A global evaluation of multi-model ensemble tropical cyclone track probability forecasts

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 (ECMWF ENS) 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 120km of a named tropical cyclone at any point in the next 7-days, and also broken down in to each 24-hour forecast period. This paper 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

Verification of heat stress thresholds for a health-based heatwave definition

Heatwaves represent a threat to human health and excess mortality is one of the associated negative effects. A health-based definition for heatwaves is therefore relevant, especially for early warning purposes, and it is here investigated via the Universal Thermal Climate Index (UTCI). The UTCI is a bioclimate index elaborated via an advanced model of human thermo-regulation that estimates the thermal stress induced by air temperature, wind speed, moisture and radiation on the human physiology. Using France as a testbed, the UTCI was computed from meteorological reanalysis data to assess the thermal stress conditions associated to heat-attributable excess mortality in five cities. UTCI values at different climatological percentiles were defined and evaluated in their ability to identify periods of excess mortality (PEMs) over 24 years. Using verification metrics such as the probability of detection (POD), the false alarm ratio (FAR) and the frequency bias (FB), daily minimum and maximum heat stress levels equal or above corresponding UTCI 95th percentiles (15±2°C and 34.5±1.5°C, respectively) for 3 consecutive days are demonstrated to correlate to PEMs with the highest sensitivity and specificity (0.69 ≤ POD ≤ 1, 0.19 ≤ FAR ≤ 0.46, 1 ≤ FB ≤ 1.48) than minimum, maximum and mean heat stress level singularly and other bioclimatological percentiles. This finding confirms the detrimental effect of prolonged, unusually high heat stress at day and night time and suggests the UTCI 95th percentile as a health meaningful threshold for a potential heat health watch warning system

What is going wrong with community engagement? How flood communities and flood authorities construct engagement and partnership working

In this paper, we discuss the need for flood risk management in England that engages stakeholders with flooding and its management processes, including knowledge gathering, planning and decision-making. By comparing and contrasting how flood communities experience ‘community engagement’ and ‘partnership working’, through the medium of an online questionnaire, with the process’s and ways of working that the Environment Agency use when ‘working with others’, we demonstrate that flood risk management is caught up in technocratic ways of working derived from long-standing historical practices of defending agricultural land from water. Despite the desire to move towards more democratised ways of working which enable an integrated approach to managing flood risk, the technocratic framing still pervades contemporary flood risk management. We establish that this can disconnect society from flooding and negatively impacts the implementation of more participatory approaches designed to engage flood communities in partnership working. Through the research in this paper it becomes clear that adopting a stepwise, one-size-fits-all approach to engagement fails to recognise that communities are heterogenous and that good engagement requires gaining an understanding of the social dimensions of a community. Successful engagement takes time, effort and the establishment of trust and utilises social learning and pooling of knowledge to create a better understanding of flooding, and that this can lead to increasing societal connectivity to flooding and its impacts

HESS Opinions: On forecast (in)consistency in a hydro-meteorological chain: curse or blessing?

Flood forecasting increasingly relies on numerical weather prediction forecasts to achieve longer lead times. One of the key difficulties that is emerging in constructing a decision framework for these flood forecasts is what to dowhen consecutive forecasts are so different that they lead to different conclusions regarding the issuing of warnings or triggering other action. In this opinion paper we explore some of the issues surrounding such forecast inconsistency (also known as "Jumpiness", "Turning points", "Continuity" or number of "Swings"). In thsi opinion paper we define forecast inconsistency; discuss the reasons why forecasts might be inconsistent; how we should analyse inconsistency; and what we should do about it; how we should communicate it and whether it is a totally undesirable property. The property of consistency is increasingly emerging as a hot topic in many forecasting environments

Reducing inconsistencies in point observations of maximum flood inundation level

Flood simulation models and hazard maps are only as good as the underlying data against which they are calibrated and tested. However, extreme flood events are by definition rare, so the observational data of flood inundation extent are limited in both quality and quantity. The relative importance of these observational uncertainties has increased now that computing power and accurate lidar scans make it possible to run high-resolution 2D models to simulate floods in urban areas. However, the value of these simulations is limited by the uncertainty in the true extent of the flood. This paper addresses that challenge by analyzing a point dataset of maximum water extent from a flood event on the River Eden at Carlisle, United Kingdom, in January 2005. The observation dataset is based on a collection of wrack and water marks from two postevent surveys. A smoothing algorithm for identifying, quantifying, and reducing localized inconsistencies in the dataset is proposed and evaluated showing positive results. The proposed smoothing algorithm can be applied in order to improve flood inundation modeling assessment and the determination of risk zones on the floodplain

Evidence of a topographic signal in surface soil moisture derived from ENVISAT ASAR wide swath data

The susceptibility of a catchment to flooding is affected by its soil moisture prior to an extreme rainfall event. While soil moisture is routinely observed by satellite instruments, results from previous work on the assimilation of remotely sensed soil moisture into hydrologic models have been mixed. This may have been due in part to the low spatial resolution of the observations used. In this study, the remote sensing aspects of a project attempting to improve flow predictions from a distributed hydrologic model by assimilating soil moisture measurements are described. Advanced Synthetic Aperture Radar (ASAR) Wide Swath data were used to measure soil moisture as, unlike low resolution microwave data, they have sufficient resolution to allow soil moisture variations due to local topography to be detected, which may help to take into account the spatial heterogeneity of hydrological processes. Surface soil moisture content (SSMC) was measured over the catchments of the Severn and Avon rivers in the South West UK. To reduce the influence of vegetation, measurements were made only over homogeneous pixels of improved grassland determined from a land cover map. Radar backscatter was corrected for terrain variations and normalized to a common incidence angle. SSMC was calculated using change detection. To search for evidence of a topographic signal, the mean SSMC from improved grassland pixels on low slopes near rivers was compared to that on higher slopes. When the mean SSMC on low slopes was 30–90%, the higher slopes were slightly drier than the low slopes. The effect was reversed for lower SSMC values. It was also more pronounced during a drying event. These findings contribute to the scant information in the literature on the use of high resolution SAR soil moisture measurement to improve hydrologic models

Forecast convergence score: a forecaster's approach to analysing hydro-meteorological forecast systems.

