The Integrated Multi-Satellite Retrievals for Global Precipitation Measurement Mission (IMERG) is a global precipitation product that uses precipitation retrievals from the virtual constellation of satellites with passive microwave (PMW) sensors, as available. In the absence of PMW observations, IMERG uses a Kalman filter scheme to morph precipitation from one PMW observation to the next. In this study, an analysis of convective systems observed during the Convective Process Experiment (CPEX) suggests that IMERG precipitation depends more strongly on the availability of PMW observations than previously suspected. Following this evidence, we explore systematic biases in IMERG through bulk statistics.In two CPEX case studies, cloud photographs, pilot’s radar, and infrared imagery suggest that IMERG represents the spatial extent of precipitation relatively well when there is a PMW observation but sometimes produces spurious precipitation areas in the absence of PMW observations. Also, considering an observed convective system as a precipitation object in IMERG, the maximum rain rate peaked during PMW overpasses, with lower values between them. Bulk statistics reveal that these biases occur throughout IMERG Version 06. We find that locations and times without PMW observations have a higher frequency of light precipitation rates and a lower frequency of heavy precipitation rates due to retrieval artifacts. These results reveal deficiencies in the IMERG Kalman Filter scheme, which have led to the development of the Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood (SHARPEN; described in a companion paper) that will be applied in the next version of IMERG.

A key strategy in obtaining complete global coverage of high-resolution precipitation is to combine observations from multiple fields, such as the intermittent passive microwave observations, precipitation propagated in time using motion vectors, and geosynchronous infrared observations. These separate precipitation fields can be combined through weighted averaging, which produces estimates that are generally superior to the individual parent fields. However, the process of averaging changes the distribution of the precipitation values, leading to an increase in precipitating area and decrease in the values of high precipitation rates, a phenomenon observed in IMERG. To mitigate this issue, we introduce a new scheme called SHARPEN, which recovers the distribution of the averaged precipitation field based on the idea of quantile mapping applied to the local environment. When implemented in IMERG, precipitation estimates from SHARPEN exhibit a distribution that resembles that of the original instantaneous observations, with matching precipitating area and peak precipitation rates. Case studies demonstrate its improved ability in bridging between the parent precipitation fields. Evaluation against ground observations reveals a distinct improvement in precipitation detection skill, but also a slightly reduced correlation likely because of a sharper precipitation field. The increased computational demand of SHARPEN can be mitigated by striding over multiple grid boxes, which has only marginal impacts on the accuracy of the estimates. SHARPEN can be applied to any precipitation algorithm that produces an average from multiple input precipitation fields and is being considered for implementation in IMERG V07.

Traditional tracking algorithms use a single threshold in a precipitation or infrared brightness temperature field to identify and track precipitation systems. Though valuable, these algorithms have limitations in tracking Mesoscale Convective Systems (MCSs) that sometimes occur as clusters embedded in synoptic scale disturbances such as tropical and mid-latitude waves. These embedded systems might be connected but should not be identified as a single large system since the connection is transient. The recent detect and spread (DAS) type algorithms help identify an MCS in a cluster  by identifying a core region of heavy rainfall and spreading it into adjacent regions of lower precipitation values. Also, as we move away from a single satellite and towards microsatellite constellations for various meteorological data, we need a robust method to identify and track objects of interest in a multi-satellite product. This is because each satellite in the constellation may have a different sensor that requires cross-calibration and is finally merged to create a single product.  We present the improved version of the Forward in Time (FIT) tracking program, a multi-threshold, detect and spread type algorithm, to track MCSs in Integrated MultisatellitE Retrievals for Global Precipitation mission (IMERG), NASA's global precipitation product. IMERG is a  multi-satellite precipitation product that combines rain retrievals from passive microwave sensors on a virtual constellation of satellites and rain retrievals from Infrared sensors onboard geostationary satellites. Using the FiT algorithm, we track MCSs in the IMERG precipitation field for ten years (2011-2020) and store MCSs' properties in a publicly available dataset called Tracked IMERG Mesoscale Precipitation Sytems (TIMPS). Leveraging this dataset, we present the regional variability of MCSs properties (frequency, lifetime, and propagation velocity) and some preliminary results from ongoing studies.