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

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.

Assessment of wind energy potential over India using high-resolution global reanalysis data

2021 ◽  
Vol 130 (2) ◽  
Author(s):  
China Satyanarayana Gubbala ◽  
Venkata Bhaskar Rao Dodla ◽  
Srinivas Desamsetti
Keyword(s):  
High Resolution ◽  
Wind Energy ◽  
Reanalysis Data ◽  
Energy Potential ◽  
Wind Energy Potential ◽  
Global Reanalysis

scholarly journals The Value of Accurate High-Resolution and Spatially Continuous Snow Information to Streamflow Forecasts

2019 ◽  
Vol 20 (4) ◽  
pp. 731-749 ◽  
Author(s):  
Dongyue Li ◽  
Dennis P. Lettenmaier ◽  
Steven A. Margulis ◽  
Konstantinos Andreadis
Keyword(s):  
High Resolution ◽  
Sierra Nevada ◽  
Snow Accumulation ◽  
Forecast Errors ◽  
Streamflow Forecasting ◽  
Streamflow Forecast ◽  
Physically Based ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow ◽  
The Impact

Abstract Previous studies have shown limited success in improving streamflow forecasting for snow-dominated watersheds using physically based models, primarily due to the lack of reliable snow water equivalent (SWE) information. Here we use a hindcasting approach to evaluate the potential benefit that a high-resolution, spatiotemporally continuous, and accurate SWE reanalysis product would have on the seasonal streamflow forecast in the snow-dominated Sierra Nevada mountains of California if such an SWE product were available in real time. We tested the efficacy of a physically based ensemble streamflow prediction (ESP) framework when initialized with the reanalysis SWE. We reinitialized the SWE over the Sierra Nevada at the time when the Sierra Nevada had domain-wide annual maximum SWE for each year in 1985–2015, and on 1 February of the driest years within the same period. The early season forecasts on 1 February provide valuable lead time for mitigating the impact of drought. In both experiments, initializing the ESP with the reanalysis SWE reduced the seasonal streamflow forecast errors; compared with existing operational statistical forecasts, the peak-annual SWE insertion and the 1 February SWE insertion reduced the overall root-mean-square error of the seasonal streamflow forecasts by 13% and 23%, respectively, over the 13 major rivers draining the Sierra Nevada. The benefits of the reanalysis SWE insertion are more pronounced in areas with greater snow accumulation, while the complex snow and runoff-generation processes in low-elevation areas impede the forecasting skill improvement through SWE reinitialization alone.

scholarly journals Dynamical Global Downscaling of Global Reanalysis

2008 ◽  
Vol 136 (8) ◽  
pp. 2983-2998 ◽  
Author(s):  
Kei Yoshimura ◽  
Masao Kanamitsu
Keyword(s):  
High Resolution ◽  
Wind Speed ◽  
Surface Temperature ◽  
Dynamical Downscaling ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
The United States ◽  
Full Data ◽  
Global Spectral Model ◽  
Global Reanalysis

Abstract With the aim of producing higher-resolution global reanalysis datasets from coarse-resolution reanalysis, a global version of the dynamical downscaling using a global spectral model is developed. A variant of spectral nudging, the modified form of scale-selective bias correction developed for regional models is adopted. The method includes 1) nudging of temperature in addition to the zonal and meridional components of winds, 2) nudging to the perturbation field rather than to the perturbation tendency, and 3) no nudging and correction of the humidity. The downscaling experiment was performed using a T248L28 (about 50-km resolution) global model, driven by the so-called R-2 reanalysis (T62L28 resolution, or about 200-km resolution) during 2001. Evaluation with high-resolution observations showed that the monthly averaged global surface temperature and daily variation of precipitation were much improved. Over North America, surface wind speed and temperature are much better, and over Japan, the diurnal pattern of surface temperature is much improved, as are wind speed and precipitation, but not humidity. Three well-known synoptic/subsynoptic-scale weather patterns over the United States, Europe, and Antarctica were shown to become more realistic. This study suggests that the global downscaling is a viable and economical method for obtaining high-resolution reanalysis without rerunning a very expensive high-resolution full data assimilation.

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 The new Italian regional reanalysis SPHERA: benefits of the convection-permitting resolution in detecting severe-weather events

2021 ◽  
Author(s):  
Antonio Giordani ◽  
Ines Cerenzia ◽  
Tiziana Paccagnella ◽  
Silvana Di Sabatino
Keyword(s):  
High Resolution ◽  
Dynamical Downscaling ◽  
Temporal Frequency ◽  
Severe Weather ◽  
Added Value ◽  
Limited Area ◽  
Reanalysis Dataset ◽  
Weather Events ◽  
Regional Reanalysis ◽  
Global Reanalysis

