Quantitative Precipitation Estimation from a C-Band Dual-Polarized Radar for the 8 July 2013 Flood in Toronto, Canada

Abstract A heavy rainfall event over a 2-h period on 8 July 2013 caused significant flash flooding in the city of Toronto and produced 126 mm of rain accumulation at a gauge located near the Toronto Pearson International Airport. This paper evaluates the quantitative precipitation estimates from the nearby King City C-band dual-polarized radar (WKR). Horizontal reflectivity Z and differential reflectivity ZDR were corrected for attenuation using a modified ZPHI rain profiling algorithm, and rain rates R were calculated from R(Z) and R(Z, ZDR) algorithms. Specific differential phase KDP was used to compute rain rates from three R(KDP) algorithms, one modified to use positive and negative KDP, and an R(KDP, ZDR) algorithm. Additionally, specific attenuation at horizontal polarization A was used to calculate rates from the R(A) algorithm. High-temporal-resolution rain gauge data at 44 locations measured the surface rainfall every 5 min and produced total rainfall accumulations over the affected area. The nearby NEXRAD S-band dual-polarized radar at Buffalo, New York, provided rain-rate and storm accumulation estimates from R(Z) and S-band dual-polarimetric algorithm. These two datasets were used as references to evaluate the C-band estimates. Significant radome attenuation at WKR overshadowed the attenuation correction techniques and resulted in poor rainfall estimates from the R(Z) and R(Z, ZDR) algorithms. Rainfall estimation from the Brandes et al. R(KDP) and R(A) algorithms were superior to the other methods, and the derived storm total accumulation gave biases of 2.1 and −6.1 mm, respectively, with correlations of 0.94. The C-band estimates from the Brandes et al. R(KDP) and R(A) algorithms were comparable to the NEXRAD S-band estimates.

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Improving Multisensor Precipitation Estimation via Adaptive Conditional Bias–Penalized Merging of Rain Gauge Data and Remotely Sensed Quantitative Precipitation Estimates

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0129.1 ◽

2019 ◽

Vol 20 (12) ◽

pp. 2347-2365 ◽

Cited By ~ 1

Author(s):

Ali Jozaghi ◽

Mohammad Nabatian ◽

Seongjin Noh ◽

Dong-Jun Seo ◽

Lin Tang ◽

...

Keyword(s):

Computational Cost ◽

Mean Field ◽

Remotely Sensed ◽

Rain Gauge ◽

Rain Gauge Data ◽

Gauge Data ◽

Precipitation Estimation ◽

Precipitation Estimates ◽

Radar Satellite ◽

Quantitative Precipitation Estimates

Abstract We describe and evaluate adaptive conditional bias–penalized cokriging (CBPCK) for improved multisensor precipitation estimation using rain gauge data and remotely sensed quantitative precipitation estimates (QPE). The remotely sensed QPEs used are radar-only and radar–satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for 13–30 September 2015 and 7–9 October 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar–satellite fusion, conditional bias–penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root-mean-square error (RMSE) of radar-only QPE by 9%–16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for September 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4% and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite larger computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE.

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A Method for Evaluating the Accuracy of Quantitative Precipitation Estimates from a Hydrologic Modeling Perspective

Journal of Hydrometeorology ◽

10.1175/jhm408.1 ◽

2005 ◽

Vol 6 (2) ◽

pp. 115-133 ◽

Cited By ~ 60

Author(s):

Jonathan J. Gourley ◽

Baxter E. Vieux

Keyword(s):

Hydrologic Modeling ◽

Initial Conditions ◽

Rain Gauge ◽

Streamflow Prediction ◽

Hydrologic Models ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

Precipitation Estimates ◽

Primary Focus ◽

Quantitative Precipitation Estimates

Abstract A major goal in quantitative precipitation estimation and forecasting is the ability to provide accurate initial conditions for the purposes of hydrologic modeling. The accuracy of a streamflow prediction system is dependent upon how well the initial hydrometeorological states are characterized. A methodology is developed to objectively and quantitatively evaluate the skill of several different precipitation algorithms at the scale of application—a watershed. Thousands of hydrologic simulations are performed in an ensemble fashion, enabling an exploration of the model parameter space. Probabilistic statistics are then utilized to compare the relative skill of hydrologic simulations produced from the different precipitation inputs to the observed streamflow. The primary focus of this study is to demonstrate a methodology to evaluate precipitation algorithms that can be used to supplement traditional radar–rain gauge analyses. This approach is appropriate for the evaluation of precipitation estimates or forecasts that are intended to serve as inputs to hydrologic models.

