Daytime Rainy Cloud Detection and Convective Precipitation Delineation Based on a Deep Neural Network Method Using GOES-16 ABI Images
Abstract
:1. Introduction
2. Data and Spectral Parameters
2.1. Study Area
2.2. GPM-IMERG Precipitation Estimates and Gauge Data
2.3. GOES-16 ABI Data
2.4. Spectral Parameters
3. Methodology and Models
3.1. Data Processing
- Data preprocessing. First, the 16 bands of ABI radiance are gridded to the spatial resolution of 0.1°, the same as IMERG precipitation estimates. Second, six individual ABI data with scanning time ranges included in one IMERG time step are averaged to complete the spatial and temporal collocation between the two datasets. Third, Ref, BT, and sun zenith angle (z) are calculated, with reflectance being derived as follows (Equation (2)):
- Spectral parameters are calculated and normalized.
- Each set of parameters is sorted into rainy and non-rainy clouds according to the associated IMERG rain rate (r). If r < 0.1 mm/hr, the sample is classified as non-rainy; otherwise, it is labeled as rainy. The rainy cloud detection models are built using DNN models, through which rainy samples are separated automatically from non-rainy ones.
- After rainy or non-rainy samples are distinguished, the convective and stratiform rain clouds are split based on their rain rates. The adopted threshold to discriminate convection or stratus cloud is calculated through the Z–r relation [49]:
- Convective precipitation delineation models are built using training samples derived from step 4 with DNN models.
- Accuracy is evaluated using validation data and case studies.
3.2. Model Development
4. Model Performance Evaluation
4.1. Evaluation Metrics
- The probability of detection (POD). The POD is the rate of testing samples correctly recognized as rainy/convective by the model:
- The probability of false detection (POFD). The POFD indicates the fraction of rainy/convective events incorrectly predicted by the model:
- The false alarm ratio (FAR). The FAR is the ratio of the incorrect detection of rainy/convective pixels and the total pixels recognized as rainy/convective:
- The bias. Bias represents model over- or underestimates of reality:
- The critical success index (CSI). The CSI is the fraction between the correct prediction of rainy/convective pixels by the model and the total number of pixels detected as rainy/convective by both IMERG and the model:
- The model accuracy (MA). The MA is the probability of a correct prediction of both rainy/convective and non-rainy/stratiform pixels by the model:
4.2. Comparison of DNN, SVM, and RF Performance on Testing Data
5. Case Study
5.1. Normal Precipitation Events
5.2. Hurricane Florence
6. Discussion
7. Conclusions
- In the detection of rainy areas, the system provides reliable results of normal precipitation events and precipitation extremes such as hurricanes with a tendency toward overestimation;
- The DNN achieves better performance than the two ML methods, with higher accuracies of the assessors on testing data;
- The system performs better over the ocean versus land;
- This study is offered as a contribution to combine the advantages of AI methodology with the modeling of atmospheric phenomena, a relatively innovative domain needing more exploration. More specifically, the system combines DNN-classifier and spectral features of rainy clouds to investigate precipitation properties. This research establishes an essential step with which to estimate precipitation rates further.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
ABI | Advanced Baseline Imager |
AVHRR | Advanced Very High Resolution Radiometer |
AVNNET | Averaged neural networks |
BP | Back-propagation |
CCD | Cold cloud duration |
CCPD | Cold cloud phase duration |
CMORPH | Climate Prediction Center Morphing Technique |
CONUS | Contiguous U.S. |
CSI | Critical success index |
CTT | Cloud-top temperature |
dBZ | Decibel relative to reflectivity factor of the radar |
DL | Deep learning |
DNN | Deep neural network |
FAR | False alarm ratio |
GBRT | Gradient-boosted regression trees |
IMERG | Integrated Multi-Satellite Retrievals for Global Precipitation Measurement |
LWP | Liquid water path |
MA | Model accuracy |
ML | Machine learning |
