Comprehensive Evaluation of the IFloodS Radar Rainfall Products for Hydrologic Applications

Abstract This study describes the generation and testing of a reference rainfall product created from field campaign datasets collected during the NASA Global Precipitation Measurement (GPM) mission Ground Validation Iowa Flood Studies (IFloodS) experiment. The study evaluates ground-based radar rainfall (RR) products acquired during IFloodS in the context of building the reference rainfall product. The purpose of IFloodS was not only to attain a high-quality ground-based reference for the validation of satellite rainfall estimates but also to enhance understanding of flood-related rainfall processes and the predictability of flood forecasting. We assessed the six RR estimates (IFC, Q2, CSU-DP, NWS-DP, Stage IV, and Q2-Corrected) using data from rain gauge and disdrometer networks that were located in the broader field campaign area of central and northeastern Iowa. We performed the analyses with respect to time scales ranging from 1 h to the entire campaign period in order to compare the capabilities of each RR product and to characterize the error structure at scales that are frequently used in hydrologic applications. The evaluation results show that the Stage IV estimates perform superior to other estimates, demonstrating the need for gauge-based bias corrections of radar-only products. This correction should account for each product’s algorithm-dependent error structure that can be used to build unbiased rainfall products for the campaign reference. We characterized the statistical error structures (e.g., systematic and random components) of each RR estimate and used them for the generation of a campaign reference rainfall product. To assess the hydrologic utility of the reference product, we performed hydrologic simulations driven by the reference product over the Turkey River basin. The comparison of hydrologic simulation results demonstrates that the campaign reference product performs better than Stage IV in streamflow generation.

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Evaluation of Three Satellite Precipitation Products TRMM 3B42, CMORPH, and PERSIANN over a Subtropical Watershed in China

Advances in Meteorology ◽

10.1155/2015/151239 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 36

Author(s):

Junzhi Liu ◽

Zheng Duan ◽

Jingchao Jiang ◽

A-Xing Zhu

Keyword(s):

Comprehensive Evaluation ◽

Tropical Rainfall Measuring Mission ◽

Rain Gauge ◽

Lake Basin ◽

Satellite Precipitation ◽

Precipitation Estimation ◽

Daily Scale ◽

Using Data ◽

Hydrological Applications ◽

Trmm 3B42

This study conducted a comprehensive evaluation of three satellite precipitation products (TRMM (Tropical Rainfall Measuring Mission) 3B42, CMORPH (the Climate Prediction Center (CPC) Morphing algorithm), and PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks)) using data from 52 rain gauge stations over the Meichuan watershed, which is a representative watershed of the Poyang Lake Basin in China. All the three products were compared and evaluated during a 9-year period at different spatial (grid and watershed) and temporal (daily, monthly, and annual) scales. The results showed that at daily scale, CMORPH had the best performance with coefficients of determination (R2) of 0.61 at grid scale and 0.74 at watershed scale. For precipitation intensities larger than or equal to 25 mm, RMSE% of CMORPH and TRMM 3B42 were less than 50%, indicating CMORPH and TRMM 3B42 might be useful for hydrological applications at daily scale. At monthly and annual temporal scales, TRMM 3B42 had the best performances, with highR2ranging from 0.93 to 0.99, and thus was deemed to be reliable and had good potential for hydrological applications at monthly and annual scales. PERSIANN had the worst performance among the three products at all cases.

