Microwave Integrated Retrieval System MIRS Performances Summary

1
Microwave Integrated Retrieval System (MIRS)
Performances Summary
June 15th 2008

• S.-A. Boukabara, K. Garrett, C. Kongoli, W. Chen,
  F. Iturbide-Sanchez

2
Context

• MIRS Fourth Release scheduled June 15th 2008
• Several updates introduced in the MIRS algorithm
• Extension of retrieval in precipitating
  conditions
• Extension to multiple sensors (NOAA-18, Metop-A
  and DMSP SSMIS)
• Covariance Matrix (cross-correlations added)
• Bias Fine Tuning
• Regression-based First Guess
• QC
• Extension to other products (T,Q, Sfc Properties,
  cloud/rain parameters, etc)
• Need to keep track of performances
• Some depend on season
• Some depend on vertical layer
• Some depend on ground truth used
• Some are only qualitative (lack of ground truth)
• Credit due to the Integrated Collocation Data
  Base (ICDB), provided by Tony Reale and Co

3
List of products (Official)

• Metop-A and NOAA-18
• Temperature profile (ocean)
• Moisture (ocean and non-costal land)
• Total Precipitable Water (TPW) (ocean and
  non-costal land)
• Land Surface Temperature (LST)
• Emissivity Spectrum (All surfaces)
• Surface Type (sea, land, snow, sea-ice)
• Emissivity-based Snow Water Equivalent (SWE)
• Emissivity-based Snow Cover Extent (SCE)
• Emissivity-based Sea Ice Concentration (SIC)
• Vertically Integrated Non-precipitating Cloud
  Liquid Water (CLW)
• Vertically Integrated Ice Water Path (IWP)
• Vertically Integrated Rain Water Path (RWP)
• DMSP F16 SSMIS
• Temperature profile (ocean)
• Moisture (ocean and non-costal land)
• Total Precipitable Water (TPW) (ocean and
  non-costal land)
• Land Surface Temperature (LST)

Note The hydrometeor profiles dropped from
official list (lack of information content in
radiances, see next slide)
Total 30 products
4
List of unofficial products (Delivered For
Testing purposes)
Note Cloud profile made available for testing
purposes (see next slide)

• Metop-A and NOAA-18
• Cloud Liquid Water Profile (CLWP) over ocean
• Surface Temperature (skin) of snow-covered land
• Sea Surface Temperature (SST)
• Effective grain size of snow (over snow-covered
  land surface)
• Multi-Year (MY) Type Sea Ice concentration
• First-Year (FY) Type Sea Ice Concentration
• DMSP F16 SSMIS
• Extended Total Precipitable Water over
  non-coastal Land
• Emissivity-based Snow Water Equivalent (SWE)
• Emissivity-based Snow Cover Extent (SCE)
• Emissivity-based Sea Ice Concentration (SIC)
• Surface Temperature (skin) of snow-covered land
• Sea Surface Temperature (SST)
• Effective grain size of snow (over snow-covered
  land surface)
• Multi-Year (MY) Type Sea Ice concentration
• First-Year (FY) Type Sea Ice Concentration

Total 21 test products
5
Important Note

• This summary is a snapshot of the performances.
• For more details, look at the STAR MIRS web site
  mirs.nesdis.noaa.gov

6
Assessing performances

• Go through all products performances, for all
  sensors (30 official products and 21 test
  products 51 total from 3 sensors)
• Performances assessments depend on product
  sometimes qualitative.
• Keep in mind these performances are impacted by
  errors in ground truth data themselves, by
  time/space collocation, by representativeness
  errors, etc.
• Intra-truth variability assessed by comparing to
  multiple sources
• These performances are assessed in cloudy
  conditions so greater spatial coverage of
  applicability of these assessments

7
Note about Hydrometeor Vertical Distribution
(Atmospheric Profile) -1st Case-

• Profiles of Hydrometeors retrieved by MIRS Used
  to get integrated values (CLW, IWP, RWP)
• Vertical distribution sometimes not very
  accurate due to lack of information content in
  Radiances. See examples
• Sometimes vertical distribution is accurate (need
  assessment by users if it fits requirements
• Suggested to make available for testing purposes

Convergence reached (in two iterations)
Temp
Iter0
Iter2
Iter1
Iter0
Iter1
Iter2
WV
Iter0
Iter1
Iter2
Truth
Background
Cloud
Retrieval
8
Note about Hydrometeor Vertical Distribution
(Atmospheric Profile) -2nd case-
A case where convergence was reached but the
vertical distribution of retrieved is
significantly different from truth
9
NOAA-18 AMSU/MHS
10
Temperature Profile (1/4)(over open water ocean,
against GDAS)
MIRS
GDAS
MIRS GDAS Diff

