scholarly journals A Completeness and Complementarity Analysis of the Data Sources in the NOAA In Situ Sea Surface Temperature Quality Monitor (iQuam) System

2021 ◽  
Vol 13 (18) ◽  
pp. 3741
Author(s):  
Haifeng Zhang ◽  
Alexander Ignatov
Keyword(s):  
Sea Surface ◽  
Data Sets ◽  
Data Set ◽  
In Situ Data ◽  
Satellite Sst ◽  
Complementarity Analysis ◽  
The Cost ◽  
Marine Environment Monitoring ◽  
Drifting Buoys

In situ sea surface temperatures (SST) are the key component of the calibration and validation (Cal/Val) of satellite SST retrievals and data assimilation (DA). The NOAA in situ SST Quality Monitor (iQuam) aims to collect, from various sources, all available in situ SST data, and integrate them into a maximally complete, uniform, and accurate dataset to support these applications. For each in situ data type, iQuam strives to ingest data from several independent sources, to ensure most complete coverage, at the cost of some redundancy in data feeds. The relative completeness of various inputs and their consistency and mutual complementarity are often unknown and are the focus of this study. For four platform types customarily employed in satellite Cal/Val and DA (drifting buoys, tropical moorings, ships, and Argo floats), five widely known data sets are analyzed: (1) International Comprehensive Ocean-Atmosphere Data Set (ICOADS), (2) Fleet Numerical Meteorology and Oceanography Center (FNMOC), (3) Atlantic Oceanographic and Meteorological Laboratory (AOML), (4) Copernicus Marine Environment Monitoring Service (CMEMS), and (5) Argo Global Data Assembly Centers (GDACs). Each data set reports SSTs from one or more platform types. It is found that drifting buoys are more fully represented in FNMOC and CMEMS. Ships are reported in FNMOC and ICOADS, which are best used in conjunction with each other, but not in CMEMS. Tropical moorings are well represented in ICOADS, FNMOC, and CMEMS. Some CMEMS mooring reports are sampled every 10 min (compared to the standard 1 h sampling in all other datasets). The CMEMS Argo profiling data set is, as expected, nearly identical with those from the two Argo GDACs.

scholarly journals Adequacy of the In Situ Observing System in the Satellite Era for Climate SST

2006 ◽  
Vol 23 (1) ◽  
pp. 107-120 ◽  
Author(s):  
Huai-Min Zhang ◽  
Richard W. Reynolds ◽  
Thomas M. Smith
Keyword(s):  
High Resolution ◽  
Satellite Observations ◽  
Sea Surface ◽  
Bias Error ◽  
Random Errors ◽  
In Situ Data ◽  
Satellite Sst ◽  
Maximum Bias ◽  
Very High

Abstract A method is presented to evaluate the adequacy of the recent in situ network for climate sea surface temperature (SST) analyses using both in situ and satellite observations. Satellite observations provide superior spatiotemporal coverage, but with biases; in situ data are needed to correct the satellite biases. Recent NOAA/U.S. Navy operational Advanced Very High Resolution Radiometer (AVHRR) satellite SST biases were analyzed to extract typical bias patterns and scales. Occasional biases of 2°C were found during large volcano eruptions and near the end of the satellite instruments’ lifetime. Because future biases could not be predicted, the in situ network was designed to reduce the large biases that have occurred to a required accuracy. Simulations with different buoy density were used to examine their ability to correct the satellite biases and to define the residual bias as a potential satellite bias error (PSBE). The PSBE and buoy density (BD) relationship was found to be nearly exponential, resulting in an optimal BD range of 2–3 per 10° × 10° box for efficient PSBE reduction. A BD of two buoys per 10° × 10° box reduces a 2°C maximum bias to below 0.5°C and reduces a 1°C maximum bias to about 0.3°C. The present in situ SST observing system was evaluated to define an equivalent buoy density (EBD), allowing ships to be used along with buoys according to their random errors. Seasonally averaged monthly EBD maps were computed to determine where additional buoys are needed for future deployments. Additionally, a PSBE was computed from the present EBD to assess the in situ system’s adequacy to remove potential future satellite biases.

scholarly journals Comparisons of Satellite and Modeled Surface Temperature and Chlorophyll Concentrations in the Baltic Sea with In Situ Data

