Spatial interpolation of experimental raindrop size distribution spectra

Abstract

We present a new approach for spatial interpolation of experimental raindrop size distribution (DSD) spectra. The DSD is fundamental to the study and understanding of precipitation and its monitoring and modelling. It is measured insitu using disdrometers at point locations. Disdrometers provide a (non‐parametric) DSD spectrum in which drop concentrations are provided per class of drop diameter. Our approach uses geostatistics to estimate the same non‐parametric DSD at unmeasured locations. Non‐stationarity due to intermittency is taken into account through estimation of the dry drift of drop concentrations, using a rain occurrence field. Principal component analysis is used to express the DSD spectra in terms of uncorrelated components that can be interpolated independently at a requested point. These interpolated components can then be recombined into the full DSD. Estimation uncertainty for the interpolated DSD spectra is provided. Because all bulk rainfall variables can be calculated from the DSD and the entire DSD is estimated, the technique effectively interpolates all bulk variables at once. Leave‐one‐out testing shows that the technique estimates the DSD with minimal bias, and it is shown that the technique can be easily adapted to perform stochastic simulation of the non‐parametric DSD.

Estimating raindrop size distributions using microwave link measurements

“…For both the TS96 approach and the numerical retrieval, values higher than 20 occur regularly for both the µ and the Λ parameter, which is not consistent with what is typically found in the literature (e.g. Raupach and Berne (2016) for the Ardèche, Zhang et al (2003) for Florida and Atlas and Ulbrich (2006) for Kapingamarangi Atoll (western tropical Pacific)).…”

“…Our first test-case consists of a 2-dimensional interpolated DSD field based on polarimetric radar data as well as disdrometer data measured in the Ardèche, France as part of the HyMeX campaign (Raupach and Berne, 2016). This field was generated using an advection-based temporal interpolation technique (Raupach and Berne, 2017).…”

Comparison of precipitation measurements by OTT Parsivel<sup>2</sup> and Thies LPM optical disdrometers

“…Rainfall estimation by remote sensing, radar and satellite also rely on PSD information (Olsen et al, 1978;Atlas et al, 1999;Uijlenhoet and Sempere-Torres, 2006;Tapiador et al, 2017b). Disdrometer observations of PSD are also used to derive relationships between radar reflectivity and rainfall rates (known usually as Z-R relationships), despite the difficulties due to differences in altitude of the measurement (surface vs. cloud base) and the sensing area (a few cm 2 vs. km 2 ; Krajewski et al, 1998;Löffler-Mang and Blahak, 2001;Miriousky et al, 2004;Thurai and Bringi, 2008;Marzano et al, 2010;Jaffrain and Berne, 2012;Jameson et al, 2015;Raupach and Berne, 2016;Gires et al, 2016). Many of these studies took place within the Precipitation Measurement Missions helping the development of better sensors and algorithms for precipitation detection and quantification; some examples are Ioannidou et al (2016) for the Tropical Rainfall Measurement Mission (TRMM), Liao et al (2014) and Tan et al (2016) for the Global Precipitation Measurement mission (GPM), Adirosi et al (2016) for the Hydrological cycle in the Mediterranean Experiment (HyMex) or Calheiros and Machado (2014) for the Cloud Processes of the Main Precipitation Systems in Brazil (CHUVA) campaign.…”

The Role of Weather Types in Assessing the Rainfall Key Factors for Erosion in Two Different Climatic Regions

“…The influence of the Atlantic Ocean affects not only the number but also the size of raindrops, and this in turn affects other aspects as diverse as the erosive power of the raindrops [12][13][14] or radio wave propagation [15]. The use of disdrometers or other similar devices to detect the size and velocity of raindrops is therefore of considerable interest in order to measure variables such as the kinetic energy and momentum registered during a particular rain event [16][17][18].…”