scholarly journals A Neural Network-Based Rain Effect Correction Method for HY-2A Scatterometer Backscatter Measurements

2020 ◽  
Vol 12 (10) ◽  
pp. 1648
Author(s):  
Xuetong Xie ◽  
Jing Wang ◽  
Mingsen Lin
Keyword(s):  
Neural Network ◽  
Systematic Error ◽  
Surface Wind ◽  
Correction Method ◽  
Reanalysis Data ◽  
Systematic Deviation ◽  
Wind Speeds ◽  
The Neural Network ◽  
Rain Effect ◽  
Ku Band

The backscattering coefficients measured by Ku-band scatterometers are strongly affected by rainfall, resulting in a systematic error in sea surface wind field retrieval. In rainy conditions, the radar signals are subject to absorption by the raindrops in their round-trip propagation through the atmosphere, while the backscatter of raindrops raises the echo energy. In addition, raindrops give rise to roughness by impinging the ocean surface, resulting in an increase in the echo energy measured by a scatterometer. Under moderate wind conditions, the comprehensive impact of rainfall causes the wind speeds retrieved by the scatterometer to be higher than their actual values. The HY-2A scatterometer is a Ku-band, pencil-beam, conically scanning scatterometer. To correct the systematic error of the HY-2A scatterometer measurement in rainy conditions, a neural network model is proposed according to the characteristics of the backscatter coefficients measured by the HY-2A scatterometer in the presence of rain. With the neural network, the wind fields of the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data were used as the reference to correct the deviation in backscatter coefficients measured by the HY-2A scatterometer in rainy conditions, and the accuracy in wind speeds retrieved using the corrected backscatter coefficients was significantly improved. Compared with the cases of wind retrieval without rain effect correction, the wind speeds retrieved from the corrected backscatter coefficients by the neural network show a much lower systematic deviation, which indicates that the neural network can effectively remove the systematic deviation in the backscatter coefficients and the retrieved wind speeds caused by rain.

scholarly journals Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network

2020 ◽  
Vol 12 (20) ◽  
pp. 3291 ◽  
Author(s):  
Xiao-Ming Li ◽  
Tingting Qin ◽  
Ke Wu
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
The Arctic ◽  
Sea Surface Wind Speed ◽  
Sea Surface Wind ◽  
The Neural Network ◽  
Marginal Ice Zone

In this paper, we presented a method for retrieving sea surface wind speed (SSWS) from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide (EW) swath mode, which have been extensively acquired over the Arctic for polar monitoring. In contrast to the conventional algorithm, i.e., using a geophysical model function (GMF) to retrieve SSWS by spaceborne SAR, we introduced an alternative retrieval method based on a GMF-guided neural network. The SAR normalized radar cross section, incidence angle, and wind direction are used as the inputs of a back propagation (BP) neural network, and the output is the SSWS. The network is developed based on 11,431 HH-polarized EW images acquired in the marginal ice zone (MIZ) of the Arctic from 2015 to 2018 and their collocated scatterometer wind measurements. Verification of the neural network based on the testing dataset yields a bias of 0.23 m/s and a root mean square error (RMSE) of 1.25 m/s compared to the scatterometer wind data for wind speeds less than approximately 30 m/s. Further comparison of the SAR retrieved SSWS with independent buoy measurements shows a bias and RMSE of 0.12 m/s and 1.42 m/s, respectively. We also analyzed the uncertainty of the retrieval when reanalysis model wind direction data are used as inputs to the neural network. By combining the detected sea ice cover information based on SAR data, sea ice and marine-meteorological parameters can be derived simultaneously by spaceborne SAR at a high spatial resolution in the Arctic.

scholarly journals Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network

2020 ◽  
Author(s):  
Xiao-ming Li ◽  
Tingting Qin ◽  
Ke Wu
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Sea Ice ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
The Arctic ◽  
Sea Surface Wind Speed ◽  
Sea Surface Wind ◽  
The Neural Network

