steve weygandt stan benjamin forecast systems laboratory noaa
Download
Skip this Video
Download Presentation
Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA

Loading in 2 Seconds...

play fullscreen
1 / 37

Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA - PowerPoint PPT Presentation


  • 78 Views
  • Uploaded on

Hourly RUC Convective Probability Forecasts using Ensembles and Radar Assimilation. Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA. AUTOMATED CONVECTIVE WEATHER GUIDANCE. PRESENT 0-2 h forecasts from radar extrapolation with growth and decay (nowcasting techniques)

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA' - wang-jensen


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
automated convective weather guidance
AUTOMATED CONVECTIVE WEATHER GUIDANCE
  • PRESENT
  • 0-2 h forecasts from radar extrapolation with
  • growth and decay (nowcasting techniques)
  • Beyond 2 h guidance from model output helpful
  • FUTURE
    • A seamless convective guidance product utilizing a variety of inputs including nowcasts and model ensemble informationto provide guidance to humans and automated decision support systems
model based probability forecasts for convective weather
Model-based Probability Forecasts for Convective Weather
  • Principle:
  • Convective forecasts at specific model grid points from a single deterministic model run less likely to be correct than averages of model outputs.
  • Procedure:
  • Aggregate model convective information to
  • larger time/space scales (~1-2 h, 80-100 km)
    • Scales should increase with increasing lead time
    • Scales will decrease as models get better
ensembles provide technique for aggregating forecast information
Ensembles provide technique for aggregating forecast information
  • Types of ensembles
  • Multi-model ensembles
  • Initial/boundary condition ensembles
  • Model physics ensembles
  • Time-lagged model ensembles (2004)
  • Model gridpoint ensembles (2003)
ruc convective precipitation forecast
RUC convective precipitation forecast

5-h fcst valid

19z

4 Aug 2003

3-h conv.

precip.

(mm)

ruc convective probability forecast
RUC convective probability forecast

(2003 --

gridpoint

ensemble)

Threshold > 2 mm/3h

Length Scale = 60 km

Box size = 7 GPs

7 pt, 2 mm

5-h fcst valid

19z

4 Aug 2003

Prob. of

convection

within 60 km

% 10 20 30 40 50 60 70 80 90

slide7

Does probability beat model precip?

----- probability

----- conv precip

  • Relative
  • Operating
  • Characteristic
  • (ROC) curves
  • Show tradeoff:
  • “detection” vs.
  • “false-alarm”
  • “Left and high”
  • curve best

Low prob

POD

Low

precip

25%

High

prob

detection

9 pt, 4 mm

High

precip

Sample:

5-h fcst from

14z 04 Aug 2003

POFD

false detection

gridpoint ensembles
Gridpoint Ensembles
  • Adjustable parameters
  • Length scale
  • Precipitation Threshold
  • Inherent weaknesses
  • Constrained to single model run
  • Non-zero probability can only extend
  • out as far as the characteristic distance

More ensemble information

 better probabilities

slide9

Different box

sizes and

convective

precip.

thresholds

give different

probability

fields

5 pt, 1 mm

7 pt, 2 mm

% 10 20 30 40 50 60 70 80 90

Need to

calculate

statistical

reliability

to calibrate

probabilities

9 pt, 4 mm

9 pt, 2 mm

slide10

Optimal threshold and length scale?

40%

25%

5-h fcst valid 19z 4 Aug 2003

ruc convective probabilistic forecast rcpf evolution
RUC Convective Probabilistic Forecast (RCPF) evolution
  • Automated convective probability forecast
  • Gridded fields derived from model ensembles
  • Real-time forecasts started 2003 (RCPFv2003)
  • Testing/improvements during 2004 (RCPFv2004)
  • 2-, 4-, 6-h forecasts every 2 hours (CCFP guidance)
  • Verification of forecasts by RTVS
  • AWC evaluation of product during 2005
  • Merge with short-range techniques (NCAR/MIT)
slide12

