Frequency Scaling Relying on the Estimation of the Drop Size Distribution from Dual Frequency Beacon Measurements

2021 ◽  
Author(s):  
Julien Queyrel ◽  
Liz-Angelica Ramos-Medina ◽  
Laurent Castanet
Keyword(s):  
Size Distribution ◽  
Drop Size ◽  
Drop Size Distribution ◽  
Dual Frequency ◽  
Frequency Scaling

scholarly journals Validation of GPM Rainfall and Drop Size Distribution Products through Disdrometers in Italy

2021 ◽  
Vol 13 (11) ◽  
pp. 2081
Author(s):  
Elisa Adirosi ◽  
Mario Montopoli ◽  
Alessandro Bracci ◽  
Federico Porcù ◽  
Vincenzo Capozzi ◽  
...  
Keyword(s):  
Size Distribution ◽  
Drop Size ◽  
Global Scale ◽  
Drop Size Distribution ◽  
Single Frequency ◽  
Dual Frequency ◽  
Near Surface ◽  
Active Sensor ◽  
Long Time ◽  
Global Precipitation

The high relevance of satellites for collecting information regarding precipitation at global scale implies the need of a continuous validation of satellite products to ensure good data quality over time and to provide feedback for updating and improving retrieval algorithms. However, validating satellite products using measurements collected by sensors at ground is still a challenging task. To date, the Dual-frequency Precipitation Radar (DPR) aboard the Core Satellite of the Global Precipitation Measurement (GPM) mission is the only active sensor able to provide, at global scale, vertical profiles of rainfall rate, radar reflectivity, and Drop Size Distribution (DSD) parameters from space. In this study, we compare near surface GPM retrievals with long time series of measurements collected by seven laser disdrometers in Italy since the launch of the GPM mission. The comparison shows limited differences in the performances of the different GPM algorithms, be they dual- or single-frequency, although in most cases, the dual-frequency algorithms present the better performances. Furthermore, the agreement between satellite and ground-based estimates depends on the considered precipitation variable. The agreement is very promising for rain rate, reflectivity factor, and the mass-weighted mean diameter (Dm), while the satellite retrievals need to be improved for the normalized gamma DSD intercept parameter (Nw).

scholarly journals Retrieving Rain Drop Size Distribution Moments from GPM Dual-Frequency Precipitation Radar

2021 ◽  
Vol 13 (22) ◽  
pp. 4690
Author(s):  
Merhala Thurai ◽  
Viswanathan Bringi ◽  
David Wolff ◽  
David A. Marks ◽  
Patrick N. Gatlin ◽  
...  
Keyword(s):  
Size Distribution ◽  
Drop Size ◽  
Drop Size Distribution ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Distribution Moments ◽  
The Us ◽  
Rain Drop ◽  
Global Precipitation Mission ◽  
Global Precipitation

A novel method for retrieving the moments of rain drop size distribution (DSD) from the dual-frequency precipitation radar (DPR) onboard the global precipitation mission satellite (GPM) is presented. The method involves the estimation of two chosen reference moments from two specific DPR products, namely the attenuation-corrected Ku-band radar reflectivity and (if made available) the specific attenuation at Ka-band. The reference moments are then combined with a function representing the underlying shape of the DSD based on the generalized gamma model. Simulations are performed to quantify the algorithm errors. The performance of methodology is assessed with two GPM-DPR overpass cases over disdrometer sites, one in Huntsville, Alabama and one in Delmarva peninsula, Virginia, both in the US. Results are promising and indicate that it is feasible to estimate DSD moments directly from DPR-based quantities.

Do the rain microphysics provide information on the overlying ice cloud?

2020 ◽  
Author(s):  
Kamil Mroz ◽  
Alessandro Battaglia ◽  
Stefan Kneifel ◽  
Jose Dias Neto
Keyword(s):  
Remote Sensing ◽  
Size Distribution ◽  
Drop Size ◽  
Microwave Remote Sensing ◽  
Drop Size Distribution ◽  
Radar Signal ◽  
Dual Frequency ◽  
Melting Layer ◽  
Sensing Applications ◽  
Melting Region

<p>This study investigates to what degree the information about the Drop Size Distribution (DSD) of rain can be used to narrow down uncertainty associated with complex ice microphysics. For this purpose, measurements from vertically pointing multi-frequency Doppler radar are thoroughly analysed. Linear Depolarization Ratio information is used to unambiguously separate hydrometeor phases. Within radar volumes where pure rain is identified multi-frequency Doppler spectra are utilised to retrieve a binned DSD with a high degree of confidence (Tridon et al. 2017). By assuming no breakup and negligible interaction between melting particles (Szyrmer and Zawadzki 1999, Olson et al. 2001, Matrosov 2008) the rain drop size distribution closest to the melting region is used to predict the particle size distribution (PSD) in the overlying snow. With these assumptions the resulting shape of the ice PSD depends solely on the hydrodynamical properties of snow that are dictated by its microphysics.  Several ice models are considered in the analysis, ranging from aggregates of columns, dendrites, needles and plates to different stages of rimed snow. Their scattering properties are simulated with Self-Similar-Rayleigh-Gans approximation (Leinonen et al. 2018) whereas falling velocities are modelled after Khvorostyanov and Curry (2005). Doppler spectra are simulated for the predicted ice PSD and compared to the measurements above the melting region. Results suggest that, if appropriate snow model used, the predicted reflectivity differs by less than 3 dB from the measured values as has been tentatively suggested by Fabry and Zawadzki (1995).</p><p>Tridon, F., A. Battaglia, E. Luke, P. Kollias, 2017. Rain retrieval from dual-frequency radar Doppler spectra: validation and potential for a midlatitude precipitating case study. Q. J. Roy. Meteorol. Soc. 143, 1364-1380. DOI: 10.1002/qj.3010</p><p>Szyrmer, W. and I. Zawadzki, 1999: Modeling of the Melting Layer. Part I: Dynamics and Microphysics. J. Atmos. Sci., 56, 3573–3592, https://doi.org/10.1175/1520-0469(1999)056<3573:MOTMLP>2.0.CO;2</p><p>S. Olson, P. Bauer, N. F. Viltard, D. E. Johnson, W-K. Tao, R. Meneghini, and L. Liao, “A melting layer model for passive/active microwave remote sensing applications—Part I: Model formulation and comparisons with observations,” J. Appl. Meteorol., vol. 40, no. 7, pp. 1145–1163, Jul. 2001</p><p>Y. Matrosov, "Assessment of Radar Signal Attenuation Caused by the Melting Hydrometeor Layer," in IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 4, pp. 1039-1047, April 2008. doi: 10.1109/TGRS.2008.915757</p><p>Fabry, F., and I. Zawadzki, 1995: Long-term radar observations of the melting layer of precipitation and their interpretation. J. Atmos. Sci., 52, 838–851.</p><p>Jussi, Leinonen, Kneifel, Stefan, Hogan, Robin J.. Evaluation of the Rayleigh–Gans approximation for microwave scattering by rimed snowflakes. Q J R Meteorol Soc 2018; 144 ( Suppl. 1): 77– 88. https://doi.org/10.1002/qj.3093</p>

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