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Machine learning (ML) provides a powerful tool for investigating the relationship between the large-scale flow and unresolved processes, which need to be parameterized in climate models. The current work explores the performance of the random forest regressor (RF) as a nonparametric model in the reconstruction of nonorographic gravity waves (GWs) over midlatitude oceanic areas. The ERA5 dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs is employed in its full resolution to derive GW variations in the lower stratosphere. Coarse-grained variables in a column-based configuration of the atmosphere are used to reconstruct the GWs variability at the target level. The first important outcome is the relative success in reconstructing the GW signal (coefficient of determination R2 ≈ 0.85 for “E3” combination). The second outcome is that the most informative explanatory variable is the local background wind speed. This questions the traditional framework of gravity wave parameterizations, for which, at these heights, one would expect more sensitivity to sources below than to local flow. Finally, to test the efficiency of a relatively simple, parametric statistical model, the efficiency of linear regression was compared to that of random forests with a restricted set of only five explanatory variables. Results were poor. Increasing the number of input variables to 15 hardly changes the performance of the linear regression (R2 changes slightly from 0.18 to 0.21), while it leads to better results with the random forests (R2 increases from 0.29 to 0.37).

This article deals with the detection of small-scale turbulence from in situ meteorological measurements performed under superpressure balloons (SPBs). These balloons allow long-duration flights (several months) at a prerequisite height level. The data set is gathered from the Strateole-2 probationary campaign during which eights SPBs flew in the tropical tropopause layer at altitudes of around 19 and 20.5 km from November 2019 to March 2020. Turbulence is not directly measured by the instrument set onboard the SPBs. Nonetheless, there is the potential to derive information about the occurrence of turbulence from the temporally well-resolved measurements of pressure, temperature, and position. It constitutes a challenge to extract the aforementioned information from a measurement set that was not designed for quantifying turbulence, and the paper explains the methodology developed to overcome this difficulty. It is observed that SPBs oscillate quasi-periodically around their equilibrium positions. The oscillation periods, which are 220 s on average with a range of 130 to 500 s, are close to but noticeably smaller than the Brunt–Väisälä period (∼300 s). The amplitude of these vertical motions is ∼±15 m, inducing large fluctuations in all quantities, whether measured (e.g., pressure, temperature and position) or inferred (e.g., density and potential temperature). The relationships between the changes in these quantities and the vertical displacements of the balloons are used to infer properties of the flow in which the SPBs drift. In the case of active turbulence, the vertical stratification as well as the wind shear are likely to be reduced by mixing. Hence, the increments of potential temperature, δθ, and of the vertical displacements of the balloon, δzB, are expected to be uncorrelated because ∂θ/∂z→0. Moreover, the local vertical gradients of measured quantities, temperature (T) and horizontal velocities (u and v), are estimated from the covariance of the increments of the considered quantity with δzB. The Richardson number of the flow is deduced. Several binary indexes (true or false) to describe the state of the flow, laminar or turbulent, are evaluated. These turbulence indexes, based either on correlations between δθ and δzB or on estimates of the local Richardson number, are found to be consistent, as they differ in less than 3 % of cases. The flow is observed to be turbulent for about 5 % of the time, with strong inhomogeneities along the longitude.

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.

The role of gravity waves on microphysics of tropical cirrus clouds and air parcel dehydration was studied using the combination of Lagrangian observations of temperature fluctuations and a 1.5 dimension model. High frequency measurements during isopycnal balloon flights were used to resolve the gravity wave signals with periods ranging from a few days to 15 min. The detailed microphysical simulations with homogeneous freezing, sedimentation and a crude horizontal mixing represent the slow ascent of air parcels in the Tropical Tropopause Layer. A reference simulation describes the slow ascent of air parcels in the tropical tropopause layer, with nucleation occurring only below the cold point tropopause with a small ice crystals density. The inclusion of the gravity waves modifies drastically the low ice concentration vertical profile and weak dehydration found during the ascent alone, with the increased ice crystal number and size distribution agreeing better with observations. Numerous events of nucleation occur below and above the cold point tropopause, efficiently restoring the relative humidity over ice to equilibrium with respect to the background temperature, as well as increase the cloud fraction in the vicinity of the cold-point tropopause. The corresponding decrease in water vapor is estimated at 2 ppmv around the cold point tropopause.

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.

Combining ensemble forecasts from several models has been shown to improve the skill of S2S predictions. One of the most used method for such combination is the “pooled ensemble” method, i.e. the concatenation of the ensemble members from the different models. The members of the new multi-model ensemble can simply have the same weights or be given different weights based on the skills of the models. If one sees the ensemble forecasts as discrete probability distributions, then the “pooled ensemble” is their (weighted-)barycenter with respect to the L2 distance.Here, we investigate whether a different metric when computing the barycenter may help improve the skill of S2S predictions. We consider in this work a second barycenter with respect to the Wasserstein distance. This distance is defined as the cost of the optimal transport between two distributions and has interesting properties in the distribution space, such as the possibility to preserve the temporal consistency of the ensemble members.We compare the L2 and Wasserstein barycenters for the combination of two models from the S2S database, namely ECMWF and NCEP. Their performances are evaluated for the weekly 2m-temperature over seven winters in Europe (land) in terms of different scores. The weights of the models in the barycenters are estimated from the data using grid search with cross-validation. We show that the estimation of these weights is critical as it greatly impacts the score of the barycenters. Although the NCEP ensemble generally has poorer skills than the ECMWF one, the barycenter ensembles are able to improve on both single-model ensembles (although not for all scores). At the end, the best ensemble depends on the score and on the location. These results constitute a promising first step before implementing this methodology with more than two ensembles, and ensembles having less contrasting skills.

Anticyclonically-trapped plumes were first discovered following the 2020 Australian fires. Since then, they have been reported after several extreme wildfires and volcanic eruptions, including the 2017 Canadian wildfires, the 2019 Raikoke and the 2022 Hunga Tonga-Hunga Ha’apai eruptions. They appear as coherent plumes of aerosols and combustion/volcanic compounds confined within mesoscale anticyclones (100s to 1000 km diameter), which for several months resist dispersion and dilution by the large-scale flow. Due to their unusual composition, large radiative forcing is prevailing inside the plumes, generating significant diabatic responses in terms of vertical motions and potential vorticity.In this presentation, we propose a conceptual model of the anticyclonic plumes. We will explore ramifications through idealized numerical simulations and theoretical investigations. Particular focus will be put on the condition of their formation and the dynamics of their maintenance and diabatic motions in the stratosphere.