• The temperature is officially delivered over
  ocean only. But over non-ocean (land, snow, sea
  ice), temperature is still valid.
• Validation is performed by comparing to
• GDAS
• ECMWF
• RAOB

MIRS GDAS Diff
N18
11
Temperature Profile (2/4)(over open water ocean,
against ECMWF)
Angle dependence taken care of very well, without
any limb correction
MIRS
ECMWF
Note Retrieval is done over all surface
backgrounds but also in all weather conditions
(clear, cloudy, rainy, ice)
MIRS ECMWF Diff
MIRS ECMWF Diff
N18
12
Temperature Profile (3/4)(over open water ocean,
against RAOBs, COSMIC, ATOVS, Forecast)
Stdev Ocean
Bias Ocean
Original 100 layers resolution
Bias of roughly 1 K noticed at the surface
Stdev Ocean
Vertical Averaging (IORD-II requirements (moving
window of 1, 1.5 and 5 km)
Collocation criteria (COSMIC, ATOVS, SSMIS,
RAOB) /- 5 hours, /- 100 Kms Data spanning 42
days
N18
13
Temperature Profile (4/4)(Performances)
Ocean
Land

• Note IORD-II requirements for temperature in
  cloudy
• Uncertainty (surface to 700 mb 2.5K per 1km
  layer, 700 mb to 300 mb 1.5K per 1 km layer, 300
  to 30 mb 1.5K per 3km layer, 30 to 1mb 1.5K per
  5km layer)

These requirements are for CrIS and ATMS, which
have more channels and higher sensing skills in
general than AMSU, MHS or SSMIS
N18
14
Moisture Profile (1/4)(over open water and land,
against GDAS)

• Validation of WV done by comparing to
• GDAS
• ECMWF
• RAOB
• Retrieval done over all surfaces in all weather
  conditions

MIRS
GDAS
land
Bias

• Assessment includes
• Angle dependence
• Statistics profiles
• Difference maps

Sea
Stdev
N18
15
Moisture Profile (2/4)(over open water and land,
against ECMWF)

• Validation of WV done by comparing to
• GDAS
• ECMWF
• RAOB
• Retrieval done over all surfaces in all weather
  conditions

MIRS
ECMWF

• Assessment includes
• Angle dependence
• Statistics profiles
• Difference maps

land
Bias
Sea
Stdev
When assessing, keep in mind all ground truths
(wrt GDAS, ECMWF, RAOB)
N18
16
Moisture Profile (3/4)(over open water and land,
against RAOB, COSMIC, Forecast, ATOVS)
N18
Stdev is found very good over land and ocean
Ocean Stdev
Ocean Bias
MIRS is compared to Raob (along with COSMIC,
ATOVS and Forecast)
Land Stdev
Land Bias
Bias wrt RAOB (over land) not consistent with
ECMWF and GDAS
17
Moisture Profile (4/4)(Performances)
Ocean
Land

• Note IORD-II requirements for Water Vapor
  Mixing Ratio (in g/Kg), for cloudy
• Uncertainty (surface to 600 mb greater of 20 or
  0.2 g/Kg, 600 mb to 100 mb greater of 40 or 0.1
  g/Kg)
• expressed as percent error of average mixing
  ratio in 2km layers
• - No measurement precision

These requirements are for CrIS and ATMS, which
have more channels and higher sensing skills in
general than AMSU, MHS or SSMIS
N18
18
Total Precipitable Water (TPW) (1/3) (over open
water and land)
ECMWF
MIRS extends TPW over other surface types (for
the first time for an operational algorithm)
MIRS

• Validation done by comparison to
• GDAS
• MSPPS
• ECMWF
• Radiosondes

GDAS
MSPPS
N18
19
Total Precipitable Water (TPW) (2/3) (over open
water and land)
Global
Global
Global
MIRS vs RAOB
MIRS vs GDAS
MIRS vs ECMWF
Ocean
Ocean
Ocean
MIRS vs RAOB
MIRS vs GDAS
MIRS vs ECMWF
N18
20
Total Precipitable Water (TPW) (3/3) (over open
water and land)
IORD-II Requirements - Accuracy greater of 2
mm or 10 - Precision 1mm
N18
21
Land Surface Temperature (1/3)Comparison against
GDAS
Ascending, MIRS
Ascending, GDAS
Bias 0.08K Std 6.9K
Bias -1.4K Std 5.7K
N18
Descending, MIRS
Descending, GDAS
22
Land Surface Temperature (2/3)Comparison against
ECMWF
Ascending, MIRS
Ascending, ECMWF
Bias -1.5K Std 5.5 K
Bias -0.7K Std 5.8K
N18
Descending, MIRS
Descending, ECMWF
23
Land Surface Temperature (3/3)Comparison against
Other Sources and Summary
N18
24
Emissivity Spectrum (1/3)(All surfaces)

