Investigating the forecast predictability for fluvial flooding from tropical cyclones

2021 ◽  
Author(s):  
Helen Titley ◽  
Hannah Cloke ◽  
Shaun Harrigan ◽  
Florian Pappenberger ◽  
Christel Prudhomme ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Decision Makers ◽  
Management Service ◽  
Flood Events ◽  
Key Factors ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Forecast Models ◽  
Landfall Location

<p>Global ensemble forecast models have been shown to have good skill in forecasting the track probabilities of tropical cyclones worldwide, but less well-studied is their ability to predict the hazards resulting from tropical cyclones, which in the case of fluvial flooding can extend far from the landfall location traditionally focussed on in operational tropical cyclone warnings. This work aims to investigate the key factors that influence the predictability of fluvial flood severity from tropical cyclones, using forecasts from the Global Flood Awareness System (GloFAS). GloFAS is jointly developed by the European Commission and the European Centre for Medium-Range Weather Forecasts (ECMWF) and is designed to provide a global overview of upcoming flood events to decision makers as part of the Copernicus Emergency Management Service, producing probabilistic river discharge forecasts driven by global ECMWF ensemble forecasts coupled to a hydrological model. This presentation will explore the chain of uncertainty through the forecasting process for several recent tropical cyclone flood events including Hurricane Iota and Cyclone Nivar. It investigates the influence on the overall predictability and uncertainty of the fluvial flood forecasts of various components of the forecasting chain, including the track, intensity, and precipitation forecasts for the tropical cyclone, and the hydrological catchment conditions and modelling.</p>

Exploring the links between hydrological forecast skill and multiple flood hazard drivers in southern Africa

2020 ◽  
Author(s):  
Andrea Ficchi ◽  
Hannah Cloke ◽  
Ervin Zsoter ◽  
Christel Prudhomme ◽  
Liz Stephens
Keyword(s):  
Tropical Cyclones ◽  
Southern Africa ◽  
Flood Hazard ◽  
Extreme Rainfall ◽  
Probability Of Detection ◽  
Forecast Skill ◽  
Management Service ◽  
Flood Events ◽  
Weather Forecasts ◽  
Mesoscale Convective

<p>Severe flooding in southern Africa is caused by a variety of meteorological hazards including intense tropical cyclones and depressions, mesoscale convective complexes and persistent lows which bring extreme rainfall and flood events with different characteristics. Little is known about the relative predictability of flooding associated to these different drivers, especially in operational forecasting systems. Understanding the limits of predictability for the different drivers of flooding is important to provide evidence of forecast capabilities to end-users and decision-makers and build trust in the use of the forecasting systems.</p><p>Here we explore the skill of probabilistic flood forecasts from the operational Copernicus Emergency Management Service Global Flood Awareness System (GloFAS v2) over southern Africa. GloFAS provides real-time hydrological forecasts up to 30 days ahead by coupling ensemble weather forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) with hydrological modelling. The GloFAS flood forecasts are openly available and can support humanitarians and other international organisations to trigger action before a devastating flood occurs.</p><p>Using hydrological records of past flood events over the last 20 years, the GloFAS forecast skill is assessed by analysing the probability of detection of the events over different lead-times from 1 to 30 days, as well as the consistency and accuracy of predictions of event-based characteristics such as the flood timing and duration. We stratify the analysis by the multi hazard drivers of flooding with a focus on the distinction between tropical cyclones and other types of meteorological events. We suggest that such a stratified analysis of forecast skill can help modellers better understand the sources of predictability in flood forecasts and can support humanitarians to define specific trigger levels for forecast-based action for different types of flood events.</p>

scholarly journals Machine Learning in Tropical Cyclone Forecast Modeling: A Review

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 676
Author(s):  
Rui Chen ◽  
Weimin Zhang ◽  
Xiang Wang
Keyword(s):  
Machine Learning ◽  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Numerical Models ◽  
Weather Forecasts ◽  
Dynamic Mechanisms ◽  
Forecast Models ◽  
Strong Winds ◽  
The Many ◽  
Axisymmetric Structures

