Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation

2017 ◽  
Vol 107 ◽  
pp. 340-351 ◽  
Author(s):  
William Y.Y. Cheng ◽  
Yubao Liu ◽  
Alfred J. Bourgeois ◽  
Yonghui Wu ◽  
Sue Ellen Haupt
Keyword(s):  
Data Assimilation ◽  
Wind Turbine ◽  
Weather Forecasting ◽  
Short Term ◽  
Forecasting System

Scale-Dependent Background Error Covariance Localization: Evaluation in a Global Deterministic Weather Forecasting System

2018 ◽  
Vol 146 (5) ◽  
pp. 1367-1381 ◽  
Author(s):  
Jean-François Caron ◽  
Mark Buehner
Keyword(s):  
Data Assimilation ◽  
Weather Forecasting ◽  
Spatial Scales ◽  
Winter Period ◽  
Wind Fields ◽  
Horizontal Scale ◽  
Background Error ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Forecasting System

Abstract Scale-dependent localization (SDL) consists of applying the appropriate (i.e., different) amount of localization to different ranges of background error covariance spatial scales while simultaneously assimilating all of the available observations. The SDL method proposed by Buehner and Shlyaeva for ensemble–variational (EnVar) data assimilation was tested in a 3D-EnVar version of the Canadian operational global data assimilation system. It is shown that a horizontal-scale-dependent horizontal localization leads to implicit vertical-level-dependent, variable-dependent, and location-dependent horizontal localization. The results from data assimilation cycles show that horizontal-scale-dependent horizontal covariance localization is able to improve the forecasts up to day 5 in the Northern Hemisphere extratropical summer period and up to day 7 in the Southern Hemisphere extratropical winter period. In the tropics, use of SDL results in improvements similar to what can be obtained by increasing the uniform amount of spatial localization. An investigation of the dynamical balance in the resulting analysis increments demonstrates that SDL does not further harm the balance between the mass and the rotational wind fields, as compared to the traditional localization approach. Potential future applications for the SDL method are also discussed.

Data assimilation of FY-4A dust aerosol observations for the CUACE/dust forecasting system

2020 ◽  
Author(s):  
Tao Niu ◽  
Xiaoye Zhang ◽  
Shanling Gong ◽  
Yaqiang Wang ◽  
Hongli Liu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Three Dimensional ◽  
Forecast Model ◽  
Short Term ◽  
Operational Forecasts ◽  
Forecasting System ◽  
Dust Loading ◽  
Data Assimilation System ◽  
Sand And Dust Storm

<p>A data assimilation system (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment– Dust (CUACE/Dust) forecast system and applied in the operational forecasts of sand and dust storm (SDS) in spring in Asia. The system is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility (phenomena) and dust loading retrieval from the Chinese geostationary satellite FY-2C. By a number of case studies, the DAS was found to provide corrections to both under- and over-estimates of SDS, presenting a major improvement to the forecasting capability of CUACE/Dust in the short-term variability in the spatial distribution and intensity of dust concentrations in both source regions and downwind areas.  By now The DAS was upgrade to assimilate FY-4A dust aerosol observations. The seasonal mean Threat Score (TS) over the East Asia in spring increased when DAS was used. The forecast results with DAS usually agree with the dust loading retrieved from FY and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful by the unification of observation and numerical model to improve the performance of forecast model.</p>

scholarly journals Machine learning emulation of gravity wave drag in numerical weather forecasting

2021 ◽  
Author(s):  
Matthew Chantry ◽  
Sam Hatfield ◽  
Peter Duben ◽  
Inna Polichtchouk ◽  
Tim Palmer
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Data Assimilation ◽  
Gravity Wave ◽  
Weather Forecasting ◽  
Wave Drag ◽  
Seasonal Forecasting ◽  
Numerical Weather ◽  
Forecasting System ◽  
Numerical Weather Forecasting

<p>We assess the value of machine learning as an accelerator for a kernel of an operational weather forecasting system, specifically the parameterisation of non-orographic gravity wave drag. Emulators of this scheme can be trained that produce stable and accurate results up to seasonal forecasting timescales. By training on an increased complexity version of the parameterisation scheme we build emulators that produce more accurate forecasts than the existing parameterisation scheme. Leveraging the differentiability of neural networks we generate tangent linear and adjoint versions of our parameterisation, key components in 4D-var data-assimilation. We test our tangent linear and adjoint codes within an operational-like 4D-var setup and find no degradation in skill vs hand-written tangent-linear and adjoint codes.</p>

Short Term Weather Forecasting in Real Time in a Base Weather Station Setting

1992 ◽  
Author(s):  
Frank P. Colby ◽  
Seitter Jr. ◽  
Keith L.
Keyword(s):  
Real Time ◽  
Weather Forecasting ◽  
Weather Station ◽  
Short Term

