Benchmarking ConvLSTM for One-Day-Ahead IMDAA Rainfall-Field Prediction across Four Indian Cities

AI 中文解读

ConvLSTM，这种专门处理空间图像序列的AI模型，在一天前的降雨预测中并没有展现出想象中那么神奇。一项针对印度四个城市的基准测试发现，它的表现经常不如更简单的方法——比如直接“照搬”今天的降雨数据，或者使用另一种不那么复杂的FC-LSTM模型。研究指出，ConvLSTM的优势通常需要依赖密集、高频的雷达数据才能发挥，而在只有每天一次的再分析资料这种“稀疏”条件下，复杂模型反而容易低估高降雨日的雨量，且漏测比例更高。这意味着，天气预报的精度并不必然与算法复杂度成正比。对普通人而言，这项研究的启示很直接：未来天气预报App可能不会盲目追求最前沿的AI模型，而会先做大量本地化对比测试，选出最靠谱的那一个。而作为用户，你会更频繁地看到“明天降雨概率和今天差不多”这种接地气的预测，因为对于某些城市，最简单的“持续法”反而比昂贵的机器学习模型更值得信赖。

arXiv:2607.26581v1 Announce Type: new Abstract: Convolutional long short-term memory networks (ConvLSTMs) are widely used for precipitation forecasting, but most evidence for their performance comes from dense, high-frequency radar sequences. This study tests whether convolutional recurrence improves one-day-ahead rainfall-field prediction on small daily reanalysis grids. Indian Monsoon Data Assimilation and Analysis (IMDAA) fields for June-September 1998-2020 were analysed for Bengaluru, Delhi, Kolkata and Mumbai. Ten naive, statistical, tree-based and neural approaches were compared using atmospheric-only and rainfall-history-plus-atmospheric inputs. Performance was assessed for complete fields, domain-mean rainfall, spatial anomalies and high-rainfall days. ConvLSTM did not consistently outperform simpler alternatives. FC-LSTM produced the numerically lowest domain-mean rainfall error in Bengaluru, Kolkata and Mumbai, whereas persistence performed best in Delhi. ConvLSTM produced the numerically lowest spatial-anomaly error only in Mumbai, where rainfall fields showed greater short-term spatial continuity and rainfall-history inputs improved all three neural architectures. The difference between ConvLSTM and FC-LSTM was nevertheless small. Neural models underestimated rainfall magnitude and predicted too few threshold exceedances on high-rainfall days, while persistence achieved the highest detection performance in every city. Post-hoc analyses showed that the selected models were most sensitive to the latest input day, with broader recent-lag sensitivity in Mumbai. These findings show that gridded inputs alone do not justify ConvLSTM and that architecture choice should follow strong benchmarking across average, spatial and high-rainfall performance.