Fix typos

docs/README.md

@@ -58,9 +58,9 @@ Then go to your forked repo on github.com and create a Pull Request to the `main
 
 Build webpages
 --------------
-Once the changes are merged to the `main` branch of pcmdi_metrics, Github Action will automatically build and deploy web pages using Github Pages. This process will follow what is defined in `.github/workflows/documentation.yaml`. The page will be accssible at http:https://pcmdi.github.io/pcmdi_metrics/.
+Once the changes are merged to the `main` branch of pcmdi_metrics, Github Action will automatically build and deploy web pages using Github Pages. This process will follow what is defined in `.github/workflows/documentation.yaml`. The page will be accessible at http:https://pcmdi.github.io/pcmdi_metrics/.
 
-To deploy the web pages via readthedocs, you will need to go to readthedocs project page (https://readthedocs.org/projects/pcmdi-metrics/), go to "Builds" menu, and click "Build Version" button. The page will be accessbile at https://pcmdi-metrics.readthedocs.io/en/latest/.
+To deploy the web pages via readthedocs, you will need to go to readthedocs project page (https://readthedocs.org/projects/pcmdi-metrics/), go to "Builds" menu, and click "Build Version" button. The page will be accessible at https://pcmdi-metrics.readthedocs.io/en/latest/.
 
 Resources
 ---------

docs/index.rst

@@ -9,7 +9,7 @@ PCMDI Metrics Package (PMP)
 
 The Program for Climate Model Diagnosis & Intercomparison (`PCMDI`_) Metrics Package (PMP) is used to provide "quick-look" objective comparisons of Earth System Models (ESMs) with one another and available observations. 
 Results are produced in the context of all model simulations contributed to CMIP6 and earlier CMIP phases. 
-Currently, the comparisons emphasize metrics of large- to global-scale annual cycle and both tropcial 
+Currently, the comparisons emphasize metrics of large- to global-scale annual cycle and both tropical 
 and extra-tropical modes of variability. 
 Recent release (v3) include established statistics for mean climate, ENSO, MJO, extratropical modes of variability, 
 regional monsoons, and high frequency characteristics of simulated precipitation as a part of U.S. DOE's Benchmarking of simulated precipitation. 
@@ -87,4 +87,4 @@ BSD 3-Clause License. See `LICENSE <https://github.com/PCMDI/pcmdi_metrics/blob/
  :hidden:
  :caption: Community
 
- GitHub discussions <https://github.com/PCMDI/pcmdi_metrics/discussions>
+ GitHub discussions <https://github.com/PCMDI/pcmdi_metrics/discussions>

docs/metrics.rst

@@ -4,7 +4,7 @@
 PMP Metrics
 *****************
 
-We provide documentation along with demos to assist users of the PMP. Most demos are simple examples on how to apply the PMP to one or several datasets. Example parameter files used for more complex appication of the PMP (e.g., applying the PMP across all CMIP models) via the sample setups used by PCMDI for semi-operational application to the CMIP database. 
+We provide documentation along with demos to assist users of the PMP. Most demos are simple examples of how to apply the PMP to one or several datasets. Example parameter files used for more complex application of the PMP (e.g., applying the PMP across all CMIP models) via the sample setups used by PCMDI for semi-operational application to the CMIP database. 
 
 A suite of demo scripts and interactive Jupyter notebooks are provided with `this documentation <https://github.com/PCMDI/pcmdi_metrics/blob/master/doc/jupyter/Demo/README.md>`_. 
 

docs/metrics_mean-clim.rst

@@ -7,8 +7,8 @@ Overview
 
 The mean climate summary statistics are the most routine analysis available from the PMP.
 Because they are quasi-operationally applied to large numbers of simulations and under 
-different conditions, the current mode of opertation is fairly general. 
-Before it can be applied some prepration is needed including: 
+different conditions, the current mode of operation is fairly general. 
+Before it can be applied some preparation is needed including: 
 
 * Setting-up observational climatologies
 
@@ -17,7 +17,7 @@ Before it can be applied some prepration is needed including:
 * Construction of an input parameter file to run the desired operations 
 
 
-Each of these steps are included in the 
+Each of these steps is included in the 
 `mean climate notebook <https://github.com/PCMDI/pcmdi_metrics/blob/master/doc/jupyter/Demo/Demo_1_mean_climate.ipynb>`_ 
 along with a series of examples that demonstrate the options. 
 These steps are also summarized below.
@@ -36,17 +36,17 @@ and you will be promptly provided with the database.
 
