the cobel model and processing numerical model output for weather forecasting l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
The COBEL model and processing numerical model output for weather forecasting PowerPoint Presentation
Download Presentation
The COBEL model and processing numerical model output for weather forecasting

Loading in 2 Seconds...

play fullscreen
1 / 30

The COBEL model and processing numerical model output for weather forecasting - PowerPoint PPT Presentation


  • 200 Views
  • Uploaded on

UQAM. Université du Québec à Montréal. National Scale C&V Science Meeting, NCAR, April 19, 2001. The COBEL model and processing numerical model output for weather forecasting. Dr. Peter Zwack Robert Tardif. The COBEL model. 1D (column) model ensemble-average 1D BL equations

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 'The COBEL model and processing numerical model output for weather forecasting' - jaunie


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
the cobel model and processing numerical model output for weather forecasting

UQAM

Université du Québec à Montréal

National Scale C&V Science Meeting, NCAR, April 19, 2001

The COBEL model and processing numerical model output for weather forecasting

Dr. Peter Zwack

Robert Tardif

UQÀM Atmospheric Sciences NCAR, April 19 2001

the cobel model
The COBEL model
  • 1D (column) model
    • ensemble-average 1D BL equations
    • moist physics for stratiform clouds
    • coupled with surface
  • High vertical resolution for “shallow” phenomena
    • surface inversions
    • nighttime LLJs
    • stratocumulus
    • stratus, fog etc...
  • 3D to 1D “nesting” for mesoscale influences
    • horizontal pressure force
    • horizontal advections
    • vertical motion

UQÀM Atmospheric Sciences NCAR, April 19 2001

slide3

The COBEL model (cont’)

  • Developed at Paul Sabatier Univ. to simulate the nocturnal boundary layer (Estournel and Guédalia, 1987)
    • Turbulence parameterization for very-stable stratification
    • Detailed parameterization for longwave radiative transfer
  • Further developed for radiation fog forecasting (Bergot and Guédalia, 1994)
    • Moist physics (fog)
    • Visibility
  • Adapted for marine stratus “burnoff” very short-term forecasting for west coast (UQAM)

UQÀM Atmospheric Sciences NCAR, April 19 2001

the cobel model cont
The COBEL model (cont’)

Radiation

IR : 232 channels (Vehil et al., 1989)

Solar : Fouquart and Bonnel (1980)

Turbulence (1.5 order closure) :

Neutral : Delage (1974)

Stable and very stable: Estournel and Guédalia (1987)

Unstable: Bougeault and Lacarrère (1989)

Soil :

Ts through diffusion eq. (Bergot, 1993)

Moisture following Mahrt and Pan (1984)

Microphysics :

Gravitational settling velocity (cloud) constant

Precip. (drizzle) following Kessler (1969)

External forcings(obs. or mesoscale model):

Horiz.pressure force, horiz adv. (T and Hum), vertical motion and pressure tendencies

UQÀM Atmospheric Sciences NCAR, April 19 2001

what has been achieved
What has been achieved...

Fog onset forecasting (an example):

Visibility (Kunkel,1984)

  • North of France - flat & homogeneous terrain
  • Winter, anticyclonic circulation (calm winds)
  • Moist soil
  • COBEL profiles initialized from “on-site” sounding
  • COBEL driven with mesoscale advections from surface mesonet
  • Soil temp. from “on-site” sensors

(taken from Bergot, 1993)

Good at forecasting fog onset

when accurate initial cond. and advections

UQÀM Atmospheric Sciences NCAR, April 19 2001

what has been achieved cont

Regional Wx Center

COBEL + meso. model

Probability of Detection

67%

85%

False Alarm Ratio

25%

57%

Most errors could be related to mesoscale model error (erroneous advections + mid-level & high-level cloudiness)

What has been achieved ...(cont’)

Pre-op. evaluation:

Init. cond. & advections from operational mesoscale model

Statistical verification of fog forecasts. 229 days, 54 observed fog events (Lille field experiments - 3 winters)

(from Bergot and Guédalia, 1996)

UQÀM Atmospheric Sciences NCAR, April 19 2001

what has been achieved cont7
What has been achieved ...(cont’)

Morning BL transition forecasting

  • Adapted COBEL for daytime conditions
  • Very short-term forecasts
  • Coupling with Meso-ETA forecasts (advections)
  • Init. cond. from “on-site” soundings and in-situ sensors
  • Real-time prototype system deployed at DFW Sept.-Oct. 1997 (wake vortex experiment)

ETA 12hr fcst

COBEL 3hr fcst

  • Able to forecast short-term morning evolution of BL
  • Soil moisture important
  • Better structure than available op. meso. model

