Development of a Precipitation Climate Record from Spaceborne Precipitation Radar Data. Part I: Mitigation of the Effects of Switching to Redundancy Electronics in the TRMM Precipitation Radar
1. Introduction
Precipitation observation from space utilizing the precipitation radar (PR) started for the first time with the Tropical Rainfall Measuring Mission (TRMM), which was launched on 27 November 1997 (Kummerow et al. 2000; Kozu et al. 2001). The TRMM PR continuously collected precipitation data for more than 17 years until its termination on 1 April 2015 with a decline in the TRMM orbit altitude. On 28 February 2014, on the other hand, the Core Observatory of the Global Precipitation Measurement (GPM) mission was launched (Hou et al. 2014), and the GPM dual-frequency precipitation radar (DPR) started providing precipitation data succeeding the TRMM PR observation, with one year overlap of observations. The DPR consists of a Ku-band precipitation radar (KuPR, 13.6 GHz), which is similar to the PR (13.8 GHz), and a Ka-band precipitation radar (KaPR, 35.5 GHz). These radars adopt very similar designs but vary in terms of sensitivity, accuracy, and frequency, among others. PR and DPR not only estimate precipitation rate accurately over both land and the oceans but also provide adequate information to derive precipitation characteristics (e.g., storm top height and precipitation vertical profile). Homogeneity of long-term PR/DPR data is essential to study water cycle changes related to interannual variability and decadal change. Long-term precipitation datasets— that is, data spanning 20–30 years—estimated by infrared and microwave imagers are also available (e.g., Xie and Arkin 1997; Adler et al. 2003; Wentz et al. 2007). However, it is known that interannual variability of precipitation associated with El Niño–Southern Oscillation (ENSO) shows a large difference between the estimated rainfall from the TRMM PR and that from the TRMM microwave imager (TMI) (Robertson et al. 2003; Wang et al. 2008; Lau and Wu 2011). Differences in precipitation estimates were inferred to arise from the individual assumptions of each retrieval in previous studies (e.g., Masunaga et al. 2002; Ikai and Nakamura 2003; Shige et al. 2006), which indicate that uncertainty of algorithm assumptions by each sensor appears as differences in the interannual variability of precipitation. A difference in assumptions may also cause a difference in long-term precipitation variability derived from different sensors and algorithms so that an intercomparison of precipitation data is required to understand the plausible long-term precipitation variability.
The quality of the PR data obtained by the TRMM satellite was observed to change over the 17-yr observation period. For example, the PR data have discontinuities in quality associated with an increase in the TRMM satellite altitude (TRMM boost) in August 2001 and with the switching to redundant electronics in June 2009. The change in precipitation amount of the PR estimate attributed to the TRMM boost is evaluated as a decrease in the range from 5.9% to 8.8% (Wang et al. 2008; Shimizu et al. 2009) and is mostly caused by mainlobe contamination and beam mismatch correction errors; this change is interpreted as a bias of the angle-bin dependence (Shimizu et al. 2009; Hirose et al. 2012). At near-nadir angles, the impact of the TRMM boost is mostly mitigated and the change in the precipitation total due to sensitivity degradation is estimated as a decrease of 0.5% (Shimizu et al. 2009). Although the impact of the TRMM boost has been evaluated in previous studies, the change attributable to the switching to redundant electronics has yet to be evaluated. These jumps in quality cause artificial errors and affect the verification of long-term changes in natural variabilities. The stability of sensor calibration is another factor that explains the variations in PR data quality. The absolute calibration of the PR is evaluated by external calibrations (Kozu et al. 2001; Takahashi et al. 2003), and the stability between the PR and a radar calibrator over four years has been determined to be about ±0.5 dB (Takahashi et al. 2003). Takahashi et al. (2003) discussed the long-term stability of the PR via the normalized radar cross section (NRCS; 
