NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG) is a widely used high resolution global precipitation product. IMERG relies on retrievals from passive microwave (PMW) sensors as the primary input precipitation estimates, so it is vital that they are first homogenously calibrated to the core observatory GMI radiometer and finally to the GPM Combined Radar–Radiometer Analysis (CORRA-G/T) for the GPM/TRMM eras respectively. Inspections of IMERG V07 illustrate a very close calibration of the PMW estimates to V07 CORRA-G/T as result of several improvements in V07 IMERG calibrations, however the final CORRA-G/T calibration of all V07 GPROF PMW precipitation did not account for surface-type dependencies, and the GMI/TMI-to-other-satellite calibration did not fine tune regional dependencies. Also, both calibrations did not take advantage of limiting the spatial comparison domain for regions of high precipitation detection by both sensors. Despite the improved calibration procedure, systematic biases and inhomogeneities remain in the satellite precipitation products used as input for V07 IMERG. Evaluations of V07 IMERG indicate discontinuities in certain regions near coastlines relative to CORRA-G/T. Specifically, for these regions the differential character of the respective CORRA-G/T land/ocean algorithms are not always captured correctly in the final calibration of the GPROF land/ocean algorithms. In V08 IMERG the final CORRA-G/T calibration of all PMW differentiates a land/ocean calibration by only using matchup retrievals from each surface type. Also in certain regions, noticeable disparities of spatiotemporal matches of GMI to other satellite GMI-calibrated GPROF precipitation is certainly a result of latitude band calibrations used in V07 [and previous versions] that do not necessarily capture the regional relationships. In V08 IMERG the regional/seasonal GMI-to-other-satellite calibrations markedly improve the regional/seasonal relationships between GMI and other sensors by regionally restricting matchups. Unlike previous versions, in V08 IMERG a spatial search restriction of precipitation frequency detection is used for both calibrations. By using minimum thresholds of precipitation detection, regional dependencies are preserved by terminating the outward spatial search of precipitation occurrences from both the calibrating source and precipitation set for calibration, once the criteria are met by both for stable calibrations. We plan to work with the GPROF and CORRA teams to finalize these corrections as part of V08.

Abstract NASA's Time‐Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission provides rapid‐refresh microwave measurements through a low‐cost smallsat constellation, each equipped with the TROPICS Millimeter‐wave Sounder (TMS). To assess TROPICS‐derived precipitation for potential integration into the Global Precipitation Measurement (GPM) mission's Integrated Multi‐satellite Retrievals for GPM (IMERG), this study evaluates precipitation retrievals from four of the TROPICS satellites using Precipitation Retrieval and Profiling Scheme (PRPS), relative to the GPM radar‐radiometer combined product (2BCMB) over ocean and land, as well as five sensors in the GPM radiometer constellation, including two cross‐track scanning sensors [Advanced Technology Microwave Sounder (ATMS) and Microwave Humidity Sounder (MHS)], and three conical‐scanning sensors [Special Sensor Microwave Imager/Sounder (SSMIS), GPM Microwave Imager (GMI), and Advanced Microwave Scanning Radiometer 2 (AMSR2)]. Over ocean, the PRPS‐TMS precipitation intensity estimates correlate better (0.52–0.56) with 2BCMB than ATMS (0.39) and MHS (0.38), which exhibit unrealistic multi‐peak intensity distributions. However, TMS performs worse than conically scanning sensors, with greater bias, most likely due to the lack of low‐frequency channels and the availability of vertical and horizontal polarizations. Over land, TMS underestimates heavy precipitation to a greater extent than all sensors in the GPM constellation except SSMIS, with similar coarser spatial resolution. Furthermore, TMS retrievals show a clear scan‐position dependence, with performance degrading toward the swath edge. Among the four TMS sensors, precipitation detection performance and precipitation intensity estimation are generally consistent, with precipitation occurrence, correlation, and bias statistics all indicating stable retrieval performance throughout the TROPICS mission duration.