2021 ◽  
Vol 13 (15) ◽  
pp. 3049
Author(s):  
Malgorzata Stramska ◽  
Marta Konik ◽  
Paulina Aniskiewicz ◽  
Jaromir Jakacki ◽  
Miroslaw Darecki
Keyword(s):  
Baltic Sea ◽  
Satellite Data ◽  
In Situ Measurements ◽  
Data Sets ◽  
The Baltic Sea ◽  
Data Set ◽  
In Situ Data ◽  
The Baltic ◽  
Good Agreement

Among the most frequently used satellite data are surface chlorophyll concentration (Chl) and temperature (SST). These data can be degraded in some coastal areas, for example, in the Baltic Sea. Other popular sources of data are reanalysis models. Before satellite or model data can be used effectively, they should be extensively compared with in situ measurements. Herein, we present results of such comparisons. We used SST and Chl from model reanalysis and satellites, and in situ data measured at eight open Baltic Sea stations. The data cover time interval from 1 January 1998 to 31 December 2019, but some satellite data were not always available. Both the model and the satellite SST data had good agreement with in situ measurements. In contrast, satellite and model estimates of Chl concentrations presented large errors. Modeled Chl presented the lowest bias and the best correlation with in situ data from all Chl data sets evaluated. Chl estimates from a regionally tuned algorithm (SatBaltic) had smaller errors in comparison with other satellite data sets and good agreement with in situ data in summer. Statistics were not as good for the full data set. High uncertainties found in chlorophyll satellite algorithms for the Baltic Sea highlight the importance of continuous regional validation of such algorithms with in situ data.

scholarly journals VARIABILITY AND VALIDATION OF SEA SURFACE TEMPERATURE ESTIMATED BY PATHFINDER ALGORITHM OF NOAA-AVHRR SATELLITE IN THE NORTH PAPUA WATERS

2013 ◽  
Vol 8 (1) ◽  
Author(s):  
Bisman Nababan ◽  
Bidawi Hasyim ◽  
Hilda I.N. Bada
Keyword(s):  
Satellite Data ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Night Time ◽  
In Situ Data ◽  
The North ◽  
Satellite Sst ◽  
Noaa Avhrr ◽  
Valid Estimate

Variability and validation of sea surface temperatures (SST) in north Papua waters were conducted using SST estimated by Pathfinder algorithm of NOAA AVHRR satellite and SST measurements from TAO buoy in 2001-2009. Satellite data (SST Pathfinder) were daily, weekly, and monthly composite with 4x4 km2 resolution and downloaded from http://poet.jpl.nasa.gov. In situ data (SST measurement from buoy TAO) were measured at a depth of 1.5 m and recorded every hour (http://www.pmel.noaa.gov/tao_deliv). The in situ data then converted into daily, weekly, and monthly average data. In general, the SST values of both satellite and in situ SST in the north Papua waters ranged between 27.10 - 31.90 °C. During the east season (June-September), SST values (27.90-31.90 °C) were generally higher than the SST values ( 27.10-30.13 °C) during the west season (December-February). In general, the SST values both day-time and night-time from in situ and the satellite measurements showed no significant differences except in waters close to the shore. The results also showed that the coefficient of determination values (R2) between the satellite and the in situ SST measurements were relatively low (65%) and up to 5% of RMSE. The relatively low correlation between in situ dan satellite SST measurements may be due to high cloud coverage (90-96%) in the north Papua waters so that SST satellite data become less representative of the in situ data. These results also indicated that the Pathfinder algorithm can not be used as a valid estimate of SST NOAA AVHRR satellite for the north Papua waters. Keywords: SST Pathfinder, NOAA AVHRR, Validation, TAO buoy, North Papua Waters

scholarly journals An Evaluation of Autonomous In Situ Temperature Loggers in a Coastal Region of the Eastern Mediterranean Sea for Use in the Validation of Near-Shore Satellite Sea Surface Temperature Measurements

2020 ◽  
Vol 12 (7) ◽  
pp. 1140
Author(s):  
Dimitrios N. Androulakis ◽  
Andrew Clive Banks ◽  
Costas Dounas ◽  
Dionissios P. Margaris
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Close Correlation ◽  
Coastal Ocean ◽  
Sea Surface ◽  
Human Society ◽  
In Situ Data ◽  
Satellite Sst ◽  
Near Shore