Tropical cyclones have always been a concern of meteorologists, and there are many studies regarding the axisymmetric structures, dynamic mechanisms, and forecasting techniques from the past 100 years. This research demonstrates the ongoing progress as well as the many remaining problems. Machine learning, as a means of artificial intelligence, has been certified by many researchers as being able to provide a new way to solve the bottlenecks of tropical cyclone forecasts, whether using a pure data-driven model or improving numerical models by incorporating machine learning. Through summarizing and analyzing the challenges of tropical cyclone forecasts in recent years and successful cases of machine learning methods in these aspects, this review introduces progress based on machine learning in genesis forecasts, track forecasts, intensity forecasts, extreme weather forecasts associated with tropical cyclones (such as strong winds and rainstorms, and their disastrous impacts), and storm surge forecasts, as well as in improving numerical forecast models. All of these can be regarded as both an opportunity and a challenge. The opportunity is that at present, the potential of machine learning has not been completely exploited, and a large amount of multi-source data have also not been fully utilized to improve the accuracy of tropical cyclone forecasting. The challenge is that the predictable period and stability of tropical cyclone prediction can be difficult to guarantee, because tropical cyclones are different from normal weather phenomena and oceanographic processes and they have complex dynamic mechanisms and are easily influenced by many factors.

Predicting Wind Power with Reforecasts

2015 ◽  
Vol 30 (6) ◽  
pp. 1655-1662 ◽  
Author(s):  
Markus Dabernig ◽  
Georg J. Mayr ◽  
Jakob W. Messner
Keyword(s):  
Wind Power ◽  
Systematic Errors ◽  
Decision Makers ◽  
Energy Market ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Medium Range ◽  
Operational Models ◽  
Nwp Model

Abstract Energy traders and decision-makers need accurate wind power forecasts. For this purpose, numerical weather predictions (NWPs) are often statistically postprocessed to correct systematic errors. This requires a dataset of past forecasts and observations that is often limited by frequent NWP model enhancements that change the statistical model properties. Reforecasts that recompute past forecasts with a recent model provide considerably longer datasets but usually have weaker setups than operational models. This study tests the reforecasts from the National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium-Range Weather Forecasts (ECMWF) for wind power predictions. The NOAA reforecast clearly performs worse than the ECMWF reforecast, the operational ECMWF deterministic and ensemble forecasts, and a limited-area model of the Austrian weather service [Zentralanstalt für Meteorologie und Geodynamik (ZAMG)]. On the contrary, the ECMWF reforecast has, of all tested models, the smallest squared errors and one of the highest financial values in an energy market.

scholarly journals Predictability of a Mediterranean Tropical-Like Storm Downstream of the Extratropical Transition of Hurricane Helene (2006)

2013 ◽  
Vol 141 (6) ◽  
pp. 1943-1962 ◽  
Author(s):  
Florian P. Pantillon ◽  
Jean-Pierre Chaboureau ◽  
Patrick J. Mascart ◽  
Christine Lac
Keyword(s):  
Tropical Cyclone ◽  
Lead Time ◽  
Large Scale ◽  
Synoptic Situation ◽  
British Isles ◽  
Extratropical Transition ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Warm Core ◽  
Medium Range

Abstract The extratropical transition (ET) of a tropical cyclone is known as a source of forecast uncertainty that can propagate far downstream. The present study focuses on the predictability of a Mediterranean tropical-like storm (Medicane) on 26 September 2006 downstream of the ET of Hurricane Helene from 22 to 25 September. While the development of the Medicane was missed in the deterministic forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) initialized before and during ET, it was contained in the ECMWF ensemble forecasts in more than 10% of the 50 members up to 108-h lead time. The 200 ensemble members initialized at 0000 UTC from 20 to 23 September were clustered into two nearly equiprobable scenarios after the synoptic situation over the Mediterranean. In the first and verifying scenario, Helene was steered northeastward by an upstream trough during ET and contributed to the building of a downstream ridge. A trough elongated farther downstream toward Italy and enabled the development of the Medicane in 9 of 102 members. In the second and nonverifying scenario, Helene turned southeastward during ET and the downstream ridge building was reduced. A large-scale low over the British Isles dominated the circulation in Europe and only 1 of 98 members forecasted the Medicane. The two scenarios resulted from a different phasing between Helene and the upstream trough. Sensitivity experiments performed with the Méso-NH model further revealed that initial perturbations targeted on Helene and the upstream trough were sufficient in forecasting the warm-core Medicane at 84- and 108-h lead time.

scholarly journals Tropical Cyclone Track Sensitivity in Deformation Steering Flow

