tempest2fast package

Submodules

tempest2fast.defaults module

Created on Thu Mar 13 11:34:26 2025

@author: dphilippus

This provides default values.

tempest2fast.dem module

Created on Tue Mar 4 13:39:52 2025

@author: dphilippus

This file handles elevation data retrieval from Copernicus GLO90, and averaging to a 0.01-degree grid.

tempest2fast.dem.get_full_dem(file, tiles_fn=<function <lambda>>)

Retrieve a full km-resolution DEM. Reads the file if available; otherwise downloads and builds it.

tempest2fast.dem.list_tiles(bbox=(-125, 25, -65, 50))

List all relevant tiles within the bbox. The format is whatever/whatever.tif.

Parameters:

bbox ((float, float, float, float)) – Bounding box with (left, bottom, right, top) lat/lon.

Returns:

Paths for relevant DEM tiles.

Return type:

list of str

tempest2fast.dem.make_groups(data)

Prepare 0.01 degree bins for a dataset containing x and y coordinates.

Returns:

Longitude centroids, longitude bounds, latitude centroids, latitude bounds.

Return type:

(np.array, np.array, np.array, np.array)

tempest2fast.dem.make_steps(left, right, width=0.01)

Create centroids and boundaries from left (minimum) to right (maximum). Centroids are at width intervals from an integer.

Returns:

Centroids and bounds.

Return type:

(np.array, np.array)

tempest2fast.dem.resample_all_tiles(tiles=['s3://copernicus-dem-90m/Copernicus_DSM_COG_30_N25_00_W077_00_DEM/Copernicus_DSM_COG_30_N25_00_W077_00_DEM.tif', 's3://copernicus-dem-90m/Copernicus_DSM_COG_30_N25_00_W078_00_DEM/Copernicus_DSM_COG_30_N25_00_W078_00_DEM.tif', 's3://copernicus-dem-90m/Copernicus_DSM_COG_30_N25_00_W079_00_DEM/Copernicus_DSM_COG_30_N25_00_W079_00_DEM.tif', 's3://copernicus-dem-90m/Copernicus_DSM_COG_30_N25_00_W080_00_DEM/Copernicus_DSM_COG_30_N25_00_W080_00_DEM.tif', 's3://copernicus-dem-90m/Copernicus_DSM_COG_30_N25_00_W081_00_DEM/Copernicus_DSM_COG_30_N25_00_W081_00_DEM.tif', 's3://copernicus-dem-90m/Copernicus_DSM_COG_30_N25_00_W082_00_DEM/Copernicus_DSM_COG_30_N25_00_W082_00_DEM.tif', 's3://copernicus-dem-90m/Copernicus_DSM_COG_30_N25_00_W098_00_DEM/Copernicus_DSM_COG_30_N25_00_W098_00_DEM.tif', 's3://copernicus-dem-90m/Copernicus_DSM_COG_30_N25_00_W099_00_DEM/Copernicus_DSM_COG_30_N25_00_W099_00_DEM.tif', 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's3://copernicus-dem-90m/Copernicus_DSM_COG_30_N49_00_W118_00_DEM/Copernicus_DSM_COG_30_N49_00_W118_00_DEM.tif', 's3://copernicus-dem-90m/Copernicus_DSM_COG_30_N49_00_W119_00_DEM/Copernicus_DSM_COG_30_N49_00_W119_00_DEM.tif', 's3://copernicus-dem-90m/Copernicus_DSM_COG_30_N49_00_W120_00_DEM/Copernicus_DSM_COG_30_N49_00_W120_00_DEM.tif', 's3://copernicus-dem-90m/Copernicus_DSM_COG_30_N49_00_W121_00_DEM/Copernicus_DSM_COG_30_N49_00_W121_00_DEM.tif', 's3://copernicus-dem-90m/Copernicus_DSM_COG_30_N49_00_W122_00_DEM/Copernicus_DSM_COG_30_N49_00_W122_00_DEM.tif', 's3://copernicus-dem-90m/Copernicus_DSM_COG_30_N49_00_W123_00_DEM/Copernicus_DSM_COG_30_N49_00_W123_00_DEM.tif', 's3://copernicus-dem-90m/Copernicus_DSM_COG_30_N49_00_W124_00_DEM/Copernicus_DSM_COG_30_N49_00_W124_00_DEM.tif'])

Convert all available tiles to lower resolution and combine them. This just applies resample_tile for all tiles.

tempest2fast.dem.resample_tile(tile_path)

Take an S3FS tile path, resample to 0.01 degrees, and return DataArray. This could be done all at once, but no harm in “pre-chunking”.