In this paper, we presented a method of retrieving sea surface wind speed from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide mode, which have been extensively acquired over the Arctic for sea ice monitoring. In contrast to the conventional algorithm, i.e., using a geophysical model function (GMF) to retrieve sea surface wind by spaceborne SAR, we introduced an alternative method based on physical model guided neural network. Parameters of SAR normalized radar cross section, incidence angle, and wind direction are used as the inputs of the backward propagation (BP) neural network, and the output is the sea surface wind speed. The network is developed based on more than 11,000 HH-polarized EW images acquired in the marginal ice zone (MIZ) of the Arctic and their collocations with scatterometer measurements. Verification of the neural network based on the testing dataset yields a bias of 0.23 m/s and a root mean square error (RMSE) of 1.25 m/s compared to the scatterometer wind speed. Further comparison of the SAR retrieved sea surface wind speed with independent buoy measurements shows a bias and RMSE of 0.12 m/s and 1.42 m/s, respectively. We also analyzed the uncertainty of retrieval when the wind direction data of a reanalysis model are used as inputs to the neural network. By combining the detected sea ice cover information based on the EW data, one can expect to derive simultaneously sea ice and marine-meteorological parameters by spaceborne SAR in a high spatial resolution in the Arctic.

scholarly journals Intercalibration of Backscatter Measurements among Ku-Band Scatterometers Onboard the Chinese HY-2 Satellite Constellation

2021 ◽  
Vol 13 (23) ◽  
pp. 4783
Author(s):  
Zhixiong Wang ◽  
Juhong Zou ◽  
Youguang Zhang ◽  
Ad Stoffelen ◽  
Wenming Lin ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Surface Wind ◽  
Double Difference ◽  
Satellite Orbits ◽  
Wind Speeds ◽  
Sea Surface Wind ◽  
Geophysical Model ◽  
Geophysical Model Function ◽  
Nwp Model ◽  
Ku Band

The Chinese HY-2D satellite was launched on 19 May 2021, carrying a Ku-band scatterometer. Together with the operating scatterometers onboard the HY-2B and HY-2C satellites, the HY-2 series scatterometer constellation was built, constituting different satellite orbits and hence opportunity for mutual intercomparison and intercalibration. To achieve intercalibration of backscatter measurements for these scatterometers, this study presents and performs three methods including: (1) direct comparison using collocated measurements, in which the nonlinear calibrations can also be derived; (2) intercalibration over the Amazon rainforest; (3) and the double-difference technique based on backscatter simulations over the global oceans, in which a geophysical model function and numerical weather prediction (NWP) model winds are needed. The results obtained using the three methods are comparable, i.e., the differences among them are within 0.1 dB. The intercalibration results are validated by comparing the HY-2 series scatterometer wind speeds with NWP model wind speeds. The curves of wind speed bias for the HY-2 series scatterometers are quite similar, particularly in wind speeds ranging from 4 to 20 m/s. Based on the well-intercalibrated backscatter measurements, consistent sea surface wind products from HY-2 series scatterometers can be produced, and greatly benefit data applications.

scholarly journals Retrieving Aerosol Optical Depth and High Spatial Resolution Ocean Surface Wind Speed From CALIPSO: A Neural Network Approach

2021 ◽  
Vol 1 ◽  
Author(s):  
Anna Murphy ◽  
Yongxiang Hu
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Surface Wind ◽  
Ocean Surface ◽  
Satellite Observation ◽  
Surface Wind Speed ◽  
Neural Network Approach ◽  
Backscatter Signal ◽  
The Neural Network ◽  
Ocean Surface Wind

A neural network nonlinear regression algorithm is developed for retrieving ocean surface wind speed from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements. The neural network is trained with CALIPSO ocean surface and atmospheric backscatter measurements together with collocated Advanced Microwave Scanning Radiometer for EOS (AMSR-E) ocean surface wind speed. Ocean surface wind speeds are derived by applying the neural network algorithm to CALIPSO measurements between 2008 and 2020. CALIPSO wind speed measurements of 2015 are also compared with Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements on the Global Change Observation Mission–Water “Shizuku” (GCOM-W) satellite. Aerosol optical depths are then derived from CALIPSO’s ocean surface backscatter signal and theoretical ocean surface reflectance calculated from CALIPSO wind speed and Cox-Munk wind–surface slope variance relation. This CALIPSO wind speed retrieval technique is an improvement from our previous studies, as it can be applied to most clear skies with optical depths up to 1.5 without making assumptions about aerosol lidar ratio.