The coastal ocean is one of the most important environments on our planet, home to some of the most bio-diverse and productive ecosystems and providing key input to the livelihood of the majority of human society. It is also a highly dynamic and sensitive environment, particularly susceptible to damage from anthropogenic influences such as pollution and over-exploitation as well as the effects of climate change. These have the added potential to exacerbate other anthropogenic effects and the recent change in sea temperature can be considered as the most pervasive and severe cause of impact in coastal ecosystems worldwide. In addition to open ocean measurements, satellite observations of sea surface temperature (SST) have the potential to provide accurate synoptic coverage of this essential climate variable for the near-shore coastal ocean. However, this potential has not been fully realized, mainly because of a lack of reliable in situ validation data, and the contamination of near-shore measurements by the land. The underwater biotechnological park of Crete (UBPC) has been taking near surface temperature readings autonomously since 2014. Therefore, this study investigated the potential for this infrastructure to be used to validate SST measurements of the near-shore coastal ocean. A comparison between in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra SST data is presented for a four year (2014–2018) in situ time series recorded from the UBPC. For matchups between in situ and satellite SST data, only nighttime in situ extrapolated to the sea surface (SSTskin) data within ±1 h from the satellite’s overpass are selected and averaged. A close correlation between the in situ data and the MODIS SST was found (squared Pearson correlation coefficient-r2 > 0.9689, mean absolute error-Δ < 0.51 both for Aqua and Terra products). Moreover, close correlation was found between the satellite data and their adjacent satellite pixel’s data further from the shore (r2 > 0.9945, Δ < 0.23 for both Aqua and Terra products, daytime and nighttime satellite SST). However, there was also a consistent positive systematic difference in the satellite against satellite mean biases indicating a thermal adjacency effect from the land (e.g., mean bias between daytime Aqua satellite SST from the UBPC cell minus the respective adjacent cell’s data is δ = 0.02). Nevertheless, if improvements are made in the in situ sensors and their calibration and uncertainty evaluation, these initial results indicate that near-shore autonomous coastal underwater temperature arrays, such as the one at UBPC, could in the future provide valuable in situ data for the validation of satellite coastal SST measurements.

Daily High-Resolution-Blended Analyses for Sea Surface Temperature

2007 ◽  
Vol 20 (22) ◽  
pp. 5473-5496 ◽  
Author(s):  
Richard W. Reynolds ◽  
Thomas M. Smith ◽  
Chunying Liu ◽  
Dudley B. Chelton ◽  
Kenneth S. Casey ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temporal Resolution ◽  
Large Scale ◽  
Sea Surface ◽  
In Situ Data ◽  
Satellite Sst ◽  
Avhrr Data

Abstract Two new high-resolution sea surface temperature (SST) analysis products have been developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25° and a temporal resolution of 1 day. One product uses the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data. The other uses AVHRR and Advanced Microwave Scanning Radiometer (AMSR) on the NASA Earth Observing System satellite SST data. Both products also use in situ data from ships and buoys and include a large-scale adjustment of satellite biases with respect to the in situ data. Because of AMSR’s near-all-weather coverage, there is an increase in OI signal variance when AMSR is added to AVHRR. Thus, two products are needed to avoid an analysis variance jump when AMSR became available in June 2002. For both products, the results show improved spatial and temporal resolution compared to previous weekly 1° OI analyses. The AVHRR-only product uses Pathfinder AVHRR data (currently available from January 1985 to December 2005) and operational AVHRR data for 2006 onward. Pathfinder AVHRR was chosen over operational AVHRR, when available, because Pathfinder agrees better with the in situ data. The AMSR–AVHRR product begins with the start of AMSR data in June 2002. In this product, the primary AVHRR contribution is in regions near land where AMSR is not available. However, in cloud-free regions, use of both infrared and microwave instruments can reduce systematic biases because their error characteristics are independent.

scholarly journals Global carbon monoxide products from combined AIRS, TES and MLS measurements on A-train satellites

2014 ◽  
Vol 14 (1) ◽  
pp. 103-114 ◽  
Author(s):  
J. X. Warner ◽  
R. Yang ◽  
Z. Wei ◽  
F. Carminati ◽  
A. Tangborn ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Data Fusion ◽  
Satellite Data ◽  
Lower Troposphere ◽  
Data Sets ◽  
Data Set ◽  
Upper Troposphere ◽  
In Situ Data ◽  
Spatial Coverage

