From b8a24fb38ee6edbdfc81f6bd0fcb15370434c0d6 Mon Sep 17 00:00:00 2001 From: Luke Marsden Date: Fri, 28 Jun 2024 16:09:27 +0200 Subject: [PATCH] Update documentation --- 03_extracting_data_to_different_formats.html | 577 +- ...extracting_data_to_different_formats.ipynb | 27532 +++++++++++++++- genindex.html | 1 + intro.html | 2 + ...tracting_data_to_different_formats.err.log | 4 +- search.html | 1 + searchindex.js | 2 +- 7 files changed, 27846 insertions(+), 273 deletions(-) diff --git a/03_extracting_data_to_different_formats.html b/03_extracting_data_to_different_formats.html index 7a6b01b..560dc2e 100644 --- a/03_extracting_data_to_different_formats.html +++ b/03_extracting_data_to_different_formats.html @@ -173,6 +173,7 @@
  • 01: Opening and understanding
  • 02: Creating Plots
  • 03: Extracting data to different formats
    • +
  • 04: Creating a CF-NetCDF file
    • @@ -367,10 +368,17 @@

    Contents

    @@ -393,6 +401,7 @@

    03: Extracting data to different formats

    Variables with 1 dimension#

    In this section, we will use some CTD data from the Nansen Legacy project. If you use these data, please cite them as recommended below:

    @@ -851,121 +860,469 @@

    Variables with 1 dimension -

    Variables with multiple dimensions#

    -

    What is a variable has multiple dimensions? Let’s look at the same data we used in tutorial #01.

    +
    +

    Variables with 2 dimensions#

    +

    Let’s look at some sea ice concentration data.

    If you use these data, please cite them as recommended below:

    -

    H.-M. Zhang, B. Huang, J. H. Lawrimore, M. J. Menne, and T. M. Smith (2019): NOAA Global Surface Temperature Dataset (NOAAGlobalTemp), Version 5.0. NOAA National Centers for Environmental Information. doi:10.25921/9qth-2p70 Accessed 2024-04-09.

    -

    Let’s load in the data, and extract the data from the anom varaible for a given date.

    +

    Lavergne, T.; Sørensen, A.; Tonboe, R.T.; Kreiner, M.; Saldo, R.; Birkedal, A.; Baordo, F.; Aspenes, T.; Eastwood, S. (2023): ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): High(er) Resolution Sea Ice Concentration Climate Data Record Version 3 (SSM/I and SSMIS). NERC EDS Centre for Environmental Data Analysis, 17 May 2023. doi:10.5285/eade27004395466aaa006135e1b2ad1a. https://dx.doi.org/10.5285/eade27004395466aaa006135e1b2ad1a

    -
    url <- 'https://www.ncei.noaa.gov/thredds/dodsC/noaa-global-temp-v5/NOAAGlobalTemp_v5.0.0_gridded_s188001_e202212_c20230108T133308.nc'
+
    url <- 'https://dap.ceda.ac.uk/thredds/dodsC/neodc/esacci/sea_ice/data/sea_ice_concentration/L4/ssmi_ssmis/12.5km/v3.0/NH/2007/09/ESACCI-SEAICE-L4-SICONC-RE_SSMI_12.5kmEASE2-NH-20070915-fv3.0.nc'
 data <- open.nc(url)
+print.nc(data)
+
    
+
    
+
    +
    +
    netcdf classic {
+dimensions:
+	time = UNLIMITED ; // (1 currently)
+	nv = 2 ;
+	xc = 864 ;
+	yc = 864 ;
+variables:
+	NC_INT Lambert_Azimuthal_Grid ;
+		NC_CHAR Lambert_Azimuthal_Grid:grid_mapping_name = "lambert_azimuthal_equal_area" ;
+		NC_DOUBLE Lambert_Azimuthal_Grid:longitude_of_projection_origin = 0 ;
+		NC_DOUBLE Lambert_Azimuthal_Grid:latitude_of_projection_origin = 90 ;
+		NC_DOUBLE Lambert_Azimuthal_Grid:false_easting = 0 ;
+		NC_DOUBLE Lambert_Azimuthal_Grid:false_northing = 0 ;
+		NC_DOUBLE Lambert_Azimuthal_Grid:semi_major_axis = 6378137 ;
+		NC_DOUBLE Lambert_Azimuthal_Grid:inverse_flattening = 298.257223563 ;
+		NC_CHAR Lambert_Azimuthal_Grid:proj4_string = "+proj=laea +lon_0=0 +datum=WGS84 +ellps=WGS84 +lat_0=90.0" ;
+	NC_DOUBLE time(time) ;
+		NC_CHAR time:units = "seconds since 1978-01-01 00:00:00" ;
+		NC_CHAR time:long_name = "reference time of product" ;
+		NC_CHAR time:standard_name = "time" ;
+		NC_CHAR time:axis = "T" ;
+		NC_CHAR time:calendar = "standard" ;
+		NC_CHAR time:bounds = "time_bnds" ;
+		NC_INT time:_ChunkSizes = 512 ;
+	NC_DOUBLE time_bnds(nv, time) ;
+		NC_INT time_bnds:_ChunkSizes = 1, 2 ;
+	NC_DOUBLE xc(xc) ;
+		NC_CHAR xc:units = "km" ;
+		NC_CHAR xc:long_name = "x coordinate of projection (eastings)" ;
+		NC_CHAR xc:standard_name = "projection_x_coordinate" ;
+		NC_INT xc:_ChunkSizes = 864 ;
+	NC_DOUBLE yc(yc) ;
+		NC_CHAR yc:units = "km" ;
+		NC_CHAR yc:long_name = "y coordinate of projection (northings)" ;
+		NC_CHAR yc:standard_name = "projection_y_coordinate" ;
+		NC_INT yc:_ChunkSizes = 864 ;
+	NC_FLOAT lat(xc, yc) ;
+		NC_CHAR lat:units = "degrees_north" ;
+		NC_CHAR lat:long_name = "latitude coordinate" ;
+		NC_CHAR lat:standard_name = "latitude" ;
+		NC_INT lat:_ChunkSizes = 864, 864 ;
+	NC_FLOAT lon(xc, yc) ;
+		NC_CHAR lon:units = "degrees_east" ;
+		NC_CHAR lon:long_name = "longitude coordinate" ;
+		NC_CHAR lon:standard_name = "longitude" ;
+		NC_INT lon:_ChunkSizes = 864, 864 ;
+	NC_INT ice_conc(xc, yc, time) ;
+		NC_INT ice_conc:_FillValue = -32767 ;
+		NC_CHAR ice_conc:long_name = "fully filtered concentration of sea ice using atmospheric correction of brightness temperatures and open water filters" ;
+		NC_CHAR ice_conc:standard_name = "sea_ice_area_fraction" ;
+		NC_CHAR ice_conc:units = "%" ;
+		NC_INT ice_conc:valid_min = 0 ;
+		NC_INT ice_conc:valid_max = 10000 ;
+		NC_CHAR ice_conc:grid_mapping = "Lambert_Azimuthal_Grid" ;
+		NC_CHAR ice_conc:ancillary_variables = "total_standard_uncertainty status_flag" ;
+		NC_DOUBLE ice_conc:scale_factor = 0.01 ;
+		NC_CHAR ice_conc:comment = "this field is the primary sea ice concentration estimate for this climate data record" ;
+		NC_CHAR ice_conc:coordinates = "time lat lon" ;
+		NC_INT ice_conc:_ChunkSizes = 1, 864, 864 ;
+	NC_INT raw_ice_conc_values(xc, yc, time) ;
+		NC_INT raw_ice_conc_values:_FillValue = -32767 ;
+		NC_CHAR raw_ice_conc_values:long_name = "sea ice concentration estimates as retrieved by the algorithm, and that were edited away by the various filters" ;
+		NC_CHAR raw_ice_conc_values:units = "%" ;
+		NC_CHAR raw_ice_conc_values:grid_mapping = "Lambert_Azimuthal_Grid" ;
+		NC_DOUBLE raw_ice_conc_values:scale_factor = 0.01 ;
+		NC_CHAR raw_ice_conc_values:comment = "this field can be used in combination with 'ice_conc' to access un-bounded normal distribution of sea ice concentration estimates corresponding to the uncertainty estimate in 'total_standard_uncertainty'. It does also feature some amount of un-physical data (below 0% and above 100%)." ;
+		NC_CHAR raw_ice_conc_values:coordinates = "time lat lon" ;
+		NC_INT raw_ice_conc_values:_ChunkSizes = 1, 864, 864 ;
+	NC_INT total_standard_uncertainty(xc, yc, time) ;
+		NC_INT total_standard_uncertainty:_FillValue = -32767 ;
+		NC_CHAR total_standard_uncertainty:long_name = "total uncertainty (one standard deviation) of concentration of sea ice" ;
+		NC_CHAR total_standard_uncertainty:standard_name = "sea_ice_area_fraction standard_error" ;
+		NC_CHAR total_standard_uncertainty:units = "%" ;
+		NC_CHAR total_standard_uncertainty:grid_mapping = "Lambert_Azimuthal_Grid" ;
+		NC_DOUBLE total_standard_uncertainty:scale_factor = 0.01 ;
+		NC_CHAR total_standard_uncertainty:coordinates = "time lat lon" ;
+		NC_INT total_standard_uncertainty:_ChunkSizes = 1, 864, 864 ;
+	NC_INT smearing_standard_uncertainty(xc, yc, time) ;
+		NC_INT smearing_standard_uncertainty:_FillValue = -32767 ;
+		NC_CHAR smearing_standard_uncertainty:long_name = "smearing uncertainty (one standard deviation) of concentration of sea ice" ;
+		NC_CHAR smearing_standard_uncertainty:units = "%" ;
+		NC_CHAR smearing_standard_uncertainty:grid_mapping = "Lambert_Azimuthal_Grid" ;
+		NC_DOUBLE smearing_standard_uncertainty:scale_factor = 0.01 ;
+		NC_CHAR smearing_standard_uncertainty:comment = "this is one of the two components contributing to 'total_standard_uncertainty'" ;
+		NC_CHAR smearing_standard_uncertainty:coordinates = "time lat lon" ;
+		NC_INT smearing_standard_uncertainty:_ChunkSizes = 1, 864, 864 ;
+	NC_INT algorithm_standard_uncertainty(xc, yc, time) ;
+		NC_INT algorithm_standard_uncertainty:_FillValue = -32767 ;
+		NC_CHAR algorithm_standard_uncertainty:long_name = "algorithm uncertainty (one standard deviation) of concentration of sea ice" ;
+		NC_CHAR algorithm_standard_uncertainty:units = "%" ;
+		NC_CHAR algorithm_standard_uncertainty:grid_mapping = "Lambert_Azimuthal_Grid" ;
+		NC_DOUBLE algorithm_standard_uncertainty:scale_factor = 0.01 ;
+		NC_CHAR algorithm_standard_uncertainty:comment = "this is one of the two components contributing to 'total_standard_uncertainty'" ;
+		NC_CHAR algorithm_standard_uncertainty:coordinates = "time lat lon" ;
+		NC_INT algorithm_standard_uncertainty:_ChunkSizes = 1, 864, 864 ;
+	NC_SHORT status_flag(xc, yc, time) ;
+		NC_SHORT status_flag:_FillValue = -32768 ;
+		NC_CHAR status_flag:long_name = "status flag bit array for sea ice concentration retrievals" ;
+		NC_CHAR status_flag:standard_name = "sea_ice_area_fraction status_flag" ;
+		NC_SHORT status_flag:valid_min = 0 ;
+		NC_SHORT status_flag:valid_max = 32767 ;
+		NC_CHAR status_flag:grid_mapping = "Lambert_Azimuthal_Grid" ;
+		NC_SHORT status_flag:flag_masks = 1, 2, 4, 8, 16, 32, 64, 128 ;
+		NC_CHAR status_flag:flag_meanings = "land lake open_water_filtered land_spill_over high_t2m spatial_interp temporal_interp max_ice_climo" ;
+		NC_CHAR status_flag:flag_descriptions = "
+all bits to 0 (flag 0): Nominal retrieval by the SIC algorithm
+bit 1 (flag 1): Position is over land
+bit 2 (flag 2): Position is lake
+bit 3 (flag 4): SIC is set to zero by the open water filter
+bit 4 (flag 8): SIC value is changed for correcting land spill-over effects
+bit 5 (flag 16): Handle with caution, the 2m air temperature is high at this position, and this might be false ice
+bit 6 (flag 32): Value is the result of spatial interpolation
+bit 7 (flag 64): Value is the result of temporal interpolation
+bit 8 (flag 128): SIC is set to zero since position is outside maximum sea ice climatology." ;
+		NC_CHAR status_flag:comment = "Flag values found in the map might be combinations of those listed above. For example flag value 80 (= 2^4 + 2^6 = 16 + 64) indicates high_t2m AND temporal_interp" ;
+		NC_CHAR status_flag:coordinates = "time lat lon" ;
+		NC_INT status_flag:_ChunkSizes = 1, 864, 864 ;
 
