scholarly journals The accuracy of weather radar in heavy rain: a comparative study for Denmark, the Netherlands, Finland and Sweden

2020 ◽  
Vol 24 (6) ◽  
pp. 3157-3188
Author(s):  
Marc Schleiss ◽  
Jonas Olsson ◽  
Peter Berg ◽  
Tero Niemi ◽  
Teemu Kokkonen ◽  
...  
Keyword(s):  
The Netherlands ◽  
Weather Radar ◽  
Heavy Rain ◽  
Radar Data ◽  
Small Scale ◽  
Differential Phase Shift ◽  
Urban Flood ◽  
Raindrop Size Distribution ◽  
Invaluable Tool ◽  
Urban Pluvial Flooding

Abstract. Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by measuring at higher resolutions and making use of dual-polarization radar, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. However, since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 h. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events in a 10-year database of radar data were used to quantify the overall agreement between radar and gauges as well as the bias affecting the peaks. Results show that the overall agreement in heavy rain is fair (correlation coefficient 0.7–0.9), with apparent multiplicative biases on the order of 1.2–1.8 (17  %–44  % underestimation). However, after taking into account the different sampling volumes of radar and gauges, actual biases could be as low as 10 %. Differences in sampling volumes between radar and gauges play an important role in explaining the bias but are hard to quantify precisely due to the many post-processing steps applied to radar. Despite being adjusted for bias by gauges, five out of six radar products still exhibited a clear conditional bias, with intensities of about 1 %–2 % per mmh−1. As a result, peak rainfall intensities were severely underestimated (factor 1.8–3.0 or 44 %–67 %). The most likely reason for this is the use of a fixed Z–R relationship when estimating rainfall rates (R) from reflectivity (Z), which fails to account for natural variations in raindrop size distribution with intensity. Based on our findings, the easiest way to mitigate the bias in times of heavy rain is to perform frequent (e.g., hourly) bias adjustments with the help of rain gauges, as demonstrated by the Dutch C-band product. An even more promising strategy that does not require any gauge adjustments is to estimate rainfall rates using a combination of reflectivity (Z) and differential phase shift (Kdp), as done in the Finnish OSAPOL product. Both approaches lead to approximately similar performances, with an average bias (at 10 min resolution) of about 30 % and a peak intensity bias of about 45 %.

scholarly journals The accuracy of weather radar in heavy rain: a comparative study for Denmark, the Netherlands, Finland and Sweden

2019 ◽  
Author(s):  
Marc Schleiss ◽  
Jonas Olsson ◽  
Peter Berg ◽  
Tero Niemi ◽  
Teemu Kokkonen ◽  
...  
Keyword(s):  
The Netherlands ◽  
Mean Field ◽  
Correlation Coefficients ◽  
Weather Radar ◽  
Heavy Rain ◽  
Small Scale ◽  
Dual Polarization ◽  
Urban Flood ◽  
Invaluable Tool ◽  
Urban Pluvial Flooding

Abstract. Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by moving to higher resolution and making use of dual-polarization, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. But since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 hours. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, 6 different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events for each country were used to quantify the overall agreement between radar and gauges and the errors affecting the peaks. Results show that the overall agreement between radar and gauges in heavy rain is fair, with multiplicative biases in the order of 1.41–1.66 (i.e., radar underestimates by 29–39.8 %) and correlation coefficients of 0.71–0.83 across countries. However, the bias increases with intensity, reaching 45.9 %–66.2 % during the peaks. Only part of the bias (i.e., roughly 13 %–30 % depending on the radar product) can be explained by differences in measurement areas between gauges and radar. Radar products with higher spatial and temporal resolutions agreed better with the gauges, highlighting the importance of high-resolution radar for urban hydrology. However, for capturing peak intensity and reducing the bias during the most intense part of a storm, the ability to combine measurements from multiple overlapping radars to help mitigate attenuation seemed to play a more important role than resolution. The use of dual-polarization and phase information (e.g., Kdp) in the experimental Finnish OSAPOL product also seemed to provide a slight advantage in heavy rain. But improvements were hard to quantify and similarly good results were achieved in the Netherlands by applying a simple Z–R relation together with a mean field bias-correction.

scholarly journals Influence of the Subgrid Variability of the Raindrop Size Distribution on Radar Rainfall Estimators

2012 ◽  
Vol 51 (4) ◽  
pp. 780-785 ◽  
Author(s):  
Joël Jaffrain ◽  
Alexis Berne
Keyword(s):  
Size Distribution ◽  
Rain Rate ◽  
Power Laws ◽  
Radar Data ◽  
Sampling Uncertainty ◽  
Radar Rainfall ◽  
Differential Phase Shift ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
Pixel Scale

