The Self-Assessment Manikin (SAM) is one of the most widely used tools for measuring affect along the dimensions of valence and arousal, yet its abstract humanoid icons have been criticized for ambiguity, especially in representing arousal, and for lacking full gender neutrality. Despite newer alternatives, challenges remain in achieving clarity, inclusivity, and dimensional precision. To address this, we developed the Weather-Based Emotion Reporting (WER), a visual digital scale that represents affective states using familiar weather scenes. Grounded in normative affective data, WER was constructed to systematically map weather imagery onto the valence-arousal circumplex. In a within-subjects online experiment (N = 100), participants rated affective words using either WER or the SAM, allowing comparison of convergent validity, reaction times, and subjective usability.WER demonstrated a strong convergence with SAM, particularly for valence, while arousal showed moderate and more variable agreement, replicating well-documented asymmetry between affective dimensions. Reaction time analyses showed that WER responses were slightly faster than SAM responses, although the effect size was small. Participants consistently preferred WER, reporting greater clarity, comfort, and ease of use. Visual complexity analyses confirmed that WER and SAM differ qualitatively in their visual structure. Complementary image-complexity analyses highlighted the role of visual richness as a contextual factor in affective reporting. Together, these findings support WER as a valid and intuitive alternative to traditional schematic affective rating tools, particularly for communicating valence while maintaining comparable performance for arousal, and suggest that ecologically grounded visual metaphors may address some limitations of existing instruments.