-lat <- var.get.nc(data, 'lat')
-lon <- var.get.nc(data, 'lon')
-
-desired_date <- as.Date('2020-01-01')
-days_since_1800 <- as.numeric(difftime(desired_date, as.Date('1800-01-01'), units = 'days'))
-
-time <- var.get.nc(data, "time") 
-# Finding index of the value
-time_index <- which(time == days_since_1800)
-
-anom <- var.get.nc(data, 'anom', start=c(NA, NA, 1, time_index), count=c(NA,NA,1,1))
+// global attributes:
+		NC_CHAR :title = "High(er) Resolution Sea Ice Concentration Climate Data Record Version 3 (SSM/I and SSMIS) from the ESA CCI+" ;
+		NC_CHAR :summary = "This climate data record of sea ice concentration is obtained from passive microwave satellite data (SSM/I and SSMIS) over the polar regions (Arctic and Antarctic). The processing chain features: 1) dynamic tuning of tie-points and algorithms,  2) correction of atmospheric noise using a Radiative Transfer Model,  3) computation of per-pixel uncertainties, 4) an optimal hybrid sea ice concentration algorithm, and 5) pan-sharpening of the SIC fields using the near-90GHz imagery channels. This dataset was generated by the ESA Climate Change Initiative (CCI+) Sea Ice Phase 1 project. This dataset is an enhanced-resolution version of the OSI SAF OSI-450-a CDR over the period 1991-2020." ;
+		NC_CHAR :topiccategory = "Oceans ClimatologyMeteorologyAtmosphere" ;
+		NC_CHAR :keywords = "GCMDSK:Earth Science > Cryosphere > Sea Ice > Sea Ice Concentration, GCMDSK:Earth Science > Oceans > Sea Ice > Sea Ice Concentration, GCMDSK:Earth Science > Climate Indicators > Cryospheric Indicators > Sea Ice Concentration, GCMDLOC:Geographic Region > Northern Hemisphere, GCMDLOC:Vertical Location > Sea Surface, GCMDPROV: Government Agencies-non-US > Norway > NO/MET > Norwegian Meteorological Institute" ;
+		NC_CHAR :keywords_vocabulary = "GCMDSK:GCMD Science Keywords:https://gcmd.earthdata.nasa.gov/kms/concepts/concept_scheme/sciencekeywords, GCMDPROV:GCMD Providers:https://gcmd.earthdata.nasa.gov/kms/concepts/concept_scheme/providers, GCMDLOC:GCMD Locations:https://gcmd.earthdata.nasa.gov/kms/concepts/concept_scheme/locations" ;
+		NC_DOUBLE :geospatial_lat_min = 16.62393 ;
+		NC_DOUBLE :geospatial_lat_max = 90 ;
+		NC_DOUBLE :geospatial_lon_min = -180 ;
+		NC_DOUBLE :geospatial_lon_max = 180 ;
+		NC_DOUBLE :geospatial_vertical_min = 0 ;
+		NC_DOUBLE :geospatial_vertical_max = 0 ;
+		NC_CHAR :sensor = "SSM/I,SSMIS" ;
+		NC_CHAR :platform = "DMSP-F<10,11,13,14,15>,DMSP-F<16,17,18>" ;
+		NC_CHAR :source = "FCDR of SMMR / SSMI / SSMIS Brightness Temperatures R4 from EUMETSAT Climate Monitoring SAF (doi:10.5676/EUM_SAF_CM/FCDR_MWI/V004), ERA5" ;
+		NC_CHAR :time_coverage_start = "2007-09-15T00:00:00Z" ;
+		NC_CHAR :time_coverage_end = "2007-09-16T00:00:00Z" ;
+		NC_CHAR :time_coverage_duration = "P1D" ;
+		NC_CHAR :time_coverage_resolution = "P1D" ;
+		NC_CHAR :project = "Climate Change Initiative - European Space Agency" ;
+		NC_CHAR :institution = "Norwegian Meteorological Institute" ;
+		NC_CHAR :creator_name = "Norwegian Meteorological Institute" ;
+		NC_CHAR :creator_type = "institution" ;
+		NC_CHAR :creator_url = "http://met.no" ;
+		NC_CHAR :creator_email = "forskning@met.no" ;
+		NC_CHAR :license = "Free and open access" ;
+		NC_CHAR :references = "A Product User Guide (PUG), an Algorithm Theoretical Basis Document (ATBD) and a Validation Report (PVASR) are available from the doi landing page and from https://climate.esa.int/seaice" ;
+		NC_CHAR :history = "2023-04-20T21:46:46Z creation" ;
+		NC_CHAR :date_created = "2023-04-20" ;
+		NC_CHAR :cdm_data_type = "Grid" ;
+		NC_CHAR :spatial_resolution = "12.5 km grid spacing" ;
+		NC_CHAR :algorithm = "reSICCI3LF: SIC from 19/37GHz channels, pan-sharpened with SIC from near-90GHz channels." ;
+		NC_CHAR :geospatial_bounds_crs = "EPSG:6931" ;
+		NC_CHAR :contributor_name = "Thomas Lavergne,Atle Soerensen,Rasmus Tonboe,Matilde Kreiner,Roberto Saldo,Anton Birkedal,Fabrizio Baordo,Trygve Aspenes,Steinar Eastwood" ;
+		NC_CHAR :contributor_role = "PrincipalInvestigator,author,author,author,author,author,author,author,author" ;
+		NC_CHAR :product_version = "3.0" ;
+		NC_CHAR :tracking_id = "0a5f4360-8c19-40aa-a09a-bab17eb0b6fc" ;
+		NC_CHAR :naming_authority = "NERC EDS Centre for Environmental Data Analysis" ;
+		NC_CHAR :Conventions = "CF-1.7,ACDD-1.3" ;
+		NC_CHAR :standard_name_vocabulary = "CF Standard Name Table (Version 80, 07 February 2023)" ;
+		NC_CHAR :product_name = "cci_HRssmi_sea_ice_conc_climate_data_record" ;
+		NC_CHAR :id = "10.5285/eade27004395466aaa006135e1b2ad1a" ;
+		NC_CHAR :format_version = "CCI Data Standards v2.3" ;
+		NC_CHAR :key_variables = "ice_conc" ;
+		NC_CHAR :DODS_EXTRA.Unlimited_Dimension = "time" ;
+}
    -

    lat and lon are 1D arrays whilst anom is a 2D matrix.

    -

    We can’t just write latitude and longitude to a dataframe like this:

    +

    The ice_conc variable has 3 dimensions; xc, yc and time. However, since there is only one time slice in this dataset, this is essentially a 2D variable.

    +

    Let’s write the ice_conc variable to a dataframe alongside the xc and yc coordinate variables.

    -
    df <- data.frame(lon = lon, lat = lat)
-head(df)
+
    xc <- var.get.nc(data, 'xc')
+yc <- var.get.nc(data, 'yc')
+ice_conc <- var.get.nc(data, 'ice_conc')
+dim(ice_conc) # Showing that this is 2D, not 3D.
    -
    - - - - - - - - - - - - - -
    A data.frame: 6 × 2
    lonlat
    <dbl><dbl>
    1 2.5-87.5
    2 7.5-82.5
    312.5-77.5
    417.5-72.5
    522.5-67.5
    627.5-62.5
    +
    +
    1. 864
    2. 864
    -

    Instead we need to create a 2D grid of values, like this:

    +

    The order of the dimensions is important. xc and then yc.

    +

    So the values are sorted such that all the values cooresponding to the first xc coordinate are first, before moving onto the second xc coordinate.

    +

    Now let’s create a 1D vector of the ice_conc data.

    -
    df <- expand.grid(lon = lon, lat = lat)
+
    ice_conc_vector <- as.vector(ice_conc)
+
    
+
    
+
    +
    +

    We then need to extend the xc coordinate values by repeating the first xc value 864 times before moving onto the second value.

    +
    +
    +
    xc_repeat <- rep(xc, each = length(yc))
+xc_repeat
+
    +
    +
    +
    +
    +
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    194. -5393.75
    195. -5393.75
    196. -5393.75
    197. -5393.75
    198. -5393.75
    199. -5393.75
    200. -5393.75
    201. ⋯
    202. 5393.75
    203. 5393.75
    204. 5393.75
    205. 5393.75
    206. 5393.75
    207. 5393.75
    208. 5393.75
    209. 5393.75
    210. 5393.75
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    242. 5393.75
    243. 5393.75
    244. 5393.75
    245. 5393.75
    246. 5393.75
    247. 5393.75
    248. 5393.75
    249. 5393.75
    250. 5393.75
    251. 5393.75
    252. 5393.75
    253. 5393.75
    254. 5393.75
    255. 5393.75
    256. 5393.75
    257. 5393.75
    258. 5393.75
    259. 5393.75
    260. 5393.75
    261. 5393.75
    262. 5393.75
    263. 5393.75
    264. 5393.75
    265. 5393.75
    266. 5393.75
    267. 5393.75
    268. 5393.75
    269. 5393.75
    270. 5393.75
    271. 5393.75
    272. 5393.75
    273. 5393.75
    274. 5393.75
    275. 5393.75
    276. 5393.75
    277. 5393.75
    278. 5393.75
    279. 5393.75
    280. 5393.75
    281. 5393.75
    282. 5393.75
    283. 5393.75
    284. 5393.75
    285. 5393.75
    286. 5393.75
    287. 5393.75
    288. 5393.75
    289. 5393.75
    290. 5393.75
    291. 5393.75
    292. 5393.75
    293. 5393.75
    294. 5393.75
    295. 5393.75
    296. 5393.75
    297. 5393.75
    298. 5393.75
    299. 5393.75
    300. 5393.75
    301. 5393.75
    302. 5393.75
    303. 5393.75
    304. 5393.75
    305. 5393.75
    306. 5393.75
    307. 5393.75
    308. 5393.75
    309. 5393.75
    310. 5393.75
    311. 5393.75
    312. 5393.75
    313. 5393.75
    314. 5393.75
    315. 5393.75
    316. 5393.75
    317. 5393.75
    318. 5393.75
    319. 5393.75
    320. 5393.75
    321. 5393.75
    322. 5393.75
    323. 5393.75
    324. 5393.75
    325. 5393.75
    326. 5393.75
    327. 5393.75
    328. 5393.75
    329. 5393.75
    330. 5393.75
    331. 5393.75
    332. 5393.75
    333. 5393.75
    334. 5393.75
    335. 5393.75
    336. 5393.75
    337. 5393.75
    338. 5393.75
    339. 5393.75
    340. 5393.75
    341. 5393.75
    342. 5393.75
    343. 5393.75
    344. 5393.75
    345. 5393.75
    346. 5393.75
    347. 5393.75
    348. 5393.75
    349. 5393.75
    350. 5393.75
    351. 5393.75
    352. 5393.75
    353. 5393.75
    354. 5393.75
    355. 5393.75
    356. 5393.75
    357. 5393.75
    358. 5393.75
    359. 5393.75
    360. 5393.75
    361. 5393.75
    362. 5393.75
    363. 5393.75
    364. 5393.75
    365. 5393.75
    366. 5393.75
    367. 5393.75
    368. 5393.75
    369. 5393.75
    370. 5393.75
    371. 5393.75
    372. 5393.75
    373. 5393.75
    374. 5393.75
    375. 5393.75
    376. 5393.75
    377. 5393.75
    378. 5393.75
    379. 5393.75
    380. 5393.75
    381. 5393.75
    382. 5393.75
    383. 5393.75
    384. 5393.75
    385. 5393.75
    386. 5393.75
    387. 5393.75
    388. 5393.75
    389. 5393.75
    390. 5393.75
    391. 5393.75
    392. 5393.75
    393. 5393.75
    394. 5393.75
    395. 5393.75
    396. 5393.75
    397. 5393.75
    398. 5393.75
    399. 5393.75
    400. 5393.75
    401. 5393.75
    +
    +
    +

    For the yc coordinate, we need to repeat the whole list 864 times.