Parameters:

tile_path (str) – Path of DEM tile on S3. See list_tiles.

Returns:

Elevation raster at 1-km resolution.

Return type:

xr.DataArray

tempest2fast.humidity module

Created on Tue Mar 4 13:39:37 2025

@author: dphilippus

This file handles humidity data retrieval from NLDAS2. NLDAS2 is on a 14-km grid, so it’s more efficient to process hourly –> daily means before converting to the 0.01-deg grid.

tempest2fast.humidity.process_day(date, bbox=(-125, 25, -65, 50), prog=True)

Process a single day of data. To avoid timeouts, run in 1-degree increments.

Parameters:

• date (str) – Date of interest.

• bbox ((float, float, float, float), optional) – Bounding box of interest.

• prog (Bool, optional) – Print a progress bar.

Returns:

NLDAS humidity raster for a single day.

Return type:

xr.DataArray

Notes

Using PyNLDAS2 here is quite slow. The recommended approach is to pre-download NLDAS2 data and process it with other functions here; see README for download instructions.

tempest2fast.humidity.process_days(outdir, start='2001-10-01', end='today', bbox=(-125, 25, -65, 50), outer_prog=True, inner_prog=False)

Process all days from start to end, exclusive. outer_prog = track progress over days inner_prog = process_day progress bar

tempest2fast.humidity.process_downloaded(indir, outdir, start='2001-10-01', end='today')

Take downloaded NLDAS humidity and convert to the FAST grid.

Parameters:

• indir (str) – Location of downloaded humidity datasets.

• outdir (str) – Location to save combined humidity datasets, one file per day.

• start (str, optional) – Start date. The default is 2001.

• end (str, optional) – End date. The default is today.

Return type:

Nothing.

Notes

For a large dataset, this has a long spinup time, so I recommend running it for the full period of record all at once. There is no penalty to running extra dates, and it doesn’t seem to substantially leak memory. Actual processing is fairly quick, around 10 days per minute. Memory use for the CONUS domain is under 2 GB.

tempest2fast.humidity.rechunk(basedir, outdir=None, delete=True)

Reprocess rasters in basedir to 1x1-pixel chunks in space.

Xarray/Dask is effective, but it’s not magic, particularly if the chunks are misaligned. Loading a 1.5 TB dataset with variable chunking still requires hundreds of GB of RAM, even if you only want a smaller subset.

So, here we have a script to automatically rechunk everything, if, like the developer, you did not think to do so initially, or if you retrieved data before TE2FAST got smart about chunking downloads.

tempest2fast.humidity.resample(humidity)

Interpolate humidity to 1-km resolution.

tempest2fast.lcov module

Created on Tue Mar 4 13:39:44 2025

@author: dphilippus

This file handles land cover data retrieval from ESA WorldCover, and averaging on a 0.01-degree grid.

Required land cover categories:

80, “water” 20, “shrubland” 60, “barren” 30, “grassland”

Output: Dataset with layers for the total abundance of each in the usual grid.

The user should manually download WorldCover macrotiles and provide a directory that contains all the TIFs.

tempest2fast.lcov.parse_tile_coordinates(filename)

Parse filename to get bottom left coordinates. Internal utility function.

tempest2fast.lcov.process(lcdir, tmpdir, outf, bbox=(-125, 25, -65, 50))

Bulk process land cover tiles. To save memory, this uses a two-pass approach. First, process each tile individually and save as its own NetCDF. Then load all the NetCDFs as a multifile dataset and write that to a single NetCDF. On my machine, each tile takes about 5 minutes using 10% CPU and ~1GB memory. Output tiles (3x3 deg) are just under 3 MB each, so the total size should be a bit under a gigabyte. There are 634 tiles in the relevant macrotiles, but the actual bounding box is 60x25 (20x9 3x3 tiles), so there should be <200 tiles resulting.

tempest2fast.lcov.process_tile(tile)

Remap a tile to 0.01 degrees, then generate abundances for each TE2 pixel therein.

