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Florian Pappenberger
and
Roberto Buizza

Abstract

In this paper the suitability of ECMWF forecasts for hydrological applications is evaluated. This study focuses on three spatial scales: the upper Danube (which is upstream of Bratislava, Slovakia), the entire Danube catchment, and the whole of Europe. Two variables, 2-m temperature and total precipitation, are analyzed. The analysis shows that precipitation forecasts follow largely in pattern the observations. The timing of the peaks between forecasted and observed precipitation and temperature is good although precipitation amounts are often underestimated. The catchment scale influences the skill scores significantly. Small catchments exhibit a larger variance as well as larger extremes. A water balance analysis suggest a 10% underestimation by the ensemble mean and an overestimation by the high-resolution forecast over the past few years. Precipitation and temperature predictions are skillful up to days 5–7. Forecasts accumulated over a longer time frame are largely more skillful than forecasts accumulated over short time periods.

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Rene Orth
,
Emanuel Dutra
, and
Florian Pappenberger

Abstract

The land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogeneous hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model.

Focusing on ECMWF’s land surface model Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), the authors present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. The authors select six poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, and total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration, and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally, the authors investigate the possibility to construct ensembles from the multiple land surface parameters.

In the uncoupled runs the authors find that minimum stomatal resistance and total soil depth have the most influence on model performance. Forecast skill scores are moreover sensitive to the same parameters as HTESSEL performance in the uncoupled analysis. The authors demonstrate the robustness of these findings by comparing multiple best-performing parameter sets and multiple randomly chosen parameter sets. The authors find better temperature and precipitation forecast skill with the best-performing parameter perturbations demonstrating representativeness of model performance across uncoupled (and hence less computationally demanding) and coupled settings.

Finally, the authors construct ensemble forecasts from ensemble members derived with different best-performing parameterizations of HTESSEL. This incorporation of parameter uncertainty in the ensemble generation yields an increase in forecast skill, even beyond the skill of the default system.

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Stephan Hemri
,
Thomas Haiden
, and
Florian Pappenberger

Abstract

This paper presents an approach to postprocess ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical postprocessing of ensemble predictions are tested: the first approach is based on multinomial logistic regression and the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on stationwise postprocessing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model.

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Claudia Di Napoli
,
Florian Pappenberger
, and
Hannah L. Cloke

Abstract

Heat waves represent a threat to human health and excess mortality is one of the associated negative effects. A health-based definition for heat waves 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 thermoregulation that estimates the thermal stress induced by air temperature, wind speed, moisture, and radiation on the human physiology. Using France as a test bed, the UTCI was computed from meteorological reanalysis data to assess the thermal stress conditions associated with 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 to 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 nighttime and suggests the UTCI 95th percentile as a health-meaningful threshold for a potential heat-health watch warning system.

Open access
David A. Lavers
,
Ervin Zsoter
,
David S. Richardson
, and
Florian Pappenberger

Abstract

Early awareness of extreme precipitation can provide the time necessary to make adequate event preparations. At the European Centre for Medium-Range Weather Forecasts (ECMWF), one tool that condenses the forecast information from the Integrated Forecasting System ensemble (ENS) is the extreme forecast index (EFI), an index that highlights regions that are forecast to have potentially anomalous weather conditions compared to the local climate. This paper builds on previous findings by undertaking a global verification throughout the medium-range forecast horizon (out to 15 days) on the ability of the EFI for water vapor transport [integrated vapor transport (IVT)] and precipitation to capture extreme observed precipitation. Using the ECMWF ENS for winters 2015/16 and 2016/17 and daily surface precipitation observations, the relative operating characteristic is used to show that the IVT EFI is more skillful than the precipitation EFI in forecast week 2 over Europe and western North America. It is the large-scale nature of the IVT, its higher predictability, and its relationship with extreme precipitation that result in its potential usefulness in these regions, which, in turn, could provide earlier awareness of extreme precipitation. Conversely, at shorter lead times the precipitation EFI is more useful, although the IVT EFI can provide synoptic-scale understanding. For the whole globe, the extratropical Northern Hemisphere, the tropics, and North America, the precipitation EFI is more useful throughout the medium range, suggesting that precipitation processes not captured in the IVT are important (e.g., tropical convection). Following these results, the operational implementation of the IVT EFI is currently being planned.

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Francesca Di Giuseppe
,
Samuel Rémy
,
Florian Pappenberger
, and
Fredrik Wetterhall

Abstract

In the absence of a dynamical fire model that could link the emissions to the weather dynamics and the availability of fuel, atmospheric composition models, such as the European Copernicus Atmosphere Monitoring Services (CAMS), often assume persistence, meaning that constituents produced by the biomass burning process during the first day are assumed constant for the whole length of the forecast integration (5 days for CAMS). While this assumption is simple and practical, it can produce unrealistic predictions of aerosol concentration due to an excessive contribution from biomass burning. This paper introduces a time-dependent factor , which modulates the amount of aerosol emitted from fires during the forecast. The factor is related to the daily change in fire danger conditions and is a function of the fire weather index (FWI). The impact of the new scheme was tested in the atmospheric composition model managed by the CAMS. Experiments from 5 months of daily forecasts in 2015 allowed for both the derivation of global statistics and the analysis of two big fire events in Indonesia and Alaska, with extremely different burning characteristics. The results indicate that time-modulated emissions based on the FWI calculations lead to predictions that are in better agreement with observations.

