Rain over Africa. An application of quantile regression neural networks to retrieve precipitation from geostationary satellites
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2022
Författare
Hee, Lilian
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
With an increasing change in Earth’s climate, global precipitation assessments are
essential to understand the hydrological cycle and prepare society for extreme weather
events. As of today, many countries are equipped with networks of weather stations
that individually measure precipitation over small areas. However, adequate spatial
coverage is not feasible for large regions like the African continent. This problem
can be overcome by utilising geostationary satellites. They scan large surfaces of
the Earth with a short revisit period, making them suitable for global nowcasting.
Unfortunately, direct measurements cannot be made as surface precipitation rates
need to be derived from an indirect relation to measured infrared (IR) and/or visible
radiation.
The problem is addressed by applying a Quantile Regression Neural Network (QRNN)
to predict the a posteriori distribution of precipitation rates over Africa. The prob abilistic retrievals yield not only estimated precipitation rate values but also their
associated uncertainties. Surface precipitation rate estimates with a 3 km × 3 km
spatial resolution are achieved for Africa every 15 minutes. The QRNN is imple mented with a convolutional neural network (CNN) trained on thermal IR data from
the SEVIRI instrument onboard the geostationary Meteosat-11 satellite. Addition ally, the GPM DPR and GMI combined precipitation product retrieved from the
low Earth orbit GPM Core Observatory satellite is used as reference data.
Two set-ups are trained, first using all thermal IR channels of the SEVIRI instru ment, and secondly using only two (6.2 µm and 10.8 µm). The second set-up indi cates promising use of historical data, while the first set-up shows the overall best
performance. Comparisons are made with IMERG, a leading global precipitation
product. The posterior distributions retrieved by the QRNN are shown to be more
revealing than the scalar-valued estimations from IMERG. Moreover, it is possible
to increase the spatiotemporal resolution as well as reduce the latency of the re trievals. The relative simplicity of the methodology hints at potential improvements
in global precipitation products available today.
Beskrivning
Ämne/nyckelord
quantile regression, neural networks, CNN, precipitation, SEVIRI, GPM, nowcasting, Africa.