    +
    +
    +
    yc_repeat <- rep(yc, times = length(xc))
+yc_repeat
+
    +
    +
    +
    +
    +
    1. 5393.75
    2. 5381.25
    3. 5368.75
    4. 5356.25
    5. 5343.75
    6. 5331.25
    7. 5318.75
    8. 5306.25
    9. 5293.75
    10. 5281.25
    11. 5268.75
    12. 5256.25
    13. 5243.75
    14. 5231.25
    15. 5218.75
    16. 5206.25
    17. 5193.75
    18. 5181.25
    19. 5168.75
    20. 5156.25
    21. 5143.75
    22. 5131.25
    23. 5118.75
    24. 5106.25
    25. 5093.75
    26. 5081.25
    27. 5068.75
    28. 5056.25
    29. 5043.75
    30. 5031.25
    31. 5018.75
    32. 5006.25
    33. 4993.75
    34. 4981.25
    35. 4968.75
    36. 4956.25
    37. 4943.75
    38. 4931.25
    39. 4918.75
    40. 4906.25
    41. 4893.75
    42. 4881.25
    43. 4868.75
    44. 4856.25
    45. 4843.75
    46. 4831.25
    47. 4818.75
    48. 4806.25
    49. 4793.75
    50. 4781.25
    51. 4768.75
    52. 4756.25
    53. 4743.75
    54. 4731.25
    55. 4718.75
    56. 4706.25
    57. 4693.75
    58. 4681.25
    59. 4668.75
    60. 4656.25
    61. 4643.75
    62. 4631.25
    63. 4618.75
    64. 4606.25
    65. 4593.75
    66. 4581.25
    67. 4568.75
    68. 4556.25
    69. 4543.75
    70. 4531.25
    71. 4518.75
    72. 4506.25
    73. 4493.75
    74. 4481.25
    75. 4468.75
    76. 4456.25
    77. 4443.75
    78. 4431.25
    79. 4418.75
    80. 4406.25
    81. 4393.75
    82. 4381.25
    83. 4368.75
    84. 4356.25
    85. 4343.75
    86. 4331.25
    87. 4318.75
    88. 4306.25
    89. 4293.75
    90. 4281.25
    91. 4268.75
    92. 4256.25
    93. 4243.75
    94. 4231.25
    95. 4218.75
    96. 4206.25
    97. 4193.75
    98. 4181.25
    99. 4168.75
    100. 4156.25
    101. 4143.75
    102. 4131.25
    103. 4118.75
    104. 4106.25
    105. 4093.75
    106. 4081.25
    107. 4068.75
    108. 4056.25
    109. 4043.75
    110. 4031.25
    111. 4018.75
    112. 4006.25
    113. 3993.75
    114. 3981.25
    115. 3968.75
    116. 3956.25
    117. 3943.75
    118. 3931.25
    119. 3918.75
    120. 3906.25
    121. 3893.75
    122. 3881.25
    123. 3868.75
    124. 3856.25
    125. 3843.75
    126. 3831.25
    127. 3818.75
    128. 3806.25
    129. 3793.75
    130. 3781.25
    131. 3768.75
    132. 3756.25
    133. 3743.75
    134. 3731.25
    135. 3718.75
    136. 3706.25
    137. 3693.75
    138. 3681.25
    139. 3668.75
    140. 3656.25
    141. 3643.75
    142. 3631.25
    143. 3618.75
    144. 3606.25
    145. 3593.75
    146. 3581.25
    147. 3568.75
    148. 3556.25
    149. 3543.75
    150. 3531.25
    151. 3518.75
    152. 3506.25
    153. 3493.75
    154. 3481.25
    155. 3468.75
    156. 3456.25
    157. 3443.75
    158. 3431.25
    159. 3418.75
    160. 3406.25
    161. 3393.75
    162. 3381.25
    163. 3368.75
    164. 3356.25
    165. 3343.75
    166. 3331.25
    167. 3318.75
    168. 3306.25
    169. 3293.75
    170. 3281.25
    171. 3268.75
    172. 3256.25
    173. 3243.75
    174. 3231.25
    175. 3218.75
    176. 3206.25
    177. 3193.75
    178. 3181.25
    179. 3168.75
    180. 3156.25
    181. 3143.75
    182. 3131.25
    183. 3118.75
    184. 3106.25
    185. 3093.75
    186. 3081.25
    187. 3068.75
    188. 3056.25
    189. 3043.75
    190. 3031.25
    191. 3018.75
    192. 3006.25
    193. 2993.75
    194. 2981.25
    195. 2968.75
    196. 2956.25
    197. 2943.75
    198. 2931.25
    199. 2918.75
    200. 2906.25
    201. ⋯
    202. -2906.25
    203. -2918.75
    204. -2931.25
    205. -2943.75
    206. -2956.25
    207. -2968.75
    208. -2981.25
    209. -2993.75
    210. -3006.25
    211. -3018.75
    212. -3031.25
    213. -3043.75
    214. -3056.25
    215. -3068.75
    216. -3081.25
    217. -3093.75
    218. -3106.25
    219. -3118.75
    220. -3131.25
    221. -3143.75
    222. -3156.25
    223. -3168.75
    224. -3181.25
    225. -3193.75
    226. -3206.25
    227. -3218.75
    228. -3231.25
    229. -3243.75
    230. -3256.25
    231. -3268.75
    232. -3281.25
    233. -3293.75
    234. -3306.25
    235. -3318.75
    236. -3331.25
    237. -3343.75
    238. -3356.25
    239. -3368.75
    240. -3381.25
    241. -3393.75
    242. -3406.25
    243. -3418.75
    244. -3431.25
    245. -3443.75
    246. -3456.25
    247. -3468.75
    248. -3481.25
    249. -3493.75
    250. -3506.25
    251. -3518.75
    252. -3531.25
    253. -3543.75
    254. -3556.25
    255. -3568.75
    256. -3581.25
    257. -3593.75
    258. -3606.25
    259. -3618.75
    260. -3631.25
    261. -3643.75
    262. -3656.25
    263. -3668.75
    264. -3681.25
    265. -3693.75
    266. -3706.25
    267. -3718.75
    268. -3731.25
    269. -3743.75
    270. -3756.25
    271. -3768.75
    272. -3781.25
    273. -3793.75
    274. -3806.25
    275. -3818.75
    276. -3831.25
    277. -3843.75
    278. -3856.25
    279. -3868.75
    280. -3881.25
    281. -3893.75
    282. -3906.25
    283. -3918.75
    284. -3931.25
    285. -3943.75
    286. -3956.25
    287. -3968.75
    288. -3981.25
    289. -3993.75
    290. -4006.25
    291. -4018.75
    292. -4031.25
    293. -4043.75
    294. -4056.25
    295. -4068.75
    296. -4081.25
    297. -4093.75
    298. -4106.25
    299. -4118.75
    300. -4131.25
    301. -4143.75
    302. -4156.25
    303. -4168.75
    304. -4181.25
    305. -4193.75
    306. -4206.25
    307. -4218.75
    308. -4231.25
    309. -4243.75
    310. -4256.25
    311. -4268.75
    312. -4281.25
    313. -4293.75
    314. -4306.25
    315. -4318.75
    316. -4331.25
    317. -4343.75
    318. -4356.25
    319. -4368.75
    320. -4381.25
    321. -4393.75
    322. -4406.25
    323. -4418.75
    324. -4431.25
    325. -4443.75
    326. -4456.25
    327. -4468.75
    328. -4481.25
    329. -4493.75
    330. -4506.25
    331. -4518.75
    332. -4531.25
    333. -4543.75
    334. -4556.25
    335. -4568.75
    336. -4581.25
    337. -4593.75
    338. -4606.25
    339. -4618.75
    340. -4631.25
    341. -4643.75
    342. -4656.25
    343. -4668.75
    344. -4681.25
    345. -4693.75
    346. -4706.25
    347. -4718.75
    348. -4731.25
    349. -4743.75
    350. -4756.25
    351. -4768.75
    352. -4781.25
    353. -4793.75
    354. -4806.25
    355. -4818.75
    356. -4831.25
    357. -4843.75
    358. -4856.25
    359. -4868.75
    360. -4881.25
    361. -4893.75
    362. -4906.25
    363. -4918.75
    364. -4931.25
    365. -4943.75
    366. -4956.25
    367. -4968.75
    368. -4981.25
    369. -4993.75
    370. -5006.25
    371. -5018.75
    372. -5031.25
    373. -5043.75
    374. -5056.25
    375. -5068.75
    376. -5081.25
    377. -5093.75
    378. -5106.25
    379. -5118.75
    380. -5131.25
    381. -5143.75
    382. -5156.25
    383. -5168.75
    384. -5181.25
    385. -5193.75
    386. -5206.25
    387. -5218.75
    388. -5231.25
    389. -5243.75
    390. -5256.25
    391. -5268.75
    392. -5281.25
    393. -5293.75
    394. -5306.25
    395. -5318.75
    396. -5331.25
    397. -5343.75
    398. -5356.25
    399. -5368.75
    400. -5381.25
    401. -5393.75
    +
    +
    +

    And now let’s combing them all in a dataframe and export the data.

    +
    +
    +
    df <- data.frame(
+  Xc = xc_repeat,
+  Yc = yc_repeat,
+  SeaIceConcentration = ice_conc_vector
+)
 head(df)
    - + - - + + - - - - - - + + + + + +
    A data.frame: 6 × 2A data.frame: 6 × 3
    lonlat
    <dbl[1d]><dbl[1d]>
    XcYcSeaIceConcentration
    <dbl><dbl><dbl>
    1 2.5-87.5
    2 7.5-87.5
    312.5-87.5
    417.5-87.5
    522.5-87.5
    627.5-87.5
    1-5393.755393.750
    2-5393.755381.250
    3-5393.755368.750
    4-5393.755356.250
    5-5393.755343.750
    6-5393.755331.250
    -

    Now adding the anom variable can be done like this:

    +

    Let’s add the latitude and longitude values as separate columns.

    -
    df$anom <- as.vector(anom)
+
    latitude <- var.get.nc(data, "lat")
+latitude_vector <- as.vector(latitude)
+df$latitude <- latitude_vector
+
+longitude <- var.get.nc(data, "lon")
+longitude_vector <- as.vector(longitude)
+df$longitude <- longitude_vector
+
 head(df)
+write_xlsx(df, '../data/exported_from_notebooks/2d_data.xlsx')
+write.csv(df, '../data/exported_from_notebooks/2d_data.csv', row.names = FALSE)
    - + - - + + - - - - - - + + + + + +
    A data.frame: 6 × 3A data.frame: 6 × 5
    lonlatanom
    <dbl[1d]><dbl[1d]><dbl>
    XcYcSeaIceConcentrationlatitudelongitude
    <dbl><dbl><dbl><dbl><dbl>
    1 2.5-87.5NA
    2 7.5-87.5NA
    312.5-87.5NA
    417.5-87.5NA
    522.5-87.5NA
    627.5-87.5NA
    1-5393.755393.75016.52435-135.0000
    2-5393.755381.25016.62387-135.0665
    3-5393.755368.75016.72321-135.1331
    4-5393.755356.25016.82236-135.1999
    5-5393.755343.75016.92134-135.2668
    6-5393.755331.25017.02014-135.3339
    -

    Finally, we can write out the data just like before

    +
    + +
    +

    Variables with 3 dimensions#

    +

    Let’s look at the same data we used in tutorial #01.

    +

    If you use these data, please cite them as recommended below:

    +

    H.-M. Zhang, B. Huang, J. H. Lawrimore, M. J. Menne, and T. M. Smith (2019): NOAA Global Surface Temperature Dataset (NOAAGlobalTemp), Version 5.0. NOAA National Centers for Environmental Information. doi:10.25921/9qth-2p70 Accessed 2024-04-09.

    +

    Let’s load in the data, and extract the data from the anom varaible.

