| 1. |
- Dance, Sarah L., et al.
(författare)
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Improvements in Forecasting Intense Rainfall : Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project
- 2019
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Ingår i: Atmosphere. - : MDPI. - 2073-4433 .- 2073-4433. ; 10:3
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Forskningsöversikt (refereegranskat)abstract
- The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the project's achievements. We highlight new radar techniques: correcting for attenuation of the radar return; correction for beams that are over 90% blocked by trees or towers close to the radar; and direct assimilation of radar reflectivity and refractivity. We discuss the treatment of uncertainty in data assimilation: new methods for estimation of observation uncertainties with novel applications to Doppler radar winds, Atmospheric Motion Vectors, and satellite radiances; a new algorithm for implementation of spatially-correlated observation error statistics in operational data assimilation; and innovative treatment of moist processes in the background error covariance model. We present results indicating a link between the spatial predictability of convection and convective regimes, with potential to allow improved forecast interpretation. The research was carried out as a partnership between University researchers and the Met Office (UK). We discuss the benefits of this approach and the impact of our research, which has helped to improve operational forecasts for convective rainfall events.
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| 2. |
- Emerton, R., et al.
(författare)
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Predicting the unprecedented : forecasting the June 2021 Pacific Northwest heatwave
- 2022
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Ingår i: Weather. - : John Wiley & Sons. - 0043-1656 .- 1477-8696. ; 77:8, s. 272-279
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Tidskriftsartikel (refereegranskat)abstract
- In June 2021, an unprecedented extreme heatwave impacted the Pacific Northwest of North America, resulting in more than 1000 excess deaths and affecting infrastructure and wildlife. Predicting such extreme events is key for preparedness and early action, and beyond temperature, it is important to consider biometeorological forecasts, accounting for the effects of the environment on the human body. The performance of ECMWF's (the European Centre for Medium-Range Weather Forecasts) temperature and heat stress (Humidex and the Universal Thermal Climate Index) forecasts during this heatwave is explored, and highlights that several days in advance, an event surpassing the maximum of climatology was predicted with extremely high confidence (100% of ensemble members) – successfully predicting the unprecedented.
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| 3. |
- Mason, David C., et al.
(författare)
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Floodwater detection in urban areas using Sentinel-1 and WorldDEM data
- 2021
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Ingår i: Journal of Applied Remote Sensing. - : SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS. - 1931-3195. ; 15:3
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Tidskriftsartikel (refereegranskat)abstract
- Remote sensing using synthetic aperture radar (SAR) is an important tool for emergency flood incident management. At present, operational services are mainly aimed at flood mapping in rural areas, as mapping in urban areas is hampered by the complicated backscattering mechanisms occurring there. A method for detecting flooding at high resolution in urban areas that may contain dense housing is presented. This largely uses remotely sensed data sets that are readily available on a global basis, including open-access Sentinel-1 SAR data, the WorldDEM digital surface model (DSM), and open-accessWorld Settlement Footprint data to identify urban areas. The method is a change detection technique that locally estimates flood levels in urban areas. It searches for increased SAR backscatter in the post-flood image due to double scattering between water (rather than unflooded ground) and adjacent buildings, and reduced SAR backscatter in areas away from high slopes. Areas of urban flooding are detected by comparing an interpolated flood level surface to the DSM. The method was tested on two flood events that occurred in the UK during the storms of Winter 2019-2020. High urban flood detection accuracies were achieved for the event in moderate density housing. The accuracy was reduced for the event in dense housing, when street widths became comparable to the DSM resolution, though it would still be useful for incident management. The method has potential for operational use for detecting urban flooding in near real-time on a global basis. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.
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| 4. |
- Mason, David C., et al.
(författare)
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Improving Urban Flood Mapping by Merging Synthetic Aperture Radar-Derived Flood Footprints with Flood Hazard Maps
- 2021
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Ingår i: Water. - : MDPI. - 2073-4441. ; 13:11
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Tidskriftsartikel (refereegranskat)abstract
- Remotely sensed flood extents obtained in near real-time can be used for emergency flood incident management and as observations for assimilation into flood forecasting models. High-resolution synthetic aperture radar (SAR) sensors have the potential to detect flood extents in urban areas through clouds during both day- and night-time. This paper considers a method for detecting flooding in urban areas by merging near real-time SAR flood extents with model-derived flood hazard maps. This allows a two-way symbiosis, whereby currently available SAR urban flood extent improves future model flood predictions, while flood hazard maps obtained after the SAR overpasses improve the SAR estimate of urban flood extents. The method estimates urban flooding using SAR backscatter only in rural areas adjacent to urban ones. It was compared to an existing method using SAR returns in both rural and urban areas. The method using SAR solely in rural areas gave an average flood detection accuracy of 94% and a false positive rate of 9% in the urban areas and was more accurate than the existing method.
