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Anders Persson

Abstract

The Hovmöller diagram or the trough–ridge diagram, a simple longitude–time diagram, was designed in 1948 by Ernest Hovmöller (1912–2008) to help understand certain features in the dynamics of the atmosphere, in particular the “downstream development” phenomenon. Originally depicting the 500-hPa geopotential, today many other parameters are used, and Hovmöller diagrams have during the last 25 years found a rapidly increasing use in a wide range of atmospheric research.

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Mark J Rodwell
,
Linus Magnusson
,
Peter Bauer
,
Peter Bechtold
,
Massimo Bonavita
,
Carla Cardinali
,
Michail Diamantakis
,
Paul Earnshaw
,
Antonio Garcia-Mendez
,
Lars Isaksen
,
Erland Källén
,
Daniel Klocke
,
Philippe Lopez
,
Tony McNally
,
Anders Persson
,
Fernando Prates
, and
Nils Wedi

Medium-range weather prediction has become more skillful over recent decades, but forecast centers still suffer from occasional very poor forecasts, which are often referred to as “dropouts” or “busts.” This study focuses on European Centre for Medium-Range Weather Forecasts (ECMWF) day-6 forecasts for Europe. Although busts are defined by gross scores, bust composites reveal a coherent “Rex type” blocking situation, with a high over northern Europe and a low over the Mediterranean. Initial conditions for these busts also reveal a coherent flow, but this is located over North America and involves a trough over the Rockies, with high convective available potential energy (CAPE) to its east. This flow type occurs in spring and is often associated with a Rossby wave train that has crossed the Pacific. A composite on this initial flow type displays enhanced day-6 random forecast errors and some-what enhanced ensemble forecast spread, indicating reduced inherent predictability.

Mesoscale convective systems, associated with the high levels of CAPE, act to slow the motion of the trough. Hence, convection errors play an active role in the busts. The subgrid-scale nature of convection highlights the importance of the representation of model uncertainty in probabilistic forecasts. The cloud and extreme conditions associated with mesoscale convective systems also reduce the availability and utility of observations provided to the data assimilation.

A question of relevance to the wider community is, do we have observations with sufficient accuracy to better constrain the important error structures in the initial conditions? Meanwhile, improvements to ensemble prediction systems should help us better predict the increase in forecast uncertainty.

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