scholarly journals Comprehensive Methodology for the Evaluation of High-Resolution WRF Multi-Physics Precipitation Simulations for Small, Topographically Complex Domains 

2021 ◽  
Author(s):  
Ioannis Sofokleous ◽  
Adriana Bruggeman ◽  
Silas Michaelides ◽  
Panos Hadjinicolaou ◽  
George Zittis ◽  
...  
Keyword(s):  
High Resolution ◽  
Evaluation Method ◽  
Dynamical Downscaling ◽  
Extreme Event ◽  
Eastern Mediterranean ◽  
Wrf Model ◽  
Monthly Precipitation ◽  
Complete Evaluation ◽  
Simulated Precipitation ◽  
Physics Parameterizations

<p> </p><p>A stepwise evaluation method and a comprehensive scoring approach are proposed and applied to select a model setup and physics parameterizations of the Weather Research and Forecasting (WRF) model for high-resolution precipitation simulations. The ERA5 reanalysis data were dynamically downscaled to 1-km resolution for the topographically complex domain of the eastern Mediterranean island of Cyprus. The performance of the simulations was examined for three domain configurations, two model initialization frequencies and 18 combinations of atmospheric physics parameterizations (members). Two continuous scores, i.e., Bias and Mean Absolute Error (MAE) and two categorical scores, i.e., the Pierce Skill Score (PSS) and a new Extreme Event Score (EES) were used for the evaluation. The EES combines hits and frequency bias and it was compared with other commonly used verification scores. A composite scaled score (CSS) was used to identify the five best performing members.</p><p>The EES was shown to be a complete evaluator of the simulation of extremes. The least errors in mean daily and monthly precipitation amounts and daily extremes were found for the domain configuration with the largest extent and three nested domains. A 5-day initialization frequency did not improve precipitation, relative to 30-day continuous simulations. The use of multiple and comprehensive evaluation measures for the assessment of WRF performance allowed a more complete evaluation of the different properties of simulated precipitation, such as daily and monthly volumes and daily extremes, for different dynamical downscaling options and model configurations. The scores obtained for the selected five members for a three-month simulation period ranged for BIAS from zero to -25%, for MAE around 2 mm, for PSS from 0.25 to 0.52 and for EES from 0.19 to 0.26. The CSS ranged from 0.56 to 0.83 for the same members. The proposed stepwise approach can be applied to select an efficient set of WRF multi-physics configurations that accounts for these properties of precipitation and that can be used as input for hydrologic applications.</p>

scholarly journals Comprehensive Methodology for the Evaluation of High-Resolution WRF Multiphysics Precipitation Simulations for Small, Topographically Complex Domains

2021 ◽  
Vol 22 (5) ◽  
pp. 1169-1186
Author(s):  
Ioannis Sofokleous ◽  
Adriana Bruggeman ◽  
Silas Michaelides ◽  
Panos Hadjinicolaou ◽  
George Zittis ◽  
...  
Keyword(s):  
High Resolution ◽  
Evaluation Method ◽  
Extreme Event ◽  
Eastern Mediterranean ◽  
Wrf Model ◽  
Reanalysis Data ◽  
Cumulus Parameterization ◽  
Monthly Precipitation ◽  
Physics Parameterizations ◽  
The Impact

ABSTRACTA stepwise evaluation method and a comprehensive scoring approach are proposed and implemented to select a model setup and physics parameterizations of the Weather Research and Forecasting (WRF) Model for high-resolution precipitation simulations. The ERA5 reanalysis data were dynamically downscaled to 1-km resolution for the topographically complex domain of the eastern Mediterranean island of Cyprus. The performance of the simulations was examined for three domain configurations, two model initialization approaches and 18 combinations of atmospheric physics parameterizations. Two continuous and two categorical scores were used for the evaluation. A new extreme event score, which combines hits and frequency bias, was introduced as a complementary evaluator of extremes. A composite scaled score was used to identify the overall best performing parameterizations. The least errors in mean daily and monthly precipitation amounts and daily extremes were found for the domain configuration with the largest extent and three nested domains. A 5-day initialization frequency did not improve precipitation, relative to 30-day continuous simulations. The parameterization type with the largest impact on precipitation was microphysics. The cumulus parameterization was also found to have an impact on the 1-km nested domain, despite that it was only activated in the coarser “parent” domains. Comparison of simulations with 12-, 4-, and 1-km resolution revealed the better skill of the model at 1 km. The impact of the various model configurations in the small-sized domain was different from the impact in larger model domains; this could be further explored for other atmospheric variables.

