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Morten Køltzow
,
Barbara Casati
,
Eric Bazile
,
Thomas Haiden
, and
Teresa Valkonen

Abstract

Increased human activity in the Arctic calls for accurate and reliable weather predictions. This study presents an intercomparison of operational and/or high-resolution models in an attempt to establish a baseline for present-day Arctic short-range forecast capabilities for near-surface weather (pressure, wind speed, temperature, precipitation, and total cloud cover) during winter. One global model [the high-resolution version of the ECMWF Integrated Forecasting System (IFS-HRES)], and three high-resolution, limited-area models [Applications of Research to Operations at Mesoscale (AROME)-Arctic, Canadian Arctic Prediction System (CAPS), and AROME with Météo-France setup (MF-AROME)] are evaluated. As part of the model intercomparison, several aspects of the impact of observation errors and representativeness on the verification are discussed. The results show how the forecasts differ in their spatial details and how forecast accuracy varies with region, parameter, lead time, weather, and forecast system, and they confirm many findings from mid- or lower latitudes. While some weaknesses are unique or more pronounced in some of the systems, several common model deficiencies are found, such as forecasting temperature during cloud-free, calm weather; a cold bias in windy conditions; the distinction between freezing and melting conditions; underestimation of solid precipitation; less skillful wind speed forecasts over land than over ocean; and difficulties with small-scale spatial variability. The added value of high-resolution limited area models is most pronounced for wind speed and temperature in regions with complex terrain and coastlines. However, forecast errors grow faster in the high-resolution models. This study also shows that observation errors and representativeness can account for a substantial part of the difference between forecast and observations in standard verification.

Open access
Vincent Vionnet
,
Ingrid Dombrowski-Etchevers
,
Matthieu Lafaysse
,
Louis Quéno
,
Yann Seity
, and
Eric Bazile

Abstract

Numerical weather prediction (NWP) systems operating at kilometer scale in mountainous terrain offer appealing prospects for forecasting the state of snowpack in support of avalanche hazard warning, water resources assessment, and flood forecasting. In this study, daily forecasts of the NWP system Applications of Research to Operations at Mesoscale (AROME) at 2.5-km grid spacing over the French Alps were considered for four consecutive winters (from 2010/11 to 2013/14). AROME forecasts were first evaluated against ground-based measurements of air temperature, humidity, wind speed, incoming radiation, and precipitation. This evaluation shows a cold bias at high altitude partially related to an underestimation of cloud cover influencing incoming radiative fluxes. AROME seasonal snowfall was also compared against output from the Système d’Analyse Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN) specially developed for alpine terrain. This comparison reveals that there are regions of significant difference between the two, especially at high elevation, and possible causes for these differences are discussed. Finally, AROME forecasts and SAFRAN reanalysis have been used to drive the snowpack model Surface Externalisée (SURFEX)/Crocus (SC) and to simulate the snowpack evolution over a 2.5-km grid covering the French Alps during four winters. When evaluated at the experimental site of Col de Porte, both simulations show good agreement with measurements of snow depth and snow water equivalent. At the scale of the French Alps, AROME-SC exhibits an overall positive bias, with the largest positive bias found in the northern and central French Alps. This study constitutes the first step toward the development of a distributed snowpack forecasting system using AROME.

Full access
Juan Jesús González-Alemán
,
Damián Insua-Costa
,
Eric Bazile
,
Sergi González-Herrero
,
Mario Marcello Miglietta
,
Pieter Groenemeijer
, and
Markus G. Donat

A record-breaking marine heatwave and anthropogenic climate change have substantially contributed to the development of an extremely anomalous and vigorous convective windstorm in August 2022 over the Mediterranean Sea.

Open access
Pablo Ortega
,
Edward W. Blockley
,
Morten Køltzow
,
François Massonnet
,
Irina Sandu
,
Gunilla Svensson
,
Juan C. Acosta Navarro
,
Gabriele Arduini
,
Lauriane Batté
,
Eric Bazile
,
Matthieu Chevallier
,
Rubén Cruz-García
,
Jonathan J. Day
,
Thierry Fichefet
,
Daniela Flocco
,
Mukesh Gupta
,
Kerstin Hartung
,
Ed Hawkins
,
Claudia Hinrichs
,
Linus Magnusson
,
Eduardo Moreno-Chamarro
,
Sergio Pérez-Montero
,
Leandro Ponsoni
,
Tido Semmler
,
Doug Smith
,
Jean Sterlin
,
Michael Tjernström
,
Ilona Välisuo
, and
Thomas Jung

Abstract

The Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and providing recommendations for future model and forecast system development.

