Characterising and reducing uncertainties in all-sky radiative transfer - Vasileios Barlakas Annual Fellow Day 01.03.2021
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Characterising and reducing uncertainties in all-sky radiative transfer Vasileios Barlakas Annual Fellow Day 01.03.2021
Preface
Project description !
CREATE – Characterizing and REducing uncertainties in All-sky
microwave radiative TransfEr
⚈ Sensors ⚈ Applications
o Sensors with frequencies up to ~183 GHz o Stand–alone retrievals
o Ice Cloud Imager (ICI): 183.31 – 664 GHz o Data assimilation (DA)
⚈ Open issues
o Three-dimensional radiative transfer is ingored
o Hydrometeor orientation and polarization are ignored
o RTTOV-SCATT accuracy
o Two-stream delta-Eddington approximation
o Sub-grid variability and cloud overlap scheme
Preface
Project description !
CREATE – Characterizing and REducing uncertainties in All-sky
microwave radiative TransfEr
⚈ Sensors ⚈ Applications
o Sensors with frequencies up to ~183 GHz o Stand–alone retrievals
o Ice Cloud Imager (ICI): 183.31 – 664 GHz o Data assimilation (DA)
⚈ Open issues
þ Three-dimensional radiative transfer is ingored 1st year1
þ Hydrometeor orientation and polarization are ignored 1st & 2nd year2
o RTTOV-SCATT accuracy 1st & 2nd year2
o Two-stream delta-Eddington approximation
o Sub-grid variability and cloud overlap scheme
1Barlakas and Eriksson., Remote Sens., 2020
2Barlakas et al., AMTD, 2020
2Geer, Barlakas et al., to be submitted to GMD, 2021
2Barlakas et al., in preparation, JQSRT, 2021
Overview
Part 1 – Hydrometeor orientation !!!!!!!!!!!
Introducing hydrometeor orientation into all-sky
millimeter/sub-millimeter assimilation
Barlakas et al., AMTD, 2020
Geer, Barlakas et al., to be submitted to GMD, 2021
Introduction
Motivation !!!!!!!!!!!
⚈ Why ice clouds?
Nasa/JPL-Caltech
IWP = 2 gm−2
o Model uncertainties (shape and orientation).
⚈ Why MW and sub-mm?
o Sensitive to large and small ice hydrometeors
o Ice Cloud Imager (ICI)
o 183–664 GHz + dual polarization
o Improved ice retrievals + extend data assimilation
Introduction
Motivation !!!!!!!!!!!
⚈ Why ice clouds?
Nasa/JPL-Caltech
IWP = 2 gm−2
o Model uncertainties (shape and orientation).
⚈ Why MW and sub-mm?
o Sensitive to large and small ice hydrometeors
o Ice Cloud Imager (ICI)
o 183–664 GHz + dual polarization
o Improved ice retrievals + extend data assimilation
+ Polarization
6 July 2019
Introduction
Hydrometeor orientation !!!!!!!!!!!
⚈ Assumptions in DA
o Totally randomly oriented (TRO) hydrometeors only
o “Scalar” simulations, i.e., V– or H–polarization
o Invariant scattering properties between V– and H–simulations
Oriented ice hydrometeors
induce polarization
PD = TBV − TBH
Introduction
Hydrometeor orientation !!!!!!!!!!!
o Attenuation driven by the extinction matrix ⎛ K 11 K 12 K 13 K 14 ⎞
⎜ ⎟
⎜ K 21 K 22 K 23 K 24 ⎟
✓ incident direction K= ⎜ ⎟
K 31 K 32 K 33 K 34 ⎟
✓ orientation ⎜
⎜
⎝ K 41 K 42 K 43 K 44 ⎟⎠
Introduction
Hydrometeor orientation !!!!!!!!!!!
o Attenuation driven by the extinction matrix ⎛ K 11 K 12 K 13 K 14 ⎞
⎜ ⎟
⎜ K 21 K 22 K 23 K 24 ⎟
✓ incident direction K= ⎜ ⎟
K 31 K 32 K 33 K 34 ⎟
✓ orientation ⎜
⎜
⎝ K 41 K 42 K 43 K 44 ⎟⎠
o Total random orientation (TRO)
✗ favored direction
✗ orientation
⎛ K 11 0 0 0 ⎞
⎜ ⎟
⎜ 0 K 11 0 0 ⎟
K= ⎜ ⎟
⎜ 0 0 K 11 0 ⎟
⎜ 0 0 0 K 11 ⎟⎠
⎝
K11, extinction cross section
Introduction
Hydrometeor orientation !!!!!!!!!!!