In the current study, we focus on the discontinuity arising from the switching to redundant electronics in June 2009. The PR experienced a major anomaly on 29 May 2009, resulting in a data loss. The Japan Aerospace Exploration Agency (JAXA) and the National Aeronautics and Space Administration (NASA) inferred that the frequency converter intermediate frequency (FCIF)/system control data processing (SCDP) units were not working normally and switched these units from the original A side or FCIF-A/SCDP-A to the redundant B side or FCIF-B/SCDP-B (TRMM Precipitation Radar Team 2011). The B-side observations began on 19 June 2009 and continued until the end of the TRMM mission on 1 April 2015. This event (the “A-to-B event”) caused a change in the level-0 PR count value data, so the calibration coefficients of the B-side PR were somewhat different from those of the A-side PR. Differences in the characteristics of the FCIF-B/SCDP-B and/or their calibrations resulted in a noise power jump in the PR version 7 (V7) product. Figure 1 shows a monthly time series of the PR’s noise power obtained by an average at near-nadir (NN; 21st–29th) angle bins during the post-TRMM-boost period from September 2001 to July 2014 over the TRMM coverage area (35°S–35°N) of the oceans. The noise power is derived from the level-1 PR power data (1B21) V7 product. The background noise from the ocean surface is lower than that over land, so the temporal change of the system noise power that originated from the PR instrument appears in the noise power over the oceans more clearly than over land. The noise power obtained after the TRMM boost gradually increases during the A-side period and slightly decreases 2 months before the anomaly of the A-side PR. After the switching from the A side to the B side, the noise power drastically decreases compared with the A side period, and it changes stably during the B-side period. The change in noise power causes the difference in the signal-to-noise ratio (SNR) between the A and B sides, such that the B-side PR is expected to be more sensitive to light precipitation than the A-side PR. Therefore, the impact of the A-to-B event on precipitation estimates should be evaluated.
Time series of monthly noise power after the TRMM boost from September 2001 to July 2014 over the TRMM coverage area (35°S–35°N) of the oceans. Data at NN (21st to 29th) angle bins.
Citation: Journal of Atmospheric and Oceanic Technology 34, 9; 10.1175/JTECH-D-17-0026.1
To use the PR data for climate studies, the quality of the PR data should remain stable over the 17-yr observation period. In this paper, the effect of the A-to-B event is evaluated by generating simulation data. The method of adjustment and data used are introduced in section 2. The results are presented in section 3 and discussed in section 4. Finally, a summary is presented in section 5.
2. Method and data
a. Noise power adjustment
Average of noise power on the A side in 2008 and on the B side 2010 over the TRMM coverage area of the oceans. Data used are taken at NN angle bins.
SNR dependence as a function of received power in decibel scale. Shown are the original SNR (solid line) and adjusted SNR (dashed line), the original B-side dB
Citation: Journal of Atmospheric and Oceanic Technology 34, 9; 10.1175/JTECH-D-17-0026.1
b. Data used
The level-2 rainfall (2A25) product (Iguchi et al. 2000, 2009) is produced from the 1B21 product via the products of level-1 radar reflectivity (1C21), level-2 surface cross section (2A21), and rain characteristics (2A23). In this simulation study, the noise adjustment and resultant precipitation judgment are conducted in the 1B21 product, and the other algorithms (1C21/2A21/2A23/2A25) to produce precipitation estimates are not modified at all. The simulated or adjusted data of the PR products are generated for 1.5 years from June 2009 to December 2010 and quantitatively assessed for the A-to-B event.
The TMI hydrometeor profile product (2A12) V7 (Kummerow et al. 2001; Gopalan et al. 2010; Kummerow et al. 2011) is also analyzed to estimate the possible bias of the A-to-B event. The precipitation retrieval algorithms are different between the PR and the TMI, but the a priori database for the precipitation estimate by the TMI in V7 is generated from precipitation profiles observed by the PR estimate and those produced by a cloud-resolving model. The TMI data are used only within the PR’s NN (about 45 km wide) swath to reduce sampling biases due to differences in swath width between the PR and the TMI. The analysis period of the comparison between the PR and the TMI is the post-TRMM-boost period from September 2001 to July 2014. The quality of the TMI data during this period is stable.
