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Massimo Bonavita
,
Mats Hamrud
, and
Lars Isaksen

Abstract

The desire to do detailed comparisons between variational and more scalable ensemble-based data assimilation systems in a semioperational environment has led to the development of a state-of-the-art EnKF system at ECMWF, which has been described in Part I of this two-part study. In this part the performance of the EnKF system is evaluated compared to a 4DVar of similar resolution. It is found that there is not a major difference between the forecast skill of the two systems. However, similarly to the operational hybrid 4DVar–EDA, a hybrid EnKF–variational system [which we refer to as the hybrid gain ensemble data assimilation (HG-EnDA)] is capable of significantly outperforming both component systems. The HG-EnDA has been implemented with relatively little effort following Penny’s recent study. Results of numerical experimentation comparing the HG-EnDA with the hybrid 4DVar–EDA used operationally at ECMWF are presented, together with diagnostic results, which help characterize the behavior of the proposed ensemble data assimilation system. A discussion of these results in the context of hybrid data assimilation in global NWP is also provided.

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Mats Hamrud
,
Massimo Bonavita
, and
Lars Isaksen

Abstract

The desire to do detailed comparisons between variational and more scalable ensemble-based data assimilation systems in a semioperational environment has led to the development of a state-of-the-art EnKF system at ECMWF. A broad description of the ECMWF EnKF is given in this paper, focusing on highlighting differences compared to standard EnKF practice. In particular, a discussion of the novel algorithm used to control imbalances between the mass and wind fields in the EnKF analysis is given. The scalability and computational properties of the EnKF are reviewed and the implementation choices adopted at ECMWF described. The sensitivity of the ECMWF EnKF to ensemble size, horizontal resolution, and representation of model errors is also discussed. A comparison with 4DVar will be found in Part II of this two-part study.

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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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Massimo Bonavita
,
Alan J. Geer
, and
Mats Hamrud

Abstract

Recent success in assimilating cloud- and precipitation-affected satellite observations using the “all-sky” approach is thought to have benefitted from variational data assimilation, particularly its ability to handle moderate nonlinearity and non-Gaussianity and to extract wind information through the generalized tracer effect. Ensemble assimilation relies on assumptions including linearity and Gaussianity that might cause difficulties when using all-sky observations. Here, all-sky assimilation is evaluated in a global ensemble Kalman filter (EnKF) system of near-operational quality, derived from an operational four-dimensional variational (4D-Var) system. To get EnKF working successfully required a new all-sky observation error model (the most successful approach was to inflate error as a multiple of the ensemble spread) and adjustments to localization. With these improvements, assimilation of eight microwave humidity instruments gave 2%–4% improvement in forecast scores whether using EnKF or 4D-Var. Correlations from the ensemble showed that all-sky observations generated sensitivity to wind, temperature, and humidity. EnKF increments shared many similarities with those in 4D-Var. Hence both 4D-Var and ensemble data assimilation were able to make good use of all-sky observations, including the extraction of wind information. In absolute terms the EnKF forecast performance in the troposphere was still worse than that that with 4D-Var, although the gap could be reduced by going from 50 to 100 ensemble members. EnKF errors were larger in the stratosphere, where there are excessive gravity wave increments that are not connected with all-sky assimilation.

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Massimo Bonavita
,
Rossella Arcucci
,
Alberto Carrassi
,
Peter Dueben
,
Alan J. Geer
,
Bertrand Le Saux
,
Nicolas Longépé
,
Pierre-Philippe Mathieu
, and
Laure Raynaud
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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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Paul Poli
,
Hans Hersbach
,
Dick P. Dee
,
Paul Berrisford
,
Adrian J. Simmons
,
Frédéric Vitart
,
Patrick Laloyaux
,
David G. H. Tan
,
Carole Peubey
,
Jean-Noël Thépaut
,
Yannick Trémolet
,
Elías V. Hólm
,
Massimo Bonavita
,
Lars Isaksen
, and
Michael Fisher

Abstract

The ECMWF twentieth century reanalysis (ERA-20C; 1900–2010) assimilates surface pressure and marine wind observations. The reanalysis is single-member, and the background errors are spatiotemporally varying, derived from an ensemble. The atmospheric general circulation model uses the same configuration as the control member of the ERA-20CM ensemble, forced by observationally based analyses of sea surface temperature, sea ice cover, atmospheric composition changes, and solar forcing. The resulting climate trend estimations resemble ERA-20CM for temperature and the water cycle. The ERA-20C water cycle features stable precipitation minus evaporation global averages and no spurious jumps or trends. The assimilation of observations adds realism on synoptic time scales as compared to ERA-20CM in regions that are sufficiently well observed. Comparing to nighttime ship observations, ERA-20C air temperatures are 1 K colder. Generally, the synoptic quality of the product and the agreement in terms of climate indices with other products improve with the availability of observations. The MJO mean amplitude in ERA-20C is larger than in 20CR version 2c throughout the century, and in agreement with other reanalyses such as JRA-55. A novelty in ERA-20C is the availability of observation feedback information. As shown, this information can help assess the product’s quality on selected time scales and regions.

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