scholarly journals Comparison and Evaluation of Statistical Rainfall Disaggregation and High-Resolution Dynamical Downscaling over Complex Terrain

2018 ◽  
Vol 19 (12) ◽  
pp. 1973-1982 ◽  
Author(s):  
B. Poschlod ◽  
Ø. Hodnebrog ◽  
R. R. Wood ◽  
K. Alterskjær ◽  
R. Ludwig ◽  
...  
Keyword(s):  
High Resolution ◽  
Statistical Method ◽  
Extreme Precipitation ◽  
Complex Terrain ◽  
Dynamical Downscaling ◽  
Precipitation Data ◽  
Maximum Rainfall ◽  
Method Of Fragments ◽  
Hourly Data ◽  
Wet Spells

Abstract Representative methods of statistical disaggregation and dynamical downscaling are compared in terms of their ability to disaggregate precipitation data into hourly resolution in an urban area with complex terrain. The nonparametric statistical Method of Fragments (MoF) uses hourly data from rain gauges to split the daily data at the location of interest into hourly fragments. The high-resolution, convection-permitting Weather Research and Forecasting (WRF) regional climate model is driven by reanalysis data. The MoF can reconstruct the variance, dry proportion, wet hours per month, number and length of wet spells per rainy day, timing of the maximum rainfall burst, and intensities of extreme precipitation with errors of less than 10%. However, the MoF cannot capture the spatial coherence and temporal interday connectivity of precipitation events due to the random elements involved in the algorithm. Otherwise, the statistical method is well suited for filling gaps in subdaily historical records. The WRF Model is able to reproduce dry proportion, lag-1 autocorrelation, wet hours per month, number and length of wet spells per rainy day, spatial correlation, and 6- and 12-h intensities of extreme precipitation with errors of 10% or less. The WRF approach tends to underestimate peak rainfall of 1- and 3-h aggregates but can be used where no observations are available or when areal precipitation data are needed.

scholarly journals An Assessment of High-Resolution Gridded Temperature Datasets over California

2018 ◽  
Vol 31 (10) ◽  
pp. 3789-3810 ◽  
Author(s):  
Daniel Walton ◽  
Alex Hall
Keyword(s):  
High Resolution ◽  
Complex Terrain ◽  
Dynamical Downscaling ◽  
Station Data ◽  
Daily Data ◽  
Historical Climatology ◽  
Systematic Biases ◽  
Global Historical Climatology Network ◽  
Lapse Rates ◽  
Physical Principles

Abstract High-resolution gridded datasets are in high demand because they are spatially complete and include important finescale details. Previous assessments have been limited to two to three gridded datasets or analyzed the datasets only at the station locations. Here, eight high-resolution gridded temperature datasets are assessed two ways: at the stations, by comparing with Global Historical Climatology Network–Daily data; and away from the stations, using physical principles. This assessment includes six station-based datasets, one interpolated reanalysis, and one dynamically downscaled reanalysis. California is used as a test domain because of its complex terrain and coastlines, features known to differentiate gridded datasets. As expected, climatologies of station-based datasets agree closely with station data. However, away from stations, spread in climatologies can exceed 6°C. Some station-based datasets are very likely biased near the coast and in complex terrain, due to inaccurate lapse rates. Many station-based datasets have large unphysical trends (>1°C decade−1) due to unhomogenized or missing station data—an issue that has been fixed in some datasets by using homogenization algorithms. Meanwhile, reanalysis-based gridded datasets have systematic biases relative to station data. Dynamically downscaled reanalysis has smaller biases than interpolated reanalysis, and has more realistic variability and trends. Dynamical downscaling also captures snow–albedo feedback, which station-based datasets miss. Overall, these results indicate that 1) gridded dataset choice can be a substantial source of uncertainty, and 2) some datasets are better suited for certain applications.

scholarly journals A classification algorithm for selective dynamical downscaling of precipitation extremes

2018 ◽  
Vol 22 (8) ◽  
pp. 4183-4200 ◽  
Author(s):  
Edmund P. Meredith ◽  
Henning W. Rust ◽  
Uwe Ulbrich
Keyword(s):  
High Resolution ◽  
Extreme Precipitation ◽  
Clustering Algorithm ◽  
Dynamical Downscaling ◽  
Classification Algorithm ◽  
Climate Data ◽  
Catchment Scale ◽  
Computational Expense ◽  
Circulation Patterns ◽  
Climate Simulations

