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.     

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.     

Intraseasonal signals in the daily high resolution blended Reynolds sea surface temperature product over the tropical Indian Ocean and their validation

National Oceanic and Atmospheric Administration daily sea surface temperature (SST) products based on Advanced Microwave Scanning Radiometer (AMSR) and Advanced Very High Resolution Radiometer (AVHRR) have been used to understand the variability in the tropical Indian Ocean SST. These products are comparable with the deep sea moored buoy observations and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) SST in the tropical Indian Ocean. However considerable difference is noticed between these satellite SST products and deep sea buoys, especially at the intraseasonal time scale. Further the first Complex Empirical Orthogonal Function (CEOF) mode of TMI and AVHRR SST explains respectively 46.49% and 46.19% of the total variance. The second CEOF mode of TMI and AVHRR SST explains respectively 23.19% and 18.94% of the total SST variance in the tropical Indian Ocean. The AVHRR SST product is important because this daily product has been available since 1985. The analysis shows that AMSR measurements are contributing considerably to the understanding of the tropical Indian Ocean SST variability. Though satellite SST products are able to capture the observed intraseasonal variability reasonably well, more accurate satellite SST products are therefore necessary to understand the climatologically important Indian Ocean region and its air–sea interaction processes.     

How consistent is the satellite derived SST–LHF relationship in comparison with observed values?

Time series data for sea surface temperature (moored buoy), wind speed, air temperature, sea level pressure, relative humidity, short wave radiation and rainfall were collected close to the Lakshadweep islands for five months from July 2000 to cover two seasons, namely summer monsoon and autumn. Day and night passes of TMI data for the same period were analysed to compare with the observed values. Daily mean values were then generated from both satellite‐derived as well as observed parameters and daily latent heat flux (LHF) values computed using the advanced COARE‐3.0 version of the model. In concurrence with earlier studies, the observed LHF–SST relationship was inverse as the SST during this season seldom fell below 27°C. On the contrary, the satellite derived LHF–SST relationship exhibited a direct correlation. It is also observed that the satellite underestimation of SST increases linearly on either side of a threshold value of 28.5°C. Although the SST over the eastern Arabian Sea was generally above 27°C, the satellite underestimation often produced SSTs less than 27°C, thereby supporting a linear relationship with LHF, as suggested by Zhang and McPhaden. Similarly for SSTs higher than 28°C, the satellite underestimation prevented a further decrease of LHF (to sustain the linear relationship) by virtue of the inverse relationship for SSTs higher than 28°C. The overestimation of SST and wind speed in the satellite scenario generates a virtual enhancement of LHF values without cooling the sea surface. The linear relationship between SST and LHF is thus nothing but a virtual display of the observed inverse SST–LHF relationship.     

Bimodal variation of SST and related physical processes over the North Indian Ocean: special emphasis on satellite observations

Using sea surface temperature (SST) and wind speed retrieved by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), for the period of 1998–2003, we have studied the annual cycle of SST and confirmed the bimodal distribution of SST over the north Indian Ocean. Detailed analysis of SST revealed that the summer monsoon cooling (winter cooling) over the eastern Arabian Sea (Bay of Bengal) is more prominent than winter cooling (summer monsoon cooling). A sudden drop in surface short wave radiation by 57 W m^?2 (74 W m^?2) and rise in kinetic energy per unit mass by 24 J kg^?1 (26 J kg^?1) over the eastern Arabian Sea (Bay of Bengal) is observed in summer monsoon cooling period. The subsurface profiles of temperature and density for the spring warming and summer monsoon cooling phases are studied using the Arabian Sea Monsoon Experiment (ARMEX) data. These data indicate a shallow mixed layer during the spring warming and a deeper mixed layer during the summer monsoon cooling. Deepening of the mixed layer by 30 to 40 m with corresponding cooling of 2°C is found from warming to summer monsoon cooling in the eastern Arabian Sea. The depth of the 28°C isotherm in the eastern Arabian Sea during the spring warming is 80 m and during summer monsoon cooling it is about 60 m, while over the Bay of Bengal the 28°C isotherm is very shallow (35 m), even during the summer monsoon cooling. The time series of the isothermal layer depth and mixed layer depth during the warming phase revealed that the formation of the barrier layer in the spring warming phase and the absence of such layers during the summer cooling over the Arabian Sea. However, the barrier layer does exist over the Bay of Bengal with significant magnitude (20–25 m). The drop in the heat content with in first 50 m of the ocean from warming to the cooling phase is about 2.15 × 10⁸ J m^?2 over the Arabian Sea.     

