Estimation of sea surface temperature from SEVIRI data: algorithm testing and comparison with AVHRR products

Three surface temperature (ST) algorithms for Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data are developed and tested. A general split window algorithm for ST estimation, a sea surface temperature (SST) algorithm and a nonlinear algorithm (NLSST) developed for SEVIRI data. The test was carried out by comparing SEVIRI data with two types of data: (a) in situ and (b) obtained with the NLSST algorithm applied to Advanced Very High Resolution Radiometer (AVHRR). The field campaign was carried out over sea using a thermal radiometer. The algorithms were applied to SEVIRI images in coincidence with the field campaign and the results show an rms error lower than 0.7 K. The comparison with AVHRR data was carried out in six test regions and provided an rms error lower than 1.3 K. The best results were obtained for the SST algorithm 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.     

Infrared sensing of sea surface temperature from space

Error sources in infrared remote sensing of sea surface temperature are discussed, e.g., imperfect transmittance models, uncertain or unknown atmospheric pressure-temperature-humidity vertical profiles, temperature discontinuities at the air-sea interface, temperature differences between surface and bulk water, and neglect of surface emissivity and reflectance. Some of these are analyzed using a simplified version of the transmittance function of Prabhakara et al. (1974). The rms error in conventional sea surface temperature retrievals, in which computers are used to integrate the equation of radiative transfer over many atmospheric layers, has thus far been reduced to about ±1 K (Maul, 1980). This error is for optimum conditions, and seems irreducible. Unless the accuracy can be improved it seems impractical to spend so much effort on lengthy computer retrievals. Prabhakara et al. (1974) have devised a much simpler retrieval method using three infrared bands, which yields an rms error of ±1.1 K. A very simple method yielding ±1.0 K with two infrared bands is described here.     

Generating global surface albedo products from multiple geostationary satellites

Operational weather geostationary satellites have acquired data for more than two decades and offer thus the possibility to generate long time series of Essential Climate Variables like surface albedo as suggested by GCOS. This paper investigates the possibility to generate consistent global, to the exception of the polar regions, surface broadband albedo product from these satellites. In this context, the paper addresses two specific issues. Firstly, the spatial consistency of surface albedo derived from five different geostationary satellites is examined in detail. Secondly, this product is compared with the equivalent MODIS one to define the temporal consistency between surface albedo derived with old geostationary instruments and technologically advanced radiometers like MODIS. The analysis of the surface albedo products has revealed a good agreement between the products derived from the various geostationary satellites. Comparison of this global product with the one routinely derived from MODIS shows that on the average, the mean relative difference between these two data sets agree within 10%. These first encouraging results open therefore a new avenue for the exploitation of the archived data for the generation of long time series, covering the last 25 years or so, of global surface albedo from geostationary weather satellites.     

Optimal estimation of sea surface temperature from split-window observations

Optimal estimation (OE) improves sea surface temperature (SST) estimated from satellite infrared imagery in the “split-window”, in comparison to SST retrieved using the usual multi-channel (MCSST) or non-linear (NLSST) estimators. This is demonstrated using three months of observations of the Advanced Very High Resolution Radiometer (AVHRR) on the first Meteorological Operational satellite (Metop-A), matched in time and space to drifter SSTs collected on the global telecommunications system. There are 32,175 matches. The prior for the OE is forecast atmospheric fields from the Météo-France global numerical weather prediction system (ARPEGE), the forward model is RTTOV8.7, and a reduced state vector comprising SST and total column water vapour (TCWV) is used. Operational NLSST coefficients give mean and standard deviation (SD) of the difference between satellite and drifter SSTs of 0.00 and 0.72 K. The “best possible” NLSST and MCSST coefficients, empirically regressed on the data themselves, give zero mean difference and SDs of 0.66 K and 0.73 K respectively. Significant contributions to the global SD arise from regional systematic errors (biases) of several tenths of kelvin in the NLSST. With no bias corrections to either prior fields or forward model, the SSTs retrieved by OE minus drifter SSTs have mean and SD of − 0.16 and 0.49 K respectively. The reduction in SD below the “best possible” regression results shows that OE deals with structural limitations of the NLSST and MCSST algorithms. Using simple empirical bias corrections to improve the OE, retrieved minus drifter SSTs are obtained with mean and SD of − 0.06 and 0.44 K respectively. Regional biases are greatly reduced, such that the absolute bias is less than 0.1 K in 61% of 10°-latitude by 30°-longitude cells. OE also allows a statistic of the agreement between modelled and measured brightness temperatures to be calculated. We show that this measure is more efficient than the current system of confidence levels at identifying reliable retrievals, and that the best 75% of satellite SSTs by this measure have negligible bias and retrieval error of order 0.25 K.     

