Results 211 to 220 of about 2,368 (248)

Changing drivers of the Great Atlantic Sargassum Belt from physical forcing to ecological control. [PDF]

Nat Commun
Zhou X, Novi L, Hay ME, Montoya JP, Aliu A, Realff MJ, Bracco A.   +6 more
europepmc   +1 more source

‏Real-time validation of advanced control strategies for grid-connected PV-wind hybrid systems using adaptive starfish and greedy man optimization algorithms. [PDF]

Sci Rep
Abdelwahab SAM, Ali MA, Mohamed WI, Abdellatif WSE.   +3 more
europepmc   +1 more source

A Global ERA5-based Tropical Cyclone Wind Field Dataset Enhanced by Integrated Parametric Correction Methods. [PDF]

Sci Data
Liu G, Jiang S, Zheng M, Lin S, Kong Y, Zhan P.   +5 more
europepmc   +1 more source

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Multi-Frequency SAR Retrieval of Sea Surface Wind Field

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023
This study is to present the lesson learned during the activ- ities related to the Italian Space Agency (ASI) funded AP- PLICAVEMARS project which aims at estimating sea surface wind field from L-, C- and X-band Synthetic Aperture Radar (SAR) imagery.
Nunziata F., Migliaccio M., Verlanti A., Buono A., Ferrentino E., Alparone M., Zecchetto S., Zanchetta A., Portabella M., Grieco G.   +9 more
openaire   +3 more sources

SEA SURFACE WIND SPEED RETRIEVAL FROM MTVZA-GYA DATA

Meteorologiya i Gidrologiya, 2023
A neural network (NN) algorithm for the sea surface wind speed retrieval from the MTVZA-GYa Russian satellite microwave radiometer measurements is presented. The algorithm is based on the physical modeling of the brightness temperature of microwave radiation in the ocean-atmosphere system using new theoretical geophysical model functions of the ...
E. V. Zabolotskikh, S. M. Azarov, M. A. Zhivotovskaya   +2 more
openaire   +1 more source

GNSS-R Global Sea Surface Wind Speed Retrieval Based on Deep Learning

IEEE Transactions on Geoscience and Remote Sensing, 2023
Cong Yin, Junming Xia, Feixiong Huang
exaly   +2 more sources

Assimilation of ASCAT Sea Surface Wind Retrievals with Correlated Observation Errors

Journal of Meteorological Research, 2021
Data assimilation systems usually assume that the observation errors of wind components, i.e., u (the longitudinal component) and v (the latitudinal component), are uncorrelated. However, since wind components are derived from observations in the form of wind speed and direction (spd and dir), the observation errors of u and v are correlated.
Boheng Duan, Weimin Zhang, Xiaofeng Yang, Mengbin Zhu   +3 more
openaire   +1 more source

New Approach to Sea Surface Wind Retrieval From GNSS-R Measurements

2011 IEEE International Geoscience and Remote Sensing Symposium, 2011
So far, GNSS-R studies on sea surface wind retrieval from Delay-Doppler Maps (DDM) have focused on the best fitting of the model to the measurements. This paper proposes a new approach to retrieve the sea surface wind using DDM deconvolution. The proposed method is based on the direct restoration of scattering coefficients from two measured DDMs. Using
Xavier Bosch-Lluis, Nereida Rodriguez-Alvarez, Adriano Camps   +2 more
exaly   +2 more sources

Wind retrieval over the china seas using satellite synthetic aperture radar

IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004, 2004
The high spatial resolution and large coverage of synthetic aperture radar (SAR) offers a good opportunity to retrieve detailed wind vector information over the oceans, especially in the coast areas. In the work presented here, the wind speeds estimate is based on the data from ScanSAR aboard the Canadian satellite RADARSAT, operating at C-band with ...
Qian Feng, Mingqiang Fang, Yingjian Liu, Laibu Wang   +3 more
openaire   +1 more source