Key Parameters Filecoverage Available Latest Product Version
Environmental dataextracted KuPR
swath[DU2/ENV]
Temperature, Air Pressure,Cloud Water Vapor, LiquidWater
Gorbit Ver. 04
Environmental dataextracted KaPR
swath[DA2/ENV]
Temperature, Air Pressure,Cloud Water Vapor, LiquidWater
Gorbit Ver. 04
Environmental dataextracted DPR
swath[DD2/ENV]
Temperature, Air Pressure,Cloud Water Vapor, LiquidWater
Gorbit Ver. 04
AUX. Auxiliary Data(JMA/GANAL)
(Near real-time data can be downloaded using SFTP after G-Portal user registration and public key authentication.SFTP directory tree is shown in page 3. *4)
1R
2R
GPM/DPR
3R Multi/Multi/GSMaP
GPM Available Products [As of Mar. 27, 2017]
Mar. 27, 2017 Page-3
Notes
*1 File Naming ConventionGPM product file naming conventions is as below, and algorithm key is corresponding to (7).
*2 Product Identifier for Constellation L1CSatellite Instrument Product Identifier /
Satellite / InstrumentTRMM/TMIDMSP-F16/SSMISDMSP-F17/SSMISDMSP-F18/SSMISGCOM-W/AMSR2METOP-A/AMSU-A, MHSMETOP-B/AMSU-A, MHSNOAA-18/AMSU-A, MHSNOAA-19/AMSU-A, MHSGPM/GMI (No data during Oct.22-24,2014)TRMM/TMI (No data from Apr.8,2015 onward)DMSP-F16/SSMISDMSP-F17/SSMISDMSP-F18/SSMISDMSP-F19/SSMIS (No data from Feb.11,2016 onward)GCOM-W/AMSR2METOP-A/AMSU-A, MHS * (No MHS data from Mar.27 to May 20, 2014)METOP-B/AMSU-A, MHSNOAA-18/AMSU-A, MHSNOAA-19/AMSU-A, MHS
GPMxxx _ sss _ YYMMDDhhmm _ hhmm _ nnnnnn _ LLS _ aaa _ VVv . h5 (1)Mission ID (3) Scene Start (4) Scene End (6) Process Level (8)Product Version (2) Sensor ID (5) Orbit Number (7)Algorithm Key skip for NRT data indicated in product list start and end time for L3 product and below note (*2) for L1C
Release Notes For Use of GPM GMI and Partner L1 Data
March 2016
The GPM project science office is pleased to announce the release of V04 of the GPM Microwave Imager (GMI) L1 (L1B, L1Base, L1C) and GPM Partner Radiometer data to the General Public. This new release involves significant changes in the calibration of the GPM radiometer constellation from the previous release. As such, we would like for all Users to keep the following in mind while using the data.
1. The Level 1C brightness temperature (Tb) data for all of the constellation radiometers has been intercalibrated to be consistent with the Tb from GMI on board the GPM core satellite. Note that the GMI V04 calibration differs from V03 by up to 2-3 K for some channels due to updated spillover corrections derived from on-orbit calibration maneuvers. Comparisons with other well calibrated radiometers and with radiative transfer simulations indicate that GMI is extremely well calibrated and stable with an absolute calibration accuracy of well within 1K for all channels.
2. For the constellation radiometers V04 moves from the use of TRMM TMI and METOP-A MHS as the calibration reference for the window and sounder channels respectively to GPM GMI as the reference for all channels. This results in changes to the Level 1C Tb by up to 2.5K depending on channel, but with significantly improved consistency between channels and with radiative transfer models. In addition, a number of calibration biases and artifacts have been identified and removed from the Level 1C Tb for the constellation radiometers. These include, but are not limited to, issues such as emissive reflectors, solar and lunar intrusions, and biases across the scan.
3. RFI is currently being flagged at the 1B level for GPM GMI, with the quality flag in the Level 1C files set to a value of 2 for the potentially affected pixels. Note that the affected Tb are currently not set to missing, but left to the user to screen based on the data quality flag. RFI impacts on the observed Tb can easily exceed 10K, although impacts are currently only observed and flagged for the 10 and 18 GHz channels.
4. Users should be cautious when using the data to draw any climate inferences or conclusions. While the level 1 products appear very reasonable, corrections to constellation radiometers in particular are based on a limited data. These issues will be re-examined as the duration of the GMI data record is extended.
For questions regarding data access and availability please contact: helpdesk@pps- mail.nascom.nasa.gov
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List of V4 GMI BASE update against V3 GMI BASE
1. Calibration
a. Adjustment of spillover coefficients of all GMI channels. This adjustment is the major improvement from V3 to V4 in GMI antenna pattern correction (APC). The adjustment of spillover is based on the data from GMI inertial hold and refinements of the analysis performed by GMI manufacture. Table 1 (Table 2.12 in ATBD) shows comparisons of APC coefficients reflecting the changes due to spillover adjustments. Tb changes vary from channel to channel and are functions of brightness temperatures. Figure 1 (Figure 2.32 in ATBD) demonstrates the Tb changes for all channels in their normal temperature range. For channels 1-5, Tb reduced ~3 – 6 K at their maximums. For channels 10-13, Tb increased ~2 – 4 K at their maximums. For channels 6-9, Tb increased ~0.1 K at their maximums.
b. Adjustment of Antenna-induced along-scan bias correction. This is a minor adjustment and may result Tb changes less than 0.1 K.
c. Adjustment of magnetic correction coefficients. This is also a minor adjustment and may result Tb changes less than 0.1K.
All these corrections are implemented in V4 as well as ITE043 and ITE057. No code adjustments for these updates.
