scholarly journals Radar-based characterisation of heavy precipitation in the eastern Mediterranean and its representation in a convection-permitting model

2020 ◽  
Vol 24 (3) ◽  
pp. 1227-1249 ◽  
Author(s):  
Moshe Armon ◽  
Francesco Marra ◽  
Yehouda Enzel ◽  
Dorita Rostkier-Edelstein ◽  
Efrat Morin
Keyword(s):  
High Resolution ◽  
Eastern Mediterranean ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Spatial Location ◽  
Radar Data ◽  
Weather Research And Forecasting ◽  
Model Simulations ◽  
Rain Gauges ◽  
Rainfall Patterns

Abstract. Heavy precipitation events (HPEs) can lead to natural hazards (e.g. floods and debris flows) and contribute to water resources. Spatiotemporal rainfall patterns govern the hydrological, geomorphological, and societal effects of HPEs. Thus, a correct characterisation and prediction of rainfall patterns is crucial for coping with these events. Information from rain gauges is generally limited due to the sparseness of the networks, especially in the presence of sharp climatic gradients. Forecasting HPEs depends on the ability of weather models to generate credible rainfall patterns. This paper characterises rainfall patterns during HPEs based on high-resolution weather radar data and evaluates the performance of a high-resolution, convection-permitting Weather Research and Forecasting (WRF) model in simulating these patterns. We identified 41 HPEs in the eastern Mediterranean from a 24-year radar record using local thresholds based on quantiles for different durations, classified these events into two synoptic systems, and ran model simulations for them. For most durations, HPEs near the coastline were characterised by the highest rain intensities; however, for short durations, the highest rain intensities were found for the inland desert. During the rainy season, the rain field's centre of mass progresses from the sea inland. Rainfall during HPEs is highly localised in both space (less than a 10 km decorrelation distance) and time (less than 5 min). WRF model simulations were accurate in generating the structure and location of the rain fields in 39 out of 41 HPEs. However, they showed a positive bias relative to the radar estimates and exhibited errors in the spatial location of the heaviest precipitation. Our results indicate that convection-permitting model outputs can provide reliable climatological analyses of heavy precipitation patterns; conversely, flood forecasting requires the use of ensemble simulations to overcome the spatial location errors.

Radar-based characterization of heavy precipitation in the eastern Mediterranean and its representation in a convection-permitting model

2020 ◽  
Author(s):  
Efrat Morin ◽  
Moshe Armon ◽  
Francesco Marra ◽  
Yehouda Enzel ◽  
Dorita Rostkier-Edelstein
Keyword(s):  
High Resolution ◽  
Eastern Mediterranean ◽  
Center Of Mass ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Spatial Location ◽  
Radar Data ◽  
Model Simulations ◽  
Rain Gauges ◽  
Rainfall Patterns

<p>Heavy precipitation events (HPEs) can lead to natural hazards (floods, debris flows) and contribute to water resources. Spatiotemporal rainfall patterns govern the hydrological, geomorphological and societal effects of HPEs. Thus, a correct characterization and prediction of rainfall patterns is crucial for coping with these events. However, information from rain gauges suitable for these goals is generally limited due to the sparseness of the networks, especially in the presence of sharp climatic gradients and small precipitating systems. Forecasting HPEs depends on the ability of weather models to generate credible rainfall patterns. In this study we characterize rainfall patterns during HPEs based on high-resolution weather radar data and evaluate the performance of a high-resolution (1 km<sup>2</sup>), convection-permitting Weather Research and Forecasting (WRF) model in simulating these patterns. We identified 41 HPEs in the eastern Mediterranean from a 24-year long radar record using local thresholds based on quantiles for different durations, classified these events into two synoptic systems, and ran model simulations for them. For most durations, HPEs near the coastline were characterized by the highest rain intensities; however, for short storm durations, the highest rain intensities were characterized for the inland desert. During the rainy season, center of mass of the rain field progresses from the sea inland. Rainfall during HPEs is highly localized in both space (<10 km decorrelation distance) and time (<5 min). WRF model simulations accurately generate the structure and location of the rain fields in 39 out of 41 HPEs. However, they showed a positive bias relative to the radar estimates and exhibited errors in the spatial location of the heaviest precipitation. Our results indicate that convection-permitting model outputs can provide reliable climatological analyses of heavy precipitation patterns; conversely, flood forecasting requires the use of ensemble simulations to overcome the spatial location errors.</p>

scholarly journals Characterising patterns of heavy precipitation events in the eastern Mediterranean using a weather radar and convection-permitting WRF simulations

