scholarly journals Numerical rainfall simulation with different spatial and temporal evenness by using WRF multi-physics ensembles

2016 ◽  
Author(s):  
Jiyang Tian ◽  
Jia Liu ◽  
Chuanzhe Li ◽  
Fuliang Yu
Keyword(s):  
Relative Error ◽  
Wrf Model ◽  
Temporal Distribution ◽  
Northern China ◽  
Rainfall Simulation ◽  
Average Relative Error ◽  
Storm Events ◽  
Cumulative Rainfall ◽  
Study Sites

Abstract. The Weather Research and Forecasting (WRF) model is used in this study to simulate six storm events in two semi-humid and semi-arid catchments of Northern China. The six storm events are classified into four types based on the rainfall evenness in the spatial and temporal dimensions. Two microphysics, two planetary boundary layers (PBL) and three cumulus parameterizations are combined to develop 12 physical ensembles for rainfall generation. The WRF model performs the best for Type 1 event with relatively even distributions of rainfall in both space and time. The average relative error (ARE) for the cumulative rainfall amount is 16.98 %. For the spatial rainfall simulation, the lowest root mean square error (RMSE) is found with event II (0.3989) which has the most even spatial distribution, and for the temporal simulation the lowest RMSE is found with event I (1.0171) which has the most even temporal distribution. It is found to be the most difficult to reproduce the very convective storm with uneven spatiotemporal distributions (Type 4 event) and the average relative error (ARE) for the cumulative rainfall amounts is up to 68.07 %. The RMSE results of Event III with the most uneven spatial and temporal distribution are 0.9363 for the spatial simulation and 2.7769 for the temporal simulation, which are much higher than the other storms. The general performance of the current WRF physical parameterisations is discussed. The Betts-Miller-Janjic (BMJ) is found to be unsuitable for rainfall simulation in the study sites. For Type 1, 2, and 4 storms, ensemble 4 performs the best. For Type 3 storms, ensemble 5 and 7 are the better choice. More guidance is provided for choosing among the physical parameterisations for accurate rainfall simulations of different storm types in the study area.

scholarly journals Numerical rainfall simulation with different spatial and temporal evenness by using a WRF multiphysics ensemble

2017 ◽  
Vol 17 (4) ◽  
pp. 563-579 ◽  
Author(s):  
Jiyang Tian ◽  
Jia Liu ◽  
Denghua Yan ◽  
Chuanzhe Li ◽  
Fuliang Yu
Keyword(s):  
Relative Error ◽  
Wrf Model ◽  
Temporal Distribution ◽  
Northern China ◽  
Rainfall Simulation ◽  
Average Relative Error ◽  
Storm Events ◽  
Cumulative Rainfall ◽  
Physical Parameterizations

Abstract. The Weather Research and Forecasting (WRF) model is used in this study to simulate six storm events in two semi-humid catchments of northern China. The six storm events are classified into four types based on the rainfall evenness in the spatial and temporal dimensions. Two microphysics, two planetary boundary layers (PBL) and three cumulus parameterizations are combined to develop an ensemble containing 16 members for rainfall generation. The WRF model performs the best for type 1 events with relatively even distributions of rainfall in both space and time. The average relative error (ARE) for the cumulative rainfall amount is 15.82 %. For the spatial rainfall simulation, the lowest root mean square error (RMSE) is found with event II (0.4007), which has the most even spatial distribution, and for the temporal simulation the lowest RMSE is found with event I (1.0218), which has the most even temporal distribution. The most difficult to reproduce are found to be the very convective storms with uneven spatiotemporal distributions (type 4 event), and the average relative error for the cumulative rainfall amounts is up to 66.37 %. The RMSE results of event III, with the most uneven spatial and temporal distribution, are 0.9688 for the spatial simulation and 2.5327 for the temporal simulation, which are much higher than the other storms. The general performance of the current WRF physical parameterizations is discussed. The Betts–Miller–Janjic (BMJ) scheme is found to be unsuitable for rainfall simulation in the study sites. For type 1, 2 and 4 storms, member 4 performs the best. For type 3 storms, members 5 and 7 are the better choice. More guidance is provided for choosing among the physical parameterizations for accurate rainfall simulations of different storm types in the study area.

scholarly journals A coupled atmospheric-hydrologic modeling system with variable grid sizes for rainfall-runoff simulation in semi-humid and semi-arid watersheds: How does the coupling scale affects the results?

