Comparative Study of Optimization Methods for Estimation of Sea Surface Temperature and Ocean Wind with Microwave Radiometer Data

A dataset of sea surface temperature (SST) estimates is generated from the temperature observations of surface drifting buoys of NOAA’s Global Drifter Program. Estimates of SST at regular hourly time steps along drifter trajectories are obtained by fitting to observations a mathematical model representing simultaneously SST diurnal variability with three harmonics of the daily frequency, and SST low-frequency variability with a first degree polynomial. Subsequent estimates of non-diurnal SST, diurnal SST anomalies, and total SST as their sum, are provided with their respective standard uncertainties. This Lagrangian SST dataset has been developed to match the existing and on-going hourly dataset of position and velocity from the Global Drifter Program.

An effectiveness of the 8.3 [μm] band in the ASTER/TIR (Advanced Spaceborne Thermal Emission and Reflection radiometer / Thermal Infrared Radiometer) on Sea Surface Temperature (SST) estimation is evaluated together with a re-evaluation of SST estimation accuracy for ASTER/TIR by using MODTRAN 3.7 rather than LOWTRAN 7. It is found that SST estimation error for MODTRAN 3.7 is 3 to 9 [%] better than that for LOWTRAN 7 because the atmospheric transparency of the atmospheric window bands are increased by around 2 [%] while the transparency of the water vapor absorption band is decreased by approximately 3 [%] due to the fact that the wavenumber step of MODTRAN 3.7 is better than that of LOWTRAN 7 (1 [cm-1] for MODTRAN 3.7, 5 [cm-1] for LOWTRAN 7) . Furthermore, it is found that 1.5 to 39.2 [%] of improvement on SST estimation can be achieved by adding band 10 to the rest of ASTER/TIR bands (11 to 14) . The band 10 is getting 59.3 [%] more effective on SST estimation by shifting to larger wave number by 25 [cm-1] from the current center wave number.

Total faunal analyses of planktic foraminifer assemblages are used to derive sea surface temperature estimates for the last interglacial from Ocean Drilling Program Sites 1018 and 1020 off northern and central California. Foraminifer assemblage data were transformed to sea-surface temperature (SST) estimates by using the modern analog technique (MAT). In order to improve our ability to estimate SST in this area, the coretop calibration data base used in the MAT was augmented by 13 new age-validated coretop assemblages from the U.S. Pacific Margin.

Pliocene sea surface temperatures of the north atlantic ocean at 3.0 Ma

By referring to the sea surface temperature profiler buoy (SSTPB) data observed in Mutsu Bay, this study showed under calm and strong sunshine conditions, the vertical water temperature profile near the sea surface bends abruptly, and sea surface temperature detected by satellite remote sensing is not necessarily coincident to the bulk sea surface temperature. Besides the atmospheric effect, this effect causes another error in the estimation of sea surface temperature by remote sensing data known as the sea surface effect (SSE). As a sequel to a former paper, this paper is concerned with the investigation of the conditions which occur in the apparent SSE. Statistical analyses were directed to the total data set of SSTPB. The amount of SSE was evaluated by the water temperature difference between the uppermost surface and 1 m depth, and apparent SSE was identified to when the absolute value of the difference is larger than 0·5 °C. Apparent SSE was observed in the season from May to September. Its ...

A comparison between shipborne observations and sea surface temperatures measured by the satellites (NOAA-3, NOAA-4, NOAA-5, NOAA-6 and TIROS-N) off the Pacific coast of Canada indicates that in each case a linear regression is sufficient to relate the two sets of data. The standard error of estimate for the temperatures observed by the above satellites were: ±0.5°C, ±0.6°C, ±0.3 – ±0.4°C, ±0.6°C and ±0.5°C, respectively. When reliable ground truth data is available, sea surface temperatures in a limited region and time can be measured by the satellites to the above accuracy using this “field-calibration” method. Estimates of errors as large as ±2°C reported in literature are suspected to be mainly due to errors in shipborne measurements, failure to correct adequately for atmospheric effects, and uncertainty in the radiometer calibration. Eventually, these errors are likely to be reduced considerably but there still remains a problem of the proper interpretation of satellite-determined sea surface temperatures in the presence of marine haze, thin clouds and light mist. Until these problems are resolved the method of applying field-calibrations appears to be the only practical way of obtaining reliable estimates of sea surface temperatures in a given region.

