Luke Edgar Whitehead

• McDonald, A.J.; Kuma, P.; Panell, M.; Petterson, O.K.L.; Plank, G.E. & Rozliaiani, M.A.H. [Show all 7 contributors for this article] (2025). Evaluating Cloud Properties at Scott Base: Comparing Ceilometer Observations With ERA5, JRA55, and MERRA2 Reanalyses Using an Instrument Simulator. Journal of Geophysical Research (JGR): Atmospheres. ISSN 2169-897X. 130(2). doi: 10.1029/2024JD041754. Full text in Research Archive Show summary

  This study compares CL51 ceilometer observations made at Scott Base, Antarctica, with statistics from the ERA5, JRA55, and MERRA2 reanalyses. To enhance the comparison we use a lidar instrument simulator to derive cloud statistics from the reanalyses which account for instrumental factors. The cloud occurrence in the three reanalyses is slightly overestimated above 3 km, but displays a larger underestimation below 3 km relative to observations. Unlike previous studies, we see no relationship between relative humidity and cloud occurrence biases, suggesting that the cloud biases do not result from the representation of moisture. We also show that the seasonal variation of cloud occurrence and cloud fraction, defined as the vertically integrated cloud occurrence, are small in both the observations and the reanalyses. We also examine the quality of the cloud representation for a set of weather states derived from ERA5 surface winds. The variability associated with grouping cloud occurrence based on weather state is much larger than the seasonal variation, highlighting weather state is a strong control of cloud occurrence. All the reanalyses continue to display underestimates below 3 km and overestimates above 3 km for each weather state. But the variability in ERA5 statistics matches the changes in the observations better than the other reanalyses. We also use a machine learning scheme to estimate the quantity of supercooled liquid water cloud from the ceilometer observations. Ceilometer low-level supercooled liquid water cloud occurrences are considerably larger than values derived from the reanalyses, further highlighting the poor representation of low-level clouds in the reanalyses.

• Whitehead, Luke Edgar; McDonald, Adrian James & Guyot, Adrien (2024). Supercooled liquid water cloud classification using lidar backscatter peak properties. Atmospheric Measurement Techniques. ISSN 1867-1381. 17(19), p. 5765–5784. doi: 10.5194/amt-17-5765-2024. Full text in Research Archive Show summary

  The use of depolarization lidar to measure atmospheric volume depolarization ratio (VDR) is a common technique to classify cloud phase (liquid or ice). Previous work using a machine learning framework, applied to peak properties derived from co-polarized attenuated backscatter data, has been demonstrated to effectively detect supercooled-liquid-water-containing clouds (SLCCs). However, the training data from Davis Station, Antarctica, include no warm liquid water clouds (WLWCs), potentially limiting the model's accuracy in regions where WLWCs are present. In this work, we apply the same framework used on the Davis data to a 9-month micro-pulse lidar dataset collected in Ōtautahi / Christchurch, Aotearoa / New Zealand, a location which includes WLWC. We then evaluate the results relative to a reference VDR cloud-phase mask. We found that the Davis model performed relatively poorly at detecting SLCC with a recall score of 0.18, often misclassifying WLWC as SLCC. The performance of our new model, trained using data from Ōtautahi / Christchurch, displays recall scores as high as 0.88 for identification of SLCC, although it generally underestimates SLCC occurrence. The overall performance of the new model highlights the effectiveness of the machine learning technique when appropriate training data relevant to the location are used.

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