scholarly journals An artificial neural network technique for downscaling GCM outputs to RCM spatial scale

2011 ◽  
Vol 18 (6) ◽  
pp. 1013-1028 ◽  
Author(s):  
R. Chadwick ◽  
E. Coppola ◽  
F. Giorgi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regional Climate ◽  
Correction Method ◽  
Lapse Rate ◽  
Climate Modelling ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Relationship

Abstract. An Artificial Neural Network (ANN) approach is used to downscale ECHAM5 GCM temperature (T) and rainfall (R) fields to RegCM3 regional model scale over Europe. The main inputs to the neural network were the ECHAM5 fields and topography, and RegCM3 topography. An ANN trained for the period 1960–1980 was able to recreate the RegCM3 1981–2000 mean T and R fields with reasonable accuracy. The ANN showed an improvement over a simple lapse-rate correction method for T, although the ANN R field did not capture all the fine-scale detail of the RCM field. An ANN trained over a smaller area of Southern Europe was able to capture this detail with more precision. The ANN was unable to accurately recreate the RCM climate change (CC) signal between 1981–2000 and 2081–2100, and it is suggested that this is because the relationship between the GCM fields, RCM fields and topography is not constant with time and changing climate. An ANN trained with three ten-year "time-slices" was able to better reproduce the RCM CC signal, particularly for the full European domain. This approach shows encouraging results but will need further refinement before becoming a viable supplement to dynamical regional climate modelling of temperature and rainfall.

scholarly journals Extreme windstorms and sting jets in convection-permitting climate simulations over Europe

2021 ◽  
Author(s):  
Colin Manning ◽  
Elizabeth J. Kendon ◽  
Hayley J. Fowler ◽  
Nigel M. Roberts ◽  
Ségolène Berthou ◽  
...  
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Low Cost ◽  
Global Climate Model ◽  
Surface Wind ◽  
Reanalysis Data ◽  
Large Contribution ◽  
High Wind ◽  
Wind Speeds ◽  
Wind Gusts

AbstractExtra-tropical windstorms are one of the costliest natural hazards affecting Europe, and windstorms that develop a sting jet are extremely damaging. A sting jet is a mesoscale core of very high wind speeds that occurs in Shapiro–Keyser type cyclones, and high-resolution models are required to adequately model sting jets. Here, we develop a low-cost methodology to automatically detect sting jets, using the characteristic warm seclusion of Shapiro–Keyser cyclones and the slantwise descent of high wind speeds, within pan-European 2.2 km convection-permitting climate model (CPM) simulations. The representation of wind gusts is improved with respect to ERA-Interim reanalysis data compared to observations; this is linked to better representation of cold conveyor belts and sting jets in the CPM. Our analysis indicates that Shapiro–Keyser cyclones, and those that develop sting jets, are the most damaging windstorms in present and future climates. The frequency of extreme windstorms is projected to increase by 2100 and a large contribution comes from sting jet storms. Furthermore, extreme wind speeds and their future changes are underestimated in the global climate model (GCM) compared to the CPM. We conclude that the CPM adds value in the representation of extreme winds and surface wind gusts and can provide improved input for impact models compared to coarser resolution models.

scholarly journals Extreme windstorms and sting-jets in convection-permitting climate simulations over Europe

2021 ◽  
Author(s):  
Colin Manning ◽  
Elizabeth J. Kendon ◽  
Hayley J. Fowler ◽  
Nigel M. Roberts ◽  
Ségolène Berthou ◽  
...  
Keyword(s):  
Climate Model ◽  
Low Cost ◽  
Surface Wind ◽  
Reanalysis Data ◽  
Large Contribution ◽  
High Wind ◽  
Wind Speeds ◽  
Wind Gusts ◽  
High Resolution Models ◽  
Extreme Wind Speeds

