scholarly journals Close-range radar rainfall estimation and error analysis

2016 ◽  
Author(s):  
Remco van de Beek ◽  
Hidde Leijnse ◽  
Pieter Hazenberg ◽  
Remko Uijlenhoet
Keyword(s):  
Positive Impact ◽  
Rain Gauge ◽  
Minor Role ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Sources Of Error ◽  
Ground Clutter ◽  
Received Power ◽  
Radar Calibration ◽  
Many Sources

Abstract. Quantitative precipitation estimation (QPE) using ground-based weather radar is affected by many sources of error. The most important of these are 1) radar calibration, 2) ground clutter, 3) wet radome attenuation, 4) rain induced attenuation, 5) vertical profile of reflectivity, 6) non-uniform beam filling, and 7) variations in rain drop size distribution (DSD). This study presents an attempt to separate and quantify these sources of error in flat terrain very close to the radar (1–2 km), where 4), 5), and 6) only play a minor role. A 3-day rainfall event (25–27 August 2010) that produced more than 50 mm of precipitation in De Bilt, The Netherlands is analyzed using radar, rain gauge, and disdrometer data. Without any correction it is found that the radar severely underestimates the total rain amount (by more than 50 %). The calibration of the radar receiver is operationally monitored by analyzing the received power from the sun. This turns out to cause a 1 dB of underestimation. The operational clutter filter applied by KNMI is found to incorrectly identify precipitation as clutter, especially at near-zero Doppler velocities. An alternative simple clutter removal scheme using a clear sky clutter map improves the rainfall estimation slightly. To investigate the effect of wet radome attenuation, stable returns from buildings close to the radar are analyzed. It is shown that this may have caused an underestimation of up to 4 dB. Finally, a disdrometer is used to derive event and intra-event specific Z-R relations due to variations in the observed DSDs. Such variations may result in errors when applying the operational Marshall-Palmer Z-R relation. Correcting for all of these effects has a large positive impact on the radar derived precipitation estimates and yields a good match between radar QPE and gauge measurements, with a difference of 5 to 8 %. This shows the potential of radar as a tool for rainfall estimation, especially at close ranges, but also underlines the importance of applying radar correction methods as individual errors can have a large detrimental impact on the QPE performance of the radar.

scholarly journals Close-range radar rainfall estimation and error analysis

2016 ◽  
Vol 9 (8) ◽  
pp. 3837-3850 ◽  
Author(s):  
C. Z. van de Beek ◽  
H. Leijnse ◽  
P. Hazenberg ◽  
R. Uijlenhoet
Keyword(s):  
Positive Impact ◽  
Rain Gauge ◽  
Minor Role ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Sources Of Error ◽  
Ground Clutter ◽  
Received Power ◽  
Radar Calibration ◽  
Many Sources

Abstract. Quantitative precipitation estimation (QPE) using ground-based weather radar is affected by many sources of error. The most important of these are (1) radar calibration, (2) ground clutter, (3) wet-radome attenuation, (4) rain-induced attenuation, (5) vertical variability in rain drop size distribution (DSD), (6) non-uniform beam filling and (7) variations in DSD. This study presents an attempt to separate and quantify these sources of error in flat terrain very close to the radar (1–2 km), where (4), (5) and (6) only play a minor role. Other important errors exist, like beam blockage, WLAN interferences and hail contamination and are briefly mentioned, but not considered in the analysis. A 3-day rainfall event (25–27 August 2010) that produced more than 50 mm of precipitation in De Bilt, the Netherlands, is analyzed using radar, rain gauge and disdrometer data. Without any correction, it is found that the radar severely underestimates the total rain amount (by more than 50 %). The calibration of the radar receiver is operationally monitored by analyzing the received power from the sun. This turns out to cause a 1 dB underestimation. The operational clutter filter applied by KNMI is found to incorrectly identify precipitation as clutter, especially at near-zero Doppler velocities. An alternative simple clutter removal scheme using a clear sky clutter map improves the rainfall estimation slightly. To investigate the effect of wet-radome attenuation, stable returns from buildings close to the radar are analyzed. It is shown that this may have caused an underestimation of up to 4 dB. Finally, a disdrometer is used to derive event and intra-event specific Z–R relations due to variations in the observed DSDs. Such variations may result in errors when applying the operational Marshall–Palmer Z–R relation. Correcting for all of these effects has a large positive impact on the radar-derived precipitation estimates and yields a good match between radar QPE and gauge measurements, with a difference of 5–8 %. This shows the potential of radar as a tool for rainfall estimation, especially at close ranges, but also underlines the importance of applying radar correction methods as individual errors can have a large detrimental impact on the QPE performance of the radar.

