Remote sensing of wind speed at sea surface level using HF skywave echoes from decametric waves

1990 ◽  
Vol 17 (5) ◽  
pp. 615-618 ◽  
Author(s):  
C. Gaffard ◽  
J. Parent
Keyword(s):  
Remote Sensing ◽  
Wind Speed ◽  
Sea Surface ◽  
Surface Level

CATDS CEC-LOCEAN debiased version 4 Sea Surface Salinity

2020 ◽  
Author(s):  
Clovis Thouvenin-Masson ◽  
Jacqueline Boutin ◽  
Jean-Luc Vergely ◽  
Dimitry Khvorostyanov ◽  
Stéphane Tarot
Keyword(s):  
Remote Sensing ◽  
Wind Speed ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
The Mean ◽  
Analysis System ◽  
Areas Of Interest

<p>The Centre Aval de Traitement des Données SMOS (CATDS), developped by the CNES in collaboration with the CESBIO and IFREMER, produces and continuously improves SMOS sea surface salinity (SSS) products.</p><p>The aim of this poster is to present the last version of CATDS L3 products developed by the LOCEAN CATDS Expertise Center (CEC-LOCEAN debiased v4, https://www.catds.fr/Products/Available-products-from-CEC-OS/CEC-Locean-L3-Debiased-v4), and to highlight its main improvements with respect to previous version 3.</p><p>The L3 products are available for 9-day and 18-day Gaussian averaging. Both versions 3 and 4 contain a bias correction based on internal consistency of SMOS SSS retrieved in various locations across swath, and on seasonal variability of salinity. The main evolutions of version 4 consist in refining the absolute correction methodology, limiting wind speed to 16m/s, add a refined filtering for sea ice and radio frequency contamination based on SMOS retrieved pseudo dielectric constant, the so-called ACARD (Waldteufel et al. 2004) and an improved sea surface temperature (SST) correction in cold waters based on Dinnat et al. (2019) observed dependency.</p><p>Improvements with respect to version 3 are assessed through systematic validation that consists in two main stages: (1) Comparison with respect to in-situ measurements (repetitive ship transects across Atlantic and Arctic regions, and Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) moorings); (2) Comparison with the In-Situ Analysis System (ISAS) monthly fields (Kolodziejczyk, 2017), in terms of both mean spatial maps and time series of key statistics parameters. The key statistics parameters are computed both over the global ocean and for individual areas of interest. Thus, both the mean spatial patterns and temporal variability in various regions are evaluated.</p><p>Comparisons between the two last versions exposed in this poster are based on relevant examples from this systematic validation: main improvements are observed in high latitudes (over 45° latitude).In the Southern Ocean modification of wind speed filtering and SST correction lead to a decrease in the mean difference between SMOS  and ISAS SSS south of 45S from 0.16+/-0.07 to 0.02+/-0.05pss. Std of the differences and r2 are also improved over global ocean. Statistics obtained with this new version are close to the ones obtained with SMAP RemSS v4 SSS.</p><p> </p><p>Dinnat, E.P.; Le Vine, D.M.; Boutin, J.; Meissner, T.; Lagerloef, G. Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters. Remote Sens. 2019, 11, 750.</p><p>Kolodziejczyk Nicolas, Prigent-Mazella Annaig, Gaillard Fabienne (2017). ISAS-15 temperature and salinity gridded fields. SEANOE. https://doi.org/10.17882/52367</p><p>Waldteufel, P., J. L. Vergely, and C. Cot, A modified cardioid model for Processing multiangular radiometric observations, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.5, pp.1059-1063, 2004. DOI : 10.1109/TGRS.2003.821698.</p>

Sea-Surface Specular Multipath for Surface-Level Antennas: Phase 1

2005 ◽  
Author(s):  
J. C. Allen ◽  
R. E. Goshorn ◽  
B. Zeidler ◽  
A. A. Beex
Keyword(s):  
Phase 1 ◽  
Sea Surface ◽  
Surface Level

scholarly journals Measurement of the Sea Surface Height with Airborne GNSS Reflectometry and Delay Bias Calibration

2021 ◽  
Vol 13 (15) ◽  
pp. 3014
Author(s):  
Feng Wang ◽  
Dongkai Yang ◽  
Guodong Zhang ◽  
Jin Xing ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Arrival Time ◽  
Elevation Angle ◽  
Satellite System ◽  
Sea Surface Height ◽  
Antenna Pattern ◽  
Sea Surface ◽  
Observation Method ◽  
Global Navigation Satellite ◽  
The Impact

Sea surface height can be measured with the delay between reflected and direct global navigation satellite system (GNSS) signals. The arrival time of a feature point, such as the waveform peak, the peak of the derivative waveform, and the fraction of the peak waveform is not the true arrival time of the specular signal; there is a bias between them. This paper aims to analyze and calibrate the bias to improve the accuracy of sea surface height measured by using the reflected signals of GPS CA, Galileo E1b and BeiDou B1I. First, the influencing factors of the delay bias, including the elevation angle, receiver height, wind speed, pseudorandom noise (PRN) code of GPS CA, Galileo E1b and BeiDou B1I, and the down-looking antenna pattern are explored based on the Z-V model. The results show that (1) with increasing elevation angle, receiver height, and wind speed, the delay bias tends to decrease; (2) the impact of the PRN code is uncoupled from the elevation angle, receiver height, and wind speed, so the delay biases of Galileo E1b and BeiDou B1I can be derived from that of GPS CA by multiplication by the constants 0.32 and 0.54, respectively; and (3) the influence of the down-looking antenna pattern on the delay bias is lower than 1 m, which is less than that of other factors; hence, the effect of the down-looking antenna pattern is ignored in this paper. Second, an analytical model and a neural network are proposed based on the assumption that the influence of all factors on the delay bias are uncoupled and coupled, respectively, to calibrate the delay bias. The results of the simulation and experiment show that compared to the meter-level bias before the calibration, the calibrated bias decreases the decimeter level. Based on the fact that the specular points of several satellites are visible to the down-looking antenna, the multi-observation method is proposed to calibrate the bias for the case of unknown wind speed, and the same calibration results can be obtained when the proper combination of satellites is selected.

