scholarly journals Pre-Operational Application of a WRF-Hydro-Based Fluvial Flood Forecasting System in the Southeast Mediterranean

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 437-446
Author(s):  
Christos Giannaros ◽  
Elissavet Galanaki ◽  
Vassiliki Kotroni ◽  
Konstantinos Lagouvardos ◽  
Christina Oikonomou ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Weather Prediction ◽  
Warning System ◽  
Flood Forecasting ◽  
Numerical Weather Prediction Model ◽  
Operational Mode ◽  
Related Information ◽  
Modeling Methodology ◽  
Forecasting System ◽  
Warning Services

The Southeast Mediterranean (SEM) is characterized by increased vulnerability to river/stream flooding. However, impact-oriented, operational fluvial flood forecasting is far away from maturity in the region. The current paper presents the first attempt at introducing an operational impact-based warning system in the area, which is founded on the coupling of a state-of-the-art numerical weather prediction model with an advanced spatially-explicit hydrological model. The system’s modeling methodology and forecasting scheme are presented, as well as prototype results, which were derived under a pre-operational mode. Future developments and challenges needed to be addressed in terms of validating the system and increasing its efficiency are also discussed. This communication highlights that standard approaches used in operational weather forecasting in the SEM for providing flood-related information and alerts can, and should, be replaced by advanced coupled hydrometeorological systems, which can be implemented without a significant cost on the operational character of the provided services. This is of great importance in establishing effective early warning services for fluvial flooding in the region.

scholarly journals Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

2018 ◽  
Vol 18 (8) ◽  
pp. 2183-2202 ◽  
Author(s):  
Ekrem Canli ◽  
Martin Mergili ◽  
Benni Thiebes ◽  
Thomas Glade
Keyword(s):  
High Performance ◽  
Weather Forecasting ◽  
Flood Forecasting ◽  
Ensemble Prediction ◽  
Operational Mode ◽  
Physically Based ◽  
Landslide Forecasting ◽  
Forecasting System ◽  
Prediction Systems ◽  
Performance Computing

Abstract. Landslide forecasting and early warning has a long tradition in landslide research and is primarily carried out based on empirical and statistical approaches, e.g., landslide-triggering rainfall thresholds. In the last decade, flood forecasting started the operational mode of so-called ensemble prediction systems following the success of the use of ensembles for weather forecasting. These probabilistic approaches acknowledge the presence of unavoidable variability and uncertainty when larger areas are considered and explicitly introduce them into the model results. Now that highly detailed numerical weather predictions and high-performance computing are becoming more common, physically based landslide forecasting for larger areas is becoming feasible, and the landslide research community could benefit from the experiences that have been reported from flood forecasting using ensemble predictions. This paper reviews and summarizes concepts of ensemble prediction in hydrology and discusses how these could facilitate improved landslide forecasting. In addition, a prototype landslide forecasting system utilizing the physically based TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability) model is presented to highlight how such forecasting systems could be implemented. The paper concludes with a discussion of challenges related to parameter variability and uncertainty, calibration and validation, and computational concerns.

Urban Finescale Forecasting Reveals Weather Conditions with Unprecedented Detail

2017 ◽  
Vol 98 (12) ◽  
pp. 2675-2688 ◽  
Author(s):  
R. J. Ronda ◽  
G. J. Steeneveld ◽  
B. G. Heusinkveld ◽  
J. J. Attema ◽  
A. A. M. Holtslag
Keyword(s):  
Local Governments ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Morphological Characteristics ◽  
Weather Conditions ◽  
Human Thermal Comfort ◽  
Forecasting System ◽  
The Impact ◽  
Urban Weather ◽  
Neighborhood Level