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On the Performance of Satellite-Based Precipitation Products in Simulating Streamflow and Water Quality During Hydrometeorological Extremes

Frontiers in Environmental Science ◽

10.3389/fenvs.2020.585451 ◽

2020 ◽

Vol 8 ◽

Author(s):

Jennifer Solakian ◽

Viviana Maggioni ◽

Adil N. Godrej

Keyword(s):

Water Quality ◽

Quality Indicators ◽

Extreme Event ◽

Comprehensive Evaluation ◽

Oxygen Demand ◽

Rain Gauge ◽

Spatiotemporal Variability ◽

Hydrologic Simulation ◽

Quality Model ◽

Water Quality Indicators

This study provides a comprehensive evaluation of streamflow and water quality simulated by a hydrological model using three different Satellite Precipitation Products (SPPs) with respect to observations from a dense rain gauge network over the Occoquan Watershed, located in Northern Virginia, suburbs to Washington, D.C., U.S. Eight extreme hydrometeorological events within a 5-year period between 2008 and 2012 are evaluated using SPPs, TMPA 3B42-V7, CMORPH V1. 0, and PERSIANN-CCS, which are based on different retrieval algorithms with varying native spatial and temporal resolutions. A Hydrologic Simulation Program FORTRAN (HSPF) hydrology and water quality model was forced with the three SPPs to simulate output of streamflow (Q), stream temperature (TW), and concentrations of total suspended solids (TSS), orthophosphate phosphorus (OP), total phosphorus (TP), ammonium-nitrate (NH4-N), nitrate-nitrogen (NO3-N), dissolved oxygen (DO), and biochemical oxygen demand (BOD) at six evaluation points within the watershed. Results indicate fairly good agreement between gauge- and SPP-simulated Q for TMPA and CMORPH, however, PERSIANN-simulated Q is lowest among SPPs, due to its inability to accurately measure stratiform precipitation between intense periods of precipitation during an extreme event. Correlations of water quality indicators vary considerably, however, TW has the strongest positive linear relationship compared to other indicators evaluated in this study. SPP-simulated TSS, a flow-dependent variable, has the weakest relationship to gauge-simulated TSS among all water quality indicators, with CMORPH performing slightly better than TMPA and PERSIANN. This study demonstrated that the spatiotemporal variability of SPPs, along with their algorithms to estimate precipitation, have an influence on water quality simulations during extreme hydrometeorological events.

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NEXRAD Quantitative Precipitation Estimations for Hydrologic Simulation Using a Hybrid Hydrologic Model

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0013.1 ◽

2016 ◽

Vol 18 (1) ◽

pp. 25-47 ◽

Cited By ~ 10

Author(s):

Younghyun Cho ◽

Bernard A. Engel

Keyword(s):

Model Simulation ◽

Model Performance ◽

Stage Iv ◽

Rain Gauge ◽

Hydrologic Model ◽

Feature Model ◽

Coefficient Of Determination ◽

Hydrologic Simulation ◽

Direct Runoff ◽

Spatially Distributed

Abstract A hybrid hydrologic model (lumped conceptual and distributed feature model), Distributed-Clark, is introduced to perform hydrologic simulations using spatially distributed NEXRAD quantitative precipitation estimations (QPEs). In Distributed-Clark, spatially distributed excess rainfall estimated with the Soil Conservation Service (SCS) curve number method and a GIS-based set of separated unit hydrographs are utilized to calculate a direct runoff flow hydrograph. This simple approach using few modeling parameters reduces calibration complexity relative to physically based distributed (PBD) models by only focusing on integrated flow estimation at watershed outlets. Case studies assessed the quality of NEXRAD stage IV QPEs for hydrologic simulation compared to gauge-only analyses. NEXRAD data validation against rain gauge observations and performance evaluation with model simulation result comparisons for inputs of spatially distributed stage IV and spatially averaged gauged data for four study watersheds were conducted. Results show significant differences in NEXRAD QPEs and gauged rainfall amounts, with NEXRAD data overestimated by 7.5% and 9.1% and underestimated by 15.0% and 11.4% accompanied by spatial variability. These differences affect model performance in hydrologic applications. Rainfall–runoff flow simulations using spatially distributed NEXRAD stage IV QPEs demonstrate relatively good fit [direct runoff: Nash–Sutcliffe efficiency ENS = 0.85, coefficient of determination R2 = 0.89, and percent bias (PBIAS) = 3.92%; streamflow: ENS = 0.91, R2 = 0.93, and PBIAS = 1.87%] against observed flow as well as better fit (ENS of 3.7% and R2 of 6.0% increase in direct runoff) than spatially averaged gauged rainfall for the same model calibration approach, enabling improved estimates of flow volumes and peak rates that can be underestimated in hydrologic simulations for spatially averaged rainfall.