UQÀM Atmospheric Sciences NCAR, April 19 2001

what has been achieved cont8

Init. with OAK sounding

+

local data

(sodar, surface T, Td)

burnoff

Re-init. with

updated local data

(sodar, radiometer,

surface T, Td)

Re-init. with

updated local data

(sodar, radiometer,

surface T, Td)

What has been achieved ...(cont’)

Marine stratus burnoff forecasting for SFO

Hourly cloud water forecasts (an example)

SFO August 12 1997

UQÀM Atmospheric Sciences NCAR, April 19 2001

slide9

COBEL forecasts-summer 1999, 47 cases

RMS

Mean error (hours)

advections important !

Mean error

(COBEL - obs.)

Forecast init. times

Use of op. models (ETA, Canadian model…) advections deteriorated results !

What has been achieved ...(cont’)

SFO “batch” results

(without

advections)

UQÀM Atmospheric Sciences NCAR, April 19 2001

ongoing development

T at 10m

Ongoing development

Horizontal advection retrieval methodology

temperature and humidity advections

Difference between model & obs :

Horizontal advection + model error

  • Iterative method: Which advection so model converges toward obs. ?
  • Constrain model with obs. to minimize error
  • Do for T and Hum.

UQÀM Atmospheric Sciences NCAR, April 19 2001

ongoing development11

Temperature advection retrieval

Temperature advection (C/h)

Hour (UTC)

Ongoing development

Horizontal advection retrieval methodology

UQÀM Atmospheric Sciences NCAR, April 19 2001

ongoing development12

10%

forecast error

reduction

Ongoing development

Horizontal advection retrieval methodology

Correlation with COBEL burnoff forecast error

Rel. hum. “advection” = Fct (T advection, Hum. advection)

Summer 1999 (37 days)

UQÀM Atmospheric Sciences NCAR, April 19 2001

ongoing development cont
Ongoing development (cont’)
  • Navy’s COAMPS high-resolution (nest 3 ~ 5km) forecasts for horizontal advections & vertical motion
  • Coupling strategy with 3D mesoscale model for coastal clouds forecasting
  • COBEL initialization with in-situ measurements
  • Coupled COBEL/COAMPS system better than COBEL alone and COAMPS alone for cloud ceiling & burnoff forecasts ??

5x5 high-res. grid

UQÀM Atmospheric Sciences NCAR, April 19 2001

ongoing development cont14
Ongoing development(cont’)

Coupling with COAMPS model for coastal clouds forecasting over Pt. Mugu California

  • COBEL/COAMPS “combo” can improve results
    • Burnoff forecasts
    • Ceiling forecasts
  • Meso. model must do arelatively good job at cloud forecasting...
    • If no burnoff over whole grid -> weak horiz. gradients -> weak advections -> no improvement over COBEL without advections (BO too early)

METAR COBELCOAMPSCOBEL/COAMPS

2000Z 1812Z none 1845Z

Ceiling RMSE : June 20th 2000

COAMPS : ~200m,

COBEL/COAMPS : ~75m

UQÀM Atmospheric Sciences NCAR, April 19 2001

coupling with 3d mesoscale models

Nest 3 00Z “cold start”

Vertical motion every 2 time steps

  • Model spin-up

0

24

  • Fluctuations in derived output

June 27 2000

Coupling with 3D mesoscale models

Processing numerical model output

Be careful about :

UQÀM Atmospheric Sciences NCAR, April 19 2001

coupling with 3d mesoscale models16
Coupling with 3D mesoscale models

Processing numerical model output

Other lessons learned :

System sensitivity to :

  • filtering options :

1hr burnoff forecast difference obtained with COAMPS advections filtered using 2 different filter configuration !

  • 3D model spatial variability

1hr burnoff forecast difference obtained with COAMPS advections from 2 adjacent grid points !

UQÀM Atmospheric Sciences NCAR, April 19 2001

ongoing development17
Ongoing development
  • Mixed-phase microphysics being added to extend range of applications (available MM5 code of Rasmussen parameterization)
    • Adapt code to 1D model
    • Test various code configuration
    • Validation on idealized winter BL cloud
    • Simulation of fall/winter (near & below freezing) stratocumulus clouds over eastern US

UQÀM Atmospheric Sciences NCAR, April 19 2001

slide18

Looking ahead...