Parameters:

tile (xr.DataArray) – Original WorldCover DataArray with land cover categories.

Returns:

Dataset aggregated to 1-km and summarized as abundances.

Return type:

xr.Dataset

Notes

Cutting off at -1: tiles are adjacent; we don’t want them to overlap

tempest2fast.lcov.summarize_cell(cell)

Convert a single cell to abundances.

Parameters:

cell (xr.DataArray) – DataArray with numeric WorldCover categories for a single cell.

Returns:

Dataset with categorical abundances for the cell.

Return type:

xr.Dataset

tempest2fast.lst module

Created on Tue Mar 4 13:39:22 2025

@author: dphilippus

This file handles Land Surface Temperature (LST) data retrieval and processing from MODIS. NASA Earthdata account required.

See: https://github.com/nasa/LPDAAC-Data-Resources/tree/main/python/scripts/daac_data_download_python

URLs are of the form:

https://e4ftl01.cr.usgs.gov/MOLT/MOD11A1.061/2002.03.31/MOD11A1.A2002090.h31v07.061.2020114050748.hdf

Through MOD11A1.061/: unchanged - 2002.03.31: yyyy.mm.dd - .A2002090: YYYYJJJ (Julian day) - h31v07: coordinates - Probably best to list the URLs for each date, then download all .hdf and .xml, or just everything with hdf in it.

The scaling factor is 0.02, and the band of interest is LST_Day_1km. Fill value is 0.

CONUS is approx. h08v03-h12v06. Test tile: h09v04

Reproject to WGS84 before returning.

Notes

• Datasets concatenate left to right, bottom to top (from bottom left). MODIS

tiles go from the top left to bottom right. - EarthData search includes spatial coverage:

result[“umm”][“SpatialExtent”][“HorizontalSpatialDomain”][“Geometry”][“GPolygons”][0][“Boundary”][“Points”] Which has a list of {“Longitude”, “Latitude”} dictionaries.

• Suggested: sort tiles by longitude and latitude, then correct bounds,

  then reproject.

• The downloaded filename is local_path/result[“umm”][“GranuleUR”].hdf

tempest2fast.lst.build_multids(outdir, start='2001-10-01', end='today', basename='modis_lst_', crash=False, **kwargs)

Prepare a multi-file dataset containing a full timeseries, stored as one NetCDF per day. This is about a terabyte of data for default arguments.

Parameters:

• outdir (str) – Output directory to store the NetCDFs.

• start (str | datetime, optional) – Start date. The default is “2001-10-01”.

• end (str | datetime, optional) – End date, exclusive. The default is “today”.

• basename (str, optional) – Prefix of the file name. The default is “modis_lst_”.

• crash (bool, optional) – Whether to crash if loading a date fails twice. The default is False.

• **kwargs (named arguments) – Passed on to load_datasets. bbox=(left, bottom, right, top) may be especially relevant.

Return type:

None.

tempest2fast.lst.find_datasets(time=('2020-01-01', '2020-01-01'), bbox=(-125, 25, -65, 50), dsname='MOD11A1')

This is a simple wrapper around ea.search_data, providing defaults.

tempest2fast.lst.hvxy_to_lonlat(h, v, i, j)

Convert MODIS coordinates to lat/lon. We can use this to build out an array of “corrected” latitude/longitude. This all uses numpy, so you can use this on a 2D array of coordinates as well as a single set.

Parameters:

• h (int) – MODIS horizontal tile index.

• v (int) – MODIS vertical tile index.

• j (int) – column of grid cell within the tile.

• i (int) – row of grid cell within the tile.

Returns:

Longitude and latitude.

Return type:

(float, float)

Notes

See: https://modis-fire.umd.edu/files/MODIS_C6_BA_User_Guide_1.2.pdf The math: first, compute the full coordinates in the MODIS grid. x = (j + 0.5)w + HT + xmin y = ymax − (i + 0.5)w − V T Now compute latitude and longitude. Latitude is simple, longitude a little less so. Latitude = y/R Longitude =x/(Rcos(lat))

Constants are also taken from the above link.

tempest2fast.lst.load_datasets(time='2020-01-01', bbox=(-125, 25, -65, 50), dsname='MOD11A1', cache='cache/lst', trim=True, cleanup=True, interpolate=True)

Load all datasets for a given date and bounding box, convert to lat/lon, and return the combined DataArray.

