Downscaling of forcing dataTemperature, Shortwave (Solar) & Longwave (Thermal)

CHARIS meeting, Dehra Dun, India, October 2014

Presented by: Karl Rittger

Spatial variability in forcing variables

Temperature and precip. gradients

North and south facing slopes

Kanjin, Langtang Himal, November 2013

Along valley gradients

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Snow heterogeneity

Wind redistribution (D. Marks)

Differential ablation (E. Bair)

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Meteorological stations

• Sparse coverage• Few high elevation

stations• Possibly short series• How representative

are they• Need to be

“upscaled”/extrapolated

• Needs knowledge of lapse rates for P and T.

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Snow cover from Landsat 5 & in-situ data

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July 02

Fractional snow cover

July 18

Fractional snow cover

Automated SWE observations

Streamflow observations

CHARIS meeting, Dehra Dun, India, October 2014

Climate reanalysis

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•Numerical description of the recent climate

•Combines models of physical processes in the atmosphere and oceans with observations

•Observations

•Air temperature, pressure, wind

•Rainfall, soil moisture, sea-surface temp• Simplified

topography

• May be biased

• Needs to be downscaled

•Globally complete

•Long time series

•consistent

CHARIS meeting, Dehra Dun, India, October 2014

Atmospheric reanalysis used in NASA’s Land Data Assimilation System (LDAS)

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http://ldas.gsfc.nasa.gov/index.php

GLDAS-1: CurrentHigher

resolution

GLDAS-2: Long time scale

Consistent

CHARIS meeting, Dehra Dun, India, October 2014

Atmospheric reanalysis used in NASA’s Land Data Assimilation System (LDAS)

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http://ldas.gsfc.nasa.gov/index.php

Drives multiple land surface models from simple to complex

CHARIS meeting, Dehra Dun, India, October 2014

Downscaling of forcing data

• Temperature Based on lapse rates and elevation differences between

coarse and fine DEMs

• Shortwave and Longwave Scale (time and space) Elevation Topography Forest

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Downscaling of forcing data

• Temperature Based on lapse rates and elevation differences between

coarse and fine DEMs

• Shortwave and Longwave Scale (time and space) Elevation Topography Forest

10 CHARIS meeting, Dehra Dun, India, October 2014

GLDAS-1-NOAH: air temperature

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Sierra Nevada, California (Elevation)

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500m(1/8° or about 14km)

CHARIS meeting, Dehra Dun, India, October 2014

Sierra Nevada, California (Elevation)

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500m scale

(1/8° or about 14km)

CHARIS meeting, Dehra Dun, India, October 2014

Downscaling temperature using a lapse rate and change in elevation

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Air Temperature, 1999-01-01 0100

Air Temperature (Ta), 1999-01-01 0100 (Video)

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• But environmental lapse rates change through time and from place to place.

• Reanalysis data has air temperatures for many levels in the atmosphere

• Atmospheric profile can be used to estimate lapse rate.

A more rigorous approach to the lapse rate, γ

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• Simplest approach is to use a standard lapse rate to disaggregate model temperature to finer grid.

CHARIS meeting, Dehra Dun, India, October 2014

Downscaling of forcing data

• Temperature Based on lapse rates and elevation differences between

coarse and fine DEMs

• Shortwave and Longwave Scale (time and space) Elevation Topography Forest

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GLDAS: incoming solar

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Net Shortwave Radiation:Tuolumne-Merced River basinsMarch 1, 2006 12:00

Painter et al., 2009;Dozier et al., 2008

Link and Marks, 1999; Garren and Marks, 2005

Dozier and Frew, 1990Erbs et al., 1982;Olyphant et al., 1984

Dubayah and Loechel., 1997

Cosgrove et al., 2003;Pinker et al., 2003;Mitchell et al., 2004

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GLDAS: incoming thermal

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Net Longwave Radiation:Tuolumne-Merced River basinsMarch 1, 2006 12:00

Dozier and Frew, 1990

Link and Marks, 1999; Garren and Marks, 2005

Dubayah and Loechel, 1997

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Summary

• Need to capture processes at small scale in modeling snow accumulation and melt

• Global re-analysis data available from several sources

• Coupled with land surface models to provide surface fluxes

• Downscaling based on Elevation, slope, aspect, solar angles, vegetation,

shading, dynamic lapse rates

22 CHARIS meeting, Dehra Dun, India, October 2014