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National Ice Center Science and Applied Technology Program. Dr. Michael Van Woert, Chief Scientist. weekly. global. manual. Planned Nowcast Product Evolution: “NIC 5 Year Plan”. GLOBAL NOWCAST PRODUCT. CURRENT PRODUCT. daily. global. Science makes the next step to

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national ice center science and applied technology program

National Ice CenterScience and Applied Technology Program

Dr. Michael Van Woert, Chief Scientist

planned nowcast product evolution nic 5 year plan

weekly

global

manual

Planned Nowcast Product Evolution: “NIC 5 Year Plan”

GLOBAL NOWCAST

PRODUCT

CURRENT PRODUCT

daily

global

Science makes

the next step to

NOWCAST products

possible.

model / assimilation-based

low resolution (10 km)

REGIONAL NOWCAST

PRODUCT

daily

non-global

manual, some automation

high resolution (<1km)

planned forecast product evolution nic 10 year plan

regional

manual

heuristic

Planned Forecast Product Evolution: “NIC 10 Year Plan”

PLANNED GLOBAL

FORECAST PRODUCT

CURRENT

FORECAST PRODUCT

Short-term (24-120 Hours)

Global

Science makes

the next step to

FORECAST products

possible.

Coupled Dynamical Model

Data Assimilation Support

PLANNED REGIONAL

FORECAST PRODUCT

Seasonal (30, 90 day)

Non-global

Statistical Model

Climate Indices

pips 2 0 ocean ice model
PIPS 2.0 Ocean/Ice Model

Coupled Ice-Ocean Model

(Hibler/Cox)

0.28 degree grid resolution

(17-34 km)

15 vertical levels

Solid wall boundaries

Ocean loosely constrained

to Levitus climatology

Forced by NOGAPS

Initialized with SSM/I

PIPS 2.0 domain. Hatched lines

drawn every 4th grid point

slide5

Forecast Skill Scores #1

Af= accuracy of the forecast system

Ap= accuracy of a perfect forecast

Ar= accuracy of a reference forecast

In this formulation SS represents the improvement in accuracy of the forecasts over the reference forecasts relative to the total improvement in accuracy.

slide6

Forecast Skill Scores #2

Accuracy defined as:

slide7

Forecast Skill Scores #3

SS>0 (skillful) when MSE(R,O) > MSE(f,O).

SS<0 (unskillful) when MSE(R,O) <MSE(f,O)

Perfect forecast SS=1; MSE(f,O)=0

No forecast skill SS=0; MSE(f,O)=MSE(R,O)

pips 24 hour forecast validation
PIPS 24-Hour Forecast Validation

PIPS much better than climo

But with respect to persistence?

for more info
For More Info

See also – M. Van Woert et al., “Satellite validation of the May 2000 sea ice

concentration fields from the Polar Ice Prediction System”, Canadian Journal

of Remote Sensing, 443-456, 2001

nic forecast requirements

Product

Resolution

Precision Tolerances

Range

Ice Concen.

10 km

+/- .5 Tenths

0-10/10ths

Ice Thickness

10 km

Flag Old Ice (2nd Year and Multiyear +/- 25% Non-Multiyear Ice

0-5 meters

Ice Drift (Speed)

10 km

(< 10cm/sec) +/- 5cm (>10cm/sec) +/- 20%

0 – 100

km day-1

Ice Drift(Direction)

10 km

+/- 20%

360 Deg

Ice Edge

10 km

+/- 10 km

N/A

Ice Deformation

10 km

+/- 25% of Range

+/-5X10-8 sec-1

Fracture (Lead) Orientation

100 km2

+/- 45o

360 deg

NIC Forecast Requirements
slide11

Navy ice modeling effort to use Los Alamos C-ICE model for operational sea ice analysis and forecasting

  • Plan to couple to Global NCOM Ocean Model
  • Provide end-user guidance to Technical Validation Panel

Polar Ice Prediction System 3.0

slide12

National Weather Service Support

Daily weather in the United States

is strongly linked to Arctic sea ice conditions.

