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LINUX Cluster Project

Arbeiten der Uni Augsburg am LRZ


  • Name: Universität Augsburg;Rechenzentrum
  • Address: Universitätsstraße 9, 86159 Augsburg
  • Project Proposal Date: 2018-10-05 21:05:15


Seasonal predictions of hydrometeorological variables for water resources management have become popular in recent years [7, 13, 15, 14, 2, 10]. In the past, water management and drought forecasts were mainly based on statistical predictions [5]. In recent years, however, decision makers tend to increasingly consider seasonal forecast products offered by the leading weather services and meteorological forecast centers. The European Center for Medium-Range Weather Forecasts (ECMWF) advertises its latest version of seasonal forecast system (SEAS5) launched in November 2017 with best horizontal resolution among all available global seasonal forecast systems. It further provides better realization of large-scale climate teleconnections, improved atmosphere-ocean-sea-ice coupling and better land initialization compared to earlier versions (e. g., S4). The resulting increased skill of the seasonal forecast system even more encourages the use of seasonal forecasts. Especially in semiarid regions, improved knowledge of the coming rainy season can be the basis of sustain- able water management. On subseasonal to seasonal timescales, decision makers have to plan in advance for water allocation during drought, manage reservoir levels for flood control and manage irrigation. However, also the 35 km horizontal resolution of the global seasonal forecasts of ECMWF Integrated Forecast System's (IFS) SEAS5 is not suited for the use for water management in individual river basins. Therefore, the SaWaM (Seasonal Water Resources Management for Semiarid Regions) project and the therein embedded doctoral research address the hydrometeorological applicability of global seasonal fore- casts and their regionalization to semiarid river catchments in Iran, Sudan, Northeast Brazil, West Africa and Ecuador. For eight river basins, SEAS5 ensemble forecasts with lead times up to six months will be dynamically downscaled using the mesoscale numerical Weather Research and Forecasting (WRF) Model. The objective of this work is the performance analysis of regionalized seasonal predictions in the SaWaM target regions. [1] Cretat, J., Pohl, B., Richard, Y., and Drobinski, P. Uncertainties in simulating regional climate of Southern Africa: sensitivity to physical parameterizations using WRF. Clim. Dyn. 38 (2012), 613-634. [2] Dutra, E., Pozzi, W., Wetterhall, F., Di Giuseppe, F., Magnusson, L., Naumann, G., Barbosa, P., Vogt, J., and Pappenberger, F. Global meteorological drought - Part 2: Seasonal forecasts. Hydrol. Earth Syst. Sci. 18, 7 (2014), 2669-2678. [3] Efstathiou, G., Zoumakis, N., Melas, D., Lolis, C., and Kassomenos, P. Sensitivity of WRF to boundary layer parameterizations in simulating a heavy rainfall event using different microphysical schemes. Effect on large-scale processes. Atmos. Res. 132-133 (2013), 125-143. [4] Flaounas, E., Bastin, S., and Janicot, S. Regional climate modelling of the 2006 West African monsoon: sensitivity to convection and planetary boundary layer parameterisation using WRF. Clim. Dynam. 36, 5 (2011), 1083-1105. [5] Mishra, A. K., and Singh, V. P. Drought modeling ? a review. J. Hydrol. 403 (2011), 157-175. [6] Que, L.-J., Que, W.-L., and Feng, J.-M. Intercomparison of different physics schemes in the WRF model over the Asian summer monsoon region. Atmos. Oceanic Sci. Lett. 9, 3 (2016), 169-177. [7] Siegmund, J. J., Laux, P., and Kunstmann, H. Toward a seasonal precipitation prediction system for West Africa. J. Geophys. Res. Atmos. 120 (2015), 7316-7339. [8] Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, M., Duda, M. G., Huang, X. Y., Wang, W., and Powers, J. G. A description of the advanced research WRF version 3. NCAR technical note NCAR/TN475+STR (2008), 113. [9] Solman, S. A., and Pessacg, N. L. Evaluating uncertainties in regional climate simulations over South America at the seasonal scale. Clim. Dyn. 39 (2012), 59-76. [10] Tian, D., Wood, E. F., and Yuan, X. CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States. Hydrol. Earth Syst. Sci. 21 (2017), 1477-1490. [11] Xu, J., and Small, E. Simulating summertime rainfall variability in the North American monsoon region: The influence of convection and radiation parameterizations. J. Geophys. Res. 107, 4727 (2002). [12] Yang, B., Qian, Y., Lin, G., Leung, R., and Zhang, Y. Some issues in uncertainty quan- tification and parameter tuning: A case study of convective parameterization scheme in the WRF regional climate model. Atmos. Chem. Phys. 12 (2012), 2409-2427. [13] Yuan, X., Wood, E. F., Chaney, N. W., Sheffield, J., Kam, J., Liang, M., and Guan, K. Probabilistic seasonal forecasting of African drought by dynamical models. J. Hydrometeor. 14, 6 (2013), 1706-1720. [14] Yuan, X., Wood, E. F., and Ma, Z. A review on climate-model-based seasonal hydrologic forecasting: physical understanding and system development. WIREs Water 2 (2015), 523-536. [15] Yuan, X., Wood, E. F., Roundy, J. K., and Pan, M. CFSv2-based seasonal hydroclimatic forecasts over the conterminous United States. J. Clim. 26, 13 (2013), 4828-4847.