scholarly journals High-resolution ensemble precipitation simulations over a small domain with complex topography

2020 ◽  
Author(s):  
Ioannis Sofokleous ◽  
Adriana Bruggeman ◽  
Corrado Camera ◽  
George Zittis
Keyword(s):  
High Resolution ◽  
Dynamical Downscaling ◽  
Eastern Mediterranean ◽  
Added Value ◽  
Computational Time ◽  
Complex Topography ◽  
Catchment Scale ◽  
Simulated Precipitation ◽  
Small Domain ◽  
Model Setup

<p>The reconstruction of detailed past weather and climate conditions, such as precipitation, is an essential part of hydrometeorological impact studies. Although this can be achieved through dynamical downscaling of reanalysis datasets, different model setup options can result in significantly different simulated fields. To select an efficient ensemble of the WRF atmospheric model for the simulation of precipitation at high resolution, suitable for hydrological studies at catchment scale, a series of simulation experiments is performed. The model experiments center on Cyprus, in the Eastern Mediterranean, a small domain with an area of 225×145 km<sup>2</sup> with complex topography. The simulations are made for the hydrologic year 2011-2012. Initial and boundary conditions are provided by the ERA5 reanalysis dataset. A stepwise approach is followed for the evaluation of monthly simulations for an ensemble comprised of 18 combinations of various model physics parameterizations. In the first step, the model ensemble is evaluated for three domain setups with different extends and nested downscaling steps, i.e. 19·10<sup>5</sup> km<sup>2</sup> with 12-, 4- and 1-km grids (12-4-1), 19·10<sup>5</sup> km<sup>2</sup> with 6- and 1-km grids (6-1a) and 7.28 ·10<sup>5</sup> km<sup>2</sup> with 6- and 1-km grids (6-1b). The ensemble performance is then investigated for two initialization frequencies, 30 and 5 days, both with 6-hour spin-up. In the last step, the performance of the individual ensemble members is evaluated and the five best performing members are selected. A gridded precipitation dataset for the area over Cyprus is developed for the evaluation of the simulated precipitation. The statistical indicators used are bias, mean absolute error (MAE), Nash-Sutcliffe efficiency and Kling-Gupta efficiency. The four indicators are scaled and combined in a single composite metric score (CMS), ranging from 0 to 1.</p><p>The best overall performance was achieved with the 12-4-1 domain setup. This setup resulted in the lowest bias of accumulated precipitation of the 18-member ensemble, i.e. 1%, compared to 8% for 6-1a and 10% for 6-1b, for the wet month of January. The 12-4-1 setup was also found to add value, in terms of computational time, to the least computationally demanding 6-1b setup by reducing the monthly bias by 47 mm per 1000 cpu hours. The statistical metrics for the ensemble with 5-day initialization exhibited very small variation from the metrics for the monthly initialization, with less than 4% difference in the MAE of the accumulated precipitation. The added value of the 5-day initialization, relative to the monthly initialization, was found to be negative for all four metrics in January and for two of the metrics in May. Despite the variable performance of individual ensemble members in different months, the combined metric showed that the overall highest (lowest) ranked members, with a CMS value of 0.63 (0.43), were those using the Ferrier and WRF-Double-Moment-6<sup>th</sup>-class (WRF-Single-Moment-6<sup>th</sup>-class) microphysical schemes. The proposed stepwise evaluation approach allows the identification of a reduced number of ensemble members, out of the initial ensemble, with a model setup that can simulate precipitation at high resolution and under different atmospheric conditions.</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.

