Sensitivity of initial‐condition and cloud microphysics to the forecasting of monsoon rainfall in South Asia

Sikder, Safat; Hossain, Faisal

doi:10.1002/met.1716

Cited by 9 publications

(15 citation statements)

References 56 publications

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“…More specifically, the CTL and ZTD mean absolute errors exceed 15 mm for the larger precipitation threshold, showing that both numerical experiments cannot capture the magnitude of severe rainfall (Figure 4). A similar extent of errors for intense precipitation thresholds have also been found in previous studies (e.g., see References [53,65,66]), showing that quantitative precipitation forecasting (QPF) remains a challenge for regional NWP systems due to uncertainties associated with physics parameterizations, primarily microphysics and convection, domain configuration (e.g., resolution and size), and initial conditions [67,68]. The improvement of a model's initial state through data assimilation results in more accurate QPF.…”

Section: Daily Precipitationsupporting

confidence: 81%

Assessing the Impact of GNSS ZTD Data Assimilation into the WRF Modeling System during High-Impact Rainfall Events over Greece

et al. 2020

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The derivation of global navigation satellite systems (GNSSs) tropospheric products is nowadays a state-of-the-art technique that serves both research and operational needs in a broad range of applications in meteorology. In particular, GNSS zenith tropospheric delay (ZTD) data assimilation is widely applied in Europe to enhance numerical weather predictions (NWPs). The current study presents the first attempt at introducing assimilation of ZTDs, derived from more than 48 stations of the Hellenic GNSS network, into the operational NWP system of the National Observatory of Athens (NOA) in Greece, which is based on the mesoscale Weather Research and Forecasting (WRF) model. WRF was applied during seven high-impact precipitation events covering the dry and wet season of 2018. The simulation employing the ZTD data assimilation reproduces more accurately, compared to the control experiment, the observed heavy rainfall (especially for high precipitation events, exceeding 20 mm in 24h) during both dry and wet periods. Assimilating ZTDs also improves the simulation of intense (>20 mm) convective precipitation during the time window of its occurrence in the dry season, and provides a beneficial influence during synoptic-scale events in the wet period. The above results, which are statistically significant, highlight an important positive impact of ZTD assimilation on the model's precipitation forecast skill over Greece. Overall, the modelling system's configuration, including the assimilation of ZTD observations, satisfactorily captures the spatial and temporal distribution of the observed rainfall and can therefore be used as the basis for examining further improvements in the future. meteorological applications, including nowcasting and numerical weather prediction (NWP) [8][9][10][11], as well as weather monitoring, including extreme events [12][13][14]. Special meteorological interest derives from the near-real time (NRT) ZTDs, which are estimated based on raw GNSS observations. The ZTD is a standard GNSS product expressing the total signal delay in the zenith direction above a receiver [6,15]. This vertical lag contains information on the total columnar amount of water vapor [16].In Europe, collaborative scientific efforts over the past two decades substantially contributed to the development of networks and analysis centers collecting and processing, respectively, GNSS data to compute tropospheric delays. The establishment of the European GNSS water vapor program (E-GVAP; [17]) in 2005 allowed for the operational distribution of NRT ZTD estimates to the meteorological community [6]. This service encouraged the implementation of precipitation forecast impact studies involving the assimilation of NRT ZTD observations into NWP models. Poli et al. [18] found an improvement in the prediction of precipitation patterns over France during spring and summer when assimilating ZTD data into a global four-dimensional variational (4D-var) assimilation and forecasting system. Positive impacts of ZTD data assimilation on precipita...

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Section: Daily Precipitationsupporting

confidence: 81%

Assessing the Impact of GNSS ZTD Data Assimilation into the WRF Modeling System during High-Impact Rainfall Events over Greece

et al. 2020

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“…The sensitivity of certain parameterization combinations for rainfall simulation has been well-documented by Refs. [16][17][18]. These findings concluded that the primary parameterizations and their combinations determining the simulated rainfall amount and its distribution are microphysics (MP), Cumulus parametrization (CP), and planetary boundary layer (PBL).…”

Section: Introductionmentioning

confidence: 91%

“…To select the most likely optimum parametrization combinations representing the overall best model performance for the rainfall event, we used a multi-criteria decision analysis technique using the relative closeness to the ideal solution proposed by Refs. [17,19,60]. It is called the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) Relative closeness Value (RCV).…”

Section: Verification Indicesmentioning

confidence: 99%

“…Based on these scores, we have a set of 11 criteria for each of the 24 MP-CP-PBL simulations (3 for the temporal dimension, i.e., MBE, RMSE, and SD; 7 for the spatial dimension, i.e., MBE, RMSE, SD, POD, FBI, FAR and CSI; and RE). To compute TOPSIS RCV, first, the calculated indices have to be re-scaled [17] (Table 5). The re-scaled values are related to the original error by defining the threshold values based on the original indices' minimum and maximum values.…”

