scholarly journals Radar-rainfall estimation algorithms of Hydro-NEXRAD

2010 ◽  
Vol 13 (2) ◽  
pp. 277-291 ◽  
Author(s):  
Bong-Chul Seo ◽  
Witold F. Krajewski ◽  
Anton Kruger ◽  
Piotr Domaszczynski ◽  
James A. Smith ◽  
...  
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Hydrologic Modelling ◽  
Estimation Algorithms ◽  
Data Archives ◽  
The Us ◽  
Reflectivity Data ◽  
Prototype Software

Hydro-NEXRAD is a prototype software system that provides hydrology and water resource communities with ready access to the vast data archives of the U.S. weather radar network known as NEXRAD (Next Generation Weather Radar). This paper describes radar-rainfall estimation algorithms and their modular components used in the Hydro-NEXRAD system to generate rainfall products to be delivered to users. A variety of customized modules implemented in Hydro-NEXRAD perform radar-reflectivity data processing, produce radar-rainfall maps with user-requested space and time resolution, and combine multiple radar data for basins covered by multiple radars. System users can select rainfall estimation algorithms that range from simple (‘Quick Look’) to complex and computing-intensive (‘Hi-Fi’). The ‘Pseudo NWS PPS’ option allows close comparison with the algorithm used operationally by the US National Weather Service. The ‘Custom’ algorithm enables expert users to specify values for many of the parameters in the algorithm modules according to their experience and expectations. The Hydro-NEXRAD system, with its rainfall-estimation algorithms, can be used by both novice and expert users who need rainfall estimates as references or as input to their hydrologic modelling and forecasting applications

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 empirical method to improve rainfall estimation of dual polarization radar using ground measurements

2016 ◽  
Author(s):  
Jungsoo Yoon ◽  
Mi-Kyung Suk ◽  
Kyung-Yeub Nam ◽  
Jeong-Seok Ko ◽  
Hae-Lim Kim ◽  
...  
Keyword(s):  
Empirical Method ◽  
Dual Polarization ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rainfall Events ◽  
High Rainfall ◽  
Estimation Algorithms ◽  
Hourly Rainfall ◽  
Constant Altitude ◽  
Position Indicator

Abstract. This study presents an easy and convenient empirical method to optimize polarimetric variables and produce more accurate dual polarization radar rainfall estimation. Weather Radar Center (WRC) in Korea Meteorological Administration (KMA) suggested relations between polarimetric variables (Z–ZDR and Z–KDP) based on a 2-D Video Distrometer (2DVD) measurements in 2014. Observed polarimetric variables from CAPPI (Constant Altitude Plan Position Indicator) images composed at 1 km of height were adjusted using the WRC's relations. Then dual polarization radar rainfalls were estimated by six different radar rainfall estimation algorithms, which are using either Z, Z and ZDR, or Z, ZDR and KDP. Accuracy of radar rainfall estimations derived by the six algorithms using the adjusted variables was assessed through comparison with raingauge observations. As a result, the accuracy of the radar rainfall estimation using adjusted polarimetric variables has improved from 50 % to 70 % approximately. Three high rainfall events with more than 40 mm of maximum hourly rainfall were shown the best accuracy on the rainfall estimation derived by using Z, ZDR and KDP. Meanwhile stratiform event was gained better radar rainfalls estimated by algorithms using Z and ZDR.

scholarly journals Detecting and Tracking Communal Bird Roosts in Weather Radar Data

2020 ◽  
Vol 34 (01) ◽  
pp. 378-385
Author(s):  
Zezhou Cheng ◽  
Saadia Gabriel ◽  
Pankaj Bhambhani ◽  
Daniel Sheldon ◽  
Subhransu Maji ◽  
...  
Keyword(s):  
Latent Variable ◽  
Weather Radar ◽  
Radar Data ◽  
Learning System ◽  
Variable Model ◽  
Detection Model ◽  
Radar Images ◽  
Biological Phenomena ◽  
The Us ◽  
Spatio Temporal

