Results of the meteorological model WRF-ARW over Catalonia, using different parameterizations of convection and cloud microphysics

The meteorological model WRF-ARW (Weather Research and Forecasting - Advanced ResearchWRF) is a new generation model that has a worldwide growing community of users. In theframework of a project that studies the feasibility of implementing it operationally at the Mete-orological Service of Catalonia, a verification of the forecasts produced by the model in severalcases of precipitation observed over Catalonia has been carried out. Indeed, given the impor-tance of precipitation forecasts in this area, one of the main objectives was to study the sensitivityof the model in different configurations of its parameterizations of convection and cloud micro-physics. In this paper, we present the results of this verification for two domains, a 36-km gridsize and one of 12 km grid size, unidirectionally nested to the previous one. In the externaldomain, the evaluation was based on the analysis of the main statistical parameters (ME andRMSE) for temperature, relative humidity, geopotential and wind, and it has been determinedthat the combination using the Kain-Fritsch convective scheme with the WSM5 microphysicalscheme has provided the best results. Then, with this configuration set for the external domain,some forecasts at the nested domain have been done, by combining different convection andcloud microphysics schemes, leading to the conclusion that the most accurate configuration isthe one combining the convective parameterization of Kain-Fritsch and the Thompson micro-physics scheme

Improving QPF by blending techniques at the Meteorological Service of Catalonia

The current operational very short-term and short-term quantitative precipitation forecast (QPF) at the Meteorological Service of Catalonia (SMC) is made by three different methodologies: Advection of the radar reflectivity field (ADV), Identification, tracking and forecasting of convective structures (CST) and numerical weather prediction (NWP) models using observational data assimilation (radar, satellite, etc.). These precipitation forecasts have different characteristics, lead time and spatial resolutions. The objective of this study is to combine these methods in order to obtain a single and optimized QPF at each lead time. This combination (blending) of the radar forecast (ADV and CST) and precipitation forecast from NWP model is carried out by means of different methodologies according to the prediction horizon. Firstly, in order to take advantage of the rainfall location and intensity from radar observations, a phase correction technique is applied to the NWP output to derive an additional corrected forecast (MCO). To select the best precipitation estimation in the first and second hour (t+1 h and t+2 h), the information from radar advection (ADV) and the corrected outputs from the model (MCO) are mixed by using different weights, which vary dynamically, according to indexes that quantify the quality of these predictions. This procedure has the ability to integrate the skill of rainfall location and patterns that are given by the advection of radar reflectivity field with the capacity of generating new precipitation areas from the NWP models. From the third hour (t+3 h), as radar-based forecasting has generally low skills, only the quantitative precipitation forecast from model is used. This blending of different sources of prediction is verified for different types of episodes (convective, moderately convective and stratiform) to obtain a robust methodology for implementing it in an operational and dynamic wa

Predictive ability of severe rainfall events over Catalonia for the year 2008

This paper analyses the predictive ability of quantitative precipitation forecasts (QPF) and the so-called "poor-man" rainfall probabilistic forecasts (RPF). With this aim, the full set of warnings issued by the Meteorological Service of Catalonia (SMC) for potentially-dangerous events due to severe precipitation has been analysed for the year 2008. For each of the 37 warnings, the QPFs obtained from the limited-area model MM5 have been verified against hourly precipitation data provided by the rain gauge network covering Catalonia (NE of Spain), managed by SMC. For a group of five selected case studies, a QPF comparison has been undertaken between the MM5 and COSMO-I7 limited-area models. Although MM5's predictive ability has been examined for these five cases by making use of satellite data, this paper only shows in detail the heavy precipitation event on the 9¿10 May 2008. Finally, the "poor-man" rainfall probabilistic forecasts (RPF) issued by SMC at regional scale have also been tested against hourly precipitation observations. Verification results show that for long events (>24 h) MM5 tends to overestimate total precipitation, whereas for short events (¿24 h) the model tends instead to underestimate precipitation. The analysis of the five case studies concludes that most of MM5's QPF errors are mainly triggered by very poor representation of some of its cloud microphysical species, particularly the cloud liquid water and, to a lesser degree, the water vapor. The models' performance comparison demonstrates that MM5 and COSMO-I7 are on the same level of QPF skill, at least for the intense-rainfall events dealt with in the five case studies, whilst the warnings based on RPF issued by SMC have proven fairly correct when tested against hourly observed precipitation for 6-h intervals and at a small region scale. Throughout this study, we have only dealt with (SMC-issued) warning episodes in order to analyse deterministic (MM5 and COSMO-I7) and probabilistic (SMC) rainfall forecasts; therefore we have not taken into account those episodes that might (or might not) have been missed by the official SMC warnings. Therefore, whenever we talk about "misses", it is always in relation to the deterministic LAMs' QPFs

