Case Study: Development of the CNN Model Considering Teleconnection for Spatial Downscaling of Precipitation in a Climate Change Scenario
<p>Target area and GCM data.</p> "> Figure 2
<p>Monthly time series comparing the reference data and GCM data in the historical period. (The black dotted line: the range of the GCM precipitation data).</p> "> Figure 3
<p>The procedure of the CNN model and a flow chart of this study.</p> "> Figure 4
<p>Concept of the CNN model and the difference between Conv1D and 2D.</p> "> Figure 5
<p>CDF before and after calibration of the GCM using QM and reference data (subfigure: (<b>a</b>) is Incheon, (<b>b</b>) is Seoul, (<b>c</b>) is Suwon).</p> "> Figure 6
<p>Monthly time series of the reference data and calibrated GCM data using QM (subfigure: (<b>a</b>) is Incheon, (<b>b</b>) is Seoul, (<b>c</b>) is Suwon).</p> "> Figure 7
<p>Result of teleconnection analysis for SST delayed by 6 months.</p> "> Figure 8
<p>Architecture of the CNN model in this study.</p> "> Figure 9
<p>Result of CNN model optimization.</p> "> Figure 10
<p>Monthly time series of the reference data and calibrated GCM data (using the CNN model).</p> "> Figure 11
<p>Time series of the calibrated GCM data by each method in the test set.</p> "> Figure 12
<p>Taylor diagram for each region: (<b>a</b>) Incheon, (<b>b</b>) Seoul, and (<b>c</b>) Suwon.</p> "> Figure 13
<p>Future precipitation forecasting time series using the final model.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Datasets
2.3. Downscaling Methods
2.3.1. Quantile Mapping
2.3.2. The CNN Model Considering Teleconnection
Convolutional Neural Network
Teleconnection
2.4. Evaluation Metrics
3. Results
3.1. Application of Quantile Mapping (QM)
3.2. Application of the CNN
3.2.1. Teleconnection Analysis Using SST
3.2.2. CNN Model Training
3.3. Performance Evaluation for Each Method
3.4. Forecasting Precipitation Using the Final Model
4. Conclusions and Discussions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data Type | Description | Resolution (Lon. × Lat.) | Cell Size | Period (Monthly) |
---|---|---|---|---|
Historical data | - | 1.41° × 1.41° | 256 × 128 | 1850-01-01–2014-12-31 |
SSP 8.5 scenario data | Extreme scenario | 1.41° × 1.41° | 256 × 128 | 2015-01-01–2100-12-31 |
Station Name | Location | Elevation | Observation Start Date | Institutions |
---|---|---|---|---|
Incheon | lat: 37.4777° lon: 126.6249° | 68.99 m | 1904-08-29 | Korea Meteorological Administration |
Seoul | lat: 37.5714° lon: 126.9658° | 85.67 m | 1907-10-01 | Korea Meteorological Administration |
Suwon | lat: 37.2723° lon: 126.9853° | 34.84 m | 1964-01-01 | Korea Meteorological Administration |
Data Type | Min | Max | Mean | Standard Deviation |
---|---|---|---|---|
GCM data | 0 mm | 743 mm | 106.9 mm | 113.3 mm |
Incheon station | 0 mm | 1364 mm | 111.3 mm | 147.6 mm |
Seoul station | 0 mm | 1105 mm | 94 mm | 119.2 mm |
Suwon station | 0 mm | 967 mm | 111 mm | 137.8 mm |
Station | Most Positively Correlated Grid | Most Negatively Correlated Grid |
---|---|---|
Incheon | Longitude: 114° E Latitude: 65° S | Longitude: 126° E Latitude: 10° N |
Seoul | Longitude: 112° E Latitude: 65° S | Longitude: 126° E Latitude: 10° N |
Suwon | Longitude: 58° E Latitude: 66° S | Longitude: 135° E Latitude: 20° N |
Variable | Abbreviation | Description |
---|---|---|
Target variable | Obs. pr | Reference precipitation data for each station |
Explanatory variable | Clt | Cloud area fraction of the GCM |
Hurs | Relative humidity of the GCM | |
Pr | Precipitation of the GCM | |
Prw | Atmosphere mass content of water vapor of the GCM | |
Ps | Surface air pressure | |
Psl | Sea level pressure | |
Tas | Air temperature of the GCM | |
SST+ | Most positively correlated SST | |
SST- | Most negatively correlated SST |
Station | Clt | Hurs | Pr | Prw | Ps | Psl | Tas | SST+ | SST- |
---|---|---|---|---|---|---|---|---|---|
Incheon | 56% | 47% | 52% | 69% | −59% | −59% | 57% | 71% | −66% |
Seoul | 55% | 47% | 51% | 68% | −58% | −58% | 57% | 68% | −66% |
Suwon | 56% | 47% | 51% | 71% | −60% | −60% | 60% | 74% | −68% |
List | Parameter |
---|---|
Loss function | Mean Squared Error |
Activation function | Conv1D layer: Relu, Output layer: Linear |
Kernel size | 3 |
Optimizer function | Adam |
Batch size | 128 |
Epoch | 100 |
Stations | QM | CNN | ||
---|---|---|---|---|
CC (%) | RMSE | CC (%) | RMSE | |
Incheon | 36.68 | 199.67 mm | 60.92% | 146.92 mm |
Seoul | 40.87 | 152.55 mm | 72.71% | 102.16 mm |
Suwon | 45.25 | 157.64 mm | 73.35% | 102.46 mm |
Station | Reference Data (For 30 Years) | 2015–2040 (Short-Term) | 2041–2070 (Medium-Term) | 2071–2100 (Long-Term) |
---|---|---|---|---|
Incheon | 1187.5 mm | 1227.0 mm | 1356.8 mm | 1338.4 mm |
3.3% | 14.3% | 12.7% | ||
Seoul | 1125.5 mm | 1486.4 mm | 1475.9 mm | 1516.6 mm |
32.1% | 31.1% | 34.7% | ||
Suwon | 1177.9 mm | 1109.7 mm | 1091.63 mm | 1198.2 mm |
−5.8% | −7.3% | 1.7% |
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Kim, J.; Lee, M.; Han, H.; Kim, D.; Bae, Y.; Kim, H.S. Case Study: Development of the CNN Model Considering Teleconnection for Spatial Downscaling of Precipitation in a Climate Change Scenario. Sustainability 2022, 14, 4719. https://doi.org/10.3390/su14084719
Kim J, Lee M, Han H, Kim D, Bae Y, Kim HS. Case Study: Development of the CNN Model Considering Teleconnection for Spatial Downscaling of Precipitation in a Climate Change Scenario. Sustainability. 2022; 14(8):4719. https://doi.org/10.3390/su14084719
Chicago/Turabian StyleKim, Jongsung, Myungjin Lee, Heechan Han, Donghyun Kim, Yunghye Bae, and Hung Soo Kim. 2022. "Case Study: Development of the CNN Model Considering Teleconnection for Spatial Downscaling of Precipitation in a Climate Change Scenario" Sustainability 14, no. 8: 4719. https://doi.org/10.3390/su14084719