FA-RDN: A Hybrid Neural Network on GNSS-R Sea Surface Wind Speed Retrieval

Based on deep learning, this paper proposes a new hybrid neural network model, a recurrent deep neural network using a feature attention mechanism (FA-RDN) for GNSS-R global sea surface wind speed retrieval. FA-RDN can process data from the Cyclone Global Navigation Satellite System (CYGNSS) satellite mission, including characteristics of the signal, spatio-temporal, geometry, and instrument. FA-RDN can receive data extended in temporal dimension and mine the temporal correlation information of features through the long-short term memory (LSTM) neural network layer. A feature attention mechanism is also added to improve the model’s computational efficiency. To evaluate the model performance, we designed comparison and validation experiments for the retrieval accuracy, enhancement effect, and stability of FA-RDN by comparing the evaluation criteria results. The results show that the wind speed retrieval root mean square error (RMSE) of the FA-RDN model can reach 1.45 m/s, 10.38%, 6.58%, 13.28%, 17.89%, 20.26%, and 23.14% higher than that of Backpropagation Neural Network (BPNN), Recurrent Neural Network (RNN), Artificial Neural Network (ANN), Random Forests (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR), respectively, confirming the feasibility and effectiveness of the designed method. At the same time, the designed model has better stability and applicability, serving as a new research idea of data mining and feature selection, as well as a reference model for GNSS-R-based sea surface wind speed retrieval.

Retrieval of TPW over ocean from locally received AMSU measurements

Total Precipitable Water (TPW) in a column of atmosphere is one of the important parameters useful for a number of meteorological applications. In the present study, a neural network based algorithm has been developed for the retrieval of TPW using NOAA-16 AMSU measurements. The TPW has been derived experimentally using NOAA-16 AMSU measurements locally received from High Resolution Picture Transmission (HRPT) station at India Meteorological Department (IMD) separately over ocean only. The validation of TPW has been carried out against the TPW derived from Radiosonde (RAOB) data. The bias and rms errors against the RAOB derived TPW have been found to about 0.11 mm and 2.98 mm respectively. The inter comparisons of TPW derived using NOAA AMSU data have also been made with that of NOAA/NESDIS derived TPW. Further, case study for the potential use of TPW derived from NOAA AMSU data has been carried out. This case study has revealed that the concentration of maximum precipitable water values in conjunction with high Sea surface wind speed data from Quickscat Scatterometer were found very useful for forecasting the heavy to very heavy rainfall event along the west coast of India. Therefore, AMSU derived TPW could be used as an important parameter for the operational weather forecasting on a real time basis.

Improved CYGNSS Wind Speed Retrieval Using Significant Wave Height Correction

This article presents the methodology for an improved estimation of the sea surface wind speed measured by the cyclone global navigation satellite system (CYGNSS) constellation of satellites using significant wave height (SWH) information as external reference data. The methodology consists of a correcting 2D look-up table (LUT) with inputs: (1) the CYGNSS wind speed given by the geophysical model function (GMF); and (2) the collocated reference SWH given by the WW3 model, which is forced by winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) organization. In particular, the analyzed CYGNSS wind speeds are the fully developed seas (FDS) obtained with the GMF 3.0, and the forcing winds are the ECMWF forecast winds. Results show an increase in sensitivity to large winds speeds and an overall reduction in the root mean square difference (RMSD) with respect to the ECMWF winds from 2.05 m/s to 1.74 m/s. The possible influence of the ECWMF winds on the corrected winds (due to their use in the WW3 model) is analyzed by considering the correlation between: (1) the difference between the ECMWF winds and those from another reference; and (2) the difference between the corrected CYGNSS winds and those from the same reference. Results using ASCAT, WindSat, Jason, and AltiKa as references show no significant influence.

Alat Ukur Kecepatan Angin Permukaan Air Laut Berbasis Mikrokontroler

The microcontroller is one technology that is developing so rapidly with various types and functions, one of which is Arduino Uno which can be used as a microcontroller for various functions in the field of electronics technology. This research was conducted at the Laboratory of Ocean Engineering Modeling, Marine and Fisheries Polytechnic of Karawang in March-June 2020. The purpose of this study was to create a microcontroller-based sea surface wind speed measuring instrument. Based on the results of the acquisition of wind data using a fan simulation and natural wind gusts with different wind speeds in the field show a significant tool response. The results of the comparison of data recording between the results of research with the existing wind speed measuring instrument show that there is an average tool error of 3.24%, a relative error of 3.78%, and an instrument accuracy rate of 96.76%. Thus it can be said that the ability of the tool is able to record wind data with high accuracy.