Prediction of sea surface temperature is important for the study of marine ecosystems in the oceanic area. The sea surface temperature is a major parameter for analysing extreme atmospheric events. Sea surface temperature determines how heat from the sun is redistributed across the world’s oceans and will directly affect large- and small-scale weather and climate patterns. Comparison studies of two different learning (shallow and deep) models are employed for the forecasting of sea surface temperature. In most cases, the deep learning model also manages to overcome the limitations of the prior shallow learning models depending on the complexity of the problem, which helps to impede efficient training and discrete representations of multi-dimensional training data. The deep learning-based LSTM model is considered for the quick prediction of sea surface temperature with higher accuracy than the usual shallow learning-based feedforward model. Two different monthly SST time series data are applied for the estimation of sea surface temperature in the eastern Arabian Sea (AS) as well as Bay of Bengal (BOB) whose grid points are (70°E, 15°N) and (90°E, 15°N) respectively. LSTM and feedforward models are trained with 80% data and tested with different hidden units and epoch values to acquire a minimum error. For the 20% testing set, AS time-series data using the feedforward model gave more error. Performances of the prediction models are assessed by using root mean square error (RMSE). The results suggest that the temporal LSTM model outperforms the feedforward model specifically on sea surface temperature time series forecasting.KeywordsSea surface temperature (SST)Deep learningShallow learningArabian SeaBay of Bengal

Estimation of Sea Surface Temperature (SST) from split- window algorithms for NOAA-AVHRR data can be determined with rms values of 0.7 K on a global basis. However, this figure is not compatible with the stringent accuracy of 0.3 K required by climate studies. Among the different sources of errors, the presence of tropospheric aerosols in the satellite field of view prevents the retrieval of accurate satellite SSTs. Still, the effect of aerosols on temperature measurements derived from remote sensing techniques has been traditionally overlooked. Very few studies have addressed the problem of giving split-window algorithms which incorporate aerosol correction, although retrieving algorithms of the aerosol loading from the images do exist. The aim of this study is the evaluation of the effect of the aerosols on the SST MODTRAN code. Such code was used to compute the upwelling radiances and, subsequently k, the brightness temperatures under cloud-free conditions. The filter response functions for the NOAA14 instrument are used to produce theoretical brightness temperatures for the zenith angles: 0 degrees, 30 degrees and 55 degrees. The results show that for most of all the atmospheres that we have considered, deviations as far as 0.8 K are reached compared with the case in which the aerosols are not considered. It is important to point up that deviations higher than 0.4K are able to mask the improvement introduced by a diminution of the Noise Equivalent Temperature in the new sensors as a consequence of error propagation.

Planktonic foraminifera response to orbital and millennial-scale climate variability at the southern Iberian Margin (IODP Site U1385) during Marine Isotope Stages 20 and 19

Near real-time estimation of Sea and Land surface temperature for MSG SEVIRI sensors

Satellite remote sensing of coastal water quality using the sensors that operate in the visible and near-infrared (NIR) bands for measuring ocean colour and estimation of Sea Surface Temperature (SST), is extensively carried out across various regions. The present study focuses on the analysis of water quality on the east coast of India using Landsat 8 OLI/TIRS (Level-1C), 30 m resolution data for the period 2016–2019. The parameters that could be measured using remote sensing through various band combinations are Chlorophyll-a, Coloured Dissolved Organic Matter (CDOM), Total Suspended Matter (TSM), Turbidity. Reflectance data is converted into radiance, later on land masking and atmospheric corrections were carried out for the cloud free images. Algorithm used for the estimation of chlorophyll-a is OC2, which is a ratio of band 2 (483 nm) and band 3 (561 nm) that varies between 0 and 6 mg/m3. CDOM is derived using ratio of band 3 (561 nm) and band 4 (655 nm), wherein the values range from 0.785 to 8.58 m−1. Turbidity is derived using ratio of band 3 (561 nm) and band 4 (655 nm). Turbidity along the coast is observed to be in the range of 4–21 FTU (Formazin Turbidity Unit). CDOM is predominant near the mouth of River ‘Mahanadi’ inferring that this has originated in the catchment area rather than the one generated in the Bay of Bengal. The impact of rainfall, discharge and cyclones on the water quality variability along the coasts of West Bengal, Odisha and Andhra Pradesh is assessed in the present study. Similarly, the anthropogenic impact on the turbidity, suspended matter and chlorophyll is assessed.