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Incorporating Satellite Precipitation Estimates into a Radar-Gage Multi-Sensor Precipitation Estimation Algorithm

10.20944/preprints201709.0134.v1 ◽

2017 ◽

Author(s):

Yuxiang He ◽

Yu Zhang ◽

Robert Kuligowski ◽

Robert Cifelli ◽

David Kitzmiller

Keyword(s):

Estimation Algorithm ◽

Rain Gauge ◽

Systematic Bias ◽

Effective Coverage ◽

Region I ◽

Precipitation Estimation ◽

Precipitation Estimates ◽

Satellite Radar ◽

Radar Satellite ◽

Quantitative Precipitation Estimates

This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE) that would objectively blend real-time satellite quantitative precipitation estimates (SQPE) with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and a two-way blending routine that statistically fuses adjusted SQPE with radar estimates. The preprocessor not only corrects systematic bias in SQPE, but also improves the spatial distribution of precipitation based on SQPE and makes it closely resemble that of radar-based observations. It uses a more sophisticated radar-satellite merging technique to blend preprocessed datasets, and provides a better overall QPE product. The performance of the new satellite-radar-gauge blending module is assessed using independent rain gauge data over a 5-year period between 2003-2007, and the assessment evaluates the accuracy of newly developed satellite-radar-gauge (SRG) blended products versus that of radar-gauge products (which represents MPE algorithm currently used in the NWS operations) over two regions: I) inside radar effective coverage and II) immediately outside radar coverage. The outcomes of the evaluation indicate a) ingest of SQPE over areas within effective radar coverage improve the quality of QPE by mitigating the errors in radar estimates in region I; and b) blending of radar, gauge, and satellite estimates over region II leads to reduction of errors relative to bias-corrected SQPE. In addition, the new module alleviates the discontinuities along the boundaries of radar effective coverage otherwise seen when SQPE is used directly to fill the areas outside of effective radar coverage.

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Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge datasets (2002–2012)

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-11489-2014 ◽

2014 ◽

Vol 11 (10) ◽

pp. 11489-11531 ◽

Cited By ~ 3

Author(s):

O. P. Prat ◽

B. R. Nelson

Keyword(s):

Real Time ◽

Stage Iv ◽

Rain Gauge ◽

Contingency Analysis ◽

Western Us ◽

Precipitation Estimates ◽

Satellite Radar ◽

Quantitative Precipitation Estimates ◽

Rain Rates ◽

Surface Observations

Abstract. We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over CONUS for the period 2002–2012. This comparison effort includes satellite multi-sensor datasets (bias-adjusted TMPA 3B42, near-real time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (± 6%). However, differences at the RFC are more important in particular for near-real time 3B42RT precipitation estimates (−33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near real time counterpart 3B42RT. However, large biases remained for 3B42 over the Western US for higher average accumulation (&geq; 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in day-1) over the Northwest. Furthermore, the conditional analysis and the contingency analysis conducted illustrated the challenge of retrieving extreme precipitation from remote sensing estimates.

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Evolving Multisensor Precipitation Estimation Methods: Their Impacts on Flow Prediction Using a Distributed Hydrologic Model

Journal of Hydrometeorology ◽

10.1175/jhm-d-10-05038.1 ◽

2011 ◽

Vol 12 (6) ◽

pp. 1414-1431 ◽

Cited By ~ 31

Author(s):

David Kitzmiller ◽

Suzanne Van Cooten ◽

Feng Ding ◽

Kenneth Howard ◽

Carrie Langston ◽

...

Keyword(s):

High Resolution ◽

Positive Impact ◽

Radar Data ◽

Rain Gauge ◽

Hydrologic Model ◽

Estimation Methods ◽

Freezing Level ◽

Distributed Hydrologic Model ◽

Precipitation Estimation ◽

Precipitation Estimates

Abstract This study investigates evolving methodologies for radar and merged gauge–radar quantitative precipitation estimation (QPE) to determine their influence on the flow predictions of a distributed hydrologic model. These methods include the National Mosaic and QPE algorithm package (NMQ), under development at the National Severe Storms Laboratory (NSSL), and the Multisensor Precipitation Estimator (MPE) and High-Resolution Precipitation Estimator (HPE) suites currently operational at National Weather Service (NWS) field offices. The goal of the study is to determine which combination of algorithm features offers the greatest benefit toward operational hydrologic forecasting. These features include automated radar quality control, automated Z–R selection, brightband identification, bias correction, multiple radar data compositing, and gauge–radar merging, which all differ between NMQ and MPE–HPE. To examine the spatial and temporal characteristics of the precipitation fields produced by each of the QPE methodologies, high-resolution (4 km and hourly) gridded precipitation estimates were derived by each algorithm suite for three major precipitation events between 2003 and 2006 over subcatchments within the Tar–Pamlico River basin of North Carolina. The results indicate that the NMQ radar-only algorithm suite consistently yielded closer agreement with reference rain gauge reports than the corresponding HPE radar-only estimates did. Similarly, the NMQ radar-only QPE input generally yielded hydrologic simulations that were closer to observations at multiple stream gauging points. These findings indicate that the combination of Z–R selection and freezing-level identification algorithms within NMQ, but not incorporated within MPE and HPE, would have an appreciable positive impact on hydrologic simulations. There were relatively small differences between NMQ and HPE gauge–radar estimates in terms of accuracy and impacts on hydrologic simulations, most likely due to the large influence of the input rain gauge information.

