european regional reanalysis activities · cosmo-rea6, cosmo-rea2 cosmo-rea6 forced by era-interim,...

European Regional Reanalysis

activities

Andrea Kaiser-Weiss

with input from Per Undén, Richard Renshaw,

Frank Kaspar, Lilo Bach, Eric Bazile

Andrea Kaiser-Weiss, Copernicus Regional Reanalysis Workshop Reading 2016 2

Outline

1) Motivation

2) Previous regional/national/European efforts

3) Current UERRA productions and archive

4) Users and applications

5) Summary


1) Motivation

for reanalysis:

many users are looking for time variability of

meteorological parameters

for regional reanalysis:

higher temporal and spatial resolution

focus on near-ground variables

interest in extremes, frequency distributions

ensembles


Why we are interest in wind and solar radiation data …

… some headlines

from last week


8.5.2016: approx 90 %

Contribution of Renewables [%] to Total Power Production

in Germany, planned

2010: 18%

2013: 25%

?

?

?2015: 30%

?

and actual


Time variability in energy production

source: https://www.agora-energiewende.de/en/topics/-agothem-/Produkt/produkt/76/Agorameter

Time variability depends (also) on weather and climate


An economically ‘extreme’ weather event

Regional reanalysis might be a powerful data

source.

2) Previous regional/national/European

efforts


Approaches for providing time variability of needed

parameters

adding dynamical constraint from global reanalysis

downscaling

with a local

area model

forcing a regional

reanalysis ( regional

data assimilation)

observations alone

(carefully interpolated)

statistical downscaling

seldom sufficient

limited benefit


Regional Downscaling

KNMI wind atlas: downscaled ERA-

Interim with HIRLAM and tuning wind sheer to

Cabauw obs:

http://projects.knmi.nl/knw/index.html, horizontal

grid of 2.5 km, 1979-2013

CoastDat2: downscaled NCEP with

COSMO: http://www.coastdat.de/ 25 km, 1948-

2012 (HZG Inst. F. Kuestenforschung)

Various other downscaling

projects (with RCMs, NWPs)

http://projects.knmi.nl/knw/index.html

http://www.coastdat.de/


Regional Reanalyses projects

Arctic Reanalysis ASR (Ohio Uni, US) based on WRF-3D-Var, forced by ERA-Interim, 2000-2010, 30 km resolution.

Baltic Sea reanalyses BaltAn65+ (Uni Tarfu, Estonia) with HILAM 3D-Var, forced byERA-40, 1965-2005, 11 km resolution

High resolution regional reanalysis overIreland (Met Éireann) using HARMONIE, 3D-Var, forced by ERA-Interim, 35yrs, 2.5 km

COSMO-REA (German effort)


ERA-Interim

COSMO-REA6, COSMO-REA2COSMO-REA6 forced by

ERA-Interim, nudging,

1997-2014,

6km resolution

COSMO-REA2 forced by

COSMO-REA6, latent heat

nudging (radar), 2007-2014,

2 km resolution

credits: Jan Keller, DWD


EURO4M regional reanalyses developments

FP7 project 2010-2014

http://www.euro4m.eu/

extended to 1979, from 2014


FP7 project UERRA,

led by Per Undén, SMHI

Building on EURO4M, extending on resolution, time span, more models,

DA developments and ensembles:

http://www.smhi.se/

http://www.smhi.se/

http://met.no/

http://met.no/


- Met Office

- University Bonn / DWD

- SMHI

- Météo France

all covering Europe CORDEX-domain,

all approx same resolution (5...25 km),

all relying on ERA-Interim.

3) Current UERRA reanalysis productions:


Met Office reanalyses

• Ensemble using static 4DVAR

• Provides lower resolution fields with uncertainty estimation

• i.e. mean and spread at 24km

• Deterministic reanalysis using hybrid 4DVAR

• Uses ensemble reanalysis uncertainty to improve assimilation (B)

• system running, production start Q3 2016

• Provides higher resolution deterministic fields at 12km

• system running, production start Q3 2016

Satellite era (1978 – present)

Credits:Peter Jeremy, Met Office


Method

UM

UM

UM

4DVAR

4DVAR

4DVAR

UM

H 4Y DB VR AI RD

Bc + Be

• Ensemble & Deterministic systems coupled

• Ensemble uses fixed bg error cov (B=Bc)

• Ensemble provides EOTD to ensemble

• EDA - "hybrid" 4DVAR - weighted sum of bg error covs (B=bcBc+beBe)

• 4DVAR minimises weighted sum of differences with background & obs

• Weights are dependent on background error covariance matrix (B)

Credits:Peter Jeremy, Met Office

20

ensembles

1

deterministic


COSMO-REA6

WP3 Motivation: assessing uncertainties

(production finished)


COSMO-EN-REA12

ensemble production

Ensemble generated by:

perturbed observations ensemble nudging

20+1 ensemble members, 12 km resolution,

coverage 2006 to 2010,

production started April 2016.

Evaluation of ensemble with:

Analysis Rank Histogramm, Reliability Diagram,

Roc Curve, Brier Score, CRPS etc.

Credits: Lilo Bach, Univ. of Bonn


SMHI regional reanalysis production

5 year HARMONIE mini-ensemble:

• 2 members using ALADIN and ALARO physics,

11 km, 2006-2010. Production on-going.

credits: Esbjorn Olsson, Heiner Körnich, SMHI

Verification against observations (2m Temp, 10m

winds, precip) overall favours ALADIN.