The parameterization scheme that represents gravity waves due to convection in LMDz-6A, the atmospheric components of the IPSL coupled climate model (IPSLCM6), is directly compared to Strateole-2 balloon observations made in the lower tropical stratosphere from November 2019 to February 2020. The input meteorological fields necessary to run the parameterization offline are extracted from the ERA5 reanalysis and correspond to the instantaneous meteorological conditions found underneath the balloons. In general, we find a fair agreement between measurements of the momentum fluxes due to waves with periods less than 1hr and the parameterization. The correlation of the daily values between the observations and the results of the parameterization is around 0.4, which is statistically elevated considering that we analyse around 600 days of data and surprisingly good considering that the parameterization has not been tuned: the scheme is just the standard one that helps producing a Quasi-Biennial Oscillation in the IPSLCM6 model. Online simulations also show that the measured values of momentum fluxes are well representative of the zonally and averaged values of momentum fluxes needed in LMDz-6A to simulate a QBO. The observations also show that longer waves with periods smaller than a day carry about twice as much flux as waves with periods smaller than an hour, which is a challenge since the low period waves that make the difference are potentially in the “grey zone” of most climate models

The role played by turbulent mixing in the vertical transport of constituents in the UTLS is still poorly understood: there is a lack of knowledge of turbulence due to the limited number of observations in this region as well as to the limitations of current observation techniques.The first part of the present work deals with the detection of small-scale turbulence in the tropical upper troposphere - lower stratosphere from in-situ meteorological measurements collected under super-pressure balloons (SPBs). Eight SPBs were launched during the first Strateole-2 campaign, from November 2019 to March 2020 and flying for several weeks (∼ 3 months). Turbulence detection methods relies on the quasi-periodic vertical oscillations (∼ ±15 m) of the SPBs around their equilibrium positions, such oscillations inducing large fluctuations of measured quantities (pressure, temperature, positions) and inferred quantities (density, potential temperature). A first method of detection is based on correlations between the increments of potential temperature δθ and the vertical displacements of the balloons (i.e. of the sensors) δz. Such correlations are expected to be null as ∂θ/∂z → 0 in case of turbulent mixing. A second method relies on the Richardson number criterion, Ri < 0.25. Ri is deduced from the vertical gradients of measured quantities (T , u, v), estimated from covariances between the increments of the considered quantities and the vertical displacements δz. Turbulence indexes (true of false) to describe the different states of the flow encountered by the SPBs during their flights (laminar or turbulent), are evaluated. These different indexes, based on independent measurements and on various methods, correlations or linear regressions, are found to be consistent: they differ for less than 3% of the cases. The flow is observed to be turbulent for about 5% of the time, with strong inhomogeneities along the longitude.The second part of the present work aims to improve our understanding of turbulence, and its impacts, in the tropical UTLS by studying small- to meso-scale processes, i.e. atmospheric waves, deep convection and associated observed turbulence. These are all key processes of the dynamics of the equatorial UTLS. One can evaluate the probability of turbulence occurrences as a function of the distance to deep convection. Such a distance seems to be a good proxy of wave activity generated by deep convection. The occurrence frequency of turbulence is significantly larger when the distance to deep convection is small, i.e. smaller than ~ 200 km.This research should contribute to a better parametrization of these processes in climate models, and to a better estimation of their impact to vertical transport in the tropical UTLS.

Recent extreme events associated with forest fires and large volcanic eruptions have demonstrated that dense aerosol clouds in the stratosphere often wraps up as persistent compact structures which rotate as anticyclones and also move vertically. One of these vortices has been observed over 3 months and experienced a 20 km rise. Such observations were made after the 2020 Australian wildfires, the 2017 British Columbia fire and more recently after the 2022 Tonga eruption and a few other cases. For all these events, the link was made with anomalous warming or cooling due to the composition of the clouds. This presentation will summarize the observed events and demonstrate the general characters of the stratospheric aerosol vortices. It will also discuss how they are detected by the weather assimilation systems through their signature in temperature, the conditions of their stability and how they can be reproduced experimentally with simple experimental models. Their impact on the transport of long-lived species will be discussed.Such structures seem so far proper to the Earth stratosphere and have found analogies nowhere else.Ref: DOIs: 10.1038/s43247-020-00022-5, 10.5194/acp-21-7113-2021, 10.5194/acp-22-14957-2022

France is committed to achieving climate neutrality by 2050. In this respect, the heating sector, one of the largest energy-consuming sectors in France, is undergoing rapid electrification. In 2022, electricity contributed to the heating of more than 40% of French dwellings. As a result, the French electricity demand is increasingly becoming thermosensitive. Accordingly, for every 1°C drop in temperature below the threshold (i.e., 15°C) during winter, the electricity demand increases by ~2.4 GW in France. With a notable share of French nuclear reactors reaching their end of service life, several recent episodes of widespread cold spells over France have raised concerns about energy security. Hence, anticipating cold spells well in advance is increasingly becoming important for the smooth operation of the energy sector. In this regard, we assess the predictability of several recent episodes of cold spells on seasonal timescales over France using the seasonal predictions from the European Centre for Medium-Range Weather Forecasts. Additionally, we test a recently developed statistical downscaling methodology in forecasting cold spells over France, using the forecasts of upper-level fields, which are better predicted than the surface fields. On comparing the dynamical and statistical predictions, we show that the statistical predictions, relying upon the information contained in the better-predicted upper-level fields, perform significantly better than the dynamical counterparts in predicting cold spells beyond a month.

Bimodality and other types of non-Gaussianity arise in ensemble forecasts of the atmosphere as a result of nonlinear spread across ensemble members. In this paper, bimodality in 50-member ECMWF ENS-extended ensemble forecasts is identified and characterized. Forecasts of 2 m temperature are found to exhibit widespread bimodality well over a derived false-positive rate. In some regions bimodality occurs in excess of 30 % of forecasts, with the largest rates occurring during lead times of 2 to 3 weeks. Bimodality occurs more frequently in the winter hemisphere with indications of baroclinicity being a factor to its development. Additionally, bimodality is more common over the ocean, especially the polar oceans, which may indicate development caused by boundary conditions (such as sea ice). Near the equatorial region, bimodality remains common during either season and follows similar patterns to the Intertropical Convergence Zone (ITCZ), suggesting convection as a possible source for its development. Over some continental regions the modes of the forecasts are separated by up to 15 ∘C. The probability density for the modes can be up to 4 times greater than at the minimum between the modes, which lies near the ensemble mean. The widespread presence of such bimodality has potentially important implications for decision makers acting on these forecasts. Bimodality also has implications for assessing forecast skill and for statistical postprocessing: several commonly used skill-scoring methods and ensemble dressing methods are found to perform poorly in the presence of bimodality, suggesting the need for improvements in how non-Gaussian ensemble forecasts are evaluated.