Abstract Urban landscapes impact the lives of urban dwellers by influencing local weather conditions. However, weather forecasting down to the street and neighborhood scale has been beyond the capabilities of numerical weather prediction (NWP) despite the fact that observational systems are now able to monitor urban climate at these scales. In this study, weather forecasts at intra-urban scales were achieved by exploiting recent advances in topographic element mapping and aerial photography as well as looking at detailed mappings of soil characteristics and urban morphological properties, which were subsequently incorporated into a specifically adapted Weather Research and Forecasting (WRF) Model. The urban weather forecasting system (UFS) was applied to the Amsterdam, Netherlands, metropolitan area during the summer of 2015, where it produced forecasts for the city down to the neighborhood level (a few hundred meters). Comparing these forecasts to the dense network of urban weather station observations within the Amsterdam metropolitan region showed that the forecasting system successfully determined the impact of urban morphological characteristics and urban spatial structure on local temperatures, including the cooling effect of large water bodies on local urban temperatures. The forecasting system has important practical applications for end users such as public health agencies, local governments, and energy companies. It appears that the forecasting system enables forecasts of events on a neighborhood level where human thermal comfort indices exceeded risk thresholds during warm weather episodes. These results prove that worldwide urban weather forecasting is within reach of NWP, provided that appropriate data and computing resources become available to ensure timely and efficient forecasts.

scholarly journals Application of data-based mechanistic modelling for flood forecasting at multiple locations in the Eden catchment in the National Flood Forecasting System (England and Wales)

2013 ◽  
Vol 17 (1) ◽  
pp. 177-185 ◽  
Author(s):  
D. Leedal ◽  
A. H. Weerts ◽  
P. J. Smith ◽  
K. J. Beven
Keyword(s):  
Warning System ◽  
Open Shell ◽  
Flood Forecasting ◽  
Mechanistic Modelling ◽  
Forecasting Models ◽  
Forecasting System ◽  
Environment Agency ◽  
The Uk ◽  
Modelling Approach

Abstract. The Delft Flood Early Warning System provides a versatile framework for real-time flood forecasting. The UK Environment Agency has adopted the Delft framework to deliver its National Flood Forecasting System. The Delft system incorporates new flood forecasting models very easily using an "open shell" framework. This paper describes how we added the data-based mechanistic modelling approach to the model inventory and presents a case study for the Eden catchment (Cumbria, UK).

Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill

2018 ◽  
Vol 146 (4) ◽  
pp. 1157-1180 ◽  
Author(s):  
Gregory C. Smith ◽  
Jean-Marc Bélanger ◽  
François Roy ◽  
Pierre Pellerin ◽  
Hal Ritchie ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Large Scale ◽  
Weather Forecasting ◽  
Positive Impact ◽  
Weather Prediction ◽  
Ocean Model ◽  
Forecast Skill ◽  
Medium Range ◽  
Forecasting System ◽  
The Impact

The importance of coupling between the atmosphere and the ocean for forecasting on time scales of hours to weeks has been demonstrated for a range of physical processes. Here, the authors evaluate the impact of an interactive air–sea coupling between an operational global deterministic medium-range weather forecasting system and an ice–ocean forecasting system. This system was developed in the context of an experimental forecasting system that is now running operationally at the Canadian Centre for Meteorological and Environmental Prediction. The authors show that the most significant impact is found to be associated with a decreased cyclone intensification, with a reduction in the tropical cyclone false alarm ratio. This results in a 15% decrease in standard deviation errors in geopotential height fields for 120-h forecasts in areas of active cyclone development, with commensurate benefits for wind, temperature, and humidity fields. Whereas impacts on surface fields are found locally in the vicinity of cyclone activity, large-scale improvements in the mid-to-upper troposphere are found with positive global implications for forecast skill. Moreover, coupling is found to produce fairly constant reductions in standard deviation error growth for forecast days 1–7 of about 5% over the northern extratropics in July and August and 15% over the tropics in January and February. To the authors’ knowledge, this is the first time a statistically significant positive impact of coupling has been shown in an operational global medium-range deterministic numerical weather prediction framework.

