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.

scholarly journals Combinational Optimization of the WRF Physical Parameterization Schemes to Improve Numerical Sea Breeze Prediction Using Micro-Genetic Algorithm

2021 ◽  
Vol 11 (23) ◽  
pp. 11221
Author(s):  
Ji Won Yoon ◽  
Sujeong Lim ◽  
Seon Ki Park
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Land Surface ◽  
Wrf Model ◽  
Longwave Radiation ◽  
Sea Breeze ◽  
Meteorological Variables ◽  
Parameterization Schemes ◽  
Micro Genetic Algorithm ◽  
Physical Parameterization

This study aims to improve the performance of the Weather Research and Forecasting (WRF) model in the sea breeze circulation using the micro-Genetic Algorithm (micro-GA). We found the optimal combination of four physical parameterization schemes related to the sea breeze system, including planetary boundary layer (PBL), land surface, shortwave radiation, and longwave radiation, in the WRF model coupled with the micro-GA (WRF-μGA system). The optimization was performed with respect to surface meteorological variables (2 m temperature, 2 m relative humidity, 10 m wind speed and direction) and a vertical wind profile (wind speed and direction), simultaneously for three sea breeze cases over the northeastern coast of South Korea. The optimized set of parameterization schemes out of the WRF-μGA system includes the Mellor–Yamada–Nakanishi–Niino level-2.5 (MYNN2) for PBL, the Noah land surface model with multiple parameterization options (Noah-MP) for land surface, and the Rapid Radiative Transfer Model for GCMs (RRTMG) for both shortwave and longwave radiation. The optimized set compared with the various other sets of parameterization schemes for the sea breeze circulations showed up to 29 % for the improvement ratio in terms of the normalized RMSE considering all meteorological variables.

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 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 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 The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

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