olsen_randerson.fisher module

Modifications to the downscaling by Fisher et al. (2016).

Changes from the Olsen-Randerson downscaling are primarily to prevent discontinuities at month change.

I do not know whether the rolling windows used in Fisher et al. (2016) were centered on the given time or ending on the given time. At present, this code uses rolling windows ending on the given day, because that is easy to get pandas to do.

olsen_randerson.fisher.INPUT_FREQUENCY = '1M'

The frequency at which the input data are given.

Used to ensure the downscaled fluxes match the predictions of the original fluxes.

olsen_randerson.fisher.downscale_gpp_timeseries(flux_gpp, par)[source]

Downscale the columns of flux_nee.

Parameters
flux_gpppd.DataFrame[N_large, M]

GPP at the larger timesteps. Must have a datetime index. Units must have time in the denominator.

parpd.DataFrame[N, M]

PAR at the small timesteps. Must be greather than or equal to zero. Must have datetime index with a set frequency.

Returns
flux_gpppd.DataFrame[N, M]

The GPP downscaled to the smaller time steps.

References

Fisher, J. B., Sikka, M., Huntzinger, D. N., Schwalm, C., and Liu, J., 2016: Technical note: 3-hourly temporal downscaling of monthly global terrestrial biosphere model net ecosystem exchange, Biogeosciences, vol. 13, no. 14, 4271–4277, doi:10.5194/bg-13-4271-2016.

olsen_randerson.fisher.downscale_resp_timeseries(flux_resp, temperature)[source]

Downscale the columns of flux_resp.

Parameters
flux_resppd.DataFrame[N_large, M]

Respiration fluxes at the larger timesteps. Must have a datetime index. Units must have time in denominator.

temperaturepd.DataFrame[N, M]

Temperature at the small timesteps. Must have datetime index with a set frequency Unit is expected to be degrees Celsius.

Returns
flux_resppd.DataFrame[N, M]

The respiration fluxes downscaled to the smaller time steps.

References

Fisher, J. B., Sikka, M., Huntzinger, D. N., Schwalm, C., and Liu, J., 2016: Technical note: 3-hourly temporal downscaling of monthly global terrestrial biosphere model net ecosystem exchange, Biogeosciences, vol. 13, no. 14, 4271–4277, doi:10.5194/bg-13-4271-2016.

olsen_randerson.fisher.downscale_timeseries(flux_nee, temperature, par)[source]

Downscale the columns of flux_nee.

The parts of the downscaled flux corresponding to the first and last time periods in the original flux will be incomplete. It is recommended to provide an extra time period on each end to avoid this.

Parameters
flux_neepd.DataFrame[N_large, M]

NEE, at the large timesteps. Must have datetime index. Positive indicates carbon is entering the atmosphere. Units must have time in denominator.

temperaturepd.DataFrame[N, M]

Temperature at the small timesteps. Must have datetime index with a set frequency Unit is expected to be degrees Celsius.

parpd.DataFrame[N, M]

PAR at the small timesteps. Must be greather than or equal to zero. Must have datetime index with a set frequency.

Returns
flux_neepd.DataFrame[N, M]

The downscaled NEE.

References

Fisher, J. B., Sikka, M., Huntzinger, D. N., Schwalm, C., and Liu, J., 2016: Technical note: 3-hourly temporal downscaling of monthly global terrestrial biosphere model net ecosystem exchange, Biogeosciences, vol. 13, no. 14, 4271–4277, doi:10.5194/bg-13-4271-2016.