Spaceborne passive microwave (PMW) radiometry is central to global precipitation monitoring, yet retrieval uncertainties remain substantial, particularly for cross-track sounders whose variable footprints and channel configurations are optimized for atmospheric temperature and moisture profiling rather than precipitation. Consequently, existing operational products often exhibit angular-dependent biases, limited effective swath utilization, unrealistic rainfall probability distributions, and systematic misclassification of precipitation phase. These limitations are further compounded by the scarcity of globally accurate and representative precipitation observations, as training data from the dual-frequency precipitation radar (DPR) and the cloud profiling radar (CPR) are spatially sparse, lack uniform global coverage, and exhibit heterogeneous error characteristics across precipitation regimes. To address these challenges, this study presents a supervised retrieval algorithm that incrementally trains an ensemble of extreme gradient-boosted decision trees by augmenting base learners with pretraining on reanalysis data and post-training on coincident DPR and CPR observations matched with the advanced technology microwave sounder (ATMS). By transferring prior information from reanalysis to posterior constraints from radar observations and adopting a sequential detection-estimation strategy for precipitation phase and rate retrieval, the proposed approach yields retrievals across the full ATMS swath that are largely free from persistent deficiencies in current global precipitation measurement (GPM) PMW operational products. In particular, the method resolves bimodal artifacts in rainfall retrievals and mitigates systematic high-latitude snowfall biases, including overestimation across the Arctic and underestimation across the Antarctic. Validation against independent Multi-Radar/Multi-Sensor (MRMS) data over the Contiguous United States (CONUS) further demonstrates improved performance in precipitation phase detection and rate estimation relative to both reanalysis and current GPM PMW products.

Abstract The Global Precipitation Climatology Project (GPCP) Version 3.3 provides an upgraded global precipitation record from January 1983 (monthly) and January 1998 (daily) to the delayed present, both at 0.5° × 0.5° resolution. Major updates include new input datasets from orbital passive microwave (PMW) and combined PMW–radar retrievals, geostationary infrared (IR) imagery, GPCC Version 2022 gauge analyses, and an updated Merged CloudSat–TRMM–GPM (MCTG) climatology, with IR estimates recalibrated using updated Special Sensor Microwave Imager (SSMI) and Special Sensor Microwave Imager/Sounder (SSMIS) adjustments. A new absolute bias error field provides a direct estimate of systematic uncertainty. The Daily product inherits these improvements and integrates IMERG Version 07B Final Run, with GridSat IR data extending coverage back to January 1998. Relative to Version 3.2, GPCP V3.3 exhibits systematically higher precipitation, primarily over the oceans, yielding a global mean of 3.06 mm day −1 for 1992–2023 (an 8.5% increase over V3.2 and 4.4% over ERA5). Comparisons with independent oceanic benchmarks (PALs and PACRAIN atolls) indicate a modest positive bias in V3.3 but more consistent behavior than ERA5, which is more sensitive to the choice of which of the two reference datasets are used. GPCP V3.3 shows weak global trends with regionally significant changes and modest increases in heavy precipitation frequency and intensity relative to V3.2.

Current merged precipitation products such as IMERG, GSMAP, and CMORPH combine satellite estimates from passive microwave (PMW) and infrared (IR) observations. However, the different information content of these sensors makes it challenging to produce consistent precipitation estimates, even for coincident observations. The resulting inconsistencies between PMW and IR retrievals can introduce artifacts in the temporal evolution of merged precipitation fields and lead to an overreliance on time-propagated PMW estimates. We introduce GPROF-IR, a novel IR precipitation retrieval that leverages a convolutional neural network to improve precipitation estimates from single-channel IR observations. We demonstrate that the proposed model is able to leverage the temporal information in half-hourly IR observations to improve precipitation estimates. GPROF-IR is designed for integration into the upcoming release of the Integrated Multi-Satellite Retrieval for GPM (IMERG V08) and produces estimates that are climatologically consistent with the GPROF-NN PMW retrieval. We evaluate GPROF-IR using independent, global reference measurements and demonstrate substantial improvements over conventional IR retrievals. GPROF-IR provides lower mean squared error and higher correlation coefficient than IMERG V07 PMW estimates over continental land masses but remains below the accuracy of PMW precipitation estimates over sea surfaces and climate regimes with a greater influence from shallow precipitation. By expoiting both spatial and temporal information content in geostationary IR observations, GPROF-IR establishes a new state of the art for single-channel IR precipitation retrievals. GPROF-IR can be used to quasi-global precipitation estimates at half-hourly resolution from 1998 onward, providing a consistent and accurate foundation for improving merged precipitation products.

