Derivation of a 10-Year Radar-Based Climatology of Rainfall

2009 ◽  
Vol 48 (7) ◽  
pp. 1448-1463 ◽  
Author(s):  
Aart Overeem ◽  
Iwan Holleman ◽  
Adri Buishand
Keyword(s):  
Land Surface ◽  
Mean Field ◽  
Rain Gauge ◽  
Data Availability ◽  
Annual Rainfall ◽  
Radar Rainfall ◽  
Maximum Rainfall ◽  
Bias Adjustment ◽  
Weather Radars ◽  
Field Bias

Abstract Weather radars give quantitative precipitation estimates over large areas with high spatial and temporal resolutions not achieved by conventional rain gauge networks. Therefore, the derivation and analysis of a radar-based precipitation “climatology” are highly relevant. For that purpose, radar reflectivity data were obtained from two C-band Doppler weather radars covering the land surface of the Netherlands (≈3.55 × 104 km2). From these reflectivities, 10 yr of radar rainfall depths were constructed for durations D of 1, 2, 4, 8, 12, and 24 h with a spatial resolution of 2.4 km and a data availability of approximately 80%. Different methods are compared for adjusting the bias in the radar precipitation depths. Using a dense manual gauge network, a vertical profile of reflectivity (VPR) and a spatial adjustment are applied separately to 24-h (0800–0800 UTC) unadjusted radar-based precipitation depths. Further, an automatic rain gauge network is employed to perform a mean-field bias adjustment to unadjusted 1-h rainfall depths. A new adjustment method is developed (referred to as MFBS) that combines the hourly mean-field bias adjustment and the daily spatial adjustment methods. The record of VPR gradients, obtained from the VPR adjustment, reveals a seasonal cycle that can be related to the type of precipitation. A verification with automatic (D ≤ 24 h) and manual (D = 24 h) rain gauge networks demonstrates that the adjustments remove the systematic underestimation of precipitation by radar. The MFBS adjustment gives the best verification results and reduces the residual (radar minus rain gauge depth) standard deviation considerably. The adjusted radar dataset is used to obtain exceedance probabilities, maximum rainfall depths, mean annual rainfall frequencies, and spatial correlations. Such a radar rainfall climatology is potentially valuable for the improvement of rainfall parameterization in weather and climate models and the design of hydraulic structures.

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 A climatological benchmark for operational radar rainfall bias reduction

2021 ◽  
Vol 25 (7) ◽  
pp. 4061-4080
Author(s):  
Ruben Imhoff ◽  
Claudia Brauer ◽  
Klaas-Jan van Heeringen ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
...  
Keyword(s):  
Real Time ◽  
Mean Field ◽  
Grid Cell ◽  
Rain Gauge ◽  
Bias Reduction ◽  
Annual Rainfall ◽  
Spatial And Temporal Patterns ◽  
Radar Rainfall ◽  
Derivation Method ◽  
Rainfall Estimates

Abstract. The presence of significant biases in real-time radar quantitative precipitation estimations (QPEs) limits its use in hydrometeorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5 min radar and reference rainfall data for the Netherlands. CARROTS is both operationally available and independent of real-time rain gauge availability and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for 12 Dutch catchments and polders. We validated the results against the operational mean field bias (MFB)-adjusted rainfall estimates and a reference dataset. This reference consists of the radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 32 automatic and 319 manual rain gauges. Only the automatic gauges of this network are available in real time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher away from the radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. The MFB-adjusted QPE outperforms the CARROTS-corrected QPE when the country-average rainfall estimates are compared to the reference. However, annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB-adjusted rainfall estimates for catchments away from the radars, where the MFB-adjusted QPE generally underestimates the rainfall amounts. This difference is absent for catchments closer to the radars. QPE underestimations are amplified when used in the hydrological model simulations. Discharge simulations using the QPE from CARROTS outperform those with the MFB-adjusted product for all but one basin. Moreover, the proposed factor derivation method is robust. It is hardly sensitive to leaving individual years out of the historical set and to the moving window length, given window sizes of more than a week.

scholarly journals A climatological benchmark for operational radar rainfall bias reduction

2021 ◽  
Author(s):  
Ruben Imhoff ◽  
Claudia Brauer ◽  
Klaas-Jan van Heeringen ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
...  
Keyword(s):  
Real Time ◽  
Mean Field ◽  
Grid Cell ◽  
Rain Gauge ◽  
Bias Reduction ◽  
Annual Rainfall ◽  
Spatial And Temporal Patterns ◽  
Radar Rainfall ◽  
Derivation Method ◽  
Rainfall Estimates