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| 5. |
- Mason, David C., et al.
(författare)
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Robust algorithm for detecting floodwater in urban areas using synthetic aperture radar images
- 2018
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Ingår i: Journal of Applied Remote Sensing. - : SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS. - 1931-3195. ; 12:4
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Tidskriftsartikel (refereegranskat)abstract
- Flooding is a major hazard in both rural and urban areas worldwide, but it is in urban areas that the impacts are most severe. High-resolution synthetic aperture radar (SAR) sensors are able to detect flood extents in urban areas during both day- and night-time. If obtained in near real time, these flood extents can be used for emergency flood relief management or as observations for assimilation into flood forecasting models. A method for detecting flooding in urban areas using near real-time SAR data is developed and extensively tested under a variety of scenarios involving different flood events and different images. The method uses an SAR simulator in conjunction with LiDAR data of the urban area to predict areas of radar shadow and layover in the image caused by buildings and taller vegetation. Of the urban water pixels visible to the SAR, the flood detection accuracy averaged over the test examples is 83%, with a false alarm rate of 9%. The results indicate that flooding can be detected in the urban area to reasonable accuracy but that this accuracy is limited partly by the SAR's poor visibility of the urban ground surface due to shadow and layover. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.
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| 6. |
- Mason, David C., et al.
(författare)
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Toward improved urban flood detection using Sentinel-1 : dependence of the ratio of post- to preflood double scattering cross sections on building orientation
- 2023
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Ingår i: Journal of Applied Remote Sensing. - : SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS. - 1931-3195. ; 17:1
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Tidskriftsartikel (refereegranskat)abstract
- High-resolution synthetic aperture radar (SAR) sensors are now commonly used for flood detection. Automated detection tends to be limited to rural areas owing to the complicated backscattering mechanisms occurring in urban areas. Flooding can be identified in urban areas by searching for increased SAR backscatter in a postflood image due to double scattering between water and adjacent buildings, compared with a preflood image where double scattering is between unflooded ground and buildings. For co-polarized data, if f is the angle between the building wall and the satellite direction of travel, double scattering is strongest for f = 0 deg and falls off as f increases. Theoretical studies estimating the ratio of flooded-to-unflooded double scatterer (DS) radar cross section (RCS) using X-band SAR data, found that the ratio was high at f = 0 deg but only small at f > 10 deg. Ostensibly, this implies that few DSs might be detected in an urban area. However, experiments on real images have called into question the veracity of the modeling. We describe an empirical study to examine the relationship between the flooded-to-unflooded DS RCS ratio and f in Sentinel-1 (S-1) C-band data. We use high-resolution light detection and ranging and aerial photographs so that f can be measured accurately and is based on S-1 images from flood events that occurred in the United Kingdom during the storms of winter 2019 to 2020. Results indicate that vertical-vertical polarization is better than vertical-horizontal at distinguishing flooded from unflooded DS; that the theoretical model used underestimates the number of DS with high RCS ratios in the f range 10 deg to 30 deg; and that sufficient DS ground heights can be determined to estimate an accurate local average flood level, although in high density housing there are less of these due to the presence of adjacent buildings.
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| 7. |
- Matthews, Gwyneth, et al.
