Applications Of Research To Operations At Mesoscale Research Articles

Development of “Physical Parametrizations with PYthon” (PPPY, version 1.1) and its usage to reduce the time-step dependency in a microphysical scheme

Abstract. To help develop and compare physical parametrizations such as those found in a numerical weather or climate model, a new tool was developed. This tool provides a framework with which to plug external parametrizations, run them in an offline mode (using one of the two time-advance methods available), save the results and plot diagnostics. The software can be used in a 0-D and a 1-D mode with schemes originating from various models. As for now, microphysical schemes from the Meso-NH model, the AROME (Applications of Research to Operations at Mesoscale) model and the Weather Research and Forecasting model have been successfully plugged. As an application, Physical Parametrizations with PYthon (PPPY) is used in this paper to suppress the origin of the time-step dependency of the microphysical scheme used in the Météo-France small-scale operational numerical weather model. The tool helped to identify the origin of the dependency and to check the efficiency of the introduced corrections.

An NWP Model Intercomparison of Surface Weather Parameters in the European Arctic during the Year of Polar Prediction Special Observing Period Northern Hemisphere 1

AbstractIncreased human activity in the Arctic calls for accurate and reliable weather predictions. This study presents an intercomparison of operational and/or high-resolution models in an attempt to establish a baseline for present-day Arctic short-range forecast capabilities for near-surface weather (pressure, wind speed, temperature, precipitation, and total cloud cover) during winter. One global model [the high-resolution version of the ECMWF Integrated Forecasting System (IFS-HRES)], and three high-resolution, limited-area models [Applications of Research to Operations at Mesoscale (AROME)-Arctic, Canadian Arctic Prediction System (CAPS), and AROME with Météo-France setup (MF-AROME)] are evaluated. As part of the model intercomparison, several aspects of the impact of observation errors and representativeness on the verification are discussed. The results show how the forecasts differ in their spatial details and how forecast accuracy varies with region, parameter, lead time, weather, and forecast system, and they confirm many findings from mid- or lower latitudes. While some weaknesses are unique or more pronounced in some of the systems, several common model deficiencies are found, such as forecasting temperature during cloud-free, calm weather; a cold bias in windy conditions; the distinction between freezing and melting conditions; underestimation of solid precipitation; less skillful wind speed forecasts over land than over ocean; and difficulties with small-scale spatial variability. The added value of high-resolution limited area models is most pronounced for wind speed and temperature in regions with complex terrain and coastlines. However, forecast errors grow faster in the high-resolution models. This study also shows that observation errors and representativeness can account for a substantial part of the difference between forecast and observations in standard verification.

Analysis of Mountain Wave Effects on a Hard Landing Incident in Pico Aerodrome Using the AROME Model and Airborne Observations

A hard landing incident in Pico Aerodrome (LPPI) involving an Airbus A320-200 aircraft is investigated using airborne observations and forecasts of the AROME (Applications of Research to Operations at Mesoscale) model. A second flight is also analyzed. The severity of the wind shear during both flights is quantified using the intensity factor “I” that is based on aerial data and recommended by ICAO (International Civil Aviation Organization). During Flight 1, 36% of the landing phase (below 2100 ft) occurred under “severe” wind shear conditions and 16% occurred under “strong” conditions. Upstream characteristics included southwest winds, stable stratification and a Froude number close to 1. According to the AROME model, these circumstances triggered the development of vertically propagating mountain waves, with maximum vertical velocities above 400 ft/min and exceeding 200 ft/min in the flight path. These conditions, together with the severe wind shear, may have caused the incident. During the second flight, a wake with lee vortices and reversed flow developed in the region of the flight path, which is consistent with a low upstream Froude number and/or with the flow regime diagram of previous studies. During the approach phase of this flight, “severe” wind shear conditions were absent, with “strong” ones occurring 4% of the time. It predominantly displayed “light” conditions during 68% of this phase. As a result of the comparison between “I” and the AROME turbulence indicators, preliminary thresholds are proposed for these indexes. Lastly, this study provides an objective verification of AROME wind forecasts, showing a good agreement with airborne observations for wind speeds above 10 kt, but a poor skill for weaker winds.

