scholarly journals Implementation of WRF-Hydro at two drainage basins in the region of Attica, Greece

2020 ◽  
Author(s):  
Elissavet Galanaki ◽  
Konstantinos Lagouvardos ◽  
Vassiliki Kotroni ◽  
Theodore Giannaros ◽  
Christos Giannaros
Keyword(s):  
Wrf Model ◽  
Calibration Method ◽  
Flood Forecasting ◽  
Precipitation Forecast ◽  
Drainage Basins ◽  
In Situ Data ◽  
Model Configuration ◽  
Simulated Precipitation ◽  
Terrestrial Hydrology ◽  
Fine Resolution

Abstract. An integrated modeling approach for simulating flood events is presented in the current study. An advanced flood forecasting model, which is based on the coupling of hydrological and atmospheric components, was used for a twofold objective: first to investigate the potential of a coupled hydrometeorological model to be used for flood forecasting at two drainage basins in the area of Attica (Greece) and second to investigate the influence of the use of the coupled hydrometeorological model on the improvement of the precipitation forecast skill. For this reason, we used precipitation and hydrometric in-situ data for 7 events at two selected drainage regions of Attica. The simulations were carried out with WRF-Hydro model, which is an enhanced version of the Weather Research and Forecasting (WRF) model complemented with the feedback of terrestrial hydrology on the atmosphere, where surface and subsurface runoff were computed at a fine resolution grid of 95 m. Results showed that WRF-Hydro is capable to produce the observed discharge after the adequate calibration method at the studied basins. Besides, the WRF-Hydro has the tendency to slightly improve the simulated precipitation in comparison to the simulated precipitation produced the atmospheric only version of the model. These outcomes provide confidence that the model configuration is robust and, thus, can be used for flood research and operational forecasting purposes in the area of Attica.

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.

scholarly journals A micro-genetic algorithm for combinatorial optimization of physics parameterizations in Weather Research and Forecasting model for quantitative precipitation forecast in Korea

2021 ◽  
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 biggest uncertainties in Numerical Weather Predictions (NWPs) comes from treating the subgrid-scale physical processes. For the more accurate regional weather/climate prediction by improving physics parameterizations, it is important to optimize a combination of physics schemes as well as unknown parameters in NWP models. We have developed an interface system between 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 non-linear 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 has demonstrated the combinatorial optimization of physics schemes in the WRF model is one of possible solutions to enhance the forecast skill of precipitation.

Simulation of karst floods with a hydrological model improved by meteorological model coupling

2021 ◽  
Keyword(s):  
Flood Control ◽  
Underground Structure ◽  
Wrf Model ◽  
Precipitation Forecast ◽  
Karst Area ◽  
Peak Time ◽  
Flood Prediction ◽  
Meteorological Model ◽  
Flood Peak ◽  
Flood Warning

Abstract Karst basins are prone to rapid flooding because of their geomorphic complexity and exposed karst landforms with low infiltration rates. Accordingly, simulating and forecasting floods in karst regions can provide important technical support for local flood control. The study area, the Liujiang karst river basin, is the most well-developed karst area in South China, and its many mountainous areas lack rainfall gauges, limiting the availability of precipitation information. Quantitative precipitation forecast (QPF) from the Weather Research and Forecasting model (WRF) and quantitative precipitation estimation (QPE) from remote sensing information by an artificial neural network cloud classification system (PERSIANN-CCS) can offer reliable precipitation estimates. Here, the distributed Karst-Liuxihe (KL) model was successfully developed from the terrestrial Liuxihe model, as reflected in improvements to its underground structure and confluence algorithm. Compared with other karst distributed models, the KL model has a relatively simple structure and small modeling data requirements, which are advantageous for flood prediction in karst areas lacking hydrogeological data. Our flood process simulation results suggested that the KL model agrees well with observations and outperforms the Liuxihe model. The average Nash coefficient, correlation coefficient, and water balance coefficient increased by 0.24, 0.19, and 0.20, respectively, and the average flood process error, flood peak error, and peak time error decreased by 13%, 11%, and 2 hours, respectively. Coupling the WRF model and PERSIANN-CCS with the KL model yielded a good performance in karst flood simulation and prediction. Notably, coupling the WRF and KL models effectively predicted the karst flood processes and provided flood prediction results with a lead time of 96 hours, which is important for flood warning and control.

scholarly journals Assimilation of Lightning Data Using a Nudging Method Involving Low-Level Warming

