Partitioning of cloud water and rainwater content by ground‐based observations with the Advanced Microwave Radiometer for Rain Identification (ADMIRARI) in synergy with a micro rain radar

Saavedra, Pablo; Battaglia, Alessandro; Simmer, Clemens

doi:10.1029/2011jd016579

Cited by 21 publications

(14 citation statements)

References 47 publications

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“…It is easy to operate due to its compact, light design and plug-and-play installation and is increasingly used for monitoring purposes and for studying liquid precipitation (Peters et al, 2002(Peters et al, , 2005Yuter et al, 2008). In addition to that, MRRs were used to study the bright band (Cha et al, 2009) and supported the passive microwave radiometer ADMIRARI in partitioning cloud and rain liquid water (Saavedra et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Improved Micro Rain Radar snow measurements using Doppler spectra post-processing

2012

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Abstract. The Micro Rain Radar 2 (MRR) is a compact Frequency Modulated Continuous Wave (FMCW) system that operates at 24 GHz. The MRR is a low-cost, portable radar system that requires minimum supervision in the field. As such, the MRR is a frequently used radar system for conducting precipitation research. Current MRR drawbacks are the lack of a sophisticated post-processing algorithm to improve its sensitivity (currently at +3 dBz), spurious artefacts concerning radar receiver noise and the lack of high quality Doppler radar moments. Here we propose an improved processing method which is especially suited for snow observations and provides reliable values of effective reflectivity, Doppler velocity and spectral width. The proposed method is freely available on the web and features a noise removal based on recognition of the most significant peak. A dynamic dealiasing routine allows observations even if the Nyquist velocity range is exceeded. Collocated observations over 115 days of a MRR and a pulsed 35.2 GHz MIRA35 cloud radar show a very high agreement for the proposed method for snow, if reflectivities are larger than −5 dBz. The overall sensitivity is increased to −14 and −8 dBz, depending on range. The proposed method exploits the full potential of MRR's hardware and substantially enhances the use of Micro Rain Radar for studies of solid precipitation.

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Section: Introductionmentioning

confidence: 99%

Improved Micro Rain Radar snow measurements using Doppler spectra post-processing

2012

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“…Cloud‐resolving model simulations have been extensively applied to establish LUTs for developing retrieving algorithms, particularly when real measurements are not available [e.g., Tao et al ., ; Kummerow et al , ; Shige et al ., ; Olson et al ., ; Saavedra et al ., ; Min et al , ; Li et al , ]. In this study, the Advance Research WRF model version 3.4 was used to conduct cloud simulations.…”

Section: The Model Simulations Of Warm Rainsmentioning

confidence: 99%

“…Saavedra et al . [] used combined passive and active microwave measurements to retrieve rainwater path and cloud water path simultaneously. To observe the column‐integrated cloud‐only water path from rainy cloud is now possible from ground‐based measurements.…”

Section: Introductionmentioning

confidence: 99%

“…The cloud water content (CWC) is one of the key parameters to determine the onset and the growth rate of precipitation. The associated autoconversion and accretion processes [Beheng, 1994;Kessler, 1969;Rogers and Yau, 1989] are parameterized as nonlinear functions of CWC in most state-of-art weather and climate numerical models. Small errors of CWC may introduce amplified bias in the simulations of precipitation [Lebsock et al, 2013;Weber and Quaas, 2012].…”

Section: Introductionmentioning

confidence: 99%

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Estimating the vertical profiles of cloud water content in warm rain clouds

Guo

Min

et al. 2015

JGR Atmospheres

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The cloud water content (CWC) in rainy clouds is a crucial parameter to determine the onset and the growth rate of precipitation and to quantify the associated latent heating rate. No direct retrieval of CWC in rainy cloud from satellite observations is reported due to the difficulties of separating cloud particles from precipitation‐sized particles. However, based on multiple cloud simulations from the Weather and Research Forecasting (WRF) model, we have found that the CWC profile in warm rains can be well determined by three macrophysical cloud properties of cloud water path (CWP), cloud top height (CTH), and cloud bottom height (CBH). The CBH can be estimated using CWP, CTH, and near‐surface rain rate. We proposed an algorithm with a look‐up table for estimating the CWC profile using CWP, CTH, and near‐surface rain rate as inputs. The performance of this algorithm was tested with WRF model simulations and a real drizzle case observed by the CloudSat satellite. Testing verified that the algorithm can retrieve the vertical distribution of CWC correctly with few errors at different spatiotemporal scales. In addition, the algorithm is not confined to particular microphysics schemes and is valid for multiple cloud systems in different areas over the world. This algorithm is expected to improve current knowledge of cloud water content in rainy clouds.

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“…Radars provide information on winds, particle size distributions, precipitation and cloud, while radiometers will give information on the profiles of temperature and water vapour, and the integrated amount of liquid water content. Thus, the complementary information of these instruments can lead to a better understanding and characterisation of clouds (Frisch et al, 1995;Shupe et al, 2008;Saavedra et al, 2012) and thermodynamic profiles (Westwater et al, 1983;Bianco et al, 2005;Gaffard et al, 2003;Löhnert et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Biases caused by the instrument bandwidth and beam width on simulated brightness temperature measurements from scanning microwave radiometers

et al. 2013

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Abstract. More so than the traditional fixed radiometers, the scanning radiometer requires a careful design to ensure high quality measurements. Here the impact of the radiometer characteristics (e.g., antenna beam width and receiver bandwidth) and atmospheric propagation (e.g. curvature of the Earth and vertical gradient of refractive index) on scanning radiometer measurements are presented. A forward radiative transfer model that includes all these effects to represent the instrument measurements is used to estimate the biases. These biases are estimated using differences between the measurement with and without these characteristics for three commonly used frequency bands: K, V and W-band. The receiver channel bandwidth errors are less important in K-band and W-band. Thus, the use of a wider bandwidth to improve detection at low signal-to-noise conditions is acceptable at these frequencies. The biases caused by omitting the antenna beam width in measurement simulations are larger than those caused by omitting the receiver bandwidth, except for V-band where the bandwidth may be more important in the vicinity of absorption peaks. Using simple regression algorithms, the effects of the bandwidth and beam width biases in liquid water path, integrated water vapour, and temperature are also examined. The largest errors in liquid water path and integrated water vapour are associated with the beam width errors.

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Partitioning of cloud water and rainwater content by ground‐based observations with the Advanced Microwave Radiometer for Rain Identification (ADMIRARI) in synergy with a micro rain radar

Cited by 21 publications

References 47 publications

Improved Micro Rain Radar snow measurements using Doppler spectra post-processing

Improved Micro Rain Radar snow measurements using Doppler spectra post-processing

Estimating the vertical profiles of cloud water content in warm rain clouds

Biases caused by the instrument bandwidth and beam width on simulated brightness temperature measurements from scanning microwave radiometers

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