Analysis: ENSO

We begin with the best-known of these phenomena: ENSO. Data are available from 1854 onwards; however, for comparability with AO, we discard all records prior to January, 1950. For analysis, we pick NINO34_MEAN, the monthly average sea surface temperature in the Niño 3.4 region (i.e., the area between 5° South, 5° North, 190° East, and 240° East). Finally, we convert to a tsibble, the format expected by feasts::STL().

library(tidyverse)
library(tsibble)

download.file(
  "https://bmcnoldy.rsmas.miami.edu/tropics/oni/ONI_NINO34_1854-2022.txt",
  destfile = "ONI_NINO34_1854-2022.txt"
)

enso <- read_table("ONI_NINO34_1854-2022.txt", skip = 9) %>%
  mutate(x = yearmonth(as.Date(paste0(YEAR, "-", `MON/MMM`, "-01")))) %>%
  select(x, enso = NINO34_MEAN) %>%
  filter(x >= yearmonth("1950-01"), x <= yearmonth("2022-09")) %>%
  as_tsibble(index = x)

enso

# A tsibble: 873 x 2 [1M]
          x  enso
      <mth> <dbl>
 1 1950 Jan  24.6
 2 1950 Feb  25.1
 3 1950 Mar  25.9
 4 1950 Apr  26.3
 5 1950 May  26.2
 6 1950 Jun  26.5
 7 1950 Jul  26.3
 8 1950 Aug  25.9
 9 1950 Sep  25.7
10 1950 Oct  25.7
# … with 863 more rows

As already announced, we want to look at seasonal decomposition, as well. In terms of seasonal periodicity, what do we expect? Unless told otherwise, feasts::STL() will happily pick a window size for us. However, there’ll likely be several important frequencies in the data. (Not wanting to ruin the suspense, but for AO and NAO, this will definitely be the case!). Besides, we want to compute the Fourier Transform anyway, so why not do that first?

Here is the power spectrum:

library(torch)
fft <- torch_fft_fft(as.numeric(scale(enso$enso)))

In the below plot, the x axis corresponds to frequencies, expressed as “number of times per year”. We only display frequencies up to and including the Nyquist frequency, i.e., half the sampling rate, which in our case is 12 (per year).

num_samples <- nrow(enso)
nyquist_cutoff <- ceiling(num_samples / 2) # highest discernible frequency
bins_below_nyquist <- 0:nyquist_cutoff

sampling_rate <- 12 # per year
frequencies_per_bin <- sampling_rate / num_samples
frequencies <- frequencies_per_bin * bins_below_nyquist

df <- data.frame(f = frequencies, y = as.numeric(fft[1:(nyquist_cutoff + 1)]$abs()))
df %>% ggplot(aes(f, y)) +
  geom_line() +
  xlab("frequency (per year)") +
  ylab("magnitude") +
  ggtitle("Spectrum of Niño 3.4 data")

There is one dominant frequency, corresponding to about once a year. From this component alone, we’d expect one El Niño event – or equivalently, one La Niña – per year. But let’s locate important frequencies more precisely. With not many other periodicities standing out, we may as well restrict ourselves to three:

strongest <- torch_topk(fft[1:(nyquist_cutoff/2)]$abs(), 3)
strongest

[[1]]
torch_tensor
233.9855
172.2784
142.3784
[ CPUFloatType{3} ]

[[2]]
torch_tensor
74
21
7
[ CPULongType{3} ]

What we have here are the magnitudes of the dominant components, as well as their respective bins in the spectrum. Let’s see which actual frequencies these correspond to:

important_freqs <- frequencies[as.numeric(strongest[[2]])]
important_freqs

[1] 1.00343643 0.27491409 0.08247423 

That’s once per year, once per quarter, and once every twelve years, approximately. Or, expressed as periodicity, in terms of months (i.e., how many months are there in a period):

num_observations_in_season <- 12/important_freqs  
num_observations_in_season

[1] 11.95890  43.65000 145.50000  

We now pass these to feasts::STL(), to obtain a five-fold decomposition into trend, seasonal components, and remainder.

