Time dependent wind fields

Two tasks were performed: (1) determination of the accuracy of Seasat scatterometer, altimeter, and scanning multichannel microwave radiometer measurements of wind speed; and (2) application of Seasat altimeter measurements of sea level to study the spatial and temporal variability of geostrophic flow in the Antarctic Circumpolar Current. The results of the first task have identified systematic errors in wind speeds estimated by all three satellite sensors. However, in all cases the errors are correctable and corrected wind speeds agree between the three sensors to better than 1 ms sup -1 in 96-day 2 deg. latitude by 6 deg. longitude averages. The second task has resulted in development of a new technique for using altimeter sea level measurements to study the temporal variability of large scale sea level variations. Application of the technique to the Antarctic Circumpolar Current yielded new information about the ocean circulation in this region of the ocean that is poorly sampled by conventional ship-based measurements

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

Challenges to Satellite Sensors of Ocean Winds: Addressing Precipitation Effects

Measurements of global ocean surface winds made by orbiting satellite radars have provided valuable information to the oceanographic and meteorological communities since the launch of the Seasat in 1978, by the National Aeronautics and Space Administration (NASA). When Quick Scatterometer (QuikSCAT) was launched in 1999, it ushered in a new era of dual-polarized, pencil-beam, higher-resolution scatterometers for measuring the global ocean surface winds from space. A constant limitation on the full utilization of scatterometer-derived winds is the presence of isolated rain events, which affect about 7% of the observations. The vector wind sensors, the Ku-band scatterometers [NASA\u27s SeaWinds on the QuikSCAT and Midori-II platforms and Indian Space Research Organisation\u27s (ISRO\u27s) Ocean Satellite (Oceansat)-2], and the current C-band scatterometer [Advanced Wind Scatterometer (ASCAT), on the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)\u27s Meteorological Operation (MetOp) platform] all experience rain interference, but with different characteristics. Over this past decade, broad-based research studies have sought to better understand the physics of the rain interference problem, to search for methods to bypass the problem (using rain detection, flagging, and avoidance of affected areas), and to develop techniques to improve the quality of the derived wind vectors that are adversely affected by rain. This paper reviews the state of the art in rain flagging and rain correction and describes many of these approaches, methodologies, and summarizes the results

Wind speed retrieval from the Gaofen-3 synthetic aperture radar for VV- and HH-polarization using a re-tuned algorithm

In this study, a re-tuned algorithm based on the geophysical model function (GMF) C-SARMOD2 is proposed to retrieve wind speed from Synthetic Aperture Radar (SAR) imagery collected by the Chinese C-band Gaofen-3 (GF-3) SAR. More than 10,000 Vertical-Vertical (VV) and Horizontal-Horizontal (HH) polarization GF-3 images acquired in quad-polarization stripmap (QPS) and wave (WV) modes have been collected during the last three years, in which wind patterns are observed over open seas with incidence angles ranging from 18° to 52°. These images, collocated with wind vectors from the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis at 0.125° resolution, are used to re-tune the C-SARMOD2 algorithm to specialize it for the GF-3 SAR (CSARMOD-GF). In particular, the CSARMOD-GF performs differently from the C-SARMOD2 at low-to-moderate incidence angles smaller than about 34°. Comparisons with wind speed data from the Advanced Scatterometer (ASCAT), Chinese Haiyang-2B (HY-2B) and buoys from the National Data Buoy Center (NDBC) show that the root-mean-square error (RMSE) of the retrieved wind speed is approximately 1.8 m/s. Additionally, the CSARMOD-GF algorithm outperforms three state-of-the-art methods – C-SARMOD, C-SARMOD2, and CMOD7 – that, when applied to GF-3 SAR imagery, generating a RMSE of approximately 2.0–2.4 m/s

Observational studies of scatterometer ocean vector winds in the presence of dynamic air-sea interactions

