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Collect in advance:


  1. The EarthCARE retrieved geophysical parameters and their correlation with each other (e.g. IWC & PSD) and the model assumption used for the retrievals (e.g. optical properties)

2. Please provide a ranked list of the major uncertainties in each product, either based on evaluation of the synthetic scenes , or identifying regimes/processes that aren't well-represented in the scenes. What are  remaining gaps in the algorithms performance (e.g. in understanding, in a-priori etc) with focus on "where the cal/val could contribute”.

3. What you consider useful measurements to have, which could help you in the products (for calibration, validation, improving the parameterisations in the algorithms etc.).

4. If you have in mind some facilities already which could provide such measurements (or parts of them).


Processor




APRIL:

ATLID, MSI, and ATLID/MSI synergy

A-FM





A-PRO

Aerosol and cloud profiles of :

  • Extinction
  • Lidar-ratio
  • Target-type (e.g. ice cloud,water cloud, aerosol etc..)
  • Aerosol type (Marine, etc..)
Verification of target classification scheme.
  • High-quality profiles of extinction, backscatter and depolarization at 355 nm.
  • In-situ aerosol sampling co-located with lidar measurements.

A-LAYMoritz Haarig

Clouds (A-CTH)

  • Cloud top height
    and classification of thick or thin clouds with layering information

Aerosol (A-ALD)

  • Aerosol layer boundaries
  • Aerosol layer mean optical properties
  • AOT (columnar, stratospheric, tropospheric)
  • Column aerosol classification probabilities

Products provided on ATLID track.

Layer detection based on Wavelet Covariance Transform technique combined with a threshold approach is applied to the Mie co-polar signal.

Aerosol-cloud discrimination using real EarthCARE data and SNR.

Adapt thresholds to determine boundaries of clouds and aerosol using real data and SNR for all heights, but especially for stratospheric clouds and aerosol.


tbc

Ground-based or airborne lidar measurements:

  • Detection of the planetary boundary layer height.
  • Multilayer aerosol scenarios
    (two distinct layers or aerosol layers with a strong internal structure)
    Aerosol optical properties and layer boundaries

  • Brocken cloud scenes embedded in aerosol layer to test the cloud detection algorithm and the aerosol layer mean optical properties.

Airborne lidar measurements of:

  • Multilayer cloud scenarios (cirrus above opaque water clouds) to test the multilayer detection.





AM-COL Moritz Haarig

Clouds (AM-CTH)

  • Synergistic cloud top height difference (ATL-MSI)

Aerosol (AM-ACD)

  • Spectral AOT
  • Ångström exponent (355/670, and over ocean only 355/865)
  • Aerosol type

Products provided on MSI swath.

Combining vertical information at 355 nm from ATLID and spatial and radiative information on MSI swath.

Proper detection of multilayer cloud scenarios with MSI to consider them in the synergistic cloud top height.


Extension of a specific aerosol plume from the track to the swath in order to prescribe the aerosol type detected on the track to the swath. 


tbc

As a synergistic product, it requires the validation of ATLID and MSI products as described in the corresponding sections.
M-AOT

Aerosol

Retrieved quantities:

  • Aerosol optical thickness at 670 nm (over ocean and land)
  • Aerosol optical thickness at 865 nm (over ocean only)

Diagnostic quantities:

  • Angstrom parameter (670 nm, 865 nm) (over ocean only)

Priors and underlying assumptions:

  • Internal, predefined mixing of HETEAC components in LUTs
  • Aerosol climatology (MAC v1 Kinne et al., 2013)
  • Fixed vertical distribution of HETAC types according to Aerosol cci (Holzer-Popp et al., 2013)
  • De-coupling of gases (H2O, O3, CO2, CH4)
  • Ocean surface parameterization following Cox and Munk (1954)
  • Lambertian surfaces over land using black sky albedo of MODIS MCD43 climatology as prior for SWIR-2
  • Higher AOT uncertainties expected over land than over ocean due to the stronger TOA signal contribution of the surface than the aerosol
  • Aerosol type assumption can lead to additional large uncertainties over land

AOT at 670 nm over land (in different biomes) and over ocean and

AOT at 865 nm over ocean based on:

  • Ground-based measurements (e.g. AERONET, AERONET-OC)
  • Ship-based sun photometer measurements (e.g. MAN)
  • Satellite based imager measurements (e.g. MODIS, VIIRS, 3MI)



M-CLDAnja Hunerbein

Clouds (M-CLD)

Identification and classification of clouds on a pixel basis (500x500m) per frame for the entire MSI swath (150km)

