Water

Research

21 pages, 4503 KiB

Open AccessArticle

SWING, The Score-Weighted Improved NowcastinG Algorithm: Description and Application

by Martina Lagasio, Lorenzo Campo, Massimo Milelli, Vincenzo Mazzarella, Maria Laura Poletti, Francesco Silvestro, Luca Ferraris, Stefano Federico, Silvia Puca and Antonio Parodi

Water 2022, 14(13), 2131; https://doi.org/10.3390/w14132131 - 4 Jul 2022

Cited by 4 | Viewed by 2110

Abstract

Because of the ongoing climate change, the frequency of extreme rainfall events at the global scale is expected to increase, resulting in higher social and economic impacts. Thus, improving the forecast accuracy and the risk communication is a fundamental goal to limit social [...] Read more.

Because of the ongoing climate change, the frequency of extreme rainfall events at the global scale is expected to increase, resulting in higher social and economic impacts. Thus, improving the forecast accuracy and the risk communication is a fundamental goal to limit social and economic damages. Both Numerical Weather Prediction (NWP) and radar-based nowcasting systems still have open issues, mainly in terms of precipitation correct time/space localization predictability and rapid forecast accuracy decay, respectively. Trying to overcome these issues, this work aims to present a nowcasting system combining an NWP model (WRF), using a 3 h rapid update cycling 3DVAR assimilation of radar reflectivity data, with the radar-based nowcasting system PhaSt through a blending technique. Moreover, an innovative post-processing algorithm named SWING (Score-Weighted Improved NowcastinG) has been developed in order to take into account the timely and spatial uncertainty in the convective field simulation. The overarching goal is to pave the way for an easy and automatic communication of the heavy rainfall warning derived by the nowcasting procedure. The results obtained applying the SWING algorithm over a case study of 22 days in the fall 2019 season suggest that the algorithm could improve the predictive capability of a traditional deterministic nowcasting forecast system, kee** a useful forecast timing and thus integrating the current forecast procedures. Eventually, the main advantage of the SWING algorithm is also its very high versatility, since it could be used with any meteorological model also in a multi-model forecast approach. Full article

(This article belongs to the Special Issue Hydrometeorological Forecasting Using the Weather Research and Forecasting Model)

► Show Figures

Figure 1

14 pages, 5526 KiB

Open AccessArticle

Detecting Extreme Rainfall Events Using the WRF-ERDS Workflow: The 15 July 2020 Palermo Case Study

by Paola Mazzoglio, Andrea Parodi and Antonio Parodi

Water 2022, 14(1), 86; https://doi.org/10.3390/w14010086 - 3 Jan 2022

Viewed by 2969

Abstract

In this work, we describe the integration of Weather and Research Forecasting (WRF) forecasts produced by CIMA Research Foundation within ITHACA Extreme Rainfall Detection System (ERDS) to increase the forecasting skills of the overall early warning system. The entire workflow is applied to [...] Read more.

In this work, we describe the integration of Weather and Research Forecasting (WRF) forecasts produced by CIMA Research Foundation within ITHACA Extreme Rainfall Detection System (ERDS) to increase the forecasting skills of the overall early warning system. The entire workflow is applied to the heavy rainfall event that affected the city of Palermo on 15 July 2020, causing urban flooding due to an exceptional rainfall amount of more than 130 mm recorded in about 2.5 h. This rainfall event was not properly forecasted by meteorological models operational at the time of the event, thus not allowing to issue an adequate alert over that area. The results highlight that the improvement in the quantitative precipitation scenario forecast skills, supported by the adoption of the H2020 LEXIS computing facilities and by the assimilation of in situ observations, allowed the ERDS system to improve the prediction of the peak rainfall depths, thus paving the way to the potential issuing of an alert over the Palermo area. Full article

(This article belongs to the Special Issue Hydrometeorological Forecasting Using the Weather Research and Forecasting Model)

► Show Figures

Figure 1

26 pages, 15465 KiB

Open AccessArticle

Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy

by Valerio Capecchi, Andrea Antonini, Riccardo Benedetti, Luca Fibbi, Samantha Melani, Luca Rovai, Antonio Ricchi and Diego Cerrai

Water 2021, 13(13), 1727; https://doi.org/10.3390/w13131727 - 22 Jun 2021

Cited by 4 | Viewed by 3139

Abstract

During the night between 9 and 10 September 2017, multiple flash floods associated with a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours was recorded. This rainfall intensity is associated with a [...] Read more.

