A tool for downscaling weather data from large-grid reanalysis products to finer spatial scales for distributed hydrological applications

Gupta, A. Sen; Tarboton, David G.

doi:10.1016/j.envsoft.2016.06.014

Cited by 55 publications

(57 citation statements)

References 45 publications

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“…It is likely that the parameterization put forward here can be further improved in its ability to predict fine-scale patterns and its suitability for transferring parameters between areas and thus the suitability for application in data-sparse regions. Nevertheless, REDCAPP and similar methods (Fiddes et al, 2015;Gupta and Tarboton, 2016) demonstrate that coarse-scale information on atmospheric variables can contribute to better prediction at finer scales without the need for increased resolution in the atmospheric model.…”

Section: Discussionmentioning

confidence: 99%

“…T sa is extrapolated by using a fixed lapse rate of −6.5 • C km −1 (REF1) and by using variable lapse rate modeled from T pl (REF3) (Giorgi et al, 2003;Gao et al, 2012). Linearly interpolated T pl is referenced as REF2 (Fiddes and Gruber, 2014;Gupta and Tarboton, 2016). Since only the upper-air temperatures are used in REF2, this is equivalent to setting LSCF uniformly to 0 (no land-surface influence), while LSCF is uniformly considered to be 1 in REF1 and REF3, which use T sa as their base temperature.…”

Section: Reference Methodsmentioning

confidence: 99%

“…In order to make predictions at the fine scale (∼ 10-100 m), required for representing topography, the coarse-scale (∼ 10-100 km) reanalysis data need to be downscaled (Bürger et al, 2012). Previous studies (Fiddes and Gruber, 2014;Gupta and Tarboton, 2016;Gao et al, 2012) have reported how reanalysis data can be used to represent the elevation dependency of T in downscaling. This study investigates how to further refine corresponding predictions and outlines a REanalysis Downscaling Cold Air Pooling Parameterization (REDCAPP) method.…”

Section: Introductionmentioning

confidence: 99%

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REDCAPP (v1.0): parameterizing valley inversions in air temperature data downscaled from reanalyses

2017

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Abstract. In mountain areas, the use of coarse-grid reanalysis data for driving fine-scale models requires downscaling of near-surface (e.g., 2 m high) air temperature. Existing approaches describe lapse rates well but differ in how they include surface effects, i.e., the difference between the simulated 2 m and upper-air temperatures. We show that different treatment of surface effects result in some methods making better predictions in valleys while others are better in summit areas. We propose the downscaling method RED-CAPP (REanalysis Downscaling Cold Air Pooling Parameterization) with a spatially variable magnitude of surface effects. Results are evaluated with observations (395 stations) from two mountain regions and compared with three reference methods. Our findings suggest that the difference between near-surface air temperature and pressure-level temperature ( T ) is a good proxy of surface effects. It can be used with a spatially variable land-surface correction factor (LSCF) for improving downscaling results, especially in valleys with strong surface effects and cold air pooling during winter. While LSCF can be parameterized from a fine-scale digital elevation model (DEM), the transfer of model parameters between mountain ranges needs further investigation.

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Section: Discussionmentioning

confidence: 99%

Section: Reference Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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REDCAPP (v1.0): parameterizing valley inversions in air temperature data downscaled from reanalyses

2017

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“…Some forcing data preprocessing efforts currently exist to downscale coarser scale datasets, e.g., climate model reanalyses in high varying terrainbased regions. Examples include the MERRA Spatial Downscaling for Hydrology tool (MSDH; Sen Gupta and Tarboton, 2016), which uses the R statistical software package (e.g., https://cran.r-project.org/); TopoSCALE, v.1.0 (Fiddes and 5 Gruber, 2014); and the "eartH2Observe" data portal, which provides a suite of python scripts that downscale meteorological fields from the European Union's eartH2Observe dataset (https://github.com/earth2observe/downscaling-tools). However, these script-based or software toolkits typically only serve a select set of different meteorological forcing datasets.…”

Section: Introductionmentioning

confidence: 99%

The Land surface Data Toolkit (LDTv7.2) – a data fusion environment for land data assimilation systems

Arsenault¹,

Kumar²,

Geiger³

et al. 2018

Preprint

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Abstract. The effective applications of land surface model (LSM) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically 20 for processing input data to execute LSMs and hydrological models. LDT not only serves as a pre-processor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand-alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions, meteorological 25 boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational data sets and data analytics algorithms to aid land surface modelling and data assimilation. 30Geosci. Model Dev. Discuss., https://doi

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“…To extend its application in this area for spatial distributed modelling, the grid version of UEB (UEBGrid) (Sen Gupta and Tarboton, 2013;Sen Gupta and Tarboton, 2016) was used in this study. It models the snow pack as a single layer to avoid over-parameterisation (Sen Gupta and Tarboton, 2013).…”

Section: Uebgridmentioning

confidence: 99%

Winter hydrology and soil erosion processes in an agricultural catchment in Norway

Starkloff¹

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A tool for downscaling weather data from large-grid reanalysis products to finer spatial scales for distributed hydrological applications

Abstract: , (2016), "A tool for downscaling weather data from large-grid reanalysis products to finer spatial scales for distributed hydrological applications,"

Cited by 55 publications

References 45 publications

REDCAPP (v1.0): parameterizing valley inversions in air temperature data downscaled from reanalyses

REDCAPP (v1.0): parameterizing valley inversions in air temperature data downscaled from reanalyses

The Land surface Data Toolkit (LDTv7.2) – a data fusion environment for land data assimilation systems

Winter hydrology and soil erosion processes in an agricultural catchment in Norway

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