2014 ◽  
Vol 142 (12) ◽  
pp. 4850-4871 ◽  
Author(s):  
Max R. Marchand ◽  
Henry E. Fuelberg
Keyword(s):  
Numerical Models ◽  
Deep Convection ◽  
Computational Cost ◽  
Wrf Model ◽  
Stage Iv ◽  
Simulated Precipitation ◽  
Synoptic Forcing ◽  
Total Lightning ◽  
Mu Method ◽  
Short Time

Abstract This study presents a new method for assimilating lightning data into numerical models that is suitable at convection-permitting scales. The authors utilized data from the Earth Networks Total Lightning Network at 9-km grid spacing to mimic the resolution of the Geostationary Lightning Mapper (GLM) that will be on the Geostationary Operational Environmental Satellite-R (GOES-R). The assimilation procedure utilizes the numerical Weather Research and Forecasting (WRF) Model. The method (denoted MU) warms the most unstable low levels of the atmosphere at locations where lightning was observed but deep convection was not simulated based on the absence of graupel. Simulation results are compared with those from a control simulation and a simulation employing the lightning assimilation method developed by Fierro et al. (denoted FO) that increases water vapor according to a nudging function that depends on the observed flash rate and simulated graupel mixing ratio. Results are presented for three severe storm days during 2011 and compared with hourly NCEP stage-IV precipitation observations. Compared to control simulations, both the MU and FO assimilation methods produce improved simulated precipitation fields during the assimilation period and a short time afterward based on subjective comparisons and objective statistical scores (~0.1, or 50%, improvement of equitable threat scores). The MU generally performs better at simulating isolated thunderstorms and other weakly forced deep convection, while FO performs better for the case having strong synoptic forcing. Results show that the newly developed MU method is a viable alternative to the FO method, exhibiting utility in producing thunderstorms where observed, and providing improved analyses at low computational cost.

scholarly journals Effects of Dynamic Crop Growth on the Simulated Precipitation Response to Irrigation*

2015 ◽  
Vol 19 (14) ◽  
pp. 1-31 ◽  
Author(s):  
Keith J. Harding ◽  
Tracy E. Twine ◽  
Yaqiong Lu
Keyword(s):  
Environmental Variability ◽  
Wrf Model ◽  
Crop Growth ◽  
Climate Impacts ◽  
Rapid Expansion ◽  
Area Index ◽  
Community Land Model ◽  
Precipitation Response ◽  
Simulated Precipitation ◽  
Dynamic Crop Growth

Abstract The rapid expansion of irrigation since the 1950s has significantly depleted the Ogallala Aquifer. This study examines the warm-season climate impacts of irrigation over the Ogallala using high-resolution (6.33 km) simulations of a version of the Weather Research and Forecasting (WRF) Model that has been coupled to the Community Land Model with dynamic crop growth (WRF-CLM4crop). To examine how dynamic crops influence the simulated impact of irrigation, the authors compare simulations with dynamic crops to simulations with a fixed annual cycle of crop leaf area index (static crops). For each crop scheme, simulations were completed with and without irrigation for 9 years that represent the range of observed precipitation. Reduced temperature and precipitation biases occur with dynamic versus static crops. Fundamental differences in the precipitation response to irrigation occur with dynamic crops, as enhanced surface roughness weakens low-level winds, enabling more water from irrigation to remain over the region. Greater simulated rainfall increases (12.42 mm) occur with dynamic crops compared to static crops (9.08 mm), with the greatest differences during drought years (+20.1 vs +5.9 mm). Water use for irrigation significantly impacts precipitation with dynamic crops (R2 = 0.29), but no relationship exists with static crops. Dynamic crop growth has the largest effect on the simulated impact of irrigation on precipitation during drought years, with little impact during nondrought years, highlighting the need to simulate the dynamic response of crops to environmental variability within Earth system models to improve prediction of the agroecosystem response to variations in climate.