Pliocene climate of Japan and environs between 4.8 and 2.8 Ma: A joint pollen and marine faunal study

Mare Frigoris is the fifth largest and almost northernmost mare located on the near side of the Moon. Mare Frigoris has an elongated shape, with a length of approximately 1500 km and a width of approximately 200 km, which makes it susceptible to becoming contaminated by the impact ejecta from the nearby highlands. Comparatively speaking, microwave radiometer (MRM) data have good penetration capabilities. Therefore, the MRM data from Chang’e-2 satellite were employed to study the volumetric thermal emission features of basaltic deposits in Mare Frigoris. Combining the MRM data with the basaltic units with FeO and TiO2 abundances identified using the small crater rim and ejecta probing (SCREP) methodology and with the gravity from Gravity Recovery and Interior Laboratory (GRAIL), the four potential conclusions that were obtained are as follows: (1) The MRM data are strongly related to the (FeO + TiO2) abundance of pristine basalts and are less influenced by ejecta contamination; (2) in every quadrant of Mare Frigoris, the (FeO + TiO2) abundance of the basalt decreases with an increase in age; (3) at least in Mare Frigoris, the main influencing factor regarding the brightness temperature remains the (FeO + TiO2) abundance of surface deposits; (4) a warm microwave anomaly was revealed in the western-central and eastern-central areas of Mare Frigoris which has a strong relationship with the positive Bouguer gravity anomaly derived from GRAIL data in terms of spatial distribution. The results are significant in the context of improving our understanding the basaltic igneous rock and thermal evolution of the Moon using MRM data.

Noise control is essential in an enclosed machine room where the noise level has to comply with the occupational safety and health act. In order to overcome a pure tone noise with a high peak value that is harmful to human hearing, a traditional reactive muffler has been used. However, the traditional method for designing a reactive muffler has proven to be time-consuming and insufficient. In order to efficiently reduce the peak noise level, interest in shape optimization of a Helmholtz muffler is coming to the forefront. Helmholtz mufflers that deal with a pure tone have been adequately researched. However, the shape optimization of multi-chamber Helmholtz mufflers that deal with a broadband noise hybridized with multiple tones within a constrained space has been mostly ignored. Therefore, this study analyzes the sound transmission loss (STL) and the best optimized design for a hybrid Helmholtz muffler under a space- constrained situation. On the basis of the plane wave theory, the four-pole system matrix used to evaluate the acoustic performance of a multi-tone hybrid Helmholtz muffler is presented. Two numerical cases for eliminating one/two tone noises emitted from a machine room using six kinds of mufflers (muffler A~F) is also introduced. To find the best acoustical performance of a space-constrained muffler, a numerical assessment using a simulated annealing (SA) method is adopted. Before the SA operation can be carried out, the accuracy of the mathematical model has been checked using the experimental data. Eliminating a broadband noise hybridized with a pure tone (130 Hz) in Case I reveals that muffler C composed of a one- chamber Helmholtz Resonator and a one-chamber dissipative element has a noise reduction of 54.9 (dB). Moreover, as indicated in Case II, muffler F, a two-chamber Helmholtz Resonator and a one-chamber dissipative element, has a noise reduction of 69.7 (dB). Obviously, the peak values of the pure tones in Case I and Case II are efficiently reduced after the muffler is added. Consequently, a successful approach in eliminating a broadband noise hybridized with multiple tones using optimally shaped hybrid Helmholtz mufflers and a simulated annealing method within a constrained space is demonstrated.

Different estimates of the monthly mean sea surface temperature (SST) fields are intercompared. Fields derived from ship data alone differ substantially in high SST gradient regions and in areas of low data density. These differences exhibit seasonal and interannual variations which are spatially correlated. The large bias and variability preclude interchangeable use of different SST products derived from ships alone.