Sample 2003 RUC product

7-h fcst valid 21z 3 Aug 2003

RUC Convective

Probability Forecast

POD=0.55

Bias = 1.4

CSI = 0.30

5 pt, 1 mm / 3h, 40% thresh

Threshold probability

forecast to get a categorical forecast

Verification display

from RTVS

slide13

2003 verification of RCPFv2003

  • RCPF most useful
  • for initial convective
  • development
  • RCPF bias too
  • large all times
  • except evening

Threshold probability

forecast at 40% to get categorical forecast

6h Fcst

RCPF

v2003

Forecast length

Forecast

Valid Time

GMT

EDT

Diurnal cycle of convection

improvements to rcpf for 2004
Improvements to RCPF for 2004
  • GOALS (maximize skill)
  • Reduce large bias (diurnal effects, western differences)
  • Improve spatial coherency, temporal consistency
  • Improve robustness
  • Reduce latency
  • ALGORITHM CHANGES
  • Increase filter size (9 GP east, 7 GP west)
  • Time-lagged ensemble (multiple hourly projections
  • from multiple RUC forecast cycles)
  • Diurnal cycle for precip. thresh. (maximum daytime,
  • minimum nightime; smaller value in the west)
  • Increase forecast lead time one hour (eg: 6-h fcst
  • from 13z valid 19z available at 1245z instead of 1345z)
diurnal variation of precipitation threshold rate
Diurnal variation of Precipitation Threshold Rate

West of 104 deg. longitude,

multiply threshold by 0.6

Lower threshold

to increase

coverage

Higher threshold

to reduce

coverage

GMT

EDT

Forecast

Valid Time

  • Threshold adjusted to optimize the forecast bias

- Threshold likely too low at night (bias still too large)

comparison of rcpfv2003 and rcpfv2004
Comparison of RCPFv2003 and RCPFv2004

6h Forecast

Diurnal cycle of convection

Forecast length

GMT

EDT

Forecast

Valid Time

  • Verification for 26 day period (6-31 Aug. 2004)
  • RCPFv2004fcst is a 1-h older than RCPFv2003
  • RCPFv2004 has similar CSI, much improved bias
csi by lead time time of day

Fcst

Lead

Time

CSI by lead-time, time of day

(Verifiation 6-31 Aug. 2004)

6-h

4-h

2-h

RCPF

v2003

.24, .25

.22, .23

.20, .21

.18, .19

.16, .17

.14, .15

.12, .13

.10, .11

6-h

4-h

2-h

RCPF

v2004

6-h

4-h

2-h

CCFP

Diurnal cycle of convection

GMT

EDT

Forecast

Valid Time

bias by lead time time of day

Fcst

Lead

Time

Bias by lead-time, time of day

(Verifiation 6-31 Aug. 2004)

6-h

4-h

2-h

2.75-3.0

2.5-2.75

2.25-2.5

2.0-2.25

1.75-2.0

1.5-1.75

1.25-1.5

1.0-1.25

0.75-1.0

0.5-0.75

v2003

6-h

4-h

2-h

v2004

6-h

4-h

2-h

CCFP

Diurnal cycle of convection

GMT

EDT

Forecast

Valid Time

slide19

CSI vs. bias for 2003 vs. 2004(6-h forecasts valid 19z)

Low Probabilities

Points at

5% intervals

40%

40%

High Probabilities

  • RCPFv2004fcst is a 1-h older than RCPFv2003
  • RCPFv2004 has better CSI for given bias value
slide20

Sample RCPFv2004 product

At fcst

Time...

13z convection

25 – 49%

50 – 74%

75 – 100%

13z + 6h

Forecast

19z

verif

RCPF

v2004

Verification

19z NCWD

10 Aug 2004

slide21

Sample RCPFv2004 product

At fcst

Time...