Over the past half century since 1975, the development and improvement of meteorological geostationary (GEO) satellites have played a pivotal role in observing cloud dynamics and subsequently in advancing spaceborne precipitation estimation. Infrared (IR) observation from GEO satellites offers unique advantages, such as broad spatial coverage, high temporal resolution, and long-term consistency, motivating extensive research to unlock the potential of GEO IR-based data for capturing precipitation structure and dynamics. This article reviews the major developments and milestone achievements of GEO IR-based precipitation estimation over the past five decades and summarizes the future prospects, including potential directions and remaining challenges. By examining the history of global GEO satellites and more than 100 references in this domain, we categorize the development of GEO IR-based precipitation estimation methodology and technology into three distinct stages: 1) the Exploration Phase (1975 to ca. 1995), 2) the Growth Phase (ca. 1995 to ca. 2015), and 3) the Exploitation Phase (ca. 2015 to the present). As we transition into an emerging new stage, these efforts collectively point toward multispectral retrievals, lifecycle-aware machine learning (ML), smart sensing, and advanced multisource integration as key directions shaping the future of GEO IR-based precipitation estimation. In summary, GEO IR-based precipitation estimation has made substantial contributions over the past half century and will play an increasingly important role with great potential in future precipitation science.

Satellite passive microwave (PMW) radiometry is essential for global precipitation monitoring, yet retrieval uncertainties remain large, particularly for high-latitude snowfall. These limitations arise from scarce globally representative radar observations, strong class imbalance, and heterogeneous training labels. In particular, rainfall estimates from the Dual-frequency Precipitation Radar (DPR) are two-dimensional, whereas snowfall observations from the Cloud Profiling Radar (CPR) are one-dimensional along-track profiles, creating a dimensional mismatch that limits deep-learning approaches. To address these challenges, we develop a supervised PMW retrieval framework based on an ensemble of extreme gradient-boosted decision trees that accommodates heterogeneous training data, with emphasis on CPR snowfall observations. The algorithm employs an incremental training strategy, pre-training on reanalysis precipitation and post-training on coincident DPR rainfall and CPR snowfall, to mitigate sampling sparsity. Applied to Global Precipitation Measurement (GPM) Microwave Imager (GMI) and Special Sensor Microwave Imager/Sounder (SSMIS) observations using a sequential phase-detection and rate-estimation scheme, the method improves high-latitude snowfall retrievals and reduces systematic biases in current GPM PMW products. Validation against Multi-Radar Multi-Sensor (MRMS) data demonstrates improved phase detection and rate estimation relative to reanalysis-based retrievals and existing GPM products. The source code is available at https://github.com/Buddha-subedi/PMWPrecip TLP-R2S.

Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster response. Traditional tools such as rain gauges and radar networks, though effective, are limited by sparse coverage in remote areas and radar constraints such as beam blockage and increasing beam height with range, which reduce near-surface accuracy. Satellite observations address these challenges by providing global coverage with fine spatial and temporal resolution. Many precipitation products combine geosynchronous thermal infrared (IR) and passive microwave (PMW) data. PMW sensors offer detailed atmospheric profiles but are restricted to infrequent overpasses and increasing reliance on smaller satellites with higher-frequency channels, which are less sensitive to liquid precipitation. In contrast, IR sensors provide consistent, high-frequency global observations, making them valuable for near-real-time estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-U-Net (PU-Net or PERSIANN V3), a quasi-global algorithm covering 60 degrees N-60 degrees S that combines IR data, monthly climatology, and the U-Net architecture to produce half-hourly precipitation estimates at 0.04 degrees resolution. The product is evaluated against Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and PERSIANN Dynamic Infrared-Rain Rate (PDIR-Now) for 2022-23. Results show that PU-Net closely matches its training target, IMERG V07 Final, at the global scale, and its performance is further evaluated against Stage IV as a reference over contiguous United States (CONUS). Training PU-Net on IMERG (2016-21) leverages a high-quality, integrated PMW-IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PU-Net avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.