Classification de la végétation sur la France à l'aide de l'AVHRR de NOAA-11

ABSTRACT NOAA-11 / AVHRR data from march 1990 to february 1991 have been processed to map the vegetation cover over France. After an automatic cloud detection, the vegetation index (NDVI) is computed over the whole period using the visible and near infrared channels corrected from atmospheric effects. A monthly NDVI value is obtained for each point of the area by keeping the maximum value among the data corresponding to low satellite zenith angles. These annual profiles have been classified with a clustering algorithm and the resulting classification has been associated to vegetation cover by comparison with in-situ data of a 1988 vegetation survey from the French Agriculture Ministry.RESUME Les données NOAA-11 /AVHRR de la période mars 1990-février 1991 sont traitées afin d'obtenir une cartographie de l'occupation du sol sur la France et une partie de l'Espagne à une résolution de 4 km. Après que les nuages aient été détectés, l'indice de végétation (NDVI) est calculé sur toute la période à partir des canaux visible et proche infrarouge corrigés des effets atmosphériques. Des profils temporels de NDVI sont ainsi obtenus en limitant l'angle de visée et en ne gardant que la valeur maximale de chaque mois. Ces profils sont classés d'une manière automatique par un algorithme de type " Nuées Dynamiques ". La confrontation des résultats de cette classification à des données de terrain provenant du Recensement Général de l'Agriculture effectué en 1988 en France à l'échelle du canton permet d'évaluer la sensibilité des profils AVHRR à l'occupation du sol.Derrien M, Farki B, Le Gleau H, Sairouni A. Classification de la végétation sur la France à l'aide de l'AVHRR de NOAA-11. In: Norois, n°155, Juillet-Septembre 1992. pp. 269-282

Predictive ability of severe rainfall events over Catalonia for the year 2008

This paper analyses the predictive ability of quantitative precipitation forecasts (QPF) and the so-called "poor-man" rainfall probabilistic forecasts (RPF). With this aim, the full set of warnings issued by the Meteorological Service of Catalonia (SMC) for potentially-dangerous events due to severe precipitation has been analysed for the year 2008. For each of the 37 warnings, the QPFs obtained from the limited-area model MM5 have been verified against hourly precipitation data provided by the rain gauge network covering Catalonia (NE of Spain), managed by SMC. For a group of five selected case studies, a QPF comparison has been undertaken between the MM5 and COSMO-I7 limited-area models. Although MM5's predictive ability has been examined for these five cases by making use of satellite data, this paper only shows in detail the heavy precipitation event on the 9¿10 May 2008. Finally, the "poor-man" rainfall probabilistic forecasts (RPF) issued by SMC at regional scale have also been tested against hourly precipitation observations. Verification results show that for long events (>24 h) MM5 tends to overestimate total precipitation, whereas for short events (¿24 h) the model tends instead to underestimate precipitation. The analysis of the five case studies concludes that most of MM5's QPF errors are mainly triggered by very poor representation of some of its cloud microphysical species, particularly the cloud liquid water and, to a lesser degree, the water vapor. The models' performance comparison demonstrates that MM5 and COSMO-I7 are on the same level of QPF skill, at least for the intense-rainfall events dealt with in the five case studies, whilst the warnings based on RPF issued by SMC have proven fairly correct when tested against hourly observed precipitation for 6-h intervals and at a small region scale. Throughout this study, we have only dealt with (SMC-issued) warning episodes in order to analyse deterministic (MM5 and COSMO-I7) and probabilistic (SMC) rainfall forecasts; therefore we have not taken into account those episodes that might (or might not) have been missed by the official SMC warnings. Therefore, whenever we talk about "misses", it is always in relation to the deterministic LAMs' QPFs