Estimation of Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager, improved using numerical weather prediction

Most of the operational Sea Surface Temperature (SST) products derived from satellite infrared radiometry use multi-spectral algorithms. They show, in general, reasonable performances with root mean square (RMS) residuals around 0.5 K when validated against buoy measurements, but have limitations, particularly a component of the retrieval error that relates to such algorithms' limited ability to cope with the full variability of atmospheric absorption and emission. We propose to use forecast atmospheric profiles and a radiative transfer model to simulate the algorithmic errors of multi-spectral algorithms. In the practical case of SST derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), we demonstrate that simulated algorithmic errors do explain a significant component of the actual errors observed for the non linear (NL) split window algorithm in operational use at the Centre de Météorologie Spatiale (CMS). The simulated errors, used as correction terms, reduce significantly the regional biases of the NL algorithm as well as the standard deviation of the differences with drifting buoy measurements. The availability of atmospheric profiles associated with observed satellite-buoy differences allows us to analyze the origins of the main algorithmic errors observed in the SEVIRI field of view: a negative bias in the inter-tropical zone, and a mid-latitude positive bias. We demonstrate how these errors are explained by the sensitivity of observed brightness temperatures to the vertical distribution of water vapour, propagated through the SST retrieval algorithm.     

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.     

Intercomparison of microwave remote-sensing soil moisture data sets based on distributed eco-hydrological model simulation and in situ measurements over the North China Plain

Intercomparisons of microwave-based soil moisture products from active ASCAT (Advanced Scatterometer) and passive AMSR-E (Advanced Microwave Scanning Radiometer for the Earth Observing System) is conducted based on surface soil moisture (SSM) simulations from the eco-hydrological model, Vegetation Interface Processes (VIP), after it is carefully validated with in situ measurements over the North China Plain. Correlations with VIP SSM simulation are generally satisfactory with average values of 0.71 for ASCAT and 0.47 for AMSR-E during 2007–2009. ASCAT and AMSR-E present unbiased errors of 0.044 and 0.053 m³ m^?3 on average, with respect to model simulation. The empirical orthogonal functions (EOF) analysis results illustrate that AMSR-E provides more consistent SSM spatial structure with VIP than ASCAT; while ASCAT is more capable of capturing SSM temporal dynamics. This is supported by the facts that ASCAT has more consistent expansion coefficients corresponding to primary EOF mode with VIP (R = 0.825, p < 0.1). However, comparison based on SSM anomaly demonstrates that AMSR-E and ASCAT have similar skill in capturing SSM short-term variability. Temporal analysis of SSM anomaly time series shows that AMSR-E provides best performance in autumn, while ASCAT provides lower anomaly bias during highly-vegetated summer with vegetation optical depth of 0.61. Moreover, ASCAT retrieval accuracy is less influenced by vegetation cover, as it is in relatively better agreement with VIP simulation in forest than in other land-use types and exhibits smaller interannual fluctuation than AMSR-E. Identification of the error characteristics of these two microwave soil moisture data sets will be helpful for correctly interpreting the data products and also facilitate optimal specification of the error matrix in data assimilation at a regional scale.     

Impact of sub-daily wind forcing on mixed-layer depth and sea surface temperature: study using observations and a one-dimensional upper ocean model