The Integrated Multi-satellitE Retrievals for global precipitation measurement (GPM) mission (IMERG) is a global precipitation product suite consisting of both near-real-time and research-grade products with high spatiotemporal resolution. However, the IMERG developers note that it is designed as a high resolution precipitation product (HRPP), not a Climate Data Record, and its ability to capture climate trends remains uncertain. Therefore, it is imperative to explore and quantify IMERG's capability in capturing climate trends for our community. This study examines the climatological performance of IMERG Final (Version 06) by analyzing annual precipitation trends over land from 2001 to 2019 using the Mann-Kendall analysis, taking gauge records as ground truth. Three different matching strategies are applied: at gauge locations, at 0.1 degrees pixels, and at 1.0 degrees pixels. Additionally, this study compares the performances during the TRMM (2001-2014) and GPM eras (2015-2019). Our results find: (1) IMERG daily data exhibits high spatial consistency with gauge records at both gauge locations and 0.1 degrees resolution, consistent with its Global Precipitation Climatology Centre calibration, with a conversion rate of approximately 89.3%; (2) IMERG performs much better overall in the GPM era than in the tropical rainfall measuring mission (TRMM) era, evidenced by a lower proportion of unreliable samples (similar to 10.2% vs. similar to 21.2%); (3) The proportion of samples showing consistent trends with gauge data is 86.7% in the GPM era, much higher than the 70.5% and 75.3% shown by the entire record and the TRMM era, respectively. This improvement in the GPM era suggests that the within-mission consistency of IMERG is higher than the between-mission consistency, likely due to residual differences in the calibration methods used during the TRMM and GPM missions. This study broadens the perspective on IMERG, showcasing its additional potential for analyzing climate trends despite its design only as a HRPP. Crucially, it highlights and reconfirms how the GPM era has enhanced IMERG's capacity for accurately tracking global precipitation trends.

Quantifying intensification/suppression of precipitation over urban areas relative to their rural surroundings can inform efforts to reduce urban flooding. Few studies have systematically addressed whether urban areas exhibit a higher/lower probability of precipitation and/or higher/lower annual total precipitation and/or intensification/weakening of intense precipitation events relative to nearby rural areas across a range of hydroclimatic conditions and urban contexts. Here we address this literature gap using the IMERG V07 data set and analyses of rural and urban samples drawn from 47 conurbations across North America. Specifically, we quantify whether/how precipitation regimes over the urban grid cells differ from those in rural grid cells located 100–250 km from the city center and at a similar elevation. As in previous research, there is evidence that both the probability of precipitation and annual total precipitation are typically higher in the urban grid cells. However, most conurbations have lower upper percentile precipitation rates in the urban sample and lower median precipitation rates above the 95th percentile than are present in samples drawn from rural grid cells. Thus, these conurbations are not, on average, intensifying high-magnitude precipitation events over urban grid cells. Further, the total volume of water accumulated at the surface during events of equivalent duration is not systematically higher over the urban areas, and 20 year return period values of 30 min and wettest pentad precipitation are also not systematically higher over the urban areas. The nature of urban modification of precipitation is a strong function of the prevailing hydroclimate. For example, the heaviest rainfall periods are enhanced over urban grid cells within regional hydroclimates where the overall probability of precipitation and annual total precipitation are low. Conversely, there is evidence for urban suppression of the highest percentile precipitation rates in wetter hydroclimates.

The multisatellite precipitation product from the U.S. Global Precipitation Measurement (GPM) mission Science Team, the Integrated Multi-satellitE Retrievals for GPM (IMERG), is a widely used GPM product. It merges precipitation retrievals from passive microwave (PMW) and infrared (IR) sensors on board a collection of satellites operated by several agencies around the globe. IMERG retrospectively provides over two decades' global precipitation data at fine temporal-spatial resolutions. This overland study evaluates the satellite-based precipitation statistics in the latest versions of the IMERG (V06B and V07B) products over the conterminous United States. High-resolution, ground-based radar precipitation estimates from a quality-controlled version of the Multi-Radar Multi-Sensor system developed under the GPM Ground Validation effort are used as the reference product. The relative performance of the two IMERG versions is evaluated using volumetric and categorical statistical metrics. The precipitation retrieval errors are further separated into three individual components: hit bias, missed-precipitation bias, and false-precipitation bias, and all are traced back to the sensor-specific sources, as well as different levels of the IR input usage. The evaluation highlights the clear improvement in the IMERG V07B precipitation product for all seasons, especially for winter, with reduced systematic bias and uncertainty and increased precipitation detectability in comparison with V06B. The changes in V07B include the use of input data processed with improved algorithms for both PMW and IR retrievals, the addition of PMW retrievals over frozen surfaces, and upgrades in the intercalibration that address sources of bias in V06B.

