Elevated Moist Layers and Relative Humidity in the Tropics

 
 
 
1
Universität Hamburg
2
IMPRS-ESM, Hamburg
Marc Prange
1,2
Manfred Brath
1
, Stefan Buehler
1
Elevated Moist Layers – Retrieval of
Complex Humidity Structures with IASI
 
Romps, et al. (2014), 
Journal of Climate
ERA – Interim 500 hPa relative humidity
1
 
Relative Humidity in the tropics
Romps, et al. (2014), 
Journal of Climate
ERA – Interim 500 hPa relative humidity
1 year average ERA - Interim
1
•
Tropical mean vertical RH structure: C-shape
Relative Humidity in the tropics
-- tropical mean
    warm pool
Romps, et al. (2014), 
Journal of Climate
ERA – Interim 500 hPa relative humidity
1 year average ERA - Interim
1
Relative Humidity in the tropics
-- tropical mean
    warm pool
•
Tropical mean vertical RH structure: C-shape
•
Western Pacific warm pool: 
•
Moistened mid troposphere
•
Secondary RH maximum near freezing level
 
Elevated Moist Layers
 (
EMLs
) detrained
     from deep convection
0˚C
Retrieval case study of an EML
2
Stevens et al. (2017), 
Surv. Geophys.
HALO Research Flight during NARVAL-2
2
Stevens et al. (2017), 
Surv. Geophys.
0˚C
Retrieval case study of an EML
HALO Research Flight during NARVAL-2
2
0˚C
0˚C
Retrieval case study of an EML
HALO Research Flight during NARVAL-2
EML
no EML
3
Stevens et al. (2017), 
Surv. Geophys.
Retrieval case study of an EML
HALO Research Flight during NARVAL-2
Stevens et al. (2017), 
Surv. Geophys.
EML
no EML
Is there an 
inherent
 EML blindspot?
Use “synthetic“ retrieval setup 
to check hypothesis for blindspot
3
Retrieval case study of an EML
HALO Research Flight during NARVAL-2
Stevens et al. (2017), 
Surv. Geophys.
EML
no EML
Is there an 
inherent
 EML blindspot?
Use “synthetic“ retrieval setup 
to check hypothesis for blindspot
Note: 
In 
Prange et al. (2021, AMT) 
we discuss more retrieval literature 
in this context.
 Regression retrievals struggle
 Physical retrievals do better
3
Retrieval case study of an EML
HALO Research Flight during NARVAL-2
4
Synthetic retrieval case study of an EML
Retrieval setup:
•
Optimal Estimation with 
Levenberg-Marquardt scheme
•
Atmospheric Radiative 
Transfer Simulator (ARTS)
•
Assume clear-sky ocean scene
4
Synthetic retrieval case study of an EML
Retrieval setup:
•
Optimal Estimation with 
Levenberg-Marquardt scheme
•
Atmospheric Radiative 
Transfer Simulator (ARTS)
•
Assume clear-sky ocean scene
•
Simultaneous retrieval of 
log(VMR
H2O
) and temperature
•
Use same spectral ranges as 
Stevens et al. (2017)
4
Synthetic retrieval case study of an EML
O
3
H
2
O
CO
2
CO
2
Stevens et al. (2017), 
Surv. Geophys.
Retrieval setup:
•
Optimal Estimation with 
Levenberg-Marquardt scheme
•
Atmospheric Radiative 
Transfer Simulator (ARTS)
•
Assume clear-sky ocean scene
•
Simultaneous retrieval of 
log(VMR
H2O
) and temperature
•
Use same spectral ranges as 
Stevens et al. (2017)
5
Synthetic retrieval case study of an EML
Retrieval setup:
•
Optimal Estimation with 
Levenberg-Marquardt scheme
•
Atmospheric Radiative 
Transfer Simulator (ARTS)
•
Assume clear-sky ocean scene
•
Simultaneous retrieval of 
log(VMR
H2O
) and temperature
•
Use same spectral ranges as 
Stevens et al. (2017)
6
Synthetic retrieval case study of an EML
O
3
H
2
O
CO
2
CO
2
Add H
2
O independent temperature information
Retrieval setup:
•
Optimal Estimation with 
Levenberg-Marquardt scheme
•
Atmospheric Radiative 
Transfer Simulator (ARTS)
•
Assume clear-sky ocean scene
•
Simultaneous retrieval of 
log(VMR
H2O
) and temperature
•
 
