members of an ensemble use slightly different initial conditions.

Observation

Ensemble

mean

Slide44

Spread is a measure of uncertainty in the forecast.

Observation

Ensemble

mean

Slide45

Spread is a measure of uncertainty in the forecast.

2°C

Day1

2

3

4

2°C

Slide46

Spread is a measure of uncertainty in the forecast.

11°C

2°C

Day1

2

3

4

5

2

9

6

Slide47

Spread is a measure of uncertainty in the forecast.

11°C

2°C

Day1

2

3

4

5

2

9

6

13

21°C

Slide48

European Centre for Medium-Range Weather Forecasts

84-h Forecast of Sea-Level Pressure

51-member ensemble: “Postage stamp plot”

Slide49

European Centre for Medium-Range Weather Forecasts

51-member ensemble: “Postage stamp plot”

84-h Forecast of Sea-Level Pressure

Similar forecasts

Same cluster

Slide50

European Centre for Medium-Range Weather Forecasts

84-h Forecast of Sea-Level Pressure

51-member ensemble: “Postage stamp plot”

Different forecasts

Different clusters

Slide51

US National Weather

Service Ensemble

“Spaghetti plot”

of two contours of

path of jet stream

Slide52

US National Weather

Service Ensemble

“Spaghetti plot”

of two contours of

path of jet stream

Less certainty

More certainty

Slide53

Met Office Ensemble

Olympic Showcase

Probability (%) that rain (>0.2 mm/h) will fall sometime within 18 h

>95%

<5%

>95%

Slide54

US National Weather Service National Hurricane Center

Slide55

US National Weather Service National Hurricane Center

Slide56

The Goal of US National Weather Service: “

Warn on Forecast”

(Hirschberg et al. 2011)

Slide57

(Hirschberg et al. 2011)

84-h forecast of risk to pirates in small boats due to adverse winds and seas

HIGH RISK

TO PIRATES

LOW

RISK

US NavyFleet NumericalMeteorology and Oceanography Center

Slide58

Themes for Today

In principle, weather forecasting is easy.

Initial state of atmosphere (observations)Laws of atmosphere (physics)

Slide59

Themes for Today

In principle, weather forecasting is easy.

Initial state of atmosphere (observations)Laws of atmosphere (physics)In practice, it is more difficult.Errors in initial state

Approximations

Chaos

Slide60

Themes for Today

In principle, weather forecasting is easy.

Initial state of atmosphere (observations)Laws of atmosphere (physics)In practice, it is more difficult.Errors in initial state

Approximations

Chaos

Given the extent of the unknown initial state and the approximations used to solve the equations, that we succeed so well in weather forecasting is

an amazing human accomplishment.

Slide61


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