scholarly journals Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 874
Author(s):  
Mingkun Sun ◽  
Zhijia Li ◽  
Cheng Yao ◽  
Zhiyu Liu ◽  
Jingfeng Wang ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Weather Research And Forecasting ◽  
Xinanjiang Model ◽  
Flood Prediction ◽  
Model Driven ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Mean Square Errors ◽  
Prediction Capability

The Weather Research and Forecasting (WRF)-Hydro model as a physical-based, fully-distributed, multi-parameterization modeling system easy to couple with numerical weather prediction model, has potential for operational flood forecasting in the small and medium catchments (SMCs). However, this model requires many input forcings, which makes it difficult to use it for the SMCs without adequate observed forcings. The Global Land Data Assimilation System (GLDAS), the WRF outputs and the ideal forcings generated by the WRF-Hydro model can provide all forcings required in the model for these SMCs. In this study, seven forcing scenarios were designed based on the products of GLDAS, WRF and ideal forcings, as well as the observed and merged rainfalls to assess the performance of the WRF-Hydro model for flood simulation. The model was applied to the Chenhe catchment, a typical SMC located in the Midwestern China. The flood prediction capability of the WRF-Hydro model was also compared to that of widely used Xinanjiang model. The results show that the three forcing scenarios, including the GLDAS forcings with observed rainfall, the WRF forcings with observed rainfall and GLDAS forcings with GLDAS-merged rainfall, are optimal input forcings for the WRF-Hydro model. Their mean root mean square errors (RMSE) are 0.18, 0.18 and 0.17 mm/h, respectively. The performance of the WRF-Hydro model driven by these three scenarios is generally comparable to that of the Xinanjiang model (RMSE = 0.17 mm/h).

scholarly journals Evaluation of the WRF Model to Simulate a High-Intensity Rainfall Event over Kampala, Uganda

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 873
Author(s):  
Yakob Umer ◽  
Janneke Ettema ◽  
Victor Jetten ◽  
Gert-Jan Steeneveld ◽  
Reinder Ronda
Keyword(s):  
High Intensity ◽  
Flood Hazard ◽  
Weather Prediction ◽  
Wrf Model ◽  
Numerical Weather Prediction Model ◽  
Rainfall Event ◽  
Weather Research And Forecasting ◽  
Event Triggering ◽  
Rain Events

Simulating high-intensity rainfall events that trigger local floods using a Numerical Weather Prediction model is challenging as rain-bearing systems are highly complex and localized. In this study, we analyze the performance of the Weather Research and Forecasting (WRF) model’s capability in simulating a high-intensity rainfall event using a variety of parameterization combinations over the Kampala catchment, Uganda. The study uses the high-intensity rainfall event that caused the local flood hazard on 25 June 2012 as a case study. The model capability to simulate the high-intensity rainfall event is performed for 24 simulations with a different combination of eight microphysics (MP), four cumulus (CP), and three planetary boundary layer (PBL) schemes. The model results are evaluated in terms of the total 24-h rainfall amount and its temporal and spatial distributions over the Kampala catchment using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. Rainfall observations from two gauging stations and the CHIRPS satellite product served as benchmark. Based on the TOPSIS analysis, we find that the most successful combination consists of complex microphysics such as the Morrison 2-moment scheme combined with Grell-Freitas (GF) and ACM2 PBL with a good TOPSIS score. However, the WRF performance to simulate a high-intensity rainfall event that has triggered the local flood in parts of the catchment seems weak (i.e., 0.5, where the ideal score is 1). Although there is high spatial variability of the event with the high-intensity rainfall event triggering the localized floods simulated only in a few pockets of the catchment, it is remarkable to see that WRF is capable of producing this kind of event in the neighborhood of Kampala. This study confirms that the capability of the WRF model in producing high-intensity tropical rain events depends on the proper choice of parametrization combinations.

Numerical Prediction of Submesoscale Flow in the Nocturnal Stable Boundary Layer over Complex Terrain

2012 ◽  
Vol 140 (3) ◽  
pp. 956-977 ◽  
Author(s):  
Nelson L. Seaman ◽  
Brian J. Gaudet ◽  
David R. Stauffer ◽  
Larry Mahrt ◽  
Scott J. Richardson ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Stable Boundary Layers ◽  
Stable Boundary ◽  
Central Pennsylvania ◽  
Stable Conditions ◽  
Improved Model ◽  
Mean Square Errors ◽  
Numerical Weather Prediction Models

