slide 1 ipwg, beijing, 13-17 october 2008 slide 1 assimilation of rain and cloud-affected microwave...
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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