The ARM Millimeter Wave Cloud Radars (MMCRs) and the Active Remote Sensing of Clouds (ARSCL) Value Added Product (VAP)

Clothiaux, Eugene E.; Miller, Mark A.; Perez, Robin; Turner, David D.; Moran, K. P.; Martner, Brooks E.; Ackerman, Thomas P.; Mace, Gerald G.; Marchand, Roger; Widener, Kevin B.; Rodriguez, Daniel J.; Uttal, Taneil; Mather, J. H.; Flynn, Connor; Gaustad, Krista; Ermold, Brian

doi:10.2172/1808567

Cited by 65 publications

(22 citation statements)

References 12 publications

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“…Specifically, we use the method described in Heffter (1980) that uses thresholds in the vertical gradient of potential temperature to locate the inversion layer and thereby, the boundary layer depth. Low-level cloud frequencies (LCFs) were estimated from the Active Remote Sensing of Clouds (ARSCL) VAP (Clothiaux et al, 2000(Clothiaux et al, , 2001 which combines observations from vertically pointing cloud radar, lidar, and microwave radiometer to produce best estimates of cloud boundaries and radar moments. The LCF is estimated as the number of timesteps when cloud is detected in the lowest four km divided by the total number of ARSCL observations (Xie et al, 2010).…”

Section: Methodsmentioning

confidence: 99%

Marine Boundary Layer Decoupling and the Stable Isotopic Composition of Water Vapor

2022

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Marine boundary layer (MBL) stratocumulus clouds play a crucial role in the climate system, radiatively cooling the Earth through strong reflectance of incoming shortwave radiation compared to the underlying ocean, with comparatively little impact in the longwave portion of the spectrum due to cloud top temperatures that are not significantly different than the surface. These important cloud types are maintained by turbulence driven by cloud top radiative cooling that generates mixing between the oceanic source of water vapor and the cloudy layer (Hartmann et al., 1992;Klein & Hartmann, 1993;Wood, 2012). When the MBL is shallow (below around 1 km), the boundary layer is generally well-mixed, capped by a strong temperature inversion, and the MBL cloud fractions are greatest (Albrecht et al., 1995). Cloud top evaporative cooling and entrainment of free-tropospheric air into the MBL, among other mechanisms, leads to a deepening of the MBL beyond 1 km, and the longwave cooling at the cloud tops cannot mix the negatively buoyant entrained air through the full depth of the MBL. The cloudy layer can thus become decoupled from the oceanic moisture source, reducing or eliminating mixing through the full depth of the MBL, and reducing cloud fractions (Bretherton & Blossey, 2014;Bretherton & Wyant, 1997). The MBL can also become decoupled through radiative heating of the cloud layer and evaporative cooling in the sub-cloud layer, both processes further stabilizing the MBL and suppressing vertical mixing (Ghate et al., 2013;Nicholls, 1984;Xiao et al., 2011). The details of this decoupling process and how it impacts water vapor transport within the MBL and the exchanges of water vapor between the subcloud layer (SCL), the cloudy layer, and the

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Section: Methodsmentioning

confidence: 99%

Marine Boundary Layer Decoupling and the Stable Isotopic Composition of Water Vapor

2022

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“…The MMCR is a zenith-pointing radar that operates at a frequency of 35 GHz [28,29]. It can provide the detection of hydrometeor reflectivity as low as −50 dBz up to 20 km above the ground.…”

Section: Raman Lidar and Cloud Radarsmentioning

confidence: 99%

“…We use the ARSCL VAP of the MMCR [29] from August 2008 to January 2011 and the KAZR [32] from January 2011 to December 2014. It should be noted that the top and bottom heights of hydrometeors used in this study in the ARSCLs VAP of MMCR and KAZR are based on combined radar and micropulse lidar observations.…”

Section: Raman Lidar and Cloud Radarsmentioning

confidence: 99%

“…Ground-based remote sensing provides near-continuous cloud measurements and has been widely used to investigate cloud properties and evaluate satellite observations [16][17][18][19][20][21][22][23][24]. The Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, located in Lamont, Oklahoma, is heavily instrumented, including the Raman lidar (RL) [25][26][27] and the millimeter cloud radar (MMCR) [28][29][30][31] and the Ka-band Zenith radar (KAZR) [32]. The RL operates at 355 nm and thus can detect all types of hydrometeors when the lidar beam can penetrate to the locations of hydrometeors.…”