Ocean vector wind measurements produced by satellite scatterometers are used in many applications across many disciplines, from forcing ocean circulation models and improving weather forecasts, to aiding in rescue operations and helping marine management services, and even mapping energy resources. However, a scatterometer does not in fact measure wind directly; received radar backscatter is proportional to the roughness of the ocean\u27s surface, which is primarily modified by wind speed and direction. As scatterometry has evolved in recent decades, highly calibrated geophysical model functions have been designed to transform this received backscatter into vector winds. Because these products are used in so many applications, it is crucial to understand any limitations of this process. For instance, a number of assumptions are routinely invoked when interpreting scatterometer retrievals in areas of complex air-sea dynamics without, perhaps, sufficient justification from supporting observations. This dissertation uses satellite data, in situ measurements, and model simulations to evaluate these assumptions. Robustness is assured by using multiple types of satellite scatterometer data from different sensors and of different resolutions, including an experimental ultra-high resolution product that first required validation in the region of study. After this validation survey, a subsequent investigation used the multiple data resolutions to focus on the influence of ocean surface currents on scatterometer retrievals. Collocated scatterometer and buoy wind data along with buoy surface current measurements support the theory that scatterometer winds respond to the relative motion of the ocean surface; in other words, that they can effectively be considered current-relative, as has been generally assumed. Another major control on scatterometer retrievals is atmospheric stability, which affects both surface roughness and wind shear. A study using wind, stress, temperature, and pressure measurements at a mooring in the Gulf Stream as well as collocated scatterometer data proved that the scatterometer responds as expected to changes in stability. Therefore, scatterometer retrievals can effectively be used to evaluate changes in wind due to speed adjustment over temperature fronts. Given the conclusions of these individual studies, this work collectively solidifies decades of theory and validates the use of scatterometer winds in areas of complex air-sea interaction

An Algorithm to Assess the Accuracy of NSCAT Ambiguity Removal

A wind field model can be used to evaluate the accuracy of pointwise ambiguity removal for NASA Scatterometer (NSCAT) data. Errors in pointwise ambiguity removal result in large model-fit errors when the pointwise wind estimates are assimilated into the model. By thresholding the error, regions containing ambiguity removal error can be identified. For these regions, the ambiguity selection can be improved using the model-fit field. I have developed a new automated algorithm for evaluating the quality of the pointwise ambiguity selection and for correcting the ambiguity selection. This paper presents this correction algorithm, which is generally applicable to other scatterometers, and the results for NSCAT data

