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Grazzini F.,ARPA SIMC Bologna Italy | Vitart F.,European Center for Medium Range Weather Forecasts Reading
Quarterly Journal of the Royal Meteorological Society | Year: 2015

Historically, the objective identification of atmospheric wave-packets has been very elusive. However, interest in these important sources of atmospheric variability has recently increased, and some automated tracking methods have been proposed. The Rossby wave packet (RWP) tracking algorithms opened the way to different types of investigation, ranging from climatology and predictability to assessing the impact of climate change on wave packet characteristics. The present study investigates the relationship between predictability (intrinsic and practical, i.e. predictive skill in a numerical weather prediction model) and the properties of RWPs, such as temporal duration, spatial extension and their area of genesis. Results suggest a significant correlation between RWP length and medium-range skill over Europe and the Northern Hemisphere. Analysis of an ensemble system shows that the spread decreases when long-living RWPs are present in the forecast, supporting the hypothesis that part of the observed increase in skill could indeed be attributed to higher intrinsic predictability induced by RWPs. Higher than average medium-range forecast skill scores are often associated with the presence of long-lasting RWPs (duration of at least 8 days) in the initial conditions, with a source often located in the west Pacific. On the contrary, bad medium-range forecast skill scores tend to be associated with shorter RWPs coming from the central USA or western Atlantic. An analysis of the probabilistic skill scores confirms that predictive skill increases with the presence of long RWPs from the west Pacific, up to week 3. © 2015 Royal Meteorological Society. Source

Hawkins E.,University of Reading | Tietsche S.,University of Reading | Day J.J.,University of Reading | Melia N.,University of Reading | And 2 more authors.
Quarterly Journal of the Royal Meteorological Society | Year: 2015

Using lessons from idealised predictability experiments, we discuss some issues and perspectives on the design of operational seasonal to inter-annual Arctic sea-ice prediction systems. We first review the opportunities to use a hierarchy of different types of experiment to learn about the predictability of Arctic climate. We also examine key issues for ensemble system design, such as measuring skill, the role of ensemble size and generation of ensemble members. When assessing the potential skill of a set of prediction experiments, using more than one metric is essential as different choices can significantly alter conclusions about the presence or lack of skill. We find that increasing both the number of hindcasts and ensemble size is important for reliably assessing the correlation and expected error in forecasts. For other metrics, such as dispersion, increasing ensemble size is most important. Probabilistic measures of skill can also provide useful information about the reliability of forecasts. In addition, various methods for generating the different ensemble members are tested. The range of techniques can produce surprisingly different ensemble spread characteristics. The lessons learnt should help inform the design of future operational prediction systems. © 2015 Royal Meteorological Society. Source

Janiskova M.,European Center for Medium Range Weather Forecasts Reading
Quarterly Journal of the Royal Meteorological Society | Year: 2015

Space-borne active instruments, providing a vertically resolved characterization of clouds, promise a new dimension of information to be used in numerical weather prediction systems. Research activities are ongoing at the European Centre for Medium-Range Weather Forecasts to exploit these data for monitoring and assimilation purposes. Using currently available observations from CloudSat and CALIPSO, a technique combining one-dimensional variational (1D-Var) assimilation with four-dimensional variational (4D-Var) data assimilation has been used to study the impact of cloud-related observations on analyses and subsequent forecasts. Temperature and specific humidity vertical profiles retrieved from 1D-Var using observations of cloud radar reflectivity and lidar backscatter, either separately or in combination, were used as pseudo-observations in the 4D-Var system. Results indicate that 1D-Var analyses get closer to assimilated and also independent observations when appropriate quality control, bias correction and error estimate are applied. The performed 1D+4D-Var assimilation experiments also suggest a slight positive impact of the new observations on the subsequent forecast. Generally, the impact of lidar backscatter from clouds is smaller than that of cloud radar reflectivity. © 2015 Royal Meteorological Society. Source

Fujii Y.,Meteorological Research Institute Japan Meteorological Agency Tsukuba Japan | Cummings J.,U.S. Navy | Xue Y.,Climate Prediction Center NCEP College Park | Schiller A.,CSIRO | And 10 more authors.
Quarterly Journal of the Royal Meteorological Society | Year: 2015

The drastic reduction in the number of observation data from the Tropical Atmospheric Ocean (TAO)/Triangle Trans-Ocean Buoy Network (TRITON) array since 2012 has given rise to a need to assess the impact of those data in ocean data assimilation (DA) systems. This article provides a review of existing studies evaluating the impacts of data from the TAO/TRITON array and other components of the Tropical Pacific Observing System (TPOS) on current ocean DA systems used for a variety of operational and research applications. It can be considered as background information that can guide the evaluation exercise of TPOS. Temperature data from TAO/TRITON array are assimilated in most ocean DA systems which cover the tropical Pacific in order to constrain the ocean heat content, stratification, and circulation. It is shown that the impacts of observation data depend considerably on the system and application. The presence of model error often makes the results difficult to interpret. Nevertheless there is consensus that the data from TAO/TRITON generally have positive impacts complementary to Argo floats. In the equatorial Pacific, the impacts are generally around the same level or larger than those of Argo. We therefore conclude that, with the current configuration of TPOS, the loss of the TAO/TRITON data is having a significant detrimental impact on many applications based on ocean DA systems. This conclusion needs to be kept under review because the equatorial coverage by Argo is expected to improve in the future. © 2015 Royal Meteorological Society. Source

Brugnara Y.,University of Bern | Auchmann R.,University of Bern | Bronnimann S.,University of Bern | Bozzo A.,European Center for Medium Range Weather Forecasts Reading
Journal of Geophysical Research: Atmospheres | Year: 2016

We describe the recovery of three daily meteorological records for the southern Alps (Domodossola, Riva del Garda, and Rovereto), all starting in the second half of the nineteenth century. We use these new data, along with additional records, to study regional changes in the mean temperature and extreme indices of heat waves and cold spells frequency and duration over the period 1874-2015. The records are homogenized using subdaily cloud cover observations as a constraint for the statistical model, an approach that has never been applied before in the literature. A case study based on a record of parallel observations between a traditional meteorological window and a modern screen shows that the use of cloud cover can reduce the root-mean-square error of the homogenization by up to 30% in comparison to an unaided statistical correction. We find that mean temperature in the southern Alps has increased by 1.4°C per century over the analyzed period, with larger increases in daily minimum temperatures than maximum temperatures. The number of hot days in summer has more than tripled, and a similar increase is observed in duration of heat waves. Cold days in winter have dropped at a similar rate. These trends are mainly caused by climate change over the last few decades. © 2016. American Geophysical Union. All Rights Reserved. Source

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