Limiting Accuracy of Height Measurement for a Precision Radar Altimeter in a Low Altitude Flying Vehicle above the Sea Surface

This study presents mathematical analysis and numerical modeling for the estimation of measurement errors of height estimation over the sea surface for a precision radar altimeter installed in a low altitude flying vehicle. Reflective properties of the electromagnetic signals from the sea surface are determined by the local backscattering patterns of the sea surface illuminated. The height estimation of the flying vehicle from the received echo signals at the output of its tracking system is the sum of three factors: the first factor is the height to the average sea level the second is the bias of the estimation of the height, which is time-varying and depends on the slope of large-scale roughness; the third is the terms related to the surface topography. For the calculation of the estimation errors of the height measurement of a low altitude precision radar altimeter, a reasonable approximation of the large roughness of the sea surface by a deterministic function is necessary. In this study, we performed the derivation of the estimation function and the analysis of the limiting accuracy of the height measurement using the calculation of the estimation errors in spectral domain method describing the large-scale sea surface roughness. The results obtained for the limiting accuracy of a flying vehicle at low altitude above the sea surface, allows to obtain reasonable system parameters minimizing height errors of the flight altitude.

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Effect of the Local Backscattering Pattern of the Sea Surface to the Reflected Signal of a Precision Airborne Radar Altimeter at Low Altitude

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2016.2578935 ◽

2016 ◽

Vol 13 (9) ◽

pp. 1246-1249

Author(s):

Min-Ho Ka ◽

Aleksandr I. Baskakov ◽

Alexey Artemenko

Keyword(s):

Sea Surface ◽

Airborne Radar ◽

Radar Altimeter ◽

Reflected Signal ◽

Low Altitude

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Improved Analyses of Changes and Uncertainties in Sea Surface Temperature Measured In Situ since the Mid-Nineteenth Century: The HadSST2 Dataset

Journal of Climate ◽

10.1175/jcli3637.1 ◽

2006 ◽

Vol 19 (3) ◽

pp. 446-469 ◽

Cited By ~ 602

Author(s):

N. A. Rayner ◽

P. Brohan ◽

D. E. Parker ◽

C. K. Folland ◽

J. J. Kennedy ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Bias Correction ◽

Measurement Errors ◽

Large Scale ◽

Sea Surface ◽

Data Set ◽

Total Uncertainty ◽

Hadley Centre ◽

Measurement Practices

Abstract A new flexible gridded dataset of sea surface temperature (SST) since 1850 is presented and its uncertainties are quantified. This analysis [the Second Hadley Centre Sea Surface Temperature dataset (HadSST2)] is based on data contained within the recently created International Comprehensive Ocean–Atmosphere Data Set (ICOADS) database and so is superior in geographical coverage to previous datasets and has smaller uncertainties. Issues arising when analyzing a database of observations measured from very different platforms and drawn from many different countries with different measurement practices are introduced. Improved bias corrections are applied to the data to account for changes in measurement conditions through time. A detailed analysis of uncertainties in these corrections is included by exploring assumptions made in their construction and producing multiple versions using a Monte Carlo method. An assessment of total uncertainty in each gridded average is obtained by combining these bias-correction-related uncertainties with those arising from measurement errors and undersampling of intragrid box variability. These are calculated by partitioning the variance in grid box averages between real and spurious variability. From month to month in individual grid boxes, sampling uncertainties tend to be most important (except in certain regions), but on large-scale averages bias-correction uncertainties are more dominant owing to their correlation between grid boxes. Changes in large-scale SST through time are assessed by two methods. The linear warming between 1850 and 2004 was 0.52° ± 0.19°C (95% confidence interval) for the globe, 0.59° ± 0.20°C for the Northern Hemisphere, and 0.46° ± 0.29°C for the Southern Hemisphere. Decadally filtered differences for these regions over this period were 0.67° ± 0.04°C, 0.71° ± 0.06°C, and 0.64° ± 0.07°C.

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A New Shooting Bouncing Ray Method for Composite Scattering from a Target above the Electrically Large Scope Sea Surface

Mathematical Problems in Engineering ◽

10.1155/2017/6201745 ◽

2017 ◽

Vol 2017 ◽

pp. 1-7

Author(s):

Peng Peng ◽

Guo Lixin

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Sea Surface ◽

Radar Target ◽

Hybrid Scheme ◽

Ray Method ◽

Electromagnetic Interactions ◽

X Band ◽

Large Scope ◽

Low Altitude

A new shooting and bouncing ray (SBR) simulator based on the hybrid scheme of GO/PO/SDFM/EEC method is developed for the accurate prediction of composite scattering from a low altitude target above the electrically very-large-scale sea surface. It can adequately deal with the complex local electromagnetic interactions between the target and the large scope sea surface. The method is compared with the exact computational electromagnetic solver FEKO-MLFMM to validate its accuracy and efficiency. Then, it is applied to simulate the bistatic and monostatic scattering characteristics of an airplane above the electrically large sea surface at X-band, for different sea states. The results reveal the contributions from the target, sea surface, and interactions, which are of significance for radar target detection and remote sensing in real maritime environments.