Parameters:

• time (str, optional) – The date requested. The default is “2020-01-01”.

• bbox ((float, float, float, float), optional) – Bounding box coordinates in lat/lon: left, bottom, right, top. The default is (-125, 25, -65, 50).

• dsname (str, optional) – Dataset name. The default is “MOD11A1”.

• cache (str, optional) – Caching directory. The default is “cache/lst”.

• trim (bool, optional) – Trim dataset to the exact bounding box. The default is True.

• cleanup (bool, optional) – Delete cached files after processing to save space. The default is True.

• interpolate (bool, optional) – Interpolate small gaps to infill. The default is True.

Returns:

A DataArray of LST in lat/lon. Also has a date coordinate with just the one entry.

Return type:

DataArray

tempest2fast.lst.login()

tempest2fast.lst.modis_to_latlon(data, h, v)

Convert raw MODIS downloads to a lat/lon dataframe at 0.01 degree resolution. This is done by rounding cell centroids to the nearest 0.01 degree, then averaging.

Parameters:

• data (DataArray) – Raw MODIS dataset, containing just the target variable (LST_Day_1km) and at 1-km resolution.

• h (int) – MODIS tile coordinate.

• v (int) – MODIS tile coordinate.

Returns:

res_df – DataFrame containing reorganized values.

Return type:

DataFrame of [“lon”, “lat”, “LST”]

tempest2fast.lst.parse_hv_filename(filename)

This is a simple helper to extract h/v coordinates from a file name.

tempest2fast.prediction module

Created on Tue Mar 4 13:40:21 2025

@author: dphilippus

This file handles the actual prediction process. Namely:

• Load spatial surfaces and coefficients

• Retrieve all data inputs

• Process LST and humidity to generate the required summaries

• Estimate coefficients

• Compute seasonality

• Compute anomalies

• Store result

As a matter of general procedure, summaries and coefficients will be quite compact (no timesteps) and can therefore be pre-generated and stored. The coefficients themselves may be a useful data result, since that amounts to a reasonably thorough description of thermal regimes.

Prediction, on the other hand, generates a lot of data, expected to be around 300 GB. We don’t want to have a lot of intermediate data floating around, and I think reducing storage requirements is likely a higher priority than reducing runtime. Thus, although it involves some redundant computation, each of the three primary outputs (temp.doy, temp.anom, temp.plus) is generated in a single pass (one ufunc):

• temp.doy: (doy, coefficients) -> temp.doy

• temp.plus and temp.anom: (doy, LST, humidity, coefficients) ->

  (lst anom, humidity anom, coefficients) -> temp.plus/temp.anom

This gives us three variables of the result dataset. The other two, then, are just sums of the relevant variables.

Testing paths:

../Rivers.nc ../conus_1km.nc ../lcov.nc X:/Rio.Data/StreamTemperature/FAST/LST/modis_lst_yyyy-mm-dd.nc X:/Rio.Data/StreamTemperature/FAST/Humidity/humidity_yyyymmdd.nc ../SpatialModels.nc

Thus:

fixed = pd.read_csv(“../FixedModel.csv”) pco = predict_coefficients(make_masked_dataset(“../Rivers.nc”, “../conus_1km.nc”, “../lcov.nc”, “X:/Rio.Data/StreamTemperature/FAST/LST/modis_lst_2002.nc”, “X:/Rio.Data/StreamTemperature/FAST/Humidity/humidity_2002*.nc”, “../SpatialModels.nc”, (-110, 40, -105, 45)), fixed); pco.to_netcdf(“../tests/testcoef.nc”)

tempest2fast.prediction.compute_summaries(dyndata: Dataset)

Computes required summary statistics for coefficient estimation from dynamic data.

Parameters:

dyndata (Dataset) – Dataset with LST (Kelvin) and humidity (kg/kg).