15z convection

25 – 49%

50 – 74%

75 – 100%

21z

verif

15z + 6h

Forecast

RCPF

v2004

Verification

21z NCWD

23 July 2004

interpreting reliability plots
Interpreting Reliability Plots

RELIABILITY

For all 60% fcsts, event

occurs 60% of time

(45 deg line)

RESOLUTION

Strong change in obs

freq for given change

in fcst probability

(vertical line)

SHARPNESS

Tendency for forecast probabilities to be near

extreme values (0%, 100%)

(not hedging)

Under forecast

perfect reliability

Actual

reliability

OBSERVED frequency (/100)

Climatology

Over forecast

FORECAST probability (/100)

Tradeoffs between reliability, resolution, sharpness

ruc ncwf 6 h fcsts valid 19z
RUC-NCWF 6-h fcsts valid 19z
  • RELIABILITY
  • Better reliability
  • for 2004 vs. 2003
  • Underfcst low prob.,
  • overfcst high prob.
  • 2004 has many fewer
  • 0% prob. pts that
  • have convection
  • Fractional Coverage
  • 2004 has more low
  • prob. pts, fewer high
  • prob. pts
  • 2004 has fewer 0%
  • prob. pts (not shown)

6-31 Aug. 2004

perfect reliability

OBSERVED frequency (/100)

Under

Over

Climatology

FORECAST probability (/100)

0.10

0.08

0.06

0.04

0.02

0.00

FCST fract.

areal cover.

FORECAST probability (/100)

activities for 2005
ACTIVITIES FOR 2005
  • Dissemination and evaluation

Realtime use and evaluation by AWC

Hourly output and update frequency

NCAR password protected web-site

(model and radar extrapolation)

  • Ongoing product development

Ensemble-based potential echo top information

Use of ensemble cumulus closure information

Upgrade from 20-km RUC to 13-km RUC

Use of other RUC fields

  • Merge RCPF with NCWF2 (E-NCWF)
slide25

Sample RCPF 2005 product

25 – 49%

50 – 74%

75 – 100%

18z + 6h

Forecast

16z + 8h

Forecast

2005

RCPF

Verification

00z NCWD

8 Mar 2005

CCFP

slide26

Sample Probability/Echo Top Display

Probabilities shown with color shading

Potential

echo top

height shown with black

Lines (kft)

-- Echo top from parcel overshoot level

-- Contour echo top height at desired interval

(3kft or 6kft?)

grell devenyi cumulus parameterization
Grell-Devenyi Cumulus Parameterization
  • Uses ensemble of closures:
    • Cape removal
    • Moisture convergence
    • Low-level vertical mass flux
    • Stability equilibrium
  • Includes multiple values for parameters:
    • Cloud radius (entrainment)
    • Detrainment (function of stability)
    • Precipitation efficiency (function of shear)
    • Convective inhibition threshold

PRESENT:Mean from ensembles fed back to model

FUTURE:Optimally weight ensembles closures,

Use ensemble information to inprove probabilities

slide28

Closures groups in RUC

Grell-Devenyi ensemble cumulus scheme

2 hr Nowcast

(scale - 60 km)

Forecast

Radar

2100 UTC

10 July, 2002

9-h fcst valid

21z

10 Jul 2002

Performance

slide29

STRENGTHS OF MODEL GUIDANCE

    • Capturing initial convective development
    • Long lead-time and early morning forecasts
  • Improvements to the model and assimilation system lead directly to
  • improvements in probability forecasts
  • For RUC model:
  • Assimilate surface obs throughout PBL
  • 13-km horizontal resolution (June 2005)
  • Radar data assimilation
  • Full North American coverage (2007)
slide30

ISSUES FOR MODEL GUIDANCE

    • Short-range forecasts (spin-up problem)
  • Poor performance for short-range forecast
  • does not invalidate longer-range forecasts
    • Propagation of convective systems
    • Robustness (spurious convection, complete misses)
    • Model bias issues

Differences for parameterized vs.

