Sea-Surface Temperature Analysis: From Data Collection to Visualization

Sea-Surface Temperature Analysis: From Data Collection to Visualization

by | Apr 5, 2024 | DS Articles

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Sea-surface temperatures in the North Atlantic have been in the news recently as they continue to break records. While there are already a number of excellent summaries and graphs of the data, I thought I’d have a go at making some myself. The starting point is the detailed data made available by the National Centers for Environmental Information, part of NOAA. As always, the sheer volume of high-quality data agencies like this make available to the public is astonishing.

Getting the Data

The specific dataset is the NOAA 0.25-degree Daily Optimum Interpolation Sea Surface Temperature (OISST), Version 2.1, which takes a global network of daily temperature observations (from things like buoys and platforms, but also satellites), and then interpolates and aggregates them to a regular spatial grid of observations at 0.25 degrees resolution.

The data is available as daily global observations doing back to September 1st, 1981. Each day’s data is available as a single file in subdirectories organized by year-month. It’s all here. Each file is about 1.6MB in size. There are more than fifteen thousand of them.

We can make a folder called raw in our project and then get all the data, preserving is subdirectory structure, with a wget command like this:

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wget --no-parent -r -l inf --wait 5 --random-wait 'https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/'

This tries to be polite with the NOAA. (I think its webserver is also lightly throttled in any case, but it never hurts to be nice.) The switches --no-parent -r -l inf tell wget not to move upwards in the folder hierarchy, but to recurse downwards indefinitely. The --wait 5 --random-wait jointly enforce a five-second wait time between requests, randomly varying between 0.5 and 1.5 times the wait period. Downloading the files this way will take several days of real time downloading, though of course much less in actual file transfer time.

The netCDF data format

The data are netCDF files, an interesting and in fact quite nice self-documenting binary file format. These files have regular arrays of data of some n-dimensional size, e.g. latitude by longitude, with measures that can be thought of as being stacked on the array. (E.g. a grid of points with a measure at each point for surface temperature, sea ice extent, etc, etc). So you have the potential to have big slabs of data that you can then summarize by aggregating on some dimension or other.

The ncdf4 package can read them in R, though as it turns out we won’t use this package for the analysis. Here’s what one file looks like:

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ncdf4::nc_open(all_fnames[1000])

File /Users/kjhealy/Documents/data/misc/noaa_ncei/raw/www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/198405/oisst-avhrr-v02r01.19840527.nc (NC_FORMAT_NETCDF4):

     4 variables (excluding dimension variables):
        short anom[lon,lat,zlev,time]   (Chunking: [1440,720,1,1])  (Compression: shuffle,level 4)
            long_name: Daily sea surface temperature anomalies
            _FillValue: -999
            add_offset: 0
            scale_factor: 0.00999999977648258
            valid_min: -1200
            valid_max: 1200
            units: Celsius
        short err[lon,lat,zlev,time]   (Chunking: [1440,720,1,1])  (Compression: shuffle,level 4)
            long_name: Estimated error standard deviation of analysed_sst
            units: Celsius
            _FillValue: -999
            add_offset: 0
            scale_factor: 0.00999999977648258
            valid_min: 0
            valid_max: 1000
        short ice[lon,lat,zlev,time]   (Chunking: [1440,720,1,1])  (Compression: shuffle,level 4)
            long_name: Sea ice concentration
            units: %
            _FillValue: -999
            add_offset: 0
            scale_factor: 0.00999999977648258
            valid_min: 0
            valid_max: 100
        short sst[lon,lat,zlev,time]   (Chunking: [1440,720,1,1])  (Compression: shuffle,level 4)
            long_name: Daily sea surface temperature
            units: Celsius
            _FillValue: -999
            add_offset: 0
            scale_factor: 0.00999999977648258
            valid_min: -300
            valid_max: 4500

     4 dimensions:
        time  Size:1   *** is unlimited ***
            long_name: Center time of the day
            units: days since 1978-01-01 12:00:00
        zlev  Size:1
            long_name: Sea surface height
            units: meters
            actual_range: 0, 0
            positive: down
        lat  Size:720
            long_name: Latitude
            units: degrees_north
            grids: Uniform grid from -89.875 to 89.875 by 0.25
        lon  Size:1440
            long_name: Longitude
            units: degrees_east
            grids: Uniform grid from 0.125 to 359.875 by 0.25

    37 global attributes:
        title: NOAA/NCEI 1/4 Degree Daily Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1 - Final
        source: ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfinder_AVHRR, Navy_AVHRR
        id: oisst-avhrr-v02r01.19840527.nc
        naming_authority: gov.noaa.ncei
        summary: NOAAs 1/4-degree Daily Optimum Interpolation Sea Surface Temperature (OISST) (sometimes referred to as Reynolds SST, which however also refers to earlier products at different resolution), currently available as version v02r01, is created by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field. The sources of data are satellite (AVHRR) and in situ platforms (i.e., ships and buoys), and the specific datasets employed may change over time. At the marginal ice zone, sea ice concentrations are used to generate proxy SSTs.  A preliminary version of this file is produced in near-real time (1-day latency), and then replaced with a final version after 2 weeks. Note that this is the AVHRR-ONLY DOISST, available from Oct 1981, but there is a companion DOISST product that includes microwave satellite data, available from June 2002
        cdm_data_type: Grid
        history: Final file created using preliminary as first guess, and 3 days of AVHRR data. Preliminary uses only 1 day of AVHRR data.
        date_modified: 2020-05-08T19:05:13Z
        date_created: 2020-05-08T19:05:13Z
        product_version: Version v02r01
        processing_level: NOAA Level 4
        institution: NOAA/National Centers for Environmental Information
        creator_url: https://www.ncei.noaa.gov/
        creator_email: oisst-help@noaa.gov
        keywords: Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature
        keywords_vocabulary: Global Change Master Directory (GCMD) Earth Science Keywords
        platform: Ships, buoys, Argo floats, MetOp-A, MetOp-B
        platform_vocabulary: Global Change Master Directory (GCMD) Platform Keywords
        instrument: Earth Remote Sensing Instruments > Passive Remote Sensing > Spectrometers/Radiometers > Imaging Spectrometers/Radiometers > AVHRR > Advanced Very High Resolution Radiometer
        instrument_vocabulary: Global Change Master Directory (GCMD) Instrument Keywords
        standard_name_vocabulary: CF Standard Name Table (v40, 25 January 2017)
        geospatial_lat_min: -90
        geospatial_lat_max: 90
        geospatial_lon_min: 0
        geospatial_lon_max: 360
        geospatial_lat_units: degrees_north
        geospatial_lat_resolution: 0.25
        geospatial_lon_units: degrees_east
        geospatial_lon_resolution: 0.25
        time_coverage_start: 1984-05-27T00:00:00Z
        time_coverage_end: 1984-05-27T23:59:59Z
        metadata_link: https://doi.org/10.25921/RE9P-PT57
        ncei_template_version: NCEI_NetCDF_Grid_Template_v2.0
        comment: Data was converted from NetCDF-3 to NetCDF-4 format with metadata updates in November 2017.
        sensor: Thermometer, AVHRR
        Conventions: CF-1.6, ACDD-1.3
        references: Reynolds, et al.(2007) Daily High-Resolution-Blended Analyses for Sea Surface Temperature (available at https://doi.org/10.1175/2007JCLI1824.1). Banzon, et al.(2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies (available at https://doi.org/10.5194/essd-8-165-2016). Huang et al. (2020) Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version v02r01, submitted.Climatology is based on 1971-2000 OI.v2 SST. Satellite data: Pathfinder AVHRR SST and Navy AVHRR SST. Ice data: NCEP Ice and GSFC Ice.

