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Tony McNally
,
Massimo Bonavita
, and
Jean-Noël Thépaut

Abstract

The excellent forecasts made by ECMWF predicting the devastating landfall of Hurricane Sandy attracted a great deal of publicity and praise in the immediate aftermath of the event. The almost unprecedented and sudden “left hook” of the storm toward the coast of New Jersey was attributed to interactions with the large-scale atmospheric flow. This led to speculation that satellite observations may play an important role in the successful forecasting of this event. To investigate the role of satellite data a number of experiments have been performed at ECMWF where different satellite observations are deliberately withheld and forecasts of the hurricane rerun. Without observations from geostationary satellites the correct landfall of the storm is still reasonably well predicted albeit with a slight timing shift compared to the control forecast. On the other hand, without polar-orbiting satellites (which represent 90% of the volume of currently ingested observations) the ECMWF system would have given no useful guidance 4–5 days ahead that the storm would make landfall on the New Jersey coast. Instead the hurricane is predicted to stay well offshore in the Atlantic and hit the Maine coast 24 h later. If background errors estimated from the ECMWF Ensemble of Data Assimilations (EDA) are allowed to evolve and adapt to the depleted observing system, then some of the performance loss suffered by withholding polar satellite data can be recovered. The use of the appropriate EDA errors results in a more enhanced use of geostationary satellite observations, which partly compensates for the loss of polar satellite data.

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William Bell
,
Sabatino Di Michele
,
Peter Bauer
,
Tony McNally
,
Stephen J. English
,
Nigel Atkinson
,
Fiona Hilton
, and
Janet Charlton

Abstract

The sensitivity of NWP forecast accuracy with respect to the radiometric performance of microwave sounders is assessed through a series of observing system experiments at the Met Office and ECMWF. The observing system experiments compare the impact of normal data from a single Advanced Microwave Sounding Unit (AMSU) with that from an AMSU where synthetic noise has been added. The results show a measurable reduction in forecast improvement in the Southern Hemisphere, with improvements reduced by 11% for relatively small increases in radiometric noise [noise-equivalent brightness temperature (NEΔT) increased from 0.1 to 0.2 K for remapped data]. The impact of microwave sounding data is shown to be significantly less than was the case prior to the use of advanced infrared sounder data [Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI)], with microwave sounding data now reducing Southern Hemisphere forecast errors by approximately 10% compared to 40% in the pre-AIRS/IASI period.

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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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