# Climate Data Operators (CDO) WPS Processes¶

Page Contents

- Climate Data Operators (CDO) WPS Processes
- Introduction
- Disclaimer
- Known Issues
- Process Descriptions
- Process: CDOGetSingleFileInfo
- Process: CDOGetMultipleFileInfo
- Process: CDOCalculateSingleFileClimateIndices
- Process: CDOCalculateTwoFileClimateIndicesWithArgument
- Process: CDOCalculateSingleFileStatistics
- Process: CDOCalculateSingleFileStatisticsWithArgument
- Process: CDOCalculateMultipleFileStatistics
- Process: CDOCalculateSingleFileClimateIndicesWithArgument

## Introduction¶

CDO is a large tool set for working on climate and NWP model data. Apart from that CDO can be used to analyse many types of gridded data not related to climate science. CDO is open source and released under the terms of the GNU General Public License v2 (GPL).

Some processes have been developed within the COWS WPS that wrap some of the CDO functionality. This section describes those processes.

## Disclaimer¶

CDO is a third-party tool developed at the Max Planck Institute for Meteorology. Whilst CDO is used widely in the atmospheric and climate research communities there may be applications, working with certain files or grids, in which the output will not be valid. The use of CDO operations within the COWS WPS carries the same risk that certain applications of the tool will not produce valid outputs. The authors hold no liability for any scientific results produced using the these CDO-related WPS processes. Individuals accessing these WPS processes do so a their own risk. **You are strongly advised to validate your outputs using other tools to ensure that the appropriate calculations have been performed.**

## Known Issues¶

When CDO operators are provided with inappropriate inputs there are two possible outcomes:

- Either the software is able to detect that the inputs are not appropriate and the user is informed about it,
- Or the process appears to run to completion producing apparently normal output files. Users must therefore be careful to check the inputs are appropriate for each process they run. It is not feasible for each process to check that the inputs are appropriate.

For example, the “cdd” operator, which is wrapped by the “CDOCalculateSingleFileClimateIndices” process, expects to work with precipitation data. It computes the consecutive dry days (cdd) and number of consecutive dry days. If, instead of precipitation, a user provides an input file containing temperature data, the process proceeds regardless and produces a file where: “cdd” is “1” over the ocean; the “number of cdd” is “0” over the ocean; both are “1.e20” over the continents.

## Process Descriptions¶

This section provides an overview of each of the CDO-related processes supported within the COWS WPS.

### Process: CDOGetSingleFileInfo¶

**Long Name:** CDO Get Single File Info

**Short Description:** Outputs information about the contents of a single data file.

**Long Description:** Calls the Climate Data Operators (CDO) tool with the single file path provided and uses the chosen operator to extract information that is written to a text file.

**Operators:** npar, nlevel, nyear, nmon, ndate, ntime, showformat, showcode, showname, showstdname, showlevel, showltype, showyear, showmon, showdate, showtime, showtimestamp, pardes, griddes, zaxisdes, vct

Name | Synopsis | Parameters | Short Description | Extra Info | General Description | Environment |
---|---|---|---|---|---|---|

npar | npar ifile | Number of parameters | Prints the number of parameters (variables). | This module prints the number of variables, levels or times of the input dataset. | ||

nlevel | nlevel ifile | Number of levels | Prints the number of levels for each variable. | This module prints the number of variables, levels or times of the input dataset. | ||

nyear | nyear ifile | Number of years | Prints the number of different years. | This module prints the number of variables, levels or times of the input dataset. | ||

nmon | nmon ifile | Number of months | Prints the number of different combinations of years and months. | This module prints the number of variables, levels or times of the input dataset. | ||

ndate | ndate ifile | Number of dates | Prints the number of different dates. | This module prints the number of variables, levels or times of the input dataset. | ||

ntime | ntime ifile | Number of timesteps | Prints the number of timesteps. | This module prints the number of variables, levels or times of the input dataset. | ||

showformat | showformat ifile | Show file format | Prints the file format of the input dataset. | This module prints the format, variables, levels or times of the input dataset. | ||

showcode | showcode ifile | Show code numbers | Prints the code number of all variables. | This module prints the format, variables, levels or times of the input dataset. | ||

showname | showname ifile | Show variable names | Prints the name of all variables. | This module prints the format, variables, levels or times of the input dataset. | ||

showstdname | showstdname ifile | Show standard names | Prints the standard name of all variables. | This module prints the format, variables, levels or times of the input dataset. | ||

showlevel | showlevel ifile | Show levels | Prints all levels for each variable. | This module prints the format, variables, levels or times of the input dataset. | ||

showltype | showltype ifile | Show GRIB level types | Prints the GRIB level type for all z-axes. | This module prints the format, variables, levels or times of the input dataset. | ||

showyear | showyear ifile | Show years | Prints all years. | This module prints the format, variables, levels or times of the input dataset. | ||

showmon | showmon ifile | Show months | Prints all months. | This module prints the format, variables, levels or times of the input dataset. | ||

showdate | showdate ifile | Show date information | Prints date information of all timesteps (format YYYY-MM-DD). | This module prints the format, variables, levels or times of the input dataset. | ||

showtime | showtime ifile | Show time information | Prints time information of all timesteps (format hh:mm:ss). | This module prints the format, variables, levels or times of the input dataset. | ||

showtimestamp | showtimestamp ifile | Show timestamp | Prints timestamp of all timesteps (format YYYY-MM-DDThh:mm:ss). | This module prints the format, variables, levels or times of the input dataset. | ||

pardes | pardes ifile | Parameter description | Prints a table with a description of all variables. For each variable the operator prints one line listing the code, name, description and units. | This module prints the description of the parameters, the grids, the z-axis or the vertical coordinate table. | ||

griddes | griddes ifile | Grid description | Prints the description of all grids. | This module prints the description of the parameters, the grids, the z-axis or the vertical coordinate table. | ||

zaxisdes | zaxisdes ifile | Z-axis description | Prints the description of all z-axes. | This module prints the description of the parameters, the grids, the z-axis or the vertical coordinate table. | ||

vct | vct ifile | Vertical coordinate table | Prints the vertical coordinate table. |

### Process: CDOGetMultipleFileInfo¶

**Long Name:** CDO Get Multiple File Info

**Short Description:** Outputs information about the contents of multiple data files.

**Long Description:** Calls the Climate Data Operators (CDO) tool with the file paths provided and uses the chosen operator to extract information that is written to a text file.

**Operators:** info, infov, sinfo, sinfov

Name | Synopsis | Parameters | Short Description | Extra Info | General Description | Environment |
---|---|---|---|---|---|---|

info | info ifiles | Dataset information listed by parameter identifier | Prints information and simple statistics for each field of all input datasets. For each field the operator prints one line with the following elements: - Date and Time - Parameter identifier and Level - Size of the grid and number of Missing values - Minimum, Mean and Maximum The mean value is computed without the use of area weights! | This module writes information about the structure and contents of all input files to standard output. All input files need to have the same structure with the same variables on different timesteps. The information displayed depends on the chosen operator. | ||

infov | infov ifiles | This module writes information about the structure and contents of all input files to standard output. All input files need to have the same structure with the same variables on different timesteps. The information displayed depends on the chosen operator. | ||||

sinfo | sinfo ifiles | Short information listed by parameter identifier | Prints short information of a dataset. The information is divided into 4 sections. Section 1 prints one line per parameter with the following information: - institute and source - parameter identifier - horizontal grid size and number - number of vertical levels and z-axis number Section 2 and 3 gives a short overview of all horizontal and vertical grids. And the last section contains short information of the time axis. | This module writes information about the structure of ifiles to standard output. ifiles is an unlimited number of input files. All input files need to have the same structure with the same variables on different timesteps. The information displayed depends on the chosen operator. | ||

sinfov | sinfov ifiles | This module writes information about the structure of ifiles to standard output. ifiles is an unlimited number of input files. All input files need to have the same structure with the same variables on different timesteps. The information displayed depends on the chosen operator. |

### Process: CDOCalculateSingleFileClimateIndices¶

**Long Name:** CDO Calculate Single File Climate Indices

**Short Description:** Outputs a NetCDF file of climate indices calculated from a single input file.

**Long Description:** Calls the Climate Data Operators (CDO) tool with the single file path provided and uses the chosen operator to calculate climate indices written to a NetCDF file.