2. Geolocation
There are no pixel geolocation changes between Version 3 and 4, however there is a notable change affecting Sun angles. This change is due to the correction of a typographic error in the calculation of sun angle in the V3 geoTK code which causes maximum error of about 6 degrees in the vector directions, reported solar beta angles, and Sun glint angles. This significant change was implemented in December 4, 2014 for V03 processing. This implementation results a change of V3 GMI Base version from V03B to V03C. The fix is included in the GMI Base V03C and ITE043 data from December 4, 2014 and not included in V03B and ITE043 data before December 4, 2014.
Another bug in computation of sun glint angles in V3 geoTK was found and fixed in V4 geoTK. This is due to a bug in the code that rejects computing sun glint angle when a scan time coincidence at noon UT. This error has a very remote chance of occurring with a scan time coincidence at noon UT within microseconds
All these geoTK corrections are implemented in V4 GMIBase and in ITE057.
3. Others
NEDT computation is added to the GMIBase code and the data format is revised to include the NEDT parameter.
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Table 1. Coefficients Change for computing Tb from Ta: Cn, Dn, and En. Tb=Cn*Ta - Dn*Ta* - En Channel Number
Figure 1: Tb changes from V3 to V4 (Tb(V4)-Tb(V3)) as functions of Tb.
GPROF2014.V2 Release Notes
Version 4 of the GPROF algorithm was intentionally not changed from the previous version. The only change is thus the replacement of the a-priori database of precipitation profiles from a pre-GPM collection to GPM generated data.
Over ocean, the new database is taken from the Combined Algorithm’s dual frequency (MS product). Rain rates were not altered but is some cases, cloud ice was added to CMB MS V4 products in order to get better agreement with GMI’s high frequency channels. Results in the tropics are quite consistent with the previous version as well as older TRMM versions of the algorithm latitudes (N and S. of 40°), the new algorithm produces less rain than previous versions, including GPCP as shown in figure 1. This is thought to be related to the radar’s inability to see the frequent drizzle, particularly in the southern hemisphere. The combined algorithm does not produce rainfall when there is no radar echo in the profiles. With little or no quantitative validation data, it was thought best to follow the GPM combined algorithm until there is specific evidence to justify a change in procedure.
Over land, the algorithm was adapted to use the DPR Ku product instead of the Combined dual frequency (i.e. MS) product. This was done because the Ku product was validating significantly better than the other GPM products when compared against the ground based radar network over the continental United States. The product differs more substantially from the previous version that used ground based radar over the continental US to construct the a-priori database. Many artificial features that resulted from sparse databases are no longer there. The validation against the ground based radar, expectedly, is a bit worse than before but global comparisons against rain gauge climatologies from GPCC are significantly improved. Results for an annual comparison against the graind radar are shown in figure 2. Results against global GPCC gauges (V3 and V4) are shown in figure 3. There is unexplained behavior in the pdf of rain rates in that the V4 appears to have a slight preference for rain rates around 0.5 and 8 mm/hr. This is shown in Figure 4.
The output format for Version 4 remains the same as Version 3 but it the hydrometeor profiles are now associated with each Field of view. Hydrometeor profiles are derived from the Combined product and written out as an integer representing a shape function (archived in the file header) and hydrometeor multiplicative value that scales the shape function. This saves considerable file space and better represents the basic hydrometeor profiles available from passive microwave radiometers.
Mar. 27, 2017 Page-7
Figure 1: Zonal means comparing GMI V3 (GPROF V1.4 in plot) to V4 (labeled GPROF V2 in plot) to Combined Algorithm and GPCP product.
Figure 2: GPROF V4 (ITE062 in figure) compared to coincident rain from the Multi-radar multi-sensor rainfall product serving as validation data.
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Figure 3: GPROF V4 compared with global GPCC rain gauge accumulations.
Figure4: The Probability Distribution function of rain rates from GPROF V4 (labeled GPF) compared to MRMS data averaged to various resolutions as well as DPR data for comparisons.
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Release Notes for the DPR Level 1 products
All users should keep them in mind when they use the data. ＜Major changes in the DPR Level 1 product V04＞ 1. Improvement in noise power calculation
Based on a concept that noise echo should be handled as continuous wave, the noise echo power is adjusted by about -2dB from product version V03 in both KuPR and KaPR. These correction values are determined according to the band path filter and log amp characteristics. Calculation of radar reflectivity and surface normalized radar cross section (sigma^0) in DPR Level 2 algorithm assumes the signal as pulse wave. Since the noise in received power cannot be handled separately, when the noise power is subtracted from the received power, the value of noise power is re-adjusted in PRE module in DPR Level 2 algorithm.
2. Empty granule correction In the case that quality flag is not nominal in all scans in a granule, the product is designated as ‘Empty Granule’.
3. Geolocation toolkit update A typographic error in the calculation of the sun angle in the DPR Level 1 algorithm was corrected. The redundant precession term for the sun and moon angles was removed.
4. Data format change Following variables are added. ‘rxGain’ that indicates the DPR total receiver gain ‘fcifFlagAB’ and ‘scdpFlagAB’ that indicate which channel is used for FCIF
and SCDP, respectively, in DPR.
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＜Caveats for DPR Level 1 products by JAXA＞ 1. Calibration of DPR
The calibration coefficients are the same as in V03 for both KuPR and KaPR. The analysis of sigma^0 of DPR shows that the current calibration coefficients that were determined before launch give consistent values of sigma^0 with those from TRMM/PR. Although some gain offsets of the DPR transmitter and receiver powers are detected by the external calibrations after launch, JAXA has decided not to adapt the gain offsets this time.