2019 ◽  
Author(s):  
Moshe Armon ◽  
Francesco Marra ◽  
Yehouda Enzel ◽  
Dorita Rostkier-Edelstein ◽  
Efrat Morin
Keyword(s):  
High Resolution ◽  
Eastern Mediterranean ◽  
Wrf Model ◽  
Weather Radar ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Weather Research And Forecasting ◽  
Rain Gauges ◽  
Rainfall Patterns ◽  
Precipitation Events

Abstract. Heavy precipitation events (HPEs) can lead to natural hazards (floods, debris flows) and contribute to water resources. Rainfall patterns govern HPEs effects. Thus, a correct characterisation and prediction of rainfall patterns is crucial for coping with HPEs. Information from rain gauges is generally limited due to the sparseness of the networks, especially in presence of sharp climatic gradients. Forecasting HPEs depends on the ability of weather models to generate credible rainfall patterns. This paper characterises rainfall patterns during HPEs based on high-resolution weather radar data and evaluates the performance of a high-resolution, convection-permitting, Weather Research and Forecasting (WRF) model in simulating these patterns. We identified 41 HPEs in the eastern Mediterranean from a 24-year radar record using local thresholds based on quantiles for different durations, and we ran model simulations of these events. For most durations, HPEs near the coastline are characterised by the highest rain intensities, however, for short durations, the highest rain intensities characterise the inland desert. During the rainy season, the centre-of-mass of the rain field progresses from the sea inland. Rainfall during HPEs is highly localised both in space (

scholarly journals Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1727
Author(s):  
Valerio Capecchi ◽  
Andrea Antonini ◽  
Riccardo Benedetti ◽  
Luca Fibbi ◽  
Samantha Melani ◽  
...  
Keyword(s):  
Extreme Event ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Flash Floods ◽  
Precipitation Event ◽  
Weather Research And Forecasting ◽  
Limited Area ◽  
Heavy Precipitation Event ◽  
The Town

During the night between 9 and 10 September 2017, multiple flash floods associated with a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours was recorded. This rainfall intensity is associated with a return period of higher than 200 years. As a consequence, all the largest streams of the Livorno municipality flooded several areas of the town. We used the limited-area weather research and forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. A more accurate description of the low-level flows and a better assessment of the atmospheric water vapor field showed how the assimilation of radar data can improve quantitative precipitation forecasts.

scholarly journals Assimilating X- and S-band Radar Data for a Heavy Precipitation Event in Italy

2021 ◽  
Author(s):  
Valerio Capecchi ◽  
Andrea Antonini ◽  
Riccardo Benedetti ◽  
Luca Fibbi ◽  
Samantha Melani ◽  
...  
Keyword(s):  
Extreme Event ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Flash Floods ◽  
Precipitation Event ◽  
Weather Research And Forecasting ◽  
Limited Area ◽  
Heavy Precipitation Event ◽  
The Town

During the night between 9 and 10 September 2017, multiple flash floods associated to a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours, associated with a return period higher than 200 years, caused all the largest streams of the Livorno municipality to flood several areas of the town. We used the limited-area Weather Research and Forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. By providing more accurate descriptions of the low-level flow and a better assessment of the atmospheric water vapour, the results demonstrate that assimilating radar data improved the quantitative precipitation forecasts.

scholarly journals Precipitation Forecast Experiments Using the Weather Research and Forecasting (WRF) Model at Gray-Zone Resolutions

2018 ◽  
Vol 33 (6) ◽  
pp. 1605-1616 ◽  
Author(s):  
Ji-Young Han ◽  
Song-You Hong
Keyword(s):  
High Resolution ◽  
Model Simulation ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Grid Size ◽  
Cumulus Parameterization ◽  
Precipitation Forecast ◽  
Weather Research And Forecasting ◽  
Gray Zone ◽  
Weather Research