A previous study has shown that a large portion of subseasonal-to-seasonal European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts for 2-meter temperature exhibit properties of bimodality, in some locations reaching occurrence rates of over 30 %. This study introduces novel methodology to help identify `bimodal events', meteorological events which trigger the development of widespread bimodality in forecasts. Understanding such events not only provides insight into the dynamics of the meteorological phenomena causing bimodal events, but also has drastic implications for the skill of forecasts affected. The methodology that is developed allows one to systematically characterize the spatial and temporal scales of the derived bimodal events, and thus uncover the flow states that lead to them. Three distinct regions that exhibit high occurrence rates of bimodality are studied: one in South America, one in the Southern Ocean and one in the North Atlantic. It is found that each region's bimodality appears to be triggered by large-scale atmospheric processes interacting with geographically specific processes: In South America bimodality is related to an atmospheric wave interacting with the Andes, in the Southern Ocean bimodality is related to an atmospheric wave interacting with sea ice, and in the North Atlantic bimodality is connected to an atmospheric wave deforming near the Gulf Stream. This common pattern of large-scale circulation anomalies interacting with local boundary conditions suggests that any deeper dynamical understanding of these events should incorporate such interactions.

This article deals with the detection of small-scale turbulence from in-situ meteorological measurements performed under superpressure balloons (SPBs). These balloons allow long duration flights (several months) at a prerequisite height level. The dataset is gathered from the Strateole-2 probationary campaign during which eights SPBs flew in the tropical tropopause layer at around 19 and 20.5 km altitudes, from November 2019 to March 2020. Turbulence is not directly measured by the instrument set onboard the SPBs. Nonetheless, there is a potential to derive information about the occurrence of turbulence from the well-resolved in time measurements of pressure, temperature and position. It constitutes a challenge to extract that information from a measurement set that was not designed for quantifying turbulence, and the manuscript explains the methodology developed to overcome this difficulty. It is observed that SPBs oscillate quasi-periodically around their equilibrium positions. The oscillation periods, 220 s in the average, range from 130 to 500 s, close to, but noticeably smaller than, the Brunt-Väisälä period (∼ 300 s). The amplitude of these vertical motions is ∼ ±15 m, inducing large fluctuations in all quantities, whether measured (pressure, temperature, positions) of inferred (density, potential temperature). The relationships between the changes in these quantities and vertical displacements of the balloons are used to infer properties of the flow in which the SPBs drift. In case of active turbulence, the vertical stratification as well as the wind shear are likely to be reduced by mixing. Hence, the increments of potential temperature, δθ, and of the vertical displacements of the balloon, δzB, are expected to be uncorrelated since ∂θ / ∂z → 0. Also, the local Richardson number is expected to be less than ∼ 0.25. Several binary indexes (true of false) to describe the state of the flow, laminar or turbulent, are evaluated. They are based either on correlations between δθ and δzB, or on estimations of the local Richardson number. Correlation coefficients are computed and compared, by using different measures of δθ and δzB and by estimating either the Pearson or the Spearman coefficients. Turbulence indexes based on a null-correlation are built using a randomisation test to check whether these correlations are significantly non-positive. It is also shown than a linear regression between the increments of a quantity and the increments of vertical displacements allows to estimate the vertical gradient of this quantity. Least square fit and Theil-Sen fit are used to estimate time series of vertical gradients ∂T / ∂z, ∂u / ∂z, ∂v / ∂z. Related quantities such as the Brunt-Väisälä frequency, or the local Richardson number, Ri, are inferred, allowing to establish turbulence indexes from Ri. These different indexes, based on independent measurements and on various methods, correlations or linear regressions, are found to be consistent: they differ for less than 3 % of the cases. The flow is observed to be turbulent for about 5 % of the time, with strong inhomogeneities along the longitude.

The relationship between the wind speed derived from the outputs of a numerical-weather-prediction model and from observations is explored using statistical and machine-learning models. Eight years of wind-speed measurements at a height of 10 m (from 2010 to 2017) from 171 stations spread over mainland France and Corsica are used for reference. Operational analyses from the European Center for Medium Range Weather Forecasts (ECMWF) provide the model information not only on the surface flow, but on other aspects of the atmospheric state at the location (or above) each station. In a first step, a large number of explanatory variables are used as input to several models (linear regressions, k-nearest neighbours, random forests, and gradient boosting). The modelled wind speed in the ECMWF analyses, by itself, has root-mean-square errors over all stations distributed widely around a median of 1.42 m s $$^{-1}$$ . Using statistical post-processing and making use of a historical dataset for training, the median of the root-mean-square errors at all stations can be reduced down to 1.07 m s $$^{-1}$$ when modelled with linear regressions, and down to 0.94 m s $$^{-1}$$ with the machine-learning models (random forests or gradient boosting). Yet more significant decreases are found for coastal stations where the errors are largest. The random-forest models are further explored to reduce the list of explanatory variables: a list of 25 explanatory variables, mainly consisting of flow variables (wind speed, velocity components, horizontal gradients of geopotential on different isobaric surfaces, wind shear between 10 and 100 m) and including marginally some temperature variables, appears as a good compromise between performance and simplicity. Finally, as a preliminary test for further work, the relation thus captured between the model outputs and the observed wind speed at a given time is applied to forecasts of the numerical-weather-prediction model, for lead times up to 24 h. The machine-learning model is found to be essentially as relevant on the forecasts as it was on the analyses, encouraging further use and development of these approaches for local wind-speed forecasts.

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.

Four state-of-the-science numerical weather prediction (NWP) models were used to perform mountain wave (MW)-resolving hindcasts over the Drake Passage of a 10-day period in 2010 with numerous observed MW cases. The Integrated Forecast System (IFS) and the Icosahedral Nonhydrostatic (ICON) model were run at Δx ≈ 9 and 13 km globally. The Weather Research and Forecasting (WRF) Model and the Met Office Unified Model (UM) were both configured with a Δx = 3-km regional domain. All domains had tops near 1 Pa (z ≈ 80 km). These deep domains allowed quantitative validation against Atmospheric Infrared Sounder (AIRS) observations, accounting for observation time, viewing geometry, and radiative transfer. All models reproduced observed middle-atmosphere MWs with remarkable skill. Increased horizontal resolution improved validations. Still, all models underrepresented observed MW amplitudes, even after accounting for model effective resolution and instrument noise, suggesting even at Δx ≈ 3-km resolution, small-scale MWs are underresolved and/or overdiffused. MW drag parameterizations are still necessary in NWP models at current operational resolutions of Δx ≈ 10 km. Upper GW sponge layers in the operationally configured models significantly, artificially reduced MW amplitudes in the upper stratosphere and mesosphere. In the IFS, parameterized GW drags partly compensated this deficiency, but still, total drags were ≈6 times smaller than that resolved at Δx ≈ 3 km. Meridionally propagating MWs significantly enhance zonal drag over the Drake Passage. Interestingly, drag associated with meridional fluxes of zonal momentum (i.e., ) were important; not accounting for these terms results in a drag in the wrong direction at and below the polar night jet. Significance Statement This study had three purposes: to quantitatively evaluate how well four state-of-the-science weather models could reproduce observed mountain waves (MWs) in the middle atmosphere, to compare the simulated MWs within the models, and to quantitatively evaluate two MW parameterizations in a widely used climate model. These models reproduced observed MWs with remarkable skill. Still, MW parameterizations are necessary in current Δx ≈ 10-km resolution global weather models. Even Δx ≈ 3-km resolution does not appear to be high enough to represent all momentum-fluxing MW scales. Meridionally propagating MWs can significantly influence zonal winds over the Drake Passage. Parameterizations that handle horizontal propagation may need to consider horizontal fluxes of horizontal momentum in order to get the direction of their forcing correct.

