Marc Schleiss

Quantification and Modeling of Wet-Antenna Attenuation for Commercial Microwave Links

Data from a new experimental setup deployed in Dübendorf (Switzerland) are used to quantify and model the magnitude and dynamics of the wet-antenna attenuation (WAA) affecting a 1.85-km commercial microwave link at 38 GHz. The results show that the WAA exhibits the following properties: 1) It is bounded by a maximum value of about 2.3 dB; 2) it increases exponentially toward 2.3 dB during the first 5-20 min of rainfall; and 3) it decreases exponentially as soon as the rain stops. A new dynamic WAA model that reproduces these three features and can be calibrated using solely link measurements is proposed. Its performance is evaluated at different temporal resolutions and compared with other wet-antenna models from the literature. The results show that the dynamic model outperforms all other models and significantly reduces the uncertainty of the retrieved path-averaged rain rates.

Stochastic simulation of intermittent rainfall using the concept of “dry drift”†

A stochastic rainfall simulator based on the concept of “dry drift” is proposed. It is characterized by a new and nonstationary representation of rainfall in which the average rain rate (in log-space) depends on the distance to the closest surrounding dry areas. The result is a more realistic transition between dry and rainy areas and a better distribution of low and high rain rates inside the simulated rainy areas. The proposed approach is very general and can be used to simulate both unconditional and conditional rain rate time series, two-dimensional fields, and space-time fields. The parameterization is intuitive and can be done using time series and/or radar rain-rate maps. Several examples illustrating the simulators capabilities are given. The results show that the simulated time series and rain rate fields look realistic and that they are difficult to distinguish from real observations.

Stochastic Simulation of Intermittent DSD Fields in Time

A method for the stochastic simulation of (rain)drop size distributions (DSDs) in space and time using geostatistics is presented. At each pixel, the raindrop size distribution is described by a Gamma distribution with two or three stochastic parameters. The presence or absence of rainfall is modeled using an indicator field. Separable space-time variograms are used to estimate and reproduce the spatial and temporal structures of all these parameters. A simple and user-oriented procedure for the parameterization of the simulator is proposed. The only data required are DSD time series and radar rain-rate (or reflectivity) measurements. The proposed simulation method is illustrated for both frontal and convective precipitation using real data collected in the vicinity of Lausanne, Switzerland. The spatial and temporal structures of the simulated fields are evaluated and validated using DSD measurements from eight independent disdrometers.

Nonstationarity in Intermittent Rainfall: The “Dry Drift”

A particular aspect of the nonstationary nature of intermittent rainfall is investigated. It manifests itself in the fact that the average rain rate varies with the distance to the surrounding dry areas. The authors call this fundamental link between the rainfall intensity and the rainfall occurrence process the ‘‘dry drift.’’ Using high-resolution radar rain-rate maps and disdrometer data, they show how the dry drift affects the structure and the variability of intermittent rainfall fields. They provide a rigorous geostatistical framework to describe it and propose an extension of the concept to more general quantities like the (rain)drop size distribution.

Spatial downscaling of precipitation using adaptable random forests

This paper introduces Prec-DWARF (Precipitation Downscaling With Adaptable Random Forests), a novel machine-learning based method for statistical downscaling of precipitation. Prec-DWARF sets up a nonlinear relationship between precipitation at fine resolution and covariates at coarse/fine resolution, based on the advanced binary tree method known as Random Forests (RF). In addition to a single RF, we also consider a more advanced implementation based on two independent RFs which yield better results for extreme precipitation. Hourly gauge-radar precipitation data at 0.125° from NLDAS-2 are used to conduct synthetic experiments with different spatial resolutions (0.25°, 0.5° and 1°). Quantitative evaluation of these experiments demonstrates that Prec-DWARF consistently outperforms the baseline (i.e., bi-linear interpolation in this case) and can reasonably reproduce the spatial and temporal patterns, occurrence and distribution of observed precipitation fields. However, Prec-DWARF with a single RF significantly underestimates precipitation extremes and often cannot correctly recover the fine-scale spatial structure, especially for the 1° experiments. Prec-DWARF with a double RF exhibits improvement in the simulation of extreme precipitation as well as its spatial and temporal structures, but variogram analyses show that the spatial and temporal variability of the downscaled fields are still strongly underestimated. Covariate feature importance analysis shows that the most important predictors for the downscaling are the coarse scale precipitation values over adjacent grid cells as well as the distance to the closest dry grid cell (i.e., the dry drift). The encouraging results demonstrate the potential of Prec-DWARF and machine-learning based techniques in general for the statistical downscaling of precipitation. This article is protected by copyright. All rights reserved.