Emittance effect on the remotely sensed sea surface temperature

A systemtic underestimation of remotely sensed sea surface temperature occurs in calculations which assume a value of the surface emittance equal to unity in the atmospheric window, where the measurements are taken. The paper includes a detailed examination of the effect on the sea surface temperature estimation caused by assuming a wrong value of the emittance. Results show that this effect is a function of the atmospheric transmittance and the surface temperature. The angular dependence and the influence of the sea state on this effect are also investigated     

Diurnal variability in sea surface temperature in the Arctic

The formation of diurnal warming events in sea surface temperature (SST) observations in the Arctic is investigated using multiple satellite derived SST products and in situ buoy temperature measurements. Significant diurnal warming events (of the order of several K) are shown to occur even in the Arctic during summer months, when the total daily insolation at high latitudes is, in fact, higher than that at low and mid latitudes. The observed Arctic diurnal warming events are shown to usually happen in persistent low wind conditions, and are more frequent in shallow waters than deep waters. During the studied period of June and July 2008, significant diurnal warming events were observed over most of the studied area, although with smaller spatial extent and reoccurring less often when compared to events reported at low and mid latitudes.     

Sea surface effects on the sea surface temperature estimation by remote sensing

By using a sea surface temperature profiler buoy, the behaviour of the vertical temperature profile near the sea surface was observed in Mutsu Bay. In the daytime under calm and strong sunshine condition, there occurred a large temperature difference between the uppermost sea surface and the 1 m depth. The difference disappeared when the wind began to blow with a speed greater than 4ms^?1. Besides the atmospheric effects the inhomogeneity of the vertical temperature distribution near the sea surface must be another major error factor in the sea surface temperature estimation by satellite remote sensing.     

Evaluation of MODIS land surface temperature products for daily air surface temperature estimation in northwest Vietnam

Recently, the MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) is considered one of the most suitable ways to retrieve air surface temperature (T_a) – one of the most important and widely used climate variables for a wide range of applications. In fact, many successful studies have been reported in many regions of the world. Each day, four MODIS LST data are available; from two sensors (Terra and Aqua) at two local overpass times (daytime and night-time). However, due to their different overpass times, most studies have used LST daytime and night-time for daily maximum (T_max) and minimum (T_min) air surface temperature estimation, respectively. Therefore, the performance of each individual LST data, the effect of the dynamic combination of these four LST data, the effect of land surface characteristics, and the effect of LST quality on the same estimation in the same region on the accuracy of estimated T_a remains unclear. In this study, we evaluated and tested all individual LST data as well as all possible combinations of the four MODIS LST data from two separate stations with distinct land surface characteristics in northwest Vietnam for 10 years (from 2004 to 2013) under two sky conditions (all clear sky conditions and only good data – i.e. Quality Control (QC) value of 0) for daily T_a (T_max, T_min, and T_mean) estimations. In addition, the mixed data of the two stations were also evaluated. Our results showed that Terra LST data have a higher correlation with T_a than Aqua LST; which is consistent for both stations and both quality conditions (all clear sky and only good data). A closer overpass time with T_max or T_min occurrence time did not guarantee a higher accuracy of T_a estimation. Using only good LST data produced a higher accuracy of T_a estimation than using all clear sky data. However, if the percentage of good data is low (i.e. less than 30%), then the all clear sky data will provide better results for T_max estimation. Comparing the performance of the different combinations when using the single station and mixed station data, combinations including at least one night-time LST produced stable and high accuracy T_min and T_mean estimates, while the combinations with only daytime LST produced very low accuracy results. For T_max estimation, the results were less impacted by LST quality; however, they were strongly impacted by different combinations and land surface characteristics.     