We present first results aiming at understanding the impact of gravity waves on homogeneous ice nucleation in a simplified microphysics set-up. We use a 1D model of homogeneous ice nucleation, growth, sedimentation, and mixing due to wind shear. This model describes the evolution of ice crystals number and mass with 36 bins of size, after freezing on particles of ammonium sulfate. 420 supersaturated air parcels of 20m thickness and 200m length, distributed in a 7 columns grid, are simulated simultaneously, allowing exchanges of ice crystals between the air masses by sedimentation and horizontal mixing. A first simple set-up represents the large-scale ascending motion of air parcels in the tropics, with a vertical speed of 0.5 mm/s. Air parcels follow a typical tropical temperature profile, between 16400 and 17700 m of altitude. The maximum of nucleated ice crystals is found just below the cold point and nucleation occurs in a layer of 400m, whereas sedimented ice crystals are found down to the bottom of the columns of air masses, showing that the width of cirrus clouds is different from the nucleation layer. The majority of nucleated ice crystals is small enough to be carried up with the air parcels. Yet the fall streaks of a few bigger crystals deplete the humidity of air parcels beneath, preventing new nucleation events from happening. We find that more than half of the total ice mass in air parcels is from the sedimented crystals only, even though they represent less than a third of the number of crystals counted in our system. These few crystals are responsible for more than half of the diminution of the humidity within the air parcels. A second experiment is designed to take into account smaller scale perturbations induced by gravity waves, by coupling the microphysics model with lagrangian temperature measurements from superpressure balloons of the first Stratéole-2 campaign. Gravity waves are found to create more nucleation events, in time and at all altitude levels of our experiment, expanding the nucleation layer up to one kilometer. The larger cooling rates create more small crystals, but the growth of ice is slowed down by the waves’s warming phases and the decreased humidity from more nucleation events. Furthermore, gravity waves prevent the biggest ice crystals from appearing. Last, the addition of gravity waves removes on average less humidity from the air parcels than the sedimentation of ice crystals nucleated during the slow unperturbed ascent.

With the continuously increasing share of renewables in the electricity mix, the sub-seasonal predictions of 100 m wind speed and surface temperature, if skillful, can provide significant socio-economic value to the energy sector. In this study, we develop a novel hybrid statistical-dynamical probabilistic prediction model to improve the skill of sub-seasonal predictions of 100 m wind speed (U100) and 2 m temperature (T2m). For the statistical part, multivariate statistical analysis is carried out between the observed gridded large-scale fields such as the geopotential height at 500 hPa (Z500) and the gridded predictand (U100 or T2m) over Europe to obtain weather regimes conditioned on the targeted predictand. The relationship between the predictor and the predictand is then used to 'reconstruct' sub-seasonal predictions of U100 and T2m based on the predictions of Z500, which are more skillful than the surface variables. This is applied on sub-seasonal predictions from the European Centre for Medium-Range Weather Forecasts. The new 50 ensemble members of 'reconstructed' surface fields are combined with the original 50 members of dynamical/direct predictions. The resulting hybrid prediction ensemble is found to be generally more skillful than the dynamical predictions on sub-seasonal timescales.

Anticyclonically-trapped plumes were discovered following the unprecedented 2020 Australian fires, which saw the rise of a 1,000-km diameter, 6-km deep bubble of tropospheric air enriched in combustion products from 19 to 35 km asl over 3 months. Since then, a number of previous occurrences has been reported, notably in the aftermath of the 2017 Canadian fires. Lifted by solar heating from black carbon aerosols, the long-lived anticyclonic plumes are characterized by a joint upward motion of plume material and anticyclonic potential vorticity (PV). These newly discovered objects raise fundamental questions from a dynamical standpoint. In particular, although the similar evolution of tracers and PV is a well-known property of quasi-adiabatic flows, it has no reason to hold in the presence of diabatic heating. Hence, there is seemingly a contradiction between the observed preservation of the low PV-aerosol-tracer relationship over time and fundamental properties of PV in this diabatically-forced flow.In this presentation, we propose a conceptual model for the formation and evolution of smoked-charged anticyclones. The mechanisms at play will first be illustrated using idealized numerical simulations with the Weather Research and Forecast (WRF) model where we explore for the first time the flow response to a Lagrangian tracer locally heating the atmosphere. We will then analyze key features of the observed anticyclonic structures reproduced by the model, including the maintenance of the anticyclonic tracer bubble along its ascent, the formation of a tracer front at its top and of a tail at its lower bound, and a very low, almost-vanishing PV within the vortex. Finally, we will discuss some implications of our findings, in particular regarding the dynamical conditions favoring the formation and maintenance of such structures.

We develop a method for forecasting the distribution of the daily surface wind speed at timescales from 15-days to 3-months in France. On such long-term timescales, ensemble predictions of the surface wind speed have poor performance, however, the wind speed distribution may be related to the large-scale circulation of the atmosphere, for which the ensemble forecasts have better skill. The information from the large-scale circulation, represented by the 500 hPa geopotential height, is summarized into a single index by first running a PCA and then a polynomial regression. We estimate, over 20 years of daily data, the conditional probability density of the wind speed at a specific location given the index. We then use the ECMWF seasonal forecast ensemble to predict the index for horizons from 15-days to 3-months. These predictions are plugged into the conditional density to obtain a distributional forecast of surface wind. These probabilistic forecasts remain sharper than the climatology up to 1-month forecast horizon. Using a statistical postprocessing method to recalibrate the ensemble leads to further improvement of our probabilistic forecast, which then remains calibrated and sharper than the climatology up to 3-months horizon, particularly in the north of France in winter and fall.

