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This Special Issue “Atmospheric Conditions for Wind Energy Applications” hosts papers on aspects of remote sensing for atmospheric conditions for wind energy applications. Wind lidar technology is presented from a theoretical view on the coherent focused Doppler lidar principles. Furthermore, wind lidar for applied use for wind turbine control, wind farm wake, and gust characterizations is presented, as well as methods to reduce uncertainty when using lidar in complex terrain. Wind lidar observations are used to validate numerical model results. Wind Doppler lidar mounted on aircraft used for observing winds in hurricane conditions and Doppler radar on the ground used for very short-term wind forecasting are presented. For the offshore environment, floating lidar data processing is presented as well as an experiment with wind-profiling lidar on a ferry for model validation. Assessments of wind resources in the coastal zone using wind-profiling lidar and global wind maps using satellite data are presented..

complex flow --- Floating Lidar System (FLS) --- mesoscale --- wind energy resources --- variational analysis --- wind turbine --- wind sensing --- wind energy --- wind gusts --- wake --- wind structure --- complex terrain --- global ocean --- remote sensing forecasting --- detached eddy simulation --- five-minute ahead wind power forecasting --- tropical cyclones --- fetch effect --- aerosol --- vertical Light Detection and Ranging --- range gate length --- resource assessment --- field experiments --- remote sensing --- optical flow --- turbulence --- atmospheric boundary layer --- Doppler Wind Lidar --- offshore --- empirical equation --- Lidar --- WindSAT --- coastal wind measurement --- offshore wind speed forecasting --- Doppler wind lidar --- Doppler --- wind --- wind lidar --- cross-correlation --- QuikSCAT --- wind resource assessment --- detecting and tracking --- single-particle --- gust prediction --- NWP model --- velocity-azimuth-display algorithm --- lidar-assisted control (LAC) --- Doppler lidar --- motion estimation --- power performance testing --- lidar --- large-eddy simulations --- wind farm --- coherent Doppler lidar --- wake modeling --- probabilistic forecasting --- control --- NeoWins --- wind turbine controls --- impact prediction --- wind turbine wake --- Hazaki Oceanographical Research Station --- VAD --- virtual lidar --- Doppler radar --- IEA Wind Task 32 --- ASCAT --- wind atlas --- turbulence intensity

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Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones.

Research & information: general --- wildfire --- satellite vegetation indices --- live fuel moisture --- empirical model function --- Southern California --- chaparral ecosystem --- forest fire --- forest recovery --- satellite remote sensing --- vegetation index --- burn index --- gross primary production --- South Korea --- land subsidence --- PS-InSAR --- uneven settlement --- building construction --- Beijing urban area --- floodplain delineation --- inaccessible region --- machine learning --- flash flood --- risk --- LSSVM --- China --- Himawari-8 --- threshold-based algorithm --- remote sensing --- dryness monitoring --- soil moisture --- NIR–Red spectral space --- Landsat-8 --- MODIS --- Xinjiang province of China --- SDE --- PE --- groundwater level --- compressible sediment layer --- tropical cyclone formation --- WindSat --- disaster monitoring --- wireless sensor network --- debris flow --- anomaly detection --- deep learning --- accelerometer sensor --- total precipitable water --- Himawari-8 AHI --- random forest --- deep neural network --- XGBoost --- n/a --- NIR-Red spectral space