Sensitivity analysis of the physical parameterizations in the WRF model on the prediction accuracy of meteorological parameters

2021 ◽  
Author(s):  
Alexander V. Starchenko ◽  
Lubov I. Kizhner ◽  
Artem I. Svarovsky ◽  
Sergey A. Prokhanov
Keyword(s):  
Sensitivity Analysis ◽  
Prediction Accuracy ◽  
Meteorological Parameters ◽  
Wrf Model ◽  
Physical Parameterizations

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.

Sensitivity analysis of WRF model PBL schemes in simulating boundary-layer variables in southern Italy: An experimental campaign

2017 ◽  
Vol 192 ◽  
pp. 58-71 ◽  
Author(s):  
E. Avolio ◽  
S. Federico ◽  
M.M. Miglietta ◽  
T. Lo Feudo ◽  
C.R. Calidonna ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Sensitivity Analysis ◽  
Southern Italy ◽  
Wrf Model ◽  
Experimental Campaign

Burnup Sensitivity Analysis in a Fast Breeder Reactor—Part II: Prediction Accuracy of Burnup Characteristics

1985 ◽  
Vol 91 (1) ◽  
pp. 11-33 ◽  
Author(s):  
Takanobu Kamei ◽  
Tadashi Yoshida ◽  
Toshikazu Takeda ◽  
Takuya Umano
Keyword(s):  
Sensitivity Analysis ◽  
Prediction Accuracy ◽  
Fast Breeder Reactor

scholarly journals The Performance of Different Boundary-Layer Parameterisations in Meteorological Modelling in a Southwestern Coastal Area of the Iberian Peninsula

2012 ◽  
Vol 2012 ◽  
pp. 1-13
Author(s):  
M. A. Hernández-Ceballos ◽  
J. A. Adame ◽  
J. P. Bolivar ◽  
B. A. De la Morena
Keyword(s):  
Boundary Layer ◽  
Coastal Area ◽  
Meteorological Parameters ◽  
Wrf Model ◽  
Near Surface ◽  
Diurnal Cycles ◽  
Medium Range Forecast ◽  
Critical Richardson Number ◽  
Standard Configuration ◽  
Meteorological Modelling

The performance of four atmospheric boundary layer (ABL) schemes in reproducing the diurnal cycles of surface meteorological parameters as well as the ABL structure and depth over a coastal area of southwestern Iberia was assessed using the mesoscale meteorological Weather Research and Forecasting (WRF) model. The standard configuration of the medium-range forecast (MRF) and the Yonsei University (YSU) ABL schemes were employed. Modified versions of each, in which the values of the bulk critical Richardson number () and the coefficient of proportionality () were varied, were also used. The results were compared to meteorological measurements representative of SW-NW and NE synoptic flows. The WRF model in its basic configuration was found to yield satisfactory forecasting results for nearly all near-surface atmospheric variables. Modifications in and did not influence the simulation of surface meteorological parameters. Both parameterisations appeared to be optimal predictors of ABL structure, and all four ABL schemes tended to produce a cold ABL during both periods, although this ABL was drier in the SW-NW flow season and wetter in the NE flow season. Considering all the parameters analysed, the MRF ABL parameterisation with the lowest values of and coefficients tested (0.25 and 0.0, resp.) tends to show a realistic simulation.

scholarly journals Ensemble Sensitivity Analysis Applied to a Southern Plains Convective Event

2015 ◽  
Vol 143 (1) ◽  
pp. 230-249 ◽  
Author(s):  
Christopher N. Bednarczyk ◽  
Brian C. Ancell
Keyword(s):  
Sensitivity Analysis ◽  
High Resolution ◽  
Wrf Model ◽  
Region Of Interest ◽  
Thermodynamic Characteristics ◽  
Forecast Errors ◽  
Synoptic Scale ◽  
Southern Plains ◽  
Synoptic Features ◽  
Upper Level

Abstract Forecast sensitivity of an April 2012 severe convection event in northern Texas is investigated with a high-resolution Weather Research and Forecasting (WRF) Model–based ensemble Kalman filter (EnKF). Through ensemble sensitivity analysis (ESA), which relates a forecast metric to initial and early forecast errors by linear regression, features of the flow are revealed that reflect dynamical relationships with the forecast convection. Results indicate that ESA can be successfully applied to high-resolution forecasts of convection, and the most important features are related to the synoptic-scale flow such as positioning of an upper-level low and lower-level thermodynamic characteristics of air masses. Comparisons of the maximum and minimum convectively active members in the region of interest show that the fields generated by ESA are consistent with the actual evolution of the event: members with more eastward progression of the synoptic-scale system produced a stronger convection forecast. The forecast metric of interest is modified in several ways to further evaluate the strength of the results of the sensitivity analysis. Three different variables acting as convection proxies (reflectivity, vertical velocity, and precipitation) are tested along with changing the location of the forecast metric and its spatial size. These additional tests highlight the same synoptic features of the flow with the only major differences reflecting the importance of magnitude versus position of the convective forecast.