The parameterization of inertia–gravity waves (IGWs) is of considerable importance in general circulation models. Among the challenging issues faced in studies concerned with parameterization of IGWs is the estimation of diabatic forcing in a way independent of the physics parameterization schemes, in particular, convection. The requirement is to estimate the diabatic heating associated with balanced motion. This can be done by comparing estimates of balanced vertical motion with and without diabatic effects. The omega equation provides the natural method of estimating balanced vertical motion without diabatic effects, and several methods for including diabatic effects are compared. To this end, the assumption of spatial-scale separation between IGWs and balanced flows is combined with a suitable form of the balanced omega equation. To test the methods constructed for estimating diabatic heating, an idealized numerical simulation of the moist baroclinic waves is performed using the Weather Research and Forecasting (WRF) Model in a channel on thefplane. In overall agreement with the diabatic heating of the WRF Model, in the omega-equation-based estimates, the maxima of heating appear in the warm sector of the baroclinic wave and in the exit region of the upper-level jet. The omega-equation-based method with spatial smoothing for estimating balanced vertical motion is thus presented as the proper way to evaluate diabatic forcing for parameterization of IGWs.

Bimodality and other types of non-Gaussianity arise in ensemble forecasts of the atmosphere as a result of non-linear spread across ensemble members. In this paper, bimodality in 50-member ECMWF ENS-extended ensemble forecasts is identified and characterized. Forecasts of 2-meter temperature are found to exhibit widespread bimodality well over a derived false-positive rate. In some regions bimodality occurs in excess of 30 % of forecasts, with the largest rates occurring during lead times of 2 to 3 weeks. Bimodality occurs more frequently in the winter hemisphere with indications of baroclinicity being a factor to its development. Additionally, bimodality is more common over the ocean, especially the polar oceans, which may indicate development caused by boundary conditions (such as sea ice). Near the equatorial region, bimodality remains common during either season and follows similar patterns to the intertropical convergence zone (ITCZ) suggesting convection as a possible source for its development. Over some continental regions the modes of the forecasts are separated by up to 15 °C. The probability density for the modes can be up to four times greater than at the minimum between the modes, which lies near the ensemble mean. The widespread presence of such bimodality has potentially important implications for decision makers acting on these forecasts. Bimodality also has implications for assessing forecast skill and for statistical post-processing: several commonly used skill scoring methods and ensemble dressing methods are found to perform poorly in the presence of bimodality.

The response of the atmospheric boundary layer to mesoscale sea surface temperature (SST) is often characterized by a link between wind stress divergence and downwind SST gradients. In this study, an idealized simulation representative of a storm track above a prescribed stationary SST field is examined in order to determine in which background wind conditions that relationship occurs. The SST field is composed of a midlatitude large-scale frontal zone and mesoscale SST anomalies. It is shown that the divergence of the surface wind can correlate either with the Laplacian of the atmospheric boundary layer temperature or with the downwind SST gradient. The first case corresponds to background situations of weak winds or of unstable boundary layers, and the response is in agreement with an Ekman balance adjustment in the boundary layer. The second case corresponds to background situations of stable boundary layers, and the response is in agreement with downward mixing of momentum. Concerning the divergence of the wind stress, it generally resembles downwind SST gradients for stable and unstable boundary layers, in agreement with past studies. For weak winds, a correlation with the temperature Laplacian is, however, found to some extent. In conclusion, our study reveals the importance of the large-scale wind conditions in modulating the surface atmospheric response with different responses in the divergences of surface wind and wind stress.

Large-scale oceanic fronts, such as in western boundary currents, have been shown to play an important role in the dynamics of atmospheric storm tracks. Little is known about the influence of mesoscale oceanic eddies on the free troposphere, although their imprint on the atmospheric boundary layer is well documented. The present study investigates the response of the tropospheric storm track to the presence of sea surface temperature (SST) anomalies associated with an eddying ocean. Idealized experiments are carried out in a configuration of a zonally reentrant channel representing the midlatitudes. The SST field is composed of a large-scale zonally symmetric front to which are added mesoscale eddies localized close to the front. Numerical simulations show a robust signal of a poleward shift of the storm track and of the tropospheric eddy-driven jet when oceanic eddies are taken into account. This is accompanied by more intense air–sea fluxes and convective heating above oceanic eddies. Also, a mean heating of the troposphere occurs poleward of the oceanic eddying region, within the storm track. A heat budget analysis shows that it is caused by a stronger diabatic heating within storms associated with more water advected poleward. This additional heating affects the baroclinicity of the flow, which pushes the jet and the storm track poleward.

Near-inertial waves (NIWs) with intrinsic frequency close to the local Coriolis parameter f constitute a striking component of the kinetic energy spectrum in both the atmosphere and the ocean. However, contrary to the oceanic case, the strong and variable background atmospheric winds tend to shift the frequency of the waves (Doppler effect). As a consequence, atmospheric NIWs cannot generally be observed directly as a kinetic energy peak at ground-based frequency f but are instead diagnosed indirectly (e.g. using the polarisation and dispersion relations). This complication does not appear when analyzing quasi-lagrangian observations from superpressure balloons (SPB), which drift together with the flow and are thus exempt from Doppler shift. Past SPB observations in the lower stratosphere have revealed the magnitude of the kinetic energy peak associated with NIWs and it was recently shown that state-of-the-art reanalyses partly represent this feature.In this presentation, we will investigate the variability of NIWs using ECMWF (re)analysis products (the operational analysis and ERA5) and balloon observations from recent CNES campaigns (2005, 2010 and 2019-2020) at various latitudes ranging from the equator to the pole (and hence different inertial frequencies). As in Podglajen et al. (2020), NIWs are extracted from the (re)analyses by computing Lagrangian trajectories using the analyzed wind and temperature fields. We will illustrate the remarkable realism of model NIWs, both statistically and for specific case studies. Then, we will characterize the geographic and seasonal variability of NIW properties. In light of those results, possible factors influencing the near-inertial energy peak (horizontal wave propagation, refraction near critical levels, tide interactions) and the parallel with the oceanic situation will be discussed, as well as the ability of the model and data assimilation system to simulate them.Reference :Podglajen, A., Hertzog, A., Plougonven, R., and Legras, B.: Lagrangian gravity wave spectra in the lower stratosphere of current (re)analyses, Atmos. Chem. Phys., 20, 9331–9350, https://doi.org/10.5194/acp-20-9331-2020, 2020.

Strateole-2 is a project aimed at studying the coupling between the troposphere and the stratosphere in the deep tropics. The project originality pertains to the use of long-duration ballons, which can fly for several months at 18 or 20 km altitude. The first Strateole-2 campaign took place from November 2019 to February 2020: 8 balloons with various instrumental configurations were released in the lower stratosphere from Seychelles Islands, in the Indian Ocean.This first campaign was primarily devoted to testing all systems (balloons, gondolas, and instruments) developed for the project, and was very successful: the balloons flew for 85 days onaverage over the whole tropical band, and most instruments performed nominally. In-situ meteorological measurements performed every 30-s on each flight provide a unique description of gravity-wave activity in the tropics and its relation to deep convection. The first observations of aerosols and water vapor onboard long-duration balloons were also achieved, which e.g. highlighted the tape recorder signal in the tropical lower stratosphere. Very innovative instruments also premiered during the campaign: RACHuTS, a light reeled payload, for instance performed 50 high-resolution vertical profiles of temperature, aerosols and water vapor down to 2km below the balloon, crossing several times the cold-point tropopause. ROC collected hundreds of temperature profiles down to the middle troposphere through GPS radio-occultations. Last, one balloon also carried a nadir-pointing backscatter lidar, which has described the underlying convection at unprecedented temporal resolution. An overview of the flights and first results will be presented.Two forthcoming balloon campaigns are planned within Strateole-2, in 2021-22 and 2024-25. Each will release 20 balloons.

