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Towards a Convective Scale Reanalysis with a Soil-Vegetation-Atmosphere- Transfer-Model International Symposium on Regional Reanalyses Clarissa Figura, Insa Thiele-Eich, Jan D. Keller, Wolfgang Kurtz, Clemens Simmer, Andreas Hense 19.07.2018

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International Symposium on Regional Reanalyses

Clarissa Figura, Insa Thiele-Eich, Jan D. Keller, Wolfgang Kurtz, Clemens Simmer, Andreas Hense

19.07.2018

Towards a Convective Scale Reanalysis with a Soil-Vegetation-Atmosphere- Transfer-Model

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Reanalyses offer spatially and temporally consistent data sets for global or regional grids and a certain vertical exent within the land surface and atmosphere. Applications:

  • Renewable Energy Sector
  • Regional climate analyses
  • Hydrology
  • Agricultural economics

Motivation

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Reanalyses offer spatially and temporally consistent data sets for global or regional grids and a certain vertical exent within the land surface and atmosphere. Applications:

  • Renewable Energy Sector
  • Regional climate analyses
  • Hydrology
  • Agricultural economics

Demand for regional reanalyses is growing!

Motivation

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Challenges 1

Misrepresentation of hydrology (soil moisture-evaporation- precipitation feedback) in atmospheric reanalyses, especially at catchment scale

  • Betts et al. 1998 and 2003: Investigations of water and

energy budget in ECMWFs reanalysis for Mississippi and subbasins → Coupled reanalysis approach

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Challenges 2

There exists a coupling between convection triggering and soil moisture (Cioni and Hohenegger, 2017)

  • idealized LES-Simulations show a strong coupling between

soil moisture and diurnal precipitation cycle  Convective scale setup

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Challenges 3

Models are not perfect due to parametrizations and the chaotic nature of the systems they represent (Lorenz, 1969).  Ensemble approach with perturbed realizations

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Experimental setup TerrSysMP

With kind permissions of P. Shrestha and M. Sulis

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KENDA (Kilometre Scale ENsemble Data Assimilation) is a Local Ensemble Transform Kalman Filter

Experimental setup

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Experimental setup

KENDA

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Experimental setup

KENDA

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Experimental setup

KENDA → Basic Cycling System (BaCy) Since March 2017 operational at Deutscher Wetterdienst COSMO Ensemble COSMO Ensemble LETKF LETKF

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Observations Observations COSMO Ensemble COSMO Ensemble

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Experimental setup

KENDA with TerrSysMP ...work in progress LETKF LETKF

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Observations Observations TerrSysMP Ensemble TerrSysMP Ensemble TerrSysMP Ensemble TerrSysMP Ensemble

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Experimental setup

EMVORADO (Efficient Modular VOlume RADar Operator) → Radar forward operator

QR QS … HEMVORADO Model

  • utput

= xb Simulation of radar beams = yb

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Experimental setup

TerrSysMP TerrSysMP TerrSysMP TerrSysMP LETKF LETKF

?

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Experimental setup

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Preliminary work

COSMO-REA6

  • COSMO 4.25/TERRA
  • ∆xy= 6 km
  • Atmospheric boundary: ERA-Interim
  • Data assimilation:

Nudging of conventional observations (e.g., buoys, radio soundings, aircraft) Soil Moisture / Snow / SST Analysis

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Preliminary work

Long-term deterministic TerrSysMP simulation (Mauro Sulis)

  • TerrSysMP fully coupled (COSMO 4.x, CLM 3.5)
  • ∆xy=1000 m/ 500 m
  • Atmospheric boundary: COSMO-DE-Analysis
  • Downscaling (no data assimilation)
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First results

Ensemble TerrSysMP downscaling

  • TerrSysMP fully coupled (COSMO 5.1, CLM 3.5)
  • ∆xy=1000 m/ 500 m
  • Atmospheric boundary: COSMO-DE-KENDA-Analysis
  • Time period: 16.05.-13.06.2014
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First results

Case study: Front

12 h accumulated rain [mm] for 21.05.2014 00 UTC - 21.05.2014 12 UTC

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First results

Case study: Convection

12 h accumulated rain [mm] for 22.05.2014 12 UTC - 23.05.2014 00 UTC

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First results

Feedback precipitation – soil moisture

IQR Stdev Precipitation (mm/24h) Soil moisture (pressure) 28.05.2014

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First results

Feedback precipitation – soil moisture

IQR Stdev Precipitation (mm/24h) Soil moisture (pressure) 26.05.2014

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First results

Contingency table

hit false alarm miss correct negatives Observed Model yes no yes no Observations: 68 DWD rain gauges stations in NRW domain

www.bremerhaven-wetter.de

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First results

Contingency table

Model BIAS ETS Log Odds COSMO-REA6 1.71

  • 0.03
  • 2.36

TSMP det 1.13

  • 0.04
  • 2.09

TSMP ens 1.04

  • 0.05
  • 1.78

Threshold: 0.1 mm/h

Model BIAS ETS Log Odds COSMO-REA6 2.95

  • 0.01
  • ∞

TSMP det 1.06

  • 0.03
  • ∞

TSMP ens 0.95

  • 0.03
  • 2.62

Threshold: 0.2 mm/h

16.-22.05.2014

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First results

Contingency table

Model BIAS ETS Log Odds COSMO-REA6 1.71

  • 0.03
  • 2.36

TSMP det 1.13

  • 0.04
  • 2.09

TSMP ens 1.04

  • 0.05
  • 1.78

Threshold: 0.1 mm/h

Model BIAS ETS Log Odds COSMO-REA6 2.95

  • 0.01
  • ∞

TSMP det 1.06

  • 0.03
  • ∞

TSMP ens 0.95

  • 0.03
  • 2.62

Threshold: 0.2 mm/h

16.-22.05.2014

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First results

Shortcomings in representation of precipitation: → Data assimilation (LETKF) is expected to enhance the results

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Conclusion

  • Qualitatively TerrSysMP with dynamic downscaling of

analyses is able to better reproduce small scale precipitation events in comparison to COSMO-REA6

  • Extended simulation time period necessary to evaluate the

quantitative accuracy of precipitation with verification scores

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Outlook

  • Extended time period with TerrSysMP-KENDA with

EMVORADO planned

  • Quantitative model comparison and verification in terms of

precipitation and soil moisture with independent observations

  • Evaluation of feedback of soil moisture on precipitation
  • Evaluate impact of initial conditions
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International Symposium on Regional Reanalyses

Clarissa Figura, Insa Thiele-Eich, Jan D. Keller, Wolfgang Kurtz, Clemens Simmer, Andreas Hense

19.07.2018

Towards a Convective Scale Reanalysis with a Soil-Vegetation-Atmosphere- Transfer-Model

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First results

Contingency table

Frequency Bias (BIAS):

  • BIAS=(hits+false alarms)/(hits+misses)
  • ratio of the frequency of modeled events to the frequency of
  • bserved events

→ BIAS<1: underforecast, BIAS>1: overforecast, perfect: 1 Equitable Threat Score (ETS):

  • ETS=(hits+hitsrandom)/(hits+misses+false alarms-hitsrandom)
  • fraction of observed and/or modeled events that were correctly

predicted, adjusted for hits associated with random chance → Range: -1/3 to 1, no skill:0, perfect: 1 Log Odds Ratio (LOR):

  • LOR=log((hits+correct negatives)/(misses+false alarms))
  • ratio of the odds of making a hit to the odds of making a false

alarm → Range: -∞ - +∞ no skill:0, perfect: +∞