• Validation (qualitative and quantitative) of
  emissivity done by comparing to
• MSPPS emissivity (23.8, 31.4 and 50.3 GHz
  channels) not available for all channels
• Analytical emissivities (over land, sea ice and
  snow)
• Ocean emissivity model applied to GDAS wind
  fields
• Ocean emissivity model applied to ECMWF wind
  fields
• Analytical emissivities (all sfcs) using RAOB
  profiles

Heavily dependent on validity of raob models
fields (wind speed, skin temperature, water vapor
and temperature profiles)
N18
Analytical emissivity not valid when specular
assumption not applicable (high topography)
25
Emissivity Spectrum (2/3)(All surfaces)
MSPPS
MIRS
MIRS vs ECMWF
ECMWF
MIRS vs MSPPS
N18
26
Emissivity Spectrum (3/3)(All surfaces)
31.4 GHz
50.3 GHz
23.8 GHz
No IORD-II requirement for emissivity
Large uncertainty over snow (bias large) when
comparing to both GDAS and ECMWF analytical
emissivity. But large uncertainties exist for the
GDAS and ECMWF Tskin over snow, which served to
determine the analytical emissivity
N18
27
Surface Type Classification
Types detected ocean, sea ice, land and snow
TB-based Pre-classification
Emissivity-based Post-classification

• Both made available to users
• When two coincide, high confidence
• When two differ, ambiguity possible

N18
28
Snow Water Equivalent (SWE)(Emissivity-based)
MSPPS
MIRS
N18
29
Example of MIRS, MSPPS, AMSR-E and IMS snow
cover retrievals. MIRS overall shows more
consistency with IMS. Noted is Central Asia,
with MSPPS and AMSR-E showing more false alarm
than MIRS
Snow Cover Extent (SCE) (1/2)(Emissivity-based)
MIRS Snow Cover
IMS Snow Cover
AMSR-E Snow Cover
MSPPS Snow Cover
30
Snow Cover Extent (SCE) (2/2)(Emissivity-based)
N18
31
Example of MIRS, MSPPS and IMS Sea Ice Conc.
Retrievals over the Northern Hemisphere MIRS
shows more consistency with IMS
Sea Ice Concentration (SIC) (1/4)(Emissivity-base
d, Comparison to IMS, MSPPS)
MIRS SIC on April 2, 2007
MSPPS SIC on April 2, 2007
IMS SIC on April 2, 2007
N18
32
Example of on-line MIRS and MSPPS Sea Ice Conc.
Inter-comparisons and Stats MIRS shows a
higher mean sea ice concentration than MSPPS,
consistent with statistical results with respect
to IMS
Sea Ice Concentration (SIC) (2/4)(Emissivity-base
d, Comparison to MSPPS)
N18
33
Sea Ice Concentration (SIC) (3/4)(Emissivity-base
d, Comparison to IMS)
Current version MIRS N18 AMSU-MHS Sea Ice
Concentration Performance Assessment over
Northern Hemisphere 4-km IMS as Reference
N18
34
Current and DAP MIRS Sea Ice Concentration
Performance over high-concentration areas (100
ice all-year around) case study
-------------------------------------------------
--------------------------------------------Latit
ude 84-86 N Longitude 135-137 W
Year 2006
Sea Ice Concentration (SIC) (4/4)(Emissivity-base
d, Assessment of SIC in 100 ice-covered sea
areas)
35
Cloud Liquid Water (CLW) (1/4)(Non-Precipitating,
in simulation)
Mean 0.006 mm Stdev 0.043 mm CorrFact
0.991
Experiment set up
ECMWF Data (cloudy)
MIRS Retrievals (CLW)
Performances of cloud retrieval are very good in
simulation
CRTM
MIRS
When comparing to real data, most errors are
believed due to assumptions in other cloud
parameters (vertical distribution, density, size
distribution, etc) as well as due to collocation
errors (time, space and representativeness)
Synthetic TBs (cloudy)
N18
36
Cloud Liquid Water (CLW) (2/4)(Non-Precipitating,
over open water ocean)

