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 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.

Impact of Soil Moisture on Winter 2-m Temperature Forecast in Northern China

2020 ◽  
Author(s):  
Ji-Qin Zhong ◽  
Bing Lu ◽  
Wei Wang ◽  
Cheng-Cheng Huang ◽  
Yang Yang
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Northern China ◽  
Heat Fluxes ◽  
Specific Humidity ◽  
Hydraulic Parameters ◽  
Temperature Range ◽  
Temperature Forecast ◽  
Soil Hydraulic ◽  
Soil Moisture Initialization

<p> The causes of the underestimated diurnal 2-m temperature range and the overestimated 2-m specific humidity in Northern China’s winter in the Rapid-refresh Multi-scale Analysis and Prediction System - Short Term (RMAPS-ST) system are investigated. Three simulations based on RMAPS-ST are conducted from Nov. 1st, 2016 to Feb. 28th, 2017. Further analyses show that the partitioning of surface upward sensible heat fluxes and downward ground heat fluxes might be the main contributing factor in 2-m temperature forecast biases. In this study, two simulations are conducted to examine the effect of soil moisture initialization and soil hydraulic property on the 2-m temperature and 2-m specific humidity forecast biases. Firstly, the High-Resolution Land Data Assimilation System (HRLDAS) is used to provide an alternative soil moisture initialization, and the result shows that the drier soil moisture leads to noticeable change in energy partition at the land surface, which in turn results in improved prediction of the diurnal 2-m temperature range, although it also enlarges the 2-m specific humidity bias in some parts of the domain. Secondly, a soil texture dataset developed by Beijing Normal University (BNU) and a revised hydraulic parameters are applied to provide a more detailed description of soil properties, which could further improve the 2-m specific humidity biases. In summary, the combination of using optimized soil moisture initialization, updated soil map and revised soil hydraulic parameters can help improve the 2-m temperature and 2-m specific humidity prediction in RMAPS-ST.</p>

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 A New Land-Use Dataset for the Weather Research and Forecasting (WRF) Model

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 350
Author(s):  
Huoqing Li ◽  
Hailiang Zhang ◽  
Ali Mamtimin ◽  
Shuiyong Fan ◽  
Chenxiang Ju
Keyword(s):  
Land Use ◽  
Heat Flux ◽  
Sensible Heat Flux ◽  
Wrf Model ◽  
Northwest China ◽  
Specific Humidity ◽  
Weather Research And Forecasting ◽  
United States Geological Survey ◽  
Parameter Values ◽  
Weather Research

The USGS (United States Geological Survey) land-use data used in the Weather Research and Forecasting (WRF) model have become obsolete as they are unable to accurately represent actual underlying surface features. Therefore, this study developed a new multi-satellite remote-sensing land-use dataset based on the latest GLC2015 (Global Land Cover, 2015) land-use data, which had 300 m spatial resolution. The new data were used to update the default USGS land-use dataset. Based on observational data from national meteorological observing stations in Xinjiang, northwest China, a comparison of the old USGS and new GLC2015 land-use datasets in the WRF model was performed for July 2018, where the simulated variables included the sensible heat flux (SHF), latent heat flux (LHF), surface skin temperature (Tsk), two-meter air temperature (T2), wind speed (Winds), specific humidity (Q2) and relative humidity (RH). The results indicated that there were significant differences between the two datasets. For example, our statistical verification results found via in situ observations made by the MET (model evaluation tools) illustrated that the bias of T2 decreased by 2.54%, the root mean square error (RMSE) decreased by 1.48%, the bias of Winds decreased by 10.46%, and the RMSE decreased by 6.77% when using the new dataset, and the new parameter values performed a net positive effect on land–atmosphere interactions. These results suggested that the GLC2015 land-use dataset developed in this study was useful in terms of improving the performance of the WRF model in the summer months.

Impact of Soil Moisture on Winter 2-m Temperature Forecasts in Northern China

2020 ◽  
Vol 21 (4) ◽  
pp. 597-614 ◽  
Author(s):  
Ji-Qin Zhong ◽  
Bing Lu ◽  
Wei Wang ◽  
Cheng-Cheng Huang ◽  
Yang Yang
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Northern China ◽  
Heat Fluxes ◽  
Specific Humidity ◽  
Hydraulic Parameters ◽  
Temperature Range ◽  
Temperature Forecast ◽  
Soil Hydraulic ◽  
Soil Moisture Initialization

AbstractIn this study, the causes of the underestimated diurnal 2-m temperature range and the overestimated 2-m specific humidity in the winter of northern China in the Rapid-Refresh Multiscale Analysis and Prediction System–Short Term (RMAPS-ST) are investigated. Three simulations based on RMAPS-ST are conducted from 1 November 2016 to 28 February 2017. Further analyses show that the partitioning of surface upward sensible heat fluxes and downward ground heat fluxes might be the main contributing factor to the 2-m temperature forecast bias. In this study, two simulations are conducted to examine the effect of soil moisture initialization and soil hydraulic property on the 2-m temperature and 2-m specific humidity forecasts. First, the High-Resolution Land Data Assimilation System (HRLDAS) is used to provide an alternative soil moisture initialization. The results show that the drier soil moisture could lead to noticeable change in energy partitioning at the land surface, which in turn results in improved prediction of the diurnal 2-m temperature range, although it also enlarges the 2-m specific humidity bias in some parts of the domain. Second, a soil texture dataset developed by Beijing Normal University and the revised hydraulic parameters are applied to provide a more detailed description of soil properties, which could further improve the 2-m specific humidity bias. In summary, the combination of using optimized soil moisture initialization, an updated soil map, and revised soil hydraulic parameters can help improve the 2-m temperature and 2-m specific humidity prediction in RMAPS-ST.

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.

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