USE OF PRECIPITATION INFORMATION FROM SPACEBORNE RADAR FOR VERIFICATION AND ASSIMILATION IN THE ECMWF MODEL A.Benedetti, P. Lopez, E. Moreau, P. Bauer,

USE OF PRECIPITATION INFORMATION FROM SPACEBORNE RADAR FOR VERIFICATION AND ASSIMILATION

IN THE ECMWF MODEL

A.Benedetti, P. Lopez, E. Moreau, P. Bauer, F. Chevallier, M. Janiskova’, A. Tompkins

Outline

• Precipitation assimilation activities at ECMWF •Brief overview of the Tropical Rainfall Measuring Mission (TRMM)

• Overview of the 1D-Var retrievals from the TRMM Microwave Imager (TMI)

• Validation of Rainrate/Brightness Temperature retrievals using the TRMM Precipitation Radar

•Outline of the 1D+4DVar approach

• Use of radar reflectivities for assimilation

• Preliminary results

• Discussion and conclusions

Precipitation assimilation at ECMWF

More recent developments:

New simplified convection scheme (Lopez 2003) New simplified cloud scheme (Tompkins & Janisková 2003) used in 1D-Var Microwave Radiative Transfer Model (Bauer & Moreau 2002)Assimilation experiments of direct measurements from TRMM and SSM/I (TB or Z) instead of indirect retrievals of rainfall rates, in a 1D+ 4D-Var framework. Use of Precipitation Radar data to validate 1D-Var and 1D+4D-Var results.

A bit of history:

Work on precipitation assimilation at ECMWF initiated by Mahfouf and Marécal

1D-Var on TMI and SSM/I rainfall rates (RR) (M&M 2000).

Indirect 1D+4D-Var assimilation of RR more robust than direct 4D-Var.

1D+4D-Var assimilation of RR is able to improve humidity but also the dynamics in the forecasts (M&M 2002).

Goal: To assimilate observations related to precipitation and clouds in ECMWF’s 4D-Var system including parameterizations of atmospheric moist processes.

TROPICAL RAINFALL MEASURING MISSION

• Operational since 1997; provides rain observations between 35S-35N• Instruments on board (still working): - Microwave Imager (TMI) : surface rainrate from Brightness Temperatures (Tb) - Precipitation Radar (PR) : rainrate profiles from Reflectivities (Z) - Visible and Infrared Scanner (VIRS) - Lightning Imaging Sensor (LIS)PR IMAGE OF TROPICAL CYCLONE ZOE, December 2002, 165-180E/0-20S

http://trmm.gsfc.nasa.gov/

1D-Var retrievals from TRMM data

Evaluation of 1D-var

1D-Var (TCWV, snow and rainfall rates)

moist physics

moist physics + radiative transfer

background T,qv

background T,qv

“Observed” rainfall rates

Retrieval algorithm (2A12,PATER)

1D-Var on Brightness Temp. 1D-Var on TMI rain rates

Observations interpolated on model’s T511 Gaussian grid

TMI Brightness Temp (Tb)

Radar Forward Model

PR reflectivity

RETRIVAL

VALIDATION

Rainfall from TRMMAlgorithms

(2A12, PATER, etc.)

Observed Radiances

(TMI)

Model FG T, q

Forward radar model=equivalent reflectivity

1D-Var retrievals of rainfall

and snowfall rate

FG ‘rainy radiance’

1D-Var retrievals of rainfall

and snowfall rate

TRMM-PRobservations

1D-Var retrievalevaluation

Validation of 1D-Var retrievals of rainfall from TMI radiances and TRMM Rainrates

Moist physics

Moist physics + radiative

transfer

FG rain and snow rates

+

Model FG T, q

• Based on Mie look-up tables for the computation of reflectivity, assumes a Marshall-Palmer distribution for rain and snow particles and includes treatment of bright band at 273K

• 3D radar reflectivity at 14 GHz is computed via bilinear interpolation at the given model temperature and rain/snow content at each model grid point and vertical level

• Model rain/snow contents are computed from precipitation fluxes assuming a fixed fall velocity

Forward radar model

Background

1D-Var results

PATER obs

1D-Var/RR1D-Var/BT

Case of tropical cyclone ZOE (26 December 2002 @1200 UTC)TMI data

Surface rainfall rates (mm hr-1)

1D-Var results

1D-Var/RR PATER 1D-Var/BT

Case of tropical cyclone ZOE (26 December 2002 @1200 UTC)

Total Column Water Vapour increments (top , kg m-2) and mean profiles of temperature and specific humidity increments (bottom)

Evaluation of 1D-Var results using PR data

Case of tropical cyclone ZOE (26 December 2002 @1200 UTC)

