Aire Limitée Adaptation Dynamique Développement International Research Articles

FOCUSED-Short-Term Wind Speed Forecast Correction Algorithm Based on Successive NWP Forecasts for Use in Traffic Control Decision Support Systems.

In this paper, we propose a new algorithm, called FOCUSED (FOrecast Correction Using Successive prEDictions), for forecast correction of short-term wind speed predictions. We developed FOCUSED with the aim of improving the forecast of bora gusts, which frequently result in high-speed wind situations dangerous for traffic. The motivation arises from occasionally ambiguous results of the currently deployed decision support system, which aids traffic management in strong and gusty wind conditions at the coast of Croatia. The proposed correction algorithm uses characteristics of numerical weather prediction models to iteratively forecast the wind speed multiple times for the same future window. We use these iterative predictions as input features of the FOCUSED algorithm and get the corrected predictions as the output. We compared the proposed algorithm with artificial neural networks, random forests, support vector machines, and linear regression to demonstrate the superiority of the algorithm’s performance on a data set comprising five years of real data measurements at the Croatian bridge “Krk” and complementary historical forecasts by ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) numerical weather prediction model.

Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales

Abstract. Ideally, perturbation schemes in ensemble forecasts should be based on the statistical properties of the model errors. Often, however, the statistical properties of these model errors are unknown. In practice, the perturbations are pragmatically modelled and tuned to maximize the skill of the ensemble forecast. In this paper a general methodology is developed to diagnose the model error, linked to a specific physical process, based on a comparison between a target and a reference model. Here, the reference model is a configuration of the ALADIN (Aire Limitée Adaptation Dynamique Développement International) model with a parameterization of deep convection. This configuration is also run with the deep-convection parameterization scheme switched off, degrading the forecast skill. The model error is then defined as the difference of the energy and mass fluxes between the reference model with scale-aware deep-convection parameterization and the target model without deep-convection parameterization. In the second part of the paper, the diagnosed model-error characteristics are used to stochastically perturb the fluxes of the target model by sampling the model errors from a training period in such a way that the distribution and the vertical and multivariate correlation within a grid column are preserved. By perturbing the fluxes it is guaranteed that the total mass, heat and momentum are conserved. The tests, performed over the period 11–20 April 2009, show that the ensemble system with the stochastic flux perturbations combined with the initial condition perturbations not only outperforms the target ensemble, where deep convection is not parameterized, but for many variables it even performs better than the reference ensemble (with scale-aware deep-convection scheme). The introduction of the stochastic flux perturbations reduces the small-scale erroneous spread while increasing the overall spread, leading to a more skillful ensemble. The impact is largest in the upper troposphere with substantial improvements compared to other state-of-the-art stochastic perturbation schemes. At lower levels the improvements are smaller or neutral, except for temperature where the forecast skill is degraded.

The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

Abstract. In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche à l'Opérationnel à Meso-Echelle) and ALADIN (Aire Limitée Adaptation Dynamique Développement International); and COSMO–EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU–GPU arrangements.

The use of ALADIN model and MERRA-2 reanalysis to represent dust seasonal dry deposition from 2006 to 2010 in Senegal, West Africa

The objective of this work is to study the seasonal distribution of dust dry deposition in West Africa particularly in Senegal. This initiative is part of the efforts to improve the performance of photovoltaic panels (PV) in dusty environments such as Sahel region. It will help to evaluate the impact of dust dry deposition on these PV. Two climate models including dust modules during 2006–2010, were used: MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) reanalysis and ALADIN (Aire Limitee Adaptation dynamique Developpement InterNational) model. In Mbour (Senegal), the aerosol optical depth (AOD) from these two products has been validated by in situ data. Indeed, MERRA-2 and ALADIN fairly simulate the AOD with a maximum in March and June. However, these products tend to overestimate measurements, especially for ALADIN. The correlation coefficient between in situ AOD and products is evaluate almost 0.83 for MERRA-2 and 0.72 for ALADIN. From 2007 to 2009, dust deposition measurement campaign was conducted in Mbour. The comparison between these seasonal data and simulations show a right coherence even if MERRA-2 underestimates measurement and ALADIN overestimates it. The correlation compared to in situ measurements is estimate to 0.71 for MERRA-2 and 0.72 for ALADIN. However, the results showed that ALADIN better describes the seasonal dry deposition during dry season which lasts 9 months despite a strong overestimation in winter. Finally, long-term simulation with ALADIN show that dry deposition maximum occurs from December to May in Senegal and throughout the West Africa region.