• Based on the Cloud Liquid Water Profile retrieval
  (just like TPW is based on the Q profile)
• Much higher correlation between MIRS and MSPPS
  noticed in descending than in descending nodes
  Note MSPPS uses Ts and wind speed from forecast
  data as inputs
• MIRS CLW is for non-precipitating clouds. MSPPS
  is for all clouds.
• MSPPS is the reference is microwave cloud amounts
• MIRS relies on physical constraints to retrieve
  cloud, rain and ice Jacobians and
  forward-simulated radiances based on Community
  Radiative Transfer Model (CRTM)

Descending
Ascending
MIRS vs MSPPS
MIRS vs MSPPS
Corr Factor 0.63
Corr Factor 0.47
N18
37
Cloud Liquid Water (CLW) (3/4)(Non-Precipitating,
over open water ocean)
MSPPS (official)
MIRS
Note the different scales MIRS 0-0.3 MSPPS
0-0.7 mm
Higher small amounts of clouds noticed from MIRS
(as opposed to MSPPS), due to use of high
frequencies?
38
Cloud Liquid Water (CLW) (4/4)(Comparison to
ECMWF analysis field of clouds)
Wrt-MSPPS uncertainty (best estimate) 0.15 mm
IORD-II Requirements Uncertainty of 0.25 mm
over ocean
Histograms comparisons (MIRS vs ECMWF)
Corr Factor 0.50
MIRS vs ECMWF
39
Ice Water Path (IWP)(over open water ocean and
land)

• Based on the Frozen Water Profile retrieval (just
  like TPW is based on the Q profile)
• The Ice Water Path is retrieved globally (over
  ocean and land)

MSPPS
MIRS
Note the different scales
N18
40
Ice Water Path (IWP)(over open water ocean and
land)
Ascending
Descending
MIRS vs MSPPS
MIRS vs MSPPS
Noticed Systematic bias of 0.1 mm between MSPPS
and MIRS (MSPPS higher)
IORD-II requirement Precision greater of 0.05
mm or 10, Accuracy greater of 0.1 mm or 25
Important Note(s) 1) There is a large
uncertainty on the actual amounts of cloud, ice,
liquid. MIRS is using physical constraints
(fitting the radiances with a forward model that
has Scattering included, use of Jacobians wrt
ice, liquid, as well from CRTM, etc) 2)
Measurement information is dependent on (a)
number of particles, (b) their sizes, and (c)
their densities IORD-II text. We rely on CRTM
for the assumptions made regarding number, size,
density of ice particles. 3) MIRS retrievals are
at AMSU footprint resolution while MSPPS are at
MHS resolution. Due to this, it is expected that
the MIRS retrievals are smoothed lower.
Retrievals is relative to amounts within
footprint.
41
Ice Water Path (IWP)(Impact of spatial
resolution)
MIRS IWP Retrieval _at_ AMSU resolution
Field of ice gets smoothed out because of
footprint size
MIRS IWP Retrieval _at_ MHS resolution
Case of Katrina (very heavy precipitation) -August
2005-
42
Rain Water Path (RWP) 1/2(over open water ocean
and land)

• Based on the Liquid (Rain) Water Profile
  retrieval (just like TPW is based on the Q
  profile)
• RWP is retrieved globally
• Qualitative comparison with MSPPS Rainfall Rate
  (RR)

MSPPS Rainfall rate
MIRS Rain Water Path
N18
Some areas are missing Retrieved as cloud in
MIRS (see next slide)
43
Rain Water Path (RWP) 2/2(over open water ocean
and land)
MIRS Liquid Water Path (Precipnonprecip)
MSPPS Rainfall rate
MIRS Rain Water Path
N18
44
METOP-A AMSU/MHS
45
Temperature Profile (1/4)(over open water ocean
and other, against GDAS)
MIRS
GDAS
MIRS GDAS Diff

• The temperature is officially delivered over
  ocean only. But over non-ocean (land, snow, sea
  ice), temperature is still valid.
• Validation is performed by comparing to
• GDAS
• ECMWF
• RAOB

MIRS GDAS Diff
METOP-A
46
Temperature Profile (2/4)(over open water ocean)
Angle dependence taken care of very well, without
any limb correction
MIRS
ECMWF
Note Retrieval is done over all surface
backgrounds but also in all weather conditions
(clear, cloudy, rainy, ice)
MIRS ECMWF Diff
MIRS ECMWF Diff
METOP-A
47
Temperature Profile (3/4)(over open water ocean,
against RAOBs, COSMIC, ATOVS, Forecast)
Bias Ocean
Stdev Ocean
Original 100 layers resolution
Stdev Ocean
Bias of roughly 1 K noticed at the surface
Vertical Averaging (IORD-II requirements (moving
window of 1, 1.5 and 5 km)
Collocation criteria (COSMIC, ATOVS, SSMIS,
RAOB) /- 5 hours, /- 100 Kms Data spanning 42
days
METOP-A
48
Temperature Profile (4/4)(Performances)
Ocean
Land