Diagnosing errors in spaceborne oceanic precipitation estimates is difficult due to complicated multisatellite algorithms and limited surface-based measurements. The Global Precipitation Measurement (GPM) mission helps to alleviate these challenges with NASA's Integrated Multi-satellitE Retrievals for GPM (IMERG) product, which is transparently designed to encourage community validation activities, and the GPM Validation Network, which collects observations across global precipitation regimes from over 100 ground-based weather radars to serve as reference datasets for the GPM precipitation products. This study uses the GPM Validation Network's oceanic precipitation observations from 32 island and coastal radars to diagnose the performance of IMERG V06B and V07B Final Run products during GPM Microwave Imager (GMI) overpasses (i.e., IMERG-GMI) in the period June 2014-September 2021. Errors are traced from the input level 2 (satellite footprint) Goddard profiling algorithm climate (GPROF-CLIM) GMI product through the successive gridding, calibration, and precipitation distribution restoration steps of IMERG's level 3 (gridded) algorithm. Results highlight that IMERG-GMI V07B outperforms V06B in detecting and quantifying oceanic precipitation, with a significant improvement over high-latitude ocean (V06B: +143%; V07B: +50%). Furthermore, there is a clear oceanic latitudinal trend in the mean relative bias of IMERG-GMI V07B (high latitude: +50%; midlatitude: +10%; tropical:-41%), which largely traces back to GPROF-CLIM V07 (high latitude: +22%; midlatitude:-8%; tropical:-44%), with bias differences driven by IMERG's passive microwave calibration scheme. This error tracing approach supports future IMERG algorithm developments by disentangling how algorithm steps enhance or mitigate errors. SIGNIFICANCE STATEMENT: Most precipitation occurs over the ocean, yet precipitation products are rarely evaluated there due to a lack of surface-based measurements. This study utilizes the GPM Validation Network's datasets from coastal and island radars to conduct the first oceanic evaluation of the NASA IMERG V07B multi-satellite precipitation product. The performance of IMERG at different successive algorithm steps is assessed to understand which processing steps reduce or increase errors and to support future algorithm improvements. The latest IMERG version provides more accurate and reliable precipitation estimates than the preceding version. However, IMERG-GMI is biased high by +50% and low by-41% over the high-latitude and tropical oceans, respectively.

NASA's multisatellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Inte-grated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network's island-based radars to assess IMERG when the GPM Core Observatory's Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 cli-mate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the esti-mates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm's calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1 degrees eastward in the latitude band 75 degrees N-75 degrees S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach en-ables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the impor-tance of collaboration between evaluation studies and algorithm developers.

The Global Precipitation Measurement (GPM) mission combines passive microwave (PMW) and infrared (IR) satellite data, together with other data to create the Integrated Multi-satellitE Retrievals for (IMERG) precipitation product on a (nearly) global 0.1° half-hour grid. Experience with Version 06 datasets revealed deficiencies that the algorithm team has addressed in creating the new Version 07 datasets.Input precipitation estimates from the Goddard Profiling (GPROF) algorithm (which retrieves precipitation from passive microwave sensor data), the GPM Combined Radar-Radiometer Algorithm (CORRA), and the new Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR) algorithm (retrieving precipitation from IR data) all represent advances over the V06 inputs. For V07, several issues have been addressed, and many algorithm improvements have been implemented. These include refining the Kalman filter to approximately preserve the local histogram of precipitation rates (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN), and applying the Kalman filter even when a PMW overpass occurs. Furthermore, a long-standing bug in the geolocation that shifted grid values 0.1° to the east in the latitude band 70°N-S has been corrected. Other changes in V07 include a hierarchical selection among motion vector sources to address deficiencies in the precipitation propagation near orography, an update to the precipitation phase specification for improved consistency with current inputs, and climatological adjustment of the near-real-time Early and Late Runs to the Final Run (which includes monthly precipitation gauge analyses). Extensive development work was directed at unexpected biases in the V06 products, leading to 1) calibrations that now employ the entire swath widths of CORRA and GPROF GPM Microwave Imager (GMI) precipitation estimates (rather than spatially coincident data), and 2) coarsening the CORRA resolution to approximately match the GPROF-GMI footprint scale. The latter provides more consistent histograms for building the calibrations.It is anticipated that the retrospective analysis for V07 will be well underway at the time of the meeting. Changes between V06 and V07 will illustrate the cumulative result of the improvements implemented in V07. The current status of processing and plans for future development will also be discussed.