2. Scan flip of DPR JAXA uploaded a proper set of phase code to the DPR on March 18th, 2014 at 13:20 UTC. Until that time, the beam scan direction of DPR had been reversed from the proper direction. After the proper code was uploaded, the beam has been scanned in the proper direction, i.e., from left to right with respect to the +X forward direction of the satellite. The DPR Level 1 algorithm was modified to accommodate this change so that the geolocations in the products are correct from the beginning of the mission.
3. Special operations of DPR The following caveats describe special operations of DPR. You can use these data with your discretion. You can also refer to the DPR invalid data lists at the following web site. <DPR operation status (missing data list)> https://www.gportal.jaxa.jp/gportal_file/qty/GPM/gpmom_vrfy_DPR_ope_st
3.1 Operation with the DPR transmitters off JAXA carried out the receiving only mode to check the DPR receiver system. The orbits in which this operation was performed are shown in Appendix-A.
3.2 Change of the DPR receiver attenuator (RX ATT) setting JAXA has checked the dynamic range of the radar system by changing the
attenuator setting in the DPR receivers. The received power in the DPR Level 1 products is not affected, because the offset caused by the receiver attenuator is accounted for in the DPR Level 1 algorithm. The orbits in which this operation was performed are shown Appendix-A.
3.3 Operation of GPM satellite maneuver NASA has carried out several maneuver operations such as a delta-V maneuver and a Yaw maneuver. In addition, pitch offset maneuvers have also been conducted to check the GPM satellite status. The orbits in which this operation was performed are shown Appendix-A.
3.4 Test operation for adjusting the phase code in the KuPR instrument The JAXA DPR project team has conducted several test operations using different phase codes in the phase shifters in order to mitigate the effects of sidelobe clutter in KuPR. Please be cautious of the periods in these test operations. The orbits in which this operation was performed are shown Appendix-A.
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＜Appendix A: Major DPR events＞ Major DPR events until September 2, 2014 are as follows. After September 2, you can visit the following web site to check the DPR status. https://www.gportal.jaxa.jp/gportal_file/qty/GPM/gpmom_vrfy_DPR_ope_status_make_2014.csv Orbit No. UTC DPR Event #144 2014/3/8 21:54 DPR observation start #171 2014/3/10 16:29 Change DPR FCIF-B to A #201 2014/3/12 14:24 GPM Delta-V Maneuver #206 2014/3/12 22:43 DPR power OFF #207-231 2014/3/13-14 GPM EEPROM change #232 2014/3/14 14:14 DPR SCDP-A ON #232 2014/3/14 14:41 DPR check out restart #236 2014/3/14 20:02 DPR observation restart #263
2014/3/16 14:08 Change DPR FCIF-A to B 2014/3/16 14:59 DPR transmitters off (f1/f2 off) test
#923 2014/4/28 0:25 #924 2014/4/28 1:10 Ku/Ka RX ATT change 6dB to 9dB #933 2014/4/28 15:04 Upload new test phase code of KuPR(#3) #935 2014/4/28 18:13 Return to phase code(#1) #964 2014/4/30 15:50 GPM Delta-V Maneuver #994
2014/5/2 13:20 Upload new test phase code of KuPR (#4) 2014/5/2 13:21 Ku/Ka RX ATT change 9dB to 6dB
#996 2014/5/2 16:36 Upload new test phase code of KuPR(#5)
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Orbit No. UTC DPR Event #998
2014/5/2 19:44 Ku/Ka RX ATT change 6dB to 9dB 2014/5/2 19:45 Return to phase code (#1)
#1059 2014/5/6 17:35 GPS both A and B ON #1103 2014/5/14 13:44
#1073 2014/5/7 15:57 GPM Delta-V Maneuver #1088
2014/5/8 14:15 Ku SSPA analysis mode (5min) 2014/5/8 15:08 Ka SSPA analysis mode (5min)
#1089
2014/5/8 15:48 Ku LNA analysis mode (5min) 2014/5/8 16:44 Ka LNA analysis mode (5min)
#1090 2014/5/8 17:23 Upload new test phase code of KuPR (#6) #1092
2014/5/8 20:21 Ka SSPA analysis mode (5min) 2014/5/8 21:12 Upload new test phase code of KuPR (#7)
#1094 2014/5/9 0:16 Return to phase code(#1) #1150 2014/5/12 14:58 Ku/Ka RX ATT change 9dB to 12dB #1182 2014/5/14 16:07 GPM Delta-V Maneuver #1274 2014/5/20 13:30 GMI Deep Space Calibration
#1277 2014/5/20 18:44 #1288 2014/5/21 11:30 Upload new test phase code of KuPR (#8) #1290 2014/5/21 14:43 Upload new test phase code of KuPR (#9) #1292 2014/5/21 17:59 Upload new test phase code of KuPR (#10) #1294 2014/5/21 21:07 Upload new test phase code of KuPR (#11) #1296 2014/5/22 0:16 Return to phase code(#1) #1319 2014/5/23 11:38 Upload new test phase code of KuPR (#12) #1322 2014/5/23 15:03 Upload new test phase code of KuPR (#13) #1324 2014/5/23 15:03 Upload new test phase code of KuPR (#14) #1326 2014/5/23 21:37 Upload new test phase code of KuPR (#15) #1328 2014/5/24 0:57 Return to phase code(#1) #1351
2014/5/25 11:44
Change DPR FCIF-B to A (For External Cal.) Ku/Ka RX ATT change 12dB to 6dB
Change DPR FCIF-A to B Ku/Ka RX ATT change 6dB to 12dB
#1414 2014/5/29 13:59 GPM Delta-V Maneuver #1430 2014/5/30 13:50 Upload new test phase code of KuPR (#16)