Operational Precipitation Forecast Over China Using the Weather Research and Forecasting (WRF) Model at a Gray-Zone Resolution: Impact of Convection Parameterization

2021 ◽  
Author(s):  
XU ZHANG ◽  
YUHUA YANG ◽  
BAODE CHEN ◽  
WEI HUANG
Keyword(s):  
Case Studies ◽  
Southern China ◽  
Wrf Model ◽  
Warm Season ◽  
Precipitation Forecast ◽  
Weather Research And Forecasting ◽  
Operational System ◽  
Quantitative Precipitation Forecast ◽  
Geographical Regions ◽  
Convection Parameterization

AbstractThe quantitative precipitation forecast in the 9 km operational modeling system (without the use of a convection parameterization scheme) at the Shanghai Meteorological Service (SMS) usually suffers from excessive precipitation at the grid scale and less-structured precipitation patterns. Two scale-aware convection parameterizations were tested in the operational system to mitigate these deficiencies. Their impacts on the warm-season precipitation forecast over China were analyzed in case studies and two-month retrospective forecasts. The results from case studies show that the importance of convection parameterization depends on geographical regions and weather regimes. Considering a proper magnitude of parameterized convection can produce more realistic precipitation distribution and reduce excessive grid-scale precipitation in southern China. In the northeast and southwest China, however, the convection parameterization plays an insignificant role in precipitation forecast because of strong synoptic-scale forcing. A statistical evaluation of the two-month retrospective forecasts indicates that the forecast skill for precipitation in the 9-km operational system is improved by choosing proper convection parameterization. This study suggests that improvement in contemporary convection parameterizations is needed for their usage for various meteorological conditions and reasonable partitioning between parameterized and resolved convection.

scholarly journals Expanding the Goddard CSH Algorithm for GPM: New Extratropical Retrievals

2019 ◽  
Vol 58 (5) ◽  
pp. 921-946 ◽  
Author(s):  
W.-K. Tao ◽  
T. Iguchi ◽  
S. Lang
Keyword(s):  
High Latitude ◽  
Wrf Model ◽  
Tropical Rainfall Measuring Mission ◽  
Consistency Check ◽  
New Classification ◽  
Surface Precipitation ◽  
Winter Conditions ◽  
Simulated Precipitation ◽  
Combined Algorithm ◽  
Self Consistency

AbstractThe Goddard convective–stratiform heating (CSH) algorithm has been used to retrieve latent heating (LH) associated with clouds and cloud systems in support of the Tropical Rainfall Measuring Mission and Global Precipitation Measurement (GPM) mission. The CSH algorithm requires the use of a cloud-resolving model to simulate LH profiles to build lookup tables (LUTs). However, the current LUTs in the CSH algorithm are not suitable for retrieving LH profiles at high latitudes or winter conditions that are needed for GPM. The NASA Unified-Weather Research and Forecasting (NU-WRF) Model is used to simulate three eastern continental U.S. (CONUS) synoptic winter and three western coastal/offshore events. The relationship between LH structures (or profiles) and other precipitation properties (radar reflectivity, freezing-level height, echo-top height, maximum dBZ height, vertical dBZ gradient, and surface precipitation rate) is examined, and a new classification system is adopted with varying ranges for each of these precipitation properties to create LUTs representing high latitude/winter conditions. The performance of the new LUTs is examined using a self-consistency check for one CONUS and one West Coast offshore event by comparing LH profiles retrieved from the LUTs using model-simulated precipitation properties with those originally simulated by the model. The results of the self-consistency check validate the new classification and LUTs. The new LUTs provide the foundation for high-latitude retrievals that can then be merged with those from the tropical CSH algorithm to retrieve LH profiles over the entire GPM domain using precipitation properties retrieved from the GPM combined algorithm.

scholarly journals Assessment of a Regional-Scale Weather Model for Hydrological Applications in South Korea

2016 ◽  
Vol 6 (2) ◽  
pp. 28
Author(s):  
Yong Jung ◽  
Yuh-Lang Lin
Keyword(s):  
Weather Prediction ◽  
Regional Scale ◽  
Wrf Model ◽  
Domain Size ◽  
Cumulus Parameterization ◽  
Precipitation Forecast ◽  
Mountainous Region ◽  
Parameterization Schemes ◽  
Plain Region ◽  
Cumulus Parameterization Schemes