The skill of predicted wind speed at 100 m and temperature at 2 m has been assessed in extended-range forecasts and hindcasts of the European Center for Medium-Range Weather Forecasts, starting from December 2015 to November 2019. The assessment was carried out over Europe grid-point wise and also by considering several spatially averaged country-sized domains, using standard scores such as the Continuous Ranked Probability Score and Anomaly Correlation Coefficient. The (re-)forecasts showed skill over climatology in predicting weekly mean wind speed and temperature well beyond two weeks. Even at a lead time of 6 weeks, the probability of the (re-)forecasts being skillful is greater than 50%, encouraging the use of operational subseasonal forecasts in the decision making value chain. The analysis also exhibited​ significant differences in skill in the predictability of different variables, with temperature being more skillful than wind speed, and for different seasons, with winter allowing more skillful forecasts. The predictability also displayed a clear spatial pattern with forecasts for temperature having more skill for Eastern than for Western Europe, and wind speed forecasts having more skill in Northern than Southern Europe.

Tropical gravity wave activity is investigated using measurements of momentum flux obtained by superpressure balloons. The dataset contains 8 balloons that flew in the equatorial band from November 2019 to February 2020, for 2 to 3 months each, collecting data every 30s. The relation between gravity waves and deep convection was investigated using geostationary satellite data from the NOAA/NCEP GPM\_MERGEIR satellite data product, at 1 hour resolution. The amplitude of gravity wave momentum fluxes shows a clear dependence on the distance to the nearest convection site, with a strong decay as distance to convection increases. The largest values of momentum flux (more than 5 mPa) are only found in the vicinity of deep convection (< 200 km). The sensitivity to distance from convection is stronger for high frequency gravity waves (periods shorter than 30 minutes). Lower frequency waves tend to a non-zero, background value away from convection, supporting some background value in gravity-wave drag parameterizations. On the other hand, the wide range of momentum flux values close to the convection sites emphasizes the intermittent nature of gravity waves. This intermittency was also studied on a larger scale, using a 20° longitudinal grid of the recorded momentum flux in the deep tropics. The results highlight spatial variations of gravity wave activity, with the highest momentum flux recorded over the continent, and associated to higher intermittency.

International audience; Tropical gravity wave activity is investigated using measurements of momentum fluxes gathered during Strateole-2 superpressure balloon flights. The data set consists of eight balloon flights performed in the deep tropics from November 2019 to February 2020. The flights lasted for 2–3 months each, and in-situ meteorological data were collected every 30 s. The relation between gravity waves and deep convection is investigated using geostationary satellite data from the NOAA/NCEP GPM_MERGIR satellite data product, at 1 h resolution. The amplitude of gravity wave momentum fluxes shows a clear dependence on the distance to the nearest convective system, with a strong decay as distance to convection increases. The largest momentum-flux values (>urn:x-wiley:2169897X:media:jgrd57204:jgrd57204-math-0001mPa) are only found less than 200 km away from deep convection. The sensitivity of the wave flux to distance from convection is stronger for high frequency gravity waves (periods shorter than 60 min). Lower frequency waves tend to a non-zero, background value away from convection, supporting some background value in gravity-wave drag parameterizations. On the other hand, the wide range of momentum flux values observed close to the convection emphasizes the intermittent nature of the gravity-wave source. The large scale variation of gravity-wave intermittency within the equatorial belt is also studied. The results highlight spatial variations of gravity wave activity, with the highest momentum flux recorded over land.

Transmission system operator (TSOs) need to project the system state at the seasonal scale to evaluate the risk of supply-demand imbalance for the season to come. Seasonal planning of the electricity system is currently mainly adressed using climatological approach to handle variability of consumption and production. Our study addresses the need for quantitative measures of the risk of supply-demand imbalance, exploring the use of sub-seasonal to seasonal forecasts which have hitherto not been exploited for this purpose. In this study, the risk of supply-demand imbalance is defined using exclusively the wind energy production and the consumption peak at 7 pm. To forecast the risks of supply-demand imbalance at monthly to seasonal time horizons, a statistical model is developed to reconstruct the joint probability of consumption and production. It is based on a the conditional probability of production and consumption with respect to indexes obtained from a linear regression of principal components of large-scale atmospheric predictors. By integrating the joint probability of consumption and production over different areas, we define two kind of risk measures: one quantifies the probablity of deviating from the climatological means, while the other, which is the value at risk at 95% confidence level (VaR95) of the difference between consumption and production, quantifies extreme risks of imbalance. In the first case, the reconstructed risk accurately reproduces the actual risk with over 0.80 correlation in time, and a hit rate around 70–80%. In the second case, we find a mean absolute error (MAE) between the reconstructed and real extreme risk of 2.5 to 2.8 GW, a coefficient of variation of the root mean square error (CV-RMSE) of 3.8% to 4.2% of the mean actual VaR95 and a correlation of 0.69 and 0.66 for winter and fall, respectively. By applying our model to ensemble forecasts performed with a numerical weather prediction model, we show that forecasted risk measures up to 1 month horizon can outperform the climatology often used as the reference forecast (time correlation with actual risk ranging between 0.54 and 0.82). At seasonal time horizon (3 months), our forecasts seem to tend to the climatology.

Orographic gravity wave (OGW) drag is one of the fundamental physics parametrizations employed in every global numerical model across timescales from weather to climate. These parameterizations have significant influences, both direct and indirect, on the atmosphere’s general circulation from the troposphere at least through the mesosphere. Despite their significant influence, observational constraints on these parameterizations are still largely lacking.Presented here is a team project jointly supported by SPARC and the International Space Science Institute with the overall objective of providing new quantitative constraints for OGW drag parameterizations. Specific objectives are to evaluate methods that quantify vertical fluxes of horizontal momentum (MF) from satellite observations via an observing system simulation experiment (OSSE), a validation of WRF, UKMO, ECMWF, and ICON models against satellite and balloon observations, and an inter-comparison of OGW properties (e.g. MF and drag) within these models. Evaluation of satellite-based estimates of MF and model validation/inter-comparison will help to better quantify actual MF in the stratosphere, providing the best stratospheric MF and drag estimates for parameterizations to reproduce to date.Two unique aspects of the project are that all models involved are deep, extending up to 1 Pa. The motivations for doing so was to include entire instrument weighting functions for AIRS observations, allowing direct, quantitative comparison between AIRS (and other satellite-borne) observations and the models. The second is the effort to perform an OSSE within the simulations, allowing comparison between MF from satellite-based methods within the models to the true MF in the models.Preliminary results show that higher-res models (dx = 3 km) compare well and produce significantly more MF than lower-res global models, but the higher-res models still underrepresent OGW amplitudes. Mesospheric tides in analyses used to force the models significantly modulate resolved GWs and their drags.