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>

scholarly journals A micro-genetic algorithm (GA v1.7.1a) for combinatorial optimization of physics parameterizations in the Weather Research and Forecasting model (v4.0.3) for quantitative precipitation forecast in Korea

2021 ◽  
Vol 14 (10) ◽  
pp. 6241-6255
Author(s):  
Sojung Park ◽  
Seon K. Park
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Wrf Model ◽  
Precipitation Forecast ◽  
Subgrid Scale ◽  
Unknown Parameters ◽  
Quantitative Precipitation Forecast ◽  
Simulated Precipitation ◽  
Micro Genetic Algorithm ◽  
Physics Parameterizations

Abstract. One of the biggest uncertainties in numerical weather predictions (NWPs) comes from treating the subgrid-scale physical processes. For more accurate regional weather and climate prediction by improving physics parameterizations, it is important to optimize a combination of physics schemes and unknown parameters in NWP models. We have developed an interface system between a micro-genetic algorithm (µ-GA) and the WRF model for the combinatorial optimization of cumulus (CU), microphysics (MP), and planetary boundary layer (PBL) schemes in terms of quantitative precipitation forecast for heavy rainfall events in Korea. The µ-GA successfully improved simulated precipitation despite the nonlinear relationship among the physics schemes. During the evolution process, MP schemes control grid-resolving-scale precipitation, while CU and PBL schemes determine subgrid-scale precipitation. This study demonstrates that the combinatorial optimization of physics schemes in the WRF model is one possible solution to enhance the forecast skill of precipitation.

Sensitivity of high-resolution precipitation to physics parameterization options in WRF over equatorial regions

2020 ◽  
Author(s):  
Martina Messmer ◽  
Santos J. González-Rojí ◽  
Christoph C. Raible ◽  
Thomas F. Stocker
Keyword(s):  
Boundary Layer ◽  
High Resolution ◽  
Planetary Boundary Layer ◽  
Large Scale ◽  
Wrf Model ◽  
Cumulus Parameterization ◽  
Wave Radiation ◽  
Strong Increase ◽  
Simulated Precipitation ◽  
The Impact

<p>Precipitation patterns and climate variability in East Africa and Western South America present high heterogeneity and complexity. This complexity is a result of large-scale and regional controls, such as surrounding oceans, lakes and topography. The combined effect of these controls has implications on precipitation and temperature, and hence, on water availability, biodiversity and ecosystem services. This study focuses on the impact of different physics parameterization in high-resolution experiments performed over equatorial regions with the Weather Research and Forecasting (WRF) model, and how these options affect the representation of precipitation in those regions.</p><p>As expected, weather and climate in equatorial regions are driven by physical processes different to those important in the mid-latitudes. Hence, it is necessary to test the parameterizations available in the WRF model. Several sensitivity simulations are performed over Kenya and Peru nesting the WRF model inside the state-of-the-art ERA5 reanalysis. A cascade of increasing grid resolutions is used in these simulations, reaching the spatial resolutions of 3 and 1 km in the innermost domains, and thus, convection permitting scales. Parameterization options of the planetary boundary layer (PBL), lake model, radiation, cumulus and microphysics schemes are changed, and their sensitivity to precipitation is tested. The year 2008 is simulated for each of the sensitivity simulations. This year is chosen as a good representative of precipitation dynamics and temperature, as it is neither abnormally wet or hot, nor dry or cold over Kenya and Peru. The simulated precipitation driven by the ERA5 reanalysis is compared against station data obtained from the WMO, and over Kenya additionally against observations from the Centre for Training and Integrated Research in ASAL Development (CETRAD).</p><p>Precipitation is strongly underestimated when adopting a typical parameterization setup for the mid-latitudes. However, results indicate that precipitation amounts and also patterns are substantially improved when changing the cumulus and PBL parameterisations. This strong increase in the simulated precipitation is obtained when using the Grell-Freitas ensemble, RRTM and the Yonsei University schemes for cumulus, long-wave radiation and planetary boundary layer, respectively. During some summer months, the accumulated precipitation is improved by up to 100 mm (80 %) compared to mid-latitudes configuration in several regions of the domains (near the Andes in Peru and over the flatlands in Kenya). Additionally, because the 1- and 2-way nesting options show a similar performance with respect to precipitation, the 1-way nesting option is preferred, as it does not overwrite the solutions in the parent domains. Hence, discontinuous solutions related to switching off the cumulus parameterization can be avoided.</p>

