Calibration of the 2007–2017 record of Atmospheric Radiation Measurements cloud radar observations using CloudSat

Kollias, Pavlos; Treserras, Bernat Puigdomènech; Protat, Alain

doi:10.5194/amt-12-4949-2019

Cited by 34 publications

(31 citation statements)

References 36 publications

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“…Observations collected by the second generation Ka‐band ARM Zenith Radar (KAZR2; enakazrgeC1.a1) and the Vaisala Ceilometer lidar (enaceilC1.b1) are noise and clutter filtered, calibrated, and combined to characterize cloud and precipitation at 30‐m, 2‐s spatiotemporal resolution at all heights between 60 m and 4 km above the surface where both sensors record signals above their detection limit (Kollias et al, 2019; Lamer et al, 2019). The maximum height of 4 km is selected to maintain our focus on marine boundary layer clouds; to avoid contamination from deep clouds extending beyond the boundary layer or midlevel clouds precipitating in the boundary layer, hourly periods that include more than 1‐min worth of radar reflectivity echoes at 4 km are not used in this analysis.…”

Section: Data Sets and Methodologymentioning

confidence: 99%

Relationships Between Precipitation Properties and Large‐Scale Conditions During Subsidence at the Eastern North Atlantic Observatory

2020

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Three years of reanalysis and ground‐based observations collected at the Eastern North Atlantic (ENA) observatory are analyzed to document the properties of rain and boundary layer clouds and their relationship with the large‐scale environment during general subsidence conditions and following cold front passages. Clouds in the wake of cold fronts exhibit on average a 10% higher propensity to precipitate and higher rain‐to‐cloud fraction than cloud found in general subsidence conditions. Similarities in the seasonal cycle of rain and of large‐scale properties suggest that the large‐scale conditions created by the cold front passage are responsible for the unique properties of the rain forming in its wake. The identification of monotonic relationships between rain‐to‐cloud fraction and rain rate with surface forcing and boundary layer stability parameters as well as between virga base height with stability and humidity measures further supports that large‐scale conditions impact precipitation variability. That being said, these relationships between the large‐scale and rain properties are less clear than those established between cloud and rain properties, suggesting that cloud macrophysics have a more direct impact on the properties of rain than the large‐scale environment. The applicability of previously documented relationships between cloud thickness and rain properties is tested and the relationships adjusted to accommodate the complex shallow clouds and melting precipitation observed to occur in the ENA region. Establishing these relationships opens up opportunities for parametrization development and suggests that a realistic representation of precipitation properties in models relies on the accurate representation of both clouds and the large‐scale environment.

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Section: Data Sets and Methodologymentioning

confidence: 99%

Relationships Between Precipitation Properties and Large‐Scale Conditions During Subsidence at the Eastern North Atlantic Observatory

2020

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“…Also plotted are the CloudSat radar reflectivity (c) raw, (d) for significant returns (CPR_mask ≥ 5), (e) for echoes deemed very weak and stronger (CPR_mask ≥ 6), and (f) for echoes deemed weak and stronger (CPR_mask ≥ 20). calibrated using observations collected during light precipitation events by the collocated surface-based Parsivel laser disdrometer as well as using observations from the CloudSat CPR collected over a small radius around the site following Kollias et al (2019).…”

Section: Arm Ground-based Observationsmentioning

confidence: 99%

Mind the gap – Part 1: Accurately locating warm marine boundary layer clouds and precipitation using spaceborne radars

