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Mozhgan Amiramjadi
,
Ali R. Mohebalhojeh
,
Mohammad Mirzaei
,
Christoph Zülicke
, and
Riwal Plougonven

Abstract

The way the large-scale flow determines the energy of the nonorographic mesoscale inertia–gravity waves (IGWs) is theoretically significant and practically useful for source parameterization of IGWs. The relations previously developed on the f plane for tropospheric sources of IGWs including jets, fronts, and convection in terms of associated secondary circulations strength are generalized for application over the globe. A low-pass spatial filter with a cutoff zonal wavenumber of 22 is applied to separate the large-scale flow from the IGWs using the ERA5 data of ECMWF for the period 2016–19. A comparison with GRACILE data based on satellite observations of the middle stratosphere shows reasonable representation of IGWs in the ERA5 data despite underestimates by a factor of smaller than 3. The sum of the energies, which are mass-weighted integrals in the troposphere from the surface to 100 hPa, as given by the generalized relations is termed initial parameterized energy. The corresponding energy integral for the IGWs is termed the diagnosed energy. The connection between the parameterized and diagnosed IGW energies is explored with regression analysis for each season and six oceanic domains distributed over the globe covering the Northern and Southern Hemispheres and the tropics. While capturing the seasonal cycle, the domain area-average seasonal mean initial parameterized energy is weaker than the diagnosed energy by a factor of 3. The best performance in regression analysis is obtained by using a combination of power and exponential functions, which suggests evidence of exponential weakness.

Full access
Naveen Goutham
,
Riwal Plougonven
,
Hiba Omrani
,
Alexis Tantet
,
Sylvie Parey
,
Peter Tankov
,
Peter Hitchcock
, and
Philippe Drobinski

Abstract

Owing to the increasing share of variable renewable energies in the electricity mix, the European energy sector is becoming more weather sensitive. In this regard, skillful subseasonal predictions of essential climate variables can provide considerable socioeconomic benefits to the energy sector. The aim of this study is therefore to improve the European subseasonal predictions of 100-m wind speed and 2-m temperature, which we achieve through statistical downscaling. We employ redundancy analysis (RDA) to estimate spatial patterns of variability from large-scale fields that allow for the best prediction of surface fields. We compare explanatory powers between the patterns obtained using RDA against those derived using principal component analysis (PCA), when used as predictors in multilinear regression models to predict surface fields, and show that the explanatory power of the former is superior to that of the latter. Subsequently, we employ the estimated relationship between RDA patterns and surface fields to produce statistical probabilistic predictions of gridded surface fields using dynamical ensemble predictions of RDA patterns. We finally demonstrate how a simple combination of dynamical and statistical predictions of surface fields significantly improves the accuracy of subseasonal predictions of both variables over a large part of Europe. We attribute the improved accuracy of these combined predictions to improvements in reliability and resolution.

Open access
Naveen Goutham
,
Riwal Plougonven
,
Hiba Omrani
,
Sylvie Parey
,
Peter Tankov
,
Alexis Tantet
,
Peter Hitchcock
, and
Philippe Drobinski

Abstract

Subseasonal forecasts of 100-m wind speed and surface temperature, if skillful, can be beneficial to the energy sector as they can be used to plan asset availability and maintenance, assess risks of extreme events, and optimally trade power on the markets. In this study, we evaluate the skill of the European Centre for Medium-Range Weather Forecasts’ subseasonal predictions of 100-m wind speed and 2-m temperature. To the authors’ knowledge, this assessment is the first for the 100-m wind speed, which is an essential variable of practical importance to the energy sector. The assessment is carried out on both forecasts and reforecasts over European domain gridpoint wise and also by considering several spatially averaged domains, using several metrics to assess different attributes of forecast quality. We propose a novel way of synthesizing the continuous ranked probability skill score. The results show that the skill of the forecasts and reforecasts depends on the choice of the climate variable, the period of the year, and the geographical domain. Indeed, the predictions of temperature are better than those of wind speed, with enhanced skill found for both variables in the winter relative to other seasons. The results also indicate significant differences between the skill of forecasts and reforecasts, arising mainly due to the differing ensemble sizes. Overall, depending on the choice of the geographical domain and the forecast attribute, the results show skillful predictions beyond 2 weeks, and in certain cases, up to 6 weeks for both variables, thereby encouraging their implementation in operational decision-making.

Open access