scholarly journals Evaluation of daily precipitation analyses in E‐OBS (v19.0e) and ERA5 by comparison to regional high‐resolution datasets in European regions

2021 ◽  
Author(s):  
Moritz Bandhauer ◽  
Francesco Isotta ◽  
Monika Lakatos ◽  
Cristian Lussana ◽  
Line Båserud ◽  
...  
Keyword(s):  
High Resolution ◽  
Daily Precipitation ◽  
European Regions

scholarly journals A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging

2021 ◽  
Vol 13 (11) ◽  
pp. 2040
Author(s):  
Xin Yan ◽  
Hua Chen ◽  
Bingru Tian ◽  
Sheng Sheng ◽  
Jinxing Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Spatial Resolution ◽  
Daily Precipitation ◽  
Machine Learning Algorithms ◽  
Precipitation Data ◽  
Large Area ◽  
Tree Model ◽  
Daily Precipitation Data ◽  
Precipitation Estimates

High-spatial-resolution precipitation data are of great significance in many applications, such as ecology, hydrology, and meteorology. Acquiring high-precision and high-resolution precipitation data in a large area is still a great challenge. In this study, a downscaling–merging scheme based on random forest and cokriging is presented to solve this problem. First, the enhanced decision tree model, which is based on random forest from machine learning algorithms, is used to reduce the spatial resolution of satellite daily precipitation data to 0.01°. The downscaled satellite-based daily precipitation is then merged with gauge observations using the cokriging method. The scheme is applied to downscale the Global Precipitation Measurement Mission (GPM) daily precipitation product over the upstream part of the Hanjiang Basin. The experimental results indicate that (1) the downscaling model based on random forest can correctly spatially downscale the GPM daily precipitation data, which retains the accuracy of the original GPM data and greatly improves their spatial details; (2) the GPM precipitation data can be downscaled on the seasonal scale; and (3) the merging method based on cokriging greatly improves the accuracy of the downscaled GPM daily precipitation data. This study provides an efficient scheme for generating high-resolution and high-quality daily precipitation data in a large area.

scholarly journals seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day

2018 ◽  
Vol 10 (1) ◽  
pp. 235-249 ◽  
Author(s):  
Cristian Lussana ◽  
Tuomo Saloranta ◽  
Thomas Skaugen ◽  
Jan Magnusson ◽  
Ole Einar Tveito ◽  
...  
Keyword(s):  
High Resolution ◽  
Daily Precipitation ◽  
Spatial Scales ◽  
National Level ◽  
Calibration Procedure ◽  
Geographical Information ◽  
Optimal Interpolation ◽  
Small Scale ◽  
Meteorological Forcing ◽  
Atmospheric Sciences

Abstract. The conventional climate gridded datasets based on observations only are widely used in atmospheric sciences; our focus in this paper is on climate and hydrology. On the Norwegian mainland, seNorge2 provides high-resolution fields of daily total precipitation for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The dataset constitutes a valuable meteorological input for snow and hydrological simulations; it is updated daily and presented on a high-resolution grid (1 km of grid spacing). The climate archive goes back to 1957. The spatial interpolation scheme builds upon classical methods, such as optimal interpolation and successive-correction schemes. An original approach based on (spatial) scale-separation concepts has been implemented which uses geographical coordinates and elevation as complementary information in the interpolation. seNorge2 daily precipitation fields represent local precipitation features at spatial scales of a few kilometers, depending on the station network density. In the surroundings of a station or in dense station areas, the predictions are quite accurate even for intense precipitation. For most of the grid points, the performances are comparable to or better than a state-of-the-art pan-European dataset (E-OBS), because of the higher effective resolution of seNorge2. However, in very data-sparse areas, such as in the mountainous region of southern Norway, seNorge2 underestimates precipitation because it does not make use of enough geographical information to compensate for the lack of observations. The evaluation of seNorge2 as the meteorological forcing for the seNorge snow model and the DDD (Distance Distribution Dynamics) rainfall–runoff model shows that both models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The seNorge2 dataset 1957–2015 is available at https://doi.org/10.5281/zenodo.845733. Daily updates from 2015 onwards are available at http://thredds.met.no/thredds/catalog/metusers/senorge2/seNorge2/provisional_archive/PREC1d/gridded_dataset/catalog.html.

