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Yawen Shao
,
Quan J. Wang
,
Andrew Schepen
,
Dongryeol Ryu
, and
Florian Pappenberger

Abstract

Climate trends have been observed over the recent decades in many parts of the world, but current global climate models (GCMs) for seasonal climate forecasting often fail to capture these trends. As a result, model forecasts may be biased above or below the trendline. In our previous research, we developed a trend-aware forecast postprocessing method to overcome this problem. The method was demonstrated to be effective for embedding observed trends into seasonal temperature forecasts. In this study, we further develop the method for postprocessing GCM seasonal precipitation forecasts. We introduce new formulation and evaluation features to cater for special characteristics of precipitation amounts, such as having a zero lower bound and highly positive skewness. We apply the improved method to calibrate ECMWF SEAS5 forecasts of seasonal precipitation for Australia. Our evaluation shows that the calibrated forecasts reproduce observed trends over the hindcast period of 36 years. In some regions where observed trends are statistically significant, forecast skill is greatly improved by embedding trends into the forecasts. In most regions, the calibrated forecasts outperform the raw forecasts in terms of bias, skill, and reliability. Wider applications of the new trend-aware postprocessing method are expected to boost user confidence in seasonal precipitation forecasts.

Restricted access
Feyera A. Hirpa
,
Peter Salamon
,
Lorenzo Alfieri
,
Jutta Thielen-del Pozo
,
Ervin Zsoter
, and
Florian Pappenberger

Abstract

The Global Flood Awareness System (GloFAS) is a preoperational suite performing daily streamflow simulations to detect severe floods in large river basins. GloFAS defines the severity of a flood event with respect to thresholds estimated based on model-simulated streamflow climatology. Hence, determining accurate and consistent critical thresholds is important for its skillful flood forecasting. In this work, streamflow climatologies derived from two global meteorological inputs were compared, and their impacts on global flood forecasting were assessed. The first climatology is based on precipitation-corrected reanalysis data (ERA-Interim), which is currently used in the operational GloFAS forecast, while the second is derived from reforecasts that are routinely produced using the latest weather model. The results of the comparison indicate that 1) flood thresholds derived from the two datasets have substantial dissimilarities with varying characteristics across different regions of the globe; 2) the differences in the thresholds have a spatially variable impact on the severity classification of a flood; and 3) ERA-Interim produced lower flood threshold exceedance probabilities (and flood detection rates) than the reforecast for several large rivers at short forecast lead times, where the uncertainty in the meteorological forecast is smaller. Overall, it was found that the use of reforecasts, instead of ERA-Interim, marginally improved the flood detection skill of GloFAS forecasts.

Full access
Louise Arnal
,
Andrew W. Wood
,
Elisabeth Stephens
,
Hannah L. Cloke
, and
Florian Pappenberger

Abstract

Seasonal streamflow prediction skill can derive from catchment initial hydrological conditions (IHCs) and from the future seasonal climate forecasts (SCFs) used to produce the hydrological forecasts. Although much effort has gone into producing state-of-the-art seasonal streamflow forecasts from improving IHCs and SCFs, these developments are expensive and time consuming and the forecasting skill is still limited in most parts of the world. Hence, sensitivity analyses are crucial to funnel the resources into useful modeling and forecasting developments. It is in this context that a sensitivity analysis technique, the variational ensemble streamflow prediction assessment (VESPA) approach, was recently introduced. VESPA can be used to quantify the expected improvements in seasonal streamflow forecast skill as a result of realistic improvements in its predictability sources (i.e., the IHCs and the SCFs)—termed “skill elasticity”—and to indicate where efforts should be targeted. The VESPA approach is, however, computationally expensive, relying on multiple hindcasts having varying levels of skill in IHCs and SCFs. This paper presents two approximations of the approach that are computationally inexpensive alternatives. These new methods were tested against the original VESPA results using 30 years of ensemble hindcasts for 18 catchments of the contiguous United States. The results suggest that one of the methods, end point blending, is an effective alternative for estimating the forecast skill elasticities yielded by the VESPA approach. The results also highlight the importance of the choice of verification score for a goal-oriented sensitivity analysis.