o Attenuation driven by the extinction matrix ⎛ K 11 K 12 K 13 K 14 ⎞
⎜ ⎟
⎜ K 21 K 22 K 23 K 24 ⎟
✓ incident direction K= ⎜ ⎟
K 31 K 32 K 33 K 34 ⎟
✓ orientation ⎜
⎜
⎝ K 41 K 42 K 43 K 44 ⎟⎠
o Total random orientation (TRO) o Azimuthal random orientation (ARO)
✗ favored direction ✓ orientation based on tilt angle β
✗ orientation ✗ orientation in the azimuth
⎛ K 11 0 0 0 ⎞ ⎛ K K 12 0 0 ⎞
⎜ ⎟ ⎜ 11 ⎟
⎜ 0 K 11 0 0 ⎟ ⎜ K 12 K 11 0 0 ⎟
K= ⎜ ⎟ K= ⎜ ⎟
⎜ 0 0 K 11 0 ⎟ ⎜ 0 0 K 11 K 34 ⎟
⎜ 0 0 0 K 11 ⎟⎠ ⎜ 0 0 −K 34 K 11 ⎟
⎝ ⎝ ⎠
K11, extinction cross section K12, cross section for linear polarisation
K34, cross section for circular polarisationPolarized scattering correction
Extinction at 166.9 GHz !!!!!!!!!!!
⚈ TRO vs ARO
Large plate aggregate
Brath et al., 2020
Barlakas et al., AMTD, 2020Polarized scattering correction
Extinction at 166.9 GHz !!!!!!!!!!!
⚈ TRO vs ARO
Large plate aggregate
Brath et al., 2020
Conical scanners
view
o Extinction cross section, K11 => At θinc ~ 55o, the differences are close to 0.
o Linear polarization cross section, K12 => Large differences up to 18 % between V & H
Barlakas et al., AMTD, 2020Polarized scattering correction
ARO approximation !!!!!!!!!!!
⚈ Orientation approximation
o The parameterization is governed by
the polarization ratio ρ: Large plate aggregate
Brath et al., 2020
τ H τ TRO (1+ α )
ρ= = ,
τ V τ TRO (1− α )
α is a correction factor approximating the
cross section for linear polarization, i.e., K12
* Control (current framework):
ρ (α = 0) = 1
Barlakas et al., AMTD, 2020
Geer, Barlakas et al., to be submitted to GMD, 2021Data assimilation experiments
Setup !!!!!!!!!!!
⚈ Based on the IFS1 of ECMWF2
1) Passive monitoring experiments
o Sensors: GMI3 Which is the best ρ?
o Duration: Jun. – Jul. 2019
2) Cycled DA experiments
o Sensors: AMSR-24, GMI3, SSMIS5
o Duration: Febr. – May 2019 & What is impact of ρ on the forecast?
Jun. – Aug. 2019
o Microphysical setup following:
Geer, to be submitted to AMT, 2021
1IntegratedForecast System
2European Centre for Medium-Range Weather Forecasts
3Global Precipitation Mission microwave imager
4Advanced Microwave Scanning Radiometer-2 Barlakas et al., AMTD, 2020
5Special Sensor Microwave Imager/Sounder Geer, Barlakas et al., to be submitted to GMD, 2021Fitting model to observations
Choosing the best ρ !!!!!!!!!!!
⚈ Polarization differences (PD)
o Observed (o): PDo = TBV
o o
− TBH
PDo − PD b !
o Simulated (b): b
PD = T b
BV
−Tb
BH
* Careful cloud screening method
Barlakas et al., AMTD, 2020Fitting model to observations
Choosing the best ρ !!!!!!!!!!!
