scholarly journals An Investigation of the Short-Term Predictability of Precipitation Using High-Resolution Composite Radar Observations

2012 ◽  
Vol 51 (5) ◽  
pp. 912-925 ◽  
Author(s):  
Evan Ruzanski ◽  
V. Chandrasekar
Keyword(s):  
High Resolution ◽  
Space Time ◽  
Radar Reflectivity ◽  
Short Term ◽  
Spatial And Temporal Scales ◽  
Precipitation Patterns ◽  
Upper Limit ◽  
Space Time Structure ◽  
Meteorological Radar ◽  
Reflectivity Data

AbstractThe short-term predictability of precipitation patterns observed by meteorological radar is an important concept as it establishes a means to characterize precipitation and provides an upper limit on the extent of useful nowcasting. Predictability also varies on the basis of spatial and temporal scales of the observed meteorological phenomena. This paper describes an investigation of the short-term predictability of precipitation patterns containing microalpha (0.2–2 km) to mesobeta (20–200 km) scales using high-resolution (0.5 km–1 min–1 dBZ) composite radar reflectivity data, extending the analysis presented in previous work to smaller space and time scales. An experimental approach is used in which continuous and categorical lifetimes of radar reflectivity fields in Eulerian and Lagrangian space are used to quantify short-term predictability. The space–time scale dependency of short-term predictability is analyzed, and a practical upper limit on the extent of Lagrangian persistence-based nowcasting is estimated. Connections to the predictability of larger scales are made within the context of previous work. The results show that short-term predictability estimates in terms of lifetime are approximately 14–15 and 20–21 min in Eulerian and Lagrangian space, respectively, and suggest that a linear relationship exists between predictability and space–time structure from microalpha to macrobeta (2000–10 000 km) scales.

scholarly journals Constructing Three-Dimensional Multiple-Radar Reflectivity Mosaics: Examples of Convective Storms and Stratiform Rain Echoes

2005 ◽  
Vol 22 (1) ◽  
pp. 30-42 ◽  
Author(s):  
Jian Zhang ◽  
Kenneth Howard ◽  
J. J. Gourley
Keyword(s):  
High Resolution ◽  
Coordinate System ◽  
Real Time ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Raw Data ◽  
Spherical Coordinate ◽  
Base Level ◽  
Analysis Scheme ◽  
Reflectivity Data

Abstract The advent of Internet-2 and effective data compression techniques facilitates the economic transmission of base-level radar data from the Weather Surveillance Radar-1988 Doppler (WSR-88D) network to users in real time. The native radar spherical coordinate system and large volume of data make the radar data processing a nontrivial task, especially when data from several radars are required to produce composite radar products. This paper investigates several approaches to remapping and combining multiple-radar reflectivity fields onto a unified 3D Cartesian grid with high spatial (≤1 km) and temporal (≤5 min) resolutions. The purpose of the study is to find an analysis approach that retains physical characteristics of the raw reflectivity data with minimum smoothing or introduction of analysis artifacts. Moreover, the approach needs to be highly efficient computationally for potential operational applications. The appropriate analysis can provide users with high-resolution reflectivity data that preserve the important features of the raw data, but in a manageable size with the advantage of a Cartesian coordinate system. Various interpolation schemes were evaluated and the results are presented here. It was found that a scheme combining a nearest-neighbor mapping on the range and azimuth plane and a linear interpolation in the elevation direction provides an efficient analysis scheme that retains high-resolution structure comparable to the raw data. A vertical interpolation is suited for analyses of convective-type echoes, while vertical and horizontal interpolations are needed for analyses of stratiform echoes, especially when large vertical reflectivity gradients exist. An automated brightband identification scheme is used to recognize stratiform echoes. When mosaicking multiple radars onto a common grid, a distance-weighted mean scheme can smooth possible discontinuities among radars due to calibration differences and can provide spatially consistent reflectivity mosaics. These schemes are computationally efficient due to their mathematical simplicity. Therefore, the 3D multiradar mosaic scheme can serve as a good candidate for providing high-spatial- and high-temporal-resolution base-level radar data in a Cartesian framework in real time.

scholarly journals Operational Implementation of the 1D+3D-Var Assimilation Method of Radar Reflectivity Data in the AROME Model

2014 ◽  
Vol 142 (5) ◽  
pp. 1852-1873 ◽  
Author(s):  
Eric Wattrelot ◽  
Olivier Caumont ◽  
Jean-François Mahfouf
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Three Dimensional ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Short Term ◽  
Background Error ◽  
Reflectivity Data

AbstractThis paper presents results from radar reflectivity data assimilation experiments with the nonhydrostatic limited-area model Application of Research to Operations at Mesoscale (AROME) in an operational context. A one-dimensional (1D) Bayesian retrieval of relative humidity profiles followed by a three-dimensional variational data assimilation (3D-Var) technique is adopted. Several preprocessing procedures of raw reflectivity data are presented and the use of the nonrainy signal in the assimilation is widely discussed and illustrated. This two-step methodology allows the authors to build up a screening procedure that takes into account the evaluation of the results from the 1D Bayesian retrieval. In particular, the 1D retrieval is checked by comparing a pseudoanalyzed reflectivity to the observed reflectivity. Additionally, a physical consistency between the reflectivity innovations and the 1D relative humidity increments is imposed before assimilating relative humidity pseudo-observations with other observations. This allows the authors to counteract the difficulty of the current 3D-Var system to correct strong differences between model and observed clouds from the crude specification of background-error covariances. Assimilation experiments of radar reflectivity data in a preoperational configuration are first performed over a 1-month period. Positive impacts on short-term precipitation forecast scores are systematically found. The evaluation shows improvements on the analysis and also on objective conventional forecast scores, in particular for the model wind field up to 12 h. A case study for a specific precipitating system demonstrates the capacity of the method for improving significantly short-term forecasts of organized convection.