Prediction of daily sea surface temperature using artificial neural networks

We present an artificial neural network model to predict the sea surface temperature (SST) and delineate SST fronts in the northe-astern Arabian Sea. The predictions are made one day in advance, using current day’s SST for predicting the SST of the next day. The model is used to predict the SST map for every single day during 2013–2015. The results show that more than 75% of the time the model error is ≤ ±0.5ºC. For the years 2014 and 2015, 80% of the predictions had an error ≤±0.5ºC. The model performance is dependent on the availability of data during the previous days. Thus during the summer monsoon months, when the data availability is comparatively less, the errors in the prediction are slightly higher. The model is also able to capture SST fronts.     

A non-regression-coefficients method of sea surface temperature retrieval from space

For several years now NOAA/NESDIS have derived an operational global sea surface temperature (SST) product from the AVHRR instrument on the NOAA satellites. This is done using the MCSST and CPSST algorithms which contain coefficients that are determined from a regression analysis of satellite data against in situ surface data. The current algorithms are used to provide global SST data without taking into account the latitude, climate or location of the satellite data, although the CPSST coefficients do have a weak dependence on the satellite brightness temperatures. Because of this global application the current SST algorithms have inherent errors due to local climate influences. In this paper a new SST algorithm is developed that does not rely on regression analysis to derive its coefficients. By using the spatial variation of the brightness temperatures in a small area (50 km by 50 km) it is possible to derive the appropriate coefficients to use in the algorithm. The SST field can thus be derived at any location without need for prior determination of the algorithm coefficients. In a simulation study, data from twenty-five radiosonde ascents-arc use with an atmospheric transmission model to derive a range of atmospheric transmittances and satellite brightness temperatures. Coincident AVHRR data and ship data are used to assess the accuracy of the new algorithm. The various dependencies of the terms in the SST algorithm are investigated. As with the MCSST and CPSST algorithms, the new method has largest errors when applied in situations of abnormal atmospheric structure. The improvement over the MCSST product may initially be only marginal, but with the advent of the more precise data from the Along Track Scanning Radiometer (ATSR) a more accurate global SST product may be possible.     

Mesoscale variability of sea surface temperature in the North Atlantic

Abstract

Analyses of mesoscale horizontal distributions of temperature were performed for an area of the North Atlantic using data from the NOAA-7 and NOAA-6 Advanced Very High Resolution Radiometer (AVHRR). The zonal and meridianal variance spectra have.slopes between —1.4 and —2.5 with a clear maximum at -2.0. This is also true for the direction-dependent structure functions. The isotropic part of the variance spectra has a mean slope of —2.2±0.17 at scales of 10— 100km; this lies between the slopes of -1 and —3 predicted by the theories of two-dimensional and geostrophic turbulence. A comparison between measurements and theories is difficult because of the insufficient applicability of these theories to boundary layers. Moreover, in some cases there are significant maxima in the variance spectra at scales between 50 km and 250 km.     

Retrieving ocean surface current by 4-D variational assimilation of sea surface temperature images

In this article we propose a new method to estimate ocean mesoscale structures of the surface current velocity by processing sea surface satellite images. Assuming that the intensity level can be described by a transport-diffusion equation, the proposed approach is based on variational assimilation of image observations within a simple transport-diffusion model. This approach permits to retrieve the current velocity field from a sequence of satellite images. Results of processing synthetic data and real NOAA-AVHRR satellite images are presented and commented.     

海面温度栅格图的锋面提取与矢量化

提出一种海面温度栅格图的锋面提取方法。针对海洋表层温度(SST)锋面强度分布不均匀的特点,利用低通滤波对表温梯度图像进行平滑。再利用迭代法确定出梯度图像的分割阈值,将图像分割成目标与背景两部分。通过数学形态学中图像细化的方法,提取海洋温度锋面的骨架并对细小分枝进行修剪。经矢量化生成锋面线后,利用抹角法对折线进行光滑。最后以西太平洋为例,给出了一个表温锋面提取的实例,表明利用此方法进行海表温度锋面的提取是可行与有效的。     

Provincial nature of chlorophyll and sea surface temperature observed by satellite

