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 General Application of the Relative Calibration Adjustment (RCA) Technique for Monitoring and Correcting Radar Reflectivity Calibration

2015 ◽  
Vol 32 (3) ◽  
pp. 496-506 ◽  
Author(s):  
David B. Wolff ◽  
David A. Marks ◽  
Walter A. Petersen
Keyword(s):  
Real Time ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Absolute Calibration ◽  
Ground Clutter ◽  
Original Application ◽  
Using Data ◽  
Anomalous Propagation ◽  
The Stability ◽  
Reflectivity Data

AbstractAccurate calibration of radar reflectivity is integral to quantitative radar measurements of precipitation and a myriad of other radar-based applications. A statistical method was developed that utilizes the probability distribution of clutter area reflectivity near a stationary, ground-based radar to provide near-real-time estimates of the relative calibration of reflectivity data. The relative calibration adjustment (RCA) method provides a valuable, automated near-real-time tool for maintaining consistently calibrated radar data with relative calibration uncertainty of ±0.5 dB or better. The original application was to S-band data in a tropical oceanic location, where the stability of the method was thought to be related to the relatively mild ground clutter and limited anomalous propagation (AP). This study demonstrates, however, that the RCA technique is transferable to other S-band radars at locations with more intense ground clutter and AP. This is done using data from NASA’s polarimetric (NPOL) surveillance radar data during the Iowa Flood Studies (IFloodS) Global Precipitation Measurement (GPM) field campaign during spring of 2013 and other deployments. Results indicate the RCA technique is well capable of monitoring the reflectivity calibration of NPOL, given proper generation of an areal clutter map. The main goal of this study is to generalize the RCA methodology for possible extension to other ground-based S-band surveillance radars and to show how it can be used both to monitor the reflectivity calibration and to correct previous data once an absolute calibration baseline is established.

scholarly journals A study on WRF radar data assimilation for hydrological rainfall prediction

2013 ◽  
Vol 17 (8) ◽  
pp. 3095-3110 ◽  
Author(s):  
J. Liu ◽  
M. Bray ◽  
D. Han
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Meteorological Data ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Radar Reflectivity ◽  
Storm Event ◽  
Hydrological Applications ◽  
Radar Data Assimilation

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.

An Approach of Radar Reflectivity Data Assimilation and Its Assessment with the Inland QPF of Typhoon Rusa (2002) at Landfall

2007 ◽  
Vol 46 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Qingnong Xiao ◽  
Ying-Hwa Kuo ◽  
Juanzhen Sun ◽  
Wen-Chau Lee ◽  
Dale M. Barker ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Mesoscale Model ◽  
Control Variable ◽  
Three Dimensional ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Mixing Ratio ◽  
Warm Rain Process ◽  
Reflectivity Data

Abstract A radar reflectivity data assimilation scheme was developed within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) three-dimensional variational data assimilation (3DVAR) system. The model total water mixing ratio was used as a control variable. A warm-rain process, its linear, and its adjoint were incorporated into the system to partition the moisture and hydrometeor increments. The observation operator for radar reflectivity was developed and incorporated into the 3DVAR. With a single reflectivity observation, the multivariate structures of the analysis increments that included cloud water and rainwater mixing ratio increments were examined. Using the onshore Doppler radar data from Jindo, South Korea, the capability of the radar reflectivity assimilation for the landfalling Typhoon Rusa (2002) was assessed. Verifications of inland quantitative precipitation forecasting (QPF) of Typhoon Rusa (2002) showed positive impacts of assimilating radar reflectivity data on the short-range QPF.

Improved radar data processing algorithms for quantitative rainfall estimation in real time

2009 ◽  
Vol 60 (1) ◽  
pp. 175-184 ◽  
Author(s):  
S. Krämer ◽  
H.-R. Verworn
Keyword(s):  
Real Time ◽  
Radar Data ◽  
Real Time Control ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Microphysical Properties ◽  
Time Control ◽  
Reflectivity Data ◽  
Radar Data Processing

This paper describes a new methodology to process C-band radar data for direct use as rainfall input to hydrologic and hydrodynamic models and in real time control of urban drainage systems. In contrast to the adjustment of radar data with the help of rain gauges, the new approach accounts for the microphysical properties of current rainfall. In a first step radar data are corrected for attenuation. This phenomenon has been identified as the main cause for the general underestimation of radar rainfall. Systematic variation of the attenuation coefficients within predefined bounds allows robust reflectivity profiling. Secondly, event specific R–Z relations are applied to the corrected radar reflectivity data in order to generate quantitative reliable radar rainfall estimates. The results of the methodology are validated by a network of 37 rain gauges located in the Emscher and Lippe river basins. Finally, the relevance of the correction methodology for radar rainfall forecasts is demonstrated. It has become clearly obvious, that the new methodology significantly improves the radar rainfall estimation and rainfall forecasts. The algorithms are applicable in real time.