scholarly journals Radar rainfall image repair techniques

2004 ◽  
Vol 8 (2) ◽  
pp. 220-234 ◽  
Author(s):  
Stephen M. Wesson ◽  
Geoffrey G. S. Pegram
Keyword(s):  
Singular Value Decomposition ◽  
Ordinary Kriging ◽  
Singular Systems ◽  
Ground Level ◽  
Singular Value ◽  
Rainfall Data ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Ground Clutter ◽  
Value Decomposition

Abstract. There are various quality problems associated with radar rainfall data viewed in images that include ground clutter, beam blocking and anomalous propagation, to name a few. To obtain the best rainfall estimate possible, techniques for removing ground clutter (non-meteorological echoes that influence radar data quality) on 2-D radar rainfall image data sets are presented here. These techniques concentrate on repairing the images in both a computationally fast and accurate manner, and are nearest neighbour techniques of two sub-types: Individual Target and Border Tracing. The contaminated data is estimated through Kriging, considered the optimal technique for the spatial interpolation of Gaussian data, where the "screening effect" that occurs with the Kriging weighting distribution around target points is exploited to ensure computational efficiency. Matrix rank reduction techniques in combination with Singular Value Decomposition (SVD) are also suggested for finding an efficient solution to the Kriging Equations which can cope with near singular systems. Rainfall estimation at ground level from radar rainfall volume scan data is of interest and importance in earth bound applications such as hydrology and agriculture. As an extension of the above, Ordinary Kriging is applied to three-dimensional radar rainfall data to estimate rainfall rate at ground level. Keywords: ground clutter, data infilling, Ordinary Kriging, nearest neighbours, Singular Value Decomposition, border tracing, computation time, ground level rainfall estimation

scholarly journals Radar Rainfall Estimation for Ground Validation Studies of the Tropical Rainfall Measuring Mission

1997 ◽  
Vol 36 (6) ◽  
pp. 735-747 ◽  
Author(s):  
Grzegorz J. Ciach ◽  
Witold F. Krajewski ◽  
Emmanouil N. Anagnostou ◽  
Mary L. Baeck ◽  
James A. Smith ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Estimation Algorithm ◽  
Rain Gauge ◽  
Performance Criterion ◽  
Local Network ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Tropical Rainfall ◽  
Rain Gauges ◽  
Ground Validation

Abstract This study presents a multicomponent rainfall estimation algorithm, based on weather radar and rain gauge network, that can be used as a ground-based reference in the satellite Tropical Rainfall Measuring Mission (TRMM). The essential steps are constructing a radar observable, its nonlinear transformation to rainfall, interpolation to rectangular grid, constructing several timescale accumulations, bias adjustment, and merging of the radar rainfall estimates and rain gauge data. Observations from a C-band radar in Darwin, Australia, and a local network of 54 rain gauges were used to calibrate and test the algorithm. A period of 25 days was selected, and the rain gauges were split into two subsamples to apply cross-validation techniques. A Z–R relationship with continuous range dependence and a temporal interpolation scheme that accounts for the advection effects is applied. An innovative methodology was used to estimate the algorithm controlling parameters. The model was globally optimized by using an objective function on the level of the final products. This is equivalent to comparing hundreds of Z–R relationships using a uniform and representative performance criterion. The algorithm performance is fairly insensitive to the parameter variations around the optimum. This suggests that the accuracy limit of the radar rainfall estimation based on power-law Z–R relationships has been reached. No improvement was achieved by using rain regime classification prior to estimation.

scholarly journals The New French Operational Radar Rainfall Product. Part II: Validation

2007 ◽  
Vol 22 (3) ◽  
pp. 409-427 ◽  
Author(s):  
P. Tabary ◽  
J. Desplats ◽  
K. Do Khac ◽  
F. Eideliman ◽  
C. Gueguen ◽  
...  
Keyword(s):  
Radar Data ◽  
Rain Gauge ◽  
Radiosonde Data ◽  
Radar Rainfall ◽  
Freezing Level ◽  
Ground Clutter ◽  
Precipitation Estimation ◽  
Upper Level ◽  
Quality Indexes ◽  
Precipitation Events

