Assimilation of (satellite) cloud-information at the convective scale in an Ensemble Kalman Filter...

Assimilation of (satellite) cloud-Assimilation of (satellite) cloud-

information at the convective scale in information at the convective scale in

an Ensemble Kalman Filteran Ensemble Kalman Filter

Annika Schomburg1, Christoph Schraff1, Africa Perianez1, Jason Otkin2, Roland Potthast1

1 DWD (German Meteorological Service)2 University of Wisconsin

annika.schomburg@dwd.de

WWOSC 16-21 August 2014, Montreal

annika.schomburg@dwd.de

Motivation: Growing renewable energy sector

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• Weather dependencey of renewable energy: Increasing demands for accurate power predictions for a safe and cost-effective power system Project to improve forecast models

for the grid integration of weather dependent energy sources

Source: energymap.info

solarWindhydroelectricbiomassgasgeothermal

Energy production in Germany for week 30, 2014

Source: Fraunhofer ISE

Development of average renewable energy production in Germany since 2002:

For photovoltaic power a realistic simulation of cloud cover is crucial this talk: exploit cloud information sources for data assimilation

annika.schomburg@dwd.de

Motivation

• Difficult weather situations for photovoltaic power predictions:

• Cloud cover after cold front passes

• Convective situations

• Low stratus clouds / fog

• Snow cover on solar panels

Convective scale models needed to capture relief and atmospheric stability locally

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annika.schomburg@dwd.de

Modelling system

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COSMO-DE :

•Limited-area short-range numerical model weather prediction model•x 2.8 km / 50 vertical layers •Explicit deep convection

•New data assimilation system : Implementation of the Ensemble Kalman Filter: LETKF after Hunt et al. (2007)

annika.schomburg@dwd.de 5

Local Ensemble Transform Kalman Filter

LETKF

Analysis perturbations: linear combination of background

perturbations

First guess ensemble members are weighted according to their departure from the observations

OBS-FG

OBS-FG

OBS-FG

OBS-FG

R

Tbibk

i

bibb

k))((

)1(

1 )(

1

)( xxxxP

Background error covariance

Observation errors

annika.schomburg@dwd.de

Outline

• Assimilation of additional data to improve cloud cover:

• Satellite cloud products

• Cloudy infrared satellite radiances

• Photovoltaic power

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Satellite cloud products

annika.schomburg@dwd.de 8

Satellite product: cloud top height

contains information on horizontal and vertical distributions of clouds

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Satellite cloud product information

Geostationary satellite data: Meteosat-SEVIRI (Δx ~ 5km over central Europe, Δt=15 min)

Source: EUMETSAT

Height [km]

Cloud top height

annika.schomburg@dwd.de

MODEL EQUIVALENT

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OBSERVATION: Satellite product: cloud top height

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Method

• Extract information if a pixel is observed as cloudy:

Height [km]

Cloud top height

Cloud top height

Relative humidity at cloud top

height

Determine cloud top model equivalent: top of most humid layer k close to observation

100%

see Schomburg et al., QJRMS, 2014

Layer kRH(k)

height(k)

Observation

Model RH profile

Assimilated variables:

annika.schomburg@dwd.de

relative humiditycloud covercloud water

cloud iceobserved cloud top

3 lines in one colour indicate ensemble mean and mean +/- spread

• 1 analysis step, 17 Nov. 2011, 6 UTC (wintertime low stratus)

vertical profiles

Example: Single-observation experiments: missed low stratus cloud

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First guess Analysis

annika.schomburg@dwd.de

Model equivalent:

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Observation:Satellite product: cloud top height

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Method

• Extract information if a pixel is observed as cloud-free:

Height [km]

Cloud top height

Cloud coverhigh clouds

Cloud covermid-level

clouds

Cloud coverlow clouds

0%

0%

0%

Z [km]

CLC

Maximum cloud cover in

high levels

Maximum cloud cover in

medium levels

Maximum cloud cover in

low levels

see Schomburg et al., QJRMS, 2014

Assimilated variables:

annika.schomburg@dwd.de

low clouds mid-level clouds high clouds‘false alarm’ cloud cover

(after 20 hrs cycling)

conventional+ cloud

conventionalobs only

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Comparison “only conventional“ versus “conventional + cloud obs"

[octa]

annika.schomburg@dwd.de13

conventional only conventional + cloud

Total cloud cover after 12 h free forecast

Observed cloud top height

Comparison of free forecast results

satellite obs

15. Nov 2011, 6:00 UTC

Cloudy infrared satellite radiances

annika.schomburg@dwd.de

Approach: Assimilate water vapour and window channels of Meteosat SEVIRI

• The goal is to obtain information on the cloud cover in the atmosphere

assimilate all-sky radiances from water-vapour channels and window channel(s)

•Challenges: Cloud dependent bias correction?

How to specify observation error

Thinning, localization in LETKF

.. etc..

