In the last years coupled atmospheric ocean climate
models have remarkably improved medium range seasonal forecasts, especially
on middle latitude areas such as Europe and the Mediterranean basin. In this
study a new framework for medium range seasonal forecasts is proposed. It is
based on circulation types extracted from long range global ensemble models
and it aims at two goals: (i) an easier use of the information contained in
the complex system of atmospheric circulations, through their reduction to a
limited number of circulation types and (ii) the computation of high spatial
resolution probabilistic forecasts for temperature and precipitation. The
proposed framework could be also useful to lead predictions of
weather-derived parameters, such as the risk of heavy rainfall, drought or
heat waves, with important impacts on agriculture, water management and
severe weather risk assessment. Operatively, starting from the ensemble
predictions of mean sea level pressure and geopotential height at 500
In this paper the model operative chain, some output examples and a first attempt of qualitative verification are shown. In particular three case studies (June 2003, February 2012 and July 2014) were examined, assuming that the ensemble seasonal model correctly predicts the circulation type occurrences. At least on this base, the framework here proposed has shown promising performance.
Diagram of the proposed framework for seasonal forecasts.
Seasonal forecasting attempts to provide useful probabilistic information about the climate that can be expected in the coming few months. The seasonal forecasts target is not a possible snapshot of continually changing atmospheric conditions, but rather a likely preview of the main weather events occurring in a given season. Consequently long-term predictions fall into the realm of essentially probabilistic problems. Due to their chaotic nature, atmospheric states just a few weeks forward in time are predictable only in terms of a probability cloud, possibly conditioned to slowly changing variables, such as the ocean's surface temperatures. Global climate models based on ocean-atmosphere coupling are frequently used with an ensemble approach to sample the inherent atmospheric uncertainty (Leutbecher and Palmer, 2007). There is a growing interest in a wide swath of user communities for the seasonal forecasts on both global and local scale, but the skill of the forecasts, as well as the type of the provided information, needs to be improved.
Remarkably the importance of a probabilistic way to communicate predictions, for instance weather warning, was already stressed at the beginning of the twentieth century:
“The most appropriate system seems therefore to be to leave to the clients concerned by the warning to form an idea of the value of loss/cost and to issue the warnings in such a form that the larger or smaller probability of the event gets clear from the formulation. The client may then himself consider if it is worth while to make arrangements of protection, or to disregard a given warning.” (Angström, 1922).
The method proposed here for probabilistic seasonal forecasts at high resolution starts from a climatological ensemble global model (the NCEP-NCAR CFSv2) and reduces the atmospheric complexity through a circulation types classification approach. The European project Cost Action 733 (2008–2010) gave a significant contribution for atmospheric circulation type classifications, in order to evaluate their skill in stratifying surface climate elements or other weather related environmental variables. The adopted technique rely on a discrete characterization of atmospheric circulations grouped into subsets (Huth et al., 2015; Philipp et al., 2014). In addition, the other key factor of this framework is the increase of the spatial resolution, through a sort of statistical downscaling (see for instance Nikulin et al., 2018; and Manzanas et al., 2018) to improve the dependence of climatic factors on geographical characteristics and orographic complexities. This goal was achieved thanks to the availability of a consistent high-resolution surface data, such as the E-OBS gridded datasets from the European Climate Assessment & Dataset project (Haylock et al., 2008). Furthermore, a Bayesian procedure was used for producing probabilistic forecasts suitable for decision-making processes and risk assessment. The Bayesian algorithm merges the information coming from the ensemble monthly forecast (i.e the predicted probability for the possible circulation types) with the climatology of the predictands for each circulation type. The final output is the probability for each predictand given both the ensemble forecast and the climatological data, that is a single product which preserves the possibility to separately evaluate the different sources of uncertainty.
