Scanning radar altimeter measurements of sea surface mean square slope during TOGA COARE and its relationship to SST and internal waves

2002 ◽  
Author(s):  
E. Walsh ◽  
D. Hagan ◽  
D. Rogers ◽  
D. Vandemark ◽  
R. Swift ◽  
...  
Keyword(s):  
Internal Waves ◽  
Sea Surface ◽  
Mean Square ◽  
Radar Altimeter ◽  
Toga Coare

scholarly journals Airborne Rain-Rate Measurement with a Wide-Swath Radar Altimeter

2014 ◽  
Vol 31 (4) ◽  
pp. 860-875 ◽  
Author(s):  
Edward J. Walsh ◽  
Ivan PopStefanija ◽  
Sergey Y. Matrosov ◽  
Jian Zhang ◽  
Eric Uhlhorn ◽  
...  
Keyword(s):  
Drop Size ◽  
Rain Rate ◽  
Theoretical Calculations ◽  
Microwave Radiometer ◽  
Rain Attenuation ◽  
Sea Surface ◽  
Mean Square ◽  
Radar Altimeter ◽  
Sea Surface Topography ◽  
Wide Swath

Abstract The NOAA Wide-Swath Radar Altimeter (WSRA) uses 80 narrow beams spread over ±30° in the cross-track direction to generate raster lines of sea surface topography at a 10-Hz rate from which sea surface directional wave spectra are produced. A ±14° subset of the backscattered power data associated with the topography measurements is used to produce independent measurements of rain rate and sea surface mean square slope at 10-s intervals. Theoretical calculations of rain attenuation at the WSRA 16.15-GHz operating frequency using measured drop size distributions for both mostly convective and mostly stratiform rainfall demonstrate that the WSRA absorption technique for rain determination is relatively insensitive to both ambient temperature and the characteristics of the drop size distribution, in contrast to reflectivity techniques. The variation of the sea surface radar reflectivity in the vicinity of a hurricane is reviewed. Fluctuations in the sea surface scattering characteristics caused by changes in wind speed or the rain impinging on the surface cannot contaminate the rain measurement because they are calibrated out using the WSRA measurement of mean square slope. WSRA rain measurements from a NOAA WP-3D hurricane research aircraft off the North Carolina coast in Hurricane Irene on 26 August 2011 are compared with those from the stepped frequency microwave radiometer (SFMR) on the aircraft and the Next Generation Weather Radar (NEXRAD) National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (QPE) system.

scholarly journals Measuring sea surface mean square slope with a 36-GHz scanning radar altimeter

1998 ◽  
Vol 103 (C6) ◽  
pp. 12587-12601 ◽  
Author(s):  
Edward J. Walsh ◽  
Douglas C. Vandemark ◽  
Carl A. Friehe ◽  
Sean P. Burns ◽  
Djamal Khelif ◽  
...  
Keyword(s):  
Sea Surface ◽  
Mean Square ◽  
Radar Altimeter

scholarly journals Spatially Broad Observations of Internal Waves in the Upper Ocean at the Hawaiian Ridge

2006 ◽  
Vol 36 (6) ◽  
pp. 1085-1103 ◽  
Author(s):  
Joseph P. Martin ◽  
Daniel L. Rudnick ◽  
Robert Pinkel
Keyword(s):  
Numerical Model ◽  
Energy Density ◽  
Potential Energy ◽  
Internal Waves ◽  
Upper Ocean ◽  
Mean Square ◽  
The South ◽  
South Side ◽  
Topographic Slope ◽  
Hawaiian Ridge

Abstract The density and current structure at the Hawaiian Ridge was observed using SeaSoar and Doppler sonar during a survey extending from Oahu to Brooks Banks. Across- and along-ridge changes in internal wave statistics in the upper ocean within 200 km of the ridge are investigated. Internal waves with trough-to-crest amplitude as large as 60 m and horizontal wavelength of about 50 km are observed repeatedly in across-ridge sections of potential density. Within 150 km of the ridge, kinetic and potential energy density exceed open-ocean values with maxima about 10 times Garrett–Munk levels. In the Kauai Channel (KC), the kinetic energy density is largest along an M2 internal tide ray. The ray originates at the northern edge of the ridge peak at a large across-ridge change in topographic slope and terminates at the ocean surface about 30–40 km south of the ridge peak. Kinetic and potential energy density are larger on the south side of the ridge at KC, the side with larger topographic slope. Energy density is also larger on the south side of the ridge at KC in numerical model results and on the side of steeper topographic slope in analytical model results. Along the ridge, the largest observed values of mean-square shear and mean-square slope of isopycnal depth are collocated with the largest energy density in numerical model results. Mean-square shear and mean-square slope increase with decreasing bottom depth and with increasing M2 barotropic tidal forcing.

