On the scale-dependent propagation of hydrologic uncertainty using high-resolution X-band radar rainfall estimates

AbstractIt remains a challenge to provide accurate and timely flood warnings in many parts of the western United States. As part of the Advanced Quantitative Precipitation Information (AQPI) project, this study explores the potential of using the AQPI gap-filling radar network for streamflow simulation of selected storm events in the San Francisco Bay Area under a WRF-Hydro modeling system. Two types of watersheds including natural and human-affected among the most flood-prone region of the Bay Area are investigated. Based on the high-resolution AQPI X-band radar rainfall estimates, three basic routing configurations, including Grid, Reach, and National Water Model (NWM), are used to quantify the impact of different model physics options on the simulated streamflow. It is found that the NWM performs better in terms of reproducing streamflow volumes and hydrograph shapes than the other routing configurations when reservoirs exist in the watershed. Additionally, the AQPI X-band radar rainfall estimates (without gauge correction) provide reasonable streamflow simulations, and they show better performance in reproducing the hydrograph peaks compared with the gauge-corrected rainfall estimates based on the operational S-band Next Generation Weather Radar network. Also, sensitivity test reveals that surficial conditions have a significant influence on the streamflow simulation during the storm: the discharge increases to a higher level as the infiltration factor (REFKDT) decreases, and its peak goes down and lags as surface roughness coefficient (Mann) increases. The time delay analysis of precipitation input on the streamflow at the two outfalls of the surveyed watersheds further demonstrates the link between AQPI gap-filling radar observations and streamflow changes in this urban region.

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Evaluation of Incremental Improvements to Quantitative Precipitation Estimates in Complex Terrain

Journal of Hydrometeorology ◽

10.1175/2009jhm1125.1 ◽

2009 ◽

Vol 10 (6) ◽

pp. 1507-1520 ◽

Cited By ~ 19

Author(s):

Jonathan J. Gourley ◽

David P. Jorgensen ◽

Sergey Y. Matrosov ◽

Zachary L. Flamig

Keyword(s):

Complex Terrain ◽

Pearson Correlation ◽

Radar Rainfall ◽

X Band ◽

American River ◽

Precipitation Estimates ◽

Research Findings ◽

Low Altitude ◽

Quantitative Precipitation Estimates ◽

Rainfall Estimates

Abstract Advanced remote sensing and in situ observing systems employed during the Hydrometeorological Testbed experiment on the American River basin near Sacramento, California, provided a unique opportunity to evaluate correction procedures applied to gap-filling, experimental radar precipitation products in complex terrain. The evaluation highlighted improvements in hourly radar rainfall estimation due to optimizing the parameters in the reflectivity-to-rainfall (Z–R) relation, correcting for the range dependence in estimating R due to the vertical variability in Z in snow and melting-layer regions, and improving low-altitude radar coverage by merging rainfall estimates from two research radars operating at different frequencies and polarization states. This evaluation revealed that although the rainfall product from research radars provided the smallest bias relative to gauge estimates, in terms of the root-mean-square error (with the bias removed) and Pearson correlation coefficient it did not outperform the product from a nearby operational radar that used optimized Z–R relations and was corrected for range dependence. This result was attributed to better low-altitude radar coverage with the operational radar over the upper part of the basin. In these regions, the data from the X-band research radar were not available and the C-band research radar was forced to use higher-elevation angles as a result of nearby terrain and tree blockages, which yielded greater uncertainty in surface rainfall estimates. This study highlights the challenges in siting experimental radars in complex terrain. Last, the corrections developed for research radar products were adapted and applied to an operational radar, thus providing a simple transfer of research findings to operational rainfall products yielding significantly improved skill.

