RGP geolocation analysis. The geolocation problem We don’t have all the necessary information: –Optical model needs tuning Can prob. do this now but not.

RGP geolocation analysis

The geolocation problem

• We don’t have all the necessary information:

– Optical model needs tuning• Can prob. do this now but not sufficient because…..

– Require spin axis misalignment details– Start of line accuracy out of spec.

• Have per image correction derived from SEVIRI by column to column jitter remains

The geolocation problem

• What does geolocation accuracy mean for the data?EXAMPLE 1: clear sky coast– Land point contaminated with ocean and ocean with

land (ignoring unfiltering error which exacerbates the problem)

Ocean SW radiance 20Wm-2 sr-1 and land 70Wm-2sr-1

0.5 pixel error implies: Ocean and land 45Wm-2sr-1

If this occurs 25% or time average radiances become:

Ocean: 26.25 (31% bias)

Land: 85Wm-2sr-1 (21% bias)

0.1 pixel error 25% of time reduces this to 6% and 4% biases resp.

The geolocation problem

• What does geolocation accuracy mean for the data?EXAMPLE 2: Clear ocean and cloud– Cloud point contaminated with ocean and ocean with

cloud (ignoring unfiltering error which exacerbates the problem)

Ocean SW radiance 20Wm-2 sr-1 and cloud 150Wm-2sr-1

0.5 pixel error implies: Ocean and land 85Wm-2sr-1

If this occurs 5% or time average radiances become:

Ocean: 23.25 (16% bias)

Land: 147Wm-2sr-1 (2% bias)

0.1 pixel error 25% of time reduces this to 3.2% and 0.4% biases resp.

NOTE cloud forcing calculations: errors compound

How good is the reprocessing geo?Reprocessing

Compared to optimal

How does this compare to NRT geoReprocessed

compare to NRT

One pixel geolocation difference

Dark blue 5%

Light blue 10%

Cyan 20%

Green 30%

Yellow 40%

Reed 50%

White 100%

• Dark blue 5%• Light blue 10%• Cyan 20%• Green 30%• Yellow 40%• Reed 50%• White 100%

Dark blue 5Wm-2

Light blue 10Wm-2

Cyan 20Wm-2

Green 30Wm-2

Yellow 40Wm-2

Reed 50Wm-2

White 100Wm-2

DERIVING the BANANA

• Pixel azimuth and elevation derived from lon-lat determined by RMIB reprocessed geolocation matching and NANRG satellite position

DERIVING the BANANA

• Pixel azimuth and elevation derived from lon-lat determined by RMIB reprocessed geolocation matching and NANRG satellite position

• Probability distribution of pixel azimuth and elevation built up from the full dataset

Distribution of pixel positionWe can then look at the proportion of the time the pixel is a given distance from the most probable position

1% < Purple < 5%5% < Blue < 10% 10% < Cyan < 25%25% < Green < 50%50% < Orange < 60%60% < Red

Distribution of pixel positionWe can then look at the proportion of the time the pixel is a given distance from the most probable position

1% < Purple < 5%5% < Blue < 10% 10% < Cyan < 25%25% < Green < 50%50% < Orange < 60%60% < Red

Summary• Reprocessing geo very close to optimal

– Within 0.25 pixel except towards disk edge

– Not possible in NRT • Cost 32,000€, or slower than real time archive or more work solution (still

non-ideal as level 1.5 and level 2 geo disconected)

• NRT geo often more than 0.5 pixel different from reprocessing

• Updated optical model in level 1.5 NANRG + current paramters

• with per column azimuth and elevation correction better than 0.2 pixel to reprocessing 90% of time

• With per image azimuth and elevation correction better than 0.3 pixel to reprocessing 90% of time

• Need to asses what final decision means on products