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Alexander Kann
,
Christoph Wittmann
,
Benedikt Bica
, and
Clemens Wastl

Abstract

The capability to accurately analyze the spatial distribution of temperature and wind at very high spatial (2.5–1 km) and temporal (60–5 min) resolutions is of interest in many modern techniques (e.g., nowcasting and statistical downscaling). In addition to observational data, the generation of such analyses requires background information to adequately resolve nonstatic, small-scale phenomena. Numerical weather prediction (NWP) models are of continuously increasing skill and are more capable of providing valuable information on convection-resolving scales. The present paper discusses the impact of two operational NWP models on hourly 2-m temperature and 10-m wind analyses as created by the Integrated Nowcasting through Comprehensive Analysis (INCA) system, which includes a topographic downscaling procedure. The NWP models used for this study are a revised version of ARPEGE–ALADIN (ALARO; 4.8-km resolution) and the Applications of Research to Operations at Mesoscale (AROME; 2.5-km resolution). Based on a case study and a longer-term validation, it is shown that, generally, the finer the grid spacing of the background model and the higher the resolution of the target grid in the downscaling procedure, the slightly more accurate is the analysis. This is especially true for wind analyses in mountainous regions, where a realistic simulation of topographic effects is crucial. In the case of 2-m temperature, the impact is less pronounced, but the topographic downscaling at very high resolution at least adds detail in complex terrain. However, in the vicinity of station observations, the analysis algorithm is capable of spatially adjusting the larger biases found in the ALARO model while having a lesser effect on the downscaled AROME model.

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Alexander Kann
,
Christoph Wittmann
,
Yong Wang
, and
Xulin Ma

Abstract

Although the quality of numerical ensemble prediction systems (EPS) has greatly improved during the last few years, these systems still show systematic deficiencies. Specifically, they are underdispersive and lack both reliability and sharpness. A variety of statistical postprocessing methods allows for improving direct model output. Since 2007, Aire Limitée Adaptation Dynamique Développement International Limited Area Ensemble Forecasting (ALADIN-LAEF) has been in operation at the Central Institute for Meteorology and Geodynamics (ZAMG), and its 2-m temperature model output subject to calibration. This work follows the approach of nonhomogeneous Gaussian regression (NGR) that addresses a statistical correction of the first and second moment (mean bias and dispersion) for Gaussian-distributed continuous variables. It is based on the multiple linear regression technique and provides a predictive probability density function (PDF) in terms of a normal distribution. Fitting the regression coefficients, a minimum continuous ranked probability score (CRPS) estimation has been chosen instead of the more traditional maximum likelihood technique. The use of high-resolution analysis data on a 1 km × 1 km grid as training data improves the forecast skill in terms of CRPS by about 35%, especially on the local scale. The percentage of outliers decreases significantly without loss of sharpness. Sensitivity studies confirm that about half of the total improvement can be attributed to the effect of a bias correction. The training length plays a minor role, at least for the chosen verification period. A rescaling of the predictive PDF is important in order to obtain sharp forecasts, especially in the short range. Applying the same method to the global ensemble from the European Centre for Medium-Range Weather Forecasts (ECMWF) gives improvements of similar magnitude. However, the calibrated 2-m temperature of ALADIN-LAEF still remains slightly better than the 2-m temperature from calibrated ECMWF-EPS, which leads to the conclusion that statistical downscaling of EPS cannot replace dynamical downscaling. Finally, an advanced version of NGR, the so-called NGR-TD, which uses time-weighted averaging within minimum CRPS estimation, is able to yield a further improvement of about 5% in terms of the CRPS.

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Clemens Wastl
,
Yong Wang
,
Aitor Atencia
, and
Christoph Wittmann

Abstract

Model error in ensemble prediction systems is often represented by either a tendency perturbation approach or a process-based parameter perturbation scheme. In this paper a novel hybrid stochastically perturbed parameterization (HSPP) scheme is proposed and implemented in the Convection Permitting Limited Area Ensemble Forecasting (C-LAEF) system developed at the Zentralanstalt für Meteorologie und Geodynamik (ZAMG). In HSPP, the individual parameterization tendencies of the physical processes radiation, shallow convection, and microphysics are perturbed stochastically by a spatially and temporally varying pattern. Uncertainties in the turbulence scheme are considered by perturbing key parameters on the process level. The proposed scheme HSPP features several advantages compared to the popular stochastically perturbed parameterization tendencies (SPPT) scheme: it considers a more physically consistent relationship between different parameterization schemes, deals with uncertainties especially adapted to the individual physical processes, respects conservation laws of energy and moisture, and eliminates the tapering function that has to be introduced to the SPPT scheme because of mainly numerical reasons. The hybrid scheme HSPP is evaluated over one summer and one winter month and compared to a reference ensemble without any stochastic physics perturbations and to two versions of the SPPT scheme. The results show that HSPP significantly increases the ensemble spread of temperature, humidity, wind speed, and pressure, especially in the lower levels of the atmosphere where a tapering function is active in the original SPPT approach. Precipitation verification yields a generally improved probabilistic performance of the HSPP scheme in summer when convection is dominating, which has also been demonstrated in a case study.