library(feasts)
enso %>%
  model(STL(enso ~ season(period = 12) + season(period = 44) +
              season(period = 145))) %>%
  components() %>%
  autoplot()

According to Loess decomposition, there still is significant noise in the data – the remainder remaining high despite our hinting at important seasonalities. In fact, there is no big surprise in that: Looking back at the DFT output, not only are there many, close to one another, low- and lowish-frequency components, but in addition, high-frequency components just won’t cease to contribute. And really, as of today, ENSO forecasting – tremendously important in terms of human impact – is focused on predicting oscillation state just a year in advance. This will be interesting to keep in mind for when we proceed to the other series – as you’ll see, it’ll only get worse.

By now, we’re well informed about how dominant temporal rhythms determine, or fail to determine, what actually happens in atmosphere and ocean. But we don’t know anything about whether, and how, those rhythms may have varied in strength over the time span considered. This is where wavelet analysis comes in.

In torchwavelets, the central operation is a call to wavelet_transform(), to instantiate an object that takes care of all required operations. One argument is required: signal_length, the number of data points in the series. And one of the defaults we need to override: dt, the time between samples, expressed in the unit we’re working with. In our case, that’s year, and, having monthly samples, we need to pass a value of 1/12. With all other defaults untouched, analysis will be done using the Morlet wavelet (available alternatives are Mexican Hat and Paul), and the transform will be computed in the Fourier domain (the fastest way, unless you have a GPU).

library(torchwavelets)
enso_idx <- enso$enso %>% as.numeric() %>% torch_tensor()
dt <- 1/12
wtf <- wavelet_transform(length(enso_idx), dt = dt)

A call to power() will then compute the wavelet transform:

power_spectrum <- wtf$power(enso_idx)
power_spectrum$shape

[1]  71 873

The result is two-dimensional. The second dimension holds measurement times, i.e., the months between January, 1950 and September, 2022. The first dimension warrants some more explanation.

Namely, we have here the set of scales the transform has been computed for. If you’re familiar with the Fourier Transform and its analogue, the spectrogram, you’ll probably think in terms of time versus frequency. With wavelets, there is an additional parameter, the scale, that determines the spread of the analysis pattern.

Some wavelets have both a scale and a frequency, in which case these can interact in complex ways. Others are defined such that no separate frequency appears. In the latter case, you immediately end up with the time vs. scale layout we see in wavelet diagrams (scaleograms). In the former, most software hides the complexity by merging scale and frequency into one, leaving just scale as a user-visible parameter. In torchwavelets, too, the wavelet frequency (if existent) has been “streamlined away”. Consequently, we’ll end up plotting time versus scale, as well. I’ll say more when we actually see such a scaleogram.

For visualization, we transpose the data and put it into a ggplot-friendly format:

times <- lubridate::year(enso$x) + lubridate::month(enso$x) / 12
scales <- as.numeric(wtf$scales)

df <- as_tibble(as.matrix(power_spectrum$t()), .name_repair = "universal") %>%
  mutate(time = times) %>%
  pivot_longer(!time, names_to = "scale", values_to = "power") %>%
  mutate(scale = scales[scale %>%
    str_remove("[\\.]{3}") %>%
    as.numeric()])
df %>% glimpse()

Rows: 61,983
Columns: 3
$ time  <dbl> 1950.083, 1950.083, 1950.083, 1950.083, 195…
$ scale <dbl> 0.1613356, 0.1759377, 0.1918614, 0.2092263,…
$ power <dbl> 0.03617507, 0.05985500, 0.07948010, 0.09819…

There is one additional piece of information to be incorporated, still: the so-called “cone of influence” (COI). Visually, this is a shading that tells us which part of the plot reflects incomplete, and thus, unreliable and to-be-disregarded, data. Namely, the bigger the scale, the more spread-out the analysis wavelet, and the more incomplete the overlap at the borders of the series when the wavelet slides over the data. You’ll see what I mean in a second.

