The new Version 2.3 of the Global Precipitation Climatology Project (GPCP) Monthly analysis is described in terms of changes made to improve the homogeneity of the product, especially after 2002. These changes include corrections to cross-calibration of satellite data inputs and updates to the gauge analysis. Over-ocean changes starting in 2003 resulted in an overall precipitation increase of 1.8% after 2009. Updating the gauge analysis to its final, high-quality version increases the global land total by 1.8% for the post-2002 period. These changes correct a small, incorrect dip in the estimated global precipitation over the last decade given by the earlier Version 2.2. The GPCP analysis is also used to describe global precipitation in 2017. The general La Niña pattern for 2017 is noted and the evolution from the early 2016 El Niño pattern is described. The 2017 global value is one of the highest for the 1979–2017 period, exceeded only by 2016 and 1998 (both El Niño years), and reinforces the small positive trend. Results for 2017 also reinforce significant trends in precipitation intensity (on a monthly scale) in the tropics. These results for 2017 indicate the value of the GPCP analysis, in addition to research, for climate monitoring.

Precipitation estimates from satellite infrared (IR) radiometers are typically based on cloud top temperatures. However, these temperatures are weakly related to surface rainfall, particularly for shallow or warm clouds. This study classifies precipitating clouds into five cloud groups. The classification uses three brightness temperature differences (BTDs) and one BTD difference (ΔBTD) from Himawari‐8 Advanced Himawari Imager (AHI): BTD1 (6.2–11.2 µm), BTD2 (8.6–11.2 µm), BTD3 (11.2–12.4 µm), and ΔBTD (BTD2 − BTD3). BTD1 is found to be effective for separating shallow and non‐shallow clouds in reference to the Global Precipitation Measurement Dual‐frequency Precipitation Radar (DPR) level 2 data. Once this separation is complete, non‐shallow clouds are further classified. The negative and positive values of ΔBTD usually indicate more water and more ice in clouds, respectively, distinguishing non‐shallow clouds with tall and taller cloud heights. Subsequently, BTD1 is applied to non‐shallow‐tall/taller clouds. Because these clouds can be considered as optically thick, BTD1 identifies the relative coldness of the cloud top based on the extent of water vapour over the cloud top. The final classification yields four non‐shallow cloud types: non‐shallow‐tall‐cold, non‐shallow‐tall‐colder, non‐shallow‐taller‐cold, and non‐shallow‐taller‐colder clouds. The relationships between IR brightness temperatures and surface rainfall obtained from the classified cloud groups over four latitude bands reveal clear differences, implying that separating cloud types and accounting for regional differences are desirable to improve the accuracy of IR‐based precipitation measurements.

Multichannel microwave sensors make it possible to construct physically based rainfall retrieval algorithms. In these schemes, errors arising from the inaccuracy of the physical modeling of the cloud system under observation have to be accounted for. The melting layer has recently been identified as a possible source of bias when stratiform events are considered. In fact, Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations reveal systematic differences in the observed brightness temperatures between similar rain profiles that often differ only by the presence or absence of a bright band. A sensitivity study of the scattering properties of the melting layer with different one-dimensional steady-state microphysical and electromagnetic models is performed. The electromagnetic modeling of the ice particle density and assumption of the ventilation coefficient parameterization is found to have the greatest impact on the extinction profiles. Data taken from a 0.915-GHz National Oceanic and Atmospheric Administration (NOAA) profiler during the Kwajalein Experiment (KWAJEX) field campaign are used to reduce the uncertainties in the modeling of the bright band. The profiler data reduce the number of viable parameterizations, which in turn leads to a reduction in the variability of the upwelling radiances (simulated at TMI angle) for different cloud simulations. Using the parameterizations that best match the profiler data, the brightness temperatures T_B generally increase if mixed-phase precipitation is included in the model atmosphere. The effect is most pronounced for systems with low freezing levels, such as a midlatitude cold front simulation. For TMI footprints at 10.65 GHz, the increase in the T_B from the bright band generally increases with rain rate and changes by as much as ∼15–20 K. At 19.35 GHz the maximum effect is found around 3–5 mm h⁻¹ (∼15 K), and at 37 GHz the maximum effect is around 1 mm h⁻¹ (∼10 K), while at 85.5 GHz the effect is always lower than 3 K. Despite the reduction of uncertainties achieved by using 915-MHz profiler data, differences between parameterizations are still significant, especially for the higher TMI frequencies. A validation experiment is proposed to solve this issue and to further reduce the uncertainties in brightband modeling.

Physically based rainfall retrievals from passive microwave sensors often make use of cloud-resolving models (CRMs) to build a priori databases of potential rain structures. Each CRM, however, has its own cloud microphysics assumptions. Hence, approximated microphysics may cause uncertainties in the a priori information resulting in inaccurate rainfall estimates. This study first builds a priori databases by combining the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) observations and simulations from the Weather Research and Forecasting (WRF) model with six different cloud microphysics schemes. The microphysics schemes include the Purdue–Lin (LIN), WRF Single-Moment 6 (WSM6), Goddard Cumulus Ensemble (GCE), Thompson (THOM), WRF Double-Moment 6 (WDM6), and Morrison (MORR) schemes. As expected, the characteristics of the a priori databases are inherited from the individual cloud microphysics schemes. There are several distinct differences in the databases. Particularly, excessive graupel and snow exist with the LIN and THOM schemes, while more rainwater is incorporated into the a priori information with WDM6 than with any of the other schemes. Major results show that convective rainfall regions are not well captured by the LIN and THOM schemes-based retrievals. Rainfall distributions and their quantities retrieved from the WSM6 and WDM6 schemes-based estimations, however, show relatively better agreement with the PR observations. Based on the comparisons of the various microphysics schemes in the retrievals, it appears that differences in the a priori databases considerably affect the properties of rainfall estimations.