Use same spectral ranges as 
Stevens et al. (2017)
 
Adjust spectral range
7
Synthetic retrieval case study of an EML
Retrieval setup:
•
Optimal Estimation with 
Levenberg-Marquardt scheme.
•
Atmospheric Radiative 
Transfer Simulator (ARTS)
•
Assume clear-sky ocean scene.
•
Simultaneous retrieval of 
log(VMR
H2O
) and temperature
•
 
Use same spectral ranges as 
Stevens et al. (2017)
 
Adjust spectral range
7
Synthetic retrieval case study of an EML
Case study shows that blindspot 
can be reproduced and circumvented. 
 
Retrieval setup:
•
Optimal Estimation with 
Levenberg-Marquardt scheme
•
Atmospheric Radiative 
Transfer Simulator (ARTS)
•
Assume clear-sky ocean scene
•
Simultaneous retrieval of 
log(VMR
H2O
) and temperature
•
 
Use same spectral ranges as 
Stevens et al. (2017)
 
Adjust spectral range
7
Synthetic retrieval case study of an EML
Case study shows that blindspot 
can be reproduced and circumvented. 
How do these results apply to real observations?
Retrieval setup:
•
Optimal Estimation with 
Levenberg-Marquardt scheme
•
Atmospheric Radiative 
Transfer Simulator (ARTS)
•
Assume clear-sky ocean scene
•
Simultaneous retrieval of 
log(VMR
H2O
) and temperature
•
 
Use same spectral ranges as 
Stevens et al. (2017)
 
Adjust spectral range
8
Retrieval of EMLs by operational L2 products
Procedure: 
Identify EML cases and quantify them in retrieval and reference dataset
⏤
   h
umidity profile
---   reference profile
       dry anomaly
       moist anomaly 
 
8
Retrieval of EMLs by operational L2 products
Procedure: 
Identify EML cases and quantify them
 in retrieval and reference dataset
⏤
   h
umidity profile
---   reference profile
       dry anomaly
       moist anomaly 
 
8
Reference profile:
•
2nd order least square fit of
log-humidity profile:
•
Fit from surface to 100 hPa.
•
Only consider anomalies between
900 and 100 hPa.
Retrieval of EMLs by operational L2 products
Procedure: 
Identify EML cases and quantify them
 in retrieval and reference dataset
⏤
   h
umidity profile
---   reference profile
       dry anomaly
       moist anomaly 
 
8
Reference profile:
•
2nd order least square fit of
log-humidity profile:
•
Fit from surface to 100 hPa.
•
Only consider anomalies between
900 and 100 hPa.
Retrieval of EMLs by operational L2 products
Procedure: 
Identify EML cases and quantify them
 in retrieval and reference dataset
⏤
   h
umidity profile
---   reference profile
       dry anomaly
       moist anomaly 
 
8
Reference profile:
•
2nd order least square fit of
log-humidity profile:
•
Fit from surface to 100 hPa.
•
Only consider anomalies between
900 and 100 hPa.
Apply to retrieval product and reference
dataset, then compare metrics statistically.
Retrieval of EMLs by operational L2 products
Procedure: 
Identify EML cases and quantify them
 in retrieval and reference dataset
 
9
GRUAN radiosondes:
•
long-term high quality
radiosonde data records
https://www.gruan.org/network/sites, 30.11.2021
Retrieval of EMLs by operational L2 products
Procedure: 
Identify EML cases and quantify them in retrieval and 
reference dataset
 
9
GRUAN radiosondes:
•
long-term high quality
radiosonde data records
•
Western Pacific warm 
pool well suited
 Deep convection
https://www.gruan.org/network/sites, 30.11.2021
Retrieval of EMLs by operational L2 products
Procedure: 
Identify EML cases and quantify them in retrieval and 
reference dataset
https://www.gruan.org/network/sites, 30.11.2021
 
9
GRUAN radiosondes:
•
long-term high quality
radiosonde data records
•
Western Pacific warm 
pool well suited
 Deep convection
Manus Island:
•
2011 – 2014
•
min. 2 soundings/day
•
UTC +10 hours
 00/12 UTC soundings
     coincide with IASI 
     crossing time within 30 min
Retrieval of EMLs by operational L2 products
Procedure: 
Identify EML cases and quantify them in retrieval and 
reference dataset
 
