11B.2

CURRENT STATUS OF THE DUAL-FREQUENCY PRECIPITATION RADAR (DPR)
LEVEL-2 STANDARD ALGORITHM
Shinta Seto*1), Toshio Iguchi2), Robert Meneghini3),
Jun Awaka4), Takuji Kubota5) and Naofumi Yoshida5)1) Institute of Industrial Science, University of Tokyo, Tokyo, Japan
2) National Institute of Information and Communications Technology, Tokyo, Japan3) NASA Goddard Space Flight Center, Greenbelt, MD4) Tokai University, Sapporo, Japan
5) JAXA Earth Observation Research and Application Center, Tsukuba, Japan.

1.

INTRODUCTION

The launch of the core satellite of theGlobal Precipitation Measurement (GPM) mission
with the Dual-frequency Precipitation Radar (DPR)
on is scheduled for July 2013. DPR consists of KuPR,
frequency of which is similar to the Tropical Rainfall
Measuring Mission (TRMM) Precipitation Radar (PR;13.8GHz while 13.6GHz is used for KuPR), andKaPR (35.5GHz). As KuPR and KaPR are different
from each other in terms of scanning geometry (Fig.
1), there are three types of pixels: observed by KuPR
only (indicated by blue: type-A), observed by KaPR
only (indicated by red: type-B), and observed byKuPR and KaPR simultaneously (indicated by purple: type-C).
The DPR standard algorithm Level-2 (L2)
consists of three algorithms: KuPR algorithm which
can use KuPR measurement only and is applied for
type-A and type-C pixels, KaPR algorithm which canuse KaPR measurement only and is applied for
type-B and type-C pixels, and dual-frequency algorithm which can use both KuPR and KaPR measurement and is applied for type-A, type-B, andtype-C pixels. Later in this paper, dual-frequency
algorithm is explained for application to type-C pixels.
The vertical profiles of echo power givenby the DPR standard algorithm Level-1 (L1) are inputted to L2, and the vertical profiles of DSD, precipitation rates, and other variables related to precipitation are outputted by L2. Figure 2 shows theframework of L2. Please note that this framework is* Corresponding author address: Shinta Seto,
Institute of Industrial Science, University ofTokyo, 4-6-1, Komaba, Meguro-ku, Tokyo, Japan,
153-8505; e-mail: seto@iis.u-tokyo.ac.jp

Fig. 1The scanning geometry of KuPR andKaPR.

Fig. 2A flow chart of the DPR L2 algorithm.

common to KuPR, KaPR, and dual-frequency algorithms. L2 is divided into 6 sub modules. Preparation
module is for rain/no-rain classification, clutter detection, calculation of radar reflectivity factors (Zm’s)
and surface backscattering cross sections (0). Vertical Profile module is to derive the vertical profiles ofatmospheric variables (e.g. air temperature) fromGANAL, and to correct for attenuation by
non-precipitation particles (clouds, water vapor, andoxygen). Classification module is for precipitation
type classification and bright band detection. DSD
module is to assume characteristics of precipitation
particles and to set scattering tables. SRT module isto apply Surface Reference Technique (SRT) for thefirst guess of Path Integrated Attenuation (PIA).
Solver module is to correct attenuation for precipitation particles and to retrieve DSD and calculate precipitation rates and other variables.
L2 is developed by the authors, delegatedby JAXA and NASA. By this autumn, a baseline codeis produced with some basic and important functions.
By next autumn, functions are added and the baseline code will be modified to an at-launch code, whichshould be close to the first version of the operationcode.
Considering that DPR is a successor toPR, KuPR algorithm is developed based on theTRMM/PR standard algorithm (Iguchi et al. 2009). At
the same time, by taking the advantage of additional
measurement at Ka-band, dual-frequency algorithm
should be better than single-frequency algorithms.
For example, SRT module estimates the differenceof PIAs between the two frequencies (dPIA; Meneghini et al. 2011). This method is called
Dual-frequency SRT (DSRT) and the estimates ofdPIA are expected to be more accurate than those of
PIA as the biased caused by the changes in surface
conditions are largely cancelled between KuPR andKaPR.
For solver module, a new retrieval method(called HB-DFR method) is developed and theunique code is applied both for single-frequency anddual-frequency algorithms. In the next section,
HB-DFR method is introduced.
2.

