Automated cloud screening algorithm for MFRSR data

Alexandrov, Mikhail D.; Marshak, Alexander; Cairns, Brian; Lacis, Andrew A.; Carlson, Barbara E.

doi:10.1029/2003gl019105

Cited by 76 publications

(79 citation statements)

References 16 publications

(20 reference statements)

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“…[6] Originally, LIP was defined as a function of the optical thickness of clouds [Alexandrov et al, 2004]. Here we modify this definition by describing LIP in terms of satellite reflectances:…”

Section: Analyses Of Spatial Variabilitymentioning

confidence: 99%

“…Although STD is commonly used in satellite remote sensing applications, spatial variability can be quantified by applying different measures. For example, Alexandrov et al [2004] used the so-called inhomogeneity parameter to detect presence of clouds from ground-based observations. In this study we expand this concept to satellite radiances by introducing the local inhomogeneity parameter (LIP).…”

Section: Introductionmentioning

confidence: 99%

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Spatial variability of satellite visible radiances in dust and dust‐cloud mixed conditions: Implications for dust detection

Darmenov¹,

Sokolik²

2009

Geophysical Research Letters

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[1] Using data from MODIS for the 2000 -2004 time period, we performed a statistical analysis of visible radiances observed in the presence of mineral dust in cloud-free and cloudy conditions over oceans. Spatial variability of the 555 nm radiances was examined by introducing two different measures: standard deviation (STD) and a local inhomogeneity parameter (LIP). We demonstrate that introducing the probability density function (PDF) of STD offers a new framework for probabilistic dust detection. We show that the probabilistic approach gives more accurate discrimination of dust from clouds compared with the MODIS fixed STD threshold method. Furthermore, the probabilistic approach enables one to determine the confidence level and skill of dust detection. In addition, we examined the capability of the probabilistic detection of dust-cloud mixed pixels currently not considered by operational algorithms. A low classification skill of dust-cloud pixels was found. Introducing multivariate PDFs by including multispectral data might help to overcome this problem. Citation: Darmenov, A., and I. N. Sokolik (2009), Spatial variability of satellite visible radiances in dust and dust-cloud mixed conditions: Implications for dust detection, Geophys. Res. Lett., 36, L14811,

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Section: Analyses Of Spatial Variabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial variability of satellite visible radiances in dust and dust‐cloud mixed conditions: Implications for dust detection

Darmenov¹,

Sokolik²

2009

Geophysical Research Letters

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“…Similar to the AERONET-based cloud-screening [21], ARM-supported algorithms [12,16] consider AOD temporal variability derived from ground-based high-resolution (20-s) measurements of the direct normal irradiance at the surface. To illustrate scenarios where these well-known algorithms are susceptible to failure, we show several representative examples (Figure 1), which depict two important features.…”

Section: Problemmentioning

confidence: 99%

“…Observations of these important aerosol parameters-AOD and AE-have been performed at Atmospheric Radiation Measurement (ARM) sites around the world over the past decade and longer [1,[12][13][14] using spectrally resolved measurements from the Multifilter Rotating Shadowband Radiometer (MFRSR; [15]) and the Normal Incidence Multifilter Radiometer (NIMFR; https://www.arm.gov/instruments). Notably, a new and representative (over 10 years) climatology of AOD and AE at the ARM Southern Great Plains site (SGP, a mid-continental site located far away from major urban source regions) has been developed [12] under both clear and partly cloudy conditions using multi-year MFRSR measurements and standard cloud-screening algorithms [16]. This climatology captures important signatures of atmospheric aerosols, such as the day-to-day and seasonal variability of mass loading and particle size.…”