**Operators:** eca_cdd, eca_cfd, eca_cwd, eca_fd, eca_id, eca_r10mm, eca_r20mm, eca_rr1, eca_sdii

Name | Synopsis | Parameters | Short Description | Extra Info | General Description | Environment |
---|---|---|---|---|---|---|

eca_cdd | eca_cdd[,R] ifile ofile | R DOUBLE Precipitation threshold (mm, default: R = 1 mm) | Let ifile be a time series of daily precipitation amounts RR, then the largest number of consecutive days where RR is less than R is counted. R is an optional parameter with default R = 1 mm. A further output variable is the number of dry periods of more than 5 days. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. The following variables are created: - consecutive_dry_days_index_per_time_period - number_of_cdd_periods_with_more_than_5days_per_time_period | |||

eca_cfd | eca_cfd ifile ofile | Let ifile be a time series of daily minimum temperatures TN, then the largest number of consecutive days whereTN < 0 °C is counted. Note that TN have to be given in units of Kelvin. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. The following variables are created: - consecutive_frost_days_index_per_time_period | ||||

eca_cwd | eca_cwd[,R] ifile ofile | R DOUBLE Precipitation threshold (mm, default: R = 1 mm) | Let ifile be a time series of daily precipitation amounts RR, then the largest number of consecutive days where RR is at least R is counted. R is an optional parameter with default R = 1 mm. A further output variable is the number of wet periods of more than 5 days. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. The following variables are created: - consecutive_wet_days_index_per_time_period - number_of_cwd_periods_with_more_than_5days_per_time_period | |||

eca_fd | eca_fd ifile ofile | Let ifile be a time series of daily minimum temperatures TN, then the number of days where TN < 0 °C is counted. Note that TN have to be given in units of Kelvin. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. The following variables are created: - frost_days_index_per_time_period | ||||

eca_id | eca_id ifile ofile | Let ifile be a time series of daily maximum temperatures TX, then the number of days where TX < 0 °C is counted. Note that TX have to be given in units of Kelvin. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. The following variables are created: - ice_days_index_per_time_period | ||||

eca_r10mm | eca_r10mm ifile ofile | x FLOAT Daily precipitation amount threshold in [mm] | Heavy precipitation days index per time period | Specific ECA operator with daily precipitation sum exceeding 10 mm. | Let ifile be a time series of daily precipitation amounts RR in [mm] (or alternatively in [kg m-2]), then the number of days where RR is at least x mm is counted. eca_r10mm and eca_r20mm are specific ECA operators with a daily precipitation amount of 10 and 20 mm respectively. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. The following variables are created: - precipitation_days_index_per_time_period | |

eca_r20mm | eca_r20mm ifile ofile | x FLOAT Daily precipitation amount threshold in [mm] | Very heavy precipitation days index per time period | Specific ECA operator with daily precipitation sum exceeding 20 mm. | Let ifile be a time series of daily precipitation amounts RR in [mm] (or alternatively in [kg m-2]), then the number of days where RR is at least x mm is counted. eca_r10mm and eca_r20mm are specific ECA operators with a daily precipitation amount of 10 and 20 mm respectively. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. The following variables are created: - precipitation_days_index_per_time_period | |

eca_rr1 | eca_rr1[,R] ifile ofile | R DOUBLE Precipitation threshold (mm, default: R = 1 mm) | Let ifile be a time series of daily precipitation amounts RR, then the number of days where RR is at least R is counted. R is an optional parameter with default R = 1 mm. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. The following variables are created: - wet_days_index_per_time_period | |||

eca_sdii | eca_sdii[,R] ifile ofile | R DOUBLE Precipitation threshold (mm, default: R = 1 mm) | Let ifile be a time series of daily precipitation amounts RR, then the mean precipitation amount at wet days (RR > R) is written to ofile. R is an optional parameter with default R = 1 mm. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. The following variables are created: - simple_daily_intensitiy_index_per_time_period |

### Process: CDOCalculateTwoFileClimateIndicesWithArgument¶

**Long Name:** CDO Calculate Two File Climate Indices With Argument

**Short Description:** Outputs a NetCDF file of climate indices calculated from the two input files.

**Long Description:** Calls the Climate Data Operators (CDO) tool with the two file paths and argument provided and uses the chosen operator to calculate climate indices written to a NetCDF file.

**Operators:** eca_cwfi, eca_etr, eca_hwfi, eca_r75p, eca_r75ptot, eca_r90p, eca_r90ptot, eca_r95p, eca_r95ptot, eca_r99p, eca_r99ptot, eca_tg10p, eca_tg90p, eca_tn10p, eca_tn90p, eca_tx10p, eca_tx90p

Name | Synopsis | Parameters | Short Description | Extra Info | General Description | Environment |
---|---|---|---|---|---|---|

eca_cwfi | eca_cwfi[,nday] ifile1 ifile2 ofile | nday INTEGER Number of consecutive days (default: nday = 6) | Let ifile1 be a time series of daily mean temperatures TG, and ifile2 be the 10th percentile TGn10 of daily mean temperatures for any period used as reference. Then counted is the number of days where, in intervals of at least nday consecutive days, TG < TGn10. The number nday is an optional parameter with default nday = 6. A further output variable is the number of cold-spell periods longer than or equal to nday days. TGn10 is calculated as the 10th percentile of daily mean temperatures of a five day window centred on each calendar day of a given climate reference period. Note that both TG and TGn10 have to be given in the same units. The date information of a timestep in ofile is the date of the last contributing timestep in ifile1. The following variables are created: - cold_spell_days_index_wrt_10th_percentile_of_reference_period - cold_spell_periods_per_time_period | |||

eca_etr | eca_etr ifile1 ifile2 ofile | Let ifile1 and ifile2 be time series of maximum and minimum temperatures TX and TN, respectively. Then the extreme temperature range is the difference of the maximum of TX and the minimum of TN. Note that TX and TN have to be given in the same units. The date information of a timestep in ofile is the date of the last contributing timesteps in ifile1 and ifile2. The following variables are created: - intra_period_extreme_temperature_range | ||||

eca_hwfi | eca_hwfi[,nday] ifile1 ifile2 ofile | nday INTEGER Number of consecutive days (default: nday = 6) | Let ifile1 be a time series of daily mean temperatures TG, and ifile2 be the 90th percentile TGn90 of daily mean temperatures for any period used as reference. Then counted is the number of days where, in intervals of at least nday consecutive days, TG > TGn90. The number nday is an optional parameter with default nday = 6. A further output variable is the number of warm-spell periods longer than or equal to nday days. TGn90 is calculated as the 90th percentile of daily mean temperatures of a five day window centred on each calendar day of a given climate reference period. Note that both TG and TGn90 have to be given in the same units. The date information of a timestep in ofile is the date of the last contributing timestep in ifile1. The following variables are created: - warm_spell_days_index_wrt_90th_percentile_of_reference_period - warm_spell_periods_per_time_period | |||

eca_r75p | eca_r75p ifile1 ifile2 ofile | Let ifile1 be a time series of daily precipitation amounts RR, and ifile2 be the 75th percentile RRn75 of daily precipitation amounts at wet days for any period used as reference. Then the percentage of wet days with RR > RRn75 is calculated. RRn75 is calculated as the 75th percentile of all wet days of a given climate reference period. The date information of a timestep in ofile is the date of the last contributing timestep in ifile1. The following variables are created: - moderate_wet_days_wrt_75th_percentile_of_reference_period | ||||

eca_r75ptot | eca_r75ptot ifile1 ifile2 ofile | Let ifile1 be a time series of daily precipitation amounts RR, and ifile2 be the 75th percentile RRn75 of daily precipitation amounts at wet days for any period used as reference. Then the ratio of the precipitation sum at wet days with RR > RRn75 to the total precipitation sum is calculated. RRn75 is calculated as the 75th percentile of all wet days of a given climate reference period. The date information of a timestep in ofile is the date of the last contributing timestep in ifile1. The following variables are created: - precipitation_percent_due_to_R75p_days | ||||

