BibTex format
@article{Gryspeerdt:2022:10.5194/amt-15-3875-2022,
author = {Gryspeerdt, E and McCoy, DT and Crosbie, E and Moore, RH and Nott, GJ and Painemal, D and Small-Griswold, J and Sorooshian, A and Ziemba, L},
doi = {10.5194/amt-15-3875-2022},
journal = {Atmospheric Measurement Techniques},
pages = {3875--3892},
title = {The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data},
url = {http://dx.doi.org/10.5194/amt-15-3875-2022},
volume = {15},
year = {2022}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - Cloud droplet number concentration (Nd) is of central importance to observation-based estimates of aerosol indirect effects, being used to quantify both the cloud sensitivity to aerosol and the base state of the cloud. However, the derivation of Nd from satellite data depends on a number of assumptions about the cloud and the accuracy of the retrievals of the cloud properties from which it is derived, making it prone to systematic biases.A number of sampling strategies have been proposed to address these biases by selecting the most accurate Nd retrievals in the satellite data. This work compares the impact of these strategies on the accuracy of the satellite retrieved Nd, using a selection of in situ measurements. In stratocumulus regions, the MODIS Nd retrieval is able to achieve a high precision (r2 of 0.5–0.8). This is lower in other cloud regimes but can be increased by appropriate sampling choices. Although the Nd sampling can have significant effects on the Nd climatology, it produces only a 20 % variation in the implied radiative forcing from aerosol–cloud interactions, with the choice of aerosol proxy driving the overall uncertainty. The results are summarised into recommendations for using MODIS Nd products and appropriate sampling.
AU - Gryspeerdt,E
AU - McCoy,DT
AU - Crosbie,E
AU - Moore,RH
AU - Nott,GJ
AU - Painemal,D
AU - Small-Griswold,J
AU - Sorooshian,A
AU - Ziemba,L
DO - 10.5194/amt-15-3875-2022
EP - 3892
PY - 2022///
SN - 1867-1381
SP - 3875
TI - The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data
T2 - Atmospheric Measurement Techniques
UR - http://dx.doi.org/10.5194/amt-15-3875-2022
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000819425100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR - https://amt.copernicus.org/preprints/amt-2021-371/
UR - http://hdl.handle.net/10044/1/96877
VL - 15
ER -