IPWG, Beijing, 13-17 October 2008

Slide 1

Slide 1

Assimilation of rain and cloud-affected microwave radiances at ECMWF

Alan Geer, Peter Bauer, Philippe Lopez

Thanks to: Deborah Salmond, Niels Bormann, Bill Bell, Chris O’Dell, Graeme Kelly

IPWG, Beijing, 13-17 October 2008

Slide 2

Slide 2

Rain and cloud in NWP

Improved initial conditions lead to improved forecasts

Variational assimilation (e.g. 4D-Var) is used to generate these initial conditions by combining a first guess forecast with observations:

- Conventional: Weather stations, radiosondes, aircraft

- Satellite: Infrared, microwave, scatterometer, atmospheric motion vectors

Need more information on temperature, pressure, winds and humidity everywhere, but particularly in cloudy and rainy regions

Need information on the cloud and rain themselves. But aren’t clouds and rain transient phenomenon?

- Directly useful for short range forecasting

- The presence or absence of cloud or rain can be used to help infer the temperature, pressure, wind and moisture structure of the atmosphere to benefit longer term forecasts

- If comparison to the observations reveals shortcomings in the cloud and rain models, they will have to be improved

IPWG, Beijing, 13-17 October 2008

Slide 3

Slide 3

Assimilation of microwave imagers at ECMWF

1998 - SSM/I TCWV assimilation from 1D-Var in clear skies over oceans

2003 - Direct 4D-Var of clear-sky SSM/I

2005 - 1D+4D-Var of rainy SSM/I

- Bauer et al. , QJRMetS, 2006a,b,c

2007-8 - AMSR-E, TMI added in rainy and clear sky

2009? - direct assimilation of all-sky radiances in 4D-Var

IPWG, Beijing, 13-17 October 2008

Slide 4

Slide 4

ECMWF’s current rain and cloud assimilation approach: 1D+4D-Var

Clear sky SSM/I radiances are directly assimilated in 4D-Var

Cloudy and rainy SSM/I radiances have been assimilated operationally at ECMWF since 28th June 2005, over sea only, using a 1D+4D-Var method:

- 1D-Var retrieves T and q profiles and surface windspeed

- 1D-Var observation operator includes:

simplified large-scale and convective cloud schemes

Microwave radiative transfer

- TCWV retrievals are assimilated in 4D-Var

IPWG, Beijing, 13-17 October 2008

Slide 5

Slide 5

TCWV /kgm-2

Tb departure (observation minus simulated) /K

SSMI channel

19v 19h 22v 37v 37h 85v 85h

FG 7.6 4.5 7.9 2.0 4.2 8.8 4.1 12.0analysis

8.8 2.0 1.3 -1.1 3.4 -1.8 13.3 5.5

Tephigram – temperature and humidity

Rain/snow

Cloud ice / water

Cloud fraction

IPWG, Beijing, 13-17 October 2008

Slide 6

Slide 6

Quality of 1D+4D-Var rain retrievals: near-instantaneous colocations

First guess Retrieval

Geer, Bauer, Lopez, QJRMetS, latest issue, 2008

SSM/I retrieval compared to mean of PR footprints within ± 7.5 minutes and 25km

IPWG, Beijing, 13-17 October 2008

Slide 7

Slide 7

Quality of 1D+4D-Var rain retrievals: correlation coefficients

Geer, Bauer, Lopez, QJRMetS, latest issue, 2008

Rain water path Surface rain rate

First guess Retrieval First guess Retrieval

Log(rain) 0.5 0.64 0.39 0.54

rain 0.49 0.83 0.41 0.73

IPWG, Beijing, 13-17 October 2008

Slide 8

Slide 8

RMSE against

operational analyses

Vector wind

Relativehumidity

South Tropics North

Forecast scores: 1D+4D-Var rainy assimilation

Kelly et al., Mon. Weath. Rev., July, 2008

Limited observing systemLimited observing system plus 1D+4D-VarFull observing system without 1D+4D-VarFull observing system

IPWG, Beijing, 13-17 October 2008

Slide 9

Slide 9

Emissive reflector biases

All conical-scanning microwave imagers (TMI, SSMI, SSMIS, AMSR-E … ) incorporate a spinning reflector