The Version 06 Global Precipitation Measurement (GPM) mission products were completed over the last year, capping five years of development since the launch of the GPM Core Observatory, and covering the joint Tropical Rainfall Measuring Mission (TRMM) and GPM eras with consistently processed algorithms. The U.S. GPM team’s Integrated Multi-satellitE Retrievals for GPM (IMERG) merged precipitation product enforces a consistent intercalibration for all precipitation products computed from individual satellites with the TRMM and GPM Core Observatory sensors as the TRMM- and GPM-era calibrators, respectively, and incorporates monthly surface gauge data in the Final (research) product. Mid-latitude calibrations during the TRMM era necessarily are more approximate because TRMM only covered the latitude band 35°N-S, while GPM covers 65°N-S. Starting in V06, IMERG employs precipitation motion vectors (used to drive the quasi-Lagrangian interpolation, or “morphing”) that are computed by tracking the vertically integrated vapor as analyzed in MERRA2 and GEOS FP. This approach covers the entire globe, expanding coverage beyond the 60°N-S latitude band provided by IR-based vectors in previous versions, although we choose to mask out microwave-based precipitation over snowy/icy surfaces as unreliable. We will provide examples of performance for the V06 IMERG products, including comparison with the long-term record of GPCP and TMPA, showing higher values by about 8% in the latitude band 50°N-S over oceans; diurnal cycle, demonstrating improvement over previous versions; and daily precipitation PDFs for the entire record, showing a shift at the TRMM/GPM boundary, as well as interannual variations. These analyses have important implications for the utility of V06 IMERG data for long-record calculations. Finally, we will review the retirement of the predecessor TMPA multi-satellite dataset.

This paper describes the development, evaluation, and applications of the CMORPH satellite global precipitation estimates.

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) is a US GPM Science Team precipitation product. IMERG uses inter-calibrated estimates from the international constellation of precipitation-relevant satellites and other data, including monthly surface precipitation gauge analyses, to compute half hour, 0.1° × 0.1° gridded datasets over 60°N-S (and partially outside of that latitude band) in three “Runs”—Early (4 h after obs time), Late (14 h after obs time), and Final (3.5 months after obs time). The concepts behind IMERG are briefly reviewed, together with major shifts related to changes in versions from the at-launch Version 03 to Version 05, and an outline of Version 06, which was released in late 2019.

Possible effects of the diurnal cycle in land convection on propagation of the Madden-Julian Oscillation over the Indo-Pacific Maritime Continent (MC) were investigated using satellite observations. Four features distinguishable from their respective climatology are uniquely associated with MJO events that cross the MC: strong precipitation over land as their convection centers approach the MC, subsequent increased soil moisture, reduced diurnal amplitude of land convection, and the dominance of precipitation over water by nondiurnal convection as their convection centers move over the MC. These results provide observational evidence for a proposed MAritime Continent Convective diurnal Cycle mechanism in which the diurnal cycle in land convection acts as an intrinsic barrier effect on MJO propagation over the MC. Plain Language Summary By influencing global weather and climate, the Madden-Julian Oscillation (MJO) plays a central role in intraseasonal prediction. But when it propagates over the Indo-Pacific Maritime Continent (MC), the MJO often weakens and sometimes breaks down and ceases to exist. This is known as the barrier effect of the MC. The reason for this barrier effect is not well understood. Through diagnosing satellite data of precipitation and soil moisture, this study provides observational evidence for several steps in a MAritime Continent Convective diurnal Cycle mechanism, in which the diurnal cycle in land convection acts as an intrinsic barrier effect that must be overcome for the MJO to propagate through the MC. The observations show that increased soil moisture reduces the amplitude of diurnal convection over land, allowing convective systems over water of the MC to develop and carry MJO signals through the MC.

