scholarly journals Overview of the first HyMeX Special Observation Period over Italy: observations and model results

2014 ◽  
Vol 18 (5) ◽  
pp. 1953-1977 ◽  
Author(s):  
R. Ferretti ◽  
E. Pichelli ◽  
S. Gentile ◽  
I. Maiello ◽  
D. Cimini ◽  
...  
Keyword(s):  
Prediction Models ◽  
Hydrological Cycle ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Central Italy ◽  
Weather Forecast ◽  
Western Mediterranean ◽  
Target Area ◽  
Northeastern Italy ◽  
Observation Period

Abstract. The Special Observation Period (SOP1), part of the HyMeX campaign (Hydrological cycle in the Mediterranean Experiments, 5 September–6 November 2012), was dedicated to heavy precipitation events and flash floods in the western Mediterranean, and three Italian hydro-meteorological monitoring sites were identified: Liguria–Tuscany, northeastern Italy and central Italy. The extraordinary deployment of advanced instrumentation, including instrumented aircrafts, and the use of several different operational weather forecast models, including hydrological models and marine models, allowed an unprecedented monitoring and analysis of high-impact weather events around the Italian hydro-meteorological sites. This activity has seen strong collaboration between the Italian scientific and operational communities. In this paper an overview of the Italian organization during SOP1 is provided, and selected Intensive Observation Periods (IOPs) are described. A significant event for each Italian target area is chosen for this analysis: IOP2 (12–13 September 2012) in northeastern Italy, IOP13 (15–16 October 2012) in central Italy and IOP19 (3–5 November 2012) in Liguria and Tuscany. For each IOP the meteorological characteristics, together with special observations and weather forecasts, are analyzed with the aim of highlighting strengths and weaknesses of the forecast modeling systems, including the hydrological impacts. The usefulness of having different weather forecast operational chains characterized by different numerical weather prediction models and/or different model set up or initial conditions is finally shown for one of the events (IOP19).

scholarly journals Overview of the first HyMeX Special Observation Period over Italy: observations and model results

2013 ◽  
Vol 10 (9) ◽  
pp. 11643-11710 ◽  
Author(s):  
R. Ferretti ◽  
E. Pichelli ◽  
S. Gentile ◽  
I. Maiello ◽  
D. Cimini ◽  
...  
Keyword(s):  
Central Italy ◽  
Weather Forecast ◽  
Heavy Precipitation ◽  
Western Mediterranean ◽  
Target Area ◽  
Weather Forecasts ◽  
Weather Events ◽  
North Eastern ◽  
Forecast Models ◽  
Observation Period

Abstract. During the first Hymex campaign (5 September–6 November 2012) referred to as Special Observation Period (SOP-1), dedicated to heavy precipitation events and flash floods in Western Mediterranean, three Italian hydro-meteorological monitoring sites were activated: Liguria-Tuscany, North-Eastern Italy and Central Italy. The extraordinary deployment of advanced instrumentation, including instrumented aircrafts, and the use of several different operational weather forecast models has allowed an unprecedented monitoring and analysis of high impact weather events around the Italian hydro-meteorological sites. This activity has seen the strict collaboration between the Italian scientific and operational communities. In this paper, an overview of the Italian organization during the SOP-1 is provided, and selected Intensive Observation Periods (IOPs) are described. A significant event for each Italian target area is chosen for this analysis: IOP2 (12–13 September 2012) in North-Eastern Italy, IOP13 (15–16 October 2012) in Central Italy and IOP19 (3–5 November 2012) in Liguria and Tuscany. For each IOP the meteorological characteristics, together with special observations and weather forecasts, are analyzed with the aim of highlighting strengths and weaknesses of the forecast modeling systems. Moreover, using one of the three events, the usefulness of different operational chains is highlighted.

scholarly journals The AROME-WMED reanalyses of the first special observation period of the Hydrological cycle in the Mediterranean experiment (HyMeX)

