Improved the Impact of SST for HY-2A Scatterometer Measurements by Using Neural Network Model

The variation of sea surface temperature (SST) can change the backscatter coefficient measured by a scatterometer, resulting in a decrease in the accuracy of the sea surface wind measurement. This study proposed a new approach to correct the effect of SST on the backscatter coefficient. The method focuses on the Ku-band scatterometer HY-2A SCAT, which is more sensitive to SST than C-band scatterometers, can improve the wind measurement accuracy of the scatterometer without relying on reconstructed geophysical model function (GMF), and is more suitable for operational scatterometers. Through comparisons to WindSat wind data, we found that the Ku-band scatterometer HY-2A SCAT wind speeds are systemically lower under low SST and higher under high SST conditions. We trained a neural network model called the temperature neural network (TNNW) using HY-2A data and WindSat data. TNNW-corrected backscatter coefficients retrieved wind speed with a small systematic deviation from WindSat wind speed. In addition, we also carried out a validation of HY-2A wind and TNNW wind using European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data as a reference, and the results showed that the retrieved TNNW-corrected backscatter coefficient wind speed is more consistent with ECMWF wind speed, indicating that the method is effective in correcting SST impact on HY-2A scatterometer measurements.

High Wind Geophysical Model Function Modeling for the HY-2A Scatterometer Using Neural Network

Under low to medium wind speeds and no rainfall, the retrieved vector wind from a scatterometer is accurate and reliable. However, under high wind conditions, the currently used geophysical model function (GMF), such as NSCAT-2, for wind vector retrieval has the disadvantage of overestimating the backscattering coefficient, which leads to a decrease in the quality of the retrieved ocean surface winds. To enhance the wind retrieval precision of the HY-2A scatterometer under high wind conditions, a new GMF for high wind (HW-GMF) is established by using the neural network method based on the backscattering coefficient data of the HY-2A scatterometer combined with the wind speed data of the Special Sensor Microwave Imager (SSM/I) and the Final (FNL) operational global analysis wind direction data from the National Centers for Environmental Prediction (NCEP). The absolute value of the mean deviation between the predicted σ0 by the HW-GMF and the measured σ0 by the HY-2A scatterometer is less than 0.1 dB, indicating that the HW-GMF has high accuracy. To verify the HW-GMF performance, the wind field inversion accuracy of the HW-GMF is compared with that of the NSCAT-2 GMF, a GMF currently used in the data processing of the HY-2A scatterometer. The experimental results show that the deviation between the HW-GMF retrieved wind speed and the SSM/I wind speed is within 2 m/s in the high wind speed range of 15–35 m/s, indicating that the HW-GMF improves the precision of the wind speed inversion of the HY-2A scatterometer under high wind speed conditions.

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

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.

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

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.

Ultrahigh Resolution Scatterometer Winds near Hawaii

Hawaii regional climate model (HRCM), QuikSCAT, and ASCAT wind estimates are compared in the lee of Hawaii’s Big Island with the goal of understanding ultrahigh resolution (UHR) scatterometer wind retrieval capabilities in this area, which includes a reverse-flow toward the island in the lee of the predominate flow. A comparison of scatterometer measured σ 0 and model predicted σ 0 suggests that scatterometers can detect the reverse flow in the lee of the island; however, neither QuikSCAT- nor ASCAT-estimated winds consistently report this flow. Furthermore, the scatterometer UHR winds do not resolve the wind direction features predicted by the HRCM. Differences between scatterometer measured σ 0 and HRCM predicted σ 0 indicate possible error in the placement of key reverse flow features predicted by the HRCM. We find that coarse initialization fields and a large size median filter windows used in ambiguity selection can impede the accuracy of the UHR wind direction retrieval in this area, suggesting the need for further development of improved near-coastal ambiguity selection algorithms.

Systematic Scatterometer Wind Errors Near Coastal Mountains

Satellite scatterometers provide the only regular observations of surface wind vectors over vast swaths of the world oceans, including coastal regions, which are of great scientific and societal interest but still present challenges for remote sensing. Here we demonstrate systematic scatterometer wind errors near Hawaii's Big Island: Two counter-rotating lee vortices, which are clear in the International Comprehensive Ocean-Atmosphere Data Set ship-based wind climatology and in aircraft observations, are absent in the Jet Propulsion Laboratory and Remote Sensing Systems scatterometer wind climatologies. We demonstrate similar errors in the representation of transient Catalina Eddy events in the Southern California Bight. These errors likely arise from the nonuniqueness of scatterometer wind observations, that is, an "ambiguity removal" is required during processing to select from multiple wind solutions to the geophysical model function. We discuss strategies to improve the ambiguity selection near coastal mountains, where small-scale wind reversals are common.

Fast Median Filtering for Phase or Orientation Data

Median filtering is among the most utilized tools for smoothing real-valued data, as it is robust, edge-preserving, value-preserving, and yet can be computed efficiently. For data living on the unit circle, such as phase data or orientation data, a filter with similar properties is desirable. For these data, there is no unique means to define a median; so we discuss various possibilities. The arc distance median turns out to be the only variant which leads to robust, edge-preserving and value-preserving smoothing. However, there are no efficient algorithms for filtering based on the arc distance median. Here, we propose fast algorithms for filtering of signals and images with values on the unit circle based on the arc distance median. For non-quantized data, we develop an algorithm that scales linearly with the filter size. The runtime of our reference implementation is only moderately higher than the Matlab implementation of the classical median filter for real-valued data. For quantized data, we obtain an algorithm of constant complexity w.r.t. the filter size. We demonstrate the performance of our algorithms for real life data sets: phase images from interferometric synthetic aperture radar, planar flow fields from optical flow, and time series of wind directions.