27 Years of Regional Cooperation for Limited Area Modelling in Central Europe

AbstractThis paper describes 27 years of scientific and operational achievement of Regional Cooperation for Limited Area Modelling in Central Europe (RC LACE), which is supported by the national (hydro-) meteorological services of Austria, Croatia, the Czech Republic, Hungary, Romania, Slovakia, and Slovenia. The principal objectives of RC LACE are to 1) develop and operate the state-of-the-art limited-area model and data assimilation system in the member states and 2) conduct joint scientific and technical research to improve the quality of the forecasts.In the last 27 years, RC LACE has contributed to the limited-area Aire Limitée Adaptation Dynamique Développement International (ALADIN) system in the areas of preprocessing of observations, data assimilation, model dynamics, physical parameterizations, mesoscale and convection-permitting ensemble forecasting, and verification. It has developed strong collaborations with numerical weather prediction (NWP) consortia ALADIN, the High Resolution Limited Area Model (HIRLAM) group, and the European Centre for Medium-Range Weather Forecasts (ECMWF). RC LACE member states exchange their national observations in real time and operate a common system that provides member states with the preprocessed observations for data assimilation and verification. RC LACE runs operationally a common mesoscale ensemble system, ALADIN–Limited Area Ensemble Forecasting (ALADIN-LAEF), over all of Europe for early warning of severe weather.RC LACE has established an extensive regional scientific and technical collaboration in the field of operational NWP for weather research, forecasting, and applications. Its 27 years of experience have demonstrated the value of regional cooperation among small- and medium-sized countries for success in the development of a modern forecasting system, knowledge transfer, and capacity building.

Impacts of the direct radiative effect of aerosols in numerical weather prediction over Europe using the ALADIN-HIRLAM NWP system

Aerosol feedbacks are becoming more accepted as physical mechanisms that should be included in numerical weather prediction models in order to improve the accuracy of the weather forecasts. The default set-up in the Aire Limitee Adaptation dynamique Developpement INternational (ALADIN) — High Resolution Limited Area Model (HIRLAM) numerical weather prediction system includes monthly aerosol climatologies to account for the average direct radiative effect of aerosols. This effect was studied using the default aerosol climatology in the system and compared to experiments run using the more up-to-date Max-Planck-Institute Aerosol Climatology version 1 (MACv1), and time-varying aerosol data from the Monitoring Atmospheric Composition and Climate (MACC) reanalysis aerosol dataset. Accounting for the direct radiative effect using monthly aerosol climatologies or near real-time aerosol distributions improved the accuracy of the simulated radiative fluxes and temperature and humidity forecasts in the lower troposphere. However, the dependency of forecast meteorological conditions on the aerosol dataset itself was found to be weak.

Modeling the ocean and atmosphere during an extreme bora event in northern Adriatic using one-way and two-way atmosphere–ocean coupling

Abstract. We have studied the performances of (a) a two-way coupled atmosphere–ocean modeling system and (b) one-way coupled ocean model (forced by the atmosphere model), as compared to the available in situ measurements during and after a strong Adriatic bora wind event in February 2012, which led to extreme air–sea interactions. The simulations span the period between January and March 2012. The models used were ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) (4.4 km resolution) on the atmosphere side and an Adriatic setup of Princeton ocean model (POM) (1°∕30 × 1°∕30 angular resolution) on the ocean side. The atmosphere–ocean coupling was implemented using the OASIS3-MCT model coupling toolkit. Two-way coupling ocean feedback to the atmosphere is limited to sea surface temperature. We have compared modeled atmosphere–ocean fluxes and sea temperatures from both setups to platform and CTD (conductivity, temperature, and depth) measurements from three locations in the northern Adriatic. We present objective verification of 2 m atmosphere temperature forecasts using mean bias and standard deviation of errors scores from 23 meteorological stations in the eastern part of Italy. We show that turbulent fluxes from both setups differ up to 20 % during the bora but not significantly before and after the event. When compared to observations, two-way coupling ocean temperatures exhibit a 4 times lower root mean square error (RMSE) than those from one-way coupled system. Two-way coupling improves sensible heat fluxes at all stations but does not improve latent heat loss. The spatial average of the two-way coupled atmosphere component is up to 0.3 °C colder than the one-way coupled setup, which is an improvement for prognostic lead times up to 20 h. Daily spatial average of the standard deviation of air temperature errors shows 0.15 °C improvement in the case of coupled system compared to the uncoupled. Coupled and uncoupled circulations in the northern Adriatic are predominantly wind-driven and show no significant mesoscale differences.