Abstract. High-resolution climate data O(1 km) at the catchment scale can be of great value to both hydrological modellers and end users, in particular for the study of extreme precipitation. While dynamical downscaling with convection-permitting models is a valuable approach for producing quality high-resolution O(1 km) data, its added value can often not be realized due to the prohibitive computational expense. Here we present a novel and flexible classification algorithm for discriminating between days with an elevated potential for extreme precipitation over a catchment and days without, so that dynamical downscaling to convection-permitting resolution can be selectively performed on high-risk days only, drastically reducing total computational expense compared to continuous simulations; the classification method can be applied to climate model data or reanalyses. Using observed precipitation and the corresponding synoptic-scale circulation patterns from reanalysis, characteristic extremal circulation patterns are identified for the catchment via a clustering algorithm. These extremal patterns serve as references against which days can be classified as potentially extreme, subject to additional tests of relevant meteorological predictors in the vicinity of the catchment. Applying the classification algorithm to reanalysis, the set of potential extreme days (PEDs) contains well below 10 % of all days, though it includes essentially all extreme days; applying the algorithm to reanalysis-driven regional climate simulations over Europe (12 km resolution) shows similar performance, and the subsequently dynamically downscaled simulations (2 km resolution) well reproduce the observed precipitation statistics of the PEDs from the training period. Additional tests on continuous 12 km resolution historical and future (RCP8.5) climate simulations, downscaled in 2 km resolution time slices, show the algorithm again reducing the number of days to simulate by over 90 % and performing consistently across climate regimes. The downscaling framework we propose represents a computationally inexpensive means of producing high-resolution climate data, focused on extreme precipitation, at the catchment scale, while still retaining the advantages of convection-permitting dynamical downscaling.

scholarly journals A classification algorithm for selective dynamical downscaling of precipitation extremes

2017 ◽  
Author(s):  
Edmund P. Meredith ◽  
Henning W. Rust ◽  
Uwe Ulbrich
Keyword(s):  
High Resolution ◽  
Extreme Precipitation ◽  
Clustering Algorithm ◽  
Dynamical Downscaling ◽  
Classification Algorithm ◽  
Climate Data ◽  
Catchment Scale ◽  
Computational Expense ◽  
Circulation Patterns ◽  
Climate Simulations

Abstract. High-resolution climate data [O(1 km)] at the catchment scale can be of great value to both hydrological modellers and end users, in particular for the study of extreme precipitation. Despite the well-known advantages of dynamical downscaling for producing quality high-resolution data, the added value of dynamically downscaling to O(1 km) resolutions can often not be realised due to the prohibitive computational expense. Here we present a novel and flexible classification algorithm for discriminating between days with an elevated potential for extreme precipitation over a catchment and days without, so that dynamical downscaling to convection-permitting resolution can be selectively performed on high-risk days only, drastically reducing total computational expense compared to continuous simulations; the classification method can be applied to climate model data or reanalyses. Using observed precipitation and the corresponding synoptic-scale circulation patterns from reanalysis, characteristic extremal circulation patterns are identified for the catchment via a clustering algorithm. These extremal patterns serve as references against which days can be classified as potentially extreme, subject to additional tests of relevant meteorological variables in the vicinity of the catchment. Applying the classification algorithm to reanalysis, the set of potential extreme days (PEDs) contains well below 10 % of all days, though includes essentially all extreme days; applying the algorithm to reanalysis-driven regional climate simulations over Europe (12 km resolution) shows similar performance and the subsequently dynamically downscaled simulations (2 km resolution) well reproduce the observed precipitation statistics of the PEDs from the training period. Additional tests on continuous 12- and 2 km resolution historical and future (RCP8.5) climate simulations show the algorithm again reducing the number of days to simulate by over 90 % and performing consistently across climate regimes. The downscaling framework we propose represents a computationally inexpensive means of producing high-resolution climate data, focused on extreme precipitation, at the catchment scale, while still retaining the advantages of the physically-based dynamical downscaling approach.

scholarly journals SPREAD: a high-resolution daily gridded precipitation dataset for Spain – an extreme events frequency and intensity overview

2017 ◽  
Vol 9 (2) ◽  
pp. 721-738 ◽  
Author(s):  
Roberto Serrano-Notivoli ◽  
Santiago Beguería ◽  
Miguel Ángel Saz ◽  
Luis Alberto Longares ◽  
Martín de Luis
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Extreme Events ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Mediterranean Coast ◽  
Extreme Precipitation Events ◽  
Precipitation Indices ◽  
Peninsular Spain ◽  
Wet Spells