10
Moist anomaly characteristics – GRUAN / IASI L2
Data base:
•
2012 GRUAN soundings for 
Manus Island (829 soundings)
•
EUMETSAT IASI L2 product
 
10
Moist anomaly characteristics – GRUAN / IASI L2
Data base:
•
2012 GRUAN soundings for 
Manus Island (829 soundings)
•
EUMETSAT IASI L2 product
Procedure:
•
Collocate IASI L2 data with 
GRUAN soundings using critieria:
•
50 km distance
•
30 min interval
 Yields 2061 collocations, 
      551 after quality filtering
•
Calculate moist anomaly 
characteristics for each 
collocated profile.
 
10
Moist anomaly characteristics – GRUAN / IASI L2
Data base:
•
2012 GRUAN soundings for 
Manus Island (829 soundings)
•
EUMETSAT IASI L2 product
Procedure:
•
Collocate IASI L2 data with 
GRUAN soundings using critieria:
•
50 km distance
•
30 min interval
 Yields 2061 collocations, 
      551 after quality filtering
•
Calculate moist anomaly 
characteristics for each 
collocated profile.
 
11
Moist anomaly characteristics – GRUAN / IASI L2
Data base:
•
2012 GRUAN soundings for 
Manus Island (829 soundings)
•
EUMETSAT IASI L2 product
Procedure:
•
Collocate IASI L2 data with 
GRUAN soundings using critieria:
•
50 km distance
•
30 min interval
 Yields 2061 collocations, 
      551 after quality filtering
•
Calculate moist anomaly 
characteristics for each 
collocated profile.
 
12
Moist anomaly characteristics – ERA5 / IASI L2
Data base:
•
2012 ERA5 reanalysis data on
137 vertical levels.
•
EUMETSAT IASI L2 product
Procedure:
•
Collocate IASI L2 data with 
ERA5 using critieria:
•
50 km distance
•
30 min interval
 Yields 65181 collocations,
      17970 after quality filtering
•
Calculate moist anomaly 
characteristics for each 
collocated profile.
 
12
Moist anomaly characteristics – ERA5 / IASI L2
Data base:
•
2012 ERA5 reanalysis data on
137 vertical levels.
•
EUMETSAT IASI L2 product
Procedure:
•
Collocate IASI L2 data with 
ERA5 using critieria:
•
50 km distance
•
30 min interval
 Yields 65181 collocations,
      17970 after quality filtering
•
Calculate moist anomaly 
characteristics for each 
collocated profile.
 