Section: Introductionmentioning

confidence: 99%

“…The lidar beam, however, can become fully attenuated during the period with optically thick clouds, which are not uncommon [27]. On the other hand, the millimeter-wavelength radar can penetrate all cloud types (except during periods of very heavy rains) but have lower detection sensitivities, thus leaving some clouds undetected, especially those with small cloud optical depth (COD) [28,29,32,33]. Borg et al [34] investigated the sensitivity of the MMCR to optically thin single-layer ice Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

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An Investigation of the Ice Cloud Detection Sensitivity of Cloud Radars Using the Raman Lidar at the ARM SGP Site

et al. 2022

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The ice cloud detection sensitivity of the millimeter cloud radar (MMCR) and the Ka-band Zenith radar (KAZR) is investigated using a collocated Raman lidar (RL) at the Atmospheric Radiation Measurement Program Southern Great Plains site. Only profiles that are transparent to the RL with ice clouds only are considered in this study. The MMCR underestimates the RL ice cloud optical depth (COD) by 20%. The MMCR detects no ice clouds in 37% of the profiles. These profiles where ice cloud goes undetected by the MMCR typically contain very optically thin clouds, with a mean RL ice COD of 0.03. Higher ice cloud detection sensitivity is found for the KAZR, which underestimates the RL ice COD by 15%. The decrease in the ice COD bias for the KAZR compared to the MMCR is largely due to a decrease in the ice COD bias for the situation where the transparent profiles with ice clouds are detected by both the RL and cloud radar. The climatic net ice cloud radiative effects (CREs) from the RL at the top of the atmosphere (TOA) and the surface are 3.2 W m−2 and −0.6 W m−2, respectively. The ice CREs at the TOA and surface are underestimated for the MMCR by 0.7 W m−2 and 0.16 W m−2 (21% and 29%) and underestimated for the KAZR by 0.6 W m−2 and 0.14 W m−2 (17% and 24%). The ice clouds undetected by the cloud radars led to underestimating the climatic net cloud heating rates below 150 hPa by about 0–0.04 K day−1.

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Radar observations of MJO and Kelvin wave interactions during DYNAMO/CINDY2011/AMIE

2014

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Radar and sounding data collected during the Dynamics of the Madden-Julian Oscillation (DYNAMO), the Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY2011), and the Atmospheric Radiation Measurement (ARM) Madden-Julian Oscillation Investigation Experiment (AMIE) field campaigns in the equatorial Indian Ocean to study the initiation of the Madden-Julian Oscillation (MJO) are used to examine the precipitation, cloud, and moisture characteristics during the MJO and convectively coupled Kelvin waves (KWs). Three MJO events and 10 KWs were identified from satellite data using different wave number frequency filters, although event identification varied based on the chosen range of latitude, frequency, and outgoing longwave radiation threshold. Radar and sounding data were composited for the three MJO events, four KWs during the active MJO, five KWs during the suppressed MJO, and one KW during the developing MJO. The MJO composite was generally consistent with past studies, although an increase in convective rain appeared to precede relative humidity increases at low-to middle-levels. The active and developing MJO KWs produced more rain and cloud than suppressed MJO KWs and had a secondary peak in stratiform rain potentially associated with subsynoptic-scale cloud clusters. The suppressed MJO KW composite displayed previously documented structure of vertical moisture buildup prior to the KW passage, whereas the developing MJO KW did not. The KW moisture signature during the active MJO was somewhat overwhelmed by the moist environment associated with the active MJO. Upper level moisture was enhanced after KW passage, regardless of MJO phase. However, upper level moisture was most enhanced after the developing MJO KW passage, providing deep tropospheric moisture that may have assisted MJO onset. Nonprecipitating upper level cloud and midlevel altocumulus/altostratus also persisted after most KW passages.

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The ARM Millimeter Wave Cloud Radars (MMCRs) and the Active Remote Sensing of Clouds (ARSCL) Value Added Product (VAP)

Cited by 65 publications

References 12 publications

Marine Boundary Layer Decoupling and the Stable Isotopic Composition of Water Vapor

Marine Boundary Layer Decoupling and the Stable Isotopic Composition of Water Vapor

An Investigation of the Ice Cloud Detection Sensitivity of Cloud Radars Using the Raman Lidar at the ARM SGP Site

Radar observations of MJO and Kelvin wave interactions during DYNAMO/CINDY2011/AMIE

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