⚈ Polarization differences (PD)
o Observed (o): PDo = TBV
o o
− TBH
PDo − PD b !
o Simulated (b): b
PD = T b
BV
−T b
BH
* Careful cloud screening method
#simulated
h = ∑ log10 / # bins
Control bins #observed
o Best fit:
166.5 GHz, ρ = 1.4 (α = 0.167)
89.0 GHz, ρ = 1.5 (α = 0.2)
Barlakas et al., AMTD, 2020Fitting model to observations
Global performance of ρ = 1.4 !!!!!!!!!!!
o Universal arch-like shape of relation
o Control run completely fails to reproduce it
o A ρ of 1.4 gives a reasonable representation of such relation
Barlakas et al., AMTD, 2020Fitting model to observations
Global performance of ρ = 1.4 !!!!!!!!!!!
Observations Simulations ρ = 1.4
o Universal arch-like shape of relation
o Control run completely fails to reproduce it
o A ρ of 1.4 gives a reasonable representation of such relation
Barlakas et al., AMTD, 2020Forecast impact
Forecast quality – ρ = 1.4 !!!!!!!!!!!
Equal & opposite Unilateral in V
α = 0.167 γ = 0.286
τ τ (1+ α ) τ TRO (1+ β ) τ (1)
ρ = H = TRO = = TRO = 1.4
τ V τ TRO (1− α ) τ TRO (1) τ TRO (1− γ )
Unilateral in H
β = 0.4
1Advanced Technology Microwave Sounder
2Winds at 850 hPa, AMSUA (53.7H GHz),
geostationary H2O radiances Barlakas et al., AMTD, 2020Forecast impact
Forecast quality – ρ = 1.4 !!!!!!!!!!!
Equal & opposite Unilateral in V
α = 0.167 γ = 0.286
τ τ (1+ α ) τ TRO (1+ β ) τ (1)
ρ = H = TRO = = TRO = 1.4
τ V τ TRO (1− α ) τ TRO (1) τ TRO (1− γ )
Unilateral in H
β = 0.4
⚈ Validation
o Independent: ATMS1 & conventional2 data
þ Neutral impact
1Advanced Technology Microwave Sounder
2Winds at 850 hPa, AMSUA (53.7H GHz),
geostationary H2O radiances Barlakas et al., AMTD, 2020Forecast impact
Forecast quality – ρ = 1.4 !!!!!!!!!!!
Equal & opposite Unilateral in V Instrument(s):Instrument(s):
GPM − GMI(ALL−SKY) − TB
DMSP−17,18 Area(s): N.Hemis
− SSMIS(ALL−SKY) S.Hemis Tropics
− TB
α = 0.167 γ = 0.286 (a)
From 00Z 15−Feb−2019
Area(s): to 12Z 31−Aug−2019
N.Hemis S.Hemis Tropics
From 00Z 15−Feb−2019 to 12Z 31−Aug−2019
13 190.3V
τ H τ TRO (1+ α ) τ TRO (1+ β ) τ TRO (1) (b)
17
ρ= = = = = 1.4 12 186.9V
τ V τ TRO (1− α ) τ TRO (1) τ TRO (1− γ ) 16
11
Frequency (pol.) [GHz]
166.5H
14
Unilateral in H
number
10 166.5V
number
β = 0.4 13
8 89.0V
Channel
12
Channel
⚈ Validation
6
11
5
10
36.5V
23.8V
o Independent: ATMS1 & conventional2 data 49 18.7V
GMI
þ Neutral impact 38 18.7H
90
96 92 98 94 10096 98102 100 104102 104
106
o Dependent: AMSR-2, GMI, SSMIS Background
Background std.
std. dev.
dev. [%,
[%, normalised]
normalised]
Pol.
Pol. ratio
ratio of
of 1.4
1.4 applied
applied to
to V
þ Increased consistency between V & H channels
(b) Pol.
Pol. ratio
ratio of
of 1.4
1.4 applied
applied to
V
to V
V and
and H
H
Pol. ratio of 1.4 applied to
Pol. ratio of 1.4 applied to HH
þ Increased consistency between instruments 100%
100% =
= Control
Control
+ Tuning the overall level of scattering
1Advanced Technology Microwave Sounder
2Winds at 850 hPa, AMSUA (53.7H GHz),
geostationary H2O radiances Barlakas et al., AMTD, 2020Conclusions
Summary & Outlook !!!!!!!!!!!