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Orbit No. UTC DPR Event #1431 2014/5/30 15:26 Upload new test phase code of KuPR (#17) #1432 2014/5/30 17:01 Upload new test phase code of KuPR (#18) #1433 2014/5/30 18:34 Upload new test phase code of KuPR (#19) #1434 2014/5/30 20:07 Return to phase code(#1) #1447 2014/5/31 16:06 Upload new test phase code of KuPR (#20) #1448 2014/5/31 17:53 Upload new test phase code of KuPR (#21) #1449 2014/5/31 19:59 Return to phase code(#1) #1477 2014/6/2 15:06 DPR External calibration #1502 2014/6/4 5:15 DPR External calibration #1508 2014/6/4 14:13 Upload new test phase code of KuPR (#22) #1508 2014/6/4 14:56 Upload new test phase code of KuPR (#23) #1509 2014/6/4 16:39 Upload new test phase code of KuPR (#22) #1511 2014/6/4 18:59 Return to phase code(#1) #1539 2014/6/6 14:09 Upload new test phase code of KuPR (#22) #1541 2014/6/6 17:26 Return to phase code(#1) #1600 2014/6/4 5:15 DPR External calibration #1603 2014/6/10 17:38 GPM 180deg Yaw Maneuver (+X to -X) #1625 2014/6/12 2:58 DPR External calibration #1646 2014/6/13 11:46 DPR External calibration #1648 2014/6/13 14:08 Upload new test phase code of KuPR (#24) #1649 2014/6/13 15:45 Upload new test phase code of KuPR (#25) #1650 2014/6/13 17:36 Upload new test phase code of KuPR (#26) #1651 2014/6/13 19:12 Upload new test phase code of KuPR (#27) #1652 2014/6/13 20:54 Upload new test phase code of KuPR (#28) #1653 2014/6/13 22:33 Upload new test phase code of KuPR (#29) #1654 2014/6/14 0:21 Upload new test phase code of KuPR (#30) #1655 2014/6/14 1:39 Return to phase code(#1) #1726 2014/6/18 15:17 GPM Delta-V Maneuver #1769 2014/6/21 9:33 DPR External calibration #1794 2014/6/22 23:42 DPR External calibration (Yaw + pitch) #1892 2014/6/29 7:18 DPR External calibration #1917 2014/6/30 21:27 DPR External calibration #1942 2014/7/2 12:42 Upload new test phase code of KuPR (#31) #1944 2014/7/2 14:38 Upload new test phase code of KuPR (#32)
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Orbit No. UTC DPR Event #1945 2014/7/2 16:30 Return to phase code(#1) #1975 2014/7/4 15:07 Upload new test phase code of KuPR (#33) #1976 2014/7/4 16:44 Upload new test phase code of KuPR (#34) #1977 2014/7/4 18:24 Return to phase code(#1) #2015 2014/7/7 5:01 DPR External calibration #2040 2014/7/8 19:08 DPR External calibration (Yaw + pitch) #2053 2014/7/9 16:17 GPM Delta-V Maneuver #2163 2014/7/16 16:32 GPM 180deg Yaw Maneuver (-X to +X) #2176 2014/7/17 13:22 Upload new test phase code of KuPR (#35) #2177 2014/7/17 15:03 Upload new test phase code of KuPR (#36) #2178 2014/7/17 16:37 Upload new test phase code of KuPR (#37) #2180 2014/7/17 18:47 Return to phase code(#1) #2184 2014/7/18 1:42 DPR External calibration #2209 2014/7/19 15:51 DPR External calibration #2286 2014/7/24 14:54 Change Ku timing delay #2289 2014/7/24 19:11 Upload new test phase code of KuPR (#38) #2290 2014/7/24 20:49 Return to phase code(#1) #2304 2014/7/25 18:07 Upload new test phase code of KuPR (#39) #2307 2014/7/25 23:26 DPR External calibration #2332 2014/7/27 13:34 DPR External calibration #2380 2014/7/30 16:04 GPM Delta-V Maneuver #2430 2014/8/2 21:12 DPR External calibration (Yaw + pitch) #2455 2014/8/4 11:21 DPR External calibration #2455 2014/8/6 20:48 Upload new phase code of KaPR #2599 2014/8/13 17:55 DPR External calibration #2624 2014/8/15 8:03 DPR External calibration #2706 2014/8/20 15:09 GPM Delta-V Maneuver #2722 2014/8/21 15:40 DPR External calibration #2747 2014/8/23 5:48 DPR External calibration #2782 2014/8/25 12:15 Change DPR FCIF-B to A #2782 2014/8/25 12:30 Upload new test phase code of KuPR
(FCIF-A#1) #2784 2014/8/25 14:34 Upload new test phase code of KuPR
(FCIF-A#2)
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Orbit No. UTC DPR Event #2785 2014/8/25 16:13 Upload new test phase code of KuPR
(FCIF-A#3) #2786 2014/8/25 17:51 Upload new test phase code of KuPR
(FCIF-A#4) #2787 2014/8/25 19:22 Change DPR FCIF-A to B #2787 2014/8/25 19:24 Return to phase code(#39)
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Release Notes for the DPR Level 2 and 3 products
DPR Level 2 and 3 Version 4 products have been released to public users since March 2016. Caveats for these products are described as follows. All users can keep them in mind when they use the data. ＜Changes for DPR Level 2 products from Version 3 to Version 4＞ Preparation module (PRE)
Noise power calculation (no net change). Clutter-free bin moved up one 125m bin away from surface. Add sea-ice information for Ku. Rain/no-rain judgment adjustment to find lower rain. Improve Ku sidelobe correction.
Vertical Profile Module (VER) Revised cloud liquid water database Bug-fixed
Classification module (CSF) Ku: update of BB detection DPR: Bug fix and new DFRm parameter values.
Difference between Ku-only result and a dual frequency result (DFRm result) becomes smaller.