scholarly journals MEAN SEA SURFACE (MSS) MODEL DETERMINATION FOR MALAYSIAN SEAS USING MULTI-MISSION SATELLITE ALTIMETER

2016 ◽  
Vol XLII-4/W1 ◽  
pp. 247-252
Author(s):  
N. A. Z. Yahaya ◽  
T. A. Musa ◽  
K. M. Omar ◽  
A. H. M. Din ◽  
A. H. Omar ◽  
...  
Keyword(s):  
Monsoon Season ◽  
Sea Surface ◽  
Dynamic Topography ◽  
Level Variation ◽  
Radar Altimeter ◽  
Satellite Altimeter ◽  
Sea Level Variation ◽  
Mission Satellite ◽  
Mean Sea Surface ◽  
Model Determination

The advancement of satellite altimeter technology has generated many evolutions to oceanographic and geophysical studies. A multi-mission satellite altimeter consists with TOPEX, Jason-1 and Jason-2, ERS-2, Envisat-1, CryoSat-2 and Saral are extracted in this study and has been processed using Radar Altimeter Database System (RADS) for the period of January 2005 to December 2015 to produce the sea surface height (hereinafter referred to SSH). The monthly climatology data from SSH is generated and averaged to understand the variation of SSH during monsoon season. Then, SSH data are required to determine the localised and new mean sea surface (MSS). The differences between Localised MSS and DTU13 MSS Global Model is plotted with root mean square error value is 2.217 metres. The localised MSS is important towards several applications for instance, as a reference for sea level variation, bathymetry prediction and derivation of mean dynamic topography.

Development of an adaptive neuro-fuzzy inference system (ANFIS) model to predict sea surface temperature (SST)

2020 ◽  
Vol 49 (4) ◽  
pp. 354-373
Author(s):  
Semih Kale
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mean Square Error ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Sea Surface ◽  
Mean Square ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy

Abstract An accurate estimation of the sea surface temperature (SST) is of great importance. Therefore, the objective of this work was to develop an adaptive neuro-fuzzy inference system (ANFIS) model to predict SST in the Çanakkale Strait. The observed monthly air temperature, evaporation and precipitation data from the Çanakkale meteorological observation station were used as input data. The Takagi–Sugeno fuzzy inference system was applied. The grid partition method (ANFIS-GP) and the subtractive clustering partitioning method (ANFIS-SC) were used with Gaussian membership functions to generate the fuzzy inference system. Six performance evaluation criteria were used to evaluate the developed SST prediction models, including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE) and correlation of determination (R2). The dataset was randomly divided into training and testing datasets for the machine learning process. Training data accounted for 75% of the dataset, while 25% of the dataset was allocated for testing in ANFIS. The hybrid algorithm was selected as a training algorithm for the ANFIS. Simulation results revealed that the ANFIS-SC4 model provided a higher correlation coefficient of 0.96 between the observed and predicted SST values. The results of this study suggest that the developed ANFIS model can be applied for predicting sea surface temperature around the world.

Operational marine products from Copernicus Sentinel-3 missions

2021 ◽  
Author(s):  
Estelle Obligis ◽  
Ewa Kwiatkowska ◽  
Anne O'Carroll ◽  
Remko Scharroo
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Sea Surface ◽  
Current Status ◽  
Radar Altimeter ◽  
Future Evolution ◽  
Sea Surface Topography ◽  
Ground Segment ◽  
Marine Products ◽  
Level 2

<p>The first Copernicus Sentinel-3 satellite, Sentinel-3A, was launched in early 2016, and its twin Sentinel-3B in April 2018. The Sentinel-3 constellation is now fully operational with Sentinel-3B satellite flying in the same orbit plan with a phase difference of 140°. This constellation provides a unique consistent, long-term collection of marine and land data for operational analysis, forecasting and environmental and climate monitoring. The marine centre is part of the Sentinel-3 Payload Data Ground Segment, located at EUMETSAT. This centre together with the existing EUMETSAT facilities provides a routine centralised service for operational meteorology, oceanography, and other Sentinel-3 marine users as part of the European Commission's Copernicus programme. The EUMETSAT marine centre delivers operational Sea Surface Temperature, Ocean Colour and Sea Surface Topography data products based on the measurements from the Sea and Land Surface Temperature Radiometer (SLSTR), Ocean and Land Colour Instrument (OLCI) and Synthetic Aperture Radar Altimeter (SRAL), all aboard Sentinel-3 satellites. All products have been developed together with ESA and industry partners and EUMETSAT is responsible for the production, distribution, performance and future evolution of Level-2 marine products. We will give an overview of the scientific characteristics and algorithms of all marine Level-2 products, as well as instrument calibration and product validation results based on on-going Sentinel-3 Cal/Val activities. Information will be also provided about the current status of the product dissemination and the future evolutions that are envisaged. Also, we will provide information how to access Sentinel-3 data from EUMETSAT and where to look for further information.</p>

scholarly journals Reconstruction of the Surface Inshore Labrador Current from SWOT Sea Surface Height Measurements