Moving towards a fully probabilistic, end-to-end, impact-based, flood forecasting and warning system

2020 ◽  
Author(s):  
Charlie Pilling
Keyword(s):  
Warning System ◽  
Flood Forecasting ◽  
Incident Management ◽  
Probabilistic Forecasting ◽  
Worst Case ◽  
Forecasting System ◽  
End To End ◽  
Forecasting And Warning ◽  
Environment Agency ◽  
The Uk

<p>Set up in 2009, the UK Flood Forecasting Centre (FFC), is a successful partnership between the Environment Agency and the Met Office to provide national, operational, flood risk guidance. At the same time, we have a development programme to continuously improve flood forecasting. Operational for over a decade, the FFC has a strong portfolio and reputation amongst its users and customers. For example, the 2019 Responder Survey reported that 94% of those who have had contact with the FFC within the last 12 months are satisfied with the services provided.  </p><p>High impact, low probability events have been a feature of the first 10 years of the Flood Forecasting Centre. Probabilistic forecasting and risk-based approaches provide approaches to identify, forecast and warn for such events. Indeed, whilst these are currently successfully employed by various National Meteorological Hydrological Centres, there is also recognition (for example, World Meteorological Organisation) that effective forecasting and warning systems should be:</p><ul><li>‘<strong>impact-based’</strong>;</li> <li>driven by ensembles or realistic scenarios through an <strong>‘end-to-end’</strong> system (rather than precipitation ranges being simplified);</li> <li>more <strong>objective</strong>, so using new tools such as ensemble ‘sub-setting’, pattern recognition and machine learning to extract most value.</li> </ul><p>The Environment Agency is implementing a new Delft-FEWS forecasting system this year, termed Incident Management Forecasting System (IMFS). This will introduce a step change in capability for probabilistic impact-based forecasting. Initially, rainfall and coastal scenarios (termed ‘best-estimate’ and ‘reasonable worst case’) will be used to drive end-to-end forecasting, which includes for example impact data bases for property, infrastructure and communities. This is very much a stepping stone in the technical (systems) and adaptive (people, culture) transformation to a <strong>fully probabilistic, end-to-end, impact-based, flood forecasting. </strong></p><p>I will share some of our recent approaches to:</p><ul><li>objective, ensemble based, forecasting, including the Natural Hazards Partnership surface water hazard impact model (driven by the Met Office MOGREPS precipitation ensembles) which goes live this year;</li> <li>scenario generation and ensemble sub-setting to provide input to end-to-end, impact-based forecasting (IMFS);</li> <li>next steps in moving to a fully probabilistic, end-to-end, impact-based, flood forecasting and warning system</li> </ul><p>I will also highlight some of our current challenges that we would love to work with others to solve.</p>

scholarly journals Solar Irradiance Nowcasting Case Studies near Sacramento

2017 ◽  
Vol 56 (1) ◽  
pp. 85-108 ◽  
Author(s):  
Jared A. Lee ◽  
Sue Ellen Haupt ◽  
Pedro A. Jiménez ◽  
Matthew A. Rogers ◽  
Steven D. Miller ◽  
...  
Keyword(s):  
Solar Irradiance ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Cumulus Parameterization ◽  
Lead Times ◽  
Shallow Cumulus ◽  
Optimal Dynamic ◽  
Forecasting System ◽  
Component Models ◽  
Solar Power Forecasting

AbstractThe Sun4Cast solar power forecasting system, designed to predict solar irradiance and power generation at solar farms, is composed of several component models operating on both the nowcasting (0–6 h) and day-ahead forecast horizons. The different nowcasting models include a statistical forecasting model (StatCast), two satellite-based forecasting models [the Cooperative Institute for Research in the Atmosphere Nowcast (CIRACast) and the Multisensor Advection-Diffusion Nowcast (MADCast)], and a numerical weather prediction model (WRF-Solar). It is important to better understand and assess the strengths and weaknesses of these short-range models to facilitate further improvements. To that end, each of these models, including four WRF-Solar configurations, was evaluated for four case days in April 2014. For each model, the 15-min average predicted global horizontal irradiance (GHI) was compared with GHI observations from a network of seven pyranometers operated by the Sacramento Municipal Utility District (SMUD) in California. Each case day represents a canonical sky-cover regime for the SMUD region and thus represents different modeling challenges. The analysis found that each of the nowcasting models perform better or worse for particular lead times and weather situations. StatCast performs best in clear skies and for 0–1-h forecasts; CIRACast and MADCast perform reasonably well when cloud fields are not rapidly growing or dissipating; and WRF-Solar, when configured with a high-spatial-resolution aerosol climatology and a shallow cumulus parameterization, generally performs well in all situations. Further research is needed to develop an optimal dynamic blending technique that provides a single best forecast to energy utility operators.

