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ACP_DHR_2023

Scripts used for the data analysis for ACP paper 2023. Including function files, and Jupyter notebooks.

See below for figure and corresponding notebook:

Figures Notebook
Figure1 Harmonisation_inc_figure1
Figure2 Trend_plots_in_abs_ATP_inc_figures2_6_S8_S13
Figure3 Spatial_trends_using_CWT_arrays_inc_figure3
Figure4 Cluster_mappings_inc_figure4
Figure5 Cluster_plots_inc_figure5_S12_S6
Figure6 Trend_plots_in_abs_ATP_inc_figures2_6_S8_S13
Figure7 Calculating_estimated_abs_from_ATP_figure7
Table1 Collocate_back_trajs_with_GFED_inc_table1
FigureS1 Data_availability_inc_figureS1
FigureS2 Compare_data_with_aethalomter_inc_figureS2_S3
FigureS3 Compare_data_with_aethalomter_inc_figureS2_S3
FigureS4 Read_aethalometer_cal_AAE_inc_figureS4
FigureS5 Collocate_ERA5_and_back_trajectorys_vectorized_inc_S5
FigureS6 Cluster_plots_inc_figure5_S12_S6
FigureS7 Active_fires_MODIS_gridding_with_hysplit_inc_figureS7
FigureS8 Trend_plots_in_abs_ATP_inc_figures2_6_S8_S13
FigureS9 Calculates_SSA_inc_S9
FigureS10 Concentration_weighted_plots_inc_figureS10
FigureS11 Masks_eclipse_trend_array_inc_figureS11
FigureS12 Cluster_plots_inc_figure5_S12_S6
FigureS13 Trend_plots_in_abs_ATP_inc_figures2_6_S8_S13
FigureS14 Seasonality_plot_inc_figureS14
FigureS15 Extremes_values_inc_figureS15
FigureS16 Arithmetic_trends_impact_of_fires_inc_figureS16
FigureS17 Calculating_estimated_abs_from_ATP_figure7

Notebooks Description

Harmonisation_inc_figure1 compare instruments:

  • applying correction factors
  • produce harmonised timeseries

Data_availability_inc_figureS1:

  • loads the data sets
  • plots the data availability as a simple bar chart

Cluster_mappings_inc_figure4:

  • load the data for the clusters
  • plot all the lat and lon endpoints as frequency plots

Concentration_weighted_plots_inc_figureS10:

  • Reads in full years’ worth of data
  • generates the CWT arrays, saves them.
  • Also, loads the arrays that were made for the manuscript

Extremes_values_inc_figureS15:

  • Generates the extreme values by defining them using a rolling percentile of 15-days 99th, 95th etc...

Active_fires_MODIS_gridding_with_hysplient_inc_figureS7:

  • Uses the MODIS Satellite data
  • Grids the number active fires
  • Counts the number of fires in grids traversed

Seasonality_plot_inc_figureS14:

  • plot for the annual cycle
  • for ECLIPSE emission inventory
  • Accumulated back trajectory precipitation (ATP)

Spatial_trends_using_CWT_arrays_inc_figure3:

  • Produces the spatial trend plots using the CWT arrays, which are loaded in

Masks_eclipse_trend_array_inc_figureS11:

  • Uses the trend arrays to apply a mask for the eclipse array

Trend_plots_in_abs_ATP_inc_figures2_6_S8_S13:

  • Subplot for the trends in the absorption coefficient
  • Trend for all seasons for the precipitation

Cluster_plots_inc_figure5_S12_S6:

  • Reads in the data files containing the cluster data sets

Compare_data_with_aethalomter_inc_figureS2_S3:

  • comparison between with Aethalometer and all the different instruments
  • comparisons with 3-month intervals of the PSAPs and MAAP

Read_aethalometer_cal_AAE_inc_figureS4:

  • read in the Aeth data and calculate the Absorbing Ångström Exponent.
  • produce the data file for the full time series of the Aethalometer data at 660 nm
  • Arithmetic_trends_impact_of_fires_inc_figureS16 Removes the extreme B.B. events from the data set to see the impact

Calculates_SSA_inc_S9:

  • Calculates SSA
  • Timeseries of the absorption coefficient, scattering coefficient and single scattering albedo

Collocate_back_trajs_with_GFED_inc_table1:

  • Here we read in the HYSPLIT data then we collocate it with the Global Fire Emission Database, which we have pre-processed and saved as .nc files
  • Saves as a GFED.dat file

Calculating_estimated_abs_from_ATP_figure7:

  • Compare absorption and precipitation
  • Map values to estimate time series

Collocate_ERA5_and_back_trajectorys_vectorized_inc_S5:

  • Collocates the ERA data with the HYSPLIT output
  • produces figureS5

Processing scripts only:

Converts_global_fire_emission_database_tonetcdf:

  • Converts hdf5 files to netcdf for later
  • Processes_hysplit_output takes the 'raw' HYSPLIT data i.e. what you get from the output, processes them one by one and saves them in a form with also distance calculated and also the rotated latitudes and longitudes as grid cells.

Generate_trend_mapping_for_ECLIPSE_and_array:

  • Plots the trend array for the ECLIPSE emissions and generates the trend array .txt file

Looks_for_missing_hysplit_output:

  • look at the folders where you have saved your runs.
  • Create a dataframe which lists the missing runs
  • Once the dataframe of the missing datetimes is created.
  • loop through them and generate HYSPLIT files for them.

Generates_back_trajectories_using_pysplit_and_GDAS:

  • Use Pysplit to generate trajectories with metrological data from GDAS

Generates_back_trajectories_using_pysplit_and_FNL:

  • Use Pysplit to generate trajectories with metrological data from FNL (different due to the resolution)

Reads_in_and_processes_MAAP_data:

  • process the MAAP data

Reads_in_ecotech_data:

  • read in and process the Ecotech nephelometer

Compare_TSI_Ecotech:

  • compare the TSI and Ecotech data

Reading_in_the_automatic_PSAP_data_applying_Bond:

  • reads the raw PSAP data processes it and applies bond

Reading_in_the_manual_PSAP_data_applying_Bond:

  • reads the raw PSAP data processes it and applies bond