Visual　object　tracking　is　essentially　crucial　for　unmanned　aerial　vehicles　(UAVs).　Despite　the　substantial　progress,　most　of　the　existing　UAV　trackers　are　designed　for　well-conditioned　daytime　data,　while　for　the　scenarios　in　challenging　weather　condition,　e.g.　foggy　or　nighttime　environment,　the　tremendous　domain　gap　leads　to　significant　performance　degradation.　To　address　this　issue,　in　this　paper,　we　propose　a　novel　robust　UAV　tracker　termed　LVPTrack,　which　conducts　high　quality　label-aligned　visual　prompt　tuning　to　adapt　to　various　challenging　weather　conditions.　Specifically,　we　first　synthesize　the　sequential　foggy　and　nighttime　video　frames　to　assist　the　model　training.　A　domain　adaptive　teacher-student　network　is　utilized　to　distill　the　hierarchical　visual　semantic　of　the　target　objects　in　cross-domain　scenarios.　Then　we　propose　a　target-aware　pseudo-label　voting　(PLV)　strategy　to　alleviate　the　target-level　misalignment　in　the　dual　domains.　Furthermore,　we　propose　a　dynamic　aggregated　prompt　(DAP)　module　to　facilitate　the　appearance　variation　adaptation　of　the　target　object　in　challenging　scenarios.　Extensive　experiments　demonstrate　that　our　tracker　achieves　superior　performance　over　existing　state-of-the-art　UAV　trackers.　Copyright　©　2025,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.