Methods for automatized detection of rapid changes in lateral boundary condition fields for NWP limited area models

Abstract. Three-hourly temporal resolution of lateral boundary data for limited area models (LAMs) can be too infrequent to resolve rapidly moving storms. This problem is expected to be worse with increasing horizontal resolution. In order to detect intensive disturbances in surface pressure moving rapidly through the model domain, a filtered surface pressure field (MCUF) is computed operationally in the ARPEGE global model of Météo France. The field is distributed in the coupling files along with conventional meteorological fields used for lateral boundary conditions (LBCs) for the operational forecast using limited area model ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) in the Meteorological and Hydrological Service of Croatia (DHMZ). Here an analysis is performed of the MCUF field for the LACE coupling domain for the period from 23 January 2006, when it became available, until 15 November 2014. The MCUF field is a good indicator of rapidly moving pressure disturbances (RMPDs). Its spatial and temporal distribution can be associated with the usual cyclone tracks and areas known to be supporting cyclogenesis. An alternative set of coupling files from the IFS operational run in the European Centre for Medium-Range Weather Forecasts (ECMWF) is also available operationally in DHMZ with 3-hourly temporal resolution, but the MCUF field is not available. Here, several methods are tested that detect RMPDs in surface pressure a posteriori from the IFS model fields provided in the coupling files. MCUF is computed by running ALADIN on the coupling files from IFS. The error function is computed using one-time-step integration of ALADIN on the coupling files without initialization, initialized with digital filter initialization (DFI) or scale-selective DFI (SSDFI). Finally, the amplitude of changes in the mean sea level pressure is computed from the fields in the coupling files. The results are compared to the MCUF field of ARPEGE and the results of same methods applied to the coupling files from ARPEGE. Most methods give a signal for the RMPDs, but DFI reduces the storms too much to be detected. The error functions without filtering and amplitude have more noise, but the signal of a RMPD is also stronger. The methods are tested for NWP LAM ALADIN, but could be applied to other LAMs and benefit the performance of climate LAMs.

Three-dimensional dust aerosol distribution and extinction climatology over northern Africa simulated with the ALADIN numerical prediction model from 2006 to 2010

Abstract. The seasonal cycle and optical properties of mineral dust aerosols in northern Africa were simulated for the period from 2006 to 2010 using the numerical atmospheric model ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) coupled to the surface scheme SURFEX (SURFace EXternalisée). The particularity of the simulations is that the major physical processes responsible for dust emission and transport, as well as radiative effects, are taken into account on short timescales and at mesoscale resolution. The aim of these simulations is to quantify the dust emission and deposition, locate the major areas of dust emission and establish a climatology of aerosol optical properties in northern Africa. The mean monthly aerosol optical thickness (AOT) simulated by ALADIN is compared with the AOTs derived from the standard Dark Target (DT) and Deep Blue (DB) algorithms of the Aqua-MODIS (MODerate resolution Imaging Spectroradiometer) products over northern Africa and with a set of sun photometer measurements located at Banizoumbou, Cinzana, Soroa, Mbour and Cape Verde. The vertical distribution of dust aerosol represented by extinction profiles is also analysed using CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations. The annual dust emission simulated by ALADIN over northern Africa is 878 Tg year−1. The Bodélé Depression appears to be the main area of dust emission in northern Africa, with an average estimate of about 21.6 Tg year−1. The simulated AOTs are in good agreement with satellite and sun photometer observations. The positions of the maxima of the modelled AOTs over northern Africa match the observed positions, and the ALADIN simulations satisfactorily reproduce the various dust events over the 2006–2010 period. The AOT climatology proposed in this paper provides a solid database of optical properties and consolidates the existing climatology over this region derived from satellites, the AERONET network and regional climate models. Moreover, the 3-D distribution of the simulated AOTs also provides information about the vertical structure of the dust aerosol extinction.