Abstract In the Weather Research and Forecasting (WRF) community, a standard model setup at a grid size smaller than 5 km excludes cumulus parameterization (CP), although it is unclear how to determine a cutoff grid size where convection permitting can be assumed adequate. Also, efforts to improve high-resolution precipitation forecasts in the range of 1–10 km (the so-called gray zone for parameterized precipitation physics) have recently been made. In this study, we attempt to statistically evaluate the skill of a gray-zone CP with a focus on the quantitative precipitation forecast (QPF) in the summertime. A WRF Model simulation with the gray-zone simplified Arakawa–Schubert (GSAS) CP at 3-km spatial resolution over East Asia is evaluated for the summer of 2013 and compared with the results from a conventional setup without CP. A statistical evaluation of the 3-month simulations shows that the GSAS demonstrates a typical distribution of the QPF skill, with high (low) scores and bias in the light (heavy) precipitation category. The WRF without CP seriously suppresses light precipitation events, but its skill for heavier categories is better. Meanwhile, a new set of precipitation data, which is simply averaged precipitation from the two simulations, demonstrates the best skill in all precipitation categories. Bearing in mind that high-resolution QPF requires essential challenges in model components, along with complexity in precipitating convection mechanisms over geographically different regions, this proposed method can serve as an alternative for improving the QPF for practical usage.

scholarly journals High-Resolution Solar Climate Atlas for Greece under Climate Change Using the Weather Research and Forecasting (WRF) Model

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 761 ◽  
Author(s):  
Theodoros Katopodis ◽  
Iason Markantonis ◽  
Nadia Politi ◽  
Diamando Vlachogiannis ◽  
Athanasios Sfetsos
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Energy Demand ◽  
General Circulation ◽  
Wrf Model ◽  
General Circulation Models ◽  
Weather Research And Forecasting ◽  
Clear Sky ◽  
The Future ◽  
Weather Research

In the context of climate change and growing energy demand, solar technologies are considered promising solutions to mitigate Greenhouse Gas (GHG) emissions and support sustainable adaptation. In Greece, solar power is the second major renewable energy, constituting an increasingly important component of the future low-carbon energy portfolio. In this work, we propose the use of a high-resolution regional climate model (Weather Research and Forecasting model, WRF) to generate a solar climate atlas for the near-term climatological future under the Representative Concentration Pathway (RCPs) 4.5 and 8.5 scenarios. The model is set up with a 5 × 5 km2 spatial resolution, forced by the ERA-INTERIM for the historic (1980–2004) period and by the EC-EARTH General Circulation Models (GCM) for the future (2020–2044). Results reaffirm the high quality of solar energy potential in Greece and highlight the ability of the WRF model to produce a highly reliable future climate solar atlas. Projected changes between the annual historic and future RCPs scenarios indicate changes of the annual Global Horizontal Irradiance (GHI) in the range of ±5.0%. Seasonal analysis of the GHI values indicates percentage changes in the range of ±12% for both scenarios, with winter exhibiting the highest seasonal increases in the order of 10%, and autumn the largest decreases. Clear-sky fraction fclear projects increases in the range of ±4.0% in eastern and north continental Greece in the future, while most of the Greek marine areas might expect above 220 clear-sky days per year.

A Comparison of Scan Speedup Strategies and Their Effect on Rapid-Scan Weather Radar Data Quality

2020 ◽  
Vol 37 (11) ◽  
pp. 1955-1972
Author(s):  
Andrew Mahre ◽  
Tian-You Yu ◽  
David J. Bodine
Keyword(s):  
High Resolution ◽  
Data Quality ◽  
Wrf Model ◽  
Weather Radar ◽  
Radar Data ◽  
Radar Imaging ◽  
Strong Argument ◽  
Scan Time ◽  
Replacement System ◽  
Scanning Strategies

AbstractAs the existing NEXRAD network nears the end of its life cycle, intense study and planning are underway to design a viable replacement system. Ideally, such a system would offer improved temporal resolution compared to NEXRAD, without a loss in data quality. In this study, scan speedup techniques—such as beam multiplexing (BMX) and radar imaging—are tested to assess their viability in obtaining high-quality rapid updates for a simulated long-range weather radar. The results of this study—which uses a Weather Research and Forecasting (WRF) Model–simulated supercell case—show that BMX generally improves data quality for a given scan time or can provide a speedup factor of 1.69–2.85 compared to NEXRAD while maintaining the same level of data quality. Additionally, radar imaging is shown to improve data quality and/or decrease scan time when selectively used; however, deleterious effects are observed when imaging is used in regions with sharp reflectivity gradients parallel to the beam spoiling direction. Consideration must be given to the subsequent loss of sensitivity and beam broadening. Finally, imaging is shown to have an effect on the radar-derived mesocyclone strength (ΔV) of a simulated supercell. Because BMX and radar imaging are most easily achieved with an all-digital phased array radar (PAR), these results make a strong argument for the use of all-digital PAR for high-resolution weather observations. It is believed that the results from this study can inform decisions about possible scanning strategies and design of a NEXRAD replacement system for high-resolution weather radar data.

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