Cloud Mask (M-CM):

  • Cloud flag distinguish between clear-sky and cloudy pixels
  • Cloud type classifier differentiate between thin and thick clouds, as well as describing ISCCP related cloud type
  • Cloud top thermodynamical phase distinguish between water, ice phased clouds Surface classification

Cloud Optical and Physical properties (M-COP):

for water and ice clouds

  • Cloud optical thickness
  • Effective radius/particle size
  • Cloud top temperature/pressure/height
  • Liquid/ice/cloud water path

Cloud mask spectral  thresholds should be verify by variety of defined scene types and compared to different cloud detection method from various instruments

Validation needs:

M-CM:

  • cloud edge detection over different surfaces (e.g. snow, desert)
  • day/night
  • MSI smile effect- MSI viewing zenith angle dependency should be taken into account


  • imagers of geostationary satellites and polar orbiting satellite
  • synoptic observations from ground to validate time series
  • ground-based remote sensing along track - to get cloud properties
  • Assessment of M-CM comparison of cloud size distribution with high resolution satellite images, e.g., from collocated Sentinel-2 scenes

DORSY:

CPR and ATLID/CPR/MSI synergy

C-PROPavlos Kollias



C-CLDPavlos Kollias



C-APC

Pavlos Kollias

Bernat Puigdomènech 

CPR Antenna Pointing Correction
Characterization of the antenna mispointing angle (mispointing model parameters, residuals and evaluation of the goodness of fit)

Assumptions:
- The mispointing due to the attitude control system errors and temperature variations has an harmonic behaviour and the mispointing angle can be parametrized by a regression fit taking into account the residuals (Battaglia et al. 2015)
- The climatology of high cloud dynamics (Kalesse et al. 2013)

The ice clouds reflectivity-Doppler velocity climatology from space measurements. This unknown relationship brings uncertainties in the C-APC "ice clouds" correction technique


  • Ground-based reflectivity and Doppler velocity measurements along the EarthCare track that would help validate and intercompare the ice clouds velocity climatology measured from space.

  • The onboard temperature telemetries that will model the thermoelastic antenna distortions. This will allow us to have a better understanding of the temperature variations associated with the sun-illumination changes along the orbit and the associated antenna mispointing corrected in the L1 products. Reverse-engineering is important if errors in the sensors impact the antenna pointing calibration

AC-TCJulien Delanoe



ACM-CAPShannon Mason

Ice clouds and snow: 

Retrieved (independent) quantities:

  • extinction (geometric optics approximation) 
  • primed number concentration (Delanoe & Hogan, 2008) 
  • extinction-to-backscatter ratio 
  • density factor (Mason et al., 2018)

Key derived quantities ( & correlations with retrieved quantities): 

  • Ice water content (extinction & primed number concentration) 
  • Snow rate (extinction, primed number concentration, density factor) 
  • Effective radius (extinction, primed number concentration, density factor) 
  • Median volume diameter (extinction, primed number concentration, density factor) 

Priors and underlying assumptions: 

  • Normalized PSD with shape factor mu=2 (Field et al., 2005) 
  • mass-size relation (Brown & Francis, 1995) 
  • area-size relation (Frances et al., 1998) 
  • terminal velocity (Heymsfield & Westbrook 2010); assuming vertical air velocity contribution to mean Doppler velocity sums to zero with spatial smoothing 
  • microwave: horizontally-aligned oblate spheroidal aggregates of bullet rosettes with aspect ratio 0.6 (SSRGA; Hogan et al. 2017), transitioning to spheroids of solid ice (Mason et al. 2018) 
  • Infrared & visible: Baran (2003)

Uncertainties & assumptions:

  • Undiagnosed supercooled liquid: supercooled cloud-tops in layered scenes; mixed-phase layers embedded in stratiform cloud; convective cores; melting snow; supercooled drizzle & rain processes. 
  • Continuity of mass flux (& size distribution) across the melting layer (interactions with “cold” rain) 
  • Scattering and particle properties across the melting layer 
  • Aerosol/ice misdetection at cloud edge


Liquid clouds: 

Retrieved (independent) quantities: 

  • Liquid water content 
  • Total number concentration

Key derived quantities ( & correlations with retrieved quantities): 

  • extinction (water content & number concentration) 
  • effective radius (water content & number concentration)

Priors and underlying assumptions: 

  • Log-normal size distribution (shape factor 0.38) 
  • Land/sea flags used for cloud droplet number concentration priors 
  • Microwave, infrared & visible: Mie 

Uncertainties & assumptions: 

  • Physical depth of liquid cloud layers (i.e. cloud base height) after lidar is extinguished 
  • Liquid clouds often completely undiagnosed in deep and layered scenes
  • When can we assume liquid cloud within cold rain?
  • When can we assume supercooled liquid in convective cores?
  • Are there any conditions under which embedded mixed-phase clouds can be diagnosed? 