Impact of new aircraft observations Mode‐S MRAR in a mesoscale NWP model

AbstractThe impact of recently available high‐resolution Mode‐S Meteorological Routine Air Report (MRAR) wind and temperature observations is evaluated in the mesoscale numerical weather prediction (NWP) model Aire Limitée Adaptation dynamique Développement InterNational (ALADIN). Data available from the airspace communicating with the Ljubljana Airport in Slovenia are assimilated by using the three‐dimensional variational assimilation procedure on top of all other observations assimilated operationally. A data selection method based on aircraft type was shown to be important for the first application of the new observations in ALADIN. The evaluation of Mode‐S MRAR impact included both winter and summer periods. In both seasons a clear improvement of wind and temperature forecasts was found for in the short forecast range, 1–3 h. The impact in the 24 h forecast range depends on season, with a consistent positive improvement of the boundary layer temperature forecasts obtained for the stable anticyclonic winter situations. In summer, the impact was mixed and it was found to be sensitive to the multivariate aspects of the moisture analysis. Overall presented results suggest that the new aircraft‐derived observations Mode‐S MRAR have a significant potential for mesoscale NWP and improved data assimilation modeling.

Reconstruction of the Spring 2011 Richelieu River Flood by Two Regional Climate Models and a Hydrological Model

Abstract Climate simulations made with two regional climate models (RCMs), the French Aire Limitée Adaptation Dynamique Développement International (ALADIN) and the Canadian Regional Climate Model, version 5 (CRCM5), operating on 10-km meshes for the period 1989–2011, and the Hydro-Québec hydrological model (HSAMI), are used to reconstruct the spring 2011 Richelieu River flood in the southern region of the province of Québec, Canada. The analysis shows that the simulated fields of 2-m air temperature, precipitation, and snow water equivalent by the RCMs closely match the observations with similar multiyear means and a high correlation of the monthly anomalies. The climatic conditions responsible for the 2011 flood are generally well simulated by the RCMs. The use of multidecadal RCM simulations facilitates the identification of anomalies that contributed to the flood. The flood was linked to a combination of factors: the 2010/11 winter was cold and snowy, the snowmelt in spring was fast, and there was a record amount of precipitation in April and May. Driven by outputs from the RCMs, HSAMI was able to reproduce the mean hydrograph of the Richelieu River, but it underestimated the peak of the 2011 flood. HSAMI adequately computes the water transport from the mountains to the river mouth and the storage effect of Lake Champlain, which dampens the flood over a long period. Overall, the results suggest that RCM simulations can be useful for reconstructing high-resolution climate information and providing new variables that can help better understand the causes of extreme climatic events.

Comparison of static mesoscale background‐error covariances estimated by three different ensemble data assimilation techniques

AbstractThree ensemble‐based analysis‐error representation techniques are applied and compared with the aim of simulating background errors of the mesoscale limited‐area model (LAM) Aire Limitée Adaptation Dynamique Développement International (ALADIN) and thus computing appropriate mesoscale static background‐error covariances for its three‐dimensional variational (3DVAR) data assimilation system. Real data assimilation experiments are performed for the simulation of the background errors, where uncertainties of the initial conditions and lateral boundary conditions are represented, while model errors are not accounted for. The three analysis error‐simulation techniques compared are as follows: (i) the downscaling of the analysis error represented by the stochastic ensemble data assimilation (EDA) system of a global model (DSC EDA); (ii) the representation of the analysis error by a LAM stochastic ensemble data assimilation (LAM EDA) system; and (iii) the representation of the analysis error by a LAM ensemble transform Kalman filter (LAM ETKF) system. Spectral diagnostics have been developed in the study, allowing an examination of the simulated background errors at different spatial scales. It is shown that LAM EDA provides more realistic simulated background errors than DSC EDA, particularly at mesoscales. This is also partly the case for LAM ETKF at mesoscales (although to a lesser extent than for LAM EDA). The error variance is underestimated by LAM ETKF, which suggests that the applied inflation technique could be improved. Analysis and forecast impact studies using background‐error covariance matrices based on the DSC EDA and LAM EDA error samples confirm the superiority of the latter simulation technique.