scholarly journals Remote Sensing Supported Sea Surface pCO2 Estimation and Variable Analysis in the Baltic Sea

2021 ◽  
Vol 13 (2) ◽  
pp. 259
Author(s):  
Shuping Zhang ◽  
Anna Rutgersson ◽  
Petra Philipson ◽  
Marcus B. Wallin
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Mean Squared Error ◽  
Numerical Models ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
The Baltic Sea ◽  
Chl A ◽  
Marginal Seas ◽  
The Baltic

Marginal seas are a dynamic and still to large extent uncertain component of the global carbon cycle. The large temporal and spatial variations of sea-surface partial pressure of carbon dioxide (pCO2) in these areas are driven by multiple complex mechanisms. In this study, we analyzed the variable importance for the sea surface pCO2 estimation in the Baltic Sea and derived monthly pCO2 maps for the marginal sea during the period of July 2002–October 2011. We used variables obtained from remote sensing images and numerical models. The random forest algorithm was employed to construct regression models for pCO2 estimation and produce the importance of different input variables. The study found that photosynthetically available radiation (PAR) was the most important variable for the pCO2 estimation across the entire Baltic Sea, followed by sea surface temperature (SST), absorption of colored dissolved organic matter (aCDOM), and mixed layer depth (MLD). Interestingly, Chlorophyll-a concentration (Chl-a) and the diffuse attenuation coefficient for downwelling irradiance at 490 nm (Kd_490nm) showed relatively low importance for the pCO2 estimation. This was mainly attributed to the high correlation of Chl-a and Kd_490nm to other pCO2-relevant variables (e.g., aCDOM), particularly in the summer months. In addition, the variables’ importance for pCO2 estimation varied between seasons and sub-basins. For example, the importance of aCDOM were large in the Gulf of Finland but marginal in other sub-basins. The model for pCO2 estimate in the entire Baltic Sea explained 63% of the variation and had a root of mean squared error (RMSE) of 47.8 µatm. The pCO2 maps derived with this model displayed realistic seasonal variations and spatial features of sea surface pCO2 in the Baltic Sea. The spatially and seasonally varying variables’ importance for the pCO2 estimation shed light on the heterogeneities in the biogeochemical and physical processes driving the carbon cycling in the Baltic Sea and can serve as an important basis for future pCO2 estimation in marginal seas using remote sensing techniques. The pCO2 maps derived in this study provided a robust benchmark for understanding the spatiotemporal patterns of CO2 air-sea exchange in the Baltic Sea.

scholarly journals A Novel Sea Surface Roughness Parameterization Based on Wave State and Sea Foam

2021 ◽  
Vol 9 (3) ◽  
pp. 246
Author(s):  
Difu Sun ◽  
Junqiang Song ◽  
Xiaoyong Li ◽  
Kaijun Ren ◽  
Hongze Leng
Keyword(s):  
Surface Roughness ◽  
Wind Speed ◽  
Drag Coefficient ◽  
Sea Surface ◽  
High Wind Speed ◽  
Wave State ◽  
Wind Speeds ◽  
Sea Surface Roughness ◽  
Extreme Wind ◽  
The Impact

A wave state related sea surface roughness parameterization scheme that takes into account the impact of sea foam is proposed in this study. Using eight observational datasets, the performances of two most widely used wave state related parameterizations are examined under various wave conditions. Based on the different performances of two wave state related parameterizations under different wave state, and by introducing the effect of sea foam, a new sea surface roughness parameterization suitable for low to extreme wind conditions is proposed. The behaviors of drag coefficient predicted by the proposed parameterization match the field and laboratory measurements well. It is shown that the drag coefficient increases with the increasing wind speed under low and moderate wind speed conditions, and then decreases with increasing wind speed, due to the effect of sea foam under high wind speed conditions. The maximum values of the drag coefficient are reached when the 10 m wind speeds are in the range of 30–35 m/s.

scholarly journals SST Anomalies in the Mozambique Channel Using Remote Sensing and Numerical Modeling Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1112
Author(s):  
Guoqing Han ◽  
Changming Dong ◽  
Junde Li ◽  
Jingsong Yang ◽  
Qingyue Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mixed Layer Depth ◽  
Surface Wind ◽  
Radiant Heat ◽  
Heat Fluxes ◽  
Sea Surface ◽  
Surface Mixed Layer ◽  
Mozambique Channel ◽  
Sea Surface Wind ◽  
Sst Anomalies

Based on both satellite remote sensing sea surface temperature (SST) data and numerical model results, SST warming differences in the Mozambique Channel (MC) west of the Madagascar Island (MI) were found with respect to the SST east of the MI along the same latitude. The mean SST west of the MI is up to about 3.0 °C warmer than that east of the MI. The SST differences exist all year round and the maximum value appears in October. The area of the highest SST is located in the northern part of the MC. Potential factors causing the SST anomalies could be sea surface wind, heat flux and oceanic flow advection. The presence of the MI results in weakening wind in the MC and in turn causes weakening of the mixing in the upper oceans, thus the surface mixed layer depth becomes shallower. There is more precipitation on the east of the MI than that inside the MC because of the orographic effects. Different precipitation patterns and types of clouds result in different solar radiant heat fluxes across both sides of the MI. Warm water advected from the equatorial area also contribute to the SST warm anomalies.

Sign in / Sign up

Export Citation Format

Share Document