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Correction of Radar Reflectivity and Differential Reflectivity for Rain Attenuation at X Band. Part II: Evaluation and Application

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech1804.1 ◽

2005 ◽

Vol 22 (11) ◽

pp. 1633-1655 ◽

Cited By ~ 117

Author(s):

S-G. Park ◽

M. Maki ◽

K. Iwanami ◽

V. N. Bringi ◽

V. Chandrasekar

Keyword(s):

Drop Size ◽

Earth Science ◽

Radar Data ◽

Rain Gauge ◽

Radar Rainfall ◽

X Band ◽

Rain Events ◽

Differential Reflectivity ◽

Rainfall Estimates ◽

Good Agreement

Abstract In this paper, the attenuation-correction methodology presented in Part I is applied to radar measurements observed by the multiparameter radar at the X-band wavelength (MP-X) of the National Research Institute for Earth Science and Disaster Prevention (NIED), and is evaluated by comparison with scattering simulations using ground-based disdrometer data. Further, effects of attenuation on the estimation of rainfall amounts and drop size distribution parameters are also investigated. The joint variability of the corrected reflectivity and differential reflectivity show good agreement with scattering simulations. In addition, specific attenuation and differential attenuation, which are derived in the correction procedure, show good agreement with scattering simulations. In addition, a composite rainfall-rate algorithm is proposed and evaluated by comparison with eight gauges. The radar-rainfall estimates from the uncorrected (or observed) ZH produce severe underestimation, even at short ranges from the radar and for stratiform rain events. On the contrary, the reflectivity-based rainfall estimates from the attenuation-corrected ZH does not show such severe underestimation and does show better agreement with rain gauge measurements. More accurate rainfall amounts can be obtained from a simple composite algorithm based on specific differential phase KDP, with the R(ZH_cor) estimates being used for low rainfall rates (KDP ≤ 0.3° km−1 or ZH_cor ≤ 35 dBZ). This improvement in accuracy of rainfall estimation based on KDP is a result of the insensitivity of the rainfall algorithm to natural variations of drop size distributions (DSDs). The ZH, ZDR, and KDP data are also used to infer the parameters (median volume diameter D0 and normalized intercept parameter Nw) of a normalized gamma DSD. The retrieval of D0 and Nw from the corrected radar data show good agreement with those from disdrometer data in terms of the respective relative frequency histograms. The results of this study demonstrate that high-quality hydrometeorological information on rain events such as rainfall amounts and DSDs can be derived from X-band polarimetric radars.

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Assessment of High-Resolution Satellite-Based Rainfall Estimates over the Mediterranean during Heavy Precipitation Events

Journal of Hydrometeorology ◽

10.1175/jhm-d-12-0167.1 ◽

2013 ◽

Vol 14 (5) ◽

pp. 1500-1514 ◽

Cited By ~ 37

Author(s):

Dimitrios Stampoulis ◽

Emmanouil N. Anagnostou ◽

Efthymios I. Nikolopoulos

Keyword(s):

High Resolution ◽

Geographical Area ◽

Heavy Precipitation ◽

Type Systems ◽

Economic Losses ◽

Error Statistics ◽

Radar Rainfall ◽

Precipitation Estimation ◽

Rainfall Estimates ◽

Precipitation Events

Abstract Heavy precipitation events (HPE) can incur significant economic losses as well as losses of lives through catastrophic floods. Evidence of increasing heavy precipitation at continental and global scales clearly emphasizes the need to accurately quantify these phenomena. The current study focuses on the error analysis of two of the main quasi-global, high-resolution satellite products [Climate Prediction Center (CPC) morphing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)], using rainfall data derived from high-quality weather radar rainfall estimates as a reference. This analysis is based on seven major flood-inducing HPEs that developed over complex terrain areas in northern Italy (Fella and Sessia regions) and southern France (Cevennes–Vivarais region). The storm cases were categorized as convective or stratiform based on their characteristics, including rainfall intensity, duration, and area coverage. The results indicate that precipitation type has an effect on the algorithm's ability to capture rainfall effectively. Convective storm cases exhibited greater rain rate retrieval errors, while low rain rates in stratiform-type systems are not well captured by the satellite algorithms investigated in this study, thus leading to greater missed rainfall volumes. Overall, CMORPH exhibited better error statistics than PERSIANN for the HPEs of this study. Similarities are also shown in the two satellite products' error characteristics for the HPEs that occurred in the same geographical area.