Added value to 6th bit (0-based) of typePrecip for DFRm precip type: 8: DFRm skipped at Part A,
Format changes : DPR HS: Added two items: binDFRmMLBottom and binDFRmMLTop DPR MS: Changed names from binDFRmBBBottom and
binDFRmBBTop to binDFRmMLBottom and binDFRmMLTop, respectively.
Note: ML stands for Melting Layer. Surface Reference Technique (SRT) module
Temporal reference files implemented. DJF, MAM, JJA, SON
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Solver module (SLV) R-Dm relation as new constraint of DSD Adjustment (epsilon) fixed per ray. Non-Uniform Beam Filling (NUBF) added. dPIA directly used in SLV module. Missing echo compensation added for Ka.
＜Changes for DPR Level 3 products from Version 3 to Version 4＞ L3DPR full daily and monthly product
Instrument and Channel index increased. Added statistics for Ku data restricted to MS swath. Instrument: Ku, Ka, KaMS, KuMS Channel: Ku, Ka, KaMS, DPRMS, KuMS
＜Caveats for DPR Level 2 products by DPR Level 2 algorithm development team＞ 1. Preparation module (PRE)
Mainlobe clutter may be occasionally misjudged as a strong precipitation echo in some areas, in particular in Greenland and in Antarctica where the accuracy of the digital elevation map (DEM) used in the algorithm is not good. It is expected that such misjudgment is very infrequent. Sidelobe clutter contamination has been reduced to a satisfactory level at most occasions. However, significant sidelobe clutter remains at exceptional places such as over a very calm sea and some ice-covered land, for example, in Northern Canada. “flagEcho” provides information related to the mainlobe and sidelobe clutter. Please see Appendix A for details.
2. Classification module (CSF) The detection of bright band (BB) in the outer swath of Ku-band data is not effective yet. Improvement of BB detection in the outer swath remains to be an issue. The Ka-band BB detection and rain type classification may not be reliable because of attenuation, sensitivity, and so on.
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All the shallow rain is classified as convective in the unified rain type. Sidelobe clutter influences the Ku-band shallow rain statistics significantly. When taking rain type statistics such as the dependence of each type count on the angle bin, you should treat the shallow rain count separately. (This suggestion applies to the Ku-only products and DPR NS products.) Rain type classification in the snow-only or near snow-only case has become a big issue. It is planned to solve this problem in the next version.
3. Solver module (SLV) The upper limit of Dm estimate is 3.0 mm, but heavy precipitation may have Dm of larger than
3.0mm. This may cause underestimation of Dm and overestimation of precipitation rate R. When Dm
takes the value of upper limit, the 5th and 6th bits of flagSLV are 0 and 1 (or the remainder of
flagSLV divided by 64 is between 32 and 47). In the next version, the upper limit of Dm should be
set higher.
The parameter epsilon (ε), which is used to adjust R-Dm relation in version 04, never change along
the beam, though it can change along the beam in the version 03 of dual-frequency algorithm. To
estimate ε at each range bin, HB-DFR method was used for version 03, but the results were not very
stable and not so good as expected. Therefore, in version 04, ε is estimated for a beam (pixel). The
idea of HB-DFR method is partly used to determine the value of ε in dual-frequency algorithm. In
the future version, dual-frequency technique should be used to estimate the vertical change of ε.
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＜Appendix A: Details of “flagEcho”＞ flagEcho is a 1-byte integer variable, and its array size is nbin x nray x nscan. Here, nbin is the number of range-bins, nray is the number of angle bins, and nscan is the number of scans in the granule. The meaning assigned to each bit in the flagEcho is summarized in Table 1. flagEcho provides the following information. Classification of precipitation/no-precipitation at each range-bin (bit 0-3).
However, the final judgment of precipitation/no-precipitation in the L2 product is provided by flagSLV (its bit 1).
Detection of mainlobe clutter (bit 4-5). Application of a routine to reduce the sidelobe clutter (bit 6-7)
Figure 1 shows an example of a vertical cross section of the flagEcho. Table 1. Meaning assigned to each bit in flagEcho
Bit 0 1
bit 0 - precipitation @ DPR or Ku or Ka
bit 1 - precipitation @ DPR
bit 2 - precipitation @ Ku
bit 3 - precipitation @ Ka
bit 4 - mainlobe clutter @ Ku
bit 5 - mainlobe clutter @ Ka
bit 6 - sidelobe clutter @ Ku
bit 7 - sidelobe clutter @ Ka
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Figure 1. An example of a vertical cross section of flagEcho. The horizontal axis
denotes the angle bin number and the vertical axis denotes the range-bin number. While sidelobe clutter remains at some places, the bits in flagEcho that indicate the existence of possible sidelobe clutter may be useful for analyses of KuPR radar reflectivity.
Range-bin where a routine to reduce the sidelobe clutter is applied
Mainlobe clutter
Precipitation
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Release Notes for GPM SLH V4
GPM SLH V4 is the same as TRMM SLH V7A (see below) except for using GPM/KuPR information instead of TRMM/PR information as an input. Analysis showed consistency between GPM SLH V4 and TRMM SLH V7A estimates over the coverage of TRMM/PR during a GPM and TRMM overlapping observation period (April-June 2014). It should be noted: 1. Shallow non-isolated echo has been classified as stratiform by rain type
classification algorithm for TRMM/PR, but as convective by that for GPM/KuPR, affecting SLH estimates. To give consistent SLH estimates from GPM/KuPR with those from TRMM/PR, shallow non-isolated echo is classified as stratiform in GPM SLH V4.
