Improving high-resolution quantitative precipitation estimation via fusion of multiple radar-based precipitation products

Rafieeinasab, A.; Norouzi, Amir Mohammad; Seo, Dong Jun; Nelson, Brian R.

doi:10.1016/j.jhydrol.2015.04.066

Cited by 20 publications

(12 citation statements)

References 42 publications

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“…The QPE products include instantaneous rain rate and 1 h and 3 h rainfall accumulations. Recently, comparative evaluation of different radar based QPE products was carried out based on a limited period of record of about 1 y (Rafieei Nasab et al 2014;Rafieei Nasab et al 2015). The results show that, in general, the CASA QPE is more accurate for larger precipitation amounts whereas the MPE estimates are more accurate for smaller amounts in the study area.…”

Section: Figure 1 the Hl-rdhm Domain Encompassing Fortmentioning

confidence: 92%

High Resolution Flash Flood Forecasting for the Dallas-Fort Worth Metroplex

Habibi

Nasab

Norouzi

et al. 2016

JWMM

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Urban flash flooding is a serious problem in large highly populated areas such as the Dallas-Fort Worth metroplex (DFW). Being able to monitor and predict flash flooding at a high spatiotemporal resolution is critical to mitigating its threats and for cost effective emergency management. In this work, the prototype high resolution flash flood warning system under de velopment for DFW is described and a case study

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Section: Figure 1 the Hl-rdhm Domain Encompassing Fortmentioning

confidence: 92%

High Resolution Flash Flood Forecasting for the Dallas-Fort Worth Metroplex

Habibi

Nasab

Norouzi

et al. 2016

JWMM

Self Cite

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“…The -relation is a precipitation estimation method based on the relationship between the radar reflectivity factor ( ) and rainfall intensity ( ). The merging methods actually start from -relation estimation procedure but have additional correction procedures, such as Hu et al [8] using Kalman filter to determine the coefficients of the variation equation and calibrating precipitation; more details can be seen in [3,[8][9][10][11][12]. For the operational monitoring of rainfall, the widely used radar precipitation estimation methods include (radar reflectivity factor)-(rainfall intensity) relationship method [7] and radar-gauge merging methods, such as average calibration (AC) method [10], Kalman filter (KF) method [8,13], optimal interpolation (OI) method [14,15], and integrated Kalman filter and optimal interpolation (KFOI) [9].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Method of Multiradar Quantitative Precipitation Estimation Based on Cloud Classification and Dynamic Error Analysis

Huang

Liu

Yun

et al. 2017

Advances in Meteorology

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Relationships between radar reflectivity factor and rainfall are different in various precipitation cloud systems. In this study, the cloud systems are firstly classified into five categories with radar and satellite data to improve radar quantitative precipitation estimation (QPE) algorithm. Secondly, the errors of multiradar QPE algorithms are assumed to be different in convective and stratiform clouds. The QPE data are then derived with methods of Z-R, Kalman filter (KF), optimum interpolation (OI), Kalman filter plus optimum interpolation (KFOI), and average calibration (AC) based on error analysis on the Huaihe River Basin. In the case of flood on the early of July 2007, the KFOI is applied to obtain the QPE product. Applications show that the KFOI can improve precision of estimating precipitation for multiple precipitation types.

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“…Precipitation estimation has been improved using new methods such as a combination of satellite-based precipitation products [16][17][18][19], merging radar and satellite precipitation [20], machine learning [18,21], the fusion of multiple radar-based precipitation products method [22], probabilistic quantitative precipitation estimation (PQPE) [23,24], and the Climatological Vertical Profiles of Reflectivity Identification and Enhancement (CVPR-IE) method [25]. On the basis of Meteosat Second Generation (MSG) and Tropical Rainfall Measuring Mission (TRMM) data, Ouallouche and Ameur [18] used an artificial neural network (ANN) for modelling and presented a new method to delineate rain areas in Algeria.…”

Section: Introductionmentioning

confidence: 99%

“…The results suggested that successful merging appears to be closely related to the quality of the satellite precipitation estimates. Rafieeinasab et al [22] evaluated four procedures for fusing QPEs of different resolutions based on Fisher estimation and its conditional bias-penalized variant. They searched for a fusion algorithm that can be implemented as a postprocessor to the QPE operation in which multiple gridded QPE products are processed.…”

Section: Introductionmentioning

confidence: 99%

Two Different Integration Methods for Weather Radar-Based Quantitative Precipitation Estimation

Ren

Huang

Guan

et al. 2017

Advances in Meteorology

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We discuss two different integration methods for radar-based quantitative precipitation estimation (QPE): the echo intensity integral and the rain intensity integral. Theoretical analyses and simulations were used to test differences between these two methods. Cumulative rainfall calculated by the echo intensity integral is usually greater than that from rain intensity integral. The difference of calculated precipitation using these two methods is generally smaller for stable precipitation systems and larger for unstable precipitation systems. If the echo intensity signal is sinusoidal, the discrepancy between the two methods is most significant. For stratiform and convective precipitation, the normalized error ranges from −0.138 to −0.15 and from −0.11 to −0.122, respectively. If the echo intensity signal is linear, the normalized error ranges from 0 to −0.13 and from 0 to −0.11, respectively. If the echo intensity signal is exponential, the normalized error ranges from 0 to −0.35 and from 0 to −0.30, respectively. When both the integration scheme and real radar data were used to estimate cumulative precipitation for one day, their spatial distributions were similar.

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Improving high-resolution quantitative precipitation estimation via fusion of multiple radar-based precipitation products

Cited by 20 publications

References 42 publications

High Resolution Flash Flood Forecasting for the Dallas-Fort Worth Metroplex

High Resolution Flash Flood Forecasting for the Dallas-Fort Worth Metroplex

An Integrated Method of Multiradar Quantitative Precipitation Estimation Based on Cloud Classification and Dynamic Error Analysis

Two Different Integration Methods for Weather Radar-Based Quantitative Precipitation Estimation

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