Topic
Disdrometer
About: Disdrometer is a research topic. Over the lifetime, 930 publications have been published within this topic receiving 23092 citations.
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Proceedings Article•
01 Mar 2019TL;DR: In this article, long-term measurements from a vertical Doppler radar (MRR-2) and an optical disdrometer are used to obtain and compare the statistics of Drop Size Distributions (DSDs) in Madrid in periods of at least ten years.
Abstract: The Drop Size Distributions (DSDs) allow the characterization of rainfall phenomena and prediction of rain attenuation. In this paper, long-term measurements from a vertical Doppler radar (MRR-2) and an optical disdrometer are used to obtain and compare the statistics of DSDs in Madrid in periods of at least ten years. The process to obtain the DSDs from the optical disdrometer spectrum is analyzed in detail, providing recommendations about the most appropriate particles filtering. Rain attenuation is calculated from the DSDs obtained from both instruments and the results are compared with rain attenuation time series measured in a W-band horizontal radio link at 75 GHz and 85 GHz.
2 citations
TL;DR: It is concluded that building separate Z–R retrieval equations for regional wind direction classes should improve radar-based QPE, especially for stratiform rain events.
Abstract: Quantitative precipitation estimation (QPE) through remote sensing has to take rain microstructure into consideration, because it influences the relationship between radar reflectivity Z and rain intensity R. For this reason, separate equations are used to estimate rain intensity of convective and stratiform rain types. Here, we investigate whether incorporating synoptic scale meteorology could yield further QPE improvements. Depending on large-scale weather types, variability in cloud condensation nuclei and the humidity content may lead to variation in rain microstructure. In a case study for Bavaria, we measured rain microstructure at ten locations with laser-based disdrometers, covering a combined 18,600 h of rain in a period of 36 months. Rain was classified on a temporal scale of one minute into convective and stratiform based on a machine learning model. Large-scale wind direction classes were on a daily scale to represent the synoptic weather types. Significant variations in rain microstructure parameters were evident not only for rain types, but also for wind direction classes. The main contrast was observed between westerly and easterly circulations, with the latter characterized by smaller average size of drops and a higher average concentration. This led to substantial variation in the parameters of the radar rain intensity retrieval equation Z–R. The effect of wind direction on Z–R parameters was more pronounced for stratiform than convective rain types. We conclude that building separate Z–R retrieval equations for regional wind direction classes should improve radar-based QPE, especially for stratiform rain events.
2 citations
TL;DR: The use of the standard deviation σm of the drop mass distribution as one of the three parameters of raindrop size distribution (DSD) functions was introduced for application to disdrometer data in this article.
Abstract: Use of the standard deviation σm of the drop mass distribution as one of the three parameters of raindrop size distribution (DSD) functions was introduced for application to disdrometer dat...
2 citations
TL;DR: Wang et al. as mentioned in this paper collected data from the Tianshan Mountains, China, to assess various corresponding characteristics for a typical arid region in China across different seasons, rainfall types, and rainfall rate classes.
2 citations
TL;DR: In this paper , the authors examined the impact of DSD variability on the ability of satellite measurements to accurately estimate warm rainfall rates and found that the assumed DSD shape can have a large impact on retrieved rain rate.
Abstract:
A significant part of the uncertainty in satellite-based precipitation products stems from differing assumptions about drop size distributions (DSDs). Satellite radar-based retrieval algorithms rely on DSD assumptions that may be overly simplistic, while radiometers further struggle to distinguish cloud water from rain. We utilize the OceanRAIN-1.0 dataset to examine the impact of DSD variability on the ability of satellite measurements to accurately estimate warm rainfall rates. We use the binned disdrometer counts and a simple model of the atmosphere to simulate observations for three satellite architectures. Two are similar to existing instrument combinations on the GPM Core Observatory and CloudSat, while the third is a theoretical triple frequency radar/radiometer architecture. Using an optimal estimation framework, we find that the assumed DSD shape can have a large impact on retrieved rain rate. A 3-parameter normalized gamma DSD model is sufficient for describing and retrieving the DSDs observed in the OceanRAIN dataset. Assuming simpler single moment DSD models can lead to significant biases in retrieved rain rate, on the order of 100%. Differing DSD assumptions could thus plausibly explain a large portion of the disagreement in satellite-based precipitation estimates.
2 citations