  • Areas for further R&D(for improved forecasting ability):
    • Microphysics of low clouds-fog
      • drop size distribution characterization and evolution during burnoff (has impact on cloud burnoff forecasts)
    • Better representation of local advections
      • Improve retrieval technique using more complete data sets from field campaigns - define data required
      • Coupling with RUC II and/or COAMPS - define improved synergy between models
    • Improve surface characterization
      • Soil temperature-moisture
      • Influence of surface heterogeneity on evolution of local atmosphere
    • Data assimilation !

UQÀM Atmospheric Sciences NCAR, April 19 2001

slide19

Looking ahead...

Integrated “1D array” system for C&V

forecasting in precipitating systems

UQÀM Atmospheric Sciences NCAR, April 19 2001

what has been achieved cont21
What has been achieved ...(cont’)

Evolution of profiles during morning BL transition

(in support of wake vortex prediction system)

  • Added components to COBEL for daytime simulations (solar radiation, soil model for surface evaporation…)
  • Evaluation using data from Memphis Wake Vortex field experiment
  • Initial conditions from on-site sounding
  • Low soil moisture
  • External forcings from Canadian regional operational forecast model

UQÀM Atmospheric Sciences NCAR, April 19 2001

what has been achieved cont22
What has been achieved ...(cont’)

Marine stratus burnoff forecasting for SFO

Developments

  • Initialization procedure
    • Oakland sounding + SFO sodar BL height + SFO obs. T & Td at surface  COBEL initial profiles
  • COBEL “re-initialization” methodology
    • sodar BL height + obs. T & Td at surface + obs. incoming solar rad.  updated initial cond. for updated hourly forecasts
  • Horizontal advection retrieval methodology

UQÀM Atmospheric Sciences NCAR, April 19 2001

initialization
Initialization

UQÀM Atmospheric Sciences NCAR, April 19 2001

very short term forecasts

sounding

Oakland

+

local data

(12Z)

Very short-term forecasts

12 Z

forecast

13 Z

forecast

14 Z

forecast

Re-initialization

15 Z

forecast

Re-initialization

16 Z

forecast

profiles

COBEL

+

local data

(13Z)

profiles

COBEL

+

Local data

(14Z)

time

UQÀM Atmospheric Sciences NCAR, April 19 2001

re initialization
Re-initialization

UQÀM Atmospheric Sciences NCAR, April 19 2001

ongoing development26

“advRH” vs COBEL error (COBEL - obs)

Summer 1999 (37 days)

Retrieval Forecasts

14Z 15Z 16Z 17Z

12-14Z -0.17-0.20

13-15Z -0.40-0.45

14-16Z -0.28 -0.19

15-17Z -0.04-0.28

If advRH (+) (RH ), COBEL without advection should be too early (error “-”)

Ongoing development

Horizontal advection retrieval methodology

Correlation with COBEL burnoff forecast error

Rel. hum. “advection” = Fct (T advection, Hum. advection)

UQÀM Atmospheric Sciences NCAR, April 19 2001

ongoing development cont27

METAR COBEL alone COAMPS alone COBEL/COAMPS

from 12Z from 00Z combo

2000Z 1812Z none 1845Z

Ceiling RMSE

COAMPS : ~200m

COBEL/COAMPS : ~75m

ceiling

Ongoing development(cont’)

Coupling with COAMPS model for coastal clouds forecasting over Pt. Mugu California

…an example...

June 20th 2000

burnoff

UQÀM Atmospheric Sciences NCAR, April 19 2001

coupling with 3d mesoscale models28

-> Filtering required ->

(40 min centered average)

Coupling with 3D mesoscale models

Processing numerical model output

Be careful about :

  • Unwanted fluctuations in model derived fields (such as advections)

~ -2 C/h

UQÀM Atmospheric Sciences NCAR, April 19 2001

coupling with 3d mesoscale models29

COBEL with COAMPS advections

cloud burnoff forecasts

45 min. 180 min.

19990913 1719Z 1718Z

(1745Z)

19990927 2225Z 2128Z

(none)

Coupling with 3D mesoscale models

Processing numerical model output

  • Other lessons learned :

System sensitivity to :

Filtering options

(averaging period)

COAMPS output filtered (in time) using 45 min. or 180 min.

centered moving average

UQÀM Atmospheric Sciences NCAR, April 19 2001

coupling with 3d mesoscale models30

COBEL with COAMPS advections

cloud burnoff forecasts

(2,2) (3,3)

19990913 1719Z >2400Z

19990914 1605Z 1620Z

19990927 2225Z >2400Z

Coupling with 3D mesoscale models

Processing numerical model output

  • Other lessons learned :

System sensitivity to :

3D model spatial variability

(advections from various grid points)

5x5 high-res. grid

-> Need for “ensemble” forecasting ?

UQÀM Atmospheric Sciences NCAR, April 19 2001