The Convective Rainfall Rate from Cloud Physical Properties (CRPh) for Meteosat Second Generation Satellites is a day-only precipitation algorithm developed at the Spanish Meteorological Agency (AEMET) for EUMETSAT’ Satellite Application Facility in support to Nowcasting and Very Short Range Forecasting (NWC SAF). It is therefore mainly intended to provide input for monitoring and near-real-time forecasts for the next few hours. This paper critically discusses the theoretical basis of the algorithm with special emphasis in the empirical values and assumptions in the microphysics of precipitation and compares the performances of the CRPh with its antecessor, the Convective Rainfall Rate algorithm (CRR), using an object-based method. The analyses show that AEMET’s CRPh is physically consistent and that outperforms the CRR. The applicability of the algorithm for nowcasting and the challenges to evolve the product to an all-day algorithm are also presented.

The availability of high quality surface observations of precipitation and volume observations by polarimetric operational radars make it possible to constrain, evaluate and validate numerical models with a wide variety of microphysical schemes. In this article, a novel particle-based Monte-Carlo microphysical model (called “McSnow”) is used to simulate the outer rain bands of Hurricane Dorian which traversed the densely instrumented precipitation research facility operated by NASA at Wallops Island, Virginia. The rain bands showed steady stratiform vertical profiles with radar signature of dendritic growth layers near −15 °C and peak reflectivity in the bright band of 55 dBZ along with polarimetric signatures of wet snow with sizes inferred to exceed 15 mm. A 2D-video disdrometer measured frequent occurrences of large drops >5 mm and combined with an optical array probe the drop size distribution was well-documented in spite of uncertainty for drops <0.5 mm due to high wind gusts and turbulence. The 1D McSnow control run and four numerical “experiments” were conducted and compared with observations. One of the main findings is that even at the moderate rain rate of 10 mm/h collisional breakup is essential for the shape of the drop size distribution.

The performance of Integrated Multisatellite Retrievals for GPM (V06B-IMERG) is evaluated at subdaily and daily scale using 10 years of heavy precipitation over a region in the Western Med-iterranean for different temporal aggregations with a dense network of rain gauges. On a half-hourly scale, the contribution of passive microwave (PMW) and infrared (IR) sources in the satellite estimates was considered, as well as the relation of various microphysical properties of cloud tops using Cloud Microphysics (CMIC- NWC SAF) data. IMERG shows a marked tendency to underestimate precipitation compared to rain gauges as the rainfall intensity threshold and temporal resolution increases. Results indicate that the negative bias is weaker when retrievals are due to PMW data. On the other hand, the benefits of filling the PMW gaps of IMERG by including IR information come at the expense of increasing the bias. IMERG performs dramatically better in the presence of precipitating ice clouds compared to warm and mixed phase clouds, which seems to be related with microphysical properties such as cloud optical thickness and cloud top effective radius. This work contributes to the understanding of the factors affecting satellite estimates of extreme precipitation and their relationship with the microphysical characteristics of clouds, which generates added value for further downstream applications and users’ decision making.