Global Sea Surface Temperature

Merchant, Christopher J.; Minnett, Peter J.; Beggs, Helen; Corlett, Gary K.; Gentemann, Chelle; Harris, Andrew R.; Høyer, Jacob L.; Maturi, Eileen

doi:10.1016/b978-0-12-814458-9.00002-2

Cited by 9 publications

(5 citation statements)

References 139 publications

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“…Four main terms can contribute to the difference between the satellite observations and in situ measurements: the satellite uncertainty, the in situ uncertainty, geophysical uncertainty, and cloud contamination [1]. A critical improvement on cloud detection and SST retrieval algorithm was applied to the H8-SST/JAXA (version 1.2) by JAXA Team to reduce the uncertainty of SST.…”

Section: Discussionmentioning

confidence: 99%

Validation of Sea Surface Temperature Derived From Himawari-8 by JAXA

Hao

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hourly sea surface temperature (SST) retrieved from Himawari-8 by the Japan Aerospace Exploration Agency (H8-SST/JAXA, latest version 1.2) is becoming an important data source for data merging as well as for resolving diurnal variation (DV). However, the spatial and temporal variation of the errors for the full disk is still unclear. In this article, two years of H8-SSTs/JAXA are validated against in situ measurements from iQuam2. In general, H8-SSTs/JAXA shows small biases between −0.11 and −0.03 K with root mean square error (RMSE) between 0.58 and 0.73 K. The spatial distributions of the errors reveal the following patterns: 1) a small median bias close to 0.1 K and RMSE of 0.4-0.6 K comparing to drifters are found at satellite zenith angle (SZA) 0°-35°; 2) negative biases (∼−0.3 K) are detected at SZA s 35°-58°; and 3) larger positive biases exceeding 0.3 K are also found along the viewing boundaries. The temporal variations of the errors show that 1) there is no prominent seasonal variation; 2) the amplitude of the DV of the errors is only ∼0.1 K for the statistical of all matchups, and 3) the maximum errors appears in the morning rather than in the noon. The statistics will be used in future work for DV analysis and merging purposes.

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Section: Discussionmentioning

confidence: 99%

Validation of Sea Surface Temperature Derived From Himawari-8 by JAXA

Hao

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Sea Surface Temperature (SST) is an essential climate variable for understanding the climate system and quantifying ongoing climatic change. Global mean SST has risen from decade-to-decade since the 1970s, with consequences for global weather patterns and oceanic ecosystems (Merchant et al, 2019). Sea surface temperature anomalies influence the atmosphere by altering the flux of latent and sensible heat from the ocean, and thus providing anomalous heating patterns (Holton & Hakim, 2013).…”

Section: Results and Discussion Mwoi-sst Oceanic Nino Indexmentioning

confidence: 99%

Satellite Oceanographic Data Processing and Analysis: Correlation Between Nino 3.4 Sea Surface Temperature & Sea Surface High and Wind

Lin¹,

Tang²,

Wijaya³

2022

Idn. J. Earth Sci.

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El Nino Southern Oscillation (ENSO) is an irregular climate oscillation induced by sea surface temperature anomalies (SSTA) in the Equatorial Pacific Ocean. An anomalous warming in this area is known as El Nino, while an anomalous cooling bears the name of La Nina. The objectives of this study are to: reproduce Oceanic Nino Index (ONI) based on MW OISST; produce Nino Region 3.4 wind and Sea Surface High (SSH); analyze the correlation between SST and Wind & SSH; discuss Typhoon Soudelor based on SST, Wind, and SSH; and analyze the correlation of El Nino and Precipitation in specific area. MWOI-SST was used to produce monthly mean SST over Nino 3.4 region from January 1998 – May 2020. Monthly wind data was obtained from QuickScat. Daily Sea Surface High (SSH) was obtained from Copernicus Marine Environment Monitoring Service (CMEMS). Daily precipitation from TRMM 3B42 over Bandung City (Indonesia) was used to assess the correlation between El Nino and precipitation in specific area. The results show that in Nino 3.4 region, 2015 is the hottest year during 1998-2020 period with average SST of 28.5oC, and 1999 is the coldest year with average SST of 25.7oC. The result shows that the MWOI-SST Ocean Nino Index has very strong correlation with ERSST.v5 with coefficient of correlation is 0.92 and RMSE is 0.36oC. the wind speed of Nino 3.4 region is range from 5.23 m/s to 7.97 m/s. Unlike Sea Surface Temperature (SST), annual average wind speed is more stable with monthly variation. The wind speed is observed high in the beginning and the end of years. Sea Surface Height (SSH) over Nino 3.4 region varied from 65.8 cm to 106.8 cm. 2015 is the highest SSH with annual average of 96 cm, whereas 1999 is the lowest SSH with annual average of 71.4 cm. It is observed that Sea Surface Temperature (SST) has negative correlation with wind speed with coefficient of correlation of 0.28. Conversely, Sea Surface Temperature (SST) over Nino 3.4 region has positive correlation with Sea Surface High (SSH) with coefficient of correlation of 0.30. which mean the higher temperature, the higher Sea Surface Height. During the passage of Typhoon Soudelor, there is evident cool trail along its track with rightward bias. We can assume that the decreasing precipitation in Bandung City might be affected by strong El Nino occurrence in 2015.

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“…Figure 8 displays the climatological environmental conditions of SST and VWS averaged over TC seasons from 2003 to 2019. The results depict that background SST and VWS decreased and increase poleward, which is due to the poleward decreasing solar radiation and changes of atmospheric circulation [44,45]. In areas equatorward 15 • N, SST (Figure 8a) and VWS (Figure 8b) are above 28 • C and below 15 m/s, respectively, both providing favorable conditions for TC development.…”

Section: Mechanism Analysismentioning

confidence: 91%

Poleward Migration of Spatial Asymmetry in Tropical Cyclone Precipitation over the Western North Pacific

Sun

et al. 2023

Atmosphere

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Comprehensive understanding of the spatial characteristics of tropical cyclone (TC) precipitation is essential for effective socioeconomic planning and scientific research. The present study examines the spatial asymmetry in TC precipitation over the western North Pacific (WNP) concerning various factors, including latitudes, sea surface temperature (SST), TC intensity, and translation speed, based on satellite observations. The results reveal a significant poleward migration of TC precipitation asymmetry particularly above 15° N. Furthermore, the asymmetry exhibits considerable sensitivity to changes in TC center latitudes and SSTs, characterized by anticlockwise and northeastward migration, respectively. The poleward migration of TC precipitation spatial asymmetry is primarily due to the poleward decreasing SST, increasing vertical wind shear and increasing TC translation speed. These findings contribute to a comprehensive understanding of TC behavior over the WNP and provide valuable insights for disaster preparedness and mitigation efforts.

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Global Sea Surface Temperature

Cited by 9 publications

References 139 publications

Validation of Sea Surface Temperature Derived From Himawari-8 by JAXA

Validation of Sea Surface Temperature Derived From Himawari-8 by JAXA

Satellite Oceanographic Data Processing and Analysis: Correlation Between Nino 3.4 Sea Surface Temperature & Sea Surface High and Wind

Poleward Migration of Spatial Asymmetry in Tropical Cyclone Precipitation over the Western North Pacific

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