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 Bayesian Network Model for Flood Forecasting Based on Atmospheric Ensemble Forecasts

2019 ◽  
Author(s):  
Leila Goodarzi ◽  
Mohammad Ebrahim Banihabib ◽  
Abbas Roozbahani ◽  
Jörg Dietrich
Keyword(s):  
Bayesian Network ◽  
Cumulus Parameterization ◽  
Small Data ◽  
Ensemble Forecasts ◽  
Validation Data ◽  
Data Set ◽  
Flood Peak ◽  
Absolute Relative Error ◽  
Cumulus Parameterization Schemes ◽  
Artificial Neural Network Ann

Abstract. The purpose of this study is to propose the Bayesian Network (BN) model to estimate flood peak from Atmospheric Ensemble Forecasts (AEFs). The Weather Research and Forecasting model was used to simulate historic storms using five cumulus parameterization schemes. The BN model was trained to forecast flood peak from AEFs. Mean Absolute Relative Error was calculated as 0.076 for validation data while it was calculated as 0.39 in artificial neural network (ANN) as a widely used model. It seems that BN is less sensitive to small data set, thus it is more suited for forecasting flood peak than ANN.

Ensemble-Based Sensitivity Analysis Applied to African Easterly Waves

2010 ◽  
Vol 25 (1) ◽  
pp. 61-78 ◽  
Author(s):  
Ryan D. Torn
Keyword(s):  
Sensitivity Analysis ◽  
Initial Conditions ◽  
Wrf Model ◽  
Cumulus Parameterization ◽  
Initial Condition ◽  
Ensemble Forecasts ◽  
Large Variability ◽  
Multidisciplinary Analysis ◽  
Meridional Winds ◽  
Cumulus Parameterization Schemes

Abstract An ensemble Kalman filter (EnKF) coupled to the Advanced Research version of the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts of a strong African easterly wave (AEW) during the African Monsoon Multidisciplinary Analysis field campaign. Ensemble sensitivity analysis is then used to evaluate the impacts of initial condition errors on AEW amplitude and position forecasts at two different initialization times. WRF forecasts initialized at 0000 UTC 8 September 2006, prior to the amplification of the AEW, are characterized by large variability in evolution as compared to forecasts initialized 48 h later when the AEW is within a denser observation network. Short-lead-time amplitude forecasts are most sensitive to the midtropospheric meridional winds, while at longer lead times, midtropospheric θe errors have equal or larger impacts. For AEW longitude forecasts, the largest sensitivities are associated with the θe downstream of the AEW and, to a lesser extent, the meridional winds. Ensemble predictions of how initial condition errors impact the AEW amplitude and position compare qualitatively well with perturbed integrations of the WRF model. Much of the precipitation associated with the AEW is generated by the Kain–Fritsch cumulus parameterization, thus the initial-condition sensitivities are also computed for ensemble forecasts that employ the Betts–Miller–Janjić and Grell cumulus parameterization schemes, and for a high-resolution nested domain with explicit convection, but with the same initial conditions. While the 12-h AEW amplitude forecast is characterized by consistent initial-condition sensitivity among the different schemes, there is greater variability among methods beyond 24 h. In contrast, the AEW longitude forecast is sensitive to the downstream thermodynamic profile with all cumulus schemes.

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 Seasonal Forecasting of Onset of Summer Rains over South Africa

2018 ◽  
Vol 57 (12) ◽  
pp. 2697-2711 ◽  
Author(s):  
J. V. Ratnam ◽  
Takeshi Doi ◽  
Willem A. Landman ◽  
Swadhin K. Behera
Keyword(s):  
South Africa ◽  
Root Mean Square ◽  
Wrf Model ◽  
Seasonal Forecasting ◽  
Cumulus Parameterization ◽  
Mean Square ◽  
Mpumalanga Province ◽  
Temporal Representation ◽  
Cumulus Parameterization Schemes ◽  
Mean Square Errors

AbstractIn this study, we attempted to forecast the onset of summer rains over South Africa using seasonal precipitation forecasts generated by the Scale Interaction Experiment–Frontier Research Center for Global Change, version 2 (SINTEX-F2), seasonal forecasting system. The precipitation forecasts of the 12-member SINTEX-F2 system, initialized on 1 August and covering the period 1998–2015, were used for the study. The SINTEX-F2 forecast precipitation was also downscaled using dynamical and statistical techniques to improve the spatial and temporal representation of the forecasts. The Weather Research and Forecasting (WRF) Model with two cumulus parameterization schemes was used to dynamically downscale the SINTEX-F2 forecasts. The WRF and SINTEX-F2 precipitation forecasts were corrected for biases using a linear scaling method with a 31-day moving window. The results indicate the onset dates derived from the raw and bias-corrected model precipitation forecasts to have realistic spatial distribution over South Africa. However, the forecast onset dates have root-mean-square errors of more than 30 days over most parts of South Africa except over the northeastern province of Limpopo and over the Highveld region of Mpumalanga province, where the root-mean-square errors are about 10–15 days. The WRF Model with Kain–Fritsch cumulus scheme (bias-corrected SINTEX-F2) has better performance in forecasting the onset dates over Limpopo (the Highveld region) compared to other models, thereby indicating the forecast of onset dates over different regions of South Africa to be model dependent. The results of this study are important for improving the forecast of onset dates over South Africa.

