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Improving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands
Research article (Artificial Intelligence for the Earth Systems, 2023) · cited 13× · AI/ML
Improving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands
Summary
Improving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands is a scholarly article[1].
Key Facts
Improving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands's instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). Improving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands. Retrieved May 24, 2026, from https://4ort.xyz/entity/improving-precipitation-nowcasting-for-high-intensity-events-using-deep-generative-models-with-balanced-loss-and-tempera
MLA“Improving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/improving-precipitation-nowcasting-for-high-intensity-events-using-deep-generative-models-with-balanced-loss-and-tempera.
BibTeX@misc{4ortxyz_improving-precipitation-nowcasting-for-high-intensity-events-using-deep-generative-models-with-balanced-loss-and-tempera_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Improving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands}}, year = {2026}, url = {https://4ort.xyz/entity/improving-precipitation-nowcasting-for-high-intensity-events-using-deep-generative-models-with-balanced-loss-and-tempera}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Improving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands — https://4ort.xyz/entity/improving-precipitation-nowcasting-for-high-intensity-events-using-deep-generative-models-with-balanced-loss-and-tempera (retrieved 2026-05-24)