eca_r90p | eca_r90p ifile1 ifile2 ofile | Let ifile1 be a time series of daily precipitation amounts RR, and ifile2 be the 90th percentile RRn90 of daily precipitation amounts at wet days for any period used as reference. Then the percentage of wet days with RR > RRn90 is calculated. RRn90 is calculated as the 90th percentile of all wet days of a given climate reference period. The date information of a timestep in ofile is the date of the last contributing timestep in ifile1. The following variables are created: - wet_days_wrt_90th_percentile_of_reference_period | ||||

eca_r90ptot | eca_r90ptot ifile1 ifile2 ofile | Let ifile1 be a time series of daily precipitation amounts RR, and ifile2 be the 90th percentile RRn90 of daily precipitation amounts at wet days for any period used as reference. Then the ratio of the precipitation sum at wet days with RR > RRn90 to the total precipitation sum is calculated. RRn90 is calculated as the 90th percentile of all wet days of a given climate reference period. The date information of a timestep in ofile is the date of the last contributing timestep in ifile1. The following variables are created: - precipitation_percent_due_to_R90p_days | ||||

eca_r95p | eca_r95p ifile1 ifile2 ofile | Let ifile1 be a time series of daily precipitation amounts RR, and ifile2 be the 95th percentile RRn95 of daily precipitation amounts at wet days for any period used as reference. Then the percentage of wet days with RR > RRn95 is calculated. RRn95 is calculated as the 95th percentile of all wet days of a given climate reference period. The date information of a timestep in ofile is the date of the last contributing timestep in ifile1. The following variables are created: - very_wet_days_wrt_95th_percentile_of_reference_period | ||||

eca_r95ptot | eca_r95ptot ifile1 ifile2 ofile | Let ifile1 be a time series of daily precipitation amounts RR, and ifile2 be the 95th percentile RRn95 of daily precipitation amounts at wet days for any period used as reference. Then the ratio of the precipitation sum at wet days with RR > RRn95 to the total precipitation sum is calculated. RRn95 is calculated as the 95th percentile of all wet days of a given climate reference period. The date information of a timestep in ofile is the date of the last contributing timestep in ifile1. The following variables are created: - precipitation_percent_due_to_R95p_days | ||||

eca_r99p | eca_r99p ifile1 ifile2 ofile | Let ifile1 be a time series of daily precipitation amounts RR, and ifile2 be the 99th percentile RRn99 of daily precipitation amounts at wet days for any period used as reference. Then the percentage of wet days with RR > RRn99 is calculated. RRn99 is calculated as the 99th percentile of all wet days of a given climate reference period. The date information of a timestep in ofile is the date of the last contributing timestep in ifile1. The following variables are created: - extremely_wet_days_wrt_99th_percentile_of_reference_period | ||||

eca_r99ptot | eca_r99ptot ifile1 ifile2 ofile | Let ifile1 be a time series of daily precipitation amounts RR, and ifile2 be the 99th percentile RRn99 of daily precipitation amounts at wet days for any period used as reference. Then the ratio of the precipitation sum at wet days with RR > RRn99 to the total precipitation sum is calculated. RRn99 is calculated as the 99th percentile of all wet days of a given climate reference period. The date information of a timestep in ofile is the date of the last contributing timestep in ifile1. The following variables are created: - precipitation_percent_due_to_R99p_days | ||||

eca_tg10p | eca_tg10p ifile1 ifile2 ofile | Let ifile1 be a time series of daily mean temperatures TG, and ifile2 be the 10th percentile TGn10 of daily mean temperatures for any period used as reference. Then the percentage of time where TG < TGn10 is calculated. TGn10 is calculated as the 10th percentile of daily mean temperatures of a five day window centred on each calendar day of a given climate reference period. Note that both TG and TGn10 have to be given in the same units. The date information of a timestep in ofile is the date of the last contributing timestep in ifile1. The following variables are created: - cold_days_percent_wrt_10th_percentile_of_reference_period | ||||

eca_tg90p | eca_tg90p ifile1 ifile2 ofile | Let ifile1 be a time series of daily mean temperatures TG, and ifile2 be the 90th percentile TGn90 of daily mean temperatures for any period used as reference. Then the percentage of time where TG > TGn90 is calculated. TGn90 is calculated as the 90th percentile of daily mean temperatures of a five day window centred on each calendar day of a given climate reference period. Note that both TG and TGn90 have to be given in the same units. The date information of a timestep in ofile is the date of the last contributing timestep in ifile1. The following variables are created: - warm_days_percent_wrt_90th_percentile_of_reference_period | ||||

eca_tn10p | eca_tn10p ifile1 ifile2 ofile | Let ifile1 be a time series of daily minimum temperatures TN, and ifile2 be the 10th percentile TNn10 of daily minimum temperatures for any period used as reference. Then the percentage of time where TN < TNn10 is calculated. TNn10 is calculated as the 10th percentile of daily minimum temperatures of a five day window centred on each calendar day of a given climate reference period. Note that both TN and TNn10 have to be given in the same units. The date information of a timestep in ofile is the date of the last contributing timestep in ifile1. The following variables are created: - cold_nights_percent_wrt_10th_percentile_of_reference_period | ||||

eca_tn90p | eca_tn90p ifile1 ifile2 ofile | Let ifile1 be a time series of daily minimum temperatures TN, and ifile2 be the 90th percentile TNn90 of daily minimum temperatures for any period used as reference. Then the percentage of time where TN > TNn90 is calculated. TNn90 is calculated as the 90th percentile of daily minimum temperatures of a five day window centred on each calendar day of a given climate reference period. Note that both TN and TNn90 have to be given in the same units. The date information of a timestep in ofile is the date of the last contributing timestep in ifile1. The following variables are created: - warm_nights_percent_wrt_90th_percentile_of_reference_period | ||||

eca_tx10p | eca_tx10p ifile1 ifile2 ofile | Let ifile1 be a time series of daily maximum temperatures TX, and ifile2 be the 10th percentile TXn10 of daily maximum temperatures for any period used as reference. Then the percentage of time where TX < TXn10 is calculated. TXn10 is calculated as the 10th percentile of daily maximum temperatures of a five day window centred on each calendar day of a given climate reference period. Note that both TX and TXn10 have to be givenin the same units. The date information of a timestep in ofile is the date of the last contributing timestep in ifile1. The following variables are created: - very_cold_days_percent_wrt_10th_percentile_of_reference_period | ||||

eca_tx90p | eca_tx90p ifile1 ifile2 ofile | Let ifile1 be a time series of daily maximum temperatures TX, and ifile2 be the 90th percentile TXn90 of daily maximum temperatures for any period used as reference. Then the percentage of time where TX > TXn90 is calculated. TXn90 is calculated as the 90th percentile of daily maximum temperatures of a five day window centred on each calendar day of a given climate reference period. Note that both TX and TXn90 have to be given in the same units. The date information of a timestep in ofile is the date of the last contributing timestep in ifile1. The following variables are created: - very_warm_days_percent_wrt_90th_percentile_of_reference_period |

### Process: CDOCalculateSingleFileStatistics¶

**Long Name:** CDO Calculate Single File Statistics

**Short Description:** Outputs a NetCDF file of statistics calculated from a single input file.

**Long Description:** Calls the Climate Data Operators (CDO) tool with the single file path provided and uses the chosen operator to calculate statistics written to a NetCDF file.