3. Results
a. Changes in 1B21/1C21 products
The impact of adjustment in the current method is primarily confirmed in the level-1 product. Figure 3 shows a case study of the vertical cross section obtained from the adjusted and original data in the 1B21 product. The original (Fig. 3b) and adjusted (Fig. 3a) data are quite similar but differ in terms of the background noise. The difference between the adjusted and original data is also shown in Fig. 3c. While the background of the 
Snapshot of the vertical cross sections obtained by the 1B21 product of TRMM orbit 69094 at 0040 UTC 1 Jan 2010. (a) Received power (dBm) of the adjusted data. (b) Received power (dBm) of the original data. (c) Difference (dB) between the adjusted and original (adjusted minus original) data. (d) Change in precipitation echo detection between the adjusted and original data. White (gray) area indicates detection of rain (no rain) in the adjusted and original data; blue (red) areas indicate detection of rain only in the original (adjusted).
Citation: Journal of Atmospheric and Oceanic Technology 34, 9; 10.1175/JTECH-D-17-0026.1
Frequency histogram of 
Citation: Journal of Atmospheric and Oceanic Technology 34, 9; 10.1175/JTECH-D-17-0026.1
b. Changes in 2A23 products
From section 3a the difference in level-1 data between the A and B sides is mitigated by creating the simulation data. The difference in level-2 data between these sides is described in this subsection. The differences in precipitation parameters among the A side, the original B side, and the adjusted B side are listed in Table 2. Figure 5a shows a time series of the storm height detected by the PR. The discontinuity of the storm height derived from the original 2A23 product (Awaka et al. 1997, 2009) is clearly seen after the switching to the B side and its difference between A side in 2008 and B side in 2010 (B side minus A side) is 137.7 m. This discontinuity suggests that the sensitivity change caused by the noise power change results in a quality change of the PR’s storm height statistics. The adjusted data decrease the storm height by 103.5 m and mitigate this discontinuity from 137.7 to 34.2 m (see storm height in Table 2). Figure 5b shows a time series of the precipitation fraction. The precipitation fraction determined from the original B-side data is somewhat higher than that obtained from the A-side data. On the other hand, the precipitation fraction of the adjusted B-side data is close to the original A-side data. The difference in precipitation fraction between the A side and the original B side is found to be 0.112%, and the adjusted B-side data decrease by 0.209% from the original B side [see total precipitation fraction (PF) in Table 2]. The adjusted B side is 0.097% lower than the A side, which may be caused by some natural interannual variabilities. These results suggest that the SNR of the adjusted B-side PR is close to that of the A-side PR, as seen in Fig. 4, and that the difference in noise power is a major factor contributing to the discontinuity of the A-to-B event. The PR 2A23 product also provides the rain type classification so that the change of the precipitation type is also examined and shown in Figs. 5c and 5d. A time series of the stratiform precipitation fraction is similar to the precipitation fraction in Fig. 5b, because stratiform precipitation is dominant over tropical regions (Schumacher and Houze 2003). The jump in the stratiform precipitation fraction derived from the original data in the A-to-B event is mitigated by the use of the adjusted data. The stratiform precipitation fraction of the adjusted B-side data decreases by 0.134% against the original B side and by 0.050% from the A side, while the original B-side data are 0.084% higher than the A-side data (see stratiform PF in Table 2). On the other hand, the convective precipitation fraction decreases by 0.054% with the adjustment, while the original A and B sides differ by only 0.003% (see convective PF in Table 2). In the 2A23 algorithm, convective precipitation is categorized by the threshold of decibels of reflectivity so that a calibration error of absolute value may be linked to a residual jump in the convective precipitation fraction. The regional change of adjustment is mapped in Fig. 6. Averages using all (from 1st to 49th) angle bins in 2010 are determined for the precipitation fraction (Fig. 6a) and the storm top height (Fig. 6b). The difference ratio of the precipitation fraction between the adjusted data and the original data (Fig. 6c) is inversely proportional to the average of the precipitation fraction (Fig. 6a), which reveals that the adjustment exerts large impacts on light precipitation areas located off the shores of Peru; California; Benguela, Angola; and the Sahara Desert. This impact is also shown in relative difference of storm heights (Fig. 6d). The relative difference in storm height is similar to the precipitation fraction, and a large change is located in regions with less frequent precipitation. These results demonstrate that degradation of the SNR particularly affects areas with regions of less frequent precipitation.