The COI gets its own data frame:

coi <- wtf$coi(times[1], times[length(enso_idx)])
coi_df <- data.frame(x = as.numeric(coi[[1]]), y = as.numeric(coi[[2]]))

And now we’re ready to create the scaleogram:

labeled_scales <- c(0.25, 0.5, 1, 2, 4, 8, 16, 32, 64)
labeled_frequencies <- round(as.numeric(wtf$fourier_period(labeled_scales)), 1)

ggplot(df) +
  scale_y_continuous(
    trans = scales::compose_trans(scales::log2_trans(), scales::reverse_trans()),
    breaks = c(0.25, 0.5, 1, 2, 4, 8, 16, 32, 64),
    limits = c(max(scales), min(scales)),
    expand = c(0, 0),
    sec.axis = dup_axis(
      labels = scales::label_number(labeled_frequencies),
      name = "Fourier period (years)"
    )
  ) +
  ylab("scale (years)") +
  scale_x_continuous(breaks = seq(1950, 2020, by = 5), expand = c(0, 0)) +
  xlab("year") +
  geom_contour_filled(aes(time, scale, z = power), show.legend = FALSE) +
  scale_fill_viridis_d(option = "turbo") +
  geom_ribbon(data = coi_df, aes(x = x, ymin = y, ymax = max(scales)),
              fill = "black", alpha = 0.6) +
  theme(legend.position = "none")

What we see here is how, in ENSO, different rhythms have prevailed over time. Instead of “rhythms”, I could have said “scales”, or “frequencies”, or “periods” – all those translate into one another. Since, to us humans, wavelet scales don’t mean that much, the period (in years) is displayed on an additional y axis on the right.

So, we see that in the eighties, an (approximately) four-year period had exceptional influence. Thereafter, yet longer periodicities gained in dominance. And, in accordance with what we expect from prior analysis, there is a basso continuo of annual similarity.

Also, note how, at first sight, there seems to have been a decade where a six-year period stood out: right at the beginning of where (for us) measurement starts, in the fifties. However, the dark shading – the COI – tells us that, in this region, the data is not to be trusted.

Summing up, the two-dimensional analysis nicely complements the more compressed characterization we got from the DFT. Before we move on to the next series, however, let me just quickly address one question, in case you were wondering (if not, just read on, since I won’t be going into details anyway): How is this different from a spectrogram?

In a nutshell, the spectrogram splits the data into several “windows”, and computes the DFT independently on all of them. To compute the scaleogram, on the other hand, the analysis wavelet slides continuously over the data, resulting in a spectrum-equivalent for the neighborhood of each sample in the series. With the spectrogram, a fixed window size means that not all frequencies are resolved equally well: The higher frequencies appear more frequently in the interval than the lower ones, and thus, will allow for better resolution. Wavelet analysis, in contrast, is done on a set of scales deliberately arranged so as to capture a broad range of frequencies theoretically visible in a series of given length.

Analysis: NAO

The data file for NAO is in fixed-table format. After conversion to a tsibble, we have:

download.file(
 "https://crudata.uea.ac.uk/cru/data//nao/nao.dat",
 destfile = "nao.dat"
)

# needed for AO, as well
use_months <- seq.Date(
  from = as.Date("1950-01-01"),
  to = as.Date("2022-09-01"),
  by = "months"
)

nao <-
  read_table(
    "nao.dat",
    col_names = FALSE,
    na = "-99.99",
    skip = 3
  ) %>%
  select(-X1, -X14) %>%
  as.matrix() %>%
  t() %>%
  as.vector() %>%
  .[1:length(use_months)] %>%
  tibble(
    x = use_months,
    nao = .
  ) %>%
  mutate(x = yearmonth(x)) %>%
  fill(nao) %>%
  as_tsibble(index = x)

nao

# A tsibble: 873 x 2 [1M]
          x   nao
      <mth> <dbl>
 1 1950 Jan -0.16
 2 1950 Feb  0.25
 3 1950 Mar -1.44
 4 1950 Apr  1.46
 5 1950 May  1.34
 6 1950 Jun -3.94
 7 1950 Jul -2.75
 8 1950 Aug -0.08
 9 1950 Sep  0.19
10 1950 Oct  0.19
# … with 863 more rows