2019 ◽  
Vol 12 (7) ◽  
pp. 2657-2678 ◽  
Author(s):  
Nadia Fourrié ◽  
Mathieu Nuret ◽  
Pierre Brousseau ◽  
Olivier Caumont ◽  
Alexis Doerenbecher ◽  
...  
Keyword(s):  
Real Time ◽  
Hydrological Cycle ◽  
Weather Prediction ◽  
Western Mediterranean ◽  
Forecast Errors ◽  
Background Error ◽  
The Real ◽  
Western Mediterranean Basin ◽  
The Mediterranean ◽  
Observation Period

Abstract. To study key processes of the water cycle, two special observation periods (SOPs) of the Hydrological cycle in the Mediterranean experiment (HyMeX) took place during autumn 2012 and winter 2013. The first SOP aimed to study high precipitation systems and flash flooding in the Mediterranean area. The AROME-WMED (western Mediterranean) model (Fourrié et al., 2015) is a dedicated version of the mesoscale Numerical Weather Prediction (NWP) AROME-France model, which covers the western Mediterranean basin providing the HyMeX operational center with daily real-time analyses and forecasts. These products allowed for adequate decision-making for the field campaign observation deployment and the instrument operation. Shortly after the end of the campaign, a first reanalysis with more observations was performed with the first SOP operational software. An ensuing comprehensive second reanalysis of the first SOP, which included field research observations (not assimilated in real time) and some reprocessed observation datasets, was made with AROME-WMED. Moreover, a more recent version of the AROME model was used with updated background error statistics for the assimilation process. This paper depicts the main differences between the real-time version and the benefits brought by HyMeX reanalyses with AROME-WMED. The first reanalysis used 9 % additional data and the second one 24 % more compared to the real-time version. The second reanalysis is found to be closer to observations than the previous AROME-WMED analyses. The second reanalysis forecast errors of surface parameters are reduced up to the 18 and 24 h forecast range. In the middle and upper troposphere, fields are also improved up to the 48 h forecast range when compared to radiosondes. Integrated water vapor comparisons indicate a positive benefit for at least 24 h. Precipitation forecasts are found to be improved with the second reanalysis for a threshold up to 10 mm (24 h)−1. For higher thresholds, the frequency bias is degraded. Finally, improvement brought by the second reanalysis is illustrated with the Intensive Observation Period (IOP8) associated with heavy precipitation over eastern Spain and southern France.

scholarly journals Effects of Multiple Doppler Radar data assimilation on the numerical simulation of a Flash Flood Event during the HyMeX campaign

2016 ◽  
Author(s):  
Ida Maiello ◽  
Sabrina Gentile ◽  
Rossella Ferretti ◽  
Luca Baldini ◽  
Nicoletta Roberto ◽  
...  
Keyword(s):  
Hydrological Cycle ◽  
Initial Conditions ◽  
Flash Flood ◽  
Weather Prediction ◽  
Central Italy ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Horizontal Resolution ◽  
Precipitation Event ◽  
The Impact

Abstract. An analysis to evaluate the impact of assimilating multiple radar data with a three dimensional variational (3D-Var) system on a heavy precipitation event is presented. The main goal is to establish a general methodology to quantitatively assess the performance of flash-flood numerical weather prediction at mesoscale. In this respect, during the first Special Observation Period (SOP1) of HyMeX (Hydrological cycle in the Mediterranean Experiment) campaign several Intensive Observing Periods (IOPs) were launched and nine occurred in Italy. Among them IOP4 is chosen for this study because of its low predictability. This event hit central Italy on 14 September 2012 producing heavy precipitation and causing several damages. Data taken from three C-band radars running operationally during the event are assimilated to improve high resolution initial conditions. In order to evaluate the impact of the assimilation procedure at different horizontal resolution and to assess the impact of assimilating multiple radars data, several experiments using Weather Research and Forecasting (WRF) model are performed. Finally, the statistical indexes as accuracy, equitable threat score, false alarm ratio and frequency bias are used to objectively compare the experiments, using rain gauges data as benchmark.

