scholarly journals A 1/4°-Spatial-Resolution Daily Sea Surface Temperature Climatology Based on a Blended Satellite and in situ Analysis

2014 ◽  
Vol 27 (21) ◽  
pp. 8221-8228 ◽  
Author(s):  
Viva F. Banzon ◽  
Richard W. Reynolds ◽  
Diane Stokes ◽  
Yan Xue
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Spatial Resolution ◽  
Sea Surface ◽  
Temperature Cycle ◽  
Western Boundary ◽  
Good Representation ◽  
Grid Points ◽  
Environmental Prediction

Abstract A new sea surface temperature (SST) climatological mean was constructed using the first 30 years (1982–2011) of the NOAA daily optimum interpolation (OI) SST. The daily analysis blends in situ and satellite data on a ¼° (~25 km) spatial grid. Use of an analysis allows computation of a climatological value for all ocean grid points, even those without observations. Comparisons were made with a monthly, 1°-spatial-resolution climatology produced by the National Centers for Environmental Prediction, computed primarily from the NOAA weekly OISST. Both climatologies were found to provide a good representation of major oceanic features and the annual temperature cycle. However, the daily climatology showed tighter gradients along western boundary currents and better resolution along coastlines. The two climatologies differed by over 0.6°C in high-SST-gradient regions because of resolution differences. The two climatologies also differed at very high latitudes, where the sea ice processing differed between the OISST products. In persistently cloudy areas, the new climatology was generally cooler by approximately 0.4°C, probably reflecting differences between the input satellite SSTs to the two analyses. Since the new climatology represents mean conditions at scales that match the daily analysis, it would be more appropriate for computing the corresponding daily anomalies.

scholarly journals Climate estimates for Eastern Amazon with OLAM model

2014 ◽  
Vol 29 (spe) ◽  
pp. 2-10
Author(s):  
Renato Ramos da Silva ◽  
Maria Isabel Vitorino ◽  
Paulo Kuhn ◽  
Daniela dos Santos Ananias
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Case Studies ◽  
Rainy Season ◽  
Initial Conditions ◽  
Regional Climate ◽  
Sea Surface ◽  
Temperature Cycle ◽  
Good Representation ◽  
Grid Scheme

The OLAM model has as its characteristics the advantage to represent simultaneously the regional and global meteorological phenomena using a refining grid scheme. During REMAM project OLAM was applied for a few case studies with the goal to evaluate its performance to estimate the regional climate for the eastern Amazon during periods of El Niño and La Niña. Case studies were performed for the rainy periods of the years 2010 and 2011 that were driven by distinct oceanic conditions. Initially, the model results were compared with local observations. The results demonstrated that OLAM was able to represent well the major precipitating regions, the diurnal temperature cycle evolution, and the wind dynamics. After that, analysis of the results demonstrated that if we provide good initial conditions and a good representation of the sea surface temperature evolution, OLAM is able to forecast with two or three months in advance if a rainy season would be wet or dry.

scholarly journals Deep Learning of Sea Surface Temperature Patterns to Identify Ocean Extremes

2021 ◽  
Vol 13 (4) ◽  
pp. 744
Author(s):  
J. Xavier Prochaska ◽  
Peter C. Cornillon ◽  
David M. Reiman
Keyword(s):  
Deep Learning ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Space Distribution ◽  
Western Boundary ◽  
Learning Modules ◽  
Ocean Science ◽  
The Many ◽  
Future Work

We performed an out-of-distribution (OOD) analysis of ∼12,000,000 semi-independent 128 × 128 pixel2 sea surface temperature (SST) regions, which we define as cutouts, from all nighttime granules in the MODIS R2019 Level-2 public dataset to discover the most complex or extreme phenomena at the ocean’s surface. Our algorithm (ULMO) is a probabilistic autoencoder (PAE), which combines two deep learning modules: (1) an autoencoder, trained on ∼150,000 random cutouts from 2010, to represent any input cutout with a 512-dimensional latent vector akin to a (non-linear) Empirical Orthogonal Function (EOF) analysis; and (2) a normalizing flow, which maps the autoencoder’s latent space distribution onto an isotropic Gaussian manifold. From the latter, we calculated a log-likelihood (LL) value for each cutout and defined outlier cutouts to be those in the lowest 0.1% of the distribution. These exhibit large gradients and patterns characteristic of a highly dynamic ocean surface, and many are located within larger complexes whose unique dynamics warrant future analysis. Without guidance, ULMO consistently locates the outliers where the major western boundary currents separate from the continental margin. Prompted by these results, we began the process of exploring the fundamental patterns learned by ULMO thereby identifying several compelling examples. Future work may find that algorithms such as ULMO hold significant potential/promise to learn and derive other, not-yet-identified behaviors in the ocean from the many archives of satellite-derived SST fields. We see no impediment to applying them to other large remote-sensing datasets for ocean science (e.g., SSH and ocean color).

scholarly journals Validation of Sea Surface Temperature from GCOM-C Satellite Using iQuam Datasets and MUR-SST in Indonesian Waters

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Bambang Sukresno ◽  
Dinarika Jatisworo ◽  
Rizki Hanintyo
Keyword(s):  
High Resolution ◽  
Error Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Important Variable ◽  
Sea Surface ◽  
Analysis Method ◽  
Validation Process ◽  
Indonesian Seas