The US weather radar archive holds detailed information about biological phenomena in the atmosphere over the last 20 years. Communally roosting birds congregate in large numbers at nighttime roosting locations, and their morning exodus from the roost is often visible as a distinctive pattern in radar images. This paper describes a machine learning system to detect and track roost signatures in weather radar data. A significant challenge is that labels were collected opportunistically from previous research studies and there are systematic differences in labeling style. We contribute a latent-variable model and EM algorithm to learn a detection model together with models of labeling styles for individual annotators. By properly accounting for these variations we learn a significantly more accurate detector. The resulting system detects previously unknown roosting locations and provides comprehensive spatio-temporal data about roosts across the US. This data will provide biologists important information about the poorly understood phenomena of broad-scale habitat use and movements of communally roosting birds during the non-breeding season.

scholarly journals An empirical QPE method based on polarimetric variable adjustments

2017 ◽  
Author(s):  
Jungsoo Yoon ◽  
Jong-Sook Park ◽  
Hae-Lim Kim ◽  
Mi-Kyung Suk ◽  
Kyung-Yeub Nam
Keyword(s):  
Empirical Method ◽  
Convective Precipitation ◽  
Estimation Accuracy ◽  
Dual Polarization ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Estimation Algorithms ◽  
Hourly Rainfall ◽  
Using Data ◽  
Constant Altitude

Abstract. This study presents an empirical method for optimizing polarimetric variables in order to improve the accuracy of dual-polarization radar rainfall estimation using data derived from radars operated by different agencies. The empirical method was developed using the Yong-In Testbed (YIT) radar operated by the Korea Meteorological Administration (KMA). The method is based on the determination of relations between polarimetric variables. Relations for Z – ZDR and Z – KDP are derived from the measurements of a two-dimensional video disdrometer installed about 30 km away from the YIT radar. These relations were used to adjust the polarimetric variables of the dual-polarization constant altitude plan position indicator (CAPPI) at a height of 1.5 km. The CAPPI data with the adjusted polarimetric variables were used to estimate rainfalls using three different radar rainfall estimation algorithms. The first algorithm is based on Z, the second on Z and ZDR, and the third on Z, ZDR, and KDP. The accuracy of the radar-estimated rainfall was then assessed using raingauge observations. Three rainfall events with more than 40 mm of maximum hourly rainfall were shown to have the best estimation when the method using Z, ZDR, and KDP was used. However, stratiform precipitation events were better estimated by the algorithm using Z and ZDR. The method was also applied to the data of three radars that belong to KMA and the Ministry of Land, Infrastructure, and Transport. The evaluation was done for six months (May–October) in 2015. The results show an improvement in radar rainfall estimation accuracy for stratiform, frontal, and convective precipitation from approximately 50 % to 70 %.

scholarly journals Quality-based generation of weather radar Cartesian products

2015 ◽  
Vol 8 (5) ◽  
pp. 2173-2181 ◽  
Author(s):  
K. Ośródka ◽  
J. Szturc
Keyword(s):  
Liquid Water ◽  
Quality Index ◽  
Spatial Interpolation ◽  
Interpolation Method ◽  
Weather Radar ◽  
Radar Data ◽  
Cartesian Products ◽  
Reflectivity Data ◽  
Position Indicator

Abstract. Weather radar data volumes are commonly processed to obtain various 2-D Cartesian products based on the transfer from polar to Cartesian representations through a certain interpolation method. In this research an algorithm of the spatial interpolation of polar reflectivity data employing quality index data is applied to find the Cartesian reflectivity as plan position indicator products. On this basis, quality-based versions of standard algorithms for the generation of the following products have been developed: ETOP (echo top), MAX (maximum of reflectivity), and VIL (vertically integrated liquid water). Moreover, as an example of a higher-level product, a CONVECTION (detection of convection) has been defined as a specific combination of the above-listed standard products. A corresponding quality field is determined for each generated product, taking into account the quality of the pixels from which a given product was determined and how large a fraction of the investigated heights was scanned. Examples of such quality-based products are presented in the paper.