This is the file for May 27th, 1984. As you can see, there’s a lot of metadata. Each variable is admirably well-documented. The key information for our purposes is that we have a grid of 1440 by 720 lat-lon points. There are two additional dimensions—time and elevation (zlev)—but these are both just 1 for each particular file, because every file is observations at elevation zero on a particular day. There are four measures at each point: sea surface temperature anomalies, the standard deviation of the sea surface temperature estimate, sea ice concentration (as a percentage), and sea surface temperature (in degrees Celsius). So we have four bits of data for each grid point on our 1440 * 720 grid, which makes for just over 4.1 million data points per day since 1981.

Processing the Data

We read in the filenames and see how many we have:

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all_fnames <- fs::dir_ls(here("raw"), recurse = TRUE, glob = "*.nc")
length(all_fnames)
#> [1] 15549

What we want to do is read in all this data and aggregate it so that we can take, for instance, the global average for each day and plot that trend for each year. Or perhaps we want to do that for specific regions of the globe, either defined directly by us in terms of some latitude and longitude polygon, or taken from the coordinates of some conventional division of the world’s oceans and seas into named areas.

Our tool of choice is the Terra package, which is designed specifically for this kind of data. It has a number of tools for conveniently aggregating and cutting into arrays of geospatial data. The netCDF4 package has a lot of useful features, too, but for the specific things we want to do Terra’s toolkit is quicker. One thing it can do, for example, is naturally aggregate over-time layers into single “bricks” of data, and then quickly summarize or calculate on these arrays.

So, let’s chunk our filenames into units of 25 days or so:

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## This one gives you an unknown number of chunks each with approx n elements
chunk <- function(x, n) split(x, ceiling(seq_along(x)/n))
chunked_fnames <- chunk(all_fnames, 25)

Next, we write a function to process a raster file that terra creates. It calculates the area-weighted means of the layer variables.

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layerinfo <- tibble(
  num = c(1:4),
  raw_name = c("anom_zlev=0", "err_zlev=0",
               "ice_zlev=0", "sst_zlev=0"),
  name = c("anom", "err",
           "ice", "sst"))

process_raster <- function(fnames, crop_area = c(-80, 0, 0, 60), layerinfo = layerinfo) {

  tdf <- terra::rast(fnames) |>
    terra::rotate() |>   # Convert 0 to 360 lon to -180 to +180 lon
    terra::crop(crop_area) # Manually crop to a defined box.  Default is roughly N. Atlantic lat/lon box

  wts <- terra::cellSize(tdf, unit = "km") # For scaling. Because the Earth is round.

  # global() calculates a quantity for the whole grid on a particular SpatRaster
  # so we get one weighted mean per file that comes in
  out <- data.frame(date = terra::time(tdf),
                    means = terra::global(tdf, "mean", weights = wts, na.rm=TRUE))
  out$var <- rownames(out)
  out$var <- gsub("_.*", "", out$var)
  out <- reshape(out, idvar = "date",
                 timevar = "var",
                 direction = "wide")

  colnames(out) <- gsub("weighted_mean.", "", colnames(out))
  out
}

For a single file, this gives us one number for each variable:

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# World box (60S to 60N)
world_crop_bb <- c(-180, 180, -60, 60)

process_raster(all_fnames[10000], crop_area = world_crop_bb)
#>                   date       anom       err       ice      sst
#> anom_zlev=0 2009-01-20 0.01397327 0.1873972 0.6823713 20.26344

For 25 filenames, 25 rows:

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process_raster(chunked_fnames[[1]], crop_area = world_crop_bb)
#>                      date       anom       err       ice      sst
#> anom_zlev=0    1981-09-01 -0.1312008 0.2412722 0.4672954 20.12524
#> anom_zlev=0.1  1981-09-02 -0.1383695 0.2483428 0.4933853 20.11629
#> anom_zlev=0.2  1981-09-03 -0.1419441 0.2583364 0.4807980 20.11095
#> anom_zlev=0.3  1981-09-04 -0.1434012 0.2627574 0.5125643 20.10772
#> anom_zlev=0.4  1981-09-05 -0.1527941 0.2520100 0.4889709 20.09655
#> anom_zlev=0.5  1981-09-06 -0.1590382 0.2421610 0.5253917 20.08851
#> anom_zlev=0.6  1981-09-07 -0.1603969 0.2406726 0.4959906 20.08539
#> anom_zlev=0.7  1981-09-08 -0.1530743 0.2437756 0.5203092 20.09094
#> anom_zlev=0.8  1981-09-09 -0.1503720 0.2483605 0.5062930 20.09187
#> anom_zlev=0.9  1981-09-10 -0.1532902 0.2574440 0.5275545 20.08718
#> anom_zlev=0.10 1981-09-11 -0.1409007 0.2548919 0.5111582 20.09779
#> anom_zlev=0.11 1981-09-12 -0.1459493 0.2438222 0.5395167 20.09097
#> anom_zlev=0.12 1981-09-13 -0.1540702 0.2341866 0.5259677 20.08107
#> anom_zlev=0.13 1981-09-14 -0.1719063 0.2322755 0.5650545 20.06144
#> anom_zlev=0.14 1981-09-15 -0.1879679 0.2319289 0.5357815 20.04363
#> anom_zlev=0.15 1981-09-16 -0.2021128 0.2330142 0.5718586 20.02638
#> anom_zlev=0.16 1981-09-17 -0.2163771 0.2371551 0.5434053 20.00766
#> anom_zlev=0.17 1981-09-18 -0.2317916 0.2366315 0.5757664 19.98781
#> anom_zlev=0.18 1981-09-19 -0.2321086 0.2388878 0.5458579 19.98307
#> anom_zlev=0.19 1981-09-20 -0.2478310 0.2388981 0.5682817 19.96289
#> anom_zlev=0.20 1981-09-21 -0.2477164 0.2366739 0.5428888 19.95858
#> anom_zlev=0.21 1981-09-22 -0.2315305 0.2369557 0.5636612 19.97033
#> anom_zlev=0.22 1981-09-23 -0.2079270 0.2401278 0.5423280 19.98950
#> anom_zlev=0.23 1981-09-24 -0.1803567 0.2397868 0.5666913 20.01262
#> anom_zlev=0.24 1981-09-25 -0.1704838 0.2376401 0.5437584 20.01805

We need to do this for all the files so we get complete dataset. We take advantage of the futureverse to parallelize the operation, because doing this with 15,000 files is going to take a bit of time. Then at the end we clean it up a little bit.

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season <-  function(in_date){
  br = yday(as.Date(c("2019-03-01",
                      "2019-06-01",
                      "2019-09-01",
                      "2019-12-01")))
  x = yday(in_date)
  x = cut(x, breaks = c(0, br, 366))
  levels(x) = c("Winter", "Spring", "Summer", "Autumn", "Winter")
  x
}

world_df <- future_map(chunked_fnames, process_raster,
                       crop_area = world_crop_bb) |>
  list_rbind() |>
  as_tibble() |>
  mutate(date = ymd(date),
         year = lubridate::year(date),
         month = lubridate::month(date),
         day = lubridate::day(date),
         yrday = lubridate::yday(date),
         season = season(date))

world_df
#> # A tibble: 15,549 × 10
#>    date         anom   err   ice   sst  year month   day yrday season
#>    <date>      <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#>  1 1981-09-01 -0.131 0.241 0.467  20.1  1981     9     1   244 Summer
#>  2 1981-09-02 -0.138 0.248 0.493  20.1  1981     9     2   245 Autumn
#>  3 1981-09-03 -0.142 0.258 0.481  20.1  1981     9     3   246 Autumn
#>  4 1981-09-04 -0.143 0.263 0.513  20.1  1981     9     4   247 Autumn
#>  5 1981-09-05 -0.153 0.252 0.489  20.1  1981     9     5   248 Autumn
#>  6 1981-09-06 -0.159 0.242 0.525  20.1  1981     9     6   249 Autumn
#>  7 1981-09-07 -0.160 0.241 0.496  20.1  1981     9     7   250 Autumn
#>  8 1981-09-08 -0.153 0.244 0.520  20.1  1981     9     8   251 Autumn
#>  9 1981-09-09 -0.150 0.248 0.506  20.1  1981     9     9   252 Autumn
#> 10 1981-09-10 -0.153 0.257 0.528  20.1  1981     9    10   253 Autumn
#> # ℹ 15,539 more rows

Now we have a time series for each of the variables daily from 1981 to yesterday.

Calculating values for all the world’s seas and oceans

We can do a little better though. What if we wanted to get these average values for the seas and oceans of the world? For that we’d need a map defining the conventional boundaries of those areas of water, which we’d then need to covert to raster format. After that, we’d slice up our global raster by the ocean and sea boundaries, and calculate averages for those areas.

I took the maritime boundaries from the IHO Sea Areas Shapefile.

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seas <- sf::read_sf(here("raw", "World_Seas_IHO_v3"))

seas
#> Simple feature collection with 101 features and 10 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -180 ymin: -85.5625 xmax: 180 ymax: 90
#> Geodetic CRS:  WGS 84
#> # A tibble: 101 × 11
#>    NAME                   ID    Longitude Latitude min_X  min_Y max_X  max_Y   area MRGID                  geometry
#>    <chr>                  <chr>     <dbl>    <dbl> <dbl>  <dbl> <dbl>  <dbl>  <dbl> <dbl>        <MULTIPOLYGON [°]>
#>  1 Rio de La Plata        33        -56.8   -35.1  -59.8 -36.4  -54.9 -31.5  3.18e4  4325 (((-54.94302 -34.94791, …
#>  2 Bass Strait            62A       146.    -39.5  144.  -41.4  150.  -37.5  1.13e5  4366 (((149.9046 -37.54325, 1…
#>  3 Great Australian Bight 62        133.    -36.7  118.  -43.6  146.  -31.5  1.33e6  4276 (((143.5325 -38.85535, 1…
#>  4 Tasman Sea             63        161.    -39.7  147.  -50.9  175.  -30    3.34e6  4365 (((159.0333 -30, 159.039…
#>  5 Mozambique Channel     45A        40.9   -19.3   32.4 -26.8   49.2 -10.5  1.39e6  4261 (((43.38218 -11.37021, 4…
#>  6 Savu Sea               48o       122.     -9.48 119.  -10.9  125.   -8.21 1.06e5  4343 (((124.5562 -8.223565, 1…
#>  7 Timor Sea              48i       128.    -11.2  123.  -15.8  133.   -8.18 4.34e5  4344 (((127.8623 -8.214911, 1…
#>  8 Bali Sea               48l       116.     -7.93 114.   -9.00 117.   -7.01 3.99e4  4340 (((115.7522 -7.143594, 1…
#>  9 Coral Sea              64        157.    -18.2  141.  -30.0  170.   -6.79 4.13e6  4364 (((168.4912 -16.79469, 1…
#> 10 Flores Sea             48j       120.     -7.51 117.   -8.74 123.   -5.51 1.03e5  4341 (((120.328 -5.510677, 12…
#> # ℹ 91 more rows

Then we rasterize the polygons with a function from terra:

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## Rasterize the seas polygons using one of the nc files
## as a reference grid for the rasterization process
one_raster <- all_fnames[1]
seas_vect <- terra::vect(seas)
tmp_tdf_seas <- terra::rast(one_raster)["sst"] |>
  rotate()
seas_zonal <- rasterize(seas_vect, tmp_tdf_seas, "NAME")

Now we can use this data as the grid to do zonal calculations on our data raster. To use it in a parallelized calculation we need to wrap it, so that it can be found by the processes that future_map() will spawn. We write a new function to do the zonal calculation. It’s basically the same as the global one above.