In this paper the properties of a hydro-meteorological forecasting system for forecasting river flows have been analysed using a probabilistic forecast convergence score (FCS). The focus on fixed event forecasts provides a forecaster's approach to system behaviour and adds an important perspective to the suite of forecast verification tools commonly used in this field. A low FCS indicates a more consistent forecast. It can be demonstrated that the FCS annual maximum decreases over the last 10 years. With lead time, the FCS of the ensemble forecast decreases whereas the control and high resolution forecast increase. The FCS is influenced by the lead time, threshold and catchment size and location. It indicates that one should use seasonality based decision rules to issue flood warnings

Impact of the representation of the infiltration on the river flow during intense rainfall events in JULES

Intense rainfall can lead to flash flooding and may cause disruption, damage and loss of life. Since flooding from intense rainfall (FFIR) events are of a short duration and occur within a limited area, they are generally poorly predicted by Numerical Weather Prediction (NWP) models. This is because of the high spatio-temporal resolution required and because of the way the convective rainfall is described in the model. Moreover, the hydrological process descriptions of Land Surface Models (LSMs) are not necessarily suitable to deal with cases of intense rainfall. In this study different representations of infiltration into the soil were developed in the JULES land surface scheme with the aim of improving prediction of the amount of surface runoff, and thus ultimately river flow. Infiltration and surface runoff are explored in a test case of intense rainfall with a variable maximum infiltration. The modelled hydraulic conductivity profile is modified with depth to reduce the rate of outgoing fluxes. The new infiltration scheme is then applied to different UK catchments. The resulting river flow is evaluated against a benchmark river flow calculated using default infiltration in JULES and also observations. The results demonstrate improved representation of the highest flows with this new variable maxiumum infiltration scheme in some catchments but limited improvement elsewhere. This scheme shows best improvement in the wettest areas of the UK where the annual mean precipitation is above 1200 mm. This work highlights the requirement for substantial further work on the hydrological process representation in JULES

Performance of GloFAS Flood Forecasts using proxy data in Uganda

Capabilities to forecast fluvial flooding are not equality spread across the globe and forecasting systems are especially limited in flood-prone low-income countries (Revilla Romero et al., 2014). The availability of higher spatial and temporal resolution remote sensing data and the increase in post processing technology have opened opportunities for fluvial forecasting at a continental and global scale (Emerton et al., 2016a) (Revilla-Romero et al., 2015). This means flood forecasts are available for regions where previously there were no forecasting capabilities. The availability of flood forecasts for flood-prone low-income countries does not directly lead to action being taken in case of flooding. The forecast based financing program of the Red Cross Climate Centre enables early action to be taken using probabilistic forecast information, with the aim of reducing the impacts of flooding (Coughlan de Perez et al 2015). The program uses a combination of forecast models including the Global Flood Awareness System (GLoFAS) and is active in multiple location including Tongo, Peru and Uganda. There are many factors at play to create an effective early warning system, including the performance of the forecast. Analysing the performance of forecasts is essential for the further improvement and development of an effective early warning system. However, in low-income countries with a low data availability this is a major challenge. This poster shows the performance of the GloFAS forecast using proxy flood event data in the North East of Uganda and poses the question: “How can the performance of forecasts be analysed when data is limited and uncertain?”

Estimating flood forecast performance using inundation data in Soroti, Uganda

This work explores the question “How can data on flood extents derived from Earth Observations (EO) be used to assess the performance of a global flood forecasting model in the ungauged catchment of the Okere and Okok Rivers in Uganda?”. The Global Flood Awareness System (GloFAS), jointly developed by the European Commission and the European Centre for Medium-Range Weather Forecasts (ECMWF), is a global hydrological forecast and monitoring system. In many parts of sub-Saharan Africa the performance of GloFAS has not been assessed. GloFAS is being used in some parts of Uganda to forecast floods. Recently Africa Risk Capacity has been developing a pan-African flood model for use in underpinning parametric flood insurance. The African Flood Extent Depiction Model (AFED) is a daily depiction of temporarily flooded areas everywhere in Africa over the past 20 years. The AFED uses satellite remote sensing from microwave sensors to map floods. The AFED data set was used to assess the performance of GloFAS for two rivers in Uganda. The AFED flood data consists of a flooded fraction per pixel which ranges from 0 to 1. This is not directly comparable to the river discharges produced by the GloFAS flood forecasting model. In order to compare both datasets and assess GloFAS’s performance, the following steps were taken: Extracting the flooded fraction of the Okok and Okere Rivers. Five methods were explored: Flooded fraction of the most downstream pixel; Catchment average flooded fraction for all non-zero pixels; Maximum flooded fraction in catchment; Number of pixels that are non-zero in the catchment; Sum of flooded fraction of all the pixels in the catchment. Comparing the recorded floods derived from newspaper articles with the EO data to establish if the AFED captures the flooding of the Okok and Okere Rivers. Establishing the range of the flood fraction that signifies flooding in recorded events. Extracting flood events using the peaks of the AFED data and the range of flooding from step 3. Assessing the performance of GloFAS and calculating its skill scores using this extended flood events. Results show that AFED data successfully identifies flooding for the two rivers and can be used to assess GloFAS’s performance