Sampling errors in satellite-derived infrared sea-surface temperatures. Part I: Global and regional MODIS fields

Yang Liu, Peter J Minnett

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Long time series of accurate Sea Surface Temperatures (SSTs) are needed to resolve subtle signals that may be indicative of a changing climate. Motivated by the stringent requirements on SST accuracy required for Climate Data Records (CDR) we quantify sampling errors in satellite SSTs. Infrared sensors, including the Moderate Resolution Imaging Spectroradiometer (MODIS), have sampling errors caused by incomplete coverage primarily due to clouds and inter-swath gaps (gaps between successive swaths/orbits). Unlike retrieval errors, the sampling errors are introduced when calculating mean values and in generating gap-free SST fields. We generate MODIS-sampled SST fields by superimposing MODIS cloud masks on top of the Multi-scale Ultrahigh Resolution (MUR) SST field for the same day. Based on the MODIS-sampled fields, we calculate sampling errors at different temporal and spatial resolutions to examine the impacts at different scales. Our results indicate that sampling errors are significant, more so in the high latitudes, especially the Arctic. The 30°N-30°S zonal band is found to have the smallest errors; a notable exception is the persistent negative errors found in the Tropical Instability Wave area, where the mesoscale ocean-atmosphere interaction leads to a more frequently satellite sampling above the cold sections of the wave area. The global mean sampling error is generally positive and increases approximately exponentially with missing data fraction at a fixed averaging interval, while error variability is mainly controlled by SST variability. Areas with persistent cloud cover have large sampling errors in temporally averaged SSTs. We conclude that the sampling error can be an important or even dominant component of the error budget of mean and gap-free SST fields. Climate data generation and interpretation of satellite-derived SST CDRs and their application must be conducted with due regard to the sampling error.

Original languageEnglish (US)
Pages (from-to)48-64
Number of pages17
JournalRemote Sensing of Environment
Volume177
DOIs
StatePublished - May 1 2016

Fingerprint

moderate resolution imaging spectroradiometer
MODIS
surface temperature
sea surface temperature
Satellites
Sampling
Infrared radiation
Imaging techniques
sampling
Temperature
Temperature distribution
climate
orbits
cloud cover
Arctic region
time series analysis
spatial resolution
oceans
climate change
time series

Keywords

  • Climate Data Records (CDR)
  • MODIS
  • Sampling errors
  • Sea Surface Temperature (SST)

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Soil Science
  • Geology

Cite this

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title = "Sampling errors in satellite-derived infrared sea-surface temperatures. Part I: Global and regional MODIS fields",
abstract = "Long time series of accurate Sea Surface Temperatures (SSTs) are needed to resolve subtle signals that may be indicative of a changing climate. Motivated by the stringent requirements on SST accuracy required for Climate Data Records (CDR) we quantify sampling errors in satellite SSTs. Infrared sensors, including the Moderate Resolution Imaging Spectroradiometer (MODIS), have sampling errors caused by incomplete coverage primarily due to clouds and inter-swath gaps (gaps between successive swaths/orbits). Unlike retrieval errors, the sampling errors are introduced when calculating mean values and in generating gap-free SST fields. We generate MODIS-sampled SST fields by superimposing MODIS cloud masks on top of the Multi-scale Ultrahigh Resolution (MUR) SST field for the same day. Based on the MODIS-sampled fields, we calculate sampling errors at different temporal and spatial resolutions to examine the impacts at different scales. Our results indicate that sampling errors are significant, more so in the high latitudes, especially the Arctic. The 30°N-30°S zonal band is found to have the smallest errors; a notable exception is the persistent negative errors found in the Tropical Instability Wave area, where the mesoscale ocean-atmosphere interaction leads to a more frequently satellite sampling above the cold sections of the wave area. The global mean sampling error is generally positive and increases approximately exponentially with missing data fraction at a fixed averaging interval, while error variability is mainly controlled by SST variability. Areas with persistent cloud cover have large sampling errors in temporally averaged SSTs. We conclude that the sampling error can be an important or even dominant component of the error budget of mean and gap-free SST fields. Climate data generation and interpretation of satellite-derived SST CDRs and their application must be conducted with due regard to the sampling error.",
keywords = "Climate Data Records (CDR), MODIS, Sampling errors, Sea Surface Temperature (SST)",
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AU - Minnett, Peter J

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N2 - Long time series of accurate Sea Surface Temperatures (SSTs) are needed to resolve subtle signals that may be indicative of a changing climate. Motivated by the stringent requirements on SST accuracy required for Climate Data Records (CDR) we quantify sampling errors in satellite SSTs. Infrared sensors, including the Moderate Resolution Imaging Spectroradiometer (MODIS), have sampling errors caused by incomplete coverage primarily due to clouds and inter-swath gaps (gaps between successive swaths/orbits). Unlike retrieval errors, the sampling errors are introduced when calculating mean values and in generating gap-free SST fields. We generate MODIS-sampled SST fields by superimposing MODIS cloud masks on top of the Multi-scale Ultrahigh Resolution (MUR) SST field for the same day. Based on the MODIS-sampled fields, we calculate sampling errors at different temporal and spatial resolutions to examine the impacts at different scales. Our results indicate that sampling errors are significant, more so in the high latitudes, especially the Arctic. The 30°N-30°S zonal band is found to have the smallest errors; a notable exception is the persistent negative errors found in the Tropical Instability Wave area, where the mesoscale ocean-atmosphere interaction leads to a more frequently satellite sampling above the cold sections of the wave area. The global mean sampling error is generally positive and increases approximately exponentially with missing data fraction at a fixed averaging interval, while error variability is mainly controlled by SST variability. Areas with persistent cloud cover have large sampling errors in temporally averaged SSTs. We conclude that the sampling error can be an important or even dominant component of the error budget of mean and gap-free SST fields. Climate data generation and interpretation of satellite-derived SST CDRs and their application must be conducted with due regard to the sampling error.

AB - Long time series of accurate Sea Surface Temperatures (SSTs) are needed to resolve subtle signals that may be indicative of a changing climate. Motivated by the stringent requirements on SST accuracy required for Climate Data Records (CDR) we quantify sampling errors in satellite SSTs. Infrared sensors, including the Moderate Resolution Imaging Spectroradiometer (MODIS), have sampling errors caused by incomplete coverage primarily due to clouds and inter-swath gaps (gaps between successive swaths/orbits). Unlike retrieval errors, the sampling errors are introduced when calculating mean values and in generating gap-free SST fields. We generate MODIS-sampled SST fields by superimposing MODIS cloud masks on top of the Multi-scale Ultrahigh Resolution (MUR) SST field for the same day. Based on the MODIS-sampled fields, we calculate sampling errors at different temporal and spatial resolutions to examine the impacts at different scales. Our results indicate that sampling errors are significant, more so in the high latitudes, especially the Arctic. The 30°N-30°S zonal band is found to have the smallest errors; a notable exception is the persistent negative errors found in the Tropical Instability Wave area, where the mesoscale ocean-atmosphere interaction leads to a more frequently satellite sampling above the cold sections of the wave area. The global mean sampling error is generally positive and increases approximately exponentially with missing data fraction at a fixed averaging interval, while error variability is mainly controlled by SST variability. Areas with persistent cloud cover have large sampling errors in temporally averaged SSTs. We conclude that the sampling error can be an important or even dominant component of the error budget of mean and gap-free SST fields. Climate data generation and interpretation of satellite-derived SST CDRs and their application must be conducted with due regard to the sampling error.

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