During the night between 9 and 10 September 2017, multiple flash floods associated with a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours was recorded. This rainfall intensity is associated with a return period of higher than 200 years. As a consequence, all the largest streams of the Livorno municipality flooded several areas of the town. We used the limited-area weather research and forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. A more accurate description of the low-level flows and a better assessment of the atmospheric water vapor field showed how the assimilation of radar data can improve quantitative precipitation forecasts. Full article

(This article belongs to the Special Issue Hydrometeorological Forecasting Using the Weather Research and Forecasting Model)

► Show Figures

Figure 1

25 pages, 16547 KiB

Open AccessArticle

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

by Yakob Umer, Janneke Ettema, Victor Jetten, Gert-Jan Steeneveld and Reinder Ronda

Water 2021, 13(6), 873; https://doi.org/10.3390/w13060873 - 23 Mar 2021

Cited by 15 | Viewed by 4094

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Hydrometeorological Forecasting Using the Weather Research and Forecasting Model)

► Show Figures

Figure 1

18 pages, 2754 KiB

Open AccessArticle

A Method to Account for QPF Spatial Displacement Errors in Short-Term Ensemble Streamflow Forecasting

by Bradley Carlberg, Kristie Franz and William Gallus, Jr.

Water 2020, 12(12), 3505; https://doi.org/10.3390/w12123505 - 13 Dec 2020

Cited by 8 | Viewed by 2491

Abstract

To account for spatial displacement errors common in quantitative precipitation forecasts (QPFs), a method using systematic shifting of QPF fields was tested to create ensemble streamflow forecasts. While previous studies addressed spatial displacement using neighborhood approaches, shifting of QPF accounts for those errors [...] Read more.

To account for spatial displacement errors common in quantitative precipitation forecasts (QPFs), a method using systematic shifting of QPF fields was tested to create ensemble streamflow forecasts. While previous studies addressed spatial displacement using neighborhood approaches, shifting of QPF accounts for those errors while maintaining the structure of predicted systems, a feature important in hydrologic forecasts. QPFs from the nine-member High-Resolution Rapid Refresh Ensemble were analyzed for 46 forecasts from 6 cases covering 17 basins within the National Weather Service North Central River Forecast Center forecasting region. Shifts of 55.5 and 111 km were made in the four cardinal and intermediate directions, increasing the ensemble size to 81 members. These members were input into a distributed hydrologic model to create an ensemble streamflow prediction. Overall, the ensemble using the shifted QPFs had an improved frequency of non-exceedance and probability of detection, and thus better predicted flood occurrence. However, false alarm ratio did not improve, likely because shifting multiple QPF ensembles increases the potential to place heavy precipitation in a basin where none actually occurred. A weighting scheme based on a climatology of displacements was tested, improving overall performance slightly compared to the approach using non-weighted members. Full article

(This article belongs to the Special Issue Hydrometeorological Forecasting Using the Weather Research and Forecasting Model)

► Show Figures

Figure 1

17 pages, 3392 KiB

Open AccessArticle

Evaluation of an Application of Probabilistic Quantitative Precipitation Forecasts for Flood Forecasting

by Andrew R. Goenner, Kristie J. Franz, William A. Gallus Jr and Brett Roberts

Water 2020, 12(10), 2860; https://doi.org/10.3390/w12102860 - 14 Oct 2020

Cited by 2 | Viewed by 2560

Abstract

Probabilistic streamflow forecasts using precipitation derived from ensemble-based Probabilistic Quantitative Precipitation Forecasts (PQPFs) are examined. The PQPFs provide rainfall amounts associated with probabilities of exceedance for all grid points, which are averaged to the watershed scale for input to the operational Sacramento Soil [...] Read more.

Probabilistic streamflow forecasts using precipitation derived from ensemble-based Probabilistic Quantitative Precipitation Forecasts (PQPFs) are examined. The PQPFs provide rainfall amounts associated with probabilities of exceedance for all grid points, which are averaged to the watershed scale for input to the operational Sacramento Soil Moisture Accounting hydrologic model to generate probabilistic streamflow predictions. The technique was tested using both the High-Resolution Rapid Refresh Ensemble (HRRRE) and the High-Resolution Ensemble Forecast version 2.0 (HREF) for 11 river basins across the upper Midwest for 109 cases. The resulting discharges associated with low probability of exceedance values were too large; no events were observed having discharges above the 10% exceedance value predicted from the technique applied to both ensembles, and no events were observed having discharges above the 25% exceedance value from the HREF-based forecast. The large differences are due to using the same precipitation exceedance value at all points; it is unlikely that all watershed points would experience the heavy rainfall associated with the 5% probability of exceedance. The technique likely can be improved through calibration of the basin-average precipitation forecasts based on typical distributions of precipitation within the convective systems that dominate warm-season precipitation events or calibration of the resulting probabilistic discharge forecasts. Full article