Open access
Jeff Wilson
,
Thomas Jung
,
Eric Bazile
,
David Bromwich
,
Barbara Casati
,
Jonathan Day
,
Estelle De Coning
,
Clare Eayrs
,
Robert Grumbine
,
Jun Ioue
,
Siri Jodha S. Khalsa
,
Jorn Kristiansen
,
Machiel Lamers
,
Daniela Liggett
,
Steffen M. Olsen
,
Donald Perovich
,
Ian Renfrew
,
Vasily Smolyanitsky
,
Gunilla Svensson
,
Qizhen Sun
,
Taneil Uttal
, and
Qinghua Yang
Open access
David H. Bromwich
,
Kirstin Werner
,
Barbara Casati
,
Jordan G. Powers
,
Irina V. Gorodetskaya
,
Francois Massonnet
,
Vito Vitale
,
Victoria J. Heinrich
,
Daniela Liggett
,
Stefanie Arndt
,
Boris Barja
,
Eric Bazile
,
Scott Carpentier
,
Jorge F. Carrasco
,
Taejin Choi
,
Yonghan Choi
,
Steven R. Colwell
,
Raul R. Cordero
,
Massimo Gervasi
,
Thomas Haiden
,
Naohiko Hirasawa
,
Jun Inoue
,
Thomas Jung
,
Heike Kalesse
,
Seong-Joong Kim
,
Matthew A. Lazzara
,
Kevin W. Manning
,
Kimberley Norris
,
Sang-Jong Park
,
Phillip Reid
,
Ignatius Rigor
,
Penny M. Rowe
,
Holger Schmithüsen
,
Patric Seifert
,
Qizhen Sun
,
Taneil Uttal
,
Mario Zannoni
, and
Xun Zou
Full access
David H. Bromwich
,
Kirstin Werner
,
Barbara Casati
,
Jordan G. Powers
,
Irina V. Gorodetskaya
,
François Massonnet
,
Vito Vitale
,
Victoria J. Heinrich
,
Daniela Liggett
,
Stefanie Arndt
,
Boris Barja
,
Eric Bazile
,
Scott Carpentier
,
Jorge F. Carrasco
,
Taejin Choi
,
Yonghan Choi
,
Steven R. Colwell
,
Raul R. Cordero
,
Massimo Gervasi
,
Thomas Haiden
,
Naohiko Hirasawa
,
Jun Inoue
,
Thomas Jung
,
Heike Kalesse
,
Seong-Joong Kim
,
Matthew A. Lazzara
,
Kevin W. Manning
,
Kimberley Norris
,
Sang-Jong Park
,
Phillip Reid
,
Ignatius Rigor
,
Penny M. Rowe
,
Holger Schmithüsen
,
Patric Seifert
,
Qizhen Sun
,
Taneil Uttal
,
Mario Zannoni
, and
Xun Zou

Abstract

The Year of Polar Prediction in the Southern Hemisphere (YOPP-SH) had a special observing period (SOP) that ran from 16 November 2018 to 15 February 2019, a period chosen to span the austral warm season months of greatest operational activity in the Antarctic. Some 2,200 additional radiosondes were launched during the 3-month SOP, roughly doubling the routine program, and the network of drifting buoys in the Southern Ocean was enhanced. An evaluation of global model forecasts during the SOP and using its data has confirmed that extratropical Southern Hemisphere forecast skill lags behind that in the Northern Hemisphere with the contrast being greatest between the southern and northern polar regions. Reflecting the application of the SOP data, early results from observing system experiments show that the additional radiosondes yield the greatest forecast improvement for deep cyclones near the Antarctic coast. The SOP data have been applied to provide insights on an atmospheric river event during the YOPP-SH SOP that presented a challenging forecast and that impacted southern South America and the Antarctic Peninsula. YOPP-SH data have also been applied in determinations that seasonal predictions by coupled atmosphere–ocean–sea ice models struggle to capture the spatial and temporal characteristics of the Antarctic sea ice minimum. Education, outreach, and communication activities have supported the YOPP-SH SOP efforts. Based on the success of this Antarctic summer YOPP-SH SOP, a winter YOPP-SH SOP is being organized to support explorations of Antarctic atmospheric predictability in the austral cold season when the southern sea ice cover is rapidly expanding.

Free access