Abstract. The presence of significant biases in real-time radar quantitative precipitation estimations (QPE) limits its use in hydro-meteorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5-min radar and reference rainfall data for the Netherlands. CARROTS is both operationally available and independent of real-time rain gauge availability, and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for twelve Dutch catchments and polders. We validated the results against the operational mean field bias (MFB) adjusted rainfall estimates and a reference dataset. This reference consists of the radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 31 automatic and 325 manual rain gauges. Only the automatic gauges of this network are available in real-time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher far from the radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. Annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB adjusted rainfall estimates for catchments far from the radars. This difference is absent for catchments closer to the radars. QPE underestimations are amplified when used in the hydrological model simulations. Discharge simulations using the QPE from CARROTS outperform those with the MFB adjusted product for all but one basin. Moreover, the proposed factor derivation method is robust. It is hardly sensitive to leaving individual years out of the historical set and to the moving window length, given window sizes of more than a week.

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.

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 First-Year Evaluation of GPM Rainfall over the Netherlands: IMERG Day 1 Final Run (V03D)

2016 ◽  
Vol 17 (11) ◽  
pp. 2799-2814 ◽  
Author(s):  
M. F. Rios Gaona ◽  
A. Overeem ◽  
H. Leijnse ◽  
R. Uijlenhoet
Keyword(s):  
The Netherlands ◽  
Land Surface ◽  
Tropical Rainfall Measuring Mission ◽  
Ground Truth ◽  
Rain Gauge ◽  
First Year ◽  
Radar Rainfall ◽  
High Resolution Data ◽  
Precipitation Estimates ◽  
Global Precipitation

Abstract The Global Precipitation Measurement (GPM) mission is the successor to the Tropical Rainfall Measuring Mission (TRMM), which orbited Earth for ~17 years. With Core Observatory launched on 27 February 2014, GPM offers global precipitation estimates between 60°N and 60°S at 0.1° × 0.1° resolution every 30 min. Unlike during the TRMM era, the Netherlands is now within the coverage provided by GPM. Here the first year of GPM rainfall retrievals from the 30-min gridded Integrated Multisatellite Retrievals for GPM (IMERG) product Day 1 Final Run (V03D) is assessed. This product is compared against gauge-adjusted radar rainfall maps over the land surface of the Netherlands at 30-min, 24-h, monthly, and yearly scales. These radar rainfall maps are considered to be ground truth. The evaluation of the first year of IMERG operations is done through time series, scatterplots, empirical exceedance probabilities, and various statistical indicators. In general, there is a tendency for IMERG to slightly underestimate (2%) countrywide rainfall depths. Nevertheless, the relative underestimation is small enough to propose IMERG as a reliable source of precipitation data, especially for areas where rain gauge networks or ground-based radars do not offer these types of high-resolution data and availability. The potential of GPM for rainfall estimation in a midlatitude country is confirmed.

scholarly journals Evaluation of radar-gauge merging methods for quantitative precipitation estimates

2008 ◽  
Vol 5 (5) ◽  
pp. 2975-3003 ◽  
Author(s):  
E. Goudenhoofdt ◽  
L. Delobbe
Keyword(s):  
Bias Correction ◽  
Mean Field ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Rain Gauge ◽  
Radar Observations ◽  
Geostatistical Methods ◽  
Precipitation Estimates ◽  
Field Bias ◽  
Quantitative Precipitation Estimates

Abstract. Accurate quantitative precipitation estimates are of crucial importance for hydrological studies and applications. When spatial precipitation fields are required, rain gauge measurements are often combined with weather radar observations. In this paper, we evaluate several radar-gauge merging methods with various degrees of complexity: from mean field bias correction to geostatical merging techniques. The study area is the Walloon region of Belgium, which is mostly located in the Meuse catchment. Observations from a C-band Doppler radar and a dense rain gauge network are used to retrieve daily rainfall accumulations over this area. The relative performance of the different merging methods are assessed through a comparison against daily measurements from an independent gauge network. A 3-year verification is performed using several statistical quality parameters. It appears that the geostatistical merging methods perform best with the mean absolute error decreasing by 40% with respect to the original data. A mean field bias correction still achieves a reduction of 25%. A seasonal analysis shows that the benefit of using radar observations is particularly significant during summer. The effect of the network density on the performance of the methods is also investigated. For this purpose, a simple approach to remove gauges from a network is proposed. The analysis reveals that the sensitivity is relatively high for the geostatistical methods but rather small for the simple methods. The geostatistical methods give the best results for all network densities except for a very low density of 1 gauge per 500 km2 where a range-dependent adjustment complemented with a static local bias correction performs best.

scholarly journals Geostatistical Merging of a Single-Polarized X-Band Weather Radar and a Sparse Rain Gauge Network over an Urban Catchment