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Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

Remote Sensing ◽

10.3390/rs13152922 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2922

Author(s):

Yang Song ◽

Patrick D. Broxton ◽

Mohammad Reza Ehsani ◽

Ali Behrangi

Keyword(s):

Snow Water Equivalent ◽

Stage Iv ◽

Microwave Remote Sensing ◽

Rain Gauge ◽

Snow Accumulation ◽

Correction Factors ◽

Rainfall Analysis ◽

Rain Gauges ◽

Snow Properties ◽

Precipitation Estimates

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that correction factors applied to rain gauges are effective for improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill for capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill for capturing the geographical distribution of snowfall and precipitation accumulation; therefore, bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.

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Model-Informed Optimization of a Pediatric Clinical Pharmacokinetic Trial of a New Spironolactone Liquid Formulation

Pharmaceutics ◽

10.3390/pharmaceutics13060849 ◽

2021 ◽

Vol 13 (6) ◽

pp. 849

Author(s):

Manasa Tatipalli ◽

Vijay Kumar Siripuram ◽

Tao Long ◽

Diana Shuster ◽

Galina Bernstein ◽

...

Keyword(s):

Pharmacokinetic Model ◽

Allometric Scaling ◽

Pediatric Patients ◽

Reference Product ◽

Population Pharmacokinetic ◽

Liquid Formulation ◽

Sampling Schemes ◽

Quantitative Pharmacology ◽

Using Data ◽

Drug Pharmacokinetics

Quantitative pharmacology brings important advantages in the design and conduct of pediatric clinical trials. Herein, we demonstrate the application of a model-based approach to select doses and pharmacokinetic sampling scenarios for the clinical evaluation of a novel oral suspension of spironolactone in pediatric patients with edema. A population pharmacokinetic model was developed and qualified for spironolactone and its metabolite, canrenone, using data from adults and bridged to pediatrics (2 to <17 years old) using allometric scaling. The model was then used via simulation to explore different dosing and sampling scenarios. Doses of 0.5 and 1.5 mg/kg led to target exposures (i.e., similar to 25 and 100 mg of the reference product in adults) in all the reference pediatric ages (i.e., 2, 6, 12 and 17 years). Additionally, two different sampling scenarios were delineated to accommodate patients into sparse sampling schemes informative to characterize drug pharmacokinetics while minimizing phlebotomy and burden to participating children.

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Impacts of Polarimetric Radar Observations on Hydrologic Simulation

Journal of Hydrometeorology ◽

10.1175/2010jhm1218.1 ◽

2010 ◽

Vol 11 (3) ◽

pp. 781-796 ◽

Cited By ~ 22

Author(s):

Jonathan J. Gourley ◽

Scott E. Giangrande ◽

Yang Hong ◽

Zachary L. Flamig ◽

Terry Schuur ◽

...

Keyword(s):