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seas_zonal_wrapped <- wrap(seas_zonal)

process_raster_zonal <- function(fnames) {

  d <- terra::rast(fnames)
  wts <- terra::cellSize(d, unit = "km") # For scaling

  layer_varnames <- terra::varnames(d) # vector of layers
  date_seq <- rep(terra::time(d)) # vector of dates

  # New colnames for use post zonal calculation below
  new_colnames <- c("sea", paste(layer_varnames, date_seq, sep = "_"))

  # Better colnames
  tdf_seas <- d |>
    terra::rotate() |>   # Convert 0 to 360 lon to -180 to +180 lon
    terra::zonal(unwrap(seas_zonal_wrapped), mean, na.rm = TRUE)
  colnames(tdf_seas) <- new_colnames

  # Reshape to long
  tdf_seas |>
    tidyr::pivot_longer(-sea,
                        names_to = c("measure", "date"),
                        values_to = "value",
                        names_pattern ="(.*)_(.*)") |>
    tidyr::pivot_wider(names_from = measure, values_from = value)

}

And we feed our chunked vector of filenames to it:

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## Be patient
seameans_df <- future_map(chunked_fnames, process_raster_zonal) |>
  list_rbind() |>
  mutate(date = ymd(date),
         year = lubridate::year(date),
         month = lubridate::month(date),
         day = lubridate::day(date),
         yrday = lubridate::yday(date),
         season = season(date))

write_csv(seameans_df, file = here("data", "oceans_means_zonal.csv"))
save(seameans_df, file = here("data", "seameans_df.Rdata"), compress = "xz")

seameans_df
#> # A tibble: 1,570,449 × 11
#>    sea          date         anom   err   ice   sst  year month   day yrday season
#>    <chr>        <date>      <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#>  1 Adriatic Sea 1981-09-01 -0.737 0.167    NA  23.0  1981     9     1   244 Summer
#>  2 Adriatic Sea 1981-09-02 -0.645 0.176    NA  23.1  1981     9     2   245 Autumn
#>  3 Adriatic Sea 1981-09-03 -0.698 0.176    NA  22.9  1981     9     3   246 Autumn
#>  4 Adriatic Sea 1981-09-04 -0.708 0.248    NA  22.9  1981     9     4   247 Autumn
#>  5 Adriatic Sea 1981-09-05 -1.05  0.189    NA  22.5  1981     9     5   248 Autumn
#>  6 Adriatic Sea 1981-09-06 -1.02  0.147    NA  22.4  1981     9     6   249 Autumn
#>  7 Adriatic Sea 1981-09-07 -0.920 0.141    NA  22.4  1981     9     7   250 Autumn
#>  8 Adriatic Sea 1981-09-08 -0.832 0.140    NA  22.5  1981     9     8   251 Autumn
#>  9 Adriatic Sea 1981-09-09 -0.665 0.162    NA  22.6  1981     9     9   252 Autumn
#> 10 Adriatic Sea 1981-09-10 -0.637 0.268    NA  22.5  1981     9    10   253 Autumn
#> # ℹ 1,570,439 more rows

Now we have properly-weighted daily averages for every sea and ocean since September 1981. Time to make some pictures.

Global Sea Surface Mean Temperature Graph

To make a graph of the global daily mean sea surface temperature we can use the world_df object we made. The idea is to put the temperature on the y-axis, the day of the year on the x-axis, and then draw a separate line for each year. We highlight 2023 and (the data to date for) 2024. And we also draw a ribbon underlay showing plus or minus two standard deviations of the global mean.

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colors <- ggokabeito::palette_okabe_ito()

## Labels for the x-axis
month_labs <- seameans_df |>
  filter(sea == "North Atlantic Ocean",
         year == 2023,
         day == 15) |>
  select(date, year, yrday, month, day) |>
  mutate(month_lab = month(date, label = TRUE, abbr = TRUE))


## Average and sd ribbon data
world_avg <- world_df |>
  filter(year > 1981 & year < 2012) |>
  group_by(yrday) |>
  filter(yrday != 366) |>
  summarize(mean_8211 = mean(sst, na.rm = TRUE),
            sd_8211 = sd(sst, na.rm = TRUE)) |>
  mutate(fill = colors[2],
         color = colors[2])

## Flag years of interest
out_world <- world_df |>
  mutate(year_flag = case_when(
    year == 2023 ~ "2023",
    year == 2024 ~ "2024",
    .default = "All other years"))


out_world_plot <- ggplot() +
  geom_ribbon(data = world_avg,
              mapping = aes(x = yrday,
                            ymin = mean_8211 - 2*sd_8211,
                            ymax = mean_8211 + 2*sd_8211,
                            fill = fill),
              alpha = 0.3,
              inherit.aes = FALSE) +
  geom_line(data = world_avg,
            mapping = aes(x = yrday,
                          y = mean_8211,
                          color = color),
            linewidth = 2,
            inherit.aes = FALSE) +
  scale_color_identity(name = "Mean Temp. 1982-2011, ±2SD", guide = "legend",
                       breaks = unique(world_avg$color), labels = "") +
  scale_fill_identity(name = "Mean Temp. 1982-2011, ±2SD", guide = "legend",
                      breaks = unique(world_avg$fill), labels = "") +
  ggnewscale::new_scale_color() +
  geom_line(data = out_world,
            mapping = aes(x = yrday, y = sst, group = year, color = year_flag),
            inherit.aes = FALSE) +
  scale_color_manual(values = colors[c(1,6,8)]) +
  scale_x_continuous(breaks = month_labs$yrday, labels = month_labs$month_lab) +
  scale_y_continuous(breaks = seq(19.5, 21.5, 0.5),
                     limits = c(19.5, 21.5),
                     expand = expansion(mult = c(-0.05, 0.05))) +
  geom_line(linewidth = rel(0.7)) +
  guides(
    x = guide_axis(cap = "both"),
    y = guide_axis(minor.ticks = TRUE, cap = "both"),
    color = guide_legend(override.aes = list(linewidth = 2))
  ) +
  labs(x = "Month", y = "Mean Temperature (°Celsius)",
       color = "Year",
       title = "Mean Daily Global Sea Surface Temperature, 1981-2024",
       subtitle = "Latitudes 60°N to 60°S; Area-weighted NOAA OISST v2.1 estimates",
       caption = "Kieran Healy / @kjhealy") +
  theme(axis.line = element_line(color = "gray30", linewidth = rel(1)),
        plot.title = element_text(size = rel(1.9)))