explicit treatments of convection

slide31

RUC Radar Data Assimilation Plans

  • Reflectivity:mosaic data
    • NSSL pre-processing code transferred to NCEP
    • Integrate mosaic data into RUC cloud analysis
    • Couple to ensemble cumulus parameterization
    • Couple to model velocity fields
  • Radial Velocity:level II data
    • Generalized 3DVAR solver from lidar OSSE
    • Use horizontal projection of 3D radial velocity
  • Outstanding Issues
  • - Data thinning/superobbing
  • - Quality Control (AP, 2nd trip, unfolding, birds,)
  • - Optimal uses (clear-air, stratiform precip., t-storms)
slide32

Sample 3DVAR analysis with radial velocity

0800 UTC

10 Nov 2004

Cint =

2 m/s

Dodge

City, KS

*

*

Analysis

WITH

radial

velocity

*

*

*

K = 15

wind

Vectors

and speed

*

Cint =

1 m/s

Amarillo, TX

Vr

Dodge City, KS

*

Analysis

difference

(WITH

radial

velocity

minus

without)

*

*

*

Vr

500 mb Height/Vorticity

Amarillo, TX

thoughts and questions
Thoughts and questions
  • Predictability very limited for small-scale convective precipitation features
    • Smoothing improves many scores
    • Smoothing alters spectra, probability information
  • Many “radar” approaches applicable
  • to model forecast precipitation fields
    • Probabilities from spatial variability of model precip.
    • Model depicts “displacement”, and “temporal evolution”
    • Apply “tracking” algorithms to model precipitation fields?
  • Many opportunities for blending model- and
  • radar-based techniques
    • Need extensive comparison to find “break even” points
    • Assess ability of radar and model for different tasks
    • Merge radar structure with model favored regions?
slide35

CONVECTIVE STORM TYPE

  • Squall-line
  • Discernible from probability shape

30%

50%

  • Not as clear for other shapes
  • Scattered storms
  • (high likelihood, 20% coverage)
  • MCS
  • (20% likelihood, significant coverage)

70%

30%

Storm-type affects correlation

of adjacent probabilities,

cumulative probability for

flight track

how is the rcpf created
How is the RCPF created?

1. Gridpoint ensemble (for each model GP)

- Fraction of 20-km model gridpoints within 9 x 9 box

with 1-h convective precipitation exceeding threshold

(use 7 x 7 km box west of 104 deg. Longitude)

- Diurnal variation to 1-h convective precipitation threshold

(smaller value for threshold west of 104 deg. longitude)

2. Time-lagged ensemble

- Use up to six forecasts

bracketing valid time

- 9-h RUC forecast every

hour with hourly output

- 2-h latency to RUC model

forecast output

4-h RCPF inputs

M0+4 M1+5 M2+6

M0+5 M1+6 M2+7

6-h RCPF inputs

M0+6 M1+7

M0+7 M1+8

8-h RCPF inputs

M0+8 M1+9

M0+9

M# = # hours

back to model initial time

slide37

Time-lagged ensemble inputs

RCPF has

2h latency

RUC model

forecasts

(HHz+F)

HHz = model intial time

F = forecast length (h)

14z 15z 16z 17z 18z 19z 20z 21z 22z 23z

12z 13z 14z 15z 16z 17z 18z 19z 20z 21z

15+6,7

14+7,8

13+8,9

15+8,9

14+9,10

13+10,11

15+4,5

14+5,6

13+6,7

15+7,8

14+8,9

13+9,10

15+9,10

14+10,11

13+11,12

15+3,4

14+4,5

13+5,6

15+5,6

14+6,7

13+7,8

15+2,3

14+3,4

13+4,5

15z RCPF

(17z CCFP)

2 3 4 5 6 7 8 9

2 4 6 8

14z+8,9

13z+9

14z+6,7

13z+7,8

14z+2,3

13z+3,4

12z+4,5

14z RCPF

(16z CCFP)

14z+4,5

13z+5,6

12z+6,7

Available

Initial Time

12z 13z 14z 15z 16z 17z 18z 19z 20z 21z 22z 23z 00z

Forecast Valid Time (UTC)

ad