Abstract. A high-resolution daily gridded precipitation dataset was built from raw data of 12 858 observatories covering a period from 1950 to 2012 in peninsular Spain and 1971 to 2012 in Balearic and Canary islands. The original data were quality-controlled and gaps were filled on each day and location independently. Using the serially complete dataset, a grid with a 5 × 5 km spatial resolution was constructed by estimating daily precipitation amounts and their corresponding uncertainty at each grid node. Daily precipitation estimations were compared to original observations to assess the quality of the gridded dataset. Four daily precipitation indices were computed to characterise the spatial distribution of daily precipitation and nine extreme precipitation indices were used to describe the frequency and intensity of extreme precipitation events. The Mediterranean coast and the Central Range showed the highest frequency and intensity of extreme events, while the number of wet days and dry and wet spells followed a north-west to south-east gradient in peninsular Spain, from high to low values in the number of wet days and wet spells and reverse in dry spells. The use of the total available data in Spain, the independent estimation of precipitation for each day and the high spatial resolution of the grid allowed for a precise spatial and temporal assessment of daily precipitation that is difficult to achieve when using other methods, pre-selected long-term stations or global gridded datasets. SPREAD dataset is publicly available at https://doi.org/10.20350/digitalCSIC/7393.

scholarly journals Iberian autumnal precipitation characterization through observed, simulated and reanalysed data

2008 ◽  
Vol 16 ◽  
pp. 49-54 ◽  
Author(s):  
A. Morata ◽  
M. Y. Luna ◽  
M. L. Martín ◽  
M. G. Sotillo ◽  
F. Valero
Keyword(s):  
Principal Component Analysis ◽  
Spatial Distribution ◽  
Comparative Analysis ◽  
High Resolution ◽  
Dynamical Downscaling ◽  
Principal Component ◽  
Reanalysis Data ◽  
Data Sets ◽  
Precipitation Data ◽  
Global Reanalysis

Abstract. A 44-year (1958–2001) homogeneous Mediterranean high-resolution atmospheric database was generated through dynamical downscaling within the HIPOCAS Project framework. The present work attempts to provide a validation of the monthly 41-autumnal (1961–2001) HIPOCAS precipitation over the Iberian Peninsula, being also provided an evaluation of its improvement versus current global reanalysis data sets. A statistical comparative analysis between observed, HIPOCAS and global reanalyses precipitation data sets was carried out, highlighting the noticeable agreement existing between the observed and the HIPOCAS precipitation data sets in terms of not only time and spatial distribution, but also in terms of total amount of precipitation. A principal component analysis is carried out showing that the patterns derived from the HIPOCAS data largely capture the main characteristics of the studied field. Moreover, it is worth to note that the HIPOCAS patterns reproduce accurately the observed regional characteristics linked to the main orographic features of the study domain.

scholarly journals High-Resolution Downscaled Simulations of Warm-Season Extreme Precipitation Events in the Colorado Front Range under Past and Future Climates*

2013 ◽  
Vol 26 (21) ◽  
pp. 8671-8689 ◽  
Author(s):  
Kelly Mahoney ◽  
Michael Alexander ◽  
James D. Scott ◽  
Joseph Barsugli
Keyword(s):  
High Resolution ◽  
Extreme Events ◽  
Extreme Precipitation ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Wrf Model ◽  
Model Errors ◽  
Grid Spacing ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract A high-resolution case-based approach for dynamically downscaling climate model data is presented. Extreme precipitation events are selected from regional climate model (RCM) simulations of past and future time periods. Each event is further downscaled using the Weather Research and Forecasting (WRF) Model to storm scale (1.3-km grid spacing). The high-resolution downscaled simulations are used to investigate changes in extreme precipitation projections from a past to a future climate period, as well as how projected precipitation intensity and distribution differ between the RCM scale (50-km grid spacing) and the local scale (1.3-km grid spacing). Three independent RCM projections are utilized as initial and boundary conditions to the downscaled simulations, and the results reveal considerable spread in projected changes not only among the RCMs but also in the downscaled high-resolution simulations. However, even when the RCM projections show an overall (i.e., spatially averaged) decrease in the intensity of extreme events, localized maxima in the high-resolution simulations of extreme events can remain as strong or even increase. An ingredients-based analysis of prestorm instability, moisture, and forcing for ascent illustrates that while instability and moisture tend to increase in the future simulations at both regional and local scales, local forcing, synoptic dynamics, and terrain-relative winds are quite variable. Nuanced differences in larger-scale and mesoscale dynamics are a key determinant in each event's resultant precipitation. Very high-resolution dynamical downscaling enables a more detailed representation of extreme precipitation events and their relationship to their surrounding environments with fewer parameterization-based uncertainties and provides a framework for diagnosing climate model errors.

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