Multiscale Performance of the ALARO-0 Model for Simulating Extreme Summer Precipitation Climatology in Belgium

AbstractDaily summer precipitation over Belgium from the Aire Limitée Adaptation Dynamique Développement International (ALADIN) model and a version of the model that has been updated with physical parameterizations, the so-called ALARO-0 model [ALADIN and AROME (Application de la Recherche à l'Opérationnel à Meso-Echelle) combined model, first baseline version released in 1998], are compared with respect to station observations for the period 1961–90. The 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) is dynamically downscaled using both models on a horizontal resolution of 40 km, followed by a one-way nesting on high spatial resolutions of 10 and 4 km. This setup allows us to explore the relative importance of spatial resolution versus parameterization formulation on the model skill to correctly simulate extreme daily precipitation. Model performances are assessed through standard statistical errors and density, frequency, and quantile distributions as well as extreme value analysis, using the peak-over-threshold method and generalized Pareto distribution. The 40-km simulations of ALADIN and ALARO-0 show similar results, both reproducing the observations reasonably well. For the high-resolution simulations, ALARO-0 at both 10 and 4 km is in better agreement with the observations than ALADIN. The ALADIN model consistently produces too high precipitation rates. The findings demonstrate that the new parameterizations within the ALARO-0 model are responsible for a correct simulation of extreme summer precipitation at various horizontal resolutions. Moreover, this study shows that ALARO-0 is a good candidate model for regional climate modeling.

Importance of the surface size distribution of erodible material: an improvement on the Dust Entrainment And Deposition (DEAD) Model

Abstract. This paper is based on dust aerosol cycle modelling in the atmospheric model ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) coupled with the EXternalised SURFace scheme SURFEX. Its main goal is to create an appropriate mineral dust emission parameterization compatible with the global database of land surface parameters ECOCLIMAP, and the Food and Agriculture Organization (FAO) soil type database in SURFEX. An improvement on the Dust Entrainment And Deposition scheme (DEAD) is proposed in this paper by introducing the geographical variation of surface soil size distribution, the Marticorena and Bergametti (1995) formulation of horizontal saltation flux and the Shao et al. (1996) formulation of sandblasting efficiency α. To show the importance of the modifications introduced in the DEAD, both sensitivity and comparative studies are conducted in 0 dimensions (0-D) and then in 3 dimensions (3-D) between the old DEAD and the new DEAD. The results of the 0-D simulations indicate that the revised DEAD scheme represents the dust source emission better, particularly in the Bodélé depression, and provides a reasonable friction threshold velocity. In 3-D simulations, small differences are found between the DEAD and the revised DEAD for the simulated Aerosol Optical Depth (AOD) compared with the AErosol RObotic NETwork (AERONET) photometer measurements available in the African Monsoon Multidisciplinary Analyses (AMMA) databases. For the surface concentration, a remarkable improvement is noted for the revised DEAD scheme.

The uncertainty of UTCI due to uncertainties in the determination of radiation fluxes derived from numerical weather prediction and regional climate model simulations

