EarthCARE radiative transfer simulation using A-Train satellite dataZhipeng Qu1, Jason Cole1, Howard Barker2, Meriem Kacimi1
Shannon Mason3, Robin Hogan3, Ben Courtier4,
1: Environment and Climate Change Canada, Toronto, Ontario, Canada
2: Environment and Climate Change Canada, Victoria, British Columbia, Canada
3: European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
4: University of Leicester, Leicester, United Kingdom

ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop, 13-17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

NWP simulated test frames

L2a products (CPR, ATLID, MSI)

1. Background

CAPTIVATE→ ACM-CAP

Radiative closure assessmentof EarthCARE mission,algorithm chart:Halifax
BajaHawaii

RT
RT-1: Cloud, aerosols, etc.

Mason et al. 2023
Mroz et al. 2023
van Zadelhoff et al. 2023
etc.

(Qu et al. 2023
Donovan et al. 2023)+other test data

- Construct 3D scenes (~5 x 21 km) for ACM-RT - Create L2a composite atmosphere & surface properties- Prepare data for ACM-RT, including ACM-CAP- Screen and sample assessment domains
Cole et al. 2023
Qu et al. 2023BBR’- Perform 1D and 3D RT calculations (HR profiles)- TOA radiances and fluxes to be used in ACMB-DF
Cole et al. 2023

RTBBR

- Compare model radiances to BBR obs- Compare model fluxes to BMA-FLX: ∆𝑥 < 10 𝑊 𝑚−2Barker et al. 2023 (in preparation)

Velázquez Blázquez et al. 2023
2

2. RT simulation based on A-Train data
➢ Different objectives at different stages of satellite algorithm development & validation
NWP simulated test frames•••••High resolution (250 m) for subgrid-scale variability
3D atmospheric scenes of cloud & aerosols (including off-nadir regions)
Excellent for 3D RT simulation and assessment of its impact
Reference truth available for verification
Only fractions of orbits•••••

Large number of orbit to test the robustness of satellite algorithm
Real-world studies available
Facilitates robust statistical analyses
Lack reference truth
Still allows for evaluation of 3D v. 1D RT?

Data from A-Train satellites

33. Input data from A-Train satellites

A-Train Satellites
CCM-CAPCAPTIVATE➢ CCM-CAP: 260 orbits available: Jan 1 to Mar 13, 2007
➢ More orbits will be processed later➢ Data will be available freely

CloudSat

Mason et al. 2023

CALIPSO
RT-1: Cloud, aerosols, etc.

MODIS (Aqua)
Radiances for scene construction

ERA5 &Surface/trace gases
climatology

MODIS (Aqua)- Construct 3D scenes (~5 x 21 km) for ACM-RT - Create L2a composite atmosphere & surface properties- Prepare data for ACM-RT, including ACM-CAP- Screen and sample assessment domains

CERES CCCM’- Perform 1D and 3D RT calculations (HR profiles)- TOA radiances and fluxes to be used in ACMB-DF

CERES CCCM

- Compare model radiances to BBR obs- Compare model fluxes to BMA-FLX: ∆𝑥 < 10 𝑊 𝑚−2

CERES CCCM
Integration of measurements:➢ CALIPSO-CALIOP➢ CloudSat-CPR➢ CERES➢ MODIS4

4. Case study (03609D)

Monte Carlo simulation for both ICA & 3D RT

Frame 03609D, 2007-01-01➢ Large land area with clear sky (Australia)➢ Deep convective system + broken clouds+ mixed phase clouds + low liquid clouds➢ Low solar zenith angle (SZA) conditions➢ Good agreement (SW & LW) over deepconvection➢ Good agreement over ocean (cloudy orclear sky)
➢ Over-estimation of SW fluxes over mixed
phase cloud (cloud properties?)➢ Underestimation of LW fluxes overAustralian desert (Surface T/type?)

SZA

Frame orbit

5

4. Case study (03609E)
Frame 03609E, 2007-01-01➢ Mostly oceanic➢ Multiple deep convective events +multilayer clouds + frontal system➢ Larger SZA➢ Good agreement for most conditions!
➢ ICA values (+) have more outliers thanthose of 3D RT (o)Frame orbit

SZA

6

5. Statistics (Shortwave 3D -1D fluxes at TOA)
Leakage?

Ocean

Ac(1.0): large negative MB for
overcast conditions over ocean &land (tropical region!): 3D effectof textured & convective cloud
top with ‘photon entrapment’?

Ac = ‘Cloud Fraction’Ac(0.5, 1.0): larger negative MBover land than over ocean: moreconvective over land for partiallycloudy conditions?

All: larger RMS for tropicalregion: more convective,textured, multi-layered clouds?

Small sample for AC=0 (10 cases)

Land

Ac(0): positive MB over ocean &land: leakage from clouds
adjacent to the assessmentdomain? (over land, only 10cases near -5°)

(Bin size: 10°)7

5. Statistics (Longwave 3D -1D fluxes at TOA)

Ocean:• Both MB and RMS are small relative to SW• Smaller MB for overcast conditions• Larger MB for partially cloudy conditions (MB ~ -1 W m-2, upwelling diffuse beam intercepted by cloud sides)• Largest RMS for cloudier conditions (Ac > 0.5) in the tropical region

86. Conclusion and perspectives
Satellite based input data are used in EarthCARE radiative closure assessment processors for algorithm evaluation• A-Train based synergetic retrievals of cloud & aerosols using CAPTIVATE algorithm from Jan 1 to Mar 13, 2007
➢ 260 frames provided by ECMWF & ULeicester• CERES CCCM upwelling SW & LW fluxes• MODIS radiances for scene construction
3D – 1D fluxes:• SW: larger differences for cloudier & more convective conditions (influence of turbulent cloud tops on RT fluxes?)• SW: differences for tropical clear-sky conditions (influence of surrounding cloudy environment?)• LW: differences are generally small for all cases (MBE < 2 W m-2, RMSE < 5 W m-2)
• LW: larger differences for cloudier & more convective conditions (similar to SW case)
Perspectives:• Further investigations of different causes of the discrepancy between ACM-RT fluxes and CERES CCCM fluxes• Further evaluation of the impact of 3D RT w.r.t 1D ICA RT• Simulate more frames to cover all seasons (at least a year)• Continued improvement of the radiative closure assessment algorithm9