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Improving Spatial Rainfall Estimates at Mt. Merapi Area Using Radar-Rain Gauge Conditional Merging

Journal of Disaster Research ◽

10.20965/jdr.2019.p0069 ◽

2019 ◽

Vol 14 (1) ◽

pp. 69-79 ◽

Cited By ~ 1

Author(s):

Roby Hambali ◽

Djoko Legono ◽

Rachmad Jayadi ◽

Satoru Oishi ◽

◽

...

Keyword(s):

Temporal Resolution ◽

Early Warning ◽

Interpolation Method ◽

Rain Gauge ◽

Value Distribution ◽

Rainfall Data ◽

Warning Systems ◽

Radar Rainfall ◽

X Band ◽

Rainfall Estimates

Rainfall monitoring is important for providing early warning of lahar flow around Mt. Merapi. The X-band multi-parameter radar developed to support these warning systems provides rainfall information with high spatial and temporal resolution. However, this method underestimates the rainfall compared with rain gauge measurements. Herein, we performed conditional radar-rain gauge merging to obtain the optimal rainfall value distribution. By using the cokriging interpolation method, kriged gauge rainfall, and kriged radar rainfall data were obtained, which were then combined with radar rainfall data to yield the adjusted spatial rainfall. Radar-rain gauge conditional merging with cokriging interpolation provided reasonably well-adjusted spatial rainfall pattern.

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Advancing Precipitation Estimation and Streamflow Simulations in Complex Terrain with X-Band Dual-Polarization Radar Observations

Remote Sensing ◽

10.3390/rs10081258 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1258 ◽

Cited By ~ 8

Author(s):

Marios Anagnostou ◽

Efthymios Nikolopoulos ◽

John Kalogiros ◽

Emmanouil Anagnostou ◽

Francesco Marra ◽

...

Keyword(s):

Complex Terrain ◽

Rain Gauge ◽

Storm Events ◽

Error Statistics ◽

Radar Rainfall ◽

Radar Observations ◽

X Band ◽

Precipitation Estimation ◽

Rainfall Estimates

In mountain basins, the use of long-range operational weather radars is often associated with poor quantitative precipitation estimation due to a number of challenges posed by the complexity of terrain. As a result, the applicability of radar-based precipitation estimates for hydrological studies is often limited over areas that are in close proximity to the radar. This study evaluates the advantages of using X-band polarimetric (XPOL) radar as a means to fill the coverage gaps and improve complex terrain precipitation estimation and associated hydrological applications based on a field experiment conducted in an area of Northeast Italian Alps characterized by large elevation differences. The corresponding rainfall estimates from two operational C-band weather radar observations are compared to the XPOL rainfall estimates for a near-range (10–35 km) mountainous basin (64 km2). In situ rainfall observations from a dense rain gauge network and two disdrometers (a 2D-video and a Parsivel) are used for ground validation of the radar-rainfall estimates. Ten storm events over a period of two years are used to explore the differences between the locally deployed XPOL vs. longer-range operational radar-rainfall error statistics. Hourly aggregate rainfall estimates by XPOL, corrected for rain-path attenuation and vertical reflectivity profile, exhibited correlations between 0.70 and 0.99 against reference rainfall data and 21% mean relative error for rainfall rates above 0.2 mm h−1. The corresponding metrics from the operational radar-network rainfall products gave a strong underestimation (50–70%) and lower correlations (0.48–0.81). For the two highest flow-peak events, a hydrological model (Kinematic Local Excess Model) was forced with the different radar-rainfall estimations and in situ rain gauge precipitation data at hourly resolution, exhibiting close agreement between the XPOL and gauge-based driven runoff simulations, while the simulations obtained by the operational radar rainfall products resulted in a greatly underestimated runoff response.