Like before, we start with the spectrum:

fft <- torch_fft_fft(as.numeric(scale(nao$nao)))

num_samples <- nrow(nao)
nyquist_cutoff <- ceiling(num_samples / 2)
bins_below_nyquist <- 0:nyquist_cutoff

sampling_rate <- 12 
frequencies_per_bin <- sampling_rate / num_samples
frequencies <- frequencies_per_bin * bins_below_nyquist

df <- data.frame(f = frequencies, y = as.numeric(fft[1:(nyquist_cutoff + 1)]$abs()))
df %>% ggplot(aes(f, y)) +
  geom_line() +
  xlab("frequency (per year)") +
  ylab("magnitude") +
  ggtitle("Spectrum of NAO data")

Have you been wondering for a tiny moment whether this was time-domain data – not spectral? It does look a lot more noisy than the ENSO spectrum for sure. And really, with NAO, predictability is much worse - forecast lead time usually amounts to just one or two weeks.

Proceeding as before, we pick dominant seasonalities (at least this still is possible!) to pass to feasts::STL().

strongest <- torch_topk(fft[1:(nyquist_cutoff/2)]$abs(), 6)
strongest

[[1]]
torch_tensor
102.7191
80.5129
76.1179
75.9949
72.9086
60.8281
[ CPUFloatType{6} ]

[[2]]
torch_tensor
147
99
146
59
33
78
[ CPULongType{6} ]

important_freqs <- frequencies[as.numeric(strongest[[2]])]
important_freqs

[1] 2.0068729 1.3470790 1.9931271 0.7972509 0.4398625 1.0584192

num_observations_in_season <- 12/important_freqs  
num_observations_in_season

[1]  5.979452  8.908163  6.020690 15.051724 27.281250 11.337662

Important seasonal periods are of length six, nine, eleven, fifteen, and twenty-seven months, approximately - pretty close together indeed! No wonder that, in STL decomposition, the remainder is even more significant than with ENSO:

nao %>%
  model(STL(nao ~ season(period = 6) + season(period = 9) +
              season(period = 15) + season(period = 27) +
              season(period = 12))) %>%
  components() %>%
  autoplot()

Now, what will we see in terms of temporal evolution? Much of the code that follows is the same as for ENSO, repeated here for the reader’s convenience:

nao_idx <- nao$nao %>% as.numeric() %>% torch_tensor()
dt <- 1/12 # same interval as for ENSO
wtf <- wavelet_transform(length(nao_idx), dt = dt)
power_spectrum <- wtf$power(nao_idx)

times <- lubridate::year(nao$x) + lubridate::month(nao$x)/12 # also same
scales <- as.numeric(wtf$scales) # will be same because both series have same length

df <- as_tibble(as.matrix(power_spectrum$t()), .name_repair = "universal") %>%
  mutate(time = times) %>%
  pivot_longer(!time, names_to = "scale", values_to = "power") %>%
  mutate(scale = scales[scale %>%
    str_remove("[\\.]{3}") %>%
    as.numeric()])

coi <- wtf$coi(times[1], times[length(nao_idx)])
coi_df <- data.frame(x = as.numeric(coi[[1]]), y = as.numeric(coi[[2]]))

labeled_scales <- c(0.25, 0.5, 1, 2, 4, 8, 16, 32, 64) # same since scales are same 
labeled_frequencies <- round(as.numeric(wtf$fourier_period(labeled_scales)), 1)

ggplot(df) +
  scale_y_continuous(
    trans = scales::compose_trans(scales::log2_trans(), scales::reverse_trans()),
    breaks = c(0.25, 0.5, 1, 2, 4, 8, 16, 32, 64),
    limits = c(max(scales), min(scales)),
    expand = c(0, 0),
    sec.axis = dup_axis(
      labels = scales::label_number(labeled_frequencies),
      name = "Fourier period (years)"
    )
  ) +
  ylab("scale (years)") +
  scale_x_continuous(breaks = seq(1950, 2020, by = 5), expand = c(0, 0)) +
  xlab("year") +
  geom_contour_filled(aes(time, scale, z = power), show.legend = FALSE) +
  scale_fill_viridis_d(option = "turbo") +
  geom_ribbon(data = coi_df, aes(x = x, ymin = y, ymax = max(scales)),
              fill = "black", alpha = 0.6) +
  theme(legend.position = "none")

