Validation Needs

EarthCARE
Data
Calibration/Validation
Validation Needs
Validation Needs Overview

The following table outlines the measurement needs for the different applications making use of EarthCARE data. Descriptions of the products referred to in this table are available in the EarthCARE ESA Product List document

*EarthCARE correlative measurement needs*
Application	Uncertainty	Locations/scenes/ regimes	Measurements needed	Products
Macrophysics	Aerosol layer detection & classification	Multiple aerosol layers Aerosol layers Aerosol layers with strong internal structure Attributing aerosol plumes at nadir to features in the across-track imagery	Ground-based (& scanning?) lidars Collocated in-situ aerosol sampling	A-ALD, AM-ACD, A-TC
	Aerosol/cloud discrimination	Cloud embedded in aerosol layers	Ground-based lidars Collocated in-situ aerosol sampling	A-ALD, AM-ACD, A-TC
	Cloud layer detection and structure	Multiple aerosol layers Aerosol layers Cirrus over liquid cloud Non-precipitating liquid clouds	Non-precipitating liquid clouds	M-CLD, AM-CTH, A-TC, C-TC, AC-TC
	Liquid clouds not fully resolved by radar/lidar synergy	Physical depth of liquid clouds (i.e. cloud base height) Mixed-phase layers embedded in ice Liquid (and liquid-topped mixed-phase) clouds below optically thick ice clouds Warm liquid clouds within cold rain	Ground-based ceilometers/ lidars Multiple-frequency radars to constrain W-band attenuation Microwave radiometers for LWP Aircraft profiles of LWC	A-TC, C-TC, AC-TC
	CPR surface clutter removal	Range of surface types	Ground-based radars	C-TC, C-FMR
Ice cloud and snow	Snow microphysics (e.g. PSDs, microwave scattering, density & fall speed)	Stratiform vs convective Rimed snow	Bulk precipitation and particle imaging Profiles of in-situ particle properties in ice clouds	C-CLD, ACM-CAP
	Ice microphysics (e.g. PSDs, ice optics, mass-size rel’n)	Cloud-tops across range of temperatures, locations & cloud types	In situ aircraft measurements Visible & IR radiances	A-ICE, ACM-CAP
	Surface snow rate	Range of meteorological conditions	Ground-based radars and in-situ at surface	C-CLD, ACM-CAP
Liquid cloud	How to account for radiatively important liquid clouds not detected by ATLID	Deep ice clouds: embedded mixed-phase layers Layered cloud scenes Warm liquid clouds within cold rain	Microwave radiometer for LWP Profiles of liquid water content	ACM-CAP
Rain	Relation between rain rate and drop size distribution	Warm rain (maritime) Convective/stratiform rain	Ground-based multiple-frequency radars Dual-pol weather radars over ocean	C-CLD, ACM-CAP
Rain	Melting layer structure & radar attenuation	Dependence on snow properties aloft Continuity of mass flux & size distributions across melting layer	Ground-based & airborne multiple-frequency radar	C-CLD, ACM-CAP
Aerosol	Large AOT uncertainties over land; sensitivity to aerosol classification	Range of different land classes (biomes) & ocean Range of different aerosol classes	Ground-based, e.g. AERONET(-OC) Ship-based sun photometers, e.g. MAN Satellite imagers, e.g. MODIS, VIIRS, 3MI	M-AOT
Radiation	Detection of and/or representation of fluxes over snow-covered surfaces	Range of snow-covered surfaces	High-latitude ground stations	BM-RAD, BMA-FLX

Campaign targets, scenes, and scenarios

EarthCARE retrieval challenges and scenes to target in Cal/Val campaigns

Robin Hogan (r.j.hogan@ecmwf.int), ECMWF, October 2023

Thanks to Bjorn Stevens, Ulla Wandinger, and Rob Koopman

The purpose of this article is to list a set of meteorological scenes that present specific challenges from the point of view of EarthCARE retrieval algorithms. While the list is not exhaustive, it will hopefully be useful in planning EarthCARE Cal/Val activities to ensure that the meteorological conditions sampled in all campaigns involving in-situ aircraft observations span at least those listed in this article. It should not be used to judge that a campaign focussing on conditions not specifically mentioned below is of less value than one that is. There are of course many previous science-focussed campaigns targeting these conditions, but the unique set of instruments aboard EarthCARE, and the specific challenges for a spaceborne platform (such as radar multiple scattering and additional noise), necessitate underflights of the satellite specifically for the purpose of evaluating retrievals. Moreover, we expect situations where the radiative closure exhibits a bias, and in-situ observations are the only way to identify the cause.

The list was formulated in the specific context of a proposal for a flight campaign in which in-situ sampling would evaluate EarthCARE's synergy retrievals. However, the retrieval challenges identified are general enough to be relevant to single-instrument retrievals as well. Examples of successful underflights of CloudSat and CALIPSO were provided by Noh et al. (2011), Cesana et al. (2016) and Qu et al. (2018).

