WRF hourly evaluation for extreme precipitation events

Merino, Andrés; García‐Ortega, Eduardo; Navarro, Andrés; Sánchez, José Luis; Tapiador, Francisco J.

doi:10.1016/j.atmosres.2022.106215

Cited by 24 publications

(10 citation statements)

References 59 publications

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“…The model structure also has some influence (lumped > gridded), although this influence is negligible compared to the simulated rainfall error. The results show minimal differences in depends on the quality of the driven data (i.e., FNL data), necessarily causes parallel uncertainty in hydrological forecasts (Merino et al, 2022). We analyzed the temporal and spatial errors inherent in the simulated WRF output rainfall, influencing the simulation results of the coupled systems.…”

Section: Discussionmentioning

confidence: 99%

Coupling WRF with HEC-HMS and WRF-Hydro for flood forecasting in typical mountainous catchments of northern China

Jam-Jalloh,

Liu,

Wang

et al. 2024

Preprint

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Abstract. The atmospheric-hydrological coupling systems are essential in flood forecasting because they allow for more improved and comprehensive prediction of flood events with an extended forecast lead time. Achieving this goal relies on a reliable hydrological model system that enhances both rainfall predictions and hydrological forecasts. This study evaluated the potential of coupling the mesoscale numerical weather prediction model, i.e., the weather research and forecasting (WRF) model, with different hydrological modeling systems to improve the accuracy of flood simulation. The fully-distributed WRF-Hydro and a simi-distributed Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) modeling systems were coupled with the WRF model, and the lumped HEC-HMS model was also adopted using the observed gauge precipitation as a benchmark to test the model uncertainty. Four distinct storm events from two mountainous catchments in northern China, characterized by varying spatial and temporal rainfall patterns were selected as case studies. Comparative analyses of the simulated flooding processes were carried out to evaluate and compare the performances of the coupled systems with different complexities. The coupled WRF/HEC-HMS system performed better for long-duration storm events and obtained optimal performance for storm events uniformly distributed both temporally and spatially, as it adapted to more rapid recession processes of floods. However, the coupled WRF/HEC-HMS system did not adequately capture the magnitude of the storm events as it had a larger flow peak error. On the other hand, the fully distributed WRF/WRF-Hydro system performed better for shorter-duration floods with higher flow peaks as it can adapt to the simulation of flash floods. However, the performance of the system became poor as uniformity decreased. The performance of the lumped HEC-HMS indicates some source of uncertainty in the hydrological model when compared with the coupled WRF/HEC-HMS, but a larger magnitude error was found in the WRF output rainfall. The results of this study can help establish an adaptive atmospheric-hydrologic coupling system to improve flood forecasting for different watersheds and climatic characteristics.

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Section: Discussionmentioning

confidence: 99%

Coupling WRF with HEC-HMS and WRF-Hydro for flood forecasting in typical mountainous catchments of northern China

Jam-Jalloh,

Liu,

Wang

et al. 2024

Preprint

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“…The investigation was also similar to previous evaluation studies. Merino et al (2022) found that the Thompson scheme had the best performance in the verification of extreme precipitation in the Mediterranean by using the station data. For the microphysical parameter evaluation of extreme precipitation in the central Himalayas, the model results driven by the Thompson scheme were found to have the best consistency with the observation results (Karki et al, 2018).…”

Section: Applicability Of Physical Schemesmentioning

confidence: 99%

Sensitivity of WRF multiple parameterization schemes to extreme precipitation event over the Poyang Lake Basin of China

Deng

Chi²,

Huang³

et al. 2023

Front. Environ. Sci.