That, literally, is a much more colorful picture than with ENSO! High frequencies are present, and continually dominant, over the whole time period.

Interestingly, though, we see similarities to ENSO, as well: In both, there is an important pattern, of periodicity four or slightly more years, that exerces influence during the eighties, nineties, and early two-thousands – only with ENSO, it shows peak impact during the nineties, while with NAO, its dominance is most visible in the first decade of this century. Also, both phenomena exhibit a strongly visible peak, of period two years, around 1970. So, is there a close(-ish) connection between both oscillations? This question, of course, is for the domain experts to answer. At least I found a recent study (Scaife et al. (2014)) that not only suggests there is, but uses one (ENSO, the more predictable one) to inform forecasts of the other:

Previous studies have shown that the El Niño–Southern Oscillation can drive interannual variations in the NAO [Brönnimann et al., 2007] and hence Atlantic and European winter climate via the stratosphere [Bell et al., 2009]. […] this teleconnection to the tropical Pacific is active in our experiments, with forecasts initialized in El Niño/La Niña conditions in November tending to be followed by negative/positive NAO conditions in winter.

Will we see a similar relationship for AO, our third series under investigation? We might expect so, since AO and NAO are closely related (or even, two sides of the same coin).

Analysis: AO

First, the data:

download.file(
 "https://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/monthly.ao.index.b50.current.ascii.table",
 destfile = "ao.dat"
)

ao <-
  read_table(
    "ao.dat",
    col_names = FALSE,
    skip = 1
  ) %>%
  select(-X1) %>%
  as.matrix() %>% 
  t() %>%
  as.vector() %>%
  .[1:length(use_months)] %>%
  tibble(x = use_months,
         ao = .) %>%
  mutate(x = yearmonth(x)) %>%
  fill(ao) %>%
  as_tsibble(index = x) 

ao

# A tsibble: 873 x 2 [1M]
          x     ao
      <mth>  <dbl>
 1 1950 Jan -0.06 
 2 1950 Feb  0.627
 3 1950 Mar -0.008
 4 1950 Apr  0.555
 5 1950 May  0.072
 6 1950 Jun  0.539
 7 1950 Jul -0.802
 8 1950 Aug -0.851
 9 1950 Sep  0.358
10 1950 Oct -0.379
# … with 863 more rows

And the spectrum:

fft <- torch_fft_fft(as.numeric(scale(ao$ao)))

num_samples <- nrow(ao)
nyquist_cutoff <- ceiling(num_samples / 2)
bins_below_nyquist <- 0:nyquist_cutoff

sampling_rate <- 12 # per year
frequencies_per_bin <- sampling_rate / num_samples
frequencies <- frequencies_per_bin * bins_below_nyquist

df <- data.frame(f = frequencies, y = as.numeric(fft[1:(nyquist_cutoff + 1)]$abs()))
df %>% ggplot(aes(f, y)) +
  geom_line() +
  xlab("frequency (per year)") +
  ylab("magnitude") +
  ggtitle("Spectrum of AO data")

Well, this spectrum looks even more random than NAO’s, in that not even a single frequency stands out. For completeness, here is the STL decomposition:

strongest <- torch_topk(fft[1:(nyquist_cutoff/2)]$abs(), 5)

important_freqs <- frequencies[as.numeric(strongest[[2]])]
important_freqs
# [1] 0.01374570 0.35738832 1.77319588 1.27835052 0.06872852

num_observations_in_season <- 12/important_freqs  
num_observations_in_season
# [1] 873.000000  33.576923   6.767442   9.387097 174.600000 

ao %>%
  model(STL(ao ~ season(period = 33) + season(period = 7) +
              season(period = 9) + season(period = 174))) %>%
  components() %>%
  autoplot()

