Abstract
Quantitative precipitation estimation and forecasting (QPE and QPF) are among the most challenging tasks in atmospheric sciences. In this work, QPE based on numerical modelling and data assimilation is investigated. Key components are the Weather Research and Forecasting (WRF) model in combination with its 3D variational assimilation scheme, applied on the convection-permitting scale with sophisticated model physics over central Europe. The system is operated in a 1-hour rapid update cycle and processes a large set of in situ observations, data from French radar systems, the European GPS network and satellite sensors. Additionally, a free forecast driven by the ECMWF operational analysis is included as a reference run representing current operational precipitation forecasting. The verification is done both qualitatively and quantitatively by comparisons of reflectivity, accumulated precipitation fields and derived verification scores for a complex synoptic situation that developed on 26 and 27 September 2012. The investigation shows that even the downscaling from ECMWF represents the synoptic situation reasonably well. However, significant improvements are seen in the results of the WRF QPE setup, especially when the French radar data are assimilated. The frontal structure is more defined and the timing of the frontal movement is improved compared with observations. Even mesoscale band-like precipitation structures on the rear side of the cold front are reproduced, as seen by radar. The improvement in performance is also confirmed by a quantitative comparison of the 24-hourly accumulated precipitation over Germany. The mean correlation of the model simulations with observations improved from 0.2 in the downscaling experiment and 0.29 in the assimilation experiment without radar data to 0.56 in the WRF QPE experiment including the assimilation of French radar data.
Highlights
Due to its high variability in space and time, precipitation strongly influences the spatial and temporal patterns of hydrologic catchment response, especially when thresholddominated processes such as infiltration, overland flow or erosion are involved (Winchell et al, 1998)
Since a comparison of the whole development during the 2-day case study is beyond the scope of the manuscript, we focus on representative snapshots during the development of the synoptic situation
This study investigated the performance of model-based quantitative precipitation estimation (QPE) with Weather Research and Forecasting (WRF) and its 3DVAR data assimilation system for a complex case study
Summary
Due to its high variability in space and time, precipitation strongly influences the spatial and temporal patterns of hydrologic catchment response, especially when thresholddominated processes such as infiltration, overland flow or erosion are involved (Winchell et al, 1998). Rainfall was only observed at points in space with rain gauges. Various interpolation techniques of different complexity were developed, ranging from Thiessen Polygons (Thiessen, 1911) or Inverse Distance Weighting (Shepard, 1968) to more advanced geostatistical approaches such as Kriging Even for medium rainfall intensities, gauge correlations may drop as low as 0.4 at distances of only 6 km from each other (Moreau et al, 2009). This effect is even more pronounced for convective rainfall due to the higher spatial heterogeneity and intensity. Gauge interpolation techniques are only adequate for large-scale applications
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