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 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.

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.

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.

Enhancing WRF Model Forecasts by Assimilating High-Resolution GPS-Derived Water-Vapor Maps combined with METEOSAT-11 Data 

2021 ◽  
Author(s):  
Yuval Reuveni ◽  
Anton Leontiev ◽  
Dorita Rostkier-Edelstein
Keyword(s):  
Water Vapor ◽  
High Resolution ◽  
Numerical Models ◽  
Eastern Mediterranean ◽  
Wrf Model ◽  
In Situ Measurements ◽  
Suggested Approach ◽  
Positioning Systems ◽  
The Mediterranean

<p>Improving the accuracy of numerical weather predictions still poses a challenging task. The lack of sufficiently detailed spatio-temporal real-time in-situ measurements constitutes a crucial gap concerning the adequate representation of atmospheric moisture fields, such as water vapor, which are critical for improving weather predictions accuracy. Information on total vertically integrated water vapor (IWV), extracted from global positioning systems (GPS) tropospheric path delays, can enhance various atmospheric models at global, regional, and local scales. Currently, numerous existing atmospheric numerical models predict IWV. Nevertheless, they do not provide accurate estimations compared with in-situ measurements such as radiosondes. In this work, we demonstrate a novel approach for assimilating 2D IWV regional maps estimations, extracted from GPS tropospheric path delays combined with METEOSAT satellite imagery data, to enhance Weather Research and Forecast (WRF) model predictions accuracy above the Eastern Mediterranean area. Unlike previous studies, which assimilated IWV point measurements, here, we assimilate quasi-continuous 2D GPS IWV maps, augmented by METEOSAT-11 data, over Israel and its surroundings. Using the suggested approach, our results show a decrease of more than 30% in the root mean square error (RMSE) of WRF forecasts after assimilation relative to the standalone WRF when verified against in-situ radiosonde measurements near the Mediterranean coast. Furthermore, substantial improvements along the Jordan Rift Valley and Dead Sea Valley areas are achieved when compared to 2D IWV regional maps. Improvements in these areas suggest the importance of the assimilated high resolution IWV maps, in particular when assimilation and initialization times coincide with the Mediterranean Sea Breeze propagation from the coastline to highland stations.</p>

Statistical postprocessing of heavy precipitation

2020 ◽  
Author(s):  
Reinhold Hess ◽  
Peter Schaumann ◽  
Volker Schmidt
Keyword(s):  
Point Processes ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Rain Gauge ◽  
Observation System ◽  
Small Areas ◽  
Rain Gauges ◽  
Long Time ◽  
Synoptic Observations ◽  
Rain Rates

<p>Heavy precipitation rates of more than 15 mm per hour are captured only about once a year at each rain gauge within Germany. More extreme events are even less frequent. Point by point verifications show that forecasts of heavy precipitation of the ensemble system COSMO-D2-EPS of DWD can be improved by statistical postprocessing. This is done in a MOS approach using long time series of synoptic observations and numerical forecasts that are required in or­der to gather a significant number of heavy precipitation events for reliable statistical model­ling.</p><p>Highest precipitation rates of convective events usually realise more likely in the surrounding of rain gauges rather than exactly above their small funnels. Statistical forecasts modelling these point observations usually underestimate maximal rain rates and result in low probabili­ties for the occurrence of heavy precipitation at a given location.</p><p>Point processes of stochastic geometry can be used to model area probabilities that provide the probability that precipitation occurs anywhere (at least at one point) within that area. Verifications with gauge adjusted radar data reveal that point probabilities are representative for very small areas, but area probabilities are significantly larger already for areas of 20*20 km<sup>2</sup>.</p><p>The use of radar data as area observation system allows to statistically generate calibrated precipitation forecasts for arbitrary areas. However, the question remains, which size of area is most relevant for the public and most suitable for weather warnings.</p>

scholarly journals An Evaluation of QPF from the WRF, NAM, and GFS Models Using Multiple Verification Methods over a Small Domain

2016 ◽  
Vol 31 (4) ◽  
pp. 1363-1379 ◽  
Author(s):  
Haifan Yan ◽  
William A. Gallus
Keyword(s):  
High Resolution ◽  
Wrf Model ◽  
Radar Data ◽  
Early Morning ◽  
Small Scale ◽  
Time Interval ◽  
Verification Methods ◽  
Small Domain ◽  
Arw Model ◽  
Neighborhood Scale

Abstract The ARW model was run over a small domain centered on Iowa for 9 months with 4-km grid spacing to better understand the limits of predictability of short-term (12 h) quantitative precipitation forecasts (QPFs) that might be used in hydrology models. Radar data assimilation was performed to reduce spinup problems. Three grid-to-grid verification methods, as well as two spatial techniques, neighborhood and object based, were used to compare the QPFs from the high-resolution runs with coarser operational GFS and NAM QPFs to verify QPFs for various precipitation accumulation intervals and on two grid configurations with different resolutions. In general, NAM had the worst performance not only for model skill but also for spatial feature attributes as a result of the existence of large dry bias and location errors. The finer resolution of NAM did not offer any advantage in predicting small-scale storms compared to the coarser GFS. WRF had a large advantage for high precipitation thresholds. A greater improvement in skill was noted when the accumulation time interval was increased, compared to an increase in the spatial neighborhood size. At the same neighborhood scale, the high-resolution WRF Model was less influenced by the grid on which the verification was done than the other two models. All models had the highest skill from midnight to early morning, because the least wet bias, location, and coverage errors were present then. The lowest skill was shown from late morning through afternoon. The main cause of poor skill during this period was large displacement errors.

A Case Study of Radar-Derived Quantitative Precipitation Estimation

2013 ◽  
Vol 726-731 ◽  
pp. 4541-4546 ◽  
Author(s):  
Li Li Yang ◽  
Yi Yang ◽  
You Cun Qi ◽  
Xue Xing Qiu ◽  
Zhong Qiang Gong
Keyword(s):  
Doppler Radar ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Convective Precipitation ◽  
Precipitation Process ◽  
Fuzzy Logic Algorithm ◽  
Rain Gauges ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

The convective and stratiform precipitations have different precipitation mechanisms. Different reflectivityrainfall rate (ZR) relations should be used for them. A heavy precipitation process on 22nd July, 2009(UTC) in Anhui Province is analyzed with Hefei Doppler radar and 269 rain gauges. First, the type of precipitation is obtained by a fuzzy logic algorithm with radar data. Then the reflectivity values are converted to rainfall rates using an adaptive Z-R relation according to different rain types. It is tested with the case and showed significant improvements over the current operational Z-R QPE when compared with gauges. Results also show that the precipitation process is caused by stratiform and convective precipitation; the rain estimated from radar corresponds well with cloud classification.

Sign in / Sign up

Export Citation Format

Share Document