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Réchou, Anne; Flores, Olivier; Jumaux, Guillaume; Duflot, Valentin; Bousquet, Olivier; Pouppeville, Chloé; Bonnardot, Francois
doi: 10.1002/qj.3485pmid: N/A
Weather forecasting is challenging because of the complex interplay between local conditions and regional atmospheric forcings. In this article, we analyse the relationships between local daily rainfall and large‐scale synoptic patterns in the geographical context of Réunion Island, a high volcanic island in the southwestern Indian Ocean basin. Given the critical role of trade winds on weather conditions at island scale, we analyse those relationships across seasons defined with respect to yearly trade‐wind regimes. The analysis of the distribution of inversion events' elevation and frequency allows us to characterize the trade‐wind inversion layer (TWIL) and identify four seasons with homogeneous distributions. We characterize the spatio‐temporal variability of rainfall measured at island scale by a dense network of rain‐gauges over 37 years and relate it to large‐scale weather regimes identified using geopotential height meteorological data. After seasonal signal removal using Fourier transforms and dimension reduction via Principal Component Analysis, we perform Canonical Correlation Analysis to identify canonical variables relating rainfall and geopotential height patterns at the two different studied spatial scales. We then combine Ascending Hierarchical Classification and partitioning k‐means methods to identify homogeneous large‐scale synoptic conditions within each season. From this information, we build composite maps of geopotential height that characterize weather regimes and further analyse rainfall patterns at island scale in each regime. Elevation and orientation are used as descriptive variables at island scale as they strongly structure the patterns. Overall, four homogeneous seasons were identified based on trade‐wind regimes, within which six large‐scale weather regimes were identified for summer, the most variable season, and four for the others. Finally, rainfall patterns at island scale are described in relation to the highlighted synoptic regimes and local descriptive variables.
Mercier, François; Michel, Yann; Montmerle, Thibaut; Jolivet, Pierre; Gürol, Selime
doi: 10.1002/qj.3428pmid: N/A
Ensembles of data assimilations (EDA) in numerical weather prediction (NWP) are frequently used for both initialization of ensemble prediction systems and provision of background‐error statistics to a deterministic variational data assimilation scheme. The EDA consists of running multiple data assimilation schemes in parallel with perturbed observations and backgrounds. This kind of ensemble is computationally expensive, in particular because it requires the solution of as many linear systems as there are members in the ensemble.
Baas, Peter; van de Wiel, Bas J. H.; van Meijgaard, Erik; Vignon, Etienne; Genthon, Christophe; van der Linden, Steven J. A.; de Roode, Stephan R.
doi: 10.1002/qj.3450pmid: 31068734
In this work we study the dynamics of the surface‐based temperature inversion over the Antarctic Plateau during the polar winter. Using 6 years of observations from the French–Italian Antarctic station Concordia at Dome C, we investigate sudden regime transitions in the strength of the near‐surface temperature inversion. Here we define “near‐surface” as being within the domain of the 45‐m measuring tower. In particular, we consider the strongly nonlinear relation between the 10‐m inversion strength (T10m – Ts) and the 10‐m wind speed.
doi: 10.1002/qj.3468pmid: N/A
There is no consensus on the physical mechanisms controlling the scale at which convective activity organizes near the Equator. Here, we introduce a diagnostic framework relating the evolution of the length‐scale of convective aggregation to the net radiative heating, the surface enthalpy flux, and horizontal energy transport. We evaluate these expansion tendencies of convective aggregation in 20 high‐resolution cloud‐permitting simulations of radiative‐convective equilibrium. While both radiative fluxes contribute to convective aggregation, the net long‐wave radiative flux operates at large scales (1,000–5,000 km) and stretches the size of moist and dry regions, while the net short‐wave flux operates at smaller scales (500–2,000 km) and shrinks it. The surface flux expansion tendency is dominated by convective gustiness, which acts to aggregate convective activity at smaller scales (500–3,000 km).
Ngae, Pierre; Kouichi, Hamza; Kumar, Pramod; Feiz, Amir‐Ali; Chpoun, Amer
doi: 10.1002/qj.3471pmid: N/A
The aim of this study is to optimize sensor networks for fast deployment in order to reconstruct an unknown source of intentional or accidental release in local urban topography. In such emergency circumstances, only the meteorological conditions are available in real time and the network deployed must be efficient enough regardless of a source's position and intensity. To determine the optimal positions to be instrumented by the sensors, an adequate cost function is defined based on the renormalization inversion method. This function, named the entropic criterion, quantifies the amount of information contained in a network of the sensors to estimate the intensity and the location of an unknown source. The optimal design is approached as combinatorial optimization (NP‐Hard) and a stochastic algorithm (simulated annealing, SA) is employed to solve this problem. The computation is performed by coupling the CFD adjoint fields in an urban environment, the renormalization algorithm and the SA. The optimization is evaluated with 20 trials of the Mock Urban Setting Test (MUST) tracer field experiment for the reconstruction of a continuous point release in an idealized urban geometry using optimal networks of sizes 10 and 13 sensors. The process is achieved successfully and the results showed that the reduction of an original network of 40 sensors to one third (13) and one quarter (10) does not degrade the performance of this network. Also, a comparison of the optimal design efficiency based on apriori information and without apriori information about the source showed that the present entropic criterion leads to network design and performance that can accurately retrieve an unknown emission source in an urban environment.
