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.

scholarly journals A Methodology for Verifying Cloud Forecasts with VIIRS Imagery and Derived Cloud Products—A WRF Case Study

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 521 ◽  
Author(s):  
Keith D. Hutchison ◽  
Barbara D. Iisager ◽  
Sudhakar Dipu ◽  
Xiaoyan Jiang ◽  
Johannes Quaas ◽  
...  
Keyword(s):  
Climate Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Region Of Interest ◽  
Weather Research And Forecasting ◽  
The North ◽  
Infrared Imager ◽  
Internal Procedures ◽  
Initial Results

A methodology is presented to evaluate the accuracy of cloud cover fraction (CCf) forecasts generated by numerical weather prediction (NWP) and climate models. It is demonstrated with a case study consisting of simulations from the Weather Research and Forecasting (WRF) model. In this study, since the WRF CCf forecasts were initialized with reanalysis fields from the North American Mesoscale (NAM) Forecast System, the characteristics of the NAM CCf products were also evaluated. The procedures relied extensively upon manually-generated, binary cloud masks created from VIIRS (Visible Infrared Imager Radiometry Suite) imagery, which were subsequently converted into CCf truth at the resolution of the NAM and WRF gridded data. The initial results from the case study revealed biases toward under-clouding in the NAM CCf analyses and biases toward over-clouding in the WRF CCf products. These biases were evident in images created from the gridded NWP products when compared to VIIRS imagery and CCf truth data. Thus, additional simulations were completed to help assess the internal procedures used in the WRF model to translate moisture forecast fields into layered CCf products. Two additional sets of WRF CCf 24 h forecasts were generated for the region of interest using WRF restart files. One restart file was updated with CCf truth data and another was not changed. Over-clouded areas in the updated WRF restart file that were reduced with an update of the CCf truth data became over-clouded again in the WRF 24 h forecast, and were nearly identical to those from the unchanged restart file. It was concluded that the conversion of WRF forecast fields into layers of CCf products deserves closer examination in a future study.

WRF model moisture adjustment method; a case study with winter-time cloudy biases in Xinjiang, China.

2021 ◽  
Author(s):  
Guiting Song ◽  
Robert Huva ◽  
Yu Xing ◽  
Xiaohui Zhong
Keyword(s):  
Weather Prediction ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Adjustment Method ◽  
Surface Irradiance ◽  
Convective Processes ◽  
Systematic Biases ◽  
Nwp Model ◽  
Winter Time

AbstractFor most locations on Earth the ability of a Numerical Weather Prediction (NWP) model to accurately simulate surface irradiance relies heavily on the NWP model being able to resolve cloud coverage and thickness. At horizontal resolutions at or below a few kilometres NWP models begin to explicitly resolve convection and the clouds that arise from convective processes. However, even at high resolutions, biases may remain in the model and result in under- or over-prediction of surface irradiance. In this study we explore the correction of such systematic biases using a moisture adjustment method in tandem with the Weather Research and Forecasting model (WRF) for a location in Xinjiang, China. After extensive optimisation of the configuration of the WRF model we show that systematic biases still exist—in particular for wintertime in Xinjiang. We then demonstrate the moisture adjustment method with cloudy days for January 2019. Adjusting the relative humidity by 12% through the vertical led to a Root Mean Square Error (RMSE) improvement of 57.8% and a 90.5% reduction in bias for surface irradiance.

scholarly journals Cloud Computing Efforts for the Weather Research and Forecasting Model

2021 ◽  
pp. 1-38
Author(s):  
Jordan G. Powers ◽  
Kelly K. Werner ◽  
David O. Gill ◽  
Yuh-Lang Lin ◽  
Russ S. Schumacher
Keyword(s):  
Cloud Computing ◽  
Service Providers ◽  
Weather Prediction ◽  
Wrf Model ◽  
Cloud Service ◽  
Numerical Weather Prediction Model ◽  
Weather Research And Forecasting ◽  
Model Code ◽  
Cloud Environments ◽  
Weather Research

AbstractThe Weather Research and Forecasting (WRF) Model is a numerical weather prediction model supported by the National Center for Atmospheric Research (NCAR) to a worldwide community of users. In recognition of the growing use of cloud computing, NCAR is now supporting the model in cloud environments. Specifically, NCAR has established WRF setups with select cloud service providers and produced documentation and tutorials on running WRF in the cloud. Described here are considerations in WRF cloud use and the supported resources, which include cloud setups for the WRF system and a cloud-based tool for model code testing.

On the effective resolution of WRF simulations at microscale grid resolution.

