Quantitative Precipitation Estimation by Combining Rain gauge and Meteorological Radar Network in Viet Nam

Fourteen-month precipitation measurements from a second-generation PARSIVEL disdrometer deployed in Beijing, northern China, were analyzed to investigate the microphysical structure of raindrop size distribution and its implications on polarimetric radar applications. Rainfall types are classified and analyzed in the domain of median volume diameter D0 and the normalized intercept parameter Nw. The separation line between convective and stratiform rain is almost equivalent to rain rate at 8.6 mm h–1 and radar reflectivity at 36.8 dBZ. Convective rain in Beijing shows distinct seasonal variations in log10Nw–D0 domain. X-band dual-polarization variables are simulated using the T-matrix method to derive radar-based quantitative precipitation estimation (QPE) estimators, and rainfall products at hourly scale are evaluated for four radar QPE estimators using collocated but independent rain gauge observations. This study also combines the advantages of individual estimators based on the thresholds on polarimetric variables. Results show that the blended QPE estimator has better performance than others. The rainfall microphysical analysis presented in this study is expected to facilitate the development of a high-resolution X-band radar network for urban QPE applications.

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Assessing the benefits of specific attenuation for quantitative precipitation estimation with a C-band radar network

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0299.1 ◽

2021 ◽

Author(s):

Ju-Yu Chen ◽

Silke Trömel ◽

Alexander Ryzhkov ◽

Clemens Simmer

Keyword(s):

Rain Gauge ◽

Horizontal Polarization ◽

Specific Attenuation ◽

Quantitative Precipitation Estimation ◽

Radar Network ◽

X Band ◽

Precipitation Estimation ◽

Drop Size Distributions ◽

Differential Reflectivity ◽

Rainfall Retrieval

AbstractRecent advances demonstrate the benefits of radar-derived specific attenuation at horizontal polarization (AH) for quantitative precipitation estimation (QPE) at S and X band. To date the methodology has, however, not been adapted for the widespread European C-band radars such as installed in the network of the German Meteorological Service (DWD, Deutscher Wetterdienst). Simulations based on a large dataset of drop size distributions (DSDs) measured over Germany are performed to investigate the DSD dependencies of the attenuation parameter αH for the AH estimates. The normalized raindrop concentration (Nw) and the change of differential reflectivity (ZDR) with reflectivity at horizontal polarization (ZH) are used to categorize radar observations into regimes for which scan-wise optimized αH values are derived. For heavier continental rain with ZH > 40 dBZ, the AH-based rainfall retrieval R(AH) is combined with a rainfall estimator using a substitute of specific differential phase (). We also assess the performance of retrievals based on specific attenuation at vertical polarization (AV). Finally, the regime-adapted hybrid QPE algorithms are applied to four convective cases and one stratiform case from 2017 to 2019, and compared to DWD’s operational RAdar-OnLine-ANeichung (RADOLAN) RW rainfall product, which is based on Zh only but adjusted to rain gauge measurements. For the convective cases, our hybrid retrievals outperform the traditional R(Zh) and pure R(AH/V) retrievals with fixed αH/V values when evaluated with gauge measurements and outperform RW when evaluated by disdrometer measurements. Potential improvements using ray-wise αH/V and segment-wise applications of the ZPHI method along the radials are discussed.

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Weather Radar Network Benefit Model for Flash Flood Casualty Reduction

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-19-0176.1 ◽

2020 ◽

Vol 59 (4) ◽

pp. 589-604 ◽

Cited By ~ 2

Author(s):

John Y. N. Cho ◽

James M. Kurdzo

Keyword(s):

Decision Process ◽

Flash Flood ◽

Weather Radar ◽

Regression Analyses ◽

Flood Warning ◽

Quantitative Precipitation Estimation ◽

Radar Network ◽

Radar Networks ◽

Precipitation Estimation ◽

Meteorological Radar

ABSTRACTA monetized flash flood casualty reduction benefit model is constructed for application to meteorological radar networks. Geospatial regression analyses show that better radar coverage of the causative rainfall improves flash flood warning performance. Enhanced flash flood warning performance is shown to decrease casualty rates. Consequently, these two effects in combination allow a model to be formed that links radar coverage to flash flood casualty rates. When this model is applied to the present-day contiguous U.S. weather radar network, results yield a flash flood–based benefit of $316 million (M) yr−1. The remaining benefit pools are more modest ($13 M yr−1 for coverage improvement and $69 M yr−1 maximum for all areas of radar quantitative precipitation estimation improvements), indicative of the existing weather radar network’s effectiveness in supporting the flash flood warning decision process.