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 Effects of Meteorology Nudging in Regional Hydroclimatic Simulations of the Eastern Mediterranean

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 470 ◽  
Author(s):  
George Zittis ◽  
Adriana Bruggeman ◽  
Panos Hadjinicolaou ◽  
Corrado Camera ◽  
Jos Lelieveld
Keyword(s):  
Eastern Mediterranean ◽  
Hot Spot ◽  
Wrf Model ◽  
Weather Conditions ◽  
Specific Humidity ◽  
Spectral Nudging ◽  
Precipitation Characteristics ◽  
Simulated Precipitation ◽  
Computational Resources ◽  
Wrf Modeling

In this study, we investigated the effects of grid and spectral nudging in regional hydroclimatic simulations over the Eastern Mediterranean climate change hot-spot. We performed year-long simulations for the hydrological year October 2001–September 2002 using the Weather Research and Forecasting (WRF) model at 12-km resolution, driven by the ERA-Interim reanalyses. Six grid and three spectral nudging options were tested using a number of model configurations. Due to the large uncertainty of regional observations, we compared the model with various satellite- and station-based meteorological datasets. The effect of nudging was tested for mean weather conditions and precipitation characteristics and extremes. For certain parts of the study domain, WRF was found to reproduce both aspects of rainfall over the Eastern Mediterranean reasonably well. Our findings highlighted that, for the WRF modeling system, nudging is critical for the simulation of rainfall; however, the application of interior constraint methods was found to have different impacts on various locations and climatic regimes. For the hyperarid parts of the domain, nudging did not improve the simulation of precipitation amounts (about 20% additional drying was introduced), while it added much value for the wetter rainfall regimes of the Eastern Mediterranean (corrections of about 30%). Improvements in the simulated precipitation were mostly introduced by spectral nudging; however, this option required significant computational resources. For these ERA-Interim-driven simulations, grid nudging that involves specific humidity within the planetary boundary layer is not recommended for the simulation of precipitation since it introduces dry biases up to 75–80%.

scholarly journals High-Resolution Dynamical Downscaling of Seasonal Precipitation Forecasts for the Hanjiang Basin in China Using the Weather Research and Forecasting Model

2017 ◽  
Vol 56 (5) ◽  
pp. 1515-1536 ◽  
Author(s):  
Yuan Li ◽  
Guihua Lu ◽  
Zhiyong Wu ◽  
Hai He ◽  
Jian He
Keyword(s):  
Correlation Coefficient ◽  
Dynamical Downscaling ◽  
Daily Precipitation ◽  
Wrf Model ◽  
Water Diversion ◽  
Weather Research And Forecasting ◽  
Monthly Precipitation ◽  
Seasonal Precipitation ◽  
Daily Precipitation Data ◽  
Weather Research

AbstractManagement of water resources may benefit from seasonal precipitation forecasts, but for obtaining high enough resolution, dynamical downscaling is necessary. This study investigated the downscaling capability of the Weather Research and Forecasting (WRF) Model ARW, version 3.5, on seasonal precipitation forecasts for the Hanjiang basin in China during 2001–09, which was the water source of the middle route of the South-to-North Water Diversion Project (SNWDP). The WRF Model is forced by the National Centers for Environmental Prediction Operational Climate Forecast System, version 2 (CFSv2), and it performs at a high horizontal resolution of 10 km with four selected convection schemes. The National Oceanic and Atmospheric Administration’s Climate Prediction Center global daily precipitation data were employed to evaluate the WRF Model on multiple scales. On average, when large biases were removed, the WRF Model slightly outperformed the CFSv2 in all seasons, especially summer. In particular, the Kain–Fritsch convective scheme performed best in summer, whereas little difference was found in winter. The WRF Model showed similar results in monthly precipitation, but no time-dependent characteristics were observed for all months. The spatial anomaly correlation coefficient showed greater uncertainty than the bias and the temporal correlation coefficient. In addition, the performance of the WRF Model showed considerable regional variations. The upper basin always showed better agreement with observations than did the middle and lower parts of the basin. A comparison of the forecast and observed daily precipitation revealed that the WRF Model can provide more accurate extreme precipitation information than the CFSv2.

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