In this study we examine the determination accuracy of both the mean radiant temperature (Tmrt) and the Universal Thermal Climate Index (UTCI) within the scope of numerical weather prediction (NWP), and global (GCM) and regional (RCM) climate model simulations. First, Tmrt is determined and the so-called UTCI-Fiala model is then used for the calculation of UTCI. Taking into account the uncertainties of NWP model (among others the HIgh Resolution Limited Area Model HIRLAM) output (temperature, downwelling short-wave and long-wave radiation) stated in the literature, we simulate and discuss the uncertainties of Tmrt and UTCI at three stations in different climatic regions of Europe. The results show that highest negative (positive) differences to reference cases (under assumed clear-sky conditions) of up to -21°C (9°C) for Tmrt and up to -6°C (3.5°C) for UTCI occur in summer (winter) due to cloudiness. In a second step, the uncertainties of RCM simulations are analyzed: three RCMs, namely ALADIN (Aire Limitée Adaptation dynamique Développement InterNational), RegCM (REGional Climate Model) and REMO (REgional MOdel) are nested into GCMs and used for the prediction of temperature and radiation fluxes in order to estimate Tmrt and UTCI. The inter-comparison of RCM output for the three selected locations shows that biases between 0.0 and ±17.7°C (between 0.0 and ±13.3°C) for Tmrt (UTCI), and RMSE between ±0.5 and ±17.8°C (between ±0.8 and ±13.4°C) for Tmrt (UTCI) may be expected. In general the study shows that uncertainties of UTCI, due to uncertainties arising from calculations of radiation fluxes (based on NWP models) required for the prediction of Tmrt, are well below ±2°C for clear-sky cases. However, significant higher uncertainties in UTCI of up to ±6°C are found, especially when prediction of cloudiness is wrong.

Some Experimental Lessons on Digital Filtering in the ALADIN-France 3DVAR Based on Near-Ground Examination

Abstract This paper deals with the characteristics and effects of digital filter initialization, as implemented in the operational three-dimensional variational data assimilation (3DVAR) system of the Aire Limitée Adaptation Dynamique Développement International (ALADIN)-France regional weather forecast model. First, a series of findings on the properties of the initialization of the model are discussed. Examples of initial spinup linked with inertia–gravity wave occurrence are shown, and the major sources for their generation are listed. These experimental results are compared with past and present experiences concerning the use and need for digital filter initialization. Furthermore, the impacts of switching to an incremental formulation of the filter in data assimilation mode are demonstrated. Second, the effects of the filter formulation on the results of an observation impact study are illustrated. The latter consists of implementing screen-level, 10-m horizontal wind information into the ALADIN 3DVAR analysis. There can, indeed, be some delicate interference between observation impact evaluation and the effects of filtering, at least on short-term forecasts. The paper is concluded with some general considerations on the experimental evaluation of spinup and the link between the assimilation system design and model state filtering.

Advances in Predicting Continental Low Stratus with a Regional NWP Model

Abstract In the eastern Alpine region, subinversion cloudiness associated with elevated temperature inversions is a frequent phenomenon in autumn and winter, which often persists for several days. Although the prediction of fog and low stratus by numerical weather prediction (NWP) models has improved in recent years, these models still show deficiencies in the spatial and temporal evolution of such wintertime weather phenomena. In spite of sophisticated current assimilation schemes or simply due to unknown conditions, even the analysis shows large discrepancies compared to the true atmospheric state. Inversions are often “smeared out” and the moist layer below the inversion is too far from saturation. Model integration from such an initial state leads to strong biases in the total cloudiness and, due to erroneous radiative response, in 2-m temperature forecasts. In the present paper, an empirical enhancement scheme for subinversion cloudiness is introduced within the framework of Aire Limitée Adaptation Dynamique Développement International (ALADIN), the operational limited area model (LAM) at the Austrian Central Institute for Meteorology and Geodynamics (ZAMG). The scheme attempts to compensate for model deficiencies in the vertical temperature and humidity profiles in order to enhance or keep preexisting signals of inversions and associated low cloudiness. Thus, a positive feedback due to radiative reaction is activated, which finally leads to more realistic vertical profiles, low (and total) cloudiness, and improved 2-m temperature predictions. Case studies demonstrate the impacts of the scheme on predictions of the spatial distribution of low cloudiness and on the vertical profiles of temperature and humidity. Verification over stratus episodes within a 2-month period comparing a reference model run without the scheme with a modified model version with the subinversion cloudiness scheme confirms the ability of the scheme to improve stratus-related wintertime weather prediction.