Further investigations will be carried out to characterize each circulations type in terms of occurrences of heavy precipitation, cold spells, heat waves, landslides, snowfall, or dry spells, which are very important variables for many activities like agriculture, water management, energy provision and severe weather risk assessment. Indeed the relationship between circulation weather type classifications and high-impact weather events was also shown in previous studies concerning extreme precipitation (Fernandez-Montes et al., 2014), extreme temperature episodes (Kysely, 2008), floods (Prudhomme and Genevier, 2011), droughts (Russo et al., 2015), and even lightning activity (Ramos et al., 2011).
The main core of the operative chain for the proposed methodology of seasonal forecasts is based on the weather type classification software package, developed within the project COST733 (Philipp et al., 2014). This software has been used for both the calibration and the forecast module of the operative chain, as illustrated in Fig. 1.
The calibration was carried out on NCEP-NCAR Reanalysis 2 data (Kanamitsu et al., 2002) between 1979
and 2015 through a sensitivity analysis detailed in a specific study
(Vallorani et al., 2017). In summary several circulation type
classifications were computed with different classification methods, number
of types and classification variables (i.e. predictands). Then such
classifications were compared through the use of proper statistical indexes
in order to assess the stratification of the ground-level precipitation and
the surface air temperature across Italian peninsula. The PCT (Principal
Component Transversal) and the SAN (Simulated Annealing) methods with 9
classes computed on MSLP and 500HGT (Vallorani et al., 2018) were selected as the best performing
classifications for precipitation and temperature respectively (see in Fig. 2
the centroid maps for PCT09 and SAN09 classifications as a result of the
calibration module, where the centroids are the central value of the
class/cluster). Climatological values of rainfall amount, wet days (number
of days with a daily rainfall exceeding the 0.4
Centroids of PCT09 classification implemented on mean sea
level pressure (Fig. 2a, values in
June 2003: observations E-OBS versus the operational
chain re-forecast: the E-OBS occurrence frequency of rainy days
The weather type classifier is also used operatively in the forecast module. Each of the 40 members of MSLP and 500HGT extracted from the NCEP – CFSv2 global model are converted into daily series of circulation types for the future 90 days. For each record of the 40 members the dissimilarity/distance between the record and the centroids are calculated and the circulation type number is chosen to be the one with the minimum distance. The probability output maps are finally computed trough a Bayesian algorithm (described in Sect. 2.1) which combine ensemble forecasts, circulation types and climatology.
In this paper a preliminary test is carried out on three case studies on
Italy, all with important temperature or rain anomalies: June 2003, February 2012, and July 2014. The obtained results are then qualitatively compared
with the observed values during these three months, taken from the
E-OBS
gridded dataset. Since the purpose of this first test is to verify the
predicting capability of the climatological and circulation types
contribution, rather than the goodness of the ensemble forecast (EF), the
probabilistic weights coming from EF (the terms
The searched probability for the value
The first term can be explicitly computed assuming that the ensemble
forecast is significantly more informative than climatology in determining
the circulation type occurrence, so making the climatology nearly irrelevant
(indeed any seasonal forecast unable to improve climatological predictions
is in practice useless). Hence
Thus the conditional probability (1) can be definitely rewritten as:
As concerns precipitation, the dry days (precipitation below the 0.4
Frequency climatological anomaly for the 3 case studies. Those anomalies with an occurrence of greater than four days per month are shown in bold.
A first, little more than qualitative, verification was carried out on three months that are all characterized by an unusual occurrence of some circulation types, generating important temperature or precipitation anomalies. The monthly occurrences of each CT with respect to the reference climatological period (1981–2010) are reported in Table 1.
