scholarly journals Country-wide CML rainfall estimation and CML-Radar combination in Germany

2021 ◽  
Author(s):  
Christian Chwala ◽  
Tanja Winterrath ◽  
Maximilian Graf ◽  
Julius Polz ◽  
Harald Kunstmann
Keyword(s):  
Large Scale ◽  
Rain Event ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Large Scale Analysis ◽  
Seasonal And Diurnal Variations ◽  
Hourly Rainfall ◽  
First Results ◽  
Microwave Link

<p>Commercial microwave links (CMLs) have emerged as a valuable source of rainfall information that can complement existing observations. In Germany, we acquire attenuation data from 4000 CMLs with a temporal resolution of one minute. In this contribution we present our results of deriving country-wide rainfall information from these CML data and show the first long-term application of CML data for adjusting the radar rainfall field.</p><p>We present results of a large-scale analysis of our country-wide dataset for one full year (Graf et al. 2020) and compare it with the gauge adjusted radar product RADOLAN-RW from the German Weather Service and the climatologically corrected radar product RADKLIM-YW. Our analysis also compares several different methods for processing CML data, including our recent improvements for the separation of dry and rainy periods in noisy CML attenuation time series based on a convolutional neural network (Polz et al. 2020). We show seasonal and diurnal variations of the performance of CML-derived rainfall data. Promising results are achieved year-round except for periods with solid precipitation. Pearson correlations for the comparison of the hourly rainfall sums reach up to 0.7 for summer months.</p><p>Furthermore, we present results from using the CML rainfall estimates to adjust radar rainfall fields. We extended the RADOLAN-method for radar-gauge adjustment for this purpose. The path-averaged CML rainfall information is compared to the gridded radar rainfall information at the path-intersecting grids. This information is then used in addition to the adjustments derived from rain gauges. We show first results of an hourly adjustment over several months. We further discuss the envisaged operational system for this application and give an outlook on the potential for radar rainfall field adjustments with higher temporal resolutions.</p><p>Graf, M., Chwala, C., Polz, J., and Kunstmann, H.: Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data, Hydrol. Earth Syst. Sci., 24, 2931–2950, https://doi.org/10.5194/hess-24-2931-2020, 2020</p><p>Polz, J., Chwala, C., Graf, M., and Kunstmann, H.: Rain event detection in commercial microwave link attenuation data using convolutional neural networks, Atmos. Meas. Tech., 13, 3835–3853, https://doi.org/10.5194/amt-13-3835-2020, 2020</p>

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 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 %.

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 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 Rainfall estimation using moving cars as rain gauges – laboratory experiments

2013 ◽  
Vol 17 (11) ◽  
pp. 4701-4712 ◽  
Author(s):  
E. Rabiei ◽  
U. Haberlandt ◽  
M. Sester ◽  
D. Fitzner
Keyword(s):  
Optical Sensors ◽  
Laboratory Experiments ◽  
Rainfall Intensity ◽  
Computer Experiments ◽  
Rainfall Estimation ◽  
Time Step ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Wide Range ◽  
Measurement Devices

Abstract. The spatial assessment of short time-step precipitation is a challenging task. Low density of observation networks, as well as the bias in radar rainfall estimation motivated the new idea of exploiting cars as moving rain gauges with windshield wipers or optical sensors as measurement devices. In a preliminary study, this idea has been tested with computer experiments (Haberlandt and Sester, 2010). The results have shown that a high number of possibly inaccurate measurement devices (moving cars) provide more reliable areal rainfall estimations than a lower number of precise measurement devices (stationary gauges). Instead of assuming a relationship between wiper frequency (W) and rainfall intensity (R) with an arbitrary error, the main objective of this study is to derive valid W–R relationships between sensor readings and rainfall intensity by laboratory experiments. Sensor readings involve the wiper speed, as well as optical sensors which can be placed on cars and are usually made for automating wiper activities. A rain simulator with the capability of producing a wide range of rainfall intensities is designed and constructed. The wiper speed and two optical sensors are used in the laboratory to measure rainfall intensities, and compare it with tipping bucket readings as reference. Furthermore, the effect of the car speed on the estimation of rainfall using a car speed simulator device is investigated. The results show that the sensor readings, which are observed from manual wiper speed adjustment according to the front visibility, can be considered as a strong indicator for rainfall intensity, while the automatic wiper adjustment show weaker performance. Also the sensor readings from optical sensors showed promising results toward measuring rainfall rate. It is observed that the car speed has a significant effect on the rainfall measurement. This effect is highly dependent on the rain type as well as the windshield angle.