Differences among the A side (A), the original B side (Borg), and the adjusted B side (Badj). Data of the A side in 2008 and the B side in 2010 are analyzed over the TRMM coverage area and subtracted by the monthly climatology calculated from 2002 to 2008. Symbol ± shows one standard deviation of the monthly average. In the case of the difference between the A side and the B side, the square root of the sum of squares of these standard deviations is indicated in parentheses.
(a) Time series of the storm height monthly anomaly (m) at the NN angles from September 2001 to July 2014 over the TRMM coverage area. Data are smoothed by a 3-month running mean. A monthly climatology was calculated for the period from 2002 to 2008 (7 years). The solid black line indicates the original A and B sides. The black dashed line indicates that the original data are corrected by a bias obtained from the difference between original and adjusted data. (b) As in (a), but for PF. (c) As in (a), but for stratiform PF. (d) As in (a), but for convective PF. The unit for (b)–(d) is in percent.
Citation: Journal of Atmospheric and Oceanic Technology 34, 9; 10.1175/JTECH-D-17-0026.1
(a) Map of PF (%) determined from original data using all (from 1st to 49th) angle bins in 2010. Data are gridded into 2.5° 
Citation: Journal of Atmospheric and Oceanic Technology 34, 9; 10.1175/JTECH-D-17-0026.1
c. Changes in 2A25 products
In this subsection the change in precipitation estimate is evaluated by comparing the estimates between the adjusted and the original B sides. Figure 7a shows a time series of unconditional mean for surface precipitation 
As in Fig. 5, but for (a) unconditional total 
Citation: Journal of Atmospheric and Oceanic Technology 34, 9; 10.1175/JTECH-D-17-0026.1
The change in 
Reverse cumulative histograms of (a) the surface PF (%) and (b) its volume (mm day−1) over the TRMM coverage area. Data at NN angle bins. Bin size is 1 dBR, and the unit of 
Citation: Journal of Atmospheric and Oceanic Technology 34, 9; 10.1175/JTECH-D-17-0026.1
Regional changes in precipitation estimate attributable to the adjustment are shown in the TRMM coverage map in Fig. 9. In Fig. 9, all angle bins (from 1st to 49th) are used to reduce sampling errors. The angle-bin dependence of the adjusted data is relatively steady in comparison with the dependence of the original data (not shown). The regional distribution of the unconditional 
(a) Map of the PF (%) obtained from the original data using all (from 1st to 49th) angle bins in 2010. (b) As in (a), but for precipitation amount (mm day−1). (c) Difference ratio of the original and the adjusted PF data. (d) As in (c), but for precipitation amount.
Citation: Journal of Atmospheric and Oceanic Technology 34, 9; 10.1175/JTECH-D-17-0026.1
Table 3 summarizes the averages of the precipitation parameters for the original and adjusted B sides and their relative biases. Although the relative bias of the unconditional 
Summary of the averages of original (Org) and adjusted (Adj) and the relative biases (%). Original and adjusted B-side data are analyzed over the TRMM coverage area in 2010. Relative bias is defined as (Adj − Org)/Org. Symbol ± shows one standard deviation of the monthly average.
4. Discussion
a. Bias estimate from TMI
Statistical results of the PR and TMI estimates on the A side (September 2001–May 2009) and B side (June 2009–July 2014) over the TRMM coverage area. Month indicates the number of normal conditions determined by ONI. Differences in PR and TMI are normalized by the average of the B-side PR. Symbol ± shows one standard deviation of the monthly average. In the case of the difference between the A side and the B side, the square root of the sum of squares of these standard deviations is indicated in parentheses.