Drizzle and rain: 

Retrieved (independent) quantities: 

  • Rain rate  
  • Number concentration scaling parameter (divergence from Abel & Boutle 2012 DSD) 

Key derived quantities ( & correlations with retrieved quantities): 

  • Rain water content (rain rate & number concentration scaling) 
  • Median volume diameter (rain rate & number concentration scaling) 

Priors and underlying assumptions: 

  • Normalized gamma DSD (mu=5) 
  • Spherical drops (Mie scattering)  
  • Terminal velocity: Beard (1976) 

Uncertainties & assumptions: 

  • Profile of rain rate is near-constant through the ground clutter to the surface 
  • Mass flux (& size distribution) continuity with snow across the melting layer (“cold” rain) 
  • Treatment of melting (e.g. do rimed ice particles take longer to melt?) 
  • Retrievals of rain when radar is extinguished (PIA saturated) 
  • Retrievals when PIA is not available/has large observational error (over land, sea ice, etc.)

Aerosols: 

Retrieved (independent) quantities: 

  • Number concentration 

Key derived quantities ( & correlations with retrieved quantities) 

  • Extinction (number concentration) 

Priors and underlying assumptions: 

  • Extinction-to-backscatter ratio prescribed according to aerosol classification  
  • Pre-defined admixtures of log-normal size distributions of HETEAC aerosols  
  • Vertical and horizontal smoothing constraints 

1. Liquid cloud co-located with rain and embedded in ice clouds 

  • Evaluation of AC-TC in the nominal scenes suggests EarthCARE active instruments detect about 25% of liquid clouds by volume, and less than 10% by liquid water content. 
  • In ACM-CAP we can assume the presence of liquid clouds in rain, but need observational support for physical representation: e.g. vertical distribution of liquid clouds, profile of liquid water content, relation of liquid water path to rain rate. 
  • Note that ACM-CAP is the only product including an active retrieval of liquid water content (C-CLD very rarely identifies liquid cloud), and that un-/misdiagnosed liquid cloud will result in large biases in shortwave cloud radiative effect. It is therefore very important to evaluate these assumptions. 

2. Drizzling stratocumulus & warm rain (not included in nominal scenes): 

  • Simultaneous retrieval of drizzle/rain and liquid cloud; both cloud and rain contribute to CPR backscatter and attenuation. Need to evaluate rain DSD and liquid cloud properties. 
  • Cloud-base is undetected by EarthCARE active instruments, so vertical structure of cloud and warm rain is poorly constrained. 

3. Stratiform rimed snowfall (not included in nominal scenes): 

  • ACM-CAP can use mean Doppler velocity to diagnose faster-falling rimed snowfall when velocity measurements are not dominated by convective vertical air motion (Mason et al. 2018), but this capability---and its sensitivity to CPR Doppler velocity measurements---has not been evaluated in the nominal scenes. 

4. Polar mixed-phase clouds: 

  • ACM-CAP retrievals of high-latitude mixed-phase clouds were subject to under-estimates of LWP (over-estimates of IWC) in the test scenes. 
  • Large uncertainties in retrieved mixed-phase cloud properties, with higher uncertainties in information from MSI visible channel at high solar zenith angles.  
  • Many different microphysical growth and multiplication processes can be at play in mixed-phase clouds, so the cloud and precipitation properties will have very high uncertainties in this regime. 

5. Ice microphysics: 

  • Ice particle and snowflake structure, scattering properties and size distributions are inherently uncertain, have significant impacts on cloud radiative effects, and require ongoing evaluation and observational constraints across all locations and regimes. 

Aircraft underflights of EarthCARE: 

In situ measurements of hydrometeor and bulk cloud properties through the vertical profile will provide critical evaluation datasets, especially in ice and mixed-phase clouds where retrieval uncertainties are greatest.