Section: Verification Indicesmentioning

confidence: 99%

“…Using the TOPSIS technique, the overall model performance in the temporal dimension is calculated by a single score, the so-called "Temporal Extent Score (TES)," which is calculated as the weighted average of the values of three re-scaled 2D continuous indices (Equation ( 9)). The model performance in the spatial dimension is computed by using a single score, the so-called "Spatial Extent Score (SES)", which is calculated by taking the weighted average of the re-scaled spatial 2D categorical and 2D continuous indices (Equation ( 10)), see [17,19]. The threshold values for RMSEmax, RMSEmin, MBEmax, MBEmin, Sdmax, and Sdmin are in mm.…”

Section: Verification Indicesmentioning

confidence: 99%

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Evaluation of the WRF Model to Simulate a High-Intensity Rainfall Event over Kampala, Uganda

Umer

Ettema

Jetten

et al. 2021

Water

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Simulating high-intensity rainfall events that trigger local floods using a Numerical Weather Prediction model is challenging as rain-bearing systems are highly complex and localized. In this study, we analyze the performance of the Weather Research and Forecasting (WRF) model’s capability in simulating a high-intensity rainfall event using a variety of parameterization combinations over the Kampala catchment, Uganda. The study uses the high-intensity rainfall event that caused the local flood hazard on 25 June 2012 as a case study. The model capability to simulate the high-intensity rainfall event is performed for 24 simulations with a different combination of eight microphysics (MP), four cumulus (CP), and three planetary boundary layer (PBL) schemes. The model results are evaluated in terms of the total 24-h rainfall amount and its temporal and spatial distributions over the Kampala catchment using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. Rainfall observations from two gauging stations and the CHIRPS satellite product served as benchmark. Based on the TOPSIS analysis, we find that the most successful combination consists of complex microphysics such as the Morrison 2-moment scheme combined with Grell-Freitas (GF) and ACM2 PBL with a good TOPSIS score. However, the WRF performance to simulate a high-intensity rainfall event that has triggered the local flood in parts of the catchment seems weak (i.e., 0.5, where the ideal score is 1). Although there is high spatial variability of the event with the high-intensity rainfall event triggering the localized floods simulated only in a few pockets of the catchment, it is remarkable to see that WRF is capable of producing this kind of event in the neighborhood of Kampala. This study confirms that the capability of the WRF model in producing high-intensity tropical rain events depends on the proper choice of parametrization combinations.

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Application of the WRF model rainfall product for the localized flood hazard modeling in a data-scarce environment

et al. 2022

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Urban flood hazard model needs rainfall with high spatial and temporal resolutions for flood hazard analysis to better simulate flood dynamics in complex urban environments. However, in many developing countries, such high-quality data are scarce. Data that exist are also spatially biased toward airports and urban areas in general, where these locations may not represent flood-prone areas. One way to gain insight into the rainfall data and its spatial patterns is through numerical weather prediction models. As their performance improves, these might serve as alternative rainfall data sources for producing optimal design storms required for flood hazard modeling in data-scarce areas. To gain such insight, we developed Weather Research and Forecasting (WRF) design storms based on the spatial distribution of high-intensity rainfall events simulated at high spatial and temporal resolutions. Firstly, three known storm events (i.e., 25 June 2012, 13 April 2016, and 16 April 2016) that caused the flood hazard in the study area are simulated using the WRF model. Secondly, the potential gridcell events that are able to trigger the localized flood hazard in the catchment are selected and translated to the WRF design storm form using a quantile expression. Finally, three different WRF design storms per event are constructed: Lower, median, and upper quantiles. The results are compared with the design storms of 2- and 10-year return periods constructed based on the alternating-block method to evaluate differences from a flood hazard assessment point of view. The method is tested in the case of Kampala city, Uganda. The comparison of the design storms indicates that the WRF model design storms properties are in good agreement with the alternating-block design storms. Mainly, the differences between the produced flood characteristics (e.g., hydrographs and the number of flood gird cells) when using WRF lower quantiles (WRFLs) versus 2-year and WRF upper quantiles (WRFUs) versus 10-year alternating-block storms are very minimal. The calculated aggregated performance statistics (F scores) for the simulated flood extent of WRF design storms benchmarked with the alternating-block storms also produced a higher score of 0.9 for both WRF lower quantiles versus 2-year and WRF upper quantile versus 10-year alternating-block storm. The result suggested that the WRF design storms can be considered an added value for flood hazard assessment as they are closer to real systems causing rainfall. However, more research is needed on which area can be considered as a representative area in the catchment. The result has practical application for flood risk assessment, which is the core of integrated flood management.

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Sensitivity of initial‐condition and cloud microphysics to the forecasting of monsoon rainfall in South Asia

Cited by 9 publications

References 56 publications

Assessing the Impact of GNSS ZTD Data Assimilation into the WRF Modeling System during High-Impact Rainfall Events over Greece

Assessing the Impact of GNSS ZTD Data Assimilation into the WRF Modeling System during High-Impact Rainfall Events over Greece

Evaluation of the WRF Model to Simulate a High-Intensity Rainfall Event over Kampala, Uganda

Application of the WRF model rainfall product for the localized flood hazard modeling in a data-scarce environment

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