14 GHz Radar Reflectivity at ~2km (dBZ)

Background

1D-Var/RR 1D-Var/BT

PR obs

Evaluation of 1D-Var results using PR data

Case of tropical cyclone ZOE (26 December 2002 @1200 UTC)

14 GHz Radar Reflectivity Cross section (dBZ)

Background

1D-Var/RR 1D-Var/BT

PR obs

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166°WTRMM-PR obs (dBZ) AMI 2003-01-14 18:00:00

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166°WZ guess (dBZ) AMI 2003-01-14 18:00:00

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166°WZ RR analysis (dBZ) AMI 2003-01-14 18:00:00

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166°WZ BT analysis (dBZ)AMI 2003-01-14 18:00:00

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Evaluation of 1D-Var results using PR data

1D-Var/RR 1D-Var/BT

PR obs Background

Case of tropical cyclone AMI (14 January 2003 @1800 UTC)

14 GHz Radar Reflectivity at ~2km (dBZ) and Mean Sea Level Pressure (hPa)

174 OW 172 OW 170 OW 168 OW

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TRMM-PR obs (dBZ) AMI 2003-01-14 18:00:00

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Z guess (dBZ) AMI 2003-01-14 18:00:00

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Z RR analysis (dBZ) AMI 2003-01-14 18:00:00

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Z BT analysisAMI 2003-01-14 18:00:00

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Evaluation of 1D-Var results using PR data

1D-Var/RR 1D-Var/BT

PR obs Background

14 GHz Radar Reflectivity Cross Section (dBZ)

Statistical evaluation of 1D-Var results

1D-Var/RR

1D-Var/BT

Background

•PR Data from 21 tropical cyclones that were observed between January and April 2003) were used to evaluate the retrieval results.•The 1D-Var/BT and 1D-Var RR were run for all cases and statistics were collected

Bias (solid) and rms (dashed) as a function of reflectivity

• Background has higher bias than retrievals• Observations tend to show larger values (this could be also due to the fact that PR only ‘sees’ rain ) • Little difference between 1D-Var/RR and 1D-Var/BT

Scatterplot of model Z vs obs

Statistical evaluation of 1D-Var results

1D-Var/RR

1D-Var/BT

Background

Heidke Skill Score

• Retrievals are more skillful than background• 1D-Var/BT slightly more skillful than 1D-Var/RR at large reflectivity values

HSS=1 good skillHSS=0 poor skill

PR obs

Probability distribution functions

• TRMM-Precipitation Radar data is a viable tool to make quantitative assessments regarding the quality of ECMWF precipitation retrievals.

• Global PR data analysis with an improved averaging to obtain more robust statistics is currently being investigated.

• PR data will be further used for evaluation of the TMI 1D+4D-Var analysis

and subsequent forecast

• Plans to use the PR data to study the spatial distribution of precipitation for

verification of the forecast model are also ongoing research

Ongoing Research and Future Validation Work

1D+4D-Var assimilation of TRMM data

4D-Var

1D-Var (T,q increments)

moist physics

moist physics + radiative transfer or

reflectivity model

background T,qv

background T,qv

“Observed” rainfall rates

Retrieval algorithm (2A12,2A25)

1D-Var on TBs or reflectivities 1D-Var on TMI or PR rain rates

Observations interpolated on model’s T511 Gaussian grid

TMI TBs or

TRMM-PR reflectivities

1D-Var on TRMM/Precipitation Radar data

Tropical Cyclone Zoe (26 December 2002 @1200 UTC)Vertical cross-section of rain rates (top, mm h-1) and reflectivities (bottom, dBZ):

observed (left), background (middle), and analysed (right).Black isolines on right panels = 1D-Var specific humidity increments.

2A25 Rain Background Rain 1D-Var Analysed Rain

2A25 Reflect. Background Reflect. 1D-Var Analysed Reflect.

Close-ups on 1D-Var using PR reflectivities with different error assumptions on obs

173OE 174OE 175OE 176OE 177OE 178OE

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TRMM PR reflectivity (dBZ)

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Model reflectivity (dBZ) fist guess

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1D-Var 25% error at all levels

1D-Var 50% error at all levels

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Model reflectivity (dBZ) and humidity increments (g/kg) err=constant 25%, level 28 only

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1D-Var retrievals using PR: observations at one level only vs full profile

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TRMM PR reflectivity (dBZ)

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1D-Var obs at all levels

1D-Var obs at level 48 (~2km)

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TCWV increments (kg/m2) .ec09.25r4.pr.hpca.0.2_Allkg/m2

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Background and 1D-Var increments in Total Column Water Vapour (pseudo-obs for 4D-Var) from PR reflectivities

TCWV guess (kg/m^2)

Increments indicate an overall moistening confined along the satellite track

TCWV increments (kg/m^2)

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4D-Var differences in Total Column Water Vapour and Mean Sea Level Pressure (MSLP)Between experiment with PR data andcontrol experiment (no PR data)

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Analysis: 26 Dec. 2003, 0300UTC

Forecast: 26 Dec. 2003, 1200UTC

No initial impact on the dynamics is evidentin the analysis. At 1200UTC, changes in MeanSea Level Pressure are developing and appearto persist well into the forecast indicating a shift in the location of the storm with respectto the control run.