Initial Condition Sensitivity of Western Pacific Extratropical Transitions Determined Using Ensemble-Based Sensitivity Analysis

2009 ◽  
Vol 137 (10) ◽  
pp. 3388-3406 ◽  
Author(s):  
Ryan D. Torn ◽  
Gregory J. Hakim
Keyword(s):  
Sensitivity Analysis ◽  
Tropical Cyclone ◽  
Initial Conditions ◽  
Forecast Error ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Forecast Errors ◽  
Initial Condition ◽  
Ensemble Forecasts ◽  
The Relationship

Abstract An ensemble Kalman filter based on the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts for the extratropical transition (ET) events associated with Typhoons Tokage (2004) and Nabi (2005). Ensemble sensitivity analysis is then used to evaluate the relationship between forecast errors and initial condition errors at the onset of transition, and to objectively determine the observations having the largest impact on forecasts of these storms. Observations from rawinsondes, surface stations, aircraft, cloud winds, and cyclone best-track position are assimilated every 6 h for a period before, during, and after transition. Ensemble forecasts initialized at the onset of transition exhibit skill similar to the operational Global Forecast System (GFS) forecast and to a WRF forecast initialized from the GFS analysis. WRF ensemble forecasts of Tokage (Nabi) are characterized by relatively large (small) ensemble variance and greater (smaller) sensitivity to the initial conditions. In both cases, the 48-h forecast of cyclone minimum SLP and the RMS forecast error in SLP are most sensitive to the tropical cyclone position and to midlatitude troughs that interact with the tropical cyclone during ET. Diagnostic perturbations added to the initial conditions based on ensemble sensitivity reduce the error in the storm minimum SLP forecast by 50%. Observation impact calculations indicate that assimilating approximately 40 observations in regions of greatest initial condition sensitivity produces a large, statistically significant impact on the 48-h cyclone minimum SLP forecast. For the Tokage forecast, assimilating the single highest impact observation, an upper-tropospheric zonal wind observation from a Mongolian rawinsonde, yields 48-h forecast perturbations in excess of 10 hPa and 60 m in SLP and 500-hPa height, respectively.

scholarly journals Sensitivity Analysis of the High-Resolution WISE-WRF Model with the Use of Surface Roughness Length in Seoul Metropolitan Areas

Atmosphere ◽  
2016 ◽  
Vol 26 (1) ◽  
pp. 111-126 ◽  
Author(s):  
Joon-Bum Jee ◽  
Min Jang ◽  
Chaeyeon Yi ◽  
Il-Sung Zo ◽  
Bu-Yo Kim ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Surface Roughness ◽  
High Resolution ◽  
Roughness Length ◽  
Metropolitan Areas ◽  
Wrf Model ◽  
Surface Roughness Length

scholarly journals Sensitivity Analysis for Predicting Sub-Micron Aerosol Concentrations Based on Meteorological Parameters

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2876
Author(s):  
Martha A. Zaidan ◽  
Ola Surakhi ◽  
Pak Lun Fung ◽  
Tareq Hussein
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Meteorological Parameters ◽  
Quality Measurement ◽  
Air Pollutant ◽  
Feed Forward Neural Network ◽  
Modeling Framework ◽  
Concentration Data ◽  
Modeling Tools ◽  
Diurnal Cycles

Sub-micron aerosols are a vital air pollutant to be measured because they pose health effects. These particles are quantified as particle number concentration (PN). However, PN measurements are not always available in air quality measurement stations, leading to data scarcity. In order to compensate this, PN modeling needs to be developed. This paper presents a PN modeling framework using sensitivity analysis tested on a one year aerosol measurement campaign conducted in Amman, Jordan. The method prepares a set of different combinations of all measured meteorological parameters to be descriptors of PN concentration. In this case, we resort to artificial neural networks in the forms of a feed-forward neural network (FFNN) and a time-delay neural network (TDNN) as modeling tools, and then, we attempt to find the best descriptors using all these combinations as model inputs. The best modeling tools are FFNN for daily averaged data (with R 2 = 0.77 ) and TDNN for hourly averaged data (with R 2 = 0.66 ) where the best combinations of meteorological parameters are found to be temperature, relative humidity, pressure, and wind speed. As the models follow the patterns of diurnal cycles well, the results are considered to be satisfactory. When PN measurements are not directly available or there are massive missing PN concentration data, PN models can be used to estimate PN concentration using available measured meteorological parameters.

Sign in / Sign up

Export Citation Format

Share Document