Returns:

humidity_sd (std dev of smoothed anomaly, not of overall timeseries) amphum (coefficient of DOY mean humidity in terms of 210-centered cosine) humidity_01 (mean January humidity) humidity_03 humidity_05 humidity_07 humidity_08 humidity_09 humidity_11 minT (min daily mean LST) maxT freeze_days (total days of year with mean LST < 0) lst_05 lst_09 lst_10 cdd_09 (cumulative degree days from January 1) cdd_10

Return type:

Dataset with required summary layers

Notes

Before doing anything, Kelvin is converted to Celsius.

tempest2fast.prediction.full_run(fixed: str | DataFrame, rivers: str | DataArray, dem: str | DataArray, lcov: str | Dataset, lst: str | DataArray, humidity: str | DataArray, spatmod: str | Dataset, bbox: tuple[float] = None, coef_file: str = None, logfile: str = None)

Run all the way through coefficient estimation and timeseries generation. See make_masked_dataset docs for detailed input info.

Parameters:

• fixed (str or DataFrame) – Either a DataFrame of fixed coefficients or the path thereof.

• rivers (str or DataArray) – True/False array indicating the presence of a river in the pixel.

• dem (str or DataArray) – Elevations in meters.

• lcov (str or Dataset) – Land cover layers as fractional abundances (water, shrubland, grassland, barren).

• lst (str or DataArray) – Land surface temperature in Kelvin.

• humidity (str or DataArray) – Specific humidity in kg/kg.

• spatmod (str or Dataset) – Spatial component of coefficient krigs.

• bbox (optional (float, float, float, float)) – (left, bottom, right, top). The default is None. If provided, clip to the specified bounding box. Only applied if inputs are paths, not datasets.

• coef_file (str, optional) – Location of intermediate coefficient file; it is best practice to store intermediate results and load from disk. If not provided, then there will not be a storage/retrieval step. The default is None.

• logfile (str, optional) – Write logging info to this file (will overwrite).

Returns:

• DataSet (lon x lat x date) containing the five predicted timeseries

• temp.doy, temp.anom, temp.plus, temp.mod, temp.max

tempest2fast.prediction.full_run_dataframes(fixed: str | DataFrame, inpdata: str | DataFrame, spatmod: str | Dataset, idcol: str = None)

Run all the way through coefficient estimation and timeseries generation, but with dataframes as inputs. Allows using TE2FAST as regular TempEst 2-but-in-Python. Since this version is not grid-based, it is assumed that all input coordinates are only for points of interest. See make_masked_dataset docs for detailed input info.

Parameters:

• fixed (str or DataFrame) – Either a DataFrame of fixed coefficients or the path thereof.

• inpdata (str or DataFrame) – DataFrame containing all input columns: lat, lon, date, elevation, water, shrubland, grassland, barren, LST, humidity; or path thereof.

• spatmod (str or DataSet) – Spatial component of coefficient krigs. Still a DataSet, unlike the rest.

• idcol (str, optional) – Name of the ID column, if any. The default is None.

Returns:

• DataFrame (lon x lat x date, possibly idcol) containing the five predicted timeseries

• temp.doy, temp.anom, temp.plus, temp.mod, temp.max

Notes

Just expanding the input points into a grid does not work well, since you end up with N^2 lat/lon pairs. Instead, we build a <space x time> (2D) grid where the space index is assigned from lat/lon, and include lat/lon as data.

tempest2fast.prediction.make_masked_dataset(rivers: str | DataArray, dem: str | DataArray, lcov: str | Dataset, lst: str | DataArray, humidity: str | DataArray, spatmod: str | Dataset, bbox: tuple[float, float, float, float] = None)

Generate a combined dataset of all required inputs, masked to the river network. All inputs can be either a string (the path to the file[s]) or an xarray DataSet/DataArray. Strings are assumed to be the full path for static datasets (rivers, dem, lcov) and to contain a wildcard for a multifile dataset (separated by date) for the dynamic datasets (lst, humidity).

All datasets should be on the 0.01-degree grid. Coordinates should be lat, lon, and (if applicable) date.

Everything except lcov and spatmodel should be a DataArray, if provided pre-loaded. lcov should be a dataset with variables “water”, “shrubland”, “grassland”, and “barren” (all abundances). spatmodel should be a dataset with variables corresponding to each coefficient.

Parameters:

• rivers (str or DataArray) – True/False array indicating the presence of a river in the pixel.

• dem (str or DataArray) – Elevations in meters.

• lcov (str or Dataset) – Land cover layers as fractional abundances (water, shrubland, grassland, barren).

• lst (str or DataArray) – Land surface temperature in Kelvin.

• humidity (str or DataArray) – Specific humidity in kg/kg.