Kelly, Mark; Cersosimo, Roberto Alessio; Berg, Jacob
doi: 10.1002/qj.3472pmid: N/A
Wind profiles above the atmospheric surface layer are not accurately described by classic similarity theories. Far from the surface, the underlying assumptions of such surface‐layer theories break down due to the stronger influence of buoyancy forces induced by the temperature inversion that caps the atmospheric boundary layer (ABL), as well as the Coriolis force. This paper examines the influence of these forces on the mean flow and presents a new similarity theory to predict mean wind profiles in and above the surface layer for an ABL with zero surface heat flux and capped by an inversion of potential temperature, that is, the conditionally neutral ABL. The analysis here is based on the results of 17 large‐eddy simulations (LES) over a flat homogeneous rough surface, which leads to and supports the new similarity theory. The development is based on two applications of the Buckingham Π theorem. A first application allows determination of the entrainment‐induced heat flux profile through the ABL and into the surface layer, which is then used within a second dimensional argument for the vertical shear of mean wind speed. We subsequently find a new dimensionless group (Π2) depending on the capping inversion strength, the Coriolis parameter, the surface stress and the ABL depth, which is correlated to the dimensionless shear (Π1) through a universal function β. Integrating the functional relation between Π1 and Π2 produces an equation for the mean wind speed profile; it effectively includes an additive “correction” to the log‐law in terms of Π2, analogous to the Monin–Obukhov profile correction function. Unlike surface‐layer similarity, the new form accounts for the influences of both the surface and the ABL top. Relative to the LES, the new profile form exhibits errors in mean wind speed below 5% for heights below 90% of the ABL depth; this is relevant for applications above the surface layer (e.g., wind energy).
Keresturi, Endi; Wang, Yong; Meier, Florian; Weidle, Florian; Wittmann, Christoph; Atencia, Aitor
doi: 10.1002/qj.3473pmid: N/A
One of the main challenges presented by a limited‐area model ensemble prediction system (LAMEPS) concerns the limited capacity for its initial condition perturbations to correctly represent large‐scale flow uncertainties due to its limited‐size domain and deficiencies in formulating lateral boundary conditions. In addition, a mismatch between LAMEPS (initial condition) and host EPS lateral boundary perturbations can form spurious gravity waves at the boundaries. In the present work, an ensemble Jk blending method is proposed for improving representation of large‐scale uncertainties and for addressing consistent initial conditions and lateral boundary perturbations. Our approach involves employing Jk blending within a framework of three‐dimensional variation (3D‐Var) ensemble data assimilation (EDA). In such a system, small‐scale perturbations are generated from 3D‐Var EDA, while large‐scale perturbations are generated from the host ensemble via Jk blending.
Kwasniok, Frank; Beaumont, Robin; Thuburn, John
doi: 10.1002/qj.3474pmid: N/A
The dynamics of the polar vortex underlying stratospheric sudden warming (SSW) events are investigated in a data‐based diagnostic study. Potential vorticity (PV) contour integral quantities on isentropic surfaces are discussed in a unified framework; new expressions for their time evolution, particularly suitable for evaluation from data, are presented and related to previous work. Diagnostics of mass and circulation are calculated from ERA‐40 reanalysis data for the stratosphere in case‐studies of seven winters with different SSW behaviour. The edge of the polar vortex is easily identifiable in these diagnostics as an abrupt transition from high to low gradients of PV, and the warming events are clearly visible. The amount of air stripped from the vortex as part of a preconditioning leading up to the warming events is determined using the balance equation of the mass integral. Significant persistent removal of mass from the vortex is found, with several such stripping events identifiable throughout the winter, especially in those during which a major sudden warming event occurred. These stripping episodes are visible in corresponding PV maps, where tongues of high PV are being stripped from the vortex and mixed into the surrounding surf zone. An attempt is made to diagnose from the balance equation of the circulation the effect of frictional forces such as gravity wave dissipation on the polar vortex.
doi: 10.1002/qj.3475pmid: N/A
The magnitude of water‐vapour content and its temporal variability are factors that influence the thermodynamics of the atmosphere significantly and result in different meteorological phenomena or hazards. High‐quality observations of water‐vapour spatial and temporal distribution enable precise weather forecasts to be made. Global Navigation Satellite System (GNSS) troposphere tomography is a technique that enables derivation of a three‐dimensional (3D) distribution of the wet refractivity with low cost in all weather conditions, based on GNSS slant observations of tropospheric delay. The tomographic estimations of the wet refractivity distribution have the potential to improve numerical weather prediction (NWP) models. In this study, we established a near‐real‐time (NRT) tomographic solution in the area of Poland using the TOMO2 model in order to verify whether tomographic products can attain the required accuracy and be assimilated into operational NWP models. The assimilation of the TOMO2 output into a weather research and forecasting (WRF) model was performed, using the WRF Data Assimilation (WRFDA) system and a GPSREF observation operator dedicated to radio occultation (RO) total refractivity assimilation. Two selected analysis periods covered summer storms and autumn rainfalls. The validation of the WRF model analysis with GNSS integrated water vapour (IWV) data, synoptic observations, radiosonde profiles, and ERA‐Interim reanalysis indicated an improvement in the relative humidity in the top tropospheric layers (the bias decreased by 1.4–4.6% and the standard deviation by 0.8–2.8%). In the middle troposphere, a positive impact was noticed in the summer (the standard deviation of the relative humidity decreased by 0.15%) but not in the autumn. The forecast at lead times of 6–18 hr was visibly improved in the autumn (reduction of root‐mean‐square error (RMSE) by 0.5% in relative humidity and 0.25 °C in temperature, reduction in standard deviation of surface pressure by 0.5 hPa), while in the summer the results were neutral or negative (RMSE of relative humidity increased by 1.0%).
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