OSI-SAF VIIRS SST Pierre Le Borgne, Gérard Legendre, Anne Marsouin, Sonia Péré, Hervé Roquet...

OSI-SAF VIIRS SST

Pierre Le Borgne, Gérard Legendre, Anne Marsouin, Sonia Péré, Hervé Roquet Centre de Météorologie Spatiale, Météo-France, Lannion, France

With contribution of Alexander Ignatov, NOAA

SPIE Defense, Security and Sensing, Baltimore, 29 April-3 May 2013

2

Outline

Objectives Operational chain overview and status Quality Level definition Algorithm determination Validation results Conclusions


3

Objectives

CMS is commited to produce SST over the NAR area for the EUMETSAT/OSISAF

We aim to start delivering operational L3C products (GDSV2.0 compliant in netcdf 4) in mid 2013

The VIIRS SST chain will be a test chain for our next operational METOP/AVHRR SST chain


4

The OSI SAF VIIRS SST chain overview

ExperimentalOE SST

Radiometric data

Maia cloud mask

Cloud mask controlNo masking!

Multispectral SST calculations

Granule workfile

Algorithm correctionNWP profilesEvery 86 s granule

Remapping and format

NAR SST product MDB


5

From radiom. dataTo workfile

From workfile to NAR

MDB extraction

Buoy!


6

Adjacency algorithm

VIIRS pixel neighborhood is needed for:– T11-T12 smoothing

– Gradient computation

– Matchup Database extraction

Trimmed pixels and scan overlapping must be taken into account -> adjacency algorithm needed!

JPSS OAD document for Common Adjacency Software describes the common Adjacency algorithm

An extended algorithm has been derived to allow neighborhood determination to extend beyond adjacent scans


7

Raw data

Pixel of interest


8

After local adjacency has been determined

Pixel of interest


9

OSI-SAF VIIRS SST chain: status

Preoperational since mid October 2012

Cloud mask control completed

Final product ready (GDSV2.0 in netcdf4 )

Operational delivery to start in mid 2013

NWP derived correction will be introduced afterwards


10

Quality levels?


11

Quality levels

?


12

Indicators

?

Error as a function ofTs-clim. Minimum,On the MDB

Gradient indicatorAerosol indicatorIce indicator…..

Mask indicator= mean of all indicators

Limit valueCritical value

Indicators defined between 0=clear et 100=cloudyBy limit and critical values such as:

Tlimit-TsTemp. Indicator= -------------- x 100

Tlimit-Tcritical


13

Quality levels

Cloud mask indicator : mean of all indicators– Temperature indicator (distance to minimum SST clim)

– Gradient indicator (distance to maximum SST gradient clim)

– Dust indicator (derived from SEVIRI SDI)

– Ice indicator (derived from OSI SAF ice information)

Algorithm risk:– Derived from algorithm potential problems

Mask indicator

Algorithm risk


14

VIIRS SST field on the 20 March 2013 at 02:00


15

Undetected cloudiness

VIIRS SST – Minimum SST Climatology (OSTIA) 20 March 2013 at 02:00


16

Cloudiness detected by low SST values

Temperature indicator derived from the SST- mini clim difference 20 March 2013 at 02:00


17

Cloudiness detected by high gradient values

VIIRS SST gradients on the 20 March 2013 at 02:00


18

Univ. Rhode Island Pathfinderderived maximum gradient climatology


19

Climatology missing

Erroneous location of max gradients

Gradient indicator


20

Mask indicator (mean of all indicators)0= no risks; 100=critical


21

SST confidence levels:Increasing quality from 2 to 5


22

VIIRS SST field on the 22 September 2012 at 13:00


23

VIIRS SST – Mean SST Climatology (OSTIA) 22 September 2012 at 13:00


24

SEVIRI derived SDI


25

Dust indicator


26

Algorithm determination

RTTOV-10 applied on the SAFREE (radiosonde profiles) data base

Daytime algorithms NLC : SST = (a + b S) T11 + (c + d Tsclim + e S) (T11 - T12) + f S + g (1)

Night-time algorithmsT37_1 : SST = (a + b S) T37 + (c + d S) (T11 - T12) + e S + f (2)where :

– TSclim = first guess climatologic SST

– S = 1/cos () – 1

In practice T11-T12 are smoothed in a 11x11 pixel box


27

Overall validation results

NAR area; 15 October 2012 till 15th March 2013Qual levels 3-5

Daytime qual. 3-5

Daytime qual. 5

Nighttime qual. 3-5

Nighttime qual. 5

number of cases

3088 990 3648 1678

bias -0.13 -0.10 -0.05 0.03

standard-deviation

0.46 0.34 0.37 0.29


28

NPP/VIIRS cloud detection is more efficient than the equivalent NOAA-A9/AVHRR


29

Overall validation results:Daytime

Qual 5: =-0.10; =0.34


30

Overall validation results:Nighttime

Qual 5: =0.03; =0.29


31

Conclusion

A VIIRS SST chain has been run in preoperational mode since mid October 2012

Validation results are OK– Cloud free coverage significantly larger than NOAA-19

– Daytime and nighttime standard deviation: 0.46 and 0.37 K resp.

Next phases:– Starting operational delivery (mid 2013)

– NWP derived BT simulations and bias correction/Optimal Estimation


32

Example of a granule over NE Atlantic…..


33

THE END

….Remapped at 1 km resolution over Brittany

Date post:	13-Dec-2015
Category:	Documents
Upload:	david-sanders
View:	227 times
Download:	8 times

OSI-SAF VIIRS SST Pierre Le Borgne, Gérard Legendre, Anne Marsouin, Sonia Péré, Hervé Roquet...

Documents