 The PMP's mean climate summary statistics can be applied to many fields and 
 in most cases there is more than one reference data set available. 
-To accomodate this, the observational climatologies used by the PMP a
-re managed via `a simple catalogue in the form of a JSON file <https://github.com/PCMDI/pcmdi_metrics/blob/master/doc/pcmdiobs2_clims_byVar_catalogue_v20201210.json>`_. 
+To accommodate this, the observational climatologies used by the PMP are 
+managed via `a simple catalogue in the form of a JSON file <https://github.com/PCMDI/pcmdi_metrics/blob/master/doc/pcmdiobs2_clims_byVar_catalogue_v20201210.json>`_. 
 For many of the variables there are 'default' and 'alternate1' 
 datasets and for some there is also an 'alternate2'. 
 To simplify the use of the different options in the mean climate, 
 the mean_climate_driver.py (see below) expects to be pointed to observational catalogue. 
 Currently, if a user wants to add additional observational data this can be done by 
-including it in the JSON cataloge. However, this most be done carefully to ensure 
+including it in the JSON catalogue. However, this must be done carefully to ensure 
 the file retains JSON compliant structure. 
 
-A recent observational climatology catalogue is included as part of the PMP as a default, so it does not need to be explicitly idenified when using the mean_climate_driver.py (unless the catalogue has been modified to include new observations). However, as described below, the user must provide the base path to the observational database. As indicated in the catalogue, the actual database does incorporate futher directory structure and defined filenames which should not be modified. If changes are made to the catalogue, this can be done with input parameter settings (below) using the "custom_observations" option. 
+A recent observational climatology catalogue is included as part of the PMP as a default, so it does not need to be explicitly identified when using the mean_climate_driver.py (unless the catalogue has been modified to include new observations). However, as described below, the user must provide the base path to the observational database. As indicated in the catalogue, the actual database does incorporate further directory structure and defined filenames which should not be modified. If changes are made to the catalogue, this can be done with input parameter settings (below) using the "custom_observations" option. 
 
 
 Preparation of model climatologies
@@ -60,7 +60,7 @@ via the `mean climate metrics notebook <https://github.com/PCMDI/pcmdi_metrics/b
 or the `PMP github repository <https://github.com/PCMDI/pcmdi_metrics/tree/master/sample_setups/pcmdi_parameter_files/mean_climate/make_clims>`_. 
 
 
-Construction of an input paramater file
+Construction of an input parameter file
 #######################################
 
 The PMP mean climate metrics can be controlled via an input parameter file, the command line, or both. With the command line only it is executed via: ::
@@ -84,12 +84,12 @@ where the list of variables (vars) to run the analysis on includes 'rlut' (outgo
 * **regrid_tool**: options include 'esmf' and 'regrid2' 
 * **metric_output_path**: the full path to the metrics output in JSON files, e.g., '~/demo_data/PMP_metrics/' 
 
-In addition to the above required input parameters, if the default cataolgue of observational climatologies is not being used its replacement needs to be specified, e.g.: ::
+In addition to the above required input parameters, if the default catalogue of observational climatologies is not being used its replacement needs to be specified, e.g.: ::
 
  custom_observations = './pcmdiobs2_clims_byVar_catalogue_v20200615.json'
 
 
-The output of the mean climate summary statistics are saved in a JSON file. `An example result <https://github.com/PCMDI/pcmdi_metrics/blob/master/sample_setups/jsons/mean_climate/CMIP5/historical/v20190724/tas/ACCESS1-0.tas.CMIP5.historical.regrid2.2p5x2p5.v20190724.json>`_ demonstrates that multiple statistics are computed for different conditions including regions and seasons. The resulting JSON files include the data, software and hardware information on how the summary statistics. 
+The output of the mean climate summary statistics are saved in a JSON file. `An example result <https://github.com/PCMDI/pcmdi_metrics/blob/master/sample_setups/jsons/mean_climate/CMIP5/historical/v20190724/tas/ACCESS1-0.tas.CMIP5.historical.regrid2.2p5x2p5.v20190724.json>`_ demonstrates that multiple statistics are computed for different conditions including regions and seasons. The resulting JSON files include the data, software and hardware information on the summary statistics. 
 
 
 In addition to the minimum set of parameters noted above, the following **additional options can be controlled** for the mean climate:

docs/overview.rst

@@ -9,11 +9,11 @@ The primary application of the PMP is to evaluate simulations from the `Coupled
 It can also be used to provide objective performance summaries during the model development process as well as selected research purposes. 
 The notes below provide a brief summary of some of the key aspects of the PMP design. 
 
-Software framework and dependancies
+Software framework and dependencies
 -----------------------------------
 
 Most of the PMP is based on `Python 3 <https://www.python.org/>`_ and built upon the Climate Data Analysis Tools (`CDAT <https://cdat.llnl.gov>`_). 
-The key component of CDAT used by the PMP is the Community Data Management System (`CDMS <https://cdms.readthedocs.io/en/latest/manual/cdms_1.html>`_) which provides access to a powerful collection of climate specific utilites, inclduing cdutil, genutil and cdtime.
+The key component of CDAT used by the PMP is the Community Data Management System (`CDMS <https://cdms.readthedocs.io/en/latest/manual/cdms_1.html>`_) which provides access to a powerful collection of climate specific utilites, including cdutil, genutil and cdtime.
 To modernize, PMP is in transition to implement Xarray Climate Data Analysis Tools (`xCDAT`_) as its primary building block.
 