Full access
Francesca Di Giuseppe
,
Florian Pappenberger
,
Fredrik Wetterhall
,
Blazej Krzeminski
,
Andrea Camia
,
Giorgio Libertá
, and
Jesus San Miguel

Abstract

A global fire danger rating system driven by atmospheric model forcing has been developed with the aim of providing early warning information to civil protection authorities. The daily predictions of fire danger conditions are based on the U.S. Forest Service National Fire-Danger Rating System (NFDRS), the Canadian Forest Service Fire Weather Index Rating System (FWI), and the Australian McArthur (Mark 5) rating systems. Weather forcings are provided in real time by the European Centre for Medium-Range Weather Forecasts forecasting system at 25-km resolution. The global system’s potential predictability is assessed using reanalysis fields as weather forcings. The Global Fire Emissions Database (GFED4) provides 11 yr of observed burned areas from satellite measurements and is used as a validation dataset. The fire indices implemented are good predictors to highlight dangerous conditions. High values are correlated with observed fire, and low values correspond to nonobserved events. A more quantitative skill evaluation was performed using the extremal dependency index, which is a skill score specifically designed for rare events. It revealed that the three indices were more skillful than the random forecast to detect large fires on a global scale. The performance peaks in the boreal forests, the Mediterranean region, the Amazon rain forests, and Southeast Asia. The skill scores were then aggregated at the country level to reveal which nations could potentially benefit from the system information to aid decision-making and fire control support. Overall it was found that fire danger modeling based on weather forecasts can provide reasonable predictability over large parts of the global landmass.

Full access
Ervin Zsótér
,
Florian Pappenberger
,
Paul Smith
,
Rebecca Elizabeth Emerton
,
Emanuel Dutra
,
Fredrik Wetterhall
,
David Richardson
,
Konrad Bogner
, and
Gianpaolo Balsamo

Abstract

In the last decade operational probabilistic ensemble flood forecasts have become common in supporting decision-making processes leading to risk reduction. Ensemble forecasts can assess uncertainty, but they are limited to the uncertainty in a specific modeling system. Many of the current operational flood prediction systems use a multimodel approach to better represent the uncertainty arising from insufficient model structure. This study presents a multimodel approach to building a global flood prediction system using multiple atmospheric reanalysis datasets for river initial conditions and multiple TIGGE forcing inputs to the ECMWF land surface model. A sensitivity study is carried out to clarify the effect of using archive ensemble meteorological predictions and uncoupled land surface models. The probabilistic discharge forecasts derived from the different atmospheric models are compared with those from the multimodel combination. The potential for further improving forecast skill by bias correction and Bayesian model averaging is examined. The results show that the impact of the different TIGGE input variables in the HTESSEL/Catchment-Based Macroscale Floodplain model (CaMa-Flood) setup is rather limited other than for precipitation. This provides a sufficient basis for evaluation of the multimodel discharge predictions. The results also highlight that the three applied reanalysis datasets have different error characteristics that allow for large potential gains with a multimodel combination. It is shown that large improvements to the forecast performance for all models can be achieved through appropriate statistical postprocessing (bias and spread correction). A simple multimodel combination generally improves the forecasts, while a more advanced combination using Bayesian model averaging provides further benefits.

Full access
Thomas C. Pagano
,
Andrew W. Wood
,
Maria-Helena Ramos
,
Hannah L. Cloke
,
Florian Pappenberger
,
Martyn P. Clark
,
Michael Cranston
,
Dmitri Kavetski
,
Thibault Mathevet
,
Soroosh Sorooshian
, and
Jan S. Verkade

Abstract

Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human–natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1) making the most of available data, 2) making accurate predictions using models, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using human-generated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors.

Full access
Richard Swinbank
,
Masayuki Kyouda
,
Piers Buchanan
,
Lizzie Froude
,
Thomas M. Hamill
,
Tim D. Hewson
,
Julia H. Keller
,
Mio Matsueda
,
John Methven
,
Florian Pappenberger
,
Michael Scheuerer
,
Helen A. Titley
,
Laurence Wilson
, and
Munehiko Yamaguchi

Abstract

The International Grand Global Ensemble (TIGGE) was a major component of The Observing System Research and Predictability Experiment (THORPEX) research program, whose aim is to accelerate improvements in forecasting high-impact weather. By providing ensemble prediction data from leading operational forecast centers, TIGGE has enhanced collaboration between the research and operational meteorological communities and enabled research studies on a wide range of topics.

The paper covers the objective evaluation of the TIGGE data. For a range of forecast parameters, it is shown to be beneficial to combine ensembles from several data providers in a multimodel grand ensemble. Alternative methods to correct systematic errors, including the use of reforecast data, are also discussed.

TIGGE data have been used for a range of research studies on predictability and dynamical processes. Tropical cyclones are the most destructive weather systems in the world and are a focus of multimodel ensemble research. Their extratropical transition also has a major impact on the skill of midlatitude forecasts. We also review how TIGGE has added to our understanding of the dynamics of extratropical cyclones and storm tracks.