• Note IORD-II requirements for temperature in
  cloudy
• Uncertainty (surface to 700 mb 2.5K per 1km
  layer, 700 mb to 300 mb 1.5K per 1 km layer, 300
  to 30 mb 1.5K per 3km layer, 30 to 1mb 1.5K per
  5km layer)

These requirements are for CrIS and ATMS, which
have more channels and higher sensing skills in
general than AMSU, MHS or SSMIS
METOP-A
49
Moisture Profile (1/4)(over open water and land,
against GDAS)

• Validation of WV done by comparing to
• GDAS
• ECMWF
• RAOB
• Retrieval done over all surfaces in all weather
  conditions

MIRS
GDAS
Bias
land

• Assessment includes
• Angle dependence
• Statistics profiles
• Difference maps

Sea
Stdev
METOP-A
50
Moisture Profile (2/4)(over open water and land,
against ECMWF)

• Validation of WV done by comparing to
• GDAS
• ECMWF
• RAOB
• Retrieval done over all surfaces in all weather
  conditions

MIRS
ECMWF

• Assessment includes
• Angle dependence
• Statistics profiles
• Difference maps

land
Bias
Sea
Stdev
When assessing, keep in mind all ground truths
(wrt GDAS, ECMWF, RAOB)
METOP-A
51
Moisture Profile (3/4)(over open water and land,
against RAOB, COSMIC, Forecast, ATOVS)
METOP-A
Stdev is found very good over land and ocean
(even better than N18 and COSMIC)
Ocean Stdev
Ocean Bias
MIRS is compared to Raob (along with COSMIC,
ATOVS and Forecast)
Land Bias
Land Stdev
Bias wrt RAOB (over land) not consistent with
ECMWF and GDAS
52
Moisture Profile (4/4)(Performances)
Ocean
Land

• Note IORD-II requirements for Water Vapor
  Mixing Ratio (in g/Kg), for cloudy
• Uncertainty (surface to 600 mb greater of 20 or
  0.2 g/Kg, 600 mb to 100 mb greater of 40 or 0.1
  g/Kg)
• expressed as percent error of average mixing
  ratio in 2km layers
• - No measurement precision

These requirements are for CrIS and ATMS, which
have more channels and higher sensing skills in
general than AMSU, MHS or SSMIS
METOP-A
53
Total Precipitable Water (TPW) (1/2)(over open
water and land)
MIRS extends TPW over other surface types (for
the first time for an operational algorithm)
MIRS/METOP
GDAS

• Validation done by comparison to
• GDAS
• MSPPS
• ECMWF
• Radiosondes

MSPPS
RAOB/OCEAN
RAOB/Land
METOP-A
54
Total Precipitable Water (TPW) (2/2) (over open
water and land)
IORD-II Requirements - Accuracy greater of 2
mm or 10 - Precision 1mm
METOP-A
55
Land Surface Temperature (1/3)Comparison against
GDAS
Ascending, GDAS
Ascending, MIRS
Bias -0.8K Std 5.3K
Bias -0.6K Std 5.3K
Descending, GDAS
Descending, MIRS
METOP-A
56
Land Surface Temperature (2/3)Comparison against
ECMWF
Ascending, MIRS
Ascending, ECMWF
Bias -1.5K Std 5.9 K
Descending, ECMWF
Bias -2.5K Std 5.3K
Descending, MIRS
METOP-A
57
Land Surface Temperature (3/3)Comparison against
Other Sources and Summary
Larger bias noticed with HIRS than when
comparing to ECMWF/GDAS
METOP-A
58
Emissivity Spectrum (1/3)(All surfaces)

• Validation (qualitative and quantitative) of
  emissivity done by comparing to
• MSPPS emissivity (23.8, 31.4 and 50.3 GHz
  channels) not available for all channels
• Analytical emissivities (over land, sea ice and
  snow)
• Ocean emissivity model applied to GDAS wind
  fields
• Ocean emissivity model applied to ECMWF wind
  fields
• Analytical emissivities (all sfcs) using RAOB
  profiles

