An experiment to measure the spatial variability of rain drop size distribution using sixteen laser disdrometers

Tapiador, Francisco J.; Checa, Ramiro; Castro, Manuel de

doi:10.1029/2010gl044120

Cited by 97 publications

(80 citation statements)

References 23 publications

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“…Germann et al, 2009;Rossa et al, 2009), the investigation of the usefulness of additional observations on the spacetime structure of rainfall (e.g. Lee and Zawadzki, 2005;Lee et al, 2007;Berenguer and Zawadzki, 2008;Niu et al, 2010;Tapiador et al, 2010) and a detailed understanding of radar signals and their interaction with hydrometeors to retrieve their microphysical properties (e.g. Dotzek and Beheng, 2001;Brandes et al, 2004aBrandes et al, , 2004bPeters et al, 2005;Cao et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Quantitative precipitation estimation based on high-resolution numerical weather prediction and data assimilation with WRF – a performance test

Bauer¹,

Schwitalla²,

Wulfmeyer³

et al. 2015

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T Quantitative precipitation estimation and forecasting (QPE and QPF) are among the most challenging tasks in atmospheric sciences. In this work, QPE based on numerical modelling and data assimilation is investigated. Key components are the Weather Research and Forecasting (WRF) model in combination with its 3D variational assimilation scheme, applied on the convection-permitting scale with sophisticated model physics over central Europe. The system is operated in a 1-hour rapid update cycle and processes a large set of in situ observations, data from French radar systems, the European GPS network and satellite sensors. Additionally, a free forecast driven by the ECMWF operational analysis is included as a reference run representing current operational precipitation forecasting. The verification is done both qualitatively and quantitatively by comparisons of reflectivity, accumulated precipitation fields and derived verification scores for a complex synoptic situation that developed on 26 and 27 September 2012. The investigation shows that even the downscaling from ECMWF represents the synoptic situation reasonably well. However, significant improvements are seen in the results of the WRF QPE setup, especially when the French radar data are assimilated. The frontal structure is more defined and the timing of the frontal movement is improved compared with observations. Even mesoscale bandlike precipitation structures on the rear side of the cold front are reproduced, as seen by radar. The improvement in performance is also confirmed by a quantitative comparison of the 24-hourly accumulated precipitation over Germany. The mean correlation of the model simulations with observations improved from 0.2 in the downscaling experiment and 0.29 in the assimilation experiment without radar data to 0.56 in the WRF QPE experiment including the assimilation of French radar data.

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Section: Introductionmentioning

confidence: 99%

Quantitative precipitation estimation based on high-resolution numerical weather prediction and data assimilation with WRF – a performance test

Bauer¹,

Schwitalla²,

Wulfmeyer³

et al. 2015

Tellus A: Dynamic Meteorology and Oceanography

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“…The raindrop size distribution (DSD) describes the microstructure of liquid precipitation and is highly variable (Jameson and Kostinski, 2001;Uijlenhoet et al, 2003;Tapiador et al, 2010;Jaffrain and Berne, 2012). The DSD is measured at the point scale by disdrometers.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of the raindrop size distribution from polarimetric radar data using double-moment normalisation

Raupach¹,

Berne²

2017

Atmos. Meas. Tech.

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Abstract.A new technique for estimating the raindrop size distribution (DSD) from polarimetric radar data is proposed. Two statistical moments of the DSD are estimated from polarimetric variables, and the DSD is reconstructed using a double-moment normalisation. The technique takes advantage of the relative invariance of the double-moment normalised DSD. The method was tested using X-band radar data and networks of disdrometers in three different climatic regions. Radar-derived estimates of the DSD compare reasonably well to observations. In the three tested domains, in terms of DSD moments, rain rate, and characteristic drop diameter, the proposed method performs similarly to and often better than a state-of-the-art DSD-retrieval technique. The approach is flexible because no specific DSD model is prescribed. In addition, a method is proposed to treat noisy radar data to improve DSD-retrieval performance with radar measurements.

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“…To minimize the spatial decorrelation with distance (Berne et al 2012;Tapiador et al 2010;Tokay and Bashor 2010), the disdrometers were placed as close as possible within a ;6 m 2 area (Fig. 1).…”

Section: A Setupmentioning

confidence: 99%

“…1). For such small distances, the spatial decorrelation is negligible for the purposes of the experiment [see the results in Tapiador et al (2010)] and turbulence is assumed to equally affect all the instruments; the same applies to the individual instrument error factor. (Toledo, Spain).…”

Section: A Setupmentioning

confidence: 99%

On the Optimal Measuring Area for Pointwise Rainfall Estimation: A Dedicated Experiment with 14 Laser Disdrometers

Tapiador

Navarro

Moreno

et al. 2017

Journal of Hydrometeorology

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Laser disdrometers measure the particle size distribution (PSD) of hydrometeors through a small cross-sectional (tens of square centimeters) surface. Such a limited area induces a sampling effect in the estimates of the PSD, which translates to error in the reflectivity-rain-rate (Z-R) relationship used for ground radar estimates of rainfall, estimates of kinetic energy of precipitation, and any other hydrometeorological application relying on particle size information. Here, the results of a dedicated experiment to estimate the extent of the effect of limited area sampling of rainfall are presented. Using 14 Parsivel, version 1 (Parsivel-1), disdrometers placed within 6 m 2 , it was found that the combined area of at least seven disdrometers is required for the estimates to start converging to a stable value. The results can be used to quantify the degree of over-/underestimation of precipitation parameters for a single instrument due to the limited collecting area effect. It has been found that a single disdrometer may underestimate instantaneous rain rate by 70%.

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An experiment to measure the spatial variability of rain drop size distribution using sixteen laser disdrometers

Cited by 97 publications

References 23 publications

Quantitative precipitation estimation based on high-resolution numerical weather prediction and data assimilation with WRF – a performance test

Quantitative precipitation estimation based on high-resolution numerical weather prediction and data assimilation with WRF – a performance test

Retrieval of the raindrop size distribution from polarimetric radar data using double-moment normalisation

On the Optimal Measuring Area for Pointwise Rainfall Estimation: A Dedicated Experiment with 14 Laser Disdrometers

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