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.

scholarly journals Surface waters properties in the Laptev and the East-Siberian Seas in summer 2018 from in situ and satellite data

2019 ◽  
Author(s):  
Anastasiia Tarasenko ◽  
Alexandre Supply ◽  
Nikita Kusse-Tiuz ◽  
Vladimir Ivanov ◽  
Mikhail Makhotin ◽  
...  
Keyword(s):  
Surface Water ◽  
Satellite Data ◽  
Water Masses ◽  
Sea Surface ◽  
Ekman Transport ◽  
Water Displacement ◽  
Wind Speeds ◽  
In Situ Data ◽  
Ocean Salinity

Abstract. Variability of surface water masses of the Laptev and the East-Siberian seas in August–September 2018 is studied using in situ and satellite data. In situ data was collected during ARKTIKA-2018 expedition and then completed with satellite estimates of sea surface temperature (SST) and salinity (SSS), sea surface height, satellite-derived wind speeds and sea ice concentrations. Derivation of SSS is still challenging in high latitude regions, and the quality of Soil Moisture and Ocean Salinity (SMOS) SSS retrieval was improved by applying a threshold on SSS weekly error. The validity of SST and SSS products is demonstrated using ARKTIKA-2018 continuous thermosalinograph measurements and CTD casts. The surface gradients and mixing of river and sea waters in the free of ice and ice covered areas is described with a special attention to the marginal ice zone. The Ekman transport was calculated to better understand the pathway of surface water displacement. T-S diagram using surface satellite estimates shows a possibility to investigate the surface water masses transformation in detail.

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.

High-resolution sea surface salinity and temperature in coastal areas from Sentinel-2 and Copernicus Marine in situ data

2020 ◽  
Author(s):  
Encarni Medina-Lopez
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
In Situ Data ◽  
The Neural Network ◽  
Sentinel 2

&lt;p&gt;The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m x 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82% and 84% for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 C and 0.4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.&lt;/p&gt;

scholarly journals Nitrous oxide emissions from the Arabian Sea: A synthesis

2001 ◽  
Vol 1 (1) ◽  
pp. 61-71 ◽  
Author(s):  
H. W. Bange ◽  
M. O. Andreae ◽  
S. Lal ◽  
C. S. Law ◽  
S. W. A. Naqvi ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Arabian Sea ◽  
Sea Surface ◽  
Nitrous Oxide Emissions ◽  
N2o Emissions ◽  
Coastal Zones ◽  
N2o Flux ◽  
In Situ Data ◽  
Data Coverage

Abstract. We computed high-resolution (1º latitude x  1º longitude) seasonal and annual nitrous oxide (N2O) concentration fields for the Arabian Sea surface layer using a database containing more than 2400 values measured between December 1977 and July 1997. N2O concentrations are highest during the southwest (SW) monsoon along the southern Indian continental shelf. Annual emissions range from 0.33 to 0.70 Tg N2O and are dominated by fluxes from coastal regions during the SW and northeast monsoons. Our revised estimate for the annual N2O flux from the Arabian Sea is much more tightly constrained than the previous consensus derived using averaged in-situ data from a smaller number of studies. However, the tendency to focus on measurements in locally restricted features in combination with insufficient seasonal data coverage leads to considerable uncertainties of the concentration fields and thus in the flux estimates, especially in the coastal zones of the northern and eastern Arabian Sea. The overall mean relative error of the annual N2O emissions from the Arabian Sea was estimated to be at least 65%.

scholarly journals ANÁLISE DE TEMPERATURA DE PELE DA SUPERFÍCIE DO MAR EM REGIÕES DE ALTA LATITUDE SIMULADA COM O MODELO WRF

2016 ◽  
Vol 38 ◽  
pp. 11
Author(s):  
Alcimoni Nelci Comin ◽  
Otávio Costa Acevedo
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Wrf Model ◽  
Sea Surface ◽  
Average Wind Speed ◽  
Shetland Islands ◽  
In Situ Data ◽  
Average Wind ◽  
Nested Grids