2018 ◽  
Vol 146 (10) ◽  
pp. 3183-3201 ◽  
Author(s):  
Ryan D. Torn ◽  
Travis J. Elless ◽  
Philippe P. Papin ◽  
Christopher A. Davis
Keyword(s):  
Tropical Cyclones ◽  
Cyclone Track ◽  
Wind Fields ◽  
Tropical Cyclone Track ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Position Errors ◽  
Steering Flow ◽  
Ensemble Mean ◽  
Medium Range

Abstract Previous studies have suggested that tropical cyclones (TCs) in deformation steering flows can be associated with large position errors and uncertainty. The goal of this study is to evaluate the sensitivity of position forecasts for three TCs within deformation wind fields [Debby (2012), Joaquin (2015), and Lionrock (2016)] using the ensemble-based sensitivity technique applied to European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts. In all three cases, the position forecasts are sensitive to uncertainty in the steering wind within 500 km of the 0-h TC position. Subsequently, the TC moves onto either side of the axis of contraction due to the ensemble perturbation steering flow. As a TC moves away from the saddle point, the ensemble members subsequently experience different ensemble-mean steering winds, which act to move the TC away from the ensemble-mean TC position along the axis of dilatation. By contrast, the position forecasts appear to exhibit less sensitivity to the steering wind more than 500 km from the initial TC position, even though the TC may interact with these features later in the forecast. Furthermore, forecasts initialized at later times are characterized by significantly lower position errors and uncertainty once it becomes clear on which side of the axis of contraction the TC will move. These results suggest that TCs in deformation steering flow could be inherently unpredictable and may benefit from densely sampling the near-storm steering flow and TC structure early in their lifetimes.

Physical Mechanisms Responsible for Track Changes and Rainfall Distributions Associated with Tropical Cyclone Landfall

2017 ◽  
Author(s):  
Johnny C.L. Chan
Keyword(s):  
Surface Roughness ◽  
Tropical Cyclone ◽  
Land Surface ◽  
Disaster Preparedness ◽  
Physical Processes ◽  
Physical Mechanisms ◽  
Model Predictions ◽  
Forecast Models ◽  
Landfall Location ◽  
Tropical Cyclone Landfall

As a tropical cyclone approaches land, its interaction with the characteristics of the land (surface roughness, topography, moisture availability, etc.) will lead to changes in its track as well as the rainfall and wind distributions near its landfall location. Accurate predictions of such changes are important in issuing warnings and disaster preparedness. In this chapter, the basic physical mechanisms that cause changes in the track and rainfall distributions when a tropical cyclone is about to make landfall are presented. These mechanisms are derived based on studies from both observations and idealized simulations. While the latter are relatively simple, they can isolate the fundamental and underlying physical processes that are inherent when an interaction between the land and the tropical cyclone circulation takes place. These processes are important in assessing the performance of the forecast models, and hence could help improve the model predictions and subsequently disaster preparedness.

scholarly journals Using subseasonal-to-seasonal (S2S) extreme rainfall forecasts for extended-range flood prediction in Australia

2015 ◽  
Vol 370 ◽  
pp. 229-234 ◽  
Author(s):  
C. J. White ◽  
S. W. Franks ◽  
D. McEvoy
Keyword(s):  
Extreme Rainfall ◽  
Relevant Information ◽  
Decision Makers ◽  
Flood Events ◽  
Climate Services ◽  
Flood Prediction ◽  
Extreme Flood ◽  
Extended Range ◽  
Forecast Models ◽  
Flooding Events

Abstract. Meteorological and hydrological centres around the world are looking at ways to improve their capacity to be able to produce and deliver skilful and reliable forecasts of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, seasonal). Making improvements to extended-range rainfall and flood forecast models, assessing forecast skill and uncertainty, and exploring how to apply flood forecasts and communicate their benefits to decision-makers are significant challenges facing the forecasting and water resources management communities. This paper presents some of the latest science and initiatives from Australia on the development, application and communication of extreme rainfall and flood forecasts on the extended-range "subseasonal-to-seasonal" (S2S) forecasting timescale, with a focus on risk-based decision-making, increasing flood risk awareness and preparedness, capturing uncertainty, understanding human responses to flood forecasts and warnings, and the growing adoption of "climate services". The paper also demonstrates how forecasts of flood events across a range of prediction timescales could be beneficial to a range of sectors and society, most notably for disaster risk reduction (DRR) activities, emergency management and response, and strengthening community resilience. Extended-range S2S extreme flood forecasts, if presented as easily accessible, timely and relevant information are a valuable resource to help society better prepare for, and subsequently cope with, extreme flood events.

