Challenges in the Assimilation of PV Power Data in the Convection-Permitting High-Resolution NWP Model COSMO-DE Stefan Declair*, Yves-Marie Saint-Drenan, Roland Potthast Erstellung innovativer Wetter- und Leistungsprognosemodelle für die Netzintegration wetterabhängiger Energieträger - Eine Kooperation von Meteorologie und Energiewirtschaft - 80. Jahrestagung der DPG und DPG-Frühjahrstagung Regensburg, March 08th 2016

Motivation: Low Stratus Planetary boundary layer height free atmosphere Inversion layer Ekman layer (1000m) Prandtl layer (100m) laminar (mm) temperature

Agenda Data Assimilation Impact Experiment Method Observation Data Monitoring: Bias and Error Assimilation: Impact?

Agenda Data Assimilation Impact Experiment Method Observation Data Monitoring: Bias and Error Assimilation: Impact?

Forecast: Can I cross the street without getting hit? Information used: Observations Knowledge about cars, street, etc Experience  statistics Forecast errors due to: Observation (estimation) errors Model errors (icy street) Case does not match statistics

Numerical Weather Forecast Model: First-guess Data assimilation tool Observations Analysis: Improved initial conditions for next integration step

Available Observation Data Blue: large-scale PV power plants (~200) Grey: small-scale PV power panels (~3.7k) Meta data: Lon/lat coordinates Tilt angle Azimuth angle Degradation coefficients Corrections (fitted 1)) for : Temperature Power 1) Source and Work: Yves-Marie Saint-Drenan, Fraunhofer IWES

Forward operator for PV module Model Equivalent: PV Power Forward Operator Forward operator for PV module Model variables: Shortwave surface irradiance (direct and diffuse downward) Panel ambient temperature Surface albedo Synthetic PV power Module meta data: Panel azimuth/tilt angles lon/lat Degradation coefficients Corrections for temperature and power Compute angle between panel normal and sun position at current time Transform horizontal model irradiation into tilted panel plane Compute losses (soiling, shading, module temperature, optical losses)

Sources of Error Inaccurate/unknown meta data Angles Large diversity in panel manufacturers Local deviations (data) from fitted corrections1) surface albedo Local deviations (model) from Aerosol optical thickness 2) Cloud positions Radiation scheme 3) 1) Source and Work: Yves-Marie Saint-Drenan, Fraunhofer IWES 2) Tegen et al., Journal of Geophysical Research., 102, pp. 23895-23915 (1997) 3) Ritter et al., Monthly Weather Review, 120, pp. 303-325 (1992).

Agenda Data Assimilation Impact Experiment Method Observation Data Monitoring: Bias and Error Assimilation: Impact

Monitoring of Observations Determination of PV power model equivalent bias and observation error Cycling over 1 week, PV power data only passive in assimilation Valid for 2014051600 - 2014052300

First Glimpse on First Results: Low Clouds Control First-guess Analysis Difference

First Glimpse on First Results: Low Clouds Experiment First-guess Analysis Difference

First Glimpse on First Results: Low Clouds 06 UTC 09 UTC 12 UTC

First Glimpse on First Results: Mid Clouds 06 UTC 09 UTC 12 UTC

Conclusion LETKF successfully utilized to assimilate PV power obserations Cloud cover correction pretty well for low and middle clouds despite strong non-locality in the morning Spread increase due to PV power data assimilation  better representation of model error Outlook Stock up on PV power data! Experiments with better localization lengths Detailed analysis of increments in other atmospheric fields Single observation experiments Impact on forecast quality Combi-experiment: add wind power data to assimilation cycle

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