Heavily dependent on validity of raob models
fields (wind speed, skin temperature, water vapor
and temperature profiles)
Analytical emissivity not valid when specular
assumption not applicable (high topography)
METOP-A
59
Emissivity Spectrum (2/3)(All surfaces)
MSPPS
MIRS
MIRS vs ECMWF
ECMWF
MIRS vs MSPPS
METOP-A
60
Emissivity Spectrum (3/3)(All surfaces)
31.4 GHz
50.3 GHz
23.8 GHz
No IORD-II requirement for emissivity
Large uncertainty over snow (bias large) when
comparing to ECMWF analytical emissivity. But
large uncertainties exist for the GDAS and ECMWF
Tskin over snow, which served to determine the
analytical emissivity
METOP-A
61
Surface Type Classification
Types detected ocean, sea ice, land and snow

• Both made available to users
• When two coincide, high confidence
• When two differ, ambiguity possible

METOP-A
62
Snow Water Equivalent (SWE)(Emissivity-based)
METOP-A
63
Example of MIRS, MSPPS, AMSR-E and IMS snow
cover retrievals. MIRS overall shows more
consistency with IMS. Noted is Central Asia,
with MSPPS and AMSR-E showing more false alarm
than MIRS
Snow Cover Extent (SCE)(Emissivity-based)
IMS Snow Cover
MIRS Snow Cover
AMSR-E Snow Cover
MSPPS Snow Cover
METOP-A
64
Snow Cover Extent (SCE)(Emissivity-based)
Statistical Assessment of current MIRS METOP-A
AMSU-MHS Snow coverover North America in 2007
4-km IMS As Reference
METOP-A
65
Example of on-line METOP-A MIRS and MSPPS Sea Ice
Conc. Inter-comparisons MIRS shows higher sea
ice concentrations, consistent with statistical
results with respect to IMS(see next slide)
Sea Ice Concentration (SIC) (1/2)(Emissivity-base
d)
METOP-A
66
Sea Ice Concentration (SIC) (2/2)(Performances)
Current version Metop-A AMSU-MHS Sea Ice
Concentration Performance Assessment over
Northern Hemisphere collocated 4-km IMS as
Reference - 2007 ---------------------------------
--------------------------------------------------
-------------
Mean Sea Ice Concentrations in
METOP-A
67
Cloud Liquid Water (CLW) (1/2)(Non-Precipitating,
over open water ocean)

• Based on the Cloud Liquid Water Profile retrieval
  (just like TPW is based on the Q profile)
• Much higher correlation between MIRS and MSPPS
  noticed in ascending than in descending nodes
  Note MSPPS uses Ts and wind speed from forecast
  data as inputs
• MIRS CLW is for non-precipitating clouds. MSPPS
  is for all clouds.
• MSPPS is the reference is microwave cloud amounts
• MIRS relies on physical constraints to retrieve
  cloud, rain and ice Jacobians and
  forward-simulated radiances based on Community
  Radiative Transfer Model (CRTM)

Wrt-MSPPS uncertainty 0.15 mm
Descending
Ascending
MIRS vs MSPPS
MIRS vs MSPPS
Corr Factor 0.64
Corr Factor 0.69
METOP-A
68
Cloud Liquid Water (CLW) (2/2)(Comparison to
ECMWF analysis field of clouds)
Histograms comparisons (MIRS vs ECMWF)
Corr Factor 0.46
MIRS vs ECMWF
METOP-A
69
Ice Water Path (IWP)(over open water ocean and
land)

• Based on the Frozen Water Profile retrieval (just
  like TPW is based on the Q profile)
• The Ice Water Path is retrieved globally (over
  ocean and land)

MSPPS
MIRS
Note the different scales
METOP-A
70
Ice Water Path (IWP)(over open water ocean and
land)
Ascending
Descending
MIRS vs MSPPS
MIRS vs MSPPS
Noticed Systematic bias of 0.1 mm between MSPPS
and MIRS (MSPPS higher)
IORD-II requirement Precision greater of 0.05
mm or 10, Accuracy greater of 0.1 mm or 25
Important Note(s) 1) There is a large
uncertainty on the actual amounts of cloud, ice,
liquid. MIRS is using physical constraints
(fitting the radiances with a forward model that
has Scattering included, use of Jacobians wrt
ice, liquid, as well from CRTM, etc) 2)
Measurement information is dependent on (a)
number of particles, (b) their sizes, and (c)
their densities IORD-II text. We rely on CRTM
for the assumptions made regarding number, size,
density of ice particles. 3) MIRS retrievals are
at AMSU footprint resolution while MSPPS are at
MHS resolution. Due to this, it is expected that
the MIRS retrievals are smoothed lower.
Retrievals is relative to amounts within
footprint.
METOP-A
71
Rain Water Path (RWP) 1/2(over open water ocean
and land)

