Radar-Based Precipitation Climatology in Germany—Developments, Uncertainties and Potentials

Kreklow, Jennifer; Tetzlaff, Björn; Burkhard, Benjamin; Kuhnt, Gerald

doi:10.3390/atmos11020217

Cited by 28 publications

(22 citation statements)

References 38 publications

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“…There is a large spatial variability in precipitation due to topography. In the rather flat Lower Rhine area in the North of the study region, the yearly precipitation is between 600-900 mm [49]. In the southern low mountain ranges the average yearly precipitation is locally 1600 mm in the Bergisches Land (South East) and 1300 mm in the Eifel (South West) [50].…”

Section: Study Areamentioning

confidence: 99%

The Importance of Subsurface Processes in Land Surface Modeling over a Temperate Region: An Analysis with SMAP, Cosmic Ray Neutron Sensing and Triple Collocation Analysis

et al. 2021

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Land surface models (LSMs) simulate water and energy cycles at the atmosphere–soil interface, however, the physical processes in the subsurface are typically oversimplified and lateral water movement is neglected. Here, a cross-evaluation of land surface model results (with and without lateral flow processes), the National Aeronautics and Space Administration (NASA) Soil Moisture Active/Passive (SMAP) mission soil moisture product, and cosmic-ray neutron sensor (CRNS) measurements is carried out over a temperate climate region with cropland and forests over western Germany. Besides a traditional land surface model (the Community Land Model (CLM) version 3.5), a coupled land surface-subsurface model (CLM-ParFlow) is applied. Compared to CLM stand-alone simulations, the coupled CLM-ParFlow model considered both vertical and lateral water movement. In addition to standard validation metrics, a triple collocation (TC) analysis has been performed to help understanding the random error variances of different soil moisture datasets. In this study, it is found that the three soil moisture datasets are consistent. The coupled and uncoupled model simulations were evaluated at CRNS sites and the coupled model simulations showed less bias than the CLM-standalone model (−0.02 cm3 cm−3 vs. 0.07 cm3 cm−3), similar random errors, but a slightly smaller correlation with the measurements (0.67 vs. 0.71). The TC-analysis showed that CLM-ParFlow reproduced better soil moisture dynamics than CLM stand alone and with a higher signal-to-noise ratio. This suggests that the representation of subsurface physics is of major importance in land surface modeling and that coupled land surface-subsurface modeling is of high interest.

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Section: Study Areamentioning

confidence: 99%

The Importance of Subsurface Processes in Land Surface Modeling over a Temperate Region: An Analysis with SMAP, Cosmic Ray Neutron Sensing and Triple Collocation Analysis

et al. 2021

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“…Yet, the temporal coverage of these products extends only to the early 2000s, which is why the sampling of extreme rainfall events is not sufficient for extreme value analysis. Furthermore, radar estimates (Goudenhoofdt and Delobbe, 2016;Kreklow et al, 2020) as well as satellite products (Stampoulis and Anagnostou, 2012) reveal biases compared to rain gauges. Reanalysis data (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…However, each of these areal precipitation products shows different limitations, which lead to uncertain or unrealistic return level estimations. Radar data (RADOLAN for Germany; Kreklow et al, 2020) and satellite products (e.g. CMORPH; Joyce et al, 2004 or PERSIANN;Hong et al, 2004) would provide the necessary temporal and spatial resolutions to capture extreme rainfall events.…”

Section: Introductionmentioning

confidence: 99%

Using high-resolution regional climate models to estimate return levels of daily extreme precipitation over Bavaria

