scholarly journals Impact of nesting strategies in dynamical downscaling of reanalysis data

2004 ◽  
Vol 31 (19) ◽  
Author(s):  
A. Beck
Keyword(s):  
Dynamical Downscaling ◽  
Reanalysis Data

scholarly journals Hydrologic modeling by means of a hybrid downscaling approach: an application to the Sai Gon–Dong Nai Rivers Basin

2021 ◽  
Author(s):  
T. Trinh ◽  
V. T. Nguyen ◽  
N. Do ◽  
K. Carr ◽  
D. H. Tran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dynamical Downscaling ◽  
Watershed Modeling ◽  
Reanalysis Data ◽  
Hydrologic Model ◽  
Substantial Contribution ◽  
Observation Data ◽  
Data Set ◽  
Hydrological Data ◽  
Calibration And Validation

Abstract The spatial and temporal availability and reliability of hydrological data are substantial contribution to the accuracy of watershed modeling; unfortunately, such data requirements are challenging and perhaps impossible in many regions of the world. In this study, hydrological conditions are simulated using the hydrologic model-WEHY, whose data input are obtained from a hybrid downscaling technique to provide reliable and high temporal and spatial resolution hydrological data. The hybrid downscaling technique is coupled a hydroclimate and a machine learning models; wherein the global atmospheric reanalysis data, including ERA-Interim, ERA-20C, and CFSR are used for initial and boundary conditions of dynamical downscaling utilizing the Weather Research and Forecasting model (WRF). The machine learning model (ANN) then follows to further downscale the WRF outputs to a finer resolution over the studied watershed. An application of the combination of mentioned techniques is applied to the third-largest river basin in Vietnam, the Sai Gon–Dong Nai Rivers Basin. The validation of hybrid model is in the ‘satisfactory’ range. After the estimation of geomorphology and land cover within the watershed, WEHY's calibration and validation are performed based on observed rainfall data. The simulation results matched well with flow observation data with respect to magnitude for both the rising and recession time segments. In comparison among the three selected reanalysis data sets, the best calibration and validation results were obtained from the CFSR data set. These results are closer to the observation data than those using only the dynamic downscaling technique in combination with the WEHY model.

scholarly journals WRF‐based dynamical downscaling of ERA5 reanalysis data for High Mountain Asia: Towards a new version of the High Asia Refined analysis

2020 ◽  
Vol 41 (1) ◽  
pp. 743-762 ◽  
Author(s):  
Xun Wang ◽  
Vanessa Tolksdorf ◽  
Marco Otto ◽  
Dieter Scherer
Keyword(s):  
Dynamical Downscaling ◽  
Reanalysis Data ◽  
High Mountain ◽  
High Asia

Fidelity of CORDEX Evaluation runs under Non-stationary climate

2020 ◽  
Author(s):  
Swati Singh ◽  
Kaustubh Salvi ◽  
Subimal Ghosh ◽  
Subhankar Karmakar
Keyword(s):  
Boundary Conditions ◽  
Statistical Methods ◽  
Statistical Downscaling ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Reanalysis Data ◽  
Observation Data ◽  
The Difference

<p>The downscaling approaches: Statistical and Dynamic, developed for regional climate predictions, have both advantages and limitations. The statistical downscaling is computationally inexpensive but suffers from the violation of the assumption of stationarity in statistical (predictor-predictand) relationship. The dynamical downscaling is assumed to take care of stationarity but suffers from the biases associated with various sources.  Here we propose a joint approach of both the methods by applying statistical methods: bias correction & statistical downscaling to <strong>Coordinated Regional Climate Downscaling Experiment (</strong>CORDEX) evaluation runs. The evaluation runs are considered as perfect simulations of CORDEX Regional Climate Models (RCMs) with the boundary conditions by ERA-Interim reanalysis data. The statistical methods are also applied to ERA-Interim reanalysis data and compared with observation data for Indian Summer Monsoon characteristics. We evaluate the ability of statistical methods under the non-stationary environment by taking the difference of years close to extreme future runs (RCP8.5) as warmer years and preindustrial runs as cooler years. We find statistical downscaling of CORDEX evaluation runs shows skill in reproducing the signal of non-stationarity. The study can be extended methods by applying statistical downscaling to CORDEX RCMs with the CMIP5 boundary conditions. </p>

High Asia Refined Analysis Version 2 (HAR v2): a New Atmospheric Data Set for the Third Pole Region

2020 ◽  
Author(s):  
Xun Wang ◽  
Vanessa Tolksdorf ◽  
Marco Otto ◽  
Dieter Scherer
Keyword(s):  
Natural Hazards ◽  
Dynamical Downscaling ◽  
Data Access ◽  
Reanalysis Data ◽  
The Tibetan Plateau ◽  
Climate Data ◽  
Data Set ◽  
The Third ◽  
High Asia ◽  
Atmospheric Data

