John Sapper

The development and operational application of nonlinear algorithms for the measurement of sea surface temperatures with the NOAA polar-orbiting environmental satellites

Since 1990, the NOAA National Environmental Satellite Data and Information Service (NESDIS) has provided satellite-derived sea surface temperature (SST) measurements based on nonlinear SST algorithms, using advanced very high resolution radiometer (AVHRR) multiple-infrared window channel data. This paper develops linear and nonlinear SST algorithms from the radiative transfer equation. It is shown that the nonlinear algorithms are more accurate than linear algorithms but that the functional dependence of the nonlinearity is data dependent. This theoretical discussion (sections 2–4) is followed with a discussion in section 5 of the accuracy over a 9-year period of the satellite-derived SST measurements provided by NOAA NESDIS when compared with coincident drifting buoys. Between 1989 and 1998 the global scatter of the daytime satellite SST against drifting buoy measurements has decreased from ∼0.8° to 0.5°C, while the nighttime scatter has remained fairly constant at 0.5°C. An exception to these accuracy measurements occurred after the eruption of Mount Pinatubo in June 1991.

Validation of coastal sea and lake surface temperature measurements derived from NOAA/AVHRR data

An interactive validation monitoring system is being used at the NOAA/NESDIS to validate the sea surface temperature (SST) derived from the NOAA-12 and NOAA-14 polar orbiting satellite AVHRR sensors for the NOAA CoastWatch program. In 1997, we validated the SST in coastal regions of the Gulf of Mexico, Southeast US and Northeast US and the lake surface temperatures in the Great Lakes every other month. The in situ

Development of a Global Validation Package for Satellite Oceanic Aerosol Optical Thickness Retrieval Based on AERONET Observations and Its Application to NOAA/NESDIS Operational Aerosol Retrievals

Abstract In this paper, a global validation package for satellite aerosol optical thickness retrieval using the Aerosol Robotic Network (AERONET) observations as ground truth is described. To standardize the validation procedure, the optimum time–space match-up window, the ensemble statistical analysis method, the best selection of AERONET channels, and the numerical scheme used to interpolate/extrapolate these observations to satellite channels have been identified through sensitivity studies. The package is shown to be a unique tool for more objective validation and intercomparison of satellite aerosol retrievals, helping to satisfy an increasingly important requirement of the satellite aerosol remote sensing community. Results of applying the package to the second-generation operational aerosol observational data (AEROBS) from the NOAA-14 Advanced Very High Resolution Radiometer (AVHRR) in 1998 and to the same year aerosol observation data [Clouds and the Earths Radiant Energy System-Single Scanner Fo...

Operational Aerosol Observations (AEROBS) from AVHRR/3 On Board NOAA-KLM Satellites

Since 1988, the National Oceanic and Atmospheric Administration (NOAA) has provided operational aerosol observations (AEROBS) from the Advanced Very High Resolution Radiometer (AVHRR/2) on board the afternoon NOAA satellites [nominal equator crossing time, (EXT) ;1330]. Aerosol optical depth (AOD) has been retrieved over oceans from channel 1 of AVHRR/2 on board NOAA-11 (1988‐94) and -14 (1995‐2000) using the first- and second-generation algorithms, respectively. With the launch of the NOAA-KLM series of satellites, in particular NOAA-16 (L) in September 2000 (EXT ;1400), and NOAA-17 (M) in June 2002 (EXT ;1000), an extended and improved third-generation algorithm was enabled. Like its predecessors, this algorithm continues to employ a single-channel methodology, by which all parameters in the retrieval algorithm (excluding AOD) are set globally as nonvariables. But now, in addition to AOD from channel 1, t1 (l1 5 0.63 mm), the algorithm also retrieves t 2 and t3 in AVHRR/3 channels 2 (l 2 5 0.83 mm) and 3A (l3 5 1.61 mm). The retrievals are made with more accurate and flexible, satellite- and channel-specific lookup tables generated with the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer code. From pairs of ti and tj, t

Reef-Scale Thermal Stress Monitoring of Coral Ecosystems: New 5-km Global Products from NOAA Coral Reef Watch

The U.S. National Oceanic and Atmospheric Administration (NOAA) Coral Reef Watch (CRW) program has developed a daily global 5-km product suite based on satellite observations to monitor thermal stress on coral reefs. These products fulfill requests from coral reef managers and researchers for higher resolution products by taking advantage of new satellites, sensors and algorithms. Improvements of the 5-km products over CRW’s heritage global 50-km products are derived from: (1) the higher resolution and greater data density of NOAA’s next-generation operational daily global 5-km geo-polar blended sea surface temperature (SST) analysis; and (2) implementation of a new SST climatology derived from the Pathfinder SST climate data record. The new products increase near-shore coverage and now allow direct monitoring of 95% of coral reefs and significantly reduce data gaps caused by cloud cover. The 5-km product suite includes SST Anomaly, Coral Bleaching HotSpots, Degree Heating Weeks and Bleaching Alert Area, matching existing CRW products. When compared with the 50-km products and in situ bleaching observations for 2013–2014, the 5-km products identified known thermal stress events and matched bleaching observations. These near reef-scale products significantly advance the ability of coral reef researchers and managers to monitor coral thermal stress in near-real-time.