Finally, what can the scaleogram tell us about dominant patterns?

ao_idx <- ao$ao %>% as.numeric() %>% torch_tensor()
dt <- 1/12 # same interval as for ENSO and NAO
wtf <- wavelet_transform(length(ao_idx), dt = dt)
power_spectrum <- wtf$power(ao_idx)

times <- lubridate::year(ao$x) + lubridate::month(ao$x)/12 # also same
scales <- as.numeric(wtf$scales) # will be same because all series have same length

df <- as_tibble(as.matrix(power_spectrum$t()), .name_repair = "universal") %>%
  mutate(time = times) %>%
  pivot_longer(!time, names_to = "scale", values_to = "power") %>%
  mutate(scale = scales[scale %>%
    str_remove("[\\.]{3}") %>%
    as.numeric()])

coi <- wtf$coi(times[1], times[length(ao_idx)])
coi_df <- data.frame(x = as.numeric(coi[[1]]), y = as.numeric(coi[[2]]))

labeled_scales <- c(0.25, 0.5, 1, 2, 4, 8, 16, 32, 64) # same since scales are same 
labeled_frequencies <- round(as.numeric(wtf$fourier_period(labeled_scales)), 1)

ggplot(df) +
  scale_y_continuous(
    trans = scales::compose_trans(scales::log2_trans(), scales::reverse_trans()),
    breaks = c(0.25, 0.5, 1, 2, 4, 8, 16, 32, 64),
    limits = c(max(scales), min(scales)),
    expand = c(0, 0),
    sec.axis = dup_axis(
      labels = scales::label_number(labeled_frequencies),
      name = "Fourier period (years)"
    )
  ) +
  ylab("scale (years)") +
  scale_x_continuous(breaks = seq(1950, 2020, by = 5), expand = c(0, 0)) +
  xlab("year") +
  geom_contour_filled(aes(time, scale, z = power), show.legend = FALSE) +
  scale_fill_viridis_d(option = "turbo") +
  geom_ribbon(data = coi_df, aes(x = x, ymin = y, ymax = max(scales)),
              fill = "black", alpha = 0.6) +
  theme(legend.position = "none")

Having seen the overall spectrum, the lack of strongly dominant patterns in the scaleogram does not come as a big surprise. It is tempting – for me, at least – to see a reflection of ENSO around 1970, all the more since by transitivity, AO and ENSO should be related in some way. But here, qualified judgment really is reserved to the experts.

El Niño, la Niña

ENSO refers to a changing pattern of sea surface temperatures and sea-level pressures occurring in the equatorial Pacific. From its three overall states, probably the best-known is El Niño. El Niño occurs when surface water temperatures in the eastern Pacific are higher than normal, and the strong winds that normally blow from east to west are unusually weak. The opposite conditions are termed La Niña. Everything in-between is classified as normal.

ENSO has great impact on the weather worldwide, and routinely harms ecosystems and societies through storms, droughts and flooding, possibly resulting in famines and economic crises. The best societies can do is try to adapt and mitigate severe consequences. Such efforts are aided by accurate forecasts, the further ahead the better.

Here, deep learning (DL) can potentially help: Variables like sea surface temperatures and pressures are given on a spatial grid – that of the earth – and as we know, DL is good at extracting spatial (e.g., image) features. For ENSO prediction, architectures like convolutional neural networks (Ham et al. (2019)) or convolutional-recurrent hybrids¹ are habitually used. One such hybrid is just our convLSTM; it operates on sequences of features given on a spatial grid. Today, thus, we’ll be training a model for ENSO forecasting. This model will have a convLSTM for its central ingredient.

Before we start, a note. While our model fits well with architectures described in the relevant papers, the same cannot be said for amount of training data used. For reasons of practicality, we use actual observations only; consequently, we end up with a small (relative to the task) dataset. In contrast, research papers tend to make use of climate simulations², resulting in significantly more data to work with.

