# Presentation: 479646

## Toward the SST Climate Data Record from Space

**Kenneth S. Casey - NOAA National Oceanographic Data Center **
**Richard W. Reynolds - NOAA National Climatic Data Center**
**Edward J. Kearns - University of Miami**
**Andrew D. Barton - NOAA National Oceanographic Data Center  **

**Notes:**

Abstract: To meet the need for a high resolution sea surface temperature (SST)  climate data record,  an improved, 20-year long data set based on NOAA Advanced Very High Resolution Radiometer (AVHRR) observations
and an enhanced Pathfinder algorithm has been developed by the NOAA National Oceanographic Data Center and the University of Miami Rosenstiel School of Marine and Atmospheric Science.  This gridded data set implements numerous
improvements over previously available global AVHRR-based SST data sets, including a finer spatial resolution, better use of ice masks in quality level determinations, and greater inclusion of coastal and inland waters.  Validation studies demonstrating the progress being made in developing this rigorous single-sensor SST climate data record from space will be presented.  Remaining steps, such as the development of accurate error estimates, will be discussed in
the context of creating a Pathfinder climate record suitable as the baseline input to the GODAE High Resolution SST (GHRSST) reanalysis program. The GHRSST reanalysis efforts seeks to blend multiple satellite sensor  observations into an accurate, temporally stable, globally complete, and high resolution SST data set with quantified uncertainties.

## The AVHRR Pathfinder Program

**Advanced Very High Resolution Radiometer (AVHRR) on NOAA Polar Orbiters**
**Heritage: NOAA/NASA AVHRR Oceans Pathfinder Program of the 1990’s (V4)**
**Collaboration: NODC, NCDC, RSMAS, NASA PO.DAAC**
** **
**To develop the SST **_***climate data record***_
**for AVHRR in a sustained environment**

**Notes:**
NODC is supporting what was originally a joint NOAA-NASA effort (though the earlier versions were almost exclusively funded by NASA).  
The concept of a climate data record is a fundamental piece of proper scientific data stewardship and involves significant and ongoing reprocessing of satellite data to optimize accuracy, temporal consistency, and applicability to climate questions.

## AVHRR Pathfinder Version 5

**Coverage: Global, 1985-near present**
**Spatial resolution: ~ 4 km**
**Averaging periods:  Daily, 5-day, 7-day, 8-day, Monthly, and Yearly (day and night)**
**Corresponding climatologies **
**FGDC and Rich Inventory Metadata**
**Satisfies most IOOS DMAC requirements**
***http://pathfinder.nodc.noaa.gov***

**Notes:**
Pathfinder V5 is the longest, most accurate, most temporally consistent, and finest resolution AVHRR dataset available globally.  Data may eventually be available back to late 1981.  The only piece of the IOOS DMAC not presently satisfied by Pathfinder is the lack of its availability through Live Access Server (now that the Pathfinder LAS demo at PMEL is over).

## Improvements over Pathfinder V4

**Finer global resolution - now 4 km was 9 km**
**Significantly improved land mask **
**Inland waters and lake observations**
**More ancillary and statistical data**
**Longer time series... now in our 21****st**** year!**
_***Climate***_**, **_***coastal,***_** *****and ***_***ecosystem***_*** *****applications now possible!**

**Notes:**
Many improvements have been achieved over the previously available Pathfinder V4.

## Improved Coastal Coverage

**ReefBase reef location database ~ 8000 sites**
**9 km Pathfinder coverage only 61%**
**4 km Pathfinder coverage:  **_**> 98%**_

**Notes:**
Actual coverage is probably closer to 100% but difficult to nail down perfectly due to errors in the ReefBase reef locations (for example, some lat/lon positions for reefs are given at the center of the controlling country). Errors in the old Pathfinder V4 land mask made it unavailable on many reefs, notably a large portion of the GBR.

## Operational NESDIS 50 km

**Operational NESDIS 50 km**

- Latitude

- Longitude

**Notes:**
From the January 50km NESDIS climatology, provided by Gang Liu and used in Coral Reef Watch.

## AVHRR Pathfinder 9 km

**AVHRR Pathfinder 9 km**

- Latitude

- Longitude

**Notes:**
The previously best available dataset, Pathfinder V4 available at 9 km resolution. 

## AVHRR Pathfinder 4 km

**AVHRR Pathfinder 4 km**

- Latitude

- Longitude

**Notes:**
Now, the best available global AVHRR SST product: Pathfinder V5.  Notice the better coverage along coasts and enhanced resolution of open ocean fronts. 

## Includes Lakes and Inland Waters

**Includes Lakes and Inland Waters**

**Notes:**
The next several slides provide some results which quantify the improvements achieved by V5 Pathfinder over operational SSTs.  These figures clearly illustrate the problem with near real time satellite data sets and highlight the absolute need to reprocess the satellite observations consistently and using the best available algorithms.

