Seasonal variation of the surface wind forecast performance of the high-resolution WRF-RTFDDA system over China

Pan, Linlin; Liu, Yubao; Roux, Gregory; Cheng, Will; Ju, Hu; Jin, Shuanggen; Feng, Shuanglei; Du, Jun; Peng, Lixia

doi:10.1016/j.atmosres.2021.105673

Cited by 12 publications

(9 citation statements)

References 38 publications

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“…The results of our analysis show that dust forecasts produced by a mesoscale model in which simulated emissions are the only source of elevated dust (with no elevated dust in the initial conditions) may show different error growth patterns than those found in meteorological forecast variables (e.g., wind, temperature) following data assimilation. Our wind-speed verification against surface observations, as well as verification of WRF-RTFDDA against surface and upper observations found in literature (e.g., Wyszogrodzki et al, 2013;Pan et al, 2021) illustrates this fact. In Figure 2 and in Pan et al (2021) error growth starts at the very few hours after free forecast initialization following data assimilation, and may saturate or continue to grow with free forecast lead time.…”

Section: Discussionsupporting

confidence: 85%

“…Our wind-speed verification against surface observations, as well as verification of WRF-RTFDDA against surface and upper observations found in literature (e.g., Wyszogrodzki et al, 2013;Pan et al, 2021) illustrates this fact. In Figure 2 and in Pan et al (2021) error growth starts at the very few hours after free forecast initialization following data assimilation, and may saturate or continue to grow with free forecast lead time. This is expected as errors in the initial conditions propagate into the forecasts and the positive impact of assimilation reduces as the atmospheric state evolves.…”

Section: Discussionsupporting

confidence: 85%

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Evaluation of WRF-Chem-RTFDDA dust forecasts over the MENA region using in-situ and remote-sensing observations

Rostkier‐Edelstein

Kunin

Sheu

et al. 2022

Front. Environ. Sci.

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We employed the combined WRF-Chem-RTFDDA model to forecast dust storms in the Middle East and North Africa (MENA). WRF-Chem simulates the emission, transport, mixing, and chemical transformation of trace gases and aerosols simultaneously with the meteorology. RTFDDA continuously assimilates both conventional and nonconventional meteorological observations and provides improved initial conditions for dust analyses and forecasts. WRF-Chem-RTFDDA was run at a horizontal resolution of 9 km using the dust only option without inclusion of anthropogenic aerosols and chemical reactions. The synoptic conditions of the dust events were characterized by a cold front at the low level and an upper-level low-pressure system over the Western Mediterranean. WRF-Chem-RTFDDA was run in continuous assimilation mode, assimilating meteorological observations only, and launching 48-h free forecasts (FF) every 6 h. Two cold starts (CSs) for data assimilation and dust emissions initiation were performed during the study period. NCEP/GFS global analyses and forecasts provided initial and lateral boundary conditions. No global dust model was used for initialization and no dust observations were assimilated. We analyzed the skill of the WRF-Chem-RTFDDA system in reproducing the horizontal and vertical distributions of dust by comparing the FF to Meteosat SEVIRI dust images, MODIS AOD retrievals, CALIPSO extinction coefficients and CAMS aerosols-reanalysis AOD calculations. The skill was analyzed as a function of FF lead time and of the period of time from the CSs. RMSE, bias and correlation between the modeled and CALIPSO measured extinction coefficients were also examined. WRF-Chem-RTFDDA reproduced the main features of the studied dust storms reasonably well. The time distance from the CSs played a more significant role in determining the dust-forecast skill than free-forecast lead time. Since no external dust information was provided to the model, dust emissions and dust spin-up by WRF-Chem played a critical role in dust forecasts. The vertical extent of the CALIPSO extinction coefficients were reasonably well reproduced once model emissions were spun-up. False alarms rates range from 0.03 to 0.26, with many below 0.15, indicating satisfactory performance as a warning system. This study shows the feasibility of dust forecasts using minimal input data over the MENA region.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionsupporting

confidence: 85%

Evaluation of WRF-Chem-RTFDDA dust forecasts over the MENA region using in-situ and remote-sensing observations

Rostkier‐Edelstein

Kunin

Sheu

et al. 2022

Front. Environ. Sci.

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“…Negative biases occur in a few windy mountainous areas of Xinjiang, northern Inner Mongolia, and the Qinghai-Tibet Plateau (Lew, 2000). Previous studies have reported similar SWS prediction errors in other regions (Duan et al, 2018;Feng, Sun, & Zhang, 2020;Misaki et al, 2019;Pan et al, 2021;Shimada et al, 2011;Wyszogrodzki et al, 2013).…”

mentioning

confidence: 70%

Improving Surface Wind Speed Forecasts Using an Offline Surface Multilayer Model With Optimal Ground Forcing

Feng

Huang

2022

J Adv Model Earth Syst

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Predicting surface wind speed (SWS) is crucial to operational weather forecasting. Accurate predictions can guide public activity planning, improve air safety, and forecast severe haze associated with stagnant air (Feng et al., 2018(Feng et al., , 2020b. They can also help improve the efficiency of wind power systems and the maintenance of wind farms, making the predictions relevant to clean energy development (Wang et al., 2020). Traditional models for the numerical weather prediction (NWP) of SWS use physical surface-layer schemes. Commonly used single-layer diagnostic schemes (Benjamin et al., 2004; European Centre for Medium-Range Forecasts, 2013;Skamarock et al., 2019) provide vertical wind profiles from the ground to the first level of the NWP model under various stability conditions Optis et al., 2014). As these schemes assume a homogeneous surface, they lose validity when the surface is a complex combination of buildings, vegetation, and variable topography (

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“…Wave height (Buoy), wind speed [38] Wave height/period, wind speed/direction, sea level pressure, gust speed, air pressure, Sea surface temperature, buoy data [39] Mean wave period (wave buoy data) [37] Offshore wind speed (light detection and ranging and seashore meteorological mast) [40] Wave height/period/direction (buoy station from NOAA) [41] Daily ocean wave height prediction [42] Wind power generation [43] Wind power forecast [44] Wind speed forecasting [45][46][47][48] Wind forecasting [49] Wind farm cluster power prediction [50] Surface wind forecast [51] Prediction of directly gained or measured parameters are more flexible and easier to achieve than calculated power. However, indirect factors such as temperature, salinity, pressure and precipitation might be used for forecasting and prediction or the exploration of correlations [13].…”

Section: Applicationsmentioning

confidence: 99%

Review on Deep Learning Research and Applications in Wind and Wave Energy

2022

Energies

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Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification.

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Seasonal variation of the surface wind forecast performance of the high-resolution WRF-RTFDDA system over China

Cited by 12 publications

References 38 publications

Evaluation of WRF-Chem-RTFDDA dust forecasts over the MENA region using in-situ and remote-sensing observations

Evaluation of WRF-Chem-RTFDDA dust forecasts over the MENA region using in-situ and remote-sensing observations

Improving Surface Wind Speed Forecasts Using an Offline Surface Multilayer Model With Optimal Ground Forcing

Review on Deep Learning Research and Applications in Wind and Wave Energy

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