diff --git a/docs/metrics.rst b/docs/metrics.rst index 1b8932255..08c1af5df 100644 --- a/docs/metrics.rst +++ b/docs/metrics.rst @@ -12,11 +12,11 @@ A suite of demo scripts and interactive Jupyter notebooks are provided with `thi :maxdepth: 1 metrics_mean-clim - subdaily-precipitation - metrics_mov metrics_enso + metrics_mov metrics_mjo metrics_monsoon metrics_ext metrics_precip-variability - metrics_precip-distribution \ No newline at end of file + metrics_precip-distribution + metrics_subdaily-precipitation \ No newline at end of file diff --git a/docs/metrics_enso.rst b/docs/metrics_enso.rst index 8f400e057..65e23fe1d 100644 --- a/docs/metrics_enso.rst +++ b/docs/metrics_enso.rst @@ -20,7 +20,7 @@ Demo * `PMP demo Jupyter notebook`_ Results -========= +======= * `Interactive graphics for PMP-calculated ENSO Metrics`_ * `Description for included metrics`_ * `Description for the results`_ diff --git a/docs/metrics_mean-clim.rst b/docs/metrics_mean-clim.rst index 25b149b01..d15160ad7 100644 --- a/docs/metrics_mean-clim.rst +++ b/docs/metrics_mean-clim.rst @@ -22,6 +22,11 @@ Each of these steps is included in the along with a series of examples that demonstrate the options. These steps are also summarized below. +Demo +==== +* `PMP demo Jupyter notebook1a`_ (Compute climatologies) +* `PMP demo Jupyter notebook1b`_ (Run mean climate driver) + Observational climatologies ########################### @@ -102,3 +107,6 @@ In addition to the minimum set of parameters noted above, the following **additi * **save_test_clims** Select to save (or not) interpolated climatologies including masking * **case_id** Save JSON and netCDF files into a subdirectory so that results from multiple tests can be readily organized +.. _PMP demo Jupyter notebook1a: https://github.com/PCMDI/pcmdi_metrics/blob/main/doc/jupyter/Demo/Demo_1a_compute_climatologies.ipynb +.. _PMP demo Jupyter notebook1b: https://github.com/PCMDI/pcmdi_metrics/blob/main/doc/jupyter/Demo/Demo_1b_mean_climate.ipynb + diff --git a/docs/metrics_monsoon.rst b/docs/metrics_monsoon.rst index a4235c687..95ea8e329 100644 --- a/docs/metrics_monsoon.rst +++ b/docs/metrics_monsoon.rst @@ -11,9 +11,13 @@ The PMP currently can be used to produce baseline metrics on the overall evoluti These evolution results are based on the work of Sperber and Annamalai (2014). Climatological pentads of precipitation in observations and CMIP5 for six monsoon-related domains (AIR: All-India Rainfall, AUS: Australian Monsoon, GoG: Gulf of Guinea, NAM: North American Monsoon, SAM: South American Monsoon, and Sahel). In the Northern Hemisphere the 73 climatological pentads run from January-December, while in the Southern Hemisphere the climatological pentads run from July-June. For each domain the precipitation is accumulated at each subsequent pentad and then divided by the total precipitation to give the fractional accumulation of precipitation as a function of pentad. Except for GoG, onset (decay) of monsoon occurs for a fractional accumulation of 0.2 (0.8). Between these fractional accumulations the accumulation of precipitation is nearly linear as the monsoon season progresses. - +Demo +==== +* `PMP demo Jupyter notebook`_ References ========== +* Sperber, K.R. and Annamalai, H., 2014. The use of fractional accumulated precipitation for the evaluation of the annual cycle of monsoons. Climate Dynamics, 43, 3219-3244, doi:10.1007/s00382-014-2099-3 + +.. _PMP demo Jupyter notebook: https://github.com/PCMDI/pcmdi_metrics/blob/main/doc/jupyter/Demo/Demo_2b_monsoon_sperber.ipynb -Sperber, K.R. and Annamalai, H., 2014. The use of fractional accumulated