Abstract A new operational radar-based rainfall product has been developed at Météo-France and is currently being deployed within the French operational network. The new quantitative precipitation estimation (QPE) product is based entirely on radar data and includes a series of modules aimed at correcting for ground clutter, partial beam blocking, and vertical profile of reflectivity (VPR) effects, as well as the nonsimultaneity of radar measurements. The surface rainfall estimation is computed as a weighted mean of the corrected tilts. In addition to the final QPE, a map of quality indexes is systematically generated. This paper is devoted to the validation of the new radar QPE. The VPR identification module has been specifically validated by analyzing 489 precipitation events observed over 1 yr by a representative eight-radar subset of the network. The conceptual model of VPR used in the QPE processing chain is shown to be relevant. A climatology of the three shape parameters of the conceptual VPR (brightband peak, brightband thickness, and upper-level decreasing rate) is established and the radar-derived freezing-level heights are shown to be in good agreement with radiosonde data. A total of 27 precipitation events observed by three S-band radars of the network during the winter of 2005 and the autumns of 2002 and 2003 are used to compare the new radar QPE to the old one. Results are stratified according to the distance to the radar and according to the height of the freezing level. The Nash criterion is increased from 0.23 to 0.62 at close range (below 50 km) and from 0.35 to 0.42 at long range (between 100 and 150 km). The relevance of the proposed quality indexes is assessed by examining their statistical relationship with long-term radar–rain gauge statistics. Mosaics of QPE and quality indexes are also illustrated.

scholarly journals An Integrated Approach to Error Correction for Real-Time Radar-Rainfall Estimation

2006 ◽  
Vol 23 (1) ◽  
pp. 67-79 ◽  
Author(s):  
Siriluk Chumchean ◽  
Ashish Sharma ◽  
Alan Seed
Keyword(s):  
Error Correction ◽  
Real Time ◽  
Mean Field ◽  
Rain Gauge ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Bias Adjustment ◽  
Field Bias ◽  
The Mean ◽  
Correction Strategies

Abstract A procedure for estimating radar rainfall in real time consists of three main steps: 1) the measurement of reflectivity and removal of known sources of errors, 2) the conversion of the reflectivity to a rainfall rate (Z–R conversion), and 3) the adjustment of the mean field bias as assessed using a rain gauge network. Error correction is associated with the first two steps and incorporates removing erroneous measurements and correcting biases in the Z–R conversion. This paper investigates the relative importance of error correction and the mean field bias–adjustment processes. In addition to the correction for ground clutter, the bright band, and hail, the two error correction strategies considered here are 1) a scale transformation function to remove range-dependent bias in measured reflectivity resulting from an increase in observation volume with range, and 2) the classification of storm types to account for the variation in Z–R relationships for convective and stratiform rainfall. The mean field bias is removed using two alternatives: 1) estimation of the bias at each time step based on the sample of observations available, and 2) use of a Kalman filter to estimate the bias under assumptions of a Markovian dependence structure. A 7-month record of radar and rain gauge rainfall for Sydney, Australia, were used in this study. The results show a stepwise decrease in the root-mean-square error (rmse) of radar rainfall with added levels of error correction using either of the two mean field bias–adjustment methods considered in our study. It was found that although the effects of the two error correction strategies were small compared to bias adjustment, they do form an important step of radar-rainfall estimation.

scholarly journals Difficulties with Correcting Radar Rainfall Estimates Based on Rain Gauge Data: A Case Study of Severe Weather in Montana on 16–17 June 2007

2009 ◽  
Vol 24 (5) ◽  
pp. 1334-1344 ◽  
Author(s):  
Steven V. Vasiloff ◽  
Kenneth W. Howard ◽  
Jian Zhang
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Flash Flood ◽  
Rain Gauge ◽  
Data Quality Control ◽  
Radar Rainfall ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Radar Calibration

Abstract The principal source of information for operational flash flood monitoring and warning issuance is weather radar–based quantitative estimates of precipitation. Rain gauges are considered truth for the purposes of validating and calibrating real-time radar-derived precipitation data, both in a real-time sense and climatologically. This paper examines various uncertainties and challenges involved with using radar and rain gauge data in a severe local storm environment. A series of severe thunderstorm systems that occurred across northeastern Montana illustrates various problems with comparing radar precipitation estimates and real-time gauge data, including extreme wind effects, hail, missing gauge data, and radar quality control. Ten radar–gauge time series pairs were analyzed with most found to be not useful for real-time radar calibration. These issues must be carefully considered within the context of ongoing efforts to develop robust real-time tools for evaluating radar–gauge uncertainties. Recommendations are made for radar and gauge data quality control efforts that would benefit the operational use of gauge data.

scholarly journals Effect of bias adjustment and rain gauge data quality control on radar rainfall estimation