    -
    write_xlsx(df, paste('../data/exported_from_notebooks/temperature_anomalies_',desired_date,'.xlsx', sep=''))
-write.csv(df, paste('../data/exported_from_notebooks/temperature_anomalies_',desired_date,'.csv', sep=''), row.names = FALSE)
+
    url <- 'https://www.ncei.noaa.gov/thredds/dodsC/noaa-global-temp-v5/NOAAGlobalTemp_v5.0.0_gridded_s188001_e202212_c20230108T133308.nc'
+data <- open.nc(url)
+print.nc(data)
    +
    +
    netcdf classic {
+dimensions:
+	lat = 36 ;
+	lon = 72 ;
+	time = 1716 ;
+	z = 1 ;
+variables:
+	NC_FLOAT time(time) ;
+		NC_CHAR time:long_name = "reference time of global temperature anomalies" ;
+		NC_CHAR time:standard_name = "time" ;
+		NC_CHAR time:coverage_content_type = "coordinate" ;
+		NC_CHAR time:units = "days since 1800-01-01 00:00:00" ;
+		NC_CHAR time:calendar = "gregorian" ;
+		NC_CHAR time:axis = "T" ;
+	NC_FLOAT lat(lat) ;
+		NC_CHAR lat:long_name = "Latitude" ;
+		NC_CHAR lat:standard_name = "latitude" ;
+		NC_CHAR lat:coverage_content_type = "coordinate" ;
+		NC_CHAR lat:units = "degrees_north" ;
+		NC_CHAR lat:grids = "Uniform grid from -87.5 to 87.5 by 5" ;
+		NC_FLOAT lat:valid_min = -87.5 ;
+		NC_FLOAT lat:valid_max = 87.5 ;
+		NC_CHAR lat:axis = "Y" ;
+		NC_CHAR lat:_CoordinateAxisType = "Lat" ;
+		NC_CHAR lat:coordinate_defines = "center" ;
+	NC_FLOAT lon(lon) ;
+		NC_CHAR lon:long_name = "Longitude" ;
+		NC_CHAR lon:standard_name = "longitude" ;
+		NC_CHAR lon:coverage_content_type = "coordinate" ;
+		NC_CHAR lon:units = "degrees_east" ;
+		NC_CHAR lon:grids = "Uniform grid from 2.5 to 357.5 by 5" ;
+		NC_CHAR lon:axis = "X" ;
+		NC_FLOAT lon:valid_min = 2.5 ;
+		NC_FLOAT lon:valid_max = 357.5 ;
+		NC_CHAR lon:_CoordinateAxisType = "Lon" ;
+		NC_CHAR lon:coordinate_defines = "center" ;
+	NC_FLOAT z(z) ;
+		NC_CHAR z:long_name = "height of surface temperature" ;
+		NC_CHAR z:standard_name = "height" ;
+		NC_CHAR z:coverage_content_type = "coordinate" ;
+		NC_CHAR z:geospatial_bounds_vertical_crs = "EPSG:5703" ;
+		NC_CHAR z:units = "meters" ;
+		NC_CHAR z:positive = "up" ;
+		NC_FLOAT z:valid_min = 0 ;
+		NC_FLOAT z:valid_max = 0 ;
+		NC_CHAR z:axis = "Z" ;
+		NC_CHAR z:_CoordinateAxisType = "Height" ;
+		NC_CHAR z:comment = "Actual measurement depth of in situ sea surface temperature varies from 0.2 to 10 m, but corrected to the nominal depth of buoy at 0.2 m; for air temperature over land is 2m." ;
+	NC_FLOAT anom(lon, lat, z, time) ;
+		NC_CHAR anom:long_name = "Global Temperature Anomalies" ;
+		NC_CHAR anom:standard_name = "surface_temperature_anomaly" ;
+		NC_CHAR anom:coverage_content_type = "physicalMeasurement" ;
+		NC_CHAR anom:units = "degrees C" ;
+		NC_FLOAT anom:missing_value = -999.900024414062 ;
+		NC_FLOAT anom:_FillValue = -999.900024414062 ;
+		NC_FLOAT anom:add_offset = 0 ;
+		NC_FLOAT anom:scale_factor = 1 ;
+		NC_FLOAT anom:valid_min = -40 ;
+		NC_FLOAT anom:valid_max = 40 ;
+		NC_CHAR anom:coordinates = "time z lat lon" ;
+
+// global attributes:
+		NC_CHAR :Conventions = "CF-1.6, ACDD-1.3" ;
+		NC_CHAR :title = "NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)" ;
+		NC_CHAR :summary = "NOAAGlobalTemp is a merged land-ocean surface temperature analysis, formerly abbreviated as MLOST. The SST dataset is the Extended Reconstructed Sea Surface Temperature (ERSST) and the land surface air temperature dataset is from the Global Historical Climatology Network Monthly (GHCN-M) database." ;
+		NC_CHAR :institution = "DOC/NOAA/NESDIS/National Centers for Environmental Information(NCEI)" ;
+		NC_CHAR :id = " gov.noaa.ncdc:C00934 " ;
+		NC_CHAR :naming_authority = " gov.noaa.ncei " ;
+		NC_CHAR :project = " NOAAGlobalTemp " ;
+		NC_CHAR :platform = "Ships, moored buoys, surface drifting buoys, Argo floats, and weather stations" ;
+		NC_CHAR :instrument = "Conventional thermometers" ;
+		NC_CHAR :history = "Version 5.0.0, blending ERSST V5 and GHCN-M V4." ;
+		NC_CHAR :comment = "Merged land ocean surface temperature anomalies." ;
+		NC_CHAR :product_version = "5.0.0" ;
+		NC_CHAR :source = "NOAAGlobalTemp combines a global sea surface temperature with a global land surface air temperature data" ;
+		NC_CHAR :ncei_template_version = "NCEI_NetCDF_Grid_Template_v2.0" ;
+		NC_CHAR :Metadata_Conventions = "Unidata Dataset Discovery v1.0" ;
+		NC_CHAR :license = "These data are available for use without restriction." ;
+		NC_CHAR :creator_name = "Boyin Huang, Xungang Yin, Ph.D" ;
+		NC_CHAR :creator_email = "Boyin.Huang@noaa.gov, Xungang.Yin@noaa.gov" ;
+		NC_CHAR :creator_type = "group" ;
+		NC_CHAR :creator_institution = "US DOC/NOAA/NESDIS/National Centers for Environmental Information(NCEI)" ;
+		NC_CHAR :contributor_name = "Huai-min Zhang, Ph.D" ;
+		NC_CHAR :contributor_role = "Chief, NOAA/NESDIS/NCEI/CCOG/OSB Ocean Surface Section" ;
+		NC_CHAR :publisher_name = "NCEI" ;
+		NC_CHAR :publisher_institution = "NCEI" ;
+		NC_CHAR :publisher_url = "https://www.ncdc.noaa.gov/" ;
+		NC_CHAR :publisher_email = "ncei.info@noaa.gov" ;
+		NC_CHAR :publisher_type = "institution" ;
+		NC_CHAR :keywords = "Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature, Earth Science > Atmosphere > Atmospheric Temperature > Surface Temperature > Air Temperature" ;
+		NC_CHAR :keywords_vocabulary = "NASA Global Change Master Directory (GCMD) Science Keywords" ;
+		NC_CHAR :standard_name_vocabulary = "Climate and Forecast (CF) Standard Name Table (Version 46, 25 July 2017)" ;
+		NC_CHAR :date_created = "2023-01-08T18:33:09Z" ;
+		NC_CHAR :necdf_version_id = "4.2.1.1" ;
+		NC_CHAR :netcdf_creator_name = "Yongsheng Zhang, Ph.D" ;
+		NC_CHAR :netcdf_creator_email = "yongsheng.zhang@noaa.gov" ;
+		NC_CHAR :dataset_citation_product = "NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)" ;
+		NC_CHAR :dataset_citation_version = "5.0.0" ;
+		NC_CHAR :dataset_citation_institution = "NOAA/NESDIS/NCEI, Asheville, NC, USA" ;
+		NC_CHAR :dataset_citation_url = "https://doi.org/10.25921/9qth-2p70" ;
+		NC_CHAR :metadata_link = "https://doi.org/10.25921/9qth-2p70" ;
+		NC_CHAR :cdm_data_type = "Grid" ;
+		NC_CHAR :grid_mapping_name = "latitude_longitude" ;
+		NC_CHAR :processing_level = "NOAA Processing Level 4" ;
+		NC_FLOAT :geospatial_lon_min = 2.5 ;
+		NC_FLOAT :geospatial_lon_max = 357.5 ;
+		NC_CHAR :geospatial_lon_units = "degrees_east" ;
+		NC_CHAR :geospatial_lon_resolution = "5.0 degree" ;
+		NC_FLOAT :geospatial_lat_min = -87.5 ;
+		NC_FLOAT :geospatial_lat_max = 87.5 ;
+		NC_CHAR :geospatial_lat_units = "degrees_north" ;
+		NC_CHAR :geospatial_lat_resolution = "5.0 degree" ;
+		NC_CHAR :geospatial_bounds = "POLYGON ((2.5 -87.5, 2.5 87.5, 357.5 87.5, 357.5 -87.5, 2.5 -87.5))" ;
+		NC_CHAR :geospatial_bounds_crs = "EPSG:4326" ;
+		NC_FLOAT :geospatial_vertical_min = 0 ;
+		NC_FLOAT :geospatial_vertical_max = 0 ;
+		NC_CHAR :geospatial_vertical_units = "meters" ;
+		NC_CHAR :geospatial_vertical_resolution = "0.f" ;
+		NC_CHAR :geospatial_vertical_positive = "up" ;
+		NC_CHAR :time_coverage_start = "1880-01" ;
+		NC_CHAR :time_coverage_end = "2022-12" ;
+		NC_CHAR :time_coverage_resolution = "P1M" ;
+		NC_CHAR :time_coverage_duration = "P143Y0M" ;
+		NC_CHAR :references = "Vose, R. S., et al., 2012: NOAAs merged land-ocean surface temperature analysis. Bulletin of the American Meteorological Society, 93, 1677-1685. doi: 10.1175/BAMS-D-11-00241.1. Huang, B., Peter W. Thorne, et. al, 2017: Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5), Upgrades, validations, and intercomparisons. J. Climate, 30, 8179-8205. doi: 10.1175/JCLI-D-16-0836.1" ;
+		NC_CHAR :climatology = "Climatology is based on 1971-2000 monthly climatology" ;
+		NC_CHAR :acknowledgment = "The NOAA Global Surface Temperature Dataset is provided by the NOAA National Centers for Environmental Information(NCEI)" ;
+		NC_CHAR :date_modified = "2023-01-08T18:33:09Z" ;
+		NC_CHAR :date_issued = "2023-01-08T18:33:09Z" ;
+}
+
    +
    -

    Now let’s try and write all the data (the whole time series) to a dataframe - so 3D data.

    +
    +

    The anom variable actually has 4 dimensions (lon, lat, z and time). However, the z dimension is equal to 1, which means all the values are from the same elevation. Therefore, the data are essentially 3D.

    +

    Let’s start by extracting the latitude, longitude and time variables as 1D vectors, and converting the time values to timestamps.

    lat <- var.get.nc(data, 'lat')
@@ -974,24 +1331,76 @@ 
    Variables with multiple dimensions# Convert time to readable dates
 start_date <- as.Date("1800-01-01")
-date <- as.Date(time, origin = start_date)
-
-anom <- var.get.nc(data, 'anom', start=c(NA, NA, 1, NA), count=c(NA,NA,1,NA))
-anom_flat <- as.vector(anom)
-
-lat_repeat <- rep(lat, each = length(lon) * length(time))
-lon_repeat <- rep(rep(lon, each = length(time)), times = length(lat))
-date_repeat <- rep(date, times = length(lat) * length(lon))
-
-df <- data.frame(
-  date = date_repeat,
-  latitude = lat_repeat,
-  longitude = lon_repeat,
-  anom = anom_flat
+timestamps <- as.Date(time, origin = start_date)
+
    +
    +
    +
    +

    Now let’s extract the temperature anomaly values. We can convert the 3D matrix into a 1D vector.

    +
    +
    +
    anom <- var.get.nc(data, 'anom')
+anom_vector <- as.vector(anom)
+
    +
    +
    +
    +

    The order of the dimensions is now important. We have longitude, latitude and then time.

    +

    This means that the anomaly values are sorted such that the all the values from the first longitude come first.

    +

    The values are next sorted by common latitude, and finally by common time.

    +

    It is a 3D grid has 72 points of longitude, 36 points of latitude, and 1716 points in time.

    +
    +
    +
    dim(anom)
+
    +
    +
    +
    +
    +
    1. 72
    2. 36
    3. 1716
    +
    +
    +

    So when flattening the first dimension, longitude, to a 1D vector, we need to repeat the first value 36 x 1716 times before moving onto the second value.

    +
    +
    +
    lon_repeat <- rep(lon, each = length(time) * length(lat))
+
    +
    +
    +
    +

    To flatten the second dimension, latitude, to a 1D vector, we need repeat the first value 1716 times before moving on to the second value. Then, after we have reached the last latitude, we need to start from the beginning and repeat 72 times.

    +
    +
    +
    lat_repeat <- rep(rep(lat, each = length(time)), times = length(lon))
+
    +
    +
    +
    +

    Finally, for the third dimension, time, we need to list all the timestamps in order first, and then repeat the list 36 x 72 times.

    +
    +
    +
    timestamps_repeat <- rep(timestamps, times = length(lon) * length(lat))
+
    +
    +
    +
    +

    Finally, let’s write the data to a dataframe and export them.

    +
    +
    +
    df <- data.frame(
+  Timestamp = timestamps_repeat,
+  Latitude = lat_repeat,
+  Longitude = lon_repeat,
+  TemperatureAnomalies = anom_vector
 )
 
 head(df)
-write.csv(df, '../data/exported_from_notebooks/temperature_anomalies_all_dates.csv', row.names = FALSE)
+write.csv(df, '../data/exported_from_notebooks/3d_data.csv', row.names = FALSE)
+# There are too many rows in this data frame to convert it to a XLSX file
    @@ -999,7 +1408,7 @@

    Variables with multiple dimensions - + @@ -1013,7 +1422,6 @@

    Variables with multiple dimensions

    How to cite this course#

    If you think this course contributed to the work you are doing, consider citing it in your list of references. Here is a recommended citation:

    @@ -1086,10 +1494,17 @@

    How to cite this course diff --git a/_sources/03_extracting_data_to_different_formats.ipynb b/_sources/03_extracting_data_to_different_formats.ipynb index 1425255..0a176e5 100644 --- a/_sources/03_extracting_data_to_different_formats.ipynb +++ b/_sources/03_extracting_data_to_different_formats.ipynb @@ -29,6 +29,14 @@ "library(writexl)" ] }, + { + "cell_type": "markdown", + "id": "46b04187-1b02-4489-93e9-233adb05b6a8", + "metadata": {}, + "source": [ + "In this notebook you'll find examples for 1D, 2D and 3D variables." + ] + }, { "cell_type": "markdown", "id": "6574a95b-abb7-4a21-8a50-2a1173e93e84", @@ -570,15 +578,13 @@ "id": "7a59409d-c0f9-4da9-874c-6b6580b26117", "metadata": {}, "source": [ - "## Variables with multiple dimensions\n", + "## Variables with 2 dimensions\n", "\n", - "What is a variable has multiple dimensions? Let's look at the same data we used in [tutorial #01](01_opening_and_understanding.ipynb).\n", + "Let's look at some sea ice concentration data.\n", "\n", "If you use these data, please cite them as recommended below:\n", "\n", - "*H.-M. Zhang, B. Huang, J. H. Lawrimore, M. J. Menne, and T. M. Smith (2019): NOAA Global Surface Temperature Dataset (NOAAGlobalTemp), Version 5.0. NOAA National Centers for Environmental Information. doi:10.25921/9qth-2p70 Accessed 2024-04-09.*\n", - "\n", - "Let's load in the data, and extract the data from the *anom* varaible for a given date." + "*Lavergne, T.; Sørensen, A.; Tonboe, R.T.; Kreiner, M.; Saldo, R.; Birkedal, A.; Baordo, F.; Aspenes, T.; Eastwood, S. (2023): ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): High(er) Resolution Sea Ice Concentration Climate Data Record Version 3 (SSM/I and SSMIS). NERC EDS Centre for Environmental Data Analysis, 17 May 2023. doi:10.5285/eade27004395466aaa006135e1b2ad1a. https://dx.doi.org/10.5285/eade27004395466aaa006135e1b2ad1a*" ] }, { @@ -586,95 +592,26942 @@ "execution_count": 5, "id": "88b9b0b0-85d3-4138-9cea-611c0d60510f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "netcdf classic {\n", + "dimensions:\n", + "\ttime = UNLIMITED ; // (1 currently)\n", + "\tnv = 2 ;\n", + "\txc = 864 ;\n", + "\tyc = 864 ;\n", + "variables:\n", + "\tNC_INT Lambert_Azimuthal_Grid ;\n", + "\t\tNC_CHAR Lambert_Azimuthal_Grid:grid_mapping_name = \"lambert_azimuthal_equal_area\" ;\n", + "\t\tNC_DOUBLE Lambert_Azimuthal_Grid:longitude_of_projection_origin = 0 ;\n", + "\t\tNC_DOUBLE Lambert_Azimuthal_Grid:latitude_of_projection_origin = 90 ;\n", + "\t\tNC_DOUBLE Lambert_Azimuthal_Grid:false_easting = 0 ;\n", + "\t\tNC_DOUBLE Lambert_Azimuthal_Grid:false_northing = 0 ;\n", + "\t\tNC_DOUBLE Lambert_Azimuthal_Grid:semi_major_axis = 6378137 ;\n", + "\t\tNC_DOUBLE Lambert_Azimuthal_Grid:inverse_flattening = 298.257223563 ;\n", + "\t\tNC_CHAR Lambert_Azimuthal_Grid:proj4_string = \"+proj=laea +lon_0=0 +datum=WGS84 +ellps=WGS84 +lat_0=90.0\" ;\n", + "\tNC_DOUBLE time(time) ;\n", + "\t\tNC_CHAR time:units = \"seconds since 1978-01-01 00:00:00\" ;\n", + "\t\tNC_CHAR time:long_name = \"reference time of product\" ;\n", + "\t\tNC_CHAR time:standard_name = \"time\" ;\n", + "\t\tNC_CHAR time:axis = \"T\" ;\n", + "\t\tNC_CHAR time:calendar = \"standard\" ;\n", + "\t\tNC_CHAR time:bounds = \"time_bnds\" ;\n", + "\t\tNC_INT time:_ChunkSizes = 512 ;\n", + "\tNC_DOUBLE time_bnds(nv, time) ;\n", + "\t\tNC_INT time_bnds:_ChunkSizes = 1, 2 ;\n", + "\tNC_DOUBLE xc(xc) ;\n", + "\t\tNC_CHAR xc:units = \"km\" ;\n", + "\t\tNC_CHAR xc:long_name = \"x coordinate of projection (eastings)\" ;\n", + "\t\tNC_CHAR xc:standard_name = \"projection_x_coordinate\" ;\n", + "\t\tNC_INT xc:_ChunkSizes = 864 ;\n", + "\tNC_DOUBLE yc(yc) ;\n", + "\t\tNC_CHAR yc:units = \"km\" ;\n", + "\t\tNC_CHAR yc:long_name = \"y coordinate of projection (northings)\" ;\n", + "\t\tNC_CHAR yc:standard_name = \"projection_y_coordinate\" ;\n", + "\t\tNC_INT yc:_ChunkSizes = 864 ;\n", + "\tNC_FLOAT lat(xc, yc) ;\n", + "\t\tNC_CHAR lat:units = \"degrees_north\" ;\n", + "\t\tNC_CHAR lat:long_name = \"latitude coordinate\" ;\n", + "\t\tNC_CHAR lat:standard_name = \"latitude\" ;\n", + "\t\tNC_INT lat:_ChunkSizes = 864, 864 ;\n", + "\tNC_FLOAT lon(xc, yc) ;\n", + "\t\tNC_CHAR lon:units = \"degrees_east\" ;\n", + "\t\tNC_CHAR lon:long_name = \"longitude coordinate\" ;\n", + "\t\tNC_CHAR lon:standard_name = \"longitude\" ;\n", + "\t\tNC_INT lon:_ChunkSizes = 864, 864 ;\n", + "\tNC_INT ice_conc(xc, yc, time) ;\n", + "\t\tNC_INT ice_conc:_FillValue = -32767 ;\n", + "\t\tNC_CHAR ice_conc:long_name = \"fully filtered concentration of sea ice using atmospheric correction of brightness temperatures and open water filters\" ;\n", + "\t\tNC_CHAR ice_conc:standard_name = \"sea_ice_area_fraction\" ;\n", + "\t\tNC_CHAR ice_conc:units = \"%\" ;\n", + "\t\tNC_INT ice_conc:valid_min = 0 ;\n", + "\t\tNC_INT ice_conc:valid_max = 10000 ;\n", + "\t\tNC_CHAR ice_conc:grid_mapping = \"Lambert_Azimuthal_Grid\" ;\n", + "\t\tNC_CHAR ice_conc:ancillary_variables = \"total_standard_uncertainty status_flag\" ;\n", + "\t\tNC_DOUBLE ice_conc:scale_factor = 0.01 ;\n", + "\t\tNC_CHAR ice_conc:comment = \"this field is the primary sea ice concentration estimate for this climate data record\" ;\n", + "\t\tNC_CHAR ice_conc:coordinates = \"time lat lon\" ;\n", + "\t\tNC_INT ice_conc:_ChunkSizes = 1, 864, 864 ;\n", + "\tNC_INT raw_ice_conc_values(xc, yc, time) ;\n", + "\t\tNC_INT raw_ice_conc_values:_FillValue = -32767 ;\n", + "\t\tNC_CHAR raw_ice_conc_values:long_name = \"sea ice concentration estimates as retrieved by the algorithm, and that were edited away by the various filters\" ;\n", + "\t\tNC_CHAR raw_ice_conc_values:units = \"%\" ;\n", + "\t\tNC_CHAR raw_ice_conc_values:grid_mapping = \"Lambert_Azimuthal_Grid\" ;\n", + "\t\tNC_DOUBLE raw_ice_conc_values:scale_factor = 0.01 ;\n", + "\t\tNC_CHAR raw_ice_conc_values:comment = \"this field can be used in combination with 'ice_conc' to access un-bounded normal distribution of sea ice concentration estimates corresponding to the uncertainty estimate in 'total_standard_uncertainty'. It does also feature some amount of un-physical data (below 0% and above 100%).\" ;\n", + "\t\tNC_CHAR raw_ice_conc_values:coordinates = \"time lat lon\" ;\n", + "\t\tNC_INT raw_ice_conc_values:_ChunkSizes = 1, 864, 864 ;\n", + "\tNC_INT total_standard_uncertainty(xc, yc, time) ;\n", + "\t\tNC_INT total_standard_uncertainty:_FillValue = -32767 ;\n", + "\t\tNC_CHAR total_standard_uncertainty:long_name = \"total uncertainty (one standard deviation) of concentration of sea ice\" ;\n", + "\t\tNC_CHAR total_standard_uncertainty:standard_name = \"sea_ice_area_fraction standard_error\" ;\n", + "\t\tNC_CHAR total_standard_uncertainty:units = \"%\" ;\n", + "\t\tNC_CHAR total_standard_uncertainty:grid_mapping = \"Lambert_Azimuthal_Grid\" ;\n", + "\t\tNC_DOUBLE total_standard_uncertainty:scale_factor = 0.01 ;\n", + "\t\tNC_CHAR total_standard_uncertainty:coordinates = \"time lat lon\" ;\n", + "\t\tNC_INT total_standard_uncertainty:_ChunkSizes = 1, 864, 864 ;\n", + "\tNC_INT smearing_standard_uncertainty(xc, yc, time) ;\n", + "\t\tNC_INT smearing_standard_uncertainty:_FillValue = -32767 ;\n", + "\t\tNC_CHAR smearing_standard_uncertainty:long_name = \"smearing uncertainty (one standard deviation) of concentration of sea ice\" ;\n", + "\t\tNC_CHAR smearing_standard_uncertainty:units = \"%\" ;\n", + "\t\tNC_CHAR smearing_standard_uncertainty:grid_mapping = \"Lambert_Azimuthal_Grid\" ;\n", + "\t\tNC_DOUBLE smearing_standard_uncertainty:scale_factor = 0.01 ;\n", + "\t\tNC_CHAR smearing_standard_uncertainty:comment = \"this is one of the two components contributing to 'total_standard_uncertainty'\" ;\n", + "\t\tNC_CHAR smearing_standard_uncertainty:coordinates = \"time lat lon\" ;\n", + "\t\tNC_INT smearing_standard_uncertainty:_ChunkSizes = 1, 864, 864 ;\n", + "\tNC_INT algorithm_standard_uncertainty(xc, yc, time) ;\n", + "\t\tNC_INT algorithm_standard_uncertainty:_FillValue = -32767 ;\n", + "\t\tNC_CHAR algorithm_standard_uncertainty:long_name = \"algorithm uncertainty (one standard deviation) of concentration of sea ice\" ;\n", + "\t\tNC_CHAR algorithm_standard_uncertainty:units = \"%\" ;\n", + "\t\tNC_CHAR algorithm_standard_uncertainty:grid_mapping = \"Lambert_Azimuthal_Grid\" ;\n", + "\t\tNC_DOUBLE algorithm_standard_uncertainty:scale_factor = 0.01 ;\n", + "\t\tNC_CHAR algorithm_standard_uncertainty:comment = \"this is one of the two components contributing to 'total_standard_uncertainty'\" ;\n", + "\t\tNC_CHAR algorithm_standard_uncertainty:coordinates = \"time lat lon\" ;\n", + "\t\tNC_INT algorithm_standard_uncertainty:_ChunkSizes = 1, 864, 864 ;\n", + "\tNC_SHORT status_flag(xc, yc, time) ;\n", + "\t\tNC_SHORT status_flag:_FillValue = -32768 ;\n", + "\t\tNC_CHAR status_flag:long_name = \"status flag bit array for sea ice concentration retrievals\" ;\n", + "\t\tNC_CHAR status_flag:standard_name = \"sea_ice_area_fraction status_flag\" ;\n", + "\t\tNC_SHORT status_flag:valid_min = 0 ;\n", + "\t\tNC_SHORT status_flag:valid_max = 32767 ;\n", + "\t\tNC_CHAR status_flag:grid_mapping = \"Lambert_Azimuthal_Grid\" ;\n", + "\t\tNC_SHORT status_flag:flag_masks = 1, 2, 4, 8, 16, 32, 64, 128 ;\n", + "\t\tNC_CHAR status_flag:flag_meanings = \"land lake open_water_filtered land_spill_over high_t2m spatial_interp temporal_interp max_ice_climo\" ;\n", + "\t\tNC_CHAR status_flag:flag_descriptions = \"\n", + "all bits to 0 (flag 0): Nominal retrieval by the SIC algorithm\n", + "bit 1 (flag 1): Position is over land\n", + "bit 2 (flag 2): Position is lake\n", + "bit 3 (flag 4): SIC is set to zero by the open water filter\n", + "bit 4 (flag 8): SIC value is changed for correcting land spill-over effects\n", + "bit 5 (flag 16): Handle with caution, the 2m air temperature is high at this position, and this might be false ice\n", + "bit 6 (flag 32): Value is the result of spatial interpolation\n", + "bit 7 (flag 64): Value is the result of temporal interpolation\n", + "bit 8 (flag 128): SIC is set to zero since position is outside maximum sea ice climatology.\" ;\n", + "\t\tNC_CHAR status_flag:comment = \"Flag values found in the map might be combinations of those listed above. For example flag value 80 (= 2^4 + 2^6 = 16 + 64) indicates high_t2m AND temporal_interp\" ;\n", + "\t\tNC_CHAR status_flag:coordinates = \"time lat lon\" ;\n", + "\t\tNC_INT status_flag:_ChunkSizes = 1, 864, 864 ;\n", + "\n", + "// global attributes:\n", + "\t\tNC_CHAR :title = \"High(er) Resolution Sea Ice Concentration Climate Data Record Version 3 (SSM/I and SSMIS) from the ESA CCI+\" ;\n", + "\t\tNC_CHAR :summary = \"This climate data record of sea ice concentration is obtained from passive microwave satellite data (SSM/I and SSMIS) over the polar regions (Arctic and Antarctic). The processing chain features: 1) dynamic tuning of tie-points and algorithms, 2) correction of atmospheric noise using a Radiative Transfer Model, 3) computation of per-pixel uncertainties, 4) an optimal hybrid sea ice concentration algorithm, and 5) pan-sharpening of the SIC fields using the near-90GHz imagery channels. This dataset was generated by the ESA Climate Change Initiative (CCI+) Sea Ice Phase 1 project. This dataset is an enhanced-resolution version of the OSI SAF OSI-450-a CDR over the period 1991-2020.\" ;\n", + "\t\tNC_CHAR :topiccategory = \"Oceans ClimatologyMeteorologyAtmosphere\" ;\n", + "\t\tNC_CHAR :keywords = \"GCMDSK:Earth Science > Cryosphere > Sea Ice > Sea Ice Concentration, GCMDSK:Earth Science > Oceans > Sea Ice > Sea Ice Concentration, GCMDSK:Earth Science > Climate Indicators > Cryospheric Indicators > Sea Ice Concentration, GCMDLOC:Geographic Region > Northern Hemisphere, GCMDLOC:Vertical Location > Sea Surface, GCMDPROV: Government Agencies-non-US > Norway > NO/MET > Norwegian Meteorological Institute\" ;\n", + "\t\tNC_CHAR :keywords_vocabulary = \"GCMDSK:GCMD Science Keywords:https://gcmd.earthdata.nasa.gov/kms/concepts/concept_scheme/sciencekeywords, GCMDPROV:GCMD Providers:https://gcmd.earthdata.nasa.gov/kms/concepts/concept_scheme/providers, GCMDLOC:GCMD Locations:https://gcmd.earthdata.nasa.gov/kms/concepts/concept_scheme/locations\" ;\n", + "\t\tNC_DOUBLE :geospatial_lat_min = 16.62393 ;\n", + "\t\tNC_DOUBLE :geospatial_lat_max = 90 ;\n", + "\t\tNC_DOUBLE :geospatial_lon_min = -180 ;\n", + "\t\tNC_DOUBLE :geospatial_lon_max = 180 ;\n", + "\t\tNC_DOUBLE :geospatial_vertical_min = 0 ;\n", + "\t\tNC_DOUBLE :geospatial_vertical_max = 0 ;\n", + "\t\tNC_CHAR :sensor = \"SSM/I,SSMIS\" ;\n", + "\t\tNC_CHAR :platform = \"DMSP-F<10,11,13,14,15>,DMSP-F<16,17,18>\" ;\n", + "\t\tNC_CHAR :source = \"FCDR of SMMR / SSMI / SSMIS Brightness Temperatures R4 from EUMETSAT Climate Monitoring SAF (doi:10.5676/EUM_SAF_CM/FCDR_MWI/V004), ERA5\" ;\n", + "\t\tNC_CHAR :time_coverage_start = \"2007-09-15T00:00:00Z\" ;\n", + "\t\tNC_CHAR :time_coverage_end = \"2007-09-16T00:00:00Z\" ;\n", + "\t\tNC_CHAR :time_coverage_duration = \"P1D\" ;\n", + "\t\tNC_CHAR :time_coverage_resolution = \"P1D\" ;\n", + "\t\tNC_CHAR :project = \"Climate Change Initiative - European Space Agency\" ;\n", + "\t\tNC_CHAR :institution = \"Norwegian Meteorological Institute\" ;\n", + "\t\tNC_CHAR :creator_name = \"Norwegian Meteorological Institute\" ;\n", + "\t\tNC_CHAR :creator_type = \"institution\" ;\n", + "\t\tNC_CHAR :creator_url = \"http://met.no\" ;\n", + "\t\tNC_CHAR :creator_email = \"forskning@met.no\" ;\n", + "\t\tNC_CHAR :license = \"Free and open access\" ;\n", + "\t\tNC_CHAR :references = \"A Product User Guide (PUG), an Algorithm Theoretical Basis Document (ATBD) and a Validation Report (PVASR) are available from the doi landing page and from https://climate.esa.int/seaice\" ;\n", + "\t\tNC_CHAR :history = \"2023-04-20T21:46:46Z creation\" ;\n", + "\t\tNC_CHAR :date_created = \"2023-04-20\" ;\n", + "\t\tNC_CHAR :cdm_data_type = \"Grid\" ;\n", + "\t\tNC_CHAR :spatial_resolution = \"12.5 km grid spacing\" ;\n", + "\t\tNC_CHAR :algorithm = \"reSICCI3LF: SIC from 19/37GHz channels, pan-sharpened with SIC from near-90GHz channels.\" ;\n", + "\t\tNC_CHAR :geospatial_bounds_crs = \"EPSG:6931\" ;\n", + "\t\tNC_CHAR :contributor_name = \"Thomas Lavergne,Atle Soerensen,Rasmus Tonboe,Matilde Kreiner,Roberto Saldo,Anton Birkedal,Fabrizio Baordo,Trygve Aspenes,Steinar Eastwood\" ;\n", + "\t\tNC_CHAR :contributor_role = \"PrincipalInvestigator,author,author,author,author,author,author,author,author\" ;\n", + "\t\tNC_CHAR :product_version = \"3.0\" ;\n", + "\t\tNC_CHAR :tracking_id = \"0a5f4360-8c19-40aa-a09a-bab17eb0b6fc\" ;\n", + "\t\tNC_CHAR :naming_authority = \"NERC EDS Centre for Environmental Data Analysis\" ;\n", + "\t\tNC_CHAR :Conventions = \"CF-1.7,ACDD-1.3\" ;\n", + "\t\tNC_CHAR :standard_name_vocabulary = \"CF Standard Name Table (Version 80, 07 February 2023)\" ;\n", + "\t\tNC_CHAR :product_name = \"cci_HRssmi_sea_ice_conc_climate_data_record\" ;\n", + "\t\tNC_CHAR :id = \"10.5285/eade27004395466aaa006135e1b2ad1a\" ;\n", + "\t\tNC_CHAR :format_version = \"CCI Data Standards v2.3\" ;\n", + "\t\tNC_CHAR :key_variables = \"ice_conc\" ;\n", + "\t\tNC_CHAR :DODS_EXTRA.Unlimited_Dimension = \"time\" ;\n", + "}\n" + ] + } + ], "source": [ - "url <- 'https://www.ncei.noaa.gov/thredds/dodsC/noaa-global-temp-v5/NOAAGlobalTemp_v5.0.0_gridded_s188001_e202212_c20230108T133308.nc'\n", + "url <- 'https://dap.ceda.ac.uk/thredds/dodsC/neodc/esacci/sea_ice/data/sea_ice_concentration/L4/ssmi_ssmis/12.5km/v3.0/NH/2007/09/ESACCI-SEAICE-L4-SICONC-RE_SSMI_12.5kmEASE2-NH-20070915-fv3.0.nc'\n", "data <- open.nc(url)\n", + "print.nc(data)" + ] + }, + { + "cell_type": "markdown", + "id": "bdc0bd4b-235c-4348-bc31-864d7884ac27", + "metadata": {}, + "source": [ + "The *ice_conc* variable has 3 dimensions; xc, yc and time. However, since there is only one time slice in this dataset, this is essentially a 2D variable.\n", "\n", - "lat <- var.get.nc(data, 'lat')\n", - "lon <- var.get.nc(data, 'lon')\n", - "\n", - "desired_date <- as.Date('2020-01-01')\n", - "days_since_1800 <- as.numeric(difftime(desired_date, as.Date('1800-01-01'), units = 'days'))\n", + "Let's write the *ice_conc* variable to a dataframe alongside the *xc* and *yc* coordinate variables." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "655fd916-723f-4a25-8ff9-1a4ac7c801e3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
    1. 864
    2. 864
    \n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 864\n", + "\\item 864\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 864\n", + "2. 864\n", + "\n", + "\n" + ], + "text/plain": [ + "[1] 864 864" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "xc <- var.get.nc(data, 'xc')\n", + "yc <- var.get.nc(data, 'yc')\n", + "ice_conc <- var.get.nc(data, 'ice_conc')\n", + "dim(ice_conc) # Showing that this is 2D, not 3D." + ] + }, + { + "cell_type": "markdown", + "id": "efa19460-6ddc-4513-ab64-dd8f60b9f111", + "metadata": {}, + "source": [ + "The order of the dimensions is important. *xc* and then *yc*. \n", "\n", - "time <- var.get.nc(data, \"time\") \n", - "# Finding index of the value\n", - "time_index <- which(time == days_since_1800)\n", + "So the values are sorted such that all the values cooresponding to the first *xc* coordinate are first, before moving onto the second *xc* coordinate. \n", "\n", - "anom <- var.get.nc(data, 'anom', start=c(NA, NA, 1, time_index), count=c(NA,NA,1,1))" + "Now let's create a 1D vector of the *ice_conc* data." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ff555277-d170-482a-92b8-a1239b98add7", + "metadata": {}, + "outputs": [], + "source": [ + "ice_conc_vector <- as.vector(ice_conc)" ] }, { "cell_type": "markdown", - "id": "be29582b-8e45-472f-bb02-6dd9c687ac12", + "id": "4551b89f-15dc-4515-a9a4-51ed37955b4b", "metadata": {}, "source": [ - "*lat* and *lon* are 1D arrays whilst *anom* is a 2D matrix. \n", - "\n", - "We can't just write latitude and longitude to a dataframe like this: " + "We then need to extend the *xc* coordinate values by repeating the first *xc* value 864 times before moving onto the second value. " ] }, { "cell_type": "code", - "execution_count": 6, - "id": "009c354f-7229-4c59-a4a8-670e957442dc", + "execution_count": 8, + "id": "56f218c6-9564-4e9b-92ac-ce38f7c40fb0", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "

    A data.frame: 6 × 4
    datelatitudelongitudeanom
    TimestampLatitudeLongitudeTemperatureAnomalies
    <date><dbl><dbl><dbl>
    \n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
    A data.frame: 6 × 2
    lonlat
    <dbl><dbl>
    1 2.5-87.5
    2 7.5-82.5
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    417.5-72.5
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    \n" + "\n", + "
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+ "[99913] -1506.25 -1518.75 -1531.25 -1543.75 -1556.25 -1568.75 -1581.25 -1593.75\n", + "[99921] -1606.25 -1618.75 -1631.25 -1643.75 -1656.25 -1668.75 -1681.25 -1693.75\n", + "[99929] -1706.25 -1718.75 -1731.25 -1743.75 -1756.25 -1768.75 -1781.25 -1793.75\n", + "[99937] -1806.25 -1818.75 -1831.25 -1843.75 -1856.25 -1868.75 -1881.25 -1893.75\n", + "[99945] -1906.25 -1918.75 -1931.25 -1943.75 -1956.25 -1968.75 -1981.25 -1993.75\n", + "[99953] -2006.25 -2018.75 -2031.25 -2043.75 -2056.25 -2068.75 -2081.25 -2093.75\n", + "[99961] -2106.25 -2118.75 -2131.25 -2143.75 -2156.25 -2168.75 -2181.25 -2193.75\n", + "[99969] -2206.25 -2218.75 -2231.25 -2243.75 -2256.25 -2268.75 -2281.25 -2293.75\n", + "[99977] -2306.25 -2318.75 -2331.25 -2343.75 -2356.25 -2368.75 -2381.25 -2393.75\n", + "[99985] -2406.25 -2418.75 -2431.25 -2443.75 -2456.25 -2468.75 -2481.25 -2493.75\n", + "[99993] -2506.25 -2518.75 -2531.25 -2543.75 -2556.25 -2568.75 -2581.25\n", + " [ reached getOption(\"max.print\") -- omitted 646497 entries ]" ] }, "metadata": {}, @@ -682,79 +27535,79 @@ } ], "source": [ - "df <- data.frame(lon = lon, lat = lat)\n", - "head(df)" + "yc_repeat <- rep(yc, times = length(xc))\n", + "yc_repeat" ] }, { "cell_type": "markdown", - "id": "133969db-da45-44a9-a5ef-5dfb7a1e5de3", + "id": "ec17144d-142b-4f9f-9d4b-d3003aff3477", "metadata": {}, "source": [ - "Instead we need to create a 2D grid of values, like this:" + "And now let's combing them all in a dataframe and export the data." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "70626b09-fb01-4f6e-9197-632098c60116", + "execution_count": 10, + "id": "8f85f904-6a50-48f0-80a5-97a87553100c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - "\n", + "\n", "\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", "\n", "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", "\n", "
    A data.frame: 6 × 2A data.frame: 6 × 3
    lonlat
    <dbl[1d]><dbl[1d]>
    XcYcSeaIceConcentration
    <dbl><dbl><dbl>
    1 2.5-87.5
    2 7.5-87.5
    312.5-87.5
    417.5-87.5
    522.5-87.5
    627.5-87.5
    1-5393.755393.750
    2-5393.755381.250
    3-5393.755368.750
    4-5393.755356.250
    5-5393.755343.750
    6-5393.755331.250
    \n" ], "text/latex": [ - "A data.frame: 6 × 2\n", - "\\begin{tabular}{r|ll}\n", - " & lon & lat\\\\\n", - " & & \\\\\n", + "A data.frame: 6 × 3\n", + "\\begin{tabular}{r|lll}\n", + " & Xc & Yc & SeaIceConcentration\\\\\n", + " & & & \\\\\n", "\\hline\n", - "\t1 & 2.5 & -87.5\\\\\n", - "\t2 & 7.5 & -87.5\\\\\n", - "\t3 & 12.5 & -87.5\\\\\n", - "\t4 & 17.5 & -87.5\\\\\n", - "\t5 & 22.5 & -87.5\\\\\n", - "\t6 & 27.5 & -87.5\\\\\n", + "\t1 & -5393.75 & 5393.75 & 0\\\\\n", + "\t2 & -5393.75 & 5381.25 & 0\\\\\n", + "\t3 & -5393.75 & 5368.75 & 0\\\\\n", + "\t4 & -5393.75 & 5356.25 & 0\\\\\n", + "\t5 & -5393.75 & 5343.75 & 0\\\\\n", + "\t6 & -5393.75 & 5331.25 & 0\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", - "A data.frame: 6 × 2\n", + "A data.frame: 6 × 3\n", "\n", - "| | lon <dbl[1d]> | lat <dbl[1d]> |\n", - "|---|---|---|\n", - "| 1 | 2.5 | -87.5 |\n", - "| 2 | 7.5 | -87.5 |\n", - "| 3 | 12.5 | -87.5 |\n", - "| 4 | 17.5 | -87.5 |\n", - "| 5 | 22.5 | -87.5 |\n", - "| 6 | 27.5 | -87.5 |\n", + "| | Xc <dbl> | Yc <dbl> | SeaIceConcentration <dbl> |\n", + "|---|---|---|---|\n", + "| 1 | -5393.75 | 5393.75 | 0 |\n", + "| 2 | -5393.75 | 5381.25 | 0 |\n", + "| 3 | -5393.75 | 5368.75 | 0 |\n", + "| 4 | -5393.75 | 5356.25 | 0 |\n", + "| 5 | -5393.75 | 5343.75 | 0 |\n", + "| 6 | -5393.75 | 5331.25 | 0 |\n", "\n" ], "text/plain": [ - " lon lat \n", - "1 2.5 -87.5\n", - "2 7.5 -87.5\n", - "3 12.5 -87.5\n", - "4 17.5 -87.5\n", - "5 22.5 -87.5\n", - "6 27.5 -87.5" + " Xc Yc SeaIceConcentration\n", + "1 -5393.75 5393.75 0 \n", + "2 -5393.75 5381.25 0 \n", + "3 -5393.75 5368.75 0 \n", + "4 -5393.75 5356.25 0 \n", + "5 -5393.75 5343.75 0 \n", + "6 -5393.75 5331.25 0 " ] }, "metadata": {}, @@ -762,79 +27615,85 @@ } ], "source": [ - "df <- expand.grid(lon = lon, lat = lat)\n", - "head(df)" + "df <- data.frame(\n", + " Xc = xc_repeat,\n", + " Yc = yc_repeat,\n", + " SeaIceConcentration = ice_conc_vector\n", + ")\n", + "head(df)\n" ] }, { "cell_type": "markdown", - "id": "dba47d26-9d01-4143-8bca-28f93d57e2e9", + "id": "3f679158-5b6b-4a44-98e2-1f7ac8aa202a", "metadata": {}, "source": [ - "Now adding the *anom* variable can be done like this:" + "Let's add the latitude and longitude values as separate columns." ] }, { "cell_type": "code", - "execution_count": 8, - "id": "c1588afa-3a68-4915-8d8a-0c4cb8721570", - "metadata": {}, + "execution_count": 11, + "id": "a1b076bd-a47b-4374-afd9-c62bd0738890", + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { "text/html": [ "\n", - "\n", + "\n", "\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", "\n", "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", "\n", "
    A data.frame: 6 × 3A data.frame: 6 × 5
    lonlatanom
    <dbl[1d]><dbl[1d]><dbl>
    XcYcSeaIceConcentrationlatitudelongitude
    <dbl><dbl><dbl><dbl><dbl>
    1 2.5-87.5NA
    2 7.5-87.5NA
    312.5-87.5NA
    417.5-87.5NA
    522.5-87.5NA
    627.5-87.5NA
    1-5393.755393.75016.52435-135.0000
    2-5393.755381.25016.62387-135.0665
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    4-5393.755356.25016.82236-135.1999
    5-5393.755343.75016.92134-135.2668
    6-5393.755331.25017.02014-135.3339
    \n" ], "text/latex": [ - "A data.frame: 6 × 3\n", - "\\begin{tabular}{r|lll}\n", - " & lon & lat & anom\\\\\n", - " & & & \\\\\n", + "A data.frame: 6 × 5\n", + "\\begin{tabular}{r|lllll}\n", + " & Xc & Yc & SeaIceConcentration & latitude & longitude\\\\\n", + " & & & & & \\\\\n", "\\hline\n", - "\t1 & 2.5 & -87.5 & NA\\\\\n", - "\t2 & 7.5 & -87.5 & NA\\\\\n", - "\t3 & 12.5 & -87.5 & NA\\\\\n", - "\t4 & 17.5 & -87.5 & NA\\\\\n", - "\t5 & 22.5 & -87.5 & NA\\\\\n", - "\t6 & 27.5 & -87.5 & NA\\\\\n", + "\t1 & -5393.75 & 5393.75 & 0 & 16.52435 & -135.0000\\\\\n", + "\t2 & -5393.75 & 5381.25 & 0 & 16.62387 & -135.0665\\\\\n", + "\t3 & -5393.75 & 5368.75 & 0 & 16.72321 & -135.1331\\\\\n", + "\t4 & -5393.75 & 5356.25 & 0 & 16.82236 & -135.1999\\\\\n", + "\t5 & -5393.75 & 5343.75 & 0 & 16.92134 & -135.2668\\\\\n", + "\t6 & -5393.75 & 5331.25 & 0 & 17.02014 & -135.3339\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", - "A data.frame: 6 × 3\n", + "A data.frame: 6 × 5\n", "\n", - "| | lon <dbl[1d]> | lat <dbl[1d]> | anom <dbl> |\n", - "|---|---|---|---|\n", - "| 1 | 2.5 | -87.5 | NA |\n", - "| 2 | 7.5 | -87.5 | NA |\n", - "| 3 | 12.5 | -87.5 | NA |\n", - "| 4 | 17.5 | -87.5 | NA |\n", - "| 5 | 22.5 | -87.5 | NA |\n", - "| 6 | 27.5 | -87.5 | NA |\n", + "| | Xc <dbl> | Yc <dbl> | SeaIceConcentration <dbl> | latitude <dbl> | longitude <dbl> |\n", + "|---|---|---|---|---|---|\n", + "| 1 | -5393.75 | 5393.75 | 0 | 16.52435 | -135.0000 |\n", + "| 2 | -5393.75 | 5381.25 | 0 | 16.62387 | -135.0665 |\n", + "| 3 | -5393.75 | 5368.75 | 0 | 16.72321 | -135.1331 |\n", + "| 4 | -5393.75 | 5356.25 | 0 | 16.82236 | -135.1999 |\n", + "| 5 | -5393.75 | 5343.75 | 0 | 16.92134 | -135.2668 |\n", + "| 6 | -5393.75 | 5331.25 | 0 | 17.02014 | -135.3339 |\n", "\n" ], "text/plain": [ - " lon lat anom\n", - "1 2.5 -87.5 NA \n", - "2 7.5 -87.5 NA \n", - "3 12.5 -87.5 NA \n", - "4 17.5 -87.5 NA \n", - "5 22.5 -87.5 NA \n", - "6 27.5 -87.5 NA " + " Xc Yc SeaIceConcentration latitude longitude\n", + "1 -5393.75 5393.75 0 16.52435 -135.0000\n", + "2 -5393.75 5381.25 0 16.62387 -135.0665\n", + "3 -5393.75 5368.75 0 16.72321 -135.1331\n", + "4 -5393.75 5356.25 0 16.82236 -135.1999\n", + "5 -5393.75 5343.75 0 16.92134 -135.2668\n", + "6 -5393.75 5331.25 0 17.02014 -135.3339" ] }, "metadata": {}, @@ -842,40 +27701,349 @@ } ], "source": [ - "df$anom <- as.vector(anom)\n", - "head(df)" + "latitude <- var.get.nc(data, \"lat\")\n", + "latitude_vector <- as.vector(latitude)\n", + "df$latitude <- latitude_vector\n", + "\n", + "longitude <- var.get.nc(data, \"lon\")\n", + "longitude_vector <- as.vector(longitude)\n", + "df$longitude <- longitude_vector\n", + "\n", + "head(df)\n", + "write_xlsx(df, '../data/exported_from_notebooks/2d_data.xlsx')\n", + "write.csv(df, '../data/exported_from_notebooks/2d_data.csv', row.names = FALSE)" + ] + }, + { + "cell_type": "markdown", + "id": "1e21719c-3e43-4db1-a0f9-6ad27d9bd63b", + "metadata": {}, + "source": [ + "# Variables with 3 dimensions\n", + "\n", + "Let's look at the same data we used in [tutorial #01](01_opening_and_understanding.ipynb).\n", + "\n", + "If you use these data, please cite them as recommended below:\n", + "\n", + "*H.-M. Zhang, B. Huang, J. H. Lawrimore, M. J. Menne, and T. M. Smith (2019): NOAA Global Surface Temperature Dataset (NOAAGlobalTemp), Version 5.0. NOAA National Centers for Environmental Information. doi:10.25921/9qth-2p70 Accessed 2024-04-09.*\n", + "\n", + "Let's load in the data, and extract the data from the *anom* varaible." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2e50aaf5-131a-4ac3-961a-5c8374f650e0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "netcdf classic {\n", + "dimensions:\n", + "\tlat = 36 ;\n", + "\tlon = 72 ;\n", + "\ttime = 1716 ;\n", + "\tz = 1 ;\n", + "variables:\n", + "\tNC_FLOAT time(time) ;\n", + "\t\tNC_CHAR time:long_name = \"reference time of global temperature anomalies\" ;\n", + "\t\tNC_CHAR time:standard_name = \"time\" ;\n", + "\t\tNC_CHAR time:coverage_content_type = \"coordinate\" ;\n", + "\t\tNC_CHAR time:units = \"days since 1800-01-01 00:00:00\" ;\n", + "\t\tNC_CHAR time:calendar = \"gregorian\" ;\n", + "\t\tNC_CHAR time:axis = \"T\" ;\n", + "\tNC_FLOAT lat(lat) ;\n", + "\t\tNC_CHAR lat:long_name = \"Latitude\" ;\n", + "\t\tNC_CHAR lat:standard_name = \"latitude\" ;\n", + "\t\tNC_CHAR lat:coverage_content_type = \"coordinate\" ;\n", + "\t\tNC_CHAR lat:units = \"degrees_north\" ;\n", + "\t\tNC_CHAR lat:grids = \"Uniform grid from -87.5 to 87.5 by 5\" ;\n", + "\t\tNC_FLOAT lat:valid_min = -87.5 ;\n", + "\t\tNC_FLOAT lat:valid_max = 87.5 ;\n", + "\t\tNC_CHAR lat:axis = \"Y\" ;\n", + "\t\tNC_CHAR lat:_CoordinateAxisType = \"Lat\" ;\n", + "\t\tNC_CHAR lat:coordinate_defines = \"center\" ;\n", + "\tNC_FLOAT lon(lon) ;\n", + "\t\tNC_CHAR lon:long_name = \"Longitude\" ;\n", + "\t\tNC_CHAR lon:standard_name = \"longitude\" ;\n", + "\t\tNC_CHAR lon:coverage_content_type = \"coordinate\" ;\n", + "\t\tNC_CHAR lon:units = \"degrees_east\" ;\n", + "\t\tNC_CHAR lon:grids = \"Uniform grid from 2.5 to 357.5 by 5\" ;\n", + "\t\tNC_CHAR lon:axis = \"X\" ;\n", + "\t\tNC_FLOAT lon:valid_min = 2.5 ;\n", + "\t\tNC_FLOAT lon:valid_max = 357.5 ;\n", + "\t\tNC_CHAR lon:_CoordinateAxisType = \"Lon\" ;\n", + "\t\tNC_CHAR lon:coordinate_defines = \"center\" ;\n", + "\tNC_FLOAT z(z) ;\n", + "\t\tNC_CHAR z:long_name = \"height of surface temperature\" ;\n", + "\t\tNC_CHAR z:standard_name = \"height\" ;\n", + "\t\tNC_CHAR z:coverage_content_type = \"coordinate\" ;\n", + "\t\tNC_CHAR z:geospatial_bounds_vertical_crs = \"EPSG:5703\" ;\n", + "\t\tNC_CHAR z:units = \"meters\" ;\n", + "\t\tNC_CHAR z:positive = \"up\" ;\n", + "\t\tNC_FLOAT z:valid_min = 0 ;\n", + "\t\tNC_FLOAT z:valid_max = 0 ;\n", + "\t\tNC_CHAR z:axis = \"Z\" ;\n", + "\t\tNC_CHAR z:_CoordinateAxisType = \"Height\" ;\n", + "\t\tNC_CHAR z:comment = \"Actual measurement depth of in situ sea surface temperature varies from 0.2 to 10 m, but corrected to the nominal depth of buoy at 0.2 m; for air temperature over land is 2m.\" ;\n", + "\tNC_FLOAT anom(lon, lat, z, time) ;\n", + "\t\tNC_CHAR anom:long_name = \"Global Temperature Anomalies\" ;\n", + "\t\tNC_CHAR anom:standard_name = \"surface_temperature_anomaly\" ;\n", + "\t\tNC_CHAR anom:coverage_content_type = \"physicalMeasurement\" ;\n", + "\t\tNC_CHAR anom:units = \"degrees C\" ;\n", + "\t\tNC_FLOAT anom:missing_value = -999.900024414062 ;\n", + "\t\tNC_FLOAT anom:_FillValue = -999.900024414062 ;\n", + "\t\tNC_FLOAT anom:add_offset = 0 ;\n", + "\t\tNC_FLOAT anom:scale_factor = 1 ;\n", + "\t\tNC_FLOAT anom:valid_min = -40 ;\n", + "\t\tNC_FLOAT anom:valid_max = 40 ;\n", + "\t\tNC_CHAR anom:coordinates = \"time z lat lon\" ;\n", + "\n", + "// global attributes:\n", + "\t\tNC_CHAR :Conventions = \"CF-1.6, ACDD-1.3\" ;\n", + "\t\tNC_CHAR :title = \"NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)\" ;\n", + "\t\tNC_CHAR :summary = \"NOAAGlobalTemp is a merged land-ocean surface temperature analysis, formerly abbreviated as MLOST. The SST dataset is the Extended Reconstructed Sea Surface Temperature (ERSST) and the land surface air temperature dataset is from the Global Historical Climatology Network Monthly (GHCN-M) database.