precipitation for the evaluation of the annual cycle of monsoons. Climate Dynamics, 43, 3219-3244, doi:10.1007/s00382-014-2099-3 diff --git a/docs/metrics_precip-distribution.rst b/docs/metrics_precip-distribution.rst index cb43ca588..9b3b91960 100644 --- a/docs/metrics_precip-distribution.rst +++ b/docs/metrics_precip-distribution.rst @@ -13,7 +13,7 @@ Demo In preparation Example parameter files -====================== +======================= A set of example parameter files for models and observations can be viewed at `this link`_. Required data sets diff --git a/docs/metrics_precip-variability.rst b/docs/metrics_precip-variability.rst index 8545b461a..6655ef14a 100644 --- a/docs/metrics_precip-variability.rst +++ b/docs/metrics_precip-variability.rst @@ -13,7 +13,7 @@ Demo * `PMP demo Jupyter notebook`_ Example parameter files -====================== +======================= A set of example parameter files for models and observations can be viewed at `this link`_. Required data sets diff --git a/docs/subdaily-precipitation.rst b/docs/metrics_subdaily-precipitation.rst similarity index 53% rename from docs/subdaily-precipitation.rst rename to docs/metrics_subdaily-precipitation.rst index 2b22fc1b7..248ba2d92 100644 --- a/docs/subdaily-precipitation.rst +++ b/docs/metrics_subdaily-precipitation.rst @@ -11,14 +11,17 @@ The PMP can be used to compare observed and simulated sub-daily precipitation, i Analysis of higher frequency data often includes multiple stages of processing. `The flow diagram of the PMP's sub-daily precipitation `_ 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 `_. +Demo +==== +* `PMP demo Jupyter notebook`_ References ========== +* Covey, C, PJ Gleckler, C Doutriaux, DN Williams, A Dai, J Fasullo, K Trenberth, and A Berg. 2016. ”Metrics for the diurnal cycle of precipitation: Toward routine benchmarks for climate models.” Journal of Climate 29(12): 4461–4471, https://doi.org/10.1175/JCLI-D-15-0664.1 +* Covey, C, C Doutriaux, PJ Gleckler, KE Taylor, KE Trenberth, and Y Zhang. 2018. “High-frequency intermittency in observed and model-simulated precipitation.” Geophysical Research Letters 45(22): 12514–12522, https://doi.org/10.1029/2018GL078926 +* Dai, A. 2006. “Precipitation characteristics coupled climate models.” Journal of Climate 19(18): 4605–4630, https://doi.org/10.1175/JCLI3884.1 +* Trenberth, KE, Y Zhang, and M Gehne. 2017. ”Intermittency in precipitation: Duration, frequency, intensity, and amounts using hourly data.” Journal of Hydrometeorology 18(5): 1393–1412, https://doi.org/10.1175/JHM-D-16-0263.1 -Covey, C, PJ Gleckler, C Doutriaux, DN Williams, A Dai, J Fasullo, K Trenberth, and A Berg. 2016. ”Metrics for the diurnal cycle of precipitation: Toward routine benchmarks for climate models.” Journal of Climate 29(12): 4461–4471, https://doi.org/10.1175/JCLI-D-15-0664.1 -Covey, C, C Doutriaux, PJ Gleckler, KE Taylor, KE Trenberth, and Y Zhang. 2018. “High-frequency intermittency in observed and model-simulated precipitation.” Geophysical Research Letters 45(22): 12514–12522, https://doi.org/10.1029/2018GL078926 -Dai, A. 2006. “Precipitation characteristics coupled climate models.” Journal of Climate 19(18): 4605–4630, https://doi.org/10.1175/JCLI3884.1 - -Trenberth, KE, Y Zhang, and M Gehne. 2017. ”Intermittency in precipitation: Duration, frequency, intensity, and amounts using hourly data.” Journal of Hydrometeorology 18(5): 1393–1412, https://doi.org/10.1175/JHM-D-16-0263.1 +.. _PMP demo Jupyter notebook: https://github.com/PCMDI/pcmdi_metrics/blob/main/doc/jupyter/Demo/Demo_3_diurnal_cycle.ipynb \ No newline at end of file