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  • English  (3)
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  • English  (3)
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  • 1
    Type of Medium: Book
    Pages: 495 S , Ill., graph. Darst
    Series Statement: Quarterly journal of the Royal Meteorological Society 136.2010,Suppl.1
    Language: English
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  • 2
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    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    Publication Date: 2023-04-20
    Description: Operational forecasts using direct output from numerical weather prediction models exhibit poor skill over northern tropical Africa compared to simple climatology-based forecasts. A recent study found potential in using Spearman’s rank correlations of gridded rainfall estimates from TRMM to predict July-September tropical African rainfall. Using the satellite-based gridded GPM-IMERG product from 2001-2019, we build on this approach using the Coefficient of Predictive Ability (CPA) developed for improved variable selection for statistical models and expanding up to 3-day lags over tropical Africa and the Atlantic Ocean. High CPAs straddling the zonally oriented rainbelt are attributed to large-scale drivers causing coherent spatio-temporal anomalies. Low CPAs over the rainbelt centre indicate the lack of dominant forcing, high stochasticity, or both. The coherent-linear-propagation factor (coh) introduced at every grid point quantifies the coherence of the identified rainfall by summarising the extent to which lagged CPAs reflect propagation with constant phase speed and direction. High coh over the Sahel suggests African easterly waves’ dominance. Stochastic precipitation driven by small-scale processes causes low coh over the rainbelt. We train a statistical model using the rainfall linked to the identified CPAs and compare it with three benchmarks: a climatology-based forecast, the ECMWF 1-day ensemble prediction-system forecast and its statistically post-processed output. The statistical model is outperformed only by the post-processed output and only in the western Sahel and central Africa. However, the Diebold-Mariano test for forecast significance suggests no significant differences between the statistical and post-processed forecasts making the former a cheaper alternative over the analysis region.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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  • 3
    Publication Date: 2023-09-29
    Description: Simulating the West African monsoon (WAM) system using numerical weather and climate models suffers from large uncertainties, which are difficult to disentangle due to highly non-linear interactions between different components of the WAM. We propose a new approach to this problem by emulating a full-blown numerical model, the ICON model of the German Weather Service, through statistical surrogate models. The ICON model was run during the rainy seasons in four years in a nested limited-area mode. The uncertainty contributions of six selected model parameters were investigated. To this end, we employed a sampling strategy to obtain model parameter combinations for a manageable number of ICON model runs. Surrogate models were then constructed to describe a relationship between the model parameters and selected Quantities of Interest (e.g. characteristics of the African and Tropical easterly jets or the Saharan heat low) to employ sensitivity and parameter studies. For better interpretation a local parameter analysis based on the output fields was conducted using the same setup. Results reveal the complex nature of the WAM system and indicate for which parameters (and thus processes) uncertainties need to be reduced to lower the spread in the outputs. Among the considered parameters, the entrainment rate and the terminal fall velocity of ice show the greatest effects, where larger values lead to a decrease of cloud cover and precipitation, and to an intensification of the Saharan heat low, despite distinct regional differences. The evaporative soil surface also shows a significant effect, mostly on temperature and cloud cover.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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