ice free

Sea Ice

http://science.natice.noaa.gov/work/ice_con_test.grb

miz model
MIZ Model

1

  • Marginal Ice Zone Model (Maksym - now at USNA)
    • Thermodynamics model driven by SSM/I data
    • Validation data obtained on Healy cruise

Ice core thick section from Healy

1

With Coon and Toudal

the model
The Model
  • Free Drift
    • 3% of the wind speed
    • 23° to the right of the wind
  • Conserve Ice
    • Single ice thickness category
    • 2nd upwind difference scheme
    • Mass conserving
  • NASA TEAM Sea Ice
    • EASE, equal area grid
    • 25 km resolution, daily
    • 435 x 435 elements ~70,000 O & I
  • Force with ECMWF wind
    • 12 hour time step
    • Interpolated to SSM/I grid: d-2

for

for

for

for

Model of c(t) written as a 2-d matrix, A(t)

Dimensions ~70,000 x 70,000 – mostly zeros!

kalman filter 1
Kalman Filter #1

Forecast step:

C is the prior estimate of the sea ice concentration field (~7,000 elements)

Cf is the forecasted sea ice concentration field

P is the prior estimate of the covariance (~7,000 x 7,000)

Pf is the forecasted covariance function

A is the matrix of model coefficients and AT is its transpose (~7,000 x 7,000)

~ indicates that the value is an estimate

C(0) is the NASA Team sea ice data for December 31, 2001 [ y(0) ]

P(0) is assumed diagonal and equal to 5%

~

kalman filter 2
Kalman Filter #2

Correction Step:

K is the Kalman gain

E is the observation design matrix (1’s on the diagonal)

y is the SSM/I sea ice concentration data vector

R is the noise covariance for the SSM/I data (assumed diagonal and 5%)

kalman filter 3
Kalman Filter #3
  • Assume single observation
  • Assume E=1

For R 0 (perfect obs), K 1 and cy (obs)

For R inf (bad obs), K  0 and c  cf (model)

preliminary results
Preliminary Results

Initial Field

December 31, 2001

Forecast

January 04, 2002

Observed

January 04, 2002

White indicates ice concentration >100% (i.e. thickness changes)

2 hours per day – 2.7 GHz PC, 512 meg, Windows XP, M/S 4.0

not yet completed
Not Yet Completed
  • Careful analysis and selection of P(t=0)
  • Careful analysis and selection of R(t=0)
  • Display and analysis of P(t)
  • Inclusion of controls in the Kalman Filter
  • Examination of forecast skill
  • Include an ice thickness equation
  • Improve satellite-derived sea ice data products
  • Incorporate data assimilation of sea ice motion
windsat coriolis mission
WindSat/Coriolis Mission

Passive Polarimetric

Microwave Radiometer

- Frequencies

6.8 GHz V, H

10.0 GHz V, H, U, V

18.7 GHz V, H, U, V

22 GHz H

37 GHz V, H, U, V

- Launch Jan 2003

- Naval Res. Lab.

- Measure Wind Speed & Dir!

- What about sea ice???

Work toward improved ice

typing with QuikScat/Windsat:

K. Partington, N. Walker,

S. Nghiem, M. Van Woert

sea ice data assimilation

50 cm s-1

Buoys

Sea Ice Data Assimilation

SSM/I Motion

OI Motion

50 cm s-1

19-Jan-92

Model Motion

  • SSM/I
    • Many missing vectors
    • Noisy
  • Model
    • Often wrong
  • Objective Interpolation
    • Constrains model
    • Interpolates between data
  • Kalman Filter
    • Moving in that direction

50 cm s-1

Meier, Unpublished

satellite derived ice motion
Satellite-Derived Ice Motion
  • Scatterometer data and radiometer data complement each other in estimating ice motion
    • Where radiometer has difficulties, scatterometer does well and visa versa
    • Enables complete coverage motion maps

Meier, unpublished

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