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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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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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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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David S. Richardson
,
Hannah L. Cloke
,
John A. Methven
, and
Florian Pappenberger

Abstract

We investigate the run-to-run consistency (jumpiness) of ensemble forecasts of tropical cyclone tracks from three global centers: ECMWF, the Met Office, and NCEP. We use a divergence function to quantify the change in cross-track position between consecutive ensemble forecasts initialized at 12-h intervals. Results for the 2019–21 North Atlantic hurricane season show that the jumpiness varied substantially between cases and centers, with no common cause across the different ensemble systems. Recent upgrades to the Met Office and NCEP ensembles reduced their overall jumpiness to match that of the ECMWF ensemble. The average divergence over the set of cases provides an objective measure of the expected change in cross-track position from one forecast to the next. For example, a user should expect on average that the ensemble mean position will change by around 80–90 km in the cross-track direction between a forecast for 120 h ahead and the updated forecast made 12 h later for the same valid time. This quantitative information can support users’ decision-making, for example, in deciding whether to act now or wait for the next forecast. We did not find any link between jumpiness and skill, indicating that users should not rely on the consistency between successive forecasts as a measure of confidence. Instead, we suggest that users should use ensemble spread and probabilistic information to assess forecast uncertainty, and consider multimodel combinations to reduce the effects of jumpiness.

Significance Statement

Forecasting the tracks of tropical cyclones is essential to mitigate their impacts on society. Numerical weather prediction models provide valuable guidance, but occasionally there is a large jump in the predicted track from one run to the next. This jumpiness complicates the creation and communication of consistent forecast advisories and early warnings. In this work we aim to better understand forecast jumpiness and we provide practical information to forecasters to help them better use the model guidance. We show that the jumpiest cases are different for different modeling centers, that recent model upgrades have reduced forecast jumpiness, and that there is not a strong link between jumpiness and forecast skill.

Open 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
Laurel L. DeHaan
,
Anna M. Wilson
,
Brian Kawzenuk
,
Minghua Zheng
,
Luca Delle Monache
,
Xingren Wu
,
David A. Lavers
,
Bruce Ingleby
,
Vijay Tallapragada
,
Florian Pappenberger
, and
F. Martin Ralph

Abstract

Atmospheric River Reconnaissance has held field campaigns during cool seasons since 2016. These campaigns have provided thousands of dropsonde data profiles, which are assimilated into multiple global operational numerical weather prediction models. Data denial experiments, conducted by running a parallel set of forecasts that exclude the dropsonde information, allow testing of the impact of the dropsonde data on model analyses and the subsequent forecasts. Here, we investigate the differences in skill between the control forecasts (with dropsonde data assimilated) and denial forecasts (without dropsonde data assimilated) in terms of both precipitation and integrated vapor transport (IVT) at multiple thresholds. The differences are considered in the times and locations where there is a reasonable expectation of influence of an intensive observation period (IOP). Results for 2019 and 2020 from both the European Centre for Medium-Range Weather Forecasts (ECMWF) model and the National Centers for Environmental Prediction (NCEP) global model show improvements with the added information from the dropsondes. In particular, significant improvements in the control forecast IVT generally occur in both models, especially at higher values. Significant improvements in the control forecast precipitation also generally occur in both models, but the improvements vary depending on the lead time and metrics used.

Significance Statement

Atmospheric River Reconnaissance is a program that uses targeted aircraft flights over the northeast Pacific to take measurements of meteorological fields. These data are then ingested into global weather models with the intent of improving the initial conditions and resulting forecasts along the U.S. West Coast. The impacts of these observations on two global numerical weather models were investigated to determine their influence on the forecasts. The integrated vapor transport, a measure of both wind and humidity, saw significant improvements in both models with the additional observations. Precipitation forecasts were also improved, but with differing results between the two models.

Restricted access
Alison Cobb
,
F. Martin Ralph
,
Vijay Tallapragada
,
Anna M. Wilson
,
Christopher A. Davis
,
Luca Delle Monache
,
James D. Doyle
,
Florian Pappenberger
,
Carolyn A. Reynolds
,
Aneesh Subramanian
,
Peter G. Black
,
Forest Cannon
,
Chris Castellano
,
Jason M. Cordeira
,
Jennifer S. Haase
,
Chad Hecht
,
Brian Kawzenuk
,
David A. Lavers
,
Michael J. Murphy Jr.
,
Jack Parrish
,
Ryan Rickert
,
Jonathan J. Rutz
,
Ryan Torn
,
Xingren Wu
, and
Minghua Zheng

Abstract

Atmospheric River Reconnaissance (AR Recon) is a targeted campaign that complements other sources of observational data, forming part of a diverse observing system. AR Recon 2021 operated for ten weeks from January 13 to March 22, with 29.5 Intensive Observation Periods (IOPs), 45 flights and 1142 successful dropsondes deployed in the northeast Pacific. With the availability of two WC-130J aircraft operated by the 53rd Weather Reconnaissance Squadron (53 WRS), Air Force Reserve Command (AFRC) and one National Oceanic and Atmospheric Administration (NOAA) Aircraft Operations Center (AOC) G-IVSP aircraft, six sequences were accomplished, in which the same synoptic system was sampled over several days.

The principal aim was to gather observations to improve forecasts of landfalling atmospheric rivers on the U.S. West Coast. Sampling of other meteorological phenomena forecast to have downstream impacts over the U.S. was also considered. Alongside forecast improvement, observations were also gathered to address important scientific research questions, as part of a Research and Operations Partnership.

Targeted dropsonde observations were focused on essential atmospheric structures, primarily atmospheric rivers. Adjoint and ensemble sensitivities, mainly focusing on predictions of U.S. West Coast precipitation, provided complementary information on locations where additional observations may help to reduce the forecast uncertainty. Additionally, Airborne Radio Occultation (ARO) and tail radar were active during some flights, 30 drifting buoys were distributed, and 111 radiosondes were launched from four locations in California. Dropsonde, radiosonde and buoy data were available for assimilation in real-time into operational forecast models. Future work is planned to examine the impact of AR Recon 2021 data on model forecasts.

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