2. Differences of sampling between TRMM/PR and GPM/KuPR affect SLH estimates. The greater global coverage of the GPM Core Observatory (65°N/S) compared to the TRMM coverage (35°N/S) decreases sampling of GPM/DPR over the coverage of TRMM/PR, especially at around the satellite inclination latitudes of 35°N/S, affecting SLH estimates there.
3. Retrieval for high mountains/winter mid-latitudes pixels will be developed.
Caveat for TRMM SLH V7A
August 25, 2015 A new mask for high mountains/winter mid-latitudes pixels has been applied to Version 7A SLH. The new mask assigns a missing value for any pixel that was classified as rainTypeSLH=4 in the previous version-7 SLH. This type was used mainly for Tibet and winter mid-latitude with the melting level close to the ground level. In order to remove suspicious extreme rainfall profiles in PR 2A25 version-7 data, a filter developed by Hamada and Takayabu (2014) has been applied. However, it cannot remove all of them, so that some suspicious extreme profiles still remain in the new version-7A SLH.
The previous version 7 SLH contained a misclassification that caused some stratiform pixels erroneously assigned to rainTypeSLH=4, and this resulted in unrealistic positive heating at altitudes lower than the freezing level [see median volumetric latent heating
March 3rd, 2016
Mar.27,2017 Page-25
from stratiform precipitation over the US and Argentina shown in Fig. 6b of Liu et al. (2015)]. This misclassification has been fixed in the new version-7A SLH.
Analysis showed some isolated abnormal reflectivity profiles, these may result in non-negligible abnormal values of SLH. We conjecture this is caused by some kind of radio wave interferences from the ground. No fixes were applied to deal with this abnormal profile. This remains as a future issue. References: Hamada, A. and Y. N. Takayabu, 2014: A removal filter for suspicious extreme rainfall
profiles in TRMM PR 2A25 version-7 data. J. Appl. Meteor. Climatol., 53, 1252–1271.
Liu, C., S. Shige, Y. N. Takayabu, E. Zipser, 2015: Latent heating contribution from precipitation systems with different sizes, depths and intensities in the tropics. J. Climate, 28, 186-203, DOI: 10.1175/JCLI-D-14-00370.1.
Mar.27, 2017 Page-26
March 27, 2017
Release note for GPM Global Rainfall Map (GPM-GSMaP) The GPM Global Rainfall Map (GPM-GSMaP) Level 3 product version 04A (Algorithm version 7) was released
to the public since January 17, 2017, and V04B was released since Mach 2, 2017. However, because of program
bugs related to “PrecipRateGC” (Gauge-corrected Precipitation rate), the GPM-GSMaP Level 3 product version
04C was released to the public since March 27, 2017.
Updates from version 04B to version 04C, connected with bug-fixing of “PrecipRateGC” in the following
products.
All standard products in V04A
Standard products since March 1, 2017 in V04B.
Updates from version 04A to version 04B are following.
Adding a missing value in “snowProbability” of the GSMaP Hourly (3GSMAPH).
Bug-fixing in “snowProbability” of the GSMaP Monthly (3GSMAPM).
Bug-fixing in “satelliteInfoFlag”.
Update from version 03 (Algorithm version 6) to version 04A (Algorithm version 7) are following.
1) Improvement of the GSMaP algorithm using GPM/DPR observations as its database
2) Implementation of a snowfall estimation method in the GMI & SSMIS data and a screening method using
NOAA multisensor snow/ice cover maps in all sensors
3) Improvement of the gauge-correction method in both near-real-time and standard products
4) Improvement of the orographic rain correction method
5) Improvement of a weak rain detection method over the ocean by considering cloud liquid water
For details, following URLs can be helpful for your reference.
1. The snowfall estimation method for the GMI & SSMIS data was installed in the V04 product, but it still needs
to be validated and improved further. In addition, several biases and/or gaps may be appeared in the
mid-latitude ocean, due to changes of the estimation method. In addition, sometimes, surface snow or sea ice
may be misidentified as precipitation signal, especially in spring season. Users should be cautious of
estimations over the cold surface (in particular, below 273.2 K).
2. The orographic/non-orographic rainfall classification scheme has been implemented in the GSMaP algorithm
for passive microwave radiometers (Yamamoto and Shige, 2014). The scheme is switched off for regions (e.g.
the Sierra Madre Mountains in the United States and Mexico) where strong lightning activity occurs in the
rainfall type database because deep convective systems for the regions are detected from the scheme involved
in the orographic rain condition. The scheme improves rainfall estimation over the entire Asian region,
particularly over the Asian region dominating shallow orographic rainfall. However, overestimation and
false-positive of orographic rainfall remain. This is because the orographic rainfall conditions have moderate
thresholds for global application. We examine to resolve their problems.
3. The precipitation estimation of gauge-calibrated hourly rainfall product (GSMaP_Gauge) depends on a large
part on the Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Global Daily Precipitation
data sets provided by NOAA. If the CPC data sets have good estimation of precipitation in a region, the
GSMaP_Gauge data sets also will show good scores in the region. However, in case the CPC data sets under
or overestimate the rain fall rate seriously or miss the rainfall event, the GSMaP_Gauge product also
estimates or misses the precipitation in a similar manner as the CPC data sets. Note that the CPC data sets and
hence the GSMaP_Gauge data do not always show accurate estimation particularly over less dense gauge
region.
4. Although the GSMaP_Gauge_NRT is a near real time version of the GSMaP_Gauge, the products does not
use the gauge measurement directly. Since the global gauge measurement takes much time to collect and
process the data from all over the world, the gauge data is not available in near real time. Hence, in the
GSMaP_Gauge_NRT product, only the error parameters derived from the GSMaP_Gauge are used to adjust
the GSMaP_NRT estimation, which is named as the GSMaP_Gague_NRT. We would like to know
evaluation and validation results of this product for improvement. We appreciate if you give us some
feedback.
