Retrieval of Surface Ozone from UV-MFRSR Irradiances using Deep Learning Maosi Chen 1
1,†
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, Zhibin Sun , John M. Davis , Melina Zempila , Chaoshun Liu , Wei Gao
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USDA UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colora do, USA 2 Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, China 3 Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, Colorado, USA
†
[email protected] +1-970-491-3604
Introduction
2.1 Dataset
High concentration of surface ozone is harmful to humans and plants. USDA UV-B Monitoring and Research Program (UVMRP) uses Ultraviolet (UV) version of Multi-Filter Rotating Shadowband Radiometer (UV-MFRSR) to measure direct, diffuse, and total irradiances every 3 minutes at 7 UV channels (i.e. 300, 305, 311, 317, 325, 332, and 368 nm channels with 2 nm full width at half maximum). There have been plenty of literatures exploring retrieval methods of total column ozone from UV-MFRSR measurements, but few has explored the retrieval of surface ozone. Under clear-sky conditions, UV irradiances absorption by ozone are significant and variable by height and wavelength. Therefore, multi-channel UV irradiances at the ground have the potential to resolve ozone concentrations at multiple vertical layers (including surface ozone). In this study, we used a deep learning algorithm (i.e. Self-Normalizing Neural Network, SNN) to retrieve surface ozone from 3-minute UV-MFRSR direct and diffuse irradiances (and the airmass) under clear-sky conditions at the UVMRP station located at Billings, Oklahoma. The 3-minute surface ozone data for training and validation are accumulated from 1-second surface ozone measured at the collocated Southern Great Plains (SGP) station by US Department of Energy Atmospheric Radiation Measurement Climate Research Facility (ARM). To cover the cloudy conditions, we also explored several spatial interpolation techniques [i.e. Triangulation-based linear interpolation, Graph Convolutional Neural Network (GCNN or ChebNet), mixture model network (MoNet), and Recurrent Neural Network (RNN)] to estimate the hourly surface ozone at the same UVMRP station from the adjacent (i.e. within the 3-degree box of) US Environmental Protection Agency (EPA) hourly surface ozone observations.
1. Surface ozone retrieval from UV irradiances
2. Surface ozone retrieval from EPA stations
FC: Fully Connected
Graph CNN (Tensorflow)
SELU: Scaled Exponential Linear Unit
(Defferrard et al. 2016)
Alpha Dropout: dropout units are set to -λα to keep the mean and variance unchanged.
(Klambauer et al. 2017)
1.1 Dataset 1. Input: UV-MFRSR 3-minute direct normal and diffuse irradiances at 7 channels and the airmass (7+7+1=15) at UV-B Monitoring and Research Program (UVMRP) Billing, Oklahoma station (36.60°N, 97.49°W) in 2012 and 2016. Cloudy data are excluded by a cloud screening algorithm (Chen et al. 2014). 2. Output: 3-minute surface ozone is averaged from 1-second surface ozone values, which are measured at the collocated Southern Great Plains (SGP) station by US Department of Energy Atmospheric Radiation Measurement Climate Research Facility (ARM). The instruments are the Thermo Environmental Instruments, Inc. 49I-A3NAB and 49I-A1NAC Ozone Analyzers in 2012 and 2016, respectively. Note: a) Randomly select 90% of available 3-min Input-Output pairs for training and use the rest samples for test. b) Datasets in 2012 and 2016 are trained and tested independently. c) Total number of samples: 7,085 (2012); 5,024 (2016).
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5
6
7
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12
14
16
2012
1.25
1.60
1.80
2.06
2.21
2.42
2.10
3.00
3.16
3.17
3.85
4.16
4.89
2016
1.12
1.23
1.41
1.61
1.72
2.14
2.10
2.18
2.50
2.32
3.00
3.09
3.69
SNN with 6 to 10 layers have the best training and test performance. Training Time increases linearly with number of SNN layers. Number of nodes in the first layer around 10-30 times of the input nodes has best performance. Decreasing number of nodes in layers as an arithmetic sequence is better than a geometric sequence. ARM surface ozone data have slightly different relationship with UVMRP irradiances in the two years.
2.2 Results Spatial Distribution of Absolute Test Error (surface ozone)
2.Calc. patch averages of points A
Time (hour) for training different number of SNN layers 400 epochs 3
1. Construct and rescale multi-level graph Laplacians. 2. Use Chebyshev polynomials, the rescaled Laplacian, and the inputs to construct K-localized kernels at the given coarsening level recursively. 3. Perform CNN-like convolution, activation, and pooling at the coarsening level. 4. Repeat steps 2 and 3 until outputs have no padded nodes (fixed length). 5. Use SNN to calculate target surface ozone.
3.Using SNN, connect the final weights (gij) for each patch with the target location, patch locations (μij), and patch averages (Df ij). 4.Calc. target surface ozone (norm.)
RNN (Tensorflow)
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EPA hourly surface ozone data within 3-degree box of the UVMRP Billings, Oklahoma station in 2016. Inputs (or reference points) and Output (or target point): In each hour, select one (interior) point as the target point and the rest as the reference points. Total number of samples: 214,802. 95% random samples for training, the rest for testing.
1.Calc. distance weights for points A with respect to patch ij (in Zone i)
(Monti et al. 2016)
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MoNet (Tensorflow)
1.2 Results
Year
H31G-1590
(Graves et al. 2013)
1.Input sequence are reference points ordered by their distances to target (features: its own norm. lat. & long., target’s norm. lat. & long., and its distance to target) 2.Use RNN (stacked Bi-LSTM) to encode spatial relationship between the target and reference points into a pair of fixed length hidden vectors from the last Bi-LSTM layer (hB,0 & hF,T: last hidden vectors from backward and forward passes). 3.Use Fully connected layers (ELU activated, with dropout) to calc. target surface ozone.
Performance Summary (20 epochs) Method
Training Time (hour)
Training |Error| (Mean, std. dev. ) (ppb)
Test |Error| (Mean, std. dev. ) (ppb)
Tri. linear interpolation
-
-
3.97, 4.25
Graph CNN
4.80
4.28, 3.80
4.38, 3.94
MoNet
4.40
3.34, 3.16
3.44, 3.27
RNN
14.53
2.43, 2.21
2.89, 2.84
References Chen et al. 2014. A new cloud screening algorithm for ground-based direct-beam solar radiation. JTECH Defferrard et al. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. arXiv:1606.09375 Graves et al. 2013. Speech recognition with deep recurrent neural networks. ICASSP. arXiv:1303.5778 Klambauer et al. 2017. Self-Normalizing Neural Networks. arXiv:1706.02515 Monti et al. 2016. Geometric deep learning on graphs and manifolds using mixture model CNNs. arXiv:1611.08402
