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GV for ECMWF's Data Assimilation Research

Peter Bauer peter.bauer@ecmwf.intECMWF, Reading, UK

NWP and data assimilation

Validation options

Requirements for GV

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What will NWP systems focus on in the GPM timeframe?

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Example of NWP Prediction Skill Development

• Improvement of model spatial/temporal resolution due to increased computer power.

• Improvement of physical parameterizations (diabatic, land/ocean-atmosphere etc.).

• Increased satellite data usage!

2-day skillimprovement

Elimination ofNH-SH discrepancy

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Expected Future Developments in NWP

ECMWF now: - 40 km (T511/T159), 60 model levels;- Two analysis suites (6-hour, 12-hour window);- Two 10-day forecasts initialized at 00 and 12 UTC;- 50-member EPS (50 km, T255);- Radiances/products from ~20 different satellite sensors

assimilated;- Assimilation of rain-affected radiances operational since

28/06/2005.

ECMWF late 2005: - 25 km (T799/T255), 91 model levels;- Two 14-day forecasts initialized at 00 and 12 UTC.

ECMWF ~2010: - 15 km (EPS 30 km);- towards longer assimilation window analyses;- towards unified ensemble prediction system (medium-range,

monthly, seasonal);- towards coupled data assimilation (land-ocean-atmosphere);- towards environmental monitoring;- towards focus on severe weather forecasting.General:

- NWP systems will become much better in (physically) resolving even meso-scale synoptic systems.- NWP systems will become much better in assimilating cloud and rain affected observations (see recent JCSDA workshop on Cloud and Precipitation, http://www.jcsda.noaa.gov/CloudPrecipWkShop/program.html ).

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Why do observations in cloud and precipitation have potential?

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Forecast sensitivity to Cloud and Rain-affected Observations

Current systems produce rather good precipitation forecasts without assimilating any (!) direct precipitation or cloud observation.

However:There are indications that key analysis errors occur in areas that are influenced by clouds and precipitation.

ANAL @ t0 + 2 days

Model HANAL @ t0 FCST @ t0 + 2 days

Sensitivity of FCST error Adjoint Model H*to perturbations FCST error @ t0 + 2 daysin ANAL @ t0

Optimum perturbations

to minimize FCST error KEY ANALYSIS ERROR TRACKING

(Rabier et al. 1996, Klinker et al. 1998)

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Mean Dec 1999 600 hPa T-perturbations modified T-perturbations modifiedT-perturbations by high cloud cover by low cloud cover

Mean profile of Dec 1999 Mean Dec 1999 high cloud cover Mean Dec 1999 low cloud coverT-perturbations

Forecast sensitivity to Cloud and Rain-affected Observations

(McNally 2002)

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What are the validation options for clouds and precipitation in NWP systems?

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Direct vs. Indirect Validation of NWP-System Performance: Small-scale

Direct validation of ECMWF cloud-cover analyses with LITEobservations

Indirect validation of ECMWFcloud/rain profiles with ground-based (ARM) 35-GHzradar observations

ECMWF

LITE

ECMWF ARM

Latitude

Time [h]Ze [dBZ]

Cloud water (solid) and ice (dashed) mixing ratio [g/kg]

(Lopez et al. 2005)

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Direct validation of ECMWF Indirect validation of ECMWFprecipitation forecasts with cloud/rain fields with SSM/I BMRC rain gauge analyses 19.35 GHz (h) observations

ECMWF

SSM/I

ECMWF CTRL

ECMWF EXP

BMRC

mm

Direct vs. Indirect Validation of NWP-System Performance: Large-scale

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What could be the specific GV requirements?

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Conceptual Model of GV for Data Assimilation Purposes

Rain assimilation at ECMWF became operational in June 2005:1) 1D-Variational retrieval of integrated moisture using SSM/I radiances over ocean.2) 4D-Variational assimilation of integrated moisture in analysis system.

(Bauer et al. 2005a, b)

Main validation requirements:Cost function minimization in variational assimilation calculates:

J (x) = B-1 (x-xb) + HTR-1[H(x) - yo]

where R includes modelling (of H) (also representativeness) and observation errors.

H comprises moist physical parameterizations and radiative transfer modelsbut R is required in units K because yo consists of radiances!

Initialize single column Validate model (H) forecast Perform validation over long timewith 3-d observations of with radiometric and other series and different forecast lengths:T, q, u, v, …. observations: model error small direct estimation of R in units K representativeness error estimate

areas of improvement for H model error growth estimate

Single column model experiment at GV site:

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Potential Ground Validation Requirements

NWP systems perform analyses to optimally initialize model forecasts of the entire atmospheric state:

Example 1: bad moisture analyses will produce bad cloud and precipitation forecasts.Example 2: good moisture analyses with bad diabatic models will produce bad cloud

and precipitation forecasts.

Ideally, the entire 3-D meteorological environment should be observed to validate the initial conditions, the model parameterizations, and the observation operators that are employed in data assimilation.

Large-scale (satellites, networks):• Direct validation with derived products from independent observations

(example: PR, GPCP, Cloudsat products, …).• Indirect validation with radiances (operational, simple, requires interpretation

but prepares for radiance assimilation).

Small-scale (GV):• Accurate and continuous (to address representativeness issue) observations of derived key meteorological parameters and radar/radiometry (example: conceptual model).