Volume 48, Issue 10 e2020GL092092
Research Letter
Open Access

Subseasonal Forecasts of Opportunity Identified by an Explainable Neural Network

Kirsten J. Mayer

Corresponding Author

Kirsten J. Mayer

Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA

Correspondence to:

K. J. Mayer,

[email protected]

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Elizabeth A. Barnes

Elizabeth A. Barnes

Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA

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First published: 04 May 2021
Citations: 27

Abstract

Midlatitude prediction on subseasonal timescales is difficult due to the chaotic nature of the atmosphere and often requires the identification of favorable atmospheric conditions that may lead to enhanced skill (“forecasts of opportunity”). Here, we demonstrate that an artificial neural network (ANN) can identify such opportunities for tropical-extratropical circulation teleconnections within the North Atlantic (40°N, 325°E) at a lead of 22 days using the network's confidence in a given prediction. Furthermore, layer-wise relevance propagation (LRP), an ANN explainability technique, pinpoints the relevant tropical features the ANN uses to make accurate predictions. We find that LRP identifies tropical hot spots that correspond to known favorable regions for midlatitude teleconnections and reveals a potential new pattern for prediction in the North Atlantic on subseasonal timescales.

Key Points

  • Neural networks can be used to identify forecasts of opportunity for subseasonal prediction

  • Neural network explainability techniques pinpoint relevant tropical regions for predictions in the North Atlantic

  • Clustering of neural network relevance heat maps reveals a potential new forecast of opportunity for the North Atlantic

Plain Language Summary

Weather forecasting on 2 week to 2 month timescales is known for its lack of predictability due to the chaotic nature of the atmosphere. One way to improve prediction skill on these timescales involves the identification of periods of atmospheric conditions that lead to enhanced predictability (“forecasts of opportunity”). Here, we show that a neural network can accurately identify these opportunities when trying to predict the atmospheric circulation over the North Atlantic Ocean 4 weeks in advance. A neural network explainability technique is then used to uncover what the network has “learned” to make these accurate predictions. We show that the network identifies known patterns of storminess ideal for midlatitude prediction and uncovers a possible new favorable region for enhanced prediction.

1 Introduction

Subseasonal timescales (2 weeks–2 months) are known for their lack of predictability (Mariotti et al., 2018), yet reliable and actionable information on these timescales are required for decision making in many sectors such as public health and water management (e.g., White et al., 2017; Vitart et al., 2012). Over the past decade, there has been a substantial research effort to improve prediction on these timescales (e.g., Pegion et al., 2019; Robertson et al., 2015; Vitart et al., 2012, 2017). One area of subseasonal prediction research focuses on forecasts of opportunity, the idea that certain earth system conditions provide opportunities for enhanced subseasonal prediction skill (Mariotti et al. 2020). When these opportunities arise, the information provided by the earth system's state can then be leveraged to improve forecast skill. For example, when the Madden-Julian Oscillation (MJO; Madden & Julian, 1971, 1972), a propagating tropical convective phenomenon, is active, its convective heating can lead to the excitation of quasi-stationary Rossby waves (Hoskins & Ambrizzi, 1993) that subsequently modulate the midlatitude circulation over the first few weeks following MJO activity (e.g., Baggett et al., 2017; Henderson et al., 2016; Hoskins & Karoly, 1981; Sardeshmukh & Hoskins, 1988; Zheng et al., 2018). When opposing convective anomalies are located over the Indian Ocean and western Pacific (defined as phases 2, 3, 6, and 7), the MJO has been shown to lead to more coherent and consistent modulations of midlatitude weather on subseasonal timescales and consequently, enhanced prediction skill (Tseng et al., 2018). Using the strength and location of tropical convective activity of the MJO to identify periods of enhanced midlatitude prediction skill is, therefore, an example of forecast of opportunity identification. Mundhenk et al. (2018) also show that an empirical model, which solely uses information about the state of the MJO and the Quasi-Biennial Oscillation, outperforms a state-of-the-art numerical prediction model for prediction of atmospheric river activity on subseasonal timescales. This highlights the importance of statistical models for enhancing subseasonal prediction.