2021 ◽  
Author(s):  
Pedro Bolgiani ◽  
Javier Díaz-Fernández ◽  
Lara Quitián-Hernández ◽  
Mariano Sastre ◽  
Daniel Santos-Muñoz ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Research Community ◽  
Weather Research And Forecasting ◽  
Large Uncertainty ◽  
Partial Explanation ◽  
Synoptic Conditions ◽  
Large Effort ◽  
Numerical Weather Prediction Models

<p>As the computational capacity has been largely improved in the last decades, the grid configuration of numerical weather prediction models has stepped into microscale resolutions. Even if mesoscale models are not originally designed to reproduce fine scale phenomena, a large effort is being made by the research community to improve and adapt these systems. However, reasonable doubts exist regarding the ability of the models to forecast this type of events, due to the unfit parametrizations and the appearance of instabilities and lack of sensitivity in the variables. Here, the Weather Research and Forecasting (WRF) model effective resolution is evaluated for several situations and grid resolutions. This is achieved by assessing the curve of dissipation for the wind kinetic energy. Results show that the simulated energy spectrum responds to different synoptic conditions. Nevertheless, when the model is forced into microscale grid resolutions the dissipation curves present an unrealistic atmospheric energy. This may be a partial explanation to the aforementioned issues and imposes a large uncertainty to forecasting at these resolutions.</p>

scholarly journals Instability indices and forecasting thunderstorms: the case of 30 April 2009

2012 ◽  
Vol 12 (2) ◽  
pp. 403-413 ◽  
Author(s):  
S. Tajbakhsh ◽  
P. Ghafarian ◽  
F. Sahraian
Keyword(s):  
Relative Humidity ◽  
Prediction Model ◽  
Numerical Weather Prediction ◽  
Vertical Velocity ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Weather Station ◽  
Using Data ◽  
Instability Indices

Abstract. In this paper, one meteorological case study for two Iranian airports are presented. Attempts have been made to study the predefined threshold amounts of some instability indices such as vertical velocity and relative humidity. Two important output variables from a numerical weather prediction model have been used to survey thunderstorms. The climatological state of thunder days in Iran has been determined to aid in choosing the airports for the case studies. The synoptic pattern, atmospheric thermodynamics and output from a numerical weather prediction model have been studied to evaluate the occurrence of storms and to verify the threshold instability indices that are based on Gordon and Albert (2000) and Miller (1972). Using data from the Statistics and Data Center of the Iran Meteorological Organization, 195 synoptic stations were used to study the climatological pattern of thunderstorm days in Iran during a 15-yr period (1991–2005). Synoptic weather maps and thermodynamic diagrams have been drawn using data from synoptic stations and radiosonde data. A 15-km resolution version of the WRF numerical model has been implemented for the Middle East region with the assistance of global data from University Corporation for Atmospheric Research (UCAR). The Tabriz airport weather station has been selected for further study due to its high frequency of thunderstorms (more than 35 thunderstorm days per year) and the existence of an upper air station. Despite the fact that storms occur less often at the Tehran weather station, the station has been chosen as the second case study site due to its large amount of air traffic. Using these two case studies (Tehran at 00:00 UTC, 31 April 2009 and Tabriz at 12:00 UTC, 31 April 2009), the results of this research show that the threshold amounts of 30 °C for KI, −2 °C for LI and −3 °C for SI suggests the occurrence and non-occurrence of thunderstorms at the Tehran and Tabriz stations, respectively. The WRF model output of vertical velocity and relative humidity are the two most important indices for examining storm occurrence, and they have a numerical threshold of 1 m s−1 and 80%, respectively. These results are comparable to other studies that have examined thunderstorm occurrence.

scholarly journals Evaluation of PBL Parameterizations in WRF at Subkilometer Grid Spacings: Turbulence Statistics in the Dry Convective Boundary Layer

2016 ◽  
Vol 144 (3) ◽  
pp. 1161-1177 ◽  
Author(s):  
Hyeyum Hailey Shin ◽  
Jimy Dudhia
Keyword(s):  
Boundary Layer ◽  
Mass Flux ◽  
Weather Prediction ◽  
Wrf Model ◽  
Eddy Diffusivity ◽  
Weather Research And Forecasting ◽  
Turbulence Statistics ◽  
Convective Boundary ◽  
Large Eddies ◽  
Horizontal Grid