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Global warming leads to more frequent and more violent extreme weather events, which cause severe natural disasters. The sensitivity of physical schemes in numerical weather prediction models for extreme precipitation is a significant challenge. To simulate a heavy precipitation process from 1 July 2020 to 10 July 2020 over the Poyang Lake Basin, where floods occur frequently, the Weather Research and Forecast model (WRF) was employed. The observation (OBS) from 92 meteorological stations was applied to evaluate WRF performance. To assess the optimal parameter, 27 combinations of multiphysics schemes based on three cumulus schemes (CUs), three microphysical schemes (MPs) and three land surface schemes (LSMs) were employed in WRF simulation. The Euclid distance (ED) was derived to evaluate the performance of the modelled total cumulative precipitation (TCP). The results showed that the simulation generally reproduced the spatial distribution of precipitation and captured the storm centre. In general, WRF underestimated the observation for most areas but overestimated the observation in the northeastern part of the basin. For total cumulative precipitation (TCP), the spatial correlation coefficients ranged from 0.6 to 0.8. M11 had the highest value of 0.796. The scores (TS, POD, FAR) of M15 were 0.66, 0.79, and 0.21, respectively. With the maximum similarity of 0.518, M7 (BMJ-WDM6-Noah) showed the best performance in ED based on six evaluation metrics. The mean values of OBS and M19 were 259.34 and 218.33 mm, respectively. M19 (NT-Thompson-Noah) was closest to the OBS for the range of the TCP. In terms of daily precipitation, the CC of M3 was maximum (0.96), and the RMSE of M24 was minimum (11.9 mm. day-1). The minimum error between the simulation and OBS was found for M3 (NT-Thompson-Noah). Therefore, with a comprehensive evaluation, five optimal combinations of physical schemes (M22, M19, M9, M3, M21) were found for the PLB, which is of great significance for extreme precipitation simulation and flood forecasting.

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“…LAMs are generally considered a valuable tool for meteorological nowcasting and are a powerful instrument for alerting [12,16,17,70]. In the recent past, extensive benchmarks have been carried out by several authors to assess the performance of LAMs and WRF to simulate orographic precipitation [71][72][73][74][75]. The main outcomes of these studies have highlighted several issues that still affect these models: strong sensitivity to microphysical parameterization [72,75,76], dependence on domain sub-grid resolution and orography discretization [71,72], dependence on convection triggering function [77,78], and model initialization [17,29,73,79].…”

Section: Introductionmentioning

confidence: 99%

“…In the recent past, extensive benchmarks have been carried out by several authors to assess the performance of LAMs and WRF to simulate orographic precipitation [71][72][73][74][75]. The main outcomes of these studies have highlighted several issues that still affect these models: strong sensitivity to microphysical parameterization [72,75,76], dependence on domain sub-grid resolution and orography discretization [71,72], dependence on convection triggering function [77,78], and model initialization [17,29,73,79]. In this regard, the usability of the LAMs model is not trivial due to their complexity that affects both their implementation (code, update and run) but also the interpretation of the output data [17,75,80].…”

Section: Introductionmentioning

confidence: 99%

“…LAM outputs may be affected by large uncertainties since they are a product of a model simulation that could be varied sensibly in the function of the initial conditions of the atmospheric state variable [17,19,68,[73][74][75]79,[81][82][83]. Their affordability sometimes is less reliable than the ground-based rain gauge records, especially if intense events are expected [16,75].…”

Section: Introductionmentioning

confidence: 99%

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Orographic Precipitation Extremes: An Application of LUME (Linear Upslope Model Extension) over the Alps and Apennines in Italy

Abbate

Papini

Longoni

2022

Water

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Critical hydrometeorological events are generally triggered by heavy precipitation. In complex terrain, precipitation may be perturbed by the upslope raising of the incoming humid airflow, causing in some cases extreme rainfall. In this work, the application of LUME—Linear Upslope Model Extension—to a group of extreme events that occurred across mountainous areas of the Central Alps and Apennines in Italy is presented. Based on the previous version, the model has been “extended” in some aspects, proposing a methodology for physically estimating the time-delay coefficients as a function of precipitation efficiency. The outcomes of LUME are encouraging for the cases studied, revealing the intensification of precipitation due to the orographic effect. A comparison between the reference rain gauge data and the results of the simulations showed good agreement. Since extreme precipitation is expected to increase due to climate change, especially across the Mediterranean region, LUME represents an effective tool to investigate more closely how these extreme phenomena originate and evolve in mountainous areas that are subject to potential hydrometeorological risks.

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WRF hourly evaluation for extreme precipitation events

Cited by 24 publications

References 59 publications

Coupling WRF with HEC-HMS and WRF-Hydro for flood forecasting in typical mountainous catchments of northern China

Coupling WRF with HEC-HMS and WRF-Hydro for flood forecasting in typical mountainous catchments of northern China

Sensitivity of WRF multiple parameterization schemes to extreme precipitation event over the Poyang Lake Basin of China

Orographic Precipitation Extremes: An Application of LUME (Linear Upslope Model Extension) over the Alps and Apennines in Italy

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