## Nighttime

**Operational - HadSST2**

**Pathfinder - HadSST2**

**Nighttime**

**NOAA9**

**NOAA11**

**NOAA14**

**NOAA16 **

**NOAA9**

**NOAA11**

**NOAA14**

**NOAA16 **

**NOAA7**

**Notes:**
Note the much more consistent behavior of the Pathfinder V5 (PFV5) data in the upper panel than in the operational, real-time AVHRR data in the lower panel.  These differences are computed on a one-degree weekly grid, then zonally averaged along the one-degree band and plotted over time to give the above diagrams.  Areas in grey are within plus or minus 0.2 deg C, which is a good as we could hope to get given the actual differences between what an infrared-observing satellite sees in the top10 micrometers or so and what a ship or buoy measures below the surface.

The colored time scales indicate which NOAA Polar Orbiter was being used during a given time period.  Note that these differ between Pathfinder and Operational.

Note that the comparisons here are with the HadSST2  dataset which is an almost completely independent, and  entirely-in situ based data set. Comparisons with the Reynolds OISSTv2 (shown in hidden slides at end of presentation) are similar.
These panels are for nighttime satellite observations. Note that Pathfinder is not perfect of course... these figures provide excellent metrics by which to gauge future improvements.  

## Daytime

**Operational - HadSST2**

**Pathfinder - HadSST2**

**Daytime**

**NOAA9**

**NOAA11**

**NOAA14**

**NOAA16 **

**NOAA9**

**NOAA11**

**NOAA14**

**NOAA16 **

**NOAA7**

**Notes:**
Same as previous but for daytime.  Hours can (and have been) spent analyzing the contents of these plots.  Note the strong seasonal cycle of warm bias most likely related to diurnal warming.   Notice the extreme biases in the operational panel (lower).

## DayNit vs Nit

**PFV5 Night - HadSST2**

**PFV5 DayNit - HadSST2**

**DayNit vs Nit**

**NOAA9**

**NOAA11**

**NOAA14**

**NOAA16 **

**NOAA9**

**NOAA11**

**NOAA14**

**NOAA16 **

**Notes:**
What about using day-night averages of Pathfinder?  This is possible and does results in generally more good “gray” areas and smaller cool biases in low latitudes.  Some more mid-latitude diurnal warming does sneak in.  Averaging day and night, you can double your data availability!

Note it would be very difficult to combine operational day and night data... for one thing, they use different algorithms (while Pathfinder is the same for both day and night).  Plus, day operational is so poor (see previous slide) you wouldn’t likely want to do this anyway.

## Nighttime

**Operational - OISSTv2**

**Pathfinder - OISSTv2**

**Nighttime**

**NOAA9**

**NOAA11**

**NOAA14**

**NOAA16 **

**NOAA9**

**NOAA11**

**NOAA14**

**NOAA16 **

**NOAA7**

**Notes:**
Note the much more consistent behavior of the Pathfinder V5 (PFV5) data in the upper panel than in the operational, real-time AVHRR data in the lower panel.  These differences are computed on a one-degree weekly grid, then zonally averaged along the one-degree band and plotted over time to give the above diagrams.  Areas in grey are within plus or minus 0.2 deg C, which is a good as we could hope to get given the actual differences between what an infrared-observing satellite sees in the top10 micrometers or so and what a ship or buoy measures below the surface.

The colored time scales indicate which NOAA Polar Orbiter was being used during a given time period.  Note that these differ between Pathfinder and Operational.

Note that the comparisons here are with the HadSST2  dataset which is an almost completely independent, and  entirely-in situ based data set. Comparisons with the Reynolds OISSTv2 (shown in hidden slides at end of presentation) are similar.
These panels are for nighttime satellite observations. Note that Pathfinder is not perfect of course... these figures provide excellent metrics by which to gauge future improvements.  

## Daytime

**Operational - OISSTv2**

**Pathfinder - OISSTv2**

**Daytime**

**NOAA9**

**NOAA11**

**NOAA14**

**NOAA16 **

**NOAA9**

**NOAA11**

**NOAA14**

**NOAA16 **

**NOAA7**

**Notes:**
Same as previous but for daytime.  Hours can (and have been) spent analyzing the contents of these plots.  Note the strong seasonal cycle of warm bias most likely related to diurnal warming.   Notice the extreme biases in the operational panel (lower).

## DayNit vs Nit

**PFV5 Night - OISSTv2**

**PFV5 DayNit - OISSTv2**

**DayNit vs Nit**

**NOAA9**

**NOAA11**

**NOAA14**

**NOAA16 **

**NOAA9**

**NOAA11**

**NOAA14**

**NOAA16 **

**Notes:**
What about using day-night averages of Pathfinder?  This is possible and does results in generally more good “gray” areas and smaller cool biases in low latitudes.  Some more mid-latitude diurnal warming does sneak in.  Averaging day and night, you can double your data availability!

