Atmospheric data for Arctic modeling
Download
1 / 19

Atmospheric data for Arctic modeling - PowerPoint PPT Presentation


  • 89 Views
  • Uploaded on

Atmospheric data for Arctic modeling. John Walsh International Arctic Research Center University of Alaska, Fairbanks Arctic System Modeling Workshop, Montreal, July 2009. Three categories of atmospheric observations:. Routine measurements for input to NWP models

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 ' Atmospheric data for Arctic modeling' - helmut


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

Atmospheric data for Arctic modeling

John Walsh

International Arctic Research Center

University of Alaska, Fairbanks

Arctic System Modeling Workshop, Montreal, July 2009


Three categories of atmospheric observations
Three categories of atmospheric observations:

  • Routine measurements for input to NWP models

    2) Special observing networks

  • Short-duration field campaigns


Three categories of atmospheric observations1
Three categories of atmospheric observations:

  • Routine measurements for input to NWP models

    2) Special observing networks

  • Short-duration field campaigns

    +

    Value-added products: reanalyses

    gridded fields (e.g., CRU)

    Polar Pathfinder products


Routine measurements in some respects the arctic is well covered
Routine measurements – in some respects, the Arctic is well-covered

  • surface synoptic network

  • rawinsonde network

  • buoy, ship reports

  • aircraft reports

  • satellite measurements (profiles of T, q, wind)

  • Archived in reanalysis ingest data banks

    (e.g., PREPBUFR files at NCAR)


Surface station observations

Upper-air rawinsonde observations



Reports from commercial, military, and reconnaissance sources

7-days: 01/01/2003 - 01/07/2003


Satellite-derived temperatures sources

7-days

Winds derived from satellite observed cloud drift analysis

7-days


01/01/2003 sources

00Z

6-hour accumulated

observations:

red: surface station

slate blue: upper-air

yellow: sat. temp.

green: sat. wind

violet: aircraft

sky blue: ship


01/01/2003 sources

06Z

6-hour accumulated

observations:

red: surface station

slate blue: upper-air

yellow: sat. temp.

green: sat. wind

violet: aircraft

sky blue: ship


01/01/2003 sources

12Z

6-hour accumulated

observations:

red: surface station

slate blue: upper-air

yellow: sat. temp.

green: sat. wind

violet: aircraft

sky blue: ship


01/01/2003 sources

18Z

6-hour accumulated

observations:

red: surface station

slate blue: upper-air

yellow: sat. temp.

green: sat. wind

violet: aircraft

sky blue: ship


Sample PrepBUFR observations: sources

SID=48582 , YOB= 82.85, XOB=194.99, ELV= 0, DHR= 1.883, RPT= 19.883, TCOR=0, TYP=180, TSB=*, T29=562, ITP=99, SQN= 203

PROCN= 2, SAID=***

PMO=******, PMQ=**

----------------------------------------------------------------------------------------------

level 1 obs qmark qc_step rcode fcst anal oberr category

----------------------------------------------------------------------------------------------

PRESSURE (MB) 988.2 2. PREPRO ***** 987.6 987.4 1.6 0.

SP HUMIDITY(MG/KG) ******** ****** ***** 234.0 239.0 ******** 0.

TEMPERATURE (C) -33.3 2. PREPRO ***** -32.8 -32.8 2.5 0.

HEIGHT (METERS) 0.0 2. PREPRO ***** -4.0 -5.0 ******** 0.

SID=BAW269 , YOB= 60.00, XOB=340.00, ELV=10058, DHR=-0.550, RPT= 17.450, TCOR=0, TYP=130, TSB=0, T29= 41, ITP=99, SQN= 26

PROCN= 0, SAID=***

RCT= 18.08, PCAT=*****, POAF=***, DGOT=***

----------------------------------------------------------------------------------------------

level 1 obs qmark qc_step rcode fcst anal oberr category

----------------------------------------------------------------------------------------------

PRESSURE (MB) 262.0 2. PREPRO ***** ******** ******** ******** 6.

SP HUMIDITY(MG/KG) ******** ****** ***** 35.0 35.0 ******** 6.

TEMPERATURE (C) -62.0 1. PREPACQC 17. -61.1 -60.9 1.7 6.

TEMPERATURE (C) -62.0 2. PREPRO ***** -61.1 -60.9 1.7 6.

HEIGHT (METERS) 10058.0 2. PREPRO ***** 9708.0 9724.0 ******** 6.

U-COMP WIND (M/S) 4.4 1. PREPACQC 17. -2.3 1.8 3.6 6.

U-COMP WIND (M/S) 4.4 2. PREPRO ***** -2.3 1.8 3.6 6.

V-COMP WIND (M/S) 2.6 1. PREPACQC 17. 4.8 2.1 3.6 6.

V-COMP WIND (M/S) 2.6 2. PREPRO ***** 4.8 2.1 3.6 6.

WIND DIR (DEG) 240.0 ****** PREPRO ***** ******** ******** ******** 6.

WIND SPEED (KNOTS) 10.0 ****** PREPRO ***** ******** ******** ******** 6.


Prepbufr data

PrepBUFR data sources

0) Keep (always assimilate)

  • Good

  • Neutral or not checked (default) -- e.g., IAOBP T and P obs are flagged as QC=2

  • Suspect

  • Rejected (don’t assimilate)

    ** Rejected obs. have an additional flag layer indicating justification (e.g., conflicts with a pre-existing QC=0; threshold test failure, etc.)

Quality Control Flags


2 examples of special observing networks
2) Examples of special observing networks sources

  • International Arctic Buoy Network

    ( + Russian NP stations)

  • Greenland automated weather stations

  • Trace gas/chemical sampling (NOAA CMDL)

  • Baseline Surface Radiation Network (BSRN), ARM

  • International Arctic System for Observing the Atmosphere (IASAO)


Iasao
IASAO sources

Tiksi, Russia

Barrow, Alaska

Eureka, Canada

Alert, Canada

Ny-Alesund, Svalbard

Summit, Greenland

IASOA Target Observatories



3 short duration field programs process studies for algorithm development model validation
3) Short-duration field programs Atmosphere-- process studies for algorithm development, model validation

  • SHEBA

  • ATLAS, LAII Flux Study

  • ARM field campaigns

  • OASIS

  • IPY cruises

    … others


Problem areas
Problem areas Atmosphere

  • Precipitation, especially solid precip (snowfall, depth, water equivalent)

  • Clouds

  • Aerosols

  • … others


ad