The impact of sub-daily wind sampling on the diurnal cycle of oceanic mixed-layer depth (MLD) and sea surface temperature (SST) is investigated using a one-dimensional upper ocean model and observations at two locations: the Central Arabian Sea (CAS) and Eastern Equatorial Indian Ocean (EEIO). Motivation to carry out this study is twofold: first, it will help in understanding the possible error in model-simulated MLD and SST due to the non-inclusion of high-temporal wind sampling; and second, it will also emphasize the requirements of temporal sampling from space-based measurements of surface winds. Temporal decorrelation analysis suggests that over a 24-hour period, auto-correlation falls rapidly in the EEIO region, whereas the fall is less even at a lag of 24 hours in CAS. Time series analysis with different sub-daily sampling rates suggests that the optimum sampling rate is three hours for MLD and SST. A suite of one-dimensional model simulations performed at the CAS and EEIO locations with sub-daily wind suggests that once-daily synoptic measurements of wind, which is the most likely scenario with one scatterometer, results in small biases but large standard deviations in MLD. In the case of SST, there is a small positive bias in the order of 0.1°C at the CAS buoy location while at the EEIO location, no such bias is observed. With two scatterometers in a constellation resulting in two observations per day, one can obtain a small standard deviation in MLD, but the bias is greater in this case. For SST, except for a small bias (about 0.1°C) at the CAS location, the distribution is mostly well-behaved Gaussian in all cases. The present study suggests the advisability of acquiring more frequent wind measurements from space-borne scatterometers. A well-coordinated satellite scatterometer constellation will help in resolving the diurnal variability and associated feedback mechanism of air–sea exchange processes, enhancing the understanding of large-scale phenomena such as the Indian summer monsoon, El Niño-southern oscillations, and the Madden–Julian oscillation.     

Use of an optimum interpolation method to construct a high-resolution global ocean surface vector wind dataset from active scatterometers and passive radiometers

This study presents a new 0.25° gridded 6-hourly global ocean surface wind vector dataset from 2000 to 2015 produced by blending satellite wind retrievals from five active scatterometers (QuikSCAT, ASCAT-A, ASCAT-B, OSCAT, and HY-2A), nine passive radiometers (four SSM/I sensors, two SSMIS sensors, TMI, AMSR-E, and AMSR2) and one polarimetric radiometer (WindSat) with reanalysis from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) employing an optimum interpolation method (OIM). The accuracy of this wind product is determined through various comparisons with buoy measurements, NCEP/NCAR reanalysis and the cross-calibrated multi-platform (CCMP) winds. The comparisons indicate that OIM winds agree well with buoys, showing a root-mean-squared difference of 1.32 m s^?1 for wind speed and 24.73° for wind direction over 0–30 m s^?1 wind speed range. And the quality of OIM winds is improved significantly relative to NCEP/NCAR reanalysis and can be comparable with CCMP winds. Furthermore, OIM winds can reveal abundant small-scale features that are not visible in reanalysis data. In addition, the wind speed and direction retrievals of most satellites are proved to play an important role in generating the high-quality product, but the procedure for including HY-2A winds and WindSat wind directions should be further explored.     

Evaluation of the MODIS-Aqua Sea-Surface Temperature product in the inner and mid-shelves of southwest Buenos Aires Province,Argentina

Validation of sea-surface temperature (SST) provided by the MODIS-Aqua sensor (Moderate Resolution Imaging Spectroradiometer) for the inner and mid-shelves of the southwest of Buenos Aires Province (Argentina), is presented for the first time. In situ data obtained with a multi-parametric sonde YSI-6600 and a CTD SBE91 between 2002 and 2011 are used for comparison with the satellite SST product. The match-up exercise was established after comparing different spatial boxes, time difference windows, wind speeds, and also a coefficient of variation. The comparison exercise was made in the coastal zone and the rest of the inner and mid-shelves separately. In the coastal zone, applying a 3 × 2 pixel box and a time window of ±3 hours led to the most accurate results, with a coefficient of determination (R²) of 0.99, a bias of 0.62°C, and a root-mean-square-error (RMSE) of 0.79°C. In the inner-mid-shelves when applying a coefficient of variability <0.3, a time window of ±3 hours, and taking only values of wind speed > 6 m s^?1, R² is 0.97, bias is 0.46°C, and RMSE is 0.95°C. Wind speed plays a major role in the inner-mid-shelves as the SST product is affected by stratification and formation of a diurnal thermocline in the ‘skin and sub-skin layer’ when wind speed is below 6 m s^?1. The results for the two shelves are very similar. Finally, the spatial and temporal variability of the SST satellite product was analysed in the study area for the period August 2002–December 2010. The results show that inter-annual variability is not significant and that there is no positive or negative trend for the 9 years of the study. Seasonality is the main component of temporal variability, with variation in amplitude signal depending on bathymetry changes, physical forcing, stability of the water column, and presence of flood plains.     

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.     

Combined remote-sensing,model, and in situ measurements of sea surface temperature as an aid to recreational navigation: crossing the Gulf Stream

Combined in situ, model, and satellite remote-sensing observations are used to determine the location of the Gulf Stream as an aid to safe navigation for small recreational vessels.