ggsave(here("figures", "global_mean.png"), out_world_plot, height = 7, width = 10, dpi = 300)

We're fucked

Global mean sea surface temperature 1981-2024

The North Atlantic

We can slice out the North Atlantic by name from seameans_df and make its graph in much the same way. For variety we can color most of the years blue, to lean into the “Great Wave off Kanagawa” (or Rockall?) vibe.

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out_atlantic <- seameans_df |>
  filter(sea == "North Atlantic Ocean") |>
  mutate(year_flag = case_when(
    year == 2023 ~ "2023",
    year == 2024 ~ "2024",
    .default = "All other years"
  )) |>
  ggplot(aes(x = yrday, y = sst, group = year, color = year_flag)) +
  geom_line(linewidth = rel(1.1)) +
  scale_x_continuous(breaks = month_labs$yrday, labels = month_labs$month_lab) +
  scale_color_manual(values = colors[c(1,6,2)]) +
  guides(
    x = guide_axis(cap = "both"),
    y = guide_axis(minor.ticks = TRUE, cap = "both"),
    color = guide_legend(override.aes = list(linewidth = 2))
  ) +
  labs(x = "Month", y = "Mean Temperature (Celsius)",
       color = "Year",
       title = "Mean Daily Sea Surface Temperature, North Atlantic Ocean, 1981-2024",
       subtitle = "Gridded and weighted NOAA OISST v2.1 estimates",
       caption = "Kieran Healy / @kjhealy") +
  theme(axis.line = element_line(color = "gray30", linewidth = rel(1)))

ggsave(here("figures", "north_atlantic.png"), out_atlantic, height = 7, width = 10, dpi = 300)

Not looking too good tbh

The North Atlantic.

All the Seas

Finally we can of course go crazy with facets and just draw everything.

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## All the world's oceans and seas
out <- seameans_df |>
  mutate(year_flag = case_when(
    year == 2023 ~ "2023",
    year == 2024 ~ "2024",
    .default = "All other years")) |>
  ggplot(aes(x = yrday, y = sst, group = year, color = year_flag)) +
  geom_line(linewidth = rel(0.5)) +
  scale_x_continuous(breaks = month_labs$yrday, labels = month_labs$month_lab) +
  scale_color_manual(values = colors[c(1,6,2)]) +
  guides(
    x = guide_axis(cap = "both"),
    y = guide_axis(minor.ticks = TRUE, cap = "both"),
    color = guide_legend(override.aes = list(linewidth = 1.4))
  ) +
  facet_wrap(~ reorder(sea, sst), axes = "all_x", axis.labels = "all_y") +
  labs(x = "Month of the Year", y = "Mean Temperature (Celsius)",
       color = "Year",
       title = "Mean Daily Sea Surface Temperatures, 1981-2024",
       subtitle = "Area-weighted 0.25° grid estimates; NOAA OISST v2.1; IHO Sea Boundaries",
       caption = "Data processed with R; Figure made with ggplot by Kieran Healy / @kjhealy") +
  theme(axis.line = element_line(color = "gray30", linewidth = rel(1)),
        strip.text = element_text(face = "bold", size = rel(1.4)),
        plot.title = element_text(size = rel(1.525)),
        plot.subtitle = element_text(size = rel(1.1)))

ggsave(here("figures", "all_seas.png"), out, width = 40, height = 40, dpi = 300)

Everything

All the world’s oceans and seas, because why not.

The full code for the data processing and the graphs is available on GitHub.

To leave a comment for the author, please follow the link and comment on their blog: R on kieranhealy.org.

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Continue reading: Make Your Own NOAA Sea Temperature Graph

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“Geographer Collects Data for Indonesia’s Coastal Climate Change Preparedness”

“Geographer Collects Data for Indonesia’s Coastal Climate Change Preparedness”

by | Apr 1, 2024 | Science

Geographer Collects Data for Indonesia's Coastal Climate Change Preparedness

Future Trends in Coastal Adaptation to Climate Change: A Comprehensive Analysis

Climate change poses significant challenges to coastal areas worldwide, necessitating the need for effective adaptation strategies to mitigate its impacts. Geographer Muh Aris Marfai, in his tireless efforts to collect reference data for Indonesia’s coastal areas, exemplifies the proactive approach required to prepare for the consequences of climate change. In this article, we will delve into the key points of Marfai’s work and explore potential future trends in coastal adaptation, offering unique predictions and recommendations for the industry.

The Significance of Geographer Muh Aris Marfai’s Work

Marfai’s collection of reference data for Indonesia’s coastal areas assumes paramount importance in light of the increasing threats posed by climate change. Rising sea levels, intensified storm events, and coastal erosion are just a few of the consequences that demand immediate attention. By meticulously gathering data on the vulnerability of the coastal regions, Marfai aims to equip decision-makers with the information necessary to implement proactive adaptation measures.

Potential Future Trends in Coastal Adaptation

1. Nature-based Solutions (NbS)

One of the most promising trends in coastal adaptation is the widespread adoption of nature-based solutions (NbS). Instead of relying solely on traditional hardened infrastructures like seawalls and breakwaters, NbS emphasizes the use of natural ecosystems to provide effective and sustainable protection against climate change impacts. These solutions involve strategies such as beach and dune restoration, wetland creation, and mangrove conservation.