After five years of development following the launch of the Global Precipitation Measurement (GPM) missionCore Observatory, the GPM data products are now being extended across the joint Tropical Rainfall MeasuringMission (TRMM) and GPM eras. Version 06 of the U.S. GPM team's Integrated Multi-satellitE Retrievals forGPM (IMERG) merged precipitation product provides a consistent intercalibration for all precipitation productscomputed from individual satellites with the TRMM and GPM Core Observatory sensors as the TRMM- andGPM-era calibrators, respectively, and incorporates monthly surface gauge data. One major change in the basicIMERG algorithm for V06 is that precipitation motion vectors (used to drive the quasi-Lagrangian interpolation,or morphing) are computed by tracking vertically integrated vapor (TQV) fields analyzed in MERRA2 andGEOS5. This innovation provides globally complete coverage, expanding IMERG's coverage beyond the 60N-Slatitude band previously provided by IR-based vectors, although precipitation over snowy/icy surfaces is stillmasked out as unreliable. A second innovation is that the Quality Index (QI) data field computed for the half-hourlydatasets has been refined to include estimates of correlation at microwave overpass times.We will summarize the processing status for V06 IMERG, for which the retrospective processing shouldbe actively advancing at meeting time. We will show early examples of performance. For example, the TQVmotion vectors are typically slightly better than the IR-based vectors at all latitudes. The transition across theTRMM/GPM data boundary will be discussed, including the necessity of filling in the TRMM-based calibrationsover the latitude band 35-65 in each hemisphere. The notional schedule for the eventual retirement of thepredecessor TRMM Multi-satellite Precipitation Analysis (TMPA) multi-satellite dataset will be updated as well.

The Climate Prediction Center (CPC) morphing technique (CMORPH) satellite precipitation estimates are reprocessed and bias corrected on an 8 km 3 8 km grid over the globe (608S-608N) and in a 30-min temporal resolution for an 18-yr period from January 1998 to the present to form a climate data record (CDR) of high-resolution global precipitation analysis. First, the purely satellite-basedCMORPH precipitation estimates (raw CMORPH) are reprocessed. The integration algorithmis fixed and the input level 2 passivemicrowave (PMW) retrievals of instantaneous precipitation rates are from identical versions throughout the entire data period. Bias correction is then performed for the raw CMORPH through probability density function (PDF) matching against the CPC daily gauge analysis over land and through adjustment against the Global Precipitation Climatology Project (GPCP) pentad merged analysis of precipitation over ocean. The reprocessed, bias-corrected CMORPH exhibits improved performance in representing the magnitude, spatial distribution patterns, and temporal variations of precipitation over the global domain from 60 degrees S to 60 degrees N. Bias in the CMORPH satellite precipitation estimates is almost completely removed over land during warm seasons (May-September), while during cold seasons (October-April) CMORPH tends to underestimate the precipitation due to the less-thandesirable performance of the current-generation PMW retrievals in detecting and quantifying snowfall and cold season rainfall. An intercomparison study indicated that the reprocessed, bias-corrected CMORPH exhibits consistently superior performance than the widely used TRMM 3B42 (TMPA) in representing both daily and 3-hourly precipitation over the contiguous United States and other global regions.

This chapter examines potential information sources for mid- and high latitude precipitation, and explores optimal strategies to expand integrated satellite precipitation estimates to cover the entire globe from pole to pole. It also explores the possibility of creating two versions of the integrated precipitation analyses: one using all available information from both satellite observations and numerical model forecasts/simulations to achieve possible coverage and accuracy and the other using satellite observations only for applications in model validations. The chapter describes the current generation climate prediction center (CPC) morphing technique (CMORPH) technique and its limitation. It presents the Kalman filter (KF)-based CMORPH. The chapter examines potential information sources for the definition of precipitation and cloud motion vectors over the globe covering the entire latitude band from the equator to the poles. It outlines strategies and demonstrates technical feasibility to integrate information of different sources for the optimal definition of global precipitation analyses.