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Postprocessing NWP model output for the

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1 Postprocessing NWP model output for the
needs of snow hydrological modelling Tobias Jonas, Adam Winstral, Nora Helbig, Michael Schirmer WSL Institute for Snow and Avalanche Research SLF Davos / Zürich, Switzerland 2nd Annual Workshop 17‐19 October, 2016 University of Grenoble LGGE Grenoble, France

2 Met Data Postprocessing
Operational snowmelt modelling in Switzerland SWE Monitoring MWP (Forecast) Meteo Observations Data Assimilation NWP (Analysis) Reanalysis Products Met Data Postprocessing EB Snowmelt Model Operational Products intro ► data assimilation ► debiasing ► downscaling ► terrain corrections ► fusion of datasets ► summary

3 Motivation We deal more frequently with complex forecast scenarios
NWP models can provide input data to EB snowmelt models but these need to be post-processed Here an example of a rain-on-snow situation in Jan/Feb 2016 intro ► data assimilation ► debiasing ► downscaling ► terrain corrections ► fusion of datasets ► summary

4 Met Data Postprocessing
Operational snowmelt modelling in Switzerland SWE Monitoring MWP (Forecast) Meteo Observations Data Assimilation NWP (Analysis) Reanalysis Products Met Data Postprocessing Data assimilation to update precipitation input Debiasing of forcing fields Downscaling of forcing fields Terrain corrections (wind, radiation, …) Fusion of Reanalysis and NWP data intro ► data assimilation ► debiasing ► downscaling ► terrain corrections ► fusion of datasets ► summary

5 Data assimilation to update precipitation input fields
 Calculate precipitation and phase from meteo input (control simulation) Rerun control simulation with disturbed meteo input fields Assume the disturbances (input errors) to be spatially correlated Optimize to match observed data (allow observational errors as well) intro ► data assimilation ► debiasing ► downscaling ► terrain corrections ► fusion of datasets ► summary

6 Debiasing input fields
 Example: air temperature of COSMO-2 forecast Issue: systematic cold bias, particularly in winter at high elevation +3 +2 +1 -1 -2 -3 Bias (K) intro ► data assimilation ► debiasing ► downscaling ► terrain corrections ► fusion of datasets ► summary

7 Debiasing input fields
 Example: air temperature of COSMO-2 forecast Issue: systematic cold bias, particularly in winter at high elevation Snowmelt Hindcast (mm/day) 16. April 2013 Snowmelt Forecast (mm/day) intro ► data assimilation ► debiasing ► downscaling ► terrain corrections ► fusion of datasets ► summary

8 COSMO – Observation (K)
Debiasing input fields  Example: air temperature of COSMO-2 forecast Issue: systematic cold bias, particularly in winter at high elevation Method: Stochastic debiasing using temperature data (mean and stdev), elevation, month COSMO – Observation (K) Altitude (m asl.) intro ► data assimilation ► debiasing ► downscaling ► terrain corrections ► fusion of datasets ► summary

9 Combined debiasing and downscaling of forcing fields
Example: wind fields of COSMO-7 Issues: coarse resolution, systematic biases with exposure, skewed PDFs Wind Speed (m/s) intro ► data assimilation ► debiasing ► downscaling ► terrain corrections ► fusion of datasets ► summary

10 Combined debiasing and downscaling of forcing fields
Example: wind fields of COSMO Issues: coarse resolution, systematic biases with exposure, skewed PDFs intro ► data assimilation ► debiasing ► downscaling ► terrain corrections ► fusion of datasets ► summary

11 Corrected Uncorrected
Combined debiasing and downscaling of forcing fields Example: wind fields of COSMO Issues: coarse resolution, systematic biases with exposure, skewed PDFs Corrected Uncorrected intro ► data assimilation ► debiasing ► downscaling ► terrain corrections ► fusion of datasets ► summary

12 Uncorrected Corrected
Combined debiasing and downscaling of forcing fields Example: wind fields of COSMO Issues: coarse resolution, systematic biases with exposure, skewed PDFs Method: Scaling using the wind-direction depended Sx exposure index improving both bias, PDF, and resolution Uncorrected Corrected Wind direction intro ► data assimilation ► debiasing ► downscaling ► terrain corrections ► fusion of datasets ► summary

13 Fusion of Reanalysis and NWP data
Example: precipitation (COSMO-analysis and RhiresD-reanalysis) Issue: reanalysis data is better but only available at daily resolution MWP (Forecast) Meteo Observations NWP (Analysis) Reanalysis Products 1h 24h Fusion Model Forecast + Update intro ► data assimilation ► debiasing ► downscaling ► terrain corrections ► fusion of datasets ► summary

14 Fusion of Reanalysis and NWP data
Example: precipitation (COSMO-analysis and RhiresD-reanalysis) Issue: reanalysis data is better but only available at daily resolution Method: force 24h-sum of hourly COSMO grids to match RhiresD data Disaggregation using weighted 20km/50km window intro ► data assimilation ► debiasing ► downscaling ► terrain corrections ► fusion of datasets ► summary

15 Fusion of Reanalysis and NWP data
Example: precipitation (COSMO-analysis and RhiresD-reanalysis) Issue: reanalysis data is better but only available at daily resolution Method: force 24h-sum of hourly COSMO grids to match RhiresD data Disaggregation using weighted 20km/50km window COSMO raw COSMO forced intro ► data assimilation ► debiasing ► downscaling ► terrain corrections ► fusion of datasets ► summary

16 Fusion of Reanalysis and NWP data
Example: precipitation (COSMO-analysis and RhiresD-reanalysis) Issue: reanalysis data is better but only available at daily resolution Method: force 24h-sum of hourly COSMO grids to match RhiresD data Disaggregation using weighted 20km/50km window COSMO raw COSMO forced intro ► data assimilation ► debiasing ► downscaling ► terrain corrections ► fusion of datasets ► summary

17 Take home message Forecast situations such as ROS require more complex snowmelt models EB snowmelt models require input fields that are difficult to produce but typically available from NWP models NWP model data need to be post-processed before using as input for subsequent EB snowmelt models Problems to deal with are: systematic bias, skewed PDFs, too coarse resolution, mismatch with reanalysis products Enhancing met data input requires a LOT of effort, data, and specific methodology for each variable/problem intro ► data assimilation ► debiasing ► downscaling ► terrain corrections ► fusion of datasets ► summary

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