slide1
Download
Skip this Video
Download Presentation
Error characterization of Atmospheric Motion Vectors

Loading in 2 Seconds...

play fullscreen
1 / 12

Error characterization of Atmospheric Motion Vectors - PowerPoint PPT Presentation


  • 130 Views
  • Uploaded on

Error characterization of Atmospheric Motion Vectors. Picture. J.Le Marshall. Quality Control (ERR) Considers       Correlation between images U acceleration V acceleration U deviation from first guess V deviation from first guess ………. Quality Indicator (QI) Considers

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 ' Error characterization of Atmospheric Motion Vectors' - nara


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
slide1

Error characterization

of Atmospheric

Motion Vectors

Picture

J.Le Marshall

slide2

Quality Control

(ERR)

Considers

      Correlation between images

U acceleration

V acceleration

U deviation from first guess

V deviation from first guess

………

slide3

Quality Indicator (QI)

Considers

      Direction consistency (pair)

      Speed consistency (pair)

      Vector consistency (pair)

      Spatial Consistency

      Forecast Consistency

QI = ∑wi.QVi/∑wi

slide4

EE - provides RMS Error (RMS)

   Estimated from

the five QI components

wind speed

vertical wind shear

temperature shear

pressure level

which are used as predictands for root mean square error

slide6

Fig. 4 (b): Predicted error using the EE regression approach

Fig. 4 (a): Predicted error using the QI lookup table

slide7

GMS-5

Table 3 AMV numbers and comparative errors in MMVD

when selecting Upper level WV AMVs by MMVD (November, 2002 )

using EE and QI. (Here vectors are chosen with Av. MMVD equal to

5 and 6 ms-1 respectively)

slide8

RT EE Computation at JCSDA

EE computed for GOES-East and West SWIR, IR, WV and VIS AMVs

EE also computed Terra and Aqua MODIS AMVs

Currently being set up for NESDIS RT Test

Length scale of correlated error to follow

ad