Error Characteristics of the Atmospheric Correction Algorithms Used in Retrieval of Sea Surface Temperatures from Infrared Satellite Measurements: Global and Regional Aspects

Kumar, Ajoy; Minnett, Peter J.; Podestá, Guillermo; Evans, Robert H.

doi:10.1175/1520-0469(2003)060<0575:ecotac>2.0.co;2

Cited by 40 publications

(27 citation statements)

References 24 publications

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“…The nighttime NLSST algorithm is responsible for the overcorrection at high water vapor in the average daily VIRS observations. From this perspective, our results do not contradict those of Kumar et al [2003].…”

Section: Analysis Of Uncertainties In Sst Retrievalssupporting

confidence: 56%

“… Kumar et al [2003] found a large negative SST bias for high water vapor conditions in the Arabian Sea, using the Pathfinder algorithm and SSMI water vapor. However, at midlatitudes they found a tendency for the algorithm to overcorrect for water vapor.…”

Section: Analysis Of Uncertainties In Sst Retrievalsmentioning

confidence: 99%

“…A comprehensive review of the current IR algorithms for SST retrievals and their limitations is presented by Barton [1995]. In addition, a recent study by Kumar et al [2003] investigates in depth the uncertainties of the atmospheric water vapor correction used in some IR algorithms for SST retrievals.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainties in sea surface temperature retrievals from space: Comparison of microwave and infrared observations from TRMM

Ricciardulli

2004

J. Geophys. Res.

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[1] A study of the uncertainties in infrared (IR) and microwave (MW) sea surface temperature (SST) retrievals by satellite is described here. The availability of both infrared (VIRS) and microwave (TMI) sensors on the Tropical Rainfall Measurement Mission (TRMM) provided an opportunity to perform this detailed SST intercomparison from two distinct but colocated sensors. Infrared retrievals provide good spatial resolution, but they are limited to clear-sky conditions. Microwave observations provide supplemental SST information in cloudy areas, but they are subject to other limitations such as poor spatial resolution and possible wind biases. One year of colocated VIRS and TMI SSTs are analyzed here, together with other colocated ancillary data sets including surface winds and water vapor, and potential sources of error are presented. The analysis shows some residual IR SST's dependence on atmospheric water vapor, despite corrective terms in the algorithm, with biases of a few tenths of a degree. The inclusion of a water vapor term in the IR algorithm reduces these biases. The results also show a higher uncertainty in MW SST retrievals for winds greater than 12 m/s, and a warm bias of MW SSTs of the order of 1°C within 50-100 km from land. Other potential sources of uncertainties discussed in the paper are undetected clouds, choice of IR SST algorithm, diurnal warming of the skin layer, and satellite maneuvers. The results of this intercomparison are valuable for the development of a future generation of a blended IR/MW SST data set, which combines the strengths of each observational method.

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Section: Analysis Of Uncertainties In Sst Retrievalssupporting

confidence: 56%

Section: Analysis Of Uncertainties In Sst Retrievalsmentioning

confidence: 99%

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Uncertainties in sea surface temperature retrievals from space: Comparison of microwave and infrared observations from TRMM

Ricciardulli

2004

J. Geophys. Res.

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“…This fact is confirmed by recent studies that compared satellite estimate to skin measurements rather than bulk measurements (e.g. Kearns et al, 2000;Kumar et al, 2003). They found that, using skin measurements, the scatter of the differences between in situ and satellite temperatures is about half of the corresponding estimate made For this particular work we selected only descending orbits in order to reduce the effect the diurnal cycle on the analysis of algorithms and interpolation performances.…”

Section: Satellite Sstmentioning

confidence: 62%

Observing the Mediterranean Sea from space: 21 years of Pathfinder-AVHRR sea surface temperatures (1985 to 2005): re-analysis and validation

et al. 2007

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Abstract. The time series of satellite infrared AVHRR data from 1985 to 2005 has been used to produce a daily series of optimally interpolated SST maps over the regular grid of the operational MFSTEP OGCM model of the Mediterranean basin. A complete validation of this OISST (Optimally Interpolated Sea Surface Temperature) product with in situ measurements has been performed in order to exclude any possibility of spurious trends due to instrumental calibration errors/shifts or algorithms malfunctioning related to local geophysical factors. The validation showed that satellite OISST is able to reproduce in situ measurements with a mean bias of less than 0.1 K and RMSE of about 0.5 K and that errors do not drift with time or with the percent interpolation error.

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“…However, these algorithms perform poorly when applied to Arctic regions, typically overestimating the SST by 2 to 3 K owing to unrealistic corrections for atmospheric absorption due to water vapor. The annual mean distribution of specific humidity near the surface is approximately 18 g kg −1 in equatorial latitudes and decreases to 1 g kg −1 or less over Polar Regions [ Kumar et al , 2003]. The relative dryness of the Arctic renders temperate SST algorithms ineffective in the region.…”

Section: Introductionmentioning

confidence: 99%

Arctic waters and marginal ice zones: A composite Arctic sea surface temperature algorithm using satellite thermal data

Vincent¹,

Marsden²,

Minnett³

et al. 2008

J. Geophys. Res.

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[1] The retrieval of Arctic sea surface temperatures (SSTs) using satellite radiometric imagery has not been well documented owing to the paucity of match-ups with in situ data. SST algorithms developed in temperate regions lead to positive biases in high latitudes due to an overestimation of atmospheric IR absorption. The composite arctic sea surface temperature algorithm (CASSTA) presented in this paper was developed from concurrent satellite and shipborne radiometric data collected in the North Water Polynya between April and July 1998. This algorithm considers three temperature regimes: seawater above freezing, the transition zones of water and ice, and primarily ice. These regimes, which are determined by advanced very high resolution radiometer (AVHRR) calibrated brightness temperatures, require different calculations for temperature estimates. For seawater above freezing, a specific Arctic SST algorithm was produced through a linear regression of AVHRR against in situ data. Areas consisting mainly of ice use an established ice surface temperature (IST) algorithm. The transition zone uses a combination of the Arctic SST and IST algorithms. CASSTA determines the Channel 4 brightness temperature for each pixel in a calibrated AVHRR image and then applies the appropriate algorithm to create a thermal image. The mean deviation of CASSTA compared to in situ data was 0.17 K with a standard deviation of 0.21 K. This represents a significant improvement over SST values using McClain coefficients for temperate waters, which overestimate the same data set by an average of 2.40 K. Application of CASSTA to the North Water imagery gives superior results compared to existing SST or IST algorithms.Citation: Vincent, R. F., R. F. Marsden, P. J. Minnett, K. A. M. Creber, and J. R. Buckley (2008), Arctic waters and marginal ice zones: A composite Arctic sea surface temperature algorithm using satellite thermal data,

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Error Characteristics of the Atmospheric Correction Algorithms Used in Retrieval of Sea Surface Temperatures from Infrared Satellite Measurements: Global and Regional Aspects

Cited by 40 publications

References 24 publications

Uncertainties in sea surface temperature retrievals from space: Comparison of microwave and infrared observations from TRMM

Uncertainties in sea surface temperature retrievals from space: Comparison of microwave and infrared observations from TRMM

Observing the Mediterranean Sea from space: 21 years of Pathfinder-AVHRR sea surface temperatures (1985 to 2005): re-analysis and validation

Arctic waters and marginal ice zones: A composite Arctic sea surface temperature algorithm using satellite thermal data

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