scholarly journals A meteo-hydrological prediction system based on a multi-model approach for precipitation forecasting

2008 ◽  
Vol 8 (1) ◽  
pp. 143-159 ◽  
Author(s):  
S. Davolio ◽  
M. M. Miglietta ◽  
T. Diomede ◽  
C. Marsigli ◽  
A. Morgillo ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Weather Prediction ◽  
Coupled System ◽  
Numerical Weather Prediction Model ◽  
Precipitation Forecast ◽  
Precipitation Forecasting ◽  
Quantitative Precipitation Forecast ◽  
Forecasting System ◽  
Hydrological Prediction ◽  
Model Approach

Abstract. The precipitation forecasted by a numerical weather prediction model, even at high resolution, suffers from errors which can be considerable at the scales of interest for hydrological purposes. In the present study, a fraction of the uncertainty related to meteorological prediction is taken into account by implementing a multi-model forecasting approach, aimed at providing multiple precipitation scenarios driving the same hydrological model. Therefore, the estimation of that uncertainty associated with the quantitative precipitation forecast (QPF), conveyed by the multi-model ensemble, can be exploited by the hydrological model, propagating the error into the hydrological forecast. The proposed meteo-hydrological forecasting system is implemented and tested in a real-time configuration for several episodes of intense precipitation affecting the Reno river basin, a medium-sized basin located in northern Italy (Apennines). These episodes are associated with flood events of different intensity and are representative of different meteorological configurations responsible for severe weather affecting northern Apennines. The simulation results show that the coupled system is promising in the prediction of discharge peaks (both in terms of amount and timing) for warning purposes. The ensemble hydrological forecasts provide a range of possible flood scenarios that proved to be useful for the support of civil protection authorities in their decision.

scholarly journals On the Orthogonalization of Bred Vectors

2010 ◽  
Vol 25 (4) ◽  
pp. 1219-1234 ◽  
Author(s):  
Jan D. Keller ◽  
Andreas Hense ◽  
Luis Kornblueh ◽  
Andreas Rhodin
Keyword(s):  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Forecast Performance ◽  
Ensemble Prediction System ◽  
Study Results ◽  
Bred Vectors ◽  
Forecasting System ◽  
Value Decomposition

Abstract The key to the improvement of the quality of ensemble forecasts assessing the inherent flow uncertainties is the choice of the initial ensemble perturbations. To generate such perturbations, the breeding of growing modes approach has been used for the past two decades. Here, the fastest-growing error modes of the initial model state are estimated. However, the resulting bred vectors (BVs) mainly point in the phase space direction of the leading Lyapunov vector and therefore favor one direction of growing errors. To overcome this characteristic and obtain growing modes pointing to Lyapunov vectors different from the leading one, an orthogonalization implemented as a singular value decomposition based on the similarity between the BVs is applied. This transformation is similar to that used in the ensemble transform technique currently in operational use at NCEP but with certain differences in the metric used and in the implementation. In this study, results of this approach using BVs generated in the Ensemble Forecasting System (EFS) based on the global numerical weather prediction model GME of the German Meteorological Service are presented. The gain in forecast performance achieved with the orthogonalized BV initialization is shown by using different probabilistic forecast scores evaluating ensemble reliability, variance, and resolution. For a 3-month period in summer 2007, the results are compared to forecasts generated with simple BV initializations of the same ensemble prediction system as well as operational ensemble forecasts from ECMWF and NCEP. The orthogonalization vastly improves the GME–EFS scores and makes them competitive with the two other centers.

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