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.

scholarly journals The Impacts of Soil Moisture Initialization on the Forecasts of Weather Research and Forecasting Model: A Case Study in Xinjiang, China

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1892
Author(s):  
Hailiang Zhang ◽  
Junjian Liu ◽  
Huoqing Li ◽  
Xianyong Meng ◽  
Ablimitijan Ablikim
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Weather Prediction ◽  
Potential Temperature ◽  
Wrf Model ◽  
Specific Humidity ◽  
Water Evaporation ◽  
Weather Research And Forecasting ◽  
Soil Moisture Initialization ◽  
Weather Research

Soil moisture is a critical parameter in numerical weather prediction (NWP) models because it plays a fundamental role in the exchange of water and energy cycles between the atmosphere and the land surface through evaporation. To improve the forecast skills of the Weather Research and Forecasting (WRF) model in Xinjiang, China, this study investigated the impacts of soil moisture initialization on the WRF forecasts by performing a series of simulations. A group of simulations was conducted using the single-column model (SCM) from 1200 UTC on 15 to 18 August 2019, at Urumchi, Xinjiang (43.78° N, 87.6° E); another was performed using the WRF model for a real weather case in Xinjiang from 0000 UTC 15 August to 1200 UTC 18 August 2019, which included an episode of heavy precipitation and gales. Our most notable findings are as follows. Specific humidity increases and potential temperature decreases persistently when soil moisture increases because of soil water evaporation. Soil moisture initialization could impact the energy budget and modulate the partition of the total available energy at the land surface significantly through evaporation and the greenhouse effect. Replacing the soil moisture with a proper multiple of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) soil moisture data could significantly improve the critical success index (CSI) and frequency bias (FBIAS) of precipitation and the root-mean-squared errors (RMSEs) of 2-m specific humidity and 2-m temperature. These findings indicate the prospect of a new way to improve the forecast skills of WRF in Xinjiang or other similar regions.

scholarly journals Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin

2018 ◽  
Vol 22 (2) ◽  
pp. 1095-1117 ◽  
Author(s):  
Ila Chawla ◽  
Krishna K. Osuri ◽  
Pradeep P. Mujumdar ◽  
Dev Niyogi
Keyword(s):  
Land Surface ◽  
Heavy Rainfall ◽  
Wrf Model ◽  
Extreme Rainfall ◽  
Weather Research And Forecasting ◽  
Grid Spacing ◽  
Ganga Basin ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Heavy Rainfall Events

Abstract. Reliable estimates of extreme rainfall events are necessary for an accurate prediction of floods. Most of the global rainfall products are available at a coarse resolution, rendering them less desirable for extreme rainfall analysis. Therefore, regional mesoscale models such as the advanced research version of the Weather Research and Forecasting (WRF) model are often used to provide rainfall estimates at fine grid spacing. Modelling heavy rainfall events is an enduring challenge, as such events depend on multi-scale interactions, and the model configurations such as grid spacing, physical parameterization and initialization. With this background, the WRF model is implemented in this study to investigate the impact of different processes on extreme rainfall simulation, by considering a representative event that occurred during 15–18 June 2013 over the Ganga Basin in India, which is located at the foothills of the Himalayas. This event is simulated with ensembles involving four different microphysics (MP), two cumulus (CU) parameterizations, two planetary boundary layers (PBLs) and two land surface physics options, as well as different resolutions (grid spacing) within the WRF model. The simulated rainfall is evaluated against the observations from 18 rain gauges and the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42RT version 7 data. From the analysis, it should be noted that the choice of MP scheme influences the spatial pattern of rainfall, while the choice of PBL and CU parameterizations influences the magnitude of rainfall in the model simulations. Further, the WRF run with Goddard MP, Mellor–Yamada–Janjic PBL and Betts–Miller–Janjic CU scheme is found to perform best in simulating this heavy rain event. The selected configuration is evaluated for several heavy to extremely heavy rainfall events that occurred across different months of the monsoon season in the region. The model performance improved through incorporation of detailed land surface processes involving prognostic soil moisture evolution in Noah scheme compared to the simple Slab model. To analyse the effect of model grid spacing, two sets of downscaling ratios – (i) 1 : 3, global to regional (G2R) scale and (ii) 1 : 9, global to convection-permitting scale (G2C) – are employed. Results indicate that a higher downscaling ratio (G2C) causes higher variability and consequently large errors in the simulations. Therefore, G2R is adopted as a suitable choice for simulating heavy rainfall event in the present case study. Further, the WRF-simulated rainfall is found to exhibit less bias when compared with the NCEP FiNaL (FNL) reanalysis data.