Abstract Planetary boundary layer (PBL) parameterizations in mesoscale models have been developed for horizontal resolutions that cannot resolve any turbulence in the PBL, and evaluation of these parameterizations has been focused on profiles of mean and parameterized flux. Meanwhile, the recent increase in computing power has been allowing numerical weather prediction (NWP) at horizontal grid spacings finer than 1 km, at which kilometer-scale large eddies in the convective PBL are partly resolvable. This study evaluates the performance of convective PBL parameterizations in the Weather Research and Forecasting (WRF) Model at subkilometer grid spacings. The evaluation focuses on resolved turbulence statistics, considering expectations for improvement in the resolved fields by using the fine meshes. The parameterizations include four nonlocal schemes—Yonsei University (YSU), asymmetric convective model 2 (ACM2), eddy diffusivity mass flux (EDMF), and total energy mass flux (TEMF)—and one local scheme, the Mellor–Yamada–Nakanishi–Niino (MYNN) level-2.5 model. Key findings are as follows: 1) None of the PBL schemes is scale-aware. Instead, each has its own best performing resolution in parameterizing subgrid-scale (SGS) vertical transport and resolving eddies, and the resolution appears to be different between heat and momentum. 2) All the selected schemes reproduce total vertical heat transport well, as resolved transport compensates differences of the parameterized SGS transport from the reference SGS transport. This interaction between the resolved and SGS parts is not found in momentum. 3) Those schemes that more accurately reproduce one feature (e.g., thermodynamic transport, momentum transport, energy spectrum, or probability density function of resolved vertical velocity) do not necessarily perform well for other aspects.

scholarly journals Potential of a geostationary geoCARB mission to estimate surface emissions of CO<sub>2</sub>, CH<sub>4</sub> and CO in a polluted urban environment: case study Shanghai

2016 ◽  
Vol 9 (9) ◽  
pp. 4633-4654 ◽  
Author(s):  
Denis M. O'Brien ◽  
Igor N. Polonsky ◽  
Steven R. Utembe ◽  
Peter J. Rayner
Keyword(s):  
Urban Environment ◽  
Mass Density ◽  
Wrf Model ◽  
Trace Gas ◽  
Weather Research And Forecasting ◽  
Retrieval Algorithm ◽  
Remotely Sensed Data ◽  
Ice Clouds ◽  
Instrument Noise

Abstract. This paper describes a numerical experiment to test the ability of the proposed geoCARB satellite to estimate emissions of trace gases (CO2, CH4 and CO) in the polluted urban environment of Shanghai. The meteorology over Shanghai is simulated with the Weather Research and Forecasting (WRF) model for a 9-day period in August 2010. The meteorology includes water and ice clouds. The chemistry version of WRF (WRF-Chem V3.6.1) is used to predict the chemical composition, mass density and number density of aerosol species. Spectra in the bands measured by geoCARB are calculated, including the effects of polarisation and multiple scattering of radiation by clouds, aerosols and molecules. Instrument noise is added, and column-averaged trace-gas mole fractions are estimated from the noisy spectra using an algorithm based on that for the Greenhouse Gases Observing Satellite (GOSAT) and the Orbiting Carbon Observatory-2 (OCO-2) but adapted to geoCARB. As expected, the high aerosol loadings are challenging. However, when the retrieval algorithm is provided with regionally adjusted aerosol optical properties, as might be determined from observations of dark targets within the field of regard, the accuracies of retrieved concentrations are comparable to those reported earlier for geoCARB. Statistics of the errors in the retrieved column-averaged concentrations are used to predict the reduction in uncertainty of surface emissions possible with remotely sensed data.

scholarly journals Using the Weather Research and Forecasting (WRF) Model for Precipitation Forecasting in an Andean Region with Complex Topography

Atmosphere ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 304 ◽  
Author(s):  
Gonzalo Yáñez-Morroni ◽  
Jorge Gironás ◽  
Marta Caneo ◽  
Rodrigo Delgado ◽  
René Garreaud
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Lead Times ◽  
Complex Topography ◽  
Front Range ◽  
Meteorological Forcing ◽  
Rainfall Events ◽  
Weather Research

The Weather Research and Forecasting (WRF) model has been successfully used in weather prediction, but its ability to simulate precipitation over areas with complex topography is not optimal. Consequently, WRF has problems forecasting rainfall events over Chilean mountainous terrain and foothills, where some of the main cities are located, and where intense rainfall occurs due to cutoff lows. This work analyzes an ensemble of microphysics schemes to enhance initial forecasts made by the Chilean Weather Agency in the front range of Santiago. We first tested different vertical levels resolution, land use and land surface models, as well as meteorological forcing (GFS/FNL). The final ensemble configuration considered three microphysics schemes and lead times over three rainfall events between 2015 and 2017. Cutoff low complex meteorological characteristics impede the temporal simulation of rainfall properties. With three days of lead time, WRF properly forecasts the rainiest N-hours and temperatures during the event, although more accuracy is obtained when the rainfall is caused by a meteorological frontal system. Finally, the WSM6 microphysics option had the best performance, although further analysis using other storms and locations in the area are needed to strengthen this result.

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