The need to have access to accurate short term forecasts is essential in order to anticipate the energy production from intermittent renewable sources, notably wind energy. For hourly and sub-hourly forecasts, benchmarks are based on statistical approaches such as time series based methods or neural networks, which are always tested against persistence. Here we discuss the performances of downscaling approaches using information from Numerical Weather Prediction (NWP) models, rarely used at those time scales, and compare them with the statistical approaches for the wind speed forecasting at hub height. The aim is to determine the added value of Model Output Statistics for sub-hourly forecasts of wind speed, compared to the classical time series based methods. Two downscaling approaches are tested: one using explanatory variables from NWP model outputs only and another which additionally includes local wind speed measurements. Results of both approaches and of the classical time series based methods, tested against persistence on a specific wind farm, are considered. For both hourly and sub-hourly forecasts, adding explanatory variables derived from observations in the downscaling models gives higher improvements over persistence than the benchmark methods and than the downscaling models using only the NWP model outputs.

Due to their increasing spatial resolution, numerical weather prediction (NWP) models and the associated analyses resolve a growing fraction of the gravity wave (GW) spectrum. However, it is unclear how well this “resolved” part of the spectrum truly compares to the actual atmospheric variability. In particular, the Lagrangian variability, relevant, for example, to atmospheric dispersion and to microphysical modeling in the upper troposphere–lower stratosphere (UTLS), has not yet been documented in recent products. To address this shortcoming, this paper presents an assessment of the GW spectrum as a function of the intrinsic (air parcel following) frequency in recent (re)analyses (ERA-Interim, ERA5, the ECMWF operational analysis and MERRA-2). Long-duration, quasi-Lagrangian balloon observations in the equatorial and Antarctic lower stratosphere are used as a reference for the atmospheric spectrum and are compared to synthetic balloon observations along trajectories calculated using the wind and temperature fields of the reanalyses. Overall, the reanalyses represent realistic features of the spectrum, notably the spectral gap between planetary and gravity waves and a peak in horizontal kinetic energy associated with inertial waves near the Coriolis frequency f in the polar region. In the tropics, they represent the slope of the spectrum at low frequency. However, the variability is generally underestimated even in the low-frequency portion of the spectrum. In particular, the near-inertial peak, although present in the reanalyses, has a reduced magnitude compared to balloon observations. We compare the observed and modeled variabilities of temperature, zonal momentum flux and vertical wind speed, which are related to low-, mid- and high-frequency waves, respectively. The probability density function (PDF) distributions have similar shapes but show increasing disagreement with increasing intrinsic frequency. Since at those altitudes they are mainly caused by gravity waves, we also compare the geographic distribution of vertical wind fluctuations in the different products, which emphasizes the increase of both GW variance and intermittency with horizontal resolution. Finally, we quantify the fraction of resolved variability and its dependency on model resolution for the different variables. In all (re)analysis products, a significant part of the variability is still missing, especially at high frequencies, and should hence be parameterized. Among the two polar balloon datasets used, one was broadcast on the Global Telecommunication System for assimilation in NWP models, while the other consists of independent observations (unassimilated in the reanalyses). Comparing the Lagrangian spectra between the two campaigns shows that the (re)analyses are largely influenced by balloon data assimilation, which especially enhances the variance at low GW frequency.

In a recent study, O’Neill et al. analyzed the divergence of surface winds above the northwest Atlantic. In the time mean, a band of convergence is found, overlying the southern flank of the Gulf Stream. To quantify the impact of synoptic storms, the authors proposed to compare the time-mean divergence with the divergence averaged in the absence of rain. In the resulting conditional-average field, divergence was found to be positive nearly everywhere. O'Neill et al. concluded that this absence of convergence precludes the Ekman-balanced mass adjustment to be responsible for the atmospheric response above the Gulf Stream. Using a simplistic toy model as well as a numerical simulation representative of a storm track, we show that the absence of negative divergence values purely results from the correlation between rain and convergence: the conditional average based on the absence of rain necessarily implies a shift toward positive divergence values. In consequence, we argue that conditional statistics (based on the absence of rain or removing extreme values in the divergence field), as produced by O’Neill et al., do not allow conclusions on the mechanisms underlying the atmospheric response to the Gulf Stream. They nevertheless highlight the essential role of synoptic storms in shaping the divergence field in instantaneous fields.

Quantification of inertia–gravity waves (IGWs) generated by upper-level jet–surface front systems and their parameterization in global models of the atmosphere relies on suitable methods to estimate the strength of IGWs. A harmonic divergence analysis (HDA) that has been previously employed for quantification of IGWs combines wave properties from linear dynamics with a sophisticated statistical analysis to provide such estimates. A question of fundamental importance that arises is how the measures of IGW activity provided by the HDA are related to the measures coming from the wave–vortex decomposition (WVD) methods. The question is addressed by employing the nonlinear balance relations of the first-order δ–γ, the Bolin–Charney, and the first- to third-order Rossby number expansion to carry out WVD. The global kinetic energy of IGWs given by the HDA and WVD are compared in numerical simulations of moist baroclinic waves by the Weather Research and Forecasting (WRF) Model in a channel on the f plane. The estimates of the HDA are found to be 2–3 times smaller than those of the optimal WVD. This is in part due to the absence of a well-defined scale separation between the waves and vortical flows, the IGW estimates by the HDA capturing only the dominant wave packets and with limited scales. It is also shown that the difference between the HDA and WVD estimates is related to the width of the IGW spectrum.

In recent years, environmental concerns have encouraged the use of wind power as a renewable energy resource. However, high penetration of the wind power in the electricity system is a challenge due to the uncertainty of wind energy forecast. Estimation of the wind energy production requires a forecast for the wind (the main source of uncertainty) but also of density, often overlooked. Measure of air density is a key for more accurate wind energy prediction. Wind farms often lack instrumentations of temperature and pressure, needed for accurate air density estimation at hub height to be used for locally debiasing air density forecast. In this study, the error budget of air density estimate is computed distinguishing temperature and pressure contributions. The analysis uses measurements for in-depth local analysis as well as meteorological reanalysis to investigate the added-value of a model-based value when measurement is missing. Meteorological reanalysis is also used to study spatial pattern of error budgets (mountainous area, coastal regions, plains, ...). The effect of altitude is carefully accounted for. Temperature is by far the variable inducing the largest errors when it is missing in the air density correction, and replaced by the standard atmosphere value (i.e. 15 °C, used as reference in power curves). It is particularly true for very cold or warm conditions (i.e. far from the standard value), for which the error on wind energy production is nearly halved when an accurate correction of temperature is performed.