AbstractThis work aims at quantifying the variability of the parameters of the power laws used for rain-rate estimation from radar data, on the basis of raindrop size distribution measurements over a typical weather radar pixel. Power laws between the rain rate and the reflectivity or the specific differential phase shift are fitted to the measured values, and the variability of the parameters is analyzed. At the point scale, the variability within this radar pixel cannot be solely explained by the sampling uncertainty associated with disdrometer measurements. When parameters derived from point measurements are applied at the radar pixel scale, the resulting error in the rain amount varies between −2% and +15%.

scholarly journals Joint statistical correction of clutters, spokes and beam height for a radar derived precipitation climatology in southern Germany

2012 ◽  
Vol 16 (11) ◽  
pp. 4101-4117 ◽  
Author(s):  
A. Wagner ◽  
J. Seltmann ◽  
H. Kunstmann
Keyword(s):  
Statistical Analysis ◽  
Cell Tracking ◽  
Weather Radar ◽  
Radar Data ◽  
Small Scale ◽  
Correction Algorithm ◽  
Rain Gauges ◽  
Radar Images ◽  
Statistical Correction ◽  
High Level

Abstract. First results of radar derived climatology have emerged over the last years, as datasets of appropriate extent are becoming available. Usually, these statistics are based on time series lasting up to ten years as continuous storage of radar data was often not achieved before. This kind of climatology demands a high level of data quality. Small deviations or minor systematic under- or overestimations in single radar images become a major cause of error in statistical analysis. Extensive corrections of radar data are a crucial prerequisite for radar derived climatology. We present a new statistical post-correction scheme based on a climatological analysis of seven years of radar data of the Munich weather radar (2000–2006) operated by DWD (German Weather Service). Original radar products are used subject only to corrections within the signal processor without any further corrections on single radar images. The aim of this statistical correction is to make up for the average systematic errors caused by clutter, propagation, or measuring effects but to conserve small-scale natural variations in space. The statistical correction is based on a thorough analysis of the different causes of possible errors for the Munich weather radar. This analysis revealed the following basic effects: the decrease of rain amount as a function of height and distance from the radar, clutter effects such as clutter remnants after filtering, holes by eliminated clutter or shading effects from obstacles near the radar, visible as spokes, as well as the influence of the bright band. The correction algorithm is correspondingly based on these results. It consists of three modules. The first one is an altitude correction which minimises measuring effects. The second module corrects clutter effects and disturbances and the third one realises a mean adjustment to selected rain gauges. Two different sets of radar products are used. The statistical analysis as well as module 1 and module 2 of the correction algorithm are based on frequencies of the six reflectivity levels within the so-called PX product. For correction module 3 and for the validation of the correction algorithm, rain amounts are calculated from the 8-bit so-called DX product. The correction algorithm is created to post-correct climatological or statistical analysis of radar data with a temporal resolution larger than one year. The correction algorithm is used for frequencies of occurrence of radar reflectivities which enables its application even for radar products such as DWD's cell-tracking-product CONRAD. Application (2004–2006) and validation (2007–2009) periods of this correction algorithm with rain gauges show an increased conformity for radar climatology after the statistical correction. In the years 2004 to 2006 the Root-Mean-Square-Error (RMSE) between mean annual rain amounts of rain gauges and corresponding radar pixels decreases from 262 mm to 118 mm excluding those pairs of values where the rain gauges are situated in areas of obviously corrupted radar data. The results for the validation period 2007 to 2009 are based on all pairs of values and show a decline of the RMSE from 322 mm to 174 mm.

scholarly journals Estimation of Dual Polarization Weather Radar Variables

2018 ◽  
Vol 28 (2) ◽  
pp. 1
Author(s):  
Dalia A. Mahmood
Keyword(s):  
Weather Radar ◽  
Heavy Rain ◽  
Radar Reflectivity ◽  
Dual Polarization ◽  
Differential Phase Shift ◽  
Light Rain ◽  
X Band ◽  
The Difference ◽  
Range Of Values ◽  
Reflectivity Factor

