Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the input and the prediction target are spatiotemporal sequences. By extending the fully connected LSTM (FC-LSTM) to have convolutional structures in both the input-to-state and state-to-state transitions, we propose the convolutional LSTM (ConvLSTM) and use it to build an end-to-end trainable model for the precipitation nowcasting problem. Experiments show that our ConvLSTM network captures spatiotemporal correlations better and consistently outperforms FC-LSTM and the state-of-theart operational ROVER algorithm for precipitation nowcasting.
The paper describes an extension to the fully connected long short-term memory models for the purpose of prediction of doppler radar echo for a region over a short period of time given the recent doppler radar echo images.The proposed framework extends LSTMs to handle the temporal evolution of spatial fields.For the experiments, the paper considers two set ups, an artificial one based on the movements of handwritten digits, and a data set of Doppler radar images for unspecified region and time.Quality: The proposed extension ConvLSTM appears to be mathematically sound and appropriate for the modeling of regularly timed spatio-temporal data.The paper provides only a basic formulation of the framework; there are few details on the training of the model -- it is assumed that the reader is familiar with how FC-LSTMs are trained.How does the training of ConvLSTM differ?There are too few details provided about the precipitation data to judge whether the set up is appropriate.It appears to be somewhat artificial to me in the following aspects: (1) storms can be fast moving (e.g., Midwest of the United States), so including the data only for the subregion of prediction may ignore the crucial information about the storm if it falls outside of the prediction area; (2) only days with precipitation are being used as a part of the data -- this appears unreasonable for a real application, (3) other covariates (e.g., time of day) are not taken into an account.Additionally, to demonstrate an impact for the application, it would be appropriate to compare the predictive ability not just to other machine learning methods, but the standard methods in the application area, numerical weather prediction models (https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/numerical-weather-prediction).Clarity: The paper tries to cover quite a bit of ground, unfortunately, at the expense of clarity.The details on the proposed model are fairly bare.I would suggest to focus either on the application (and in this case, skip the dancing digits while elaborating more on the primary application) or on the methodology (and provide more details on the property of the new model at the expense of the details on the nowcasting application).Originality: There are original contribution to the modeling framework and in applying it to the short-term precipitation forecasting.Personally, I am glad to read that deep learning techniques are reaching precipitation modeling research area.Significance: I do not have the expertise to judge whether the incremental leap between FC-LSTM and ConvLSTM is significant.For the nowcasting application, while the proposed framework can be a useful intermediate step, due to the reservations mentioned under Quality section, I doubt it is close to solving the problem.Minor: - Please check equation (3); I think some of the Hadamard products should actually be convolution operators. - Page 1, The acronym for numerical weather forecasting is NWF.Did you mean numerical weather prediction? - Footnote on page 1, "plan"->"plane"?