Comparison of AMSR‐E and TMI sea surface temperature with Argo near‐surface temperature over the Indian Ocean

Sea surface temperature (SST) measurements from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are compared with near‐surface temperature (foundation SST) in situ measurements obtained from Argo floats over the Indian Ocean. Spatial variation was compared for 2002–2006 and 11 floats were used for temporal variation collocated observations. The results show that TMI and AMSR‐E SST measurements are slightly overestimated during the pre‐ and post‐monsoon seasons and underestimated during the monsoon season. Statistical analysis shows that the SST from the AMSR‐E is better correlated with the Argo foundation SST compared to the TMI. The standard deviation (SD) and root mean square error (RMSE) for AMSR‐E SST are 0.58°C and 0.35°C, respectively, over the Equatorial Indian Ocean (EIO). The corresponding values for the TMI are 0.66°C and 0.47°C. Over the Arabian Sea the SD values are slightly higher compared to the EIO values, whereas RMSE values are less for both TMI and AMSR‐E SST. These retrieval accuracies are above the expected retrieval accuracy. The seasonal average spatial distribution of AMSR‐E SST shows a better match with the Argo foundation SST compared to TMI SST distributions. The robustness of the good spatial match during the monsoon season may be attributed to strong winds.     

Inter-comparison of various global sea surface temperature products

In this study, eight global sea surface temperature (SST) products for 2009 are compared to clarify their characteristics. The median of eight daily values, the Ensemble Median as Reference Product (EMRP), is used as a reference product for inter-comparison. The results show that the absolute value of mean differences and the value of root mean square (RMS) differences are higher in single-microwave products such as Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E), Tropical Rainfall Measuring Mission Microwave Imager (TMI), and WindSat, than in products such as MicroWave Optimally Interpolated SST (MWOI), Merged satellite and in situ data Global Daily SST (MGD), and Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) constructed by merging several SST data. It is of note that the characteristics of SST products depend on the type of SST used within the product, rather than the data source used. A comparison of SST products was also conducted using EMRP and data observed by moored buoys. The results show that only AMSR-E has a warm bias (+0.06°C) while other products have a cool bias (maximum value ?0.10°C). The RMS error of TMI is the highest (0.57°C), and that of EMRP the lowest (0.28°C). Furthermore, the temporal variability between the data in each SST product was compared to those observed by the Kuroshio Extension Observatory (KEO) buoy. Results show that the temporal variability of EMRP corresponds well to that of buoy data, and that the RMS error of EMRP is lower than that of the other SST products.     

Observation of the Kuroshio intrusion region in the South China Sea from AVHRR data

The variability of sea surface temperature in the region of the Kuroshio intrusion into the South China Sea (SCS) through the Luzon Strait was studied using sea surface temperature (SST) derived from Advanced Very High Resolution Radiometer (AVHRR) from 1985 to 2002. The covariance empirical orthogonal function (CEOF) method was applied for analysing the temporal and spatial variability in the study area. The results show that the Kuroshio intrusion during El Niño periods is weaker than that in La Niña periods. The calculation of surface layer heat of the Kuroshio intrusion region also shows response to the El Niño-La Niña events. The variation is attributed to the changes in wind fields during those events.     

On the consistency in variations of the South China Sea Warm Pool as revealed by three sea surface temperature datasets

The areal and intensity indices of the South China Sea Warm Pool (SCSWP) derived from three datasets, the Advanced Very High Resolution Radiometer (AVHRR), Tropical Rainfall Measuring Mission's Microwave Imager (TMI) and Optimum Interpolation Version 2 (OI.v2) sea surface temperature (SST), are generally consistent with each other at monthly, seasonal and interannual scales. However, the three records are different in some cases. First, minor differences among the monthly records of intensity index are observed in the period July to September. Secondly, the interannual records of SCSWP intensity derived from AVHRR and OI.v2 are different in autumn during the period 1990-1996. The reason is not yet clear and nor is it clear which record best represents fluctuations in SCSWP intensity. These suggest that various drawbacks of the three datasets, such as low resolution of OI.v2, and cloud and rain contamination on AVHRR and TMI data, would be serious enough to allow deviation from each other to appear. Merging AVHRR and TMI SST data might be the way leading to a more convincing time series of SCSWP. In addition, changes of areal and intensity indices are not always consistent with each other, for example, they have different monthly patterns. Although the three interannual records of intensity index in three seasons all capture the main Multivariate ENSO Index (MEI) signals at a half-year lag, only those which are in the summer significantly correlated with MEI.     