• Based on the Liquid (Rain) Water Profile
  retrieval (just like TPW is based on the Q
  profile)
• RWP is retrieved globally
• Qualitative comparison with MSPPS Rainfall Rate
  (RR)

MSPPS Rainfall rate
MIRS Rain Water Path
Some areas are missing Retrieved as cloud in
MIRS (see next slide)
METOP-A
72
Rain Water Path (RWP) 2/2(over open water ocean
and land)
MIRS Liquid Water Path (Precipnonprecip)
Thanks to CRTM, MIRS is able to distinguish
precipitating from non-precipitating clouds
liquid water
MSPPS Rainfall rate
MIRS Rain Water Path
METOP-A
73
DMSP-F16 SSMI/S
74
Temperature Profile (1/4)(over open water ocean,
against GDAS)
MIRS
GDAS
MIRS GDAS Diff

• The temperature is officially delivered over
  ocean only. But over non-ocean (land, snow, sea
  ice), temperature is still valid.
• Validation is performed by comparing to
• GDAS
• ECMWF
• RAOB

MIRS GDAS Diff
DMSP SSMIS
75
Temperature Profile (2/4)(over open water ocean)
Angle dependence taken care of very well, without
any limb correction
MIRS
ECMWF
Note Retrieval is done over all surface
backgrounds but also in all weather conditions
(clear, cloudy, rainy, ice)
MIRS ECMWF Diff
MIRS ECMWF Diff
DMSP SSMIS
76
Temperature Profile (3/4)(over open water ocean,
against RAOBs, COSMIC, ATOVS, Forecast)
Bias Ocean
Stdev Ocean
Original 100 layers resolution
Stdev Ocean
Collocation criteria (COSMIC, ATOVS, SSMIS,
RAOB) /- 5 hours, /- 100 Kms Data spanning 42
days
Vertical Averaging (IORD-II requirements (moving
window of 1, 1.5 and 5 km)
DMSP SSMIS
77
Temperature Profile (4/4)(Performances)
Ocean
Land

• Note IORD-II requirements for temperature in
  cloudy
• Uncertainty (surface to 700 mb 2.5K per 1km
  layer, 700 mb to 300 mb 1.5K per 1 km layer, 300
  to 30 mb 1.5K per 3km layer, 30 to 1mb 1.5K per
  5km layer)

These requirements are for CrIS and ATMS, which
have more channels and higher sensing skills in
general than AMSU, MHS or SSMIS
DMSP SSMIS
78
Moisture Profile (1/4)(over open water, against
GDAS)

• Validation of WV done by comparing to
• GDAS
• ECMWF
• RAOB
• Retrieval done over all surfaces in all weather
  conditions

MIRS
GDAS
Bias
land

• Assessment includes
• Angle dependence
• Statistics profiles
• Difference maps

Sea
Stdev
DMSP SSMIS
79
Moisture Profile (2/4)(over open water, against
ECMWF)

• Validation of WV done by comparing to
• GDAS
• ECMWF
• RAOB
• Retrieval done over all surfaces in all weather
  conditions

ECMWF
MIRS

• Assessment includes
• Angle dependence
• Statistics profiles
• Difference maps

Bias
land
Sea
Stdev
DMSP SSMIS
80
Moisture Profile (3/4)(over open water and land,
against RAOB, COSMIC, Forecast)
DMSP SSMIS
Perfs of SSMIS sounding not bad, given
calibration anomalies
Ocean Stdev
Ocean Bias
MIRS is compared to Raob (along with COSMIC, and
Forecast)
Land Stdev
Land Bias
Bias wrt RAOB (over ocean and land) not
consistent with ECMWF and GDAS
81
Moisture Profile (4/4)(Performances)
Ocean
Land

• Note IORD-II requirements for Water Vapor
  Mixing Ratio (in g/Kg), for cloudy
• Uncertainty (surface to 600 mb greater of 20 or
  0.2 g/Kg, 600 mb to 100 mb greater of 40 or 0.1
  g/Kg)
• expressed as percent error of average mixing
  ratio in 2km layers
• - No measurement precision

These requirements are for CrIS and ATMS, which
have more channels and higher sensing skills in
general than AMSU, MHS or SSMIS
DMSP SSMIS
82
Total Precipitable Water (TPW) (1/2)(over open
water)
TPW over other surface types available (but not
officially operational in this phase)
MIRS Vs RAOB
GDAS
MIRS
MIRS Vs ECMWF
MSPPS
ECMWF
MIRS Vs GDAS