Homogeneity criteria from AVHRR information within IASI pixels in a numerical weather prediction context

Abstract. This article focuses on the selection of satellite infrared IASI (Infrared Atmospheric Sounding Interferometer) observations in the global numerical weather prediction (NWP) system ARPEGE (Action de Recherche Petite Echelle Grande Echelle). The observation simulation is performed with the sophisticated radiative transfer model RTTOV-CLD, which takes into account the cloud scattering and the multilayer clouds from atmospheric profiles and cloud microphysical profiles (liquid water content, ice content and cloud fraction). The aim of this work is to select homogeneous scenes by using the information of the collocated Advanced Very High Resolution Radiometer (AVHRR) pixels inside each IASI field of view and to retain the most favourable cases for the assimilation of IASI infrared radiances. Two methods to select homogeneous scenes using homogeneity criteria already proposed in the literature were adapted: the criteria derived from Martinet et al. (2013) for cloudy sky selection in the French mesoscale model AROME (Applications of Research to Operations at MEsoscale) and the criteria from Eresmaa (2014) for clear-sky selection in the global model IFS (Integrated Forecasting System). A comparison between these methods reveals considerable differences, in both the method to compute the criteria and the statistical results. From this comparison a revised method representing a kind of compromise between the different tested methods is proposed and it uses the two infrared AVHRR channels to define the homogeneity criteria in the brightness temperature space. This revised method has a positive impact on the observation minus the simulation statistics, while retaining 36 % of observations for the assimilation. It was then tested in the NWP system ARPEGE for the clear-sky assimilation. These criteria were added to the current data selection based on the McNally and Watts (2003) cloud detection scheme. It appears that the impact on analyses and forecasts is rather neutral.

AROME-MetCoOp: A Nordic Convective-Scale Operational Weather Prediction Model

Abstract Since October 2013 a convective-scale weather prediction model has been used operationally to provide short-term forecasts covering large parts of the Nordic region. The model is now operated by a bilateral cooperative effort [Meteorological Cooperation on Operational Numerical Weather Prediction (MetCoOp)] between the Norwegian Meteorological Institute and the Swedish Meteorological and Hydrological Institute. The core of the model is based on the convection-permitting Applications of Research to Operations at Mesoscale (AROME) model developed by Météo-France. In this paper the specific modifications and updates that have been made to suit advanced high-resolution weather forecasts over the Nordic regions are described. This includes modifications in the surface drag description, microphysics, snow assimilation, as well as an update of the ecosystem and surface parameter description. Novel observation types are introduced in the operational runs, including ground-based Global Navigation Satellite System (GNSS) observations and radar reflectivity data from the Norwegian and Swedish radar networks. After almost two years’ worth of experience with the AROME-MetCoOp model, the model’s sensitivities to the use of specific parameterization settings are characterized and the forecast skills demonstrating the benefit as compared with the global European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (ECMWF-IFS) are evaluated. Furthermore, case studies are provided to demonstrate the ability of the model to capture extreme precipitation and wind events.

Numerical Weather Forecasts at Kilometer Scale in the French Alps: Evaluation and Application for Snowpack Modeling

Abstract Numerical weather prediction (NWP) systems operating at kilometer scale in mountainous terrain offer appealing prospects for forecasting the state of snowpack in support of avalanche hazard warning, water resources assessment, and flood forecasting. In this study, daily forecasts of the NWP system Applications of Research to Operations at Mesoscale (AROME) at 2.5-km grid spacing over the French Alps were considered for four consecutive winters (from 2010/11 to 2013/14). AROME forecasts were first evaluated against ground-based measurements of air temperature, humidity, wind speed, incoming radiation, and precipitation. This evaluation shows a cold bias at high altitude partially related to an underestimation of cloud cover influencing incoming radiative fluxes. AROME seasonal snowfall was also compared against output from the Système d’Analyse Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN) specially developed for alpine terrain. This comparison reveals that there are regions of significant difference between the two, especially at high elevation, and possible causes for these differences are discussed. Finally, AROME forecasts and SAFRAN reanalysis have been used to drive the snowpack model Surface Externalisée (SURFEX)/Crocus (SC) and to simulate the snowpack evolution over a 2.5-km grid covering the French Alps during four winters. When evaluated at the experimental site of Col de Porte, both simulations show good agreement with measurements of snow depth and snow water equivalent. At the scale of the French Alps, AROME-SC exhibits an overall positive bias, with the largest positive bias found in the northern and central French Alps. This study constitutes the first step toward the development of a distributed snowpack forecasting system using AROME.

