AVHRR GAC SST Reanalysis Version 1 (RAN1)

Ignatov, Alexander; Zhou, Xinjia; Petrenko, B.; Liang, Xingming; Kihai, Yury; Dash, Prasanjit; Stroup, John; Sapper, John; DiGiacomo, Paul M.

doi:10.3390/rs8040315

Cited by 28 publications

(26 citation statements)

References 23 publications

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“…For a decadal analysis, monthly SODA reanalysis version 3.3.1 temperature, salinity, and subsurface currents from 1993 to 2012 were used, and mapped onto a 0.25°× 0.25°Mercator horizontal grid at 50 vertical levels. This version uses the global ocean/sea ice component of the GFDL CM2.5 coupled model (Delworth et al, 2012) and uses 15.4 million hydrographic profiles from the World Ocean Database 2013 and bathythermograph data as described in Levitus et al (2009) as well as in situ and remotely sensed SST (Casey et al, 2010;Ignatov et al, 2016;Woodruff et al, 2011). The data assimilation algorithm is described in Carton and Giese (2008).…”

Section: Hycom Model and Reanalysis Datamentioning

confidence: 99%

Large‐Scale Fresh and Salt Water Exchanges in the Indian Ocean

et al. 2019

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Upper‐ocean dynamics in the Northern Indian Ocean (NIO) depend on changes in the magnitude and location of the high salinity waters of the Arabian Sea and low salinity waters of the Bay of Bengal. The large sea surface salinity (SSS) differences between these two basins are related to the surface freshwater flux (evaporation minus precipitation), which is positive (negative) in the Arabian Sea (Bay of Bengal). To quantify large‐scale salinity changes on decadal time scale over the whole water column and to study trends in salinity and volume transport, we have analyzed Simple Ocean Data Assimilation (SODA) reanalysis product, HYbrid Coordinate Ocean Model (HYCOM) simulations, European Centre for Medium‐Range Weather Forecasting's ERA‐Interim reanalysis product, and riverine streamflow data from the National Centers for Atmospheric Research's Global River Flow and Continental Discharge Dataset for the NIO. We find increased freshening conditions in the Bay of Bengal and salinification conditions in the Arabian Sea that would support a stronger zonal SSS difference in the NIO but that it is partially compensated by positive (negative) salt transports into the Bay of Bengal (BoB) (Arabian Sea). Empirical orthogonal function analysis of SODA SSS indicates that the main factors of SSS variability are Indian Ocean Dipole and El Niño‐Southern Oscillation and seasonal currents. The trends in the volume transport reveal decadal changes in zonal equatorial currents in HYCOM and Somali Current in SODA.

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Section: Hycom Model and Reanalysis Datamentioning

confidence: 99%

Large‐Scale Fresh and Salt Water Exchanges in the Indian Ocean

et al. 2019

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“…The capabilities of such monitoring have expanded with the launch of a new generation instrument, the Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite (GOES)- 16 and -17 (launched on 19 November 2016 and on 1 March 2018, respectively), and the ABI's twin sensor, the Advanced Himawari Imager (AHI) flown onboard the Japan Himawari-8 and -9 satellites (launched on The ABI/AHI offers five infrared atmospheric transparency window bands (centered at 3.9, 8.4, 10.3, 11.2, and 12.3 µm) suitable for SST, with high spatial resolution (2 km at nadir, which degrades to~12 km at satellite view zenith angles, VZA~67 • ), frequent scans (every 15/10 minutes for ABI/AHI; note that NOAA also considers "Mode 6" for ABI, with a 10 minute refresh rate), and superior radiometric performance [3][4][5]. The NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) system, initially developed to retrieve SST from polar-orbiting sensors, such as NOAA and MetOp AVHRRs; S-NPP and NOAA-20 VIIRS; and Terra and Aqua MODIS [6], was modified with the launch of Himawari-8 to process data of new generation geostationary sensors [7][8][9]. SSTs retrieved from GOES-16 ABI and Himawari-8 AHI reveal a clear and smooth diurnal cycle.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Sensitivity of GOES-16 ABI Sea Surface Temperature by Matching Satellite Observations with L4 Analysis

