INM, Madrid, December 2003 Single column experiments at ECMWF, status of work, and plans for 2003 Pedro Viterbo and Gisela Seuffert European Centre for

INM, Madrid, December 2003

Single column experiments at ECMWF, status of work, and plans for 2003

Pedro Viterbo and Gisela SeuffertEuropean Centre for Medium-Range Weather Forecasts

ELDAS 2nd Progress MeetingINM, Madrid, 10-11 December 2003

Thanks to: J.F. Mahfouf, H. Wilker, M. Drusch, and J.-C. Calvet


• Introduction• Assimilation (OI vs. KF and synthetic mw Tb)• Assimilation of observed mw Tb• Additional experiments• Development of production system• Remaining work• (Scientific) conclusions

Layout



Layout


Goals

• Build a soil moisture analysis system assimilating – 2m temperature and relative humidity– Thermal IR heating rates– MW brightness temperature

• Forced by observation based estimates of– Precipitation and radiation

• Study its properties and compare to OI in a controlled environment

• Build the production system


Plans ( ELDAS 1st progress meeting)Assimilation aspects:• Minimize the combined errors in prediction of soil moisture, latent heat flux and

screen level observations

• Further mw-Tb assimilation experiments (viewing angle, times)

• Assimilation of heating rates

Reports:• Paper(s) focusing on the

- new features of assimilation method - assimilation of mw-Tb

- (assimilation of heating rates)

Technical aspects:• Summer 2003: Build production system for the annual data base

• End of 2003: Start production

Action: Completed using SCM

Action: Building …

Action: SCM test runs

Action: 2 papers-published in GRL (T,RH,Tb)-Accepted at JHM (OI, EKF)

Action: Still pending


Soil moisture analysis systems Optimal Interpolation:• Used in the operational ECMWF-

forecast since 1999 (Douville et al., 2000)

• Fixed statistically derived forecast errors

• Criteria for the applicability of the method- atmospheric and soil exceptions- corrections when T and RH error are negatively correlated

Extended Kalman Filter:• Used in the operational DWD-

forecast since 2000 (Hess, 2001) *

• Updated forecast errors

• Criteria for the applicability of the method- no ‘direct’ atmospheric exceptions- soil exceptions still to be tested

* Changes:- Assimilation of 2m- T and RH, mw-Tb- Model forecast operator accounts for water

transfer between soil layers- Test adaptive EKF


SCM experiment DesignAtm. initial conditions +dynamics forcing from

ECMWF reanalysis (ERA40)

Single-column model of theECMWF NWP model

+ microwave emissivity model

First guess: T2m,RH2m,HR(?)

Soil moisture analysis schemeOI or Extended Kalman Filter

Soil moisture Background error

Increments (daily)

Observations: T2m,RH2m,HR

Observation of precipitation + radiation


ObservationsMurex:• 1.6 – 9.10.1997 (1995- 1998)• Forcing:

SW , (unbiased) LW , precipitation• Validation:

Soil Moisture, Rnet, H, G, LE=Rnet-H-G, Ts• Assimilation/Validation:

T2m, RH2m, synthetic mw-Tb

SGP 97:- 15.6 – 19.7.1997 - Little Washita site (2) (Central Facility site(3))- Forcing: SW , (simulated) LW , precipitation- Validation: Soil Moisture, Rnet, H, G, LE, Ts- Assimilation/Validation: T2m, RH2m, mw-Tb



Layout


OI vs KF: Gain matrix (FIFE)

3

1

6

1

2)]([l m

lmbba kFNxHyKxx

160 180 200 220 240 260 280julian day

0.0000

0.0005

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Frob

eniu

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rm

OIEKF•Both systems distinguish between periods off strong and weak

influence of soil moisture on screen-level variables.•OI does that thanks to carefully selected thresholds; EKF has built-in dynamic dependency•In clear-sky, the FN of EKF is slightly larger than that of OI: EKF has a slight preference for the obs, rather than the bg.


