HydroClass configuration file contents

This section lists the distinct parameter lines with default settings for C-band radars.

Title and configuration nickname

The title comment line is machine written:

dpolapp.sConfigurationName ="dpolapp_C-band"

A descriptive name for the current configuration settings.

This parameter is the only one communicated as meta data in the header structures of IRIS/RDA data. The nickname provides a way of indicating possible evolutions and modifications made to the HydroClass configuration.

The default string "dpolapp_C-band" suggests the default settings. It is recommended that you change to a descriptive name when you modify algorithm parameters.

Main algorithm selection menu

dpolapp.bRunQuality = 1

Activates the quality considerations in the main block Quality.

dpolapp.usClassificationMethods = 222

Activates a combination of the distinct echo classifier methods. Possible settings are:

  • "1": Preclassifier, only (the JPOLE algorithm for meteo versus bio versus GC/AP)
  • "2": Meteoclassifiers, with the Preclassifier as a quality mask
  • "21": PrecipClassifier: the JPOLE warm algorithm, with the Preclassifier mask
  • "22": PrecipClassifier and Meteoclassifiers, with the Preclassifier mask
  • "201": CellClassifier for stratiform and convective rain, with the Preclassifier mask
  • "202": Meteoclassifiers and CellClassifier, with the Preclassifier mask
  • "221": CellClassifier and PrecipClassifier, with the Preclassifier mask
  • "222:" CellClassifier, PrecipClassifier, Meteoclassifiers and CellClassifier, with the Preclassifier mask

Software version information

dpolapp.sVersion = "8.13"

Machine written major IRIS/RDA version. No need to modify this.

dpolapp.sRevision = "8.13"

Machine written minor IRIS/RDA version. No need to modify this.

dpolapp.sDpolappRevision = "15"

Machine written HydroClass running version. No need to modify this.

Miscellaneous quality settings

dpolapp.Quality.Aux[0] = 1

Source of the melting layer height input:

  • "1": HydroClass uses the melting layer height value suggested in the configuration file.
  • "0": instructs HydroClass in IRIS to use the ML value in the input RAW file, and HydroClass in RDA to use the RVP10 process value of ML. The RVP10 value can be updated during operation (dynamically).
  • In case dpolapp.Quality.Aux[0] =0 but the requested values (in RAW or RVP10) appear undefined, HydroClass reports about the missing input. HydroClass in IRIS moves over to using the value specified in the configuration file, while HydroClass in RDA switches off until a well defined value is provided.
dpolapp.Quality.Aux[1] =2300

An estimate of the current ML in meters above mean sea level (MSL). This estimate is used when dpolapp.Quality.Aux[0] =1, see previous field. It is recommended to move to using IRIS/RDA current values in continued use.

dpolapp.Quality.OtherInputs[0].dParams[0] =0.0

A posteriori HydroClass specific off-set to H-/V-channel balance (Zdr). The off-set is intrinsic to HydroClass and does not affect the Zdr output data type.

Use of non-zero value is discouraged in real-time applications (HydroClass in RVP10). The recommended IRIS/RDA approach is to use the tools described in chapter Calibration considerations for dual-polarization.

General settings of PreClassifier

dpolapp.Preclassifier.iClassifierID =-1

Are the PreClassifier results formatted as in the field of PRECIP_CLASSIFIER in the bits 3,4,5 from LSB=0 of the DB_HCLASS data type?

  • "-1": NO, the legacy HydroClass format
  • "1": YES, JPOLE classes of PRECIP_CLASSIFIER used
dpolapp.Preclassifier.uiMaxNonMeteo = 1

This parameter is irrelevant in all cases, except:

dpolapp.usClassificationMethods =1

This parameter defines what to do with the bins that fail acceptance to any class of PreClassifier. It allows simplifying the class settings further through regrouping. You can re-group PreClassifier (un)identified results:

  • "1": unidentified are 'uiNonMeteoID
  • "2": in addition, 'GC/AP' are 'uiNonMeteoID'
  • "3": in addition, 'BIO' are 'uiNonMeteoID' 
dpolapp.Preclassifier.uiNonMeteoID =1s

What to do with the regrouped (un)identified results:

  • "0": set as thresholded in IRIS/RDA
  • "1": report as GC/AP
  • "2": report as BIO
  • "3": report as PRECIP
dpolapp.Preclassifier.dMinRS[0] =0

Minimum rule strength for accepting GC/AP. 

dpolapp.Preclassifier.dMinRS[1] =0

Minimum rule strength for accepting BIO.

dpolapp.Preclassifier.dMinRS[2] =0

Minimum rule strength for accepting PRECIP.

dpolapp.Preclassifier.iAux[3] =2

The preferred class of the Polarimetric Meteo Index (PMI):

  • "2": PMI gets high values when the PRECIP class is preferred
  • "1": PMI gets high values when the BIO class is preferred
  • "0": PMI gets high values when the GC/AP class is preferred

Settings for Polarimetric Meteo Index

dpolapp.Preclassifier.iAux[4] =1

PMI mathematical formula.

  • "1": the 'score'

    P M I = 1 − 1 1 + ( R U L E S T R E N G T H P R E F E R R E D max ⁡ ( R U L E S T R E N G T H ) N O N P R E F E R R E D )

  • "0": the likelihood ratio

    P M I = 1 − R U L E S T R E N G T H P R E F E R R E D Σ R U L E S T R E N G T H

Detailed settings of PreClassifier

dpolapp.Preclassifier.OtherInputs[0].iParams[0] = 5

The number of consecutive bins used to compute texture of reflectivity.

dpolapp.Preclassifier.OtherInputs[1].iParams[0] = 10

The number of consecutive bins used to compute texture of differential phase.

# MBFinputs and their use:
# Reflectivity in preclassifier:
# Input data: Zh.
# MBF(Zh) is computed as JPOLE trapetzoid; an additive factor in rule strength
# MBF: default initialization with parameter settings of Table 1 Ref.1
# Zh MBF trapetzoid for GC/AP.
dpolapp.Preclassifier.MBFinputs[0].dMBF[0][0] = 15
dpolapp.Preclassifier.MBFinputs[0].dMBF[1][0] = 20
dpolapp.Preclassifier.MBFinputs[0].dMBF[2][0] = 75
dpolapp.Preclassifier.MBFinputs[0].dMBF[3][0] = 85
# Zh MBF trapetzoid for bio scatter.
dpolapp.Preclassifier.MBFinputs[0].dMBF[0][1] = 5
dpolapp.Preclassifier.MBFinputs[0].dMBF[1][1] = 10
dpolapp.Preclassifier.MBFinputs[0].dMBF[2][1] = 20
dpolapp.Preclassifier.MBFinputs[0].dMBF[3][1] = 30
# Zh MBF trapetzoid for precipitation.
dpolapp.Preclassifier.MBFinputs[0].dMBF[0][2] = 5
dpolapp.Preclassifier.MBFinputs[0].dMBF[1][2] = 10
dpolapp.Preclassifier.MBFinputs[0].dMBF[2][2] = 75
dpolapp.Preclassifier.MBFinputs[0].dMBF[3][2] = 80
# Differential reflectivity in preclassifier:
# Input data: Zdr (In reingest: adjusted with Quality offset, internally), and Zh.
# MBF(Zdr;Zh) is computed as JPOLE 2D-trapetzoid; additive in rule strength
# Zdr MBF trapetzoid for GC/AP.
dpolapp.Preclassifier.MBFinputs[1].dMBF[0][0] = -4
dpolapp.Preclassifier.MBFinputs[1].dMBF[1][0] = -2
dpolapp.Preclassifier.MBFinputs[1].dMBF[2][0] = 1
dpolapp.Preclassifier.MBFinputs[1].dMBF[3][0] = 2
# Zdr MBF trapetzoid for bio scatter.
dpolapp.Preclassifier.MBFinputs[1].dMBF[0][1] = 0
dpolapp.Preclassifier.MBFinputs[1].dMBF[1][1] = 2
dpolapp.Preclassifier.MBFinputs[1].dMBF[2][1] = 10
dpolapp.Preclassifier.MBFinputs[1].dMBF[3][1] = 12
# Zdr MBF trapetzoid for precipitation.
dpolapp.Preclassifier.MBFinputs[1].dMBF[0][2] = -0.3
dpolapp.Preclassifier.MBFinputs[1].dMBF[1][2] = 0
dpolapp.Preclassifier.MBFinputs[1].dMBF[2][2] = 0
dpolapp.Preclassifier.MBFinputs[1].dMBF[3][2] = 0.3
# A polynomial dependence of the Zdr precipitation trapezoid on Zh
# The Zh range in which the polynomial is applied:
dpolapp.Preclassifier.MBFinputs[1].dMBF[4][2] = 0
dpolapp.Preclassifier.MBFinputs[1].dMBF[5][2] = 80
# Dependency on Zh: left tail constant term P0, linear P1, 2nd order P2, and 3rd order P3.
# The left shoulder is the same.
dpolapp.Preclassifier.MBFinputs[1].dP[2][0][0] = -0.5
dpolapp.Preclassifier.MBFinputs[1].dP[2][0][1] = 0.0025
dpolapp.Preclassifier.MBFinputs[1].dP[2][0][2] = 0.00075
dpolapp.Preclassifier.MBFinputs[1].dP[2][0][3] = 0
# The right tail constant term P0, linear P1, 2nd order P2 and 3rd order P3
# The right shoulder is the same.
dpolapp.Preclassifier.MBFinputs[1].dP[2][2][0] = 0.08
dpolapp.Preclassifier.MBFinputs[1].dP[2][2][1] = 0.0364
dpolapp.Preclassifier.MBFinputs[1].dP[2][2][2] = 0.000357
dpolapp.Preclassifier.MBFinputs[1].dP[2][2][3] = 0
# Cross correlation coefficient in preclassifier:
# Data type used as input: RhoHV.
# MBF(RhoHV) is computed as JPOLE trapetzoid; an additive factor in rule strength
# MBF: default initialization with parameter settings of Table 1 Ref.1
# RHOhv MBF trapetzoid for GC/AP.
dpolapp.Preclassifier.MBFinputs[2].dMBF[0][0]  =  0.5
dpolapp.Preclassifier.MBFinputs[2].dMBF[1][0]  =  0.6
dpolapp.Preclassifier.MBFinputs[2].dMBF[2][0]  =  0.9
dpolapp.Preclassifier.MBFinputs[2].dMBF[3][0]  =  0.95
# RHOhv MBF trapetzoid for bio scatter.
dpolapp.Preclassifier.MBFinputs[2].dMBF[0][1]  =  0
dpolapp.Preclassifier.MBFinputs[2].dMBF[1][1]  =  0
dpolapp.Preclassifier.MBFinputs[2].dMBF[2][1]  =  0.8
dpolapp.Preclassifier.MBFinputs[2].dMBF[3][1]  =  0.83
# RHOhv MBF trapetzoid for precipitation.
dpolapp.Preclassifier.MBFinputs[2].dMBF[0][2]  =  0.85
dpolapp.Preclassifier.MBFinputs[2].dMBF[1][2]  =  0.97
dpolapp.Preclassifier.MBFinputs[2].dMBF[2][2] = 1
dpolapp.Preclassifier.MBFinputs[2].dMBF[3][2] = 1.01
# Differential phase texture in preclassifier:
# The input data type is computed internally.
#--- MBF(Texture-1) is computed as JPOLE trapetzoid; an additive factor in rule strength
# MBF: default initialization with parameter settings of Table 1 Ref.1
# PHIDP texture MBF trapetzoid for GC/AP.
dpolapp.Preclassifier.MBFinputs[3].dMBF[0][0] = 30
dpolapp.Preclassifier.MBFinputs[3].dMBF[1][0] = 40
dpolapp.Preclassifier.MBFinputs[3].dMBF[2][0] = 10800
dpolapp.Preclassifier.MBFinputs[3].dMBF[3][0] = 10800
# PHIDP texture MBF trapetzoid for bio scatter.
dpolapp.Preclassifier.MBFinputs[3].dMBF[0][1] = 8
dpolapp.Preclassifier.MBFinputs[3].dMBF[1][1] = 10
dpolapp.Preclassifier.MBFinputs[3].dMBF[2][1] = 40
dpolapp.Preclassifier.MBFinputs[3].dMBF[3][1] = 60
# PHIDP texture MBF trapetzoid for precipitation.
dpolapp.Preclassifier.MBFinputs[3].dMBF[0][2] = 0
dpolapp.Preclassifier.MBFinputs[3].dMBF[1][2] = 1
dpolapp.Preclassifier.MBFinputs[3].dMBF[2][2] = 15
dpolapp.Preclassifier.MBFinputs[3].dMBF[3][2] = 30
# Reflectivity texture in preclassifier:
# Data type is computed internally.
# MBF(Texture-2) is computed as JPOLE trapetzoid; an additive factor in the rule strength
# MBF: default initialization with parameter settings of Table 1 Ref.1
# Zh texture MBF trapetzoid for GC/AP.
dpolapp.Preclassifier.MBFinputs[4].dMBF[0][0] = 2
dpolapp.Preclassifier.MBFinputs[4].dMBF[1][0] = 4
dpolapp.Preclassifier.MBFinputs[4].dMBF[2][0] = 10000
dpolapp.Preclassifier.MBFinputs[4].dMBF[3][0] = 10000
# Zh texture MBF trapetzoid for bio scatter.
dpolapp.Preclassifier.MBFinputs[4].dMBF[0][1] = 1
dpolapp.Preclassifier.MBFinputs[4].dMBF[1][1] = 2
dpolapp.Preclassifier.MBFinputs[4].dMBF[2][1] = 4
dpolapp.Preclassifier.MBFinputs[4].dMBF[3][1] = 7
# Zh texture MBF trapetzoid for precipitation.
dpolapp.Preclassifier.MBFinputs[4].dMBF[0][2] = 0
dpolapp.Preclassifier.MBFinputs[4].dMBF[1][2] = 0.5
dpolapp.Preclassifier.MBFinputs[4].dMBF[2][2] = 3
dpolapp.Preclassifier.MBFinputs[4].dMBF[3][2] = 6
# Other flags: none.

General settings of MeteoClassifiers

dpolapp.MeteoClassifiers.uiNonMeteoID = 1

This parameter defines what to do with the bins that fail the preceding quality consideration (PreClassifier), or fail the acceptance to any class of MeteoClassifiers.

  • "0": MeteoClassifiers redirects the unaccepted and unclassified bins as IRIS/RDA 'CLASS_THRESHOLD'
  • "1": MeteoClassifiers redirects the unaccepted and unclassified bins as IRIS/RDA "CLASS_NON_MET"
dpolapp.MeteoClassifiers.dMinRS[0] = 0

Minimum rule strength for accepting rain

dpolapp.MeteoClassifiers.dMinRS[1] = 0

Minimum rule strength for accepting wet snow

dpolapp.MeteoClassifiers.dMinRS[2] = 0

Minimum rule strength for accepting snow

dpolapp.MeteoClassifiers.dMinRS[3] = 0

Minimum rule strength for accepting graupel

dpolapp.MeteoClassifiers.dMinRS[4] = 0

Minimum rule strength for accepting hail

dpolapp.MeteoClassifiers.dMinRS[5] = 0

Minimum rule strength for accepting rain/hail mixture (reported as hail)

Detailed settings of MeteoClassifiers

# MBFinputs and their use:
# Reflectivity in MeteoClassifiers:
# Data type used as input: Zh.
# MBF(dBZ) is computed as the CSU beta function.
# MBF: C-band, default settings:
# Zh beta MBF for rain: central value, width, slope
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[0][0] = 30
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[1][0] = 31
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[2][0] = 40
# Zh beta MBF for wet snow.
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[0][1] = 25
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[1][1] = 21
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[2][1] = 40
# Zh beta MBF for snow.
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[0][2] = 0
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[1][2] = 36
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[2][2] = 40
# Zh beta MBF for graupel/small hail.
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[0][3] = 45
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[1][3] = 11
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[2][3] = 20
# Zh beta MBF for large hail.
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[0][4] = 57.5
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[1][4] = 14
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[2][4] = 20
# Zh beta MBF for rain+hail mixture.
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[0][5] = 60
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[1][5] = 11
dpolapp.MeteoClassifiers.MBFinputs[0].dMBF[2][5] = 20
# Altitude in MeteoClassifiers:
# The IRIS/RDA estimate of the melting level height will be used.
# MBF(altitude;melting level) is computed as the CSU beta(ML) function.
# MBF: C-band, default settings:
# Altitude beta MBF for rain: central value, width, slope
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[0][0] = 0
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[1][0] = -0.2
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[2][0] = 20
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[3][0] = 0
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[4][0] = 0.5
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[5][0] = 5
# Altitude beta MBF for wet snow.
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[0][1] = -0.3
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[1][1] = 0.5
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[2][1] = 5
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[3][1] = 0
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[4][1] = 1
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[5][1] = 5
# Altitude beta MBF for snow.
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[0][2] = 10
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[1][2] = 10.2
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[2][2] = 60
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[3][2] = 0
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[4][2] = 0
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[5][2] = 0
# Altitude beta MBF for graupel/small hail.
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[0][3] = 10
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[0][3] = 10
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[1][3] = 12
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[2][3] = 60
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[3][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[4][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[5][3] = 0
# Altitude beta MBF for hail.
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[0][4] = 10
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[1][4] = 15
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[2][4] = 20
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[3][4] = 0
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[4][4] = 0
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[5][4] = 0
# Altitude beta MBF for rain+hail mixture.
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[0][5] = 0
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[1][5] = 0.5
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[2][5] = 20
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[3][5] = 0
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[4][5] = 0
dpolapp.MeteoClassifiers.MBFinputs[1].dMBF[5][5] = 0
# Differential reflectivity in MeteoClassifiers:
# Input data: Zdr (adjusted with Quality offset, internally in reingest), and Zh
# MBF(Zdr) is computed as the CSU 2D-beta function of Zh.
# MBF: C-band, default settings:
# Zdr 2D beta MBF for rain at Zh=<Zh> dB (central value, width, slope):
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[0][0] = 0.48155
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[1][0] = 0.70578
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[2][0] = 10.958
# Polynomial dependence of rain MBF(Zdr) on Zh-<Zh>
# The center <Zh> and the Zh range in which the polynomial is applied:
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[3][0] = 30
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[4][0] = 30
# Polynomial coefficients; the linear, the 2nd, the 3rd, and the 4th order; min/max constraints:
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][0][0] = 0.047498
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][0][1] = 0.0017624
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][0][2] = 6.993E- 06
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][0][3] = -4.4975E-07
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][0][4] = 0.1
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][0][5] = 5.5
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][1][0] = 0.025917
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][1][1] = 0.00043621
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][1][2] = -4.8951E-06
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][1][3] = -5.6218E-08
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][1][4] = 0.4
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][1][5] = 8
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][2][0] = 0.22873
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][2][1] = 0.0054945
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][2][2] = -5.8275E-05
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][2][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][2][4] = 8
dpolapp.MeteoClassifiers.MBFinputs[2].dP[0][2][5] = 25
# Zdr 2D beta MBF for wet snow at Zh=<Zh> (central value, width, slope):
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[0][1] = 1.1031
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[1][1] = 1.3594
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[2][1] = 15
# Polynomial dependence of wet snow MBF(Zdr) on Zh-<Zh>
# The center <Zh> and the Zh range in which the polynomial is applied:
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[3][1] = 27.5
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[4][1] = 17.5
# Polynomial coefficients; the linear, the 2nd, the 3rd, and the 4th order; min/max constraint:
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][0][0] = 0.004623
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][0][1] = 0.00064286
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][0][2] = 2.2222E-05
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][0][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][0][4] = 1.1
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][0][5] = 2.5
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][1][0] = -0.0091144
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][1][1] = 0.00021429
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][1][2] = 3.8384E-05
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][1][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][1][4] = 1
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][1][5] = 2.5
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][2][0] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][2][1] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][2][2] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][2][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][2][4] = 5
dpolapp.MeteoClassifiers.MBFinputs[2].dP[1][2][5] = 20
# Zdr 2D beta MBF for snow at Zh=<Zh> (central value, width, slope):
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[0][2] = 0.25
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[1][2] = 0.75
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[2][2] = 8
# Zdr 2D beta MBF for graupel at Zh=<Zh> (central value, width, slope):
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[0][3] = 0.42969
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[1][3] = 0.92969
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[2][3] = 12.5
# Polynomial dependence of graupel MBF(Zdr) on Zh-<Zh>
# The center <Zh> and the Zh range in which the polynomial is applied:
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[3][3] = 42.5
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[4][3] = 12.5
# Polynomial coefficients; the linear, the 2nd, the 3rd, and the 4th orde;min/max constraints:
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][0][0] = 0.033714
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][0][1] = 0.00096429
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][0][2] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][0][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][0][4] = 0.155
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][0][5] = 1.5
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][1][0] = 0.033714
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][1][1] = 0.00096429
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][1][2] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][1][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][1][4] = 0.5
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][1][5] = 2
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][2][0] = 0.25714
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][2][1] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][2][2] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][2][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][2][4] = 9
dpolapp.MeteoClassifiers.MBFinputs[2].dP[3][2][5] = 17
# Zdr weight and 2D beta MBF for hail at Zh=<Zh> (central value, width, slope):
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[0][4] = -0.75928
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[1][4] = 1.2778
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[2][4] = 15.859
# Polynomial dependence of hail MBF(Zdr) on Zh-<Zh>
# The center <Zh> and the Zh range in which the polynomial is applied:
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[3][4] = 57.5
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[4][4] = 12.5
# Polynomial coefficients; the linear, the 2nd, the 3rd, and the 4th order;min/max constraint:
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][0][0] = -0.017336
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][0][1] = 0.0015104
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][0][2] = -8.3333E-05
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][0][3] = 4.1667E-06
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][0][4] = -2
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][0][5] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][1][0] = 0.0057788
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][1][1] = -0.0045313
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][1][2] = 2.7778E-05
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][1][3] = 1.25E- 05
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][1][4] = 0.1
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][1][5] = 1.3
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][2][0] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][2][1] = -0.14167
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][2][2] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][2][3] = 0.00066667
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][2][4] = 7
dpolapp.MeteoClassifiers.MBFinputs[2].dP[4][2][5] = 17
# Zdr weight and 2D beta MBF for rain+hail mixture at Zh=<Zh> (central value, width, slope):
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[0][5] = 0.93571
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[1][5] = 2
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[2][5] = 15
# Polynomial dependence of rain+hail mixture MBF(Zdr) on Zh- <Zh>
# The center <Zh> and the Zh range in which the polynomial is applied:
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[3][5] = 60
dpolapp.MeteoClassifiers.MBFinputs[2].dMBF[4][5] = 10
# Polynomial coefficients; the linear, the 2nd, the 3rd, and the 4th order; min/max constraint:
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][0][0] = -0.054167
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][0][1] = -0.0057143
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][0][2] = 0.00016667
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][0][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][0][4] = -0.5
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][0][5] = 1.5
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][1][0] = 0.0041667
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][1][1] = -0.02375
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][1][2] = -0.00016667
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][1][3] = 0.00015
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][1][4] = 0.5
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][1][5] = 3
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][2][0] = -5.9212E-17
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][2][1] = 0.016667
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][2][2] = 5.9212E-19
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][2][3] = -0.00066667
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][2][4] = 5
dpolapp.MeteoClassifiers.MBFinputs[2].dP[5][2][5] = 20
# Specific differential phase in MeteoClassifiers:
# Input data: Kdp, and Zh.
# MBF(Kdp) is computed as the CSU 2D-beta function of Zh.
# MBF: C-band, default settings:
# Kdp 2D beta MBF for rain at Zh=<Zh> dB (central value, width, slope):
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[0][0] = 0.0079391
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[1][0] = -0.032435
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[2][0] = 11.154
# Polynomial dependence of rain MBF(Kdp) on Zh-<Zh>
# The center <Zh> and the Zh range in which the polynomial is applied:
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[3][0] = 30
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[4][0] = 35
# Polynomial coefficients; the linear, the 2nd, the 3rd, and the 4th order; min/max constraint:
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][0][0] = -0.0049584
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][0][1] = 0.0020558
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][0][2] = 0.00019114
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][0][3] = 3.9888E-06
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][0][4] = 0.05
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][0][5] = 14
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][1][0] = 0.011085
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][1][1] = 0.0029526
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][1][2] = 0.00010385
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][1][3] = 6.1086E-07
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][1][4] = 0.2
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][1][5] = 8
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][2][0] = 0.22873
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][2][1] = 0.0054945
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][2][2] = -5.8275E-05
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][2][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][2][4] = 9
dpolapp.MeteoClassifiers.MBFinputs[3].dP[0][2][5] = 16
# Kdp 2D beta MBF for wet snow at Zh=25 dB: central value, width, slope.
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[0][1] = 0.25
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[1][1] = 1.2
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[2][1] = 10
# Kdp 2D beta MBF for snow at Zh=17.5 dB (central value, width, slope):
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[0][2] = 0
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[1][2] = 0.25
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[2][2] = 10
# Kdp 2D beta MBF for graupel at Zh=42.5 dB (central value, width, slope):
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[0][3] = 0.26875
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[1][3] = 0.76875
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[2][3] = 12.5
# Polynomial dependence of graupel MBF(Kdp) on Zh-<Zh>
# The center <Zh> and the Zh range in which the polynomial is applied:
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[3][3] = 42.5
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[4][3] = 12.5
# Polynomial coefficients; the linear, the 2nd, the 3rd, and the 4th order; min/max constraint:
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][0][0] = 0.068704
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][0][1] = 0.001
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][0][2] = -0.00014815
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][0][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][0][4] = -0.25
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][0][5] = 1.5
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][1][0] = 0.068704
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][1][1] = 0.001
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][1][2] = -0.00014815
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][1][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][1][4] = 0.3
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][1][5] = 2
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][2][0] = 0.25714
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][2][1] = 0
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][2][2] = 0
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][2][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][2][4] = 9
dpolapp.MeteoClassifiers.MBFinputs[3].dP[3][2][5] = 17
# Kdp weight and 2D beta MBF for hail at Zh=57.5 dB (central value, width, slope):
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[0][4] = 0.5
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[1][4] = 1
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[2][4] = 10
# Kdp weight and 2D beta MBF for rain+hail mixture at Zh=60 dB (central value, width, slope):
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[0][5] = 2.3714
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[1][5] = 2.3971
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[2][5] = 15
# Polynomial dependence of rain+hail mixture MBF(Kdp) on Zh- <Zh>
# The center <Zh> and the Zh range in which the polynomial is applied:
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[3][5] = 60
dpolapp.MeteoClassifiers.MBFinputs[3].dMBF[4][5] = 20
# Polynomial coefficients; the linear, the 2nd, the 3rd, and the 4th order; min/max constraint:
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][0][0] = 0.30833
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][0][1] = 0.0085714
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][0][2] = -0.00033333
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][0][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][0][4] = 0
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][0][5] = 10
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][1][0] = 0.29667
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][1][1] = 0.0088571
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][1][2] = -0.00026667
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][1][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][1][4] = -1
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][1][5] = 10
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][2][0] = -0.16667
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][2][1] = 2.4371E-17
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][2][2] = 0.0066667
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][2][3] = 0
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][2][4] = 8
dpolapp.MeteoClassifiers.MBFinputs[3].dP[5][2][5] = 25
# Cross correlation coefficient in MeteoClassifiers:
#--- Data type used as input: RhoHV.
#--- MBF(Rhohv) is computed as the CSU beta function.
# # MBF: C-band, default settings:
# RHOHV beta MBF for rain: central value, width, slope
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[0][0] = 1
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[1][0] = 0.04
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[2][0] = 10
# RHOHV beta MBF for wet snow.
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[0][1] = 0.88
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[1][1] = 0.11
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[2][1] = 20
# RHOHV beta MBF for snow.
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[0][2] = 1
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[1][2] = 0.06
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[2][2] = 10
# RHOHV beta MBF for graupel.
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[0][3] = 0.96
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[1][3] = 0.04
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[2][3] = 10
# RHOHV beta MBF for hail.
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[0][4] = 0.9
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[1][4] = 0.045
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[2][4] = 10
# RHOHV beta MBF for rain+hail mixture.
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[0][5] = 0.8
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[1][5] = 0.13
dpolapp.MeteoClassifiers.MBFinputs[4].dMBF[2][5] = 30

General settings of PrecipClassifier

dpolapp.PrecipClassifier.uiNonMeteoID = 1

This parameter defines what to do with the bins that fail the preceding quality consideration (PreClassifier), or fail the acceptance to any class of PrecipClassifier.

  • "0": PrecipClassifier redirects the unaccepted and unclassified bins as IRIS/RDA 'CLASS_THRESHOLD'
  • "1": PrecipClassifier reports the unaccepted PreClassifier classifications unchanged, and reports the unclassified bins as IRIS/ RDA "CLASS_NON_MET"
dpolapp.PrecipClassifier.dMinRS[0] = 0

Minimum rule strength for accepting light rain.

dpolapp.PrecipClassifier.dMinRS[1] = 0

Minimum rule strength for accepting moderate rain.

dpolapp.PrecipClassifier.dMinRS[2] = 0

Minimum rule strength for accepting heavy rain.

dpolapp.PrecipClassifier.dMinRS[3] = 0

Minimum rule strength for accepting large drops.

Detailed settings of PrecipClassifier

# Precip classifier uses the textures of reflectivity and differential phase.
# MBF inputs and their use:
# Reflectivity in rain classifier:
# Input data: Zh.
# MBF(Zh) is computed as JPOLE trapetzoid; an additive factor in rule strength
# MBF: default initialization with parameter settings of Table 2 Ref.2
# Zh MBF trapetzoid for light precipitation.
dpolapp.PrecipClassifier.MBFinputs[0].dMBF[0][0] = 5
dpolapp.PrecipClassifier.MBFinputs[0].dMBF[1][0] = 10
dpolapp.PrecipClassifier.MBFinputs[0].dMBF[2][0] = 35
dpolapp.PrecipClassifier.MBFinputs[0].dMBF[3][0] = 40
# Zh MBF trapetzoid for moderate precipitation.
dpolapp.PrecipClassifier.MBFinputs[0].dMBF[0][1] = 30
dpolapp.PrecipClassifier.MBFinputs[0].dMBF[1][1] = 35
dpolapp.PrecipClassifier.MBFinputs[0].dMBF[2][1] = 45
dpolapp.PrecipClassifier.MBFinputs[0].dMBF[3][1] = 50
# Zh MBF trapetzoid for heavy precipitation.
dpolapp.PrecipClassifier.MBFinputs[0].dMBF[0][2] = 40
dpolapp.PrecipClassifier.MBFinputs[0].dMBF[1][2] = 45
dpolapp.PrecipClassifier.MBFinputs[0].dMBF[2][2] = 75
dpolapp.PrecipClassifier.MBFinputs[0].dMBF[3][2] = 80
# Zh MBF trapetzoid for large drops.
dpolapp.PrecipClassifier.MBFinputs[0].dMBF[0][3] = 15
dpolapp.PrecipClassifier.MBFinputs[0].dMBF[1][3] = 20
dpolapp.PrecipClassifier.MBFinputs[0].dMBF[2][3] = 45
dpolapp.PrecipClassifier.MBFinputs[0].dMBF[3][3] = 50
# Differential reflectivity in rain classifier:
# Input data: Zdr (In reingest: adjusted with Quality offset, internally), and Zh.
# MBF(Zdr;Zh) is computed as JPOLE 2D-trapetzoid; additive in rule strength
# Zdr MBF trapetzoid for light precipitation.
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[0][0] = -0.3
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[1][0] = 0
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[2][0] = 0
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[3][0] = 0.3
# The polynomial dependence of the Zdr light precipitation trapezoid on Zh
# The Zh range in which the polynomial is applied:
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[4][0] = 0
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[5][0] = 80
# Dependency on Zh: left tail constant term P0, linear P1, 2nd order P2, and 3rd order P3.
# The left shoulder is the same.
dpolapp.PrecipClassifier.MBFinputs[1].dP[0][0][0] = -0.5
dpolapp.PrecipClassifier.MBFinputs[1].dP[0][0][1] = 0.0025
dpolapp.PrecipClassifier.MBFinputs[1].dP[0][0][2] = 0.00075
dpolapp.PrecipClassifier.MBFinputs[1].dP[0][0][3] = 0
# The right tail constant term P0, linear P1, 2nd order P2 and 3rd order P3
# The right shoulder is the same.
dpolapp.PrecipClassifier.MBFinputs[1].dP[0][2][0] = 0.08
dpolapp.PrecipClassifier.MBFinputs[1].dP[0][2][1] = 0.0364
dpolapp.PrecipClassifier.MBFinputs[1].dP[0][2][2] = 0.000357
dpolapp.PrecipClassifier.MBFinputs[1].dP[0][2][3] = 0
# Zdr MBF trapetzoid for moderate precipitation.
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[0][1] = -0.3
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[1][1] = 0
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[2][1] = 0
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[3][1] = 0.3
# The polynomial dependence of the Zdr moderate precipitation trapezoid on Zh
# The Zh range in which the polynomial is applied:
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[4][1] = 0
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[5][1] = 80
# Dependency on Zh: left tail constant term P0, linear P1, 2nd order P2, and 3rd order P3.
# The left shoulder is the same.
dpolapp.PrecipClassifier.MBFinputs[1].dP[1][0][0] = -0.5
dpolapp.PrecipClassifier.MBFinputs[1].dP[1][0][1] = 0.0025
dpolapp.PrecipClassifier.MBFinputs[1].dP[1][0][2] = 0.00075
dpolapp.PrecipClassifier.MBFinputs[1].dP[1][0][3] = 0
# The right tail constant term P0, linear P1, 2nd order P2 and 3rd order P3
# The right shoulder is the same.
dpolapp.PrecipClassifier.MBFinputs[1].dP[1][2][0] = 0.08
dpolapp.PrecipClassifier.MBFinputs[1].dP[1][2][1] = 0.0364
dpolapp.PrecipClassifier.MBFinputs[1].dP[1][2][2] = 0.000357
dpolapp.PrecipClassifier.MBFinputs[1].dP[1][2][3] = 0
# Zdr MBF trapetzoid for heavy precipitation.
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[0][2] = -0.3
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[1][2] = 0
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[2][2] = 0
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[3][2] = 0.3
# The polynomial dependence of the Zdr heavy precipitation trapezoid on Zh
# The Zh range in which the polynomial is applied:
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[4][2] = 0
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[5][2] = 80
# Dependency on Zh: left tail constant term P0, linear P1, 2nd order P2, and 3rd order P3.
# The left shoulder is the same.
dpolapp.PrecipClassifier.MBFinputs[1].dP[2][0][0] = -0.5
dpolapp.PrecipClassifier.MBFinputs[1].dP[2][0][1] = 0.0025
dpolapp.PrecipClassifier.MBFinputs[1].dP[2][0][2] = 0.00075
dpolapp.PrecipClassifier.MBFinputs[1].dP[2][0][3] = 0
# The right tail constant term P0, linear P1, 2nd order P2 and 3rd order P3
# The right shoulder is the same.
dpolapp.PrecipClassifier.MBFinputs[1].dP[2][2][0] = 0.08
dpolapp.PrecipClassifier.MBFinputs[1].dP[2][2][1] = 0.0364
dpolapp.PrecipClassifier.MBFinputs[1].dP[2][2][2] = 0.000357
dpolapp.PrecipClassifier.MBFinputs[1].dP[2][2][3] = 0
# Zdr MBF trapetzoid for large drops.
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[0][3] = -0.3
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[1][3] = 0
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[2][3] = 0
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[3][3] = 0.3
# The polynomial dependence of the Zdr large drops trapezoid on Zh
# The Zh range in which the polynomial is applied:
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[4][3] = 0
dpolapp.PrecipClassifier.MBFinputs[1].dMBF[5][3] = 80
# Dependency on Zh: left tail constant term P0, linear P1, 2nd order P2, and 3rd order P3.
# The left shoulder is the same.
dpolapp.PrecipClassifier.MBFinputs[1].dP[3][0][0] = -0.5
dpolapp.PrecipClassifier.MBFinputs[1].dP[3][0][1] = 0.0025
dpolapp.PrecipClassifier.MBFinputs[1].dP[3][0][2] = 0.00075
dpolapp.PrecipClassifier.MBFinputs[1].dP[3][0][3] = 0
# The right tail constant term P0, linear P1, 2nd order P2 and 3rd order P3
# The right shoulder is the same.
dpolapp.PrecipClassifier.MBFinputs[1].dP[3][2][0] = 0.08
dpolapp.PrecipClassifier.MBFinputs[1].dP[3][2][1] = 0.0364
dpolapp.PrecipClassifier.MBFinputs[1].dP[3][2][2] = 0.000357
dpolapp.PrecipClassifier.MBFinputs[1].dP[3][2][3] = 0
# Cross correlation coefficient in rain classifier:
# Data type used as input: RhoHV.
# MBF(RhoHV) is computed as JPOLE trapetzoid; an additive factor in rule strength
# MBF: default initialization with parameter settings of Table 2 Ref.1
# RHOhv MBF trapetzoid for light precipitation.
dpolapp.PrecipClassifier.MBFinputs[2].dMBF[0][0] = 0.85
dpolapp.PrecipClassifier.MBFinputs[2].dMBF[1][0] = 0.97
dpolapp.PrecipClassifier.MBFinputs[2].dMBF[2][0] = 1
dpolapp.PrecipClassifier.MBFinputs[2].dMBF[3][0] = 1.01
# RHOhv MBF trapetzoid for moderate precipitation.
dpolapp.PrecipClassifier.MBFinputs[2].dMBF[0][1] = 0.85
dpolapp.PrecipClassifier.MBFinputs[2].dMBF[1][1] = 0.97
dpolapp.PrecipClassifier.MBFinputs[2].dMBF[2][1] = 1
dpolapp.PrecipClassifier.MBFinputs[2].dMBF[3][1] = 1.01
# RHOhv MBF trapetzoid for heavy precipitation.
dpolapp.PrecipClassifier.MBFinputs[2].dMBF[0][2] = 0.85
dpolapp.PrecipClassifier.MBFinputs[2].dMBF[1][2] = 0.97
dpolapp.PrecipClassifier.MBFinputs[2].dMBF[2][2] = 1
dpolapp.PrecipClassifier.MBFinputs[2].dMBF[3][2] = 1.01
# RHOhv MBF trapetzoid for large drops.
dpolapp.PrecipClassifier.MBFinputs[2].dMBF[1][3] = 0.97
dpolapp.PrecipClassifier.MBFinputs[2].dMBF[0][3] = 0.85
dpolapp.PrecipClassifier.MBFinputs[2].dMBF[2][3] = 1
dpolapp.PrecipClassifier.MBFinputs[2].dMBF[3][3] = 1.01
# Differential phase texture in rain classifier:
# The input data type is computed internally.
#--- MBF(Texture-1) is computed as JPOLE trapetzoid; an additive factor in rule strength
# MBF: default initialization with parameter settings of Table 2 Ref.1
# PHIDP texture MBF trapetzoid for light precipitation.
dpolapp.PrecipClassifier.MBFinputs[3].dMBF[0][0] = 0
dpolapp.PrecipClassifier.MBFinputs[3].dMBF[1][0] = 1
dpolapp.PrecipClassifier.MBFinputs[3].dMBF[2][0] = 15
dpolapp.PrecipClassifier.MBFinputs[3].dMBF[3][0] = 30
# PHIDP texture MBF trapetzoid for moderate precipitation.
dpolapp.PrecipClassifier.MBFinputs[3].dMBF[0][1] = 0
dpolapp.PrecipClassifier.MBFinputs[3].dMBF[1][1] = 1
dpolapp.PrecipClassifier.MBFinputs[3].dMBF[2][1] = 15
dpolapp.PrecipClassifier.MBFinputs[3].dMBF[3][1] = 30
# PHIDP texture MBF trapetzoid for heavy precipitation.
dpolapp.PrecipClassifier.MBFinputs[3].dMBF[0][2] = 0
dpolapp.PrecipClassifier.MBFinputs[3].dMBF[1][2] = 1
dpolapp.PrecipClassifier.MBFinputs[3].dMBF[2][2] = 15
dpolapp.PrecipClassifier.MBFinputs[3].dMBF[3][2] = 30
# PHIDP texture MBF trapetzoid for large drops.
dpolapp.PrecipClassifier.MBFinputs[3].dMBF[0][3] = 0
dpolapp.PrecipClassifier.MBFinputs[3].dMBF[1][3] = 1
dpolapp.PrecipClassifier.MBFinputs[3].dMBF[2][3] = 15
dpolapp.PrecipClassifier.MBFinputs[3].dMBF[3][3] = 30
# Reflectivity texture in rain classifier:
# Data type is computed internally.
# MBF(Texture-2) is computed as JPOLE trapetzoid; an additive factor in the rule strength
# MBF: default initialization with parameter settings of Table 2 Ref.1
# Zh texture MBF trapetzoid for light precipitation.
dpolapp.PrecipClassifier.MBFinputs[4].dMBF[0][0] = 0
dpolapp.PrecipClassifier.MBFinputs[4].dMBF[1][0] = 0.5
dpolapp.PrecipClassifier.MBFinputs[4].dMBF[2][0] = 3
dpolapp.PrecipClassifier.MBFinputs[4].dMBF[3][0] = 6
# Zh texture MBF trapetzoid for moderate precipitation.
dpolapp.PrecipClassifier.MBFinputs[4].dMBF[0][1] = 0
dpolapp.PrecipClassifier.MBFinputs[4].dMBF[1][1] = 0.5
dpolapp.PrecipClassifier.MBFinputs[4].dMBF[2][1] = 3
dpolapp.PrecipClassifier.MBFinputs[4].dMBF[3][1] = 6
# Zh texture MBF trapetzoid for heavy precipitation.
dpolapp.PrecipClassifier.MBFinputs[4].dMBF[0][2] = 0
dpolapp.PrecipClassifier.MBFinputs[4].dMBF[1][2] = 0.5
dpolapp.PrecipClassifier.MBFinputs[4].dMBF[2][2] = 3
dpolapp.PrecipClassifier.MBFinputs[4].dMBF[3][2] = 6
# Zh texture MBF trapetzoid for large drops.
dpolapp.PrecipClassifier.MBFinputs[4].dMBF[0][3] = 0
dpolapp.PrecipClassifier.MBFinputs[4].dMBF[1][3] = 0.5
dpolapp.PrecipClassifier.MBFinputs[4].dMBF[2][3] = 3
dpolapp.PrecipClassifier.MBFinputs[4].dMBF[3][3] = 6

Settings of CellClassifier

dpolapp.CellClassifier.dMinRS[0] = 0.5

Minimum rule strength for accepting convective rain.

# MBFinputs and their use:
# Reflectivity&Height in cell classifier:
# Input data: Zh and height difference w.r.t. the current 0oC isotherm.
# MBF(Zh,height) is computed as 2D product of trapetzoids; summed up to rule strength.
# Min Zh required for convection (full fuzzy strength will be +5 dBZ from min).
dpolapp.CellClassifier.MBFinputs[0].dMBF[0][0] = 25
# Min height w.r.t. 0oC isotherm (full fuzzy strength will be +1. km higher ).
dpolapp.CellClassifier.MBFinputs[0].dMBF[4][0] = 0