Comparisons of regional satellite sea surface temperature gradients derived from MODIS and AVHRR sensors

Regional sea surface temperature (SST) gradients were examined for a 6-year (2003–2008) period using data from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and the Advanced Very High Resolution Radiometer (AVHRR) on two NOAA satellite platforms. Two regions, one in the California Current System and the other in the Gulf Stream, representing an eastern boundary upwelling region and strong western boundary current, respectively, were chosen to investigate the seasonal variability, statistical differences and similarities, and correlations with respect to the two sets of SST gradients. Results indicated higher gradient magnitudes using MODIS SST in relative comparison to those derived from AVHRR that are attributed to instrument and algorithm differences. These observed differences are important for any studies that employ SST gradients, such as fisheries investigations that have traditionally relied on AVHRR SST gradients only.     

Interannual waves in the sea surface temperatures of the Pacific Ocean

Various oscillatory modes of sea surface temperatures (SSTs) observed over a period of 8.8 years with the NASA Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) and for 13 years with the NOAA Advanced Very High Resolution Radiometer (AVHRR), the latter sensing in the thermal infrared band, are described for the Pacific Ocean. The various modes are isolated by a combination of techniques designed also to accommodate non-stationary phenomena. After detrending and removing the seasonal cycle from each grid map element of the data, singular value decomposition (SVD) is used to separate the data into spatial and temporal parts to facilitate the modal analysis. Empirical Mode Decomposition is then used to separate the temporal parts of the data into approximately seven intrinsic modal functions (IMFs) for the temporal parts of the first five principal components (PCs) resulting from the SVD. A filtered time sequence of SST grids is then obtained by selecting IMFs with periods longer than 1.5 years and then reconstructing the SST grid maps from the filtered PCs. The time sequence of SMMR SSTs in the Pacific Ocean shows El Niño Southern Oscillation (ENSO) oscillations not only along the Equator, but also in both the North and South Pacific, with, in fact, even larger amplitudes than along the Equator. A similar analysis was applied to the SST record from the AVHRR instrument. During the period of overlap with the SMMR record, similarities occur in the equatorial region, but the records are by no means identical. The AVHRR SSTs do not show any strong oscillations in the South Pacific.     

Using sea surface temperature measurements from microwave and infrared satellite measurements

The Tropical Rainfall Mapping Mission Microwave Imager (TMI) instrument Sea Surface Temperature (SST) product (v1.0) is compared with in situ observations obtained in the Atlantic Ocean. The TMI SST has a mean warm bias of 0.25?K±0.7?K when compared to in situ SST at a depth of 7?m. When TMI SST are compared to in situ skin SST measurements, the bias is 0.6?K±0.5?K. A limited global comparison between TMI SST and co-incident ERS-2 Along-Track Scanning Radiometer (ATSR/2) skin SST demonstrates a bias of 0.6?K±0.6?K consistent with the result obtained using in situ observations. These results are consistent with the predicted accuracy of the TMI SST data products. Based on these results, a simple method to merge the TMI and ATSR data is proposed.     

Inter-comparison of satellite sea ice motion with drifting buoy data

In this study, three low-resolution and three medium-resolution ice motion products were compared to ice-tethered profiler (ITP) global positioning system (GPS) data over a 2 year period. The ice motion products were the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E), merged Advanced Scatterometer + Special Sensor Microwave/Imager (ASCAT + SSM/I), advanced synthetic aperture radar (ASAR), and Advanced Very High Resolution Radiometer (AVHRR) ice motion data. The results show that the data quality of six satellite products is better than or close to expected values. The error distributions of the satellite ice motion generally have high kurtosis and heavy tails and are not normally distributed. Low-resolution ice motion generally shows large errors in the Fram Strait. AVHRR summer ice motion shows a larger bias, probably affected by inaccurate cloud masking, while the large errors in ASAR ice motion mainly occur due to occasional geolocation errors of near-real-time ASAR images used for ice motion retrieval. Inter-comparison between satellite ice motion products with different time intervals is also discussed.     

