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Manuel Rauch
,
Jan Bliefernicht
,
Marlon Maranan
,
Andreas H. Fink
, and
Harald Kunstmann

Abstract

The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé River basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like ordinary kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling.

Open access
Thomas Engel
,
Andreas H. Fink
,
Peter Knippertz
,
Gregor Pante
, and
Jan Bliefernicht

Abstract

Two extreme, high-impact events of heavy rainfall and severe floods in West African urban areas (Ouagadougou on 1 September 2009 and Dakar on 26 August 2012) are investigated with respect to their atmospheric causes and statistical return periods. In terms of the synoptic–convective dynamics, the Ouagadougou case is truly extraordinary. A succession of two slow-moving African easterly waves (AEWs) caused record-breaking values of tropospheric moisture. The second AEW, one of the strongest in recent decades, provided the synoptic forcing for the nighttime genesis of mesoscale convective systems (MCSs). Ouagadougou was hit by two MCSs within 6 h, as the strong convergence and rotation in the AEW-related vortex allowed a swift moisture refueling. An AEW was also instrumental in the overnight development of MCSs in the Dakar case, but neither the AEW vortex nor the tropospheric moisture content was as exceptional as in the Ouagadougou case. Tropical Rainfall Measuring Mission (TRMM) 3B42 precipitation data show some promise in estimating centennial return values (RVs) using the “peak over threshold” approach with a generalized Pareto distribution fit, although indications for errors in estimating extreme rainfall over the arid Sahel are found. In contrast, the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) dataset seems less suitable for this purpose despite the longer record. Notably, the Ouagadougou event demonstrates that highly unusual dynamical developments can create extremes well outside of RV estimates from century-long rainfall observations. Future research will investigate whether such developments may become more frequent in a warmer climate.

Open access
Joel Arnault
,
Sven Wagner
,
Thomas Rummler
,
Benjamin Fersch
,
Jan Bliefernicht
,
Sabine Andresen
, and
Harald Kunstmann

Abstract

The analysis of land–atmosphere feedbacks requires detailed representation of land processes in atmospheric models. The focus here is on runoff–infiltration partitioning and resolved overland flow. In the standard version of WRF, runoff–infiltration partitioning is described as a purely vertical process. In WRF-Hydro, runoff is enhanced with lateral water flows. The study region is the Sissili catchment (12 800 km2) in West Africa, and the study period is from March 2003 to February 2004. The WRF setup here includes an outer and inner domain at 10- and 2-km resolution covering the West Africa and Sissili regions, respectively. In this WRF-Hydro setup, the inner domain is coupled with a subgrid at 500-m resolution to compute overland and river flow. Model results are compared with TRMM precipitation, model tree ensemble (MTE) evapotranspiration, Climate Change Initiative (CCI) soil moisture, CRU temperature, and streamflow observation. The role of runoff–infiltration partitioning and resolved overland flow on land–atmosphere feedbacks is addressed with a sensitivity analysis of WRF results to the runoff–infiltration partitioning parameter and a comparison between WRF and WRF-Hydro results, respectively. In the outer domain, precipitation is sensitive to runoff–infiltration partitioning at the scale of the Sissili area (~100 × 100 km2), but not of area A (500 × 2500 km2). In the inner domain, where precipitation patterns are mainly prescribed by lateral boundary conditions, sensitivity is small, but additionally resolved overland flow here clearly increases infiltration and evapotranspiration at the beginning of the wet season when soils are still dry. The WRF-Hydro setup presented here shows potential for joint atmospheric and terrestrial water balance studies and reproduces observed daily discharge with a Nash–Sutcliffe model efficiency coefficient of 0.43.

Full access
Windmanagda Sawadogo
,
Jan Bliefernicht
,
Benjamin Fersch
,
Seyni Salack
,
Samuel Guug
,
Kehinde O. Ogunjobi
,
Stefanie Meilinger
, and
Harald Kunstmann

Abstract

The number of solar power plants has increased in West Africa in recent years. Reliable reanalysis data and short-term forecasting of solar irradiance from numerical weather prediction models could provide an economic advantage for the planning and operation of solar power plants, especially in data-poor regions such as West Africa. This study presents a detailed assessment of different shortwave (SW) radiation schemes from the Weather Research and Forecasting (WRF) Model option Solar (WRF-Solar), with appropriate configurations for different atmospheric conditions in Ghana and the southern part of Burkina Faso. We applied two 1-way nested domains (D1 = 15 km and D2 = 3 km) to investigate four different SW schemes, namely, the Community Atmosphere Model, Dudhia, RRTMG, Goddard, and RRTMG without aerosol and with aerosol inputs (RRTMG_AERO). The simulation results were validated using hourly measurements from different automatic weather stations established in the study region in recent years. The results show that the RRTMG_AERO_D01 generally outperforms the other SW radiation schemes to simulate global horizontal irradiance under all-sky condition [RMSE = 235 W m−2 (19%); MAE = 172 W m−2 (14%)] and also under cloudy skies. Moreover, RRTMG_AERO_D01 shows the best performance on a seasonal scale. Both the RRTMG_AERO and Dudhia experiments indicate a good performance under clear skies. However, the sensitivity study of different SW radiation schemes in the WRF-Solar model suggests that RRTMG_AERO gives better results. Therefore, it is recommended that it be used for solar irradiance forecasts over Ghana and the southern part of Burkina Faso.

Open access