Monthly composite SeaWiFS derived chlorophyll, National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) derived sea surface temperature (SST) and QuikScat derived wind data of 2003–2005 (January–December) were analysed to study the provincial nature of chlorophyll‐a (chl‐a), SST and wind speed in the Arabian sea and Bay of Bengal. The study was confined to five provinces, three in the Arabian Sea and two in the Bay of Bengal. Results indicate provincial variability in the SST‐chl‐a relation. It suggests that the correlation between chlorophyll and SST is not always negative. A negative correlation was observed in provinces 1, 2 and 3 for all the seasons, whereas, except for the month of January–February, it was positively correlated for province 4. Analysis shows the provincial specific nature of chlorophyll variability to physical forcing and suggests that treatment of such a problem should not be undertaken on the basin scale.     

Satellite based forecasting of sea surface temperature in the Tuscan Archipelago

The system described employs a nonlinear forecasting technique based on a combination of genetic algorithms and empirical orthogonal function (EOF) analysis. The genetic algorithm identifies the equations that best describe the behaviour of the different temporal orthogonal functions in the EOF decomposition and therefore, enables global forecasting of future time variability. The method is applied to obtain a one-month ahead forecast of the monthly mean space-time variability of the sea surface temperature (SST) of the Tuscan Archipelago, northwest coast of Italy. The system performance has been validated comparing forecast fields with real satellite observations. Results indicate that the system provides better predictions than those based on climatology. Future research is oriented to make the system applicable to military operations, environmental control and fisheries activities.     

Seasonal and interannual studies of vortices in sea surface temperature data

An algorithm for calculating feature displacement velocities and for detecting vortices has been applied to 13 years of sea surface temperature data derived from Advanced Very High Resolution Radiometer (AVHRR) data. A unique global event database for seasonal and interannual studies of the spatial distribution of oceanic vortices was created for the years 1986–1998. The results indicate that (1) the number of vortices in each season is fairly constant from year to year in each hemisphere—however, their preferred locations change on seasonal to interannual time-scales; (2) the maximum number of vortices were detected in the summer and in the winter in all oceans and the minimum number were detected in the autumn; and (3) the distribution of the spatial density function shows preferred localizations such as 40°?S, the tropical instability region, marginal seas, western boundary and eastern boundary current regimes.     

The satellite-measured sea surface temperature change in the Gulf of Finland

Climate change in Baltic region and in the Gulf of Finland is an accomplished fact in human brains and in science. The purpose of this research is to retrieve quantitative level of changes for sea surface temperature (SST) of the Gulf of Finland. Two space systems National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) provided satellite data about temperature of the sea surface. SST data covers period 1981–2014 and includes 444 monthly data scenes with spatial resolution about 10 km. Data quality analysis displays high reliability of NOAA/AVHRR and Aqua/MODIS satellite information. The Gulf of Finland’s average annual SST has changed from 6.8°C in 1982 up to 8.2°C in 2014. Its mean speed of warming is about 0.04°C year^–1. The growth of the temperature was irregular, in the middle of 80th year, the temperature dropped down to 5.0°C, and then sharply increased up to 7.3°C in 1989. SST growth in the Gulf of Finland coincides with air temperature and sea temperature growth. The climate change in the Gulf of Finland has special significance due to the fragility of the northern ecosystems and high anthropogenic load.     

Stabilizing global mean surface temperature: A feedback control perspective

In this paper, we develop a discrete time, state variable feedback control regime to analyze the closed-loop properties associated with stabilizing the global mean surface temperature anomaly at 2 °C within a sequential decision making framework made up of 20 year review periods beginning in 2020. The design of the feedback control uses an optimal control approach that minimizes the peak deceleration of anthropogenic CO₂ emissions whilst avoiding overshooting the 2 °C target. The peak value for emissions deceleration that satisfies the closed-loop optimization was found to be linearly related to climate sensitivity and a climate sensitivity of 3.5 °C gave a deceleration of ?1.9 GtC/a/20 years². In addition to accounting for the predicted climate dynamics, the control system design includes a facility to emulate a robust corrective action in the face of uncertainty. The behavior of the overall control action is evaluated using an uncertainty scenario for climate model equilibrium sensitivity.