The overarching goal of the NASA INvestigation of Convective UpdraftS (INCUS) mission is to enhance our understanding of why, when and where tropical convective storms form, and why only some of these storms produce extreme weather. Convective storms transport air and water between Earth's surface and the upper troposphere. This vertical transport of air and water - often referred to as convective mass flux (CMF) - plays a critical role in Earth's weather and climate system through its impacts on large-scale atmospheric circulations, upper tropospheric moistening and high cloud-radiative feedbacks, precipitation rates, and extreme weather. Potential changes to CMF with changing climates may significantly impact these processes. In spite of the critical role of this vertical transport of water and air, representation of CMF remains a major source of error in weather and climate models, thereby limiting our ability to accurately predict convective storms and their impacts in current and future climates. The observations obtained from INCUS will enhance our understanding of tropical convective storm processes and provide guidance for representing these processes in weather and climate models.

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.

Accurate precipitation estimation is critical for hydrological applications, especially in the Global South where ground-based observation networks are sparse and forecasting skill is limited. Existing satellite-based precipitation products often rely on the longwave infrared channel alone or are calibrated with data that can introduce significant errors, particularly at sub-daily timescales. This study introduces Oya, a novel real-time precipitation retrieval algorithm utilizing the full spectrum of visible and infrared (VIS-IR) observations from geostationary (GEO) satellites. Oya employs a two-stage deep learning approach, combining two U-Net models: one for precipitation detection and another for quantitative precipitation estimation (QPE), to address the inherent data imbalance between rain and no-rain events. The models are trained using high-resolution GPM Combined Radar-Radiometer Algorithm (CORRA) v07 data as ground truth and pre-trained on IMERG-Final retrievals to enhance robustness and mitigate overfitting due to the limited temporal sampling of CORRA. By leveraging multiple GEO satellites, Oya achieves quasi-global coverage and demonstrates superior performance compared to existing competitive regional and global precipitation baselines, offering a promising pathway to improved precipitation monitoring and forecasting.

The Integrated Multi-satellitE Retrievals for GPM (IMERG) product and the Global Precipitation Climatology Project (GPCP) product are two global precipitation datasets that also provide a diagnostic estimate of the probability of precipitation phase, thus enabling a quantification of snowfall rates. With recent improvements to the latest versions of the two algorithms, IMERG V07B and GPCP V3.2 represent a unique opportunity to study the global snowfall rates at an unprecedented resolution. This presentation examines the distribution of snowfall in IMERG V07B and GPCP V3.2 both globally and regionally. By leveraging IMERG’s high resolution and GPCP’s consistent record, we investigate the climatology not just from a snowfall volume point of view but also from peak snowfall intensity and snow event duration perspectives that only high-resolution data can provide. To assess the reliability of the results, we compare the IMERG and GPCP snowfall against global observations from CloudSat. For example, the comparison revealed deficiencies in passive microwave retrievals of snowfall rates in IMERG over Greenland and Antarctica. Furthermore, we leverage IMERG’s half-hourly resolution to demonstrate its unprecedented potential in tracking snowfall events around the globe. With the latest advances in the algorithms, IMERG V07 and GPCP V3.2 represent a unique opportunity to study snowfall globally using a combination of fine resolution, complete global coverage, and long record.

Numerous gridded precipitation (P) datasets have been developed to address a variety of needs and challenges. However, selecting the most suitable and reliable dataset remains a challenge for users. We conducted the most comprehensive global evaluation to date of gridded (sub-)daily $P$ datasets using hydrological modeling. A total of 23 datasets, derived from satellite, model, gauge sources, or their combinations thereof, were assessed. To evaluate their performance, we calibrated the conceptual hydrological model HBV against observed daily streamflow for 16,295 catchments (each