2019 ◽  
Vol 11 (11) ◽  
pp. 1264 ◽  
Author(s):  
Zhimin Ma ◽  
Guoqi Han
Keyword(s):  
High Resolution ◽  
Root Mean Square ◽  
Surface Current ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Root Mean Square Difference ◽  
Optimal Interpolation ◽  
Mean Square ◽  
Labrador Current ◽  
Mean Square Difference

Utilizing a high-resolution (2-km) coastal ocean model output off Eastern Newfoundland, this paper explores the potential for reconstructing the sea surface height (SSH) and the surface inshore Labrador Current from high-resolution SSH data of the upcoming Surface Water and Ocean Topography (SWOT) satellite mission. The model results are evaluated against in-situ data from tide gauges and nadir altimetry for the period from June to October, 2010. The hourly model SSH output is used as true SSH and sampled along-swath with expected measurement errors by using a SWOT simulator, which produces SWOT-like data. We reconstruct half-day SSH fields from the SWOT-like data using optimal interpolation and average them into weekly fields. The average normalized root-mean-square difference between the weekly reconstructed SSH field and the model SSH filed is 0.07 for the inshore Labrador Current. Between the geostrophic surface current derived from the reconstructed SSH field and the model surface current, the average normalized root-mean-square difference is 0.26 for the inshore Labrador Current. For the surface unit-depth transport of the inshore Labrador Current, the normalized root-mean-square differences are 0.32–0.38 between the reconstructed current and the model current.

Seasonal Variation of Surface Circulation Along Peninsular Malaysia' East Coast

2014 ◽  
Vol 71 (4) ◽  
Author(s):  
Muhammad Faiz Pa'suya ◽  
Kamaludin Mohd Omar ◽  
Benny N. Peter ◽  
Ami Hassan Md Din ◽  
Mohd Fadzil Mohd Akhir
Keyword(s):  
Sea Level ◽  
East Coast ◽  
Peninsular Malaysia ◽  
Environmental Data ◽  
Sea Surface ◽  
Northeast Monsoon ◽  
Radar Altimeter ◽  
Sea Level Anomaly ◽  
Surface Circulation ◽  
Altimetric Data

The sea surface circulation pattern over the coast of Peninsula Malaysia's East Coast during Northeast Monsoon (NE) and Southwest Monsoon (SW) are derived using the seasonally averaged sea level anomaly (SLA) data from altimetric data and 1992-2002 Mean Dynamic Ocean Topography. This altimetric data has been derived from multi-mission satellite altimeter TOPEX, ERS-1, ERS-2, JASON-1, and ENVISAT for the period of nineteen years (1993 to 2011) using the Radar Altimeter Database System (RADS). The estimated sea level anomaly (SLA) have shown similarity in the pattern of sea level variations observed by four tide gauges. Overall, the sea surface circulations during the NE and SW monsoons shows opposite patterns, northward and southward respectively. During the SW monsoon, an anti-cyclonic circulation has been detected around the Terengganu coastal area centred at (about 5.5° N 103.5° E) and nearly consistent with previous study using numerical modelling. The estimated geostrophic current field from the altimeter is consistent with the trajectories of Argos-tracked Drifting Buoys provided by the Marine Environmental Data Services (MEDS) in Canada.

scholarly journals A Novel Method for Sea Surface Temperature Prediction Based on Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xuan Yu ◽  
Suixiang Shi ◽  
Lingyu Xu ◽  
Yaya Liu ◽  
Qingsheng Miao ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Root Mean Square ◽  
Spatial Information ◽  
Sea Surface ◽  
The Yellow Sea ◽  
Percentage Error ◽  
Good Prediction ◽  
Mean Square

Sea surface temperature (SST) forecasting is the task of predicting future values of a given sequence using historical SST data, which is beneficial for observing and studying hydroclimatic variability. Most previous studies ignore the spatial information in SST prediction and the forecasting models have limitations to process the large-scale SST data. A novel model of SST prediction integrated Deep Gated Recurrent Unit and Convolutional Neural Network (DGCnetwork) is proposed in this paper. The DGCnetwork has a compact structure and focuses on learning deep long-term dependencies in SST time series. Temporal information and spatial information are all included in our procedure. Differential Evolution algorithm is applied in order to configure DGCnetwork’s optimum architecture. Optimum Interpolation Sea Surface Temperature (OISST) data is selected to conduct experiments in this paper, which has good temporal homogeneity and feature resolution. The experiments demonstrate that the DGCnetwork significantly obtains excellent forecasting result, predicting SST by different lengths flexibly and accurately. On the East China Sea dataset and the Yellow Sea dataset, the accuracy of the prediction results is above 98% on the whole and all mean absolute error (MAE) values are lower than 0.33°C. Compared with the other models, root mean square error (RMSE), root mean square percentage error (RMSPE), and mean absolute percentage Error (MAPE) of the proposed approach reduce at least 0.1154, 0.2594, and 0.3938. The experiments of SST time series show that the DGCnetwork model maintains good prediction results, better performance, and stronger stability, which has reached the most advanced level internationally.

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