scholarly journals Geographical Distribution of Variance of Intraseasonal Variations in Western Indochina as Revealed from Radar Reflectivity Data

2008 ◽  
Vol 21 (19) ◽  
pp. 5154-5161 ◽  
Author(s):  
Satoru Yokoi ◽  
Takehiko Satomura
Keyword(s):  
Geographical Distribution ◽  
Coastal Region ◽  
Radar Reflectivity ◽  
Windward Side ◽  
Synoptic Scale ◽  
Wind Anomaly ◽  
Orographic Rainfall ◽  
Intraseasonal Variations ◽  
Reflectivity Data ◽  
Reflectivity Factor

Abstract This study reveals remarkable differences in the geographical distribution of variance between two types of intraseasonal variations in daily-mean radar reflectivity data in the western part of the Indochina Peninsula. In this region, the Downa Range lies parallel to the coast and separates the inland region from the coastal region. The 30–60-day variation of reflectivity factor dominates most of the coastal region, while its variance in the inland region is less than that of background red noise with the same frequency band. Horizontal gradients in the variance are largest over the range, implying that the mountain range plays a significant role in the geographical contrast. Correlation analysis with reanalysis data shows that the variation only in the coastal region is associated with a synoptic-scale zonal wind anomaly with the same time scale, suggesting the importance of an orographic rainfall process that brings a large amount of precipitation only to the windward side of the Downa Range. In contrast, while the 10–20-day variation of reflectivity factor has larger variance in the inland region than in the coastal region, the variation in both of the regions is correlated with synoptic-scale cyclonic circulation anomaly. A possible reason for the differences between the two types is also discussed in terms of the relationship between synoptic-scale wind anomaly field and the orientation of the Downa Range.

scholarly journals Estimating Rain Rates from Tipping-Bucket Rain Gauge Measurements

2008 ◽  
Vol 25 (1) ◽  
pp. 43-56 ◽  
Author(s):  
Jianxin Wang ◽  
Brad L. Fisher ◽  
David B. Wolff
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Radar Reflectivity ◽  
Time Shift ◽  
Estimation Errors ◽  
Rate Estimation ◽  
Reflectivity Data ◽  
Rain Rates ◽  
Event Definition

Abstract This paper describes the cubic spline–based operational system for the generation of the Tropical Rainfall Measuring Mission (TRMM) 1-min rain-rate product 2A-56 from tipping-bucket (TB) gauge measurements. A simulated TB gauge from a Joss–Waldvogel disdrometer is employed to evaluate the errors of the TB rain-rate estimation. These errors are very sensitive to the time scale of rain rates. One-minute rain rates suffer substantial errors, especially at low rain rates. When 1-min rain rates are averaged over 4–7-min intervals or longer, the errors dramatically reduce. Estimated lower rain rates are sensitive to the event definition whereas the higher rates are not. The median relative absolute errors are about 22% and 32% for 1-min rain rates higher and lower than 3 mm h−1, respectively. These errors decrease to 5% and 14% when rain rates are used at the 7-min scale. The radar reflectivity–rain-rate distributions drawn from the large amount of 7-min rain rates and radar reflectivity data are mostly insensitive to the event definition. The time shift due to inaccurate clocks can also cause rain-rate estimation errors, which increase with the shifted time length. Finally, some recommendations are proposed for possible improvements of rainfall measurements and rain-rate estimations.

Fuzzy Logic Classification of S-Band Polarimetric Radar Echoes to Identify Three-Body Scattering and Improve Data Quality

2014 ◽  
Vol 53 (8) ◽  
pp. 2017-2033 ◽  
Author(s):  
Vivek N. Mahale ◽  
Guifu Zhang ◽  
Ming Xue
Keyword(s):  
Fuzzy Logic ◽  
Standard Deviation ◽  
Radar Data ◽  
Classification Algorithm ◽  
Radar Reflectivity ◽  
Membership Functions ◽  
Polarimetric Radar ◽  
Radar Echo ◽  
Three Body ◽  
Reflectivity Factor