\" ;\n", + "\t\tNC_CHAR :institution = \"DOC/NOAA/NESDIS/National Centers for Environmental Information(NCEI)\" ;\n", + "\t\tNC_CHAR :id = \" gov.noaa.ncdc:C00934 \" ;\n", + "\t\tNC_CHAR :naming_authority = \" gov.noaa.ncei \" ;\n", + "\t\tNC_CHAR :project = \" NOAAGlobalTemp \" ;\n", + "\t\tNC_CHAR :platform = \"Ships, moored buoys, surface drifting buoys, Argo floats, and weather stations\" ;\n", + "\t\tNC_CHAR :instrument = \"Conventional thermometers\" ;\n", + "\t\tNC_CHAR :history = \"Version 5.0.0, blending ERSST V5 and GHCN-M V4.\" ;\n", + "\t\tNC_CHAR :comment = \"Merged land ocean surface temperature anomalies.\" ;\n", + "\t\tNC_CHAR :product_version = \"5.0.0\" ;\n", + "\t\tNC_CHAR :source = \"NOAAGlobalTemp combines a global sea surface temperature with a global land surface air temperature data\" ;\n", + "\t\tNC_CHAR :ncei_template_version = \"NCEI_NetCDF_Grid_Template_v2.0\" ;\n", + "\t\tNC_CHAR :Metadata_Conventions = \"Unidata Dataset Discovery v1.0\" ;\n", + "\t\tNC_CHAR :license = \"These data are available for use without restriction.\" ;\n", + "\t\tNC_CHAR :creator_name = \"Boyin Huang, Xungang Yin, Ph.D\" ;\n", + "\t\tNC_CHAR :creator_email = \"Boyin.Huang@noaa.gov, Xungang.Yin@noaa.gov\" ;\n", + "\t\tNC_CHAR :creator_type = \"group\" ;\n", + "\t\tNC_CHAR :creator_institution = \"US DOC/NOAA/NESDIS/National Centers for Environmental Information(NCEI)\" ;\n", + "\t\tNC_CHAR :contributor_name = \"Huai-min Zhang, Ph.D\" ;\n", + "\t\tNC_CHAR :contributor_role = \"Chief, NOAA/NESDIS/NCEI/CCOG/OSB Ocean Surface Section\" ;\n", + "\t\tNC_CHAR :publisher_name = \"NCEI\" ;\n", + "\t\tNC_CHAR :publisher_institution = \"NCEI\" ;\n", + "\t\tNC_CHAR :publisher_url = \"https://www.ncdc.noaa.gov/\" ;\n", + "\t\tNC_CHAR :publisher_email = \"ncei.info@noaa.gov\" ;\n", + "\t\tNC_CHAR :publisher_type = \"institution\" ;\n", + "\t\tNC_CHAR :keywords = \"Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature, Earth Science > Atmosphere > Atmospheric Temperature > Surface Temperature > Air Temperature\" ;\n", + "\t\tNC_CHAR :keywords_vocabulary = \"NASA Global Change Master Directory (GCMD) Science Keywords\" ;\n", + "\t\tNC_CHAR :standard_name_vocabulary = \"Climate and Forecast (CF) Standard Name Table (Version 46, 25 July 2017)\" ;\n", + "\t\tNC_CHAR :date_created = \"2023-01-08T18:33:09Z\" ;\n", + "\t\tNC_CHAR :necdf_version_id = \"4.2.1.1\" ;\n", + "\t\tNC_CHAR :netcdf_creator_name = \"Yongsheng Zhang, Ph.D\" ;\n", + "\t\tNC_CHAR :netcdf_creator_email = \"yongsheng.zhang@noaa.gov\" ;\n", + "\t\tNC_CHAR :dataset_citation_product = \"NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)\" ;\n", + "\t\tNC_CHAR :dataset_citation_version = \"5.0.0\" ;\n", + "\t\tNC_CHAR :dataset_citation_institution = \"NOAA/NESDIS/NCEI, Asheville, NC, USA\" ;\n", + "\t\tNC_CHAR :dataset_citation_url = \"https://doi.org/10.25921/9qth-2p70\" ;\n", + "\t\tNC_CHAR :metadata_link = \"https://doi.org/10.25921/9qth-2p70\" ;\n", + "\t\tNC_CHAR :cdm_data_type = \"Grid\" ;\n", + "\t\tNC_CHAR :grid_mapping_name = \"latitude_longitude\" ;\n", + "\t\tNC_CHAR :processing_level = \"NOAA Processing Level 4\" ;\n", + "\t\tNC_FLOAT :geospatial_lon_min = 2.5 ;\n", + "\t\tNC_FLOAT :geospatial_lon_max = 357.5 ;\n", + "\t\tNC_CHAR :geospatial_lon_units = \"degrees_east\" ;\n", + "\t\tNC_CHAR :geospatial_lon_resolution = \"5.0 degree\" ;\n", + "\t\tNC_FLOAT :geospatial_lat_min = -87.5 ;\n", + "\t\tNC_FLOAT :geospatial_lat_max = 87.5 ;\n", + "\t\tNC_CHAR :geospatial_lat_units = \"degrees_north\" ;\n", + "\t\tNC_CHAR :geospatial_lat_resolution = \"5.0 degree\" ;\n", + "\t\tNC_CHAR :geospatial_bounds = \"POLYGON ((2.5 -87.5, 2.5 87.5, 357.5 87.5, 357.5 -87.5, 2.5 -87.5))\" ;\n", + "\t\tNC_CHAR :geospatial_bounds_crs = \"EPSG:4326\" ;\n", + "\t\tNC_FLOAT :geospatial_vertical_min = 0 ;\n", + "\t\tNC_FLOAT :geospatial_vertical_max = 0 ;\n", + "\t\tNC_CHAR :geospatial_vertical_units = \"meters\" ;\n", + "\t\tNC_CHAR :geospatial_vertical_resolution = \"0.f\" ;\n", + "\t\tNC_CHAR :geospatial_vertical_positive = \"up\" ;\n", + "\t\tNC_CHAR :time_coverage_start = \"1880-01\" ;\n", + "\t\tNC_CHAR :time_coverage_end = \"2022-12\" ;\n", + "\t\tNC_CHAR :time_coverage_resolution = \"P1M\" ;\n", + "\t\tNC_CHAR :time_coverage_duration = \"P143Y0M\" ;\n", + "\t\tNC_CHAR :references = \"Vose, R. S., et al., 2012: NOAAs merged land-ocean surface temperature analysis. Bulletin of the American Meteorological Society, 93, 1677-1685. doi: 10.1175/BAMS-D-11-00241.1. Huang, B., Peter W. Thorne, et. al, 2017: Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5), Upgrades, validations, and intercomparisons. J. Climate, 30, 8179-8205. doi: 10.1175/JCLI-D-16-0836.1\" ;\n", + "\t\tNC_CHAR :climatology = \"Climatology is based on 1971-2000 monthly climatology\" ;\n", + "\t\tNC_CHAR :acknowledgment = \"The NOAA Global Surface Temperature Dataset is provided by the NOAA National Centers for Environmental Information(NCEI)\" ;\n", + "\t\tNC_CHAR :date_modified = \"2023-01-08T18:33:09Z\" ;\n", + "\t\tNC_CHAR :date_issued = \"2023-01-08T18:33:09Z\" ;\n", + "}\n" + ] + } + ], + "source": [ + "url <- 'https://www.ncei.noaa.gov/thredds/dodsC/noaa-global-temp-v5/NOAAGlobalTemp_v5.0.0_gridded_s188001_e202212_c20230108T133308.nc'\n", + "data <- open.nc(url)\n", + "print.nc(data)" ] }, { "cell_type": "markdown", - "id": "16a62202-8f64-423e-bf24-33857f1ed537", + "id": "503ca6ad-e7cd-4d10-bff6-cb47c3aa3091", "metadata": {}, "source": [ - "Finally, we can write out the data just like before" + "The anom variable actually has 4 dimensions (lon, lat, z and time). However, the z dimension is equal to 1, which means all the values are from the same elevation. Therefore, the data are essentially 3D.\n", + "\n", + "Let's start by extracting the latitude, longitude and time variables as 1D vectors, and converting the time values to timestamps." ] }, { "cell_type": "code", - "execution_count": 9, - "id": "35bb27c9-9856-4cb4-a05b-d5dab3728c76", + "execution_count": 13, + "id": "dcef0f79-092f-43b8-9f20-2f5fe72140c6", "metadata": {}, "outputs": [], "source": [ - "write_xlsx(df, paste('../data/exported_from_notebooks/temperature_anomalies_',desired_date,'.xlsx', sep=''))\n", - "write.csv(df, paste('../data/exported_from_notebooks/temperature_anomalies_',desired_date,'.csv', sep=''), row.names = FALSE)" + "lat <- var.get.nc(data, 'lat')\n", + "lon <- var.get.nc(data, 'lon')\n", + "time <- var.get.nc(data, \"time\")\n", + "\n", + "# Convert time to readable dates\n", + "start_date <- as.Date(\"1800-01-01\")\n", + "timestamps <- as.Date(time, origin = start_date)" ] }, { "cell_type": "markdown", - "id": "1e21719c-3e43-4db1-a0f9-6ad27d9bd63b", + "id": "3f28c670-4f0b-4189-9bce-82fd15a8c9fb", "metadata": {}, "source": [ - "Now let's try and write all the data (the whole time series) to a dataframe - so 3D data." + "Now let's extract the temperature anomaly values. We can convert the 3D matrix into a 1D vector." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, + "id": "082c61f7-d5ea-4dd3-bba4-25c79546be32", + "metadata": {}, + "outputs": [], + "source": [ + "anom <- var.get.nc(data, 'anom')\n", + "anom_vector <- as.vector(anom)" + ] + }, + { + "cell_type": "markdown", + "id": "47b513c7-8308-4aba-9792-814ede7eb2fe", + "metadata": {}, + "source": [ + "The order of the dimensions is now important. We have longitude, latitude and then time.\n", + "\n", + "This means that the anomaly values are sorted such that the all the values from the first longitude come first.\n", + "\n", + "The values are next sorted by common latitude, and finally by common time.\n", + "\n", + "It is a 3D grid has 72 points of longitude, 36 points of latitude, and 1716 points in time." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8a19e1d0-ed35-428e-a3b9-18ac9c31c447", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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    2. 36
    3. 1716
    \n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 72\n", + "\\item 36\n", + "\\item 1716\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 72\n", + "2. 36\n", + "3. 1716\n", + "\n", + "\n" + ], + "text/plain": [ + "[1] 72 36 1716" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dim(anom)" + ] + }, + { + "cell_type": "markdown", + "id": "4e7df733-22f6-4acd-bcd1-6aca1447ddd9", + "metadata": {}, + "source": [ + "So when flattening the first dimension, longitude, to a 1D vector, we need to repeat the first value 36 x 1716 times before moving onto the second value." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "7fc26c1a-8d06-49a1-8f05-d3e3b0193977", + "metadata": {}, + "outputs": [], + "source": [ + "lon_repeat <- rep(lon, each = length(time) * length(lat))" + ] + }, + { + "cell_type": "markdown", + "id": "837c7425-e31a-4c77-b519-7034e5323828", + "metadata": {}, + "source": [ + "To flatten the second dimension, latitude, to a 1D vector, we need repeat the first value 1716 times before moving on to the second value. Then, after we have reached the last latitude, we need to start from the beginning and repeat 72 times." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "51842083-bd95-4eba-8b9d-681ef1c97f9b", + "metadata": {}, + "outputs": [], + "source": [ + "lat_repeat <- rep(rep(lat, each = length(time)), times = length(lon))" + ] + }, + { + "cell_type": "markdown", + "id": "5dfb86cc-ceb8-42e3-90a5-0b8b7cb497cd", + "metadata": {}, + "source": [ + "Finally, for the third dimension, time, we need to list all the timestamps in order first, and then repeat the list 36 x 72 times." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "10ce502f-aa51-45c4-b81a-ee50ecc5b6c7", + "metadata": {}, + "outputs": [], + "source": [ + "timestamps_repeat <- rep(timestamps, times = length(lon) * length(lat))" + ] + }, + { + "cell_type": "markdown", + "id": "a5885340-db48-4266-9bba-3e445c33b4a8", + "metadata": {}, + "source": [ + "Finally, let's write the data to a dataframe and export them." + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "98bb498b-049b-4b15-a65b-df73cbfefc1c", "metadata": {}, "outputs": [ @@ -885,7 +28053,7 @@ "\n", "\n", "\n", - "\t\n", + "\t\n", "\t\n", "\n", "\n", @@ -901,7 +28069,7 @@ "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r|llll}\n", - " & date & latitude & longitude & anom\\\\\n", + " & Timestamp & Latitude & Longitude & TemperatureAnomalies\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t1 & 1880-01-01 & -87.5 & 2.5 & NA\\\\\n", @@ -916,7 +28084,7 @@ "\n", "A data.frame: 6 × 4\n", "\n", - "| | date <date> | latitude <dbl> | longitude <dbl> | anom <dbl> |\n", + "| | Timestamp <date> | Latitude <dbl> | Longitude <dbl> | TemperatureAnomalies <dbl> |\n", "|---|---|---|---|---|\n", "| 1 | 1880-01-01 | -87.5 | 2.5 | NA |\n", "| 2 | 1880-02-01 | -87.5 | 2.5 | NA |\n", @@ -927,13 +28095,13 @@ "\n" ], "text/plain": [ - " date latitude longitude anom\n", - "1 1880-01-01 -87.5 2.5 NA \n", - "2 1880-02-01 -87.5 2.5 NA \n", - "3 1880-03-01 -87.5 2.5 NA \n", - "4 1880-04-01 -87.5 2.5 NA \n", - "5 1880-05-01 -87.5 2.5 NA \n", - "6 1880-06-01 -87.5 2.5 NA " + " Timestamp Latitude Longitude TemperatureAnomalies\n", + "1 1880-01-01 -87.5 2.5 NA \n", + "2 1880-02-01 -87.5 2.5 NA \n", + "3 1880-03-01 -87.5 2.5 NA \n", + "4 1880-04-01 -87.5 2.5 NA \n", + "5 1880-05-01 -87.5 2.5 NA \n", + "6 1880-06-01 -87.5 2.5 NA " ] }, "metadata": {}, @@ -941,30 +28109,16 @@ } ], "source": [ - "lat <- var.get.nc(data, 'lat')\n", - "lon <- var.get.nc(data, 'lon')\n", - "time <- var.get.nc(data, \"time\")\n", - "\n", - "# Convert time to readable dates\n", - "start_date <- as.Date(\"1800-01-01\")\n", - "date <- as.Date(time, origin = start_date)\n", - "\n", - "anom <- var.get.nc(data, 'anom', start=c(NA, NA, 1, NA), count=c(NA,NA,1,NA))\n", - "anom_flat <- as.vector(anom)\n", - "\n", - "lat_repeat <- rep(lat, each = length(lon) * length(time))\n", - "lon_repeat <- rep(rep(lon, each = length(time)), times = length(lat))\n", - "date_repeat <- rep(date, times = length(lat) * length(lon))\n", - "\n", "df <- data.frame(\n", - " date = date_repeat,\n", - " latitude = lat_repeat,\n", - " longitude = lon_repeat,\n", - " anom = anom_flat\n", + " Timestamp = timestamps_repeat,\n", + " Latitude = lat_repeat,\n", + " Longitude = lon_repeat,\n", + " TemperatureAnomalies = anom_vector\n", ")\n", "\n", "head(df)\n", - "write.csv(df, '../data/exported_from_notebooks/temperature_anomalies_all_dates.csv', row.names = FALSE)" + "write.csv(df, '../data/exported_from_notebooks/3d_data.csv', row.names = FALSE)\n", + "# There are too many rows in this data frame to convert it to a XLSX file" ] }, { diff --git a/genindex.html b/genindex.html index 2ed44c6..08b0498 100644 --- a/genindex.html +++ b/genindex.html @@ -168,6 +168,7 @@
  • 01: Opening and understanding
  • 02: Creating Plots
  • 03: Extracting data to different formats
    • +
  • 04: Creating a CF-NetCDF file
    • diff --git a/intro.html b/intro.html index 5501dc4..a663257 100644 --- a/intro.html +++ b/intro.html @@ -170,6 +170,7 @@
  • 01: Opening and understanding
  • 02: Creating Plots
  • 03: Extracting data to different formats
    • +
  • 04: Creating a CF-NetCDF file
    • @@ -394,6 +395,7 @@