The Impact of Vertical Resolution on Fog Forecasting in the Kilometric-Scale Model AROME: A Case Study and Statistics

Abstract The impact of vertical resolution on numerical fog forecasting is studied in detail for a specific case and evaluated statistically over a winter season. Three vertical resolutions are tested with the kilometric-scale Applications of Research to Operations at Mesoscale (AROME) numerical weather prediction model over Paris Charles de Gaulle Airport (Paris-CDG) in Paris, France. For the case studied, the vertical resolution has a strong impact on fog onset. The nocturnal jet and the turbulence created by wind shear at the top of the nocturnal boundary layer are more pronounced with a finer vertical resolution, and the turbulence close to the ground is also stronger with high vertical resolution. Local circulations created by the terrain induce different simulated processes during the fog onset. The fog is simulated as advection–radiation fog in the finer vertical resolution run and as radiation fog in the others. The vertical resolution has little impact on the mature and dissipation phases. A statistical study over a winter season confirms the results obtained in the fog case study. High vertical resolution simulates earlier onset, as well as longer-lasting and more spatially heterogeneous fogs. The high vertical resolution configuration simulates more fog events than are found at low resolution (LR); these fog events generally form north of Paris-CDG. No observations are available in this area, leading to many simulated but no observed fog events in the fine-resolution runs. The ceiling of low clouds is not well simulated by the numerical model no matter what vertical resolution is used.

Evaluation of two convection‐permitting ensemble systems in the HyMeX Special Observation Period (SOP1) framework

This study compares and evaluates two convection‐permitting ensemble systems based on Consortium for Small‐scale Modeling (COSMO) and Applications of Research to Operations at Mesoscale (AROME) models in the HyMeX framework. The performance of both AROME‐EPS and COSMO‐H2‐EPS (where EPS denotes Ensemble Prediction System) is assessed over the whole HyMeX special observation period SOP1. Afterwards, an analysis of the predictability of two heavy precipitation events observed during the intense observation period IOP16a of the first HyMeX special observation period SOP1 on 26 October 2012 is also undertaken. Ensemble discharge forecasts were carried out to reinforce the quantitative precipitation forecast evaluation.A probabilistic evaluation is conducted over a 53 day period of the HyMeX SOP1. AROME‐EPS has a more discriminating behaviour than COSMO‐H2‐EPS, especially when comparing both ensembles over a verification domain including a strong variability in precipitation events. AROME‐EPS still has slightly better reliability, but the statistical resolution is nearly the same for both convection‐permitting ensemble prediction systems (CPEPS).For the specific case of heavy precipitation occurring over the Var region (southeastern France), the fine‐scale surface precipitation prediction is strongly sensitive to the good behaviour of a surface low pressure simulated by the ensembles, focusing strong low‐level moisture towards Var. The convergence between strong moistened southerly low‐level inflow and northerly cold air blowing from the Po Valley is a key factor controlling the predictability of the heavy precipitation episode over the Liguria region (northwestern Italy). The two convection‐permitting ensembles, though different in their characteristics, exhibit a good amount of probabilistic skill in forecasting heavy precipitation at a relatively high spatial and temporal resolution. Therefore they can be regarded as promising tools for operational forecasts.