A field study was executed from Hamilton, Bermuda, to Virginia Beach, USA, over a period of 5 days, from 30 June 2010 to 4 July 2010 to test the feasibility of using remote-sensing products as an aid to cross the Gulf Stream from the point of view of a small, slow-moving (?6 knots, 3 m s^?1) sailboat. The in situ data collected were compared to NASA Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) remote-sensing data, to the Global High Resolution Sea Surface Temperature (GHRSST) microwave and infrared blended data set, to the National Oceanic and Atmospheric Administration Real-Time Ocean Forecast System (NOAARTOFS) ocean model, and to selected NOAA buoy and ship measurements.

A spatio-temporal analysis was performed by comparing the in situ measurements with observations retrieved at the same time and location in each of the data sets. The least error (correlation coefficient r?=?0.94) was obtained using MODIS data, and the largest error (r?=?0.78) was obtained using the RTOFS model data. Overall, most observations agree with the general spatio-temporal trend of the in situ data, with 95% of the errors within ±1°C and 98% of the errors within ±2°C.

The study shows that MODIS data are particularly suited to identification of the location of the Gulf Stream, which can be used by small vessels to optimize the crossing route and to minimize the risks associated with the passage.     

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.     

Spatial and temporal variability of satellite-derived sea surface temperature in the Barents Sea

The Barents Sea (BS) is an important region for studying climate change. This sea is located on the main pathway of the heat transported from low to high latitudes. Since oceanic conditions in the BS may influence vast areas of the Arctic Ocean, it is important to continue to monitor this region and analyse the available oceanographic data sets. One of the important quantities that can be used to track climate change is the sea surface temperature (SST). In this study, we have analysed the 32 years, (1982–2013) National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation SST Version 2 data for the BS. Our results indicate that the regionally averaged SST trend in the BS (about 0.03°C year^–1) is greater than the global trend. This trend varies spatially with the lowest values north from 76° N and the highest values (about 0.06°C year^–1) in proximity of Svalbard and in coastal regions near the White Sea. The SST and 2 m air temperature (AT) trends are high in winter months in the open BS region located west from Novaya Zemlya. Such trends can be linked to a significant retreat of sea ice in this area in recent years. In this article, we also documented spatial patterns in the annual cycle of SST in the BS. We have shown that the interannual variability of SST is similar in different regions of the BS and well correlated with the interannual patterns in AT variability.     

卫星遥感反演海面风矢量数据真实性检验与质量评价

微波散射计(QuikSCAT)和微波辐射计(WindSAT)的10m风矢量数据是目前覆盖范围最大、持续时间最长的海面风场卫星数据产品。利用QuikSCAT和WindSAT运行时间重叠的风矢量原始轨道资料,分别与同步的全球海洋浮标实测风矢量资料进行比较。结果显示:QuikSCAT平均风速略大于浮标,平均绝对误差约为1m/s;风向平均绝对误差在8°以下。WindSAT平均风速略大于浮标,平均绝对误差不超过0.5m/s,均方根误差约为1m/s;风向平均绝对误差在10°以下。在全球海域,QuikSCAT和WindSAT风矢量数据质量高,可信度极好。QuikSCAT和WindSAT在同一点上空的过境时间为准同步,并且二者的风矢量数据相关性极好,可以互相替代。     

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.     

Evaluation of soil moisture from satellite observations over South America

A study was performed to evaluate the surface soil moisture derived from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) sensor observations over South America. Other soil moisture and rainfall datasets were also used for the analysis. The information for the soil data came from the Eta regional climate model, and for the rainfall data from the Tropical Rainfall Microwave Mission (TRMM) satellite. Statistical analysis was used to evaluate the quality of the soil moisture and rainfall products, with estimates of the correlation coefficient (R), χ² and Cramer's phi (?_c). The results show high correlations (R > 0.8) of the AMSR-E soil moisture products with the Eta model for different regions of South America. Comparison of soil moisture products with rainfall datasets showed that the AMSR-E C-band soil moisture product was highly correlated with the TRMM satellite rainfall datasets, with the highest values of χ² and ?. The results show that the AMSR-E C-band soil moisture products contain important information that can be used for various purposes, such as monitoring floods or droughts in arid areas or as input within the framework of an assimilation scheme of numerical weather prediction models.