MLP | Multilayer perceptron network |
NIR | Near-infrared |
NNET | Neural networks |
PMW | Passive microwave |
POD | Probability of detection |
POFD | Probability of false detection |
QPE | Quantitative precipitation estimation |
TRMM | Tropical Rainfall Measuring Mission |
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Band Number | Central Wavelength (μm) | Spatial Resolution at Nadir (km) | κ-Factor (W −1 * m * μm) | Used in Study | Primary Application |
---|---|---|---|---|---|
1 | 0.47 | 1.0 | 1.5177−3 | Aerosols | |
2 | 0.64 | 0.5 | 1.8767−3 | √ | Clouds |
3 | 0.865 | 1.0 | 3.1988−3 | √ | Vegetation |
4 | 1.378 | 2.0 | 8.4828−3 | Cirrus | |
5 | 1.61 | 1.0 | 1.26225−2 | Snow/ice discrimination, cloud phase | |
6 | 2.24 | 2.0 | 3.98109−2 | √ | Cloud particle size, snow cloud phase |
7 | 3.9 | 2.0 | - | Fog, stratus, fire, volcanism | |
8 | 6.19 | 2.0 | - | √ | Various atmospheric features |
9 | 6.95 | 2.0 | - | Middle-level water vapor features | |
10 | 7.34 | 2.0 | - | √ | Lower-level water vapor features |
11 | 8.5 | 2.0 | - | √ | Cloud-top phase |
12 | 9.61 | 2.0 | - | √ | Total column of ozone |
13 | 10.35 | 2.0 | - | √ | Clouds |
14 | 11.2 | 2.0 | - | √ | Clouds |
15 | 12.3 | 2.0 | - | √ | Clouds |
16 | 13.3 | 2.0 | - | √ | Air temperature, clouds |
Spectral Parameters | Cloud Features Reflected by the Parameter |
---|---|
Ref0.64 | Cloud brightness |
Ref0.865 | Cloud brightness |
Ref2.24 | Cloud particle size |
BT10.35 | Cloud particle size, cloud-top temperature |
ΔBT6.19−10.35 | Cloud-top temperature, convective level |
ΔBT7.34−12.3 | Cloud-top temperature, convective level |
ΔBT6.19−7.34 | Cloud height and thickness |
ΔBT13.3−10.35 | Cloud-top height |
ΔBT9.61−13.3 | Cloud-top height |
ΔBT8.5−10.35 | Cloud phase (positive for thick ice clouds, negative for thin low-level water clouds) |
ΔBT8.5−12.3 | Optical thickness (negative values for thin optical thickness) |
BT6.19 | Upper-level tropospheric water vapor |
ΔBT7.34−8.5 | Cloud optical thickness |
ΔBT7.34−11.2 | Cloud-top temperature and height |
ΔBT11.2−12.3 | Cloud thickness, particle size |
Training and Validation Samples | Testing Samples |
---|---|
3–5, 17, 22 June; | 1, 10, 24 June; |
4, 7, 30, 31 July; | 11, 26 July; |
2–4, 8–12, 18–20, 22 August; | 5–7 August; |
7–8, 14, 17, 25 September | 4–6 September |
By IMERG | |||
---|---|---|---|
Yes (Rainy/Convective) | No (Non-Rainy/Stratiform) | ||
By models | Yes (Rainy/Convective) | a | b |
No (Non-rainy/Stratiform) | c | d |
Model | POD (Ideal 1) | POFD (Ideal 0) | FAR (Ideal 0) | Bias (Ideal 1) | CSI (Ideal 1) | MA (Ideal 1) |
---|---|---|---|---|---|---|
DNN | 0.86 | 0.13 | 0.20 | 1.07 | 0.71 | 0.87 |
SVM | 0.85 | 0.13 | 0.21 | 1.07 | 0.69 | 0.86 |
RF | 0.85 | 0.14 | 0.21 | 1.09 | 0.70 | 0.86 |
Model | POD (Ideal 1) | POFD (Ideal 0) | FAR (Ideal 0) | Bias (Ideal 1) | CSI (Ideal 1) | MA (Ideal 1) |
---|---|---|---|---|---|---|
DNN | 0.72 | 0.23 | 0.24 | 0.94 | 0.58 | 0.74 |
SVM | 0.86 | 0.40 | 0.44 | 1.55 | 0.51 | 0.69 |
RF | 0.78 | 0.35 | 0.43 | 1.37 | 0.49 | 0.70 |
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Liu, Q.; Li, Y.; Yu, M.; Chiu, L.S.; Hao, X.; Duffy, D.Q.; Yang, C. Daytime Rainy Cloud Detection and Convective Precipitation Delineation Based on a Deep Neural Network Method Using GOES-16 ABI Images. Remote Sens. 2019, 11, 2555. https://doi.org/10.3390/rs11212555
Liu Q, Li Y, Yu M, Chiu LS, Hao X, Duffy DQ, Yang C. Daytime Rainy Cloud Detection and Convective Precipitation Delineation Based on a Deep Neural Network Method Using GOES-16 ABI Images. Remote Sensing. 2019; 11(21):2555. https://doi.org/10.3390/rs11212555
Chicago/Turabian StyleLiu, Qian, Yun Li, Manzhu Yu, Long S. Chiu, Xianjun Hao, Daniel Q. Duffy, and Chaowei Yang. 2019. "Daytime Rainy Cloud Detection and Convective Precipitation Delineation Based on a Deep Neural Network Method Using GOES-16 ABI Images" Remote Sensing 11, no. 21: 2555. https://doi.org/10.3390/rs11212555
APA StyleLiu, Q., Li, Y., Yu, M., Chiu, L. S., Hao, X., Duffy, D. Q., & Yang, C. (2019). Daytime Rainy Cloud Detection and Convective Precipitation Delineation Based on a Deep Neural Network Method Using GOES-16 ABI Images. Remote Sensing, 11(21), 2555. https://doi.org/10.3390/rs11212555