et al. 2019

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Monitoring of the diurnal warming cycle in sea surface temperature (SST) is one of the key tasks of the new generation geostationary sensors, the Geostationary Operational Environmental Satellite (GOES)-16/17 Advanced Baseline Imager (ABI), and the Himawari-8/9 Advanced Himawari Imager (AHI). However, such monitoring requires modifications of the conventional SST retrieval algorithms. In order to closely reproduce temporal and spatial variations in SST, the sensitivity of retrieved SST to SSTskin should be as close to 1 as possible. Regression algorithms trained by matching satellite observations with in situ SST from drifting and moored buoys do not meet this requirement. Since the geostationary sensors observe tropical regions over larger domains and under more favorable conditions than mid-to-high latitudes, the matchups are predominantly concentrated within a narrow range of in situ SSTs >2 85 K. As a result, the algorithms trained against in situ SST provide the sensitivity to SSTskin as low as ~0.7 on average. An alternative training method, employed in the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear-Sky Processor for Oceans, matches nighttime satellite clear-sky observations with the analysis L4 SST, interpolated to the sensor’s pixels. The method takes advantage of the total number of clear-sky pixels being large even at high latitudes. The operational use of this training method for ABI and AHI has increased the mean sensitivity of the global regression SST to ~0.9 without increasing regional biases. As a further development towards improved SSTskin retrieval, the piecewise regression SST algorithm was developed, which provides optimal sensitivity in every SST pixel. The paper describes the global and the piecewise regression algorithms trained against analysis SST and illustrates their performance with SST retrievals from the GOES-16 ABI.

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“…Like light, temperature varies through the water column, it therefore makes sense to set the light values to be vertically at the same level in the water column as the temperature values in order to avoid mismatches between EEE and SST effects that vary with water depth. Since the satellite SST data used by CRW are derived from measurements made at the sea surface and calibrated against drifting buoys [62], which provide temperatures at 20 cm depth [63], it follows that the light should also be set to a water depth of 20 cm. The effect of light attenuation due to refraction and reflection at the sea surface was modeled prior to incorporation into the calculations.…”

Section: Keppel Islands Examplementioning

confidence: 99%

Remote Sensing of Coral Bleaching Using Temperature and Light: Progress towards an Operational Algorithm

et al. 2017

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Abstract:The National Oceanic and Atmospheric Administration's Coral Reef Watch program developed and operates several global satellite products to monitor bleaching-level heat stress. While these products have a proven ability to predict the onset of most mass coral bleaching events, they occasionally miss events; inaccurately predict the severity of some mass coral bleaching events; or report false alarms. These products are based solely on temperature and yet coral bleaching is known to result from both temperature and light stress. This study presents a novel methodology (still under development), which combines temperature and light into a single measure of stress to predict the onset and severity of mass coral bleaching. We describe here the biological basis of the Light Stress Damage (LSD) algorithm under development. Then by using empirical relationships derived in separate experiments conducted in mesocosm facilities in the Mexican Caribbean we parameterize the LSD algorithm and demonstrate that it is able to describe three past bleaching events from the Great Barrier Reef (GBR). For this limited example, the LSD algorithm was able to better predict differences in the severity of the three past GBR bleaching events, quantifying the contribution of light to reduce or exacerbate the impact of heat stress. The new Light Stress Damage algorithm we present here is potentially a significant step forward in the evolution of satellite-based bleaching products.

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AVHRR GAC SST Reanalysis Version 1 (RAN1)

Cited by 28 publications

References 23 publications

Large‐Scale Fresh and Salt Water Exchanges in the Indian Ocean

Large‐Scale Fresh and Salt Water Exchanges in the Indian Ocean

Optimization of Sensitivity of GOES-16 ABI Sea Surface Temperature by Matching Satellite Observations with L4 Analysis

Remote Sensing of Coral Bleaching Using Temperature and Light: Progress towards an Operational Algorithm

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