Abstract Numerical weather prediction models often perform poorly for weakly forced, highly variable winds in nocturnal stable boundary layers (SBLs). When used as input to air-quality and dispersion models, these wind errors can lead to large errors in subsequent plume forecasts. Finer grid resolution and improved model numerics and physics can help reduce these errors. The Advanced Research Weather Research and Forecasting model (ARW-WRF) has higher-order numerics that may improve predictions of finescale winds (scales <~20 km) in nocturnal SBLs. However, better understanding of the physics controlling SBL flow is needed to take optimal advantage of advanced modeling capabilities. To facilitate ARW-WRF evaluations, a small network of instrumented towers was deployed in the ridge-and-valley topography of central Pennsylvania (PA). Time series of local observations and model forecasts on 1.333- and 0.444-km grids were filtered to isolate deterministic lower-frequency wind components. The time-filtered SBL winds have substantially reduced root-mean-square errors and biases, compared to raw data. Subkilometer horizontal and very fine vertical resolutions are found to be important for reducing model speed and direction errors. Nonturbulent fluctuations in unfiltered, very finescale winds, parts of which may be resolvable by ARW-WRF, are shown to generate horizontal meandering in stable weakly forced conditions. These submesoscale motions include gravity waves, primarily horizontal 2D motions, and other complex signatures. Vertical structure and low-level biases of SBL variables are shown to be sensitive to parameter settings defining minimum “background” mixing in very stable conditions in two representative turbulence schemes.

La représentation des surfaces continentales pour la prévision numérique du temps

2020 ◽  
pp. 059
Author(s):  
Stéphane Bélair ◽  
Aaron Boone
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Physical Processes ◽  
Prognostic Variables ◽  
Near Surface ◽  
Land Data Assimilation ◽  
Surface Scheme ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

La représentation des processus physiques associés aux surfaces continentales, incluant les échanges de chaleur, d'humidité et de quantité de mouvement avec l'atmosphère, ainsi que l'analyse des conditions initiales pour ses principales variables influencent de manière substantielle la prévision atmosphérique près de la surface, en plus d'avoir un impact sur la production de nuages et des précipitations. Comment les surfaces continentales sont-elles représentées dans les modèles de prévision numérique du temps ? Quelles sont les problématiques propres à la prévision numérique du temps dans cette représentation ? Ces questions sont examinées dans cet article en utilisant des exemples tirées du modèle Isba (Interactions solbiosphère-atmosphère) développé à Météo-France et du système d'assimilation de surface du Service météorologique du Canada. The representation of physical processes over land, including heat, humidity, and momentum exchanges with the atmosphere, as well as accurate initialisation of its main prognostic variables, has a substantial influence on numerical prediction of the near-surface atmosphere and on the formation of clouds and precipitation. How are continental surfaces represented in numerical weather prediction (NWP) models? What are the scientific issues specif ic to NWP for this representation? These are questions examined in this study using examples from the Isba (Interactions Soil-Biosphere-Atmosphere) land surface scheme developed at Météo-France and the land data assimilation system from the Meteorological Service of Canada.

scholarly journals Wavelet-Based Methodology for the Verification of Stochastic Submeso and Meso-Gamma Fluctuations

2015 ◽  
Vol 143 (10) ◽  
pp. 4220-4235 ◽  
Author(s):  
Astrid Suarez ◽  
David R. Stauffer ◽  
Brian J. Gaudet
Keyword(s):  
Single Case ◽  
Weather Prediction ◽  
Model Performance ◽  
Numerical Weather Prediction Model ◽  
Weather Research And Forecasting ◽  
New Approach ◽  
Frequency Distributions ◽  
Use Of Data ◽  
Rock Springs ◽  
Grid Length

Abstract Numerical weather prediction model skill is difficult to assess for transient, nonstationary, nondeterministic, or stochastic motions, like submeso and small meso-gamma motions. New approaches are needed to complement traditional methods and to quantify and evaluate the variability and the errors for these high-frequency, nondeterministic modes. A new verification technique that uses the wavelet transform as a bandpass filter to obtain scale-dependent frequency distributions of fluctuations is proposed for assessing model performance or accuracy. This new approach quantifies the nondeterministic variability independent of time while accounting for the time scale and amplitude of each fluctuation. The efficacy of this wavelet decomposition technique for the verification of submeso and meso-gamma motions is first illustrated for a single case before the analysis is extended to six cases. The sensitivity of subkilometer grid-length Weather Research and Forecasting Model forecasts to the choice of three initialization strategies is assessed for both deterministic and stochastic motions using observations from a special network located at Rock Springs, Pennsylvania. It is demonstrated that the use of data assimilation in a preforecast period results in improved temperature and wind speed statistics for deterministic motions and for nondeterministic fluctuations with periods greater than ~20 min. As expected, there is little-to-no accuracy forecasting the occurrence of variability for temperature and wind in the smaller-submeso range and greater accuracy in the larger-submeso and meso-gamma ranges. Nonetheless, the model has some difficulty reproducing the observed variability with the correct amplitude. It underestimates the amplitude of observed fluctuations even for larger time scales, where better model performance could be expected.

scholarly journals A New Diffusion Scheme for Numerical Models Based on Full Irreversibility

2009 ◽  
Vol 24 (2) ◽  
pp. 595-600 ◽  
Author(s):  
C. Liu ◽  
Y. Liu ◽  
H. Xu
Keyword(s):  
Numerical Models ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Second Law Of Thermodynamics ◽  
New Physics ◽  
Numerical Weather Prediction Model ◽  
One Dimensional ◽  
Diffusion Scheme ◽  
The One ◽  
Mean Square Errors