13
Conclusion
1.
There is no inherent EML blindspot for 
hyperspectral infrared sounders. 
2.
The EUMETSAT IASI L2 retrieval shows  significantly 
different moist anomaly characteristics compared 
to GRUAN soundings and ERA5:
•
Strength 1-2 orders of magnitude lower
•
Strong overestimation of anomaly thickness
3.
Bimodality in anomaly height captured by retrieval, 
but currently disagreements between all datasets.
 To be continued…
Backup slides
Averaging kernels
Tropical mean
Moist Layer
scenario
NARVAL-2 EML in IASI L2 EUMETSAT retrieval
Trimodality of convection in the tropics
Romps, et al. (2014), 
Journal of Climate
1 year average ERA - Interim
~ 0˚  C
1
Johnson et al. (1999), 
Journal of Climate
Elevated
Moist layer
2
What is an Elevated Moist Layer (EML)?
NARVAL-2 dropsondes
What is an Elevated Moist Layer (EML)?
Elevated
Moist layer
0˚ C
2
NARVAL-2 dropsondes
Elevated
Moist layer
Elevated
Stable layer
0˚ C
2
What is an Elevated Moist Layer (EML)?
NARVAL-2 dropsondes
Elevated
Moist layer
0˚ C
2
What is an Elevated Moist Layer (EML)?
NARVAL-2 dropsondes
Elevated
Moist layer
0˚ C
Detrainment of 
moist air
2
What is an Elevated Moist Layer (EML)?
NARVAL-2 dropsondes
Elevated
Moist layer
0˚ C
Radiative Cooling
Detrainment of 
moist air
2
What is an Elevated Moist Layer (EML)?
NARVAL-2 dropsondes
Elevated
Moist layer
Elevated
Stable layer
0˚ C
3
What is an Elevated Moist Layer (EML)?
NARVAL-2 dropsondes
Maximum
radiative cooling
Moisture anomaly characteristics
height
strength
thickness
14
Moisture anomaly characteristics
height
strength
thickness
Total count of
anomalies
True: 2894
Retrieved: 2095
14
Moisture anomaly characteristics
height
strength
thickness
Total count of
anomalies
True: 2894
Retrieved: 2095
14
Note: 
EMLs around 0°C are
subset of moisture
anomalies.
Moisture anomaly characteristics
height
strength
thickness
14
Moisture anomaly characteristics
height
strength
thickness
Height:
Gap below 5 km
14
Moisture anomaly characteristics
height
strength
thickness
Height:
Gap below 5 km
Strength:
Overrepresentation
of weak anomalies
14
Moisture anomaly characteristics
height
strength
thickness
Height:
Gap below 5 km
Strength:
Overrepresentation
of weak anomalies
Thickness:
Gap for narrow
anomalies
14
Gap in retrieved anomalies below 5 km
t
hickness [km]
15
Anomaly 
height:
t
hickness [km]
t
hickness [km]
t
hickness [km]
Gap in retrieved anomalies below 5 km
15
Anomaly 
height:
t
hickness [km]
t
hickness [km]
t
hickness [km]
Gap in retrieved anomalies below 5 km
Below 5 km most
anomalies are much more
narrow, than aloft.
15
Anomaly 
height:
How do moisture anomaly characteristics
determine the cooling rate?
anomaly strength [-]
17
T
est dataset
How do moisture anomaly characteristics
determine the cooling rate?
anomaly strength [-]
17
T
est dataset
How do moisture anomaly characteristics
determine the cooling rate?
anomaly strength [-]
17
T
est dataset
How do moisture anomaly characteristics
determine the cooling rate?
anomaly strength [-]
anomaly strength [-]
17
T
est dataset
Retrieval
Gap in retrieved anomalies beneath 5 km
Test Dataset
Retrieval
 Most anomalies are missed beneath 5 km, which are also the strongest.

								

								
								

										

												
Slide Note

												
First part of the talk is about moist layers in the tropical mid troposphere, where I want to show that these layers are somewhat statistically significant, physically relevant and also intersting to study.
Second part of the talk will be more specifically about what I do in my own research project, namely to investigate wheather we can observe these moist layers from space.

										

								


								
						

							
						


							

								

									
										
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This presentation explores the retrieval of complex humidity structures and relative humidity patterns in the tropics using data from various research studies. It delves into the significance of Elevated Moist Layers (EMLs) and their relationship to deep convection, with a focus on the ERA-Interim dataset and research flights during NARVAL-2. The analysis sheds light on the vertical structures of moisture, highlighting key features in the Western Pacific warm pool region.


								
    
																		
  •   Humidity Structures
    • 
																	
  •    Tropics
    • 
																	
  •    Elevated Moist Layers
    • 
																	
  •    Research Studies
    • 
																	
  •    ERA-Interim
    • 
																

								

								

									

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Presentation Transcript

								

								
  1. Elevated Moist Layers  Retrieval of  Complex Humidity Structures with IASI Marc Prange1,2 Manfred Brath1, Stefan Buehler1 1Universit  t Hamburg 2IMPRS-ESM, Hamburg
    2. 
            
            
            
  2. Relative Humidity in the tropics Romps, et al. (2014), Journal of Climate ERA     Interim 500 hPa relative humidity 1
    3. 
            
            
            
  3. Relative Humidity in the tropics Romps, et al. (2014), Journal of Climate ERA     Interim 500 hPa relative humidity 1 year average ERA - Interim     Tropical mean vertical RH structure: C-shape -- tropical mean     warm pool 1
    4. 
            
            
            
  4. Relative Humidity in the tropics Romps, et al. (2014), Journal of Climate ERA     Interim 500 hPa relative humidity 1 year average ERA - Interim 0  C         Tropical mean vertical RH structure: C-shape Western Pacific warm pool:      Moistened mid troposphere     Secondary RH maximum near freezing level    Elevated Moist Layers (EMLs) detrained      from deep convection -- tropical mean     warm pool 1
    5. 
            