A persistent wintertime fog episode at Lisbon airport (Portugal): performance of ECMWF and AROME models

ABSTRACTThe 7–8 January 2015 persistent fog episode at Lisbon international airport is analysed. Its persistence was atypical of the local climate (second longest episode since 2004) and caused significant air traffic disruptions. Furthermore, the performance of two operational models (European Centre for Medium‐range Weather Forecasts (ECMWF) and Applications of Research to Operations at Mesoscale (AROME)) used at the Portuguese Weather Service is assessed not only for the selected fog episode, but also for the extended winters (November to March) of 2013/2014 and 2014/2015. This episode developed under the following conditions: (1) the occurrence of a strong and persistent midwinter anticyclone over Iberia, driving favourable conditions for the formation of radiation fog; (2) a short interruption of the anticyclonic circulation by a kata‐cold front on the day before, favouring the development of fog/low stratus clouds, which persisted until dawn on 7 January over the Tagus valley; (3) low‐tropospheric easterly/northeasterly winds, favouring the drift of fog patches from the Tagus towards Lisbon. At the nearest grid point to Lisbon airport, both ECMWF and AROME were able to capture fog occurrence, but were unable to predict its persistence throughout the episode. Moreover, both models overestimated the 2 m temperature and dew point depression. Low cloud cover and horizontal visibility from ECMWF revealed higher skill in fog prediction than that derived from AROME. Nonetheless, a modified version of the Fog Stability Index based on AROME was proven to be a more skilful fog predictor, also outperforming other predictors based on ECMWF. These conclusions are also supported by an objective verification over a two‐winter period.

Linear Filtering of Sample Covariances for Ensemble-Based Data Assimilation. Part II: Application to a Convective-Scale NWP Model

Abstract In Part I of this two-part study, a new theory for optimal linear filtering of covariances sampled from an ensemble of forecasts was detailed. This method, especially designed for data assimilation (DA) schemes in numerical weather prediction (NWP) systems, has the advantage of using optimality criteria that involve sample estimated quantities and filter output only. In this second part, the theory is tested with real background error covariances computed using a large ensemble data assimilation (EDA) at the convective scale coupled with a large EDA at the global scale, based respectively on the Applications of Research to Operations at Mesoscale (AROME) and ARPEGE operational NWP systems. Background error variances estimated with a subset of this ensemble are filtered and evaluated against values obtained with the remaining members, which are considered as an independent reference. Algorithms presented in Part I show relevant results, with the homogeneous filtering being quasi optimal. Heterogeneous filtering is also successfully tested with different local criteria, yet at a higher computational cost, showing the full generality of the method. As a second application, horizontal and vertical localization functions are diagnosed from the ensemble, providing pertinent localization length scales that consistently depend on the number of members, on the meteorological variables, and on the vertical levels.

Evaluation of a revised IASI channel selection for cloudy retrievals with a focus on the Mediterranean basin

AbstractThe Infrared Atmospheric Sounding Interferometer (IASI) provides 8461 channels in the infrared spectrum. In current numerical weather prediction (NWP) models, it is not feasible to assimilate all channels and it is known that the information content between adjacent channels is redundant. This issue has been addressed in NWP centres by employing a channel selection strategy. The goal of this article is to add new channels to the existing IASI operational channel selection, aimed at improving the data assimilation in cloudy conditions and the simultaneous retrieval of cloud microphysical variables, specifically liquid and ice water contents. Cloudy profiles from the French convective‐scale model Applications of Research to Operations at MEsoscale (AROME) are used in the study to focus on the retrieval of cloud variables over the Mediterranean region. Three channel selection methodologies were evaluated in this study: a statistical approach based on the degrees of freedom of the signal (DFS), a physical method based on the channel spectral sensitivity to the cloud variables and a random selection. To validate the new selections, an idealized framework is used with observing system simulation experiments (OSSE) in the context of one‐dimensional variational retrievals. The current operational IASI selection has already been shown to provide good retrievals of cloud variables. However, all the different channel selections improve the results with small differences in the 1D‐Var retrievals. Based on the physical and DFS methods, the final sets of 134 channels sensitive to cloud variables are proposed for future investigation in operational implementation. Additional tests on temperature and water‐vapour retrieval results, air‐mass dependence and cloud microphysical parametrization have also been conducted.