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Correcting Radar Rainfall Estimates Based on Ground Elevation Function

Journal of the Civil Engineering Forum ◽

10.22146/jcef.49395 ◽

2019 ◽

Vol 5 (3) ◽

pp. 300

Author(s):

Roby Hambali ◽

Djoko Legono ◽

Rachmad Jayadi

Keyword(s):

Correction Method ◽

Rain Gauge ◽

Real Time System ◽

Radar Rainfall ◽

Rainfall Condition ◽

Correction Process ◽

R Ratio ◽

X Band ◽

The Impact ◽

Rainfall Estimates

X-band radar gives several advantages for quantitative rainfall estimation, involving higher spatial and temporal resolution, also the ability to reduce attenuation effects and hardware calibration errors. However, the estimates error due to attenuation in heavy rainfall condition cannot be avoided. In the mountainous region, the impact of topography is considered to contribute to radar rainfall estimates error. To have more reliable estimated radar rainfall to be used in various applications, a rainfall estimates correction needs to be applied. This paper discusses evaluation and correction techniques for radar rainfall estimates based on ground elevation function. The G/R ratio is used as a primary method in the correction process. The novel approach proposed in this study is the use of correction factor derived from the relationship between Log (G/R) parameter and elevation difference between radar and rain gauge stations. A total of 4590 pairs of rainfall data from X-band MP radar and 15 rain gauge stations in the Mt. Merapi region were used in evaluation and correction process. The results show the correction method based on the elevation function is relatively good in correcting radar rainfall depth with values of Log (G/R) decreased up to 81.1%, particularly for light rainfall (≤ 20 mm/hour) condition. Also, the method is simple to apply in a real-time system.

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Improvement of X-band radar rainfall estimates using a microwave link

Atmospheric Research ◽

10.1016/j.atmosres.2004.10.028 ◽

2005 ◽

Vol 77 (1-4) ◽

pp. 278-299 ◽

Cited By ~ 23

Author(s):

S. Krämer ◽

H.-R. Verworn ◽

A. Redder

Keyword(s):

Radar Rainfall ◽

X Band ◽

Microwave Link ◽

Rainfall Estimates

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Analyzing the Application of X-Band Radar for Improving Rainfall Observation and Flood Forecasting in Yeongdong, South Korea

Remote Sensing ◽

10.3390/rs14010043 ◽

2021 ◽

Vol 14 (1) ◽

pp. 43

Author(s):

Seong-Sim Yoon ◽

Sang-Hun Lim

Keyword(s):

South Korea ◽

Flood Forecasting ◽

Hydraulic Modeling ◽

Rain Gauge ◽

Risk Level ◽

Radar Rainfall ◽

Risk Increase ◽

Flood Level ◽

X Band ◽

Rainfall Estimates

The mountainous Yeongdong region of South Korea contains mountains over 1 km. Owing to this topographic blockage, the region has a low-density rain-gauge network, and there is a low-altitude (~1.5 km) observation gap with the nearest large S-band radar. The Korean government installed an X-band dual-polarization radar in 2019 to improve rainfall observations and to prevent hydrological disasters in the Yeongdong region. The present study analyzed rainfall estimates using the newly installed X-band radar to evaluate its hydrological applicability. The rainfall was estimated using a distributed specific differential phase-based technique for a high-resolution 75 m grid. Comparison of the rainfall estimates of the X-band radar and the existing rainfall information showed that the X-band radar was less likely to underestimate rainfall compared to the S-band radar. The accuracy was particularly high within a 10 km observation radius. To evaluate the hydrological applicability of X-band radar rainfall estimates, this study developed a rain-based flood forecasting method—the flow nomograph—for the Samcheok-osib stream, which is vulnerable to heavy rain and resultant floods. This graph represents the flood risk level determined by hydrological–hydraulic modeling with various rainfall scenarios. Rainfall information (X-band radar, S-band radar, ground rain gauge) was applied as input to the flow nomograph to predict the flood level of the stream. Only the X-band radar could accurately predict the actual high-risk increase in the water level for all studied rainfall events.

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