Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results

NSCAT high-resolution surface wind measurements in Typhoon Violet

NASA scatterometer (NSCAT) measurements of the western Pacific Supertyphoon Violet are presented for revolutions 478 and 485 that occurred in September 1996. A tropical cyclone planetary boundary layer numerical, model, which uses conventional meteorological and geostationary cloud data, is used to estimate the winds at 10-m elevation in the cyclone. These model winds are then compared with the winds inferred from the NSCAT backscatter data by means of a novel approach that allows a wind speed to be recovered from each individual backscatter cell. This spatial adaptive (wind vector) retrieval algorithm employs several unique steps. The backscatter values are first regrouped in terms of closest neighbors in sets of four. The maximum likelihood estimates of speed and direction are then used to obtain speeds and directions for each group. Since the cyclonic flow around the tropical cyclone is known, NSCAT wind direction alias selection is easily accomplished. The selected wind directions are then used to convert each individual backscatter value to a wind speed. The results are compared to the winds obtained from the tropical cyclone boundary layer model. The NSCAT project baseline geophysical model function, NSCAT 1, was found to yield wind speeds that were systematically too low, even after editing for suspected rain areas of the cyclone. A new geophysical model function was developed using conventional NSCAT data and airborne Ku band scatterometer measurements in an Atlantic hurricane. This new model uses the neural network method and yields substantially better agreement with the winds obtained from the boundary layer model according to the statistical tests that were used

Validation of coastal forecasts

Deliverable D2.3 del Proyecto MyWave: A pan-European concerted and integrated approach to operational wave modelling and forecasting – a complement to GMES MyOcean services. Work programme topic: SPA.2011.1.5.03 – R&D to enhance future GMES applications in the Marine and Atmosphere areas.Funded under: FP7-SPACE-2011-284455

A two-parameter wind speed algorithm for Ku-band altimeters

Globally distributed crossovers of altimeter and scatterometer observations clearly demonstrate that ocean altimeter backscatter correlates with both the near-surface wind speed and the sea state. Satellite data from TOPEX/Poseidon and NSCAT are used to develop an empirical altimeter wind speed model that attenuates the sea-state signature and improves upon the present operational altimeter wind model. The inversion is defined using a multilayer perceptron neural network with altimeter-derived backscatter and significant wave height as inputs. Comparisons between this new model and past single input routines indicates that the rms wind error is reduced by 10%–15% in tandem with the lowering of wind error residuals dependent on the sea state. Both model intercomparison and validation of the new routine are detailed, including the use of large independent data compilations that include the SeaWinds and ERS scatterometers, ECMWF wind fields, and buoy measurements. The model provides consistent improvement against these varied sources with a wind-independent bias below 0.3 m s?1. The continuous form of the defined function, along with the global data used in its derivation, suggest an algorithm suitable for operational application to Ku-band altimeters. Further model improvement through wave height inclusion is limited due to an inherent multivaluedness between any single realization of the altimeter measurement pair [?o, HS] and observed near-surface winds. This ambiguity indicates that HS is a limited proxy for variable gravity wave properties that impact upon altimeter backscatter

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

Estimating Wind Stress at the Ocean Surface From Scatterometer Observations

Abstract—Wind stress is the most important ocean forcing for driving tropical surface currents. Stress can be estimated from scatterometer-reported wind measurements at 10 m that have been extrapolated to the surface, assuming a neutrally stable atmosphere and no surface current. Scatterometer calibration is designed to account for the assumption of neutral stability; however, the assumption of a particular sea state and negligible current often introduces an error in wind stress estimations. Since the fundamental scatterometer measurement is of the surface radar backscatter (sigma-0) which is related to surface roughness and, thus, stress, we develop a method to estimate wind stress directly from the scatterometer measurements of sigma-0 and their associated azimuth angle and incidence angle using a neural network approach. We compare the results with in situ estimations and observe that the wind stress estimations from this approach are more accurate compared with those obtained from the conventional estimations using 10-m-height wind measurements. Index Terms—Atmospheric stability, neutral stability, scatterometer, wind stress. I

