...
Collect in advance: |
| 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-PRO | A-LAY | Moritz Haarig | Clouds (A-CTH)
Aerosol (A-ALD)
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. | AM-COL | Moritz Haarig | Clouds (AM-CTH)
Aerosol (AM-ACD)
Products provided on MSI swath. Combining vertical information at 355 nm from ATLID and spatial and radiative information on MSI swath. | As a synergistic product, it requires the validation of ATLID and MSI products as described in the corresponding sections. | M-AOT | Nicole Docter | Aerosol Retrieved quantities:
Diagnostic quantities:
Priors and underlying assumptions:
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| M-CLD | Anja 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 Optical and Physical properties (M-COP): for water and ice clouds
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| DORSY: CPR and ATLID/CPR/MSI synergy | C-PRO | Pavlos Kollias | C-CLD | Pavlos Kollias | C-APC | Pavlos Kollias | AC-TC | Julien Delanoe | ACM-CAP | Shannon Mason | 1. Liquid cloud co-located with rain and embedded in ice clouds
2. Drizzling stratocumulus & warm rain (not included in nominal scenes):
3. Stratiform rimed snowfall (not included in nominal scenes):
4. Polar mixed-phase clouds:
5. Ice microphysics:
| 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:
Configuration:
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:
Configuration:
| CLARA: BBR, radiation, and closure assessment | BM-RAD | Almudena Velazquez | Radiation
| Using test scenes (Baja and Halifax)
Snow covered regions present in average higher uncertainties that need to be further studied (either to improve LUT for the unfiltering or detection) | BMA-FLX | Carlos (Almudena Velazquez) | Radiation
| Using Baja scene
| ACM-COM | Howard Barker | ACM-3D | Howard Barker | ACM-RT | Howard Barker | (ACMB-DF) | Howard BarkerAerosol and cloud profiles of :
| Verification of target classification scheme. |
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A-LAY | Moritz Haarig | Clouds (A-CTH)
Aerosol (A-ALD)
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:
Airborne lidar measurements of:
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AM-COL | Moritz Haarig | Clouds (AM-CTH)
Aerosol (AM-ACD)
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:
Diagnostic quantities:
Priors and underlying assumptions:
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| AOT at 670 nm over land (in different biomes) and over ocean and AOT at 865 nm over ocean based on:
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M-CLD | Anja 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 Optical and Physical properties (M-COP): for water and ice clouds
| 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:
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DORSY: CPR and ATLID/CPR/MSI synergy | C-PRO | Pavlos Kollias | |||||||||||||||||||||||||||||||||||||||||||||||
C-CLD | Pavlos Kollias | ||||||||||||||||||||||||||||||||||||||||||||||||
C-APC | Bernat Puigdomènech | CPR Antenna Pointing Correction Assumptions: | The ice clouds reflectivity-Doppler velocity climatology from space measurements. This unknown relationship brings uncertainties in the C-APC "ice clouds" correction technique |
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AC-TC | Julien Delanoe | ||||||||||||||||||||||||||||||||||||||||||||||||
ACM-CAP | Shannon Mason | Ice clouds and snow: Retrieved (independent) quantities:
Key derived quantities ( & correlations with retrieved quantities):
Priors and underlying assumptions:
Uncertainties & assumptions:
Liquid clouds: Retrieved (independent) quantities:
Key derived quantities ( & correlations with retrieved quantities):
Priors and underlying assumptions:
Uncertainties & assumptions:
Drizzle and rain: Retrieved (independent) quantities:
Key derived quantities ( & correlations with retrieved quantities):
Priors and underlying assumptions:
Uncertainties & assumptions:
Aerosols: Retrieved (independent) quantities:
Key derived quantities ( & correlations with retrieved quantities)
Priors and underlying assumptions:
| 1. Liquid cloud co-located with rain and embedded in ice clouds
2. Drizzling stratocumulus & warm rain (not included in nominal scenes):
3. Stratiform rimed snowfall (not included in nominal scenes):
4. Polar mixed-phase clouds:
5. Ice microphysics:
| 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:
Configuration:
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:
Configuration:
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CLARA: BBR, radiation, and closure assessment | BM-RAD | Almudena Velazquez | Radiation For each telescope: FORE, NADIR, AFT
Provided at different resolutions: in the BBR grid: sampling 1km alongtrack
In the JSG grid: sampling 1JSG pixel
PSF weighted variables:
| Using test scenes (Baja and Halifax)
Snow covered regions present in average higher uncertainties that need to be further studied (either to improve LUT for the unfiltering or detection) | |||||||||||||||||||||||||||||||||||||||||||||
BMA-FLX | Carlos Almudena Velazquez | Radiation
| Using Baja scene
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ACM-COM | Howard Barker | ||||||||||||||||||||||||||||||||||||||||||||||||
ACM-3D | Howard Barker | ||||||||||||||||||||||||||||||||||||||||||||||||
ACM-RT | Howard Barker | ||||||||||||||||||||||||||||||||||||||||||||||||
(ACMB-DF) | Howard Barker |
Table 2. Correlative measurements needed in support of algorithm developent
Category | Variable | Unit | Comment | Feedback 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 | ||||
Sunphotometer | AOD | 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 product | Vertical profiles at nadir | ||||
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Quantity | At nadir | Across-track | Quantity | Products | |
Target classification | Cloud-top height | M-COP, A-CTH, A-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 path | C-CLD, ACM-CAP | Rain water content | C-CLD, ACM-CAP | ||
Median drop size | C-CLD, ACM-CAP | ||||
Aerosol (per species) | Aerosol optical thickness | M-AOT, A-ALD, A-AER, | M-AOT, AM-ACD | Aerosol extinction | A-AER, A-EBD, |
Extinction-to- backscatter ratio | A-AER, A-EBD, | ||||
Ångström exponent | M-AOT (670/865nm), AM-ACD (355/670nm) | M-AOT (670/865nm), AM-ACD (355/670nm) | Particle linear depolarization ratio | A-AER, A-EBD |