Pearson Correlation ◽

Extreme Rainfall ◽

Rain Gauge ◽

Hydrologic Model ◽

Laboratory Research ◽

Model Parameters ◽

Polarimetric Radar ◽

Storm Events ◽

Hydrologic Simulation ◽

Radar Observations

Abstract Rainfall estimated from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler [WSR-88D (KOUN)] was evaluated using a dense Micronet rain gauge network for nine events on the Ft. Cobb research watershed in Oklahoma. The operation of KOUN and its upgrade to dual polarization was completed by the National Severe Storms Laboratory. Storm events included an extreme rainfall case from Tropical Storm Erin that had a 100-yr return interval. Comparisons with collocated Micronet rain gauge measurements indicated all six rainfall algorithms that used polarimetric observations had lower root-mean-squared errors and higher Pearson correlation coefficients than the conventional algorithm that used reflectivity factor alone when considering all events combined. The reflectivity based relation R(Z) was the least biased with an event-combined normalized bias of −9%. The bias for R(Z), however, was found to vary significantly from case to case and as a function of rainfall intensity. This variability was attributed to different drop size distributions (DSDs) and the presence of hail. The synthetic polarimetric algorithm R(syn) had a large normalized bias of −31%, but this bias was found to be stationary. To evaluate whether polarimetric radar observations improve discharge simulation, recent advances in Markov Chain Monte Carlo simulation using the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) were used. This Bayesian approach infers the posterior probability density function of model parameters and output predictions, which allows us to quantify HL-RDHM uncertainty. Hydrologic simulations were compared to observed streamflow and also to simulations forced by rain gauge inputs. The hydrologic evaluation indicated that all polarimetric rainfall estimators outperformed the conventional R(Z) algorithm, but only after their long-term biases were identified and corrected.

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Stage at cancer diagnosis during the COVID-19 pandemic in western Washington state.

Journal of Clinical Oncology ◽

10.1200/jco.2020.39.28_suppl.145 ◽

2021 ◽

Vol 39 (28_suppl) ◽

pp. 145-145

Author(s):

Catherine R. Fedorenko ◽

Karma L. Kreizenbeck ◽

Li Li ◽

Laura Elizabeth Panattoni ◽

Veena Shankaran ◽

...

Keyword(s):

Medical Care ◽

Early Stage ◽

Stage Iv ◽

Washington State ◽

Stage I ◽

Tumor Type ◽

Stage At Diagnosis ◽

Stage Iv Cancer ◽

Using Data ◽

The Impact

145 Background: The COVID-19 pandemic disrupted medical care, including routine cancer screening for breast, colorectal, lung and cervical cancers. We aimed to investigate the impact of the pandemic on stage at diagnosis for cancer patients. Methods: Using data from the Washington State SEER records we compared AJCC stage for patients diagnosed with cancer in 2017-2019 to 2020 for two time periods, March to June (initial pandemic months) and July to December (later pandemic months). Patients were included if they were age 18+, diagnosed with a solid tumor, and not diagnosed at autopsy. Results: In the early phase of the pandemic, March – June 2020, there was a shift to cancers being diagnosed at a later stage compared to the same time period in 2017-2019 (Stage III: 13.5% to 14.9%, Stage IV: 16.2% to 19.7%). There was also a decrease in cancer diagnoses for cancers that are often detected through routine screening. As a percentage of all cancer diagnoses, both melanoma (13.2% to 9.8%) and colon cancer diagnoses (7.2% to. 6.7%) decreased during the early pandemic. In the later phase of the pandemic, July to December 2020, the stage at diagnosis showed an indication of returning to pre-pandemic levels with an increase in the proportion of early stage cancers (In situ: 16.6% to 19.3%, Stage I: 38.8% to 41.1%). Stage at diagnosis trends varied by tumor type. For colorectal cancer, the overall number of diagnoses decreased during the initial pandemic months. Stage I diagnoses decreased and Stage IV cancer diagnoses increased in both early and late stages of the pandemic. Conclusions: In Washington State, the COVID-19 pandemic had an impact on stage at diagnosis potentially caused by delays or interruptions in medical care. Additional studies are needed to understand how this shift in stage at diagnosis impacted treatment and outcomes for patients.