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Alexander Kann
,
Harald Seidl
,
Christoph Wittmann
, and
Thomas Haiden

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.

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Yihong Duan
,
Jiandong Gong
,
Jun Du
,
Martin Charron
,
Jing Chen
,
Guo Deng
,
Geoff DiMego
,
Masahiro Hara
,
Masaru Kunii
,
Xiaoli Li
,
Yinglin Li
,
Kazuo Saito
,
Hiromu Seko
,
Yong Wang
, and
Christoph Wittmann

The Beijing 2008 Olympics Research and Development Project (B08RDP), initiated in 2004 under the World Meteorological Organization (WMO) World Weather Research Programme (WWRP), undertook the research and development of mesoscale ensemble prediction systems (MEPSs) and their application to weather forecast support during the Beijing Olympic Games. Six MEPSs from six countries, representing the state-of-the-art regional EPSs with near-real-time capabilities and emphasizing on the 6–36-h forecast lead times, participated in the project.

The background, objectives, and implementation of B08RDP, as well as the six MEPSs, are reviewed. The accomplishments are summarized, which include 1) providing value-added service to the Olympic Games, 2) advancing MEPS-related research, 3) accelerating the transition from research to operations, and 4) training forecasters in utilizing forecast uncertainty products. The B08RDP has fulfilled its research (MEPS development) and demonstration (value-added service) purposes. The research conducted covers the areas of verification, examining the value of MEPS relative to other numerical weather prediction (NWP) systems, combining multimodel or multicenter ensembles, bias correction, ensemble perturbations [initial condition (IC), lateral boundary condition (LBC), land surface IC, and model physics], downscaling, forecast applications, data assimilation, and storm-scale ensemble modeling. Seven scientific issues important to MEPS have been identified. It is recognized that the daily use of forecast uncertainty information by forecasters remains a challenge. Development of forecaster-friendly products and training activities should be a long-term effort and needs to be continuously enhanced.

The B08RDP dataset is also a valuable asset to the research community. The experience gained in international collaboration, organization, and implementation of a multination regional EPS for a common goal and to address common scientific issues can be shared by the ongoing projects The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble—Limited Area Models (TIGGE-LAM) and North American Ensemble Forecast System—Limited Area Models (NAEFS-LAM).

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Yong Wang
,
Martin Belluš
,
Andrea Ehrlich
,
Máté Mile
,
Neva Pristov
,
Petra Smolíková
,
Oldřich Španiel
,
Alena Trojáková
,
Radmila Brožková
,
Jure Cedilnik
,
Dijana Klarić
,
Tomislav Kovačić
,
Ján Mašek
,
Florian Meier
,
Balázs Szintai
,
Simona Tascu
,
Jozef Vivoda
,
Clemens Wastl
, and
Christoph Wittmann

Abstract

This 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.

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Mathias W. Rotach
,
Stefano Serafin
,
Helen C. Ward
,
Marco Arpagaus
,
Ioana Colfescu
,
Joan Cuxart
,
Stephan F. J. De Wekker
,
Vanda Grubišic
,
Norbert Kalthoff
,
Thomas Karl
,
Daniel J. Kirshbaum
,
Manuela Lehner
,
Stephen Mobbs
,
Alexandre Paci
,
Elisa Palazzi
,
Adriana Bailey
,
Jürg Schmidli
,
Christoph Wittmann
,
Georg Wohlfahrt
, and
Dino Zardi

Abstract

In this essay, we highlight some challenges the atmospheric community is facing concerning adequate treatment of flows over mountains and their implications for numerical weather prediction (NWP), climate simulations, and impact modeling. With recent increases in computing power (and hence model resolution) numerical models start to face new limitations (such as numerical instability over steep terrain). At the same time there is a growing need for sufficiently reliable NWP model output to drive various impact models (for hydrology, air pollution, agriculture, etc.). The input information for these impact models is largely produced by the boundary layer (BL) parameterizations of NWP models. All known BL parameterizations assume flat and horizontally homogeneous surface conditions, and their performance and interaction with resolved flows is massively understudied over mountains—hence their output may be accidentally acceptable at best. We therefore advocate the systematic investigation of the so-called “mountain boundary layer” (MoBL), introduced to emphasize its many differences to the BL over flat and horizontally homogeneous terrain.

An international consortium of scientists has launched a research program, TEAMx (Multi-Scale Transport and Exchange Processes in the Atmosphere over Mountains–Program and Experiment), to address some of the most pressing scientific challenges. TEAMx is endorsed by World Weather Research Programme (WWRP) and the Global Energy and Water Exchanges (GEWEX) project as a “cross-cutting project.” A program coordination office was established at the University of Innsbruck (Austria). This essay introduces the background to and content of a recently published white paper outlining the key research questions of TEAMx.

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