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Quantitative Precipitation Estimation using Overlapped Area in Radar Network

Journal of Wetlands Research ◽

10.17663/jwr.2017.19.1.112 ◽

2017 ◽

Vol 19 (1) ◽

pp. 112-121

Author(s):

Jeongho Choi ◽

Myoungsun Han ◽

Chulsang Yoo ◽

Jiho Lee

Keyword(s):

Quantitative Precipitation Estimation ◽

Radar Network ◽

Precipitation Estimation

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Impact of the Altitudinal Gradients of Precipitation on the Radar QPE Bias in the French Alps

Atmosphere ◽

10.3390/atmos10060306 ◽

2019 ◽

Vol 10 (6) ◽

pp. 306 ◽

Cited By ~ 1

Author(s):

Dominique Faure ◽

Guy Delrieu ◽

Nicolas Gaussiat

Keyword(s):

Ground Level ◽

Radar Data ◽

Rain Gauge ◽

Altitudinal Gradients ◽

French Alps ◽

Rain Gauges ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

Radar Data Processing

In the French Alps the quality of the radar Quantitative Precipitation Estimation (QPE) is limited by the topography and the vertical structure of precipitation. A previous study realized in all the French Alps, has shown a general bias between values of the national radar QPE composite and the rain gauge measurements: a radar QPE over-estimation at low altitude (+20% at 200 m a.s.l.), and an increasing underestimation at high altitudes (until −40% at 2100 m a.s.l.). This trend has been linked to altitudinal gradients of precipitation observed at ground level. This paper analyzes relative altitudinal gradients of precipitation estimated with rain gauges measurements in 2016 for three massifs around Grenoble, and for different temporal accumulations (yearly, seasonal, monthly, daily). Comparisons of radar and rain gauge accumulations confirm the bias previously observed. The parts of the current radar data processing affecting the bias value are pointed out. The analysis shows a coherency between the relative gradient values estimated at the different temporal accumulations. Vertical profiles of precipitation detected by a research radar installed at the bottom of the valley also show how the wide horizontal variability of precipitation inside the valley can affect the gradient estimation.

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Error Analysis of Satellite Precipitation Products in Mountainous Basins

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0194.1 ◽

2014 ◽

Vol 15 (5) ◽

pp. 1778-1793 ◽

Cited By ~ 99

Author(s):

Yiwen Mei ◽

Emmanouil N. Anagnostou ◽

Efthymios I. Nikolopoulos ◽

Marco Borga

Keyword(s):

Error Analysis ◽

Warm Season ◽

Tropical Rainfall Measuring Mission ◽

Heavy Precipitation ◽

Rain Gauge ◽

Satellite Precipitation ◽

Quantitative Precipitation Estimation ◽

Precipitation Regimes ◽

Precipitation Estimation ◽

High Precipitation

Abstract Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events (HPEs). In situ observations over mountainous areas are limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for hydrological applications. In this study, four widely used satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, version 7 (3B42-V7), and in near–real time (3B42-RT); Climate Prediction Center (CPC) morphing technique (CMORPH); and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)] are evaluated with respect to their performance in capturing the properties of HPEs over different basin scales. Evaluation is carried out over the upper Adige River basin (eastern Italian Alps) for an 8-yr period (2003–10). Basin-averaged rainfall derived from a dense rain gauge network in the region is used as a reference. Satellite precipitation error analysis is performed for warm (May–August) and cold (September–December) season months as well as for different quantile ranges of basin-averaged precipitation accumulations. Three error metrics and a score system are introduced to quantify the performances of the various satellite products. Overall, no single precipitation product can be considered ideal for detecting and quantifying HPE. Results show better consistency between gauges and the two 3B42 products, particularly during warm season months that are associated with high-intensity convective events. All satellite products are shown to have a magnitude-dependent error ranging from overestimation at low precipitation regimes to underestimation at high precipitation accumulations; this effect is more pronounced in the CMORPH and PERSIANN products.