TRMM derived sea surface temperature in the wake of a cyclonic storm over the central Bay of Bengal

Sea surface cooling associated with a cyclone in the Bay of Bengal was investigated using the data derived from TRMM Microwave Imager (TMI) onboard Tropical Rainfall Measuring Mission (TRMM) spacecraft. Though the TRMM/TMI sensor has all weather capabilities, sea surface temperature (SSTs) can not be obtained during heavy rain conditions. Hence, to overcome the problem of having no observations during the cyclone day, weekly analysis was carried out during the cyclone week (27 March–2 April 2000) and pre‐cyclone week (20–26 March 2000). To compute the magnitude of SST cooling in the cyclone track, weekly SSTs of the cyclone period were subtracted from the pre‐cyclone period. Similar analysis was carried out during non‐cyclonic periods of 20–26 March and 27 March–2 April of 2001, 2002. The analysis indicated that the TMI SST was reduced by maximum of 1.57°C along the cyclone track during the passage of cyclonic storm. Such an activity was not observed during 27 March–2 April 2001 and 2002, indicating that the cooling observed in 27 March–2 April 2000 was due to the cyclonic storm. On the other hand, SST anomalies are positive during 27 March–2 April of 2001, 2002 over these regions. TRMM observations shows higher wind speed and precipitation rate associated with the storm and are responsible for decrease in SST. Analysis of Pathfinder Advanced Very High Resolution Radiometer (AVHRR) SST showed the cyclone induced cooling but the SSTs measurement was blocked by clouds during the cyclone period (27 March–2 April 2000). In the same time, Reynolds SSTs was unable to detect the cooled sea surface. In these circumstances, the cyclone induced sea surface cooling was well captured by TRMM/TMI and had distinct advantage of providing SSTs in presence of cloud as compared to infrared SSTs measurement like those from pathfinder SSTs.     

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.     

Geostatistical downscaling of AMSR2 precipitation with COMS infrared observations

This article presents a geostatistical approach for downscaling precipitation products from passive microwave satellites with geostationary Meteorological Satellite observations. More precisely, the Advanced Microwave Scanning Radiometer 2 (AMSR2) precipitation (daily level 3 product) with 0.25° spatial resolution and the Communication, Ocean and Meteorological Satellite (COMS) infrared (IR) data with 5 km spatial resolution were used for the downscaling experiment over the Korean peninsula. Brightness temperature data observed at COMS IR 1 and water vapour channels were incorporated for downscaling via area-to-point residual Kriging with non-linear regression. The evaluation results with densely sampled Automatic Weather Station data revealed that incorporating the COMS IR observations with the AMSR2 precipitation showed similar error statistics to those of the AMSR2 precipitation because of the limitations of IR observations themselves and the inherent errors of the AMSR2 precipitation product over land. However, the area-based evaluation using information entropy indicated that incorporating the COMS observations resulted in more detailed spatial variation in the final product than direct downscaling of the AMSR2 precipitation. In addition, local precipitation patterns could be captured when there were regions with missing precipitation values in the AMSR2 product. Consequently, the downscaling result is useful for understanding the local precipitation patterns with an accuracy similar to that provided by microwave satellite observations. It is also suggested that the spatial variability in the downscaling result and errors in input low-resolution data should be considered when downscaling coarse resolution data using fine resolution auxiliary variables.     

Operational measurement of sea surface temperatures at CMS Lannion from NOAA-7 AVHRR data

Abstract

Data from the NOAA-7 Advanced Very-High-Resolution Radiometer (AVHRR) have been used on a routine basis for sea surface temperature (SST) retrieval at the Centre de Meteorologie Spatiale (CMS) in Lannion (France) since September 1983. Operational SST retrieval is still practised at CMS, using NOAA-9 data. Two methods are used. The first, which is automatic, produces numerical fields (resolution: 15 × 15 nautical miles); the second is manual and produces graphic documents (resolution about 10 km). The corresponding products are published in a monthly bulletin, SATMER. The accuracy of satellite SSTs has been tested by various methods, the results of which are discussed. Some case studies of SST time variability in the Mediterranean are presented. One of the main conclusions is the need for mesoscale (10 km) numerical SST fields produced as often as possible (daily) by interactive methods.     

Multi-year sea ice mapping by thermal infrared radiometry

Marine operations in polar and subpolar regions rely on accurate sea ice information for operational planning purposes. Before venturing into operations, however, mapping of the prevailing sea ice conditions are important to feasibility analyses and planning of the operation. Multi-year sea ice information is often derived from passive microwave radiometers such as the Special Sensor Microwave Imager (SSM/I) on board the U.S. Defense Meteorological Satellite Program (DMSP) satellites, or the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite. These sources provide wide aerial coverage and all weather capability, but offer only low spatial resolution, 30 km. In contrast, the thermal infrared channel of the Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA satellites, provides a 1 1km spatial resolution at nadir with a reasonable cost. A technique for extraction of multi-year sea ice information from thermal infrared AVHRR data was thus created. It relies on surface temperature differences between first-year and multi-year sea ice. Adjustments of the absolute concentration levels were made based on a regression relation between AVHRR and SMMR based multi-year sea ice concentrations. The technique is inapplicable during periods of dark, cloud cover, or melting conditions.     

