Concatenating a gridded rainfall reanalysis dataset into a time series
Concatenating a gridded rainfall reanalysis dataset into a time series¶
General Preprocessing Standard Python
To load and extract a region of interest from a gridded rainfall reanalysis dataset, and concatenate into a time series using the Iris package.
Time series data allows us to carry out a wide range of analyses including but not limited to trend, seasonality, anomaly detection and causality. As most of the climatological datasets are gridded, we provide a general tool to preprocess them into time series. The example global dataset from NCEP/NCAR reanalysis has a fairly low resolution (T62 Gaussian grid or approximately 1.9 * 1.9 degrees lat/long) which allows easy execution. It is openly available with a variety of atmospheric variables at near surface levels in daily and monthly frequencies as well as long-term monthly mean in NetCDF format, which is described in and can be obtained from the NOAA Physical Sciences Laboratory.
This notebook uses a single sample data file for global daily precipitation rate (monthly mean) included with the notebook.
Data for the entire globe is loaded and plotted using Iris
Seasonal means are created by aggregating the data
The Indonesian Borneo region is extracted and plotted
The area-averaged time series for Indonesian Borneo region is created
A particular season and timeframe are extracted from the time series
Timothy Lam (author), University of Exeter, @timo0thy
Marlene Kretschmer (author), University of Reading, @MarleneKretschmer
Samantha Adams (author), Met Office Informatics Lab, @svadams
Rachel Prudden (author), Met Office Informatics Lab, @RPrudden
Elena Saggioro (author), University of Reading, @ESaggioro
Nick Homer (reviewer), University of Edinburgh, @NHomer
Alejandro Coca-Castro (reviewer), The Alan Turing Institute, @acocac
NOAA National Center for Environmental Prediction (creator)
E. Kalnay, M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K. C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, Roy Jenne, and Dennis Joseph. The ncep/ncar 40-year reanalysis project. Bulletin of the American Meteorological Society, 77(3):437 – 472, 1996. URL: https://journals.ametsoc.org/view/journals/bams/77/3/1520-0477_1996_077_0437_tnyrp_2_0_co_2.xml, doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
Marlene Kretschmer, Samantha V. Adams, Alberto Arribas, Rachel Prudden, Niall Robinson, Elena Saggioro, and Theodore G. Shepherd. Quantifying causal pathways of teleconnections. Bulletin of the American Meteorological Society, 102(12):E2247 – E2263, 2021. URL: https://journals.ametsoc.org/view/journals/bams/102/12/BAMS-D-20-0117.1.xml, doi:10.1175/BAMS-D-20-0117.1.
NCEP-NCAR Reanalysis 1 data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at https://psl.noaa.gov
import os import iris import iris.quickplot as qplt import iris.coord_categorisation as coord_cat import cf_units import nc_time_axis import matplotlib.pyplot as plt import urllib.request import holoviews as hv import geoviews as gv import warnings warnings.filterwarnings(action='ignore') %matplotlib inline hv.extension('bokeh')