As the scientific community continues to recognize the invaluable role of healthy ecosystems in coastal resilience, we can expect to see an increased implementation of NbS worldwide. Policymakers and coastal managers should prioritize the integration of NbS into their adaptation plans to capitalize on the numerous environmental and socio-economic benefits they offer.

2. Community Engagement and Local Knowledge

Another crucial trend is the recognition of the importance of community engagement and local knowledge in coastal adaptation efforts. Coastal communities, who are at the forefront of climate change impacts, possess invaluable insights gained from their historical experiences and intimate connection with the coastal environment.

It is essential for decision-makers to engage with these communities and incorporate their knowledge into adaptation strategies. This bottom-up approach not only ensures that adaptation measures align with the needs and aspirations of the people but also fosters a sense of ownership and empowerment within the community. Collaboration between scientists, policymakers, and local communities is key to successful coastal adaptation.

3. Technology and Data-driven Solutions

The rapid advancement of technology and data availability presents exciting opportunities for coastal adaptation. Remote sensing technologies, such as satellite imagery and LiDAR, can provide valuable data on coastal change patterns, allowing for more accurate predictions and targeted adaptation actions.

Artificial intelligence (AI) and machine learning algorithms can be harnessed to analyze vast amounts of data, enabling the identification of trends and the development of predictive models. These tools can assist in decision-making processes, optimize resource allocation, and enhance the overall efficiency of coastal adaptation efforts.

Recommendations for the Industry

  • Invest in Research and Data Collection: Governments, research institutions, and non-governmental organizations must allocate adequate resources to support comprehensive research and data collection initiatives, as demonstrated by Marfai’s work. Robust data forms the foundation for informed decision-making and effective adaptation strategies.
  • Integrate Nature-based Solutions: Policymakers and coastal managers should prioritize the incorporation of nature-based solutions into their adaptation plans. These solutions offer multiple benefits, including climate resilience, biodiversity conservation, and sustainable livelihoods for coastal communities.
  • Promote Community Engagement: It is imperative to engage coastal communities and empower them as active participants in adaptation processes. Their local knowledge, cultural values, and lived experiences contribute to the development of context-specific and socially inclusive adaptation strategies.
  • Embrace Technology: Embracing technological advancements, such as remote sensing and AI, can revolutionize the way coastal adaptation is approached. Governments and organizations should invest in these tools to enhance data analysis, prediction accuracy, and decision-making in the face of climate change.

“The future of coastal adaptation lies in the integration of nature-based solutions, community engagement, and technological innovations.”

As the world grapples with the impacts of climate change, proactive coastal adaptation measures are crucial for sustaining the resilience of vulnerable coastal areas. By leveraging nature-based solutions, incorporating local knowledge, and embracing technological advancements, we can chart a path toward a more resilient and sustainable coastal future.

References:

  1. Marfai, M. A. (2024). Collecting reference data for Indonesia’s coastal areas to prepare for the impacts of climate change. Nature, Published online: 01 April 2024. doi:10.1038/d41586-024-00908-w
  2. Burke, L., Reytar, K., Spalding, M., & Perry, A. (2012). Reefs at risk revisited. World resources institute, 1-130.
  3. Cooper, J. A., & Pilkey, O. H. (2004). Sea-level rise and shoreline retreat: time to abandon the Bruun Rule. Global and planetary change, 43(3-4), 157-171.
  4. Dahdouh-Guebas, F., Jayatissa, L. P., Di Nitto, D., Bosire, J. O., Lo Seen, D., & Koedam, N. (2005). How effective were mangroves as a defence against the recent tsunami?. Current Biology, 15(12), R443-R447.
“Filipino Scientist Leads Volcano Research and Response Organization”

“Filipino Scientist Leads Volcano Research and Response Organization”

by | Mar 25, 2024 | Science

Filipino Scientist Leads Volcano Research and Response Organization

Exploring Future Trends in Filipino Volcano Research and Response

Introduction

In recent years, the study of volcanoes and their potential hazards has gained significant attention as their eruptions pose considerable danger to human lives and infrastructure. Mariton Antonia Bornas, a notable figure in the field, leads a Filipino volcano research and response organization. In this article, we will delve into the key points of her work and analyze potential future trends in volcano research and response in the Philippines.

Key Points

1. Increasing Emphasis on Early Warning Systems

With the advancement of technology, there has been a growing emphasis on developing efficient and effective early warning systems for volcanic eruptions. Bornas, through her organization, has been at the forefront of implementing these systems in the Philippines. Such systems utilize various monitoring tools, such as seismometers, thermal imaging, and gas analyzers, to detect volcanic activity at its earliest stages.

Future trends indicate that early warning systems will become even more sophisticated, integrating real-time data analysis with artificial intelligence algorithms. This will enhance the accuracy and speed of eruption forecasts, enabling authorities to evacuate at-risk populations and prepare mitigation strategies promptly.

2. Integration of Volcano Research with Climate Change

Another crucial aspect highlighted in Bornas’s work is the integration of volcano research with climate change studies. Volcanic eruptions not only release large amounts of particulate matter and gases into the atmosphere but also have the potential to induce climate effects, such as cooling or regional weather pattern alterations. Recognizing this, future trends will see an increased focus on the relationship between volcanism and climate change.

Research in this area will not only aid in better understanding volcanic impacts on the environment but also assist in developing strategies to mitigate climate-related consequences. By studying volcanic ash deposition patterns and analyzing volcanic gases’ influence on the atmosphere, scientists can improve climate models and refine their projections of future climate scenarios.

3. Enhanced Volcano Monitoring Techniques

Bornas has been instrumental in implementing advanced volcano monitoring techniques in the Philippines, such as remote sensing, satellite monitoring, and ground deformation analysis. These methods have proven valuable in assessing volcanic activity and providing critical data for hazard assessments.

Future trends suggest that volcano monitoring techniques will continue to evolve, leveraging cutting-edge technologies. Integration of remote sensing with drones and satellite imaging will enable real-time monitoring of volcanic plumes and ground movements with higher precision and resolution. Furthermore, the use of machine learning algorithms will aid in extracting meaningful patterns from vast amounts of monitoring data, assisting scientists in making more accurate predictions about eruption behavior.