Albers and Newman (2019) demonstrate a technique for forecast of opportunity identification through the utilization of expected skill from a linear inverse model. The study demonstrates the ability of the linear statistical model to identify forecasts of opportunity, and raises the question of whether other statistical models, such as artificial neural networks (ANNs), can identify forecasts of opportunity for subseasonal prediction. ANNs are very good at nonlinear function estimation (Chen & Chen, 1995), and thus, may be able to identify both linear and nonlinear relationships that lend predictability. Recently, neural networks have been successfully applied to seasonal prediction of meteorological variables such as monthly rainfall (Abbot & Marohasy, 2014) and surface temperature (Toms et al., 2020) as well as yearly prediction of the El Nino Southern Oscillation (Ham et al., 2019), suggesting neural networks may be useful for identifying subseasonal forecasts of opportunity as well.

In this paper, we test whether an ANN can be used for subseasonal forecast of opportunity identification. To do so, we input tropical outgoing longwave radiation (OLR) anomalies into an ANN and task the network to predict the sign of 500 hPa geopotential height (z500) anomalies in the North Atlantic (40°N, 325°E) 22 days later (e.g., Week 4). Tropical OLR is used to explore the ability of an ANN to identify known relationships between the MJO and the North Atlantic via tropical-extratropical teleconnections (e.g., Cassou, 2008; Henderson et al., 2016). We demonstrate that an ANN can identify subseasonal forecasts of opportunity related to tropical OLR, and through an ANN explainability technique, demonstrate that the ANN identifies these known MJO-like OLR patterns. In addition, we find a possible new tropical OLR pattern associated with predictable behavior of the North Atlantic circulation on subseasonal timescales.

2 Data and Methods

2.1 Data

We use daily mean OLR (1979–2019) from the National Center for Atmospheric Research/National Oceanic and Atmospheric Administration (NCAR/NOAA; Liebmann and Smith (1996)) and daily mean z500 (1979–2019) from the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim reanalysis (ERA-I; Dee et al. (2011)). MJO teleconnections tend to be stronger during boreal winter (Madden, 1986), and therefore, the extended boreal winter months (November–February) are used for the OLR fields. Since we task the network to predict the sign of the z500 anomaly 22 days following a given OLR field, March is also included in the z500 analysis (see Text S1 for reasoning behind the choice of lead).

The annual cycle is removed from both the z500 and OLR data. For z500, the annual cycle is removed by subtracting the daily climatology over the record (1979–2019). A Fast Fourier Transform (FFT) high-pass filter is then applied to the z500 anomalies to remove seasonal oscillations (frequencies smaller than urn:x-wiley:00948276:media:grl62374:grl62374-math-0001) to ensure the network focuses on subseasonal anomalies. The median of the z500 anomalies for the training data (see Section 2.2.1) is subtracted to obtain an equal number of positive and negative values. These anomalies are then converted into 0 and 1s depending on the sign (negative or positive, respectively). To filter the testing data (see Section 2.2.1), z500 anomalies from 2017 to 2019 are appended to the unfiltered z500 anomalies from 1979 to 2016 and another FFT high pass filter is applied to all years. The now filtered 2017–2019 data are then subset and used as testing data. Initially, the FFT analysis is not applied to the full data set to ensure the network has no information about the testing data during training. The median of the z500 anomalies for the training data (see Section 2.2.1) is then subtracted and the anomalies are converted into 0 and 1s. For OLR, the annual cycle is removed by subtracting the first 3 harmonics of the daily climatology from the raw field. The first three harmonics are used instead of the daily mean because OLR is a noisier field than z500.

2.2 Methods

2.2.1 ANN Architecture

A two layer ANN (Figure 1) is tasked to ingest tropical OLR and predict the sign of the z500 anomaly over the North Atlantic (40°N, 325°E; red dot in Figure 1) 22 days later. The North Atlantic is chosen for this analysis since the MJO is known to force circulation anomalies over this region on subseasonal timescales and thus allows us to explore the utility of an ANN in the context of a well-known problem (e.g., Cassou, 2008; Henderson et al., 2016; Roundy et al., 2010). In addition, we find that this grid point is representative of a larger area within the North Atlantic (see Figure S1).

Details are in the caption following the image

Artificial neural network architecture for prediction of the sign of z500 anomalies over the North Atlantic 22 days following tropical outgoing longwave radiation anomalies. The neural network consists of two hidden layers of 128 and 8 nodes, respectively, and an output layer of two nodes (one node for each sign). The output layer uses the softmax activation function.