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Multifractal analysis of radar rainfall fields over the area of Rome

Advances in Geosciences ◽

10.5194/adgeo-2-293-2005 ◽

2005 ◽

Vol 2 ◽

pp. 293-299 ◽

Cited By ~ 5

Author(s):

G. Calenda ◽

E. Gorgucci ◽

F. Napolitano ◽

A. Novella ◽

E. Volpi

Keyword(s):

Scale Invariance ◽

Large Scale ◽

Doppler Radar ◽

Risk Mitigation ◽

Small Scale ◽

Radar Rainfall ◽

Invariance Properties ◽

Meteorological Radar ◽

Using Data ◽

Precipitation Events

Abstract. A scale-invariance analysis of space and time rainfall events monitored by meteorological radar over the area of Rome (Italy) is proposed. The study of the scale-invariance properties of intense precipitation storms, particularly important in flood forecast and risk mitigation, allows to transfer rainfall information from the large scale predictive meteorological models to the small scale hydrological rainfall-runoff models. Precipitation events are monitored using data collected by the polarimetric Doppler radar Polar 55C (ISAC-CNR), located 15 km Southeast from downtown. The meteorological radar provides the estimates of rainfall intensity over an area of about 10 000 km2 at a resolution of 2×2 km2 in space and 5 min in time. Many precipitation events have been observed from autumn 2001 up to now. A scale-invariance analysis is performed on some of these events with the aim at exploring the multifractal properties and at understanding their dependence on the meteorological large-scale conditions.

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Close-range radar rainfall estimation and error analysis

Atmospheric Measurement Techniques ◽

10.5194/amt-9-3837-2016 ◽

2016 ◽

Vol 9 (8) ◽

pp. 3837-3850 ◽

Cited By ~ 9

Author(s):

C. Z. van de Beek ◽

H. Leijnse ◽

P. Hazenberg ◽

R. Uijlenhoet

Keyword(s):

Positive Impact ◽

Rain Gauge ◽

Minor Role ◽

Rainfall Estimation ◽

Radar Rainfall ◽

Sources Of Error ◽

Ground Clutter ◽

Received Power ◽

Radar Calibration ◽

Many Sources

Abstract. Quantitative precipitation estimation (QPE) using ground-based weather radar is affected by many sources of error. The most important of these are (1) radar calibration, (2) ground clutter, (3) wet-radome attenuation, (4) rain-induced attenuation, (5) vertical variability in rain drop size distribution (DSD), (6) non-uniform beam filling and (7) variations in DSD. This study presents an attempt to separate and quantify these sources of error in flat terrain very close to the radar (1–2 km), where (4), (5) and (6) only play a minor role. Other important errors exist, like beam blockage, WLAN interferences and hail contamination and are briefly mentioned, but not considered in the analysis. A 3-day rainfall event (25–27 August 2010) that produced more than 50 mm of precipitation in De Bilt, the Netherlands, is analyzed using radar, rain gauge and disdrometer data. Without any correction, it is found that the radar severely underestimates the total rain amount (by more than 50 %). The calibration of the radar receiver is operationally monitored by analyzing the received power from the sun. This turns out to cause a 1 dB underestimation. The operational clutter filter applied by KNMI is found to incorrectly identify precipitation as clutter, especially at near-zero Doppler velocities. An alternative simple clutter removal scheme using a clear sky clutter map improves the rainfall estimation slightly. To investigate the effect of wet-radome attenuation, stable returns from buildings close to the radar are analyzed. It is shown that this may have caused an underestimation of up to 4 dB. Finally, a disdrometer is used to derive event and intra-event specific Z–R relations due to variations in the observed DSDs. Such variations may result in errors when applying the operational Marshall–Palmer Z–R relation. Correcting for all of these effects has a large positive impact on the radar-derived precipitation estimates and yields a good match between radar QPE and gauge measurements, with a difference of 5–8 %. This shows the potential of radar as a tool for rainfall estimation, especially at close ranges, but also underlines the importance of applying radar correction methods as individual errors can have a large detrimental impact on the QPE performance of the radar.

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

Journal of Hydrometeorology ◽

10.1175/2010jhm1265.1 ◽

2010 ◽

Vol 11 (5) ◽

pp. 1191-1198 ◽

Cited By ~ 36

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.

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