Validation and correction of rainfall data from the WegenerNet high density network in southeast Austria

Sungmin, O; Foelsche, Ulrich; Kirchengast, Gottfried; Fuchsberger, Jürgen

doi:10.1016/j.jhydrol.2016.11.049

Cited by 25 publications

(9 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Raw precipitation amount is sampled at 5‐min intervals. O et al () recently performed a thorough validation and bias estimation of the gauge data and we here use the bias‐corrected WEGN level 2 version 6 precipitation data. All WEGN observations are aggregated to 10‐min temporal resolution to match AHYD and ZAMG data resolution.…”

Section: Methodsmentioning

confidence: 99%

Strong Dependence of Extreme Convective Precipitation Intensities on Gauge Network Density

Schroeer

Kirchengast

Sungmin

2018

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

Extreme convective precipitation on subhourly scales is notoriously misrepresented in rain gauge‐based observations, but uncertainties are weakly quantified at the 1 to 30 km scale. We employ a unique observing network, the high‐density WegenerNet and surrounding operational rain gauge network in southeastern Austria, to sample convective precipitation extremes at these scales. By systematically constructing lower‐density networks, we explore how estimated maximum area precipitation depends on observing station density. Using subhourly to hourly temporal resolution, we find a d−0.5(±0.1) power law decay of the event maximum area precipitation over distances d from 1 to 30 km, showing that operational gauge networks underrate extreme convective precipitation falling over small areas. Furthermore, extremes at point scale are found underestimated by operational networks by about 20%. We consider the dependencies representative for short‐duration convective events over similar regions at midlatitudes and the results valuable for high‐resolution climate model evaluation.

show abstract

Section: Methodsmentioning

confidence: 99%

Strong Dependence of Extreme Convective Precipitation Intensities on Gauge Network Density

Schroeer

Kirchengast

Sungmin

2018

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although the difference in the numerical resolution between INCA (1 km grid spacing) and CCLM (3 km grid spacing) is only a factor of 3, CCLM is not able to resolve small-scale wind patterns. This occurs for multiple reasons: (1) due to the third-order advection scheme with its horizontal diffusion damping, the effective resolution in CCLM is several times coarser than the numeric grid spacing (Ogaja and Will, 2016); (2) the orography is smoothed as well, so that individual mountain ridge and valley structures are removed. For example, the mountain peak of the Hochtor with its 2396 m elevation at the center of the WegenerNet JBT region is lowered by about 500 m in the CCLM model.…”

Section: Discussionmentioning

confidence: 99%

“…The intercomparison of the CCLM dynamical fields with the INCA analysis fields for thermally induced wind events reflects the disadvantage of smoothed terrain, which is caused by the limited effective resolution being several times coarser than the 3 km × 3 km grid spacing of the CCLM as already noted above. Improvements can be expected from the latest developments in the numerical core of CCLM by Ogaja and Will (2016), which have enabled an improvement of the effective resolution by a factor of 2 by introducing a fourth-order advection scheme that allows us to circumvent the horizontal diffusion damping.…”

Section: Discussionmentioning

confidence: 99%

Response to Referee #2

Schlager¹

2019

Preprint

View full text Add to dashboard Cite

“…While those datasets are good alternatives to address a number of the issues relating to the scarcity of gauges, rainfall variability at subpixel scales can still not be fully resolved (Peleg et al, 2013;Tokay et al, 2014). Moreover, systematic errors can be large (Svensson and Jones, 2010), and the quality of remotely sensed data therefore strongly relies on gauge-based data that are used for their regional validation and correction (Kann et al, 2015;O et al, 2018b;Steiner et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

Assessment of spatial uncertainty of heavy rainfall at catchment scale using a dense gauge network

Sungmin

Foelsche

2019

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Hydrology and remote-sensing communities have made use of dense rain-gauge networks for studying rainfall uncertainty and variability. However, in most regions, these dense networks are only available at small spatial scales (e.g., within remote-sensing subpixel areas) and over short periods of time. Just a few studies have applied a similar approach, i.e., employing dense gauge networks to catchment-scale areas, which limits the verification of their results in other regions. Using 10-year rainfall measurements from a network of 150 rain gauges, WegenerNet (WEGN), we assess the spatial uncertainty in observed heavy rainfall events. The WEGN network is located in southeastern Austria over an area of 20 km × 15 km with moderate orography. First, the spatial variability in rainfall in the region was characterized using a correlogram at daily and sub-daily scales. Differences in the spatial structure of rainfall events between warm and cold seasons are apparent, and we selected heavy rainfall events, the upper 10 % of wettest days during the warm season, for further analyses because of their high potential for causing hazards. Secondly, we investigated the uncertainty in estimating mean areal rainfall arising from a limited gauge density. The average number of gauges required to obtain areal rainfall with errors less than a certain threshold (≤20 % normalized root-mean-square error – RMSE – is considered here) tends to increase, roughly following a power law as the timescale decreases, while the errors can be significantly reduced by establishing regularly distributed gauges. Lastly, the impact of spatial aggregation on extreme rainfall was examined, using gridded rainfall data with various horizontal grid spacings. The spatial-scale dependence was clearly observed at high intensity thresholds and high temporal resolutions; e.g., the 5 min extreme intensity increases by 44 % for the 99.9th and by 25 % for the 99th percentile, with increasing horizontal resolution from 0.1 to 0.01∘. Quantitative uncertainty information from this study can guide both data users and producers to estimate uncertainty in their own observational datasets, consequently leading to the sensible use of the data in relevant applications. Our findings could be transferred to midlatitude regions with moderate topography, but only to a limited extent, given that regional factors that can affect rainfall type and process are not explicitly considered in the study.

show abstract

Validation and correction of rainfall data from the WegenerNet high density network in southeast Austria

Cited by 25 publications

References 29 publications

Strong Dependence of Extreme Convective Precipitation Intensities on Gauge Network Density

Strong Dependence of Extreme Convective Precipitation Intensities on Gauge Network Density

Response to Referee #2

Assessment of spatial uncertainty of heavy rainfall at catchment scale using a dense gauge network

Contact Info

Product

Resources

About