scholarly journals Improving Short-Term Storm Predictions by Assimilating both Radar Radial-Wind and Reflectivity Observations

2008 ◽  
Vol 23 (3) ◽  
pp. 373-391 ◽  
Author(s):  
Qingyun Zhao ◽  
John Cook ◽  
Qin Xu ◽  
Paul R. Harasti
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Radar Data ◽  
Naval Research ◽  
Short Term ◽  
High Resolution Data ◽  
Reflectivity Data ◽  
The Impact ◽  
Data Assimilation System ◽  
Resolution Data

Abstract A high-resolution data assimilation system is under development at the Naval Research Laboratory (NRL). The objective of this development is to assimilate high-resolution data, especially those from Doppler radars, into the U.S. Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System to improve the model’s capability and accuracy in short-term (0–6 h) prediction of hazardous weather for nowcasting. A variational approach is used in this system to assimilate the radar observations into the model. The system is upgraded in this study with new capabilities to assimilate not only the radar radial-wind data but also reflectivity data. Two storm cases are selected to test the upgraded system and to study the impact of radar data assimilation on model forecasts. Results from the data assimilation experiments show significant improvements in storm prediction especially when both radar radial-wind and reflectivity observations are assimilated and the analysis incremental fields are adequately constrained by the model’s dynamics and properly adjusted to satisfy the model’s thermodynamical balance.

scholarly journals The CASA Nowcasting System

2011 ◽  
Vol 28 (5) ◽  
pp. 640-655 ◽  
Author(s):  
Evan Ruzanski ◽  
V. Chandrasekar ◽  
Yanting Wang
Keyword(s):  
High Resolution ◽  
Practical Importance ◽  
Absolute Error ◽  
Radar Reflectivity ◽  
Adaptive Sensing ◽  
Critical Success ◽  
Emergency Manager ◽  
Weather Events ◽  
Short Term Prediction ◽  
Reflectivity Data

Abstract Short-term prediction (nowcasting) of high-impact weather events can lead to significant improvement in warnings and advisories and is of great practical importance. Nowcasting using weather radar reflectivity data has been shown to be particularly useful and the Collaborative Adaptive Sensing of the Atmosphere (CASA) radar network provides high-resolution (0.5-km spatial and 1-min temporal resolution) reflectivity data that are amenable to producing valuable nowcasts. This paper describes the theory and implementation of a nowcasting system operating in the CASA Distributed Collaborative Adaptive Sensing network and shows that nowcasting can be reliably performed in such a distributed environment. In this context, nowcasting is used in a traditional sense to produce predictions of radar reflectivity fields up to 10 min into the future to support emergency manager decision making, and in a novel manner to support researchers and operational forecasters where 1–5-min nowcasts are used to steer the radar nodes to better observe moving precipitation systems. The high-resolution nature of CASA data and distributed system architecture necessitate the use of a fast nowcasting algorithm. A method is described that uses linear least squares estimation implemented in the Fourier domain for motion estimation with advection performed via a kernel-based method formulated in the spatial domain. Results of a performance evaluation during the CASA 2009 Integrative Project 1 experiment are presented that show that the nowcasting system significantly outperformed persistence forecasts of radar reflectivity in terms of critical success index and mean absolute error for lead times up to 10 min. Feedback from end users regarding the use of nowcasting for adaptive scanning was also unanimously positive.

Space-time structure of auroral radio absorption events observed with the imaging riometer at south pole

Radio Science ◽  
1991 ◽  
Vol 26 (4) ◽  
pp. 925-930 ◽  
Author(s):  
J. K. Hargreaves ◽  
D. L. Detrick ◽  
T. J. Rosenberg
Keyword(s):  
Space Time ◽  
Time Structure ◽  
South Pole ◽  
Space Time Structure

scholarly journals SPACE–TIME STRUCTURE AND SPINOR GEOMETRY

2008 ◽  
Vol 50 (2) ◽  
pp. 143-176 ◽  
Author(s):  
GEORGE SZEKERES ◽  
LINDSAY PETERS
Keyword(s):  
Cosmological Solution ◽  
Space Time ◽  
Time Structure ◽  
Variation Principle ◽  
Field Equations ◽  
Covering Group ◽  
Space Time Structure ◽  
Spinor Geometry ◽  
Complete Set ◽  
Particle Solution

AbstractThe structure of space–time is examined by extending the standard Lorentz connection group to its complex covering group, operating on a 16-dimensional “spinor” frame. A Hamiltonian variation principle is used to derive the field equations for the spinor connection. The result is a complete set of field equations which allow the sources of the gravitational and electromagnetic fields, and the intrinsic spin of a particle, to appear as a manifestation of the space–time structure. A cosmological solution and a simple particle solution are examined. Further extensions to the connection group are proposed.

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