Each input sample to the ANN consists of vectorized daily anomalous OLR from 30°N to 20°S and 45 to 210°E, where the number of input nodes is equal to the number of OLR grid points (N = 1,407). The ANN then outputs two values that describe the categorical prediction, positive or negative sign of z500, given the initial OLR input image. The softmax activation function is applied to this final layer and transforms the two output values such that they sum to 1. The output then represents an estimation of the likelihood that an input belongs to a particular category, where the predicted category is defined by a likelihood greater than 0.5. We refer to this estimation of likelihood as “model confidence.” A more confident prediction will, therefore, have a predicted category value closer to 1. We define forecasts of opportunities as the top 10% most confident predictions by the network, although we explore alternative percentages as well.

The ANN architecture consists of two hidden layers of 128 and 8 nodes, respectively, and both use the rectified linear activation function. The final layer includes two nodes and uses the rectified linear and softmax activation function. Categorical cross entropy is used for the loss function. This architecture is chosen because it was found to consistently lead to reasonably high accuracies across many combinations of training/validation sets, but our ANN approach should be equally applicable to both shallow and deep networks. The batch size is set to 256 samples (i.e., OLR vectorized images) and the ANN is trained for 50 epochs unless the validation loss increases for two epochs in a row. If this occurs, the ANN stops training early and restores the model's best weights to reduce overfitting. It is found that 50 epochs are sufficient for training as the ANN rarely completes all 50 epochs. A more detailed explanation of ANNs is provided in the supplemental material for reference along with a comparison of this ANN approach to multinomial logistic regression.

The data used to train and test the ANN is composed of three groups: training, validation, and testing. Training and validation data are used during training, where training data is used to update the weights and biases of the ANN and the validation data is used to evaluate the model. The testing data is data that has never been “seen” by the ANN to evaluate the ability of the ANN to generalize to new data. To create the testing data, we assume that the years 2017–2019 have not yet occurred when training the model. In this way, these years act as true testing data for the ANN. While the specific accuracies likely would change with different testing data, the main point of this paper is to introduce a method to identify forecasts of opportunity and then to further identify the associated relevant regions for the enhanced prediction skill, not to provide the most accurate model for this scenario.

For this analysis, the ANN validation data are from November 2007 through February 2011 (N = 481) and the testing data is from November 2017 through February 2019 (N = 240). The remaining extended boreal winter (NDJF) data are used for training (November 1979–February 2007 and November 2011–February 2016; N = 4,450; see Figure S2). All data are standardized for each grid point by the years used for training and validation. To choose a model for the following analysis, ANN training is repeated for a variety of validation years. Different consecutive 4-year chunks are removed from the training data and set aside to use as validation. For each of the nine four-year chunks, the ANN was trained 20 times with random initialized weights. We find that our conclusions are robust to our choice in training period and do not change with variations in random initialization weights. We present one model with reasonably high accuracy here and using the training, validation, and testing groups outlined above.

2.2.2 Layer-Wise Relevance Propagation (LRP)

While ANNs are a useful tool for making predictions, in doing so, they are learning how to make accurate predictions. Therefore, understanding the inner workings of a trained ANN can provide valuable information for improving prediction skill and understanding, as well as increasing user confidence in the results. Here, we utilize a relatively new neural network explainability technique to the geosciences called LRP (Bach et al., 2015; Montavon et al., 2019) to extract and visualize the features the trained ANN employs to make accurate predictions. While Toms et al. (2020) describes the use of LRP for geoscience applications in detail, we briefly provide a high-level description here (see supplemental material for a more detailed explanation). After network training is completed, a single sample is passed through the network and a prediction is made (in our case, two output values are predicted). Our implementation of LRP then takes the highest of these values (i.e., the winning category) and back-propagates this value through the network via a series of predefined rules, ultimately distributing it across the input nodes (i.e., input gridpoints). What results is a heat map of “relevance” across the input space, where input nodes that are more relevant for the network's specific prediction for that sample are given higher relevance. This process is then repeated for every sample of interest, resulting in a unique relevance heat map for each sample. In our study, since the input layer consists of maps of OLR anomalies, the LRP heat maps are maps of the relevant tropical OLR patterns for each prediction of the circulation in the North Atlantic (40°N, 325°E). These maps are discussed in detail in Section 3.2.