scholarly journals Simulation of Structure, Track and Landfall of Tropical Cyclone Bijli Using WRF-ARW Model

2015 ◽  
Vol 39 (2) ◽  
pp. 157-167 ◽  
Author(s):  
KM Zahir Rayhun ◽  
DA Quadir ◽  
MA Mannan Chowdhury ◽  
MN Ahasan ◽  
MS Haque
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Convective Available Potential Energy ◽  
Wrf Model ◽  
Relative Vorticity ◽  
Cumulus Parameterization ◽  
Arw Model ◽  
Boundary Layer Scheme ◽  
Cumulus Parameterization Schemes ◽  
Academy Of Sciences

An attempt was made to simulate the structure, track, landfall and a few dynamical aspects of the tropical cyclone Bijli that formed over the Bay of Bengal using WRF-ARW model. WRF model was run in a single domain using KF cumulus parameterization schemes with WSM 3 micro physics and YSU planetary boundary layer scheme. The ARW model was run for 24, 48, 72 and 96 hrs to simulate structure, track and landfall of tropical cyclones Bijli. The different simulated parameters viz. minimum sea level pressure, maximum wind speed, convective available potential energy and relative vorticity have been studied. The results showed that the model is capable to forecast the formation of the first depression 60 - 78 hrs in advance. This indicates the high and unique predictive power of ARW model for predicting the tropical cyclone formation. The model generates a realistic structure of the tropical cyclones with high spatial details. This was possible due to the higher spatial resolution of the regional model. One of the outstanding findings of the study is that the model was successfully predicted the tracks, recurvature and probable areas and time of landfall of the selected tropical cyclone Bijli with high accuracy even in the 96 hrs predictions.Journal of Bangladesh Academy of Sciences, Vol. 39, No. 2, 157-167, 2015

scholarly journals The Impact of Cumulus Parameterization Schemes on the Simulation of Tropical Cyclone “Roanu” Over the Bay of Bengal Using WRF Model

2020 ◽  
Vol 68 (1) ◽  
pp. 87-94
Author(s):  
Saifullah ◽  
Md Idris Ali ◽  
Ashik Imran
Keyword(s):  
Tropical Cyclone ◽  
Bay Of Bengal ◽  
Wrf Model ◽  
Absolute Error ◽  
Sensitivity Study ◽  
Cumulus Parameterization ◽  
Weather Research And Forecasting ◽  
Sea Level Pressure ◽  
Cumulus Parameterization Schemes ◽  
The Impact

A sensitivity study has been made on cumulus parameterization (CP) schemes of Weather Research and Forecasting (WRF) model for the simulation of tropical cyclone Roanu which formed over Bay of Bengal during May 2016. The model was run for 72 hours with different CP schemes such as Kain–Fritsch (KF), Betts-Miller-Janjic (BMJ), Grell–Freit as Ensemble (GFE), Grell 3D Ensemble (G3E) and Grell–Devenyi (GD) Ensemble schemes to study the variation in track, intensity. The landfall position error is minimum for BMJ scheme but the time delayed only 1.5-5 hours for all schemes except GD scheme. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of minimum sea level pressure and maximum wind speed is smaller for BMJ, GFE, GD schemes. The RMSE-MAE of rainfall is minimum for BMJ and G3E schemes. Except GD scheme all the other schemes give the better result. Dhaka Univ. J. Sci. 68(1): 87-94, 2020 (January)

The Role of Negative Buoyancy in Surface-Based Convection and Its Representation in Cumulus Parameterization Schemes

2013 ◽  
Vol 52 (12) ◽  
pp. 2887-2905 ◽  
Author(s):  
Manisha Ganeshan ◽  
Raghu Murtugudde ◽  
John Strack
Keyword(s):  
Wrf Model ◽  
Warm Season ◽  
Cumulus Parameterization ◽  
Cloud Base ◽  
Moist Convection ◽  
Shallow Convection ◽  
Parameterization Schemes ◽  
Cumulus Parameterization Schemes ◽  
Weak Constraints ◽  
Negative Buoyancy

AbstractSeveral warm season, late-afternoon precipitation events are simulated over the Chesapeake Bay watershed using the Weather Research and Forecasting (WRF) model at three different resolutions. The onset and peak of surface-based convection are predicted to occur prematurely when two popular cumulus parameterization schemes (Betts–Miller–Janjić and Kain–Fritsch) are used. Rainfall predictions are significantly improved with explicit convection. The early bias appears to be associated with the inadequacy in representing convective inhibition (CIN) or negative buoyancy in the trigger for moist convection. In particular, both schemes have weak constraints for the negative buoyancy above cloud base and below the level of free convection, leading to premature rainfall. Satellite-derived soundings suggest that, even with extremely favorable conditions, negative buoyancy in this layer may delay the onset of surface-based convection. Other factors, such as enhanced mixing due to overactive shallow convection, also appear to contribute to the early rainfall bias through the premature removal of CIN during the day.

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