OI vs KF: Average increments (FIFE)

0.00

0.05

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soil layer 1 soil layer 2 soil layer 3

OI

EKF


OI vs KF: Time series of increments (FIFE)

160 180 200 220 240 260 280julian day

-1.5-1.0

-0.5

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soil

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soil layer 1soil layer 2soil layer 3

a)

160 180 200 220 240 260 280julian day

-1.5-1.0

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soil layer 1soil layer 2soil layer 3

b)

•EKF increments are at the same order of magnitude of OI, and come at the same time•OI has soil moisture increments similar across the 3 layers.•EKF puts more weight on the deeper layers


Soilmoisture

Sensible Heat flux

(Synthetic) microwave Tb assimilation (MUREX)


Daytime fit to observations (MUREX)

Corr. Bias RMS

Control 0.92 -1.80 1.81

KTRB 0.92 -1.52 1.82

KTR 0.92 -1.28 1.82

KB 0.92 1.15 1.88

2 metre temperature

Corr. Bias RMS

Control 0.79 0.44 11.86

KTRB 0.79 -1.98 11.75

KTR 0.82 -4.01 11.34

KB 0.80 -4.94 12.04

2 metre relative humidity

•The control simulation has a cold and wet bias, but hardly any bias on sensible heat flux, (and a wet bias in root zone moisture).•The assimilation of screen-level parameters tends to reduce the cold/wet bias, reducing soil moisture (moving away from observations), and giving too much sensible heat.•EKF tends to follow mainly 2T information, in detriment of 2RH.•The assimilation of mw Tb moves the root zone moisture closer to the (ground-based) observations


Surface soil moisture

Microwave Tb

Soil moisture, Teff, mw-Tb at 6 LT

•Assimilation of mw Tb (on its own or combined with screen level parameters) brings the simulated Tb and the top soil moisture closer to the observations.



Layout


Assimilation of mw Tb: Performance of 2T/RH

170 175 180 185 190 195 200julian day

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ily m

ean

2m-te

mpe

ratu

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]a)OBS

CTRLKTRKTRBKB

Corr Bias RMS0.88 2.37 1.430.90 1.53 1.090.90 1.44 1.080.87 1.99 1.39

170 175 180 185 190 195 200julian day

30405060708090

100

daily

mea

n 2m

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]

b)

OBSCTRLKTRKTRBKB

Corr Bias RMS0.57 -10.70 8.930.57 -5.67 7.180.58 -4.71 7.030.56 -7.41 8.37

2T

2RH

SGP97


Surface soil moisture and Tb (SGP97)

170 175 180 185 190 195 200julian day

210220230240250260270280

brig

htne

ss te

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ratu

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]a)

Estar Obs

CTRL

KTRKTRB

KB

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vol

umet

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[%]

b)Grav. Obs (5cm) with error barsObs 10cmderived from Estar

Tb

Top

soil

mo i

s tu r

e

•The control simulation (indeed, all simulations) are too warm and too dry. Model day-to-day variability of humidity exceeds observations.•Top soil moisture in the control simulation compares well with observations.•Assimilation of screen-level parameters decrease the warm/dry bias by 30-40%, but deteriorate the fit to top soil moisture.•Assimilation of mw Tb, on top of screen-level parameters, improves again the top soil moisture but deteriorates the fit to screen-level observations.


Root zone soil moisture (SGP97)

170 175 180 185 190 195 200julian day

1012141618202224

root

zon

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[%]

OBSCTRLKTRBKTRKB

Corr. Bias RMS 0.97 0.01 0.31-0.20 3.82 1.91-0.22 3.44 2.01 0.89 0.87 0.51

170 175 180 185 190 195 200julian day

1012141618202224

root

zon

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[%]

a) OBSCTRLKTRBKTRKB

No precipitation simulation

•Results for root zone soil moisture are similar to those of the top layer.•The assimilation scheme responds correctly to a (very large) imposed error in the precipitation.


Evaporative fraction (SGP97)

170 175 180 185 190 195julian day

0.2

0.4

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evap

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CTRLKFTRKFTRBKB

•Evaporative fraction [EF=LE/(H+LE)], the relevant quantity for the surface impact on the atmosphere, is underestimated by the control simulation (cf. dry/warm bias).•EF is clearly improved when screen-level parameters are used.•And deteriorated again when mw Tb is added …


EKF assimilation of microwave Tb• Assimilation of mw Tb:

– Transports surface soil moisture signal from 1st layer to deeper root zone

– Improves simulated soil moisture, surface energy fluxes, T,RH– Best results for atmosphere, when T,RH and Tb are assimilated– Assimilation of Tb needs better background:

• Different soil types• More soil layers• Removal of soil temperature bias necessary (results not shown here)



Layout


Assimilation of satellite heating rate

160 180 200 220 240 260 280julian day

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mea

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ObsCtrlEKF assim. T,RH,SHREKF assim. T,RHEKF assim. SHR

Corr. Bias RMS0.94 3.46 2.000.81 0.89 2.440.79 0.97 2.530.94 2.95 1.58

Soilmoisture

Days when SHR is available (50% data missing, 25% cloudy)

•Variable SHR observation error depends on cloud fraction flag (how many hours arecloud free):

