The influence of synoptic airflow on UK daily precipitation extremes. Part II: regional climate model and E-OBS data validation

2011 ◽  
Vol 39 (1-2) ◽  
pp. 287-301 ◽  
Author(s):  
Douglas Maraun ◽  
Timothy J. Osborn ◽  
Henning W. Rust
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Daily Precipitation ◽  
Regional Climate ◽  
Precipitation Extremes ◽  
Data Validation ◽  
Obs Data ◽  
Synoptic Airflow

Evaluation of extreme precipitation over the Nordic region using a convection-permitting regional climate model

2020 ◽  
Author(s):  
Erika Toivonen ◽  
Danijel Belušić ◽  
Emma Dybro Thomassen ◽  
Peter Berg ◽  
Ole Bøssing Christensen ◽  
...  
Keyword(s):  
High Resolution ◽  
Regional Climate Model ◽  
Extreme Precipitation ◽  
Climate Model ◽  
Daily Precipitation ◽  
Regional Climate ◽  
Precipitation Extremes ◽  
Nordic Region ◽  
Mesh Resolution ◽  
Grid Mesh

<p>Extreme precipitation events have a major impact upon our society. Although many studies have indicated that it is likely that the frequency of such events will increase in a warmer climate, little has been done to assess changes in extreme precipitation at a sub-daily scale. Recently, there is more and more evidence that <span>high-resolution convection-permitting models </span><span>(CPMs)</span> (grid-mesh typically < 4 km) can represent especially short-duration precipitation extremes more accurately when compared with coarser-resolution <span>regional climate model</span><span>s </span><span>(RCMs)</span><span>.</span></p><p>This study investigates sub-daily and daily precipitation characteristics based on hourly <span>output data from the HARMONIE-Climate model </span>at 3-km and 12-km grid-mesh resolution over the Nordic region between 1998 and 2018. The RCM modelling chain uses the ERA-Interim reanalysis to drive a 12-km grid-mesh simulation which is further downscaled to 3-km grid-mesh resolution using a non-hydrostatic model set-up.</p><p>The statistical properties of the modeled extreme precipitation are compared to several sub-daily and daily observational products, including gridded and in-situ gauge data, from April to September. We investigate the skill of the model to represent different aspects of the frequency and intensity of extreme precipitation as well as intensity–duration–frequency (IDF) curves that are commonly used to investigate short duration extremes from an urban planning perspective. The high grid resolution combined with the 20-year-long simulation period allows for a robust assessment at a climatological time scale <span>and enables us to examine the added value of high-resolution </span><span>CPM</span><span> in reproducing precipitation extremes over the Nordic </span><span>region</span><span>. </span><span>Based on the tentative results, the high-resolution CPM can realistically capture the </span><span>characteristics </span><span>of precipitation extremes, </span><span>for instance, </span><span>in terms of improved diurnal cycle and maximum intensities of sub-daily precipitation.</span></p>

scholarly journals Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue

2013 ◽  
Vol 26 (6) ◽  
pp. 2137-2143 ◽  
Author(s):  
Douglas Maraun
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Model ◽  
Daily Precipitation ◽  
Temporal Structure ◽  
Regional Climate ◽  
Quantile Mapping ◽  
Model Simulations ◽  
Scale Mismatch ◽  
Climate Model Simulations

Abstract Quantile mapping is routinely applied to correct biases of regional climate model simulations compared to observational data. If the observations are of similar resolution as the regional climate model, quantile mapping is a feasible approach. However, if the observations are of much higher resolution, quantile mapping also attempts to bridge this scale mismatch. Here, it is shown for daily precipitation that such quantile mapping–based downscaling is not feasible but introduces similar problems as inflation of perfect prognosis (“prog”) downscaling: the spatial and temporal structure of the corrected time series is misrepresented, the drizzle effect for area means is overcorrected, area-mean extremes are overestimated, and trends are affected. To overcome these problems, stochastic bias correction is required.

scholarly journals Spatial Interpolation of Daily Precipitation in a High Mountainous Watershed Based on Gauge Observations and a Regional Climate Model Simulation

2017 ◽  
Vol 18 (3) ◽  
pp. 845-862 ◽  
Author(s):  
Yuhan Wang ◽  
Hanbo Yang ◽  
Dawen Yang ◽  
Yue Qin ◽  
Bing Gao ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Regional Climate Model ◽  
Spatial Interpolation ◽  
Climate Model ◽  
Daily Precipitation ◽  
Regional Climate ◽  
Heihe River ◽  
Topographic Effects ◽  
Precipitation Data ◽  
Meteorological Observations

Abstract Precipitation is a primary climate forcing factor in catchment hydrology, and its spatial distribution is essential for understanding the spatial variability of ecohydrological processes in a catchment. In mountainous areas, meteorological stations are generally too sparse to represent the spatial distribution of precipitation. This study develops a spatial interpolation method that combines meteorological observations and regional climate model (RCM) outputs. The method considers the precipitation–elevation relationship in the mountain region and the topographic effects, especially the mountain blocking effect. Furthermore, using this method, this study produced a 3-km-resolution precipitation dataset from 1960 to 2014 in the middle and upper reaches of the Heihe River basin located on the northern slope of the Qilian Mountains in the northeastern Tibetan Plateau. Cross validation based on the station observations showed that this method is reasonable. The rationality of the interpolated precipitation data was also evaluated by the catchment water balances using the observed river discharge and the actual evapotranspiration based on remote sensing. The interpolated precipitation data were compared with the China Gauge-Based Daily Precipitation Analysis product and the RCM output and was shown to be optimal. The results showed that the proposed method effectively used the information from the meteorological observations and the RCM simulations and provided the spatial distributions of daily precipitations with reasonable accuracy and high resolution, which is important for a distributed hydrological simulation at the catchment scale.

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