A new generation of early warning systems for coastal risk: the i-coast project

A new coastal early warning system CEWS called iCoast for the NW Mediterranean Sea is under development. It is composed of three numerical modules: meteorological, hydrodynamic and morphodynamic. The CEWS is designed for use on open sandy beaches, pocket beaches, secondary harbours as well as areas of coastal defences. A set of hotspots, prone to erosion or flooding, along the Catalan coast are being identified through a coastal hazard and risk mapping. Special attention is given to a set of intervention and emergency protocols based on the forecast outputs and the application of Quick Defence Measures (QDM) to diminish the risk. This paper presents the initial architecture and highlights the next step for QDM under the iCoast project.Peer Reviewe

Improving QPF by blending techniques at the Meteorological Service of Catalonia

The current operational very short-term and short-term quantitative precipitation forecast (QPF) at the Meteorological Service of Catalonia (SMC) is made by three different methodologies: Advection of the radar reflectivity field (ADV), Identification, tracking and forecasting of convective structures (CST) and numerical weather prediction (NWP) models using observational data assimilation (radar, satellite, etc.). These precipitation forecasts have different characteristics, lead time and spatial resolutions. The objective of this study is to combine these methods in order to obtain a single and optimized QPF at each lead time. This combination (blending) of the radar forecast (ADV and CST) and precipitation forecast from NWP model is carried out by means of different methodologies according to the prediction horizon. Firstly, in order to take advantage of the rainfall location and intensity from radar observations, a phase correction technique is applied to the NWP output to derive an additional corrected forecast (MCO). To select the best precipitation estimation in the first and second hour (t+1 h and t+2 h), the information from radar advection (ADV) and the corrected outputs from the model (MCO) are mixed by using different weights, which vary dynamically, according to indexes that quantify the quality of these predictions. This procedure has the ability to integrate the skill of rainfall location and patterns that are given by the advection of radar reflectivity field with the capacity of generating new precipitation areas from the NWP models. From the third hour (t+3 h), as radar-based forecasting has generally low skills, only the quantitative precipitation forecast from model is used. This blending of different sources of prediction is verified for different types of episodes (convective, moderately convective and stratiform) to obtain a robust methodology for implementing it in an operational and dynamic way

Improving QPF by blending techniques at the meteorological service of catalonia

The current operational very short-term and shortterm quantitative precipitation forecast (QPF) at the Meteorological Service of Catalonia (SMC) is made by three different methodologies: Advection of the radar reflectivity field (ADV), Identification, tracking and forecasting of convective structures (CST) and numerical weather prediction (NWP) models using observational data assimilation (radar, satellite, etc.). These precipitation forecasts have different characteristics, lead time and spatial resolutions. The objective of this study is to combine these methods in order to obtain a single and optimized QPF at each lead time. This combination (blending) of the radar forecast (ADV and CST) and precipitation forecast from NWP model is carried out by means of different methodologies according to the prediction horizon. Firstly, in order to take advantage of the rainfall location and intensity from radar observations, a phase correction technique is applied to the NWP output to derive an additional corrected forecast (MCO). To select the best precipitation estimation in the first and second hour (t+1 h and t+2 h), the information from radar advection (ADV) and the corrected outputs from the model (MCO) are mixed by using different weights, which vary dynamically, according to indexes that quantify the quality of these predictions. This procedure has the ability to integrate the skill of rainfall location and patterns that are given by the advection of radar reflectivity field with the capacity of generating new precipitation areas from the NWP models. From the third hour (t+3 h), as radar-based forecasting has generally low skills, only the quantitative precipitation forecast from model is used. This blending of different sources of prediction is verified for different types of episodes (convective, moderately convective and stratiform) to obtain a robust methodology for implementing it in an operational and dynamic way