Inter-comparison of radar rainfall rate using Constant Altitude Plan Position Indicator and hybrid surface rainfall maps

Kwon, Soo Hyun; Jung, Sung-Hwa; Lee, GyuWon

doi:10.1016/j.jhydrol.2015.08.063

Cited by 22 publications

(16 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The lowest available Cartesian QPE is implemented as a final composite QPE product. These procedures are similar to Kwon et al [18].…”

Section: Simplified Decision-tree Qpementioning

confidence: 87%

“…Radar data with the PBB effect, mainly resulting from Taiwan's complex topography, are first identified by a beam blockage simulation [10,18,34,35]. When the central height of the radar beams, estimated by 4/3 of the Earth's radius model, is lower than the top of the terrain (50% of the power is blocked), the data and the following bins along the radial will be removed.…”

Section: Pbb Removalmentioning

confidence: 99%

“…Nevertheless, the dual-pol radar measurement uncertainties contribute to QPE error significantly [11,27]. The partial beam blockage (PBB), inadequate correction for attenuation, calibration of Z HH and Z DR , and the wet radome effect (WRE) are crucial to QPE application [11,18,19,[27][28][29][30]. Furthermore, the variability of RSD also degrades the QPE performance even though dual-pol radar variables are applied.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Synthetic Quantitative Precipitation Estimation by Integrating S- and C-Band Dual-Polarization Radars over Northern Taiwan

Chen

Chang

Chang³

2021

Remote Sensing

View full text Add to dashboard Cite

The key factors, namely, the radar data quality, raindrop size distribution (RSD) variability, and the data integration method, which significantly affect radar-based quantitative precipitation estimation (QPE) are investigated using the RCWF (S-band) and NCU C-POL (C-band) dual-polarization radars in northern Taiwan. The radar data quality control (QC) procedures, including the corrections of attenuation, the systematic bias, and the wet-radome effect, have large impact on the QPE accuracy. With the proper QC procedures, the values of normalized root mean square error (NRMSE) decrease about 10~40% for R(ZHH) and about 5~15% for R(KDP). The QPE error from the RSD variability is mitigated by applying seasonal coefficients derived from eight-year disdrometer data. Instead of using discrete QPEs (D-QPE) from one radar, the synthetic QPEs are derived via discretely combined QPEs (DC-QPE) from S- and C-band radars. The improvements in DC-QPE compared to D-QPE are about 1.5–7.0% and 3.5–8.5% in R(KDP) and R(KDP, ZDR), respectively. A novel algorithm, Lagrangian-evolution adjustment (LEA), is proposed to compensate D-QPE from a single radar. The LEA-QPE shows 1–4% improvements in R(KDP, ZDR) at the C-band radar, which has a larger scanning temporal gap (up to 10 min). The synthetic LEA-QPEs by combining two radars have outperformed both D-QPEs and DC-QPEs.

show abstract

“…The lowest available Cartesian QPE is implemented as a final composite QPE product. These procedures are similar to Kwon et al [18].…”

Section: Simplified Decision-tree Qpementioning

confidence: 87%

Section: Pbb Removalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Synthetic Quantitative Precipitation Estimation by Integrating S- and C-Band Dual-Polarization Radars over Northern Taiwan

Chen

Chang

Chang³

2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…When determining the rain field, that is, eliminating non-meteorological echoes or removing artifacts, the threshold values of the following texture were used as follows [42,43]: 0.95 for ρ HV , 0.1 for (ρ HV ), 4.0 dB for (ZH), 4.0 dB for (ZDR), and 15.0° for (Φ DP ). Here, ( ) is the radial texture of variable with a window size of 10.…”

