# Presentation: 345889

## Sea Surface Temperature (SST) In Situ Network for Climate

- Richard W. Reynolds
- Huai-Min Zhang
- Thomas M. Smith
- National Climatic Data Center
- Asheville, NC

## SST Analysis Errors

- There are three types of analysis errors: Sampling, Random, Bias
- If the random observational errors are known, analysis sampling and random errors can easily be computed
- Optimum Averaging results show that random plus sampling errors on monthly 5o scales are small:   <  0.3oC
- This is due to the high density of satellite observations

## SST Data and Analysis for 1 Week14-20 December 1997: El Niño

- oC

## Bias Errors

- Biases occur with all satellite data due to instrument and algorithm problems
- Typical bias:      	0.2 to 0.5oC
- Worst case bias: 	2 to 3oC
- In situ (ship & buoy) data are needed to correct any satellite bias
- There is no convenient algorithm to compute bias
- We don't know when biases will occur
**GOAL:** Assume required SST analysis bias accuracy is  0.2-0.5oC monthly on 5o spatial grid
- Needler, et al. 1999, OceanObs’99

## AVHRR Nighttime Biases

- Volcanoes
- El Chichón: Mar '82 - Sep '83
- Mt. Pinatubo: Jun '91 - Mar '92
- NOAA Satellite Periods
- NOAA-7: Nov '81 - Feb '85
- NOAA-9: Feb '85 - Nov '88
- NOAA-11: Nov '88 - Dec '94
- NOAA-12: Dec '94 - Apr '95
- NOAA-14:Apr '95 - Feb '01
- NOAA-16: Feb '01- .........

- Zonal Difference: OI.v2-Night Satellite

**NOAA-7**

**NOAA-9**

**NOAA-11**

**NOAA-16**

**NOAA-14**

**NOAA-11**

## Simulation of Bias Errors

- OI analysis used with bias correction
- For Jan 1990 to Dec 2002
- Climatology is first guess (FG)
- Spatial satellite bias EOF patterns computed
- Bias is OI with minus OI without bias correction
- Define Gaussian Noise Functions, a(t) & b(t), with mean of 0 and variance of 1
- Satellite SSTs are simulated at actual data locations
- Satellite SSTs = FG(t) + Bias(t)
- where Bias(t) =  EOF(i) * a(t), i is the EOF (1-6)
- EOF absolute maximum scaled to 2oC

## Simulation of Bias Errors-2

- Buoy data are simulated on different grids
- Buoy SSTs = FG(t) + 0.5oC * b(t), where the buoy random observational error is 0.5oC
- Compute RMS Differences between the simulated OI and First Guess over time
- If there were no buoy data, the RMS residual would be equal to the absolute value of the EOF
- If there were complete buoy and/or ship sampling, the RMS would be 0

## Results

- A Potential Satellite SST Bias Error as a function of buoy density
- Potential is used because if satellite data have no biases, no buoy data are needed
- By definition the satellite biases are scaled so that the potential bias error without buoys is 2oC, a worst case bias error

## Potential SST Satellite Bias Error

- Number of Buoys on 10o grid

- Goal: 0.5oC

- Goal Exceeded by 2 Buoys on 10o Grid

- SST Error (Co)

- 2

- 0.2oC

- Satellite Bias Error Set to 2oC

## Buoy Equivalent Density

- "Buoy Equivalent" defined by: Number of Ships/7 + Number of Buoys
- 	Because ships are nosier than buoys, 7 ships equals 1 buoy

## Potential SST Satellite Bias Error

- Goal: 0.5oC

**To reach density of 2 per 10****o**** grid  need additional**
**	****	****60****o****S - 60****o****N  189 buoys**
**	****60****o****S - 20****o****S   102 buoys**
**	****20****o****S - 20****o****N    65 buoys**
**	****20****o****N - 60****o****N   22 buoys**

- No Buoys

## Conclusions

- Satellite data greatly reduces SST analysis sampling and random errors over analyses using ship and buoy data alone
- Bias errors were simulated with biased satellite data & unbiased buoy data
- Results show that an **equivalent buoy density** of **2 buoys on a 10****o**** grid** is required to reduce the potential satellite bias from 2oC to below 0.5oC
- An equivalent buoy is 7 ships or 1 buoy
- Present ship and buoy data distribution is not adequate
- Roughly 200 additional buoys are needed