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Evaluation of radar-gauge merging methods for quantitative precipitation estimates

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-5-2975-2008 ◽

2008 ◽

Vol 5 (5) ◽

pp. 2975-3003 ◽

Cited By ~ 9

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.

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One year of attenuation data from a commercial dual-polarized duplex microwave link with concurrent disdrometer, rain gauge and weather observations

10.5194/essd-2021-3 ◽

2021 ◽

Author(s):

Anna Špačková ◽

Vojtěch Bareš ◽

Martin Fencl ◽

Marc Schleiss ◽

Joël Jaffrain ◽

...

Keyword(s):

Remote Sensing ◽

Relevant Information ◽

Rain Gauge ◽

Telecommunication Networks ◽

High Temporal Resolution ◽

Potential Applications ◽

Dual Polarized ◽

One Year ◽

Microwave Link ◽

Commercial Microwave Links

Abstract. Commercial microwave links (CML) in telecommunication networks can provide relevant information for remote sensing of precipitation and other environmental variables, such as path-averaged drop size distribution, evaporation or humidity. To address this issue, the CoMMon field experiment (COmmercial Microwave links for urban rainfall MONitoring) monitored a 38-GHz dual-polarized CML of 1.85 km at a high temporal resolution (4 s), as well as a collocated array of five disdrometers and three rain gauges over one year. The dataset is complemented with observations from five nearby weather stations. Raw and pre-processed data, which can be explored effortlessly with a custom static HTML viewer, are available at https://doi.org/10.5281/zenodo.4524632 (Špačková et al., 2020). The data quality is generally satisfactory and potentially problematic measurements are flagged to help the analyst identify relevant periods for specific study purposes. Finally, we encourage potential applications and discuss open issues regarding future remote sensing with CMLs.

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Precipitation Estimates from Commercial Microwave Links: Practical Approaches to Wet-antenna Correction

10.31224/osf.io/nt5wg ◽

2021 ◽

Author(s):

Jaroslav Pastorek ◽

Martin Fencl ◽

Jörg Rieckermann ◽

Vojtěch Bareš

Keyword(s):

Rainfall Intensity ◽

Rain Gauge ◽

Model Parameters ◽

Rainfall Events ◽

Precipitation Estimates ◽

Attenuation Models ◽

Using Data ◽

Quantitative Precipitation Estimates ◽

Rain Gauge Network ◽

Commercial Microwave Links

An inadequate correction for wet antenna attenuation (WAA) often causes a notable bias in quantitative precipitation estimates (QPEs) from commercial microwave links (CMLs) limiting the usability of these rainfall data in hydrological applications. This paper analyzes how WAA can be corrected without dedicated rainfall monitoring for a set of 16 CMLs. Using data collected over 53 rainfall events, the performance of six empirical WAA models was studied, both when calibrated to rainfall observations from a permanent municipal rain gauge network and when using model parameters from the literature. The transferability of WAA model parameters among CMLs of various characteristics has also been addressed. The results show that high-quality QPEs with a bias below 5% and RMSE of 1 mm/h in the median could be retrieved, even from sub-kilometer CMLs where WAA is relatively large compared to raindrop attenuation. Models in which WAA is proportional to rainfall intensity provide better WAA estimates than constant and time-dependent models. It is also shown that the parameters of models deriving WAA explicitly from rainfall intensity are independent of CML frequency and path length and, thus, transferable to other locations with CMLs of similar antenna properties.

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Automatic quality control of telemetric rain gauge data for operational applications at IMGW-PIB

ITM Web of Conferences ◽

10.1051/itmconf/20182300028 ◽

2018 ◽

Vol 23 ◽

pp. 00028 ◽

Cited By ~ 1

Author(s):

Irena Otop ◽

Jan Szturc ◽

Katarzyna Ośródka ◽

Piotr Djaków

Keyword(s):

Quality Control ◽

Real Time ◽

Error Detection ◽

Rain Gauge ◽

Temporal Consistency ◽

Consistency Check ◽

Gross Error Detection ◽

Individual Measurement ◽

Precipitation Estimates ◽

Quantitative Precipitation Estimates

The automatic procedure of real-time quality control (QC) of telemetric rain gauge measurements (G) has been developed to produce quantitative precipitation estimates mainly for the needs of operational hydrology. The developed QC procedure consists of several tests: gross error detection, a range check, a spatial consistency check, a temporal consistency check, and a radar and satellite conformity check. The output of the procedure applied in real-time is quality index QI(G) that quantitatively characterised quality of each individual measurement. The QC procedure has been implemented into operational work at the Institute of Meteorology and Water Management since 2016. However, some elements of the procedure are still under development and can be improved based on the results and experience collected after about two years of real-time work on network of telemetric rain gauges

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