scholarly journals Time-dependent <i>Z-R</i> relationships for estimating rainfall fields from radar measurements

2010 ◽  
Vol 10 (1) ◽  
pp. 149-158 ◽  
Author(s):  
L. Alfieri ◽  
P. Claps ◽  
F. Laio
Keyword(s):  
Time Estimation ◽  
Rain Gauge ◽  
Rainfall Rate ◽  
Time Step ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Weather Radars ◽  
The North ◽  
Real Time Estimation ◽  
Power Law Equation

Abstract. The operational use of weather radars has become a widespread and useful tool for estimating rainfall fields. The radar-gauge adjustment is a commonly adopted technique which allows one to reduce bias and dispersion between radar rainfall estimates and the corresponding ground measurements provided by rain gauges. This paper investigates a new methodology for estimating radar-based rainfall fields by recalibrating at each time step the reflectivity-rainfall rate (Z-R) relationship on the basis of ground measurements provided by a rain gauge network. The power-law equation for converting reflectivity measurements into rainfall rates is readjusted at each time step, by calibrating its parameters using hourly Z-R pairs collected in the proximity of the considered time step. Calibration windows with duration between 1 and 24 h are used for estimating the parameters of the Z-R relationship. A case study pertaining to 19 rainfall events occurred in the north-western Italy is considered, in an area located within 25 km from the radar site, with available measurements of rainfall rate at the ground and radar reflectivity aloft. Results obtained with the proposed method are compared to those of three other literature methods. Applications are described for a posteriori evaluation of rainfall fields and for real-time estimation. Results suggest that the use of a calibration window of 2–5 h yields the best performances, with improvements that reach the 28% of the standard error obtained by using the most accurate fixed (climatological) Z-R relationship.

scholarly journals Precipitation Analysis over the French Alps Using a Variational Approach and Study of Potential Added Value of Ground-Based Radar Observations

2017 ◽  
Vol 18 (5) ◽  
pp. 1425-1451 ◽  
Author(s):  
Camille Birman ◽  
Fatima Karbou ◽  
Jean-François Mahfouf ◽  
Matthieu Lafaysse ◽  
Yves Durand ◽  
...  
Keyword(s):  
Snow Depth ◽  
Positive Impact ◽  
Weather Prediction ◽  
Rain Gauge ◽  
Added Value ◽  
Background Information ◽  
Time Step ◽  
Rain Gauges ◽  
Weather Radars ◽  
Precipitation Estimates

Abstract A one-dimensional variational data assimilation (1DVar) method to retrieve profiles of precipitation in mountainous terrain is described. The method combines observations from the French Alpine region rain gauges and precipitation estimates from weather radars with background information from short-range numerical weather prediction forecasts in an optimal way. The performance of this technique is evaluated using measurements of precipitation and of snow depth during two years (2012/13 and 2013/14). It is shown that the 1DVar model allows an effective assimilation of measurements of different types, including rain gauge and radar-derived precipitation. The use of radar-derived precipitation rates over mountains to force the numerical snowpack model Crocus significantly reduces the bias and standard deviation with respect to independent snow depth observations. The improvement is particularly significant for large rainfall or snowfall events, which are decisive for avalanche hazard forecasting. The use of radar-derived precipitation rates at an hourly time step improves the time series of precipitation analyses and has a positive impact on simulated snow depths.

scholarly journals Uncertainties in rainfall retrievals from ground-based weather radar: overview, case study, and simulation experiment

2006 ◽  
Vol 3 (4) ◽  
pp. 2385-2436
Author(s):  
R. Uijlenhoet ◽  
S. H. van der Wielen ◽  
A. Berne
Keyword(s):  
Simulation Experiment ◽  
Temporal Distribution ◽  
Weather Radar ◽  
Rain Gauge ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Weather Radars ◽  
Wide Range ◽  
Rainfall Retrieval

Abstract. Because rainfall constitutes the main source of water for the terrestrial hydrological processes, accurate and reliable measurement and prediction of its spatial and temporal distribution over a wide range of scales is an important goal for hydrology. We investigate the potential of ground-based weather radar to provide such measurements through a detailed analysis of the associated observation uncertainties. First, a historical perspective on measuring the space-time distribution of rainfall, from the rain gauge to the radar era, is presented. Subsequently, we provide an overview of the various errors and uncertainties affecting radar rainfall retrievals. As an example, we present a case study of the relation between measurements from an operational C-band weather radar and a network of tipping bucket rain gauges as a function of range. Finally, a recently developed stochastic model of range profiles of rainfall microstructure is employed in a simulation experiment designed to investigate the rainfall retrieval uncertainties associated with weather radars operating in different widely used frequency bands.