b. Uncertainty of the current simulation and other factors
The noise power adjustment successfully mitigates small differences in the sensitivity of the PR between the A and B sides. The original sensitivity difference between the original A and B sides obtained as 0.75 dB for 
Daily time series of the 
Citation: Journal of Atmospheric and Oceanic Technology 34, 9; 10.1175/JTECH-D-17-0026.1
Estimated B-side offset changes of dependence for (a) the range-bin number of 
Citation: Journal of Atmospheric and Oceanic Technology 34, 9; 10.1175/JTECH-D-17-0026.1
The difference in noise power between the A and B sides in V7 is obtained as 0.58 dB. This difference is caused by sensitivity changes attributable to the degradation of the A side over time and the calibration change of the PR. The system noise that originated from the A and B sides is identical to those determined from a prelaunch test. After the TRMM launch and the switching of the internal attenuator of the PR from 6 to 9 dB on February 1998, the digital number of the A-side noise power derived from the level-0 data increased by about 0.8 count at the end of the A-side observation (not shown). This count change corresponds to a change in noise power of about 0.30 dB, and the residual difference may be caused by calibration errors. The small calibration change during the A-side period is a source of residual error of the current adjustment between the A and B sides.
The difference in the calibration curves between the A and B sides could cause an error for continuity of the PR data. The A-to-B event switched the FCIF/SCDP system, including the analog-to-digital conversion, and changed the calibration curves slightly. Although the change in the slope of the calibration curves is taken into account by using the internal calibration data (Kozu et al. 2001), uncertainty of the calibration curves between the A and B sides may partially explain the residual error.
5. Summary
Precipitation observations by the TRMM PR were conducted for more than 17 years. Homogeneity of long-term PR data quality is essential to study changes in water and energy cycle related to interannual and decadal variabilities. In this study, we aim to develop precipitation climate record from the 17-yr PR data. The PR data show a discontinuity in quality associated with the switching to redundant electronics (A-to-B event) in June 2009. In version 7 of the level-1 PR product, a difference in noise power between before and after the A-to-B event is found so that a change in signal-to-noise ratio is expected. In this work, the noise power and the received power of the B-side PR after the A-to-B event are artificially increased to match those of the A-side PR to remove the effect of the signal-to-noise change. An obvious discontinuity of the storm height caused by the A-to-B event is mitigated with the adjusted data. Differences in other precipitation characteristics are also mitigated overall. The precipitation derived from the adjusted data over the TRMM coverage area decreased by 0.90% compared with that determined from the original data. This decrease is caused by the reduction of light precipitation. The effect of the A-to-B event on the precipitation amount for the PR estimate is examined to compare the A and B sides using the TMI estimate, which shows that the precipitation amount of the B-side PR is 1.24%–1.58% higher than that of the A side. Although some residual error remains as a calibration issue, the results of the adjustment indicate achievements mitigating discontinuities attributed to the A-to-B event.
The current study reveals that slight changes in the detectability of light precipitation associated not only with the TRMM boost but also with the A-to-B event should be considered to develop a precipitation climate record observed by the PR. It is true that the noise power appears to increase almost linearly from 2002 to 2008 during the A-side operation as seen in Fig. 1. This issue is closely related to the calibration of the system that affects the long-term change of noise power. Adjustments of the PR data by taking into account all factors, such as the TRMM boost and the long-term stability of the PR’s calibration and the continuity between the PR and KuPR, are topics in future research. Some of such topics will be explored in Part II.
Acknowledgments
The TRMM PR and TMI version 7 products were provided by the Japan Aerospace Exploration Agency (JAXA). Oceanic Niño index data are available online (at http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml). The work was supported by the Japan Aerospace Exploration Agency (JAXA) as part of authors' regular job responsibilities. The authors thank Mr. Higashiuwatoko of the Remote Sensing Technology Center of Japan (RESTEC) for generating data. They also thank Mr. Kojima of JAXA, Mr. Hanado of the National Institute of Information and Communications Technology (NICT), Mr. Yoshida of RESTEC, and Mr. Masaki of the JAXA Earth Observation Research Center (EORC) for providing valuable information and comments. The authors thank the anonymous reviewers for providing positive comments, which helped to improve this paper.
APPENDIX
Range-Bin Dependence of the Received Power and Its Correction for the B Side
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