유류오염 탐지를 위한 해양표면 마이크로파 후방산란 분석기반의 준경험적 임계모델

학위논문(석사) -- 서울대학교대학원 : 자연과학대학 지구환경과학부, 2023. 8. 김덕진.In an automatic oil spill monitoring system that utilizes SAR satellites, the dark spot detection step, which is responsible for the segmentation of potential oil spills, is undeniably significant. As the initial stage of automatic oil spill detection, this process is typically the most time-consuming and substantially influences the system performance. Considering the vast expanses of ocean that require thorough surveillance, it is crucial to have an efficient method that accurately identifies oil spill candidates at this critical early stage. In this study, a semi-empirical model was carefully proposed, grounded in a comprehensive analysis of the physical characteristics governing the interaction between electromagnetic waves and the sea surface, as well as oil spill observation data from SAR. This model utilizes wind speed, relative wind direction, and incidence angle as independent variables to calculate the threshold radar backscatter coefficient, to differentiate oil spill candidates from the ocean. To determine the parameters of the proposed model, large oil spill observational data was collected from the Sentinel-1 satellite, and the corresponding wind field data was derived from the ECMWF ERA5 reanalysis data. When compared to widely used dark spot detection methods such as the Otsu, Bradley, and active contour methods, the proposed model demonstrated outstanding performance. The model achieved an average F1 score of 0.7948 on the evaluation dataset, while the aforementioned methods showed 0.3315, 0.6400, and 0.5191, respectively. The proposed model exhibited distinguished accuracy with a straightforward implementation process, balancing effectiveness with simplicity, which makes it particularly suitable for real-time oil spill detection where efficiency is paramount. A notable feature of the proposed model is its ability to compute threshold at the pixel-level, unlike conventional patch-level methods that require iterative processes to detect oil spill candidates of varying sizes. This allows the model to identify oil spills in a single operation regardless of their sizes. While the proposed model is flexible in using diverse wind input sources such as buoys, scatterometers, or geophysical model functions, it is crucial to note that its performance depends on the accuracy of the wind field information, specifically, how well it reflects the wind conditions at the exact SAR acquisition time. In conclusion, this study has thoroughly investigated the behavior of the radar backscatter coefficient under both slick-free and slick-covered sea surfaces, leading to the development of a semi-empirical model that can enhance the efficiency of oil spill monitoring systems. The practical implications of the model extend beyond improving system performance; it can be used to create balanced deep-learning datasets by selectively choosing patches with dark spots. Moreover, the physically-grounded nature of the model enables advanced future research, such as distinguishing types of oil or estimating slick thickness.SAR 위성을 활용한 자동 유류오염 모니터링 시스템에서 유류오염 후보를 탐지하는 과정인 dark spot detection 단계는, 시스템의 첫 번째 단계로서 일반적으로 가장 많은 시간이 소요되고, 최종 탐지 성능에 결정적인 영향을 미친다. 넓은 해상 영역을 효과적으로 감시하기 위해서는, 이와 같이 초기 단계에서 유류오염 후보를 정확하고 효율적으로 식별할 필요성이 강조된다. 본 논문에서는 전자기파와 유류막으로 덮인 해양 표면 간의 상호작용에 대한 광범위한 분석과 유류오염 위성 관측값을 기반으로 준경험적 모델을 제시하였다. 제안된 모델은 풍속, 상대 풍향, 입사각을 독립변수로 가지며 바다와 유류오염 후보를 효과적으로 구분하는 레이더 후방산란 계수의 임계값을 산출한다. 모델의 매개변수를 결정하기 위해, Sentinel-1 위성에서 대량의 유류오염 관측 데이터를 수집하였고, 이에 해당하는 바람장 데이터를 ECMWF ERA5 재분석 데이터로부터 추출하여 사용하였다. 본 연구에서 제안한 모델의 segmentation 성능 평가 결과, 평균 F1 점수는 0.7948로 나타났으며, 기존의 대표적인 접근방법인 Otsu, Bradley, active contour model의 성능이 각각 0.3315, 0.6400, 0.5191인 것과 비교하여 우수한 탐지 성능을 보였다. 해당 모델은 물리적 특성을 고려한 직관적인 알고리즘과 높은 segmentation 정확도로, 특히 효율성이 강조되는 실시간 유류오염 모니터링에 매우 적합하다. 또한 제안된 모델은 픽셀 단계의 임계값 계산이 가능하여, 다른 patch 단위로 동작하는 탐지 모델들과 달리, 다양한 크기의 유류오염을 탐지하기 위해 여러 크기의 윈도우를 반복적으로 사용하여 탐지할 필요가 없다. 해당 모델은 해상 부이, 산란계와 같은 다양한 바람장 정보를 입력값으로 사용할 수 있으나, 모델의 성능은 해당 데이터의 정확도에 크게 의존하며, 특히 SAR 이미지 취득 시점의 바람 상태를 얼마나 정확히 반영하는지에 따라 달라진다는 특징을 가진다. 결론적으로, 본 논문에서는 유류오염이 없는 바다 표면과 있는 표면에서의 레이더 후방산란 계수의 변화를 세밀하게 분석하여, 유류오염 모니터링 시스템의 효율성을 강화할 수 있는 준경험적 유류오염 후보 추출 임계값 모델을 개발하였다. 제안된 모델은 모니터링 시스템의 성능을 향상시킬 수 있을 뿐만 아니라, dark spot이 있는 patch를 선별하여 균형잡힌 딥러닝 데이터셋을 생성하는 데에도 활용될 수 있다. 또한, 본 모델은 유류오염과 해양의 물리적 특성에 근거하므로, 유종 식별 또는 유류 두께추정과 같은 후속 연구의 가능성을 제시한다.Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Literature Review 4 1.3 Research Objective 9 Chapter 2. Microwave Backscattering Properties from the Sea Surface 11 2.1 Microwave backscattering from the Slick-Free Ocean Surface 11 2.1.1 Radar Scattering Model 11 2.1.2 Geophysical Model Function 13 2.2 Microwave backscattering from the Slick-Covered Ocean Surface 18 2.2.1 The Action Balance Equation 18 2.2.2 The Damping Ratio 27 Chapter 3. Development and Validation of the Semi-Empirical Model 29 3.1 Formulation of the Theoretical Framework for the Semi-Empirical Model 29 3.2 Data Acquisition 34 3.2.1 Acquisition of SAR Image Data 34 3.2.2 Acquisition of Wind Field Data 40 3.3 Parameter Determination of the Semi-Empirical Model 44 Chapter 4. Performance Evaluation of the Semi-Empirical Model 47 4.1 Experimental Design 47 4.2 Performance Evaluation with Other Methods 51 4.3 Performance Evaluation for Different Wind Conditions and Regions 60 Chapter 5. Application of the Semi-Empirical Model 69 Chapter 6. Conclusion 72 Bibliography 75 Abstract in Korean 82석