3 Results

3.1 Identifying Forecasts of Opportunity

ANNs with the architecture shown in Figure 1 are trained 100 times with random initialized weights to predict the sign of the z500 anomalies 22 days following the tropical OLR anomalies. Figure 2a shows the distribution of the testing prediction accuracy for all 100 models, where dark teal represents the distribution of all predictions and light teal represents the distribution of the 10% most confident predictions. The corresponding colored vertical dashed lines indicate a threshold for what is expected by random chance. To calculate the random chance accuracy threshold, 100,000 randomly generated groups (N = 240 for all and N = 24 for 10% most confident predictions) of zeros and ones are used to create a distribution of accuracies, and the 90th percentile of this distribution is used as the random chance threshold. In Figure 2a, the top 10% most confident prediction accuracies (light teal) are shifted toward higher accuracies compared to the distribution with all predictions (dark teal). This shift in the distributions demonstrates that in general, higher model confidence leads to substantially enhanced prediction accuracy.

Details are in the caption following the image

(a) Histograms of testing prediction accuracy for 100 trained artificial neural networks. The dark teal represents the histogram of all prediction accuracies and the light teal represents the histogram for the 10% most confident prediction accuracies. The dark teal and light teal dashed lines in (a) are the maximum accuracies expected by random chance at the 90% confidence level for the corresponding colored histogram (see text for details). (b) Accuracy of one particular model as a function of the percent most confident predictions for training and validation (black) and testing (light teal) data. The dashed lines indicate the maximum accuracies expected by random chance at the 90% confidence level for the corresponding colored lines (see text for details).

We chose one model from Figure 2a to further understand how accuracy varies when a different percent model confidence is used (Figure 2b). The solid lines represent the accuracy across various model confidence values for training and validation (black) and testing (light teal) data sets. Figure 2b shows that the testing accuracy (light teal line) barely outperforms the random chance 90% confidence bound (light teal dashed line) for all predictions (“all”) while the skill is substantially larger than random chance for the top 10% of predictions. Accuracy increasing with increasing model confidence is also apparent in the training and validation data. Together, Figure 2a and 2b illustrate that model confidence and prediction accuracy generally increase together and therefore, can be used to identify forecasts of opportunities, or periods of enhanced prediction skill. From this analysis, the 10% most confident predictions are chosen to define forecasts of opportunity since this threshold has one of the largest accuracy differences from random chance while still retaining 10% of the samples.

When evaluating the network with the training and validation data, the prediction accuracy for all predictions is 58% and for the top 10% most confident predictions is 73%. For the testing data, the prediction accuracy for all predictions is 56% and for the top 10% most confident predictions is 79%. The ANN predictions as a function of time are detailed in Figure S2, and additional skill metrics are provided in Figure S3 and Table S1.

3.2 Tropical Sources of Predictability

We have shown that ANNs can identify forecasts of opportunity using model confidence; however, understanding where this enhanced skill originates is critical for improving physical understanding as well as gaining trust in the network's predictions. To do so, LRP is used to identify the OLR patterns that lead the ANN to make correct predictions (see Section 2.2.2). The correct 10% most confident predictions from the training, validation and testing data sets are combined for this LRP analysis. All three sets of data are used instead of only testing data because all data sets have similar accuracies and LRP values (not shown). Thus, including all the data increases the sample sizes for the analysis. The shading in Figure 3c–3h shows the regions the network found relevant, on average, to make confident and correct positive (Figure 3c, 3e, and 3g) and negative (Figure 3d, 3f, and 3h) z500 predictions. The contours correspond to the average OLR anomalies for these confident and correct predictions.

Details are in the caption following the image

(a, b) Layer-wise relevance propagation (LRP) frequency of occurrence maps for average relevance values greater than 0.5. Both (a) and (b) consist of models from every 4 years validation chunk. Of these models, only average LRP maps of confident and correct predictions (training, validation, and testing) from models with testing accuracies greater than 70% are included. Maps (c)–(h) are the LRP maps associated with the artificial neural network from Figure 2b where the shading denotes smoothed composites of LRP fields for all correct forecasts of opportunity for (c) positive sign and (d) negative sign predictions across training, validation and testing periods. The associated two k-means clusters of LRP for (e),(g) positive sign predictions and (f),(h) negative sign predictions are also shown. Contours represent the corresponding smoothed outgoing longwave radiation anomalies where solid lines are positive values and dashed lines are negative values. (a) and (b) contours range from 0.4urn:x-wiley:00948276:media:grl62374:grl62374-math-0002 and −1.0– −0.4urn:x-wiley:00948276:media:grl62374:grl62374-math-0003 and (e)–(h) contours range from 0.4–1.6urn:x-wiley:00948276:media:grl62374:grl62374-math-0004 and −1.6–−0.4urn:x-wiley:00948276:media:grl62374:grl62374-math-0005, both with a contour interval of 0.2urn:x-wiley:00948276:media:grl62374:grl62374-math-0006.

