A Real-Time Automated Quality Control of Hourly Rain Gauge Data Based on Multiple Sensors in MRMS System

Abstract Automated rain gauge networks provide direct measurements of precipitation and have been used for numerous applications, such as generating regional and national precipitation maps, calibrating remote sensing quantitative precipitation estimation (QPE), and validating hydrological and meteorological model predictions. However, automated gauge observations are prone to be affected by a variety of error sources and require a careful quality-control (QC) procedure. Many previous gauge QC techniques were based on spatiotemporal checks within the gauge network itself, and their effectiveness can be dependent on gauge densities and precipitation regimes. The current study takes advantage of the multisensor data sources in the Multi-Radar Multi-Sensor (MRMS) system and develops an automated and computationally efficient gauge QC scheme based on the consistency of hourly gauge and radar QPE observations. Radar and gauge error characteristics related to radar sampling geometry, precipitation regimes, and freezing-level height is utilized within this scheme. This QC scheme is evaluated by testing its capability to identify suspect gauges and comparing the ability to quality-controlled gauges through statistical and spatial comparisons of gauge-influenced gridded QPE products. Spatial analysis of the gridded QPE products in MRMS resulted in a more physical spatial QPE distribution using quality-controlled gauges versus the same product created with non-quality-controlled gauge data.

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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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Error Analysis of Satellite Precipitation Products in Mountainous Basins

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0194.1 ◽

2014 ◽

Vol 15 (5) ◽

pp. 1778-1793 ◽

Cited By ~ 99

Author(s):

Yiwen Mei ◽

Emmanouil N. Anagnostou ◽

Efthymios I. Nikolopoulos ◽

Marco Borga

Keyword(s):

Error Analysis ◽

Warm Season ◽

Tropical Rainfall Measuring Mission ◽

Heavy Precipitation ◽

Rain Gauge ◽

Satellite Precipitation ◽

Quantitative Precipitation Estimation ◽

Precipitation Regimes ◽

Precipitation Estimation ◽

High Precipitation

Abstract Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events (HPEs). In situ observations over mountainous areas are limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for hydrological applications. In this study, four widely used satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, version 7 (3B42-V7), and in near–real time (3B42-RT); Climate Prediction Center (CPC) morphing technique (CMORPH); and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)] are evaluated with respect to their performance in capturing the properties of HPEs over different basin scales. Evaluation is carried out over the upper Adige River basin (eastern Italian Alps) for an 8-yr period (2003–10). Basin-averaged rainfall derived from a dense rain gauge network in the region is used as a reference. Satellite precipitation error analysis is performed for warm (May–August) and cold (September–December) season months as well as for different quantile ranges of basin-averaged precipitation accumulations. Three error metrics and a score system are introduced to quantify the performances of the various satellite products. Overall, no single precipitation product can be considered ideal for detecting and quantifying HPE. Results show better consistency between gauges and the two 3B42 products, particularly during warm season months that are associated with high-intensity convective events. All satellite products are shown to have a magnitude-dependent error ranging from overestimation at low precipitation regimes to underestimation at high precipitation accumulations; this effect is more pronounced in the CMORPH and PERSIANN products.

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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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Multisensor Precipitation Reanalysis

Journal of Hydrometeorology ◽

10.1175/2010jhm1210.1 ◽

2010 ◽

Vol 11 (3) ◽

pp. 666-682 ◽

Cited By ~ 37

Author(s):

Brian R. Nelson ◽

D-J. Seo ◽

Dongsoo Kim

Keyword(s):

Quality Control ◽

South Carolina ◽

Stage Iv ◽

Radar Data ◽

Rain Gauge ◽

Data Quality Control ◽

Rain Gauge Data ◽

Wide Range ◽

Gauge Data ◽

Validation Experiments

Abstract Temporally consistent high-quality, high-resolution multisensor precipitation reanalysis (MPR) products are needed for a wide range of quantitative climatological and hydroclimatological applications. Therefore, the authors have reengineered the multisensor precipitation estimator (MPE) algorithms of the NWS into the MPR package. Owing to the retrospective nature of the analysis, MPR allows for the utilization of additional rain gauge data, more rigorous automatic quality control, and post factum correction of radar quantitative precipitation estimation (QPE) and optimization of key parameters in multisensor estimation. To evaluate and demonstrate the value of MPR, the authors designed and carried out a set of cross-validation experiments in the pilot domain of North Carolina and South Carolina. The rain gauge data are from the reprocessed Hydrometeorological Automated Data System (HADS) and the daily Cooperative Observer Program (COOP). The radar QPE data are the operationally produced Weather Surveillance Radar-1988 Doppler digital precipitation array (DPA) products. To screen out bad rain gauge data, quality control steps were taken that use rain gauge and radar data. The resulting MPR products are compared with the stage IV product on a daily scale at the withheld COOP gauge locations. This paper describes the data, the MPR procedure, and the validation experiments, and it summarizes the findings.

