Accounting for rainfall evaporation using dual-polarization radar and mesoscale model data

Pallardy, Quinn; Fox, Neil I.

doi:10.1016/j.jhydrol.2017.12.058

Cited by 7 publications

(11 citation statements)

References 12 publications

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“…According to the data in Figure 6e,h, corrected Z DR and GMM K dp are characterized by low values in the widespread stratiform regime, indicating small and less-oblate raindrops. Moreover, the evaporation below the melting layer may further decrease K dp as a result of mass flux changes [64]. In the west and northwest of Figure 6h, GMM K dp shows some pockets of convective cells with moderately large values (>2 deg km −1 ).…”

Section: Prolonged Rain Event: 2-4 July 2016mentioning

confidence: 96%

The Quality Control and Rain Rate Estimation for the X-Band Dual-Polarization Radar: A Study of Propagation of Uncertainty

2020

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In this study, the specific differential phase ( K d p ) is applied to attenuation correction for radar reflectivity Z H and differential reflectivity Z D R , and then the corrected Z H , Z D R , and K d p are studied in the rain rate (R) estimation at the X-band. The statistical uncertainties of Z H , Z D R , and R are propagated from the uncertainty of K d p , leading to variability in their error characteristics. For the attenuation correction, a differential phase shift ( Φ d p )-based method is adopted, while the statistical uncertainties σ ( Z H ) and σ ( Z D R ) are related to σ ( K d p ) via the relations of K d p -specific attenuation ( A H ) and K d p -specific differential attenuation ( A D P ), respectively. For the rain rate estimation, the rain rates are retrieved by the power-law relations of R ( K d p ) , R ( Z h ) , R ( Z h , Z d r ) , and R ( Z h , Z d r , K d p ) . The statistical uncertainty σ ( R ) is propagated from Z H , Z D R , and K d p via the Taylor series expansion of the power-law relations. A composite method is then proposed to reduce the σ ( R ) effect. When compared to the existing algorithms, the composite method yields the best performance in terms of root mean square error and Pearson correlation coefficient, but shows slightly worse normalized bias than R ( K d p ) and R ( Z h , Z d r , K d p ) . The attenuation correction and rain rate estimation are evaluated by analyzing a squall line event and a prolonged rain event. It is clear that Z H , Z D R , and K d p show the storm structure consistent with the theoretical model, while the statistical uncertainties σ ( Z H ) , σ ( Z D R ) and σ ( K d p ) are increased in the transition region. The scatterplots of Z H , Z D R , and K d p agree with the self-consistency relations at X-band, indicating a fairly good performance. The rain rate estimation algorithms are also evaluated by the time-series of the prolonged rain event, yielding strong correlations between the composite method and rain gauge data. In addition, the statistical uncertainty σ ( R ) is propagated from Z H , Z D R , and K d p , showing higher uncertainty when the large gradient presents.

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Section: Prolonged Rain Event: 2-4 July 2016mentioning

confidence: 96%

The Quality Control and Rain Rate Estimation for the X-Band Dual-Polarization Radar: A Study of Propagation of Uncertainty

2020

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“…A simple analytical solution for raindrop evaporation assumes that a prescribed steady environment is derived, which is presented by Rogers and Yau (1996) and Li and Srivastava (2001) and later applied by Kumjian and Ryzhkov (2010) and Pallardy and Fox (2018). If the vapor density at the drop surface exceeds the vapor density in the environment, the water vapor is diffused away from the rain drop.…”

Section: Simulation Of the Raindrop Evaporation Processmentioning

confidence: 99%

“…In this work, we focused on two evolution processes of raindrops: horizontal motion and evaporation. There has been an increasing interest in radar rainfall uncertainty due to raindrop drift owing to developments in spatial radar rainfall resolution in recent years (Pallardy & Fox, 2018). After the out-of-sync issues with pixels between the radar and rain gauge were reported by Collier (1999), Mittermaier et al (2004) presented the first quantified method to calculate wind-induced error by simulating fall streaks of snow.…”

Section: Introductionmentioning

confidence: 99%

“…This premise is obviously incorrect because it ignores a variety of microphysical processes when hydrometeors descend from the radar observation height to the ground, such as horizontal drift, evaporation, aggregation, melting, break up, collision, and coalescence Lack & Fox, 2007;Testik & Rahman, 2016. Albeit the importance of investigating the interaction between these microphysical processes as well as the associated meteorological elements and radar rainfall measurements is well recognized (Austin, 1987;Cluckie et al, 2000;Li & Srivastava, 2001;Pallardy & Fox, 2018), only a few studies addressed the relevant quantitative and practical methods by embedding the predominant raindrop evolution processes in radar rainfall adjustment Song et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…The model was later improved by Xie et al (2016), who analyzed the amount of evaporation using a vertically pointing radar. Most recently, Pallardy and Fox (2018) simulated individual raindrops in the air using dual-polarization radar measurements and further concluded that raindrop evaporation played a significant role in radar rainfall estimation, especially in dry atmospheric environments.…”

Section: Introductionmentioning

confidence: 99%

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Adjustment of Radar‐Gauge Rainfall Discrepancy Due to Raindrop Drift and Evaporation Using the Weather Research and Forecasting Model and Dual‐Polarization Radar

Dai

Yang

Han

et al. 2019

Water Resources Research

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Radar‐gauge rainfall discrepancies are considered to originate from radar rainfall measurements while ignoring the fact that radar observes rain aloft while a rain gauge measures rainfall on the ground. Observations of raindrops observed aloft by weather radars consider that raindrops fall vertically to the ground without changing in size. This premise obviously does not stand because raindrop location changes due to wind drift and raindrop size changes due to evaporation. However, both effects are usually ignored. This study proposes a fully formulated scheme to numerically simulate both raindrop drift and evaporation in the air and reduces the uncertainties of radar rainfall estimation. The Weather Research and Forecasting model is used to simulate high‐resolution three‐dimensional atmospheric fields. A dual‐polarization radar retrieves the raindrop size distribution for each radar pixel. Three schemes are designed and implemented using the Hameldon Hill radar in Lancashire, England. The first considers only raindrop drift, the second considers only evaporation, and the last considers both aspects. Results show that wind advection can cause a large drift for small raindrops. Considerable loss of rainfall is observed due to raindrop evaporation. Overall, the three schemes improve the radar‐gauge correlation by 3.2%, 2.9%, and 3.8% and reduce their discrepancy by 17.9%, 8.6%, and 21.7%, respectively, over eight selected events. This study contributes to the improvement of quantitative precipitation estimation from radar polarimetry and allows a better understanding of precipitation processes.

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Uncertainty analysis of radar rainfall estimates induced by atmospheric conditions using long short-term memory networks

Yang

Dai

Han

et al. 2020

Journal of Hydrology

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Accounting for rainfall evaporation using dual-polarization radar and mesoscale model data

Cited by 7 publications

References 12 publications

The Quality Control and Rain Rate Estimation for the X-Band Dual-Polarization Radar: A Study of Propagation of Uncertainty

The Quality Control and Rain Rate Estimation for the X-Band Dual-Polarization Radar: A Study of Propagation of Uncertainty

Adjustment of Radar‐Gauge Rainfall Discrepancy Due to Raindrop Drift and Evaporation Using the Weather Research and Forecasting Model and Dual‐Polarization Radar

Uncertainty analysis of radar rainfall estimates induced by atmospheric conditions using long short-term memory networks

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