Poschlod

2021

Preprint

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Abstract. Extreme daily rainfall is an important trigger for floods in Bavaria. The dimensioning of water management structures as well as building codes are based on observational rainfall return levels. In this study, three high-resolution regional climate models (RCMs) are employed to produce 10-year daily rainfall return levels and their performance is evaluated by comparison to observational return levels. The study area is governed by different types of precipitation (stratiform, orographic, convectional) and a complex terrain, with convective precipitation also contributing to daily rainfall levels. The Canadian Regional Climate Model version 5 (CRCM5) at 12 km spatial resolution and the Weather and Forecasting Research model (WRF) at 5 km resolution both driven by ERA-Interim reanalysis data use parametrization schemes to simulate convection. The WRF at 1.5 km resolution driven by ERA5 reanalysis data explicitly resolves convectional processes. Applying the Generalized Extreme Value (GEV) distribution, all three model setups can reproduce the observational return levels with an areal average bias of +6.6 % or less and a spatial Spearman rank correlation of ρ > 0.72. The increase of spatial resolution between the 12 km CRCM5 and the 5 km WRF setup is found to improve the performance in terms of bias (+6.6 % and +3.2 %) and spatial correlation (ρ = 0.72 and ρ = 0.82). However, the finer topographic details of the WRF-ERA5 return levels cannot be evaluated with the observation data because their spatial resolution is too low. Hence, this comparison shows no great further improvement (bias = +1.1 %, ρ = 0.82) of the overall performance compared to the 5 km resolution setup. Uncertainties due to extreme value theory are explored by employing three different approaches for the highest-resolution WRF-ERA5 setup. The GEV distribution with fixed shape parameter (bias = +0.9 %, ρ = 0.79) and the Generalized Pareto (GP: bias = +1.3 %, ρ = 0.81) show almost equivalent results for the 10-year return period, whereas the Metastatistical Extreme Value (MEV) distribution leads to a slight underestimation (bias = -6.2 %, ρ = 0.86). From these results, it follows that high-resolution regional climate models are suitable for generating spatially homogeneous rainfall return level products. In regions with a sparse rain gauge density or low spatial representativeness of the stations due to complex topography, RCMs can support the observational data. Further, RCMs driven by global climate models with emission scenarios can project climate change-induced alterations in rainfall return levels at regional to local scales. This would allow adjustment of structural design and, therefore, adaption to future precipitation conditions.

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“…On the other hand, a general 'bias' in the weather radar when compared with stations is visible, generally increasing with rainfall intensity (e.g. Schleiss et al, 2020, Kreklow et al, 2020 as the radar precipitation is an indirect product (derived from reflectivity) integrated over a larger area. This fact is certainly another serious drawback when using radar data for the estimation of design storms.…”

mentioning

confidence: 99%

Enhancing the usability of weather radar data for the statistical analysis of extreme precipitation events

Hänsler

Weiler

2022

Preprint

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Abstract. Spatially explicit quantification on design storms are essential for flood risk assessment and planning. Since the limited temporal data availability from weather radar data, design storms are usually estimated on the basis of rainfall records of a few precipitation stations having a substantially long time coverage. To achieve a regional picture these station based estimates are spatially interpolated, incorporating a large source of uncertainty due to the typical low station density, in particular for short event durations. In this study we present a method to estimate spatially explicit design storms with a return period of up to 100 years on the basis of statistically extended weather radar precipitation estimates based on the ideas of regional frequency analyses and subsequent bias correction. Associated uncertainties are quantified using an ensemble-sampling approach and event-based bootstrapping. With the resulting dataset, we compile spatially explicit design storms for various return periods and event durations for the federal state of Baden Württemberg, Germany. We compare our findings with two reference datasets based on interpolated station estimates. We find that the transition in the spatial patterns of the design storms from a rather random short duration events, 15 minute) to a more structured, orographically influenced pattern (long duration events, 24 hours) seems to be much more realistic in the weather radar based product. However, the absolute magnitude of the design storms, although bias-corrected, is still generally lower in the weather radar product, which should be addressed in future studies in more detail.

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Radar-Based Precipitation Climatology in Germany—Developments, Uncertainties and Potentials

Cited by 28 publications

References 38 publications

The Importance of Subsurface Processes in Land Surface Modeling over a Temperate Region: An Analysis with SMAP, Cosmic Ray Neutron Sensing and Triple Collocation Analysis

The Importance of Subsurface Processes in Land Surface Modeling over a Temperate Region: An Analysis with SMAP, Cosmic Ray Neutron Sensing and Triple Collocation Analysis

Using high-resolution regional climate models to estimate return levels of daily extreme precipitation over Bavaria

Enhancing the usability of weather radar data for the statistical analysis of extreme precipitation events

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