            
            
  5. Retrieval case study of an EML HALO Research Flight during NARVAL-2 Stevens et al. (2017),  Surv. Geophys. 2
    6. 
            
            
            
  6. Retrieval case study of an EML HALO Research Flight during NARVAL-2 0  C Stevens et al. (2017),  Surv. Geophys. 2
    7. 
            
            
            
  7. Retrieval case study of an EML HALO Research Flight during NARVAL-2 0  C 0  C 2
    8. 
            
            
            
  8. Retrieval case study of an EML HALO Research Flight during NARVAL-2 no EML EML Stevens et al. (2017),  Surv. Geophys. 3
    9. 
            
            
            
  9. Retrieval case study of an EML HALO Research Flight during NARVAL-2 Is there an inherent EML blindspot?     Use    synthetic    retrieval setup  to check hypothesis for blindspot no EML EML Stevens et al. (2017),  Surv. Geophys. 3
    10. 
            
            
            
  10. Retrieval case study of an EML HALO Research Flight during NARVAL-2 Is there an inherent EML blindspot?     Use    synthetic    retrieval setup  to check hypothesis for blindspot Note: In Prange et al. (2021, AMT)  we discuss more retrieval literature  in this context.     Regression retrievals struggle     Physical retrievals do better no EML EML Stevens et al. (2017),  Surv. Geophys. 3
    11. 
            
            
            
  11. Synthetic retrieval case study of an EML Retrieval setup:     Optimal Estimation with  Levenberg-Marquardt scheme     Atmospheric Radiative  Transfer Simulator (ARTS)     Assume clear-sky ocean scene 4
    12. 
            
            
            
  12. Synthetic retrieval case study of an EML Retrieval setup:     Optimal Estimation with  Levenberg-Marquardt scheme     Atmospheric Radiative  Transfer Simulator (ARTS)     Assume clear-sky ocean scene     Simultaneous retrieval of  log(VMRH2O) and temperature     Use same spectral ranges as  Stevens et al. (2017) 4
    13. 
            
            
            
  13. Synthetic retrieval case study of an EML Retrieval setup:     Optimal Estimation with  Levenberg-Marquardt scheme     Atmospheric Radiative  Transfer Simulator (ARTS)     Assume clear-sky ocean scene     Simultaneous retrieval of  log(VMRH2O) and temperature     Use same spectral ranges as  Stevens et al. (2017) H2O O3 CO2 CO2 Stevens et al. (2017), Surv. Geophys. 4
    14. 
            
            
            
  14. Synthetic retrieval case study of an EML Retrieval setup:     Optimal Estimation with  Levenberg-Marquardt scheme     Atmospheric Radiative  Transfer Simulator (ARTS)     Assume clear-sky ocean scene     Simultaneous retrieval of  log(VMRH2O) and temperature     Use same spectral ranges as  Stevens et al. (2017) 5
    15. 
            
            
            
  15. Synthetic retrieval case study of an EML Retrieval setup:     Optimal Estimation with  Levenberg-Marquardt scheme     Atmospheric Radiative  Transfer Simulator (ARTS)     Assume clear-sky ocean scene     Simultaneous retrieval of  log(VMRH2O) and temperature      Use same spectral ranges as  Stevens et al. (2017)    Adjust spectral range H2O O3 CO2 CO2 Add H2O independent temperature information 6
    16. 
            
            
            
  16. Synthetic retrieval case study of an EML Retrieval setup:     Optimal Estimation with  Levenberg-Marquardt scheme.     Atmospheric Radiative  Transfer Simulator (ARTS)     Assume clear-sky ocean scene.     Simultaneous retrieval of  log(VMRH2O) and temperature      Use same spectral ranges as  Stevens et al. (2017)    Adjust spectral range 7
    17. 
            
            
            
  17. Synthetic retrieval case study of an EML Retrieval setup:     Optimal Estimation with  Levenberg-Marquardt scheme     Atmospheric Radiative  Transfer Simulator (ARTS)     Assume clear-sky ocean scene     Simultaneous retrieval of  log(VMRH2O) and temperature      Use same spectral ranges as  Stevens et al. (2017)    Adjust spectral range     Case study shows that blindspot  can be reproduced and circumvented.  7
    18. 
            