B. Calibration issues
5. Brightness temperatures used in rainfall retrievals of GCOM-W/AMSR2 and GPM-Core/GMI are
bias-corrected using parameters provided by JAXA. These parameters may be modified in future when
calibration of each Level 1B data is updated.
6. Scan errors may be occasionally found in rainfall retrievals of SSMIS (microwave imager/sounder) on board
the DMSP-F16, DMSP-F17 and DMSP-F18 satellites. This problem will be corrected in the future version of
L1c data.
7. MHS data used in the GSMaP product was changed form Level 1B to Level 1C. The Scattering Index (SI) in
Mar.27, 2017 Page-28
the AMSU-A/MHS algorithm is changed at altitude higher than 40 degrees. However, we have not yet fully
evaluated the effect. We would like to know evaluation and validation results of the GSMaP AMSU-A/MHS
rainfall retrievals. We appreciate if you give us some feedback.
February 24, 2016
Release Notes for the CMB Level 2 Product in the GPM V04 Public Release
The Combined Radar1 Radiometer Algorithm (CMB) L2 V04 product includes precipitation estimates over the broader, NS (Ku+GMI) swath as well as estimates over the narrower, MS (Ku+Ka+GMI) swath. The input of the CMB L2 algorithm is derived from DPR L2 and GMI L1 products. In particular, the CMB L2 algorithm depends upon inputs from the DPR L2 Preparation Module, Classification Module, Surface Reference Technique Module, and the Vertical Structure Module. From GMI L1, the CMB L2 algorithm utilizes the intercalibrated brightness temperature observations.
During the early GPM mission (prior to June 2014) many tests and modifications of the DPR performance were carried out, and these can have an impact on not only DPR products but also the CMB L2 estimates that depend on them. Therefore, CMB L2 precipitation estimates from the early mission should be used with caution. A listing of the orbits impacted by these tests and modifications can be obtained from the GPM Radar Team.
Mainlobe and sidelobe clutter contamination of DPR reflectivities has been reduced using radar beam reshaping and statistical corrections. The combination of these applications has reduced clutter successfully over most surfaces, but there are still “exceptional” regions where clutter signatures are still evident. Also, ice1 covered land surfaces produce Ku1 band radar surface cross1 sections at nadir view that sometime exceed the upper limit of the radar receiver range. Estimates of Ku1 band path1 integrated attenuation from the Surface Reference Technique Module are possibly biased in these regions. Since radar reflectivities and path1 integrated attenuations are utilized by the CMB L2 algorithm, precipitation estimates in these “exceptional” regions should be used with caution.
The current CMB L2 algorithm uses the Ku1 band radar reflectivities from the Preparation Module to detect either liquid1 or ice1 phase precipitation. The lowest detectable reflectivity for DPR at Ku band is ~13 dBZ, and so light snow or very light rainfall may not be detected and quantified by the algorithm.
In addition to the impact of input data from DPR L2, there are uncertainties due to the current limitations of the CMB L2 algorithm’s physical models and other assumptions that will also have an impact on precipitation estimates. In particular, the physical models for scattering by ice1 phase and mixed1 phase precipitation particles are simplified. These scattering models in the CMB algorithm will be improved for the purpose of generating precipitation estimates in future product releases. Also, the effects of radar footprint non1 uniform beamfilling and multiple scattering of transmitted power have been addressed in CMB L2, but are not yet
Mar.27, 2017 Page- 29
generalized and have not been analyzed in detail. Multiple scattering primarily affects Ka-‐band reflectivities, and sometimes eliminates earth surface reflection, in regions of strong radar attenuation, while footprint non-‐uniform beamfilling impacts the interpretation of both Ku-‐ and Ka-‐band radar data. As a consequence, NS and MS swath precipitation estimates associated with intense convection, in particular, should be treated with caution. Finally, the assumed a priori statistics of precipitation particle size distributions can have an influence on estimated precipitation. As particle size distribution data are collected during the mission, more appropriate assumptions regarding the a priori statistics of particle sizes will be specified in the algorithm. At this stage of the mission, however, insufficient data on particle size distributions have been collected for the purpose of updating a priori statistics, and so biases in estimated precipitation and underlying particle size distributions can occur.
It should also be noted that both precipitation estimates and retrievals of environmental parameters from CMB L2 have not yet been comprehensively validated using ground observations. Such a validation effort has begun and will continue after the V04 release of the CMB L2 product. Therefore, it is very important that users of the public release product keep in contact with the CMB Team for updates on the validation of precipitation estimates and any reprocessing’s of the CMB L2 algorithm product.
Preliminary validation of the V04 CMB L2 product has revealed good consistency between estimated surface precipitation rate and raingage-‐calibrated radar, with correlations greater than 0.80 between 0.5 degree-‐resolution instantaneous estimates of surface precipitation rate and gage-‐calibrated radar (Multi-‐Radar Multi-‐Sensor [MRMS] product) over the continental US and coastal waters. However, regional biases are seen, with some positive biases relative to gage-‐calibrated radar in convective regimes over land, and smaller negative biases over coastal waters. Zonal mean precipitation rates agree well with zonal mean precipitation rates from the Global Precipitation Climatology Project (GPCP) product within the 40 oS to 40 oN latitude band. Estimated zonal means at higher latitudes are underestimated relative to GPCP, due in part to the limited sensitivity of the DPR radar to light snow and drizzle. Although agreement of zonal means between 40 oS – 40 oN is noted, regional positive biases over land and compensating weaker negative biases over ocean relative to GPCP are evident, and these biases are consistent with the bias patterns inferred from the MRMS product over the US and coastal waters.