scholarly journals Scale Dependence of Radar Rainfall Uncertainty: Initial Evaluation of NEXRAD’s New Super-Resolution Data for Hydrologic Applications

2010 ◽  
Vol 11 (5) ◽  
pp. 1191-1198 ◽  
Author(s):  
Bong-Chul Seo ◽  
Witold F. Krajewski
Keyword(s):  
Scale Effects ◽  
Spatial Scales ◽  
Super Resolution ◽  
Rain Gauge ◽  
Radar Rainfall ◽  
Weather Radars ◽  
Level Ii ◽  
Simple Scaling ◽  
Resolution Level ◽  
Reflectivity Data

Abstract This study explores the scale effects of radar rainfall accumulation fields generated using the new super-resolution level II radar reflectivity data acquired by the Next Generation Weather Radar (NEXRAD) network of the Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars. Eleven months (May 2008–August 2009, exclusive of winter months) of high-density rain gauge network data are used to describe the uncertainty structure of radar rainfall and rain gauge representativeness with respect to five spatial scales (0.5, 1, 2, 4, and 8 km). While both uncertainties of gauge representativeness and radar rainfall show simple scaling behavior, the uncertainty of radar rainfall is characterized by an almost 3 times greater standard error at higher temporal and spatial resolutions (15 min and 0.5 km) than at lower resolutions (1 h and 8 km). These results may have implications for error propagation through distributed hydrologic models that require high-resolution rainfall input. Another interesting result of the study is that uncertainty obtained by averaging rainfall products produced from the super-resolution reflectivity data is slightly lower at smaller scales than the uncertainty of the corresponding resolution products produced using averaged (recombined) reflectivity data.

Real-Time Estimation of Directional Wave Spectra Using Non-Stationary Ship Motion Data

2009 ◽  
Author(s):  
Toshio Iseki
Keyword(s):  
Stochastic Processes ◽  
Real Time ◽  
Time Estimation ◽  
Ship Motion ◽  
Wave Spectra ◽  
Time Step ◽  
Motion Data ◽  
Stationary Stochastic Processes ◽  
Real Time Estimation ◽  
Directional Wave

The Bayesian modeling procedure is modified for real-time estimation of directional wave spectra using non-stationary ship motion data. The assumption of stationary stochastic processes is applied to the seaway, but not to ship response because it also depends on ship maneuvers. Ship response is strongly affected by changes in the encounter angle and frequency of waves. Therefore, it is need to be a real-time algorithm that can deal with non-stationary stochastic processes and estimate the directional wave spectra. In the proposed algorithm, the iterative calculations of the non-linear equations were optimized and the convergence was not achieved at every time step, but was achieved gradually over several time steps. In order to examine the reliability of the proposed method, real-time estimation was conducted by using the data of onboard experiments. Comparisons between the results of the proposed algorithm and a wave monitoring radar system show good agreements.

Influence of systematic errors from tipping bucket rain gauges on recorded rainfall data

1998 ◽  
Vol 37 (11) ◽  
pp. 121-129 ◽  
Author(s):  
Rolf Fankhauser
Keyword(s):  
Depth Resolution ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Urban Hydrology ◽  
Data Series ◽  
Data Recording ◽  
Time Step ◽  
Recording Time ◽  
Rain Gauges ◽  
Evaporation Losses

Tipping bucket rain gauges (TBR) are widely used in urban hydrology. The present study investigated the uncertainties in recorded rainfall intensity induced by the following properties of the TBR: depth resolution i.e. the bucket volume, calibration parameters, wetting and evaporation losses and the method of data recording (time between tips or tips per minute). The errors were analysed by means of a TBR simulator i.e. a simulation program that models the behaviour of a TBR. Rainfall data disaggregated to 6 seconds from measured 1-min data and randomly varied were taken as input to the simulator. Different TBR data series were produced by changing the properties of the simulated rain gauge. These data series together with the original rainfall events were used as input to a rainfall-runoff model. Computed overflow volume and peak discharge from a combined sewer overflow (CSO) weir were compared. Errors due to depth resolution (i.e. the bucket size) proved to be small. Therefore TBRs with a depth resolution up to 0.254 mm can be used in urban hydrology without inducing significant errors. Wetting and evaporation losses caused small errors. The method of data recording had also little influence. For larger bucket volumes variable time step recording induced smaller errors than tips per minute recording.

scholarly journals The New French Operational Polarimetric Radar Rainfall Rate Product