Although TIGGE is a research project, it has proved invaluable for the development of products for future operational forecasting. Examples include the forecasting of tropical cyclone tracks, heavy rainfall, strong winds, and flood prediction through coupling hydrological models to ensembles.

Finally, the paper considers the legacy of TIGGE. We discuss the priorities and key issues in predictability and ensemble forecasting, including the new opportunities of convective-scale ensembles, links with ensemble data assimilation methods, and extension of the range of useful forecast skill.

Full access
David A. Lavers
,
N. Bruce Ingleby
,
Aneesh C. Subramanian
,
David S. Richardson
,
F. Martin Ralph
,
James D. Doyle
,
Carolyn A. Reynolds
,
Ryan D. Torn
,
Mark J. Rodwell
,
Vijay Tallapragada
, and
Florian Pappenberger

Abstract

A key aim of observational campaigns is to sample atmosphere–ocean phenomena to improve understanding of these phenomena, and in turn, numerical weather prediction. In early 2018 and 2019, the Atmospheric River Reconnaissance (AR Recon) campaign released dropsondes and radiosondes into atmospheric rivers (ARs) over the northeast Pacific Ocean to collect unique observations of temperature, winds, and moisture in ARs. These narrow regions of water vapor transport in the atmosphere—like rivers in the sky—can be associated with extreme precipitation and flooding events in the midlatitudes. This study uses the dropsonde observations collected during the AR Recon campaign and the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) to evaluate forecasts of ARs. Results show that ECMWF IFS forecasts 1) were colder than observations by up to 0.6 K throughout the troposphere; 2) have a dry bias in the lower troposphere, which along with weaker winds below 950 hPa, resulted in weaker horizontal water vapor fluxes in the 950–1000-hPa layer; and 3) exhibit an underdispersiveness in the water vapor flux that largely arises from model representativeness errors associated with dropsondes. Four U.S. West Coast radiosonde sites confirm the IFS cold bias throughout winter. These issues are likely to affect the model’s hydrological cycle and hence precipitation forecasts.

Open access
Anna M. Wilson
,
Alison Cobb
,
F. Martin Ralph
,
Vijay Tallapragada
,
Chris Davis
,
James Doyle
,
Luca Delle Monache
,
Florian Pappenberger
,
Carolyn Reynolds
,
Aneesh Subramanian
,
Forest Cannon
,
Jason Cordeira
,
Jennifer Haase
,
Chad Hecht
,
David Lavers
,
Jonathan J. Rutz
, and
Minghua Zheng
Full access
F. Martin Ralph
,
Forest Cannon
,
Vijay Tallapragada
,
Christopher A. Davis
,
James D. Doyle
,
Florian Pappenberger
,
Aneesh Subramanian
,
Anna M. Wilson
,
David A. Lavers
,
Carolyn A. Reynolds
,
Jennifer S. Haase
,
Luca Centurioni
,
Bruce Ingleby
,
Jonathan J. Rutz
,
Jason M. Cordeira
,
Minghua Zheng
,
Chad Hecht
,
Brian Kawzenuk
, and
Luca Delle Monache

Abstract

Water management and flood control are major challenges in the western United States. They are heavily influenced by atmospheric river (AR) storms that produce both beneficial water supply and hazards; for example, 84% of all flood damages in the West (up to 99% in key areas) are associated with ARs. However, AR landfall forecast position errors can exceed 200 km at even 1-day lead time and yet many watersheds are <100 km across, which contributes to issues such as the 2017 Oroville Dam spillway incident and regularly to large flood forecast errors. Combined with the rise of wildfires and deadly post-wildfire debris flows, such as Montecito (2018), the need for better AR forecasts is urgent. Atmospheric River Reconnaissance (AR Recon) was developed as a research and operations partnership to address these needs. It combines new observations, modeling, data assimilation, and forecast verification methods to improve the science and predictions of landfalling ARs. ARs over the northeast Pacific are measured using dropsondes from up to three aircraft simultaneously. Additionally, airborne radio occultation is being tested, and drifting buoys with pressure sensors are deployed. AR targeting and data collection methods have been developed, assimilation and forecast impact experiments are ongoing, and better understanding of AR dynamics is emerging. AR Recon is led by the Center for Western Weather and Water Extremes and NWS/NCEP. The effort’s core partners include the U.S. Navy, U.S. Air Force, NCAR, ECMWF, and multiple academic institutions. AR Recon is included in the “National Winter Season Operations Plan” to support improved outcomes for emergency preparedness and water management in the West.

Free access