scholarly journals Model error in weather forecasting

2001 ◽  
Vol 8 (6) ◽  
pp. 357-371 ◽  
Author(s):  
D. Orrell ◽  
L. Smith ◽  
J. Barkmeijer ◽  
T. N. Palmer
Keyword(s):  
Prediction Models ◽  
Weather Forecasting ◽  
Initial Conditions ◽  
Forecast Error ◽  
Weather Prediction ◽  
Model Error ◽  
Local Model ◽  
State Dependent ◽  
Rapid Divergence ◽  
Dependent Model

Abstract. Operational forecasting is hampered both by the rapid divergence of nearby initial conditions and by error in the underlying model. Interest in chaos has fuelled much work on the first of these two issues; this paper focuses on the second. A new approach to quantifying state-dependent model error, the local model drift, is derived and deployed both in examples and in operational numerical weather prediction models. A simple law is derived to relate model error to likely shadowing performance (how long the model can stay close to the observations). Imperfect model experiments are used to contrast the performance of truncated models relative to a high resolution run, and the operational model relative to the analysis. In both cases the component of forecast error due to state-dependent model error tends to grow as the square-root of forecast time, and provides a major source of error out to three days. These initial results suggest that model error plays a major role and calls for further research in quantifying both the local model drift and expected shadowing times.

Meteorological and geographical control on stable isotope signature of precipitation in a Western Mediterranean area (Tuscany, Central Italy)

2021 ◽  
Author(s):  
Stefano Natali ◽  
Giovanni Zanchetta ◽  
Ilaria Baneschi ◽  
Marco Doveri ◽  
Roberto Giannecchini
Keyword(s):  
Stable Isotope ◽  
Hydrological Cycle ◽  
Central Italy ◽  
Precipitation Amount ◽  
Rank Correlation ◽  
Western Mediterranean ◽  
Meteorological Variables ◽  
Stable Water Isotopes ◽  
Fractionation Factor ◽  
Monthly Data

<p>Stable water isotopes of precipitation are widely used to track processes occurring within the hydrological cycle and to understand regional atmospheric patterns that influence a specific area. Moreover, the use of the oxygen isotopic composition in continental carbonates (e.g. speleothems) is a well-established practice to reconstruct climatic variations in the recent past. In the Mediterranean basin, the continental carbonate δ<sup>18</sup>O is generally used as a proxy of paleo-precipitation since the water-calcite fractionation factor is able to compensate the δ<sup>18</sup>O-T gradient of about 0.2‰/°C typical of rainfall in this area. However, few comprehensive investigations were performed in the Western Mediterranean in order to analyze the statistical relationships between measured stable isotopes in precipitation and meteorological variables, and none of them accounted for the possible seasonality in these relationships. Understanding the degree of dependence of the rainfall isotopic signature from precipitation amount and temperature at present day is of primary importance in Tuscany (Central-Western Italy), where many performed palaeohydrological studies require a more precise and quantitative interpretation. To this end, in the present study 560 isotope monthly data (δ<sup>18</sup>O, δ<sup>2</sup>H, and deuterium excess) of precipitation collected in 11 sites through Tuscany from 1971 to 2018 were gathered in a database. A large part of dataset was extracted from GNIP database (and integrated with new data) or derived from local hydrogeological studies, whereas 83 new measurements were produced at two novel sites. Then, only sites whose monthly data covered almost one year were considered for processing, resulting in 474 precipitation samples archived along with monthly mean temperature and rainfall amount. In this framework, a LMWL for Tuscany Region was determined for the first time by applying different regression techniques. A Spearman’s rank correlation analysis was performed to summarize the strength and direction of the relationship between stable isotope signatures of precipitation and meteorological variables, both at monthly and annual timescale. The monthly correlation was also investigated on seasonal basis. Finally, the influence of local geographical effects (altitude, distance to the coast, etc.) on the isotopic signals registered at different sites was evaluated.</p>

scholarly journals Impact of multiple radar reflectivity data assimilation on the numerical simulation of a flash flood event during the HyMeX campaign