Note it would be very difficult to combine operational day and night data... for one thing, they use different algorithms (while Pathfinder is the same for both day and night).  Plus, day operational is so poor (see previous slide) you wouldn’t likely want to do this anyway.

## Operational
Pathfinder

**Nighttime**

**Operational**
**Pathfinder**

- 1982

- 2004

**Satellite Biases**

- +/- 1 C

- +/- 1 C

**Daytime**

**Weekly Satellite-OISSTv2**

**Notes:**
Here is a simplified version of the previous two slides, using the Reynolds OISSTv2 as a comparison instead, created by averaging each time step.  A data set perfectly matching the OISSTv2 would fall right along the zero line.  Note that Pathfinder, for both day and night, is more consistent and closer to zero.

Note in all of these comparisons that the OISSTv2 is by no means perfect!  This issue is always a problem when trying to determine how “good” a satellite SST product is... what should you really compare it to?

## Operational
Pathfinder

**Weekly Nighttime**

**Weekly Daytime**

**Operational**
**Pathfinder**

- 1982

- 2004

**Maximum Satellite - OISSTv2**

**Notes:**
A slide showing that the operational data is also prone to much greater extreme deviations from the OISSTv2 dataset, in addition to having strong mean differences as shown in the earlier slides.  This slide is hidden.

## Operational
Pathfinder

**Weekly Nighttime**

**Weekly Daytime**

**Operational**
**Pathfinder**

- 1982

- 2004

**Minimum Satellite - OISSTv2**

**Notes:**
Another slide showing that the operational data is also prone to much greater extreme deviations from the OISSTv2 dataset, in addition to having strong mean differences as shown in the earlier slides.  This slide is hidden

## Average Differences Over Time

## Improvements Coming in PFV 6

**Wind and aerosol ancillary data**
**Improved QC including aerosol flagging and better use of sea ice**
**Improved data management (HDF5 with CF and ACDD metadata?)**
**1985-2005 (maybe back to 1981?) **
**Implement “18-line problem” correction with new global gridding scheme**

**Notes:**
Many improvements can still be implemented, depending on future available funding levels.  The goal is to eventually maintain this kind of reprocessing capability within NOAA.  Grant money could be then used for targeted research to improve  specific aspects of the data.  The knowledge gained from that research could then be brought back into NOAA’s sustained system during the next reprocessing cycle.

## Connections to GHRSST

**GODAE High Resolution SST (GHRSST)**
**GHRSST is both *****real time***** and *****retrospective***
**Optimizing the retrospective connection...**
-  Level 2
- Times of Observation
- All AVHRRs?
- Error estimates

## Acknowledgements

- Thanks to
- John Kennedy and Nick Rayner,
- Hadley Centre, UK Met Office,
-  for providing the HadSST2 data set !!

## Operational NESDIS 50 km

**Operational NESDIS 50 km**

- Latitude

- Longitude

**Notes:**
Now, the same three (50km, 9km, 4 km) for the Great Barrier Reef region off NE Australia.  Notice the extreme lack of detail in the NESDIS 50 km SST field (again, provided by Gang Liu of Coral Reef Watch).

## AVHRR Pathfinder 9 km

**AVHRR Pathfinder 9 km**

- Latitude

- Longitude

**Notes:**
Much better in 9k mV4 Pathfinder, but the GBR was flagged as land in that product!  This problem with the land mask was one of the original inspirations of the new V5 Pathfinder effort.

## AVHRR Pathfinder 4 km

**AVHRR Pathfinder 4 km**

- Latitude

- Longitude

**Notes:**
Here is the V5 Pathfinder field... note the enhanced coverage and better resolution along the GBR itself.

## Improvements over Operational AVHRR

**Climatologies: 0.22 C reduced uncertainties and smaller biases with in situ data!**
**Time Series: **
- Greater availability
- Greater consistency
- Smaller biases

**Notes:**
The next several slides provide some results which quantify the improvements achieved by V5 Pathfinder over operational SSTs.  These figures clearly illustrate the problem with near real time satellite data sets and highlight the absolute need to reprocess the satellite observations consistently and using the best available algorithms.

## Improved Climatologies

| Climatology | Number | Std. Dev. (oC) | Bias (oC) |
| --- | --- | --- | --- |
| 4km Pathfinder | 100M | 1.64 | -0.02 |
| 50km NESDIS |  98M | 1.86 | -0.10 |

*In situ* SST – Climatological SST
- 1942-1997

- Method of Casey and Cornillon (*Journal of Climate*, v 12, 1999)

**Notes:**
Following the method of Casey and Cornillon (JCLIM v12, 1999)