NEUROSAT: an overview

This report gives an overview of the work being carried out, as part of the NEUROSAT project, in the Neural Computing Research Group at Aston University. The aim is to give a general review of the work and methods, with reference to other documents which provide the detail. The document is ongoing and will be updated as parts of the project are completed. Thus some of the references are not yet present. In the broadest sense, the Aston part of NEUROSAT is about using neural networks (and other advanced statistical techniques) to extract wind vectors from satellite measurements of ocean surface radar backscatter. The work involves several phases, which are outlined below. A brief summary of the theory and application of satellite scatterometers forms the first section. The next section deals with the forward modelling of the scatterometer data, after which the inverse problem is addressed. Dealiasing (or disambiguation) is discussed, together with proposed solutions. Finally a holistic framework is presented in which the problem can be solved

Wind-stress variability over the Benguela upwelling system

Bibliography: leaves 119-133.Regional wind-stress variability over the Benguela Upwelling System is described using 16 months (01 August 1999 29 November 2000) of satellite derived QuikSCA T wind data. The QuikSCA T data are compared to the climatologies presented by Kamstra (1985) and Bakun and Nelson (1991), as well as the long-term climatology (1968-1996) of the surface vector wind speed field off the coast of southern Africa, as derived from the 2.5° resolution NCEPINCAR reanalysis dataset. Broad scale similaritie"s are found between the QuikSCA T and the long-term NCEPINCAR climatology (1968-1996) data sets. This allows one to have confidence in using this scatterometer data to investigate details of spatial and temporal variability over the Benguela System. During summer, wind-stress maxima are found at approximately 17, 29 and 34°S. These maxima strengthen in late summer. The seasonal northward migration of the South Atlantic Anticyclone becomes apparent in late autumn, when the strongest wind-stress occurs north of 28°S. A significant wind-stress minimum is observed to develop slightly north of Cape Columbine (33°S) during autumn. To the north (10-23.5°S) the Benguela is characterised by relatively strong south-easterly wind-stress during winter. To the south (24-35°S) the Benguela is characterised by relatively weak westerly to south-westerly wind-stress during winter. A southward migration of southeasterly wind-stress is observed during early spring. By November the entire Benguela Upwelling System is once again characterised by southerly to south-easterly wind stress. Wind-stress variability is investigated using both a type of artificial neural network, known as the Kohonen Self Organising Map (SOM), as well as a wavelet analysis. Two independent SOM studies are conducted. The first study produced a 6x4 SOM output array, which is used to examine seasonal variability as well as the temporal evolution of two synoptic-scale wind events. For the second study both a SOM and a wavelet analysis are applied to an extracted data set to find that the system can be divided into six discrete wind regimes, 10-15°S; 15.5-18.5°S; 19-23.5°S; 24-28.5°S; 29-32.5°S; and 33-35°S. The wavelet power spectra for these wind cells span a range of frequencies from 4 to 64 days, with each region appearing to contain distinct periodicities. To the north, 10-23.5°S, the majority of the power occurs during winter, with a 6-16 day periodicity. Further south, 24-35°S, the majority of the power occurs in the summer. Here a bi-modal distribution occurs, with peaks of 6-16 and 35-40 days. Lastly a case study sequence of the spatial distribution of wind-stress, windstress curl and SST, at a location off the west coast of southern Africa (25-300S and 12-17°E), is discussed in relation to an intense, upwelling favourable, wind event that occurred from 11-20 February 2000

Ocean Winds and Turbulent Air-Sea Fluxes Inferred From Remote Sensing

Air-sea turbulent fluxes determine the exchange of momentum, heat, freshwater, and gas between the atmosphere and ocean. These exchange processes are critical to a broad range of research questions spanning length scales from meters to thousands of kilometers and time scales from hours to decades. Examples are discussed (section 2). The estimation of surface turbulent fluxes from satellite is challenging and fraught with considerable errors (section 3); however, recent developments in retrievals (section 3) will greatly reduce these errors. Goals for the future observing system are summarized in section 4. Surface fluxes are defined as the rate per unit area at which something (e.g., momentum, energy, moisture, or CO Z ) is transferred across the air/sea interface. Wind- and buoyancy-driven surface fluxes are called surface turbulent fluxes because the mixing and transport are due to turbulence. Examples of nonturbulent processes are radiative fluxes (e.g., solar radiation) and precipitation (Schmitt et al., 2010). Turbulent fluxes are strongly dependent on wind speed; therefore, observations of wind speed are critical for the calculation of all turbulent surface fluxes. Wind stress, the vertical transport of horizontal momentum, also depends on wind direction. Stress is very important for many ocean processes, including upper ocean currents (Dohan and Maximenko, 2010) and deep ocean currents (Lee et al., 2010). On short time scales, this horizontal transport is usually small compared to surface fluxes. For long-term processes, transport can be very important but again is usually small compared to surface fluxes