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3-hourly quantitative precipitation estimation over Central and Northern Europe from rain gauge and radar data

Atmospheric Research ◽

10.1016/j.atmosres.2009.05.005 ◽

2009 ◽

Vol 94 (4) ◽

pp. 544-554 ◽

Cited By ~ 8

Author(s):

Franz Rubel ◽

Katharina Brugger

Keyword(s):

Radar Data ◽

Rain Gauge ◽

Northern Europe ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation

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The Results of Applying Different Methodologies to 10 Years of Quantitative Precipitation Estimation in Catalonia Using Weather Radar

Geomatics ◽

10.3390/geomatics1030020 ◽

2021 ◽

Vol 1 (3) ◽

pp. 347-368

Author(s):

Tomeu Rigo ◽

Maria Carmen Llasat ◽

Laura Esbrí

Keyword(s):

Quality Control ◽

Weather Radar ◽

Rain Gauges ◽

Quantitative Precipitation Estimation ◽

Radar Network ◽

Daily Data ◽

Precipitation Estimation ◽

Weather Stations ◽

Point Data ◽

Single Polarization

The single polarization C-Band weather radar network of the Meteorological Service of Catalonia covers the entire region (32,000 km2), which allows it to apply a series of corrections that improve preliminary estimations of the rainfall field (hourly and daily). In addition, an automatic re-processing using automatic weather stations helps to incorporate ground-based information. The last process of the quantitative precipitation estimation (QPE) is running the end-product again eight days later, when the data have been reviewed and corrected in the case of detecting anomalies in the radar or gauge data. These corrections are applied operationally, with the fields generated and stored automatically. The QPE fields are generated in the GeoTIFF format, allowing easy use with multiple applications and simplifying processes such as quality control. In this way, the analysis of a 10 year period of GeoTIFF QPE daily data compared with ground rainfall values is introduced. The results help to understand different points regarding the functioning of the network such as the dependance on the type of precipitation and the seasonality. In addition, the description of a heavy rainfall episode (22 October 2019) shows the variations and improvements in the different products. The main conclusions refer to how using GeoTIFF combined with point data (rain gauges), it is possible to ensure simple but effective quality control of an operational radar network.

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Product-Error-Driven Uncertainty Model for Probabilistic Quantitative Precipitation Estimation with NEXRAD Data

Journal of Hydrometeorology ◽

10.1175/2007jhm814.1 ◽

2007 ◽

Vol 8 (6) ◽

pp. 1325-1347 ◽

Cited By ~ 163

Author(s):

Grzegorz J. Ciach ◽

Witold F. Krajewski ◽

Gabriele Villarini

Keyword(s):

Agricultural Research ◽

Central Element ◽

Processing System ◽

Rain Gauge ◽

Random Errors ◽

Radar Rainfall ◽

Quantitative Precipitation Estimation ◽

Uncertainty Model ◽

Precipitation Estimation ◽

The U.S

Abstract Although it is broadly acknowledged that the radar-rainfall (RR) estimates based on the U.S. national network of Weather Surveillance Radar-1988 Doppler (WSR-88D) stations contain a high degree of uncertainty, no methods currently exist to inform users about its quantitative characteristics. The most comprehensive characterization of this uncertainty can be achieved by delivering the products in a probabilistic rather than the traditional deterministic form. The authors are developing a methodology for probabilistic quantitative precipitation estimation (PQPE) based on weather radar data. In this study, they present the central element of this methodology: an empirically based error structure model for the RR products. The authors apply a product-error-driven (PED) approach to obtain a realistic uncertainty model. It is based on the analyses of six years of data from the Oklahoma City, Oklahoma, WSR-88D radar (KTLX) processed with the Precipitation Processing System algorithm of the NEXRAD system. The modeled functional-statistical relationship between RR estimates and corresponding true rainfall consists of two components: a systematic distortion function and a stochastic factor quantifying remaining random errors. The two components are identified using a nonparametric functional estimation apparatus. The true rainfall is approximated with rain gauge data from the Oklahoma Mesonet and the U.S. Department of Agriculture (USDA) Agricultural Research Service Micronet networks. The RR uncertainty model presented here accounts for different time scales, synoptic regimes, and distances from the radar. In addition, this study marks the first time in which results on RR error correlation in space and time are presented.

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