Future Predictions

In light of the key trends discussed above, several predictions can be made regarding the future of volcano research and response in the Philippines:

  1. Timely and reliable eruption forecasts: With the integration of advanced technology and artificial intelligence in early warning systems, eruption forecasts will become increasingly accurate and timely. This will be crucial in minimizing casualties and implementing effective response strategies.
  2. Holistic approach to volcano research: The integration of volcano research with climate change studies will pave the way for a more comprehensive understanding of volcanic impacts. This interdisciplinary approach will lead to innovative solutions for mitigating climate-related consequences.
  3. Prominence of automated monitoring systems: With enhanced volcano monitoring techniques utilizing drones, satellites, and machine learning algorithms, the role of automated monitoring systems will significantly increase. These systems will provide continuous, real-time data analysis, allowing scientists to detect even subtle volcanic activity.

Recommendations for the Industry

Based on the trends and predictions outlined above, certain recommendations can be made for the volcano research and response industry:

  1. Invest in technology and infrastructure: Governments, research organizations, and stakeholders should allocate sufficient resources to develop and maintain state-of-the-art volcano monitoring systems. This includes acquiring advanced monitoring equipment, establishing communication networks, and upgrading data analysis capabilities.
  2. Promote interdisciplinary collaborations: Encourage collaborations between volcano researchers, climate scientists, and policymakers to foster a holistic approach to volcano research. This will ensure a comprehensive understanding of volcanic hazards and aid in developing effective mitigation strategies.
  3. Enhance public awareness and education: Increase public education efforts regarding volcanic risks, safety protocols, and the importance of early warning systems. Creating awareness and imparting knowledge will contribute to better public preparedness and response during volcanic crises.

Conclusion

The future of volcano research and response in the Philippines holds exciting prospects, thanks to the dedication and efforts of individuals like Mariton Antonia Bornas. Innovations in early warning systems, the integration of volcano research with climate change studies, and enhanced monitoring techniques will shape the industry’s future. By implementing the recommended strategies and fostering collaboration, the Philippines can become a global leader in volcano research and response, ensuring the safety and wellbeing of its communities in the face of volcanic hazards.

References

[1] Bornas, M.A. Running a Filipino volcano research and response organization. Nature. Published online: 25 March 2024. Available at: https://doi.org/10.1038/d41586-024-00896-x

“Roman Harbor Remains Uncovered off Coast of Portorož, Slovenia”

“Roman Harbor Remains Uncovered off Coast of Portorož, Slovenia”

by | Mar 18, 2024 | Art

Roman Harbor Remains Uncovered off Coast of Portorož, Slovenia

Potential Future Trends in Underwater Archaeology

Introduction

Underwater archaeology is a field that continues to uncover fascinating discoveries from the past. The recent findings by the research team from the University of Ljubljana’s Institute of Underwater Archaeology off the coast of Portorož, Slovenia, are just one example of the potential future trends in this field. This article will analyze these key points and provide predictions and recommendations for the industry.

Key Points

  • The discovery of the Roman harbor and its artifacts provides valuable insights into the history of the port town and its importance during the Roman Empire.
  • The findings of the wooden stakes and two ancient ship masts are unique on a global scale, highlighting the significance of the site.
  • The presence of sigallata pottery suggests trade connections and imports during the 1st century CE.
  • The preservation and storage of the artifacts at the Sergei Mašera Maritime Museum ensure their accessibility for future research and public display.
  • The excavation is part of a larger campaign to explore Slovenia’s coastline, indicating a growing interest in uncovering underwater heritage.
  • The consistently poor visibility during the study adds complexity to the research process, highlighting the need for innovative underwater archaeological techniques.
  • The presence of other Roman sites in the area suggests the possibility of a small Roman harbor, which requires further investigation.

Predictions for the Industry

The recent discoveries and ongoing exploration in the field of underwater archaeology suggest several potential future trends:

  1. Increase in Research and Exploration: The successful findings off the coast of Portorož demonstrate the importance of conducting systematic underwater archaeological surveys. This is likely to lead to an increase in research and exploration efforts in coastal regions around the world.
  2. Advancements in Underwater Archaeological Techniques: The consistently poor visibility during the study highlights the need for advancements in underwater archaeological techniques. Research teams are likely to invest in innovative technologies such as underwater drones, 3D imaging, and remote sensing to enhance their capabilities in uncovering and documenting submerged sites.
  3. Collaboration and Partnerships: As underwater archaeological projects require diverse expertise and resources, collaboration and partnerships between academic institutions, government agencies, and private organizations will become more prevalent. These collaborations will ensure the successful preservation, research, and public outreach of underwater heritage.
  4. Digital Preservation and Virtual Reconstruction: With the advancements in 3D imaging and virtual reality technologies, the industry is likely to adopt digital preservation and virtual reconstruction techniques. This will enable researchers to create immersive experiences for the public, allowing them to explore underwater sites without physically visiting them.

Recommendations for the Industry

Based on the analysis of the key points and the predicted future trends, the following recommendations can be made to enhance the development and growth of the underwater archaeology industry:

  1. Investment in Research and Training: Governments, academic institutions, and funding organizations should allocate resources for underwater archaeological research and training programs. This will ensure a skilled workforce and foster the advancement of the field.
  2. Integration of Technology and Innovation: Research teams should actively explore and adopt new technologies and innovative approaches to overcome the challenges of underwater archaeological studies. Collaborations with experts in fields such as robotics, computer science, and remote sensing will be instrumental in driving these advancements.
  3. International Cooperation: The underwater archaeology community should encourage international cooperation and knowledge sharing. This can be achieved through conferences, workshops, and collaborative projects. Sharing best practices and lessons learned will accelerate the progress of research and deepen our understanding of underwater heritage.
  4. Public Outreach and Education: Efforts should be made to raise awareness about the significance of underwater archaeology and engage the public in preserving and appreciating underwater heritage. This can be achieved through interactive exhibitions, educational programs, and the use of digital platforms to disseminate information.

Conclusion

The recent discoveries off the coast of Portorož, Slovenia, provide a glimpse into the potential future trends in underwater archaeology. Advancements in technology, increased research efforts, collaborations, and public outreach initiatives will shape the industry in the coming years. By investing in these trends and following the recommendations mentioned above, the underwater archaeology industry can continue to uncover the mysteries of the past and preserve our underwater heritage for future generations.

