Abstract
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).
Highlights
In the past two decades, the Atmospheric Radiation Measurement (ARM) Program has installed and operated a number of remote sensing instruments dedicated to observing cloud macro- and micro-physical properties
A secondary goal of this paper is to explore the impact self-organizing map (SOM) class selection and sampling have on synoptic pattern classifications
Cloud observations at the ARM Southern Great Plains (SGP) site come from the Active Remote Sensing of Clouds (ARSCL) Value-Added Product (VAP; Clothiaux et al 2000, 2001)
Summary
In the past two decades, the Atmospheric Radiation Measurement (ARM) Program has installed and operated a number of remote sensing instruments dedicated to observing cloud macro- and micro-physical properties These include millimeter-wavelength cloud radars (MMCRs; Moran et al 1998), micro-pulse lidars (MPLs; Spinhirne 1993), and laser ceilometers. Total CF is defined as the ratio of the number of vertical profiles with cloud present to the total number of profiles available This temporal calculation of CF at the ARM SGP site compares well to area-averaged CFs provided by Geostationary Operational Environmental Satellite (GOES) observations (Kennedy et al 2010; Xi et al 2009). Uncertainties of total CF were calculated using a bootstrap technique for months with instrument uptimes ≥95 % For these months, samples were randomly withheld in increasing quantities to determine the 95 % confidence interval for a specific instrument uptime. The uncertainty varies markedly by month due to varying instrument uptimes (see Fig. 3 of Kennedy et al (2014))
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