Abstract Extra-tropical windstorms are one of the costliest natural hazards affecting Europe, and windstorms that develop a sting-jet are extremely damaging. A sting-jet is a mesoscale core of very high wind speeds that occurs in Shapiro-Keyser type cyclones, and high-resolution models are required to adequately model sting-jets. Here, we develop a low-cost methodology to automatically detect sting jets, using the characteristic warm seclusion of Shapiro-Keyser cyclones and the slantwise descent of high wind speeds, within pan-European 2.2km convection-permitting climate model (CPM) simulations over Europe. The representation of wind gusts is improved with respect to ERA-Interim reanalysis data compared to observations; this is linked to better representation of cold conveyor belts and sting-jets in the CPM. Our analysis indicates that Shapiro-Keyser cyclones, and those that develop sting-jets, are the most damaging windstorms in present and future climates. The frequency of extreme windstorms is projected to increase by 2100 and a large contribution comes from sting-jet storms. Furthermore, extreme wind speeds and their future changes are underestimated in the GCM compared to the CPM. We conclude that the CPM adds value in the representation of extreme winds and surface wind gusts and can provide improved input for impact models compared to coarser resolution models.

scholarly journals Three variables are better than one: detection of european winter windstorms causing important damages

2014 ◽  
Vol 14 (4) ◽  
pp. 981-993 ◽  
Author(s):  
M.-S. Deroche ◽  
M. Choux ◽  
F. Codron ◽  
P. Yiou
Keyword(s):  
Sea Level ◽  
Large Scale ◽  
Insurance Industry ◽  
Surface Wind ◽  
Relative Vorticity ◽  
Reanalysis Data ◽  
Data Set ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Wind Speeds

Abstract. In this paper, we present a new approach for detecting potentially damaging European winter windstorms from a multi-variable perspective. European winter windstorms being usually associated with extra-tropical cyclones (ETCs), there is a coupling between the intensity of the surface wind speeds and other meso-scale and large-scale features characteristic of ETCs. Here we focus on the relative vorticity at 850 hPa and the sea level pressure anomaly, which are also used in ETC detection studies, along with the ratio of the 10 m wind speed to its 98th percentile. When analysing 10 events known by the insurance industry to have caused extreme damages, we find that they share an intense signature in each of the 3 fields. This shows that the relative vorticity and the mean sea level pressure have a predictive value of the intensity of the generated windstorms. The 10 major events are not the most intense in any of the 3 variables considered separately, but we show that the combination of the 3 variables is an efficient way of extracting these events from a reanalysis data set.

scholarly journals A More Accurate Field-to-Field Method towards the Wind Retrieval of HY-2B Scatterometer

2021 ◽  
Vol 13 (12) ◽  
pp. 2419
Author(s):  
Xinjie Shi ◽  
Boheng Duan ◽  
Kaijun Ren
Keyword(s):  
Neural Network ◽  
Wind Direction ◽  
Wind Field ◽  
Surface Wind ◽  
Field Method ◽  
Observation Data ◽  
Retrieval Method ◽  
Sea Surface Wind ◽  
The Neural Network ◽  
Mean Square Errors

In this paper, we present a method for retrieving sea surface wind field (SSWF) from HaiYang-2B (HY-2B) scatterometer data. In contrast to the conventional algorithm, i.e., using a point-to-point (P2P) method based on geophysical model functions (GMF) to retrieve SSWF by spaceborne scatterometer, we introduce a more accurate field-to-field (F2F) retrieval method based on convolutional neural network (CNN). We fully consider the spatial correlation and continuity between adjacent observation points, and input the observation data of continuous wind field within a certain range into the neural network to construct the neural network model, and then synchronously obtain the wind field within the range. The wind field obtained by our retrieval method maintains its continuity and solves the problem of ambiguity removal in traditional wind direction retrieval methods. Comparing the retrieval results with the buoy data, the results show that the root mean square errors (RMSE) of wind direction and wind speed are less than 0.18 rad (10.31°) and 0.75 m/s, respectively. The retrieval accuracy is better than the L2B product of HY-2B.

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