한반도 주변해 연안 해양현상에 대한 합성개구레이더 활용

학위논문 (박사)-- 서울대학교 대학원 : 과학교육과 (지구과학전공), 2016. 8. 박경애.In this thesis, the applicability of synthetic aperture radar (SAR) to interpretation of oceanic phenomena at the coastal regions around Korea peninsula is presented. For that, the spatial and temporal variations of SAR-derived coastal wind fields and evolution of disastrous oil spills on SAR images were analyzed in relation to atmospheric and oceanic environmental factors using in-situ measurement and satellite observations. The SAR wind fields retrieved from the east coast of Korea in August 2007 during the upwelling period revealed a spatial distinction between near and offshore regions. Low wind speeds were associated with cold water regions with dominant coastal upwelling. Time series of in-situ measurements of both wind speed and water temperature indicated that the upwelling was induced by the wind field. SAR data at the present upwelling region showed a relatively large backscattering attenuation to SST ratio of 1.2 dB ºC−1 compared the known dependence of the water viscosity on the radar backscattering. In addition, wind speed magnitude showed a positive correlation with the difference between SST and air temperature. It implies that the low wind field from SAR was mainly induced by changes in atmospheric stability due to air-sea temperature differences. Oil spills at the Hebei Spirit accident off the coast of Korea in the Yellow Sea were identified using SAR data and their evolution was investigated. To quantitatively analyze the spatial and temporal variations of oil spills, objective detection methods based on adaptive thresholding and a neural network were applied. Prior to applying, the results from two methods were compared for verification. It showed good agreement enough for the estimation of the extent of oil patches and their trajectories, with the exception of negligible errors at the boundaries. Quantitative analyses presented that the detected oil slicks moved southeastward, corresponding to the prevailing wind and tidal currents, and gradually dissipated during the spill, except for an extraordinary rapid decrease in onshore regions at the initial stage. It was identified that the initial dissipation of the spilt oil was induced by strong tidal mixing in the tidal front zone from comparison with the tidal mixing index. The spatial and temporal variations of the oil slicks confirmed the influence of atmospheric and oceanic environmental factors. The overall horizontal migration of the oil spills detected from consecutive SAR images was mainly driven by Ekman drift during the winter monsoon rather than the tidal residual current.Chapter 1. Introduction 1 1.1. Study Background 1 1.2. Objectives of the Thesis 14 Chapter 2. Data Description 15 2.1. SAR Data 15 2.2. Other Satellite Data 21 2.2.1. Wind Data 21 2.2.2. Sea Surface Temperature Data 21 2.2.3. Ocean Color Data 22 2.3. Reanalysis Data 23 2.4. In-situ Measurements 23 2.5. Land Masking Data 26 2.6. Tidal Current Data 28 Chapter 3. Methods 29 3.1. SAR Wind Retrieval 29 3.2. Noise Reduction of ScanSAR Images 37 3.3. Conversion of Wind Speed to Neutral Wind 41 3.4. Estimation of Index of the Tidal Front 43 3.5. Estimation of Ekman Drift and Tidal Residual Current 45 3.6. Feature Detection Methods 46 3.6.1. Adaptive Threshold Method 47 3.6.2. Bimodal Histogram Method 50 3.6.3. Neural Network Method 54 Chapter 4. Coastal Wind Fields and Upwelling Response 58 4.1. Variations of Wind Fields during Coastal Upwelling 58 4.2. Stability Effect on Wind Speed 65 4.3. Biological Impact of Upwelling 70 Chapter 5. Characteristics of Objective Feature Detection 74 5.1. Comparison of Thresholding Methods 74 5.2. Oil Spill of the Hebei Spirit by Thresholding Method 81 5.3. Oil Spill by the Hebei Spirit by Neural Network Method 85 5.4. Differences by Detection Methods 88 Chapter 6. Evolution of Oil Spill at the Coastal Region 90 6.1. Temporal Evolution of the Hebei Spirit Oil Spill 90 6.2. Effect of Artificial Factor on the Evolution 96 Chapter 7. Effect of Environmental Factors on the Oil Spill 98 7.1. Effect of Tidal Mixing 98 7.2. Effect of Wind and Tidal Current 103 Chapter 8. Summary and Conclusion 110 Reference 114 Abstract in Korean 142Docto