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- Author or Editor: Douglas A. May x
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Abstract
Sea surface temperature (SST) retrieval accuracy from the multispectral imager on the new generation of GOES satellites is analyzed. Equations for two and three infrared channels are empirically derived using cloud-free satellite radiances matched to buoy SST measurements obtained in 1995 and 1996. Both GOES-8 and GOES-9 demonstrate the capability to retrieve sea surface temperature at better than 1-K root-mean-square difference (rmsd) with negligible bias relative to buoy SST measurements. GOES-8 rmsd errors are found to be 0.79 K (day) and 0.81 K (night). GOES-9 rmsd errors are 0.65 K (day) and 0.59 K (night). The GOES-9 results are relatively comparable to those currently achieved operationally from the NOAA polar-orbiting satellite Advanced Very High Resolution Radiometer sensor. Investigation revealed that GOES imager multiple detector scan striping impacted SST accuracy, requiring sample array averaging for best results.
Abstract
Sea surface temperature (SST) retrieval accuracy from the multispectral imager on the new generation of GOES satellites is analyzed. Equations for two and three infrared channels are empirically derived using cloud-free satellite radiances matched to buoy SST measurements obtained in 1995 and 1996. Both GOES-8 and GOES-9 demonstrate the capability to retrieve sea surface temperature at better than 1-K root-mean-square difference (rmsd) with negligible bias relative to buoy SST measurements. GOES-8 rmsd errors are found to be 0.79 K (day) and 0.81 K (night). GOES-9 rmsd errors are 0.65 K (day) and 0.59 K (night). The GOES-9 results are relatively comparable to those currently achieved operationally from the NOAA polar-orbiting satellite Advanced Very High Resolution Radiometer sensor. Investigation revealed that GOES imager multiple detector scan striping impacted SST accuracy, requiring sample array averaging for best results.
Abstract
Efforts to monitor the Gulf of Mexico Loop Current and mesoscale ocean features using IR satellite imagery in the summertime have been significantly hindered by 1) strong surface heating that masks surface frontal gradients and 2) extremely high atmospheric water vapor attenuation that lowers effective satellite brightness-temperature values. These problems can now be addressed, provided high-quality multichannel infrared data are available during nighttime satellite passes. The National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) consists of three IR channels that include channels 3 (3.55–3.93 µm), 4 (10.3–11.3 µm), and 5 (11.5–12.5 µm). Of these, channel 3 is least affected by water vapor attenuation, making it better suited for viewing the ocean through a humid atmosphere. All satellites prior to NOAA-11, however, experienced substantial noise in channel 3 soon after launch, rendering the channel relatively useless for long-term oceanographic monitoring. NOAA-11, with a high-quality 3.7-µm channel, has enabled us to detect Loop Current and eddy features throughout the typical worst summertime conditions. A three-channel cross-product sea surface temperature (CPSST) algorithm was applied to nighttime images in August and September 1990 to monitor the Loop Current a major warm-core eddy (Quiet Eddy), and a minor warm-core eddy (Quiet Eddy II). Feature locations are verified using drifting data buoys. This capability demonstrates the importance of a low-noise AVHRR channel 3, and will increase our knowledge about Loop Current dynamics and ring periodicity during periods previously unfavorable for IR images.
Abstract
Efforts to monitor the Gulf of Mexico Loop Current and mesoscale ocean features using IR satellite imagery in the summertime have been significantly hindered by 1) strong surface heating that masks surface frontal gradients and 2) extremely high atmospheric water vapor attenuation that lowers effective satellite brightness-temperature values. These problems can now be addressed, provided high-quality multichannel infrared data are available during nighttime satellite passes. The National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) consists of three IR channels that include channels 3 (3.55–3.93 µm), 4 (10.3–11.3 µm), and 5 (11.5–12.5 µm). Of these, channel 3 is least affected by water vapor attenuation, making it better suited for viewing the ocean through a humid atmosphere. All satellites prior to NOAA-11, however, experienced substantial noise in channel 3 soon after launch, rendering the channel relatively useless for long-term oceanographic monitoring. NOAA-11, with a high-quality 3.7-µm channel, has enabled us to detect Loop Current and eddy features throughout the typical worst summertime conditions. A three-channel cross-product sea surface temperature (CPSST) algorithm was applied to nighttime images in August and September 1990 to monitor the Loop Current a major warm-core eddy (Quiet Eddy), and a minor warm-core eddy (Quiet Eddy II). Feature locations are verified using drifting data buoys. This capability demonstrates the importance of a low-noise AVHRR channel 3, and will increase our knowledge about Loop Current dynamics and ring periodicity during periods previously unfavorable for IR images.
A complete overview of the national Shared Processing Program (SPP) satellite sea surface temperature (SST) retrieval product is presented. This paper summarizes the operational processing of digital Advanced Very High Resolution Radiometer (AVHRR) satellite data into a global SST retrieval product at the Naval Oceanographic Office (NAVOCEANO). Satellite SST generation is described, detailing data processing procedures, algorithms used, and quality control techniques. User interaction and data monitoring through the SPP algorithm research panel for SST is presented along with SST products and information available to users. The NAVOCEANO national SST product consists of more than 150 000 global retrievals per day and demonstrates monthly bias errors less than 0.1 °C and root-mean-square difference errors less than 0.6°C relative to global drifting-buoy measurements. The product is important to and operationally utilized within thermal structure analyses, civilian and military maritime activities, and numerical weather prediction forecasts.
A complete overview of the national Shared Processing Program (SPP) satellite sea surface temperature (SST) retrieval product is presented. This paper summarizes the operational processing of digital Advanced Very High Resolution Radiometer (AVHRR) satellite data into a global SST retrieval product at the Naval Oceanographic Office (NAVOCEANO). Satellite SST generation is described, detailing data processing procedures, algorithms used, and quality control techniques. User interaction and data monitoring through the SPP algorithm research panel for SST is presented along with SST products and information available to users. The NAVOCEANO national SST product consists of more than 150 000 global retrievals per day and demonstrates monthly bias errors less than 0.1 °C and root-mean-square difference errors less than 0.6°C relative to global drifting-buoy measurements. The product is important to and operationally utilized within thermal structure analyses, civilian and military maritime activities, and numerical weather prediction forecasts.
Abstract
Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.
Abstract
Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.