1999 ◽  
Vol 35 (8) ◽  
pp. 2487-2503 ◽  
Author(s):  
Matthias Steiner ◽  
James A. Smith ◽  
Stephen J. Burges ◽  
Carlos V. Alonso ◽  
Robert W. Darden
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Rain Gauge ◽  
Data Quality Control ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rain Gauge Data ◽  
Bias Adjustment ◽  
Gauge Data

scholarly journals Radar rainfall estimation for the post-event analysis of a Slovenian flash-flood case: application of the mountain reference technique at C-band frequency

2009 ◽  
Vol 6 (1) ◽  
pp. 667-696
Author(s):  
L. Bouilloud ◽  
G. Delrieu ◽  
B. Boudevillain ◽  
F. Zanon ◽  
M. Borga
Keyword(s):  
Flash Flood ◽  
Heavy Rain ◽  
Radar Data ◽  
Rain Attenuation ◽  
Event Analysis ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Band Frequency ◽  
Event Time ◽  
Radar Calibration

Abstract. This article is dedicated to radar rainfall estimation for the post-event analysis of a Slovenian flash flood that occurred on 18 September 2007. The utility of the Mountain Reference Technique is demonstrated to quantify rain attenuation effects that affect C-band radar measurements in heavy rain. Maximum path-integrated attenuation between 15 and 20 dB were measured thanks to mountain returns for path-averaged rain rates between 10 and 15 mm h−1 over a 120-km path. The proposed technique allowed estimation of an effective radar calibration correction factor, assuming the reflectivity-attenuation relationship to be known. Screening effects were quantified using a geometrical calculation based on a digitized terrain model of the region. The vertical structure of the reflectivity was modelled with a normalized apparent vertical profile of reflectivity. Implementation of the radar data processing indicated that: (1) attenuation correction using the Hitschfeld Bordan algorithm allowed obtaining satisfactory radar rain estimates (Nash criterion of 0.8 at the event time scale); (2) due to the attenuation equation instability, it is however compulsory to limit the maximum path-integrated attenuation to be corrected to about 10 dB; (3) the results also proved to be sensitive on the parameterization of reflectivity-attenuation-rainrate relationships. The convective nature of the precipitation explains the rather good performance obtained. For more contrasted rainy systems with convective and stratiform regions, the combination of the vertical (VPR) and radial (attenuation, screening) sources of heterogeneity yields a still very challenging problem for radar quantitative precipitation estimation at C-band.

scholarly journals Performance of high-resolution X-band radar for rainfall measurement in The Netherlands

2009 ◽  
Vol 6 (5) ◽  
pp. 6035-6085 ◽  
Author(s):  
C. Z. van de Beek ◽  
H. Leijnse ◽  
J. N. M. Stricker ◽  
R. Uijlenhoet ◽  
H. W. J. Russchenberg
Keyword(s):  
High Resolution ◽  
The Netherlands ◽  
Weather Radar ◽  
Rain Gauge ◽  
Signal Loss ◽  
Rainfall Events ◽  
Ground Clutter ◽  
X Band ◽  
Urban Catchments ◽  
Radar Calibration

Abstract. This study presents an analysis of 195 rainfall events gathered with the X-band weather radar SOLIDAR and a tipping bucket rain gauge network near Delft, The Netherlands, between May 1993 and April 1994. The high spatial (120 m) and temporal (16 s) resolution of the radar combined with the extent of the database make this study a climatological analysis of the potential for high-resolution rainfall measurement with non-polarimetric X-band radar over completely flat terrain. An appropriate radar reflectivity – rain rate relation is derived from measurements of raindrop size distributions and compared with radar – rain gauge data. The radar calibration is assessed using a long-term comparison of rain gauge measurements with corresponding radar reflectivities as well as by analyzing the evolution of the stability of ground clutter areas over time. Three different methods for ground clutter correction as well as the effectiveness of forward and backward attenuation correction algorithms have been studied. Five individual rainfall events are discussed in detail to illustrate the strengths and weaknesses of high-resolution X-band radar and the effectiveness of the presented correction methods. X-band radar is found to be able to measure the space-time variation of rainfall at high resolution, far greater than can be achieved by rain gauge networks or a typical operational C-band weather radar. On the other hand, SOLIDAR can suffer from receiver saturation, wet radome attenuation as well as signal loss along the path. During very strong convective situations the signal can even be lost completely. In combination with several rain gauges for quality control, high resolution X-band radar is considered to be suitable for rainfall monitoring over relatively small (urban) catchments. These results offer great prospects for the new high resolution polarimetric doppler X-band radar IDRA.

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