A Time Series Weather Radar Simulator Based on High-Resolution Atmospheric Models

2008 ◽  
Vol 25 (2) ◽  
pp. 230-243 ◽  
Author(s):  
B. L. Cheong ◽  
R. D. Palmer ◽  
M. Xue
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Time Series Data ◽  
Three Dimensional ◽  
Weather Radar ◽  
Series Data ◽  
Atmospheric Conditions ◽  
Moment Maps ◽  
Weather Model ◽  
Radar Simulator

Abstract A three-dimensional radar simulator capable of generating simulated raw time series data for a weather radar has been designed and implemented. The characteristics of the radar signals (amplitude, phase) are derived from the atmospheric fields from a high-resolution numerical weather model, although actual measured fields could be used. A field of thousands of scatterers is populated within the field of view of the virtual radar. Reflectivity characteristics of the targets are determined from well-known parameterization schemes. Doppler characteristics are derived by forcing the discrete scatterers to move with the three-dimensional wind field. Conventional moment-generating radar simulators use atmospheric conditions and a set of weighting functions to produce theoretical moment maps, which allow for the study of radar characteristics and limitations given particular configurations. In contrast to these radar simulators, the algorithm presented here is capable of producing sample-to-sample time series data that are collected by a radar system of virtually any design. Thus, this new radar simulator allows for the test and analysis of advanced topics, such as phased array antennas, clutter mitigation schemes, waveform design studies, and spectral-based methods. Limited examples exemplifying the usefulness and flexibility of the simulator will be provided.

scholarly journals Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China

2020 ◽  
Vol 12 (19) ◽  
pp. 3120
Author(s):  
Luojia Hu ◽  
Nan Xu ◽  
Jian Liang ◽  
Zhichao Li ◽  
Luzhen Chen ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Mangrove Forest ◽  
Time Series Data ◽  
Landsat Imagery ◽  
Google Earth ◽  
Series Data ◽  
National Scale ◽  
Google Earth Engine ◽  
Sentinel 2

A high resolution mangrove map (e.g., 10-m), including mangrove patches with small size, is urgently needed for mangrove protection and ecosystem function estimation, because more small mangrove patches have disappeared with influence of human disturbance and sea-level rise. However, recent national-scale mangrove forest maps are mainly derived from 30-m Landsat imagery, and their spatial resolution is relatively coarse to accurately characterize the extent of mangroves, especially those with small size. Now, Sentinel imagery with 10-m resolution provides an opportunity for generating high-resolution mangrove maps containing these small mangrove patches. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and/or Sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features of random forest to classify mangroves in China. We found that Sentinel-2 (F1-Score of 0.895) is more effective than Sentinel-1 (F1-score of 0.88) in mangrove extraction, and a combination of SAR and MSI imagery can get the best accuracy (F1-score of 0.94). The 10-m mangrove map was derived by combining SAR and MSI data, which identified 20003 ha mangroves in China, and the area of small mangrove patches (<1 ha) is 1741 ha, occupying 8.7% of the whole mangrove area. At the province level, Guangdong has the largest area (819 ha) of small mangrove patches, and in Fujian, the percentage of small mangrove patches is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest map is expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of the mangrove forest.

scholarly journals Missing data imputation of high‐resolution temporal climate time series data

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
E Afrifa‐Yamoah ◽  
U. A. Mueller ◽  
S. M. Taylor ◽  
A. J. Fisher
Keyword(s):  
Time Series ◽  
Missing Data ◽  
High Resolution ◽  
Time Series Data ◽  
Series Data ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Climate Time Series

scholarly journals Probing the size of a magnetosphere of a young solar-like star

2008 ◽  
Vol 4 (S259) ◽  
pp. 413-414
Author(s):  
Catrina M. Hamilton ◽  
C. M. Johns-Krull ◽  
R. Mundt ◽  
W. Herbst ◽  
J. N. Winn
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Binary System ◽  
Line Profile ◽  
Main Sequence ◽  
Time Series Data ◽  
Young Stars ◽  
Series Data ◽  
Accretion Flows ◽  
Magnetospheric Accretion

AbstractWe have obtained high resolution spectra of the pre-main sequence binary system KH 15D (V582 Mon) while the star is fully visible, fully occulted, and during several ingress and egress events over the course of five contiguous observing seasons. The Hα line profile is a standard probe of the magnetospheric accretion flows on young stars such as KH 15D. We use these time series data to map out the size of the magnetosphere and find that it changes size from one observing season to the next.

scholarly journals Contextualizing time-series data: quantification of short-term regional variability in the San Pedro Channel using high-resolution in situ glider data

2018 ◽  
Vol 15 (20) ◽  
pp. 6151-6165 ◽  
Author(s):  
Elizabeth N. Teel ◽  
Xiao Liu ◽  
Bridget N. Seegers ◽  
Matthew A. Ragan ◽  
William Z. Haskell ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Water Column ◽  
Time Series Data ◽  
Series Data ◽  
Regional Variability ◽  
Modes Of Variability ◽  
Subsurface Chlorophyll Maximum ◽  
San Pedro

Abstract. Oceanic time series have been instrumental in providing an understanding of biological, physical, and chemical dynamics in the oceans and how these processes change over time. However, the extrapolation of these results to larger oceanographic regions requires an understanding and characterization of local versus regional drivers of variability. Here we use high-frequency spatial and temporal glider data to quantify variability at the coastal San Pedro Ocean Time-series (SPOT) site in the San Pedro Channel (SPC) and provide insight into the underlying oceanographic dynamics for the site. The dataset could be described by a combination of four water column profile types that typified active upwelling, a surface bloom, warm-stratified low-nutrient conditions, and a subsurface chlorophyll maximum. On weekly timescales, the SPOT station was on average representative of 64 % of profiles taken within the SPC. In general, shifts in water column profile characteristics at SPOT were also observed across the entire channel. On average, waters across the SPC were most similar to offshore profiles, suggesting that SPOT time series data would be more impacted by regional changes in circulation than local coastal events. These results indicate that high-resolution in situ glider deployments can be used to quantify major modes of variability and provide context for interpreting time series data, allowing for broader application of these datasets and greater integration into modeling efforts.

scholarly journals Weather Radar Time Series Simulation: Improving Accuracy and Performance

2018 ◽  
Vol 35 (11) ◽  
pp. 2169-2187 ◽  
Author(s):  
Christopher D. Curtis
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Weather Radar ◽  
Series Data ◽  
Signal Processing Algorithms ◽  
Graphical Processing ◽  
And Performance ◽  
Time Series Simulation ◽  
Improving Accuracy ◽  
Improved Technology

AbstractTime series simulation is an important tool for developing and testing new signal processing algorithms for weather radar. The methods for simulating time series data have not changed much over the last few decades, but recent advances in computing technology call for new methods. It would seem that faster computers would make better-performing simulators less necessary, but improved technology has made comprehensive, multiday simulations feasible. Even a relatively minor performance improvement can significantly shorten the time of one of these multiday simulations. Current simulators can also be inaccurate for some sets of parameters, especially narrow spectrum widths. In this paper, three new modifications to the conventional simulators are introduced to improve accuracy and performance. Two of the modifications use thresholds to optimize both the total number of samples and the number of random variates that need to be simulated. The third modification uses an alternative method for implementing the inverse Fourier transform. These new modifications lead to fast versions of the simulators that accurately match the desired autocorrelation and spectrum for a wide variety of signal parameters. Additional recommendations for using single-precision values and graphical processing units are also suggested.

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