2013 ◽  
Vol 52 (8) ◽  
pp. 1817-1835 ◽  
Author(s):  
Jordi Figueras i Ventura ◽  
Pierre Tabary
Keyword(s):  
Radar Data ◽  
Rain Gauge ◽  
Rainfall Rate ◽  
Superior Performance ◽  
Radar Rainfall ◽  
Rain Gauge Data ◽  
Frequency Bands ◽  
Radar Network ◽  
Significant Events ◽  
X Band

AbstractIn 2012 the Météo France metropolitan operational radar network consists of 24 radars operating at C and S bands. In addition, a network of four X-band gap-filler radars is being deployed in the French Alps. The network combines polarimetric and nonpolarimetric radars. Consequently, the operational radar rainfall algorithm has been adapted to process both polarimetric and nonpolarimetric data. The polarimetric processing chain is available in two versions. In the first version, now operational, polarimetry is only used to correct for attenuation and filter out clear-air echoes. In the second version there is a more extensive use of polarimetry. In particular, the specific differential phase Kdp is used to estimate rainfall rate in intense rain. The performance of the three versions of radar rainfall algorithms (conventional, polarimetric V1, and polarimetric V2) at different frequency bands (S, C, and X) is evaluated by processing radar data of significant events offline and comparing hourly radar rainfall accumulations with hourly rain gauge data. The results clearly show a superior performance of the polarimetric products with respect to the nonpolarimetric ones at all frequency bands, but particularly at higher frequency. The second version of the polarimetric product, which makes a broader use of polarimetry, provides the best overall results.

scholarly journals Statistical and neural classifiers in estimating rain rate from weather radar measurements

2007 ◽  
Vol 10 ◽  
pp. 111-115
Author(s):  
C. I. Christodoulou ◽  
S. C. Michaelides
Keyword(s):  
Electromagnetic Radiation ◽  
Rain Rate ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Meteorological Conditions ◽  
Swiss Alps ◽  
Rain Gauges ◽  
Weather Radars ◽  
Radar Measurements

Abstract. Weather radars are used to measure the electromagnetic radiation backscattered by cloud raindrops. Clouds that backscatter more electromagnetic radiation consist of larger droplets of rain and therefore they produce more rain. The idea is to estimate rain rate by using weather radar as an alternative to rain-gauges measuring rainfall on the ground. In an experiment during two days in June and August 1997 over the Italian-Swiss Alps, data from weather radar and surrounding rain-gauges were collected at the same time. The statistical KNN and the neural SOM classifiers were implemented for the classification task using the radar data as input and the rain-gauge measurements as output. The proposed system managed to identify matching pattern waveforms and the rainfall rate on the ground was estimated based on the radar reflectivities with a satisfactory error rate, outperforming the traditional Z/R relationship. It is anticipated that more data, representing a variety of possible meteorological conditions, will lead to improved results. The results in this work show that an estimation of rain rate based on weather radar measurements treated with statistical and neural classifiers is possible.

scholarly journals Radar Data Analyses for a Single Rainfall Event and Their Application for Flow Simulation in an Urban Catchment Using the SWMM Model

2018 ◽  
Author(s):  
Mariusz Barszcz
Keyword(s):  
Flow Simulation ◽  
Radar Data ◽  
Rain Gauge ◽  
Radar Reflectivity ◽  
Rainfall Rate ◽  
Correction Coefficient ◽  
Urban Catchment ◽  
Storm Water Management Model ◽  
Rain Gauges ◽  
Swmm Model

In this study, regression analyses were used to find a relationship between the rain gauge rainfall rate R and radar reflectivity Z for the urban catchment of the Służewiecki Stream in Warsaw, Poland. Rainfall totals for 18 events which were measured at two rainfall stations were used for these analyses. Various methods for determining calculational values of radar reflectivity in reference to specific rainfall cells with 1-km resolution within an event duration were applied. The influence of each of these methods on the Z-R relationship was analyzed. The correction coefficient for data from the SRI (Surface Rainfall Intensity) product was established, in which the values of rainfall rate are calculated based on parameters a and b determined by Marshall and Palmer. Relatively good agreement between measured and estimated rainfall totals for the analyzed events was obtained using the Z-R relationships as well as the correction coefficient determined in this study. Rainfall depths estimated from radar data for two selected events were used to simulate flow hydrographs in the catchment using the SWMM (Storm Water Management Model) hydrodynamic model. Different scenarios were applied to investigate the stream response to changes in rainfall depths, in which the data both for 2 existing as well as 64 virtual rain gauges assigned to appropriate rainfall cells in the catchment were included.

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.

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