Forecast: 28 Dec. 2003, 1200UTC

Comparison 1D+4D-Var assimilation of TRMM-PR rain rates/reflectivities: Impact on analysed and forecast TCWV and MSLP (Experiment – Control)

(Tropical Cyclone Zoe, 26-28 December 2002)

Analysis at 300UTC, Dec 26

wit

h P

R r

ain

rate

s

Forecast at 1200UTC, Dec 26. Forecast at 1200UTC, Dec 28.

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1D+4D-Var assimilation of TRMM-PR and TMI observations: Impact on tropical cyclone Zoe track forecast (26-31 December 2002)

Comparison of forecast tracks from:

- control run (no TRMM data),

- observations,

- 1D+4D on TMI TBs,

- 1D+4D on TMI Rain Rates,

- 1D+4D on TRMM/PR Rain Rates,

- 1D+4D on TRMM/PR Reflectivities

Coloured labels indicate forecast times (in hours)

-As suggested by the MSLP changes, the track forecasts are improved when TRMM observations are assimilated in rainy areas especially when using TMI Brightness Temperatures.-Despite the smaller spatial coverage of TRMM/PR data (200-km swath) compared to that of TMI data (780-km swath), the impact of these type of observations is non-negligible.

168E 170E 172E 174E 176E 178E

168E 170E 172E 174E 176E 178E

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CONTROL

PR-Z

PR-RAIN

TMI-RAIN

TMI-TB

ZOE TRACK FORECAST (BASE: 2002122612)

Observations pros cons

TMI RR computationally cheap only if rainy background & over ocean algorithm-dependent (2A12, PATER,…)

TMI TB sensitivity to RR, cloud and WV computational cost of RTM flexibility of channels over ocean only

TRMM/PR RR land and ocean, vertical info limited spatial coverage

TRMM/PR Z land and ocean, vertical info limited spatial coverage

All four methods manage to converge in various meteorological situations (large-

scale/convective precipitation, tropics/mid-latitudes).

1D+4D-Var assimilation of precipitation: preliminary conclusions

4D-Var is able to digest TCWV retrievals produced by 1D-Var on TMI and TRMM/PR observations in rainy areas.

The significant impact on the humidity field seen at analysis time can be kept during the forecast, and the dynamics is affected accordingly.

In the studied TC case, assimilating TMI and TRMM/PR observations improve the TC track and minimum MSLP forecasts.

TMI versus TRMM/PR ?

Including the information on the vertical distribution of rainfall contained in the TRMM/PR observations improves the 1D-Var retrieved rain rate profiles.

Despite their smaller spatial coverage, the impact of TRMM/PR data is comparable to that of TMI data.

TRMM/PR data can be used over land and ocean areas, whereas TMI data are currently restricted to ocean (surface emissivity over land).

1D+4D-Var assimilation of precipitation: preliminary conclusions (2)

TRMM/PR Rain Rates versus TRMM/PR Reflectivities ?

Observational errors may be easier to prescribe for reflectivities than for 2A25 derived rain rates.

Inclusion of vertical correlations of observation errors has a marginal impact on the 1D-Var results.

The extra computational cost for running the reflectivity model is reasonable.

1D+4D-Var assimilation of precipitation: prospects

• Cycle 1D+4D-Var assimilation of TRMM and SSM/I observations in rainy areas over several months:

global scores, study of specific events, assessment of the different 1D-Var methods.

• Improve the determination of observation and model error statistics.

• Address the issue related to the use of satellite passive microwave data over land.

• Assess the potential of the assimilation of ground-based radar data, but problem of availability (non real-time, country-dependent)?

• Until when will TRMM observations be available?

• Looking forward to GPM (global coverage, better temporal resolution, information on atmospheric ice?).

• 1D+4D-Var assimilation of SSM/I (and TMI data ?) expected to become operational in 2004.

We defined a ‘confusion matrix’ for grid points where first guessand 1D-var BT and RR retrievals hit/miss with respect to PR Observed YES Observed NO

Predicted YES A C

Predicted NO B D

Then we defined the Heidke Skill Score (HSS):

2(AD-BC) B*B + C*C + 2*A*D + (B+C)*(A+D)

Some statistics…..