⚈ Final configuration of RTTOV-SCATT v13.0
o Minor software adaption and no calculation burdens
o Maximum modeling errors are reduced by 10–15 K
o Errors are now ~ symmetrical
o Neutral forecast impact, with increasing consistency between instruments
o Polarization ratio of 1.4 in RTTOV-SCATT v13.0 + future IFS cycle
⚈ Outlook MHS ICI
o Polarization correction scheme
o Cross-track sounders
o Radar backscattering 3rd year Photo: esa.int Photo: Airbus
o Ice retrievals, e.g., in GMI
o Frequencies above 183 GHz, i.e., Ice Cloud Imager
Barlakas et al., AMTD, 2020
Geer, Barlakas et al., to be submitted to GMD, 2021Overview
Part 2 – RTTOV-SCATT accuracy !!!!
Model inter-comparison in all-sky
millimeter/sub-millimeter radiative transfer
Barlakas et al., in preparation, JQSRT, 2021
Objectives:
o Are there any systematic or random errors?
o Where does the two-stream delta-Eddington “falls apart”?
o Most studies focus on clear-sky or frequencies below ~90 GHz
o Prepare RTTOV–SCATT for ICIOverview
Part 2 – RTTOV-SCATT accuracy !!!!
Model inter-comparison in all-sky
millimeter/sub-millimeter radiative transfer
Barlakas et al., in preparation, JQSRT, 2021
Objectives:
o Are there any systematic or random errors?
o Where does the two-stream delta-Eddington “falls apart”?
o Most studies focus on clear-sky or frequencies below ~90 GHz
o Prepare RTTOV–SCATT for ICI
ARTS vs RRTOV (–SCATT):
o Clear–sky
o All–sky (i.e. snow, ice, rain)
Sensors: ARTS scattering database
o Advanced Microwave Sounding Unit A (AMSUA) o 34 freq.: 1-886.4 GHz
o Microwave Humidity Sounder (MHS) o 34 particle models (PM)
o Ice Cloud Imager (ICI) o 35-45 sizes per PM
Eriksson et al., ESSD, 2018Results
MHS: Clear–sky comparison !!!!
Settings:
o US standard atmosphere
o Surface emissivity = 1
o An excellent agreement is found ± 0.1 K Correct spectroscopy!
o Exceptions: ICI at 243.2 GHz (~0.2 K) and 448 GHz (~0.4 K) Ongoing by Met Office
Barlakas et al., in preparation, JQSRT, 2021Results
ICI: Ice–only comparison !!!!
Settings:
o Gaussian cloud: 9-11 km
o Large plate aggregate habit
o Field et al., 2007 PSD
ΔΤΒ = ΤΒ,cloudy – TB,clear
Barlakas et al., in preparation, JQSRT, 2021Results
ICI: Ice–only comparison !!!!
Settings:
o Gaussian cloud: 9-11 km
o Large plate aggregate habit
o Field et al., 2007 PSD
ΔΤΒ = ΤΒ,cloudy – TB,clear
325 GHz
Barlakas et al., in preparation, JQSRT, 2021Conclusions
Summary & Outlook !!!!
⚈ Performance of RTTOV-SCATT
o Clear–sky conditions
o An excellent agreement is found (±0.1 K). For ICI, there is still some ongoing work.
o All–sky conditions
o ICI: ΔΤΒ ~3 K, with a relative error up to ΔΤΒ ~10 %
o MHS and AMSUA: ΔΤΒ ~7 K, with a relative error up to ΔΤΒ ~15 %
⚈ Outlook
o Where does the two-stream delta-Eddington “falls apart”?
3rd year
o Finalize the manuscript
o Sub-grid variability and cloud overlap scheme.
Barlakas et al., in preparation, JQSRT, 2021To–do–list
Year 3 !
⚈ Polarized correction scheme
o Cross-track sounders
MHS
o Radar backscattering
o Ice cloud retrievals (ongoing)
Photo: esa.int
⚈ Performance of RTTOV-SCATT
o Finalize the intercomparison (ongoing)
o Sub-grid variability and cloud overlap scheme.
Thank you for your attention !You can also read