SMHI regional reanalysis production

50+ years regional reanalysis with HARMONIE:• Aladin physics, 3D-Var, 11 km, 1961-2013. Production running.• lateral boundary condition: ERA40 / ERA-Interim• large scale constraint (penalty term in cost function) from

ERA40 to 1979, and ERA-Interim from 1979• output used by: MESCAN, HYPE

credits: Martin Ridalet al., SMHI. Source: UERRA D2.5


Downscaling and/or bespoke DA

for special parameters of interest

Clouds

Precipitation

Wind

Temperature

Humidity


MESCAN

MESCAN: data assimilation (OI) with station obs for T2m, Hu2m &

RR24, two types of background:

1. Dynamical downscaling: 6h forecast with ALADIN model at

5.5km (ARPEGE physics) for wind, precipitation, T2m & Hu2m.

Period 2006-2010 production is running, ends 07/2016.

2. Static downscaling: HARMONIE-ALADIN(11km) downscaled

to 5.5km, using 6h forecast as background for OI with station obs,

period 1961-2013 production is running, expected end 11/2017.

Ensemble of 8 members for the period 2006-2010

to characterize the impact of perturbed obs, of poor density/ full

density observation network, of different background physics

(ALADIN or ALARO), production running.

credits: E. Bazile, R. Abida, F. Besson and C. Soci (09/2015)


MESCAN for SURFEX driving the hydrological model TRIP

MESCAN analysis for air temperature, humidity and precipitation (total), wind speed, incoming radiations and Surface Pressure from HARMONIE-ALADIN

MESCAN 2D Analysis at ~5 km:

T2m, Rh2m, Rain/snow

SURFEX: Surface Externalized Model

Forecast model downscaled at ~5km :

SW, LW, Wind, Ps

TRIP: Hydrological Model

(river rounting + aquifer

credits: P. LeMoigne, C. Szczypta, B. Decharme, A. Verrelle, R. Abida, and E. Bazile

Surface Runoff

and deep drainage

Modified soil moisture due

to aquifer interaction


Impact of the observation density network

density ~ 1960

MESCAN Analysis

June 2010 (mm)

density ~ 2010

Diff MESCAN

impact of the density


Assimilated observations

UKMO UBonn/

DWD

SMHI METEO

FRANCE

Satellite obs x - - -

Conventional

obs, upper air

x x x -

Conventional

obs, surface

x x x x

Radar - (x) - -

… and a common UERRA archive

https://software.ecmwf.int/wiki/display/UER/Home

https://software.ecmwf.int/wiki/display/UER/Home


Model levels

Store analysis output every six hours at

00UTC, 06UTC, 12UTC, 18UTC for all models.

Don’t store any forecast fields.


Pressure levels


Pressure levels

Analysis: six hourly

at 00 UTC, 06 UTC, 12 UTC, 18 UTC (hourly

for COSMO) Forecasts : T+1,2,3,4,5,6,9,12,15,

18,21,24,27,30 started at 00 UTC and 12 UTC

T+1,2,3,4,5,6 started at 06 UTC and 18 UTC


Height levels


Height levels

Analysis:

00 UTC, 06 UTC, 12 UTC, 18 UTC,

hourly for COSMO

Forecasts: T+1,2,3,4,5,6,9,12,15,18,21,24,27,30

00 UTC and 12 UTC, T+1,2,3,4,5,6 06 UTC and 18


Surface level analysis:

00 UTC, 06 UTC, 12 UTC, 18 UTC

hourly for COSMO, MESAN, SURFEX

Forecast: T+1,2,3,4,5,6,9,12,15,18,21,24,27,30

00 UTC and 12 UTC, T+1,2,3,4,5,6 06 UTC and 18


Accumulated fluxes and radiation


Surface temperature and wind


Surface levels pressures


Surface levels, clouds


Snow


Surface levels, Soil


Static fields


1) Motivation

2) Previous regional/national/European efforts

3) Current UERRA productions and archive

4) Users and applications


DWD example: coastal station Putbus

Bias smaller

10m wind speed:

advantage of

regional reanalysis

correlation

better

Stations COSMO-

REA6

ERA-I

ERA-20C


ERA-Interim (interpolated to 12.5 km horizontal resolution) with stations 10m wind speed

HErZ COSMO-REA6 with stations 10m wind speed

DWD example: 10m wind speeds

Kaiser-Weiss, A. K., Kaspar,

F., Heene, V., Borsche, M.,

Tan, D. G. H., Poli, P.,

Obregon, A., and Gregow, H.:

Comparison of regional and

global reanalysis near-surface

winds with station

observations over Germany,

Adv. Sci. Res., 12, 187-198,

doi:10.5194/asr-12-187-2015,

2015.


COSMO-REA6

Annual cycle ok

Monthly correlation ok

Long term variability ok

Diurnal cycle is difficult

above 60m height (low

level jet issue)

credits: Michael Borsche, DWD

Vertical wind speed profile


User wishes see project documentation of

CORE-CLIMAX, UERRA


Summary 1/2 1) Aim: to capture the state of the atmosphere and its change

over time, on the hourly to multi-decadal scale, mainly close to

the ground, with highest possible spatial resolution.

2)+3) addressed by different methods, multi-model-productions

(mainly by NMHS), several productions currently running.

3.1) efforts on ensembles for assessing uncertainties

(probability distributions)

3.2) comparison against observations for assessing

uncertainties

4) UERRA archive is a pretty good start to address user needs.


Summary 2/2 5.1) Users needs will vary, potential for: renewable energy,

hydrological, and traditional climate data applications, climate

modelling community, historical data quality control and

representativity checks.

5.2) Strengths-Opportunites: users will emerge as soon as

data are there. Weaknesses-Threats: the potential for user

frustration.

5.3) Remedy: Communicate the uncertainties and limitations

(e.g. effect of the change of the observing system, effect of

physics parameterization, amount and quality of assimilated

observations, special effects of each method).


Thank you.

Questions?

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