scholarly journals Heavy Rainfall Simulation over Sinai Peninsula Using the Weather Research and Forecasting Model

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Gamal El Afandi ◽  
Mostafa Morsy ◽  
Fathy El Hussieny
Keyword(s):  
Heavy Rainfall ◽  
Flash Flood ◽  
Wrf Model ◽  
Rain Gauge ◽  
Rainfall Simulation ◽  
Weather Research And Forecasting ◽  
Sinai Peninsula ◽  
Rainfall Events ◽  
Heavy Rainfall Events ◽  
Weather Research

Heavy rainfall is one of major severe weather over Sinai Peninsula and causes many flash floods over the region. The good forecasting of rainfall is very much necessary for providing early warning before the flash flood events to avoid or minimize disasters. In the present study using the Weather Research and Forecasting (WRF) Model, heavy rainfall events that occurred over Sinai Peninsula and caused flash flood have been investigated. The flash flood that occurred on January 18, 2010, over different parts of Sinai Peninsula has been predicted and analyzed using the Advanced Weather Research and Forecast (WRF-ARW) Model. The predicted rainfall in four dimensions (space and time) has been calibrated with the measurements recorded at rain gauge stations. The results show that the WRF model was able to capture the heavy rainfall events over different regions of Sinai. It is also observed that WRF model was able to predict rainfall in a significant consistency with real measurements. In this study, several synoptic characteristics of the depressions that developed during the course of study have been investigated. Also, several dynamic characteristics during the evolution of the depressions were studied: relative vorticity, thermal advection, and geopotential height.

scholarly journals Development and Testing of Polar Weather Research and Forecasting (WRF) Model. Part I: Greenland Ice Sheet Meteorology*

2008 ◽  
Vol 136 (6) ◽  
pp. 1971-1989 ◽  
Author(s):  
Keith M. Hines ◽  
David H. Bromwich
Keyword(s):  
Boundary Layer ◽  
Land Surface ◽  
Mesoscale Model ◽  
Ice Sheet ◽  
Wrf Model ◽  
Greenland Ice Sheet ◽  
Forecast Skill ◽  
Weather Research And Forecasting ◽  
Surface Model ◽  
Weather Research

Abstract A polar-optimized version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) was developed to fill climate and synoptic needs of the polar science community and to achieve an improved regional performance. To continue the goal of enhanced polar mesoscale modeling, polar optimization should now be applied toward the state-of-the-art Weather Research and Forecasting (WRF) Model. Evaluations and optimizations are especially needed for the boundary layer parameterization, cloud physics, snow surface physics, and sea ice treatment. Testing and development work for Polar WRF begins with simulations for ice sheet surface conditions using a Greenland-area domain with 24-km resolution. The winter month December 2002 and the summer month June 2001 are simulated with WRF, version 2.1.1, in a series of 48-h integrations initialized daily at 0000 UTC. The results motivated several improvements to Polar WRF, especially to the Noah land surface model (LSM) and the snowpack treatment. Different physics packages for WRF are evaluated with December 2002 simulations that show variable forecast skill when verified with the automatic weather station observations. The WRF simulation with the combination of the modified Noah LSM, the Mellor–Yamada–Janjić boundary layer parameterization, and the WRF single-moment microphysics produced results that reach or exceed the success standards of a Polar MM5 simulation for December 2002. For summer simulations of June 2001, WRF simulates an improved surface energy balance, and shows forecast skill nearly equal to that of Polar MM5.

scholarly journals The Weather Research and Forecasting Model: Overview, System Efforts, and Future Directions