<p>In recent years the interest towards the development of limited-area atmospheric reanalysis datasets has been growing more and more. Regional reanalyses in fact, as a consequence of the restricted domain that they cover, provide a data distribution displaced on a much finer grid compared to a coarser global dataset. This permits to better resolve those patterns related to rapid and high-impact weather events, first and foremost convection. Furthermore, with a finer horizontal resolution, a consistent increase in the level of detail in the description of the orography is also gained, that is a crucial point to achieve especially in a very complex territory such as Italy. This study presents the first application of the novel regional reanalysis dataset developed at ARPAE-SIMC: the High rEsolution ReAnalysis over Italy (SPHERA). SPHERA is a high-resolution convection-permitting reanalysis over the Italian domain and the surrounding seas covering 25 years, from 1995 to 2020, at hourly temporal frequency. SPHERA is based on the non-hydrostatic limited-area model COSMO, and produced by a dynamical downscaling of the global reanalysis ERA5, developed at ECMWF. A nudging data assimilation scheme is applied in order to steer the model outcomes towards the surface and upper-air observations. All the available conventional observations have been used.</p><p>The added value of SPHERA in representing severe-weather and convective events is evident from its preliminar validation, which was performed on the multidecadal period against various datasets of surface observations, joined with the comparison against the global reanalysis ERA5. In fact, a clear advantage of SPHERA on its driver ERA5 is found for the detection of events with moderate to intense daily and sub-daily rainfalls, which are characterized by a strong seasonal and geographical component, that is further investigated. We report also the preliminary sensitivity analysis on the dimension of the box used to operate the upscaling for the validation of SPHERA, a process necessary to reduce the errors caused by geographical mismatches between observed and simulated events localizations, which are particularly frequent in case of strongly-localized and rapid processes. Furthermore, in order to give a quantitative evaluation of the performance of the new reanalysis in particular conditions, the results of the simulations for specific case studies involving the occurrence of severe-precipitation events in recent years was performed, focusing on events having different dynamical genesis, but interrelated by the important damages they caused. From this analysis, for which also a comparison with other regional reanalyses is performed, the advantage of SPHERA in representing the most intense rainfall occurrences, in terms of location, intensity and timing, clearly emerges.</p>

scholarly journals On the sensitivity of meteorological forcing resolution on hydrologic metrics

2019 ◽  
Author(s):  
Fadji Z. Maina ◽  
Erica R. Siirila-Woodburn ◽  
Pouya Vahmani
Keyword(s):  
Sierra Nevada ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Central Valley ◽  
Snow Accumulation ◽  
Watershed Model ◽  
Meteorological Forcing ◽  
Groundwater Levels

Abstract. Projecting the spatio-temporal changes to water resources under a no-analog future climate requires physically-based integrated hydrologic models, which simulate the transfer of water and energy across the earth's surface. These models show promise in the context of unprecedented climate extremes given their reliance on the underlying physics of the system as opposed to empirical relationships. However, these techniques are plagued by several sources of uncertainty, including the inaccuracy of input datasets such as meteorological forcing. These datasets, usually derived from climate models or satellite-based products, typically have a resolution of several kilometers, while hydrologic metrics of interest (e.g. discharge, groundwater levels) require a resolution at much smaller scales. In this work, a high-resolution watershed model is forced with various resolutions (0.5 to 40.5 km) of meteorological forcing generated by a dynamical downscaling analysis based on a regional climate model (WRF) to assess how the uncertainties associated with the spatial resolution of meteorological forcing affect the simulated hydrology. The Cosumnes watershed, which spans the Sierra Nevada and Central Valley interface of California (USA), exhibits semi-natural flow conditions due to its rare un-dammed river basin and is used here as a testbed to illustrate potential impacts on snow accumulation and snowmelt, surface runoff, infiltration, evapotranspiration, and groundwater levels. Results show that localized biases in groundwater levels can be as large as 5–10 m and that other metric biases (e.g. ET and snowpack dynamics) are seasonally and spatially-dependent, but can have serious implications for model calibration and ultimately water management decisions.

A new spatial modeling and interpolation approach for high-resolution temperature maps combining reanalysis data and ground measurements

2019 ◽  
Vol 276-277 ◽  
pp. 107590 ◽  
Author(s):  
Mariassunta Viggiano ◽  
Lorenzo Busetto ◽  
Domenico Cimini ◽  
Francesco Di Paola ◽  
Edoardo Geraldi ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Modeling ◽  
Reanalysis Data ◽  
Temperature Maps

scholarly journals Sensitivity of meteorological-forcing resolution on hydrologic variables

2020 ◽  
Vol 24 (7) ◽  
pp. 3451-3474
Author(s):  
Fadji Z. Maina ◽  
Erica R. Siirila-Woodburn ◽  
Pouya Vahmani
Keyword(s):  
Sierra Nevada ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Hydrologic Model ◽  
Snow Accumulation ◽  
Test Bed ◽  
Meteorological Forcing ◽  
Groundwater Levels

Abstract. Projecting the spatiotemporal changes in water resources under a no-analog future climate requires physically based integrated hydrologic models which simulate the transfer of water and energy across the earth's surface. These models show promise in the context of unprecedented climate extremes given their reliance on the underlying physics of the system as opposed to empirical relationships. However, these techniques are plagued by several sources of uncertainty, including the inaccuracy of input datasets such as meteorological forcing. These datasets, usually derived from climate models or satellite-based products, are typically only resolved on the order of tens to hundreds of kilometers, while hydrologic variables of interest (e.g., discharge and groundwater levels) require a resolution at much smaller scales. In this work, a high-resolution hydrologic model is forced with various resolutions of meteorological forcing (0.5 to 40.5 km) generated by a dynamical downscaling analysis from the regional climate model Weather Research and Forecasting (WRF). The Cosumnes watershed, which spans the Sierra Nevada and Central Valley interface of California (USA), exhibits semi-natural flow conditions due to its rare undammed river basin and is used here as a test bed to illustrate potential impacts of various resolutions of meteorological forcing on snow accumulation and snowmelt, surface runoff, infiltration, evapotranspiration, and groundwater levels. Results show that the errors in spatial distribution patterns impact land surface processes and can be delayed in time. Localized biases in groundwater levels can be as large as 5–10 m and 3 m in surface water. Most hydrologic variables reveal that biases are seasonally and spatially dependent, which can have serious implications for model calibration and ultimately water management decisions.

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