    Course contents01: Opening and understanding
  • 02: Creating Plots
  • 03: Extracting data to different formats
    • +
  • Variables with 3 dimensions
  • 04: Creating a CF-NetCDF file
    • diff --git a/reports/03_extracting_data_to_different_formats.err.log b/reports/03_extracting_data_to_different_formats.err.log index 8baec44..159902e 100644 --- a/reports/03_extracting_data_to_different_formats.err.log +++ b/reports/03_extracting_data_to_different_formats.err.log @@ -37,8 +37,8 @@ nbclient.exceptions.CellTimeoutError: A cell timed out while it was being execut The message was: Cell execution timed out. Here is a preview of the cell contents: ------------------- -["lat <- var.get.nc(data, 'lat')", "lon <- var.get.nc(data, 'lon')", 'time <- var.get.nc(data, "time")', '', '# Convert time to readable dates'] +['latitude <- var.get.nc(data, "lat")', 'latitude_vector <- as.vector(latitude)', 'df$latitude <- latitude_vector', '', 'longitude <- var.get.nc(data, "lon")'] ... -[' anom = anom_flat', ')', '', 'head(df)', "write.csv(df, '../data/exported_from_notebooks/temperature_anomalies_all_dates.csv', row.names = FALSE)"] +['df$longitude <- longitude_vector', '', 'head(df)', "write_xlsx(df, '../data/exported_from_notebooks/2d_data.xlsx')", "write.csv(df, '../data/exported_from_notebooks/2d_data.csv', row.names = FALSE)"] ------------------- diff --git a/search.html b/search.html index 1c85ce8..a7fa128 100644 --- a/search.html +++ b/search.html @@ -170,6 +170,7 @@
  • 01: Opening and understanding
  • 02: Creating Plots
  • 03: Extracting data to different formats
    • +
  • 04: Creating a CF-NetCDF file
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    A data.frame: 6 × 4
    datelatitudelongitudeanom
    TimestampLatitudeLongitudeTemperatureAnomalies
    <date><dbl><dbl><dbl>