Stochastic Space–Time Disaggregation of Rainfall into DSD fields

AbstractA stochastic method to disaggregate rain rate fields into drop size distribution (DSD) fields is proposed. It is based on a previously presented DSD simulator that has been modified to take into account prescribed block-averaged rain rate values at a coarser scale. The integral quantity used to drive the disaggregation process can be the rain rate, the radar reflectivity, or any variable directly related to the DSD. The proposed method is illustrated and qualitatively evaluated using radar rain rate data provided by MeteoSwiss for two rain events of very contrasted type (stratiform versus convective). The evaluation shows that both types of rainfall are correctly disaggregated, although the general agreement in terms of rain rate distributions, intermittency, and space–time structures is much better for the stratiform case. Possible extensions and generalizations of the technique (e.g., using radar reflectivities at two different frequencies or polarizations to drive the disaggregation process) ar...

Two Simple Metrics for Quantifying Rainfall Intermittency: The Burstiness and Memory of Interamount Times

AbstractPrecipitation displays a remarkable variability in space and time. An important yet poorly documented aspect of this variability is intermittency. In this paper, a new way of quantifying intermittency based on the burstiness B and memory M of interamount times is proposed. The method is applied to a unique dataset of 325 high-resolution rain gauges in the United States and Europe. Results show that the M–B diagram provides useful insight into local precipitation patterns and can be used to study intermittency over a wide range of temporal scales. It is found that precipitation tends to be more intermittent in warm and dry climates with the largest observed values in the southwest of the United States (i.e., California, Nevada, Arizona, and Texas). Low-to-moderate values are reported for the northeastern United States, the United Kingdom, the Netherlands, and Germany. In the second half of the paper, the new metrics are applied to daily rainfall data for 1954–2013 to investigate regional trends in ...

A Method to Estimate the 3D–Time Structure of the Raindrop Size Distribution Using Radar and Disdrometer Data*

AbstractA geostatistical method to quantify the small-scale 3D–time structure of the drop size distribution (DSD) from the ground level up to the melting layer using radar and disdrometer data is presented. First, 3D–time radar reflectivity fields are used to estimate the large-scale properties of a rain event, such as the apparent motion, spatial anisotropy, and temporal innovation. The retrieved quantities are then combined with independent disdrometer time series to estimate the 3D–time variogram of each DSD parameter. A key point in the procedure is the use of a new metric for measuring distances in moving anisotropic rainfall fields. This metric has the property of being invariant with respect to the specific rainfall parameter being considered, that is, it is identical for the radar reflectivity, rain rate, mean drop diameter, drop concentration, or any other weighted moment of the DSD. Evidence is shown of this fact and some illustrations for a stratiform event in southern France and a convective c...

Scaling and Distributional Properties of Precipitation Interamount Times

AbstractThe scaling and distributional properties of precipitation interamount times (IATs) are investigated using 10 years of high-resolution rain gauge observations from the U.S. Climate Reference Network. Results show that IATs above 200 mm tend to be approximately uncorrelated and normally distributed. As one moves toward smaller scales, autocorrelation and skewness increase and distributions progressively evolve into Weibull, Gamma, lognormal, and Pareto. This procession is interpreted as a sign of increasing complexity from large to small scales in a system composed of many interacting components. It shows that, as one approaches finer scales, IATs take over more of the characteristics of power-law distributions and (multi)fractals. Regression analysis on the log moments reveals that IATs generally exhibit better scaling, that is, smaller departures from multifractality, than precipitation amounts over the same range of scales. The improvement is attributed to the fact that IATs, unlike rainfall rat...

Geostatistical simulation of two-dimensional fields of raindrop size distributions at the meso-¿ scale

The large variability of the drop size distribution (DSD) in space and time must be taken into account to improve remote sensing of precipitation. The ability to simulate a large number of 2D fields of DSD sharing the same statistical properties provides a very useful simulation framework that nicely complements experimental approaches based on DSD ground measurements. These simulations can be used to investigate radar beam propagation through rain and to evaluate different radar retrieval techniques. The proposed approach uses geostatistics to provide structural analysis and stochastic simulation of DSD fields. First, the DSD is assumed to follow a Gamma distribution with three parameters. Therefore, 2D fields of DSDs can be described as a three-component multivariate random function. Such 2D fields are normalized using a Gaussian anamorphosis and simulated by taking advantage of fast multivariate Gaussian simulation algorithms. Variograms and cross-variograms are used to generate fields with identical spatial structure that are consistent with the observations. To assess the proposed approach, the method is applied to data collected during intense Mediterranean rainfall. Taylor’s hypothesis is assumed to convert time series into 1D range profiles. The anisotropy of the DSD fields is derived from radar reflectivity measurements collected over the same area during the same event. A large number of DSD fields are generated and the corresponding reflectivity fields are derived. The results of the simulations are in good agreement with respect to the mean, the standard deviation and the spatial structure, demonstrating the promising potential of the proposed stochastic model of DSD fields