Measurements: 

  • in situ measurements of hydrometeor size distributions (up to mm scale for ice particles) 
  • bulk water and liquid water content 
  • Airborne microwave radiometer retrievals of liquid water path above and below aircraftif available, would help to contextualise the in-situ measurements within the profile of liquid cloud undiagnosed by EarthCARE active instruments 

Configuration: 

  • Prioritize close coordination with EarthCARE tracks 
  • Profiling flights to resolve vertical structure of clouds and precipitation 

Ground-based remote-sensing along EarthCARE track: 

EarthCARE precipitation retrievals are promising, especially over the ocean, but very difficult to validate robustly. Where EarthCARE passes near or over a scanning dual-polarization precipitation radar, there is good potential to generate large correlative datasets over O100km of flight track, including information about precipitation microphysics. 

Measurements: 

  • Precipitation radar scans of rain and snowfall along the EarthCARE track 
  • Polarimetric radar variables provide insights into hydrometeor microphysics, especially ice growth processes such as aggregation and riming. 

Configuration: 

  • A range of scanning strategies can be used to maximise correlative data: vertical (RHI) scans along the EarthCARE track for overpassesand horizontal scans (PPI) across the EarthCARE track whenever within range of the radar. 
  • Prioritize scans of precipitation over the ocean or bodies of water, where EarthCARE precipitation retrievals make use of CPR path-integrated attenuation. 
  • Zenith-pointing ground-based instruments provide additional information of interest (e.g. vertical Doppler velocity, cloud-base height from ceilometers) in conjunction with in-situ measurements at the surface, but focusing on rare EarthCARE overpasses of ground stations severely limits the amount of correlative data availableThis may be overcome by collating data across a network of sites. 

CLARA:

BBR, radiation, and closure assessment

BM-RADAlmudena Velazquez

Radiation

For each telescope: FORE, NADIR, AFT

  • solar radiances (stand-alone and msi-based)
  • thermal radiances

Provided at different resolutions:

in the BBR grid: sampling 1km alongtrack

  • Standard: 10x10 km^2
  • Small: 10x(<10)km^2
  • Full
    • 10 x 28 km^2 (along/across-track, oblique views)
    • 10 x 17 km^2 (along/across-track, nadir view)

In the JSG grid: sampling 1JSG pixel

  •  Assessment Domain (5x21 JSG) resolutions

PSF weighted variables:

  • MSI cloud cover
  • MSI cloud phase
  • X-MET snow cover
  • fraction of IGBP surface type in BBR PSF

Using test scenes (Baja and Halifax)

  • SW unfiltering → RMS ~ 0.7 W m-2 sr-1
  • LW unfiltering → RMS ~ 0.3 W m-2 sr-1

Snow covered regions present in average higher uncertainties that need to be further studied (either to improve LUT for the unfiltering or detection)



BMA-FLXCarlos Almudena Velazquez

Radiation

  • solar fluxes for each telescope (FORE, NADIR, AFT)
  • thermal fluxes (FORE, NADIR, AFT)
  • coregistered solar and thermal radiances at a Reference Level
  • views-combined solar and thermal fluxes

Using Baja scene

  • LW flux estimation → RMS ~ 6 W m-2, stddev = 5 W m-2


ACM-COM Howard Barker



ACM-3DHoward Barker



ACM-RTHoward Barker



(ACMB-DF)Howard Barker



Table 2. Correlative measurements needed in support of algorithm developent

CategoryVariableUnitCommentFeedback from Algorithm developers on needs
Radar

- radarReflectivityFactor 

[dBZ]

- dopplerVelocity 

[m/s]

- spectrumWidth 

[m/s]

- signalToNoiseRatio [dB]

Backscatter Lidar

- attenuatedBackscatter 

[sr-1 km-1]with corrections applied or respective information delivered (see below)
- depolarizationRatio or[-]with corrections/calibration applied or respective information delivered (see below)
- cross-polar attenuatedBackscatter[sr-1 km-1]with corrections applied or respective information delivered (see below)
HSRL/Raman lidar- Mie and Rayleigh/Raman attenuated Backscatter
with corrections applied or respective information delivered (see below)
HSRL/Raman multi wavelength 355. 532 (+1064)

3x- Mie and Rayleigh/Raman attenuated Backscatter

2x extinction

2x depolarization

[sr-1 km-1]

[km-1]

[-]

aerosol retrievals + typing

with corrections applied or respective information delivered (see below)


Radiometer- IR and solar radiances




ground based BSRN like

- surface direct-beam shortwave irradiance

[W m-2]

- surface diffuse shortwave irradiance

- surface longwave irradiance

- level radiative fluxes on aircraft (SW and LW)

- (spectral) surface direct & diffuse albedo[-]