2020 ◽  
Author(s):  
Jiyang Tian ◽  
Jia Liu ◽  
Yang Wang ◽  
Wei Wang ◽  
Chuanzhe Li ◽  
...  
Keyword(s):  
Hydrologic Modeling ◽  
Wrf Model ◽  
Northern China ◽  
Flood Forecasting ◽  
Storm Events ◽  
Rainfall Runoff ◽  
Infiltration Capacity ◽  
Runoff Simulation ◽  
Flood Simulation ◽  
Simulation Results

Abstract. The coupled atmospheric-hydrologic modeling system is an effective way in improving the accuracy of rainfall-runoff modeling and extending the lead time in real-time flood forecasting. The aim of this study is to explore the appropriate coupling scale of the coupled atmospheric-hydrologic modeling system, which is established by the Weather Research and Forecasting (WRF) model and the gridded Hebei model with three different sizes (1 × 1 km, 3 × 3 km and 9 × 9 km). The soil moisture storage capacity and infiltration capacity of different grids in the gridded Hebei model are obtained and dispersed using the topographic index. The lumped Hebei model is also used to establish the lumped atmospheric-hydrologic coupled system as a reference system. Four 24 h storm events occurring at two small and medium-scale sub-watersheds in northern China are selected as cases study. Contrastive analyses of the flood process simulations from the gridded and lumped systems are carried out. The results show that the flood simulation results may not always be improved with higher dimension precision and more complicated system, and the grid size selection has a great relationship with the rainfall evenness. For the storm events with uniform spatial distribution, the coupling scale has less impact on flood simulation results, and the lumped system also performs well. For the storm events with uneven spatiotemporal distribution, the corrected rainfall can improve the simulation results significantly, and higher resolution lead to better flood process simulation. The results can help to establish the appropriate coupled atmospheric-hydrologic modeling system to improve the flood forecasting accuracy.

scholarly journals The Realization of Extreme Tornadic Storm Events under Future Anthropogenic Climate Change

2016 ◽  
Vol 29 (14) ◽  
pp. 5251-5265 ◽  
Author(s):  
Robert J. Trapp ◽  
Kimberly A. Hoogewind
Keyword(s):  
Climate Change ◽  
Convective Available Potential Energy ◽  
Wrf Model ◽  
Real Data ◽  
Anthropogenic Climate Change ◽  
Basic Question ◽  
Storm Events ◽  
The Mean ◽  
Atmospheric State ◽  
Convection Initiation

Abstract This research seeks to answer the basic question of how current-day extreme tornadic storm events might be realized under future anthropogenic climate change. The pseudo global warming (PGW) methodology was adapted for this purpose. Three contributions to the CMIP5 archive were used to obtain the mean 3D atmospheric state simulated during May 1990–99 and May 2090–99. The climate change differences (or Δs) in temperature, relative humidity, pressure, and winds were added to NWP analyses of three high-end tornadic storm events, and this modified atmospheric state was then used for initial and boundary conditions for real-data WRF Model simulations of the events at high resolution. Comparison of an ensemble of these simulations with control simulations (CTRL) facilitated assessment of PGW effects. In contrast to the robust development of supercellular convection in each CTRL, the combined effects of increased convective inhibition (CIN) and decreased parcel lifting under PGW led to a failure of convection initiation in many of the experiments. Those experiments that had sufficient matching between the CIN and lifting tended to generate stronger convective updrafts than CTRL, although not in proportion to the projected higher levels of convective available potential energy (CAPE) under PGW. In addition, the experiments with enhanced updrafts also tended to have enhanced vertical rotation. In fact, such supercellular convection was even found in simulations that were driven with PGW-reduced environmental wind shear. Notably, the PGW modifications did not induce a change in the convective morphology in any of the PGW experiments with significant convective storminess.