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DESIGN AND IMPLEMENTATION OF THE OCEANOGRAPHIC MODELING AND OBSERVATION NETWORK (REMO) FOR PHYSICAL OCEANOGRAPHY STUDIES AND OCEAN FORECASTING

Brazilian Journal of Geophysics ◽

10.22564/rbgf.v31i2.18 ◽

2014 ◽

Vol 31 (2) ◽

Author(s):

Jose Antonio Moreira Lima

Keyword(s):

Atlantic Ocean ◽

Large Scale ◽

North Pacific Ocean ◽

Integrated Approach ◽

Sea Surface ◽

Global Ocean ◽

Height Anomaly ◽

The North ◽

Observation Network ◽

Ocean Forecasting

This paper is concerned with the planning, implementation and some results of the Oceanographic Modeling and Observation Network, named REMO, for Brazilian regional waters. Ocean forecasting has been an important scientific issue over the last decade due to studies related to climate change as well as applications related to short-range oceanic forecasts. The South Atlantic Ocean has a deficit of oceanographic measurements when compared to other ocean basins such as the North Atlantic Ocean and the North Pacific Ocean. It is a challenge to design an ocean forecasting system for a region with poor observational coverage of in-situ data. Fortunately, most ocean forecasting systems heavily rely on the assimilation of surface fields such as sea surface height anomaly (SSHA) or sea surface temperature (SST), acquired by environmental satellites, that can accurately provide information that constrain major surface current systems and their mesoscale activity. An integrated approach is proposed here in which the large scale circulation in the Atlantic Ocean is modeled in a first step, and gradually nested into higher resolution regional models that are able to resolve important processes such as the Brazil Current and associated mesoscale variability, continental shelf waves, local and remote wind forcing, and others. This article presents the overall strategy to develop the models using a network of Brazilian institutions and their related expertise along with international collaboration. This work has some similarity with goals of the international project Global Ocean Data Assimilation Experiment OceanView (GODAE OceanView).

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Do elite soccer players cover longer distance when losing? Differences between attackers and defenders

International Journal of Sports Science & Coaching ◽

10.1177/1747954120982270 ◽

2020 ◽

pp. 174795412098227

Author(s):

Carlos Lago-Peñas ◽

Anton Kalén ◽

Miguel Lorenzo-Martinez ◽

Roberto López-Del Campo ◽

Ricardo Resta ◽

...

Keyword(s):

New York ◽

High Speed ◽

Large Scale ◽

Tracking System ◽

Soccer Players ◽

Running Performance ◽

Situational Variables ◽

Total Distance ◽

Professional Soccer ◽

Score Line

This study aimed to evaluate the effects playing position, match location (home or away), quality of opposition (strong or weak), effective playing time (total time minus stoppages), and score-line on physical match performance in professional soccer players using a large-scale analysis. A total of 10,739 individual match observations of outfield players competing in the Spanish La Liga during the 2018–2019 season were recorded using a computerized tracking system (TRACAB, Chyronhego, New York, USA). The players were classified into five positions (central defenders, players = 94; external defenders, players = 82; central midfielders, players = 101; external midfielders, players = 72; and forwards, players = 67) and the following match running performance categories were considered: total distance covered, low-speed running (LSR) distance (0–14 km · h−1), medium-speed running (MSR) distance (14–21 km · h−1), high-speed running (HSR) distance (>21 km · h−1), very HSR (VHSR) distance (21–24 km · h−1), sprint distance (>24 km · h−1) Overall, match running performance was highly dependent on situational variables, especially the score-line condition (winning, drawing, losing). Moreover, the score-line affected players running performance differently depending on their playing position. Losing status increased the total distance and the distance covered at MSR, HSR, VHSR and Sprint by defenders, while attacking players showed the opposite trend. These findings may help coaches and managers to better understand the effects of situational variables on physical performance in La Liga and could be used to develop a model for predicting the physical activity profile in competition.