• spatmod (str or Dataset) – Spatial component of coefficient krigs.

• bbox (optional (float, float, float, float)) – (left, bottom, right, top). The default is None. If provided, clip to the specified bounding box. Only applied if inputs are paths, not datasets.

Returns:

Two datasets

Return type:

static and dynamic, masked to the rivers.

Notes

If loading from files, FAST makes some format assumptions based on the default processing procedures. This may be different if you generate the files yourself.

Even with Xarray’s lazy loading - but much improved thereby - this does have substantial memory requirements. In a 1-year test, the input size on disk is about 180 MB/day (LST + humidity), for a total size of ~66 GB. Memory usage when processing hits about 5.5 GB. With a 10-degree-square bounding box, that’s down to 2.5 GB. Static is 124 MB and dynamic is 4 GB.

tempest2fast.prediction.predict_coefficients(static: Dataset, dynamic: Dataset, fixed_coef: str | DataFrame)

This is where the magic happens: full coefficient prediction. Generate dynamic summaries, then apply fixed relationships and add spatial terms to generate coefficient estimates. Coefficient estimates themselves are a comprehensive description of river thermal regimes; the rest is just outputting the timeseries.

Parameters:

• static (Dataset) – Xarray dataset containing all static information from make_masked_dataset.

• dynamic (Dataset) – Xarray dataset containing all dynamic inputs from make_masked_dataset.

• fixed_coef (str or DataFrame) – DataFrame of fixed model coefficients, or path thereof. If it is a DataFrame, expected columns are Component (output), Variable (input), and estimate (coefficient).

Returns:

Intercept Amplitude SpringSummer AutumnWinter WinterDay SpringDay (constant: 160) AutumnDay (constant: 330) SummerDay (constant: 220) LST (sensitivity coefficients) Humidity LSTMax HumidityMax InterceptMax

Return type:

Dataset containing all TempEst 2 river coefficients

tempest2fast.prediction.predict_timeseries(coefs: Dataset, dyndata: Dataset)

Generate full timeseries from estimated coefficients and dynamic data.

Parameters:

• coefs (DataSet) – DataSet (lon x lat) containing all TempEst 2 coefficients: Intercept, Amplitude, AutumnWinter, SpringSummer, WinterDay, SpringDay, SummerDay, AutumnDay, LST, Humidity, LSTMax, HumidityMax, InterceptMax.

• dyndata (DataSet) – DataSet (lon x lat x date) containing dynamic inputs (LST and humidity).

Returns:

DataSet (lon x lat x date) containing the five predicted timeseries: temp.doy, temp.anom, temp.plus, temp.mod, temp.max

Return type:

DataSet

tempest2fast.prediction.ts_dim(Intercept, Amplitude, SpringSummer, FallWinter, SpringDay, SummerDay, FallDay, WinterDay)

tempest2fast.rivers module

Created on Tue Mar 4 13:39:57 2025

@author: dphilippus

This file handles river masking for the TE2FAST grid. What and why?

What: in each 0.01-degree grid cell, identify whether there is a river. Provide a gridded dataset of rivers that can also be used for masking.

Why:

• For user convenience: analysis can be limited to actual rivers - no

mountaintop guesses. - For processing and storage efficiency: we don’t need to analyze non-river sites.

Process the MERIT Hydro river width dataset, downloaded using Google Earth Engine as RiverWidth.tif. For every 0.01-degree cell, note whether it has nonzero mean river width (i.e., contains a river). In the original projection, 5% of cells contain a river.

tempest2fast.rivers.make_grid(raster)

tempest2fast.ufuncs module

Created on Fri Mar 28 14:52:50 2025

@author: dphilippus

This file implements all required functions as Numpy ufuncs. All summary functions are applied to DOY means. Straightforward means, etc, are not provided.

Summary functions:

amphum (coefficient of DOY mean humidity in terms of 210-centered cosine)

Prediction functions:

anomaly application anomaly smoothers coefficient predictors

tempest2fast.ufuncs.amphum(humidity, doy)

tempest2fast.ufuncs.costs(doy)

tempest2fast.ufuncs.humidity_smooth(humidity)

tempest2fast.ufuncs.lst_smooth(lst)

Module contents

Created on Tue Mar 4 13:38:16 2025

@author: dphilippus