Cloud fraction at the ARM SGP site: reducing uncertainty with self-organizing maps

摘要

Instrument downtime leads to uncertainty in the monthly and annual record of cloud fraction (CF), making it difficult to perform time series analyses of cloud properties and perform detailed evaluations of model simulations. As cloud occurrence is partially controlled by the large-scale atmospheric environment, this knowledge is used to reduce uncertainties in the instrument record. Synoptic patterns diagnosed from the North American Regional Reanalysis (NARR) during the period 1997-2010 are classified using a competitive neural network known as the self-organizing map (SOM). The classified synoptic states are then compared to the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) instrument record to determine the expected CF. A number of SOMs are tested to understand how the number of classes and the period of classifications impact the relationship between classified states and CFs. Bootstrapping is utilized to quantify the uncertainty of the instrument record when statistical information from the SOM is included. Although all SOMs significantly reduce the uncertainty of the CF record calculated in Kennedy et al. (Theor Appl Climatol 115:91-105, 2014), SOMs with a large number of classes and separated by month are required to produce the lowest uncertainty and best agreement with the annual cycle of CF. This result may be due to a manifestation of seasonally dependent biases in NARR. With use of the SOMs, the average uncertainty in monthly CF is reduced in half from the values calculated in Kennedy et al. (Theor Appl Climatol 115:91-105, 2014).