References:

Causal Graph Neural Networks for Wildfire Danger Prediction

Causal Graph Neural Networks for Wildfire Danger Prediction

by | Mar 14, 2024 | AI

Wildfire forecasting is notoriously hard due to the complex interplay of different factors such as weather conditions, vegetation types and human activities. Deep learning models show promise in…

revolutionizing wildfire forecasting by leveraging vast amounts of data and sophisticated algorithms. These models have the potential to predict the behavior and spread of wildfires with unprecedented accuracy, enabling authorities to take proactive measures to mitigate their devastating impact. By analyzing a wide range of variables, including weather patterns, fuel moisture content, and historical fire data, deep learning models can provide invaluable insights into the likelihood and severity of wildfires. This article explores the advancements in deep learning techniques for wildfire forecasting and highlights their potential to revolutionize fire management strategies, ultimately saving lives and protecting ecosystems.

Reimagining Wildfire Forecasting with Deep Learning

Wildfire forecasting has long been a challenging task for scientists and authorities, given the complex interplay of variables such as weather conditions, vegetation types, and human activities. Traditional forecasting methods often struggle to provide accurate predictions, leaving communities vulnerable to the devastating impact of wildfires. However, there is hope on the horizon as deep learning models show promise in revolutionizing the way we predict and mitigate wildfires.

The Power of Deep Learning

Deep learning, a subset of artificial intelligence, has proven its potential in various fields, from image recognition to natural language processing. By training complex neural networks on vast amounts of data, deep learning models can identify subtle patterns and correlations that human experts may overlook.

When it comes to wildfire forecasting, harnessing the power of deep learning can offer significant improvements. These models can incorporate a vast array of variables, including weather data, historical wildfire patterns, topography, vegetation maps, and even social media data. By analyzing and synthesizing this wealth of information, deep learning models can provide more accurate and timely predictions.

Integrating Real-Time Data

One of the most exciting aspects of deep learning models is their ability to integrate real-time data into the forecasting process. Traditional methods often rely on historical data and predefined rules, limiting their adaptability to rapidly changing conditions. Deep learning models, on the other hand, can constantly update their predictions as new data becomes available.

Imagine a system that continuously monitors weather conditions, satellite imagery, sensor data, and social media feeds, combining this information with historical patterns. By assessing the interplay of these variables in real-time, deep learning models can provide up-to-the-minute wildfire forecasts, empowering authorities and communities to take proactive measures to prevent or mitigate the spread of fires.

Empowering Early Intervention

Another crucial aspect of deep learning models for wildfire forecasting is their potential to enable early intervention. By accurately predicting the likelihood and potential trajectory of wildfires, authorities can mobilize resources and implement targeted preventive measures before the situation escalates.

Deep learning models can identify factors such as vulnerable areas, high-risk ignition sources, and the likelihood of rapid fire spread based on environmental conditions. This information can be used to strategize fire prevention efforts, allocate firefighting resources, and even plan evacuation routes accurately. By leveraging the power of deep learning, we can reduce the loss of lives, property, and ecological damage caused by wildfires.

Bridging the Gap: Collaboration and Data Sharing

While deep learning models offer great potential, their success relies heavily on collaboration and data sharing. To train accurate models, we need access to comprehensive and diverse datasets that encompass various geographical regions, climate types, and socio-economic factors.

Researchers, scientific institutions, governments, and technology companies must collaborate to collect and share data, ensuring that deep learning models capture the complexity of wildfire dynamics accurately. Open-source initiatives and partnerships are vital in this regard, fostering innovation and advancing the collective understanding of wildfire forecasting.

It is only through interdisciplinary collaboration and a shared commitment to data-driven solutions that we can harness the full potential of deep learning in wildfire forecasting.

A Safer, More Resilient Future

Incorporating deep learning models into wildfire forecasting holds the promise of a safer and more resilient future. By leveraging the power of artificial intelligence and real-time data integration, we can significantly improve the accuracy and timeliness of wildfire predictions. This, in turn, enables early intervention and empowers communities to take proactive measures to safeguard lives and property.

However, we must remember that deep learning models are not a panacea; they are tools that require continual refinement and adaptation. Ongoing research, validation, and improvement are essential to maximize their potential and address any limitations.

By embracing innovation, collaboration, and a data-driven approach, we can reimagine wildfire forecasting and create a future where lives and landscapes are protected from the devastating impact of wildfires.

improving wildfire forecasting by leveraging their ability to process vast amounts of data and identify complex patterns. These models have the potential to revolutionize the field of wildfire prediction and provide more accurate and timely information to firefighters, land managers, and communities at risk.

One of the key advantages of deep learning models is their ability to handle large and diverse datasets. They can incorporate data from various sources, including satellite imagery, weather forecasts, historical fire data, and even social media feeds. By analyzing these inputs, deep learning models can identify hidden relationships and patterns that may not be apparent to human experts.

Moreover, deep learning models can capture the dynamic nature of wildfires, taking into account the changing weather conditions and vegetation characteristics. This allows for real-time predictions and the ability to update forecasts as new data becomes available. By continuously learning from new information, these models can adapt and improve over time, enhancing their predictive accuracy.

However, it is important to note that deep learning models are not a silver bullet, and there are challenges that need to be addressed. One of the main challenges is the availability and quality of data. Accurate and up-to-date data is crucial for training and validating these models. Additionally, the interpretability of deep learning models can be a concern. Understanding how and why a model makes a particular prediction is essential for gaining trust and acceptance from stakeholders.

To overcome these challenges, collaborations between researchers, government agencies, and technology companies are crucial. By pooling resources and expertise, we can ensure the development of robust and reliable deep learning models for wildfire forecasting. Furthermore, efforts should be made to integrate these models into existing wildfire management systems and workflows, allowing for seamless integration and adoption.

Looking ahead, the future of wildfire forecasting lies in the continued advancement of deep learning models, coupled with the integration of other emerging technologies such as remote sensing and Internet of Things (IoT) devices. These technologies can provide real-time data on various environmental variables, further enhancing the accuracy and timeliness of wildfire predictions.

In conclusion, deep learning models hold great promise for wildfire forecasting, offering the potential to revolutionize the field and improve our ability to predict and mitigate the devastation caused by wildfires. However, ongoing research, collaboration, and data availability are crucial to harnessing the full potential of these models and ensuring their successful integration into wildfire management practices.
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