This paper investigates the accuracy of simulated long-duration super-pressure balloon trajectories in the lower stratosphere. The observed trajectories were made during the (tropical) Pre-Concordiasi and (polar) Concordiasi campaigns in 2010, while the simulated trajectories are computed using analyses and forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System model. In contrast with the polar stratosphere situation, modelling accurate winds in the tropical lower stratosphere remains challenging for numerical weather prediction systems. The accuracy of the simulated tropical trajectories are quantified with the operational products of 2010 and 2016 in order to understand the impact of model physics and vertical resolution improvements. The median errors in these trajectories are large (typically ≳ 250 km after 24 h), with a significant negative bias in longitude, for both model versions. In contrast, using analyses in which the balloon-borne winds have been assimilated reduces the median error in the balloon position after 24 h to &sim, 60 km. For future campaigns, we describe operational strategies that take advantage of the geographic distribution and the episodic nature of large error events to anticipate the amplitude of error in trajectory forecasts. We finally stress the importance of a high vertical resolution in the model, given the intense shears encountered in the tropical lower stratosphere.

Safety compliance issues for operational studies of the atmosphere with balloons require quantifying risks associated with descent and developing strategies to reduce the uncertainties at the location of the touchdown point. Trajectory forecasts are typically computed from weather forecasts produced by an operational center, for example, the European Centre for Medium-Range Weather Forecasts. This study uses past experiments to investigate strategies for improving these forecasts. Trajectories for open stratospheric balloon (OSB) short-term flights are computed using mesoscale simulations with the Weather and Research Forecasting (WRF) Model initialized with ECMWF operational forecasts and are assimilated with radio soundings using the Data Assimilation Research Testbed (DART) ensemble Kalman filter, for three case studies during the Strapolété 2009 campaign in Sweden. The results are very variable: in one case, the error in the final simulated position is reduced by 90% relative to the forecast using the ECMWF winds, while in another case the forecast is hardly improved. Nonetheless, they reveal the main source of forecasting error: during the ceiling phase, errors due to unresolved inertia–gravity waves accumulate as the balloon continuously experiences one phase of a wave for a few hours, whereas they essentially average out during the ascent and descent phases, when the balloon rapidly samples through whole wave packets. This sensitivity to wind during the ceiling phase raises issues regarding the feasibility of such forecasts and the observations that would be needed. The ensemble spread is also analyzed, and it is noted that the initial ensemble perturbations should probably be improved in the future for better forecasts.

The austral stratospheric final warming date is often predicted with substantial delay in several climate models. This systematic error is generally attributed to insufficient parameterized gravity wave (GW) drag in the stratosphere around 60°S. A simulation with a general circulation model [Laboratoire de Météorologie Dynamique zoom model (LMDZ)] with a much less pronounced bias is used to analyze the contribution of the different types of waves to the dynamics of the final warming. For this purpose, the resolved and unresolved wave forcing of the middle atmosphere during the austral spring are examined in LMDZ and reanalysis data, and a good agreement is found between the two datasets. The role of parameterized orographic and nonorographic GWs in LMDZ is further examined, and it is found that orographic and nonorographic GWs contribute evenly to the GW forcing in the stratosphere, unlike in other climate models, where orographic GWs are the main contributor. This result is shown to be in good agreement with GW-resolving operational analysis products. It is demonstrated that the significant contribution of the nonorographic GWs is due to highly intermittent momentum fluxes produced by the source-related parameterizations used in LMDZ, in qualitative agreement with recent observations. This yields sporadic high-amplitude GWs that break in the stratosphere and force the circulation at lower altitudes than more homogeneously distributed nonorographic GW parameterizations do.

International audience; Wave-induced Lagrangian fluctuations of temperature and vertical velocity in the lower stratosphere are quantified using measurements from superpressure balloons (SPBs). Observations recorded every minute along SPB flights allow the whole gravity wave spectrum to be described and provide unprecedented information on both the intrinsic frequency spectrum and the probability distribution function of wave fluctuations. The data set has been collected during two campaigns coordinated by the French Space Agency in 2010, involving 19 balloons over Antarctica and 3 in the deep tropics. In both regions, the vertical velocity distributions depart significantly from a Gaussian behavior. Knowledge on such wave fluctuations is essential for modeling microphysical processes along Lagrangian trajectories. We propose a new simple parameterization that reproduces both the non-Gaussian distribution of vertical velocities (or heating/cooling rates) and their observed intrinsic frequency spectrum.

In the Stratéole 2 program, set to launch in November 2018, instruments will ride balloons into the stratosphere and circle the world, observing properties of the air and winds in fine detail.

International audience; Atmospheric mountain flows produced when the incoming wind is small near the surface and continuously increases with altitude are evaluated with models of increasing complexity. All models confirm that foehn can be produced by a mountain gravity wave critical level mechanism, where the critical level is located below the surface. This mechanism does not involve humidity, upper level wave breaking, upstream blocking, downward wave reflections or hydraulic control as often suggested by popular theories. The first model used is a theoretical model which combines linear gravity wave dynamics with a nonlinear boundary condition: in this model the wave breaking does not feedback onto the dynamics by construction. Partial linear waves reflection are also minimized by using smooth profiles of the incident wind and a uniform stratification N 2 = cte, and can even be suppressed when the incident wind shear is also constant, U z = cst. The second model is a numerical mesoscale model (Weather Research and Forecast), and we show that it predicts mountain wave fields that can be reproduced by the theoretical model, provided that we specify an adequate boundary layer depth in the theoretical model.

Downscaling a meteorological quantity at a specific location from outputs of Numerical Weather Prediction models is a vast field of research with continuous improvement. The need to provide accurate forecasts of the surface wind speed at specific locations of wind farms has become critical for wind energy application. While classical statistical methods like multiple linear regression have been often used in order to reconstruct wind speed from Numerical Weather Prediction model outputs, machine learning methods, like Random Forests, are not as widespread in this field of research. In this paper, we compare the performances of two downscaling statistical methods for reconstructing and forecasting wind speed at a specific location from the European Center of Medium-range Weather Forecasts (ECMWF) model outputs. The assessment of ECMWF shows for 10 m wind speed displays a systematic bias, while at 100 m, the wind speed is better represented. Our study shows that both classical and machine learning methods lead to comparable results. However, the time needed to pre-process and to calibrate the models is very different in both cases. The multiple linear model associated with a wise pre-processing and variable selection shows performances that are slightly better, compared to Random Forest models. Finally, we highlight the added value of using past observed local information for forecasting the wind speed on the short term.