Dual polarization weather radar has now become a widely used as instrument in meteorological offices around the world because of its capability in distinguishing different precipitation type and in improving the accuracy of quantitative precipitation estimation. The aim of this work is to estimate the polarimetry radar variables for radars of different frequency bands and study their behavior with rainfall rates. Calculations of polarimetry radar variables were made on the basis of several assumptions. The results showed that factors at horizontal and vertical polarization, ZH,V, ranges between 20 dBz respectively, and more than 55 dBz for light rain and extreme heavy rain respectively, and radar reflectivity factor at horizontal ZH is greater than radar reflectivity factor at vertical ZV for all rainfall rates. The differential reflectivity, ZDR, also increases with increasing rainfall rates since it is the difference between ZH and Zv. Calculations of specific differential attenuation indicated that X band radars are seriously atten-uated by rain and C band radars are less affected by rain. The specific differential attenuation, S band radars is very small. In addition to this feature, the results showed that the differential phase shift between return signals of horizontal and vertical polarizations for S band radars is much less than those for C and X band radars, and also, the results showed that the co-polarization correlation coefficient for S band the radars is much higher than those of C and X bands. In order to investigate the accuracy of the calculated polarimetric weather radar variables per-formed in this research, real radar measurements were used for this purpose. Results indicated that the range of values for calculated polarimetric radar variables are very consistent with range of values for measured variables

scholarly journals Joint statistical correction of clutters, spokes and beam height for a radar climatology in Southern Germany

2012 ◽  
Vol 9 (4) ◽  
pp. 4703-4746
Author(s):  
A. Wagner ◽  
J. Seltmann ◽  
H. Kunstmann
Keyword(s):  
Statistical Analysis ◽  
Weather Radar ◽  
Radar Data ◽  
Small Scale ◽  
Correction Algorithm ◽  
Rain Gauges ◽  
Radar Images ◽  
Statistical Correction ◽  
First Results ◽  
High Level

Abstract. Extensive corrections of radar data are a crucial prerequisite for radar derived climatology. This kind of climatology demands a high level of data quality. Little deviations or minor systematic underestimations or overestimations in single radar images become a major cause of error in statistical analysis. First results of radar derived climatology have emerged over the last years, as data sets of appropriate extent are becoming available. Usually, these statistics are based on time series lasting up to ten years as storage of radar data was not achieved before. We present a new statistical post-correction scheme, which is based on seven years of radar data of the Munich weather radar (2000–2006) that is operated by DWD (German Weather Service). The typical correction algorithms for single radar images, such as clutter corrections, are used. Then an additional statistical post-correction based on the results of a climatological analysis from radar images follows. The aim of this statistical correction is to correct systematic errors caused by clutter effects or measuring effects but to conserve small-scale natural variations in space. The statistical correction is based on a thorough analysis of the different causes of possible errors for the Munich weather radar. This robust analysis revealed the following basic effects: the decrease of rain rate in relation to height and distance from the radar, clutter effects such as remaining clutter, eliminated clutter or shading effects from obstacles near the radar, visible as spokes, as well as the influence of the Bright Band. The correction algorithm is correspondingly based on these results. It consists of three modules. The first one is an altitude correction, which minimizes measuring effects. The second module corrects clutter effects and the third one realizes a mean adjustment to selected rain gauges. Two different radar products are used. The statistical analysis as well as module one and module two of the correction algorithm are based on frequencies of occurrence of the so-called PX-product with six reflectivity levels. For correction module 3 and for the validation of the correction algorithm rain rates are calculated from the 8-bit-depth so-called DX-product. An application (2004–2006) and a validation (2007–2009) of this correction algorithm with rain gauges show a much higher conformity for radar climatology after the statistical correction. In the years 2004 to 2006 the Root-Mean-Square-Error (RMSE) decreases from 262 mm to 118 mm excluding those pair of values where the rain gauges are situated in areas of obviously corrupted radar data. The results for the validation period 2007 to 2009 are based on all pairs of values and show a decline of the RMSE from 322 mm to 174 mm.

scholarly journals Utilization of Red-Green-Blue (RGB) modification composite for nighttime convective cloud monitoring over North Sumatra region

2021 ◽  
Vol 893 (1) ◽  
pp. 012019
Author(s):  
I J A Saragih ◽  
K Tarigan ◽  
M Sinambela ◽  
M Situmorang ◽  
K Sembiring ◽  
...  
Keyword(s):  
Satellite Image ◽  
Convective Cloud ◽  
Weather Radar ◽  
Heavy Rain ◽  
Radar Data ◽  
Convective System ◽  
Rainfall Analysis ◽  
The North ◽  
Observation Method ◽  
North Sumatra