2017 ◽  
Vol 21 (11) ◽  
pp. 5459-5476 ◽  
Author(s):  
Ida Maiello ◽  
Sabrina Gentile ◽  
Rossella Ferretti ◽  
Luca Baldini ◽  
Nicoletta Roberto ◽  
...  
Keyword(s):  
Hydrological Cycle ◽  
Initial Conditions ◽  
Flash Flood ◽  
Central Italy ◽  
Heavy Precipitation ◽  
Radar Reflectivity ◽  
Precipitation Event ◽  
Italian Regions ◽  
Reflectivity Data ◽  
The Impact

Abstract. An analysis to evaluate the impact of multiple radar reflectivity data with a three-dimensional variational (3-D-Var) assimilation system on a heavy precipitation event is presented. The main goal is to build a regionally tuned numerical prediction model and a decision-support system for environmental civil protection services and demonstrate it in the central Italian regions, distinguishing which type of observations, conventional and not (or a combination of them), is more effective in improving the accuracy of the forecasted rainfall. In that respect, during the first special observation period (SOP1) of HyMeX (Hydrological cycle in the Mediterranean Experiment) campaign several intensive observing periods (IOPs) were launched and nine of which occurred in Italy. Among them, IOP4 is chosen for this study because of its low predictability regarding the exact location and amount of precipitation. This event hit central Italy on 14 September 2012 producing heavy precipitation and causing several cases of damage to buildings, infrastructure, and roads. Reflectivity data taken from three C-band Doppler radars running operationally during the event are assimilated using the 3-D-Var technique to improve high-resolution initial conditions. In order to evaluate the impact of the assimilation procedure at different horizontal resolutions and to assess the impact of assimilating reflectivity data from multiple radars, several experiments using the Weather Research and Forecasting (WRF) model are performed. Finally, traditional verification scores such as accuracy, equitable threat score, false alarm ratio, and frequency bias – interpreted by analysing their uncertainty through bootstrap confidence intervals (CIs) – are used to objectively compare the experiments, using rain gauge data as a benchmark.

scholarly journals Low-Atmosphere Drifting Balloons: Platforms for Environment Monitoring and Forecast Improvement

2016 ◽  
Vol 97 (9) ◽  
pp. 1583-1599 ◽  
Author(s):  
A. Doerenbecher ◽  
C. Basdevant ◽  
P. Drobinski ◽  
P. Durand ◽  
C. Fesquet ◽  
...  
Keyword(s):  
Field Experiments ◽  
Prediction Models ◽  
Hydrological Cycle ◽  
Weather Prediction ◽  
Observation System ◽  
Convective Systems ◽  
Leading Role ◽  
Space Agency ◽  
In Situ Investigation ◽  
The Mediterranean

Abstract Balloons are one of the key observing platforms for the atmosphere. Radiosounding is the most commonly used technique and provides over a thousand vertical profiles worldwide every day. These data represent an essential cornerstone of data assimilation for numerical weather prediction systems. Although less common (but equally interesting for the in situ investigation of the atmosphere), drifting boundary layer pressurized balloons (BLPBs) offer rare observational skills. These balloons collect meteorological and/or chemical measurements at isopycnal height as they drift in a quasi-Lagrangian way. The BLPB system presented in this paper was developed by the French Space Agency [Centre National d’Études Spatiales (CNES)] and has been used in field experiments focusing on precipitation in Africa [African Monsoon Multiscale Analysis (AMMA)] and the Mediterranean [Hydrological Cycle in the Mediterranean Experiment (HyMeX)] as well as on air pollution in India [Indian Ocean Experiment (INDOEX)] and the Mediterranean [Transport a Longue Distance et Qualite de l’Air dans le bassin Méditerraneen (TRAQA) and Chemistry–Aerosol Mediterranean Experiment (ChArMeX)]. One important advantage of BLPBs is their capability to explore the lowest layers of the atmosphere above the oceans, areas that remain difficult to access. BLPB had a leading role in a complex adaptive observation system for the forecast of severe precipitation events. These balloons collected data in the marine environment of convective systems, which were assimilated in real time to improve the knowledge of the state of the atmosphere in the numerical prediction models of Météo-France.

