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.

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 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.

scholarly journals Quality-Based Combination of Multi-Source Precipitation Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1709 ◽  
Author(s):  
Anna Jurczyk ◽  
Jan Szturc ◽  
Irena Otop ◽  
Katarzyna Ośródka ◽  
Piotr Struzik
Keyword(s):  
Spatial Resolution ◽  
Flash Flood ◽  
Radar Data ◽  
Rain Gauge ◽  
Quality Information ◽  
Precipitation Data ◽  
Spatial Error ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Temporal And Spatial

A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high temporal and spatial resolution. Other sources of precipitation data, such as telemetric rain gauges and satellite observations, are also included in the QPE. All of the used data are characterized by different temporal and spatial error structures. Therefore, a combination of the data should be based on quality information quantitatively determined for each input to take advantage of a particular source of precipitation measurement. The presented work on multi-source QPE, being implemented as the RainGRS system, has been carried out in the Polish national meteorological and hydrological service for new nowcasting and hydrological platforms in Poland. For each of the three data sources, different quality algorithms have been designed: (i) rain gauge data is quality controlled and, on this basis, spatial interpolation and estimation of quality field is performed, (ii) radar data are quality controlled by RADVOL-QC software that corrects errors identified in the data and characterizes its final quality, (iii) NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting) products for both visible and infrared channels are combined and the relevant quality field is determined from empirical relationships that are based on analyses of the product performance. Subsequently, the quality-based QPE is generated with a 1-km spatial resolution every 10 minutes (corresponding to radar data). The basis for the combination is a conditional merging technique that is enhanced by involving detailed quality information that is assigned to individual input data. The validation of the RainGRS estimates was performed taking account of season and kind of precipitation.

scholarly journals The Role of the Spatial Distribution of Radar Rainfall on Hydrological Modeling for an Urbanized River Basin in Japan

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1703 ◽  
Author(s):  
Shakti P. C. ◽  
Tsuyoshi Nakatani ◽  
Ryohei Misumi
Keyword(s):  
River Basin ◽  
Spatial Resolution ◽  
Hydrological Modeling ◽  
Distribution Patterns ◽  
Rainfall Data ◽  
Model Parameters ◽  
Radar Rainfall ◽  
Rain Events ◽  
The Impact ◽  
Hydrological Applications

Recently, the use of gridded rainfall data with high spatial resolutions in hydrological applications has greatly increased. Various types of radar rainfall data with varying spatial resolutions are available in different countries worldwide. As a result of the variety in spatial resolutions of available radar rainfall data, the hydrological community faces the challenge of selecting radar rainfall data with an appropriate spatial resolution for hydrological applications. In this study, we consider the impact of the spatial resolution of radar rainfall on simulated river runoff to better understand the impact of radar resolution on hydrological applications. Very high-resolution polarimetric radar rainfall (XRAIN) data are used as input for the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) to simulate runoff from the Tsurumi River Basin, Japan. A total of 20 independent rainfall events from 2012–2015 were selected and categorized into isolated/convective and widespread/stratiform events based on their distribution patterns. First, the hydrological model was established with basin and model parameters that were optimized for each individual rainfall event; then, the XRAIN data were rescaled at various spatial resolutions to be used as input for the model. Finally, we conducted a statistical analysis of the simulated results to determine the optimum spatial resolution for radar rainfall data used in hydrological modeling. Our results suggest that the hydrological response was more sensitive to isolated or convective rainfall data than it was to widespread rain events, which are best simulated at ≤1 km and ≤5 km, respectively; these results are applicable in all sub-basins of the Tsurumi River Basin, except at the river outlet.

Mean areal rainfall improvement using radar rainfall estimation technique by considering geographic character for dam operation

2020 ◽  
Author(s):  
Seongsim Yoon ◽  
Hongjoon Shin ◽  
Gian Choi
Keyword(s):  
Nuclear Power ◽  
Heavy Rainfall ◽  
Mountainous Area ◽  
Accuracy Improvement ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Dam Operation ◽  
Areal Precipitation ◽  
Areal Rainfall ◽  
Dam Inflow

<p>Efficiently dam operation is necessary to secure water resources and to respond to floods. For the dam operation, the amount of dam inflow should be accurately calculate. Rainfall information is important for the amount of dam inflow estimation and prediction therefore rainfall should be observed accurately. However, it is difficult to observe the rainfall due to poor density of rain gauges because of the dam is located in the mountainous region. Moreover, ground raingauges are limitted to localized heavy rainfall, which is increasing in frequency due to climate changes. The advantage of radar is that it can obtain high-resolution grid rainfall data because radar can observe the spatial distribution of rainfall. The radar rainfall are less accurate than ground gauge data. For the accuracy improvement of radar rainfall, many adjustment methods using ground gauges, have been suggested. For dam basin, because the density of ground gauge is low, there are limitations when apply the bias adjustment methods. Especially, the localized heavy rainfall occurred in the mountainous area depending on the topography. In this study, we will develop a radar rainfall adjustment method considering the orographic effect. The method considers the elevation to obtain kriged rainfall and apply conditional merging skill for the accuracy improvement of the radar rainfall. Based on this method, we are going to estimate the mean areal precipitation for hydropower dam basin. And, we will compare and evaluate the results of various adjustment methods in term of mean areal precipitation and dam inflow.</p><p>This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD (No. 2018-Tech-20)</p><div> </div><div> </div>

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 Impact of Rain Gauges Distribution on the Runoff Simulation of a Small Mountain Catchment in Southern Ecuador

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1169 ◽  
Author(s):  
Adrián Sucozhañay ◽  
Rolando Célleri
Keyword(s):  
Hydrological Modeling ◽  
Rainfall Variability ◽  
Model Performance ◽  
Rainfall Data ◽  
Model Structure ◽  
Mountain Regions ◽  
Rainfall Estimation ◽  
Runoff Simulation ◽  
Rain Gauges ◽  
The Impact

In places with high spatiotemporal rainfall variability, such as mountain regions, input data could be a large source of uncertainty in hydrological modeling. Here we evaluate the impact of rainfall estimation on runoff modeling in a small páramo catchment located in the Zhurucay Ecohydrological Observatory (7.53 km2) in the Ecuadorian Andes, using a network of 12 rain gauges. First, the HBV-light semidistributed model was analyzed in order to select the best model structure to represent the observed runoff and its subflow components. Then, we developed six rainfall monitoring scenarios to evaluate the impact of spatial rainfall estimation in model performance and parameters. Finally, we explored how a model calibrated with far-from-perfect rainfall estimation would perform using new improved rainfall data. Results show that while all model structures were able to represent the overall runoff, the standard model structure outperformed the others for simulating subflow components. Model performance (NSeff) was improved by increasing the quality of spatial rainfall estimation from 0.31 to 0.80 and from 0.14 to 0.73 for calibration and validation period, respectively. Finally, improved rainfall data enhanced the runoff simulation from a model calibrated with scarce rainfall data (NSeff 0.14) from 0.49 to 0.60. These results confirm that in mountain regions model uncertainty is highly related to spatial rainfall and, therefore, to the number and location of rain gauges.

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.

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