Abstract Located between the Indian Ocean and the Malacca Strait, also the presence of the Bukit Barisan Mountains cause high convective activity in the North Sumatra region. The Himawari-8 satellite has 16 atmospheric observation channels that allow for observations of the convective system growth phase. The Red-Green-Blue (RGB) composite method is used to display a variety of satellite image composite information. The nocturnal convective system that often forms in the coastal areas of Sumatra causes heavy rains. A nocturnal convective system observation method is needed to publish early warning information on extreme weather. This research was conducted to observe the nocturnal convective system during heavy rain events in the North Sumatra region using a modification of RGB composite. This research used the Himawari-8 satellite data, Coloumn Max (CMAX) products of Medan weather radar data, and Global Satellite Mapping of Precipitation (GSMaP) rainfall estimation data. Comparison of RGB modified products with Night Microphysics RGB products and CMAX weather radar products, as well as time-series rainfall analysis. The results showed that the RGB modification product could capture the beginning of the convective system's growth, development, and spatial movement. The convective cloud distribution pattern corresponds to the area of heavy rain. There is a slight difference in cloud growth area between the satellite and radar products indicated the parallax error from the satellite image.

Uncertainty in risk analysis of urban pluvial flooding: a case study

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
J.A.E. ten Veldhuis ◽  
F.H.L.R. Clemens
Keyword(s):  
The Netherlands ◽  
Flood Risk ◽  
Flood Frequency ◽  
Urban Catchment ◽  
Urban Flood ◽  
Calculation Results ◽  
System Maintenance ◽  
Urban Pluvial Flooding ◽  
Urban Flood Risk

The interest in urban flood risk is growing steadily over the last decades. Still, in the Netherlands no data is available to quantify urban flood risk. In this paper an estimation of urban flood frequencies is made in a detailed analysis of an urban catchment. Calculation results from a theoretical model are compared with data from a complaint register. The analysis in the case study shows that insufficient system maintenance condition is an important potential cause of urban flooding. The estimated flood frequency caused by severe rainfall is 4 events in 7 years or 0.6 per year, while the flood frequency caused by maintenance problems is 13 in 4 years or 3.3 per year. This includes 2 flood events that are caused by heavy rainfall and 11 events that are related to maintenance problems. The number of locations that suffer flooding caused by severe rainfall is more than 3 or 4 per event, while the number of locations that suffer flooding caused by maintenance problems is not more than 2 per event. It is expected that these numbers are representative for the rest of the Netherlands. Further research and data collection will very this assumption.

scholarly journals Technical a Gaussian Mixture Model to Separate Birds and Insects in Single-Polarization Weather Radar Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1989
Author(s):  
Raphaël Nussbaumer ◽  
Baptiste Schmid ◽  
Silke Bauer ◽  
Felix Liechti
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Doppler Radar ◽  
Animal Movement ◽  
Weather Radar ◽  
Gaussian Mixture ◽  
Radar Data ◽  
Volume Data ◽  
Simple Method ◽  
Single Polarization

Recent and archived data from weather radar networks are extensively used for the quantification of continent-wide bird migration patterns. While the process of discriminating birds from weather signals is well established, insect contamination is still a problem. We present a simple method combining two Doppler radar products within a Gaussian mixture model to estimate the proportions of birds and insects within a single measurement volume, as well as the density and speed of birds and insects. This method can be applied to any existing archives of vertical bird profiles, such as the European Network for the Radar surveillance of Animal Movement repository, with no need to recalculate the huge amount of original polar volume data, which often are not available.

scholarly journals Comparison of Radar-Based Hail Detection Using Single- and Dual-Polarization

2019 ◽  
Vol 11 (12) ◽  
pp. 1436 ◽  
Author(s):  
Skripniková ◽  
Řezáčová
Keyword(s):  
Comparative Analysis ◽  
Heavy Rain ◽  
Radar Data ◽  
Severe Weather ◽  
Detection Methods ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Rain Events ◽  
Single Polarization ◽  
Strong Agreement

The comparative analysis of radar-based hail detection methods presented here, uses C-band polarimetric radar data from Czech territory for 5 stormy days in May and June 2016. The 27 hail events were selected from hail reports of the European Severe Weather Database (ESWD) along with 21 heavy rain events. The hail detection results compared in this study were obtained using a criterion, which is based on single-polarization radar data and a technique, which uses dual-polarization radar data. Both techniques successfully detected large hail events in a similar way and showed a strong agreement. The hail detection, as applied to heavy rain events, indicated a weak enhancement of the number of false detected hail pixels via the dual-polarization hydrometeor classification. We also examined the performance of hail size detection from radar data using both single- and dual-polarization methods. Both the methods recognized events with large hail but could not select the reported events with maximum hail size (diameter above 4 cm).

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