scholarly journals Daily precipitation statistics in a EURO-CORDEX RCM ensemble: added value of raw and bias-corrected high-resolution simulations

2015 ◽  
Vol 47 (3-4) ◽  
pp. 719-737 ◽  
Author(s):  
A. Casanueva ◽  
S. Kotlarski ◽  
S. Herrera ◽  
J. Fernández ◽  
J. M. Gutiérrez ◽  
...  
Keyword(s):  
High Resolution ◽  
Daily Precipitation ◽  
Added Value

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>

Evaluation of High-Resolution Precipitation Products over Southwest China

2020 ◽  
Vol 21 (11) ◽  
pp. 2691-2712
Author(s):  
Yanbo Nie ◽  
Jianqi Sun
Keyword(s):  
High Resolution ◽  
Root Mean Square ◽  
Extreme Precipitation ◽  
Southwest China ◽  
Daily Precipitation ◽  
The Other ◽  
Future Research ◽  
Monthly Precipitation ◽  
Mean Square ◽  
Mean Square Errors

AbstractThe evaluation of gridded high-resolution precipitation products (HRPPs) is important in areas with complex topography, because rain gauges that are unevenly and sparsely distributed over an area cannot effectively reflect the spatial variabilities of the precipitation and related extremes in detail. In this study, the applicability of six satellite-based precipitation products (TMPA 3B42V7, IMERG, GSMaP-Gauge, CMORPH-CRT, PERSIANN-CDR, and GPCP) and five gauge-based precipitation products (APHRODITE, CN05.1, GPCC-D, GPCC-M, and CRU) over southwest China from 1998 to 2016 is evaluated by performing a comparison with meteorological station observations. The results show that GPCC-M exhibits the best performances for annual, seasonal, and monthly precipitation, which is supported by the lowest root-mean-square errors (RMSEs) for annual and seasonal precipitation and the lowest normalized root-mean-square error (NRMSE) for monthly precipitation. According to the NRMSE and critical success index (CSI), CN05.1 outperforms the other HRPPs at detecting daily precipitation; however, CN05.1 tends to overestimate the frequencies of light precipitation and underestimate the frequencies of heavy precipitation, which is reflected by the probability density function (PDF) for daily precipitation. The bias ratio (BIAS) and extreme precipitation indices show that IMERG shows numerous advantages over the other HRPPs in detecting extreme precipitation and estimating the precipitation intensity. Such results are helpful for future research on precipitation/extremes and related hydrometeorological disasters that occur throughout southwest China.

scholarly journals Bridging the scale gap: Obtaining high-resolution stochastic simulations of gridded daily precipitation in a future climate

2021 ◽  
Author(s):  
Qifen Yuan ◽  
Thordis L. Thorarinsdottir ◽  
Stein Beldring ◽  
Wai Kwok Wong ◽  
Chong-Yu Xu
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Climate Model ◽  
Daily Precipitation ◽  
Regional Climate ◽  
Weather Generator ◽  
Future Climate ◽  
Stochastic Simulations ◽  
Climate Change Information ◽  
Historical Observation

Abstract. Climate change impact assessment related to floods, infrastructure networks and water resources management applications requires realistic simulations of high-resolution gridded precipitation series under a future climate. This paper proposes to produce such simulations by combining a weather generator for high-resolution gridded daily precipitation, trained on historical observation-based gridded data product, with coarser scale climate change information obtained using a regional climate model. The climate change information can be added to various components of the weather generator, related to both the probability of precipitation as well as the amount of precipitation on wet days. The information is added in a transparent manner, allowing for an assessment of the plausibility of the added information. In a case study of nine hydrological catchments in central Norway with the study areas covering 1000–5500 km2, daily simulations are obtained on a 1 km grid for a period of 19 years. The method yields simulations with realistic temporal and spatial structures and outperforms empirical quantile delta mapping in terms of marginal performance.

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