Open access
Ervin Zsoter
,
Hannah Cloke
,
Elisabeth Stephens
,
Patricia de Rosnay
,
Joaquin Muñoz-Sabater
,
Christel Prudhomme
, and
Florian Pappenberger

Abstract

Land surface models (LSMs) have traditionally been designed to focus on providing lower-boundary conditions to the atmosphere with less focus on hydrological processes. State-of-the-art application of LSMs includes a land data assimilation system (LDAS), which incorporates available land surface observations to provide an improved realism of surface conditions. While improved representations of the surface variables (such as soil moisture and snow depth) make LDAS an essential component of any numerical weather prediction (NWP) system, the related increments remove or add water, potentially having a negative impact on the simulated hydrological cycle by opening the water budget. This paper focuses on evaluating how well global NWP configurations are able to support hydrological applications, in addition to the traditional weather forecasting. River discharge simulations from two climatological reanalyses are compared: one “online” set, which includes land–atmosphere coupling and LDAS with an open water budget, and an “offline” set with a closed water budget and no LDAS. It was found that while the online version of the model largely improves temperature and snow depth conditions, it causes poorer representation of peak river flow, particularly in snowmelt-dominated areas in the high latitudes. Without addressing such issues there will never be confidence in using LSMs for hydrological forecasting applications across the globe. This type of analysis should be used to diagnose where improvements need to be made; considering the whole Earth system in the data assimilation and coupling developments is critical for moving toward the goal of holistic Earth system approaches.

Open access
Brandon L. Parkes
,
Hannah L. Cloke
,
Florian Pappenberger
,
Jeff Neal
, and
David Demeritt

Abstract

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.

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Louise Crochemore
,
Maria-Helena Ramos
,
Florian Pappenberger
,
Schalk Jan van Andel
, and
Andrew W. Wood

Abstract

The use of probabilistic forecasts is necessary to take into account uncertainties and allow for optimal risk-based decisions in streamflow forecasting at monthly to seasonal lead times. Such probabilistic forecasts have long been used by practitioners in the operation of water reservoirs, in water allocation and management, and more recently in drought preparedness activities. Various studies assert the potential value of hydrometeorological forecasting efforts, but few investigate how these forecasts are used in the decision-making process. Role-playing games can help scientists, managers, and decision-makers understand the extremely complex process behind risk-based decisions. In this paper, we present an experiment focusing on the use of probabilistic forecasts to make decisions on reservoir outflows. The setup was a risk-based decision-making game, during which participants acted as water managers. Participants determined monthly reservoir releases based on a sequence of probabilistic inflow forecasts, reservoir volume objectives, and release constraints. After each decision, consequences were evaluated based on the actual inflow. The analysis of 162 game sheets collected after eight applications of the game illustrates the importance of leveraging not only the probabilistic information in the forecasts but also predictions for a range of lead times. Winning strategies tended to gradually empty the reservoir in the months before the peak inflow period to accommodate its volume and avoid overtopping. Twenty percent of the participants managed to do so and finished the management period without having exceeded the maximum reservoir capacity or violating downstream release constraints. The role-playing approach successfully created an open atmosphere to discuss the challenges of using probabilistic forecasts in sequential decision-making.

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Nathalie Voisin
,
Florian Pappenberger
,
Dennis P. Lettenmaier
,
Roberto Buizza
, and
John C. Schaake

Abstract

A 10-day globally applicable flood prediction scheme was evaluated using the Ohio River basin as a test site for the period 2003–07. The Variable Infiltration Capacity (VIC) hydrology model was initialized with the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis temperatures and winds, and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) precipitation up to the day of forecast. In forecast mode, the VIC model was then forced with a calibrated and statistically downscaled ECMWF Ensemble Prediction System (EPS) 10-day ensemble forecast. A parallel setup was used where ECMWF EPS forecasts were interpolated to the spatial scale of the hydrology model. Each set of forecasts was extended by 5 days using monthly mean climatological variables and zero precipitation in order to account for the effects of the initial conditions. The 15-day spatially distributed ensemble runoff forecasts were then routed to four locations in the basin, each with different drainage areas. Surrogates for observed daily runoff and flow were provided by the reference run, specifically VIC simulation forced with ECMWF analysis fields and TMPA precipitation fields. The hydrologic prediction scheme using the calibrated and downscaled ECMWF EPS forecasts was shown to be more accurate and reliable than interpolated forecasts for both daily distributed runoff forecasts and daily flow forecasts. The initial and antecedent conditions dominated the flow forecasts for lead times shorter than the time of concentration depending on the flow forecast amounts and the drainage area sizes. The flood prediction scheme had useful skill for the 10 following days at all sites.

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