Improvement of Radar Rainfall Estimation Using Radar Reflectivity Data from the Hybrid Lowest Elevation Angles

2015 ◽  
Vol 36 (1) ◽  
pp. 109-124 ◽  
Author(s):  
Geunsu Lyu ◽  
◽  
Sung-Hwa Jung ◽  
Kyung-Yeub Nam ◽  
Soohyun Kwon ◽  
...  
Keyword(s):  
Radar Reflectivity ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Reflectivity Data

scholarly journals Weather radar rainfall data in urban hydrology

2017 ◽  
Vol 21 (3) ◽  
pp. 1359-1380 ◽  
Author(s):  
Søren Thorndahl ◽  
Thomas Einfalt ◽  
Patrick Willems ◽  
Jesper Ellerbæk Nielsen ◽  
Marie-Claire ten Veldhuis ◽  
...  
Keyword(s):  
Urban Areas ◽  
Numerical Models ◽  
Climate Change Impacts ◽  
Weather Radar ◽  
Radar Data ◽  
Rainfall Data ◽  
Urban Hydrology ◽  
Radar Rainfall ◽  
The Past ◽  
Hydrological Applications

Abstract. Application of weather radar data in urban hydrological applications has evolved significantly during the past decade as an alternative to traditional rainfall observations with rain gauges. Advances in radar hardware, data processing, numerical models, and emerging fields within urban hydrology necessitate an updated review of the state of the art in such radar rainfall data and applications. Three key areas with significant advances over the past decade have been identified: (1) temporal and spatial resolution of rainfall data required for different types of hydrological applications, (2) rainfall estimation, radar data adjustment and data quality, and (3) nowcasting of radar rainfall and real-time applications. Based on these three fields of research, the paper provides recommendations based on an updated overview of shortcomings, gains, and novel developments in relation to urban hydrological applications. The paper also reviews how the focus in urban hydrology research has shifted over the last decade to fields such as climate change impacts, resilience of urban areas to hydrological extremes, and online prediction/warning systems. It is discussed how radar rainfall data can add value to the aforementioned emerging fields in current and future applications, but also to the analysis of integrated water systems.

scholarly journals Rainfall estimation using Image Processing and Regression model on DWR Rainfall Product for Delhi-NCR Region

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Kuldeep Srivastava ◽  
Ashish Nigam
Keyword(s):  
Image Processing ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Weather Radar ◽  
Least Square ◽  
Rainfall Data ◽  
Doppler Weather Radar ◽  
Rainfall Estimation ◽  
Radar Rainfall

Observed rainfall is a very essential parameter for the analysis of rainfall, day to day weather forecast and its validation. The observed rainfall data is only available from five observatories of IMD; while no rainfall data is available at various important locations in and around Delhi-NCR. However, the 24-hour rainfall data observed by Doppler Weather Radar (DWR) for entire Delhi and surrounding region (up to 150 km) is readily available in a pictorial form. In this paper, efforts have been made to derive/estimate the rainfall at desired locations using DWR hydrological products. Firstly, the rainfall at desired locations has been estimated from the precipitation accumulation product (PAC) of the DWR using image processing in Python language. After this, a linear regression model using the least square method has been developed in R language. Estimated and observed rainfall data of year 2018 (July, August and September) was used to train the model. After this, the model was tested on rainfall data of year 2019 (July, August and September) and validated.With the use of linear regression model, the error in mean rainfall estimation reduced by 46.58% and the error in max rainfall estimation reduced by 84.53% for the year 2019. The error in mean rainfall estimation reduced by 81.36% and the error in max rainfall estimation reduced by 33.81% for the year 2018. Thus, the rainfall can be estimated with a fair degree of accuracy at desired locations within the range of the Doppler Weather Radar using the radar rainfall products and the developed linear regression model.

Sign in / Sign up

Export Citation Format

Share Document