Abstract. This study tests a novel methodology to add value to satellite data sets. This methodology, data fusion, is similar to data assimilation, except that the background model-based field is replaced by a satellite data set, in this case AIRS (Atmospheric Infrared Sounder) carbon monoxide (CO) measurements. The observational information comes from CO measurements with lower spatial coverage than AIRS, namely, from TES (Tropospheric Emission Spectrometer) and MLS (Microwave Limb Sounder). We show that combining these data sets with data fusion uses the higher spectral resolution of TES to extend AIRS CO observational sensitivity to the lower troposphere, a region especially important for air quality studies. We also show that combined CO measurements from AIRS and MLS provide enhanced information in the UTLS (upper troposphere/lower stratosphere) region compared to each product individually. The combined AIRS–TES and AIRS–MLS CO products are validated against DACOM (differential absorption mid-IR diode laser spectrometer) in situ CO measurements from the INTEX-B (Intercontinental Chemical Transport Experiment: MILAGRO and Pacific phases) field campaign and in situ data from HIPPO (HIAPER Pole-to-Pole Observations) flights. The data fusion results show improved sensitivities in the lower and upper troposphere (20–30% and above 20%, respectively) as compared with AIRS-only version 5 CO retrievals, and improved daily coverage compared with TES and MLS CO data.

Variability of sea surface salinity in the tropical Indian Ocean as inferred from Aquarius and in situ data sets

2015 ◽  
Vol 36 (7) ◽  
pp. 1907-1920 ◽  
Author(s):  
Imranali M. Momin ◽  
Ashis K. Mitra ◽  
Satya Prakash ◽  
D.K. Mahapatra ◽  
Anitha Gera ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Tropical Indian Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Data Sets ◽  
Surface Salinity ◽  
In Situ Data

scholarly journals Contributions of dust and biomass burning to aerosols at a Colorado mountain-top site

2015 ◽  
Vol 15 (23) ◽  
pp. 13665-13679 ◽  
Author(s):  
A. G. Hallar ◽  
R. Petersen ◽  
E. Andrews ◽  
J. Michalsky ◽  
I. B. McCubbin ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Data Sets ◽  
Data Set ◽  
Western Usa ◽  
Angstrom Exponent ◽  
In Situ Data ◽  
Ångström Exponent ◽  
Multifilter Rotating Shadowband Radiometer ◽  
Ångstrom Exponent

Abstract. Visible Multifilter Rotating Shadowband Radiometer (vis-MFRSR) data were collected at Storm Peak Laboratory (SPL), a mountain-top facility in northwest Colorado, from 1999 to 2011 and in 2013. From 2011 to 2014, in situ measurements of aerosol light scattering were also obtained. Using these data sets together, the seasonal impact of dust and biomass burning is considered for the western USA. Analysis indicates that the median contributions to spring and summer aerosol optical depth (AOD) from dust and biomass-burning aerosols across the data set are comparable. The mean AOD is slightly greater in the summer, with significantly more frequent and short-duration high AOD measurements due to biomass-burning episodes than in the spring. The Ångström exponent showed a significant increase in the summer for both the in situ and vis-MFRSR data, suggesting an increase in combustion aerosols. Spring dust events are less distinguishable in the in situ data than the column measurement, suggesting that a significant amount of dust may be found above the elevation of SPL, 3220 m a.s.l. Twenty-two known case studies of intercontinental dust, regional dust, and biomass-burning events were investigated. These events were found to follow a similar pattern, in both aerosol loading and Ångström exponent, as the seasonal mean signal in both the vis-MFRSR and ground-based nephelometer. This data set highlights the wide-scale implications of a warmer, drier climate on visibility in the western USA.

Low Wave Number Radar Image Energy Influence on Determining Current

2012 ◽  
Vol 433-440 ◽  
pp. 6054-6059
Author(s):  
Gan Nan Yuan ◽  
Rui Cai Jia ◽  
Yun Tao Dai ◽  
Ying Li
Keyword(s):  
Wave Spectrum ◽  
Surface Current ◽  
Sea Surface ◽  
Radar Image ◽  
Wave Number ◽  
Sea State ◽  
Low Frequencies ◽  
In Situ Data ◽  
Main Effects

In the radar imaging mechanism different phenomena are present, as a result the radar image is not a direct representation of the sea state. In analyzing radar image spectra, it can be realized that all of these phenomena produce distortions in the wave spectrum. The main effects are more energy for very low frequencies. This work investigates the structure of the sea clutter spectrum, and analysis the low wave number energy influence on determining sea surface current. Then the radar measure current is validated by experiments. By comparing with the in situ data, we know that the radar results reversed by image spectrum without low wave number spectrum have high precision. The low wave number energy influent determining current seriously.

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.

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