*Meteorological scene retrieval challenges*
Conditions	Key retrieval challenge
Mixed aerosol types, especially over land	Aerosol type and optical properties are estimated primarily from ATLID, with information on aerosol absorption coming from the backscatter-to-extinction ratio and asphericity from the depolarization ratio (see Fig. 8 of Illingworth et al. 2015), but with a prior constraint on possible types and mixtures. How well suited are the predefined types to the real world, and how accurate are the retrieved properties in contrasting aerosol conditions, scenes containing layers of different aerosol types, and in the presence of noise such as from solar contamination?
Cumulus and marine aerosol	Drizzle-free and sometimes optically thin cumulus are widespread over the ocean and so are radiatively important, but retrieving their properties is challenging because they are often smaller than the instrument sample volume and barely detectable by the radar. It is also a challenge to cleanly mask them out when performing retrievals of marine aerosols, which tend to be optically thinner than over land so require horizontal averaging of the lidar signal to overcome instrument noise.
Marine stratocumulus	A key synergy challenge: CPR will often be dominated by drizzle drops, ATLID detects cloud top but is rapidly attenuated, while solar radiances provide an optical depth constraint, and the radar ocean-surface return provides a path-attenuation constraint on integrated liquid water. Can the retrievals infer the location of cloud base and information on the vertical profile of liquid water content?
Large-scale rain	The CPR reflectivity signal in rain can be tricky to interpret as it is affected by melting-layer and rain attenuation, so a key constraint is path-integrated attenuation (PIA) from the ocean return (Mason et al. 2017). However, there are frequently liquid clouds embedded in the rain that also contribute to PIA and for which an assumption needs to be made. In this context, how accurate are retrieved rain rates? (Note that rain from deep convection presents severe additional difficulties, outlined in the final item of this list.)
Snow, including snow above the melting layer	The snow region in nimbostratus (temperature between –15 and 0°C) frequently contains embedded supercooled liquid clouds that cause riming and lead to significantly denser ice particles. Since these fall faster than low-density ice aggregates, we can use the CPR Doppler velocity to estimate snow density (Mason et al. 2018), but how accurate are these retrievals, and can we also get a handle on the water path of the supercooled liquid?
Altocumulus and cold-air outbreaks	The simplest mixed-phase cloud consists of a layer of supercooled liquid seen by ATLID, within and beneath which ice crystals are falling and dominate the CPR return. The need to retrieve four variables simultaneously (water content and effective radius of ice and liquid) means there is more reliance on a-priori assumptions and so evaluation of retrievals is needed.
Cirrus	The synergy of radar and lidar provides a powerful constraint on particle size as demonstrated by CloudSat and CALIPSO, but how can we make retrievals fully consistent with the extra Doppler and HSRL information, as well as MSI radiances in the solar and thermal infrared? Do the retrieval assumptions and priors need to be relaxed or changed?
Complex multi-layer scenes	It is common for multiple cloud layers to be present in a profile (the simplest being cirrus over stratocumulus), in which case as well as evaluating the microphysical retrievals in each layer, we need to determine whether the optical depth information from solar radiance measurements (and indeed PIA) has been correctly partitioned between the upper and lower layers; this can in principle be inferred from aircraft radiation measurements taken between the two layers.
Deep convection	In this situation the radar attenuation is so strong that PIA cannot be estimated, and multiple scattering makes interpretation of the reflectivity profile higher in the cloud very difficult. Except for the very top of the cloud where the lidar still has sensitivity, is there anything that can be meaningfully retrieved in such situations? Given these retrieval difficulties, and indeed the challenge of sampling deep convection in-situ with aircraft, it is not clear how much focus should be given to deep convection.

References

Cesana, G., H. Chepfer, D. Winker, B. Getzewich, X. Cai, O. Jourdan, G. Mioche, H. Okamoto, Y. Hagihara, V. Noel and M. Reverdy, 2016: Using in situ airborne measurements to evaluate three cloud phase products derived from CALIPSO. J. Geophys. Res. Atmos., 121, doi:10.1002/2015JD024334.

Illingworth, A. J., Barker, H. W., Beljaars, A., Ceccaldi, M., Chepfer, H., Clerbaux, N., Cole, J., Delanoë, J., Domenech, C., Donovan, D. P., Fukuda, S., Hirakata, M., Hogan, R. J., Huenerbein, A., Kollias, P., Kubota, T., Nakajima, T., Nakajima, T. Y., Nishizawa, T., Ohno, Y., Okamoto, H., Oki, R., Sato, K., Satoh, M., Shephard, M. W., Velázquez-Blázquez, A., Wandinger, U., Wehr, T., & van Zadelhoff, G.-J., 2015: The EarthCARE Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation. Bull. Amer. Meteorol. Soc., 96, 1311-1332.

Mason, S. L., Chiu, J. C., Hogan, R. J., & Tian, L., 2017: Improved rain rate and drop size retrievals from airborne Doppler radar. Atmos. Chem. Phys., 17, 11567-11589.

Mason, S. L., Chiu, C. J., Hogan, R. J., Moisseev, D., & Kneifel, S., 2018: Retrievals of riming and snow density from vertically pointing Doppler radars. J. Geophys. Res. Atmos., 123, 13807-13834.

Noh, Y.‐J., C. J. Seaman, T. H. Vonder Haar, D. R. Hudak, and P. Rodriguez, 2011: Comparisons and analyses of aircraft and satellite observations for wintertime mixed‐phase clouds. J. Geophys. Res., 116, D18207, doi:10.1029/2010JD015420.

Qu, Z., H. W. Barker, A. V. Korolev, J. A. Milbrandt, I. Heckman, S. Bélair, S. Leroyer, P. A. Vaillancourt, M. Wolde, A. Schwarzenböck, D. Leroy, J. W. Strapp, J. N. S. Cole, L. Nguyen and A. Heidinger, 2018: Evaluation of a high-resolution numerical weather prediction model’s simulated clouds using observations from CloudSat, GOES-13 and in situ aircraft. Q. J. R. Meteorol Soc., 144, 1681–1694, doi:10.1002/qj.3318.

Find out more about EarthCARE's Geophysical Parameters on the Geophysical Parameters page.