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 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.

Fire weather simulation skill by the Weather Research and Forecasting (WRF) model over south-east Australia from 1985 to 2009

2013 ◽  
Vol 22 (6) ◽  
pp. 739 ◽  
Author(s):  
Hamish Clarke ◽  
Jason P. Evans ◽  
Andrew J. Pitman
Keyword(s):  
Initial Conditions ◽  
Full Range ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Fire Weather ◽  
Grid Spacing ◽  
Skill Scores ◽  
Forest Fire Danger ◽  
Environmental Prediction ◽  
Weather Research

The fire weather of south-east Australia from 1985 to 2009 has been simulated using the Weather Research and Forecasting (WRF) model. The US National Oceanic and Atmospheric Administration Centers for Environmental Prediction and National Center for Atmospheric Research reanalysis supplied the lateral boundary conditions and initial conditions. The model simulated climate and the reanalysis were evaluated against station-based observations of the McArthur Forest Fire Danger Index (FFDI) using probability density function skill scores, annual cumulative FFDI and days per year with FFDI above 50. WRF simulated the main features of the FFDI distribution and its spatial variation, with an overall positive bias. Errors in average FFDI were caused mostly by errors in the ability of WRF to simulate relative humidity. In contrast, errors in extreme FFDI values were driven mainly by WRF errors in wind speed simulation. However, in both cases the quality of the observed data is difficult to ascertain. WRF run with 50-km grid spacing did not consistently improve upon the reanalysis statistics. Decreasing the grid spacing to 10km led to fire weather that was generally closer to observations than the reanalysis across the full range of evaluation metrics used here. This suggests it is a very useful tool for modelling fire weather over the entire landscape of south-east Australia.

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 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 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.

scholarly journals Implementation of a new aerosol HAM model within the Weather Research and Forecasting (WRF) modeling system

2009 ◽  
Vol 2 (2) ◽  
pp. 681-707 ◽  
Author(s):  
R. Mashayekhi ◽  
P. Irannejad ◽  
J. Feichter ◽  
A. A. Bidokhti
Keyword(s):  
Black Carbon ◽  
Radiative Forcing ◽  
Mass Concentration ◽  
Mineral Dust ◽  
Wrf Model ◽  
Pm10 Concentration ◽  
Shortwave Radiation ◽  
Weather Research And Forecasting ◽  
Particulate Emission ◽  
Weather Research

Abstract. A new coupled system of aerosol HAM model and the Weather, Research and Forecasting (WRF) model is presented in this paper. Unlike the current aerosol schemes used in WRF model, the HAM is using a "pseudomodal" approach for the representation of the particle size distribution. The aerosol components considered are sulfate, black carbon, particulate organic matter, sea salt and mineral dust. The preliminary model results are presented for two different 6-day simulation periods from 22 to 28 February 2006 as a winter period and 6 to 12 May 2006 as a mild period. The mean shortwave radiation and thermal forcing were calculated from the model simulations with and without aerosols feedback for two simulation periods. A negative radiative forcing and cooling of the atmosphere were found mainly over the regions of high emission of mineral dust. The absorption of shortwave radiation by black carbon caused warming effects in some regions with positive radiative forcing. The simulated daily mean sulfate mass concentration showed a rather good agreement with the measurements in the European EMEP network. The diurnal variation of the simulated hourly PM10 mass concentration at Tehran was also qualitatively close to the observations in both simulation periods. The model captured diurnal cycle and the magnitude of the observed PM10 concentration during most of the simulation periods. The differences between the observed and simulated PM10 concentration resulted mostly from limitation of the model in simulating the clouds and precipitation, transport errors and uncertainties in the particulate emission rates. The inclusion of aerosols feedback in shortwave radiation scheme improved the simulated daily mean shortwave radiation fluxes in Tehran for both simulation periods.

Evaluation of the Weather Research and Forecasting model in simulating fire weather for the south-west of Western Australia

2020 ◽  
Vol 29 (9) ◽  
pp. 779 ◽  
Author(s):  
Jatin Kala ◽  
Alyce Sala Tenna ◽  
Daniel Rudloff ◽  
Julia Andrys ◽  
Ole Rieke ◽  
...  
Keyword(s):  
Western Australia ◽  
Wrf Model ◽  
Metropolitan Region ◽  
Weather Research And Forecasting ◽  
Fire Weather ◽  
The South ◽  
Weather Forecasts ◽  
South West ◽  
Forest Fire Danger ◽  
Weather Research

The Weather Research and Forecasting (WRF) model was used to simulate fire weather for the south-west of Western Australia (SWWA) over multiple decades at a 5-km resolution using lateral boundary conditions from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA)-Interim reanalysis. Simulations were compared with observations at Australian Bureau of Meteorology meteorological stations and the McArthur Forest Fire Danger Index (FFDI) was used to quantify fire weather. Results showed that, overall, the WRF reproduced the annual cumulative FFDI at most stations reasonably well, with most biases in the FFDI ranging between –600 and 600. Biases were highest at stations within the metropolitan region. The WRF simulated the geographical gradients in the FFDI across the domain well. The source of errors in the FFDI varied markedly between the different stations, with no one particular variable able to account for the errors at all stations. Overall, this study shows that the WRF is a useful model for simulating fire weather for SWWA, one of the most fire-prone regions in Australia.

Sign in / Sign up

Export Citation Format

Share Document