The joint U.S.-Japan Global Precipitation Measurement (GPM) mission is approaching a decade of operations, and continues to pursue research, dataset production, and outreach related to precipitation. Key activities over the last year were the release of an improved “Version 07” of all GPM precipitation and latent heating products, boosting the orbit of the GPM Core Observatory (GPM CO) to 435 km, and improving quality control on precipitation retrievals from the GPM constellation of passive microwave satellites.This presentation summarizes key improvements to the GPM products and provides some examples of the changes between Versions 06 and 07 in algorithm performance. One important operational change that affected Version 07 is that the scanning strategy for the Ka-band radar channel changed in May 2018; all products that depend on Ka were revised to accommodate this change. For example, in Version 07 the Goddard Profiling (GPROF) algorithm has implemented improvements in regions where orographic enhancement and suppression take place and where the surface is snowy/icy, and again covers radiometers reaching back to 1987. The Combined Radar Radiometer Algorithm (CORRA) now incorporates modified drop-size distribution constraints that substantially reduce bias. Revisions to the Convective-Stratiform Heating (CSH) algorithm employ new radiative transfer retrievals as well as accounting for terrain in the vertical coordinates. Each algorithm was adjusted to ensure continuity for each product across the boundary in 2014 between the predecessor Tropical Rainfall Measuring Mission (TRMM) and the GPM CO. The U.S. Science Team’s Integrated Multi-satellitE Retrievals for GPM (IMERG) was upgraded to account for distortions in the probability density function of regional precipitation rates due to weighted averaging in the Kalman filter used for “morphing” the passive microwave data.Maintaining the GPM CO orbital altitude in the the current very active solar cycle has been forcing the use of more fuel than planned and consequently shortening the forecasted life of the mission from the early 2030's to the late 2020's. It was considered vital to regain some of this lifetime to ensure overlap with the upcoming Atmosphere Observing System mission to provide cross-calibration of instruments. To accomplish this, the orbital altitude was raised from 400 to 435 km on 7-8 November 2023. Thereafter, the primary GPM CO algorithms had to be revised to account for the change in observing parameters. By meeting time this action should be complete.Recently, a screening algorithm based on auto-encoding was developed that uncovered 162 orbits (out of the many thousands of orbits across all years and all satellites) of passive microwave retrievals that had highly anomalous values. Removing these defective retrievals has improved the integrity of both the GPROF and IMERG records. However, the nature of the IMERG processing interacted sufficiently badly with the now-discovered anomalous orbits that it was necessary to completely reprocess the IMERG Final Run record, now labeled Version 07B.The presentation also considers major issues that require continued attention, including the use of machine learning algorithms and the operational challenge of swarms of “small”, perhaps short–lived satellites.

This presentation is composed of four major parts: (1) a brief overview of the latest Global Precipitation Climatology Project (GPCP) Daily and Monthly products (V3.2) and satellite-gauge input data sets used in them, (2) comparison of the GPCP V3.2 products with the previous version of GPCP Daily (V1.3) and Monthly (V2.3) products and highlighting major changes, (3) assessment of the GPCP V3.2 products over the Oceans using Passive Aquatic Listeners (PALs) and over sea ice using snow depth data from combination of ICESat-2 and Cryosat-2 observations, and (4) a brief description of the plans towards the next generation of the GPCP products. GPCP is a popular combined satellite-gauge precipitation dataset in which the long-term CDR standards of consistency and homogeneity are emphasized, going back to 1983 for GPCP Monthly V3.2. Several major changes occurred in V3.2 including: (1) moving from Monthly 2.5°x2.5° and Daily 1.0°x 1.0° spatial resolution in V2.3 to 0.5°x0.5° for both Daily and Monthly products, (2) addition of more recent satellite data such as the Tropical Rainfall Measuring Mission (TRMM), CloudSat, Global Precipitation Measurement (GPM) mission, and the Gravity Recovery and Climate Experiment (GRACE) mass change observations, and (3) use of new precipitation retrieval and calibration methods. Compared to V2.3, GPCP V3.2 shows about 6.5% increase in global oceanic and about a 4.5% increase in global (land and ocean) precipitation rates with some major changes over the ocean between 40 oS and 60 oS. Similar to V2.3, a near-zero global precipitation trend was observed in V3.2. However, regional trends, which are substantial, remain generally similar between V2.3 and V3.2. Evaluations over the oceans using PALs showed that GPCP v3.2 substantially outperforms GPCP V2.3 in representing rain occurrence and rain intensity at a daily scale, likely due to the use of IMERG in the daily product of GPCP V3.2. Comparison of the GPCP V3.2 product over sea ice, suggests that GPCP V3.2 generally captures the snowfall accumulation pattern over sea ice, compared to that obtained from the combination of ICESat-2 and Cryosat-2 observations, as well as that from ERA5. However, the products show considerable differences in the amount of snowfall accumulation, with ERA5 often showing the highest values. We will end the presentation by briefly discussing our plans for further improvement of GPCP including higher spatial and temporal resolution, lower latency, and the use of more advanced gauge analysis and precipitation retrieval methods.