An EKF assimilation of AMSR‐E soil moisture into the ISBA land surface scheme

An Extended Kalman Filter (EKF) for the assimilation of remotely sensed near‐surface soil moisture into the Interactions between Surface, Biosphere, and Atmosphere (ISBA) model is described. ISBA is the land surface scheme in Météo‐France's Aire Limitée Adaptation Dynamique développement InterNational (ALADIN) Numerical Weather Prediction (NWP) model, and this work is directed toward providing initial conditions for NWP. The EKF is used to assimilate near‐surface soil moisture observations retrieved from C‐band Advanced Microwave Scanning Radiometer (AMSR‐E) brightness temperatures into ISBA. The EKF can translate near‐surface soil moisture observations into useful increments to the root‐zone soil moisture. If the observation and model soil moisture errors are equal, the Kalman gain for the root‐zone soil moisture is typically 20–30%, resulting in a mean net monthly increment for July 2006 of 0.025 m3 m−3 over ALADIN's European domain. To test the benefit of evolving the background error, the EKF is compared to a Simplified EKF (SEKF), in which the background errors at the time of the analysis are constant. While the Kalman gains for the EKF and SEKF are derived from different model processes, they produce similar soil moisture analyses. Despite this similarity, the EKF is recommended for future work where the extra computational expense can be afforded. The method used to rescale the near‐surface soil moisture data to the model climatology has a greater influence on the analysis than the error covariance evolution approach, highlighting the importance of developing appropriate methods for rescaling remotely sensed near‐surface soil moisture data.

Comparison of model‐simulated and observed currents in the central Adriatic during DART

Numerical simulations of the Adriatic Sea were performed with the Navy Coastal Ocean Model (NCOM) during the Dynamics of the Adriatic in Real Time (DART) Experiments conducted between October 2005 and September 2006. Grid resolution was 1 km. Model forcing included tides, surface fluxes from the Aire Limitée Adaptation Dynamique Développement International (ALADIN) atmospheric model, relaxation to a daily satellite sea surface temperature analysis, extensive river and runoff discharges, and open boundary conditions south of Otranto specified from a global model. Currents predicted by the model were compared with currents from 12 Acoustic Doppler Current Profiler (ADCP) moorings located along a line between the Gargano Peninsula, Italy, and Split, Croatia. The nontidal comparisons were performed with detided currents. Correlations between the model and ADCP currents were highest in the Western Adriatic Current (WAC), which flows southeastward along the Italian coast. Lowest correlations were in the interior of the Adriatic, likely because of instability processes. Correlations between the ALADIN winds and the model and ADCP currents at the mooring locations were also highest in the WAC. For November 2005 through August 2006, the model and ADCP mean WAC transports were 0.321 and 0.304 Sv, respectively, with a temporal correlation of 0.79. Comparison of current variance showed best agreement near the Italian and Croatian coasts. In the interior, the NCOM current variance compared fairly well with that of the ADCPs for November–January but decreased significantly relative to the ADCPs for February–August. Spectral analyses indicate most of the difference in variance to be at periods exceeding 2 days.

Downscaling of ECMWF Ensemble Forecasts for Cases of Severe Weather: Ensemble Statistics and Cluster Analysis

Abstract Dynamical downscaling has been applied to global ensemble forecasts to assess its impact for four cases of severe weather (precipitation and wind) over various parts of Croatia. It was performed with the Croatian 12.2-km version of the Aire Limitée Adaptation Dynamique Développement International (ALADIN) limited-area model, nested in the ECMWF TL255 (approximately 80 km) global ensemble prediction system (EPS). The 3-hourly EPS output was used to force the ALADIN model over the central European/northern Mediterranean domain. Results indicate that the identical clustering algorithm may yield differing results when applied to either global or to downscaled ensembles. It is argued that this is linked to the fact that a downscaled, higher-resolution ensemble resolves more explicitly small-scale features, in particular those strongly influenced by orographic forcing. This result has important implications in limited-area ensemble prediction, since it implies that downscaling may affect the interpretation or relevance of the global ensemble forecasts; that is, it may not always be feasible to make a selection (or a subset) of global lower-resolution ensemble members that might be representative of all possible higher-resolution evolution scenarios.