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 496 ◽  
Author(s):  
Ibrahim Seck ◽  
Joël Van Baelen
Keyword(s):  
Mean Field ◽  
Radar Data ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
X Band ◽  
Distance Weighting ◽  
Field Bias ◽  
The Mean ◽  
Inverse Distance

Optimal Quantitative Precipitation Estimation (QPE) of rainfall is crucial to the accuracy of hydrological models, especially over urban catchments. Small-to-medium size towns are often equipped with sparse rain gauge networks that struggle to capture the variability in rainfall over high spatiotemporal resolutions. X-band Local Area Weather Radars (LAWRs) provide a cost-effective solution to meet this challenge. The Clermont Auvergne metropolis monitors precipitation through a network of 13 rain gauges with a temporal resolution of 5 min. 5 additional rain gauges with a 6-minute temporal resolution are available in the region, and are operated by the national weather service Météo-France. The LaMP (Laboratoire de Météorologie Physique) laboratory’s X-band single-polarized weather radar monitors precipitation as well in the region. In this study, three geostatistical interpolation techniques—Ordinary kriging (OK), which was applied to rain gauge data with a variogram inferred from radar data, conditional merging (CM), and kriging with an external drift (KED)—are evaluated and compared through cross-validation. The performance of the inverse distance weighting interpolation technique (IDW), which was applied to rain gauge data only, was investigated as well, in order to evaluate the effect of incorporating radar data on the QPE’s quality. The dataset is comprised of rainfall events that occurred during the seasons of summer 2013 and winter 2015, and is exploited at three temporal resolutions: 5, 30, and 60 min. The investigation of the interpolation techniques performances is carried out for both seasons and for the three temporal resolutions using raw radar data, radar data corrected from attenuation, and the mean field bias, successively. The superiority of the geostatistical techniques compared to the inverse distance weighting method was verified with an average relative improvement of 54% and 31% in terms of bias reduction for kriging with an external drift and conditional merging, respectively (cross-validation). KED and OK performed similarly well, while CM lagged behind in terms of point measurement QPE accuracy, but was the best method in terms of preserving the observations’ variance. The correction schemes had mixed effects on the multivariate geostatistical methods. Indeed, while the attenuation correction improved KED across the board, the mean field bias correction effects were marginal. Both radar data correction schemes resulted in a decrease of the ability of CM to preserve the observations variance, while slightly improving its point measurement QPE accuracy.

Rainfall monitoring in Sri Lanka employing commercial microwave links

2021 ◽  
Author(s):  
Aart Overeem ◽  
Hidde Leijnse ◽  
Thomas van Leth ◽  
Linda Bogerd ◽  
Jan Priebe ◽  
...  
Keyword(s):  
Sri Lanka ◽  
Land Surface ◽  
Urban Areas ◽  
Weather Prediction ◽  
Rain Gauge ◽  
Cellular Communication ◽  
Dual Frequency ◽  
Middle Income ◽  
Rain Gauges ◽  
Weather Radars

&lt;p&gt;Microwave backhaul links from cellular communication networks provide a valuable &amp;#8220;opportunistic&amp;#8221; source of high-resolution space&amp;#8211;time rainfall information, complementing traditional in situ measurement devices (rain gauges, disdrometers) and remote sensors (weather radars, satellites). Over the past decade, a growing community of researchers has, in close collaboration with cellular communication companies, developed retrieval algorithms to convert the raw microwave link signals, stored operationally by their network management systems, to hydrometeorologically useful rainfall estimates. Operational meteorological and hydrological services as well as private consulting firms are showing an increased interest in using this complementary source of rainfall information to improve the products and services they provide to end users from different sectors, from water management and weather prediction to agriculture and traffic control. The greatest potential of these opportunistic environmental sensors lies in those geographical areas over the land surface of the Earth with few rain gauges and no weather radars: often mountainous and urban areas, but especially low- to middle-income regions, which are generally in (sub)tropical climates.&amp;#160;&lt;/p&gt;&lt;p&gt;Here, the open-source R package RAINLINK is employed to retrieve CML rainfall maps covering the majority of Sri Lanka, a middle-income country having a tropical climate. This is performed for a 3.5-month period based on CML data from on average 1140 link paths. CML rainfall maps are compared locally to hourly and daily rain gauge data, as well as to rainfall maps from the Dual-frequency Precipitation Radar on board the Global Precipitation Measurement Core Observatory satellite. The results confirm the potential of CMLs for real-time tropical rainfall monitoring. This holds a promise for, e.g., ground validation of or merging with satellite precipitation products.&lt;/p&gt;

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