Spatio‐temporal variability of warm rain events over southern West Africa from geostationary satellite observations for climate monitoring and model evaluation

Abstract

This article presents the spatio‐temporal variability of warm rain events over southern West Africa (SWA) during the summer monsoon season for the first time, using Spinning Enhanced Visible Infrared Radiometer (SEVIRI) observations on the Meteosat geostationary satellites. The delineation of warm rain events is based on the principle that precipitating low‐level clouds are associated with either sufficient water content or large cloud droplet size. Capitalising on the ability of space‐borne radar to resolve vertical cloud structures and detect the presence of precipitation, the delineation is trained by collocated SEVIRI and CloudSat observations. The resulting 12 years of observations from SEVIRI are used to examine the spatial, diurnal, seasonal and interannual variability of warm rain events over SWA. Warm rain events predominate during the monsoon in August, with little interannual variability, and persist over orography in the morning and the coasts after midday, likely enhanced by orographic lifting and land–sea breeze effects. Warm clouds have a much higher probability of precipitation along the coastlines of Liberia and Nigeria compared to the central SWA coastline and further inland. Finally, when evaluating an 8‐day yet high‐spatial resolution model simulation, we find that warm rain frequencies from the simulation agree well with SEVIRI near the coast but simulated warm cloud cover and thus warm rain frequencies are too low over the Gulf of Guinea. The probability of precipitation of warm clouds is also too low inland. The newly developed observations of warm rain create opportunities to further investigate the diurnal cycle of warm rain, study aerosol–cloud–precipitation interactions, and assess the role of warm rain in the water cycle across Africa and beyond.

An evaluation of operational and research weather forecasts for southern West Africa using observations from the DACCIWA field campaign in June–July 2016

“…During the latter, the main rainband has moved into the Sahel, creating somewhat drier conditions in the DACCIWA region (4.9 versus 5.5 in IMERG; not shown). The often light (and sometimes warm) rains during this period (Young et al ., ) appear to be particularly difficult to capture for IMERG (M. Maranan, personal communication, 2019). In contrast, the dry bias on forecast day 1 in the models tends to get smaller from period 1 to period 2.…”

Rainfall types over southern West Africa: Objective identification, climatology and synoptic environment

“…Huffman and Norman, ), further analysis of the rainfall types is required with a refined definition of potential warm‐rain areas. Possible options include a method introduced in Young et al () where a combination of cloud optical depth and effective radius information from SEVIRI was used to delineate these areas.…”

“…The latter may explain the enhanced pre‐noon rainfall probability that was found at the Benin coast based on rain‐gauge recordings (Fink et al ). Similarly, using continuous geostationary‐based cloud property retrievals from SEVIRI, Young et al () found the highest frequency of warm rain along the coastline around midday. At the Coast, the frequency of WCCs already peaks in the early afternoon, which is partly caused by coastal convection forming a contiguous line with strong radar echoes (not shown).…”

Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall in the Tropics

“…Over land, TRMM generally underestimates the frequency and amount of rain from warm clouds, typically found over coastal areas with onshore trade or monsoonal winds and in the vicinity of mountains (e.g., Dinku et al 2018). Another potential problem is an underestimation of extreme values, partly due to beam filling in the microwave bands (Young et al 2014;Monsieurs et al 2018). Over ocean, precipitation detection is more challenging than over land and calibration with gauges is not possible.…”

“…4c in Berg et al 2010) and a comparatively large dry bias (Huffman et al 2007) in these regions. The often light rain from warm clouds is generally challenging to detect from space (Young et al 2018). This suggests that the calibration (and skill) of the model is presumably assessed worse in these regions than it actually is-particularly in recent years, as ECMWF has addressed relevant problems in their forecast model (Ahlgrimm and Forbes 2014).…”