There could potentially be significant changes in the CMB L2 rain rate products in the transition from V04 to V05 due to expected changes in the DPR radar calibration as well as adjustments and improvements of the CMB algorithm. Again, the users of the V04 public release product should keep in contact with the CMB Team for information regarding these changes.
Mar.27, 2017 Page- 30
CMB L2 V03 to V04 Changes
Many updates have been made to the CMB L2 algorithm in the transition from V03 to V04, and the significant updates are summarized here. It may be noted at the outset, however, that the basic algorithm mechanics (i.e., estimation methodology) and output file structure have not changed. The estimation method filters ensembles of DPR Ku reflectivity-‐consistent precipitation profiles using the DPR Ka reflectivities, path integrated attenuations at Ku and Ka bands, and GMI radiances. The filtered profile ensembles are consistent with all of the observations and their uncertainties, and the mean of the filtered ensemble gives the best estimate of the precipitation profile.
In the CMB V03 and V04 algorithms, input data are passed from the DPR L2 and GMI L1C algorithms. However, to obtain better responsiveness of precipitation profile estimates to the GMI data in V04, input radiances are first resolution-‐enhanced to approximately the spatial resolution of the DPR resolution (~5 km). This enhancement is accomplished, at each channel frequency and polarization, using a statistically derived filter that predicts the DPR-‐resolution radiance from a weighted average of native-‐resolution GMI radiances in a small neighborhood of the observation to be enhanced. Filter weights are derived from regressions on synthetic radiance data, and the degree of enhancement is traded against noise amplification, with an optimal balance between enhancement and noise determined by cross-‐validation. Use of the resolution-‐enhanced data leads to a greater responsiveness of precipitation estimates to the GMI radiometer data, and a better fitting of those data. Moreover, data from all thirteen of the GMI channels are utilized in the V04 CMB algorithm, whereas data from only seven channels were used in the V03 algorithm.
In V03, the impact of multiple scattering on simulated reflectivities was crudely represented by typical reflectivity corrections (relative to single-‐scattering calculations) as functions of bulk scattering optical depth. This simple correction of reflectivities is replaced in V04 by the full simulation of multiple-‐scattering affected reflectivities using the 1D time-‐dependent radiative transfer model of Hogan and Battaglia (2008). This model is fully invoked only in situations where single-‐ and multiple-‐scattering reflectivity simulations based upon the ensemble-‐mean, Ku-‐consistent precipitation profile are significantly different, in which case the multiple-‐scattering model is applied to all ensemble member profiles to simulate the Ka reflectivities. The impact of multiple scattering on Ku reflectivities is generally much smaller than at Ka band and is not considered in V04.
The general parameterization of the effects of radar footprint non-‐uniform beamfilling by precipitation is the same in CMB V03 to V04; however, the impact of non-‐uniform beamfilling on simulations of average path-‐integrated attenuation at the earth’s surface is now properly represented in this parameterization in V04.
Mar.27, 2017 Page- 31
This allows more consistent comparisons of simulated and surface reference technique (SRT) derived path-‐integrated attenuations in the algorithm.
Further, the use of individual SRT-‐based estimates of path-‐integrated attenuation at Ka band in V03 has been replaced by differential Ka-‐Ku path-‐integrated attenuation in the MS (Ku+Ka+GMI) mode of the CMB V04 algorithm. The precipitation-‐free differential Ka-‐Ku path-‐integrated attenuation reference is much more stable than the Ka-‐band reference, particularly over land surfaces, and this leads to less uncertainty in SRT-‐derived, differential Ka-‐Ku path-‐integrated attenuation estimates in precipitation regions. The SRT differential path-‐integrated attenuation is used to directly filter the precipitation profile ensembles, rather than inferring the individual Ku and Ka path-‐integrated attenuations from the differential path-‐integrated attenuation, and then filtering with those individual path-‐integrated attenuations.
The expected uncertainties of forward model simulations (relative to observations) prescribed in the ensemble filter kernel are changed from 1.4 dB to 3 dB for Ka-‐band reflectivities and from 5 oK to 6.1 oK for GMI radiances at frequencies above 37 GHz, going from V03 to V04. Expected uncertainties of path-‐integrated attenuations are maintained at 4 dB in the filter, and uncertainties of GMI radiances at frequencies up to 37 GHz are maintained at 5 oK.
Hogan, R. J., and A. Battaglia, 2008: Fast lidar and radar multiple-‐scattering models. Part II: Wide-‐angle scattering using the time-‐dependent two-‐stream approximation. J. Atmos. Sci., 65, 3636–3651.
Mar.27, 2017 Page- 32
March 3rd, 2016
Mar.27,2017 Page-33
Release Notes for GPM CSH Products The CSH LH products strongly depend on the surface rainfall amount and its stratiform component (%). Heating depth is indirectly inferred from the use of conditional surface precipitation rates. The CSH and SLH LH products are based on heating look-up tables (LUTs). The LUTs are generated from a high-resolution cloud-resolving model (i.e., the Goddard Cumulus Ensemble model), which can typically produce/simulate Q1 profiles (i.e., LH+Eddy+Qr) that are in good agreement with sounding estimates. However, the current LUTs are only based on a limited number of cases (several tropical oceanic but only a couple continental). Please see Tao et al. (2010). For GPM LH products, the LUTs need to include cases associated with fronts and snow events, including mid-latitude synoptic and winter storms (please see the cases shown in the table in the next slide). These same cases will also be used to generate the LUTs needed for the SLH algorithm. Tao, W.-K., S. Lang, X. Zeng, S. Shige, and Y. Takayabu, 2010: Relating convective and stratiform rain to latent heating, J. Climate, 23, 1874-1893.
Red: High priority, will simulate with NU-WRF Green: Low priority
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