Catastrophic Effects of Floods on Environment and Health: Evidence from Pakistan

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Falak Shad Memon ◽  
M. Yousuf Sharjeel
Keyword(s):  
Paradigm Shift ◽  
Weather Forecasting ◽  
Human Life ◽  
National Level ◽  
Operational Cost ◽  
Site Assessment ◽  
Environment And Health ◽  
Forecasting System ◽  
Frame Work ◽  
Multiple Levels

<span>Torrential rains and floods have been causing irreplaceable losses to both human lives and environment in <span>Pakistan. This loss has reached to an extent of assively aggrieved situation to reinstate life at <span>operationally viable position. This paper unfolds the notion that only constructive paradigm shift to <span>overcome this phenomenon is vital as a strategy. Multiple levels of observations and on-site assessment <span>of various calamity-prone venues were considered to probe into this scenario. Some of the grave site in <span>Sindh and Punjab were observed and necessarily practicable measures were recommended to avoid loss to <span>human health and environment. The paper finds that a consistent drastic management authority on <span>national level with appropriate caliber and forecasting expertise can reduce the damage to human life and <span>environment to great extent. Weather forecasting system need to be installed at many appropriately <span>observed cities and towns in the country with adequate man power, funds and technical recourses. By <span>implementing the proper frame work of prevention and mitigation of floods country can save the major <span>costs cleanup and recovery. These measures are expected to reduce operational cost of state in terms of <span>GDP and GNP to restore life and environment.</span></span></span></span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span>

scholarly journals Aerodynamic-Efficiency-Constrained Short-Term Frequency Support Control of Wind Turbine Generators

2020 ◽  
Vol 53 (2) ◽  
pp. 12115-12120
Author(s):  
Zhengyang Zhang ◽  
Zaiyu Chen ◽  
Guoqiang Yu ◽  
Tianhai Zhang ◽  
Minghui Yin ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Short Term ◽  
Aerodynamic Efficiency ◽  
Term Frequency ◽  
Turbine Generators ◽  
Wind Turbine Generators

scholarly journals Evaluation of WRF-DART (ARW v3.9.1.1 and DART Manhattan release) multiphase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment

2019 ◽  
Vol 12 (9) ◽  
pp. 3939-3954
Author(s):  
Frederik Kurzrock ◽  
Hannah Nguyen ◽  
Jerome Sauer ◽  
Fabrice Chane Ming ◽  
Sylvain Cros ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Cloud Water ◽  
Specific Humidity ◽  
Reunion Island ◽  
Short Term ◽  
Water Path ◽  
Réunion Island ◽  
Cloud Water Path ◽  
The Impact

Abstract. Numerical weather prediction models tend to underestimate cloud presence and therefore often overestimate global horizontal irradiance (GHI). The assimilation of cloud water path (CWP) retrievals from geostationary satellites using an ensemble Kalman filter (EnKF) led to improved short-term GHI forecasts of the Weather Research and Forecasting (WRF) model in midlatitudes in case studies. An evaluation of the method under tropical conditions and a quantification of this improvement for study periods of more than a few days are still missing. This paper focuses on the assimilation of CWP retrievals in three phases (ice, supercooled, and liquid) in a 6-hourly cycling procedure and on the impact of this method on short-term forecasts of GHI for Réunion Island, a tropical island in the southwest Indian Ocean. The multilayer gridded cloud properties of NASA Langley's Satellite ClOud and Radiation Property retrieval System (SatCORPS) are assimilated using the EnKF of the Data Assimilation Research Testbed (DART) Manhattan release (revision 12002) and the advanced research WRF (ARW) v3.9.1.1. The ability of the method to improve cloud analyses and GHI forecasts is demonstrated, and a comparison using independent radiosoundings shows a reduction of specific humidity bias in the WRF analyses, especially in the low and middle troposphere. Ground-based GHI observations at 12 sites on Réunion Island are used to quantify the impact of CWP DA. Over a total of 44 d during austral summertime, when averaged over all sites, CWP data assimilation has a positive impact on GHI forecasts for all lead times between 5 and 14 h. Root mean square error and mean absolute error are reduced by 4 % and 3 %, respectively.

scholarly journals The Australian Air Quality Forecasting System: Exploring First Steps Towards Determining The Limits of Predictability For Short-Term Ozone Forecasting

2005 ◽  
Vol 116 (2) ◽  
pp. 363-384 ◽  
Author(s):  
M. E. Cope ◽  
G. D. Hess ◽  
S. Lee ◽  
K. J. Tory ◽  
M. Burgers ◽  
...  
Keyword(s):  
Air Quality ◽  
Short Term ◽  
Forecasting System ◽  
Air Quality Forecasting ◽  
Ozone Forecasting
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