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Automated Bayesian quality control of streaming rain gauge data

Environmental Modelling & Software ◽

10.1016/j.envsoft.2012.10.006 ◽

2013 ◽

Vol 40 ◽

pp. 289-301 ◽

Cited By ~ 7

Author(s):

David J. Hill

Keyword(s):

Quality Control ◽

Rain Gauge ◽

Rain Gauge Data ◽

Gauge Data

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Application of Satellite Rainfall Products for Flood Inundation Modelling in Kelantan River Basin, Malaysia

Hydrology ◽

10.3390/hydrology6040095 ◽

2019 ◽

Vol 6 (4) ◽

pp. 95 ◽

Cited By ~ 4

Author(s):

Tam ◽

Abd Rahman ◽

Harun ◽

Hanapi ◽

Kaoje

Keyword(s):

Rain Gauge ◽

Flood Inundation ◽

Primary Input ◽

Rain Gauge Data ◽

Flood Level ◽

Gauge Data ◽

Flood Inundation Mapping ◽

Precipitation Estimation ◽

Inundation Modelling ◽

Satellite Rainfall

The advent of satellite rainfall products can provide a solution to the scarcity of observed rainfall data. The present study aims to evaluate the performance of high spatial-temporal resolution satellite rainfall products (SRPs) and rain gauge data in hydrological modelling and flood inundation mapping. Four SRPs, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) - Early, - Late (IMERG-E, IMERG-L), Global Satellite Mapping of Precipitation-Near Real Time (GSMaP-NRT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Cloud Classification System (PERSIANN-CCS) and rain gauge data were used as the primary input to a hydrological model, Rainfall-Runoff-Inundation (RRI) and the simulated flood level and runoff were compared with the observed data using statistical metrics. GSMaP showed the best performance in simulating hourly runoff with the lowest relative bias (RB) and the highest Nash-Sutcliffe efficiency (NSE) of 4.9% and 0.79, respectively. Meanwhile, the rain gauge data was able to produce runoff with −12.2% and 0.71 for RB and NSE, respectively. The other three SRPs showed acceptable results in daily discharge simulation (NSE value between 0.42 and 0.49, and RB value between −23.3% and −31.2%). The generated flood map also agreed with the published information. In general, the SRPs, particularly the GSMaP, showed their ability to support rapid flood forecasting required for early warning of floods.

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The New French Operational Radar Rainfall Product. Part II: Validation

Weather and Forecasting ◽

10.1175/waf1005.1 ◽

2007 ◽

Vol 22 (3) ◽

pp. 409-427 ◽

Cited By ~ 50

Author(s):

P. Tabary ◽

J. Desplats ◽

K. Do Khac ◽

F. Eideliman ◽

C. Gueguen ◽

...

Keyword(s):

Radar Data ◽

Rain Gauge ◽

Radiosonde Data ◽

Radar Rainfall ◽

Freezing Level ◽

Ground Clutter ◽

Precipitation Estimation ◽

Upper Level ◽

Quality Indexes ◽

Precipitation Events

Abstract A new operational radar-based rainfall product has been developed at Météo-France and is currently being deployed within the French operational network. The new quantitative precipitation estimation (QPE) product is based entirely on radar data and includes a series of modules aimed at correcting for ground clutter, partial beam blocking, and vertical profile of reflectivity (VPR) effects, as well as the nonsimultaneity of radar measurements. The surface rainfall estimation is computed as a weighted mean of the corrected tilts. In addition to the final QPE, a map of quality indexes is systematically generated. This paper is devoted to the validation of the new radar QPE. The VPR identification module has been specifically validated by analyzing 489 precipitation events observed over 1 yr by a representative eight-radar subset of the network. The conceptual model of VPR used in the QPE processing chain is shown to be relevant. A climatology of the three shape parameters of the conceptual VPR (brightband peak, brightband thickness, and upper-level decreasing rate) is established and the radar-derived freezing-level heights are shown to be in good agreement with radiosonde data. A total of 27 precipitation events observed by three S-band radars of the network during the winter of 2005 and the autumns of 2002 and 2003 are used to compare the new radar QPE to the old one. Results are stratified according to the distance to the radar and according to the height of the freezing level. The Nash criterion is increased from 0.23 to 0.62 at close range (below 50 km) and from 0.35 to 0.42 at long range (between 100 and 150 km). The relevance of the proposed quality indexes is assessed by examining their statistical relationship with long-term radar–rain gauge statistics. Mosaics of QPE and quality indexes are also illustrated.

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