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CARPE DIEM

Bernat Codina, Miquel PicanyolDept. of Astronomy and Meteorology

University of Barcelona

6th meeting. Helsinki, June 2004.

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WP 3: Data assimilation

Contribution to WP3 (Data Assimilation):

• “A comparison experiment between nudging and incremental analysis updating (IAU) in a mesoscale model”

•The objective is to determine which assimilation scheme and what meteorological fields have the best positive impact on the forecasted precipitation field.

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WP 3: Data assimilation

Description of the experiment:

•Corrections on the T, u, v, q and ps variables are introduced via IAU and nudging methods.

•Assimilation frequency: 6 and 3 hours.

•10 different cases.

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WP 3: Data assimilation

Effects of a unique assimilation:

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WP 3: Data assimilation

Methodology:

00 06 12 18 24

“Perfect Observations”

IAU/Nudging

Control

Time (UTC)

First guess

First guess

First guess + OBS

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WP 3: Data assimilationSfc–500hPa RH

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WP 3: Data assimilation

Case CNTL IAU6 NUD6 IAU3 NUD3021210 20.7 23.8 19.4 18.0 16.0030106 23.6 22.3 20.3 18.8 19.3030213 16.3 9.8 12.8 7.7 8.1030220 29.7 23.9 26.2 20.8 21.6030227 21.4 20.4 21.0 17.4 15.8030328 23.6 19.4 18.2 18.7 14.2030409 7.0 5.2 4.8 4.3 4.2030506 26.6 26.6 26.1 23.3 23.0030817 25.1 18.2 14.8 16.7 13.1030831 7.9 4.7 3.5 3.6 2.8

Total precipitation RMSE

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WP 3: Data assimilation

Case CNTL IAU6 NUD6 IAU3 NUD3021210 -5.6 0.6 -3.7 2.1 -2.7030106 1.7 3.9 -0.3 4.1 0.2030213 -0.2 0.8 0.1 1.1 0.3030220 3.2 2.9 0.3 3.5 0.3030227 -0.8 4.7 1.1 5.6 1.5030328 -2.3 4.5 -0.8 5.3 0.0030409 1.6 1.0 0.5 0.8 0.5030506 1.6 7.0 2.1 7.5 2.6030817 3.3 1.2 -0.6 1.6 -1.1030831 1.8 1.7 0.7 0.8 0.4

Total precipitation mean error

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WP 3: Data assimilation

030106

030213

030220

030227

021210

030328

030506

030409

030817

Cases

Control

IAU 6

Nudging 6

IAU 3

Nudging 3

Assimilation method

u, v, T, q

u, v, T

u, v, q

T, q

u, v, T, q, Ps

Assimilated data

030831

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WP 3: Data assimilation

Sfc–500hPa RH

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WP 3: Data assimilation

Sfc–500hPa RH

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WP 3: Data assimilation

Total precipitation RMSE (IAU 3 h)

Case UVTQP UVTQ UVT TQ UVQ021210 18.0 17.5 17.8 19.8 17.6030106 18.8 18.6 21.1 22.9 20.4030213 7.7 7.8 10.8 13.3 7.4030220 20.8 22.1 26.5 24.7 20.1030227 17.4 17.1 20.4 25.2 15.5030328 18.7 21.1 27.4 21.6 16.1030409 4.3 4.4 9.5 4.5 4.4030506 23.3 24.1 28.1 25.9 23.9030817 16.7 17.5 28.7 16.8 16.6030831 3.6 3.8 6.6 3.9 3.2

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WP 3: Data assimilation

Total precipitation RMSE (NUD 3 h)

Case UVTQP UVTQ UVT TQ UVQ021210 16.0 16.0 17.9 19.2 16.6030106 19.3 19.2 20.9 21.5 19.4030213 8.1 7.8 12.8 14.6 8.1030220 21.6 21.7 25.6 23.3 22.5030227 15.8 15.9 18.7 22.1 16.5030328 14.2 14.8 25.3 16.2 13.9030409 4.2 4.2 7.7 4.5 4.3030506 23.3 23.5 27.9 25.9 22.8030817 13.1 13.3 21.9 14.0 13.2030831 2.8 2.8 5.0 4.1 3.2

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WP 3: Data assimilation

Total precipitation mean error (IAU 3 h)

Case UVTQP UVTQ UVT TQ UVQ021210 2.1 2.2 -5.5 2.2 1.9030106 4.1 4.1 3.3 3.9 3.9030213 1.1 1.0 0.9 1.2 1.0030220 3.5 4.0 5.4 4.3 3.7030227 5.6 5.2 3.6 6.0 5.1030328 5.3 6.6 6.0 6.6 4.9030409 0.8 0.9 2.9 0.6 0.8030506 7.5 7.8 1.7 8.7 7.2030817 1.6 1.8 5.5 0.9 1.0030831 0.8 0.9 2.5 0.7 1.5

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WP 3: Data assimilation

Total precipitation mean error (NUD 3 h)

Case UVTQP UVTQ UVT TQ UVQ021210 -2.7 -2.9 -6.0 -2.5 -2.5030106 0.2 0.0 1.4 0.0 0.3030213 0.3 0.2 0.4 0.2 0.1030220 0.3 0.5 3.4 1.1 0.9030227 1.5 1.3 1.6 2.4 1.2030328 0.0 0.5 2.8 1.1 0.1030409 0.5 0.5 2.2 0.4 0.5030506 2.6 2.8 0.8 3.7 2.5030817 -1.1 -1.1 3.0 -1.3 -0.4030831 0.4 0.5 1.8 0.3 0.7

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WP 3: Data assimilation

Conclusions:

•3-hour assimilation frequency minimizes the RMSE.

•IAU tends to overestimate the total amount of precipitation while nudging gives a bias closer to zero.

•There are not any significant differences on the forecast precipitation field when assimilating surface pressure.

•Assimilating all meteorological fields or the combination of wind and humidity produces the best impact on the precipitation field.

•The bias is not so affected by the combination chosen.