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class | mio::FilterDeGrass |
This filter is used to distinguish if snow (HS) is on the ground or not, because the ultrasonic sensor cannot distinguish between snow or vegetation/grass on the ground. The filter is based on total snow depth (HS), snow surface temperature (TSS), ground surface temperature (TSG) and reflected shortwave radiation (RSWR). Different steps to do:
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class | mio::FilterMAD |
Median Absolute Deviation. Values outside of median ± 3 σ_MAD are rejected. The σ_MAD is calculated as follows:![]() ![]()
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class | mio::FilterMax |
Max range filter. Reject all values greater than the max. Remarks: More... | |
class | mio::FilterMin |
Min range filter. Reject all values smaller than the min. Remarks: More... | |
class | mio::FilterMinMax |
Min/Max range filter. More... | |
class | mio::FilterNoChange |
This filter removes periods showing insufficient changes. It searches for time periods in which the value of the certain variable doesn't change by looking at the variance. It expects the following arguments, in order to define the data window where the variance will be computed: More... | |
class | mio::FilterOffsetsnowdepth |
Correct offset of snow depth (HS) measurements because of wrong offset justifications of the sensor First, the mean daily snow surface temperature (SST) is calculated. If it is higher than a given threshold for a certain time period, the offset of snow depth (HS) can be determined. The offset of HS is the median of the measured HS in the certain time period. This is done for the first and last week of the year with SST higher the chosen threshold. That means that normally one offset is calculated in spring and one offset in autumn. More... | |
class | mio::FilterPotentialSW |
Checks for physically reallistic incoming short wave radiation (ISWR) values. For each data point, the measured value must be: More... | |
class | mio::FilterRate |
Rate of change filter. Calculate the change rate (ie: slope) between two points, if it is above a user given value, reject the point. More... | |
class | mio::FilterStdDev |
Standard deviation filter. Values outside of mean ± 2 std_dev are rejected. More... | |
class | mio::FilterSuppr |
Suppression filter. Normally, this filter simply reject all values. This is convenient to quickly turn a parameter off without modifying the original data. It is also possible to provide a list of station ID's and timesteps where the parameter should be suppressed. More... | |
class | mio::FilterTimeconsistency |
Compare sum of differences between snow depth (HS) value and HS value before and HS value afterwards, respectively, with 4 times of the standard deviation of HS in a defined time period (something like MAD) First, the standard deviation of HS is calculated for a certain time period. Afterwards, the difference between the value and the value before and the difference between the value and the following value are calculated. Then, the sum of the two differences is calculated and compared with 4 times of the standard deviation. Is the sum lower than the standard deviation, the HS value is accepted. Otherwise the HS value gets invalid. References/Literature: Zahumensky, Igor, 2004: Guidelines on Quality Control Procedures for Data from Automatic Weather Stations, World Meteorological Organisation. More... | |
class | mio::FilterTukey |
Tukey 53H method A smooth time sequence is generated from the median, substracted from the original signal and compared with the standard deviation. see "Despiking Acoustic Doppler Velocimeter Data", Derek G. Goring and Vladimir L. Nikora, Journal of Hydraulic Engineering, 128, 1, 2002 The deviation factor coeffecient is currently hard-coded as k=1.5. More... | |
class | mio::FilterUnheatedPSUM |
Filters out snow melting in an unheated rain gauge. This filter can ONLY be applied to precipitation. Non-zero measurements are accepted only if they take place when the relative humidity is greater than 0.5 and (TA-TSS) < 3, otherwise they get reset to 0. It can take two optional arguments overwriting these thresholds. If none of these conditions could be tested (for lack of data), then the precipitation is reset to nodata. On the contrary, if the "soft" option is given, the lack of validation data keeps the precipitation as it is. More... | |
class | mio::ProcAdd |
Add an offset to the values. This adds to all values a given offset. Either a fixed value is given as single argument or a period (hourly/daily/monthly) as well as a filename (and absolute or relative path) containing the offsets to apply. This file must contain in the first column the indices (months from 1 to 12 or days from 1 to 366 or hours from 0 to 23) and the matching offset in the second column (whitespace delimited). Comments following the same syntax as in the ini file are accepted, missing indices are treated as 0. More... | |
class | mio::ProcAggregate |
Data aggregation. This aggregates the input data over the defined window with the defined aggregation algorithm. The aggregation algorithm must be declared first and can be any of the following: More... | |
class | mio::ProcIIR |
Infinite Impulse Response (IIR) filter. This filter can either be used as a low pass or high pass filter. It is based on a Critically Damped, 2 poles filter (considering that it is better avoid overshooting even at the cost of a gentler falloff). It is possible to use it as a Low Pass (LP) or High Pass (HP) More... | |
class | mio::ProcMult |
Multiply values. This multiplies all values by a given factor. Either a fixed value is given as single argument or a period (hourly/daily/monthly) as well as a filename (and absolute or relative path) containing the factors to apply. This file must contain in the first column the indices (months from 1 to 12 or days from 1 to 366 or hours from 0 to 23) and the matching factor in the second column (whitespace delimited). Comments following the same syntax as in the ini file are accepted, missing indices are treated as 1. More... | |
class | mio::ProcNoise |
Generate a noise signal to modify the input. The noise signal is either added ("add") to the input or used as a fraction and multiplied by the input signal ("mult"). This filter always takes three arguments: a type specifying if the noise is added or multiplied, a distribution specifying the random numbers distribution and a range that is the scaling factor to apply. The following random distributions are supported: More... | |
class | mio::ProcPSUMDistribute |
Distributes precipitation on the preceeding timesteps in a physically plausible way This assumes that the precipitation has been measured on intervals greater than the sampling interval of the data file (for example, 24 hours accumulations written once per day in an hourly file, the other timesteps receiving nodata). The accumulation has to be written on the last timestep of the accumulation period. The measured accumulation period is provided as argument (in seconds). If using the "soft" argument, missing accumulated values would be replaced by "0". The precipitation is distributed on the preceeding timesteps by using criterias on relative humidity and the difference between the air temperature and the surface temperature. More... | |
class | mio::ProcShade |
Apply a shading mask to the Incoming or Reflected Short Wave Radiation A shading mask that is either computed from the DEM or read from a separate file will be applied to the radiation and combined with the radiation splitting model in order to properly compute the shading effects on the measurement point. This mask will be linearly interpolated between the provided points in order to be applied to the true sun position. More... | |
class | mio::ProcUndercatch_Forland |
Correct precipitation for undercatch in winter conditions. More... | |
class | mio::ProcUndercatch_Hamon |
Correct precipitation for undercatch in winter conditions. More... | |
class | mio::ProcUndercatch_WMO |
Correct precipitation for undercatch in winter conditions. More... | |
class | mio::ProcUnventilatedT |
Filters and corrects temperatures from unventilated sensor. This either deletes all air temperature values when the wind speed is below a given threshold or corrects the air temperature data according to "Air Temperature Measurement Errors in Naturally Ventilated Radiation Shields", Reina Nakamura, L. Mahrt, J. Atmos. Oceanic Technol., 22, 2005, pp 1046–1058 with an albedo dependency as introduced in "Albedo effect on radiative errors in air temperature measurements", H. Huwald, C. W. Higgins, M.-O. Boldi, E. Bou-Zeid, M. Lehning, and M. B. Parlange, Water Resour. Res., 45, W08431, 2009. More... | |
Documentation for available data processing components. These can be used on incoming meteorological data. See Available data processing elements.