Evaluation of NWP results for wintertime nocturnal boundary‐layer temperatures over Europe and Finland

AbstractFour operational numerical weather prediction (NWP) models were evaluated in winter conditions against (a) synoptic observations in Europe, (b) observations at a 48 m high micrometeorological mast in Sodankylä, northern Finland, and (c) observations at the Helsinki Testbed stations: (i) to evaluate the skills of the models to compute nocturnal 2 m air temperature (T2m) and the temperature inversion; and (ii) to distinguish between the T2m bias and the subgrid‐scale spatial variability of T2m. The models were (1) the Integrated Forecast System (IFS) of the European Centre for Medium‐Range Weather Forecasts (ECMWF), (2) the High Resolution Limited Area Model (HIRLAM), (3) the Applications of Research to Operations at Mesoscale (AROME) developed by Météo‐France, and (4) the Global Forecasting System (GFS) of the US National Center for Environmental Predictions (NCEP). The results demonstrated a T2m bias increasing with decreasing temperature and strengthening temperature inversion. When a strong temperature inversion was observed in Sodankylä, the models underestimated it, whereas in near‐neutral conditions the stratification was overestimated. Comparison of observed and modelled 3 h temperature tendencies showed that the T2m tendency in the models was on average only 17–20% of the observed one. The warm bias in T2m forecast in Sodankylä during periods of observed temperature inversion partly resulted from a warm bias in the initial conditions. This was due to problems in data assimilation in IFS and HIRLAM, in initialization in AROME, and in either or both procedures in GFS. In particular, the IFS data assimilation increased the T2m bias. Evaluation of modelled T2m against grid‐averaged T2m observed at Helsinki Testbed demonstrated that the T2m model error dominated over the spatial variability of observed T2m. This suggests that over an almost flat terrain horizontal resolution is not a major factor for the accuracy of T2m forecast at low T2m typically associated with temperature inversions. Copyright © 2012 Royal Meteorological Society

Impact of IASI assimilation at global and convective scales and challenges for the assimilation of cloudy scenes

AbstractSince July 2008, Infrared Atmospheric Sounder Interferometer (IASI) radiances have been assimilated in the French global model Action de Recherche Petite Echelle Grande Echelle (ARPEGE) and since April 2010 at high density in the French convective‐scale model Applications of Research to Operations at MEsoscale (AROME).The impact of the assimilation of clear IASI data on forecast skill is found to be positive for both models. As many observed scenes are cloudy, several ways to characterize the clouds within the observed spectra are investigated. Firstly, a simple approach that enables the determination of both the cloud‐top pressure and the effective cloud amount of an equivalent single‐layer cloud is followed using a CO2‐slicing method. The first assimilation trials in overcast conditions lead to a small positive impact on forecast skill. Another approach would be to take advantage of the fact that cloud water variables are described at high resolution in the convective‐scale model AROME. Model cloud fields have to be used in conjunction with a cloudy radiative transfer model. The first simulations using this technique are performed and compared against observations. Copyright © 2011 Royal Meteorological Society

Application of Radar Wind Observations for Low-Level NWP Wind Forecast Validation

AbstractThe Finnish Meteorological Institute has produced a new numerical weather prediction model–based wind atlas of Finland. The wind atlas provides information on local wind conditions in terms of annual and monthly wind speed and direction averages. In the context of the wind atlas project, low-level Applications of Research to Operations at Mesoscale (AROME) model wind forecasts have been validated against radar radial wind observations and, as a comparison, against conventional radiosonde observations to confirm the realism of the wind forecasts. The results indicate that the systematic and random errors in the AROME wind forecasts are relatively small and are of the same order of magnitude independent of the validating observation type. The validation benefits from the high spatial and temporal resolution of the radar observations. There are over 4000 times as many radar observations as radiosonde observations available for the considered validation period of July 2008–May 2009.