Abstract In this work, the forecast accuracy of a numerical weather prediction model is improved by emulating physical dissipation as suggested by the second law of thermodynamics, which controls the irreversible evolutionary direction of a many-body system like the atmosphere. The ability of the new physics-based scheme to improve model accuracy is demonstrated via the case of the one-dimensional viscous Burgers equation and the one-dimensional diffusion equation, as well as a series of numerical simulations of the well-known 1998 successive torrential rains along the Yangtze River valley and 365 continuous 24-h simulations during 2005–06 with decreased root-mean-square errors and improved forecasts in all of the simulations.

scholarly journals The Canadian Land Data Assimilation System (CaLDAS): Description and Synthetic Evaluation Study

2015 ◽  
Vol 16 (3) ◽  
pp. 1293-1314 ◽  
Author(s):  
Marco L. Carrera ◽  
Stéphane Bélair ◽  
Bernard Bilodeau
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Weather Prediction ◽  
Surface Model ◽  
Land Data Assimilation ◽  
L Band ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

Abstract The Canadian Land Data Assimilation System (CaLDAS) has been developed at the Meteorological Research Division of Environment Canada (EC) to better represent the land surface initial states in environmental prediction and assimilation systems. CaLDAS is built around an external land surface modeling system and uses the ensemble Kalman filter (EnKF) methodology. A unique feature of CaLDAS is the use of improved precipitation forcing through the assimilation of precipitation observations. An ensemble of precipitation analyses is generated by combining numerical weather prediction (NWP) model precipitation forecasts with precipitation observations. Spatial phasing errors to the NWP first-guess precipitation forecasts are more effective than perturbations to the precipitation observations in decreasing (increasing) the exceedance ratio (uncertainty ratio) scores and generating flatter, more reliable ranked histograms. CaLDAS has been configured to assimilate L-band microwave brightness temperature TB by coupling the land surface model with a microwave radiative transfer model. A continental-scale synthetic experiment assimilating passive L-band TBs for an entire warm season is performed over North America. Ensemble metric scores are used to quantify the impact of different atmospheric forcing uncertainties on soil moisture and TB ensemble spread. The use of an ensemble of precipitation analyses, generated by assimilating precipitation observations, as forcing combined with the assimilation of L-band TBs gave rise to the largest improvements in superficial soil moisture scores and to a more rapid reduction of the root-zone soil moisture errors. Innovation diagnostics show that the EnKF is able to maintain a sufficient forecast error spread through time, while soil moisture estimation error improvements with increasing ensemble size were limited.

scholarly journals Assimilation of Passive L-band Microwave Brightness Temperatures in the Canadian Land Data Assimilation System: Impacts on Short-Range Warm Season Numerical Weather Prediction

2019 ◽  
Vol 20 (6) ◽  
pp. 1053-1079 ◽  
Author(s):  
Marco L. Carrera ◽  
Bernard Bilodeau ◽  
Stéphane Bélair ◽  
Maria Abrahamowicz ◽  
Albert Russell ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Weather Prediction ◽  
Warm Season ◽  
Brightness Temperatures ◽  
Screen Level ◽  
Land Data Assimilation ◽  
L Band ◽  
Land Data Assimilation System ◽  
Data Assimilation System

Abstract This study examines the impacts of assimilating Soil Moisture Active Passive (SMAP) L-band brightness temperatures (TBs) on warm season short-range numerical weather prediction (NWP) forecasts. Focusing upon the summer 2015 period over North America, offline assimilation cycles are run with the Canadian Land Data Assimilation System (CaLDAS) to compare the impacts of assimilating SMAP TB versus screen-level observations to analyze soil moisture. The analyzed soil moistures are quantitatively compared against a set of in situ sparse soil moisture networks and a set of SMAP core validation sites. These surface analyses are used to initialize a series of 48-h forecasts where near-surface temperature and precipitation are evaluated against in situ observations. Assimilation of SMAP TBs leads to soil moisture that is markedly improved in terms of correlation and standard deviation of the errors (STDE) compared to the use of screen-level observations. NWP forecasts initialized with SMAP-derived soil moistures exhibit a general dry bias in 2-m dewpoint temperatures (TD2m), while displaying a relative warm bias in 2-m temperatures (TT2m), when compared to those forecasts initialized with soil moistures analyzed with screen-level temperature errors. Largest impacts with SMAP are seen for TD2m, where the use of screen-level observations leads to a daytime wet bias that is reduced with SMAP. The overall drier soil moisture leads to improved precipitation bias scores with SMAP. A notable deterioration in TD2m STDE scores was found in the SMAP experiments during the daytime over the Northern Great Plains. A reduction in the daytime TD2m wet bias was found when the observation errors for the screen-level observations were increased.

Sign in / Sign up

Export Citation Format

Share Document