(författare)
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Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System
- 2022
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Ingår i: Hydrology and Earth System Sciences. - : Copernicus Publications. - 1027-5606 .- 1607-7938. ; 26:11, s. 2939-2968
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Tidskriftsartikel (refereegranskat)abstract
- Streamflow forecasts provide vital information to aid emergency response preparedness and disaster risk reduction. Medium-range forecasts are created by forcing a hydrological model with output from numerical weather prediction systems. Uncertainties are unavoidably introduced throughout the system and can reduce the skill of the streamflow forecasts. Post-processing is a method used to quantify and reduce the overall uncertainties in order to improve the usefulness of the forecasts. The post-processing method that is used within the operational European Flood Awareness System is based on the model conditional processor and the ensemble model output statistics method. Using 2 years of reforecasts with daily timesteps, this method is evaluated for 522 stations across Europe. Post-processing was found to increase the skill of the forecasts at the majority of stations in terms of both the accuracy of the forecast median and the reliability of the forecast probability distribution. This improvement is seen at all lead times (up to 15 d) but is largest at short lead times. The greatest improvement was seen in low-lying, large catchments with long response times, whereas for catchments at high elevation and with very short response times the forecasts often failed to capture the magnitude of peak flows. Additionally, the quality and length of the observational time series used in the offline calibration of the method were found to be important. This evaluation of the post-processing method, and specifically the new information provided on characteristics that affect the performance of the method, will aid end users in making more informed decisions. It also highlights the potential issues that may be encountered when developing new post-processing methods.
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| 8. |
- Titley, Helen A., et al.
(författare)
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A global evaluation of multi-model ensemble tropical cyclone track probability forecasts
- 2020
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Ingår i: Quarterly Journal of the Royal Meteorological Society. - : Wiley. - 0035-9009 .- 1477-870X. ; 146:726, s. 531-545
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Tidskriftsartikel (refereegranskat)abstract
- At the Met Office, dynamic ensemble forecasts from the Met Office Global and Regional Ensemble Prediction System (MOGREPS-G), the European Centre for Medium-Range Weather Forecasts Ensemble (ECMWFENS) and National Centers for Environmental Prediction Global Ensemble Forecast System (NCEP GEFS) global ensemble forecast models are post-processed to identify and track tropical cyclones. The ensemble members from each model are also combined into a 108-member multi-model ensemble. Track probability forecasts are produced for named tropical cyclones showing the probability of a location being within 120 km of a named tropical cyclone at any point in the next 7 days, and also broken down into each 24-hour forecast period. This study presents the verification of these named-storm track probabilities over a two-year period across all global tropical cyclone basins, and compares the results from basin to basin. The combined multi-model ensemble is found to increase the skill and value of the track probability forecasts over the best-performing individual ensemble (ECMWF ENS), for both overall 7-day track probability forecasts and 24-hour track probabilities. Basin-based and storm-based verification illustrates that the best performing individual ensemble can change from basin to basin and from storm to storm, but that the multi-model ensemble adds skill in every basin, and is also able to match the best performing individual ensemble in terms of overall probabilistic forecast skill in several high-profile case-studies. This study helps to illustrate the potential value and skill to be gained if operational tropical cyclone forecasting can continue to migrate away from a deterministic-focused forecasting environment to one where the probabilistic situation-based uncertainty information provided by the dynamic multi-model ensembles can be incorporated into operational forecasts and warnings.
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| 9. |
- Wagener, Thorsten, et al.
(författare)
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Knowledge gaps in our perceptual model of Great Britain's hydrology
- 2021
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Ingår i: Hydrological Processes. - : John Wiley & Sons. - 0885-6087 .- 1099-1085. ; 35:7
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Tidskriftsartikel (refereegranskat)abstract
- There is a no lack of significant open questions in the field of hydrology. How will hydrological connectivity between freshwater bodies be altered by future human alterations to the hydrological cycle? Where does water go when it rains? Or what is the future space-time variability of flood and drought events? However, the answers to these questions will vary with location due to the specific and often poorly understood local boundary conditions and system properties that control the functional behaviour of a catchment or any other hydrologic control volume. We suggest that an open, shared and evolving perceptual model of a region's hydrology is critical to tailor our science questions, as it would be for any other study domain from the plot to the continental scale. In this opinion piece, we begin to discuss the elements of and point out some knowledge gaps in the perceptual model of the terrestrial water cycle of Great Britain. We discuss six major knowledge gaps and propose four key ways to reduce them. While the specific knowledge gaps in our perceptual model do not necessarily transfer to other places, we believe that the development of such perceptual models should be at the core of the debate for all hydrologic communities, and we encourage others to have a similar debate for their hydrologic domain.
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| 10. |
- Arnal, Louise, et al.
(författare)
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Skilful seasonal forecasts of streamflow over Europe?
- 2018
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Ingår i: Hydrology and Earth System Sciences. - : Copernicus GmbH. - 1027-5606 .- 1607-7938. ; 22:4, s. 2057-2072
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Tidskriftsartikel (refereegranskat)abstract
- This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate-model-based seasonal streamflow forecasting.
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