Section: Application To Operational Radar Datamentioning

confidence: 99%

Quantitative Precipitation Estimates Using Machine Learning Approaches with Operational Dual-Polarization Radar Data

et al. 2021

Self Cite

View full text Add to dashboard Cite

Traditional radar-based rainfall estimation is typically done by known functional relationships between the rainfall intensity (R) and radar measurables, such as R–Zh, R–(Zh, ZDR), etc. One of the biggest advantages of machine learning algorithms is the applicability to a non-linear relationship between a dependent variable and independent variables without any predefined relationships. We explored the potential use of two supervised machine learning methods (regression tree and random forest) in rainfall estimation using dual-polarization radar variables. The regression tree does not require normalization and scaling of data; however, this method is quite unstable since each split depends on the parent split. Since the random forest is an ensemble method of regression trees, it has less variability in prediction compared with regression trees, but consumes more computer resources. We considered several different configurations for machine learning algorithms with different sets of dependent and independent variables. The random forest model was appropriately tuned. In the test of variable importance, the specific differential phase (differential reflectivity) was the most important variable to predict the rainfall rate (residual that is the difference between the true rainfall rate and the one estimated from the R–Z relationship). The models were evaluated by 10-fold cross-validation. The best model was the random forest model using a residual with the non-classified training set. The results indicated that the machine learning algorithms outperformed the traditional R–Z relationship. Then, we applied the best machine learning model to an S-band dual-polarization radar (Mt. Myeonbong) and validated the result with ground rain gauges. The results of the application to radar data showed that the estimates of the residuals had spatial variability. The stratiform and weak rain areas had positive residuals while convective areas had negative residuals, indicating that the spatial error structure driven by the R–Z relationship was well captured by the model. The rainfall rates of all pixels over the study area were adjusted with the estimated residuals. The rainfall rates adjusted by residual showed excellent agreement with the rain gauge, especially at high rainfall rates.

show abstract

“…For example, good representation of rainfall spatial variability (high-density, high resolution records) is required to accurately reproduce hydrological processes [9]. Radar data suffer from large uncertainties due to ground clutter and beam shielding by complex terrain [18]. Interpretation of climate data obtained through both radar and remote sensing requires validation using station-based records.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Spatial Climatic Representation on Hydrological Model Calibration and Prediction Uncertainty: A Mountainous Catchment of Three Gorges Reservoir Region, China

Thompson

2016

Water

View full text Add to dashboard Cite

Sparse climatic observations represent a major challenge for hydrological modeling of mountain catchments with implications for decision-making in water resources management. Employing elevation bands in the Soil and Water Assessment Tool-Sequential Uncertainty Fitting (SWAT2012-SUFI2) model enabled representation of precipitation and temperature variation with altitude in the Daning river catchment (Three Gorges Reservoir Region, China) where meteorological inputs are limited in spatial extent and are derived from observations from relatively low lying locations. Inclusion of elevation bands produced better model performance for 1987-1993 with the Nash-Sutcliffe efficiency (NSE) increasing by at least 0.11 prior to calibration. During calibration prediction uncertainty was greatly reduced. With similar R-factors from the earlier calibration iterations, a further 11% of observations were included within the 95% prediction uncertainty (95PPU) compared to the model without elevation bands. For behavioral simulations defined in SWAT calibration using a NSE threshold of 0.3, an additional 3.9% of observations were within the 95PPU while the uncertainty reduced by 7.6% in the model with elevation bands. The calibrated model with elevation bands reproduced observed river discharges with the performance in the calibration period changing to "very good" from "poor" without elevation bands. The output uncertainty of calibrated model with elevation bands was satisfactory, having 85% of flow observations included within the 95PPU. These results clearly demonstrate the requirement to account for orographic effects on precipitation and temperature in hydrological models of mountainous catchments.

show abstract

Inter-comparison of radar rainfall rate using Constant Altitude Plan Position Indicator and hybrid surface rainfall maps

Cited by 22 publications

References 29 publications

A Synthetic Quantitative Precipitation Estimation by Integrating S- and C-Band Dual-Polarization Radars over Northern Taiwan

A Synthetic Quantitative Precipitation Estimation by Integrating S- and C-Band Dual-Polarization Radars over Northern Taiwan

Quantitative Precipitation Estimates Using Machine Learning Approaches with Operational Dual-Polarization Radar Data

Impacts of Spatial Climatic Representation on Hydrological Model Calibration and Prediction Uncertainty: A Mountainous Catchment of Three Gorges Reservoir Region, China

Contact Info

Product

Resources

About