Deriving the operational nonlinear multichannel sea surface temperature algorithm coefficients for NOAA-15 AVHRR/3

The National Oceanic and Atmospheric Administration (NOAA) currently uses Nonlinear Sea Surface Temperature (NLSST) algorithms to estimate sea surface temperature (SST) from NOAA satellite Advanced Very High Resolution Radiometer (AVHRR) data. In this study, we created a three-month dataset of global sea surface temperature derived from NOAA-15 AVHRR data paired with coincident SST measurements from buoys (i.e. called the SST matchup dataset) between October and December 1998. The satellite sensor SST and buoy SST pairs were included in the dataset if they were coincident within 25 km and 4 hours. A regression analysis of the data in this matchup dataset was used to derive the coefficients for the operational NLSST equations applicable to NOAA-15 AVHRR sensor data. An independent matchup dataset (between January and March 1999) was also used to assess the accuracy of these day and night operational NLSST algorithms. The bias was found to be 0.14°C and 0.08°C for the day and night algorithms, respectively. The standard deviation was 0.5°C or less.

NOAA's sea surface temperature products from operational geostationary satellites

Abstract NOAAs National Environmental Satellite, Data, and Information Service (NESDIS) has generated sea surface temperature (SST) products from Geostationary Operational Environmental Satellite (GOES)-East (E) and GOES-West (W) on an operational basis since December 2000. Since that time, a process of continual development has produced steady improvements in product accuracy. Recent improvements extended the capability to permit generation of operational SST retrievals from the Japanese Multifunction Transport Satellite (MTSAT)-1R and the European Meteosat Second Generation (MSG) satellite, thereby extending spatial coverage. The four geostationary satellites (at longitudes of 75°W, 135°W, 140°E, and 0°) provide high temporal SST retrievals for most of the tropics and midlatitudes, with the exception of a region between ∼60° and ∼80°E. Because of ongoing development, the quality of these retrievals now approaches that of SST products from the polar-orbiting Advanced Very High Resolution Radiometer (AVH...

The SST Quality Monitor (SQUAM)

Abstract The National Environmental Satellite, Data, and Information Service (NESDIS) has been operationally generating sea surface temperature (SST) products (TS) from the Advanced Very High Resolution Radiometers (AVHRR) onboard NOAA and MetOp-A satellites since the early 1980s. Customarily, TS are validated against in situ SSTs. However, in situ data are sparse and are not available globally in near–real time (NRT). This study describes a complementary SST Quality Monitor (SQUAM), which employs global level 4 (L4) SST fields as a reference standard (TR) and performs statistical analyses of the differences ΔTS = TS − TR. The results are posted online in NRT. The TS data that are analyzed are the heritage National Environmental Satellite, Data, and Information Service (NESDIS) SST products from NOAA-16, -17, -18, and -19 and MetOp-A from 2001 to the present. The TR fields include daily Reynolds, real-time global (RTG), Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA), and Ocean Data Analy...

The effect of orbit drift on the calibration of the 3.7 µm channel of the AVHRR onboard NOAA-14 and its impact on night-time sea surface temperature retrievals

The orbit drift of National Oceanic & Atmospheric Administration (NOAA)-14 towards the terminator has caused the deterioration of the radiometric calibration of the Advanced Very High Resolution Radiometer (AVHRR) 3.7 µm channel at night. This deterioration is a result of solar contamination of the radiometric calibration system when the sun strikes the instrument from the spacecraft horizon. The long-term trend and seasonal variation of the contamination are analysed in this study based on trending data from 1995 to 2000. The calibration bias is evaluated and its effect on the sea surface temperature retrievals is quantified. The solar contamination in late 2000 affected as much as 25% of an orbit of data, compared to an average of 7% in 1995. The NOAA/NESDIS operational calibration algorithm partially corrects for the bias but residual effects can still contribute bias on the order of 0.5 K in scene brightness temperature.

AVHRR GAC SST Reanalysis Version 1 (RAN1)

In response to its users’ needs, the National Oceanic and Atmospheric Administration (NOAA) initiated reanalysis (RAN) of the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC; 4 km) sea surface temperature (SST) data employing its Advanced Clear Sky Processor for Oceans (ACSPO) retrieval system. Initially, AVHRR/3 data from five NOAA and two Metop satellites from 2002 to 2015 have been reprocessed. The derived SSTs have been matched up with two reference SSTs—the quality controlled in situ SSTs from the NOAA in situ Quality Monitor (iQuam) and the Canadian Meteorological Centre (CMC) L4 SST analysis—and analyzed in the NOAA SST Quality Monitor (SQUAM) online system. The corresponding clear-sky ocean brightness temperatures (BT) in AVHRR bands 3b, 4 and 5 (centered at 3.7, 11, and 12 µm, respectively) have been compared with the Community Radiative Transfer Model simulations in another NOAA online system, Monitoring of Infrared Clear-sky Radiances over Ocean for SST (MICROS). For some AVHRRs, the time series of “AVHRR minus reference” SSTs and “observed minus model” BTs are unstable and inconsistent, with artifacts in the SSTs and BTs strongly correlated. In the official “Reanalysis version 1” (RAN1), data from only five platforms—two midmorning (NOAA-17 and Metop-A) and three afternoon (NOAA-16, -18 and -19)—were included during the most stable periods of their operations. The stability of the SST time series was further improved using variable regression SST coefficients, similarly to how it was done in the NOAA/NASA Pathfinder version 5.2 (PFV5.2) dataset. For data assimilation applications, especially those blending satellite and in situ SSTs, we recommend bias-correcting the RAN1 SSTs using the newly developed sensor-specific error statistics (SSES), which are reported in the product files. Relative performance of RAN1 and PFV5.2 SSTs is discussed. Work is underway to improve the calibration of AVHRR/3s and extend RAN time series, initially back to the mid-1990s and later to the early 1980s.