scholarly journals Verification of Precipitation Forecasts from Two Limited-Area Models over Italy and Comparison with ECMWF Forecasts Using a Resampling Technique

2005 ◽  
Vol 20 (3) ◽  
pp. 276-300 ◽  
Author(s):  
Christophe Accadia ◽  
Stefano Mariani ◽  
Marco Casaioli ◽  
Alfredo Lavagnini ◽  
Antonio Speranza
Keyword(s):  
Large Scale ◽  
Nearest Neighbor ◽  
Skill Score ◽  
Convective Precipitation ◽  
Limited Area ◽  
Limited Area Model ◽  
Rainfall Analysis ◽  
Area Model ◽  
Skill Scores ◽  
Forecast Skill Score

Abstract This paper presents the first systematic limited area model (LAM) precipitation verification work over Italy. A resampling technique was used to provide skill score results along with confidence intervals. Two years of data were used, starting in October 2000. Two operational LAMs have been considered, the Limited Area Model Bologna (LAMBO) operating at the Agenzia Regionale Prevenzione e Ambiente-Servizio Meteorologico Regionale (ARPA-SMR) of the Emilia–Romagna region, and the QUADRICS Bologna Limited Area Model (QBOLAM) running at the Agenzia per la Protezione dell’Ambiente e per i Servizi Tecnici (APAT). A 24-h forecast skill score comparison was first performed on the native 0.1° high-resolution grids, using a Barnes scheme to produce the observed 24-h accumulated rainfall analysis. Two nonparametric skill scores were used: the equitable threat score (ETS) and the Hanssen and Kuipers score (HK). Frequency biases (BIA) were also calculated. LAM forecasts were also remapped on a lower-resolution grid (0.5°), using a nearest-neighbor average method; this remapping allowed for comparison with ECMWF model forecasts, and for LAM intercomparisons at lower resolution, with the advantage of reducing the skill score sensitivity to small displacements errors. LAM skill scores depend on the resolution of the verification grid, with an increase when they are verified on a lower-resolution grid. The selected LAMs have a higher BIA compared to ECMWF, showing a tendency to overforecast precipitation, especially along mountain ranges, possibly due to undesired effects from the large-scale and/or convective precipitation parameterizations. Lower ECMWF BIA accounts for skill score differences. LAMBO precipitation forecasts during winter (adjusted for BIA differences) have less misses than ECMWF over the islands of Sardinia and Sicily. Higher-resolution orography definitely adds value to LAM forecasts.

scholarly journals A limited area model intercomparison on the "Montserrat-2000" flash-flood event using statistical and deterministic methods

2005 ◽  
Vol 5 (4) ◽  
pp. 565-581 ◽  
Author(s):  
S. Mariani ◽  
M. Casaioli ◽  
C. Accadia ◽  
M. C. Llasat ◽  
F. Pasi ◽  
...  
Keyword(s):  
Flash Flood ◽  
Single Case ◽  
Forecast Error ◽  
Flood Event ◽  
False Alarms ◽  
Limited Area ◽  
Data Set ◽  
Limited Area Model ◽  
Area Model ◽  
Skill Scores

Abstract. In the scope of the European project Hydroptimet, INTERREG IIIB-MEDOCC programme, limited area model (LAM) intercomparison of intense events that produced many damages to people and territory is performed. As the comparison is limited to single case studies, the work is not meant to provide a measure of the different models' skill, but to identify the key model factors useful to give a good forecast on such a kind of meteorological phenomena. This work focuses on the Spanish flash-flood event, also known as "Montserrat-2000" event. The study is performed using forecast data from seven operational LAMs, placed at partners' disposal via the Hydroptimet ftp site, and observed data from Catalonia rain gauge network. To improve the event analysis, satellite rainfall estimates have been also considered. For statistical evaluation of quantitative precipitation forecasts (QPFs), several non-parametric skill scores based on contingency tables have been used. Furthermore, for each model run it has been possible to identify Catalonia regions affected by misses and false alarms using contingency table elements. Moreover, the standard "eyeball" analysis of forecast and observed precipitation fields has been supported by the use of a state-of-the-art diagnostic method, the contiguous rain area (CRA) analysis. This method allows to quantify the spatial shift forecast error and to identify the error sources that affected each model forecasts. High-resolution modelling and domain size seem to have a key role for providing a skillful forecast. Further work is needed to support this statement, including verification using a wider observational data set.

Lateral boundary relaxation and large scale nudging in RCM runs

2020 ◽  
Author(s):  
Fedor Mesinger ◽  
Katarina Veljovic ◽  
Sin Chan Chou
Keyword(s):  
Large Scale ◽  
Climate Modeling ◽  
Regional Climate ◽  
Regional Climate Modeling ◽  
Scientific Basis ◽  
Driver Model ◽  
Limited Area ◽  
Lateral Boundary ◽  
Limited Area Model ◽  
Area Model

<p>Almost universally, in Regional Climate Modeling (RCM) integrations, Davies’ relaxation lateral boundary conditions are applied. They force variables in a number of rows around the boundary to conform to the driver global model values, completely at the boundary, and less and less toward the inside of the integration domain. Very often, in addition, investigators apply so-called large scale or spectral nudging inside the domain, forcing the integration variables not to depart much from those of the driver model.</p><p>It is pointed out that there is no scientific basis for these two practices. So why are they used? In particular for the former of these two, it is suggested that reasons must be either a belief that this is a practice RCM should follow, or a technique to address numerical issues of the limited area model used, or a combination of the two.  For the latter, a belief only.</p><p>Examples are shown that, in the absence of these two stratagems, the limited area model can improve on large scales inside its domain. This demonstrates that their use, aimed to force variables inside the domain not to depart much from the driver model data, should be detrimental, if possible numerical issues of the model used were to be remedied.</p>

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

2018 ◽  
Vol 99 (7) ◽  
pp. 1415-1432 ◽  
Author(s):  
Yong Wang ◽  
Martin Belluš ◽  
Andrea Ehrlich ◽  
Máté Mile ◽  
Neva Pristov ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Central Europe ◽  
Regional Cooperation ◽  
Weather Prediction ◽  
Ensemble Forecasting ◽  
Limited Area ◽  
Member States ◽  
Limited Area Model ◽  
Area Model ◽  
Common System

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.

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