• Validation done by comparison to
• GDAS, MSPPS, ECMWF, Radiosondes

DMSP SSMIS
83
Total Precipitable Water (TPW) (2/2) (over open
water)
IORD-II Requirements - Accuracy greater of 2
mm or 10 - Precision 1mm
DMSP SSMIS
84
Land Surface Temperature (1/4)Comparison against
GDAS
Ascending, GDAS
Ascending, MIRS
Bias 1.8K Std 5.3K
Bias 1.3K Std 4.7K
Descending, GDAS
Descending, MIRS
DMSP SSMIS
85
Land Surface Temperature (2/4)Comparison against
ECMWF
Ascending, MIRS
Ascending, ECMWF
Bias 1.3K Std 5.4 K
Descending, ECMWF
Bias 0.5K Std 4.4K
Descending, MIRS
DMSP SSMIS
86
Land Surface Temperature (3/4)Comparison against
GOES
87
Land Surface Temperature (3/3)Comparison against
Other Sources (HIRS) Summary
Larger bias noticed with HIRS than when
comparing to ECMWF/GDAS
Much more consistent performances When comparing
to GOES Tskin.
DMSP SSMIS
88
Emissivity Spectrum (1/3)(All surfaces)
MIRS
GDAS

• Validation (qualitative and quantitative) of
  emissivity done by comparing to
• Analytical emissivities (over land, sea ice and
  snow)
• Ocean emissivity model applied to GDAS wind
  fields
• Ocean emissivity model applied to ECMWF wind
  fields
• Analytical emissivities (all sfcs) using RAOB
  profiles

Heavily dependent on validity of raob models
fields (wind speed, skin temperature, water vapor
and temperature profiles)
Analytical emissivity not valid when specular
assumption not applicable (high topography)
SSMIS
89
Emissivity Spectrum (2/3)(All surfaces)
MIRS
GDAS
MIRS vs ECMWF
ECMWF
ECMWF
MIRS vs GDAS
DMSP SSMIS
90
Emissivity Spectrum (3/3)(All surfaces)
19 V
19H
37 V
No IORD-II requirement for emissivity
DMSP SSMIS
91
Surface Type Classification
MIRS/METOP-A
MIRS/N18
MIRS/SSMIS
DMSP SSMIS
92
Conclusions Talking Points
93
Discussion (1/4)

• Performances were assessed using different
  sources. Sometimes results are different,
  reflecting inter-truth variability.
• When consistent behavior is noticed, assumed that
  MIRS is the likely reason
• SSMIS is found, as expected from radiances
  noisiness, to have slightly more degraded
  performances (EDRS TPW)
• N18 and Metop-A running at AMSUA resolution
• SSMIS running at UAS resolution
• Both resolutions could be augmented (computer
  time cost)

94
Discussion (2/4)

• TPW is extended to all surfaces Ocean, Land, Sea
  ice and Snow operationally for NOAA-18 and
  Metop-A, for the first time.
• Also for first time, retrieval is performed (and
  convergence reached) in cloudy, rainy,
  ice-impacted scenes
• False alarm / Undetection possible with the
  surface type product (always the case with this
  type of products). We make available two products
  (TB-based and EM-based) for a higher confidence.
• For SSMIS, non-convergence noticed over coastal
  areas (70 kms footprint more likely to be mixed
  and therefore more difficult to fit TBs)
• Products from Metop-A, NOAA-18 and DMSP are not
  exactly identical everywhere but are
  statistically consistent.

95
Discussion (3/4)

• Very small bias found for N18 and Metop in Tskin
  retrieval when compared to GDAS and ECMWF. Larger
  bias found when comparing to HIRS (from ICDB).
  Inconsistency.
• MIRS seems to pick smaller amounts of cloud
  liquid water, likely due to the use of high
  frequency channels in MIRS.
• Systematic Bias in IWP wrt MSPPS found (0.1 mm)
  but with a high correlation (0.68)

96
Discussion (4/4)

• Convergence is reached everywhere all surfaces,
  all weather conditions including precipitating,
  icy conditions
• This is a major achievement a radiometric
  solution is found even when precip/ice present.
  With CRTM physical constraints.

Previous version (non convergence when precip/ice
present)
Current version
97
Whats Next

• Continue validation of cloud products using
  CLOUDSAT/TRMM data
• Code checking using Valgrind and ForCheck
  (potential code changes)
• Potential improvement for T(p)

98
For Next Phase

• Extension of products to include rain rate and
  SSMIS emissivity-based sfc properties as well as
  cloud/hydrometeors properties
• Start extension of sensors to include NPP/ATMS
  (for all products)