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.

AbstractThe constellation approach underpinning precipitation products such as the Integrated Multi‐satellitE Retrievals for GPM (IMERG) is key to achieving high resolution, but the use of data from multiple sources can unintentionally incorporate instrumental artifacts. Here, we introduce a machine learning–based anomaly detection scheme called SPEEDe, which processes a two‐dimensional precipitation field into a re‐estimated precipitation field that can be compared with the input. Large differences identify IMERG fields with bad orbit data, separating most of the bad cases from the good cases. When modified to process the passive microwave inputs, SPEEDe can pick out orbits with bad data, enabling quality control on these IMERG inputs. SPEEDe works by producing a locally realistic‐looking precipitation field when given unphysical data, which results in a larger‐than‐normal difference between the input and the output. SPEEDe is implemented as an automated quality control for GPM precipitation products.

Satellites bring opportunities to quantify precipitation amount and distribution over the globe, critical to understanding how the Earth system works. The amount and spatial distribution of oceanic precipitation from the latest versions (V07 and the previous version) of the Global Precipitation Measurement (GPM) Core Observatory instruments and selected members of the constellation of passive microwave sensors are quantified and compared with other products such as the Global Precipitation Climatology Project (GPCP V3.2); the Merged CloudSat, TRMM, and GPM (MCTG) climatology; and ERA5. Results show that GPM V07 products have a higher precipitation rate than the previous version, except for the radar -only product. Within-65 degrees S-65 degrees N, covered by all of the instruments, this increase ranges from about 9% for the combined radar-radiometer product to about 16% for radiometer -only products. While GPM precipitation products still show lower mean precipitation rate than MCTG (except over the tropics and Arctic Ocean), the V07 products (except radar -only) are generally more consistent with MCTG and GPCP V3.2 than V05. Over the tropics (25 degrees S-25 degrees N), passive microwave sounders show the highest precipitation rate among all of the precipitation products studied and the highest increase (-19%) compared to their previous version. Precipitation products are least consistent in midlatitude oceans in the Southern Hemisphere, displaying the largest spread in mean precipitation rate and location of latitudinal peak precipitation. Precipitation products tend to show larger spread over regions with low and high values of sea surface temperature and total precipitable water. The analysis highlights major discrepancies among the products and areas for future research.

Remote sensing-based precipitation products face several challenges in high latitudes and specifically over frozen surfaces (i.e., snow and ice). Consequently, precipitation estimates tend to be lower in quality over these regions, including Antarctica, the coldest continent on Earth. In this study, we developed a method for adjusting precipitation estimates over Antarctica by leveraging CloudSat's ability to capture snowfall compared to other satellite products over snow and ice surfaces. We addressed limitations of CloudSat, such as poor spatiotemporal sampling, noise, and incomplete coverage near the poles. We utilized the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) to guide development, particularly during the period when the TIROS Operational Vertical Sounder (TOVS) was contributing. We adjusted monthly and seasonal TOVS and Atmospheric Infrared Sounder – Infrared (AIRS-IR) precipitation biases over Antarctica at the pixel level, as these two products are the primary inputs for the Global Precipitation Climatology Project (GPCP) in high latitudes. We assessed the adjusted TOVS and AIRS-IR through analyses of geographical maps and time series of monthly and seasonal mean precipitation rates. The results are encouraging, indicating that the proposed approach could replace the current approach used in the GPCP for adjusting precipitation estimates from AIRS-IR and TOVS over Antarctica. Adjusting precipitation estimates from TOVS and AIRS-IR using the proposed approach improves the Kling-Gupta efficiency (KGE) over their entire period by 162% and 147%, respectively. Moreover, the proposed approach can be applied to adjust other precipitation products over Antarctica at the pixel level, including satellites and reanalysis products.