Real-time 3D visualization of phased array weather radar data via concurrent processing in Science Cloud

Author(s):  
Ken T. Murata ◽  
Kazuya Muranaga ◽  
Kazunori Yamamoto ◽  
Yoshiaki Nagaya ◽  
Praphan Pavarangkoon ◽  
...  
1998 ◽  
Vol 2 (2/3) ◽  
pp. 265-281 ◽  
Author(s):  
V. A. Bell ◽  
R. J. Moore

Abstract. A practical methodology for distributed rainfall-runoff modelling using grid square weather radar data is developed for use in real-time flood forecasting. The model, called the Grid Model, is configured so as to share the same grid as used by the weather radar, thereby exploiting the distributed rainfall estimates to the full. Each grid square in the catchment is conceptualised as a storage which receives water as precipitation and generates water by overflow and drainage. This water is routed across the catchment using isochrone pathways. These are derived from a digital terrain model assuming two fixed velocities of travel for land and river pathways which are regarded as model parameters to be optimised. Translation of water between isochrones is achieved using a discrete kinematic routing procedure, parameterised through a single dimensionless wave speed parameter, which advects the water and incorporates diffusion effects through the discrete space-time formulation. The basic model routes overflow and drainage separately through a parallel system of kinematic routing reaches, characterised by different wave speeds but using the same isochrone-based space discretisation; these represent fast and slow pathways to the basin outlet, respectively. A variant allows the slow pathway to have separate isochrones calculated using Darcy velocities controlled by the hydraulic gradient as estimated by the local gradient of the terrain. Runoff production within a grid square is controlled by its absorption capacity which is parameterised through a simple linkage function to the mean gradient in the square, as calculated from digital terrain data. This allows absorption capacity to be specified differently for every grid square in the catchment through the use of only two regional parameters and a DTM measurement of mean gradient for each square. An extension of this basic idea to consider the distribution of gradient within the square leads analytically to a Pareto distribution of absorption capacity, given a power distribution of gradient within the square. The probability-distributed model theory (Moore, 1985) can then be used directly to obtain the integrated runoff production for the square for routing to the catchment outlet. justification for the simple linkage function is in part sought through consideration of variants on the basic model where (i) runoff production is based on a topographic index control on saturation and (ii) absorption capacity is related to the Integrated Air Capacity of the soil, as obtained from soil survey. An impervious area fraction is also introduced based on the use of Landsat classified urban areas. The Grid Model and its variants are assessed in Part 2 (Bell and Moore, 1998), first as simulation models and then as forecasting models, following the development of updating procedures to accommodate recent observations of flow so as to improve forecast performance in a real-time context.


2013 ◽  
Vol 17 (8) ◽  
pp. 3095-3110 ◽  
Author(s):  
J. Liu ◽  
M. Bray ◽  
D. Han

Abstract. Mesoscale numerical weather prediction (NWP) models are gaining more attention in providing high-resolution rainfall forecasts at the catchment scale for real-time flood forecasting. The model accuracy is however negatively affected by the "spin-up" effect and errors in the initial and lateral boundary conditions. Synoptic studies in the meteorological area have shown that the assimilation of operational observations, especially the weather radar data, can improve the reliability of the rainfall forecasts from the NWP models. This study aims at investigating the potential of radar data assimilation in improving the NWP rainfall forecasts that have direct benefits for hydrological applications. The Weather Research and Forecasting (WRF) model is adopted to generate 10 km rainfall forecasts for a 24 h storm event in the Brue catchment (135.2 km2) located in southwest England. Radar reflectivity from the lowest scan elevation of a C-band weather radar is assimilated by using the three-dimensional variational (3D-Var) data-assimilation technique. Considering the unsatisfactory quality of radar data compared to the rain gauge observations, the radar data are assimilated in both the original form and an improved form based on a real-time correction ratio developed according to the rain gauge observations. Traditional meteorological observations including the surface and upper-air measurements of pressure, temperature, humidity and wind speed are also assimilated as a bench mark to better evaluate and test the potential of radar data assimilation. Four modes of data assimilation are thus carried out on different types/combinations of observations: (1) traditional meteorological data; (2) radar reflectivity; (3) corrected radar reflectivity; (4) a combination of the original reflectivity and meteorological data; and (5) a combination of the corrected reflectivity and meteorological data. The WRF rainfall forecasts before and after different modes of data assimilation are evaluated by examining the rainfall temporal variations and total amounts which have direct impacts on rainfall–runoff transformation in hydrological applications. It is found that by solely assimilating radar data, the improvement of rainfall forecasts are not as obvious as assimilating meteorological data; whereas the positive effect of radar data can be seen when combined with the traditional meteorological data, which leads to the best rainfall forecasts among the five modes. To further improve the effect of radar data assimilation, limitations of the radar correction ratio developed in this study are discussed and suggestions are made on more efficient utilisation of radar data in NWP data assimilation.