For each case study two kinds of semi-quantitative comparison are shown: (i) the observed occurrences of rainy days (i.e. the fraction of rainy days
within the month) versus the simulated mean probability of rainy day
occurrences, and (ii) the observed monthly mean temperature (at 2
February 2012: observations E-OBS versus the operational
chain re-forecast: the E-OBS occurrence frequency of rainy days
July 2014: observations E-OBS versus the operational
chain re-forecast: the E-OBS occurrence frequency of rainy days
According to these characteristics, Table 1 pointed out some anomalies in
the circulation types and in particular a 6 days negative anomaly of
circulation type 7 for SAN9 and a 5 days positive anomaly of circulation
type 2 for PCT9. Concerning the percentage of rainy days (Fig. 5a and b), this case study shows less satisfactory results, despite the overall
pattern remains quite well described. A lack of rainy days was evident on
central and northern Italy (Fig. 5b). On the contrary, the tertile
probability maps for 2
The operative chain for seasonal forecast illustrated in this paper is a
flexible and exportable solution for computing mesoscale probability
distribution of surface meteorological variables, like temperature and
precipitation. Spatial resolution is strongly dependent on the surface
observation datasets. In our application of the method, the E-OBS gridded
datasets (Haylock et al., 2008) at 25 km proved to be an acceptable
solution for describing the geographical heterogeneity and orographic
complexity of the Italian peninsula. However other consistent datasets could
be used whenever they are available. For instance, a datasets at 5 km
resolution based on a dense network of long series weather observations for
central-north Italy is under development (
The circulation type classifications adopted in the operational chain were
specifically selected and calibrated for the stratification of surface
temperature and precipitation for Italy, but different types of
classifications are available in the COST 733 catalogue (
A further element of flexibility is due to the possibility to use any conceivable numerical model all over the world, provided it runs in ensemble mode and a suitable climatology is available. The Bayesian approach used to produce the output maps fully maintains the probabilistic nature of the driving ensemble global model and represents the essential link to any decision making process, such as the cost/loss model (Palmer, 2002).
The simplified approach, based on Eq. (2), merges the information coming from the ensemble monthly forecast, the climatology and the circulation type classifications into a single product. Nevertheless this does not prevent one from evaluating separately the different sources of uncertainty. The method appears suitable for risk assessment analysis of extreme events strongly related to surface temperature and precipitation. Anyway the circulation types approach can be also extended to other variables like heat waves, cold spells, heavy precipitations or dry series, producing estimates for the occurrence probability of extremes, possibly at local scale and on a seasonal time horizon. The positive impact on agriculture, water management, energy and health system is easily understood.
The first preliminary test presented in this paper gives an idea of how good can be the matching between the probabilistic output and the observed monthly anomalies, even in case when strong anomalies occurred. As shown by the results presented here, if the monthly circulation types are correctly predicted, then a reliable forecast for the expected anomalies of rainy days and surface temperature becomes possible. The uncertainty coming from the prediction of the circulation types, as made by the ensemble seasonal forecast, could deteriorate the final probability estimation for the variable of interest, even in the case of highly informative climatological circulation types. Since we have not taken into account this aspect in the present study, further investigations on this issue are surely needed.
A verification procedure based on statistically consistent samples of measurements will be carried out in the near future starting from a retrospective seasonal forecast database (i.e. hindcasts of the ensemble NCEP-NCAR CFSv2), over a period of at least 10 years (Nikulin et al., 2018; Manzanas et al., 2018). Hence, the skill of the entire framework of seasonal forecasts will be evaluated throughout a proper score, like the logarithmic score (Benedetti, 2010) based on the relative entropy between the observed occurrence frequencies and the predicted probabilities for the forecasted events.
Text files of the two circulation type classifications (pct9 and san9) and the centroid values of MSLP and 500HGT
(Fig. 2) are available to the following DOI (
GM, RB, AC and RV conceived of the presented idea, developed the theory and performed the computations. MR and RV performed post-processing and graphical output. BG and GM gave numerous suggestions for the methodology and encouraged our research. All authors discussed the results and contributed to the final manuscript.
The authors declare that they have no conflict of interest.
This article is part of the special issue “17th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2017”. It is a result of the EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2017, Dublin, Ireland, 4–8 September 2017.
A special thank full of affection, esteem and gratitude to Giampiero Maracchi for having always believed and stimulated our research on climaology and seasonal forecasts. Edited by: Rasmus Benestad Reviewed by: Ciaran Broderick and one anonymous referee