            
            
  18. Synthetic retrieval case study of an EML Retrieval setup:     Optimal Estimation with  Levenberg-Marquardt scheme     Atmospheric Radiative  Transfer Simulator (ARTS)     Assume clear-sky ocean scene     Simultaneous retrieval of  log(VMRH2O) and temperature      Use same spectral ranges as  Stevens et al. (2017)    Adjust spectral range     Case study shows that blindspot  can be reproduced and circumvented.  How do these results apply to real observations? 7
    19. 
            
            
            
  19. Retrieval of EMLs by operational L2 products Procedure: Identify EML cases and quantify them in retrieval and reference dataset 8
    20. 
            
            
            
  20. Retrieval of EMLs by operational L2 products Procedure: Identify EML cases and quantify them in retrieval and reference dataset         humidity profile --- reference profile dry anomaly moist anomaly  8
    21. 
            
            
            
  21. Retrieval of EMLs by operational L2 products Procedure: Identify EML cases and quantify them in retrieval and reference dataset Reference profile:     2nd order least square fit of  log-humidity profile:         humidity profile --- reference profile dry anomaly moist anomaly  ln ???H2O,ref = ??2+ ?? + ?         Fit from surface to 100 hPa. Only consider anomalies between  900 and 100 hPa. 8
    22. 
            
            
            
  22. Retrieval of EMLs by operational L2 products Procedure: Identify EML cases and quantify them in retrieval and reference dataset Reference profile:     2nd order least square fit of  log-humidity profile:         humidity profile --- reference profile dry anomaly moist anomaly  ln ???H2O,ref = ??2+ ?? + ?         Fit from surface to 100 hPa. Only consider anomalies between  900 and 100 hPa. 8
    23. 
            
            
            
  23. Retrieval of EMLs by operational L2 products Procedure: Identify EML cases and quantify them in retrieval and reference dataset Reference profile:     2nd order least square fit of  log-humidity profile:         humidity profile --- reference profile dry anomaly moist anomaly  ln ???H2O,ref = ??2+ ?? + ?         Fit from surface to 100 hPa. Only consider anomalies between  900 and 100 hPa.     Apply to retrieval product and reference dataset, then compare metrics statistically. 8
    24. 
            
            
            
  24. Retrieval of EMLs by operational L2 products Procedure: Identify EML cases and quantify them in retrieval and reference dataset GRUAN radiosondes:     long-term high quality radiosonde data records https://www.gruan.org/network/sites, 30.11.2021 9
    25. 
            
            
            
  25. Retrieval of EMLs by operational L2 products Procedure: Identify EML cases and quantify them in retrieval and reference dataset GRUAN radiosondes:     long-term high quality radiosonde data records     Western Pacific warm  pool well suited     Deep convection https://www.gruan.org/network/sites, 30.11.2021 9
    26. 
            
            
            
  26. Retrieval of EMLs by operational L2 products Procedure: Identify EML cases and quantify them in retrieval and reference dataset GRUAN radiosondes:     long-term high quality radiosonde data records     Western Pacific warm  pool well suited     Deep convection Manus Island:     2011     2014     min. 2 soundings/day     UTC +10 hours     00/12 UTC soundings      coincide with IASI       crossing time within 30 min https://www.gruan.org/network/sites, 30.11.2021 9
    27. 
            
            
            
  27. Moist anomaly characteristics  GRUAN / IASI L2 Data base:     2012 GRUAN soundings for  Manus Island (829 soundings)     EUMETSAT IASI L2 product 10
    28. 
            
            
            
  28. Moist anomaly characteristics  GRUAN / IASI L2 Data base:     2012 GRUAN soundings for  Manus Island (829 soundings)     EUMETSAT IASI L2 product Procedure:     Collocate IASI L2 data with  GRUAN soundings using critieria:     50 km distance     30 min interval     Yields 2061 collocations,        551 after quality filtering     Calculate moist anomaly  characteristics for each  collocated profile. 10
    29. 
            
            
            
  29. Moist anomaly characteristics  GRUAN / IASI L2 Data base:     2012 GRUAN soundings for  Manus Island (829 soundings)     EUMETSAT IASI L2 product Procedure:     Collocate IASI L2 data with  GRUAN soundings using critieria:     50 km distance     30 min interval     Yields 2061 collocations,        551 after quality filtering     Calculate moist anomaly  characteristics for each  collocated profile. 10
    30. 
            