The in situ data of sea surface temperature (SST) were measured onboard the Polar Ship Almirante Maximiano in the southern Shetland Islands between 5 and 23 February 2011. For the simulations, three concentric nested grids have been used at the 9 km, 3 km and 1 km spatial resolution in the simulations of the skin sea surface temperature (SSST) with WRF model. The grids are displaced every day, always centered in the middle position of the ship (latitude/longitude) during transect. The SSST is underestimated in comparison with SST on average 1.5°C. The real average wind speed observed was 8.7 ms-1. Therefore the amount of mixing between SST and SSST is greater, and the temperature difference between the two layers is smaller, on average 0.5°C. The underestimation of the model is mean 1°C. This underestimation directly interfere on the amount of ocean evaporation for the atmosphere, which may cause error in the energy balance. The correlation of the SSST with real SST data was 0.84 and root mean square error 1.87. 

scholarly journals The Contribution of High-Frequency Wind-Generated Surface Waves to the Stokes Drift

2020 ◽  
Vol 50 (12) ◽  
pp. 3455-3465
Author(s):  
Luc Lenain ◽  
Nick Pizzo
Keyword(s):  
Surface Waves ◽  
Wave Spectrum ◽  
Ocean Dynamics ◽  
Stokes Drift ◽  
Upper Ocean ◽  
Sea State ◽  
Instrument Measurement ◽  
Wave Models ◽  
Surface Wave Field

AbstractThe effects of nonbreaking surface waves on upper-ocean dynamics enter the wave-averaged primitive equations through the Stokes drift. Through the resulting upper-ocean dynamics, Stokes drift is a catalyst for the fluxes of heat and trace gases between the atmosphere and ocean. However, estimates of the Stokes drift rely crucially on properly resolving the wave spectrum. In this paper, using state-of-the-art spatial measurements (in situ and airborne remote sensing) from a number of different field campaigns, with environmental conditions ranging from 2 to 13 m s−1 wind speed and significant wave height of up to 4 m, we characterize the properties of the surface wave field across the equilibrium and saturation ranges and provide a simple parameterization of the transition between the two regimes that can easily be implemented in numerical wave models. We quantify the error associated with instrument measurement limitations, or incomplete numerical parameterizations, and propose forms for the continuation of these spectra to properly estimate the Stokes drift. Depending on the instrument and the sea state, predictions of surface Stokes drift may be underestimated by more than 50%.

scholarly journals An update to the traditional water mass (trans)formation framework.

2021 ◽  
Author(s):  
Aqeel Piracha ◽  
Antonio Turiel ◽  
Estrella Olmedo ◽  
Marcos Portabella
Keyword(s):  
Water Mass ◽  
Deep Convection ◽  
Sea Surface ◽  
Future Directions ◽  
In Situ Data ◽  
Water Mass Formation ◽  
Alternative Approach ◽  
Spatio Temporal ◽  
Ocean Convection

&lt;p&gt;Traditional estimates of convection/water mass formation at the sea surface rely on measurements of air-sea fluxes of heat and freshwater&lt;br&gt;(evaporation minus precipitation), that are estimated by combining in-situ data with meteorological modelisation. Satellite-based estimates of ocean convection are thus largely impacted by the relatively high uncertainties and low space-time resolution of those fluxes. However, direct satellite measurements of the ocean surface offer a unique opportunity to study convection (upwelling, downwelling) events with unprecedented spatio-temporal resolution compared to in-situ measurements. In this work, we propose an alternative approach to the traditional framework for estimating ocean convection using satellites. Instead of combining high-resolution ocean data of sea surface temperature and salinity with the much less precise, less resolved air-sea interaction data, we estimate the air-sea fluxes by computing the material derivatives (using satellite ocean currents) of the satellite sea surface variables. We therefore obtain estimates at the same resolution of the satellite products, and with much better accuracy than what was estimated before. We present some examples of application in the Atlantic ocean and in the Mediterranean sea. Future directions of this work is the study of the seasonal and interannual variability of ocean convection, and the potential changes on deep convection associated to climate variability at different time scales.&lt;/p&gt;

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