The average LRP heat map for the correct forecasts of opportunity of positive sign predictions (Figure 3c) indicates four hot spots, one over the southern Indian Ocean into the southern Maritime Continent (20–0°S, 70–130°E), one over the western Pacific (20–0°S, 155−170°E), another northwest of Hawaii (25°N, 170°W), and the fourth over Saudi Arabia (30°N, 40–60°E). The average LRP heat map for the correct forecasts of opportunity of negative sign predictions (Figure 3d) indicates four hot spots, one over the Maritime Continent (20–0°S, 110–150°E), one in the western and central Pacific Ocean (20–0°S, 155°E−170°W), another to the west of Hawaii (20°N, 170°W), and the fourth over Saudi Arabia (30°N, 40–60°E).

For both sign predictions, the hot spots over the Maritime Continent and the western Pacific have opposing signed OLR anomalies (contours) that straddle 150°E. These dipoles of convection over the Indian Ocean into the Maritime Continent and over the western Pacific have similar structures to phase 4–5 and phase 1, 7–8 of the MJO (Wheeler & Hendon, 2004). This structure of OLR is consistent with previous research of MJO teleconnections over the North Atlantic for average lead times of 10–14 and 15–19 days (e.g., Cassou, 2008; Henderson et al., 2016; Henderson & Maloney, 2018; Tseng et al., 2018). In addition, this dipole structure is known to lead to higher pattern consistency of teleconnections in the midlatitudes (Tseng et al., 2019), which has been shown to lead to enhanced prediction skill (Tseng et al., 2018). Rossby waves initiated by the MJO tend to be quasi-stationary, which suggests that these OLR anomalies may also correspond to 22 days leads as well. This Maritime Continent and western Pacific Ocean dipole highlighted in part by LRP is therefore consistent with previous research and demonstrates that the ANN has learned physically relevant structures.

To test the robustness of these average LRP results for this particular ANN, we calculated the frequency of occurrence of average relevance hotspots greater than 0.5 for models with testing accuracies greater than 70% (Figure 3a and 3b, n = 42 models). We find that all of the hotspots (i.e., the MJO-like structure, the hot spot over Saudi Arabia and the hot spot west of Hawaii) are robust features for enhanced subseasonal prediction throughout these 42 models. In the next section, we hypothesize that the hot spot over Saudi Arabia is associated with the two-way relationship between the North Atlantic Oscillation (NAO) and the MJO (Lin et al., 2009). On the other hand, the hot spot west of Hawaii in both sign predictions is discussed as a possible new region relevant for enhanced subseasonal prediction.

3.3 K-Means Clustering of LRP Maps

To further distinguish the relevant regions for the ANN's predictions, k-means clustering (Hartigan and Wong (1979), see supplemental material for more information) is applied to the LRP maps (Figure 3e–3h). This analysis reveals that the composite LRP maps for each sign (Figure 3c and 3d) actually consist of multiple distinct patterns used by the ANN. For positive sign predictions (Figure 3e and 3g), both clusters have a hot spot located between the central Indian ocean and the maritime continent, which are associated with negative OLR anomalies. While not highlighted by LRP in cluster 2 (Figure 3g), each negative OLR anomaly region is accompanied by a region of positive OLR anomalies over the western Pacific. This suggests the model is identifying an MJO-like pattern. More specifically, the clustering has identified two types of relevance for this MJO-like pattern. The LRP map for cluster 1 (Figure 3e) highlights both the positive and negative OLR anomalies. As previously mentioned, these regions lead to more consistent midlatitude teleconnections (Tseng et al., 2018) and have been shown to be associated with a positive NAO anomaly (Cassou, 2008), which corresponds to a positive z500 anomaly at the predicted location. Cluster 1, therefore, supports previously identified tropical OLR regions and patterns ideal for enhanced prediction skill on subseasonal timescales in the North Atlantic. On the other hand, the LRP map for cluster 2 (Figure 3g) focuses exclusively on the south-central Maritime Continent, which is associated with enhanced convection from the Indian Ocean to the Maritime Continent. This is more consistent with recent research that suggests that convection over the Indian Ocean dominates the formation of a positive NAO anomaly (Shao et al., 2020). This relationship is nicely illustrated in Figure 3a as the Indian Ocean is highlighted by the LRP analysis more often than the western Pacific.