scholarly journals Areal rainfall estimation using moving cars – computer experiments including hydrological modeling

2016 ◽  
Author(s):  
E. Rabiei ◽  
U. Haberlandt ◽  
M. Sester ◽  
D. Fitzner ◽  
M. Wallner
Keyword(s):  
Spatial Resolution ◽  
Measurement Errors ◽  
Laboratory Experiments ◽  
Hydrological Modeling ◽  
Computer Experiments ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Temporal And Spatial ◽  
Areal Rainfall

Abstract. The need for high temporal and spatial resolution precipitation data for hydrological analyses has been discussed in several studies. Although rain gauges provide valuable information, a very dense rain gauge network is costly. As a result, several new ideas have been emerged to help estimating areal rainfall with higher temporal and spatial resolution. Rabiei et al. (2013) observed that moving cars, called RainCars (RCs), can potentially be a new source of data for measuring rainfall amounts. The optical sensors used in that study are designed for operating the windscreen wipers and showed promising results for rainfall measurement purposes. Their measurement accuracy has been quantified in laboratory experiments. Considering explicitly those errors, the main objective of this study is to investigate the benefit of using RCs for estimating areal rainfall. For that, computer experiments are carried out, where radar rainfall is considered as the reference and the other sources of data, i.e. RCs and rain gauges, are extracted from radar data. Comparing the quality of areal rainfall estimation by RCs with rain gauges and reference data helps to investigate the benefit of the RCs. The value of this additional source of data is not only assessed for areal rainfall estimation performance, but also for use in hydrological modeling. The results show that the RCs considering measurement errors derived from laboratory experiments provide useful additional information for areal rainfall estimation as well as for hydrological modeling. Even assuming higher uncertainties for RCs as obtained from the laboratory up to a certain level is observed practical.

scholarly journals Areal rainfall estimation using moving cars – computer experiments including hydrological modeling

2016 ◽  
Vol 20 (9) ◽  
pp. 3907-3922 ◽  
Author(s):  
Ehsan Rabiei ◽  
Uwe Haberlandt ◽  
Monika Sester ◽  
Daniel Fitzner ◽  
Markus Wallner
Keyword(s):  
Spatial Resolution ◽  
Measurement Errors ◽  
Laboratory Experiments ◽  
Hydrological Modeling ◽  
Computer Experiments ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Temporal And Spatial ◽  
Areal Rainfall

Abstract. The need for high temporal and spatial resolution precipitation data for hydrological analyses has been discussed in several studies. Although rain gauges provide valuable information, a very dense rain gauge network is costly. As a result, several new ideas have emerged to help estimating areal rainfall with higher temporal and spatial resolution. Rabiei et al. (2013) observed that moving cars, called RainCars (RCs), can potentially be a new source of data for measuring rain rate. The optical sensors used in that study are designed for operating the windscreen wipers and showed promising results for rainfall measurement purposes. Their measurement accuracy has been quantified in laboratory experiments. Considering explicitly those errors, the main objective of this study is to investigate the benefit of using RCs for estimating areal rainfall. For that, computer experiments are carried out, where radar rainfall is considered as the reference and the other sources of data, i.e., RCs and rain gauges, are extracted from radar data. Comparing the quality of areal rainfall estimation by RCs with rain gauges and reference data helps to investigate the benefit of the RCs. The value of this additional source of data is not only assessed for areal rainfall estimation performance but also for use in hydrological modeling. Considering measurement errors derived from laboratory experiments, the result shows that the RCs provide useful additional information for areal rainfall estimation as well as for hydrological modeling. Moreover, by testing larger uncertainties for RCs, they observed to be useful up to a certain level for areal rainfall estimation and discharge simulation.