<p>Climatic-triggered natural hazards such as landslides and glacier lake outburst floods pose a threat to human lives in the third pole region. Availability of accurate climate data with high spatial and temporal resolution is crucial for better understanding climatic triggering mechanisms of these localized natural hazards. Within the framework of the project “Climatic and Tectonic Natural Hazard in Central Asia” (CaTeNA), High Asia Refined analysis version 2 (HAR v2) is under production, and is freely available upon request. HAR v2 is a regional atmospheric data set generated by dynamical downscaling of global ERA5 reanalysis data using the Weather Research and Forecasting (WRF) model. Compared to its predecessor (HAR), HAR v2 has an extended 10 km domain covering the Tibetan Plateau and the surrounding mountains, as well as a longer temporal coverage. It will be extended back to 1979, and will be continuously updated in the future. This presentation will contain the following aspects: (1) summarizing the WRF configuration; (2) validating HAR v2 against observational data; (3) comparing HAR v2 with other gridded data sets, such as the newly developed ERA5-Land reanalysis data; (4) providing information about data format, variable list, data access, etc.  </p>

The Geospatial and Temporal Distributions of Severe Thunderstorms from High-Resolution Dynamical Downscaling

2013 ◽  
Vol 52 (9) ◽  
pp. 2147-2161 ◽  
Author(s):  
Eric D. Robinson ◽  
Robert J. Trapp ◽  
Michael E. Baldwin
Keyword(s):  
High Resolution ◽  
Dynamical Downscaling ◽  
Warm Season ◽  
Reanalysis Data ◽  
Thunderstorm Activity ◽  
Severe Thunderstorm ◽  
The North ◽  
Severe Thunderstorms ◽  
Artificial Neural Network Ann ◽  
The Many

AbstractTrends in severe thunderstorms and the associated phenomena of tornadoes, hail, and damaging winds have been difficult to determine because of the many uncertainties in the historical eyewitness-report-based record. The authors demonstrate how a synthetic record that is based on high-resolution numerical modeling may be immune to these uncertainties. Specifically, a synthetic record is produced through dynamical downscaling of global reanalysis data over the period of 1990–2009 for the months of April–June using the Weather Research and Forecasting model. An artificial neural network (ANN) is trained and then utilized to identify occurrences of severe thunderstorms in the model output. The model-downscaled precipitation was determined to have a high degree of correlation with precipitation observations. However, the model significantly overpredicted the amount of rainfall in many locations. The downscaling methodology and ANN generated a realistic temporal evolution of the geospatial severe-thunderstorm activity, with a geographical shift of the activity to the north and east as the warm season progresses. Regional time series of modeled severe-thunderstorm occurrences showed no significant trends over the 20-yr period of consideration, in contrast to trends seen in the observational record. Consistently, no significant trend was found over the same 20-yr period in the environmental conditions that support the development of severe thunderstorms.

scholarly journals Assessment of Alaska Rain-on-Snow Events Using Dynamical Downscaling

2018 ◽  
Vol 57 (8) ◽  
pp. 1847-1863 ◽  
Author(s):  
Peter A. Bieniek ◽  
Uma S. Bhatt ◽  
John E. Walsh ◽  
Rick Lader ◽  
Brad Griffith ◽  
...  
Keyword(s):  
Dynamical Downscaling ◽  
Cold Season ◽  
Reanalysis Data ◽  
Seasonal Patterns ◽  
Climate Data ◽  
Coastal Regions ◽  
Winter Rainfall ◽  
Southwesterly Wind ◽  
Rain Events ◽  
Projected Changes

AbstractThe ice formed by cold-season rainfall or rain on snow (ROS) has striking impacts on the economy and ecology of Alaska. An understanding of the atmospheric drivers of ROS events is required to better predict them and plan for environmental change. The spatially/temporally sparse network of stations in Alaska makes studying such events challenging, and gridded reanalysis or remote sensing products are necessary to fill the gaps. Recently developed dynamically downscaled climate data provide a new suite of high-resolution variables for investigating historical and projected ROS events across all of Alaska from 1979 to 2100. The dynamically downscaled reanalysis data of ERA-Interim replicated the seasonal patterns of ROS events but tended to produce more rain events than in station observations. However, dynamical downscaling reduced the bias toward more rain events in the coarse reanalysis. ROS occurred most frequently over southwestern and southern coastal regions. Extreme events with the heaviest rainfall generally coincided with anomalous high pressure centered to the south/southeast of the locations receiving the event and warm-air advection from the resulting southwesterly wind flow. ROS events were projected to increase in frequency overall and for extremes across most of the region but were expected to decline over southwestern/southern Alaska. Increases in frequency were projected as a result of more frequent winter rainfall, but the number of ROS events may ultimately decline in some areas as a result of temperatures rising above the freezing threshold. These projected changes in ROS can significantly affect wildlife, vegetation, and human activities across the Alaska landscape.

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.

Impact of different reanalysis data on WRF dynamical downscaling over China

2018 ◽  
Vol 200 ◽  
pp. 25-35 ◽  
Author(s):  
Danlian Huang ◽  
Shibo Gao
Keyword(s):  
Dynamical Downscaling ◽  
Reanalysis Data

Dynamical downscaling of global reanalysis data for high-resolution spatial modeling of snow accumulation/melting at the central/southern Sierra Nevada watersheds

2021 ◽  
pp. 126445
Author(s):  
Yoshihiko Iseri ◽  
Andres J. Diaz ◽  
Toan Trinh ◽  
M. Levent Kavvas ◽  
Kei Ishida ◽  
...  
Keyword(s):  
High Resolution ◽  
Sierra Nevada ◽  
Spatial Modeling ◽  
Dynamical Downscaling ◽  
Reanalysis Data ◽  
Snow Accumulation ◽  
Global Reanalysis
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