scholarly journals ECMWF Activities for Improved Hurricane Forecasts

2019 ◽  
Vol 100 (3) ◽  
pp. 445-458 ◽  
Author(s):  
L. Magnusson ◽  
J.-R. Bidlot ◽  
M. Bonavita ◽  
A. R. Brown ◽  
P. A. Browne ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Weather Forecasts ◽  
Research Activities ◽  
Ocean Coupling ◽  
Hurricane Season ◽  
Medium Range ◽  
Future Challenges ◽  
Forecasting System ◽  
The Impact

AbstractTropical cyclones are some of the most devastating natural hazards and the “three beasts”—Harvey, Irma, and Maria—during the Atlantic hurricane season 2017 are recent examples. The European Centre for Medium-Range Weather Forecasts (ECMWF) is working on fulfilling its 2016–25 strategy in which early warnings for extreme events will be made possible by a high-resolution Earth system ensemble forecasting system. Several verification reports acknowledge deterministic and probabilistic tropical cyclone tracks from ECMWF as world leading. However, producing reliable intensity forecasts is still a difficult task for the ECMWF global forecasting model, especially regarding maximum wind speed. This article will put the ECMWF strategy into a tropical cyclone perspective and highlight some key research activities, using Harvey, Irma, and Maria as examples. We describe the observation usage around tropical cyclones in data assimilation and give examples of their impact. From a model perspective, we show the impact of running at 5-km resolution and also the impact of applying ocean coupling. Finally, we discuss the future challenges to tackle the errors in intensity forecasts for tropical cyclones.

scholarly journals On the Ability of Global Ensemble Prediction Systems to Predict Tropical Cyclone Track Probabilities

2010 ◽  
Vol 25 (2) ◽  
pp. 659-680 ◽  
Author(s):  
Sharanya J. Majumdar ◽  
Peter M. Finocchio
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Ensemble Prediction ◽  
Deterministic Models ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
Prediction Systems

Abstract The ability of ensemble prediction systems to predict the probability that a tropical cyclone will fall within a certain area is evaluated. Ensemble forecasts of up to 5 days issued by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Met Office (UKMET) were evaluated for the 2008 Atlantic and western North Pacific seasons. In the Atlantic, the ECMWF ensemble mean was comparable in skill to a consensus of deterministic models. Dynamic “probability circles” that contained 67% of the ECMWF ensemble captured the best track in ∼67% of all cases for 24–84-h forecasts, and were slightly underdispersive beyond 96 h. In contrast, the Goerss predicted consensus error (GPCE) was overdispersive. The addition of the UKMET ensemble yielded improvements in the short range and degradations for longer-range forecasts. The ECMWF ensemble performed similarly when the size was reduced from 50 to 20. On average, it produced a lower measure of independence between its members than an ensemble comprising different deterministic models. The 67% circles normally captured the best track during straight-line motion, but less so for sharply turning tracks. In contrast to the Atlantic, the ECMWF ensemble (and GPCE) was unable to capture sufficient verifications within the 67% probability circles in the western North Pacific, in part because of a less skillful ensemble mean (and consensus). Though further evaluations are necessary, the results demonstrate the potential for ensemble prediction systems to enhance probabilistic forecasts, and for The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) to be embraced by the operational and research communities.

Physical Mechanisms Responsible for Track Changes and Rainfall Distributions Associated with Tropical Cyclone Landfall

2017 ◽  
Author(s):  
Johnny C.L. Chan
Keyword(s):  
Surface Roughness ◽  
Tropical Cyclone ◽  
Land Surface ◽  
Disaster Preparedness ◽  
Physical Processes ◽  
Physical Mechanisms ◽  
Model Predictions ◽  
Forecast Models ◽  
Landfall Location ◽  
Tropical Cyclone Landfall

As a tropical cyclone approaches land, its interaction with the characteristics of the land (surface roughness, topography, moisture availability, etc.) will lead to changes in its track as well as the rainfall and wind distributions near its landfall location. Accurate predictions of such changes are important in issuing warnings and disaster preparedness. In this chapter, the basic physical mechanisms that cause changes in the track and rainfall distributions when a tropical cyclone is about to make landfall are presented. These mechanisms are derived based on studies from both observations and idealized simulations. While the latter are relatively simple, they can isolate the fundamental and underlying physical processes that are inherent when an interaction between the land and the tropical cyclone circulation takes place. These processes are important in assessing the performance of the forecast models, and hence could help improve the model predictions and subsequently disaster preparedness.

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