A comparative assessment of satellite-derived Adriatic Sea surface temperature

In this article, five Advanced Very High Resolution Radiometer (AVHRR) and four Moderate Resolution Imaging Spectrometer (MODIS)-based Adriatic-focused satellite sea surface temperature (SST) products are analysed and compared with two sets of in situ SST measurements: a drifter-based dataset collected in 2003, and a platform-based dataset gathered in 2004; an additional set was used to validate the new SST coefficients. Analysis of satellite minus in situ SST residuals shows similar results for both in situ datasets, with the differences being within 0.2 K. All daytime SST biases exhibited positive values (less than 0.5 K). Night-time biases for short-wave infrared (IR) algorithms exhibited near zero and small negative values with an exceptionally low standard deviation (about 0.3 K) regardless of the sensor used. Analysis of filtered residual time-series allowed direct comparison between different SST products. The seasonal change in the daytime biases was found to covary with similar changes in atmospheric water vapour and the Adriatic specific wind regime.     

Seasonal and interannual variability of oceanographic processes in the Gulf of Guinea: An investigation using AVHRR sea surface temperature data

The Gulf of Guinea is situated in a critical position for understanding Atlantic equatorial dynamics. This study investigates seasonal and interannual variability in sea surface temperature (SST) throughout this region, focusing on dynamical ocean processes. A 10.5-year time series of remotely sensed SST data with 4 km spatial resolution from the Advanced Very High Resolution Radiometer (AVHRR) were used for this investigation, as they are sufficient to resolve shelf processes. Firstly, patterns of cloud cover were assessed, then spatio-temporal variability in SST patterns was investigated. Features identified in climatological SST images were the Senegalese upwelling influence, coastal upwelling, tropical surface water, river run-off and fronts. Of particular interest is a shelf-edge cooling along the coast of Liberia and Sierra Leone in February. Interannual variability, assessed using annual mean images, time series decomposition and spectral analysis, showed a quasi-cyclic pattern of warm and cool years, perhaps related to El Niño-type forcing. The results of this study show the usefulness of infrared remote sensing for tropical oceanography, despite high levels of cloud cover and atmospheric water vapour contamination, and they provide evidence for theories of westward movement of the upwelling against the Guinea current and remote forcing of the upwelling.     

亚洲区域AMSR2与SMOS土壤水分产品对比研究

遥感反演土壤水分(SM)产品越来越多地应用于农业、气象、水文等研究,而微波土壤水分数据产品的区域适用性分析是其合理使用的必要前提。使用MERRA-2(Modern Era Retrospective-analysis for Research and Applications,Version 2)模拟土壤水分为参考数据,运用传统统计方法(原始数据相关性、距平相关性、偏差以及无偏均方根差)和TC(Triple-Collocation)不确定性误差模型分析的方法,对亚洲区域2012年7月～2016年7月两种被动微波土壤水分SMOS-L3-SM(Soil Moisture and Ocean Salinity,L3)和AMSR2-LPRM-SM(The Advanced Microwave Scanning Radiometer 2,Land Parameter Retrieval Model Product)进行对比评估。结果表明:①空间上SMOS-L3较AMSR2-LPRM数据与参考数据MERRA-2土壤水分的相关性较好,表现为SMOS-L3-SM具有较好的空间连续性,且在亚洲大多数地区有较小的无偏均方根差;②湿季条件下遥感土壤水分与参考值的相关性比干季条件下的相关性更好,且干季出现高纬地区(约55°)缺失值较多的情况;③两遥感土壤水分的TC误差呈现相似的分布,区域TC平均误差两者均为0.076 m~3/m~3。总之,SMOS-L3-SM和AMSR2-LPRM-SM在空间相关性及TC误差评价方面都具有合理性,为遥感土壤水分在农业、气象、水文等方面的应用提供参考。     

Response of Vietnam coastal upwelling to the 1997-1998 ENSO event observed by multisensor data