scholarly journals Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

2014 ◽  
Vol 21 (5) ◽  
pp. 1027-1041 ◽  
Author(s):  
K. Apodaca ◽  
M. Zupanski ◽  
M. DeMaria ◽  
J. A. Knaff ◽  
L. D. Grasso
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
New Information ◽  
Forecasting System ◽  
Maximum Likelihood Ensemble Filter ◽  
The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Geostationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), and improving initial conditions during several data assimilation cycles. However, the 6 h forecast after the assimilation did not show a clear improvement in terms of root mean square (RMS) errors.

Towards operational postprocessing of cloud cover at MeteoSwiss

2020 ◽  
Author(s):  
Stephan Hemri ◽  
Christoph Spirig ◽  
Jonas Bhend ◽  
Lionel Moret ◽  
Mark Liniger
Keyword(s):  
Cloud Cover ◽  
Prediction Models ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Forecast Errors ◽  
Observation Data ◽  
Model Output ◽  
Ensemble Forecasts ◽  
Direct Model ◽  
Ensemble Model Output Statistics

<p>Over the last decades ensemble approaches have become state-of-the-art for the quantification of weather forecast uncertainty. Despite ongoing improvements, ensemble forecasts issued by numerical weather prediction models (NWPs) still tend to be biased and underdispersed. Statistical postprocessing has proven to be an appropriate tool to correct biases and underdispersion, and hence to improve forecast skill. Here we focus on multi-model postprocessing of cloud cover forecasts in Switzerland. In order to issue postprocessed forecasts at any point in space, ensemble model output statistics (EMOS) models are trained and verified against EUMETSAT CM SAF satellite data with a spatial resolution of around 2 km over Switzerland. Training with a minimal record length of the past 45 days of forecast and observation data already produced an EMOS model improving direct model output (DMO). Training on a 3 years record of the corresponding season further improved the performance. We evaluate how well postprocessing corrects the most severe forecast errors, like missing fog and low level stratus in winter. For such conditions, postprocessing of cloud cover benefits strongly from incorporating additional predictors into the postprocessing suite. A quasi-operational prototype has been set up and was used to explore meteogram-like visualizations of probabilistic cloud cover forecasts.</p>

scholarly journals A Similarity-Based Implementation of the Schaake Shuffle

2016 ◽  
Vol 144 (5) ◽  
pp. 1909-1921 ◽  
Author(s):  
Roman Schefzik
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Similarity Criterion ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Prediction Horizon ◽  
Prediction Systems ◽  
Specific Implementation

Contemporary weather forecasts are typically based on ensemble prediction systems, which consist of multiple runs of numerical weather prediction models that vary with respect to the initial conditions and/or the parameterization of the atmosphere. Ensemble forecasts are frequently biased and show dispersion errors and thus need to be statistically postprocessed. However, current postprocessing approaches are often univariate and apply to a single weather quantity at a single location and for a single prediction horizon only, thereby failing to account for potentially crucial dependence structures. Nonparametric multivariate postprocessing methods based on empirical copulas, such as ensemble copula coupling or the Schaake shuffle, can address this shortcoming. A specific implementation of the Schaake shuffle, called the SimSchaake approach, is introduced. The SimSchaake method aggregates univariately postprocessed ensemble forecasts using dependence patterns from past observations. Specifically, the observations are taken from historical dates at which the ensemble forecasts resembled the current ensemble prediction with respect to a specific similarity criterion. The SimSchaake ensemble outperforms all reference ensembles in an application to ensemble forecasts for 2-m temperature from the European Centre for Medium-Range Weather Forecasts.

Sign in / Sign up

Export Citation Format

Share Document