2017 ◽  
Vol 98 (8) ◽  
pp. 1717-1737 ◽  
Author(s):  
Jordan G. Powers ◽  
Joseph B. Klemp ◽  
William C. Skamarock ◽  
Christopher A. Davis ◽  
Jimy Dudhia ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
System Modeling ◽  
Weather Prediction ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Earth System Modeling ◽  
Numerical Weather ◽  
Wide Range ◽  
Weather Research

Abstract Since its initial release in 2000, the Weather Research and Forecasting (WRF) Model has become one of the world’s most widely used numerical weather prediction models. Designed to serve both research and operational needs, it has grown to offer a spectrum of options and capabilities for a wide range of applications. In addition, it underlies a number of tailored systems that address Earth system modeling beyond weather. While the WRF Model has a centralized support effort, it has become a truly community model, driven by the developments and contributions of an active worldwide user base. The WRF Model sees significant use for operational forecasting, and its research implementations are pushing the boundaries of finescale atmospheric simulation. Future model directions include developments in physics, exploiting emerging compute technologies, and ever-innovative applications. From its contributions to research, forecasting, educational, and commercial efforts worldwide, the WRF Model has made a significant mark on numerical weather prediction and atmospheric science.

scholarly journals Suitability of the Weather Research and Forecasting (WRF) Model to Predict the June 2005 Fire Weather for Interior Alaska

2008 ◽  
Vol 23 (5) ◽  
pp. 953-973 ◽  
Author(s):  
Nicole Mölders
Keyword(s):  
Relative Humidity ◽  
Weather Prediction ◽  
Wrf Model ◽  
Shortwave Radiation ◽  
Maximum Temperature ◽  
Weather Research And Forecasting ◽  
Fire Weather ◽  
Forest Environment ◽  
Interior Alaska ◽  
Weather Research

Abstract Standard indices used in the National Fire Danger Rating System (NFDRS) and Fosberg fire-weather indices are calculated from Weather Research and Forecasting (WRF) model simulations and observations in interior Alaska for June 2005. Evaluation shows that WRF is well suited for fire-weather prediction in a boreal forest environment at all forecast leads and on an ensemble average. Errors in meteorological quantities and fire indices marginally depend on forecast lead. WRF’s precipitation performance for interior Alaska is comparable to that of other mesoscale models applied to midlatitudes. WRF underestimates precipitation on average, but satisfactorily predicts precipitation ≥7.5 mm day−1, the threshold considered to reduce interior Alaska’s fire risk for several days. WRF slightly overestimates wind speed, but captures the temporal mean behavior accurately. WRF predicts the temporal evolution of daily temperature extremes, mean relative humidity, air and dewpoint temperature, and daily accumulated shortwave radiation well. Daily minimum (maximum) temperature and relative humidity are slightly overestimated (underestimated). Fire index trends are suitably predicted. Fire indices derived from daily mean predicted meteorological quantities are more reliable than those based on predicted daily extremes. Indirect evaluation by observed fires suggests that WRF-derived NFDRS indices reflect the variability of fire activity.

scholarly journals Evaluation of Weather Research and Forecasting (WRF) Microphysics single moment class-3 and class-6 in Precipitation Forecast

2018 ◽  
Vol 150 ◽  
pp. 03007 ◽  
Author(s):  
Syeda Maria Zaidi ◽  
Jacqueline Isabella Anak Gisen
Keyword(s):  
Model Performance ◽  
Wrf Model ◽  
Precipitation Forecast ◽  
Weather Research And Forecasting ◽  
Microphysics Scheme ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Single Moment ◽  
Minimum Medium ◽  
Weather Research

In this study, the performance of two different Microphysics Scheme options in Weather Research and Forecasting (WRF) model were evaluated for the estimating the precipitation forecast. The schemes WRF single moment class-3 (WSM-3) and single moment class-6 (WSM-6) were employed to produce the minimum, medium and maximum precipitation for the selected events over the Kuantan River Basin (KRB). The obtained simulated results were compared with the observed data from eight different rainfall gauging stations. The results comparison indicate that WRF model provides better forecasting at some rainfall stations for minimum and medium rainfall events but did not produce good result during maximum rainfall overall. The WSM-6 scheme is found to produce better result compared to WSM-3. The study also found that to acquire accurate precipitation results, it is also required to test some other physics scheme parameterization to enhance the model performance.

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.

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