From the outset, then, we don’t expect stellar performance. Nevertheless, this should make for an interesting case study, and a useful code template for our readers to apply to their own data.

Data

We will attempt to predict monthly average sea surface temperature in the Niño 3.4 region³, as represented by the Niño 3.4 Index, plus categorization as one of El Niño, La Niña or neutral⁴. Predictions will be based on prior monthly sea surface temperatures spanning a large portion of the globe.

On the input side, public and ready-to-use data may be downloaded from Tokyo Climate Center; as to prediction targets, we obtain index and classification here.

Input and target data both are provided monthly. They intersect in the time period ranging from 1891-01-01 to 2020-08-01; so this is the range of dates we’ll be zooming in on.

purrr::walk(
   readLines("files"),
   function(f) download.file(url = f, destfile = basename(f))
)

# let's just quickly load all libraries we require to start with

library(torch)
library(tidyverse)
library(stars)
library(viridis)
library(ggthemes)

torch_manual_seed(777)

read_stars(file.path(grb_dir, "sst189101.grb"))

stars object with 2 dimensions and 1 attribute
attribute(s):
 sst189101.grb   
 Min.   :-274.9  
 1st Qu.:-272.8  
 Median :-259.1  
 Mean   :-260.0  
 3rd Qu.:-248.4  
 Max.   :-242.8  
 NA's   :21001   
dimension(s):
  from  to offset delta                       refsys point values    
x    1 360      0     1 Coordinate System importe...    NA   NULL [x]
y    1 180     90    -1 Coordinate System importe...    NA   NULL [y]

grb <- read_stars(
  file.path(grb_dir, map(readLines("files", warn = FALSE), basename)), along = "time") %>%
  st_set_dimensions(3,
                    values = seq(as.Date("1891-01-01"), as.Date("2020-12-01"), by = "months"),
                    names = "time"
                    )

grb

stars object with 3 dimensions and 1 attribute
attribute(s), summary of first 1e+05 cells:
 sst189101.grb   
 Min.   :-274.9  
 1st Qu.:-273.3  
 Median :-258.8  
 Mean   :-260.0  
 3rd Qu.:-247.8  
 Max.   :-242.8  
 NA's   :33724   
dimension(s):
     from   to offset delta                       refsys point                    values    
x       1  360      0     1 Coordinate System importe...    NA                      NULL [x]
y       1  180     90    -1 Coordinate System importe...    NA                      NULL [y]
time    1 1560     NA    NA                         Date    NA 1891-01-01,...,2020-12-01    

ggplot() +
  geom_stars(data = grb %>% filter(between(time, as.Date("2020-01-01"), as.Date("2020-12-01"))), alpha = 0.8) +
  facet_wrap("time") +
  scale_fill_viridis() +
  coord_equal() +
  theme_map() +
  theme(legend.position = "none") 

nino <- read_table2("ONI_NINO34_1854-2020.txt", skip = 9) %>%
  mutate(month = as.Date(paste0(YEAR, "-", `MON/MMM`, "-01"))) %>%
  select(month, NINO34_MEAN, PHASE) %>%
  filter(between(month, as.Date("1891-01-01"), as.Date("2020-08-01"))) %>%
  mutate(phase_code = as.numeric(as.factor(PHASE)))

nrow(nino)

1556

Preprocessing

sst <- grb %>% filter(time <= as.Date("2020-08-01"))

sst <- grb %>% filter(between(y,-55, 60))

dim(sst)

360, 115, 1560

sst_train <- sst %>% filter(time < as.Date("1990-01-01"))
sst_valid <- sst %>% filter(time >= as.Date("1990-01-01"))

sst_train <- as.tbl_cube.stars(sst_train)$mets[[1]]
sst_valid <- as.tbl_cube.stars(sst_valid)$mets[[1]]

sst_train <- sst_train + 273.15
quantile(sst_train, na.rm = TRUE)