 
@@ -22,7 +22,7 @@ Input/Output format
 
 The PMP is designed to most readily handle model output that adheres to the `Climate-Forecast (CF) data conventions <https://cfconventions.org/>`_. 
 More specifically, because the PMP is used to routinely analyze simulations contributed to CMIP it leverages `the data conventions developed in support of CMIP <https://pcmdi.llnl.gov/CMIP6/Guide/dataUsers.html>`_. 
-Many modeling groups have a workflow that conforms to CMIP or is very similiar to it, making it possible to adapt the PMP to assist in the model development process. 
+Many modeling groups have a workflow that conforms to CMIP or is very similar to it, making it possible to adapt the PMP to assist in the model development process. 
 
 The PMP statistics are output in `JSON format <https://www.json.org/json-en.html>`_, and the underlying diagnostics from which they were derived are typically saved in `netCDF format <https://www.unidata.ucar.edu/software/netcdf>`_.
 
@@ -47,6 +47,6 @@ which includes both a string variable (period) and a python list (test_data_set)
 Here, the "---variable" option is used to specify "pr" (precipitation) with other options included in the file after the "-p" flag. 
 
 
-The python standard `argparse libary <https://docs.python.org/3/library/argparse.html>`_ is implicitly used in all cases, enabling the inclusion of user-friendly interface options. As in the above example, this allows users to set input parameters on the command line **or** in an input file. However, there are circumstances where users of the PMP may want to use a combination of both (an input parameter file and command line setting) for the same execution. This useful combination is possible with the standard argeparse library however with limited functionality. We make use of the Community Diagnostics Package (`CDP <https://github.com/CDAT/cdp>`_) to enable prioritization between the two input possibilities. The CDP enables us to use command line options in combination with input parameter files, with the command line inputs overrridding options set in the parameter files. This provides convenience in setting up and maintaining large jobs. Examples of the combined use of parameter files and command line inputs are included in the PMP demos.
+The python standard `argparse libary <https://docs.python.org/3/library/argparse.html>`_ is implicitly used in all cases, enabling the inclusion of user-friendly interface options. As in the above example, this allows users to set input parameters on the command line **or** in an input file. However, there are circumstances where users of the PMP may want to use a combination of both (an input parameter file and command line setting) for the same execution. This useful combination is possible with the standard argeparse library however with limited functionality. We make use of the Community Diagnostics Package (`CDP <https://github.com/CDAT/cdp>`_) to enable prioritization between the two input possibilities. The CDP enables us to use command line options in combination with input parameter files, with the command line inputs overriding options set in the parameter files. This provides convenience in setting up and maintaining large jobs. Examples of the combined use of parameter files and command line inputs are included in the PMP demos.
 
-.. _xCDAT: https://xcdat.readthedocs.io/en/stable/
+.. _xCDAT: https://xcdat.readthedocs.io/en/stable/

docs/subdaily-precipitation.rst

@@ -7,7 +7,7 @@ Sub-daily precipitation
 Overview
 ========
 
-The PMP can be used to compare observed and simulated sub-daily precipition, including forced (the diurnal and semi-diurnal cycle) and unforced variability (often referred to as "intermittency"). Well established Fourier analysis (e.g., Dai, 2006) with well-established large scale objective performance metrics (Covey et al., 2016) to estimate the phase and amplitude of the diurnal and semi-diurnal cycle of precipitation. The unforced sub-daily variability stems from methods developed by Trenberth et al. (2017) and Covey et al. (2017). Both analysis require data at a 3hr time resolution. 
+The PMP can be used to compare observed and simulated sub-daily precipitation, including forced (the diurnal and semi-diurnal cycle) and unforced variability (often referred to as "intermittency"). Well established Fourier analysis (e.g., Dai, 2006) with well-established large scale objective performance metrics (Covey et al., 2016) to estimate the phase and amplitude of the diurnal and semi-diurnal cycle of precipitation. The unforced sub-daily variability stems from methods developed by Trenberth et al. (2017) and Covey et al. (2017). Both analysis require data at a 3hr time resolution. 
 
 Analysis of higher frequency data often includes multiple stages of processing. `The flow diagram of the PMP's sub-daily precipitation <https://github.com/PCMDI/pcmdi_metrics/blob/master/doc/Diurnal%20Cycle%20Diagram.pdf>`_ shows that is the case here. Each of the steps highlighted in the flow diagram are included in `the diurnal cycle and intermittency Jupyter notebook demo <https://github.com/PCMDI/pcmdi_metrics/blob/master/doc/jupyter/Demo/Demo_3_diurnal_cycle.ipynb>`_. 
 

docs/supporting-data.rst

@@ -17,7 +17,7 @@ A location where you want to store the demo data locally can be set: ::
  demo_data_directory = 'MyDemoPath' 
 
 
-After you have set the location for the demo_output you can downloaded it by entering the following: ::
+After you have set the location for the demo_output you can download it by entering the following: ::
 
  import cdat_info
  cdat_info.download_sample_data_files("data_files.txt", demo_data_directory)