Sea surface temperature (SST) is an important variable in oceanography. One of the SST data can be obtained from the Global Observation Mission-Climate (GCOM-C) satellite. Therefore, this data needs to be validated before being applied in various fields. This study aimed to validate SST data from the GCOM-C satellite in the Indonesian Seas. Validation was performed using the data of Multi-sensor Ultra-high Resolution sea surface temperature (MUR-SST) and in situ sea surface temperature Quality Monitor (iQuam). The data used are the daily GCOM-C SST dataset from January to December 2018, as well as the daily dataset from MUR-SST and iQuam in the same period. The validation process was carried out using the three-way error analysis method. The results showed that the accuracy of the GCOM-C SST was 0.37oC.

scholarly journals RETRIEVAL OF SEA SURFACE TEMPERATURE OVER POTERAN ISLAND WATER OF INDONESIA WITH LANDSAT 8 TIRS IMAGE: A PRELIMINARY ALGORITHM

2015 ◽  
Vol XL-2/W4 ◽  
pp. 87-90 ◽  
Author(s):  
M. A. Syariz ◽  
L. M. Jaelani ◽  
L. Subehi ◽  
A. Pamungkas ◽  
E. S. Koenhardono ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Brightness Temperature ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
In Situ Data ◽  
The North ◽  
Thermal Band

The Sea Surface Temperature (SST) retrieval from satellites data Thus, it could provide SST data for a long time. Since, the algorithms of SST estimation by using Landsat 8 Thermal Band are sitedependence, we need to develop an applicable algorithm in Indonesian water. The aim of this research was to develop SST algorithms in the North Java Island Water. The data used are in-situ data measured on April 22, 2015 and also estimated brightness temperature data from Landsat 8 Thermal Band Image (band 10 and band 11). The algorithm was established using 45 data by assessing the relation of measured in-situ data and estimated brightness temperature. Then, the algorithm was validated by using another 40 points. The results showed that the good performance of the sea surface temperature algorithm with coefficient of determination (<i>R</i><sup>2</sup>) and Root Mean Square Error (<i>RMSE</i>) of 0.912 and 0.028, respectively.

scholarly journals Adjusting for Desert-Dust-Related Biases in a Climate Data Record of Sea Surface Temperature

2020 ◽  
Vol 12 (16) ◽  
pp. 2554
Author(s):  
Christopher J. Merchant ◽  
Owen Embury
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Dust Aerosol ◽  
Sea Surface ◽  
Climate Data ◽  
Desert Dust ◽  
Data Record ◽  
Climate Data Record

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol.

scholarly journals Satellite-based time-series of sea-surface temperature since 1981 for climate applications

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Christopher J. Merchant ◽  
Owen Embury ◽  
Claire E. Bulgin ◽  
Thomas Block ◽  
Gary K. Corlett ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ocean Model ◽  
Sea Surface ◽  
Climate Data ◽  
Satellite Measurements ◽  
Data Record ◽  
The Mean ◽  
Climate Data Record

Abstract A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 1012 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km2 and 45 km2. The mean density of good-quality observations is 13 km−2 yr−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

scholarly journals Evaluation of global monitoring and forecasting systems at Mercator Océan

Ocean Science ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 57-81 ◽  
Author(s):  
J.-M. Lellouche ◽  
O. Le Galloudec ◽  
M. Drévillon ◽  
C. Régnier ◽  
E. Greiner ◽  
...  
Keyword(s):  
Quality Control ◽  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Altimeter Data ◽  
Open Boundary ◽  
Vertical Profiles ◽  
Validation Procedure

Abstract. Since December 2010, the MyOcean global analysis and forecasting system has consisted of the Mercator Océan NEMO global 1/4° configuration with a 1/12° nested model over the Atlantic and the Mediterranean. The open boundary data for the nested configuration come from the global 1/4° configuration at 20° S and 80° N. The data are assimilated by means of a reduced-order Kalman filter with a 3-D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. A 3-D-Var scheme provides a correction for the slowly evolving large-scale biases in temperature and salinity. Altimeter data, satellite sea surface temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. In addition to the quality control performed by data producers, the system carries out a proper quality control on temperature and salinity vertical profiles in order to minimise the risk of erroneous observed profiles being assimilated in the model. This paper describes the recent systems used by Mercator Océan and the validation procedure applied to current MyOcean systems as well as systems under development. The paper shows how refinements or adjustments to the system during the validation procedure affect its quality. Additionally, we show that quality checks (in situ, drifters) and data sources (satellite sea surface temperature) have as great an impact as the system design (model physics and assimilation parameters). The results of the scientific assessment are illustrated with diagnostics over the year 2010 mainly, assorted with time series over the 2007–2011 period. The validation procedure demonstrates the accuracy of MyOcean global products, whose quality is stable over time. All monitoring systems are close to altimetric observations with a forecast RMS difference of 7 cm. The update of the mean dynamic topography corrects local biases in the Indonesian Throughflow and in the western tropical Pacific. This improves also the subsurface currents at the Equator. The global systems give an accurate description of water masses almost everywhere. Between 0 and 500 m, departures from in situ observations rarely exceed 1 °C and 0.2 psu. The assimilation of an improved sea surface temperature product aims to better represent the sea ice concentration and the sea ice edge. The systems under development are still suffering from a drift which can only be detected by means of a 5-yr hindcast, preventing us from upgrading them in real time. This emphasizes the need to pursue research while building future systems for MyOcean2 forecasting.

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