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Source: Schmetz et al, BAMS, 2002

SEVIRI channels

annika.schomburg@dwd.de

First step: Monitoring

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Example for water vapour band WV7.3, sensitive to mid-level moisture (and clouds)

• Obs minus model statistics look promising with the exception of high-level clouds, semitransparent clouds should be excluded here

• Bias correction is needed: the model is 2-4 K too warm

Observation minus Background

RMSE

annika.schomburg@dwd.de

First assimilation results: 12 hours of cycling, assimilation of channel WV7.3

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Bia

sR

MS

E

Without radiance assimilation With radiance assimilation

Photovoltaic power

annika.schomburg@dwd.de 19

Assimilation of photovoltaic power

Model variables:- surface irradiance- 2m temperature

- albedo

Model variables:- surface irradiance- 2m temperature

- albedo

Source: Yves-Marie Saint-Drenan, IWES

Forward operator:

Compute radiation on tilted

plane

Compute radiation on tilted

plane

Forward operator for PV module

Challenge: Data availability - Up to now only data for a few solar parks available for DWD

Synthetic PV power (clouds

main forcing factor)

Synthetic PV power (clouds

main forcing factor)

annika.schomburg@dwd.de 20

Example of simulated and observed photovoltaic power

Model forecast solar insolation at surfaceObserved photovoltaic powerSimulated photovoltaic power (based on model forecast radiation)

No

rmal

ized

po

wer

[W

/Wp

eak]

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annika.schomburg@dwd.de

Conclusions

• Work on assimilating cloud information from various sources at the convective scale (∆x~3km) in a LETKF system ongoing:

• Satellite products, satellite radiances, PV power

• Challenges:

• Nonlinearity

• Presentation of clouds in the model

• Forward modelling

• PV data and meta-data availability and quality• Shading by trees, string failures, soiling…

• Improved cloud cover simulations is also expected to lead to a better onset of convection and temperature predictions

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Thank you for your attentionThank you for your attention!

annika.schomburg@dwd.de 22

annika.schomburg@dwd.de

Determine the model equivalent cloud top

Avoid strong penalizing of members which are dry at CTHobs but have a cloud or even only high humidity close to CTHobs

search in a vertical range hmax around CTHobs fora ‘best fitting’ model level k, i.e. with minimum ‘distance’ d:

2

max

2 )(1

)(min obskobskk

CTHhh

RHRHd

relative humidity height ofmodel level

k

= 1

use y=CTHobs H(x)=hk

and y=RHobs=1 H(x)=RHk (relative humidity over

water/ice depending on temperature)as 2 separate variables assimilated by LETKF

use y=CTHobs H(x)=hk

and y=RHobs=1 H(x)=RHk (relative humidity over

water/ice depending on temperature)as 2 separate variables assimilated by LETKF

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Z [km]

RH [%]

CTHobs

k1

k2

k3

k4

k5

Cloud top

model profile

•(make sure to choose the top of the detected cloud)

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Example: 17 Nov 2011, 6:00 UTCObservations and model equivalents

RH model level kObservation Model

„Cloud top height“

annika.schomburg@dwd.de 25

• COSMO cloud cover where observations “cloudfree”

Example: 17 Nov 2011, 6:00 UTC

High clouds (oktas)Mid-level clouds (oktas)Low clouds (oktas)

annika.schomburg@dwd.de

Example: Missed cloud case:Effect on temperature profile

temperature profile [K] (mean +/- spread)

first guess analysis

observed cloud top

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annika.schomburg@dwd.de27

conventional only conventional + cloud

Total cloud cover of first guess fields after 20 hours of cycling

Satellite cloud top height

Results: Comparison of cycled experiments

satellite obs

12 Nov 2011 17:00 UTC

Low stratus cloud cover improved through assimilation of cloud products!Low stratus cloud cover improved through assimilation of cloud products!

annika.schomburg@dwd.de 28

Results III:Forecast impact

• 24 h free forecast starting after 21h cycling• 14 Nov. 2011, 18 UTC – 15 Nov. 18 UTC• Wintertime low stratus

annika.schomburg@dwd.de 29

conventional only conventional + cloud

Observed cloud top height

Results after 12 hours of free forecast

satellite obs

Total cloud cover after 12h forecast (15. Nov 2011, 6:00 UTC)

annika.schomburg@dwd.de

• The forecast of cloud characteristics can be improved through the assimilation of the cloud information

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Results: Comparison of free forecast: time series of

errors

Conventional + cloud dataOnly conventional data

RMSE

Bias (Obs-Model)

Low cloudsMid-level cloudsHigh clouds

Mean squared error averaged over all cloud-free observations

RH (relative humidity) at observed cloud top averaged over all cloudy

observations

annika.schomburg@dwd.de 31

Assimilation of photovoltaic power

Model variables:- surface irradiance- 2m temperature

- albedo

Model variables:- surface irradiance- 2m temperature

- albedo

Source: Yves-Marie Saint-Drenan, IWES

Forward operator:

Compute radiation on tilted

plane

Compute radiation on tilted

plane

Forward operator for PV module

Sensitivities:

Direct solar irradiance [W/m²]

Diffuse part of solar irradiance

[W/m²]

Ambient air temperature [K]