2013 ◽  
Vol 30 (5) ◽  
pp. 873-895 ◽  
Author(s):  
Yong Hyun Kim ◽  
Sungshin Kim ◽  
Hye-Young Han ◽  
Bok-Haeng Heo ◽  
Cheol-Hwan You

Abstract In countries with frequent aerial military exercises, chaff particles that are routinely spread by military aircraft represent significant noise sources for ground-based weather radar observation. In this study, a cost-effective procedure is proposed for identifying and removing chaff echoes from single-polarization Doppler radar readings in order to enhance the reliability of observed meteorological data. The proposed quality control procedure is based on three steps: 1) spatial and temporal clustering of decomposed radar image elements, 2) extraction of the clusters’ static and time-evolution characteristics, and 3) real-time identification and removal (or censoring) of target echoes from radar data. Simulation experiments based on this procedure were conducted on site-specific ground-echo-removed weather radar data provided by the Korea Meteorological Administration (KMA), from which three-dimensional (3D) reflectivity echoes covering hundreds of thousands of square kilometers of South Korean territory within an altitude range of 0.25–10 km were retrieved. The algorithm identified and removed chaff clutter from the South Korean data with a novel decision support system at an 81% accuracy level under typical cases in which chaff and weather clusters were isolated from one another with no overlapping areas.


2017 ◽  
Vol 34 (7) ◽  
pp. 1607-1623 ◽  
Author(s):  
Christopher D. Curtis ◽  
Sebastián M. Torres

AbstractAdaptive range oversampling processing can be used either to reduce the variance of radar-variable estimates without increasing scan times or to reduce scan times without increasing the variance of estimates. For example, an implementation of adaptive pseudowhitening on the National Weather Radar Testbed Phased-Array Radar (NWRT PAR) led to a twofold reduction in scan times. Conversely, a proposed implementation of adaptive pseudowhitening the U.S. Next Generation Weather Radar (NEXRAD) network would reduce the variance of dual-polarization estimates while keeping current scan times. However, the original version of adaptive pseudowhitening is not compatible with radar-variable estimators for which an explicit variance expression is not readily available. One such nontraditional estimator is the hybrid spectrum-width estimator, which is currently used in the NEXRAD network. In this paper, an extension of adaptive pseudowhitening is proposed that utilizes lookup tables (rather than analytical solutions based on explicit variance expressions) to obtain range oversampling transformations. After describing this lookup table (LUT) adaptive pseudowhitening technique, its performance is compared to that of the original version of adaptive pseudowhitening using traditional radar-variable estimators. LUT adaptive pseudowhitening is then applied to the hybrid spectrum-width estimator, and simulation results are confirmed with a qualitative analysis of radar data.


2016 ◽  
Vol 19 (4) ◽  
pp. 689-705 ◽  
Author(s):  
Bong-Joo Jang ◽  
Keon-Haeng Lee ◽  
Sanghun Lim ◽  
Dong-Ryul Lee ◽  
Ki-Ryong Kwon

1990 ◽  
pp. 462-470
Author(s):  
F. P. DE Troch ◽  
J. Heynderickx ◽  
P. A. Troch ◽  
D. Van Erdeghem

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