California next-day gridded forecasts

Grid-based forecast is one of the two forecasts types used for CSEP experiments and supported by PyCSEP. For a specific forecasting period, a grid-based forecast is specified by providing the expected number of events (or rate) for each square bin in a pre-defined space-magnitude grid. In this way, grid-based forecasts can be formatted as tables with each row representing a space-magnitude bin and each column reporting informations about the bin location (e.g. space coordinates, magnitude, depth). Grid-forecasts for multiple forecasting periods are then formatted as multiple tables, one for each period.

Here, we provide access to 27 models for California that, within CSEP, produced daily grid-based forecasts (between 00:00:01 and 23:59:59) of events with magnitude above 3.95 (or 4.95 depending on the model) and depth below 30 km, covering the period from August 1, 2007 to August 30, 2018 for a total of over 50,000 forecasts. These seismicity models includes several ensemble models, non-parametric models, and different flavors of the well-established Epidemic-Type Aftershock Sequence (ETAS), and Short-Term Earthquake Probability (STEP) models.

All the grid-based forecasts provided here are formatted accordingly to the PyCSEP default format . They are a collection of .dat files (one for each day covered by the model) where row represents space-magnitude bins and column contains all the information needed to identify the bin and the rate. More specifically the columns (in order) are lower longitude extreme, upper longitude extreme, lower latitude extreme, upper latitude extreme, lower depth extreme, upper depth extreme, lower magnitude extreme, upper magnitude extreme, rate, and a flag. A more detailed explanation can be found on the PyCSEP website. All the forecasts are based on the same space-magnitude grid which consists of 0.1° x 0.1° (lat x lon), 0.1 Mw units, and one bin for the depth going from 0 to 30km.

The forecasts are provided as a .hdf5 file storing a system of nested folders. The system of nested folders is structered in the following way: one folder per year, inside each year folder we have one folder per month, and inside each month folder we have the .dat file representing the forecasts for the days of the respective month and year. We provided a tutorial (link) on how to use the .hdf5 file in combination with PyCSEP to evaluate the forecasts against observed data. This comprehends testing the consistency of the forecasts with the observations and comparing forecasts from different models.

List of Models

To download the model, the user can download the models of interest using the Download links in the table below, or visit the download page with all the models at the following link. Specific information on each model can be found following the Description links in the table.

Model nameStarting dateEnd dateMissing daysMin magnitudeFile dimensionLinks
ETAS01 August 200730 August 201803.955.8 GBDescription
Download
STEP01 August 200721 January 201303.951.78 GBDescription
Download
STEPJAVA01 September 201030 June 201803.952.39 GBDescription
Download
KJSSOneDay01 January 200930 June 201803.954.17 GBDescription
Download
KJSSFiveYears01 October 2012 30 June 2018154.952.44 GBDescription
Download
GSF_ISO01 October 2016 30 June 201813.95726 MBDescription
Download
GSF_ANISO01 October 2016 30 June 201813.95331 MBDescription
Download
K3Md301 October 201230 June 201853.952.24 GBDescription
Download
ETAS_DROneDayMd301 October 201230 June 201853.952.66 GBDescription
Download
ETAS_DROneDayMd2.9501 July 201630 June 2018183.95930 MBDescription
Download
ETAS_DROneDayMd201 October 201230 June 2018173.952.72 GBDescription
Download
ETAS_DROneDayPPEMd301 October 201230 June 201863.952.70 GBDescription
Download
ETAS_DROneDayPPEMd201 October 201230 June 2018183.952.70 GBDescription
Download
ETASSYN_DROneDayMd2.9501 July 201630 June 2018113.95924 MBDescription
Download
ETAS_HW_K3_AVERAGE_Md301 October 201230 June 2018233.952.61 GBDescription
Download
ETAS_HWMd301 October 201230 June 201853.951.97 GBDescription
Download
ETASV1.101 July 201230 June 201843.953.19 GBDescription
Download
OneDayBayesianBMA01 July 201207 August 201603.952.18 GBDescription
Download
OneDayBayesianSeqBMA01 July 201207 August 201603.952.18 GBDescription
Download
ETAS_HW_K3_AVERAGE_Md201 October 201219 September 201613.951.81 GBDescription
Download
ETAS_HWMd201 October 201219 September 201603.951.36 GBDescription
Download
JANUSOneDay01 October 201230 June 2018183.952.50 GBDescription
Download
JANUSOneDayEEPAS1F01 October 201230 June 2018173.952.45 GBDescription
Download
JANUSOneDayPPE01 October 201230 June 2018173.952.23 GBDescription
Download
JANUSOneDayTV01 October 201230 June 2018173.95982 MBDescription
Download
K3Md201 October 201219 September 201603.951.55 GBDescription
Download
SE2OneDay01 October 201230 June 2018853.951.68 GBDescription
Download

News

pyCSEP v0.6.3 is now available on PyPI and conda-forge. Visit the GitHub page for more information.

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