scholarly journals Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors

Buildings ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 458
Author(s):  
Yanan Zhao ◽  
Zihan Zang ◽  
Weirong Zhang ◽  
Shen Wei ◽  
Yingli Xuan
Keyword(s):  
Temperature Distribution ◽  
Relative Error ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Mobile Sensors ◽  
Average Relative Error ◽  
Contribution Ratio ◽  
Mobile Sensor ◽  
Indoor Climate ◽  
Indoor Temperature

In practical building control, quickly obtaining detailed indoor temperature distribution is necessary for providing satisfying personal comfort and improving building energy efficiency. The aim of this study is to propose a fast prediction method for indoor temperature distribution without knowing the thermal boundary conditions in practical applications. In this method, the index of contribution ratio of indoor climate (CRI), which represents the independent contribution of each heat source to the temperature distribution, has been combined with the air temperature collected by one mobile sensor at the height of the working area. Based on a typical office model, the effectiveness of using mobile sensors was discussed, and the influence of its acquisition height and acquisition distance on the prediction accuracy was analyzed as well. The results showed that the proposed prediction method was effective. When the sensors fixed on the wall were used to predict the indoor temperature distribution, the maximum average relative error was 27.7%, whereas when the mobile sensor was used to replace the fixed sensors, the maximum average relative error was 4.8%. This indicates that using mobile sensors with flexible acquisition location can help promote both reliability and accuracy of temperature prediction. In the human activity area, data from a set of mobile sensors were used to predict the temperature distribution at four heights. The prediction accuracy was 2.1%, 2.1%, 2.3%, and 2.7%, respectively. However, the influence of acquisition distance of mobile sensors on prediction accuracy cannot be ignored. The distance should be large enough to disperse the distribution of the acquisition points. Due to the influence of airflow, some distance between the acquisition points and the room boundaries should be given.

scholarly journals Flash Flood and Extreme Rainfall Forecast through One-Way Coupling of WRF-SMAP Models: Natural Hazards in Rio de Janeiro State

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 834
Author(s):  
Priscila da Cunha Luz Barcellos ◽  
Marcio Cataldi
Keyword(s):  
Natural Hazards ◽  
Land Surface ◽  
Rio De Janeiro ◽  
Flash Flood ◽  
Weather Prediction ◽  
Wrf Model ◽  
Temporal Distribution ◽  
Atmospheric Conditions ◽  
Streamflow Forecast ◽  
Janeiro State

Flash floods and extreme rains are destructive phenomena and difficult to forecast. In 2011, the mountainous region of Rio de Janeiro state suffered one of the largest natural hazards in Brazil, affecting more than 300,000 people, leaving more than 900 dead. This article simulates this natural hazard through Quantitative Precipitation Forecasting (QPF) and streamflow forecast ensemble, using 18 combinations of parameterizations between cumulus, microphysics, surface layer, planetary boundary layer, land surface and lateral contour conditions of the Weather Research and Forecasting (WRF) Model, coupling to the Soil Moisture Accounting Procedure (SMAP) hydrological model, seeking to find the best set of parametrizations for the forecasting of extreme events in the region. The results showed rainfall and streamflow forecast were underestimated by the models, reaching an error of 57.4% to QPF and 24.6% error to streamflow, and part of these errors are related to the lack of skill of the atmospheric model in predicting the intensity and the spatial-temporal distribution of rainfall. These results bring to light the limitations of numerical weather prediction, possibly due to the lack of initiatives involving the adaptation of empirical constants, intrinsic in the parametrization models, to the specific atmospheric conditions of each region of the country.

scholarly journals MENYELESAIKAN VEHICLE ROUTING PROBLEM MENGGUNAKAN ALGORITMA FUZZY EVOLUSI