**Operators:** fldmin, fldmax, fldsum, fldmean, fldavg, fldvar, fldstd, zonmin, zonmax, zonsum, zonmean, zonavg, zonvar, zonstd, mermin, mermax, mersum, mermean, meravg, mervar, merstd, vertmin, vertmax, vertsum, vertmean, vertavg, vertvar, vertstd, timmin, timmax, timsum, timmean, timavg, timvar, timstd, hourmin, hourmax, hoursum, hourmean, houravg, hourvar, hourstd, daymin, daymax, daysum, daymean, dayavg, dayvar, daystd, monmin, monmax, monsum, monmean, monavg, monvar, monstd, seasmin, seasmax, seassum, seasmean, seasavg, seasvar, seasstd, yearmin, yearmax, yearsum, yearmean, yearavg, yearvar, yearstd, yhourmin, yhourmax, yhoursum, yhourmean, yhouravg, yhourvar, yhourstd, ydaymin, ydaymax, ydaysum, ydaymean, ydayavg, ydayvar, ydaystd, ymonmin, ymonmax, ymonsum, ymonmean, ymonavg, ymonvar, ymonstd, yseasmin, yseasmax, yseassum, yseasmean, yseasavg, yseasvar, yseasstd

Name | Synopsis | Parameters | Short Description | Extra Info | General Description | Environment |
---|---|---|---|---|---|---|

fldmin | fldmin ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Field minimum | For every gridpoint x_1, ..., x_n of the same field it is: o(t,1) = min{i(t,x’), x_1<x’<=x_n} | This module computes statistical values of the input fields. According to the chosen operator the field minimum, maximum, sum, average, variance, standard deviation or a certain percentile is written to ofile. | |

fldmax | fldmax ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Field maximum | For every gridpoint x_1, ..., x_n of the same field it is: o(t,1) = max{i(t,x’), x_1<x’<=x_n} | This module computes statistical values of the input fields. According to the chosen operator the field minimum, maximum, sum, average, variance, standard deviation or a certain percentile is written to ofile. | |

fldsum | fldsum ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Field sum | For every gridpoint x_1, ..., x_n of the same field it is: o(t,1) = sum{i(t,x’), x_1<x’<=x_n} | This module computes statistical values of the input fields. According to the chosen operator the field minimum, maximum, sum, average, variance, standard deviation or a certain percentile is written to ofile. | |

fldmean | fldmean ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Field mean | For every gridpoint x_1, ..., x_n of the same field it is: o(t,1) = mean{i(t,x’), x_1<x’<=x_n} weighted by area weights obtained by the input field. | ||

fldavg | fldavg ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Field average | For every gridpoint x_1, ..., x_n of the same field it is: o(t,1) = avg{i(t,x’), x_1<x’<=x_n} weighted by area weights obtained by the input field. | ||

fldvar | fldvar ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Field variance | For every gridpoint x_1, ..., x_n of the same field it is: o(t,1) = var{i(t,x’), x_1<x’<=x_n} weighted by area weights obtained by the input field. | ||

fldstd | fldstd ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Field standard deviation | For every gridpoint x_1, ..., x_n of the same field it is: o(t,1) = std{i(t,x’), x_1<x’<=x_n} weighted by area weights obtained by the input field. | ||

zonmin | zonmin ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Zonal minimum | For every latitude the minimum over all longitudes is computed. | This module computes zonal statistical values of the input fields. According to the chosen operator the zonal minimum, maximum, sum, average, variance, standard deviation or a certain percentile is written to ofile. All input fields need to have the same regular lonlat grid. | |

zonmax | zonmax ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Zonal maximum | For every latitude the maximum over all longitudes is computed. | This module computes zonal statistical values of the input fields. According to the chosen operator the zonal minimum, maximum, sum, average, variance, standard deviation or a certain percentile is written to ofile. All input fields need to have the same regular lonlat grid. | |

zonsum | zonsum ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Zonal sum | For every latitude the sum over all longitudes is computed. | This module computes zonal statistical values of the input fields. According to the chosen operator the zonal minimum, maximum, sum, average, variance, standard deviation or a certain percentile is written to ofile. All input fields need to have the same regular lonlat grid. | |

zonmean | zonmean ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Zonal mean | For every latitude the mean over all longitudes is computed. | ||

zonavg | zonavg ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Zonal average | For every latitude the average over all longitudes is computed. | ||

zonvar | zonvar ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Zonal variance | For every latitude the variance over all longitudes is computed. | ||

zonstd | zonstd ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Zonal standard deviation | For every latitude the standard deviation over all longitudes is computed. | ||

mermin | mermin ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Meridional minimum | For every longitude the minimum over all latitudes is computed. | This module computes meridional statistical values of the input fields. According to the chosen operator the meridional minimum, maximum, sum, average, variance, standard deviation or a certain percentile is written to ofile. All input fields need to have the same regular lon/lat grid. | |

mermax | mermax ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Meridional maximum | For every longitude the maximum over all latitudes is computed. | This module computes meridional statistical values of the input fields. According to the chosen operator the meridional minimum, maximum, sum, average, variance, standard deviation or a certain percentile is written to ofile. All input fields need to have the same regular lon/lat grid. | |

mersum | mersum ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Meridional sum | For every longitude the sum over all latitudes is computed. | This module computes meridional statistical values of the input fields. According to the chosen operator the meridional minimum, maximum, sum, average, variance, standard deviation or a certain percentile is written to ofile. All input fields need to have the same regular lon/lat grid. | |

mermean | mermean ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Meridional mean | For every longitude the area weighted mean over all latitudes is computed. | ||

meravg | meravg ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Meridional average | For every longitude the area weighted average over all latitudes is computed. | ||

mervar | mervar ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Meridional variance | For every longitude the variance over all latitudes is computed. | ||

merstd | merstd ifile ofile | p FLOAT Percentile number in {0, ..., 100} | Meridional standard deviation | For every longitude the standard deviation over all latitudes is computed. | ||

vertmin | vertmin ifile ofile | Vertical minimum | For every gridpoint the minimum over all levels is computed. | This module computes statistical values over all levels of the input variables. According to chosen operator the vertical minimum, maximum, sum, average, variance or standard deviation is written to ofile. | ||

vertmax | vertmax ifile ofile | Vertical maximum | For every gridpoint the maximum over all levels is computed. | This module computes statistical values over all levels of the input variables. According to chosen operator the vertical minimum, maximum, sum, average, variance or standard deviation is written to ofile. | ||

vertsum | vertsum ifile ofile | Vertical sum | For every gridpoint the sum over all levels is computed. | This module computes statistical values over all levels of the input variables. According to chosen operator the vertical minimum, maximum, sum, average, variance or standard deviation is written to ofile. | ||

vertmean | vertmean ifile ofile | Vertical mean | For every gridpoint the mean over all levels is computed. | |||

vertavg | vertavg ifile ofile | Vertical average | For every gridpoint the average over all levels is computed. | |||

vertvar | vertvar ifile ofile | Vertical variance | For every gridpoint the variance over all levels is computed. | |||

vertstd | vertstd ifile ofile | Vertical standard deviation | For every gridpoint the standard deviation over all levels is computed. | |||

timmin | timmin ifile ofile | Time minimum | o(1,x) = min{i(t’,x), t_1<t’<=t_n} | This module computes statistical values over all timesteps in ifile. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of all timesteps read from ifile is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. | ||

timmax | timmax ifile ofile | Time maximum | o(1,x) = max{i(t’,x), t_1<t’<=t_n} | This module computes statistical values over all timesteps in ifile. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of all timesteps read from ifile is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. | ||

timsum | timsum ifile ofile | Time sum | o(1,x) = sum{i(t’,x), t_1<t’<=t_n} | This module computes statistical values over all timesteps in ifile. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of all timesteps read from ifile is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. | ||

timmean | timmean ifile ofile | Time mean | o(1,x) = mean{i(t’,x), t_1<t’<=t_n} | |||

timavg | timavg ifile ofile | Time average | o(1,x) = avg{i(t’,x), t_1<t’<=t_n} | |||

timvar | timvar ifile ofile | Time variance | o(1,x) = var{i(t’,x), t_1<t’<=t_n} | |||

timstd | timstd ifile ofile | Time standard deviation | o(1,x) = std{i(t’,x), t_1<t’<=t_n} | |||

hourmin | hourmin ifile ofile | Hourly minimum | For every adjacent sequence t_1, ...,t_n of timesteps of the same hour it is: o(t,x) = min{i(t’,x), t_1<t’<=t_n} | This module computes statistical values over timesteps of the same hour. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of timesteps of the same hour is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. | ||

hourmax | hourmax ifile ofile | Hourly maximum | For every adjacent sequence t_1, ...,t_n of timesteps of the same hour it is: o(t,x) = max{i(t’,x), t_1<t’<=t_n} | This module computes statistical values over timesteps of the same hour. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of timesteps of the same hour is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. | ||

hoursum | hoursum ifile ofile | Hourly sum | For every adjacent sequence t_1, ...,t_n of timesteps of the same hour it is: o(t,x) = sum{i(t’,x), t_1<t’<=t_n} | This module computes statistical values over timesteps of the same hour. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of timesteps of the same hour is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. | ||

hourmean | hourmean ifile ofile | Hourly mean | For every adjacent sequence t_1, ...,t_n of timesteps of the same hour it is: o(t,x) = mean{i(t’,x), t_1<t’<=t_n} | |||

houravg | houravg ifile ofile | Hourly average | For every adjacent sequence t_1, ...,t_n of timesteps of the same hour it is: o(t,x) = avg{i(t’,x), t_1<t’<=t_n} | |||

hourvar | hourvar ifile ofile | Hourly variance | For every adjacent sequence t_1, ...,t_n of timesteps of the same hour it is: o(t,x) = var{i(t’,x), t_1<t’<=t_n} | |||

hourstd | hourstd ifile ofile | Hourly standard deviation | For every adjacent sequence t_1, ...,t_n of timesteps of the same hour it is: o(t,x) = std{i(t’,x), t_1<t’<=t_n} | |||

daymin | daymin ifile ofile | Daily minimum | For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is o(t,x) = min{i(t’,x), t_1<t’<=t_n} | This module computes statistical values over timesteps of the same day. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of timesteps of the same day is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. | ||

daymax | daymax ifile ofile | Daily maximum | For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is o(t,x) = max{i(t’,x), t_1<t’<=t_n} | This module computes statistical values over timesteps of the same day. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of timesteps of the same day is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. | ||

daysum | daysum ifile ofile | Daily sum | For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is o(t,x) = sum{i(t’,x), t_1<t’<=t_n} | This module computes statistical values over timesteps of the same day. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of timesteps of the same day is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. | ||

daymean | daymean ifile ofile | Daily mean | For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is o(t,x) = mean{i(t’,x), t_1<t’<=t_n} | |||

dayavg | dayavg ifile ofile | Daily average | For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is o(t,x) = avg{i(t’,x), t_1<t’<=t_n} | |||

dayvar | dayvar ifile ofile | Daily variance | For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is o(t,x) = var{i(t’,x), t_1<t’<=t_n} | |||

daystd | daystd ifile ofile | Daily standard deviation | For every adjacent sequence t_1, ...,t_n of timesteps of the same day it is o(t,x) = std{i(t’,x), t_1<t’<=t_n} | |||

monmin | monmin ifile ofile | Monthly minimum | For every adjacent sequence t_1, ...,t_n of timesteps of the same month it is: o(t,x) = min{i(t’,x), t_1<t’<=t_n} | This module computes statistical values over timesteps of the same month. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of timesteps of the same month is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. | ||

monmax | monmax ifile ofile | Monthly maximum | For every adjacent sequence t_1, ...,t_n of timesteps of the same month it is o(t,x) = max{i(t’,x), t_1<t’<=t_n} | This module computes statistical values over timesteps of the same month. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of timesteps of the same month is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. | ||

monsum | monsum ifile ofile | Monthly sum | For every adjacent sequence t_1, ...,t_n of timesteps of the same month it is o(t,x) = sum{i(t’,x), t_1<t’<=t_n} | This module computes statistical values over timesteps of the same month. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of timesteps of the same month is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. | ||

monmean | monmean ifile ofile | Monthly mean | For every adjacent sequence t_1, ...,t_n of timesteps of the same month it is o(t,x) = mean{i(t’,x), t_1<t’<=t_n} | |||

monavg | monavg ifile ofile | Monthly average | For every adjacent sequence t_1, ...,t_n of timesteps of the same month it is o(t,x) = avg{i(t’,x), t_1<t’<=t_n} | |||

monvar | monvar ifile ofile | Monthly variance | For every adjacent sequence t_1, ...,t_n of timesteps of the same month it is o(t,x) = var{i(t’,x), t_1 < t’ <= t_n} | |||

monstd | monstd ifile ofile | Monthly standard deviation | For every adjacent sequence t_1, ...,t_n of timesteps of the same month it is o(t,x) = std{i(t’,x), t_1 < t’ <= t_n} | |||

seasmin | seasmin ifile ofile | Seasonal minimum | For every adjacent sequence t_1, ...,t_n of timesteps of the same season it is o(t,x) = min{i(t’,x), t1 < t’ <= tn} | This module computes statistical values over timesteps of the same season. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of timesteps of the same season is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. Be careful about the first and the last output timestep , they may be incorrect values if the seasons have incomplete timesteps. | ||

seasmax | seasmax ifile ofile | Seasonal maximum | For every adjacent sequence t_1, ...,t_n of timesteps of the same season it is o(t,x) = max{i(t’,x), t1 < t’ <= tn} | This module computes statistical values over timesteps of the same season. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of timesteps of the same season is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. Be careful about the first and the last output timestep , they may be incorrect values if the seasons have incomplete timesteps. | ||

seassum | seassum ifile ofile | Seasonal sum | For every adjacent sequence t_1, ...,t_n of timesteps of the same season it is o(t,x) = sum{i(t’,x), t1 < t’ <= tn} | This module computes statistical values over timesteps of the same season. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of timesteps of the same season is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. Be careful about the first and the last output timestep , they may be incorrect values if the seasons have incomplete timesteps. | ||

seasmean | seasmean ifile ofile | Seasonal mean | For every adjacent sequence t_1, ...,t_n of timesteps of the same season it is o(t,x) = mean{i(t’,x), t1 < t’ <= tn} | |||

seasavg | seasavg ifile ofile | Seasonal average | For every adjacent sequence t_1, ...,t_n of timesteps of the same season it is o(t,x) = avg{i(t’,x), t1 < t’ <= tn} | |||

seasvar | seasvar ifile ofile | Seasonal variance | For every adjacent sequence t_1, ...,t_n of timesteps of the same season it is o(t,x) = var{i(t’,x), t1 < t’ <= tn} | |||

seasstd | seasstd ifile ofile | Seasonal standard deviation | For every adjacent sequence t_1, ...,t_n of timesteps of the same season it is o(t,x) = std{i(t’,x), t1 < t’ <= tn} | |||

yearmin | yearmin ifile ofile | Yearly minimum | For every adjacent sequence t_1, ...,t_n of timesteps of the same year it is o(t,x) = min{i(t’,x), t_1<t’<=t_n} | This module computes statistical values over timesteps of the same year. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of timesteps of the same year is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. | ||

yearmax | yearmax ifile ofile | Yearly maximum | For every adjacent sequence t_1, ...,t_n of timesteps of the same year it is o(t,x) = max{i(t’,x), t_1<t’<=t_n} | This module computes statistical values over timesteps of the same year. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of timesteps of the same year is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. | ||

yearsum | yearsum ifile ofile | Yearly sum | For every adjacent sequence t_1, ...,t_n of timesteps of the same year it is o(t,x) = sum{i(t’,x), t_1<t’<=t_n} | This module computes statistical values over timesteps of the same year. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of timesteps of the same year is written to ofile. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. | ||

yearmean | yearmean ifile ofile | Yearly mean | For every adjacent sequence t_1, ...,t_n of timesteps of the same year it is o(t,x) = mean{i(t’,x), t_1<t’<=t_n} | |||

yearavg | yearavg ifile ofile | Yearly average | For every adjacent sequence t_1, ...,t_n of timesteps of the same year it is o(t,x) = avg{i(t’,x), t_1<t’<=t_n} | |||

yearvar | yearvar ifile ofile | Yearly variance | For every adjacent sequence t_1, ...,t_n of timesteps of the same year it is o(t,x) = var{i(t’,x), t_1 < t’ <= t_n} | |||

yearstd | yearstd ifile ofile | Yearly standard deviation | For every adjacent sequence t_1, ...,t_n of timesteps of the same year it is o(t,x) = std{i(t’,x), t_1 < t’ <= t_n} | |||

yhourmin | yhourmin ifile ofile | Multi-year hourly minimum | o(0001,x) = min{i(t,x), day(i(t)) = 0001} ... o(8784,x) = min{i(t,x), day(i(t)) = 8784} | This module computes statistical values of each hour and day of year. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of each hour and day of year in ifile is written to ofile. The date information in an output field is the date of the last contributing input field. | ||

yhourmax | yhourmax ifile ofile | Multi-year hourly maximum | o(0001,x) = max{i(t,x), day(i(t)) = 0001} ... o(8784,x) = max{i(t,x), day(i(t)) = 8784} | This module computes statistical values of each hour and day of year. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of each hour and day of year in ifile is written to ofile. The date information in an output field is the date of the last contributing input field. | ||

yhoursum | yhoursum ifile ofile | Multi-year hourly sum | o(0001,x) = sum{i(t,x), day(i(t)) = 0001} ... o(8784,x) = sum{i(t,x), day(i(t)) = 8784} | This module computes statistical values of each hour and day of year. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of each hour and day of year in ifile is written to ofile. The date information in an output field is the date of the last contributing input field. | ||

yhourmean | yhourmean ifile ofile | Multi-year hourly mean | o(0001,x) = mean{i(t,x), day(i(t)) = 0001} ... o(8784,x) = mean{i(t,x), day(i(t)) = 8784} | |||

yhouravg | yhouravg ifile ofile | Multi-year hourly average | o(0001,x) = avg{i(t,x), day(i(t)) = 0001} ... o(8784,x) = avg{i(t,x), day(i(t)) = 8784} | |||

yhourvar | yhourvar ifile ofile | Multi-year hourly variance | o(0001,x) = var{i(t,x), day(i(t)) = 0001} ... o(8784,x) = var{i(t,x), day(i(t)) = 8784} | |||

yhourstd | yhourstd ifile ofile | Multi-year hourly standard deviation | o(0001,x) = std{i(t,x), day(i(t)) = 0001} ... o(8784,x) = std{i(t,x), day(i(t)) = 8784} | |||

ydaymin | ydaymin ifile ofile | Multi-year daily minimum | o(001,x) = min{i(t,x), day(i(t)) = 001} ... o(366,x) = min{i(t,x), day(i(t)) = 366} | This module computes statistical values of each day of year. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of each day of year in ifile is written to ofile. The date information in an output field is the date of the last contributing input field. | ||

ydaymax | ydaymax ifile ofile | Multi-year daily maximum | o(001,x) = max{i(t,x), day(i(t)) = 001} ... o(366,x) = max{i(t,x), day(i(t)) = 366} | This module computes statistical values of each day of year. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of each day of year in ifile is written to ofile. The date information in an output field is the date of the last contributing input field. | ||

ydaysum | ydaysum ifile ofile | Multi-year daily sum | o(001,x) = sum{i(t,x), day(i(t)) = 001} ... o(366,x) = sum{i(t,x), day(i(t)) = 366} | This module computes statistical values of each day of year. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of each day of year in ifile is written to ofile. The date information in an output field is the date of the last contributing input field. | ||

ydaymean | ydaymean ifile ofile | Multi-year daily mean | o(001,x) = mean{i(t,x), day(i(t)) = 001} ... o(366,x) = mean{i(t,x), day(i(t)) = 366} | |||

ydayavg | ydayavg ifile ofile | Multi-year daily average | o(001,x) = avg{i(t,x), day(i(t)) = 001} ... o(366,x) = avg{i(t,x), day(i(t)) = 366} | |||

ydayvar | ydayvar ifile ofile | Multi-year daily variance | o(001,x) = var{i(t,x), day(i(t)) = 001} ... o(366,x) = var{i(t,x), day(i(t)) = 366} | |||

ydaystd | ydaystd ifile ofile | Multi-year daily standard deviation | o(001,x) = std{i(t,x), day(i(t)) = 001} ... o(366,x) = std{i(t,x), day(i(t)) = 366} | |||

ymonmin | ymonmin ifile ofile | Multi-year monthly minimum | o(01,x) = min{i(t,x), month(i(t)) = 01} ... o(12,x) = min{i(t,x), month(i(t)) = 12} | This module computes statistical values of each month of year. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of each month of year in ifile is written to ofile. The date information in an output field is the date of the last contributing input field. | ||

ymonmax | ymonmax ifile ofile | Multi-year monthly maximum | o(01,x) = max{i(t,x), month(i(t)) = 01} ... o(12,x) = max{i(t,x), month(i(t)) = 12} | This module computes statistical values of each month of year. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of each month of year in ifile is written to ofile. The date information in an output field is the date of the last contributing input field. | ||

ymonsum | ymonsum ifile ofile | Multi-year monthly sum | o(01,x) = sum{i(t,x), month(i(t)) = 01} ... o(12,x) = sum{i(t,x), month(i(t)) = 12} | This module computes statistical values of each month of year. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of each month of year in ifile is written to ofile. The date information in an output field is the date of the last contributing input field. | ||

ymonmean | ymonmean ifile ofile | Multi-year monthly mean | o(01,x) = mean{i(t,x), month(i(t)) = 01} ... o(12,x) = mean{i(t,x), month(i(t)) = 12} | |||

ymonavg | ymonavg ifile ofile | Multi-year monthly average | o(01,x) = avg{i(t,x), month(i(t)) = 01} ... o(12,x) = avg{i(t,x), month(i(t)) = 12} | |||

ymonvar | ymonvar ifile ofile | Multi-year monthly variance | o(01,x) = var{i(t,x), month(i(t)) = 01} ... o(12,x) = var{i(t,x), month(i(t)) = 12} | |||

ymonstd | ymonstd ifile ofile | Multi-year monthly standard deviation | o(01,x) = std{i(t,x), month(i(t)) = 01} ... o(12,x) = std{i(t,x), month(i(t)) = 12} | |||

yseasmin | yseasmin ifile ofile | Multi-year seasonal minimum | o(1,x) = min{i(t,x), month(i(t)) = 12, 01, 02} o(2,x) = min{i(t,x), month(i(t)) = 03, 04, 05} o(3,x) = min{i(t,x), month(i(t)) = 06, 07, 08} o(4,x) = min{i(t,x), month(i(t)) = 09, 10, 11} | This module computes statistical values of each season. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of each season in ifile is written to ofile. The date information in an output field is the date of the last contributing input field. | ||

yseasmax | yseasmax ifile ofile | Multi-year seasonal maximum | o(1,x) = max{i(t,x), month(i(t)) = 12, 01, 02} o(2,x) = max{i(t,x), month(i(t)) = 03, 04, 05} o(3,x) = max{i(t,x), month(i(t)) = 06, 07, 08} o(4,x) = max{i(t,x), month(i(t)) = 09, 10, 11} | This module computes statistical values of each season. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of each season in ifile is written to ofile. The date information in an output field is the date of the last contributing input field. | ||

yseassum | yseassum ifile ofile | Multi-year seasonal sum | o(1,x) = sum{i(t,x), month(i(t)) = 12, 01, 02} o(2,x) = sum{i(t,x), month(i(t)) = 03, 04, 05} o(3,x) = sum{i(t,x), month(i(t)) = 06, 07, 08} o(4,x) = sum{i(t,x), month(i(t)) = 09, 10, 11} | This module computes statistical values of each season. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of each season in ifile is written to ofile. The date information in an output field is the date of the last contributing input field. | ||

yseasmean | yseasmean ifile ofile | Multi-year seasonal mean | o(1,x) = mean{i(t,x), month(i(t)) = 12, 01, 02} o(2,x) = mean{i(t,x), month(i(t)) = 03, 04, 05} o(3,x) = mean{i(t,x), month(i(t)) = 06, 07, 08} o(4,x) = mean{i(t,x), month(i(t)) = 09, 10, 11} | |||

yseasavg | yseasavg ifile ofile | Multi-year seasonal average | o(1,x) = avg{i(t,x), month(i(t)) = 12, 01, 02} o(2,x) = avg{i(t,x), month(i(t)) = 03, 04, 05} o(3,x) = avg{i(t,x), month(i(t)) = 06, 07, 08} o(4,x) = avg{i(t,x), month(i(t)) = 09, 10, 11} | |||

yseasvar | yseasvar ifile ofile | Multi-year seasonal variance | o(1,x) = var{i(t,x), month(i(t)) = 12, 01, 02} o(2,x) = var{i(t,x), month(i(t)) = 03, 04, 05} o(3,x) = var{i(t,x), month(i(t)) = 06, 07, 08} o(4,x) = var{i(t,x), month(i(t)) = 09, 10, 11} | |||

yseasstd | yseasstd ifile ofile | Multi-year seasonal standard deviation | o(1,x) = std{i(t,x), month(i(t)) = 12, 01, 02} o(2,x) = std{i(t,x), month(i(t)) = 03, 04, 05} o(3,x) = std{i(t,x), month(i(t)) = 06, 07, 08} o(4,x) = std{i(t,x), month(i(t)) = 09, 10, 11} |

### Process: CDOCalculateSingleFileStatisticsWithArgument¶

**Long Name:** CDO Calculate Single File Statistics With Argument

**Short Description:** Outputs a NetCDF file of statistics calculated from a single input file.

**Long Description:** Calls the Climate Data Operators (CDO) tool with the single file path and argument provided and uses the chosen operator to calculate statistics written to a NetCDF file.

**Operators:** runmin, runmax, runsum, runmean, runavg, runvar, runstd, ydrunmin, ydrunmax, ydrunsum, ydrunmean, ydrunavg, ydrunvar, ydrunstd

Name | Synopsis | Parameters | Short Description | Extra Info | General Description | Environment |
---|---|---|---|---|---|---|

runmin | runmin,nts ifile ofile | nts INTEGER Number of timesteps | Running minimum | o(t+(nts-1)/2,x) = min{i(t,x), i(t+1,x), ..., i(t+nts-1,x)} | This module computes running statistical values over a selected number of timesteps. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of a selected number of consecutive timesteps read from ifile is written to ofile. The date information in ofile is the date of the middle contributing timestep in ifile. | RUNSTAT_DATE Sets the date information in ofile to the “first”, “last” or “middle” contributing timestep in ifile. |

runmax | runmax,nts ifile ofile | nts INTEGER Number of timesteps | Running maximum | o(t+(nts-1)/2,x) = max{i(t,x), i(t+1,x), ..., i(t+nts-1,x)} | This module computes running statistical values over a selected number of timesteps. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of a selected number of consecutive timesteps read from ifile is written to ofile. The date information in ofile is the date of the middle contributing timestep in ifile. | RUNSTAT_DATE Sets the date information in ofile to the “first”, “last” or “middle” contributing timestep in ifile. |

runsum | runsum,nts ifile ofile | nts INTEGER Number of timesteps | Running sum | o(t+(nts-1)/2,x) = sum{i(t,x), i(t+1,x), ..., i(t+nts-1,x)} | This module computes running statistical values over a selected number of timesteps. Depending on the chosen operator the minimum, maximum, sum, average, variance or standard deviation of a selected number of consecutive timesteps read from ifile is written to ofile. The date information in ofile is the date of the middle contributing timestep in ifile. | RUNSTAT_DATE Sets the date information in ofile to the “first”, “last” or “middle” contributing timestep in ifile. |

runmean | runmean,nts ifile ofile | nts INTEGER Number of timesteps | Running mean | o(t+(nts-1)/2,x) = mean{i(t,x), i(t+1,x), ..., i(t+nts-1,x)} | ||

runavg | runavg,nts ifile ofile | nts INTEGER Number of timesteps | Running average | o(t+(nts-1)/2,x) = avg{i(t,x), i(t+1,x), ..., i(t+nts-1,x)} | ||

runvar | runvar,nts ifile ofile | nts INTEGER Number of timesteps | Running variance | o(t+(nts-1)/2,x) = var{i(t,x), i(t+1,x), ..., i(t+nts-1,x)} | ||

runstd | runstd,nts ifile ofile | nts INTEGER Number of timesteps | Running standard deviation | o(t+(nts-1)/2,x) = std{i(t,x), i(t+1,x), ..., i(t+nts-1,x)} | ||

ydrunmin | ydrunmin,nts ifile ofile | nts INTEGER Number of timesteps | Multi-year daily running minimum | o(001,x) = min{i(t,x), i(t+1,x), ..., i(t+nts-1,x); day[(i(t+(nts-1)/2)] = 001} ... o(366,x) = min{i(t,x), i(t+1,x), ..., i(t+nts-1,x); day[(i(t+(nts-1)/2)] = 366} | This module writes running statistical values for each day of year in ifile to ofile. Depending on the chosen operator, the minimum, maximum, sum, average, variance or standard deviation of all timesteps in running windows of wich the medium timestep corresponds to a certain day of year is computed. The date information in an output field is the date of the medium timestep in the last contributing running window. Note that the operator have to be applied to a continuous time series of daily measurements in order to yield physically meaningful results. Also note that the output time series begins (nts-1)/2 timesteps after the first timestep of the input time series and ends (nts-1)/2 timesteps before the last one. For input data which are complete but not continuous, such as time series of daily measurements for the same month or season within different years, the operator yields physically meaningful results only if the input time series does include the (nts-1)/2 days before and after each period of interest. | |

ydrunmax | ydrunmax,nts ifile ofile | nts INTEGER Number of timesteps | Multi-year daily running maximum | o(001,x) = max{i(t,x), i(t+1,x), ..., i(t+nts-1,x); day[(i(t+(nts-1)/2)] = 001} ... o(366,x) = max{i(t,x), i(t+1,x), ..., i(t+nts-1,x); day[(i(t+(nts-1)/2)] = 366} | This module writes running statistical values for each day of year in ifile to ofile. Depending on the chosen operator, the minimum, maximum, sum, average, variance or standard deviation of all timesteps in running windows of wich the medium timestep corresponds to a certain day of year is computed. The date information in an output field is the date of the medium timestep in the last contributing running window. Note that the operator have to be applied to a continuous time series of daily measurements in order to yield physically meaningful results. Also note that the output time series begins (nts-1)/2 timesteps after the first timestep of the input time series and ends (nts-1)/2 timesteps before the last one. For input data which are complete but not continuous, such as time series of daily measurements for the same month or season within different years, the operator yields physically meaningful results only if the input time series does include the (nts-1)/2 days before and after each period of interest. | |

ydrunsum | ydrunsum,nts ifile ofile | nts INTEGER Number of timesteps | Multi-year daily running sum | o(001,x) = sum{i(t,x), i(t+1,x), ..., i(t+nts-1,x); day[(i(t+(nts-1)/2)] = 001} ... o(366,x) = sum{i(t,x), i(t+1,x), ..., i(t+nts-1,x); day[(i(t+(nts-1)/2)] = 366} | This module writes running statistical values for each day of year in ifile to ofile. Depending on the chosen operator, the minimum, maximum, sum, average, variance or standard deviation of all timesteps in running windows of wich the medium timestep corresponds to a certain day of year is computed. The date information in an output field is the date of the medium timestep in the last contributing running window. Note that the operator have to be applied to a continuous time series of daily measurements in order to yield physically meaningful results. Also note that the output time series begins (nts-1)/2 timesteps after the first timestep of the input time series and ends (nts-1)/2 timesteps before the last one. For input data which are complete but not continuous, such as time series of daily measurements for the same month or season within different years, the operator yields physically meaningful results only if the input time series does include the (nts-1)/2 days before and after each period of interest. | |