Section: Introductionmentioning

confidence: 99%

Failure and Redemption of Multifilter Rotating Shadowband Radiometer (MFRSR)/Normal Incidence Multifilter Radiometer (NIMFR) Cloud Screening: Contrasting Algorithm Performance at Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) and Southern Great Plains (SGP) Sites

et al. 2013

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Well-known cloud-screening algorithms, which are designed to remove cloud-contaminated aerosol optical depths (AOD) from Multifilter Rotating Shadowband Radiometer (MFRSR) and Normal Incidence Multifilter Radiometer (NIMFR) measurements, have exhibited excellent performance at many middle-to-low latitude sites around world. However, they may occasionally fail under challenging observational conditions, such as when the sun is low (near the horizon) and when optically thin clouds with small spatial inhomogeneity occur. Such conditions have been observed quite frequently at the high-latitude Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) sites. A slightly modified cloud-screening version of the standard algorithm is proposed here with a focus on the ARM-supported MFRSR and NIMFR data. The modified version uses approximately the same techniques as the standard algorithm, but it additionally examines the magnitude of the slant-path line of sight transmittance and eliminates points when the observed magnitude is below a specified threshold. Substantial improvement of the multi-year (1999-2012) aerosol product (AOD and its Angstrom exponent) is shown for the NSA sites when the modified version is applied. Moreover, this version reproduces the AOD product at the ARM Southern Great Plains (SGP) site, which OPEN ACCESSAtmosphere 2013, 4 300 was originally generated by the standard cloud-screening algorithms. The proposed minor modification is easy to implement and its application to existing and future cloud-screening algorithms can be particularly beneficial for challenging observational conditions.

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“…The cloud screening applied in this study is based on the method by Alexandrov et al (2004) with modifications to fit the UVPFR measurements. The Alexandrov et al (2004) cloud screening algorithm was developed for optical depth measurements at 870 nm wavelength and for a sampling interval of 20 s. Stability tests were performed with a 15-measurement window, which consequently spanned over 5 min. For the cloud screening, optical depth at the longest UVPFR wavelength (332 nm) was used.…”

Section: Calculation Of Aod From Uvpfr Measurementsmentioning

confidence: 99%

Aerosol optical depth determination in the UV using a four-channel precision filter radiometer

et al. 2017

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Abstract. The determination of aerosol properties, especially the aerosol optical depth (AOD) in the ultraviolet (UV) wavelength region, is of great importance for understanding the climatological variability of UV radiation. However, operational retrievals of AOD at the biologically most harmful wavelengths in the UVB are currently only made at very few places. This paper reports on the UVPFR (UV precision filter radiometer) sunphotometer, a stable and robust instrument that can be used for AOD retrievals at four UV wavelengths. Instrument characteristics and results of Langley calibrations at a high-altitude site were presented. It was shown that due to the relatively wide spectral response functions of the UVPFR, the calibration constants (V 0 ) derived from Langley plot calibrations underestimate the true extraterrestrial signals. Accordingly, correction factors were introduced. In addition, the instrument's spectral response functions also result in an apparent air-mass-dependent decrease in ozone optical depth used in the AOD determinations. An adjusted formula for the calculation of AOD, with a correction term dependent on total column ozone amount and ozone air mass, was therefore introduced. Langley calibrations performed 13-14 months apart resulted in sensitivity changes of ≤ 1.1 %, indicating good instrument stability. Comparison with a high-accuracy standard precision filter radiometer, measuring AOD at 368-862 nm wavelengths, showed consistent results. Also, very good agreement was achieved by comparing the UVPFR with AOD at UVB wavelengths derived with a Brewer spectrophotometer, which was calibrated against the UVPFR at an earlier date. Mainly due to non-instrumental uncertainties connected with ozone optical depth, the total uncertainty of AOD in the UVB is higher than that reported from AOD instruments measuring in UVA and visible ranges. However, the precision can be high among instruments using harmonized algorithms for ozone and Rayleigh optical depth as well as for air mass terms. For 4 months of comparison measurements with the UVPFR and a Brewer, the root mean squared AOD differences were found < 0.01 at all the 306-320 nm Brewer wavelengths.

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Automated cloud screening algorithm for MFRSR data

Cited by 76 publications

References 16 publications

Spatial variability of satellite visible radiances in dust and dust‐cloud mixed conditions: Implications for dust detection

Spatial variability of satellite visible radiances in dust and dust‐cloud mixed conditions: Implications for dust detection

Failure and Redemption of Multifilter Rotating Shadowband Radiometer (MFRSR)/Normal Incidence Multifilter Radiometer (NIMFR) Cloud Screening: Contrasting Algorithm Performance at Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) and Southern Great Plains (SGP) Sites

Aerosol optical depth determination in the UV using a four-channel precision filter radiometer

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