AbstractThe three-body scatter signature (TBSS) is a radar artifact that appears downrange from a high-radar-reflectivity core in a thunderstorm as a result of the presence of hailstones. It is useful to identify the TBSS artifact for quality control of radar data used in numerical weather prediction and quantitative precipitation estimation. Therefore, it is advantageous to develop a method to automatically identify TBSS in radar data for the above applications and to help identify hailstones within thunderstorms. In this study, a fuzzy logic classification algorithm for TBSS identification is developed. Polarimetric radar data collected by the experimental S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), are used to develop trapezoidal membership functions for the TBSS class of radar echo within a hydrometeor classification algorithm (HCA). Nearly 3000 radar gates are removed from 50 TBSSs to develop the membership functions from the data statistics. Five variables are investigated for the discrimination of the radar echo: 1) horizontal radar reflectivity factor ZH, 2) differential reflectivity ZDR, 3) copolar cross-correlation coefficient ρhv, 4) along-beam standard deviation of horizontal radar reflectivity factor SD(ZH), and 5) along-beam standard deviation of differential phase SD(ΦDP). These membership functions are added to an HCA to identify TBSSs. Testing is conducted on radar data collected by dual-polarization-upgraded operational WSR-88Ds from multiple severe-weather events, and results show that automatic identification of the TBSS through the enhanced HCA is feasible for operational use.

scholarly journals Impact of multiple radar reflectivity data assimilation on the numerical simulation of a flash flood event during the HyMeX campaign

2017 ◽  
Vol 21 (11) ◽  
pp. 5459-5476 ◽  
Author(s):  
Ida Maiello ◽  
Sabrina Gentile ◽  
Rossella Ferretti ◽  
Luca Baldini ◽  
Nicoletta Roberto ◽  
...  
Keyword(s):  
Hydrological Cycle ◽  
Initial Conditions ◽  
Flash Flood ◽  
Central Italy ◽  
Heavy Precipitation ◽  
Radar Reflectivity ◽  
Precipitation Event ◽  
Italian Regions ◽  
Reflectivity Data ◽  
The Impact

Abstract. An analysis to evaluate the impact of multiple radar reflectivity data with a three-dimensional variational (3-D-Var) assimilation system on a heavy precipitation event is presented. The main goal is to build a regionally tuned numerical prediction model and a decision-support system for environmental civil protection services and demonstrate it in the central Italian regions, distinguishing which type of observations, conventional and not (or a combination of them), is more effective in improving the accuracy of the forecasted rainfall. In that respect, during the first special observation period (SOP1) of HyMeX (Hydrological cycle in the Mediterranean Experiment) campaign several intensive observing periods (IOPs) were launched and nine of which occurred in Italy. Among them, IOP4 is chosen for this study because of its low predictability regarding the exact location and amount of precipitation. This event hit central Italy on 14 September 2012 producing heavy precipitation and causing several cases of damage to buildings, infrastructure, and roads. Reflectivity data taken from three C-band Doppler radars running operationally during the event are assimilated using the 3-D-Var technique to improve high-resolution initial conditions. In order to evaluate the impact of the assimilation procedure at different horizontal resolutions and to assess the impact of assimilating reflectivity data from multiple radars, several experiments using the Weather Research and Forecasting (WRF) model are performed. Finally, traditional verification scores such as accuracy, equitable threat score, false alarm ratio, and frequency bias – interpreted by analysing their uncertainty through bootstrap confidence intervals (CIs) – are used to objectively compare the experiments, using rain gauge data as a benchmark.

Future extreme precipitation frequency in the eastern Mediterranean: a new approach exploiting climate model projections

2020 ◽  
Author(s):  
Francesco Marra ◽  
Moshe Armon ◽  
Davide Zoccatelli ◽  
Osama Gazal ◽  
Chaim Garfinkel ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Eastern Mediterranean ◽  
Regional Climate ◽  
Coastal Region ◽  
Climatic Conditions ◽  
Synoptic Scale ◽  
Precipitation Frequency ◽  
Extreme Quantiles

<p>Understanding extreme precipitation under changing climatic conditions is crucial to manage weather- and flood-related hazards. Global and regional climate models are able to provide coarse scale information on future conditions under different emission scenarios, but large uncertainties affect the projected precipitation amounts, extremes in particular, so that frequency analyses cannot be quantitatively trusted. This study uses, for the first time, the Simplified Metastatistical Extreme Value (SMEV) approach to directly exploit synoptic scale information, better represented by climate models, for obtaining projections of future extreme precipitation frequency.</p><p>We use historical rainfall data from >400 stations in Israel and Jordan to (a) provide a climatology of extreme daily precipitation (e.g., the 100-year return period amounts) in the steep climatic gradients of the region and (b) improve understanding of the SMEV description under changing climate. We demonstrate that, using SMEV, it is possible to (c) present the sensitivity of extreme quantiles to occurrence and intensity of Mediterranean lows and other synoptic systems, and (d) project future extreme quantiles starting from synoptic scale information generated by earlier climate-model-based studies. Under our working hypotheses, we project a general decrease of extreme precipitation quantiles for the RCP8.5 scenario; an increase is detected in the coastal region and the Negev arid lands. We discuss the apparent contrast of these results with previous findings.</p>

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.

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