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Ant-Mutated Immune Particle Filter Design for Terrain Referenced Navigation with Interferometric Radar Altimeter

Remote Sensing ◽

10.3390/rs13112189 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2189

Author(s):

Suktae Kang ◽

Myeong-Jong Yu

Keyword(s):

Immune System ◽

Particle Filter ◽

Measurement Errors ◽

Filter Design ◽

Sar Interferometry ◽

Low Grade ◽

Radar Altimeter ◽

Terrain Referenced Navigation ◽

Auxiliary Particle ◽

Auxiliary Particle Filter

This study aims to design a robust particle filter using artificial intelligence algorithms to enhance estimation performance using a low-grade interferometric radar altimeter (IRA). Based on the synthetic aperture radar (SAR) interferometry technology, the IRA can extract three-dimensional ground coordinates with at least two antennas. However, some IRA uncertainties caused by geometric factors and IRA-inherent measurement errors have proven to be difficult to eliminate by signal processing. These uncertainties contaminate IRA outputs, crucially impacting the navigation performance of low-grade IRA sensors in particular. To deal with such uncertainties, an ant-mutated immune particle filter (AMIPF) is proposed. The proposed filter combines the ant colony optimization (ACO) algorithm with the immune auxiliary particle filter (IAPF) to bring individual mutation intensity. The immune system indicates the stochastic parameters of the ACO, which conducts the mutation process in one step for the purpose of computational efficiency. The ant mutation then moves particles into the most desirable position using parameters from the immune system to obtain optimal particle diversity. To verify the performance of the proposed filter, a terrain referenced navigation (TRN) simulation was conducted on an unmanned aerial vehicle (UAV). The Monte Carlo simulation results show that the proposed filter is not only more computationally efficient than the IAPF but also outperforms both the IAPF and the auxiliary particle filter (APF) in navigation performance and robustness.

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Methodology for Implementing the State Estimation in Renewable Energy Management Systems

Energies ◽

10.3390/en14082301 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2301

Author(s):

Yun-Sung Cho ◽

Yun-Hyuk Choi

Keyword(s):

Renewable Energy ◽

Power Systems ◽

State Estimation ◽

Measurement Errors ◽

Large Scale ◽

Test System ◽

The State ◽

Inner Function ◽

Outer Function ◽

Bad Data

This paper describes a methodology for implementing the state estimation and enhancing the accuracy in large-scale power systems that partially depend on variable renewable energy resources. To determine the actual states of electricity grids, including those of wind and solar power systems, the proposed state estimation method adopts a fast-decoupled weighted least square approach based on the architecture of application common database. Renewable energy modeling is considered on the basis of the point of data acquisition, the type of renewable energy, and the voltage level of the bus-connected renewable energy. Moreover, the proposed algorithm performs accurate bad data processing using inner and outer functions. The inner function is applied to the largest normalized residue method to process the bad data detection, identification and adjustment. While the outer function is analyzed whether the identified bad measurements exceed the condition of Kirchhoff’s current law. In addition, to decrease the topology and measurement errors associated with transformers, a connectivity model is proposed for transformers that use switching devices, and a transformer error processing technique is proposed using a simple heuristic method. To verify the performance of the proposed methodology, we performed comprehensive tests based on a modified IEEE 18-bus test system and a large-scale power system that utilizes renewable energy.

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Subseasonal Forecasts of the Northern Queensland Floods of February 2019: Causes and Forecast Evaluation

Atmosphere ◽

10.3390/atmos12060758 ◽

2021 ◽

Vol 12 (6) ◽

pp. 758

Author(s):

Wayne Yuan-Huai Tsai ◽

Mong-Ming Lu ◽

Chung-Hsiung Sui ◽

Yin-Min Cho

Keyword(s):

Large Scale ◽

Deep Convection ◽

Austral Summer ◽

Rainfall Event ◽

Monsoon Depression ◽

Forecast Skill ◽

Sea Surface ◽

Central Pacific ◽

Percentile Rank ◽

Extended Range

During the austral summer 2018/19, devastating floods occurred over northeast Australia that killed approximately 625,000 head of cattle and inundated over 3000 homes in Townsville. In this paper, the disastrous event was identified as a record-breaking subseasonal peak rainfall event (SPRE). The SPRE was mainly induced by an anomalously strong monsoon depression that was modulated by the convective phases of an MJO and an equatorial Rossby (ER) wave. The ER wave originated from an active equatorial deep convection associated with the El Niño warm sea surface temperatures near the dateline over the central Pacific. Based on the S2S Project Database, we analyzed the extended-range forecast skill of the SPRE from two different perspectives, the monsoon depression represented by an 850-hPa wind shear index and the 15-day accumulated precipitation characterized by the percentile rank (PR) and the ratio to the three-month seasonal (DJF) totals. The results of four S2S models of this study suggest that the monsoon depression can maintain the same level of skill as the short-range (3 days) forecast up to 8–10 days. For precipitation parameters, the conclusions are similar to the monsoon depression. For the 2019 northern Queensland SPRE, the model forecast was, in general, worse than the expectation derived from the hindcast analysis. The clear modulation of the ER wave that enhanced the SPRE monsoon depression circulation and precipitation is suspected as the main cause for the lower forecast skill. The analysis procedure proposed in this study can be applied to analyze the SPREs and their associated large-scale drivers in other regions.

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