<p class="1Body">In this study, a regional numerical weather prediction (NWP) model known as the Weather Research Forescasting (WRF) model was adopted to improve the quantitative precipitation forecasts (QPF) by optimizing combined microphysics and cumulus parameterization schemes. Four locations in two regions (plain region for Sangkeug and Imsil; mountainous region for Dongchun and Bunchun) in Korean Peninsula were examined for QPF for two heavy rainfall events 2006 and 2008. The maximum Index of Agreement (IOA) was 0.96 at Bunchun in 2006 using the combined Thompson microphysics and the Grell cumulus parameterization schemes. Sensitivity of QPF on domain size at Sangkeug indicated that the localized smaller domain had 55% (from 0.35 to 0.90) improved precipitation accuracy based on IOA of 2008. For the July 2006 Sangkeug event, the sensitivity to cumulus parameterization schemes for precipitation prediction cannot be ignored with finer resolutions. In mountainous region, the combined Thompson microphysics and Grell cumulus parameterization schemes make a better quantitative precipitation forecast, while in plain region, the combined Thompson microphysics and Kain-Frisch cumulus parameterization schemes are the best.</p>

scholarly journals Effects of Increasing Horizontal Resolution in a Convection-Permitting Model on Flood Forecasting: The 2011 Dramatic Events in Liguria, Italy

2015 ◽  
Vol 16 (4) ◽  
pp. 1843-1856 ◽  
Author(s):  
Silvio Davolio ◽  
Francesco Silvestro ◽  
Piero Malguzzi
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Flood Forecasting ◽  
Horizontal Resolution ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Flood Prediction ◽  
Meteorological Model ◽  
The Impact ◽  
Atmospheric Simulations

Abstract Coupling meteorological and hydrological models is a common and standard practice in the field of flood forecasting. In this study, a numerical weather prediction (NWP) chain based on the BOLogna Limited Area Model (BOLAM) and the MOdello LOCale in Hybrid coordinates (MOLOCH) was coupled with the operational hydrological forecasting chain of the Ligurian Hydro-Meteorological Functional Centre to simulate two major floods that occurred during autumn 2011 in northern Italy. Different atmospheric simulations were performed by varying the grid spacing (between 1.0 and 3.0 km) of the high-resolution meteorological model and the set of initial/boundary conditions driving the NWP chain. The aim was to investigate the impact of these parameters not only from a meteorological perspective, but also in terms of discharge predictions for the two flood events. The operational flood forecasting system was thus used as a tool to validate in a more pragmatic sense the quantitative precipitation forecast obtained from different configurations of the NWP system. The results showed an improvement in flood prediction when a high-resolution grid was employed for atmospheric simulations. In turn, a better description of the evolution of the precipitating convective systems was beneficial for the hydrological prediction. Although the simulations underestimated the severity of both floods, the higher-resolution model chain would have provided useful information to the decision-makers in charge of protecting citizens.

scholarly journals Comparison of Simulated Precipitation over East Asia in Two Regional Models with Hydrostatic and Nonhydrostatic Dynamical Cores

2016 ◽  
Vol 144 (10) ◽  
pp. 3579-3590 ◽  
Author(s):  
Jihyeon Jang ◽  
Song-You Hong
Keyword(s):  
Wrf Model ◽  
Local Maximum ◽  
Weather Research And Forecasting ◽  
Model Program ◽  
Grid Spacing ◽  
Dynamical Core ◽  
Simulated Precipitation ◽  
Precipitation Area ◽  
Hydrostatic Limit ◽  
Regional Forecasts

This study examines the characteristics of a nonhydrostatic dynamical core compared to a corresponding hydrostatic dynamical core in the Regional Model Program (RMP) of the Global/Regional Integrated Model system (GRIMs), a spectral model for regional forecasts, focusing on simulated precipitation over Korea. This kind of comparison is also executed in the Weather Research and Forecasting (WRF) finite-difference model with the same physics package used in the RMP. Overall, it is found that the nonhydrostatic dynamical core experiment accurately reproduces the heavy rainfall near Seoul, South Korea, on a 3-km grid, relative to the results from the hydrostatic dynamical core in both models. However, the characteristics of nonhydrostatic effects on the simulated precipitation differ between the RMP and WRF Model. The RMP with the nonhydrostatic dynamical core improves the local maximum, which is exaggerated in the hydrostatic simulation. The hydrostatic simulation of the WRF Model displaces the major precipitation area toward the mountainous region along the east coast of the peninsula, which is shifted into the observed area in the nonhydrostatic simulation. In the simulation of a summer monsoonal rainfall, these nonhydrostatic effects are negligible in the RMP, but the simulated monsoonal rainfall is still influenced by the dynamical core in the WRF Model even at a 27-km grid spacing. One of the reasons for the smaller dynamical core effect in the RMP seems to be the relatively strong horizontal diffusion, resulting in a smaller grid size of the hydrostatic limit.

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