For cluster 1 of the negative sign predictions (Figure 3f), there are two hot spots, one over the Maritime Continent and the other over the Pacific Ocean. As with the positive sign predictions, each hot spot is associated with opposing sign OLR anomalies; however, unlike cluster 1 of the positive sign predictions, the LRP analysis more strongly highlights the western Pacific region, and suggests that the network finds the region of enhanced convection more relevant. This is similar to cluster two of the positive sign predictions and is consistent with Figure 3b which shows that the region over the western Pacific is more often highlighted by the LRP analysis compared to the Maritime Continent. This suggests that the network often focuses on the region of enhanced convection for both sign predictions.

Unexpectedly, there is also a hot spot located over Saudi Arabia in cluster 1 for both positive and negative predictions. As seen in Figure 3a and 3b, this region is frequently highlighted by LRP in many ANNs. This hot spot appears to only be important when an MJO-like dipole structure is present. To the authors' knowledge, this region has not been shown to be important for tropical-extratropical teleconnections to the North Atlantic. However, previous research has shown that there is a two-way relationship between the MJO and NAO. Following the NAO, there tends to be a significant modulation of the tropical upper troposphere zonal wind over the Atlantic-Africa region (Lin et al., 2009). This modulation has been hypothesized to play a role in MJO initialization (Lin & Brunet, 2011; Lin et al., 2009). Since the NAO can persist over many weeks, the network may be identifying an influence of the NAO on the MJO and back to the NAO. We leave a deeper exploration of this possible mechanism to future work.

Unlike the other clusters, cluster 2 of the negative sign predictions (Figure 3h) has only one hot spot west of Hawaii (25°N, 170°W) and no MJO-like OLR anomalies. We hypothesize that this region is physically important as it is located south of the subtropical jet exit region and is associated with a large OLR anomaly. Rossby waves can be generated through advection of vorticity by upper level divergence or convergence associated with OLR anomalies (Sardeshmukh & Hoskins, 1988). Since this hot spot region is close to the jet exit region, these waves can more easily propagate into the midlatitudes or become trapped within the North Atlantic jet and directed into the North Atlantic (Hoskins & Ambrizzi, 1993; Hoskins & Karoly, 1981). Based on these known tropical-extratropical teleconnection dynamics, it is likely that this hot spot west of Hawaii is a new pattern identified by the ANN. This hot spot is also weakly apparent in cluster 1 of the positive sign predictions (Figure 3e), but is associated with MJO-like OLR anomalies. Given the lack of MJO-like patterns in cluster 2 of the negative sign predictions for this region, we hypothesize that this hot spot in cluster 1 of the positive sign prediction may not actually be associated with the MJO, but instead acting as an additional source of predictability.

4 Conclusions

Improving subseasonal prediction accuracy and understanding requires identifying opportunities that can lead to enhanced predictability (e.g., Mariotti et al., 2020). Here, we show that an ANN can identify forecasts of opportunity for subseasonal prediction using the network's confidence in its prediction. In addition, we demonstrate that LRP can extract knowledge gained by the ANN to identify relevant physical tropical features important for the predictions. K-means clustering of the LRP maps further provides insight into multiple distinct patterns used by the ANN for enhanced prediction and reveals a possible new forecast of opportunity for prediction over the North Atlantic.

The hot spots identified by the ANN provide a stepping stone to further our understanding of tropical-extratropical teleconnections. For example, lagged composite analysis or simplified models can be used to further explore the physical mechanisms behind enhanced midlatitude predictability associated with these regions. In addition, analysis of the incorrect predictions made by the ANN may also be useful for improving our understanding of ideal tropical patterns for enhanced subseasonal prediction. Finally, while our application is focused on subseasonal prediction, the approach outlined here should be applicable to predictions across timescales. Ultimately, this paper demonstrates that ANNs are not only a useful tool for prediction, but can also be used to gain physical insight into predictability and subsequently, improve prediction skill.​

Acknowledgments

This research is partially funded by the National Science Foundation Graduate Research Fellowship under the grant number 006784 supporting Kirsten J. Mayer and partially funded by the National Science Foundation Harnessing the Data Revolution through supporting Elizabeth A. Barnes with grant 1934668.

    Conflict of Interest

    The authors declare that they have no conflict of interest.

    Data Availability Statement

    ERA-I reanalysis data are provided by the European Centre for Medium-Range Forecasts (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim; Dee et al., 2011). The interpolated OLR data is provided by the NOAA/OAR/ESRL PSL, Boulder, CO, USA (https://psl.noaa.gov/data/gridded/data.interp_OLR.html; Liebmann & Smith, 1996).