•Cloud fraction flag of neighbouring pixels•Cloud fraction flag of pixel

•Assimilating SHR:–Low data coverage does not allow for real conclusions

•When T, RH are available no extra information–SHR error difficult to define


Winter simulations

Soilmoisture

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ObsCtrl

KF assim. T,RHKF assim. T,RH flags

1.10.97 1.4.9897/98

•Without any additional flags EKF-system computes rather large soil moisture increments in winter•Flags necessary:

a) Low radiation (zenith angle) b) Freezing


Soil temperature analysis

• Soil temperature analysis– 2m-T is assimilated at 3 and 6 LT (zenith angle dependent)– Soil temperature increments of similar magnitude to OI

• Combining soil moisture (SMA) and soil temperature (STA) analysis– Tests about the order of the SMA + STA analysis + avoidance of

SCM-runs• Cannot be based on the same background run• Almost no difference when SMA or STA is performed first• Better performance when final trajectory is calculated (expensive)



Layout


Production system for ELDAS• Requirements:

– Annual database (1.10.1999–31.12.2000) of soil moisture for Europe – 0.2 x 0.2 regular lat/lon grid (15W-38E,35N-72N) – Computer time (cost efficiency)

• Starting point:– Experiments based on Single Column version of the ECMWF’s

NWP model (SCM) • Solutions:

– Add 1: Run n x n SCMs over Europe (each SCM runs independently)

– Add 2:• Run SCMs only for land points (about 25 000 SCMs)• I/O consideration• Open MP

– Add 3: Supervisor Monitor Scheduler (SMS)


Progress of work

• Changes to the SCM +SMA source code– SCM structure has been changed to run n x n SCMs in one run– I/O netcdf I/O grib – OpenMPI parallelization (up to 8 processes on one thread)

• Forcing data– Composition of forcing data changed from one point to n x n points– Output netcdf Output grib

• Control Structure– First SMS layout

1) Soil moisture analysis

1) Get forcing data from Mars archive2) Prepare data for SCM INPUT

1) Background run

1) Get forcing data from Mars archive2) Prepare data for SCM INPUT

1) Soil moisture perturbation

1) Final (soil moisture) trajectory 2) Check success of SMA (Costfunctions)

1) Forecast run

1) Final (soil temperature) trajectory2) Check success of STA (costfunctions)

1) Soil temperature analysis

1) Soil temperature perturbation



Layout


Future work (1)• Run validation points first in SCM mode

– Satisfies the validation group needs– Early warning on system performance– Learning exercise for Janneke– But entails:

• Delays on start of production• Extra work on software development (e.g., era40 forcing, validation diagnostics)

• Production system– What is still missing?

• Interpolation from gaussian grid to reg. 0.2 x 0.2 lat/lon grid• Incorporation of ELDAS maps (e.g. land cover)• Incorporation of ELDAS forcing data (precipitation, radiation)• Archiving of output in MARS • Observation (Re-analysis) data of 2mT and 2mRH for SMA +STA• Post-processing routines for parameters especially asked for by ELDAS

validation• ECMWF orography problems (LW)

– Final tests


Production system• Estimated Production Time

– Analysis for one day• One SCM run for 1000 pixels needs 5 min on 8 nodes ~ 2 hours for 25000

pixels• 5 x SCMs are needed 10 hours for 25000 pixels

– Approx. 5-6 months for annual database• Further parallelization needed

– Splitting Europe into boxes– MPI, distributed memory

• Run the system at t511 (resolution 39 km)

• Expected Start of production

• Under normal circumstances– 6 weeks required to include missing bits and pieces

– 2 weeks final tests

?



Layout


Conclusions• An E(xtended)KF was developed for land data assimilation, for the

assimilation of observations of screen-level T/RH, mw Tb, and SHR, a flexible introduction of new observation types, and usage of observed radiation and precipitation.

• The properties of such a system were systematically explored in a controlled environment (the atmosphere acts as a buffer, but the system does not feed back to the atmosphere), and confronted to the OI system operational at ECMWF, using the Single Column Model.

• “The devil is in the details”: The ratios sigma_o/sigma_b for the different observation types determines the response of the assimilations system.

• Screen-level parameters and mw Tb contain independent, and often contradictory, information on soil moisture, with possible contradictory impacts on surface fluxes. NWP centres tend to tune the assimilation to fit the evaporative fraction, since that is the quantity impacting on the atmosphere.

• The assimilation system will face biases (in both model and observations), mismatches of soil/vegetation parameters between model and real world, …

Documents

INM, Madrid, December 2003 Single column experiments at ECMWF, status of work, and plans for 2003 Pedro Viterbo and Gisela Seuffert European Centre for