In this paper, we examine the behavior of the Vietnam coastal upwelling during the 1997-1998 El Niño-Southern Oscillation (ENSO) event. The baseline is 4 years of National Oceanic and Atmospheric Administration (NOAA) satellite Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature (SST) data taken from 1997 to 2000. Comparison of upwelling images to simultaneous ERS-2 (European Remote Sensing Satellite) wind fields indicates that the summer monsoon winds constitute a major generation forcing. During the 1997 El Niño, the monsoon winds enhanced the upwelling and induced the upwelling center to move southward. During the 1998 La Niña, the monsoon winds weakened the upwelling. In contrast with the tropical Pacific, in the study area, La Niña implies a warm event and El Niño a cold event. We use empirical orthogonal function (EOF) methods to analyze the spatial and temporal variance of the upwelling. The three principal modes account for 37%, 15%, and 8% of the total variance, respectively. The first EOF modes reveal that the SST variance in the north and south subregions underwent a positive-negative sign switch in summer 1997. The second EOF modes represent the monthly evolution in normal years. The third modes seem to be sensitive to the 1998 La Niña event. Simultaneous TOPEX/POSEIDON and ERS-2 altimeter data provide further evidence for our analysis. Comparison with California coastal upwelling and mid-Atlantic Bight (MAB) coastal upwelling indicates that the Vietnam coastal upwelling is the most intensive one.     

Thermal fronts of the southern South China Sea from satellite and in situ data

The major goal of this study was to find match-ups between thermal fronts mapped from satellite sea surface temperature (SST) imagery and from in situ data in the southern South China Sea (SSCS), using 11 ship surveys conducted by the South China Sea Institute of Oceanology (SCSIO) between 1987 and 1999. Fronts were automatically detected by the Cayula–Cornillon multi-image edge detection algorithm (CCA) applied to satellite-derived maps of the Advanced Very High Resolution Radiometer (AVHRR) SST obtained from the Pathfinder project (8364 twice-daily global fields with 9 km resolution between 1985 and 1996). Twice-daily near-instant frontal maps were composited without any averaging or smoothing to produce individual monthly frontal maps covering the period from January 1985 through December 1996 (144 maps in total). Although the SSCS is a tropical sea with little SST difference between water masses, the CCA turned out to be an effective tool for front mapping in the SSCS. Out of the 11 ship surveys analysed in this study, four surveys produced satisfactory match-ups. The percentage of match-ups is considered reasonably high given that (1) ship surveys were not optimized to cross fronts, therefore most in situ sections missed fronts; (2) satellite measurements of SST with AVHRR are hampered by cloudiness, therefore satellite-derived frontal maps might miss some fronts masked by persistent cloudiness. Fronts are more distinct in winter, when cross-frontal SST gradients are enhanced. From oceanographic vertical sections and horizontal maps, fronts are much sharper in the subsurface layer (represented here by 50 m level). Nonetheless, the CCA successfully detected SST fronts with a cross-frontal step as small as 1°C.     

Merging AVHRR and SMMR data for remote sensing of ice and cloud in polar regions

Abstract

Multispectral data from the Advanced Very High Resolution Radiometer (AVHRR) were digitally processed and merged with Scanning Multichannel Microwave Radiometer (SMMR) imagery. Five channels of AVHRR data, four channels of SMMR brightness temperatures and SMMR-derived ice concentration and ice type were co-registered to a polar stereographic grid. The merged data sets are currently being used in combination with meteorological information for integrated studies of clouds and sea ice.     

Interannual variability of NDVI over Africa and its relation to El Niño/Southern Oscillation

The array of Normalized Difference Vegetation Index (NDVI) products now being derived from satellite imagery open up new opportunities for the study of short and long-term variability in climate. Using a time series analysis procedure based on the Principal Components transform, and a sequence of monthly Advanced Very High Resolution Radiometer (AVHRR)-derived NDVI imagery from 1986 through 1990, we examine trends in variability of vegetation greenness for Africa for evidence of climatic trends. In addition to the anticipated seasonal trends, we identify signals of interannual variability. The most readily identified is one that periodically affects Southern Africa. It is shown that the temporal loadings for this component exhibit a very strong relationship with the El Nino/Southern Oscillation (ENSO) Index derived from atmospheric pressure patterns in the Pacific, Pacific sea surface temperature (SST) anomalies, and with anomalous Outgoing Longwave Radiation (OLR). However, we have also detected a second interannual variation, affecting most particularly East Africa and the Sahel, that does not exhibit a consistent ENSO relationship. The results show the teleconnection patterns between climatic conditions in the Pacific Ocean basin and vegetation conditions at specific regional locations over Africa. The comprehensive spatial character and high temporal resolution of these data offer exciting prospects for deriving a land surface index of ENSO and mapping the impacts of ENSO activity at continental scale. This study illustrates that vegetation reflectance data derived from polar orbiting satellites can serve as good proxy for the study of interannual climate variability.