If the reflector is emissive:

Unfortunately a common situation:

- SSMIS – Bill Bell, 2008, IEEE

- TMI – Frank Wentz, 2001, IEEE

REFLECTORTRUEMEASURED TTT )1(

Reflector emissivity

IPWG, Beijing, 13-17 October 2008

Slide 10

Slide 10

SSM/I

AMSR-E

TMI

Firs

t gu

ess

depa

rtur

e [K

]

IPWG, Beijing, 13-17 October 2008

Slide 11

Slide 11

TMI reflector temperature estimated from first guess departure biases Estimated

reflector temperatur

e [K]

IPWG, Beijing, 13-17 October 2008

Slide 12

Slide 12

Modelled cloud liquid water (at SSM/I observation locations; 12hrs of

data)

37v Obs – FG departure [K]

(after moist physics

improvements; CMAX cloud overlap)

37v Obs – FG departure [K]

(after moist physics

improvements; CMEAN cloud overlap)

Radiative transfer biases in cloud and rain

IPWG, Beijing, 13-17 October 2008

Slide 13

Slide 13

Surface

TOA

Cloudy column

Clear column

Cma

xClou

d

Forecast model – 1 grid point

RTTOV fast radiative transfer

Cma

x

RTTOV-SCATT – Two independent column approximation:Tb = (1 - Cmax ) × Tb(clear) + Cmax × Tb(cloudy)

IPWG, Beijing, 13-17 October 2008

Slide 14

Slide 14

Surface

TOA

Cloudy column

Clear column

CmeanCloud

Forecast model – 1 grid point

RTTOV fast radiative transfer

Cma

x

RTTOV-SCATT – revised version:Tb = (1 - Cmean ) × Tb(clear) + Cmean × Tb(cloudy)

IPWG, Beijing, 13-17 October 2008

Slide 15

Slide 15

Modelled cloud liquid water (at SSM/I observation locations; 12hrs of

data)

37v Obs – FG departure [K]

(after moist physics

improvements; CMAX cloud overlap)

37v Obs – FG departure [K]

(after moist physics

improvements; CMEAN cloud overlap)

IPWG, Beijing, 13-17 October 2008

Slide 16

Slide 16

All-sky, direct 4D-Var assimilation

In contrast to 1D+4D-Var, the full information content of the observations is assimilated:

- Surface temperature and winds

- Cloud and precipitation

- Total column water vapour

A unified assimilation:- All sky conditions (rainy, cloudy, clear) are treated in the

same assimilation stream

IPWG, Beijing, 13-17 October 2008

Slide 17

Slide 17

RMS forecast errors: relative humiditynormalised difference (all-sky 4D-Var - 33r1 control)

degradation

improvement

10th Aug to 4th Sept 2007: 18 to 26 samples verified against own analyses.

IPWG, Beijing, 13-17 October 2008

Slide 18

Slide 18

Departure statistics: SSM/I obs- FG mean

IPWG, Beijing, 13-17 October 2008

Slide 19

Slide 19

Summary 1 - issues

Emissive reflectors - TMI suffers from emissive reflector bias

AMSR-E also?

Be careful when creating multi-instrument products

- Recommendations for instrument builders:

Need to build non-emissive reflectors

Need for accurate measurements of reflector skin temperature

Radiative transfer in rain and cloud- Move from “maximum” to “weighted average” cloud fraction

- Better agreement with 10 independent column approach and with observations

IPWG, Beijing, 13-17 October 2008

Slide 20

Slide 20

Summary 2 - assimilation

1D+4D-Var assimilation of rain- and cloud- affected SSM/I radiances

- Operational since June 2005 but only the TCWV information content is currently used

- Positive impact on forecast scores for tropical moisture and winds

- Impact is comparable to clear sky microwave imager assimilation.

- Good quality rain retrievals (compared to PR)

Direct 4D-Var assimilation of all-sky radiances (clear, cloudy, rainy)

- Full information content of the observations is assimilated

- Improved forecasts compared to previous system

- To be made operational early 2009