(This article belongs to the Special Issue Hydrometeorological Forecasting Using the Weather Research and Forecasting Model)

► Show Figures

Figure 1

12 pages, 2780 KiB

Open AccessArticle

Effect of Logarithmically Transformed IMERG Precipitation Observations in WRF 4D-Var Data Assimilation System

by Jiaying Zhang, Liao-Fan Lin and Rafael L. Bras

Water 2020, 12(7), 1918; https://doi.org/10.3390/w12071918 - 5 Jul 2020

Cited by 2 | Viewed by 2319

Abstract

Precipitation estimates from numerical weather prediction (NWP) models are uncertain. The uncertainties can be reduced by integrating precipitation observations into NWP models. This study assimilates Version 04 Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) (IMERG) Final Run into the Weather Research [...] Read more.

Precipitation estimates from numerical weather prediction (NWP) models are uncertain. The uncertainties can be reduced by integrating precipitation observations into NWP models. This study assimilates Version 04 Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) (IMERG) Final Run into the Weather Research and Forecasting (WRF) model data assimilation (WRFDA) system using a four-dimensional variational (4D-Var) method. Three synoptic-scale convective precipitation events over the central United States during 2015–2017 are used as case studies. To investigate the effect of logarithmically transformed IMERG precipitation in the WRFDA system, this study reports on several experiments with six-hour and hourly assimilation windows, regular (nontransformed) and logarithmically transformed observations, and a constant observation error in regular and logarithmic spaces. Results show that hourly assimilation windows improve precipitation simulations significantly compared to six-hour windows. Logarithmically transformed precipitation does not improve precipitation estimations relative to nontransformed precipitation. However, better predictions of heavy precipitation can be achieved with a constant error in the logarithmic space (corresponding to a linearly increasing error in the regular space), which modifies the threshold of rejecting observations, and thus utilizes more observations. This study provides a cost function with logarithmically transformed observations for the 4D-Var method in the WRFDA system for future investigations. Full article

(This article belongs to the Special Issue Hydrometeorological Forecasting Using the Weather Research and Forecasting Model)

► Show Figures

Figure 1

16 pages, 6628 KiB

Open AccessArticle

The Impacts of Soil Moisture Initialization on the Forecasts of Weather Research and Forecasting Model: A Case Study in **njiang, China

by Hailiang Zhang, Junjian Liu, Huoqing Li, **anyong Meng and Ablimitijan Ablikim

Water 2020, 12(7), 1892; https://doi.org/10.3390/w12071892 - 2 Jul 2020

Cited by 12 | Viewed by 3013

Abstract

Soil moisture is a critical parameter in numerical weather prediction (NWP) models because it plays a fundamental role in the exchange of water and energy cycles between the atmosphere and the land surface through evaporation. To improve the forecast skills of the Weather [...] Read more.

Soil moisture is a critical parameter in numerical weather prediction (NWP) models because it plays a fundamental role in the exchange of water and energy cycles between the atmosphere and the land surface through evaporation. To improve the forecast skills of the Weather Research and Forecasting (WRF) model in **njiang, China, this study investigated the impacts of soil moisture initialization on the WRF forecasts by performing a series of simulations. A group of simulations was conducted using the single-column model (SCM) from 1200 UTC on 15 to 18 August 2019, at Urumchi, **njiang (43.78° N, 87.6° E); another was performed using the WRF model for a real weather case in **njiang from 0000 UTC 15 August to 1200 UTC 18 August 2019, which included an episode of heavy precipitation and gales. Our most notable findings are as follows. Specific humidity increases and potential temperature decreases persistently when soil moisture increases because of soil water evaporation. Soil moisture initialization could impact the energy budget and modulate the partition of the total available energy at the land surface significantly through evaporation and the greenhouse effect. Replacing the soil moisture with a proper multiple of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) soil moisture data could significantly improve the critical success index (CSI) and frequency bias (FBIAS) of precipitation and the root-mean-squared errors (RMSEs) of 2-m specific humidity and 2-m temperature. These findings indicate the prospect of a new way to improve the forecast skills of WRF in **njiang or other similar regions. Full article

(This article belongs to the Special Issue Hydrometeorological Forecasting Using the Weather Research and Forecasting Model)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Hydrometeorological Forecasting Using the Weather Research and Forecasting Model

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI