GPM/DPR Level-2
Algorithm Theoretical Basis Document

Authors: Toshio Iguchi, Shinta Seto, Robert Meneghini,
Naofumi Yoshida, Jun Awaka, Minda Le,
V. Chandrasekar, Stacy Brodzik and Takuji Kubota

December 2010
Revised October 2014
Revised January 2015
Revised April 2015
Revised August 2015
Revised February 2016
Revised March 2016
Revised April 2017 for V05
Revised October 2018 for V06

1

TABLE OF CONTENTS
1. INTRODUCTION AND CAVEATS
2. OBJECTIVES
3. BACKGROUND INFORMATION
2.1 Historical Perspective
2.2 Instrument Characteristics
4. ALGORITHM DESCRIPTION
3.1 Theoretical Description
3.2 Main module
3.3 Preparation module
3.4 Vertical module
3.5 Classification module
3.6 DSD module
3.7 SRT module
3.8 Solver module
5. INTERFACE TO OTHER ALGORITHMS
6. REFERENCES
7. ACRONYMS
APPENDIX: Level 2 Contents of Objects in Each Group2

0. INTRODUCTION AND CAVEATSThis document describes the basic idea of DPR data processing. It was originally written for thealgorithm used in the at-launch version (V03). The algorithm has been modified and improved
since then. Although the basic idea of data processing remains the same, the actual flow ofprocessing, in particular that in the solver module, has changed substantially. As a result, somepart of description in section 3.1 may not be relevant any more.
Major changes in the DPR algorithm from V04 to V05
In level 1 algorithm, based on the new calibration results, the system parameters of DPR werere-examined, and the oﬀset parameters for the transmitting powers, receiver’s gains, the beamwidths, and the pulse width of both KuPR and KaPR were redefined. As a result, Zm of KuPR
has increased by about +1.3 dB, and Zm of KaPR by about +1.2 dB.
In level 2 algorithm, in addition to the changes in the DPR L1 calibration, adjustment factors
were introduced in the preparation module to remove small trends in the overall system gains
in KuPR and KaPR. The adjustment factors change the measured received powers only by asmall fraction of dB. New flags, such flagHeavyIcePrecip, flagSurfaceSnowfall, flagAnvil
and snowIceCover, are introduced in V05. A DSD database that depends on the month, region,
surface type and rain type was created from the statistics of DSD parameters estimated withthe dual-frequency algorithm and is applied to the single frequency (Ku-only and Ka-only) dataprocessing. These changes are described in the relevant sections of this document.
Changes in the DPR algorithm from V05 to V06
1. A new SRT code has been modified to include calculations of Hitschfeld-Bordan PIA as well
as a hybrid PIA that combines HB and SRT results. As a result of applying the new PIA
in SLV module, erroneously large estimates of high precipitation over ocean (near coast)
are mitigated and both DPR(MS) and Ku rain estimates in V06A agree better with Gound
Validation data over USA.
2. A new classification algorithm is introduced by the University of Washington (Stacy Brodzik
and Robert Houze) to reclassify the stratiform rain type. The new algorithm improved anangle-bin dependence of rain classification and SLH profiles.
Minor changes in DPR L2 algorithm:· KaHS’s sidelobe clutter is mitigated and it is used to re-calculated the data base for KuPR’s
sidelobe clutter.
· New variables are introduced. They are flagScanPatten in PRE module, PIAhb, PIAhybrid, reliabFactorHY, reliabFlagHY, stddevEff, stddevHY and zeta in SRT module.
The meanings of these variables should be referred to the user’s manual.
· The surface snow index is modified for the winter temperature inversions.
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· The latest SRT data base is applied.
· A bug in flagEcho is fixed in dual frequency data processing.
There are cases of instantaneous retrieved precipitation rate classified as stratiform rainthat exceed 40 mm/hr. These cases are rare, and the frequency is latitude dependent with morecases near the equator than in the extra-tropics.
KaHS scan pattern change
1. As of May 21, 2018, the scan pattern of KaHS beams was changed as shown in Fig. 0.1.
The KaHS beams scanned in the inner swath before May 21 2018, but now they scan in
the outer swath and match with KuPR’s beams. Please note that the range resolution of
KaHS is 500m and diﬀers from that of the KuPR or KaMS. One scan of KuPR over thefull swath consists of 49 beams which are numbered from 1 to 49 in Fig. 0.1 below. The
array for one scan of KaMS data consists of 25 beams which correspond to KuPR’s central
beams from 13 to 37, whereas one scan of KaHS data array for 24 beams consists of twoparts. The first 12 elements in the array correspond to KuPR’s beams from 38 to 49, andthe last 12 elements correspond to KuPR’s beams from 1 to 12 of the following scan. All
KaMS beams and the first 12 KaHS beams match well with KuPR’s beams from 13 to 49.
The magnitude of misalignment between these KuPR and KaPR beams is estimated to beless than 50 m after May 21. However, KaHS’s beams from 13 to 24 are slightly shifted fromthe corresponding KuPR beams from 1 to 12 in the along-track direction. The magnitudeof misalignment in these beams is about 400 m.
The dual-frequency algorithm can be applied to the full swath of data after the change ofscan pattern. However, because it takes time to develop a new algorithm applicable to thefull swath and to adjust the necessary parameters to ensure the quality of the products,
KaHS data are not processed in V06 L2 algorithm. Missing values are stored in the output
variables that use KaHS data. KaHS’s received power data are available only in L1 productsin V05B and V06.
2. All beam directions of KuPR and KaPR were adjusted to match better with the nominal
footprint locations. The diﬀerence between KuPR and KaPR’s footprint centers is now
about 30 m at nadir. It was about 300 m before May 21, 2018. This improvement of beam
matching is not considered to make a big diﬀerence in the L2 output products except forvery heavy rain cases because the mismatch was small (300 m) from the beginning.

4

(a)

(b)

Figure 0.1 DPR’s scan pattern before May 21 2018 (a) and after May 21 2018 (b). KaHS
beams scan in the inner swath before May 21 2018, but now they scan in the outer swathand match with KuPR’s beams. Numbers in color indicate angle bin numbers for KuPR
(blue), KaMS (yellow), and KaHS (red).

51. OBJECTIVES
The objective of the level 2 DPR algorithms is to generate from the level 1 DPR products radaronly derived meteorological quantities on an instantaneous FOV (field of view) basis. A subsetof the results will be used by the level 2 combined radar-radiometer algorithm and the level 3
combined and radar-only products.
The general idea behind the algorithms is to determine general characteristics of the precipitation, correct for attenuation and estimate profiles of the precipitation water content, rainfall
rate and, when dual-wavelength data are available, information on the particle size distributions
in rain and snow. It is particularly important that dual-wavelength data will provide better
estimates of rainfall and snowfall rates than the TRMM PR data by using the particle size
information and the capability of estimating, even in convective storms, the height at which theprecipitation transitions from solid to liquid.

6

2. BACKGROUND INFORMATION
2.1 Historical Perspective
The Dual-Frequency Precipitation Radar (DPR) on the GPM core satellite will be the second
space-borne precipitation radar, following the first such radar, the Precipitation Radar (PR),launched on the TRMM satellite in November, 1997. The TRMM PR has already revolutionized
the measurement of precipitation from space by providing high resolution 3-dimensional rainechoes in the tropics and subtropics. The DPR consists of Ku-band (13.6GHz) and Ka-band(35.5GHz) channels. A major source of error in the rainfall estimates from the TRMM/PR comesfrom the uncertainty in the conversion of radar reflectivity into rainfall rate. This uncertainty
originates in the variations of the raindrop size distribution (DSD) that changes by region, season
and rain type. One of the reasons for adding the Ka-band frequency channel to the DPR is toprovide information on the DSD that can be obtained from non-Rayleigh scattering eﬀects at
the higher frequency.
Another reason for the new Ka-band channel is to provide more accurate estimates of thephase-transition height in precipitating systems. This information is very important not only
in increasing the accuracy of rain rate estimation by the DPR itself, but in improving rainestimation by passive microwave radiometers.
The third reason for the Ka-band channel arises from the fact that the GPM core satellite
will provide coverage up to about 65 degrees latitude; by increasing the sensitivity of this channel,a larger fraction of snow events will be detected.
Since the Ku-band channel of the DPR is very similar to the TRMM PR, the principal
challenge in the development of the DPR level 2 algorithms is to combine the new Ka-band datawith the Ku-band data to achieve the objectives mentioned above.
2.2 Instrument Characteristics
The DPR consists of the Ku-band precipitation radar and the Ka-band precipitation radar.
They are abbreviated as KuPR and KaPR, respectively. These Earth-pointing KuPR and
KaPR instruments will provide rain sensing over both land and ocean, both day and night. The
KuPR and KaPR design specifications, with all active phased array elements functioning, areshown in Table 2.2-1. The spacecraft orbital information is shown in Table 2.2-2.
Figure 2.2-1 shows the DPR scan pattern. KuPR’s scan pattern is similar to that of the
TRMM PR. It has 49 footprints in a scan and the footprint size is about 5 km in diameter.
The scan swath is 245 km. The KaPR also has 49 footprints, but these are divided into two
types of scan. In the first type of scan (Ka MS), the beams are matched to the central 25
beams of KuPR, providing a swath of 120 km. In the second type of scan (Ka HS), the KaPR is
operated in the high-sensitivity mode to detect light rain and snow. In this case, its beams areinterlaced within the scan pattern of the matched beams as shown in Fig. 2.2-1. The KuPR and
7

KaPR for the Ka MS scan have the same range resolution (250 m), while the range resolutionof data in Ka HS is 500m. In both cases, radar echoes are over-sampled at twice the rate of thecorresponding resolution: 125 m for the matched beams and 250 m for the Ka HS.
Table 2.2-1 DPR Design Specification
Item

KuPR

KaPR

Swath Width

245 km120 kmRange Resolution

250 m

250/500 m

Spatial Resolution

5.2 km (Nadir at the height of 407 km)

Beam Width

0.71◦ (Center Beam)Transmitter

128 Solid State Amplifiers
1

Peak Transmit Power1012.0 W

Pulse Repetition Freq.2

146.5 W
4000 to 4500 Hz

Pulse Width

two 1.6 µs pulses

two 1.6 µs pulses (matched beams)
two 3.2 µs pulses (interlaced scans)

Beam Number49

49 (25 in matched beams
and 24 in interlaced scans)Min. measurable rain rate

0.5 mm/h

0.2 mm/h

Beam matching errorLess than 1000 m

Observable range

19 km to Surface (to −5 km near nadir)

Dynamic range

From −5 dB below the system noise levelto +5 dB above the nominal maximum surface echo level±1 dB±8.5◦ Cross Track

Receiver power accuracy◦

Scan Angle±17 Cross Track

Frequencies

13.597 and 13.603 GHz

Bandwidth35.547 and 35.553 GHz
14 MHzMax. Mass

472 kg

336 kgPower (max)

446 W (orbit average)

344 W (orbit average)

Science Data Rate (max)

109 kbps
81 kbps(The Total of KuPR and KaPR is 190 kbps)

Housekeeping Data Rate3

1 kbps (nominal)

1. This parameter is for informational purposes in the ICD.
2. In nominal operation mode.
3. 1 kbps may increase up to 2 kbps during SCDP switch-overs.

Table 2.2-2 Spacecraft Orbital Information
Inclination

65◦

Mean semi-major axis

6776.14 km

S/C Altitude Control Box±1 km

Orbit Eccentricity

0.00010 (0-0.0005 tolerance)

Geodetic Altitude Variation Range

397 km to 419 km

8Figure 2.2-1. DPR scan pattern.
Figure 2.2-2 shows the observation range. The DPR’s echo sampling is designed to covera range that, at minimum, extends from the surface to 19 km above the sea level (or from the
Ellipsoid). The pulse repetition interval is adjusted according to the satellite altitude and theangle of observation. As a result, the number of independent samples changes slightly as afunction of the scan angle.

Figure 2.2-2 DPR’s data sampling range

93. ALGORITHM DESCRIPTION
3.1. Theoretical Description
3.1.1. Physical Basis of the Algorithm
The radar transmits a pulse of radio waves and receives an echo from an object. The object
in the case of precipitation radar is a distribution of rain drops in the volume defined by theantenna directivity, the pulse width and the time between the transmission and the receptionof the pulse. The received power Pr from rain at range r is proportional to the apparent radarreflectivity factor Zm0 (r).
Pr (r) =C|K|2
Zm0 (r)r2(3.1-1)

where C is the radar constant, and K is a constant defined as a function of complex refractive
index m of scattering particles by the following equation.
m2 − 1m2 + 2K=(3.1-2)

When the radar electric specifications and range r are given, Zm0 can be calculated from Pr .
Therefore, we can assume that Zm0 (r) can be derived from a measurement of the echo power.
Zm0 is related to the eﬀective radar reflectivity factor Ze by
Zm0 (r) = A(r)Ze (r)(3.1-3)where A is the attenuation factor. The eﬀective radar reflectivity factor Ze can be expressedin terms of the backscattering cross section σb (D) of a precipitation particle of diameter D andthe particle size distribution N (D):Ze =λ4
π 5 |K|2∫
σb (D)N (D) dD(3.1-4)

Here λ is the wavelength of the electromagnetic waves, and rainfall rate R can also be expressedin terms of N (D),∫R=

V (D)v(D)N (D) dD(3.1-5)where V (D) is the volume of the precipitation particle of diameter D, v(D) is its falling velocity.
If N (D) is characterized by a single parameter, for example D∗ , then Ze and D∗ are in
one-to-one correspondence. Since R and D∗ is in a one-to-one correspondence as well, once
Ze is obtained, then N (D) can be specified by D∗ corresponding to this Ze , and R can becalculated. In this case, therefore, the attenuation correction used to obtain Ze from Zm0 is theprimary problem to be solved. This is the principle of rain estimation for a single frequency radar10such as the TRMM PR. In fact, however, variations of N (D) in nature cannot be characterized
suﬃciently by a single parameter in many cases. As a result, rain estimates from a single
frequency radar often involve errors and biases.
In contrast, variations in N (D) can be well represented by two parameters as far as theconversion from Ze to R is concerned. If rain is measured by the radar at two wavelengths andif one of the wavelengths divided by 2π is smaller than or comparable to the average raindrop size,
then the corresponding backscattering cross section will deviate from that of Rayleigh scattering,and Ze at this wavelength diﬀers from Ze at the longer wavelength where Rayleigh scattering
generally applies. This situation enables us to estimate two parameters of the model functionof N (D) and results in a better estimate of rainfall rate. This is the idea of rainfall estimation
with a dual-wavelength radar. In other words, if N (D) is characterized by two parameters N ∗and D∗ , Ze at two wavelengths Ze1 and Ze2 become functions of N ∗ and D∗ ,
∫
λ41
∗ ∗
Ze1 (N , D ) = 5
σb1 (D)N (D; N ∗, D∗ ) dD
π |K|2∫λ42∗ ∗Ze2 (N , D ) = 5
σb2 (D)N (D; N ∗, D∗ ) dD
π |K|2(3.1-6)(3.1-7)

Once Ze1 and Ze2 are given, we can solve (3.1-6) and (3.1-7) for N ∗ and D∗ , and R can becalculated from N (D; N ∗, D∗ ).
As in the single-wavelength case, the attenuation corrections to obtain Ze1 and Ze2 at two
frequencies from measured or apparent radar reflectivity factors Zm1 and Zm2 are crucial. Since
the major attenuation comes from precipitation itself, and the DSD parameters can be estimatedfrom Ze1 and Ze2 , we look for a profile of pairs of DSD parameters (one pair per range gate) thatgives the attenuation corrected profiles of Ze1 and Ze2 that are consistent with the attenuations
caused by the precipitation particles whose size distributions are characterized by these DSD
parameters. This part of the algorithm is the heart of the rainfall retrieval with dual-frequency
radar data. Details of the algorithm are described in section 3.2.5.
Attenuation caused by non-precipitation particles and atmospheric gases must be compensated for beforehand. Specifically, we need to take into account the attenuation caused by cloudwater, water vapor and oxygen molecules. Meteorological data and storm models are used toestimate their profiles for this purpose.
3.1.1-1 Eﬀective radar reflectivity factor Ze , specific attenuation k and measured
radar reflectivity factor
The eﬀective radar reflectivity factor Ze is given by (3.1-4). Similarly, the specific attenuation k
is given in terms of the drop size distribution N (D) and the total extinction cross section σe (D)∫byk = ckσe (D)N (D) dD
11(3.1-8)

where ck = 0.01/ ln(10) if k is expressed in [dB (km−1 )], σe (D) in [mm2 ], and N (D) in [mm−1m−3 ]. Let r [km] denote the distance from the radar along the range. The measured radarreflectivity factor Zm0 (r) [mm6 m−3 ] involves the attenuation by precipitation particles AP (r)and by non-precipitation particles ANP (r) as is expressed in the following equation.
[

]

∫ r

Zm0 (r) = Ze (r)ANP (r)AP (r) = Ze (r)ANP (r) exp −0.2 ln(10)k(s) ds(3.1-9)0

where s is a dummy variable. Equation (3.1-9) can be rewritten in decibels as shown below.
∫ r
10 log10 Zm0 (r) = 10 log10 Ze (r) + 10 log10 ANP (r) − 2

k(s) ds(3.1-10)0After attenuation correction for non-precipitation particles, Zm0 (r) becomes Zm (r), where Zm (r)is given by.
Zm (r) = Zm0 (r)A−1
NP (r) = Ze (r)AP (r)(3.1-11)

By substituting Eq. (3.1-11) into Eq. (3.1-9), the following equation is obtained.
∫ r
10 log10 Zm (r) = 10 log10 Ze (r) − 2

k(s) ds(3.1-12)0

3.1.1-2 HB method
Let us assume a power-law relation between k and Ze as shown below.
k(r) = α(r)Zeβ (r)(3.1-13)

where α and β are coeﬃcients, α can be range dependent, but β should be constant along therange. With the help of Eq. (3.1-13), Eq. (3.1-12) can be solved for Ze and written in the formof (3.1-14),
Zm (r)Ze (r) = [
]1/β
∫r
β
1 − 0.2 ln(10)β 0 α(s)Zm(s) ds

(3.1-14)

Alternatively, the equation can be solved for the attenuation factor, AP , giving,
[
]1/β
∫ r
β
AP (r) = 1 − 0.2 ln(10)βα(s)Zm(s) ds012(3.1-15)

3.1.1-3 Path integrated attenuation (PIA)
Here, Path Integrated Attenuation (PIA) is defined as integrated attenuation caused by precipitation particles from the radar to the surface. If r = rs at the surface, PIA is given as shown
below.
PIA = −10 log10 AP (rs ) = 2∫ rs
k(s) ds(3.1-16)0

Eq. (3.1-12) can be rewritten as a function of the PIA.
10 log10 Zm (r) = 10 log10 Ze (r) − PIA + 2∫ rs
k(s) ds(3.1-17)r

By substituting Eq. (3.1-15) at r = rs into Eq. (3.1-16), the following equation is obtained.
[
]
∫ rs
10
β
PIA = − log10 1 − 0.2 ln(10)βα(s)Zm (s) ds(3.1-18)β0
The PIA can be estimated by taking the diﬀerence between the surface backscattering crosssections with and without precipitation (surface reference technique).
3.1.1-4 Parameterization of drop size distribution function
From a mathematical point of view, N (D) should be parameterized with at most two unknown
parameters in order to characterize N (D) deterministically from dual-frequency measurements.
To keep the discussion fairly general, N (D) is parameterized in the following way.
N (D) = N ∗ n(D; D∗ ),(3.1-19)

where N ∗ and D∗ are unknown parameters and n is a function of D. By substituting Eq.
(3.1-19) into Eq. (3.1-4) and (3.1-8), Ze and k are given as below.
∫
λ4
∗
∗
∗
Ze = N Ib (D ), Ib (D ) = 5
σb (D)n(D; D∗ ) dD
π |K|2∫0.01∗∗∗
k = N Ie (D ), Ie (D ) =σe (D)n(D; D∗ ) dD
ln(10)(3.1-20)(3.1-21)

which shows that Ze and k can be decomposed into N ∗ and a function of D∗ (Ib (D∗ ) andIe (D∗ )).
3.1.1-5 Retrieval
3.1.1-5-1 Dual-frequency retrieval
If Zm is available at two frequencies, N ∗ and D∗ can be obtained by solving the following two
equations.
10 log10 Zm1 (r) = 10 log10 N ∗ (r) + 10 log10 Ib1 (D∗ (r)) − 2

∫ r
0

13

k1 (N ∗ (s), D∗ (s)) ds(3.1-22)

10 log10 Zm2 (r) = 10 log10 N ∗ (r) + 10 log10 Ib2 (D∗ (r)) − 2∫ r

k2 (N ∗ (s), D∗ (s)) ds(3.1-23)

0

The third term of the right hand side of Eq. (3.1-22) or (3.1-23) is equal to the 2-way attenuationfrom the storm top to range r, which implies that the retrieval should be performed sequentiallyfrom the top range bin to the gate of interest. This attenuation can be treated as a known
quantity if the attenuation is expressed as a function of the DSD parameters derived at eachgate. Since the solution progresses from the storm top downwards, the method is called theforward retrieval method. If an independent estimate of the PIA is available, in addition to Zm ,∫ rs
10 log10 Zm1 (r) = 10 log10 N ∗ (r) + 10 log10 Ib1 (D∗ (r)) − PIA + 2
k1 (N ∗ (s), D∗ (s)) dsr

10 log10 Zm2 (r) = 10 log10 N ∗ (r) + 10 log10 Ib2 (D∗ (r)) − PIA + 2∫ rs(3.1-24)
k2 (N ∗ (s), D∗ (s)) dsr(3.1-25)
Eqs. (3.1-24) and (3.1-25) instead of Eq. (3.1-22) and (3.1-23) can be used to calculate N ∗ andD∗ . In this case, the fourth term of the right hand side of Eq. (3.1-24) or (3.1-25) is equal to the
2-way attenuation from the gate of interest (at range r) to the surface, implying that the retrieval
should be done sequentially from the bottom range bin to the gate of interest. As in the forward
method, if the attenuation is related to the DSD parameters at each range gate, the fourth terms
can be treated as known. This method is called backward retrieval method. In reality, becauseof ground clutter, the fourth term cannot be calculated without some assumptions regarding
the profile of the precipitation in the clutter region. Furthermore, because PIA estimates by
the SRT have some error, equations (3.1-24) and (3.1-25) diﬀer from (3.1-22) and (3.1-23),
and the backward retrieval method yields diﬀerent solutions from the forward retrieval method.
However, the backward retrieval method is preferable, as it gives numerically stable and uniquesolutions, while the forward retrieval method usually has two solutions at each range bin.
In the backward retrieval method, we define 10 log10 Zb1 and 10 log10 Zb2 by moving thethird and fourth term of the right hand side to the left hand side in Eqs. (3.1-24) and (3.1-25).
∫ rs
10 log10 Zb1 (r) ≡ 10 log10 Zm1 (r) + PIA − 2
k1 (N ∗ (s), D∗ (s)) ds(3.1-26)r∗∗= 10 log10 N (r) + 10 log10 Ib1 (D (r))∫ rs
10 log10 Zb2 (r) ≡ 10 log10 Zm2 (r) + PIA − 2
k2 (N ∗ (s), D∗ (s)) ds(3.1-27)r∗∗= 10 log10 N (r) + 10 log10 Ib2 (D (r))
By taking the diﬀerence between Eqs. (3.1-26) and (3.1-27), we obtain:
10 log10 Zb1 (r) − 10 log10 Zb2 (r) = Ib1 (D∗ (r)) − Ib2 (D∗ (r))(3.1-28)

As the left hand side of Eq. (3.1-28) is known, D∗ can be derived. For liquid precipitation, theright-hand side of (3.1-28) has a maximum, and Eq. (3.1-28) has generally two solutions.
143.1.1-5-2 Single-frequency retrieval
If Zm is available only at a single frequency, we need to characterize N (D) with a single parameter. This means that we assume a relationship between N ∗ and D∗ . Once such a relation isassumed, we can translate the N ∗ -D∗ relation to the k-Ze relation, and the attenuation correction can be carried out by (3.1-14). If a PIA estimate by SRT (PIASRT ) is substituted into PIA
in (3.1-18), the equality is generally not satisfied. This inequality is caused either by an error
in the SRT or by an error in the k-Ze relation. If the SRT is correct, the k-Ze relation can be
modified to k = ϵαZeβ where ϵ satisfies (3.1-29). This is called the α-adjustment method.
[
]
∫ r
10
β
α(s)Zm (s) ds
PIASRT = − log10 1 − 0.2 ln(10)βϵβ0(3.1-29)

Once the ϵ parameter is found from (3.1-29), the Hitschfeld-Bordan solution with the modified
k-Ze relation provides the attenuation-corrected radar reflectivities. From the above attenuation
correction process, k and Ze are obtained. Then, by taking the ratio of (3.1-20) to (3.1-21), D∗
can be retrieved.

kIe (D∗ )=Ze
Ib (D∗ )(3.1-30)

Generally, the right hand side of (3.1-30) is a monotonic function of D∗ so that (3.1-30) has
a unique solution. Since N (D) is characterized by a single parameter, there is one-to-one
correspondence between Ze and D∗ or k and D∗, and we can directly calculate D∗ as well.
3.1.2. Overall structure of the algorithm
3.1.2-1 Algorithms
There are three kinds of Level 2 algorithms for the DPR: DPR algorithm, Ku-only (KuPR)
algorithm, and Ka-only (KaPR) algorithm. The latter two are single-frequency (SF) algorithms.
The DPR algorithm is a dual-frequency (DF) algorithm. The DF algorithm employs both KuPR
and KaPR L2 standard products as inputs. Pixels observed by DPR can be categorized into
three types: pixels in the inner swath of normal scans (observed both by KuPR and KaPR),
pixels in the outer swath of normal scans (observed only by KuPR), and pixels in the interleaved
scans (observed only by KaPR in the high-sensitivity mode). The KuPR algorithm is executedfor pixels in both inner and outer swaths of normal scans. The KaPR algorithm is executed for
pixels in the inner swath of normal scans and in the interleaved scans. The DF algorithm is
executed for pixels of all the three kinds.
In the DF algorithm, pixels in the inner swath of normal scans are categorized into the dualbeam (DB) pixels. The other pixels in the DF algorithm and all the pixels in the SF algorithms
are categorized into the single-beam (SB) pixels. For a SB pixel, the DF algorithm can use data
in dual-frequency observations at neighboring pixels, while the SF algorithms can use data only15

in the corresponding single-frequency observations. For example, by using the characteristics ofthe drop size distribution estimated by dual-frequency measurements at DB pixels in the innerswath, the DF algorithm can give better estimates at SB pixels in outer swath than the KuPR
algorithm. Each algorithm executes data for one orbit at a time. For an orbit, only after thetwo SF algorithms are properly executed, the DF algorithm will be executed. The order of
execution between the KuPR algorithm and the KaPR algorithm is not fixed. The algorithms
are designed in such a way that the order of execution for diﬀerent orbits has no restrictions
within the same calendar month, but one month of data should be executed as a unit to produce
proper databases.
3.1.2-2 Input/Output Files
The input and output files of the KuPR, KaPR and DF algorithms are as follows.
KuPR algorithm(Input) KuPR L1B standard product, Weather data file, Database file
(Output) KuPR L2 standard product, KuPR L2 temporary file, KuPR L2 environment data
KaPR algorithm(Input) KaPR L1B standard product, Weather data file, Database file
(Output) KaPR L2 standard product, KaPR L2 temporary file, KaPR L2 environment data
Dual-frequency algorithm(Input) KuPR L2 standard product, KaPR L2 standard product, KuPR L2 temporally file, KaPR L2 temporary file, Database file, KuPR L2 environment
data, KaPR L2 environment data
(Output) DPR L2 standard product, DPR L2 environment data, DPR L2 temporary
file
The temporary files include some results of the SF algorithms, which are not written in thestandard products. The temporary products can be treated as research products, once the DPR
L2 standard product and other outputs are properly produced by the DF algorithm.
The environmental data include the profiles of atmospheric parameters assumed in the L2
algorithm. Because of its large volume, the ancillary environmental data are output into aseparate file from the standard product.
The weather data file is prepared by converting the resolution and/or variables of weather
analysis/reanalysis/forecast dataset in advance to L2 algorithm. Database files are explainedlater.

16

3.1.2-3 Modules
The framework of the L2 algorithm is shown in Fig. 3.1-1. This framework is common to the
KuPR, KaPR, and DF algorithms. The main module manages the overall procedure, and it
employs seven sub-modules. The main module will open and close files, call sub-modules, andread/write all the input and output files and variables. It will also terminate the algorithm.
Sub-modules can read/write files and variables as long as they are permitted. As a basic rule,they should process for all the pixels in the orbit, and the order of pixels processed is not fixed(can be determined by each module). Sub-modules cannot call other sub-modules. When a sub
module is terminated, the processing is returned to the main module.

Figure 3.1-1 DPR algorithm flow17

3.1.2-4 Basic roles of the modules
The L2 algorithm is to numerically solve Eq. (3.1-22) or (3.1-23) for DB pixels and Eq. (3.130) for SB pixels to obtain N ∗ and D∗ . The retrieval process is carried out in the Solver
module, but the preparation of equations is shared by the other six modules. The PIA in
Eqs. (3.1-29), (3.1-24) and (3.1-25) can be estimated in the SRT module. The DSD module
is responsible for quantifying the terms in the equations such as α, β, n(D; D∗ ), Ib (D∗ ), andIe (D∗ ) based on the physical characteristics of precipitation (precipitation types and brightband information) determined by the Classification module. Zm is converted from Zm0 throughattenuation correction for non-precipitation particles in the Vertical Profile module and Zm0 isconverted from received echo power in the Preparation module.
3.1.2-5 Variables
A tentative table of variables is attached to this document. The table is used for all the threealgorithms in the L2 algorithms. There are two types of variables: L1B variables and L2
variables. L1B variables are just copied from the L1B standard products. L2 variables are
somehow processed in the L2 algorithms. Not all but some selected variables are written in
L2 standard, temporary, or environment products. A variable with scan dependence, angle bin
dependence, range bin dependence, and frequency dependence is expressed as an array in thesource code. Some variables may have other dimensions than scan, angle bin, range bin, andfrequency. Type (bytes) of a variable can be changed when it is written into the product to savethe file size. Some variables can take diﬀerent values in the SF algorithms and DF algorithm.
Those variables are product dependent. Product dependence and frequency dependence are
explained by examples below.
• “Location of observation” is without product dependence and without frequency dependence.
• “Measured radar reflectivity factor Zm0 ” is without product dependence and with frequency
dependence.
• “Equivalent radar reflectivity factor Ze ” is with product dependence and with frequency
dependence.
In the DF algorithm, variables with product dependence should be reprocessed, but variables without product dependence can be copied from SF products. Variables without productdependence and without frequency dependence should take the same value in KuPR productand KaPR product, but there is some possibility of disagreement as the KuPR algorithm and
KaPR algorithm are processed independently. In the case of disagreement, a rule (e.g., KuPR
algorithm is more reliable) should be prepared to determine the value in the DF algorithm.

18

3.1.2-6 Databases
A database is to include useful information for the algorithm, and exists as separate files fromthe main body of the algorithm (source code). For example, the SRT database is used in the
SRT module to increase the accuracy of the PIA estimates. The L2 algorithm can refer to andupdate databases. The actual updating process is done not by directly modifying the database
file, but by creating an intermediate file. The intermediate files are summarized and the databasefile is modified oﬀ line.
3.1.2-7 Look-up tables
A look-up table has a similar role with a database, but the diﬀerence is that a look-up table
will basically not be updated. Therefore, we can treat the look up tables as part of the main
body of the algorithm, and do not list the look-up tables as input and output files. The L2
algorithm employs a DSD look-up table and a RTM look-up table in the DSD module and theSolver module, respectively.

19

3.2 Main module
The basic procedure of the main module is described in 3.2.1 below. The test version before
the data release in 2014 used the basic procedure. Version 3 algorithm adopts the advanced
procedure 3.2.2-1. In near future, advanced procedures described in 3.2.2-2 amd 3.2.2-3 shouldbe tested.
3.2.1 The basic procedure
In the basic procedure, sub-modules are executed in the following order, and each module iscalled only once. Firstly, the Preparation module is executed. This module judges the existenceof precipitation, and identifies the precipitation pixels. Secondly, the Vertical profile module is
executed. The ancillary atmospheric profile data are used to correct for the attenuation caused
by non-precipitation particles to obtain the surface backscattering cross sections both at precipitation and no-precipitation pixels. Thirdly, the Classification module classifies each precipitation
pixel into an appropriate storm type. At no-precipitation pixels, almost no processes are taken.
Fourthly, the SRT module is executed. At no-precipitation pixels, SRT database is updated
based on the measurement of surface backscattering cross sections. Note that the SRT module
can be executed anytime after the Vertical profile module and before the Solver module. Finally,
the Solver module is executed. At no-precipitation pixels, almost no processes are taken.
3.2.2 Advanced procedure
By executing some of the modules multiple times, improvement of results is expected. Twokinds of examples are given below.
3.2.2-1 Recursive procedure
Once the basic procedure is done, the same procedure is repeated again with the parameters
estimated in the first cycle. In the second cycle, for example, we can estimate the attenuation
due to cloud liquid water by referring the precipitation rate estimated in the first cycle.
3.2.2-2 Parallel procedure
In the basic procedure, the sub-modules are required to determine the values of variables incharge, but sometimes they do not have enough information to estimate the values in a deterministic way. A parallel procedure allows a sub-module to give multiple estimates and to let thefollowing sub-modules execute with multiple estimates. For example, if the precipitation type is
not determined with confidence in Classification module, then the following (DSD and Solver)
modules are executed with multiple assumptions of precipitation types, and the main module
checks which assumption gives the vertical profiles of Ze and the corresponding precipitation
rate R in accordance with the assumed precipitation type. Thus, the precipitation type can be
determined afterwards.
20

3.2.2-3 Additional processes
Some important processes may be missing in the basic procedure, for example, non-uniform
beam filling (NUBF) correction. They should be involved in the final version of the algorithm,
but currently, it has not been determined which modules are in charge of additional processes;
NUBF correction can be done inside the Solver module, or should be shared with the DSD
module and/or the SRT module, or a new module (Texture module) should be introduced.

21

3.3 Preparation module
3.3.1. Objectives and functions of the Preparation Module (PRE)The primary purposes of the preparation module are (1) to classify the pixels into either rain or′
no-rain pixels, (2) to convert the received power Pr into measured reflectivity factor Zm
without

any attenuation corrections at each range bin, and (3) to calculate apparent normalized surface′

0
cross section σm
without any attenuation correction at each pixel.

3.3.2. Algorithm descriptions of the PRE
In this section, the preparation module of Ku-band level-2 algorithm will be described in detail.
As to the preparation module of Ka-band level-2 algorithm, it is almost the same with thatfor the Ku-band. The DPR level-2 algorithm can use level-2 products and level-2 supplementary products of both Ku-only and Ka-only algorithms. As a result, it can provide necessary
information to other modules of the DPR level-2 algorithm without re-calculations.
Reading of input data
The module reads data from a Ku-band level-1B product that includes not only the radar echoes
but also other variables related to the measurements such as scan time (ScanTime), footprint
position (Latitude, Longitude), local zenith angle (scLocalZenith), operational mode (operationalMode, elevation (DEMHmean), and data quality flag (dataQuality). In the case of the
DPR level-2 algorithm, the module reads level-2 products and level-2 supplementary productsof both Ku-band and Ka-band algorithms.
Status confirmation
The module refers to dataQuality of level-1B product and reflect qualityData of level-2
product scan by scan. If dataQuality is bad (not 0), level-2 values of the corresponding scan
are overwritten by missing values.
Calculation of range distance (rangeDist)The range distance (rangeDist) is defined by the distance from the satellite to each range
bin along the radar beam. Each rangeDist is calculated from the range distance from thesatellite to (the center of) the highest range bin (startBinRange), the number of range bins
with normal sampling (echoLowResBinNumber), the number of range bins with over-sampling(echoHighResBinNumber) and the range bin size (rangeBinSize). Specifically, the range distance (rangeDist) for normal sampling ranges are calculated by using the following equation.
rangeDist = R0 + (n − 1) × ∆R,(n = 1, 2, 3, · · · , N )The rangeDist for over sampling ranges arerangeDist = R0 + {N + (m − 1)} × ∆R,
22(m = 1, 2, 3, · · · , M )whereR0 = startBinRange in L1B productN = echoLowResBinNumber in L1B productM = echoHighResBinNumber in L1B product∆R = rangeBinSize in L1B productFigure 3.3-1 Definition of rangeDist

Calculation of height (Height)
The height is defined by the vertical distance from the footprint of radar beam on the Ellipsoid to the range bin in question. In order to calculate Height, we define ellipsoidBinOffset
as follows.
ellipsoidBinOffset =scRangeEllipsoid− {R0 + (binEllipsoid − 1) × rangeBinSize}

Then, Height of a binRangeNo (as shown “p” in Fig. 3.3-3) is calculated by the following
equation.
Height[binRangeNo] ={(binEllipsoid 2A − binRangeNo) × rangeBinSize+ ellipsoidBinOffset} × cos(localZenithAngle)
23

Figure 3.3-2 Definition of ellipsoidBinOffset
where binEllipsoid 2A is the Ellipsoid range bin number in level 2. binEllipsoid 2A is 176
for Ku and KaMS, and 88 for KaHS. rangeBinSize is 125 m for Ku and KaMS, and 250 m for
KaHS.

Figure 3.3-3 Definition of Height

Extraction of level-1B range data
echoPower and echoFlag of level-1B product are given at each range bin. The array size of24

data with range-bin dependence in Ku level-1B is 260. The array sizes of data with range-bin
dependence in Ka MS and Ka HS level-1B are 260 and 130, respectively. On the other hand,the array size of data with range-bin dependence in Ku level-2 is 176, whereas the array sizes
for range bins of Ka MS and Ka HS level-2 are 176 and 88, respectively. The preparation
module renumbers the range bins relative to the Ellipsoid position in level-2 so that the range
bin number at the Ellipsoid becomes 176 in the Ku and Ka MS level-2 products, and 88 in the
Ka HS products. Thus, at the nadir angle bin, data from 0 km to +22 km altitude relative tothe Ellipsoid are extracted.
Land surface type (landSurfaceType)The land-and-ocean flag (landOceanFlag) of level-1B product gives land, ocean, inland
water, and coast classification for each footprint. The preparation module adds surface type
information for land and coast area, using footprint position (Latitude and Longitude) and an
external data base. The surface type information is stored in landSurfaceType.
Snow- or Ice-covered area (snowIceCover)In version 5, a new flag called snowIceCover is added to indicate the areas that are covered
with surface snow or sea ice. This flag is used in the SRT module when it needs to separate
diﬀerent surface types and also when it collects surface scattering data to create the σ 0 database.
The flag is based on NOAA/NESDIS autosnow.
Surface detection (binRealSurface)
The preparation module determines the surface range bin using the received power(echoPower), and DEM related data, such as binDEM, scRangeDEM, DEMHmean, binDEMHtop,
and binDEMHbottom of level-1B product. The estimated surface position is stored in binRealSurface.
Estimation of Clutter-free bottom (binClutterFreeBottom)
The range bin number of the clutter-free bottom is estimated using echoPower profiles.
binRealSurface may be used as a reference of the search window. The clutter-free bottom
range bin number is stored in binClutterFreeBottom.
In V5, binclutterFreeBottom is automatically raised up by one range bin (by adding a one
range bin margin to binClutterFreeBottom). This automatic one range bin raise-up sometimes
causes the contamination of binclutterFreeBottom with the side lobe clutter. This problem
is solved in V6 by further raising up the binClutterFreeBottom (by more than one range bin
until the contamination disappears) when the contamination with the side lobe clutter occurs.
The V5 code also has a very small bug on Ka-HS binClutterFreeBottom. This bug is fixed in
V6.
Reduction of sidelobe clutter contamination
25

A routine to reduce the sidelobe clutter was installed in the KuPR-L2 algorithm. Fig. 3.3-4
shows a flowchart of the routine (Kubota et al. 2016). Sidelobe echoes are estimated from astatistical relationship between Normalized Radar Cross Section (NRCS) and sidelobe clutter
obtained from observation data and then the routine subtracts sidelobe estimates from received
power. In the version 06 or later versions, the routine to reduce the sidelobe clutter was installed
also in the KaHS-L2. The method in the KaHS was largely similar to that in the KuPR, but it
included some modifications described in Kubota et al. (2018). When the sidelobe estimate is
subtracted at a range bin, bit 6 in the flagEcho is flagged by this routine.

Figure 3.3-4 Flowchart of the routine to reduce sidelobe clutter installed in
KuPR-L2 algorithm

Calculation of Signal power (echoSignalPower)
Calculations of signal power are applied to all range bin data except missing data which are
flagged by dataQuality in L1B product.
Pecho = pow(10.0,(double) echoPower /10)
Pnoise = pow(10.0,(double) noisePower /10)
Psignal = Pecho - Pnoise
echoSignalPower = 10*log10(Psignal)
where echoPower and noisePower are stored in dB unit in L1B product. If Psignal values arenegative, missing value is stored in echoSignalPower.
26

Rain/NoRain classification (flagEcho, flagPrecip, binStormTop, heightStormTop)
Rain/NoRain classification is carried out in two steps. At first, it is done for all range bins
above the binClutterFreeBottom except missing data which are labeled by dataQuality in L1B
product. If echoSignalPower exceeds a certain threshold, it means that rain is detected for therange bin. The results of Rain/NoRain classification for range bins are stored in flagEcho.
Then, using the surrounding flagEcho results, the Rain/NoRain classification for angle bin iscarried out for all angle bins except missing data. The results of Rain/NoRain classification foreach angle bin are stored in flagPrecip. The modules in the downstream in the flow chart may
use the flagPrecip to determine the target pixels for processing. The function also detects ahighest rain position in each angle bin and provides binStormTop and heightStormTop.
′
Calculation of Zm
factor (zFactorMeasured)′′Zm
is defined as the measured reflectivity factor without any attenuation corrections. Zm

is calculated using a radar equation at all range bins above the binClutterFreeBottom except′
missing data which are labeled by dataQuality in L1B product. The result of Zm
is stored inzFactorMeasured. The radar equation is as follows.
Pr = C · P · Lr · E[(= [Pt · Gat · Gar ]
=

λ
4πr

)4

] [
]
4π
π5
πr2 θ0a θ0c2−18
Lr 4 |K| Ze 10 cτ 4λ2
λ2 ln(2)

π3 c
Pt Gat Gar θ0a θ0c τ |K|2
Lr Ze
210 1018 ln(2)λ2 r2′(= Lr Ze ) is calculated by using the following equation.
In the preparation module, Zm

Pr =

π3 c
210 1018 ln(2)′Zm=

Pt Gat Gar θ0a θ0c τ |K|2 ′
Zm
λ2 r2

210 1018 ln(2)λ2 r2 Pr
π3 c
Gat Gar θ0a θ0c τ |K|2 Pt

where
r range distance (rangeDist)λ wave length (eqvWavelength of L1B)
|K| function of dielectric constantGt transmitter antenna gain (txAntGain of L1B)
Gr receiver antenna gain (rxAntGain of L1B)
θc Cross-track beam width (crossTrackBeamWidth of L1B)
θa Along-track beam width (alongTrackBeamWidth of L1B)
c light speed
τ transmitter pulse width (transPulseWidth of L1B)
27

Pt transmitted power (radarTransPower of L1B)
Pr received signal power (echoSignalPower)
It should be noted that |K| is a constant in the preparation module. Any adjustment of |K|by temperature is controlled in the solver module if necessary.
′0
Calculation of σm(sigmaZeroMeasured)′0
σmis defined as the measured normalized surface cross section without any attenuation cor′0
rections. Calculation of σmis done for all footprints except missing data which are labeled by′0
dataQuality in L1B product. The value of σmis stored in sigmaZeroMeasured.
′0(θz ) = Prs (r0 )
σm

where
−1
θbp
=

√

512π 2 ln(2) loss cos(θz )r02
Pt λ2 Gt Gr 1θa θbpθc−2 + θp−2 ,θp−1 =

2
r0 tan(θz )cτ

θc Cross-track beam width (CrossTrackBeamWidth of L1B)
θa Along-track beam width (AlongTrackBeamWidth of L1B)
Pt transmitted power (radarTransPower of L1B)
Prs received signal power (echoSignalPower)r0 range distance from the satellite to the geographic surface (ref. binRealSurface)θz zenith angle (localZenithAngle)
loss band path filter loss
λ wave length (eqvWavelength of L1B)
Gt transmitter antenna gain (txAntGain of L1B)
Gr receiver antenna gain (rxAntGain of L1B)
c light speed
τ received pulse width (receivedPulseWidth of L1B)
3.3.3. Interfaces to other algorithms
As to the Ku-band and Ka-band level-2 algorithms, input data for this module is Ku-band andKa-band level-1B product, respectively. For the DPR level-2 algorithm, input data for thismodule are level-2 products and level-2 supplementary products of both Ku-band and Ka-band.
The outputs will be used by the Solver module and other modules in the DPR algorithm.

28

3.3.4 Output Variables′
adjustFactor :Adjustment factor (dB) for zFactorMeasured (dBZm) and sigmaZeroMeasured′0(dBσ 0′ m ). dBZm
= dBZm − adjustFactor, dBσ 0′ m = dBσm
− adjustFactor

binClutterFreeBottom: Range bin number for clutter free bottom.
binRealSurface: Range bin number for real surface.
binStormTop: Range bin number for the storm top.
elevation: Elevation of the measurement point.
ellipsoidBinOffset: Distance between the ellipsoid and a center range bin of binEllipsoid.
flagPrecip: A flag of Rain/NoRain classification.
flagSigmaZeroSaturation: A flag of saturation of sigmaZeroMeasured.
heightStormTop: Height of storm top.
landSurfaceType: Land surface type.
localZenithAngle: Local zenith angle of each ray.
sigmaZeroMeasured: Surface backscattering cross section without attenuation correction.
snRatioAtRealSurface: Signal/Noise ratio at real surface range bin.
snRatioAtRealSurface = 10 × log10 (echoPowertrueV[mW]/noisePowertrueV[mW])snowIceCover: Snow and ice cover information. It refers to the multisensor snow/ice covermaps provided by NOAA.
zFactorMeasured: Vertical profile of reflectivity factor without attenuation correction.

29

3.4 Vertical Profile Module
3.4.1 Objectives and functions of the Vertical Profile Module (VER)
The primary purposes of the VER are to read ancillary environmental data, to provide verticalprofiles of the environmental parameters, to compute the path-integrated attenuation (PIA) due
to non-precipitation (NP) particles, and to give radar reflectivity factors corrected for attenuation by the non-precipitation particles. The VER provides environmental information suchas pressure, temperature, water vapor, and cloud liquid water at each range bin. The VER
calculates the attenuation due to water vapor, molecular oxygen, and cloud liquid water.
For Ku, Ka, Ka HS, the VER is executed using the information of the pixel lat/lon and therange bin width. For the DPR, results of the Ku, Ka, Ka HS are introduced.
Algorithm descriptions of the VER
3.4.2 Utilization of ancillary environmental data
The VER inputs ancillary environmental data, objective analysis data by Japan Meteorological
Agency (JMA) named as JMA Global Analysis (GANAL) product. By reading the ancillary
environmental product, the VER provides pressure, temperature, water vapor, and cloud liquidwater for each range bin. In addition, the VER computes the height of 0 degree centigrade andfinds out the range bin corresponding to that height.
The horizontal resolutions of the GANAL data are 0.5 degree latitude/longitude, respectively. The pressure levels such as 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100 hPa,and so on are converted to the height levels before the input of the algorithm. The temporal
resolution is 6 hourly as 00, 06, 12, and 18Z. The VER inputs two 6-hourly files and computes
the values of parameters at the time of measurement using linear temporal interpolation. We
also use a linear horizontal and vertical interpolation to calculate the location of each range
bin from the lat/lon’s of the footprint and the satellite and the range bin height. Figuree 3.4-1
shows how this interpoloation is carried out by finding the antenna beam direction from thelat/lon information and a local zenith angle of the spacecraft and the “height” provided by the
preparation module (see Section 3.3). The VER provides pressure, temperature, water vapor,and cloud liquid water at each range bin.
Calculation of attenuation by water vapor
At a frequency less than 100 GHz, the attenuation coeﬃcient due to water vapor, κH2O (f )(dB/km) is expressed as follows (Waters 1976, Ulaby et al. 1981, Meneghini and Kozu 1990),(
2

κH2O (f ) = 2f ρν

300
T

)3/2

[(
γl

300
T

)
e

−644/T

where30

1+ 1.2 × 10−62(494.4 − f )2 + 4f 2 γl2]

Figure 3.4-1 A concept of consideration for the antenna beam directions in the VER.
f : frequency (GHz)ρν : water vapor content (g/m3)
γl : parameter of line width (GHz)T : temperature (K)The line width parameter γl is given as(
γl = 2.85

P
1013

)(

300
T

)0.626 ()ρν T
1 + 0.018
P

where P : pressure (hPa).
In precipitation pixels, the water vapor content provided by the GANAL may be problematicdue to its relatively low resolution (0.5x0.5 deg. lat/lon). Therefore, a minimum threshold isused in the VER. The relative humidity is set to 90% when the GANAL gives a value less than
90% in precipitation pixels.
Calculation of attenuation by molecular oxygen
For attenuation by molecular oxygen, the following expression is valid for frequencies lessthan 45 GHz (Rosenkranz 1975, Ulaby et al. 1981, Meneghini and Kozu 1990).
−2 2

κO2 (f ) = 1.1 × 10

(

f

P
1013

)(

300
T

where
f : frequency (GHz)f0 : 60 GHz
γ: parameter of line width (GHz)T : temperature (K)
31

)2 [
γ

1
1
+ 2
(f − f0 )2 + γ 2
f + γ2]

Line width parameter γ is given by(γ = γ0
where

P
1013

)(

300
T

)0.85


for 333 ≤ P (hPa); 0.59,γ0 = 0.59[1 + 3.1 × 10−3 (333 − P )], for 25 ≤ P < 333 (hPa);
1.18,
for P < 25 (hPa).

Calculation of attenuation by cloud liquid water (CLW)
For attenuation by cloud liquid water κCLW over non-precipitation pixels, cloud water contents in the ancillary environmental data product are utilized. In the cloud scheme of the JMA
Climate Data Assimilation System (JCDAS) product (Onogi et al. 2007), the eﬀective radius(re )of cloud liquid droplets is fixed at 15 microns. In this calculation, we assume cloud particle
distribution nc (D) as mono-disperse, that is,
nc (D) = Nc δ(D − 2re )where
D: Diameterre : 15 microns
Nc : Number concentration of cloud liquid particles
For cloud liquid water content Q (kg/m3), Nc is expressed as follows,
Nc =3Q
4πre3 ρw

κCLW is computed due to the Rayleigh scattering using nc (D). The mono-disperse assumption
implies the homogeneity of the cloud distribution, although clouds generally distribute withhigh inhomogeneity within the grid-size of the reanalysis such as 1.25-degree latitude/longitude.
Therefore, the previous formula will underestimate κCLW over precipitation pixels. In the 2A25
algorithm for the TRMM/PR, κCLW over precipitation pixels is estimated based on the result of
a numerical simulation of storms with a cloud-system resolving model (CRM) that was used tocreate the database for the TRMM/TMI 2A12 algorithm (Iguchi et al. 2009). In their method,
the attenuation due to CLW is estimated as a function of surface precipitation rate, separately forconvective columns and stratiform columns. The VER adopts a method which expands Iguchi
et al. (2009) and κCLW is estimated from a CLW database as a function of surface precipitation
rate, precipitation type (convective or stratiform), temperature, latitude, and land surface type
(Kubota et al. 2012). The CLW database was generated by 3.5 km-mesh global simulations
performed using a Nonhydrostatic ICosahedral Atmospheric Model (NICAM). The NICAM is a
global cloud-system resolving model (GCRM), and explicitly calculates moist convection using
a cloud microphysical scheme. The NICAM data were provided by the University of Tokyo andJapan Agency for Marine-Earth Science and Technology (JAMSTEC).
32

3.4.3 Input Variables
Input: from MOSS or PPS
Ancillary environmental data Two 6-hourly files
Surface: Surface pressure, Mean sea level pressure
Pressure levels: Geopotential height, temperature, water vapor, and cloud liquid water
Input: from Preparation Module
Geolocation
scanTime
Elevation
landSurfaceTypelocalZenithAngle
flagPrecip
binRealSurface
binStormTopheightStormTop
binClutterFreeBottom
sigmaZeroMeasured
zFactorMeasured
rangeBottom
rangeTop
Height33

3.4.4. Definitions of Output Variables
airTemperature[80][49] Temperature
rangeZeroDeg[49] Range bin number corresponding to the level of 0 degree centigrade.
heightZeroDeg[49] Height of the level of 0 degree centigrade.
airPressure[80][49] Pressure (hPa)waterVapor[80][49] Water vapor (kg/m3)
attenuationNPwv[80][49] Attenuation by water vapor (dB/km)
attenuationNPoxygen[80][49] Attenuation by molecular oxygen (dB/km)
cloudLiquidWater[80][49] Cloud Liquid water content (kg/m3)
attenuationNPclw[80][49] Attenuation by cloud Liquid water (dB/km)
attenuationNP[80][49] Sum of Attenuations by water vapor, molecular oxygen, and cloudliquid water
AttenNP (r) = κWV (r) + κO2 (r) + κCLW (r)piaNP[80][49] PIA by the non-precipitation particles.
∫ r
PIANP (r) = 2∫ r
AttenNP (s) ds = 20

κWV (s) + κO2 (s) + κCLW (s) ds
0

zFactoNPCorrected[80][49] Radar reflectivity corrected for attenuation by the nonprecipitation particles′
Zm (r) = Zm(r)/ANP (r)ANP (r) = exp(−qPIANP (r)) where q = 0.1 ln(10)sigmaZeroNPCorrected[49] σ 0 corrected for attenuation by the non-precipitation particles′

σ 0 = σ 0 /ANP (r)ANP (rsfc ) = exp(−qPIANP (rsfc )) where q = 0.1 ln(10)3.4.5. Intermediate Files
Before the input of the algorithm, the pressure levels of the ancillary environmental data areconverted to the height levels. In addition, the attenuations and PIA estimates by water vapor,molecular oxygen, and cloud liquid water are computed in advance.

34

3.4.6. Description of the Processing Procedure
Before the input of the algorithm, the pressure levels of the ancillary environmental data will be
converted to the height levels. In addition, the attenuations and PIA estimates by water vapor,molecular oxygen, and cloud liquid water will be computed in advance.
For Ku, Ka, Ka HS, the VER is executed using the information of the pixel lat/lon and therange bin width. For the DPR, calculated results of the Ku, Ka, Ka HS are used.
3.4.7. Interfaces to other algorithms:
Input data for this algorithm is from the Preparation Module and Ancillary environmental data;
the outputs will be used by the Classification module, DSD module, Solver Module and others.

35

3.5 Classification Module
3.5.1 Introduction
In reliable rain rate retrieval using space-borne radars, information about the drop size distribution (DSD) is necessary. DSD varies depending on the rain type. Therefore, rain typeclassification plays an important role in the GPM DPR algorithm. There exist two distinctive
rain types, one is stratiform and the other is convective. Stratiform rain is characterized by
its weakness in intensity, wide extension in area, and in many cases accompanying a brightband (BB) in radar echo. Because of the last characteristic, detection of a BB can be used for
determining stratiform rain.
The rain type classification is made on pixel basis. Hence, the rain type is the same along aradar beam. Then there arises the following ambiguous situation. Suppose that, when the radarreflectivity being examined along a given radar beam, the precipitation echo happens to exist
only at altitude higher than 0◦ C height. What is the rain type for this case? Is it stratiform
or convective? This is a diﬃcult question to answer. To handle such an ambiguous case, thethird category of “other” type is introduced in a similar manner to the case of the TRMM PR
algorithm 2A23. The third category “other” means that there exists only cloud or possibly noise
when the radar echo is examined along the radar beam.
A surprise in the early TRMM PR observation was a ubiquitous shallow isolated rain system,
which may be warm rain but it still remains a puzzle what it actually is. TRMM PR observationshows that shallow isolated rain is weak, which does not agree with the concept of warm rain
which is thought to be very strong. There also exists shallow non-isolated rain, whose statisticalproperties seem to be very similar to those of shallow isolated rain. Shallow rains (both shallow
isolated and shallow non-isolated rain) are marked by a shallow rain flag, flagShallowRain,which is independent of the rain type flag, typePrecip.
In the V5 classification (CSF) module, new items (flagHeavyIcePrecip, flagAnvil, surfaceSnowfallIndex, and flagSurfaceSnowfall) were added to the output fields and a decision
on winter convective type was also made. Section 3.5.5 describes these features.
In the V6 CSF module, reclassification of precipitation type from convective to stratiform ismade by using a method developed by the UW (University of Washington) (Houze and Brodzik,2017), which examines the slope of Z profile above the 0◦ C isotherm (see Section 3.5.4-1-4-5 fordetails).

36

3.5.2 Objectives
The CSF module detects bright band (BB) and classifies rain into three major categories, which
are stratiform, convective, and other.
3.5.3 Algorithm Principles
In single frequency CSF modules, i.e., in Ku-only and Ka-only modules, rain type classification
is made by a V-method (Vertical profiling method) and by an H-method (Horizontal pattern
method) (Awaka et al., 1998, 2009). The rain types by the V-method and H-method are unified,
and the Classification (CSF) module outputs the unified rain type, which consists of three major
categories; stratiform, convective, and other.
In the dual frequency algorithm of GPM DPR, however, rain type classification is made by
a new method called measured dual frequency ratio (DFRm ) method (Le and Chandrasekar,
2013a, 2013b) and by using the single frequency result from the Ku-only module. The DFRm
method is a kind of vertical profiling method, and classifies rain type into stratiform, convective
and transition. Though transition is a new type, it is not a major category. The rain types
by the DFRm method and those by the Ku-only module are unified. Three major categories,
after the unification of rain types of DFRm method and Ku-only results, are again stratiform,
convective and other.
3.5.3-1 Single frequency: V-method
In the single frequency V-method, detection of BB is made first. Detection of a BB is made byexamining the vertical profile of the radar reflectivity factor (Z) to see if the vertical profile ofZ satisfies certain conditions which are typical to the profile of Z when a BB exists. When a
BB is detected, the rain type is stratiform if the reflectivity factor in the rain region does notexceed a special convective threshold. When a BB is not detected, and the reflectivity factor
exceeds a conventional convective threshold, the rain type is convective. When rain type isneither stratiform nor convective, the rain type is “other” in the V-method.
3.5.3-2 Single frequency: H-method
In the H-method, a horizontal pattern of a representative radar reflectivity factor is examined.
Here the representative reflectivity factor means the maximum value of reflectivity factor in therain region along the considering radar beam. Rain type is classified using a modified Universityof-Washington convective/stratiform separation method (Steiner et al. 1995, Yuter and Houze1997). In the H-method, rain type is again classified into three categories: stratiform, convective
and other. In the H-method, detection of convective rain is made first. If the rain type is notconvective, the rain type is stratiform unless the reflectivity factor is very small, being almost
identical to noise. If rain type is neither convective nor stratiform, the rain type is other.
37

3.5.3-3 Dual frequency: DFRm method
In the dual frequency module, in place of the V-method, the DFRm method is used for rain typeclassification and for BB detection in the inner swath. The DFRm method uses the diﬀerence
between the measured Ku-band Z in dB and the measured Ka-band Z in dB. To make the BB
detection reliable, BB detected by the DFRm method and that by the Ku-only algorithm are
unified in the dual frequency CSF module. Rain types by the DFRm method are stratiform,
convective and transition. In the outer swath in which only Ku-band data is available, rain
type and BB are copies of those in the Ku-only module. The DFRm method is also used forhandling HS data by interpolation, which is made vertically and horizontally. In the verticalinterpolation, the Ka-band data with a 125 m interval is interpolated from the 250 m interval
HS data. In the horizontal interpolation, four Ku-band pixels adjacent to the HS pixel are used
for the interpolation of the corresponding Ku-band data at the HS pixel.
3.5.4 Actual Algorithm (Data Processing)
3.5.4-1 Single frequency processing
In Ku-only and Ka-only algorithms, BB detection is based on a search for a typical BB peakby examining the range profile of radar reflectivity along a given antenna beam. This method
is eﬀective for the Ku-band data which show clear BB peaks. In the case of Ka-band, however,since a clear BB peak is not expected, detection of BB is not eﬀective.
3.5.4-1-1 Selection of pixels to be processed:- Skip the process for the pixel if no precipitation echo exists in Ku-band and Ka-band data.
This judgment can be made by examining a flag from the PRE module, flagPrecip.
- Skip the process for the pixel if the quality flag of Ku-band and/or that of Ka-band showsthat the pixel is a bad pixel or that the data is missing. This judgment can be madeby examining a flag from the PRE module, qualityData. (It is assumed here that the
information about missing data is also available from the qualityData flag.)
3.5.4-1-2 Determination of range where precipitation echoes exist:- Determination of the echo top and the echo bottom, where the echo top is given byrangeStormTop from the PRE module and the echo bottom is given by binClutterFreeBottom from the PRE module.
3.5.4-1-3 Detection of bright band (BB) by the single frequency method- Set the BB search window using rangeZeroDeg from the VER module. The BB searchwindow ranges from rangeZeroDeg − 4 to rangeZeroDeg + 8 with a 250 m interval, where
the center of the BB window is rangeZeroDeg + 2 which is about 0.5 km below the 0 degreeheight. This window range is reasonable from TRMM PR experience and many other radar38

observations (unfortunately, Ka-band radar data may not exhibit a clear BB peak, but the
BB search window can be the same for the Ku-band and Ka-band). Experience shows that
BB peak appears about 0.5 km below the 0 degree height.
Detection of BB is made using the NP-attenuation corrected radar reflectivity factors, zFactorNPCorrected, from the VER module, where NP-attenuation means attenuation due to nonprecipitation particles.
Detection of BB can be made by two methods: one is a vertical method and the other isa (horizontal) texture method. The vertical method examines the profile of radar reflectivity
factor. If the profile of the radar reflectivity satisfies certain conditions which characterize BB,
it is determined that there exists BB. The above characteristic conditions are diﬀerent between
Ku-band and Ka-band. In the Ku-band, a sharp BB peak should be observed in the profile ofradar reflectivity. In the Ka-band, however, a clear BB peak may not be observed, but there
must be a detectable characteristic change in the slope in the radar reflectivity profile near theBB.
The (horizontal) texture method detects BB by examining nearby pixels. The (horizontal )texture method is eﬀective for detecting a BB smeared by slanted beam observation.
When BB is detected, the upper boundary of BB (i.e., rangeBBTop) and the lower boundaries
of BB (i.e., rangeBBBottom) are determined. The lower boundary of BB is detected first. The
lower boundary of BB is defined as the point where there is the largest change in the slope of
Z in the region just below the BB peak. This definition is very close to that by Fabry and
Zawadzki (1995). The upper boundary of BB is determined by finding the following two points
A and B:
Point A: where there is the largest change in the slope of Z in the upper region of BB
peak.
Point B: where Z becomes smaller than Z at the lower boundary of BB for the firsttime when Z is examined upward in the upper part of BB starting from the BB
peak.
When points A and B are the same, the upper boundary of BB is defined as the point A(which is the same as the point B in this case). When points A and B are diﬀerent, the upperboundary of BB is defined as either A or B that is closer to the BB peak. The definition of theupper boundary is the one which is somewhere in between the definition by Fabry and Zawadzki(1995) and that by Klaassen (1998).
The width of BB (widthBB) is computed by the following empirical formula:widthBB = [(rangeBBBottom − rangeBBTop) × 125 − L sin(θz )] cos(θz )where θz is the local zenith angle, and L is given below:
L = L0 F/ cos2 (θz )39[m]Here L0 (=5000 [m] tentatively) is the footprint size of antenna beam, and F is an empiricalfactor (F = 0.5, tentatively). When the above equation gives widthBB < 250 cos(θz ) [m], thewidth is set to widthBB = 250 cos(θz ) [m]. When BB is detected, the following quantities arecomputed or given and written to the output of the single frequency CSF module.
- flagBB: This flag indicates that BB is detected. (flagBB > 0 when BB is detected.)- rangeBBPeak: Range bin number of BB peak. In the case of Ku-band, it is simple andstraight forward. In the case of Ka-band, BB peak may not be clear, and the peak position
may be displaced from that of Ku-band. Nevertheless, a simple peak search method is usedalso at Ka-band.
- rangeBBTop: Range bin number of BB top.
- rangeBBbottom: Range bin number of BB bottom.
- heightBB: Height [m] of BB which corresponds to rangeBBPeak.
- widthBB: Width of BB [m].
- qualityBB: Quality flag of BB detection. Details are still TBD. The flag qualityBB ismeaningful when BB is detected. Tentatively in V4, qualityBB=1 always when BB is
detected (which means that the quality of all the detected BB is good).
3.5.4-1-4 Rain type classification by the single frequency method
In each single frequency CSF module, that is, in Ku-only or Ka-only CSF module, rain typeclassification is made by two methods: one is a V-method and the other is an H-method. V- andH-methods classify rain into three categories; stratiform, convective and other. The rain types
by these two methods are unified, and each single frequency CSF module outputs the unified
rain type, which consists of three major categories; stratiform, convective and other.
Each single frequency DPR L2 algorithm adopts a loop structure in the flow of data as
shown in Fig. 3.5-0. The data go through the loop only twice. In the 1-st loop, the CSF module
uses the radar reflectivity factor, zFactorNPCorrected, processed by the VER module. In the
2-nd loop, the CSF module uses the updated zFactorNPCorrected for the detection of BB in
the V-method and for the H-method processing and also uses the attenuation corrected radarreflectivity factor, zFactorCorrected, processed by the SLV module for the determination of
convective type in the V-method.

40

Figure 3.5-0. Loop structures of L2 data flow (single frequency algorithms only)
3.5.4-1-4-1 V-method
In the V-method, stratiform rain is detected first.
- When BB is detected, the rain type is basically stratiform. There is an exception, however,that when the following two conditions are satisfied, it is convective even though BB is
detected:(1) Zmax below > conv th BB (=46 dBZ in V5),
(2) Zmax below > ZBBPeak ,where Zmax below is the maximum value of zFactorCorrected lower than or equal tothe height H BBBottom − (about) 0.375 km in the clutter free region and ZBBPeak is the
maximum value of zFactorCorrected in the BB. H BBBottom is the height of the BB
bottom.
- When BB is not detected, the rain type is convective if one of the following conditions is
satisfieed. Otherwise, the rain type is other.
(a) The radar reflectivity factor, zFactorCorrected, in the valid range between
rangeStormTop and binClutterFreeBottom exceeds a threshold, which is 40 dBZ inboth Ku-band and Ka-band.
(b) Storm top in Ka- or Ku-band is very high (> 15 km (TBD)). (Not implemented in V5
nor in previous versions.)
3.5.4-1-4-2 H-method
In the H-method, horizontal texture of the maximum value of zFactorNPCorrected in therain region along each radar beam, Zmax , is examined. A modified University of Washington
convective/stratiform separation method is applied to the horizontal extent of Zmax . When one
41

of the following conditions is satisfied, the considering pixel is a convective center:(a) Zmax in the pixel exceeds a convective threshold, or(b) Zmax in the pixel stands out against that in the surrounding area.
Rain type of the convective center is convective, and the rain type of pixels adjacent tothe convective center is convective. If the rain type is not convective, and if Zmax is not smallenough to be considered as noise, the rain type is stratiform. If Zmax is very small, being almost
identical to noise, the rain type is other.
3.5.4-1-4-3 Shallow rain and small cell-size rain
Detection of shallow rain is also made independently of the above mentioned methods of rain
type classification. When the following condition is satisfied, it is judged as shallow rain, which
will be marked by an internal flag:heightStormTop < heightZeroDeg − margin
where margin is currently 1000 m.
Shallow rain is separated out into shallow isolated and shallow non-isolated by examining
the horizontal extent of shallow rain. In the rain type unification, both shallow isolated andshallow non-isolated are classified as convective. (It should be noted that in the TRMM PR
rain type classification algorithm 2A23 V7, all the shallow isolated is convective, but shallow
non-isolated can be either stratiform or convective.)
Detection of rain having small cell size is also made independently. The rain having small
cell size is classified as convective in the unification of rain type.
3.5.4-1-4-4 Unification of rain type by the single frequency method
Rain types by the V-method and by the H-method are unified, and the CSF module outputs
the unified rain type, which consists of three major categories: stratiform, convective and other.
The unified rain type is written to the flag typePrecip.
Unification of single frequency rain types is made in the following way.
(1) When a BB is detected by the V-method, rain type is basically stratiform (but is convective
if Z close to the surface is exceptionally strong). When a BB is detected, the unified maintype follows the V-method decision (and the H-method decision does not aﬀect the unified
main type).
(2) When a BB is not detected, and Z examined by the V-method is strong enough to beconvective, the unified main type is convective (and the H-method decision does not aﬀect
the unified main type).
(3) If not stratiform nor convective, rain type is other by the V-method.
(4) The other type by the V-method is further classified into one of 3 main unified types byusing the H-method decision.
42

(5) For Ku-band data, some (unified) convective type is reclassified to (unified) stratiform typeby the UW reclassification method (see below).
(6) In the above unification, there are exceptions as follows:(a) All the shallow rain is convective in the unified main type.
(b) All the small cell-size rain is convective in the unified main type.
(c) When HIP is detected, the unified main type is convective unless a BB is detected. (If a
BB is detected and the V-method type is stratiform, the unified main type is stratiform.)
In the V6 CSF module, the UW slope algorithm reclassifies some of the precipitation typefrom convective to stratiform by examining the slope of Z along the beam (Houze and Brodzik,2017). The algorithm is applied to the Ku-band unified type (because the UW reclassification
method is developed for the Ku-band classification only).
The unified rain type is written to the flag typePrecip. There are many rain type subcategories depending on the combination of V- and H-mehod results. The unified rain type isexpressed by eight digit numbers as follows:
typePrecip = 1xxxxxxx

for stratiform rain,

2xxxxxxx

for convective rain,

3xxxxxxx

for other rain,

where xxxxxxx varies depending on subcategories. Details are given in 3.5.9. In a computer
program, the three main categories of unified rain type can be obtained easily from typePrecip.
Let Type be the integer variable for the main category, for example, then the main category canbe obtained by one of the following codes:
Type = typePrecip / 10000000 (in Fortran),Type = typePrecip / 10000000; (in C)
3.5.4-1-4-5 Reclassification of convective type to stratiform type by the “slope
method”
In 2017, after examining hundreds of cross-sections of V5 Ku-band data, R. Houze and S.
Brodzik of the University of Washington (UW) recognized that many convective pixels in V5
Ku-band output were classified incorrectly. They developed a method for solving the problem byexamining the slope of the Ku-band Z profile above the 0 degrees C isotherm and reclassifying
the type from convective to stratiform when certain conditions were satisfied. Since the method
developed by the UW examines the slope of Z, we refer to it as the “slope method.”The V6 CSF
module adopts the “slope method”for reclassifying some convective type to stratiform type atthe end of the Ku-only rain type unification. (Since the reclassified results are handed down to
the DPR CSF processing, the CSF DPR module does not carry out a duplicate (i.e., redundant)
reclassification using the “slope method.”)
The reclassification of convective type to stratiform type by the “slope method”is carriedout by using the Z factor corrected for attenuation by the nonprecipitation (NP) particles,43

zFactoNPCorrected, and the procedure is as follows:(1) For each beam, If the unified Ku-only rain type is convective and the shallow rain type flagis not set, find the maximum value of Ku-band zFactorNPCorrected (max refl) in the BB
search window (from rangeZeroDeg − 8 to rangeZeroDeg + 16 with a 125 m range intervalbounded by the highest BB height being 6.5km). At this stage, the V6 CSF module uses
the BB search window in order to keep the consistency with the detection of BB, However,further restriction is imposed later (see (3)).
(2) Starting at the max refl point, move up the ray bin by bin to the last point which satisfies
this condition:|(max refl − zFactorNPCrrected)| <= REFL MAX OFFSET (currently defined as 2 dBZ)
This is called the reference point.
(3) If the height of the reference point is lower than MAX BB HT (currently defined as 5km),
continue. Otherwise, no reclassification will occur for this beam.
(4) Find the comparison point along the NP corrected Z profile which is 1.5 km(DELTA HT FOR SLOPE) above the reference point. (If zFactorNPCorrected at 1.5 km above
the reference point is noise, step down the ray bin by bin until a valid value of zFactorNPCorrected is found. That point will become the comparison point.)(5) Calculate the slope between the reference point and the comparison point (defined as|delta zFactorNPCorrected|/|delta height|.
(6) If the slope is greater than or equal to SLOPE THRESHOLD (currently defined as 7.5 dBZ/km),then reclassify the type as stratiform.
NOTE: All parameter values for REFL MAX OFFSET, MAX BB HT, DELTA HT FOR SLOPE,SLOPE THRESHOLD were determined empirically. While they have worked well in testing, they
may need to be adjusted in the future.
3.5.4-2 Dual frequency processing
In the dual frequency processing, a newly developed DFRm method is used for the detection ofmelting layer (ML) and rain type classification. Details of DFRm method is given in 3.5.4-2-3.
The concept of ML has a meaning wider than that of BB. In other words, BB which appears
in the stratiform rain is a subset of ML. Since BB is a subset of ML, the DFRm method canbe used for the detection of BB. In the dual frequency processing, the DFRm result and the
Ku-only result are merged.
3.5.4-2-1 Detection of bright band (BB) by the dual frequency method
In the dual frequency method, BB detected by the Ku-only method and ML by the DFRm
method are compared. Firstly, ML detected by the DFRm method is regarded as BB. Together
44with BB detected by the Ku-only method, the median of the height of BB is computed in one
scan of data. When the considering height deviates from the median to a large extent, it isjudged that the considering BB is not a true BB and the BB decision is rejected.
If BB is detected by both the Ku-only and the DFRm methods at a given angle bin, it isjudged that the height of BB is the height which is closer to the median of BB height.
When BB is thus detected, properties of BB such as height, width, etc., for matched beamdata are then determined by using the profile of Z at Ku-band only, because Z at Ku-band
shows a clear BB peak but in most cases Z at Ka-band doesn’t. Because only the Ku-band Z
profile is used for the determination of BB properties, NS outputs of the BB properties in the
inner swath are copied to the corresponding MS outputs.
Since the concept of ML is wider than that of BB, and the upper and lower bounds of ML
are diﬀerent from those of BB in general, the following two items are added to the output of
dual frequency CSF module (for both MS and HS data):- rangeDFRmMLTop: Range bin number of ML top. (The name was rangeDFRmBBTop in V3.)
- rangeDFRmMLBottom: Range bin number of ML bottom. (The name was rangeDFRmBBBottom in V3.)
Names of the other BB related items are the same to those of single frequency items.
In the HS mode data processing, detection of BB is made by the DFRm method only.
3.5.4-2-2 Rain type classification by the dual frequency method
As is shown in the next 3.5.4-2-3, the DFRm method classifies rain into the following three
types: stratiform, convective, and transition, the latter of which is a new rain type. The rain
type by the DFRm method is merged with the single frequency Ku-band rain type, and the dual
frequency CSF module outputs unified rain type which consists of the following three major
types: stratiform, convective and other.
Outline of the unification of dual frequency rain types in the inner-swath is as follows:(1) When the DFRm rain type is convective or stratiform, the DFRm decision is respected andthe DFRm rain type is the unified main type if a BB is not detected. If a BB is detected,
however, the unified main type is basically startiform.
(2) When the DFRm rain type is transition, the single frequency Ku-band rain type is theunified main type.
(3) When the DFRm processing is skipped, the single frequency Ku-band rain type is the unified
main type.
(4) If HIP is detected, the unified main type is basically convective.
The unified rain type is expressed by eight digit numbers as follows:
typePrecip = 1xxxxxxx for stratiform rain,
2xxxxxxx for convective rain,45

3xxxxxxx for other rain,
where xxxxxxx varies depending on subcategories. (See 3.5.9 for details.) In a computer program, the three main categories of unified rain type can be obtained easily from typePrecip.
Let Rtype be the integer variable for the main category, for example, then the main category
can be obtained by one of the following codes:
Rtype = typePrecip / 10000000 (in Fortran),
Rtype = typePrecip / 10000000; (in C)
The rain type by the DFRm method is expressed in the 2nd digit of typePrecip. Hence the rain
type by the DFRm method, if we denote it by DFRmRtype for example, can be obtained asfollows:
DFRmRtype = MOD(typePrecip, 10000000) / 1000000 (in Fortran),
DFRmRtype = (typePrecip % 10000000) / 1000000; (in C).
When rain exists, DFRmRtype takes one of the following values:
1: stratiform,
2: convective,
4: transition,
5: when stratiform is changed to convective by the extended DFRm method
explained in 3.5.5-2,
8: DFRm method skipped at Part B in Fig. 3.5-3 (shown later in 3.5.4-3).
9: DFRm method skipped at Part A in Fig. 3-5-3 (shown later in 3.5.4-3).
3.5.4-2-3 Detail description of DFRm method
Dual-frequency precipitation radar DPR oﬀers Ku and Ka band dual-frequency observations
along the vertical profile which allow us additional information to investigate the microphysicalproperties using the diﬀerence between two frequency observations or so-called measured dualfrequency ratio (DFRm ) defined as
DFRm = 10 log10 (Zm (Ku )) − 10 log10 (Zm (Ka ))(3.5-1)

where Zm is measured reflectivity. DFRm profile holds rich information to assist in precipitation
type classification and melting layer detection. There are two main functions of DFRm method
in classification module: (1) precipitation type classification, and (2) melting layer detection.
Correspondingly, there are two models developed for dual-frequency profile classification method.
Figure 3.5-1 shows typical vertical profiles of reflectivity and DFRm for stratiform and convective
rain. These profiles are observations from airborne precipitation radar.
3.5.4-3 Precipitation type classification model
Precipitation type classification model classifies stratiform, transition, and convective rain types.
The main parameter used in the model is DFRm and its vertical variation. In order to quantify
46

(a)

(b)

(c)

(d)

Figure 3.5-1. Typical vertical profiles for stratiform (a)(b) and convective (c)(d) rain.
(a)(c) Measured reflectivity at Ku and Ka band; (b)(d) DFRm . DFRm (max) and
DFRm (min) marked on (b) and (d) are local max and min value.

the features of DFRm , a set of DFRm indices are defined. Let V 1 be
V1=DFRml (max) − DFRml (min)DFRml (max) + DFRml (min)(3.5-2)

DFRm (max) and DFRm (min) are shown in figure 4.2.3-1. DFRml used in (3.5-2) means DFRm in
linear scale. Let V2 be the absolute value of the mean slope for DFRm below the local minimumpoint
V 2 = abs(mean(DFRm slope))(3.5-3)

Both V 1 and V 2 are normalized values and not dependent on the height or depth of the melting
layer. V 1 values are normally larger for stratiform rain than for convective rain and V 2 values
are larger for convective than for stratiform rain. To further enlarge the diﬀerence between
stratiform and convective rain types, a third index V 3 is defined as
V3=

V1
V2

(3.5-4)The index V 3 is an eﬀective parameter and provides a separable threshold for performing precipitation type classifications. Extensive statistic studies are performed on index V 3 using both
airborne radar data and GPM real data. Cumulative density function (CDF) of index V 3 is calculated for stratiform and convective database separated using Ku only classification algorithm.
Separable thresholds of C1 and C2 can be found on index V 3 for stratiform and convective
rain types with 70% of CDF. In other words, for stratiform rain: V 3 > C2; convective rain:
V 3 < C1; transition type: C1 ≤ V 3 ≤ C2. C1 is smaller than C2. “Transition” type is neither
a stratiform, nor a convective rain type, but a type transitioning from stratiform to convective
rain. Figure 3.5-2 shows histogram of index V 3 and its cumulative density function calculated
47

using total of 73 storms data with 121859 vertical profiles. Table 3.5-1 shows the thresholds
of C1 and C2 used in the current version. In the future, further adjustment of C1 and C2 isneeded. Figure 3.5-3 illustrates the block diagram for precipitation type classification modelthat used in the current version.

(a)

(b)

Figure 3.5-2. Histogram of DFRm index V 3 with CDF line for (a) stratiform rain and(b) convective rain calculated using total of 121859 vertical profiles from GPM real data.

Table 3.5-1. C1 and C2 thresholds used in precipitation type classification model
70%
CDF

121859 verticalprofiles of GPM data

C1

0.18

C2

0.20

3.5.4-4 Melting layer detection model
Melting layer detection model detects melting layer top and bottom height for each verticalprofile. The main parameter used in the model is DFRm profile and its vertical variation. When
DFRm pair as shown in Fig. 3.5-1 is detected, the melting layer top is defined as the height at
which the slope of the DFRm profile hits a peak value. The melting layer bottom is defined as
the height at which the DFRm profile has a local minimum value. The dashed lines in Fig. 3.5-4
show an illustration of melting layer top and bottom detected.
The criteria described above have been compared with other existing criteria in the literatureusing diﬀerent radar parameters. Tilford et al. (2001) used the gradient of reflectivity (Zm ) to48

Figure 3.5-3. Block diagram of precipitation type classification model.

Figure 3.5-4. Schematic plot of DFRm profile with key points A, B, C, and D. Point
A: slope of DFRm has peak value. Point B: local maximum of DFRm . Point C: local
minimum of DFRm . Point D: DFRm value near surface.

detect the bright band top and bottom for stratiform rain type. The linear depolarization
ratio (LDR) has been pointed out by many researchers as an important signature in melting
phase detection, with certain thresholds determined for diﬀerent hydrometeor particles (Smythet al., 1998; Bandera et al., 1998; Tan and Goddard, 1995; Hines, 1983). Typical verticalprofiles of reflectivity as well as the corresponding velocity for stratiform and convective type49

were extensively studied by Fabry and Zawadzki, (1994). Baldini and Gorgucci (2006) mentionedthat the rapid change of the hydrometeor fall velocity is an implication of the melting layer. The
curvature of velocity was used by Zrnic et al. (1994) in characterizing the melting boundaries.
Klaassen (1988) found that the melting bottom can be detected by maximum of velocity. Figure
3.5-5 shows a schematic plot of the comparisons between melting layer detection criteria in
DFRm method and other existing criteria. The comparison results using airborne radar data
are listed in Table 3.5-2. From the table, estimations from the DFRm method match best withvelocity based criteria with normalized bias of 1.3% and 2.2% for melting layer top and bottom
respectively. The DFRm method also compares well with the LDR criteria, with a −28 dB
threshold, the bias between these two criteria is around −2.8%. Details can be found in Le
and Chandrasekar (2012). Figure 3.5-6 shows a profile comparison between DFRm method and
LDR as well as velocity criteria using APR-2 observations. Figure 3.5-7 is the block diagram of
melting layer detection model used in the current version of DFRm classification method.

(a)

(b)

Figure 3.5-5. Schematic plots of some current criteria for melting layer boundaries
detection and their possible relations with melting layer detection model used in the
DFRm method. (a) melting layer top criteria; (b) melting layer bottom criteria.

50

(a)

(b)
Figure 3.5-6. Sample profile from airborne radar data. Top row (stratiform rain): (a)Zm (Ku) and Zm (Ka); (b) DFRm ; (c) LDR; (d) Velocity;Dashed lines are melting layer
top and bottom decided by the DFRm method. Bottom row (convective rain): (a) to(d) are the same as in top row.

51

Table 3.5-2. Comparisons of melting layer boundaries between diﬀerent criteria
for NAMMA, GRIP and Wakasa Bay data.
DFRm slope peak DFRm slope peak DFRm slope peak
Criteria

Meltinglayer top
comparison(NAMMA)(GRIP)(Wakasa Bay)

NB

NSE

NB

NSE

NB

NSE

−2.6%

3.6%

−2.5%

3.6%

−4.9%

6.6%

Zm curvature peak

1.6%

3.3%

1.5%

3.0%

2.8%

5.2%

LDR

−2.8%
−1.3%

4.5%

−3.3%
−1.4%

4.2%

−6.0%
−1.9%

7.2%

Zm slope peak

Velocity curvature peak

3.6%

3.7%

5.6%

DFRm slope peak DFRm slope peak DFRm slope peak
Criteria

Meltinglayer bottom
comparison(NAMMA)(GRIP)(Wakasa Bay)

NB

NSE

NB

NSE

NB

NSE

Zm curvature peak

4.3%

5.5%

3.7%

5.0%

4.3%

6.9%

LDR

4.5%

5.9%

4.0%

5.4%

5.4%

11.2%

Velocity curvature min

2.2%

4.9%

1.7%

4.4%

7.0%

Velocity max

1.6%

5.9%

1.9%

4.3%

−0.08%
−2.6%

13.9%

Figure 3.5-7. Block diagram of melting layer detection model for DFRm method.

3.5.4-4b Modification on melting layer detection of dual-frequency classificationmodel
One function of the dual-frequency classification module is to detect melting layer on a profile
basis. Currently, the detection of melting layer top and bottom is done simultaneously. If either52the top or bottom is not detected, both melting layer top and bottom are not available. In order
to increase the detectability, we modify the current version of algorithm to separate the melting
layer top and bottom detection to make it independent. This is reasonable since melting layer
top is relatively easy to be detected than melting layer bottom.

Figure 3.5-3b. Modified block diagram for melting layer top and bottom detection fordual-frequency classification algorithm.

In the modification, we have threshold1 and threhsold2. When DFRm diﬀ > threshold2,
melting layer top and melting layer bottom are both detected. When DFRm diﬀ > threhsold1
and < threshold2, then only melting layer top is detected, not melting layer bottom. When
DFRm diﬀ < threshold1 or not exist, neither melting layer top or bottom is detected. The
threhsold1 and threshold2 are 0.5 dB and 2 dB in the modified version. A “FlagML” info is
temporarily stored at “qualityTypePrecip” for the purpose of testing the quality of melting layer
detection. Independent Flag info (for both MS and HS) should be added to the output of future
toolkit. Figure 3 5 3b illustrates the modified flow chart of the melting layer detection algorithm
in the dual-frequency classification module.
Figure 3.5-3c shows a section (Scan range: 3032–3070) of the GPM DPR orbit #24981,
where precipitation was captured over Buﬀalo, New York on 22 July 2018. Figure 3.5-3c (a) is
Zmku at 2km, while 2 (b) is Zmka at 2 km.
Sample vertical cut at angle bin #18 is shown in Fig. 3.5-3c (c). Black dashed lines are
53

(a)

(b)

(c)
Figure 3.5-3c. GPM DPR orbit #24981 with scan range of (3032–3070). (a) Zmku at
2km. (b) Zmka at 2 km. (3) Vertical cut at angle bin #18 of Zmku.
melting layer top and bottom before modification. Pink dashed lines are melting layer top
and bottom after modification. For both top and bottom, more detections are made after
modification of the code, some of which are circled by the dotted lines. Overall, the percentage
of melting layer detection in the scan range increases from 64.2% to 90.3% for top and 64.2% to86.5% for bottom.
Table 3.5-1b illustrates the count of rain types and melting layer top detection for one dayof GPM orbits on 01 August 2018. Column of “V6”indicates the results before modificationand column of “new”is for after modification. Changes of stratiform /convective counts occur
in the inner swath only. Those small diﬀerences between V6 and updated codes occur due tothe change of threshold1 (from 2.0 to 1.5). However, melting layer top detection counts increase
largely for both MS and HS condition. For MS, during one-day orbits, the count increases
54from 24,262 to 35,918, with increase percentage of 48%. For the HS condition, the increase
percentage is around 63%. This is a big improvement on the melting layer detection part ofthe dual-frequency classification module. More extensive analysis will be performed in the near
future.
Table 3.5-1b. Counts of stratiform, convective rain types as well as
melting layer top detection for MS and HS on one- day GPM DPR
orbits of 01 August 2018.

3.5.5 Features added in the V5 CSF module
The features added in the V5 CSF module are also available in the V6 CSF module. Most of thefeatures described in this section arise from the advantage of dual frequency data processing.
3.5.5-1 flagHeavyIcePrecip
This flag indicates that a Heavy Ice Precipitation (HIP) is detected in the upper region ofstorm where the temperature is lower than −10 degrees in Centigrade. Here, HIP is defined as
precipitation consisting of ice particles which produces a large measured Z factor (Zm ) and/or
produces a large DFRm. Detection of HIP, proposed by Dr. Iguchi, is made by examining Ku
Zm and Ka Zm in the single frequency modules and by additionally examining DFRm in the
dual frequency module (Iguchi et al. 2018). The following values of flagHeavyIcePrecip aredevised to distinguish by which decision HIP is detected (see Figure 3.5-8.)
Default value: FlagHeavyIcePrecip = 0 (HIP not detected, including the case of missing)
Ku-band decision (Ku NS)
If KuPR’s Zm > 45 dBZ, then flagHeavyIcePrecip = 12 = 0x0C
Else if KuPR’s Zm > 40 dBZ, then flagHeavyIcePrecip = 8 = 0x08
55

Else if KuPR’s Zm > 35 dBZ, then flagHeavyIcePrecip = 4 = 0x04
Ka-band decision (Ka MS)
If KaPR’s Zm > 40 dBZ, then flagHeavyIcePrecip = 3 = 0x03
Else if KaPR’s Zm > 35 dBZ, then flagHeavyIcePrecip = 2 = 0x02
Else if KaPR’s Zm > 30 dBZ, then flagHeavyIcePrecip = 1 = 0x01
DPR decision
Outer swath: The same as the outer swath value of Ku NS.
Inner swath: The addition of the above value of Ku NS, that of Ka MS, and the following
value of DFRm decision:
KuPR’s Zm > 27 dBZ and DFRm > 7 dB, then flagHeavyIcePrecip = 16 = 0x10

Figure 3.5-8. Bit assignment of flagHeavyIcePrecip

3.5.5-2 Detection of winter convection
Detection of winter convection is made by examining DFRm at the storm top and its vicinity.
The basic idea is that a large DFRm near the storm top in winter may indicate the convective
nature of precipitation. Since DFRm is examined, the method in this section can be called an‘extended DFRm method’. It is found that the above flagHeavyIcePrecip plays an importantrole in the detection of convective precipitation in winter. When the following conditions are
satisfied, it is judged that the winter convection is detected by the extended DFRm method (SeeDFRmRtype in 3.5.4-2-2.).
(1) The estimated 0C height is lower than or equal to 1 km. (Winter condition.)(2) flagHeavyIcePrecip > 0 is found near the considering pixel. Here, the term ‘near’ means
the inner swath region within −3 to +3 scans measured from the considering pixel’s scannumber (see Figure 3.5-9).
(3) Examine the DFRm of 5 consecutive range bins starting from storm top towards downward
direction. At three or more range bins, the following condition is satisfied:
DFRm at x >2.5 − 0.8
+ 0.8
5.0

where x is the relative range bin number counted downward from the storm top.
(4) If Zm at any of the examining range bins exceeds 35 dBZ, the process of detecting winterconvection is skipped because such a large Zm near the storm top may mean the existenceof BB.
56

Figure 3.5-9. Nearness of the considering pixel (marked by a circle) to the pixel offlagHeavyIcePrecip > 0 (marked by a square)

3.5.5-3 flagAnvil
Since the Ku-band PR turns out to have a higher sensitivity, the Ku-band PR data can be used
for the detection of ‘Anvil’. For the definition of ‘Anvil’, we need to introduce cloud top andcloud bottom, where cloud top is identical with storm top and cloud bottom means the range
bin below which no rain bins continue to some extent. (When precipitation exists, the cloud top
always exists but the cloud bottom does not always exist.) As shown in Figure 3.5-10, when thecloud bottom is 500 meters higher than the estimated 0C height, it is defined that the ‘Anvil’
exists. If there does not exist any precipitation echoes below the ‘Anvil’, it is judged that the‘Anvil’ type 1 is detected. If precipitation echoes exist below the ‘Anvil’, but if the no raininterval between the cloud bottom and the top of precipitation echoes below the ‘Anvil’ exceeds
1 km, it is judged that the ‘Anvil’ type 2 is detected (see Figure 3.5-10(b)). The V5 CSF module
detects type 1 ‘Anvil’ and type 2 ‘Anvil’ only. The possible values of flagAnvil are as follows:
flagAnvil=1: type 1 ‘Anvil’ is detected (there is no rain echo below the ‘Anvil’),=2: type 2 ‘Anvil’ is detected (there exists rain echoes below the ‘Anvil’,=0: ‘Anvil’ is not detected (including the case of data missing).

Figure 3.5-10 Definition of ‘Anvil’

57

3.5.5-4 Surface snowfall
The V5 CSF module introduces a process which determines whether the precipitation at thesurface is snow or rain. The process has been developed by Dr. Chandra’s group. The process
outputs the following two variables. (Though the decision is made in the CSF module, the twovariables are available in the Experimental output fields of dual frequency MS.)surfaceSnowfallIndex: If this index exceeds a predetermined threshold, it is judgedthat the surface precipitation is snow.
flagSurfaceSnowfall: This flag takes the following two values.
{
flagSurfaceSnowfall =1:0:

The surface snowfall is possible,
The surface snowfall not detected.

3.5.6 Snow and rain feature on measured dual-frequency ratio (DFRm) profile
Figure 3.5-11 (a) shows the first snow observation caught by GPM DPR with overpass #000272.
On that overpass, there are snow, stratiform and convective rain precipitation within 160 scans(around 800 km) range. In the figure, A, B and C indicates the location of snow, stratiform andconvective rain. In order to study vertical profile feature for these diﬀerent precipitation types,
we study averaged reflectivity as well as measured dual-frequency ratio profile for snow, stratiform and convective rain. The vertical profile is calculated from linear averaging of reflectivity
at corresponding heights. If data in a pixel is missing or below noise level, it is not consideredin the average. Figure 3.5-11 (b) shows the averaged reflectivity profile for snow at Ku-, Kaband and a measured dual-frequency ratio. As expected, reflectivity at Ku- band is, most of thetime, below 25 dBZ. However, the diﬀerence between Ku- and Ka- band (indicated by DFRm)
is several dBs, even when reflectivity at Ku- band remains a relatively small value. DFRm
values increase obviously when it comes toward the surface mainly due to the aggregation of
the snow particles. Figure 3.5-11 (c) shows an averaged vertical profile for stratiform rain. The
bright band is obvious from Ku- band reflectivity. Values of DFRm below the melting region(or the bright band) are very small indicating that attenuation at Ka- band is very small whenreflectivity at Ku- band is less than around 25 dBZ in the rain region. DFRm values are quite
constant with respect to height. In contrast, DFRm values above melting layer is several dBs
and DFRm slope is noticeable, which is similar to the snow profile in figure 2 (b). For convective rain, from figure 1 (d), the maximum of reflectivity at Ku- band is equal or larger than 35
dBZ, while DFRm values are considerable in the rain region. Large DFRm values in rain arecontributed from both attenuation diﬀerence and the non-Rayleigh scattering eﬀect, while theformer factor plays a more important role. DFRm slope for convective rain is large mainly due
to large attenuation diﬀerent change when it comes toward the surface.
58

One of the features for a convection storm is that it is normally formed at a higher altitudein the atmosphere (except for some warm rain or orographic rain) compared to stratiform andsnow precipitation.
Based on analysis above, three ingredients of DFRm, maximum value of reflectivity at Kuband, as well as storm top height become important, then are used in the development of thealgorithm to identify surface snowfall. To avoid calibration issues, we use the slope of DFRm
instead of DFRm itself.

(a)

(b)

(c)

(d)

Figure 3.5-11. GPM DPR overpass of rainfall rate on March 17, 2014 (#000272).
Circled A, B and C represents snow, stratiform rain, and convective rain. (b) Averaged
reflectivity profiles as well as dual-frequency ratio profile for snow. (c) Same as (b) for
stratiform rain. (d) Same as (b) for convective rain.

59

3.5.7 Algorithm description
Snow index (SI) is carefully chosen from the features discussed in section 1. The definition of
SI is
SI =

mean(|DFRmslope |)(1)
Zmkumax × Storm top height

DFRm slope (in dB/km) is used instead of DFRm value due to its immunity to calibration
change. Zmkumax (in dBZ) represents maximum of reflectivity at Ku-band along the profile.
Storm top height represents altitude of storm top in km. In general, absolute value of DFRm
slope in the numerator of (1) is larger for snow than for stratiform rain. Zmkumax value in the
denominator is larger for convective rain than for snow, while storm top height in the denominator is lower for snow and stratiform rain than for convective rain. Therefore, SI is expected to
be a larger value for snow profile than for rain. In the algorithm, we use the normalized value of
Zmkumax and Storm top height. A statistics study of snow index is performed on large scale of
GPM DPR vertical profiles. A total of 353166 rain profiles and 4935 snow profiles are used inthis study. Rain profiles are chosen from tropical regions during the summer season and snow
profiles are chosen from northern Europe and the northern US in the winter season. Informationof 0 degree isotherm is used in profile selection. Figure 3.5.12 shows the histogram of snow index(SI) defined in (1) for snow and rain profiles respectively. In general, as expected, snow index
is larger for snow profiles. The cumulative density function (CDF) is calculated for rain, andillustrated as a blue dashed curve in figure 8, while red dashed curve represents 1- CDF for snow
profiles. At around 97% of CDF (or 1-CDF), SI index can separate snow and rain profiles. In
other word, 97% of snow profiles have SI > 17, while 97% of rain profiles have SI <= 17. The
statistics study considers calibration changes that will be applied to version 5 of the GPM DPRlevel 2 algorithm.
Figure 3.5-13 shows the flow chart of the surface snowfall identification algorithm thatuses snow index. Besides snow index, other auxiliary information such as 0 degree isothermand clutter free height are used in the algorithm. Although 0 degree isotherm (or surface
temperature) plays an important role in snowfall detection, its accuracy is a challenge for spaceborne weather radars such as GPM. Therefore, in this algorithm, 0 degree isotherm is not thedominant parameter but rather a constraint parameter. Snow index is calculated for profiles
with 0 degree isotherm heights not higher than 10 range bins (≈ 1.25 km) above clutter freeheight. This serves more like a sanity check to filter out nonsense profiles.
3.5.8 Enhancement of GPM DPR level 2 dual-frequency classification module
In the current DPR level 2 classification module, the vertical profile is classified as either stratiform, convective or other type following the TRMM legacy. However, GPM orbits cover bothcold and tropical regions. It is reasonable to add snow flag to vertical profiles that indicate
whether there is snow on the surface or not. The surface snowfall identification algorithm takes
60

Figure 3.5-12. Large scale study of snow index using GPM DPR profiles. Histogram ofsnow index for rain (blue) and snow (red). Blue dashed curve is the cumulative density
function (CDF) for rain. Red dashed curve is 1- CDF for snow.

Figure 3.5-13. Flowchart to perform surface snowfall identification in profile classification module of GPM DPR level 2 algorithm.

advantage of dual-frequency measurements from GPM DPR, thus it provides a new approachto detect snow other than the method using surface temperature or surface echo. The algorithm outputs flagSurfaceSnowfall and surfaceSnowfallIndex to each vertical profile in
the DPR inner swath. This snow flag provides useful information for microphysics retrieval
for other modules such as the solver module. Therefore, this algorithm enhances the current
dual-frequency classification module in GPM DPR level 2 algorithm and has been implemented61

as an experiment version.
3.5.9 Eight digit rain type number
As mentioned in 3.5.4-1-4-4 and 3.5.4-2-2, the unified rain types for Ku-only, Ka-only, and DPR
are expressed by eight digit numbers. Figure 3.5-14 illustrates the eight digit rain type number,which has a common structure for Ku-only, Ka-only, and DPR.

Figure 3.5-14. A summary of eight digit rain type number. (*): For part A and part B,
see Figure 3.5-3.

3.5.10 Detection of orographic rain (optional)
In the CSF module, detection of orographic rain will be tried in the future. Unfortunately,
however, detection of orographic rain would require research works.
3.5.11 Input and Output Variables
Input Variables
From Preparation (PRE) module:
latlon
ScanTime information (Year, Month, DayOfMonth, Hour, Minute, Second, Millisecond, DayOfYear, SecondOfDay)
elevation
landSurfaceTypelocalZenithAngle
flagPrecip
binRealSurface
binStormTop
62

heightStormTop
binClutterFreeBottom
sigmaZeroMeasured
rangeBottom
rangeTop
HeightechoSignalPowerzFactorMeasured
From Vertical (VER) module:
airTemperature
binZeroDeg
zFactorNPCorrectedheightZeroDeg
flagEcho (R/W) (This flag marks possible sidelobe clutter positions.)
qualityData (R/W)
From Solver (SLV) module:zFactorNPCorrected (For convective decision by the V-method in the
single frequency Ku- or Ka-loop)
Output Variables
flagBB [4 byte integer (can be 2byte integer)]
binBBPeak [2 byte integer]
binBBTop [2 byte integer]
binBBBottom [2 byte integer]heightBB [4 byte real]
widthBB [4 byte real]
qualityBB [4 byte integer (can be 2 byte integer)] (Currently tentative values areset, that is, when BB is detected, qualityBB=1 (meaning good) always.)
typePrecip [4 byte integer]
qualityTypePrecip [4 byte integer (can be 2 byte integer)] (Currently tentativevalues are set, that is, when rain exists, qualityTypePrecip=1 (meaning good) always.)
flagShallowRain [4 byte integer (can be 2 byte integer)]
binDFRmMLTop [4 byte integer] (dual frequency MS and HS output fields only)
binDFRmMLBottom [4 byte integer] (dual frequency MS and HS output fields only)
flagHeavyIcePrecip [1 byte integer]
flagAnvil [1 byte integer]EEingle frequency Ku NS and dual frequency NS output
fields onlyEE63

flagSurfaceSnowfall [1 byte integer] (Experimental output field of dual frequency
MSEE
surfaceSnowfallIndex [4 byte real] (Experimental output field of dual frequency
MSEE
3.5.12 Relation to other modules
The Classification (CSF) module uses data from the Preparation (PRE) module and the vertical(VER) module. Each single frequency CSF module uses data from the corresponding Solver(SLV) module in the 2-nd part of (a kind of iteration) loop for convective decision by the Vmethod. The output data from the CSF module is used by the Solver (SLV) module.
3.5.13 Caveats on Classification Module
Most part of this section describes not on the theory itself but rather on the bug fixes and on
the diﬀerence between V4 and V5 data.
3.5.13-1 Bugs in V5
The following bugs in V5 are fixed in V6.
1) Some side-lobe peaks being misjudged as HIP in V5 are fixed in V6. (The fix is applied to
the Ku outer swath only.)2) When flagHeavyIcePrecip > 0, rain type is expected to be convective, but the DPR rain
type for this case is sometimes stratiform in V5 because of a bug. This bug is fixed in V6.
3.5.13-2 Bugs on BB quantities in V4
The V4 CSF codes for the detection of BB contain a large number of bugs, which were fixed inV5.
In Ku-only CSF module1) All widthBBs, except for those at nadir, were wrong in V4: The width of BB is calculatedby the following formula:widthBB = (rangeBBBottom − rangeBBTop − correction term) × cos(θz )However, correction term was zero in V4 because of the bug. This bug was fixed in V5.
2) Some heightBBs were not exact in V4: In some places of the V4 code, ellipsoidBinOffset
was not used. The eﬀect of the bug was small, which may be ignored from a practical pointof view. This bug was fixed in V5.
3) A rare case bug in V4: In a very small number of cases, though BB was detected (i.e.,
flagBB > 0), the following quantities were not set properly: binBBPeak, binBBTop, andbinBBBottom, which also made widthBB wrong. This bug was fixed in V5.
In Ka-only CSF module
No bugs on BB quantities were found in the V4 single frequency Ka-only CSF module.
In DPR CSF module641) BB detected in normal scan (NS) data: Since the DPR NS copied the Ku-only results ofBB, the BB bugs in the Ku-only V4 module also showed up except for the case when the
DFRm method detected BB.
2) Dual frequency HS code contained a serious bug: HS BB quantities in V4 were all wrong
except for flagBB. Detection of BB was made using the DFRm method with a 125 m(interpolated) range bin data. When BB was detected, binBBPeak was set to a 250 m range
bin number. However, binBBPeak was wrongly set to a 125 m bin number in V4, which madebinBBPeak small and heightBB very high. The bug also made binBBTop, binBBBottom, andwidthBB wrong. The bug was fixed in V5.
3.5.13-3 Bug fix on the exceptional convective type when BB is detected
As is explained in 3.5.4-1-4-1, the precipitation type is basically stratiform when BB is detected,
but there is an exception that the type can be convective even when BB is detected when certain
conditions are satisfied. In the case of TRMM V7 data, only about 0.1% of BB detected pixels
are convective. However, because of bugs, such an “exceptional”case occurred not “exceptionally”but rather noticeably in V4 (more than 1% of BB detected pixels were convective.) The
bugs were fixed in V5, where less than about 0.1% of detected pixels would be convective.
3.5.13-4 Issues
Besides the parameter tunings, the following improvements are needed in the next update.
- Improvement of BB detection, in particular the detection of low BB in winter and also thedetection of BB in the outer swath.
- It is found that the reclassification of the type from convective to stratiform by the “slope
method”has a problem when there exist winter time temperature inversions. This problem
is solved by an ad hoc fix in the V6 CSF module. However, the winter time temperature
inversion problem should be handled in more fundamental ways in the next update.
- Determination of stratiform type by examining the slope of the radar reflectivity factor in
the upper part of (not detected) BB. This is important because low BB cannot be detected
near scan edges, but if the upper Z profile exhibits a typically stratiform shape and if thealgorithm can capture the typical upper slope character, then it may allow us to judge thatthe type is stratiform.
- Development of the dual frequency codes which enable us to handle the planned new HS
scan mode.

65

3.6 DSD module
3.6.1 Objective
The objective of DSD module is to set the physical variables of precipitation particles (especially, density, dielectric constants, and falling velocity), to parameterize N (D) (in other words,
to set n(D; D∗ )), and to set the R-Dm relationship.
3.6.2 Processes
The following processes are common to single- and dual-frequency algorithms.
3.6.2-1 Target pixels and range bins
Pixels with precipitation are processed. Range bins from the storm top range bin to the land
surface range bin (including those without precipitation and those with ground clutter) areprocessed. In dual-frequency algorithm, range bins from the higher storm top range bin of thetwo frequencies to the lower land surface range bin of the two frequencies are processed.
3.6.2-2 Nodes and the physical temperature and phase of the particles
Five range bins are selected as nodal bins and designated as nodes A through E. Simultaneously,
the physical temperature of particles (particle temperature) and the phase of particles are set.
Particle temperature is related to the dielectric constants and it is not necessarily the samewith air temperature. The dielectric constant is dependent on the precipitation types and thedetection of bright band.
3.6.2-2-1 Stratiform precipitation with bright band
The range bin that corresponds to the upper edge of bright band is node B, the range binat the peak of bright band is node C, and the range bin at the lower edge of bright band isnode D. Between node B and node D, particle temperature is set to 0◦ C. Above node B, theparticle temperature is basically the same with ambient air temperature, but is 0◦ C if the airtemperature is higher than 0◦ C. Below node D, the particle temperature is basically set to thesame with the air temperature, but 0◦ C if the air temperature is lower than 0◦ C. The range binwith particle temperature closest to −20◦ C corresponds to node A, and that closest to 20◦ C isnode E. At and above node A, the phase of precipitation is Solid. At and below node D, thephase of precipitation is Liquid. At other range bins, below node A and above node D, the phaseof precipitation is Mixed (Solid and Liquid).
3.6.2-2-2 Stratiform precipitation without bright band
The process is the same as 3.6.2-2-1, but the nodes B, C, and D are at the range bin corresponding
to 0◦ C.
66

3.6.2-2-3 Convective precipitation or other-type precipitation
The process is the same as 3.6.2-2-2.
3.6.2-3 Assumption of precipitation particles
A precipitation particle is modeled as a spherical particle composed of liquid water, solid water,
and air. Its diameter (or drop size) is designated as Db [mm], and its density as ρb [g/cm3 ]. The
volume ratios of liquid water, solid water, and air to the particle are Pw , Pi , and Pa , respectively.
The following equation always holds.
Pw + Pi + Pa = 1(3.6-1)The densities of liquid water, solid water, and air are ρw , ρi , ρa , respectively. Then, the densityof the particle can be given as below.
ρb = Pw ρw + Pi ρi + Pa ρa(3.6-2)

Generally, we can assume ρw = 1.0 (g/cm3 ). ρi is also constant (for example, ρi = 0.92).
Moreover, ρa can be regarded to be 0 (g/cm3 ) for simplicity. With these constant values, Eq.
(3.6-2) can be simplified to (3.6-3).
ρb = Pw + 0.92Pi(3.6-3)

3.6.2-4 Setting of volume ratio and density
3.6.2-4-1 Liquid phase particle
Obviously, Pw = 1, Pi = Pa = 0, ρb = 1.0 (g/cm3 ).
3.6.2-4-2 Mixed phase particle
First, Pw is set based on Awaka’s model or TRMM/PR’s experience. Then, ρb can be calculatedby the following empirical equation.
ρb =

√
Pw

(3.6-4)

Then, Pi can be calculated by Eq. (3.6-2) with ρa = 0.
Pi =ρb − Pw ρw
ρi(3.6-5)3.6.2-4-3 Solid phase particle
First, ρb is set, for example, ρb = 0.10 (g/cm3 ). As Pw = 0, Pi can be calculated by Eq. (3.6-5).
67

3.6.2-5 Dielectric constants
εb is calculated by mixing rule, for example, given in Eq. (3.6-6).
εb − 1εw − 1εi − 1εa − 1
= Pw
+ Pi
+ Pa
εb + Uεw + Uεi + Uεa + U
where U is given as below,
U=

{

2.0
2.0e13.0(ρb −0.09)(3.6-6)if ρb ≤ 0.09 g/cm3 ,if ρb > 0.09 g/cm3 .

(3.6-7)

εw and εi are dielectric constants of water and ice and are functions of particle temperature,and εa is dielectric constants of air. As εa is approximated to 1, Eq. (3.6-6) can be simplified
to Eq. (3.6-8).

εb − 1εw − 1εi − 1= Pw+ Pi
εb + Uεw + Uεi + U(3.6-8)3.6.2-6 Falling velocity
Falling velocity Vb of the particle can be given as below.
3

Vb = 3.3 × [ρb − ρa ]1/2

if ρb ≤ 0.05 g/cm ;Vb = 8.8 × [0.1Db (ρb − ρa )]1/3
ρ − 0.31/3(VR − Vb0.3 ) + Vb0.3
Vb = b
1.0 − 0.31/3
1/2(3.6-9a)3

3(3.6-9b)

if 0.3 g/cm < ρb ≤ 1.0 g/cm .

(3.6-9c)

if 0.05 g/cm < ρb ≤ 0.3 g/cm ;3

3

In Eq. (3.6-9c), VR is the fall velocity of rain drops, and Vb0.3 is the falling velocity calculated in
Eq. (3.6-9b) with ρb = 0.3 g/cm3 . The relationship between Db and the diameter after melting
Dmelt is as follows.
−1/3

Db = Dmelt ρb(3.6-11)

3.6.2-7 Drop size distribution function
3.6.2-7-1 Liquid phase
Gamma distribution function may be most commonly used for the drop size distribution function
in liquid phase. The function is given as below.

[](4 + µ)DN (D) = Nm D exp −,Dmµ(3.6-12)

where Dm is the volume weighted diameter (or often called the mass weighted diameter) definedas∫ 4
D N (D) dD
Dm = ∫ 3.
D N (D) dD

Nm is the corresponding scale factor. The shape factor µ is assumed to be known and constant.
N ∗ in (3.1-19) corresponds to Nm , D∗ to Dm , and n is given as Eq. (3.6-13).
][(4 + µ)Dµ,n(D; Dm ) = D exp −Dm
68(3.6-13)

3.6.2-7-2 Mixed phase and solid phase
It is assumed that when all the particles are melted to liquid particles, the drop size distribution
obeys Eq. (3.6-12). With Eq. (3.6-11), Eq. (3.6-12) can be expressed by Db and ρb .
[(µ+1)/3 µ
N (Db ) dDb = Nm ρb
Db exp

1/3(4 + µ)Db ρb
−
Dm

]
dDb

(3.6-14)

Eq. (3.6-14) can be modified to Eq. (3.6-15), if we consider the diﬀerence of number density
caused by the diﬀerence of falling velocity as Non-break up and Non-coalescence model (N/Nmodel) assumes.
(µ+1)/3 µ VR (Dmelt )N (Db ) dDb = Nm ρbexp
Db
Vb (Db )[1/3(4 + µ)Db ρb
−
Dm

]
dDb

(3.6-15)

3.6.2-8 R-Dm relation
R-Dm relations are assumed for stratiform and convective rain separately to calculate k and Ze
for a given R at both Ka and Ku bands. In version 3 algorithm, default k-Ze relations were
given as the basic relations to constrain the DSD parameters. However, since these relations
were derived from the R-Λ relation (Kozu et al. 2009) by assuming a power law between k
and Ze , the resultant k-Ze relations did not reproduce the original R-Λ relations exactly. The
discrepancy was apparent espcially in heavy rainfall cases for KaPR and solid particles, andcaused a serious error in the algorithm. Since the R-Dm relation is directly derived from theR-Λ relation without approximation, we have consistent DSD parametes to relate k, Ze at both
freqencies and R.
6.131
The actual R-Dm realtions adopted are R = 0.401ϵ4.649 Dm
for stratiform precipitation
5.420
and R = 1.370ϵ4.258 Dm
for convective precipitation, where ϵ is an adjustment factor and is

the same with ϵ used in version 03 and in the TRMM/PR algorithm. The same equations areused for all range bins irrespective of its “phase”, while Dm is defined for particle size whenmelted.

69

3.6.3 Phase
An index called “phase” is introduced. “phase” is an integer-type variable. If “phase” is equal
to or greater than 200, particles are in liquid phase, and the diﬀerence between the value of“phase” and 200, i.e., (phase−200), indicates the particle temperature in unit of degrees C.
For example, if “phase” is 210, the particle temperature is 10 degrees C. If “phase” is between
100 and 200, particles are in a bright band. 100 is for the top of bright band, 150 is for the
peak of bright band, and 200 is for the bottom of bright band, where all particles are melted
in liquid phase. 125 and 175 are used for intermediate positions, but other numbers between
100 and 200 are not used. If “phase” is smaller than 100, particle is partly or fully frozen, andthe diﬀerence between “phase” and 100 means particle temperature in unit of degrees C. For
example, if “phase” is 80, particle temperature is −20 degrees C.
3.6.4 DSD database
ϵ used in the R-Dm relations was assumed to follow the log-normal distribution. In the version
04, the average (µ) and the standard deviation (σ) of log10 ϵ is set at 0 and 0.1, respectively. In
the version 05, the statistics of ϵ is calculated based on the outputs of ϵ at dual-frequency pixels
by the dual-frequency algorithm. The average and the standard deviation of log10 ϵ are calculated
for each precipitation type (stratiform, convective), for each land surface type (land, ocean, all),each 5 by 5 degree grid box, and each month. Not only grid values but global values are prepared.
The purpose of the DSD database is to make estimates by KuPR algorithm be closer to thoseby the dual-frequency algorithm. DSD database is applied only to single-frequency pixels (allpixels of the single-frequency algorithms and pixels in outer swath or in interleaved scan of thedual-frequency algorithm). In the dual-frequency pixels of the dual-frequency algorithm, µ andσ is set 0 and 0.1 same as in the version 04.

70

3.7 Surface Reference Technique for DPR
3.7.1. Objective of the algorithm
The primary purpose of the surface reference technique (SRT) is to compute the path-integrated
attenuation (PIA), or simply path attenuation, using the radar return from the surface. The
SRT rests on the assumption that the diﬀerence between the measurements of the normalized
radar surface cross section, σ 0 or NRCS, in dB, within and outside the rain provides an estimateof the PIA.
For the dual-frequency Precipitation Radar (DPR), the method is to be applied to the Kuband as well as the Ka-band and Ka-band high sensitivity (KaHS) data. The basic set of output
products for each of the channels (Ku, Ka and KaHS) consist of the path attenuation estimate(when rain is present) and an associated reliability. As in the TRMM algorithm 2a21, version
7, the primary output for each channel will be an eﬀective or ‘best’ PIA estimate. In addition,
however, path attenuation estimates corresponding to specific types of surface reference data
are also generated. These are described below. The standard PIA estimates will be produced
by processing the data from the 3 channels (Ku, Ka, Ka HS) independently. However, to takeadvantage of the correlation in the NRCS at the two frequencies, dual-frequency-derived pathattenuations at Ku- and Ka-band will also be generated in the inner swath. These are referred
to as the DPR-Ku and DPR-Ka estimates.
For each ‘channel’ (Ku, Ka, KaHS, DPR-Ku, DPR-Ka), a spatially or temporally-averaged
estimate of the rain-free normalized radar surface cross section (σ 0 or NRCS) is used as areference value for computing the PIA. For the DPR channel, the rain-free reference data is
obtained from a diﬀerence between the Ka- and Ku-band σ 0 data. The algorithm computes up
to six estimates of path attenuation, corresponding to six diﬀerent σ 0 reference estimates foreach of the five channels. An eﬀective PIA is obtained by weighting the individual estimates by
a factor that is inversely proportional to the variance of the estimate. The reference estimates
are described below:
Along-track Spatial Average: The along-track spatial average is obtained from an averageof the Ns most recent rain-free σ 0 measurements at same angle bin and with the same surface
type (nominally, Ns = 8) as the rain pixel of interest.
Hybrid or Cross-Track: The hybrid reference data set is generated from a quadratic fit of
the along-track spatial average data over the angle bins within the cross-track swath. This is
explained below in more detail. For the Ku-band, separate fits are done for the inner (|θ| ≤ θ0 )and outer swath (|θ| > θ0 ) where θ0 ≈ 9◦ .
Temporal Average: The temporally averaged data, which are given in the form of look-up
tables, presently consists of statistics (sample mean, sample mean square and number of data
points) of rain-free σ 0 data at 0.5◦ × 0.5◦ latitude-longitude cells, separated into incidence angle.
71

The look-up tables have been derived from the Ku, Ka, KaHS and diﬀerential (σ 0 (Ku)- σ 0 (Ka))
data categorized into 25, 13, 12, and 13 angle bins, respectively. Each of these files are further
subdivided into 5 surface-dependent look-up tables that correspond to ocean, land, coast, snowover-land, and sea-ice surface types for a total of 20 look-up tables for each 3-month period(DJF, MAM, JJA, SON). Product version 3 and earlier do not use the temporal reference data;
version 4 and later versions will use the standard temporal reference file but not the elight-rainf
temporal reference file. For version 5 and above, the original 3 surface types (ocean, land, coast)
were expanded into 5 surface types, with the additional categories of snow-over-land and sea-ice.
Further details can be found in Section 3.
Forward-Backward Processing
Diﬀerent estimates for along-track and cross-track (or hybrid) methods are obtained from forward and backward processing. This provides up to six estimates - Forward Along-track, Forward
Cross-track, Backward Along-track, Backward Cross-track, and two types of Temporal reference
files - for each rain observation. Note that the hybrid/cross-track estimates are only available
over ocean since the fitting procedure does not work well over land. (Cross-track fitting over land00
might be eﬀective for the diﬀerence field, σN
R (Ku) − σN R (Ka). Whether such an estimate is to

be used is to be determined.) The diﬀerent reference estimates are filtered according to various
criteria. For example, an along-track estimate isn’t used if the location of the reference data is
too far from the observed rain point. A temporal estimate is considered invalid if the numberof rain-free observations at a particular angle for the reference cell is too few. The surviving
estimates are weighted by the inverse of their associated variance. The weighted estimates are
combined to yield an eﬀective PIA for each of the channels, (pathAtten Ku, pathAtten Ka,
pathAtten KaHS). The individual estimates, (PIAalt Ku, PIAalt Ka, PIAalt KaHS), and theirweights, (PIAweight Ku, PIAweight Ka, PIAweight KaHS) are included in the output product.
A discussion of the weights and the eﬀective PIA, variance, and reliability factor is given in
section 5.
In the spatial (along-track) surface reference data set, the mean and standard deviation of
the NRCS are calculated over a running window of Ns fields of view before rain is encountered(currently, Ns = 8). These operations are performed separately for each of the 49 incidence
angles for the Ku-band data, corresponding to the cross-track scan from −18◦ to +18◦ withrespect to nadir. The Ka and KaHS channels are treated in the same way. For the Ka- andDPR channels 25 and 24 angle bins, respectively, are used. For the diﬀerential channel, the00same procedure is used except that the reference data are formed from the σN
R (Ku) − σN R (Ka)data; in this case, the reference data are computed for the 25 angle bins of the inner swath.
Basic Processing72When rain is encountered, at incidence angle θi , the means and standard deviations of thereference σ 0 values are retrieved from the along-track spatial (forward or backward), the hybrid(forward or backward) and the temporal surface reference data sets. If a valid surface reference
data set exists for one of the above estimates, then, denoting this by the jth estimate, the 2-way
path attenuation (PIA) is computed from the equation:
0
0
PIAj (θi ) = ⟨σNR (θi )⟩j − σ (θi )00where ⟨σNR (θi )⟩ is the mean of the jth rain-free reference estimate and σ (θi ) is the value ofthe apparent normalized radar surface cross section at the raining field of view of interest.
To obtain information as to the reliability of the jth PIA estimate we consider the ratio of
the PIA, as derived in the above equation, to the standard deviation associated with the jthrain-free reference data set. Labeling this standard deviation by σj , the reliability factor of thejth PIA estimate is defined as:
reliabFactorj =

PIAj
σj

which is equal to the inverse of the coeﬃcient of variation of the estimate.
The eﬀective PIA, PIAeﬀ , and the corresponding reliability factor, Releﬀ , can be expressedin similar ways. From section 5, we have:∑
PIAeﬀ =

u PIAj∑juj∑
uj PIAj
RELeﬀ = √∑uj
Where uj is the inverse of the variance, σj2 , associated with the jth reference data set:
uj =

1
σj2

Note that the summations are assumed to range over all valid reference data sets, even if the
PIA’s are negative. Over land, there can be a maximum of 4 valid reference data sets (forward
and backward along-track and two temporal files) while over ocean there can be as many as six
since the forward/backward cross-track reference data sets are defined over ocean but not land.
A generalized form of the above equations can be defined, where the various quantities are
replaced by the diﬀerence between the Ka and Ku-band σ 0 values. Letting δX = X(Ka) −X(Ku), then for the diﬀerential PIA, the above equation becomes
00
δPIAj (θi ) = ⟨δσNR (θi )⟩j − δσ (θi )73The individual and eﬀective dual-frequency estimates at Ku- and Ka-band are then derived from
δPIAj from
δPIAj (θi )p−1
p δPIAj (θi )PIAj (Ka, θi ) =p−1

PIAj (Ku, θi ) =where p is the ratio of the path attenuation at Ka-band to that at Ku-band and is nominally
set to 6.
Updates for Version 6
A number of new variables have been introduced in version 6. The primary set of variables
are associated with a hybrid estimate of the path attenuation that consists of a weighted sumof the SRT, Hitschfeld-Bordan (HB), and the standard dual-wavelength method (DW). For the
single-frequency Ku-band and Ka-band estimates, only the SRT and HB results are used since
the DW method is inapplicable.
The basic idea follows closely that used for the SRT itself: if n independent estimates of aquantity A are available and if the estimate is written in the form
Â =

n
∑

wj Aj

j=1

then the minimum variance estimate of A is obtained by choosing the weighting factors, wj , tobe inversely proportional to the variance of Aj :τjwj = ∑nj=1 τj;τj = var(Aj )It can also be shown that the variance of A is given by
1
var(Â) = ∑n

j=1 τjWe can write explicitly the expression for the diﬀerential hybrid path attenuation as the weighted
sum of the diﬀerential estimates from the SRT, HB and DW
δAHY = wSRT δASRT + wHB δAHB + wDW δADW
As in the SRT case, the diﬀerential PIA is converted into Ku- and Ka-band results by assuming
the ratio of Ka/Ku path attenuation is 6 so that, for the DPR results, AHY (Ku) = 0.2 × δAHY
and AHY (Ka) = 1.2 × δAHY .
For the single-frequency case, the hybrid path attenuation is given by
AHY = wSRT ASRT + wHB AHB
74

Note that the SRT estimates in the above equations should be interpreted as the eﬀective PIA
estimate described earlier, which itself is a weighted sum of the SRT estimates using diﬀerent
rain-free reference NRCS estimates. Since the procedures used to derive the SRT and hybrid
estimates are the same, the formula for the reliability factor, the ratio of the mean of AHY to
the standard deviation, can be written in the same form:∑
τj A jReleﬀ = √∑τjwhere
A1 = ASRT ;

τ1 =

1
,
var(ASRT )etc.

The AHB is given by the following formula:10
log10 [1 − ζ(r)]1/ββ∫ rβζ(r) = 0.2β ln 10α(s)Zm(s) ds; k = αZeβAHB = 10

where the range, r, is taken to be the last clutter-free range bin above the surface and Zm is themeasured radar reflectivity factor. The expression for δAHY is obtained simply by taking thediﬀerence between AHB at Ka-band and at Ku-band.
An expression for the standard dual-wavelength PIA estimate, DW, can be obtained bynoting that the diﬀerential path attenuation is equal to the diﬀerence between the measured
and actual dual-frequency ratio, DFR
δADW (r) = DFRm(r) − DFR(r)The first term is the diﬀerence of the measured reflectivity factors at Ku- and Ka-band and isa measured quantity. The second term in the above equation, the true DFR, is not known but
we approximate it by using the measured dBZm(Ku) so that
δADW (r) = [dBZm (Ku, r) − dBZm (Ka, r)] − DFR(dBZm (Ku, r))
As in the HB method, the range is evaluated at the clutter-free gate nearest the surface.
To implement the hybrid method, it is necessary to approximate the variances for the SRT,HB and DW. Expressions for the variance of the SRT are given above and in section 5; for the
HB and DW, measured drop size distributions are used to estimate the errors.
3.7.2. Input Variables
sigmaZeroMeasured [real*4]: Normalized backscattering radar cross section of the surface (dB) (NRCS) at Ku-band, Ka-band or KaHS for the angle bins in the radar
scan (unitless), from the Preparation Module.
75flagPrecip [integer*2]: rain/no-rain flag from Preparation Module.
localZenithAngle [real*4]: Incidence angle from Preparation Module.
landSurfaceType [integer*2]: surface type from Preparation Module. These are modified so that only 3 surface classes are defined: ocean, land and coast.
lat, lon [real*4]: latitude, longitude of the center of the FOV from Preparation Module.
zFactorMeasured(176/88) [real*4]: range profile of the measured radar reflectivity factor in dB from the Preparation Module. For the KuNS and KaMS the range
resolution is 0.125 km with 176 gates while for the KaHS the range sampling is0.25 km with 88 gates.
binStormTop [integer*2]: range bin number corresponding to the first gate at which
precipitation is detected, from the Preparation Module.
snowIceCover [integer*2]: an indicator flag for the presence of snow-over-land or sea-ice,from the Preparation Module.
snRatioAtRealSurface [real*4]: signal-to-noise ratio at the surface range gate, fromthe Preparation Module.
binNode(5) [integer*2] provides the range bins corresponding to storm height, top, center and bottom of melting layer and lowest bin of the precipitation, from the DSD
modulebinBBTop, binBBBottom [integer*2]: bin numbers corresponding to the top and bottomof the bright-band, from the Classification Module.
3.7.3. Output Variables
Five sets of path-integrated attenuation and associated quantities are produced. These consist of:single- and dual-frequency derived Ku-band attenuation, single- and dual-frequency derived Kaband attenuation and a single-frequency derived KaHS-band attenuation. The single-frequency
Ku-band attenuation and associated products are stored in the 49 angle bins (full swath) of
the Ku-NS structure. The single-frequency Ka/KaHS products are stored in the Ka-MS/KaHS structures and are available for the 25 angle bins of the inner swath/ 24 angle bins of
the interleaved swath. The dual-frequency PIA products for Ku- and Ka-band are stored in
the DPR-NS and DPR-MS structures, respectively, and are produced for the 25 angle bins
comprising the inner swath. (For the DPR-NS structure, the dual-frequency derived Ku-band
path attenuations are contained in angle bins 13 through 37.) Note that the single-frequency
Ku-band results are used in the outer swath of the DPR-NS structure, covering the angle binsfrom 1 to 12 and from 38 to 49.
The output variables for each of these channels are defined below.
76pathAtten [real*4]: The estimated eﬀective 2-way path-attenuation in dB where∫ r
pathAten = 2
k(s) ds0

where k(s) is the attenuation coeﬃcient in dB/km where the integral is takenfrom the storm top to the surface. The path attenuation is often designated as
the PIA, the path-integrated attenuation. In the notation used above and in∑section 5:
pathAten = PIAeﬀ =u PIAj∑juj

Where uj is equal to the inverse of the variance associated with the jth reference
data point:
uj = 1/σj2
PIAalt(6) [real*4]: The path-integrated attenuation from the jth estimate, (PIAj in
the notation above), where
PIAalt (1)= PIA derived from the forward along-track spatial reference data
PIAalt (2)= PIA derived from the backward along-track spatial reference data
PIAalt (3)= PIA derived from the forward hybrid/cross-track reference data
PIAalt (4)= PIA derived from the forward hybrid/cross-track reference data
PIAalt (5)= PIA derived from standard temporal reference data
PIAalt (6)= PIA derived from the light-rain temporal reference data
Note that for product versions 1 through 3, the temporal path-attenuation estimate, PIAalt(5), is set to missing but is defined for versions 4 and higher.
For product versions 1 through 6, the light-rain temporal estimate, PIAalt(6),is set to missing. Note also that the forward/backward hybrid/cross-track pathattenuations are defined only over ocean and are set to missing over land.
PIAweight(6) [real*4]: The weights, w, of the individual PIA estimates used in deriving
the eﬀective PIA. The weight for a particular PIA estimate is proportional to theinverse of the error variance associated with the method. The sum of the weights
should equal one. As with PIAalt(6), PIAweight(6) is set to missing.
wj =

1 1
uj
∑ 1 ≡∑
2
σj
uj
σ2
j

where
uj = 1/σj2∑
wi = 1
reliabFlag [integer*2]: Reliability Flag for the PIAeﬀ estimate,= 1 if Releﬀ > 3 ; PIAeﬀ estimate is considered reliable77

= 2 if 3 ≥ Releﬀ > 1 ; PIAeﬀ estimate is considered marginally reliable= 3 if Releﬀ ≤ 1 ; PIAeﬀ is unreliable= 4 if SNRat surface < 2 dB; provides a lower bound to the path-attenuation= 9 (no-rain case)reliabFactor [real*4]: Reliability Factor for the eﬀective PIA estimate, pathAtten.
This is defined as:
reliabFact = Releﬀ = (∑uj )−1/2∑

uj PIAj

RFactorAlt(6) [real*4]: The reliability factors associated with the individual PIA estimates in PIAalt. As with PIAalt(6), RFactorAlt(6) is set to missing.
RFactorAltj = Relj = PIAj /σj ;j = 1, . . . , 6

refScanID(2,2) [integer*2]: refScanID gives the number of scan lines between thecurrent scan and the beginning (or end) of the along-track reference data at eachangle bin. The values are computed by the equation: Current Scan Number −Reference Scan Number. The values are positive for the Forward estimates andnegative for the Backward estimates. The Fortran indices are:
1,1 - Forward - Near reference
2,1 - Forward - Far reference
1,2 - Backward - Near reference
2,2 - Backward - Far reference
PIAhybrid [real*4] (V6): For the dual-frequency output, PIAhybrid (dB) is a weighted
sum of the path attenuations from the SRT, HB, and DW methods. For the singlefrequency outputs, PIAhybrid is a weighted sum of the SRT and HB methods.
reliabFlagHY [integer*2] (V6): reliabFlagHY is the reliability flag for the PIAhybrid
and is defined in the same way as reliabFlag where PIAeﬀ is replaced by PIAhybrid.
reliabFactorHY [real*4] (V6): reliabFactorHY is the reliability factor associated with
the PIAhybrid estimate and is defined as the mean over the standard deviationof the estimate.
stddevHY [real*4] (V6): stddevHy is the standard deviation (dB) of the hybrid estimateof path attenuation.
PIAhb [real*4] (V6): PIAhb (dB) is the path attenuation estimate derived from the
Hitschfeld-Bordan equation.
78

zeta [real*4] (V6): zeta (unitless) is a parameter in the Hitschfeld-Bordan equation.
stddevEff(3) [real*4] (V6):
stddevEff(1) contains the standard deviation of the PIAeff (i.e., the composite(∑)−1/2
or eﬀective SRT path attenuation estimate). It is given by σ1 =
1/σj2
=
√
1/ uj
stddevEff(2) is a weighted root mean square error and provides a measured ofthe error of the individual PIA estimates from the eﬀective PIA estimate. It is(∑)1/2
given by σ2 =wj (Aeﬀ − Aj )2.
stddevEff(3) is given by σ3 = [σ12 + σ22 ]1/2
PIAdw [real*4] (V7): PIAdw(dB) is the path-integrated attenuation as estimated fromthe standard dual-wavelength technique. [This variable is not yet defined in the
toolkit].
PIAHYweight(3) [real*4] (V7): PIAHYweight contains the weights of the SRT, HB, andDF methods in the calculation of the hybrid PIA. For single-frequency results,
since the DF method is inapplicable, PIAHYweight(3)=0. [This variable is not
yet defined in the toolkit].
3.7.4. Temporal Reference Files
Temporal reference files or look-up tables (LUT) are needed to estimate the path attenuation
using temporal reference data. The temporal reference data are simply the mean and standard
deviations of previously measured rain-free σ 0 data that have been categorized with respect toincidence angle and location. In particular, the LUTs contain the rain-free sample mean andstandard deviation of the σ 0 data at each (lat, lon, incidence angle) bin, where the incidence
angles, with respect to the radar, consist of nadir, ±0.710, ±1.420, · · · up to the maximum angle.
Also stored is the number of σ 0 data points acquired at the particular bin in question and usedin computing the statistics.
For version 5 of the algorithm, a 0.50 × 0.50 grid is used which covers the GPM-DPR latitude
range of 67◦ S–67◦ N so that 268 latitude bins and 720 longitude bins are needed. Four LUTs
are derived from DJF (December-January-February), MAM, JJA, SON input data. For each ofthese, 5 LUTs are prepared corresponding to ocean, land, coast, sea-ice and snow-covered landbackgrounds. Note that separate tables are produced for σ 0 (Ku), σ 0 (Ka), σ 0 (KaHS), and thediﬀerence field, [σ 0 (Ka)−σ 0 (Ku)] so that a total of 20 LUT’s are generated for each three-monthseason.
As noted earlier, two types of temporal reference files have been defined: the standard
temporal reference files as described above and a light-rain reference file which, as of version 4of the code, is undefined. The results derived from the standard temporal reference file can befound in the fifth entry in the variables PIAalt, PIAweight, RFactorAlt. The sixth entry for79these variables, corresponding to the ‘light-rain’ temporal reference file, is set to missing.
3.7.5. Definitions of the Eﬀective PIA, Variance and Reliability Factor
As noted above, multiple estimates of PIA can be generated for each channel corresponding to
diﬀerent surface reference estimates. Specifically, we have the following situation
00
PIAj = ⟨σNR ⟩j − σ(3.7-1)

where the first term on the right-hand side is the jth surface reference value and the second
term is the apparent NRCS in rain. Associated with the jth reference data set is a variance, σj2 :02var(PIAj ) = var[⟨(σNR ⟩j ] = σj

(3.7-2)

From these PIA estimates we want to obtain an eﬀective or ‘best’ PIA. We assume it can be
written in the form:
PIAeﬀ =
Where the weights, wj , are such that∑∑wj PIAj(3.7-3)

wj = 1(3.7-4)

We assume that the individual PIA estimates are statistically independent so that the varianceof PIAeﬀ is:var(PIAeﬀ ) =∑wj2 σj2(3.7-5)To minimize this, subject to the side condition given by (3.7-4), we use the method of Lagrange
multipliers where the expression∑∑
wj − 1)wj2 σj2 + λ((3.7-6)is minimized with respect to the weights, wj . Taking the partial derivatives of (3.7-6) withrespect to wi , then
2wi σi2 + λ = 0⇒

wi = −λ/2σi2(3.7-7)

Also, using (3.7-4) gives∑

wj = −(λ/2)∑(1/σj2 )⇒ λ = −2/∑
(1/σj2 )(3.7-8)Substituting (3.7-8) into (3.7-7) gives an expression for the weights:
wj =

∑
1 1
∑ 1 ≡ uj /
uj
2
σj
σ2

(3.7-9)jWhere
uj = 1/σj280(3.7-10)

The eﬀective PIA is thenPIAeﬀ = (∑uj )−1∑

uj PIAj(3.7-11)

If we define the reliability factor, Rel, as the ratio of the PIA to the standard deviation of thereference estimate, then for the jth reference estimate, we can write:
Relj = PIAj /σj(3.7-12)To apply this definition to the present situation, we define Releﬀ by the equation:
PIAeﬀ = (∑uj )−1∑

uj PIAj ≡ σeﬀ Releﬀ(3.7-13)

Computing Releﬀ requires a value for the standard deviation of the eﬀective PIA. This can be2found by substituting (3.7-9) into (3.7-5) and by noting that σeﬀ= var(PIAeﬀ ). This gives
2
1/σeﬀ
=

∑
(1/σj2 )

⇒

2
σeﬀ
=[

∑
∑
(1/σj2 )]−1 = (uj )−1(3.7-14)

Using (3.7-14) in (3.7-13) gives∑∑
uj PIAj
Releﬀ = (uj )−1/2(3.7-15)Equations (3.7-9), (3.7-11) and (3.7-15) define, respectively, the weights, eﬀective PIA, andeﬀective reliability factor that are computed in the algorithm. In addition to the σeﬀ error
defined above in (3.7-14), a kind of RMS error can be defined by the following equation:
RMS2eﬀ =∑wj [PIAeﬀ − PIA]2(3.7-16)jwhere the summation runs over the j estimates of PIA. Note that when the weighting factors
are all the same then this reduces to the usual definition of RMS error.
There are several issues related to these equations. For example, what should be done if
none of the reference data sets exist? This situation can occur for measurements over small
islands or small bodies of water or at coastal fields of view. For example, over a small island,
there may be an insuﬃcient number of non-raining fields of view adjacent to the rain area toform a valid spatial reference. In most cases, the temporal reference data set would be used
and the other reference estimates would be discarded. However, in some cases, there may be an
insuﬃcient number of data points in the temporal file to provide a valid estimate. In this case,a flag is set indicating that no valid reference data are available and all the output variables are
set to −9999.9
A somewhat diﬀerent situation occurs if some of the reference data sets exist but all yield anegative PIA. For these cases, the individual variances will exist so that Releﬀ , PIAeﬀ and the81

eﬀective variance should all exist. Note that for these cases Releﬀ , PIAeﬀ will be negative but
the eﬀective variance will be positive, as it should be.
A third type of situation occurs if one or more of the PIA estimates are positive and one ormore of the PIA estimates are negative. In this case, the negative PIAs will be included in thedefinition of PIAeﬀ . In general, as long as the reference data is considered to be valid, the PIA
will be used even if the value is negative.
According to these scenarios, there will be only one type of raining situation where theoutput variables will need to be set to some default value and this occurs when none of thereference data sets exist or are valid. This is expected to be a small fraction relative to the total
number of rain cases.
3.7.6. Excluding Spatial Reference Data based on the refScanID VariableThis section relates to determining the circumstances under which we assume a spatial reference
estimate to be valid. For the forward-going spatial reference, reference data will almost always
exist. An exception is if rain is encountered at the beginning of the orbit before Ns (= 8) rain-freefields of view have been measured at a particular incidence angle. A similar exception occurs
for the backward spatial methods: this occurs, however, at the end of the orbit rather than the
beginning. In all other cases, forward and backward spatial reference data should exist. The
question is how should we exclude spatial reference estimates if the data are taken at locations far
from the raining area. To make this definite we implement the following rules in the algorithm.
See definition of refScanID(2,2) in section 3.7.3.
The forward along-track spatial reference at angle bin j will be assumed to be invalid if:|refScanID(2, 1, j)| > 50.
Similarly, the backward along-track spatial reference at angle bin j will be assumed to be invalid
if:|refScanID(2, 2, j)| > 50.
The above conditions are equivalent to stating that, for a particular incidence angle, all the
spatial reference data must be taken within 50 scans of the scan at which rain is encountered.
The criteria for the hybrid cross-track are more complicated because two quadratic fits are used
for the inner and outer portion of the swath. Nominally, we will assume that if there are 15 ormore angle bins in the inner portion of the swath for which:|refScanID(2, 1, j)| ≤ 50
then the forward hybrid cross-track method will be applied. Similarly in the outer portion of
the swath, if there are 15 or more angle bins in this portion of the swath for which:|refScanID(2, 1, j)| ≤ 50
82

then the forward hybrid cross-track method will be applied. Application of the backward hybrid
cross-track will follow the same rule, based on refScanID(2, 2, j).
3.7.7. Additional Comments and Version-dependent Variables
In version 5, sea-ice and snow-covered land surface data from NOAA are now output by the
preparation module in the variable snowIceCover. The preparation module also outputs landSurfaceType, the leading digit of which is used to determine whether the surface is ocean, landor coast. When the snowIceCover variable registers ‘sea-ice’ and landSurfaceType is ‘ocean’,then the surface type for the SRT is set to ‘sea-ice’ and the temporal and spatial reference
data for ‘sea-ice’ are used. Similarly, when the snowIceCover variable indicates snow-cover andlandSurfaceType is ‘land’, then the surface type for the SRT is set to ‘snow-cover’ and therelevant temporal and spatial reference data are used. On the other hand, if snowIceCover is
set to ‘sea-ice’ and ‘landSurfaceType’ is not ‘ocean’ then landSurfaceType is used to identify
the surface type. The landSurfaceType variable is also used to override the snowIceCovervariable if the later indicates ‘snow-cover’ but landSurfaceType is not set to ‘land’.
Version 5 of the DPR algorithm also produces saturation flags for the surface returns. These
flags are generated by the preparation module and given the name flagSigmaZeroSaturation.
A second set of temporal look-up tables has been prepared so that the ‘saturation-corrected’sigma-zero values are not included in the calculation of the statistics. A decision has been madeto use the tables that include the ‘saturation-corrected’ data. As saturation conditions primarily
aﬀect the nadir/near-nadir Ku-band data under rain-free conditions, inclusion of the ‘saturationcorrected’ data will tend to increase the magnitude of the reference data thereby increasing theestimated Ku-band path attenuation. The ‘saturation-corrected’ σ 0 (Ku) data will also tend
to decrease the dual-frequency estimates of path attenuation because this is computed as thediﬀerence between the Ka- and Ku-band path attenuations and, in nearly all cases, only the
Ku-band data are aﬀected by saturation.
Because the Ku-band has greater sensitivity than the matched-swath Ka-band, there are anumber of cases for which rain is detected at Ku-band but not at Ka-band. As dual-frequency
derived estimates at Ku- and Ka-band are produced when either or both channels detect rain,
a number of dual-frequency derived Ka-band estimates of path attenuation will be produced
without corresponding single-frequency estimates.
Versions 1 through 3 of the SRT products do not contain path attenuations based on thetemporal reference data. Versions 4 and higher will include such results and which can be foundin PIAalt(5). The light-rain/wet soil temporal reference data set has yet to be derived so thisproduct will be unavailable up to and including version 5. As a consequence PIAalt(6) will be
set to ‘missing’ (−9999.9). As already noted, the hybrid/cross-track reference data are derived
only over ocean so that over land, PIAalt(3) and PIAalt(4) are set to ‘missing’.
83

In version 4 or lower, the algorithm does not check for saturated conditions. This can leadto errors in the path-attenuation estimates.

84

3.8 Solver module
3.8.1 Objective
The primary objective of the Solver module is to retrieve the drop size distribution andcalculate some physical variables.
3.8.2 Algorithm Overview
The solver module employs an R-Dm plane, where the vertical axis is the precipitation rate(R) and the horizontal axis is the mass weighted mean diameter (Dm ). Once an R-Dm relationq) is assumed, the retrieval process goes from the top to the bottom range bin. At(as R = ϵr pDma “precipitating”range bin, a combination of R and Dm which satisfies both the R-Dm relationand the given Zf is usually selected. Zf is the radar reflectivity factor corrected for attenuation
caused at the higher range bins but not for attenuation caused at the current range bin. For
drawing the contour of Zf on the R-Dm plane, scattering tables are used. After R and Dm arecalculated at all “precipitating”range bins, the path integrated attenuation (PIA) is calculated.
When the surface reference technique (SRT) gives a reliable PIA estimate, the calculated PIA
and SRT’s PIA estimate can be compared.
The R-Dm relation is linked to ϵ, which is originally an adjustment factor for the k-Ze
realtion. The retrieval is done multiple times for diﬀerent ϵ or diﬀerent R-Dm relations corresponding ϵ ranging from 0.2 to 5.0. The best ϵ or the best R-Dm relation is selected by checking
the likelihood of ϵ and the diﬀerence between the calculated PIA and SRT’s PIA.
3.8.3 Retrieval processes
The major input data to the Solver module are radar reflectivity factor corrected for attenuation by non-precipitating particles (here it is denoted by Zm ), µ and σ of log10 ϵ, SRT’s
PIA estimates PIA SRT, its reliability, precipitation type and the information of “phase”. The
retrieval algorithm first defines range bins to be processed: It mainly processes the data between
the storm top and the actual surface.
From the storm top to the clutter free bottom, Zm is usually available for the retrieval.
By correcting the attenuation caused by higher range bins, Zm is converted to Zf . Then, thecontour of the given Zf is drawn on the R-Dm plane by reference to the scattering tables. If
there is only one crossing point between the contour of Zf and the curve for the assumed R-Dm
relation, R and Dm can be determined easily. If there are multiple crossing points, a set of
R and Dm which has smaller R and Dm , is preferred. Unfortunately if there are no crossing
points, the point that is on the curve of R-Dm relation and gives the closest Zf to the given
value is selected. In this case, given Zf (Zf 1 ) and the finally determined Zf (Zf 2 ) are diﬀerent.
In the main-lobe clutter region (below the clutter free bottom), Zm is not useful for the
retrieval. Here, the vertical profile of Ze is assumed to be constant, or the same value of Ze85

at the clutter free bottom is used in the clutter region. The same method is also applied forrange bins in which Zm ’s are smaller than the noise level if such small Zm ’s are caused by large
attenuation due to heavy rain above them. Specifically, if Zm ’s above the point in question are
reliable at 8 (4 for HS) or more liquid-phase range bins in the same pixel, Ze is extrapolated.
In the dual-frequency algorithm, the following preference is set.
(1) Zf of KuPR
(2) Zf of KaPR
(3) extrapolated Ze of KuPR
(4) extrapolated Ze of KaPR

3.8.3-1 Determination of ϵ and use of SRT
Diﬀerent R-Dm relations are tested by changing ϵ from 0.2 to 5.0. Using the following
conditions, the best R-Dm relation is selected. Generally, the three conditions a) to c) arechecked for the single-frequency algorithms and the four conditions a) to d) are checked for thedual-frequency algorithm.
a) A priori probability of ϵ; (log(ϵ) − µ)2 is minimized, where µ is given by the DSD databaseor µ = 0 if DSD database is not applied.
b) The diﬀerence of PIA calculated by the retrieval (PIA SLV) and PIA estimated by
SRT (PIA SRT); (PIA SLV − PIA SRT)2 is minimized.

When PIA SRT is saturated,(min(PIA SLV − PIA SRT, 0)) is minimized.
2c) The diﬀerence of Zf 1 and Zf 2 ; The sum of (Zf 1 − Zf 2 )2 is minimized.
d) If Zm of KuPR is used for the retrieval and Zm of KaPR is normal, estimated DSD shouldbe also agreed with KaPR; (max(Zf 2 of KaPR − Zf 1 of KaPR, 0))2 +(min(Zf 2 of KaPR −Zm of KaPR, 0))2 should be minimized. If Zf 2 of KaPR is between Zm of KaPR and Zf 1
of KaPR, the vaule is zero.
The following preference is set to select the use of PIA.
(1) δPIA SRT=KaPR’s PIA SRT−KuPR’s PIA SRT (if both are reliable and not saturated)(2) KaPR’s PIA SRT (if it is reliable and not saturated)(3) KuPR’s PIA SRT (if it is reliable and not saturated)(4) KaPR’s PIA SRT (if it is reliable and saturated)(5) KuPR’s PIA SRT (if it is reliable and saturated)
If none of PIA SRT is available, or if selected PIA SRT is saturated, abnormally high precipitation rates may be resulted. To avoid them, condition e) is used.
e) R estimated from Zm (not extrapolated Ze ) at liquid-phase range bins should be verticallyconstant; the variance of R is minimized.
86

PIA SRT is set to be not “reliable” if the standard deviation of sampled surface backscattering cross section is larger than 10 dB. PIA SRT is set to be “saturated”if the signal-to-noise
ratio of measured surface backscattering cross section is smaller than 2.
From Version 06, the following condition is added. PIA SRT is set to be not “reliable” if
PIA SRT is more than 10 times as large as PIA HB, where PIA HB is PIA calculated withϵ = 1.
3.8.3-2 NUBF correction
Two NUBF parameters (p area and rainvar) are set in the algorithm, but p area (theratio of rain part in an FOV) is fixed to 1.0 for the version 04 and version 05. rainvar (thesquare of coeﬃcient of variation of R) is estimated by a fractal approach. In the single-frequency
algorithms, NUBF correction is not applied in the first main loop. In the second (final) mainloop, the NUBF parameter is estimated from piaFinal of the first main loop. In the dualfrequency algorithm, the NUBF parameter is estimated from piaFinal of the single-frequency
algorithms. KuPR’s piaFinal is preferred to KaPR’s piaFinal. For the details, please refer toSeto et al. (2015).

87

4. VALIDATION (TEST AND VERIFICATION)
4.1 Pre-launch of the GPM core satellite
Before the launch of the GPM core satellite, we will test the algorithm test and physicallyvalidate it as much as possible with collaboration of the GPM GV team. In the algorithm
test, algorithm mechanics and robustness will be checked. More than one type of test data
will be needed. We will generate synthetic Ka-band data in the central swath from actual
TRMM PR data (Ku-band) by assuming a relationship among the DSD parameters and theattenuation by clouds and water vapor (Kubota et al. 2014). Airborne data (PR-2, APR2) will provide realistic dual-frequency radar data although with many unknown parameters(e.g., clouds). Ground-based dual-frequency radars including NASA’s dual-frequency and dualpolarized Doppler radar (D3R) and JAXA’s dual Ka-band radar system will also provide realisticdual-frequency radar data. Synthetic data created by using a numerical model have an advantagethat all parameters are known so that we can test the performance of the algorithm by checking
whether the algorithm can retrieve the parameters correctly. Note that many parameters needed
to reproduce radar echoes accurately are not handled well in most of the current numericalmodels. They include melting or partially frozen particles. Nevertheless, synthetic data createdwith simple assumptions are very helpful for sanity check of the algorithm. In the physicalvalidation, various parameters of the DPR algorithm will be tested with collaboration of the
GPM GV team. The physical validation will validate the parameters in the physical model ofthe precipitation system assumed in the algorithm. They include the DSD parameters, density
and shape of snow and melting particles, width and structure of the melting layer, supercooled
droplets, attenuation by the water vapor and cloud, inhomogeneity of rain distribution and soon.
4.2 Post-launch of the GPM core satellite
After the launch of the GPM core satellite, product validation will be performed, in addition to the physical validation. Data taken nearly simultaneously by the GPM/DPR and the
TRMM/PR enable us to make a direct comparison. Statistics such as averages and histogramsof radar echoes taken by the DPR can be compared with the corresponding statistics of the
TRMM/PR data. Ground instruments such as a dense rain gauge network and ground-radar
including NASA’s D3R and JAXA’s dual Ka-band radar system can be used to validate theDPR products. Validations by using airborne data are desirable.

88

4. INTERFACE TO OTHER ALGORITHMS
The combined DPR-GMI algorithm requires outputs from the preparation module (PRE), thevertical profile module (VER), the surface reference technique module (SRT), and the classification module (CSF), in addition to the DPR radar reflectivity profiles. Precipitation detection
from the PRE, pressure, temperature and humidity profiles from the VER, the PIA from the
SRT, and bright band detection, altitude of the bright band (if it exists), and classification as
convective or stratiform precipitation from the CSF will be used in the DPR-GMI combinedalgorithm.
The DPR products will be utilized also in the GPM Passive Microwave-Radar Enhanced
Algorithm of an Optimal Estimation approach that uses the DPR/GMI data as the a-priori
constraint.

89

5. REFERENCES
Awaka, J., T. Iguchi, H. Kumagai, and K. Okamoto, 1997: Rain type classification algorithm
for TRMM precipitation radar, Proc. IEEE IGARSS, 317–319.
Awaka, J., T. Iguchi, and K. Okamoto, 1998: Early results on rain type classification by the
Tropical Rainfall Measuring Mission (TRMM) precipitation radar, Proc. 8th URSI Commission F Open Symp., Aveiro, Portugal, 143–146.
Awaka, J., T. Iguchi, and K. Okamoto, 2009: TRMM PR standard algorithm 2A23 and its
performance on bright band detection, J. Meteor. Soc. Japan, 87A, 31–52.
Baldini, L. and E. Gorgucci, 2006: Identification of the melting layer through dual-polarization
radar measurements at vertical incidence, J. Atmos. Ocean. Technol., 23, 829–839.
Bandera, J., A. D. Papatsoris, P. A. Watson, and J. W. Goddard, 1998: Method for detecting
the extent of the melting layer, Electron. Lett., 34(22), 2104–2105.
Caylor I.J., G.M. Heymsfield, R. Meneghini, and L.S. Miller, 1997: Correction of sampling errors
in ocean surface cross-sectional estimates from nadir-looking weather radar. J. Atmos.
Oceanic Technol., 14, 203–210.
Durden, S.L., L. Li, E. Im and S.H. Yueh, 2003: A surface reference technique for airborne
Doppler radar measurements in hurricanes, J. Atmos. Oceanic Technol., 20, 269–275,
2003.
Durden, S.L., S. Tanelli, and G. Dobrowalski, 2010: CloudSat W-band radar measurements of surface backscatter, IEEE Geosci. Remote Sens. Lett., 7, 401–405, doi:
10.1109/LGRS.2010.2079314.
Durden S.L., S. Tanelli, and R. Meneghini, 2012: Using surface classification to improve surface
reference technique over land, Indian J. Radio & Space Physics, 41, 403–410.
Fabry, F. and I. Zawadzki, 1995, Long-term radar observations of the melting layer of precipitation and their interpretation, J. Atmos. Sci., 52, 838–851.
Hines, E. L., 1983: Image processing techniques for the detection of the radar bright band, Ph.D.
dissertation, Univ. Bradford, Bradford, U.K.
Iguchi, T. and R. Meneghini, 1994: Intercomparisons of single-frequency methods for retrieving a
vertical rain profile from airborne or spaceborne radar data. J. Atmos. Oceanic Technol.,11, 1507–1516.
Iguchi, T., T. Kozu, J. Kwiatkowski, R. Meneghini, J. Awaka, and K. Okamoto, 2009: Uncertainties in the Rain Profiling Algorithm for the TRMM Precipitation Radar. J. Meteor.
Soc. Japan, Vol., 87A, 1–30.
Iguchi, T., S. Seto, et al., 2012: An overview of the precipitation retrieval algorithm for the Dualfrequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM)
missionfs core satellite. SPIE, Kyoto, Japan.
Iguchi, T., N. Kawamoto and R. Oki, 2018: Detection of intense ice precipitation with
GPM/DPR. J. Atmos. Oceanic Technol., 35, 491–502, DOI: 10.1175/JTECH-D-170120.1.
Klaassen, W., 1988: Radar observations and simulation of the melting layer of precipitation, J.
Atmospheric Sciences, 45, 3741–3753.
Kozu, T., 1995: A generalized surface echo radar equation for down-looking pencil beam radar.
IEICE Trans. Commun., E78-B, 1245–1248.
90

Kubota, T., T.Iguchi, M. Kojima, L. Liao, T Masaki, H. Hanado, R. Meneghini, R. Oki, 2016:
A statistical method for reducing sidelobe clutter for the Ku-band precipitation radar
onboard the GPM Core Observatory, J. Atmos. Oceanic Technol., 33 (7), 1413-1428.
Kubota, T., T. Iguchi, T. Masaki, N. Yoshida, and R. Oki, 2018: Development of a statiscal method for reducing sidelobe clutter in high sensitivity mode of GPM/KaPR, Proc.
IGARSS2018, in press.
Kubota, T., M. Satoh, T. Nasuno, S. Seto, T. Iguchi, R. Oki , 2012: Development of cloud liquidwater database using global cloud-system resolving model for GPM/DPR algorithm. Proc.
IGARSS 2012, 350–353.
Kubota, T., N. Yoshida, S. Urita, T. Iguchi, S. Seto, R. Meneghini, J. Awaka, Member,
H. Hanado, S. Kida, and R. Oki, 2014: Evaluation of precipitation estimates by atlaunch codes of GPM/DPR algorithms using synthetic data from TRMM/PR observations. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 7, 3931–3944.
(doi.10.1109/JSTARS.2014.2320960.)
Le, M. and V. Chandrasekar, 2013a: Precipitation Type Classification Method for DualFrequency Precipitation Radar (DPR) Onboard the GPM, IEEE Trans. Geosci. Remote
Sens., 51(3), 1784–1790.
Le, M. and V. Chandrasekar, 2013b: Hydrometeor Profile Characterization Method for DualFrequency Precipitation Radar Onboard the GPM, IEEE Trans. Geosci. Remote Sens.,
51(6-2), 3648–3658.
Li, L., E. Im, L.N. Connor, and P.S. Chang, 2004: Retrieving ocean surface wind speed fromthe TRMM precipitation radar measurements, IEEE Trans. Geosci. Remote Sens., 42,
1271–1282.
Marzoug, M. and P. Amayenc, 1994: A class of single- and dual-frequency algorithms for rain rate
profiling from a spaceborne radar. Part I: Principle and tests from numerical simulations.
J. Atmos. Oceanic Technol., 11, 1480–1506.
Meneghini, R., and T. Kozu, 1990: Spaceborne weather radar. Artech House (Boston/London),Norwood, MA, 197pp.
Meneghini, R. and K. Nakamura, 1990: Range profiling of the rain rate by an airborne weather
radar. Remote Sens. Environ., 31, 193–209.
Meneghini, R., T. Iguchi, T. Kozu, L. Liao, K. Okamoto, J.A. Jones, and J. Kwiatkowski, 2000:
Use of the surface reference technique for path attenuation estimates from the TRMM
Radar. J. Appl. Meteor., 39, 2053–2070.
Meneghini, R., J.A. Jones, T. Iguchi, K. Okamoto, and J. Kwiatkowski, 2004: A hybrid surface
reference technique and its application to the TRMM Precipitation Radar, J. Atmos.
Oceanic Technol., 21, 1645–1658.
Meneghini, R. and J.A. Jones, 2011: Standard deviation of spatially-averaged surface cross section data from the TRMM Precipitation Radar, IEEE Geosc. Remote Sens. Letters, 8,
293–297.
Meneghini, R., L. Liao, S. Tanelli, and S. L. Durden, 2012: Assessment of the Performance of aDual-Frequency Surface Reference Technique Over Ocean. IEEE Trans. Geosci. Remote
Sens., 50, 2968–2977.
Meneghini, R., and L. Liao, 2013: Modified Hitschfeld-Bordan equations for attenuationcorrected rain reflectivity: Application to nonuniform beamfilling at oﬀ-nadir incidence.
91

J. Atmos. Oceanic Technol., 30, 1149–1160.
Meneghini, R., and H. Kim, 2016: Minimizing the Standard Deviation of Spatially Averaged
Surface Cross-Sectional Data From the Dual-Frequency Precipitation Radar. IEEE Trans.
Geosci. Remote Sens..
Onogi, K., J. Tsutsui, H. Koide, M. Sakamoto, S. Kobayashi, H. Hatsushika, T. Matsumoto, N.
Yamazaki, H. Kamahori, K. Takahashi, S. Kadokura, K. Wada, K. Kato, R. Oyama, T.
Ose, N. Mannoji and R. Taira , 2007 : The JRA-25 Reanalysis. J. Meteor. Soc. Japan,
85, 369–432.
Press W.H. et al., 1992: Numerical Recipes in FORTRAN, 2nd Edition. Cambridge University
Press. ISBN 0 521 43064 X.
Rosenkranz, P. W., 1975: Shape of the 5mm oxygen band in the atmosphere. IEEE Trans. Ant.
and Propag., AP-23, 498–506.
Seto, S. and T. Iguchi, 2007: Rainfall-induced changes in actual surface backscattering crosssections and eﬀects on rain-rate estimates by spaceborne precipitation radar. J. Atmos.
Oceanic Technol., 24, 1693–1709.
Seto, S., and T. Iguchi, 2011: Applicability of the iterative backward retrieval method for the
GPM Dual-frequency Precipitation Radar. IEEE Transactions on Geoscience and Remote
Sensing, 49(6), pp.1827–1838.
Seto, S., T. Iguchi, and T. Oki, 2013: The basic performance of a precipitation retrieval algorithm
for the Global Precipitation Measurement mission’s single/dual-frequency radar measurements. IEEE Transactions on Geoscience and Remote Sensing, 51(12), 5239–5251.
Seto, S., T. Iguchi, T. Shimozuma, and S. Hayashi, 2015: NUBF correction method
for the GPM/DPR level-2 algorithms. Proc. IGARSS 2015, 2612–2614, DOI:
10.1109/IGARSS.2015.7326347.
Smyth, T. J., A. J. Illingworth, and A. B. Smith, 1998: Radar estimates of rainfall rates at the
ground in bright band and non-bright band events, Q. J. R. Meteorol. Soc., 124(551),
2417–2434.
Steiner, M., R.A. Houze, Jr., and S.E. Yuter, 1995: Climatological characterization of threedimensional storm structure from operational radar and rain gauge data, J. Appl. Meteor.,
34, 1978–2007.
Tagawa, T., 2009: Derivation of sub-footprint scale σ 0 observed by TRMM Precipitation Radar.
IEEE. Trans. Geosci. Remote Sens.,IGARSS 2008, IV, 137–140.
Takahashi, N., and T. Iguchi, 2004: Estimation and correction of beam mismatch of the Precipitation Radar after an orbit boost of the Tropical Rainfall Measuring Mission Satellite,
IEEE. Trans. Geosci. Remote Sens., 42, 2362–2369.
Takahashi, N., H. Hanado, and T. Iguchi, 2005: Estimation of path-integrated attenuation and its
nonuniformity from TRMM/PR range profile data. IEEE. Trans. Geosci. Remote Sens.,
44, 3276–3283.
Tan, J. and J.W.F. Goddard, 1995: The use of dual-polarisation techniques for bright-band
detection with PPI-based radars, Eng.-Colloq. Radar Meteorol., 11/1–11/6.
Tanelli, S., S.L. Durden, and E. Im, 2006: Simultaneous measurements of Ku- and Ka-band sea
surface cross sections by an airborne radar. IEEE. Trans. Geosci. Remote Sens. Letters,
3, 359–363.

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Tanelli, S., G.F. Sacco, S.L. Durden, and Z.S. Haddad, 2012: Impact of non-uniform beam fillingon spaceborne cloud and precipitation radar retrieval algorithms. SPIE, Kyoto, Japan.
Tilford, K.A., I. D. Cluckie, R. J. Griﬃth, and A. Lane, 2001: Vertical reflectivity characteristics
and bright band correction, Proc. Radar Hydrol. Real Time Flood Forecast., Proc. Adv.
Course, 47–65, European Communities.
Ulaby, F.T., R. K. Moore, and A. K. Fung, 1981: Microwave Remote Sensing: Active and Passive.
Vol. I. Artech House, Norwood, MA, 456pp.
Waters, J.W., 1976: Absorption and emission of microwave radiation by atmospheric gases,
in Methods of Experimental Physics, M. L. Meeks, ed. 12, Part B, Radio Astronomy,
Academic Press, Section 2.3.
Yuter, S. E., and R. A. Houze, Jr., 1997: Measurements of Raindrop Size Distributions overthe Pacific Warm Pool and Implications for Z-R Relations, J. Appl. Meteorol., 36(7),847–867.
Zrnic, D.S., R. Raghavan, and V. Chandrasekar, 1994: Observations of copolar correlation coefficient through a bright band at vertical incidence, J. Appl. Meteorol., 33(1), 45–52.

93

6. ACRONYMS
BB:

Bright band

CSF module:

Classification moduleDB:

dual-beamDF:dual-frequency

DFRm:

Measured Dual Frequency Ratio

DPR:

Dual-frequency Precipitation Radar

DSD:

drop size distributionHS:high-sensitivity

KuPR:

Ku-band precipitation radar

KaPR:

Ka-band precipitation radar

ML:

Melting layer

MS:

matched scan

NP-attenuation:

Attenuation due to non-precipitation particles

NS:

normal scan

NUBF:

non-uniform beam filling

PIA:

path-integrated attenuation

PRE module:

Preparation module

PSD:

particle size distributionSB:single-beamSF:single-frequency

SLV module:

Solver module

SRT:

surface reference technique

VER module:

Vertical module94Appendix: Level 2 Contents of Objects in Each Group
Metadata
Metadata has seven elements. Figure 0-1 shows metadata structure.
FileHeader

Metadata(Attribute)

InputRecord

Metadata(Attribute)

AlgorithmRuntimeInfo

Metadata(Attribute)

NavigationRecord

Metadata(Attribute)

FileInfo

Metadata(Attribute)

JAXAInfo

Metadata(Attribute)

File

SWATH

SwathHeader

Metadata(Attribute)

Figure 0-1 L2 Metadata

FileHeader
FileHeader contains metadata of general interest. This group appears in all data products. Table 0-1
shows each metadata elements in FileHeader.

Table 0-1 FileHeader Elements

No

Element

Description

Data size(bytes)1

DOI

Digital Object Identifier.
*Value is blank currently.

2

DOIauthority

Digital Object Identifier Authority.

256

3

DOIshortName

Digital Object Identifier Short Name.
*Value is blank currently.

256

4

AlgorithmID

The algorithm that generated this product, e.g., 2A12.

50

5AlgorithmVersion

The version of the algorithm that generated this product.

50

6FileName

The file name of this granule.

50

7SatelliteName

Values are: TRMM GPM MULTI F10 ... F18 AQUA GCOMW1 CORIOLISMT1 NOAA15 ... NOAA19 METOPA NPP. More values will be added as they
are known.

10

8

InstrumentName

Values are: PR TMI VIRS PRTMI KU KA DPR GMI DPRGMI MERGED SSMI
SSMIS AMSRE AMSR2 WINDSAT MADRAS AMSUA AMSUB SAPHIRMHS ATMS. More values will be added as they are known.

10

9

GenerationDateTime

The date and time this granule was generated. The format is
YYYY-MM-DDTHH:MM:SS.sssZ, where YYYY is 4-digit year, MM is month
number, DD is day of month, T is "T", HH is hour, MM is minute, SS is second,

50

256

No

Element

Description

Data size(bytes)sss is millisecond, and Z is "Z". All fields are zero-filled. The missing value isconstructed by replacing all digits with 9, i.e., 9999-99-99T99:99:99.999Z.

StartGranuleDateTime

The start time defining this granule. The format is the same as
GenerationDateTime. DETAILS: An orbital granule starts when the satellite is at
the position defined by GranuleStart. Thus the start time is not the first scan time.
Some algorithms have overlap scans in the file before the start time as defined in
SwathHeader. A monthly granule starts on the first ms of the month, for example
March 1998 would be 1998-03-01T00:00:00.000Z.

50

StopGranuleDateTime

The stop time defining this granule. The format is the same as
GenerationDateTime. DETAILS: An orbital granule stops when the satellite is at
the position defined by GranuleStart. Thus the stop time is not the last scan time.
Some algorithms have overlap scans in the file after the stop time as defined in
SwathHeader. A monthly granule stops on the last ms of the month, for example
March 1998 would be 1998-03-31T23:59:59.999Z.

50

12GranuleNumber

The number of this granule, which starts as in GranuleStart. If the GranuleStart is
identical to the orbit start, then the GranuleNumber will be the same as the orbit
number. The GranuleNumber will have 6 digits, including leading zeroes, forexample 001234.

50

13

NumberOfSwaths

The number of swaths in this granule.

50

14

NumberOfGrids

The number of grid structures in this granule.

50

15GranuleStart

The starting place in the orbit of this granule. Currently defined values
are ”SOUTHERNMOST LATITUDE” and ”NORTHBOUND EQUATOR
CROSSING”.

50

16

TimeInterval

The time interval covered by this granule. Values are ”ORBIT”, ”HALF
ORBIT”, ”HALF HOUR”, ”HOUR”, ”3 HOUR”, ”DAY”, ”DAY ASC”, DAY
DES”, ”MONTH”, ”CONTACT”.

50

17

ProcessingSystem

The name of the processing system, e.g.,”PPS”, ”JAXA”.

50

18

ProductVersion

The data version assigned by the processing system.

50

19

EmptyGranule

Whether a granule is empty. Values are "EMPTY" or "NOT EMPTY".

50

20

MissingData

The number of missing scans.

50

10

11

InputRecord
InputRecord contains a record of input files for this granule. This group appears in Level1, Level 2,and Level 3 orbital data products. Level 3 times averaged products have the same information
separated into 3 groups since they have many inputs. Table 0-2 shows each metadata elements in
InputRecord.

No
1
2
3

Table 0-2 InputRecord Elements

Element

Description

Data size(bytes)

InputFileNames

A list of input file names for this granule.

1000

InputAlgorithmVersions

A list of algorithm versions of the input files for this granule.

1000

InputGenerationDateTimes

A list of generation date times of the input files for this granule. The format is thesame as GenerationDateTime.

1000

AlgorithmRuntimeInfo
AlgorithmRuntimeInfo contains text runtime information written by the algorithm. This group is

a ”Long Metadata Group”, which has no elements. This group appears in products if the algorithm
developer asks for it.

NavigationRecord
NavigationRecord contains navigation metadata for this granule. This group appears in Level 1, Level2, and Level 3 orbital data products. Table 0-3 shows each metadata elements in NavigationRecord.

Table 0-3 NavigationRecord Elements

No

Element

Description

Data size(bytes)1LongitudeOnEquator

The longitude where the satellite crosses the equator going fromsouth to north.

2

UTCDateTimeOnEquator

The UTC time when the satellite crosses the equator going fromsouth to north. The format is the same as GenerationDate Time.

50

3

MeanSolarBetaAngle

The average solar beta angle in this granule.

50

EphemerisFileName

Name of the ephemeris file input for processing.

50

5

AttitudeFileName

Name of the attitude file input for processing.

50

6

GeoControlFileName

Name of the GeoTK Control Parameters File input for processing.

50

7EphemerisSource

Values are”0 CONSTANT INPUT TEST VALUE”,”1 GROUND ESTIMATED STATE (GES)”,”2 GPS FILTERED SOLUTION (GEONS)”,”3 GPS POINT SOLUTION (PVT)”,”4 ON BOARD PROPAGATED (OBP)”,”5 OEM GROUND EPHEMERIS FILE”,”6 GEONS WITH FALLBACK AS FLAGGED”,”7 PVT WITH FALLBACK AS FLAGGED”,”8 OBP WITH FALLBACK AS FLAGGED”,”9 GES WITH FALLBACK AS FLAGGED”.

50

8

AttitudeSource

values are”0 CONSTANT INPUTS FOR TESTING”,”1 ON BOARD CALCULATED PITCH ROLL YAW”

50

9

GeoToolkitVersion

Version of the GeoToolkit.

50

10

SensorAlignmentFirstRotationAngle

Alignment angle, first rotation, in degrees. Rotation adjustment fromsensor coordinates to the Attitude Control System Flight
Coordinates.

50

11

SensorAlignmentSecondRotationAngle

Alignment angle, second rotation, in degrees.

50

12SensorAlignmentThirdRotationAngle

Alignment angle, third rotation, in degrees.

50

13SensorAlignmentFirstRotationAxis

Euler rotation sequence, first rotation axis. Values are "1","2", "3"(representing X, Y, Z).

50

14

SensorAlignmentSecondRotationAxis

Euler rotation sequence, second rotation axis. Values are "1","2", "3"(representing X, Y, Z).

50

15

SensorAlignmentThirdRotationAxis

Euler rotation sequence, third rotation axis. Values are "1","2", "3"(representing X, Y, Z).

50

4

FileInfo50

FileInfo contains metadata used by the PPS I/O Toolkit. This group appears in all data products. Table
0-4 shows each metadata elements in FileInfo.

Table 0-4 FileInfo Elements
No

Element

Description

Data size(bytes)No

Element

Description

Data size(bytes)DataFormatVersion

The version of the data format used to write this file. This version is separate foreach AlgorithmID. The order is: "a" "b" ... "z" "aa" "ab" ... "az" "ba" "bb".

50

2

TKCodeBuildVersion

Usually TK CodeBuildVersion is "1". If the I/O routines built by TKIO changeeven though the DataFormatVersion is unchanged, then TK CodeBuildVersion
increments to "2", "3", ...If subsequently DataFormatVersion changes,
TKCodeBuildVersion becomes "1" again.

50

3

MetadataVersion

The version of metadata used to write this file. This version is separate for each
AlgorithmID. The order is: "a" "b" ... "z" "aa" "ab" ... "az" "ba" "bb" ...

50

4FormatPackage

The underlying format of this granule.
Values are ”HDF4”,”HDF5”, ”NETCDF”, ”TKBINARY”.

50

5

BlueprintFilename

The filename of the primary blueprint file that defined the format used to write
this file.

50

6

BlueprintVersion

The BlueprintVersion of the format definition.

50

7

TKIOVersion

The version of TKIO used to create I/O routines to write this file. TKIOVersion
does not define the format used to write this file.

50

8

MetadataStyle

The style in which the metadata was written, e.g., "PVL"."PVL" means< parameter >=< value >.

50

9EndianType

The endian type of the system that wrote this file. Values are "BIG ENDIAN" and"LITTLE ENDIAN".

50

1

JAXAInfo
JAXAInfo contains metadata requested by JAXA. Used by DPR algorithms and GSMaP. Table 0-5
shows each metadata elements in JAXAInfo.

Table 0-5 JAXAInfo Elements
No

Element

1

GranuleFirstScanUTCDateTime

2

GranuleLastScanUTCDateTime

3

TotalQualityCode

Description
The date and time of first scan (incl. missing scan). The format is
YYYY-MM-DDTHH:MM:SS.sssZ, where YYYY is 4-digit year, MM ismonth number, DD is day of month, T is "T", HH is hour, MM is minute,
SS is second, sss is millisecond, and Z is "Z". All fields are zero-filled. The
missing value is constructed by replacing all digits with 9, i.e.,
9999-99-99T99:99:99.999Z.
Granule Last Scan UTC Date. Date is a 24 character string. The format is
YYYY-MM-DDTHH:MM:SS.sssZ, where YYYY is 4-digit year, MM ismonth number, DD is day of month, T is ”T”, HH is hour, MM is minute,
SS is second, sss is millisecond, and Z is ”Z”. All fields are zero-filled.
The total quality of product is defined based on the quality of input data.
Quality meaning are
GPM KuPR/KaPR TRMM PR L2 product
Good: The total quality of input data (Ku/Ka/PR L1B) is Good.
Fair: The GPM KuPR/KaPR L2 is not JMA's global weather forecast(FCST) or JMA's Global ANALsis model data (GANAL) but
weather DB file.
EG (Empty Granule): The total quality of input data (Ku/Ka/PR L1B)
is EG
GPM DPR L2 product
Good: The total quality of both Ku L2 and Ka L2 is Good.
Fair: (i)The total quality of either Ku L2 or Ka L2 is EG(ii)The input data used in GPM DPR L2 is not JMA's global
weather forecast (FCST) or JMA's Global ANALysis model
data (GANAL) but weather DB file.
EG (Empty Granule): The total quality of both Ku L2 and Ka L2 is
EG.

Data size(bytes)

50

50

50

No

Element

Description

Data size(bytes)GPM DPR SLH L2 product
Good: The total quality of input data (DPR L2) is Good
Fair: The total quality of input data is Fair.
EG (Empty Granule): The total quality of input data (DPR L2) is EG.
4FirstScanLat

Latitude of orbit first scan.

50

5FirstScanLon

Longitude of orbit first scan.

50

6

LastScanLat

Latitude of orbit last scan.

50

7

LastScanLon

Longitude of orbit last scan.

50

Number of rain pixels in the NS swath, judged at DPR L2 algorithm. At
DPR L1, value is "-9999".
Number of rain pixels in the MS swath, judged at DPR L2algorithm. At
DPR L1, value is "-9999".

8

NumberOfRainPixelsNS

50

9NumberOfRainPixelsMS

10

NumberOfRainPixelsHS

Number of rain pixels in the HS swath, judged at DPR L2algorithm. At
DPR L1, value is "-9999".

50

11

ProcessingSubSystem

The name of the processing sub-system, e.g., "ALGORITHM","PCS".

50

12ProcessingMode

The name of the processing mode, e.g., "STD","NRT".

50

13lightspeed

Constant value of light speed.

50

14dielectricConstantKu

The parameter of dielectric for Ku.

50

15

dielectricConstantKa

The parameter of dielectric for Ka.

50

50

SwathHeader

SwathHeader contains metadata for swaths. This group appears in Level 1 and Level 2 data products.
Table 0-6 shows each metadata elements in SwathHeader.

Table 0-6No

Element

1

NumberScansInSet

2

MaximumNumberScansTotal

3

NumberScansBeforeGranule

SwathHeader Elements
Description

The scans read by TKreadScan are a ”set”. For single swath data, one scan isread so NumberScansInSet=1. For multiple swath data, one TKreadScan may
read more than one scan. For example, for SSM/I data one TKreadScan reads
one low frequency scan and two high frequency scans. Therefore
NumberScansInSet=1 for the low frequency swath and Number-ScansInSet=2
for the high frequency swath.
The maximum allowed number of total scans in this swath. Total scans =overlap scans before granule + scans in granule + overlap scans after granule.

Data size(bytes)50

50

The number of overlap scans before the first scan of the granule in this swath.

50

4

NumberScansGranule

The number of scans in the granule in this swath.

50

5NumberScansAfterGranule

The number of overlap scans after the last scan of the granule in this swath.

50

6

NumberPixels

The number of IFOV in each scan in this swath.

50

ScanType

The type of scan in this swath. Values are: "CROSSTRACK" and"CONICAL".

50

7

Data Group
Elements of data group are explained in detail in this section. Each swath has 11 data group (12data group for MS swath of 2ADPR) and 2 data (Latitude and Longitude) commonly. Figure
0-1 shows data group structure.
SwathHeader

SWATH

Metadata(Attribute)

ScanTime

Group

Latitude4byte float : nray × nscan

Longitude

4byte float : nray × nscan

scanStatus

Group

navigation

Group

PRE

Group

VER

Group

CSF

Group

SRT

Group

DSD

Group

Experimental

Group

SLV

Group

FLG

Group

TRG

Group (only for DPR/MS swath)

Figure 0-1 Data Format Structure for Data Group

ScanTime (Group)
Year

Type
2-byte integer

Array
nscan

Unit
year

Type
1-byte integer

Array
nscan

Unit
month

Array
nscan

Unit
day

4-digit year, e.g., 1998. Values range from 1950 to 2100 years:
Missing Value：
-9999

Month

Month of the year. Values range from 1 to 12 months.
Missing Value：-99

DayOfMonthType
1-byte integer

Day of the month. Values range from 1 to 31 days.
Missing Value：
-99

Hour

Type
1-byte integer

Array
nscan

Unit
hour

Type
1-byte integer

Array
nscan

Unit
minuteType
1-byte integer

Array
nscan

Unit
s

Type
2-byte integer

Array
nscan

Unit
ms

Type
2-byte integer

Array
nscan

Unit
day

Array
nscan

Unit
s

UTC hour of the day. Values range from 0 to 23 hours.
Missing Value：-99

MinuteMinute of the hour. Values range from 0 to 59 minutes.
Missing Value：-99

SecondSecond of the minute. Values range from 0 to 60 s.
Missing Value：-99

MilliSecond

Thousandths of the second. Values range from 0 to 999 ms.
Missing Value：-9999

DayOfYearDay of the year. Values range from 1 to 366 days.
Missing Value：-9999

SecondOfDay

Type
8-byte float

A time associated with the scan. It is expressed as the UTC seconds of the day.
Values range from 0 to 86400 s.
Missing Value：-9999.9

LatitudeType4-byte float

Array
nray * nscan

Unit
degreesType
4-byte float

Array
nray * nscan

Unit
degrees

The earth latitude of the center of the IFOV at the altitude of the earth ellipsoid. Latitude ispositive north, negative south.
Values range from -90 to 90 degrees.
Missing Value：-9999.9

Longitude

The earth longitude of the center of the IFOV at the altitude of the earth ellipsoid. Longitude ispositive east, negative west. A point on the 180th meridian has the value -180 degrees.
Values range from -180 to 180 degrees.

Missing Value：-9999.9

scanStatus (Group)
dataQualityType
1-byte integer

Array
nscan

Unit
N/A

Type
1-byte integer

Array
nscan

Unit
N/A

Type
1-byte integer

Array
nscan

Unit
N/A

Array
nscan

Unit
N/A

A summary of data quality in the scan. Unless this is 0 (normal), the scan data is meaninglessto higher precipitation processing. Bit 0 is the least significant bit (i.e., if bit i = 1 and other bits
= 0, the unsigned integer value is 2**i).
Bit Meaning
0 : missing
5 : geoError is not zero
6 : modeStatus is not zero

dataWarning

Flag of data warning for each scan. Bit Meaning is below.
Bit Meaning
0 : beam Matching is abnormal
1 : VPRF table is abnormal
2 : surface Table is abnormal
3 : geoWarning is not Zero
4 : operational mode is not observation mode.
5 : GPS status is abnormal

missing

Indicates whether information is contained in the scan data. The values are:Bit Meaning
0 : Scan is missing
1 : Science telemetry packet missing
2 : Science telemetry segment withing packet missing
3 : Science telemetry other missing
4 : Housekeeping (HK) telemetry packet missing
5 : Spare (always 0)
6 : Spare (always 0)
7 : Spare (always 0)

modeStatusType
1-byte integer

A summary of status modes. If all status modes are routine, all bits in modeStatus = 0. Routine
means that scan data has been measured in the normal operational situation as far as the status
modes are concerned. modeStatus does not assess geolocation quality. modeStatus is broken
into 8 bit flags. Each bit = 0 if the status is routine but the bit = 1 if the status is not routine. Bit
0 is the least significant bit (i.e., if bit i = 1 and other bits = 0, the unsigned integer value is2**i). The non-routine situations follow:

Bit Meaning
0 : Spare (always 0)
1 : SCorientation not 0 or 180
2: pointingStatus not 0
3 : Non-routine limitErrorFlag
4 : Non-routine operationalMode (not 1 or 11)
5 : Spare (always 0)
6 : Spare (always 0)
7 : Spare (always 0)

geoErrorType
2-byte integer

Array
nscan

Unit
N/A

Array
nscan

Unit
N/A

A summary of geolocation errors in the scan. geoError is used to set a bit in dataQuality. A zero
integer value of geoError indicates ’good’ geolocation. A non-zero value broken down into the
bit flags below indicates the specified reason, where bit 0 is the least significant bit (i.e., if bit i= 1 and other bits = 0 the unsigned integer value is 2**i). Bits 0, 4, 5, 8 and 9 are per pixel
error flags. If the number of bad pixels (for any of the reasons specified by these flags) isgreater than the threshold then bit 7 = 1 and each of these flags is set to 1 if any pixel is bad forthat reason. At launch this threshold is zero, so data is flagged if any pixel is bad. If the numberof bad pixels is less than or equal to the threshold then bit 7 = 0 and all of these flags are also 0.
Bit Meaning
0 : Latitude limit exceeded for viewed pixel locations
1 : Negative scan time, invalid input
2 : Error getting spacecraft attitude at scan mid-time
3 : Error getting spacecraft ephemeris at scan mid-time
4 : Invalid input non-unit ray vector for any pixel
5 : Ray misses Earth for any pixel with normal pointing
6 : Nadir calculation error for subsatellite position
7 : Pixel count with geolocation error over threshold
8 : Error in getting spacecraft attitude for any pixel
9 : Error in getting spacecraft ephemeris for any pixel
10 : Spare (always 0)
11 : Spare (always 0)
12 : Spare (always 0)
13 : Spare (always 0)
14 : Spare (always 0)
15 : Spare (always 0)

geoWarningType
2-byte integer

A summary of geolocation warnings in the scan. geoWarning does not set a bit in dataQuality.
Warnings indicate unusual conditions. These conditions do not indicate bad geolocation but are
flagged as a warning that further review of the data may be useful. A zero integer value
indicates usual geolocation. A non-zero value broken down into the following bit flags
indicates the following, where bit 0 is the least significant bit (i.e., if bit i = 1 and other bits = 0
the unsigned integer value is 2**i):Bit Meaning
0 : Ephemeris Gap Interpolated
1 : Attitude Gap Interpolated
2 : Attitude jump/discontinuity
3 : Attitude out of range
4 : Anomalous Time Step
5 : GHA not calculated due to error
6 : SunData (Group) not calculated due to error
7 : Failure to calculate Sun in inertial coordinates
8 : Fallback to GES ephemeris
9 : Fallback to GEONS ephemeris
10 : Fallback to PVT ephemeris
11 : Fallback to OBP ephemeris
12 : Spare (always 0)
13 : Spare (always 0)
14 : Spare (always 0)
15 : Spare (always 0)

SCorientation

Type
2-byte integer

Array
nscan

Unit
degrees

Array
nscan

Unit
N/A

Array
nscan

Unit
N/A

Array

Unit

The positive angle of the spacecraft vector (v) from the satellite forward direction of motion,measured clockwise facing down. We define v in the same direction as the spacecraft axis +X,
which is also the center of the GMI scan. If SCorientation is not 0 or 180, a bit is set to 1 inmodeStatus.
Value Meaning
0 : +X forward (yaw 0)
180 : -X forward (yaw 180)
-8000 : Non-nominal pointing-9999 : Missing

pointingStatusType
2-byte integer

It is provided by the GeoTK. A value of zero means the pointing is good. Non-zero values
indicate non-nominal pointing. If pointingStatus is non-zero, a bit in modeStatus is set to 1.
Value Meaning
0 : Nominal pointing in Mission Science Mode
1 : GPS point solution stale and PVT ephemeris used
2 : GEONS solution stale and GEONS ephemeris used
-8000 : Non-nominal mission science orientation-9999 : Missing

acsModeMidScan

Type
1-byte integer

It is provided by the GeoTK as taken from Attitude Control System telemetry and is provided
in this format for information only.
Value Meaning
0 : LAUNCH
1 : RATENULL
2 : SUNPOINT
3 : GSPM (Gyro-less Sun Point)
4 : MSM (Mission Science Mode)
5 : SLEW
6 : DELTAH
7 : DELTAV

targetSelectionMidScan
Type

Type
1-byte integer

Array
nscan

Unit
N/A

Array
nscan

Unit
N/A

Array
nscan

Unit
N/A

It is provided by the GeoTK as taken from Attitude Control System telemetry and is provided
in this format for information only.
Value Meaning
0 : S/C Z axis nadir, +X in flight direction
1 : Flight Z axis nadir, +X in flight direction
2 : S/C Z axis nadir, -X in flight direction
3 : Flight Z axis nadir, -X in flight direction
4 : +90 yaw for DPR antenna pattern calibration
5 : -90 yaw for DPR antenna pattern calibration-99 : Missing
Other standard target orientations TBD

operationalModeType
1-byte integer

The operational mode of KuPR/KaPR/PR stored in science telemetry basically. However, if
science telemetry is not made like as stand-by mode, KuPR/KaPR L1B algorithm decides itusing HK telemetry.PR L1B algorithm stored missing value. The values range is 1 to 20 for
KuPR/KaPR. The values range is 1 to 3, 5, 6, 10, -99. The value meaning is shown below.
Value Meaning
1 : Ku/Ka/PR Observation
2 : Ku/Ka/PR External Calibration
3 : Ku/Ka/PR Internal Calibration
4 : Ku/Ka SSPA Analysis
5 : Ku/Ka/PR LNA Analysis
6 : Ku/Ka/PR Health-Check
7 : Ku/Ka Standby VPRF Table OUT
8 : Ku/Ka Standby Phase Out
9 : Ku/Ka Standby Dump Out
10 : Ku/Ka Standby (No Science Data)
11 : Ku/Ka/PR Independent Observation
12 : Ku/Ka Independent External Calibration
13 : Ku/Ka Independent Internal Calibration
14 : Ku/Ka Independent SSPA Analysis
15 : Ku/Ka Independent LNA Analysis
16 : Ku/Ka Independent Health-Check
17 : Ku/Ka Independent Standby VPRF Table OUT
18 : Ku/Ka Independent Standby Phase Out
19 : Ku/Ka Independent Standby Dump Out
20 : Ku/Ka Independent Standby (No Science Data)-99 : PR missing value (No Science Data)

limitErrorFlag

Type
1-byte integer

It has 2 error information. One is as for noise power limit, another one is as for binEllipsoid
limit. The former is defined that if there are more than 2 overlimitted rays in a swath,
limitErrorFlag(at 0bit) is adapted. On the other hand, the later is defined that if there is even an
overlimitted ray, limitErrorFlag(at 1bit) is adapted. Then, LimitErrorFlag is used in modeStatus,
dataQuality in scanStatus Group picks it up consequently.
The values are
Bit Meaning
0 : noise power limit error
1 : binEllipsoid is missing
2 : Spare (always 0)

3 : Spare (always 0)
4 : Spare (always 0)
5 : Spare (always 0)
6 : Spare (always 0)
7 : Spare (always 0)

FractionalGranuleNumberType
8-byte float

Array
nscan

Unit
N/A

Array

Unit

XYZ * nscanm

The floating point granule number. The granule begins at the Southern-most point of thespacecraft’s trajectory. For example, FractionalGranuleNumber = 10.5 means the spacecraft ishalfway through granule 10 and starting the descending half of the granule. Values range from0 to 100000. In Near Real Time (NRT) process, granule number is stored only ‘0’, so
Fractional Granule Number less than 1.0.
Missing value:N/Anavigation (Group)
scPos

Type
4-byte float

The position vector(m) of the spacecraft in Earth-Centered Earth Fixed (ECEF) Coordinates at
the Scan mid-Time (i.e., time at the middle pixel/IFOV of the active scan period). Values range
from -10000000 to 10000000 m.
Missing value:-9999.9

scVel

Type
4-byte float

Array

Unit

XYZ * nscan

m/s

The velocity vector (m/s) of the spacecraft in ECEF Coordinates at the Scan mid-Time.Values
range from -10000000 to 10000000 m/s.
Missing value:-9999.9

scLat

Type

Array

Unit

4-byte float

nscan

degrees

The geodesic latitude (decimal degrees) of the spacecraft at the Scan mid-Time. Values range
from -70 to 70 degrees.
Missing value:-9999.9

scLon

Type
4-byte float

Array

Unit

nscan

degrees

The geodesic longitude (decimal degrees) of the spacecraft at the Scan mid-Time. Values range
from -180 to 180 degrees.
Missing value:-9999.9

scAlt

Type
4-byte float

Array
scan

Unit
m

The altitude (m) of the spacecraft above the Earth Ellipsiod at the Scan mid-Time. It is
computed by GeoTK. Values range from 350000 to 500000 m.

Missing value:-9999.9

dprAltType
4-byte float

Array
nscan

Unit
m

Type

Array

Unit

4-byte float

nscan

degrees

The altitude (m) of the spacecraft above the Earth Ellipsiod at the Scan mid-Time from DPR
science telemetry. This is empty in non-DPR products. It is stored ‘GPS Altitude Data’ with
LSB equal to 10m in DPR science telemetry.Values range from 350000 to 500000 m.
Missing value:-9999.9 : at missing scan and internal calibration mode.

scAttRollGeoc

The geocentric satellite attitude Euler roll angle (degrees) at the Scan mid-Time. The order ofthe components in the file is roll, pitch, and yaw. However, the angles are computed using a
3-2-1 Euler rotation sequence representing the rotation order yaw, pitch, and roll for therotation from Orbital Coordinates to the spacecraft body coordinates. Orbital Coordinates
represent an orthogonal triad in Geocentric Inertial Coordinates where the Z-axis is toward thegeocentric nadir, the Y-axis is perpendicular to the spacecraft velocity opposite the orbit normal
direction, and the X-axis is approximately in the velocity direction for a near circular orbit.
Note this is geocentric, not geodetic, referenced, so that pitch and roll will have twice orbital
frequency components due to the onboard control system following the oblate geodetic Earthhorizon. Note also that the yaw value will show an orbital frequency component relative to the
Earth fixed ground track due to the Earth rotation relative to inertial coordinates. Values range
from -180 to 180 degrees.
Missing value:-9999.9

scAttPitchGeoc

Type

Array

Unit

4-byte float

nscan

degrees

The geocentric satellite attitude Euler pitch angle (degrees) at the Scan mid-Time.Values range
from -180 to 180 degrees.
Missing value:-9999.9

scAttYawGeoc

Type

4-byte float

Array

Unit

nscan

degrees

The geocentric satellite attitude Euler yaw angle (degrees) at the Scan mid-Time. Values range
from -135 to 225 degrees.
Missing value:-9999.9

scAttRollGeod

Type

4-byte float

Array

Unit

nscan

degrees

The geodetic satellite attitude Euler roll angle (degrees) at the Scan mid-Time. The order of thecomponents in the file is roll, pitch, and yaw. However, the angles are computed using a 3-2-1
Euler rotation sequence representing the rotation order yaw, pitch, and roll for the rotation fromGeodetic Coordinates to the spacecraft body coordinates. Geodetic Coordinates represent an
orthogonal triad in Geocentric Inertial Coordinates where the Z-axis is toward the geodetic
nadir, the Y-axis is perpendicular to the spacecraft velocity opposite the orbit normal direction,and the X-axis is approximately in the velocity direction for a near circular orbit. Values range
from -180 to 180 degrees.
Missing value:-9999.9

scAttPitchGeod

Type

Array

Unit

4-byte float

nscan

degrees

The geodedic satellite attitude Euler pitch angle (degrees) at the Scan mid-Time.Values range
from -180 to 180 degrees.
Missing value:-9999.9

scAttYawGeod

Type

Array

Unit

4-byte float

nscan

degrees

The geodedic satellite attitude Euler yaw angle (degrees) at the Scan mid-Time. Values range
from -135 to 225 degrees.
Missing value:-9999.9

greenHourAng

Type
4-byte float

Array
nscan

Unit
degrees

Array

Unit

nscan

s

The rotation angle (degrees) from Geocentric Inertial Coordinates to Earth Fixed Coordinates.
Values range from 0 to 390 degrees.
Missing value:-9999.9

timeMidScan

Type

8-byte float

The Scan mid-Time in GPS Atomic time, namely the seconds since 0000 UTC,6 Jan
1980.timeMidScan is used as the reference time for the scPos and scVel values. Values range
from 0 to 10000000000 s.
Missing value:-9999.9

timeMidScanOffset

Type

8-byte float

Array

Unit

nscan

s

Offset from the secondary header packet time to the timeMidScan.Values range from 0 to 100s.
Missing value:-9999.9

PRE (Group)
elevation

Type

Array

Unit

4-byte float

nray * nscanmElevation of the measurement point. It is a copy of DEMHmean of level 1B product.
Missing Value：-9999.9

landSurfaceType
Type

Array

Unit

4-byte integer

nray * nscan

N/A

Type

Array

Unit

4-byte float

nray * nscan

degrees

Land surface type.
The values are
Bit Meaning
0-99 : Ocean
100 - 199 : Land200 - 299 : Coast
300 - 399 : Inland water-9999 : Missing

localZenithAngle

Local zenith angle of each ray. It is a copy of scLocalZenith of level 1B product.
Missing Value：-9999.9

flagPrecip

Type

Array

Unit

4-byte integer

nray * nscan

N/A

Type

Array

Unit

2-byte integer

nray * nscan

range bin

Type

Array

Unit

2-byte integer

nray * nscan

range bin

Type

Array

Unit

4-byte float

nray * nscan

m

Type

Array

Unit

2-byte integer

nray * nscan

range bin

The values are
Bit Meaning
0 : No precipitation
1 : Precipitation-9999 : Missing

binRealSurface

Range bin number for real surface.
Missing Value：-9999

binStormTop

Range bin number for the storm top.
Missing Value：-9999

heightStormTop

Height of storm top.
Missing Value：-9999.9

binClutterFreeBottom

Range bin number for clutter free bottom.
Missing Value：-9999

sigmaZeroMeasured
Type

Array

Unit

4-byte float

nray * nscandB

Surface backscattering cross section without attenuation correction (as measured).
Missing Value：-9999.9

zFactorMeasured
Type

Array

Unit

4-byte float

nbin *nray * nscandBZ

Vertical profile of reflectivity factor (Z) without attenuation correction (as measured). 10
log10(Z) where Z is in mm^6/m^3.
Missing Value：-9999.9

ellipsoidBinOffset
Type

Array

Unit

4-byte float

nray * nscanmDistance between the ellipsoid and a center range bin of binEllipsoid defined by level 1B
algorithm.
ellipsoidBinOffset =scRangeEllipsoid – ( startBinRange + (binEllipsoid-1) x rangeBinSize)
scRangeEllipsoid : Distance between a sensor and the ellipsoid [m]startBinRange : Distance between a sensor and a center of the highest observed range bin [m]binEllipsoid : Range bin number of the Ellipsoid (1 - 260)
rangeBinSize : Range bin size [m]Missing Value：-9999

snRatioAtRealSurface
Type

Array

Unit

4-byte float

nray * nscan

N/A

Signal/Noise ratio at real surface range bin.
snRatioAtRealSurface= 10.*log10(echoPowertrueV[mW]/noisePowertrueV[mW])
Missing Value：-9999

adjustFactor

Type

Array

Unit

4-byte float

nray * nscandB

Adjustment factor (dB) for zFactorMeasured (dBZm’) and sigmaZeroMeasured (dBs0m’).
dBZm’ and dBs0m’ are used and stored as follows:
dBZm’ = dBZm – adjustFactor
dBs0m’ = dBs0m – adjustFactor
The adjustment factor is the sum of 3 components:base adjustment for instrument dependency,
angle-bin adjustment for angle-bin dependency, andtemporal adjustment for orbit number dependency.
Missing Value：-9999.9

snowIceCover

Type

Array

Unit

1-byte character

nray * nscan

N/A

Snow and ice cover information. It refers to the multisensor snow/ice cover maps provided by
NOAA.
The values are
Value Meaning
0 : Open water
1 : Land, no snow
2 : Snow cover on land
3 : Ice on water-99 : Missing

flagSigmaZeroSaturation
Type

Array

Unit

1-byte character

nray * nscandB

A flag to show whether echoPower is under a saturated level or not at a range bin with acalculation of sigmaZeroMeasured.
The values are
Value Meaning
0 : Normal (under saturated level)
1 : Possible saturated level at real surface
2 : Saturated level at real surface-99 : Missing

VER (Group)

binZeroDeg

Type

Array

Unit

2-byte integer

nray * nscan

range binRange bin number with 0 degrees C level.
Missing Value：-9999

attenuationNP

Type

Array

Unit

4-byte float

nbin *nray * nscan

dB/km

Vertical profile of attenuation by non-precipitation particles (cloud liquid water, cloud ice
water, water vapor, and oxygen molecules).
Missing Value：-9999.9

piaNP

Type

Array

Unit

4-byte floatnNP * nray * nscandB

Path integrated attenuation caused by non-precipitation particles (cloud liquid water, cloud ice
water, water vapor, and oxygen molecules).
Missing Value：-9999.9

sigmaZeroNPCorrected
Type

Array

Unit

Type

Array

Unit

4-byte float

nray * nscandB

Surface backscattering cross section with attenuation correction only for non-precipitation
particles.
Missing Value：-9999.9

heightZeroDeg

Type

Array

Unit

4-byte float

nray * nscanmHeight of freezing level (0 degrees C level) Values are in m.
Missing Value：-9999.9

CSF (Group)
flagBB

Type

Array

Unit

4-byte integer

nray * nscan

N/A

Type

Array

Unit

2-byte integer

nray * nscan

range bin

Bright band (BB) exists or not.
The values are
Value Meaning
0 : BB not detected
1 : BB detected-1111 : No rain value-9999 : Missing

binBBPeak

Range bin number for the peak of bright band.
Missing Value：-9999

binBBTop

Type

Array

Unit

2-byte integer

nray * nscan

range binRange bin number for the top of bright band.
Missing Value：-9999

binBBBottom

Type

Array

Unit

2-byte integer

nray * nscan

range binRange bin number for the bottom of bright band.
Missing Value：-9999

heightBB

Type

Array

Unit

4-byte float

nray * nscanmHeight of bright band.
Missing Value：-9999.9

widthBB

Type

Array

Unit

4-byte float

nray * nscan

m

Type

Array

Unit

4-byte integer

nray * nscan

N/A

The width of bright band.
Missing Value：-9999.9

qualityBB

Quality of the bright band.
The values are
Value Meaning
1 : Good
0 : BB not detected in the case of rain
-1111 : No rain value-9999 : Missing

typePrecip

Type

Array

Unit

4-byte integer

nray * nscan

N/A

When positive, typePrecip shows precipitation type by an 8-digit number as shown in the next
box. The negative typePrecip means as follows:-1111: No rain,-9999: Missing.
Details of 8-digit typePrecip:

The three major rain categories, stratiform, convective, and other, can be obtained fromtypePrecip as follows:When typePrecip is greater than zero,
Major rain type = typePrecip/10000000
1 : stratiform
2 : convective
3 : otherIn the DPR product, rain type by the CSU’s DFRm (measured dual frequency ratio) method is
also included in typePrecip and can be obtained as follows:
DFRm rain type = (typePrecip%10000000)/1000000 in C
DFRm rain type = (MOD(typePrecip,10000000)/1000000 in FORTRAN
DFRm rain type
1 : stratiform
2 : convective
4 : transition
5 : Decided winter precipitation as convective by extended DFRm method.
8 : DFRm method cannot be applicable at Part B (in this casethe conventional method determines the major rain type)
9 : DFRm method cannot be applicable at Part A (in this casethe conventional method determines the major rain type)-1111 : No rain value-9999 : Missing value

qualityTypePrecip
Type

Array

Unit

4-byte integer

nray * nscan

N/A

Type

Array

Unit

4-byte integer

nray * nscan

N/A

Quality of the precipitation type.
The values are
Value Meaning
1 : Good-1111 : No rain value-9999 : Missing

flagShallowRain

Type of shallow rain.
The values are
Value Meaning
0 : No shallow rain
10 : Shallow isolated (maybe)
11 : Shallow isolated (certain)
20 : Shallow non-isolated (maybe)
21 : Shallow non-isolated (certain)-1111 : No rain value-9999 : Missing

binDFRmMLBottom (MS, HS)
Type

Array

Unit

4-byte integer

nray * nscan

range bin

The DFRm method detects melting layer (ML) the meaning of which is wider than that of BB.
Since ML and BB are different, new output item binDFRmMLBottom and binDFRmMLTop
are added to MS and HS data.
Range bin number for ML bottom detected by the DFRm method.
The values areValue: Meaning> 0 : Range bin number when ML bottom is detected
0 : ML bottom is not detected-1111 : Value for no rain in MS (HS) mode at Ka band
-9999 : Missing value

binDFRmMLTop(MS, HS)
Type

Array

Unit

4-byte integer

nray * nscan

N/A

The DFRm method detects melting layer (ML) the meaning of which is wider than that of BB.
Since ML and BB are different, new output item binDFRmMLBottom and binDFRmMLTop
are added to MS and HS data.
Range bin number for ML top detected by the DFRm method.
The values are
Value: Meaning> 0 : Range bin number when ML top is detected
0 : ML top is not detected-1111 : Value for no rain in MS (HS) mode at Ka band
-9999 : Missing value

flagHeavyIcePrecip
Type

Array

Unit

1-byte character

nray * nscan

N/A

This flag denotes detection of solid ice hydrometeors which cause severely strong Z factor orhuge DFRm in the sky less than -10 degree C temperature.
Value Meaning
The case of Ka band MS:
1 (=0x01): 35dBZ >= Zm(Ka) > 30dBZ
2 (=0x02): 40dBZ >= Zm(Ka) > 35dBZ
3 (=0x03): Zm(Ka) > 40dBZ
The case of Ku band NS:
4 (=0x04): 35dBZ >= Zm(Ku) > 30dBZ
8 (=0x08): 40dBZ >= Zm(Ku) > 35dBZ
12 (=0x0c): Zm(Ku) > 40dBZ
The case of DPR NS:
Outer swaths are same as (B).
Inner swaths are addition of (A) and (B). If Zm(Ku) > 27dBZ and DFRm > 7dB in
inner swaths, the following value is added in addition to (A) and (B).
16(=0x10)
0 : Missing value

flagAnvil (Ku NS, DPR NS)
Type

Array

Unit

1-byte character

nray * nscan

N/A

flagAnvil is positive values when anvil precipitation is detected by the Ku-band radar. 0 whenanvil precipitation is not detected.
The values are
Value Meaning
1 : Type 1 (without rain downward)
2 : Type 2 (with rain downward)
0 : Missing

SRT (Group)
PIAalt

Type

Array

Unit

4-byte float

method * nray * nscandB

The two-way path integrated attenuation (PIA) at from the each method estimate. The
path-integrated attenuation from the jth method, where
PIAalt (j=1) = PIA Ku from forward along-track spatial at kth angle bin
PIAalt (j=2) = PIA Ku from backward along-track spatial at kth angle bin
PIAalt (j=3) = PIA Ku from forward hybrid at kth angle bin
PIAalt (j=4) = PIA Ku from backward hybrid at kth angle bin
PIAalt (j=5) = PIA Ku from temporal reference at kth angle bin
PIAalt (j=6) = PIA Ku from light-rain temporal reference at kth angle bin
Missing Value：-9999.9

PIAhb

Type

Array

Unit

4-byte float

nray * nscan

dB

Type

Array

Unit

4-byte float

nray * nscandB

The 2-way attenuation of HB.
Missing Value：-9999.9

PIAhybrid

The 2-way attenuation from a weighted combination of HB and SRT.
Missing Value：-9999.9

RFactorAlt

Type

Array

Unit

4-byte float

method * nray * nscan

N/A

The reliability factors associated with the individual PIA estimates corresponding to PIAalt.
The reliability factor is the ratio of the mean to the standard deviation of the PIA estimate.
Missing Value：-9999.9

PIAweight

Type

Array

Unit

4-byte float

method * nray * nscan

N/A

The weights of the individual PIA Ku estimates used in deriving the effective path attenuation
estimate, pathAtten. The sum of the weights should equal one. Where j is method and sigma j
is the standard deviation of reference data for method j.
PIAweight_j = 1/sigma_j2 * (1/Sum_j (1/sigma_j2))
Missing Value：-9999.9

pathAtten

Type

Array

Unit

4-byte float

nray * nscandB

The effective 2-way path integrated attenuation.

Missing Value：-9999.9

reliabFactor

Type

Array

Unit

4-byte float

nray * nscan

N/A

Reliability Factor for the effective PIA estimate, pathAtten.
standard deviation of the PIA estimate
Missing Value：-9999.9

reliabFactorHY

It is the ratio of the mean to the

Type

Array

Unit

4-byte float

nray * nscan

N/A

Type

Array

Unit

2-byte integer

nray * nscan

N/A

TBD.
Missing Value：-9999.9

reliabFlag

The reliability flag for the effective PIA estimate (pathAtten) based on the reliability factor(Rel_eff) in reliabFactor. Reliability Flag is:= 1 if Rel_eff > 3 ; PIAeff estimate is considered reliable= 2 if 3 >= Rel eff > 1 ; PIAeff estimate is considered marginally reliable= 3 if Rel_eff ≦1 ; PIAeff is unreliable= 4 if SNR at surface < 2dB; provides a lower bound to the path-attenuation= 9 (no-rain case)
Missing Value：-9999.9

reliabFlagHY

Type

Array

Unit

2-byte integer

nray * nscan

N/A

Type

Array

Unit

2-byte integer

nearFar * foreBack *nray * nscan

Number

TBD.
Missing Value：-9999.9

refScanID

The number of scan lines between the current scan and the beginning (or end) of thealong-track reference data at each angle bin. The values are computed by the equation:
Current Scan Number - Reference Scan Number
The values are positive for the Forward estimates and negative for the Backward estimates.
The values are
Bit Meaning
1,1 : Forward - Near reference
2,1 : Forward - Far reference
1,2 : Backward - Near reference
2,2 : Backward - Far reference
9999 : Missing

stddevEff

Type

Array

Unit

4-byte float

nsdew * nray * nscan

N/A

The effective standard deviation of PIA-SRT computed 3 ways.
Missing Value：-9999.9

stddevHY

Type

Array

Unit

2-byte integer

nray * nscan

N/A

Type

Array

Unit

4-byte float

nray * nscan

N/A

TBD.
Missing Value：-9999.9zeta

The term in the HB estimate of path attenuation.
Missing Value：-9999.9

DSD (Group)
phase

Type

Array

Unit

1-byte charnbin * nray * nscan

N/A

Phase state of the precipitation. As an unsigned byte value this represents:
phase < 100 Temperature(C)=phase-100
phase > 200 Temperature(C)=phase-200
phase = 100 Top of the bright band
phase = 200 Bottom of the bright band
phase = 125 is used for the range bins between
the top and peak of bright band
phase = 150 Peak of bright band
phase = 175 is used for the range bins between
the peak and bottom of bright band
Integer values of phase/100 =0 - solid
1 - mixed phase2 - liquid
Missing Value：
255

binNode

Type

Array

Unit

2-byte integer

nNode * nray * nscan

N/A

The bin number of the 5 nodes defined as:
0 - Bin number of storm top.
1 - Stratiform: 500m above center of bright band.
Convective: 750m above 0deg C level.
2 - Stratiform: center of bright band.
Convective: 0deg C level.

3 - Stratiform: 500m below center of bright band.
Convective: 750m below 0deg C level.
4 - Bin number of real surface equal to binRealSurface in PRE group.
For NS and MS swaths, bin numbers are 1-based ranging from 1 at the top of the data
window with 176 at the Ellipsoid. For HS swaths, bin numbers are 1-based ranging from
1 at the top of the data window with 88 at the Ellipsoid.
Missing Value：-9999

Experimental (Group)

precipRateESurface2
Type

Array

Unit

4-byte float

nray * nscan

mm/hr

Estimates Surface Precipitation using alternate method.
Missing Value：-9999.9

precipRateESurface2Status
Type

Array

Unit

1-byte charnray * nscan

N/A

Status of the estimated surface precipitation using alternate method.
Missing Value：225

sigmaZeroProfile
Type

Array

Unit

4-byte float

nbinSZP * nray * nscandB

Surface backscattering cross section profile around the current IFOV.
Missing Value：-9999.9

binDEML2

Type

Array

Unit

2-byte integer

nray * nscan

range binRange bin number of the digital elevation model surface estimate.
Missing Value：-9999

seaIceConcentration
Type

Array

Unit

4-byte float

nray * nscan%

Sea ice concentration (30.0 – 100.0%) estimated by Ku.
Missing Value：-9999.9

flagSurfaceSnowfall (DPR output only)
Type

Array

Unit

1-byte character

nray * nscan

N/A

Flag indicating snowfall on the surface, not aloft. 1 for snow on the ground, 0 for not snow.
Missing Value：0surfaceSnowfallIndex(DPR output only)
Type

Array

Unit

4-byte float

nray * nscan%

flagSurfaceSnowfall is 1 when this index exceed the defined threshold.
When no rain or skipped, the value is 0.0.
Missing Value：-9999.9

SLV (Group)
flagSLV

Type

Array

Unit

1-byte integer

nbin * nray * nscan

N/A

A flag for each range bin data. At rain range bins, flagSLV is positive. At no-rain range bins,
flagSLV is 0. If a range bin is located below ESurface, flagSLV is negative (-64). When the
retrieval is abnormally terminated or data quality is bad, flagSLV is negative (-128).
The values are
flagSLV%2
0:no rain
1:rainflagSLV%4
3:Zm is used for the retrieval(1:extrapolated Ze is used for the retrieval)(0:no rain)
flagSLV%16(0-3:no rain)
4-7:only KuPR is used for the retrieval
8-11:only KaPR is used for the retrieval
12-15:Both KuPR and KaPR’s Zm are used for the retrieval
flagSLV%64
0-15:Dm takes normal value (or no-rain)
16-31:Dm takes the minimum value
32-47:Dm takes the maximum value
48-63:Dm takes an abnormal value
flagSLV% 256
0-63:R takes an normal value (or no-rain)
64-127:R takes the maximum value(128:bad data quality)(192:below ESurface)
Missing Value：NAbinEchoBottom

Type

Array

Unit

2-byte integer

nray * nscan

N/A

The bin number of bottom of echo. For NS and MS swaths, bin numbers are 1-based ranging
from 1 at the top of the data window with 176 at the Ellipsoid. For HS swaths, bin numbers are
1-based ranging from 1 at the top of the data window with 88 at the Ellipsoid.
Missing Value：-9999

piaFinal

Type

Array

Unit

Type

Array

Unit

4-byte float

nray * nscandB

The final estimates of path integrated attenuation caused by precipitation particles. It is
calculated from the retrieved DSD profiles. It includes the attenuation only by precipitation.
Missing Value：-9999.9

sigmaZeroCorrected
Type

Array

Unit

4-byte float

nray * nscandB

Surface backscatter cross section with attenuation correction.
Missing Value：-9999.9

zFactorCorrected
Type

Array

Unit

4-byte float

nbin * nray * nscandBZ

Vertical profile of reflectivity factor Z with attenuation correction.
10 log10(Z) where Z is in mm^6/m^3.
Missing Value：-9999.9

zFactorCorrectedESurface
Type

Array

Unit

4-byte float

nray * nscandBZ

Reflectivity factor with attenuation correction at estimated surface.
10 log10(Z) where Z is in mm^6/m^3.
Missing Value：-9999.9

zFactorCorrectedNearSurface
Type

Array

Unit

4-byte float

nray * nscandBZ

Reflectivity factor with attenuation correction at near surface.
10 log10(Z) where Z is in mm^6/m^3.
Missing Value：-9999.9

paramDSD

Type

Array

4-byte floatnDSD * nbin *nray * nscan

Unit

10 log10 (Nw)mm
Parameters of DSD functions, Nw and Dm. Nw in 1/m^3 mm

Missing Value：-9999.9

precipRate

Type

Array

Unit

4-byte float

nbin * nray * nscan

mm/hr

Precipitation rate.
Missing Value：-9999.9

precipWaterIntegrated
Type

Array

Unit

4-byte float

LS * nray * nscan

g/m2

Precipitation water vertically integrated.
For LS=1, sum of liquid water (phase >= 200)
For LS=2, sum of non-liquid water (phase < 200)
Missing Value：-9999.9

precipRateNearSurface
Type

Array

Unit

4-byte float

nray * nscan

mm/hr

Type

Array

Unit

4-byte float

nray * nscan

mm/hr

Precipitation rate at the nearSurface bin, i.e., at binClutterFreeBottom.
Missing Value：-9999.9

precipRateESurface

Precipitation rate at the estimated surface, i.e., at binRealSurface.
Missing Value：-9999.9

precipRateAve24
Type

Array

Unit

4-byte float

nray * nscan

mm/hr

Average of precipitation rate for 2 to 4km height.
Missing Value：-9999.9

phaseNearSurface
Type

Array

Unit

1-byte charnray * nscan

N/A

Value of the Phase parameter in the DSD module at binClutterFreeBottom (nearSurface bin).
Missing Value：255

epsilon

Type

Array

Unit

4-byte float

nbin * nray * nscan

N/A

Epsilon is the indication of the adjustment away from the initial drop size distribution, epsilon= 1 is no adjustment.
Missing Value：-9999.9

paramNUBF

Type

Array

Unit

4-byte floatnNUBF * nray * nscan

N/A

The parameter to adjustment of None Uniform Beam Filling (NUBF).
𝜎𝜎 2 +1

paramNUBF(1) is 𝜎𝜎𝑇𝑇2 where 𝜎𝜎𝑇𝑇 = �

𝑝𝑝

−1 .

paramNUBF(2) is 𝜎𝜎 2 where σ is the coefficient of variation of 𝑁𝑁𝑤𝑤 .
paramNUBF(3) is p where p is the ratio of the raining area to the total area in FOV.

(Currently p is set to 1.)
Missing Value：-9999.9

FLG (Group)
flagEcho

Type

Array

Unit

1-byte integer

nbin * nray * nscan

N/A

The values are
Bit Meaning
0 : For L2 Ku/PR: Precipitation judged by L2 Ku algorithm (copy of bit 2)
0 : For L2 Ka: Precipitation judged by L2 Ka algorithm (copy of bit 3)
0 : For L2 DPR: Precipitation judged by L2 DPR algorithm (copy of bit 1)
1 : Precipitation judged by L2 DPR algorithm
2 : Precipitation judged by L2 Ku algorithm
3 : Precipitation judged by L2 Ka algorithm
4 : Main lobe clutter judged by L2 Ku algorithm
5 : Main lobe clutter judged by L2 Ka algorithm
6 : Side lobe clutter judged by L2 Ku algorithm
7 : Side lobe clutter judged by L2 Ka algorithm

qualityData

Type

Array

Unit

4-byte integer

nray * nscan

N/A

Normal data gives ”0”. Non-zero values mean the kinds of errors
The values are
The 2 bit flag for each module has values:[higher bit lower bit]
[0 0] : Good[0 1] : Warning but usable[1 0] : NG or error
The bits of qualityData are assigned as follows:
0 - 7 : Copy of dataQuality in level 1B product8 - 9 : Flag by input module
10 - 11 : Flag by preparation module
12 - 13 : Flag by vertical module
14 - 15 : Flag by classification module
16 - 17 : Flag by SRT module
18 - 19 : Flag by DSD module
20 - 21 : Flag by solver module
22 - 23 : Flag by output module
24 - 31 : Spare-9999 : Missing

flagSensor

Type

Array

Unit

1-byte integer

nscan

N/A

Flag of input Ku/Ka data condition
The values are
1 : Valid-99 : Invalid (judged by dataQuality)

qualityFlag

Type

Array

Unit

1-byte integer

nray*nscan

N/A

qualityFlag is a sample flag gererated by qualityData
The values are
0: High quality. No issues.
1 : Low quality. (DPR modules had warnings but still made a retrieval)
2: Bad. (DPR modules had errors or dataQuality is bad and retrieval is missing)-99: Missing value

flagScanPattern

Type

Array

Unit

2-byte integer

nscan

N/A

Flag of scan pattern information. Ku and PR are always “0”.
The values are
0 : Original scan pattern. (from the beginning of the mission until May 21, 2018)
1 : KaHS outer swath scan pattern(After May 21, 2018)-99: Others or Missing

TRG (Group)

NUBFindex

Type

Array

Unit

1-byte character

nray * nscan

N/A

Type

Array

Unit

1-byte character

nray * nscan

N/A

Type

Array

Unit

1-byte character

nray * nscan

N/A

Type

Array

Unit

1-byte character

3*nray * nscan

N/A

Type

Array

Unit

4-byte float

nray * nscan

N/A

The value is 0.

MSindexKu

The value is 0.

MSindexKa

The value is 0.

precipFrac

The value is 0.

RNUBFcond

The value is 0.

MSsurfPeakIndexKu
Type

Array

Unit

1-byte character

nray * nscan

N/A

Type

Array

Unit

1-byte character

nray * nscan

N/A

Type

Array

Unit

1-byte character

nray * nscan

N/A

Type

Array

Unit

1-byte character

nray * nscan

N/A

Type

Array

Unit

1-byte character

nray * nscan

N/A

Type

Array

Unit

1-byte character

nray * nscan

N/A

Type

Array

Unit

1-byte character

nray * nscan

N/A

Type

Array

Unit

1-byte character3 * nray * nscan

N/A

Type

Array

Unit

1-byte character3 * nray * nscan

N/A

The value is 0.

MSsurfPeakIndexKa

The value is 0.

MSthroughsurfIndexKu

The value is 0.

MSthroughsurfIndexKa

The value is 0.

MSkneeDFRindex

The value is 0.

MSthrZindex

The value is 0.

NUBFratioPIAindex

The value is 0.

NUBFnZmVarIndex

The value is 0.

NUBFnZkVarIndex

The value is 0.

NUBFnZmVarScaling
Type

Array

Unit

2-byte integer

nray * nscan

N/A

Type

Array

Unit

2-byte integer

nray * nscan

N/A

Type

Array

Unit

4-byte float

30 * nray * nscan

N/A

Type

Array

Unit

4-byte float

30 * nray * nscan

N/A

Type

Array

Unit

2-byte integer

13 * nray * nscan

N/A

Type

Array

Unit

4-byte float

10 * nray * nscan

N/A

Type

Array

Unit

4-byte float

13 * nray * nscan

N/A

Type

Array

Unit

4-byte float

6 * nray * nscan

N/A

Type

Array

Unit

1-byte char

4 * nray * nscan

N/A

The value is 0.

NUBFnZkVarScaling

The value is 0.

NUBFsurfSliceIndex

The value is 0.

NUBFprofZPC

The value is 0.

MSbreakpoints

The value is 0.

MSslopes

The value is 0.

MSslopePoints

The value is 0.

MSslopeFits

The value is 0.

MSlowSNRrangeFilter

The value is 0.

NUBFcorrPIA

Type

Array

Unit

4-byte float

2 * nray * nscan

N/A

Type

Array

Unit

4-byte float

8 * nray * nscan

N/A

The value is 0.

triggerParameters

The value is 0.