ydrunmean | ydrunmean,nts ifile ofile | nts INTEGER Number of timesteps | Multi-year daily running mean | o(001,x) = mean{i(t,x), i(t+1,x), ..., i(t+nts-1,x); day[(i(t+(nts-1)/2)] = 001} ... o(366,x) = mean{i(t,x), i(t+1,x), ..., i(t+nts-1,x); day[(i(t+(nts-1)/2)] = 366} | ||

ydrunavg | ydrunavg,nts ifile ofile | nts INTEGER Number of timesteps | Multi-year daily running average | o(001,x) = avg{i(t,x), i(t+1,x), ..., i(t+nts-1,x); day[(i(t+(nts-1)/2)] = 001} ... o(366,x) = avg{i(t,x), i(t+1,x), ..., i(t+nts-1,x); day[(i(t+(nts-1)/2)] = 366} | ||

ydrunvar | ydrunvar,nts ifile ofile | nts INTEGER Number of timesteps | Multi-year daily running variance | o(001,x) = var{i(t,x), i(t+1,x), ..., i(t+nts-1,x); day[(i(t+(nts-1)/2)] = 001} ... o(366,x) = var{i(t,x), i(t+1,x), ..., i(t+nts-1,x); day[(i(t+(nts-1)/2)] = 366} | ||

ydrunstd | ydrunstd,nts ifile ofile | nts INTEGER Number of timesteps | Multi-year daily running standard deviation | o(001,x) = std{i(t,x), i(t+1,x), ..., i(t+nts-1,x); day[i(t+(nts-1)/2)] = 001} ... o(366,x) = std{i(t,x), i(t+1,x), ..., i(t+nts-1,x); day[i(t+(nts-1)/2)] = 366} |

### Process: CDOCalculateMultipleFileStatistics¶

**Long Name:** CDO Calculate Multiple File Statistics

**Short Description:** Outputs a NetCDF file of statistics calculated from multiple input files.

**Long Description:** Calls the Climate Data Operators (CDO) tool with the file paths provided and uses the chosen operator to calculate statistics written to a NetCDF file.

**Operators:** ensmin, ensmax, enssum, ensmean, ensavg, ensvar, ensstd

Name | Synopsis | Parameters | Short Description | Extra Info | General Description | Environment |
---|---|---|---|---|---|---|

ensmin | ensmin ifiles ofile | p FLOAT Percentile number in {0, ..., 100} | Ensemble minimum | o(t,x) = min{i1(t,x), i2(t,x), ..., in(t,x)} | This module computes statistical values over an ensemble of input files. Depending on the chosen operator the minimum, maximum, sum, average, variance, standard deviation, a certain percentile over all input files or a skill score is written to ofile. All input files need to have the same structure with the same variables. The date information of a timestep in ofile is the date of the first input file. | |

ensmax | ensmax ifiles ofile | p FLOAT Percentile number in {0, ..., 100} | Ensemble maximum | o(t,x) = max{i1(t,x), i2(t,x), ..., in(t,x)} | This module computes statistical values over an ensemble of input files. Depending on the chosen operator the minimum, maximum, sum, average, variance, standard deviation, a certain percentile over all input files or a skill score is written to ofile. All input files need to have the same structure with the same variables. The date information of a timestep in ofile is the date of the first input file. | |

enssum | enssum ifiles ofile | p FLOAT Percentile number in {0, ..., 100} | Ensemble sum | o(t,x) = sum{i1(t,x), i2(t,x), ..., in(t,x)} | This module computes statistical values over an ensemble of input files. Depending on the chosen operator the minimum, maximum, sum, average, variance, standard deviation, a certain percentile over all input files or a skill score is written to ofile. All input files need to have the same structure with the same variables. The date information of a timestep in ofile is the date of the first input file. | |

ensmean | ensmean ifiles ofile | p FLOAT Percentile number in {0, ..., 100} | Ensemble mean | o(t,x) = mean{i1(t,x), i2(t,x), ..., in(t,x)} | ||

ensavg | ensavg ifiles ofile | p FLOAT Percentile number in {0, ..., 100} | Ensemble average | o(t,x) = avg{i1(t,x), i2(t,x), ..., in(t,x)} | ||

ensvar | ensvar ifiles ofile | p FLOAT Percentile number in {0, ..., 100} | Ensemble variance | o(t,x) = var{i1(t,x), i2(t,x), ..., in(t,x)} | ||

ensstd | ensstd ifiles ofile | p FLOAT Percentile number in {0, ..., 100} | Ensemble standard deviation | o(t,x) = std{i1(t,x), i2(t,x), ..., in(t,x)} |

### Process: CDOCalculateSingleFileClimateIndicesWithArgument¶

**Long Name:** CDO Calculate Single File Climate Indices With Argument

**Short Description:** Outputs a NetCDF file of climate indices calculated from multiple input files.

**Long Description:** Calls the Climate Data Operators (CDO) tool with the file paths and argument provided and uses the chosen operator to calculate climate indices written to a NetCDF file.

**Operators:** eca_csu, eca_rx1day, eca_rx5day, eca_su, eca_tr, eca_pd

Name | Synopsis | Parameters | Short Description | Extra Info | General Description | Environment |
---|---|---|---|---|---|---|

eca_csu | eca_csu[,T] ifile ofile | T DOUBLE Temperature threshold (? Celsius, default: T = 25? Celsius) | Let ifile be a time series of daily maximum temperatures TX, then the largest number of consecutive days where TX > T is counted. The number T is an optional parameter with default T = 25 °C. Note that TN have to be given in units of Kelvin, whereas T have to be given in degrees Celsius. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. The following variables are created: - consecutive_summer_days_index_per_time_period | |||

eca_rx1day | eca_rx1day[,mode] ifile ofile | mode STRING Operation mode (optional). If mode = ‘m’ then maximum daily precipitation amounts are determined for each month | Let ifile be a time series of daily precipitation amounts RR, then the maximum of RR is written to ofile. If the optional parameter mode is set to ‘m’ the maximum daily precipitation amounts are determined for each month. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. The following variables are created: - highest_one_day_precipitation_amount_per_time_period | |||

eca_rx5day | eca_rx5day[,x] ifile ofile | x FLOAT Precipitation threshold (mm, default: x = 50 mm) | Let ifile be a time series of 5-day precipitation totals RR, then the maximum of RR is written to ofile. A further output variable is the number of 5 day period with precipitation totals greater than x mm, where x is an optional parameter with default x = 50 mm. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. The following variables are created: - highest_five_day_precipitation_amount_per_time_period - number_of_5day_heavy_precipitation_periods_per_time_period | |||

eca_su | eca_su[,T] ifile ofile | T FLOAT Temperature threshold (degree Celsius, default: T = 25? Celsius) | Let ifile be a time series of daily maximum temperatures TX, then the number of days where TX > T is counted. The number T is an optional parameter with default T = 25 °C. Note that TX have to be given in units of Kelvin, whereas T have to be given in degrees Celsius. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. The following variables are created: - summer_days_index_per_time_period | |||

eca_tr | eca_tr[,T] ifile ofile | T FLOAT Temperature threshold (? Celsius, default: T = 20? Celsius) | Let ifile be a time series of daily minimum temperatures TN, then the number of days where TN > T is counted. The number T is an optional parameter with default T = 20 °C. Note that TN have to be given in units of Kelvin, whereas T have to be given in degrees Celsius. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. The following variables are created: - tropical_nights_index_per_time_period | |||

eca_pd | eca_pd,x ifile ofile | x FLOAT Daily precipitation amount threshold in [mm] | Precipitation days index per time period | Generic ECA operator with daily precipitation sum exceeding x mm. | Let ifile be a time series of daily precipitation amounts RR in [mm] (or alternatively in [kg m-2]), then the number of days where RR is at least x mm is counted. eca_r10mm and eca_r20mm are specific ECA operators with a daily precipitation amount of 10 and 20 mm respectively. The date information of a timestep in ofile is the date of the last contributing timestep in ifile. The following variables are created: - precipitation_days_index_per_time_period |