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 Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

2018 ◽  
Vol 10 (9) ◽  
pp. 1453 ◽  
Author(s):  
Serguei Ivanov ◽  
Silas Michaelides ◽  
Igor Ruban
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Original Data ◽  
Radar Data ◽  
Numerical Weather Prediction Model ◽  
Radar Reflectivity ◽  
Horizontal Structure ◽  
Cell Patterns ◽  
Reflectivity Data

This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The proposed method creates a 3D regular grid in which a horizontal size of meshes coincides with the horizontal model resolution. This minimizes the representative error associated with the discrepancy between resolutions of informational sources. After such preprocessing, horizontal structure functions and their gradients for radar reflectivity maintain the sizes and shapes of precipitation patterns similar to those of the original data. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. It redistributes precipitable water with smoothed values over the common domain since the control runs show, among several sub-domains with increased and decreased values, correspondingly. It also reproduces the mesoscale belts and cell patterns of sizes from a few to ten kilometers in precipitation fields. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 km to 6 km with major variations at 2.5 km to 3 km. It also reproduces the mesoscale belt and cell patterns of precipitation fields.

scholarly journals New Algorithm for Rain Cell Identification and Tracking in Rainfall Event Analysis

2019 ◽  
Author(s):  
Ting He ◽  
JianXin Zhang ◽  
JiYao Hua ◽  
Yang Cai
Keyword(s):  
High Resolution ◽  
Temporal Analysis ◽  
Radar Data ◽  
Life Cycles ◽  
Radar Reflectivity ◽  
Federal State ◽  
Event Analysis ◽  
Matching Rule ◽  
Rainfall Events ◽  
Cell Identification

This study proposes a new algorithm termed rain cell identification and tracking (RCIT) to identify and track rain cells from high resolution weather radar data. Previous algorithms have limitations when tracking non-consequent rain cells owing to their use of maximum correlation coefficient methods and their lack of an alternative way to handle the variation stages of rain cells during their life cycles. To address these deficiencies, various methods are implemented in the new algorithm. These include the particle image velocimetry (PIV) method for motion estimation and the rain cell matching rule to obtain the stage changes of rain cells. High resolution (5-min and 1-km) radar reflectivity data from three rainy days over the German federal state North Rhine Westphalia (NRW) are used to evaluate the proposed algorithm. The performance of the new algorithm is compared with a radar reflectivity map and verified by two object-oriented methods: structure–amplitude–location (SAL) and geometric index. The verification results suggest that the performance of the new algorithm is good. Application of the RCIT algorithm to the selected cases shows that the inner structure of rainfall events in the experimental region present extreme value distributions, with most rainfall events having a short duration with less intensity. The new algorithm can effectively capture the stage changes of rain cells during their life cycles. The proposed algorithm can serve as the basis for further hydro-meteorological applications such as spatial and temporal analysis of rainfall events and short-term flood forecasting.

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 X-Net-Based Radar Data Assimilation Study over the Seoul Metropolitan Area

2020 ◽  
Vol 12 (5) ◽  
pp. 893 ◽  
Author(s):  
Ji-Won Lee ◽  
Ki-Hong Min ◽  
Young-Hee Lee ◽  
GyuWon Lee
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Metropolitan Area ◽  
Numerical Data ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Model Verification ◽  
Seoul Metropolitan Area ◽  
X Band ◽  
Reflectivity Data

This study investigates the ability of the high-resolution Weather Research and Forecasting (WRF) model to simulate summer precipitation with assimilation of X-band radar network data (X-Net) over the Seoul metropolitan area. Numerical data assimilation (DA) experiments with X-Net (S- and X-band Doppler radar) radial velocity and reflectivity data for three events of convective systems along the Changma front are conducted. In addition to the conventional assimilation of radar data, which focuses on assimilating the radial velocity and reflectivity of precipitation echoes, this study assimilates null-echoes and analyzes the effect of null-echo data assimilation on short-term quantitative precipitation forecasting (QPF). A null-echo is defined as a region with non-precipitation echoes within the radar observation range. The model removes excessive humidity and four types of hydrometeors (wet and dry snow, graupel, and rain) based on the radar reflectivity by using a three-dimensional variational (3D-Var) data assimilation technique within the WRFDA system. Some procedures for preprocessing radar reflectivity data and using null-echoes in this assimilation are discussed. Numerical experiments with conventional radar DA over-predicted the precipitation. However, experiments with additional null-echo information removed excessive water vapor and hydrometeors and suppressed erroneous model precipitation. The results of statistical model verification showed improvements in the analysis and objective forecast scores, reducing the amount of over-predicted precipitation. An analysis of a contoured frequency by altitude diagram (CFAD) and time–height cross-sections showed that increased hydrometeors throughout the data assimilation period enhanced precipitation formation, and reflectivity under the melting layer was simulated similarly to the observations during the peak precipitation times. In addition, overestimated hydrometeors were reduced through null-echo data assimilation.

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