            
            
  30. Moist anomaly characteristics  GRUAN / IASI L2 Data base:     2012 GRUAN soundings for  Manus Island (829 soundings)     EUMETSAT IASI L2 product Procedure:     Collocate IASI L2 data with  GRUAN soundings using critieria:     50 km distance     30 min interval     Yields 2061 collocations,        551 after quality filtering     Calculate moist anomaly  characteristics for each  collocated profile. 11
    31. 
            
            
            
  31. Moist anomaly characteristics  ERA5 / IASI L2 Data base:     2012 ERA5 reanalysis data on 137 vertical levels.     EUMETSAT IASI L2 product Procedure:     Collocate IASI L2 data with  ERA5 using critieria:     50 km distance     30 min interval     Yields 65181 collocations,       17970 after quality filtering     Calculate moist anomaly  characteristics for each  collocated profile. 12
    32. 
            
            
            
  32. Moist anomaly characteristics  ERA5 / IASI L2 Data base:     2012 ERA5 reanalysis data on 137 vertical levels.     EUMETSAT IASI L2 product Procedure:     Collocate IASI L2 data with  ERA5 using critieria:     50 km distance     30 min interval     Yields 65181 collocations,       17970 after quality filtering     Calculate moist anomaly  characteristics for each  collocated profile. 12
    33. 
            
            
            
  33. Conclusion 1. There is no inherent EML blindspot for  hyperspectral infrared sounders.  2. The EUMETSAT IASI L2 retrieval shows  significantly  different moist anomaly characteristics compared  to GRUAN soundings and ERA5:     Strength 1-2 orders of magnitude lower     Strong overestimation of anomaly thickness 3. Bimodality in anomaly height captured by retrieval,  but currently disagreements between all datasets.     To be continued    13
    34. 
            
            
            
  34. Backup slides
    35. 
            
            
            
  35. Averaging kernels Tropical mean Moist Layer  scenario
    36. 
            
            
            
  36. NARVAL-2 EML in IASI L2 EUMETSAT retrieval
    37. 
            
            
            
  37. Trimodality of convection in the tropics 1 year average ERA - Interim ~ 0    C Romps, et al. (2014),  Journal of Climate Johnson et al. (1999),  Journal of Climate 1
    38. 
            
            
            
  38. What is an Elevated Moist Layer (EML)? NARVAL-2 dropsondes Elevated  Moist layer 2
    39. 
            
            
            
  39. What is an Elevated Moist Layer (EML)? NARVAL-2 dropsondes Elevated  Moist layer 0   C 2
    40. 
            
            
            
  40. What is an Elevated Moist Layer (EML)? NARVAL-2 dropsondes Elevated  Moist layer Elevated  Stable layer 0   C 2
    41. 
            
            
            
  41. What is an Elevated Moist Layer (EML)? NARVAL-2 dropsondes Elevated  Moist layer 0   C 2
    42. 
            
            
            
  42. What is an Elevated Moist Layer (EML)? NARVAL-2 dropsondes Elevated  Moist layer 0   C Detrainment of  moist air 2
    43. 
            
            
            
  43. What is an Elevated Moist Layer (EML)? NARVAL-2 dropsondes Radiative Cooling Elevated  Moist layer 0   C Detrainment of  moist air 2
    44. 
            
            
            
  44. What is an Elevated Moist Layer (EML)? NARVAL-2 dropsondes Elevated  Moist layer Elevated  Stable layer 0   C Maximum  radiative cooling 3
    45. 
            
            
            
  45. Moisture anomaly characteristics height strength thickness 14
    46. 
            
            
            
  46. Moisture anomaly characteristics Total count of  anomalies True: 2894 Retrieved: 2095 height strength thickness 14
    47. 
            
            
            
  47. Moisture anomaly characteristics Total count of  anomalies True: 2894 Retrieved: 2095 Note:  EMLs around 0  C are  subset of moisture  anomalies.  height strength thickness 14
    48. 
            
            
            
  48. Moisture anomaly characteristics height strength thickness 14
    49. 
            
            
            
  49. Moisture anomaly characteristics Height: Gap below 5 km height strength thickness 14
    50. 
            
            
            
  50. Moisture anomaly characteristics Height: Gap below 5 km Strength: Overrepresentation  of weak anomalies height strength thickness 14
    51. 
            
            
            
							

							

					

					
					
					

						
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