- (spectral) surface emissivity

SunphotometerAOD
ground based aeronet
Hyperspectral Imager

- IR and solar radiances


MSI smile
Instrument properties- radarWaveLength  [GHz](94 and/or 35GHz)
- pitchAngle / elevation [deg] (aircraft/ground-based radar, lidar)

- observation geometry

[deg](e.g. viewing angle)

- lidarWaveLength  

[nm](355, 532, 1064 nm or other)
- lidarFOV[mrad]

- calibration and correction parameters

(e.g. spectral and polarization cross talk, dead-time, dark current, overlap)
Thermodynamic

(specify source)

- temperature 

[K]

-temperature profile[K]

- surfaceTemperature 

[K]

- pressure 

[hPa]

- relativeHumidity 

[%]

- humidity profile[g/g]

- surfaceRelativeHumidity 

[%]

- winds 

[m/s]

- surfaceWinds 

[m/s]

- navigationLandSeaFlag 
(land type/water)
- atmospheric gases
(e.g. H2O,O3,O2,CH4 ...)

Retrievals

(for ice and rain)




- Detailed information about all assumptions used 


(model, PSD, etc.)

- Information about particle size and density




- meanMassDiameter 

[mm]

- waterContent 

[g/m3]

- massFlux [mm/h]

- surface precipitation occurrence


Retrievals/In situ observations

for aerosol

- particle size distribution/effective particle size

[µm]

- refractive index[-]

- absorption and scattering coefficients and/or single-scattering albedo[1/m]

- particle shape information (non-sphericity)



3) The table below (thanks Shannon Mason) relates geophysical parameters to EarthCARE data products. The table was created for the purpose of algorithm intercomparison. Please provide your recommentations (in the comment field at the bottom of this page) how this table can be re-purposed/adapted to support the Principal Investigators (for example: " indicate when/how to use which of these products, by placing them in columns per validation method" )?

Table 3: Geophysical Parameters vs EarthCARE Products


Cloud-top, vertically integrated and layerwise retrieval productVertical profiles at nadir

Quantity

At nadir

Across-track


Quantity

Products

Target classification

Cloud-top height

M-COP, A-CTH, A-TC,
C-TC, AC-TC

M-COP, AM-CTH

Cloud/precipitation fraction

A-TC, C-TC, AC-TC


Cloud-top phase

M-CM, A-TC, C-TC, AC-TC

M-CM, AM-CTH

Cloud/precipitation phase

A-TC, C-TC, AC-TC


Aerosol layer height/depth

A-ALD, A-TC

AM-ACD

Aerosol fraction

A-TC, ACM-COM


Aerosol layer classification

A-ALD, A-TC

AM-ACD

Aerosol species

A-TC, ACM-COM

Ice cloud & snow

Optical thickness

M-COP, A-EBD, ACM-CAP

M-COP

Extinction

A-EBD, ACM-COM, ACM-CAP


Effective radius

M-COP, A-ICE, ACM-CAP

M-COP

Effective radius

A-ICE, ACM-COM ACM-CAP


Water path

M-COP, A-ICE, C-CLD, ACM-CAP

M-COP

Water content

A-ICE, ACM-COM, ACM-CAP


Surface snow rate

C-CLD, ACM-CAP


Snow rate

C-CLD, ACM-CAP





Snow median diameter

C-CLD, ACM-CAP





Extinction-to- backscatter ratio

A-EBD, ACM-CAP

Liquid cloud

Optical thickness

M-COP, A-EBD, ACM-CAP

M-COP

Extinction

A-EBD, ACM-COM, ACM-CAP


Effective radius

M-COP, ACM-CAP

M-COP

Effective radius

ACM-COM, ACM-CAP


Water path

M-COP, ACM-CAP

M-COP

Water content

C-CLD, ACM-COM, ACM-CAP

Rain

Surface rain rate

C-CLD, ACM-CAP


Rain rate

C-CLD, ACM-CAP


Rain water pathC-CLD, ACM-CAP
Rain water contentC-CLD, ACM-CAP




Median drop size

C-CLD, ACM-CAP

Aerosol (per species)

Aerosol optical thickness

M-AOT, A-ALD, A-AER,
A-EBD, ACM-CAP

M-AOT, AM-ACD

Aerosol extinction

A-AER, A-EBD,
ACM-COM, ACM-CAP





Extinction-to- backscatter ratio

A-AER, A-EBD,
ACM-CAP


Ångström exponent

M-AOT (670/865nm),

AM-ACD (355/670nm)

M-AOT (670/865nm),

AM-ACD (355/670nm)

Particle linear depolarization ratioA-AER, A-EBD



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