     0%     25%     50%     75%    100% 
-1.8000 12.9975 21.8775 26.8200 34.3700 

sst_valid <- sst_valid + 273.15
quantile(sst_valid, na.rm = TRUE)

    0%    25%    50%    75%   100% 
-1.800 13.425 22.335 27.240 34.870 

train_mean <- mean(sst_train, na.rm = TRUE)
train_sd <- sd(sst_train, na.rm = TRUE)

sst_train <- (sst_train - train_mean) / train_sd

sst_valid <- (sst_valid - train_mean) / train_sd

sst_train[is.na(sst_train)] <- 0
sst_valid[is.na(sst_valid)] <- 0

nino_train <- nino %>% filter(month < as.Date("1990-01-01"))
nino_valid <- nino %>% filter(month >= as.Date("1990-01-01"))

nino_train %>% group_by(phase_code, PHASE) %>% summarise(count = n(), avg = mean(NINO34_MEAN))

# A tibble: 3 x 4
# Groups:   phase_code [3]
  phase_code PHASE count   avg
       <dbl> <chr> <int> <dbl>
1          1 E       301  27.7
2          2 L       333  25.6
3          3 N       554  26.7

nino_valid %>% group_by(phase_code, PHASE) %>% summarise(count = n(), avg = mean(NINO34_MEAN))

# A tibble: 3 x 4
# Groups:   phase_code [3]
  phase_code PHASE count   avg
       <dbl> <chr> <int> <dbl>
1          1 E        93  28.1
2          2 L        93  25.9
3          3 N       182  27.2

train_mean_nino <- mean(nino_train$NINO34_MEAN)
train_sd_nino <- sd(nino_train$NINO34_MEAN)

nino_train <- nino_train %>% mutate(NINO34_MEAN = scale(NINO34_MEAN, center = train_mean_nino, scale = train_sd_nino))
nino_valid <- nino_valid %>% mutate(NINO34_MEAN = scale(NINO34_MEAN, center = train_mean_nino, scale = train_sd_nino))

Predictions

Now losses tend to be abstract; let’s see what actually gets predicted. We obtain predictions for index values and phases like so …

net$eval()

pred_index <- c()
pred_phase <- c()

coro::loop(for (b in valid_dl) {

  output <- net(b$x$to(device = device))

  pred_index <- c(pred_index, output[[2]]$to(device = "cpu"))
  pred_phase <- rbind(pred_phase, as.matrix(output[[3]]$to(device = "cpu")))

})

… and combine these with the ground truth, stripping off the first six rows (six was the number of timesteps used as predictors):

valid_perf <- data.frame(
  actual_temp = nino_valid$NINO34_MEAN[(batch_size + 1):nrow(nino_valid)] * train_sd_nino + train_mean_nino,
  actual_nino = factor(nino_valid$phase_code[(batch_size + 1):nrow(nino_valid)]),
  pred_temp = pred_index * train_sd_nino + train_mean_nino,
  pred_nino = factor(pred_phase %>% apply(1, which.max))
)

For the phase, we can generate a confusion matrix:

yardstick::conf_mat(valid_perf, actual_nino, pred_nino)

          Truth
Prediction   1   2   3
         1  70   0  43
         2   0  47  10
         3  23  46 123

This looks better than expected (based on the losses). Phases 1 and 2 correspond to El Niño and La Niña, respectively, and these get sharply separated.

What about the Niño 3.4 Index? Let’s plot predictions versus ground truth:

valid_perf <- valid_perf %>% 
  select(actual = actual_temp, predicted = pred_temp) %>% 
  add_column(month = seq(as.Date("1990-07-01"), as.Date("2020-08-01"), by = "months")) %>%
  pivot_longer(-month, names_to = "Index", values_to = "temperature")

ggplot(valid_perf, aes(x = month, y = temperature, color = Index)) +
  geom_line() +
  scale_color_manual(values = c("#006D6F", "#B2FFFF")) +
  theme_classic()

This does not look bad either. However, we need to keep in mind that we’re predicting just a single time step ahead. We probably should not overestimate the results. Which leads directly to the discussion.