Recent improvements of CML rainfall estimation and CML-Radar combination in Germany

2021 ◽  
Author(s):  
Christian Chwala ◽  
Maximilian Graf ◽  
Julius Polz ◽  
Sebastian Rothermel ◽  
Luca Glawion ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Heavy Rain ◽  
Radar Data ◽  
Rainfall Estimation ◽  
Raw Data ◽  
Processing Methods ◽  
Data Gaps ◽  
Microwave Link ◽  
First Time

<p>During the last years we made great progress with the country-wide rainfall estimation from commerical microwave link (CML) data in Germany (Graf et al. 2020, Polz et al. 2020). Using the derived results in different applications has, however, revealed that undetected erratic behaviour of CML raw data is still limiting data quality and that data gaps during heavy rain can lead to underestimation of peak rain rates. Hence, we have extended our processing methods and, for the first time, have carried out a large-scale intercomparison with other available methods. Albeit we are constantly improving our CML rainfall estimation, we already apply these data to operationally generate rainfall maps for Germany, also in combination with radar data from the German Meteorological Service (DWD).</p><p>In this contribution we will present our current research on the following interconnected topics:</p><p><strong>1. Detecting erratic signal fluctuations</strong>: In contrast to the existing methods that focus on detecting rainy-periods in the noisy raw data we have developed a dedicated classification method for periods with erratic signal fluctuations, which can easily lead to rainfall overestimation from CMLs. Our method, which is based on an artificial neural network, is designed to reduce the number of falsely classified rainy periods during dry periods with strong signal fluctuation.</p><p><strong>2. Large scale method intercomparison</strong>: For the first time, we compare the widely used RAINLINK algorithm, which is based on analysing data from nearby CMLs, with purely time-series based processing methods. First results show that both methods have advantages that, when combined, could improve the overall processing.</p><p><strong>3. The effect and mitigation of data gaps during heavy rainfall</strong>: CML networks are designed so that very heavy rain events lead to a complete loss of signal, and hence to gaps in the data we use for rainfall estimation. We analyse the occurrence of these gaps and show the impact on CML-derived rainfall estimation as well as mitigation methods.</p><p><strong>4. Real-time application</strong>: We use the CML data that we acquire in real-time to generate rainfall maps for Germany and merge the CML rainfall estimates with DWD radar data. Our approach is an extension of the existing RADOLAN-method. Results show that merging with the path-averaged CML rainfall information provides similar results than merging with gauges. In regions where the addition of CMLs significantly increases the density of observations, the joint Radar-gauge-CML product is expected to show improved quality.</p><p>References:</p><p>Graf, M., Chwala, C., Polz, J., and Kunstmann, H.: Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data, Hydrol. Earth Syst. Sci., 24, 2931–2950, https://doi.org/10.5194/hess-24-2931-2020, 2020</p><p>Polz, J., Chwala, C., Graf, M., and Kunstmann, H.: Rain event detection in commercial microwave link attenuation data using convolutional neural networks, Atmos. Meas. Tech., 13, 3835–3853, https://doi.org/10.5194/amt-13-3835-2020, 2020</p>

scholarly journals Rainfall estimation using moving cars as rain gauges – laboratory experiments

2013 ◽  
Vol 10 (4) ◽  
pp. 4207-4236 ◽  
Author(s):  
E. Rabiei ◽  
U. Haberlandt ◽  
M. Sester ◽  
D. Fitzner
Keyword(s):  
Optical Sensors ◽  
Laboratory Experiments ◽  
Rainfall Intensity ◽  
Computer Experiments ◽  
Rainfall Estimation ◽  
Time Step ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Wide Range ◽  
Measurement Devices

Abstract. The spatial assessment of short time step precipitation is a challenging task. Low density of observation networks, as well as the bias in radar rainfall estimation motivated the new idea of exploiting cars as moving rain gauges with windshield wipers or optical sensors as measurement devices. In a preliminary study, this idea has been tested with computer experiments (Haberlandt and Sester, 2010). The results have shown that a high number of possibly inaccurate measurement devices (moving cars) provide more reliable areal rainfall estimations than a lower number of precise measurement devices (stationary gauges). Instead of assuming a relationship between wiper frequency (W) and rainfall intensity (R) with an arbitrary error, the main objective of this study is to derive valid W–R relationships between sensor readings and rainfall intensity by laboratory experiments. Sensor readings involve the wiper speed, as well as optical sensors which can be placed on cars and are usually made for automating wiper activities. A rain simulator with the capability of producing a wide range of rainfall intensities is designed and constructed. The wiper speed and two optical sensors are used in the laboratory to measure rainfall intensities, and compare it with tipping bucket readings as reference. Furthermore, the effect of the car speed on the estimation of rainfall using a car speed simulator device is investigated. The results show that the sensor readings, which are observed from wiper speed adjustment according to the front visibility, can be considered as a strong indicator for rainfall intensity. Also the optical sensors showed promising results toward measuring rainfall rate. It is observed that the car speed has a significant effect on the rainfall measurement. This effect is highly dependent on the rain type as well as the windshield angle.

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