dapper.ERA5Adapter

class dapper.ERA5Adapter

Bases: BaseAdapter

ERA5-Land → ELM adapter.

This adapter implements the BaseAdapter interface for ERA5-Land hourly data. It handles source-specific details—file discovery, unit conversions, humidity diagnostics, renaming to ELM short names, and nonnegativity enforcement, so the upstream Exporter can remain source-agnostic.

Responsibilities

• discover_files: Find CSV shards in a directory and infer the overall (start_year, end_year) using their date coverage.

• normalize_locations: Validate and normalize the locations table (adds lon_0-360, ensures/creates zone, stable sorting).

• id_column_for_csv: Declare the identifier column name in the input CSVs. For ERA5 we require gid.

• preprocess_shard: Convert one merged shard (CSV rows joined to locations) into canonical ELM columns. Steps include:

  1. time filtering and optional “noleap” removal of Feb 29

  2. ERA5→ELM unit conversions (e.g., J/hr/m² → W/m², m/hr → mm/s)

  3. optional humidity computation (RH/Q) if temperature, dewpoint, and surface pressure are available

  4. renaming raw ERA5 fields to ELM short names via a mapping

  5. clipping canonical nonnegative variables

  6. returning only required columns in a deterministic order

• required_vars: Report the canonical ELM variable names required for the requested output format.

• pack_params: Provide robust (add_offset, scale_factor) for a canonical ELM variable, given optional data to tune ranges.

Notes

• Humidity computation is performed only when temperature_2m, dewpoint_temperature_2m, and surface_pressure are present.

• Precipitation conversion uses m/hr → mm/s via division by 3.6.

__init__()

Methods

__init__()
discover_files(csv_directory, calendar)	Discover ERA5 CSV shards in a directory and infer the inclusive year range.
id_column_for_csv(df_csv, id_col)	Return the required identifier column name expected in ERA5 CSV shards ("gid").
normalize_locations(df_loc[, id_col])	Standardize df_loc to include ['gid','lat','lon','lon_0-360','zone'], sorted by (lat, lon).
pack_params(elm_var[, data])	Return (add_offset, scale_factor) used to pack a variable for NetCDF output.
preprocess_shard(df_merged, start_year, ...)
required_vars(dformat)	Return the canonical ELM variables required for the requested output format.

Attributes

DRIVER_TAG
SOURCE_NAME

DRIVER_TAG = 'ERA5'

SOURCE_NAME = 'ERA5-Land hourly reanalysis'

discover_files(csv_directory, calendar)

Discover ERA5 CSV shards in a directory and infer the inclusive year range.

id_column_for_csv(df_csv, id_col)

Return the required identifier column name expected in ERA5 CSV shards (“gid”).

pack_params(elm_var, data=None)

Return (add_offset, scale_factor) used to pack a variable for NetCDF output.

preprocess_shard(df_merged, start_year, end_year, calendar, dformat)

1. Filter time & handle no-leap

2. Apply ERA5 → ELM unit conversions

3. Compute humidities (if columns available)

4. Rename columns to canonical ELM names using RAW_TO_ELM

5. Clip canonical nonnegative variables

6. Return only the canonical vars required by elm_required_vars(dformat), plus LONGXY/LATIXY/time/gid/zone (coords/meta).

required_vars(dformat)

Return the canonical ELM variables required for the requested output format.