2018 ◽  
Vol 7 (3) ◽  
pp. 252
Author(s):  
I PUTU ARYA YOGA SUMADI ◽  
I PUTU EKA NILA KENCANA ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Evolutionary Algorithm ◽  
Vehicle Routing ◽  
Relative Error ◽  
Vehicle Routing Problem ◽  
Average Relative Error ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Optimal Value ◽  
Evolution Algorithm ◽  
Capacitated Vehicle

The purpose of this research is to know the performance of Fuzzy Evolutionary Algorithm in solving one type of Vehicle Routing Problem that is Capacitated Vehicle Routing Problem (CVRP). There are 8 different CVRP data to be solved. The performance of the algorithm can be determined by comparing the value obtained by AFE with the optimal value of the data. The result of this research is fuzzy evolution algorithm yields the best average relative error from all data for distance that is equal to 69,5855% and for minimum vehicle equal to 26%.

scholarly journals Bayesian network model for flood forecasting based on atmospheric ensemble forecasts

2019 ◽  
Vol 19 (11) ◽  
pp. 2513-2524 ◽  
Author(s):  
Leila Goodarzi ◽  
Mohammad E. Banihabib ◽  
Abbas Roozbahani ◽  
Jörg Dietrich
Keyword(s):  
Bayesian Network ◽  
Relative Error ◽  
Wrf Model ◽  
Cumulus Parameterization ◽  
Small Data ◽  
Ensemble Forecasts ◽  
Validation Data ◽  
Flood Peak ◽  
Absolute Relative Error ◽  
Cumulus Parameterization Schemes

Abstract. The purpose of this study is to propose the Bayesian network (BN) model to estimate flood peaks from atmospheric ensemble forecasts (AEFs). The Weather Research and Forecasting (WRF) model was used to simulate historic storms using five cumulus parameterization schemes. The BN model was trained to compute flood peak forecasts from AEFs and hydrological pre-conditions. The mean absolute relative error was calculated as 0.076 for validation data. An artificial neural network (ANN) was applied for the same problem but showed inferior performance with a mean absolute relative error of 0.39. It seems that BN is less sensitive to small data sets, thus it is more suited for flood peak forecasting than ANN.

scholarly journals On the Effect of Red Sea and Topography on Rainfall over Saudi Arabia: Case Study

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 669
Author(s):  
Al-Mutairi ◽  
Abdel Basset ◽  
Morsy ◽  
Abdeldym
Keyword(s):  
Saudi Arabia ◽  
Red Sea ◽  
Sea Water ◽  
Wrf Model ◽  
Temporal Distribution ◽  
Surface Fluxes ◽  
Sensitivity Experiment ◽  
Low Level ◽  
Spatio Temporal ◽  
The Impact

This paper aimed to investigate the impact of Red Sea topography and water on the development and rainfall of a case of cyclogenesis occurs over Saudi Arabia during the period 16–18 November 2015 using the Weather Research and Forecasting (WRF) model. The WRF Control Run (WRF-CR) experiment was performed with presence of actual topography and surface water of the Red Sea, while the other three sensitivity experiments were carried out without (i) Red Sea Topography (NRST), (ii) Red Sea Water (NRSW), and (iii) Red Sea Topography and Water (NRSTW). The simulated rainfall in the control experiment depicts in well agreement with Tropical Rainfall Measurement Mission (TRMM) rainfall estimates in terms of intensity as well as spatio-temporal distribution. Results demonstrate that rainfall intensity and spatio-temporal distribution significantly changes through each sensitivity experiment compared to the WRF-CR, where the significant variation was found in the NRST experiment. The absence of topography (NRST) leads to formation of strong convergence area over the middle of Red Sea which enhanced uplift motion that further strengthened the low-level jet over Red Sea and the surrounding regions, which enhanced the moisture and temperature gradient and created a conditionally unstable atmosphere that favored the development of the cyclonic system. The absence of Red Sea water (NRSW) changed rainfall spatial distribution and reduced its amount by about 30–40% due to affecting of the dynamics of the upward motion and moisture gradient, suggesting that surface fluxes play an important role in regulating the low-level moist air convergence prior to convection initiation and development.

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