et al. 2020

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Abstract. Ground-based radar observations show that, over the eastern North Atlantic, 50 % of warm marine boundary layer (WMBL) hydrometeors occur below 1.2 km and have reflectivities of < −17 dBZ, thus making their detection from space susceptible to the extent of surface clutter and radar sensitivity. Surface clutter limits the ability of the CloudSat cloud profiling radar (CPR) to observe the true cloud base in ∼52 % of the cloudy columns it detects and true virga base in ∼80 %, meaning the CloudSat CPR often provides an incomplete view of even the clouds it does detect. Using forward simulations, we determine that a 250 m resolution radar would most accurately capture the boundaries of WMBL clouds and precipitation; that being said, because of sensitivity limitations, such a radar would suffer from cloud cover biases similar to those of the CloudSat CPR. Observations and forward simulations indicate that the CloudSat CPR fails to detect 29 %–43 % of the cloudy columns detected by ground-based sensors. Out of all configurations tested, the 7 dB more sensitive EarthCARE CPR performs best (only missing 9.0 % of cloudy columns) indicating that improving radar sensitivity is more important than decreasing the vertical extent of surface clutter for measuring cloud cover. However, because 50 % of WMBL systems are thinner than 400 m, they tend to be artificially stretched by long sensitive radar pulses, hence the EarthCARE CPR overestimation of cloud top height and hydrometeor fraction. Thus, it is recommended that the next generation of space-borne radars targeting WMBL science should operate interlaced pulse modes including both a highly sensitive long-pulse mode and a less sensitive but clutter-limiting short-pulse mode.

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“…Note that the methods described in this study are also applicable to any other W-band cloud radar (FMCW or pulsed) with a proper rain mitigation system. An overview of the used radar design, operation, and the budget calibration was described in Küchler et al (2017). Typical radar specifications are summarized in Table 1.…”

Section: Radarsmentioning

confidence: 99%

“…Maintaining accurate reflectivity measurements requires temperature stabilization of the radar housing, protecting antennas and radomes from water (Hogan et al, 2003;Delanoë et al, 2016), frequent automatic internal calibrations, and regular maintenance (Chandrasekar et al, 2015). Cloud radar manufacturers typically apply a budget calibration, i.e., characterizing individual radar components separately during manufacturing and taking the results into account in the reflectivity calculations Küchler et al, 2017;Ewald et al, 2019). The budget calibration has several shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the reflectivity calibration of W-band radars based on observations in rain

2020

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Abstract. This study presents two methods for evaluating the reflectivity calibration of W-band cloud radars. Both methods use natural rain as a reference target. The first approach is based on a self-consistency method of polarimetric radar variables, which is widely used in the precipitation radar community. As previous studies pointed out, the method cannot be directly applied to higher frequencies where non-Rayleigh scattering effects and attenuation have a nonnegligible influence on radar variables. The method presented here solves this problem by using polarimetric Doppler spectra to separate backscattering and propagational effects. New fits between the separated radar variables allow one to estimate the absolute radar calibration using a minimization technique. The main advantage of the self-consistency method is its lower dependence on the spatial variability in radar drop size distribution (DSD). The estimated uncertainty of the method is ±0.7 dB. The method was applied to three intense precipitation events, and the retrieved reflectivity offsets were within the estimated uncertainty range. The second method is an improvement on the conventional disdrometer-based approach, where reflectivity from the lowest range gate is compared to simulated reflectivity using surface disdrometer observations. The improved method corrects, first, for the time lag between surface DSD observations and the radar measurements at a certain range. In addition, the effect of evaporation of raindrops on their way towards the surface is mitigated. The disdrometer-based method was applied to 12 rain events observed by vertically pointed W-band radar and showed repeatable estimates of the reflectivity offsets at rain rates below 4 mm h−1 within ±0.9 dB. The proposed approaches can analogously be extended to Ka-band radars. Although very different in terms of complexity, both methods extend existing radar calibration evaluation approaches, which are inevitably needed for the growing cloud radar networks in order to provide high-quality radar observation to the atmospheric community.

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Calibration of the 2007–2017 record of Atmospheric Radiation Measurements cloud radar observations using CloudSat

Cited by 34 publications

References 36 publications

Relationships Between Precipitation Properties and Large‐Scale Conditions During Subsidence at the Eastern North Atlantic Observatory

Relationships Between Precipitation Properties and Large‐Scale Conditions During Subsidence at the Eastern North Atlantic Observatory

Mind the gap – Part 1: Accurately locating warm marine boundary layer clouds and precipitation using spaceborne radars

Evaluation of the reflectivity calibration of W-band radars based on observations in rain

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