An Improved Ocean Vector Winds Retrieval Approach Using C- And Ku-band Scatterometer And Multi-frequency Microwave Radiometer Measurements

This dissertation will specifically address the issue of improving the quality of satellite scatterometer retrieved ocean surface vector winds (OVW), especially in the presence of strong rain associated with tropical cyclones. A novel active/passive OVW retrieval algorithm is developed that corrects Ku-band scatterometer measurements for rain effects and then uses them to retrieve accurate OVW. The rain correction procedure makes use of independent information available from collocated multi-frequency passive microwave observations provided by a companion sensor and also from simultaneous C-band scatterometer measurements. The synergy of these active and passive measurements enables improved correction for rain effects, which enhances the utility of Ku-band scatterometer measurements in extreme wind events. The OVW retrieval algorithm is based on the next generation instrument conceptual design for future US scatterometers, i.e. the Dual Frequency Scatterometer (DFS) developed by NASA’s Jet Propulsion Laboratory. Under this dissertation research, an end-to-end computer simulation was developed to evaluate the performance of this active/passive technique for retrieving hurricane force winds in the presence of intense rain. High-resolution hurricane wind and precipitation fields were simulated for several scenes of Hurricane Isabel in 2003 using the Weather Research and Forecasting (WRF) Model. Using these numerical weather model environmental fields, active/passive measurements were simulated for instruments proposed for the Global Change Observation Mission- Water Cycle (GCOM-W2) satellite series planned by the Japanese Aerospace Exploration Agency. Further, the quality of the simulation was evaluated using actual hurricane measurements from the Advanced Microwave Scanning Radiometer and iv SeaWinds scatterometer onboard the Advanced Earth Observing Satellite-II (ADEOS-II). The analysis of these satellite data provided confidence in the capability of the simulation to generate realistic active/passive measurements at the top of the atmosphere. Results are very encouraging, and they show that the new algorithm can retrieve accurate ocean surface wind speeds in realistic hurricane conditions using the rain corrected Ku-band scatterometer measurements. They demonstrate the potential to improve wind measurements in extreme wind events for future wind scatterometry missions such as the proposed GCOM-W2

Experimental and theoretical determination of sea-state bias in radar altimetry

The major unknown error in radar altimetry is due to waves on the sea surface which cause the mean radar-reflecting surface to be displaced from mean sea level. This is the electromagnetic bias. The primary motivation for the project was to understand the causes of the bias so that the error it produces in radar altimetry could be calculated and removed from altimeter measurements made from space by the Topex/Poseidon altimetric satellite. The goals of the project were: (1) observe radar scatter at vertical incidence using a simple radar on a platform for a wide variety of environmental conditions at the same time wind and wave conditions were measured; (2) calculate electromagnetic bias from the radar observations; (3) investigate the limitations of the present theory describing radar scatter at vertical incidence; (4) compare measured electromagnetic bias with bias calculated from theory using measurements of wind and waves made at the time of the radar measurements; and (5) if possible, extend the theory so bias can be calculated for a wider range of environmental conditions