HB-DFR METHOD

In the TRMM/PR standard algorithm, ahybrid method with Hitschfeld-Bordan (HB) method
and SRT is applied. In HB method, by assuming
relations between specific attenuation k and radarreflectivity factor Ze as power law (k=Ze), the vertical profile of measured radar reflectivity factor Zm(r)is corrected to Ze(r), where r is range distance. Theparameter  can be range dependent, but  should
be fixed. By integrating k along the range from theradar to the surface, PIA is calculated in HB method,
but this value is generally different from PIA estimate
by SRT. In the hybrid method,  is adjusted by multiplying a range-independent constant  ((r)=0(r);0(r) is an initial estimate).  is determined based ona-priori probability of  and the reliability of PIA esti-Fig. 3

The framework for HB-DFR method inthe baseline code.

Fig. 4

DFR as a function of D0 (rain, 0 degree
C, Gamma distribution with =3)

mates by SRT.
The hybrid method will be incorporated inat-launch codes of KuPR algorithm and KaPR algorithm, but HB method is applied in their baseline
codes. HB-DFR method is applied in baseline codeof dual-frequency algorithm (Fig. 3). The left part ofthis figure is the same as KuPR algorithm, the rightpart is the same as KaPR algorithm, and the middle
part is special to dual-frequency algorithm. In themiddle part, for each range bin, Dual-Frequency
Ratio (DFR) is calculated for Ze’s of KuPR and KaPR
estimated by HB methods. In this study, DFR is defined as Ze(Ka) minus Ze(Ku) in decibels. For liquid
spherical particles with temperature of 0 degree C, if
DSD is parameterized by a Gamma distributionfunction with D0 and N0 (=3), relation between DFR
and D0 is given in Fig. 4. There is a famous problem
that multiple solutions exist if DFR is positive. Usually,
D0 is limited to be larger than D0s, where DFR takes
the maximum. Once DSD is obtained, k and Ze arecalculated for each range bin and at each frequency,and  can be recalculated (this process is called DFR
metrhod). By iterating HB and DFR methods,  isupdated.

The HB-DFR method is tested with asimple simulation dataset based on the TRMM/PR
standard product. The simulation dataset includes
the vertical profiles of Zm’s at the two frequencies,
which are exactly calculated based on DSD information given and assumed in the TRMM/PR standard
algorithm. No measurement errors in Zm’s and noclutter and noise effects are considered. Characteristics of precipitation particles (DSD parameterization, falling velocity, scattering tables, and so on) areproperly assumed. Under the ideal condition,
HB-DFR method is applied for one-month simulation
dataset. The precipitation rates at the lowest range
bin are evaluated as summarized in Fig. 5. In case ofmedium precipitation (1-10 mm/h), the accuracy issatisfactory. But in the case of heavy precipitation(over 10 mm/h), severe underestimation are seen
unless the storm height is low. Slight negative bias isseen in the case of light precipitation (under 1 mm/h).
Errors are inherent in the dual-frequency
retrieval. Problems to retrieve two DSD parametersfrom dual-frequency Zm’s often have multiple solutions (Seto and Iguchi 2011). In this method, a solution with weaker precipitation rate is favored to beselected, which results in underestimation for heavy
precipitation events. In DFR method, D0 is limited tobe larger than D0s, and it results in underestimationof light precipitation, of which D0 is often smaller thanD0s.
3.

TOWARD AT-LAUNCH CODE
Each sub module should be improved

toward at-launch code. In solver module, SRT areincorporated into the retrieval method (HB-SRT-DFR
method). The a-priori probability of , reliability of PIA
by SRT, and reliability of DSD estimates by DFR areconsidered to optimize . The retrieval method isrequired to be robust against random errors in Zm’sand inappropriate setting of precipitation characteristics, and is also required to be flexible to estimate thevariation of .
The dual-frequency algorithm is expected
to show seasonal and regional variations in DSD
through . Successful results of DSD variations may
be useful for ground-based conventional radars.
Moreover, the parameter  can be transferred totype-A and type-B pixels.

Fig. 5

Evaluation of the HB-DFR method applied to the simulation dataset.

Acknowledgements. This study is a part of resultsof a collaborative research “Development of modules
for the GPM/DPR standard algorithm (PI: S. Seto)”
between the Japan Aerospace Exploration Agency(JAXA) and the University of Tokyo.
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