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Marco Gabella and Giovanni Perona
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Marco Gabella, Sebastiano Pavone, and Giovanni Perona

Abstract

Theories on drop formation and quantitative rain estimation would require knowledge not only of the statistical size distribution of drops, but also of their statistical spatial distribution, which, in turn, determines the statistical fluctuations of the echoes detected by meteorological radar. Of particular interest is the question of whether such a spatial distribution can be assumed to be either statistically homogeneous or fractal. To analyze the spatial patterns of raindrops, a reasonable and immediate way of proceeding in the estimation of the fractal dimension is through the computation of the correlation integral. In any experimental observation of raindrop distribution, only a finite number of drops in a given space–time volume can be observed. Consequently, in this situation, the estimated value D of the fractal dimension differs from the true value D t because of systematic (s) and random (r) errors. This paper shows that these errors can be ascribed to the finite number of raindrops and to“edge effects.” The order of magnitude of the error components that affect the estimate of D is investigated using numerical simulations for the inference of r and s, given a “reference” spatial distribution with D t = 2. It has been found that the correlation dimension, estimated on the basis of a previous experimental observation (452 raindrops collected on a square blotting paper of 1.28 m × 1.28 m during a 1-s exposure to rain), could be compatible with a uniform random spatial distribution. The paper also presents the characteristics that new experimental setups have to possess to permit better estimates of the raindrop correlation dimension.

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Peter Speirs, Marco Gabella, and Alexis Berne

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The Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR) provides a unique set of three-dimensional radar precipitation estimates across much of the globe. Both terrain and climatic conditions can have a strong influence on the reliability of these estimates. Switzerland provides an ideal testbed to evaluate the performance of the DPR in complex terrain: it consists of a mixture of very complex terrain (the Alps) and the far flatter Swiss Plateau. It is also well instrumented, covered with a dense gauge network as well as a network of four dual-polarization C-band weather radars, with the same instrument network used in both the Plateau and the Alps. Here an evaluation of the GPM DPR rainfall rate products against the MeteoSwiss radar rainfall rate product for the first two years of the GPM DPR’s operation is presented. Errors in both detection and estimation are considered, broken down by terrain complexity, season, precipitation phase, precipitation type, and precipitation rate. Errors are considered both integrated across the entire domain and spatially, and consistent underestimation of precipitation by GPM is found. This rises to −51% in complex terrain in the winter, primarily due to the predominance of DPR measurements wholly in the solid phase, where problems are caused by lower reflectivities. The smaller vertical extent of precipitation in winter is also likely a cause. Both detection and estimation performance are found to be significantly better in summer than in winter, in liquid than in solid precipitation, and in flatter terrain than in complex terrain.

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Rebecca Gugerli, Marco Gabella, Matthias Huss, and Nadine Salzmann

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The snow water equivalent (SWE) is a key component for understanding changes in the cryosphere in high mountain regions. Yet, a reliable quantification at a high spatiotemporal resolution remains challenging in such environments. In this study, we investigate the potential of an operational weather radar–rain gauge composite (CombiPrecip) to infer the daily evolution of SWE on seven Swiss glaciers. To this end, we validate cumulative CombiPrecip estimates with glacier-wide manual SWE observations (snow probing, snow pits) obtained around the time of the seasonal peak during four winter seasons (2015–19). CombiPrecip underestimates the end-of-season snow accumulation by factors of 2.2 up to 3.7, depending on the glacier site. These factors are consistent over the four winter seasons. The regional variability can be mainly attributed to the empirical visibility of the Swiss radar network within the Alps. To account for the underestimation, we investigate three approaches to adjust CombiPrecip for the applicability to glacier sites. Thereby, we combine the factor of underestimation with a precipitation-phase parameterization. For further comparison, we apply a rain gauge catch-efficiency function based on wind speed. We validate these approaches with 14 manual point observations of SWE obtained on two glaciers during three winter seasons. All approaches show a similar improvement of CombiPrecip estimates. We conclude that CombiPrecip has great potential to estimate SWE on glaciers at a high temporal resolution, but further investigations are necessary to understand the regional variability of the bias throughout the Swiss Alps.

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Stefano Mariani, Christophe Accadia, Nazario Tartaglione, Marco Casaioli, Marco Gabella, Silas Chr Michaelides, and Antonio Speranza

Abstract

This paper presents a study performed within the framework of the European Union’s (EU) VOLTAIRE project (Fifth Framework Programme). Among other tasks, the project aimed at the integration of the Tropical Rainfall Measuring Mission (TRMM) data with ground-based observations and at the comparison between water fields (precipitation and total column water vapor) as estimated by multisensor observations and predicted by NWP models. In particular, the VOLTAIRE project had as one of its main objectives the goal of assessing the application of satellite-borne instrument measures to model verification. The island of Cyprus was chosen as the main “test bed,” because it is one of the few European territories covered by the passage of the TRMM Precipitation Radar (PR) and it has a dense rain gauge network and an operational weather radar. TRMM PR provides, until now, the most reliable space-borne spatial high-resolution precipitation measurements.

Attention is focused on the attempt to define a methodology, using state-of-the-art diagnostic methods, for a comprehensive evaluation of water fields as forecast by a limited area model (LAM). An event that occurred on 5 March 2003, associated with a slow cyclone moving eastward over the Mediterranean Sea, is presented as a case study.

The atmospheric water fields were forecast over the eastern Mediterranean Sea using the Bologna Limited Area Model (BOLAM). Data from the Cyprus ground-based radar, the Cyprus rain gauge network, the Special Sensor Microwave Imager (SSM/I), and the TRMM PR were used in the comparison. Ground-based radar and rain gauge data were merged together in order to obtain a better representation of the rainfall event over the island. TRMM PR measurements were employed to range-adjust the ground-based radar data using a linear regression algorithm.

The observed total column water vapor has been employed to assess the forecast quality of large-scale atmospheric patterns; such an assessment has been performed by means of the Hoffman diagnostic method applied to the entire total column water vapor field. Subsequently, in order to quantify the spatial forecast error at the finer BOLAM scale (0.09°), the object-oriented contiguous rain area (CRA) analysis was chosen as a comparison method for precipitation. An assessment of the main difficulties in employing CRA in an operational framework, especially over such a small verification domain, is also discussed in the paper.

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Monika Feldmann, Curtis N. James, Marco Boscacci, Daniel Leuenberger, Marco Gabella, Urs Germann, Daniel Wolfensberger, and Alexis Berne

Abstract

Region-based Recursive Doppler Dealiasing (R2D2) is a novel dealiasing algorithm to unfold Doppler velocity fields obtained by operational radar measurements. It specializes in resolving issues when the magnitude of the gate-to-gate velocity shear approaches or exceeds the Nyquist velocity. This occurs either in highly sheared situations, or when the Nyquist velocity is low. Highly sheared situations, such as convergence lines or mesocyclones, are of particular interest for nowcasting and warnings. R2D2 masks high-shear areas and adds a spatial buffer around them. The areas between the buffers are then identified as continuous regions that lie within the same Nyquist interval. Each region subsequently is assigned its most likely Nyquist interval by applying vertical and temporal continuity constraints, as well as supplemental wind information from an operational mesoscale model. The shear zones are then resolved using 2D continuity in azimuth and range. This 4D procedure is repeated until no further improvement can be achieved. Each iteration with fewer folds identifies fewer but larger continuous regions and less shear zones until an optimum is reached. Residual errors, often related to shear greater than the Nyquist velocity, are contained to small areas within the buffer zones. This approach maximizes operational performance in high-shear situations and restricts errors to minimal areas to mitigate error propagation.

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