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Urs Germann and Jürg Joss

Abstract

In the Alps, the volume visible by a radar is reduced because of ground clutter, elevated horizon, and earth curvature. This often inhibits a direct view on precipitation close to the ground. When using radar measurements from aloft to estimate precipitation rates at ground level, the measurements must be corrected for the vertical change of the radar echo (the profile) caused by the growth and transformation of precipitation. In this paper a robust profile-correction scheme for operational use in complex orography is presented. The aim is to correct for the large errors related to the profile in an Alpine environment: frequent underestimation caused by the vertical decrease of the radar echo, and occasional overestimation in the bright band. The profile is determined from volumetric radar data integrated over a few hours within a 70-km range of the radar (mesobeta scale). The correction scheme is verified by comparing radar estimates to gauge measurements of 247 h of summer and winter precipitation in a highly mountainous area. During the selected period, 10 gauges collected a total of 3966 mm of water. Four concepts to estimate ground-level precipitation are compared: the vertical maximum echo, the lowest visible echo, estimates corrected with the average-event profile, and estimates corrected using the mesobeta profile. Comparisons with the ground truth show that in summer profile correction considerably reduces the bias and scatter. The root-mean-square error diminishes by a factor of 2. Thus, corrected radar images give a much better overall view of the precipitation field than uncorrected ones. In winter, the improvement is found in a very significant reduction of the bias. The algorithm is currently being implemented in the operational radar network of MeteoSwiss. Long-term verification is needed after a few years of operation.

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Jürg Joss and Robert Lee

Abstract

The analyses of data recorded during the past eight years with two Swiss radars, a network of rain gauges, and river flow measurements have helped to quantify the vertical profile of reflectivity and the influences of topography, meteorology, and radar parameters on the precision of radar precipitation estimation. The influence of the topography around the radar, the width of the radar beam, and the vertical echo structure produces a complex error distribution in space and time, with errors dependent upon storm type, distance from the radar, and the radar horizon. In spite of excellent agreement between amounts estimated by the 5-cm radar at close ranges and gauges located below the radar volume, underestimation of rainfall increases with range from the radar. The authors' experience dramatically shows how significantly errors are reduced when precipitation can be estimated close to the ground, a task made easier by choosing a radar site with a good view and by rigorously eliminating echoes contaminated by ground clutter and anomalous propagation without, however, reducing the detection capability of the radar for precipitation. Several methods of clutter detection are used together to ensure that precipitation estimates are not biased by clutter.

A physical model can correct for a large part of these errors, including brightband effects, or at least tell us something about the validity of the results, if the causes of the long-range underestimation are understood. This paper proposes a two-step approach to error correction: first, a three-dimensional map of the “visibility” from the radar of each observation point is made, initially assumed constant with time. The vertical profile of precipitation is then estimated (in real time where possible and from climatological values if not) and used together with a topographical database to estimate the precipitation reaching the (usually obscured) ground from a weighted function of all rain-rate estimates made above each point on the surface.

The results of this analysis, especially appropriate for the Alps but also valuable in ordinary terrain, are being applied to the Swiss Meteorological Institute's new generation of weather radars in order to provide improved quantitative precipitation information to support the preparation of operational flood warnings in the Swiss Alps. An optimized sun strategy with simultaneous reflectivity and Doppler processing and automatic calibration is used to allow corrections in real time and to produce products to satisfy a wide variety of user needs.

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Urs Germann and Jürg Joss

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The authors use variograms of radar reflectivity as a summary statistic to describe the spatial continuity of Alpine precipitation on mesogamma scales. First, how to obtain such variograms is discussed. Second, a set of typical variograms of Alpine precipitation is found. Third, some examples are given on how these variograms can be used to tackle several questions such as, What spatial variation of precipitation rate can be found in Alpine catchments? What difference can be expected between the measurements at two points separated by a given distance? To what accuracy can areal precipitation be estimated from point observations? Are there preferred regions for convection in Alpine precipitation? Variograms are obtained using a method-of-moments estimator together with high-resolution polar reflectivity data of well-visible regions. Depending on the application, the variogram was determined in terms of linear precipitation rate, logarithmic reflectivity, or linear reflectivity. Spatial continuity was found to vary significantly both in time and space in the various types of Alpine precipitation analyzed so far. At a separation distance of 10 km, the expected difference of reflectivity ranges from 4 dBZ (factor of 2.5 in stratiform rain or snow) to about 13 dBZ (factor of 20 in a mesoscale convective system). In a 96-h period of heavy rain in the southern European Alps, maximum variation occurred in upslope regions (frequent convection), while close to the crest of the Alps the variation was relatively weak (persistent stratiform rain). The representativeness of a point observation, which can be quantified given the variogram, therefore depends on both the time and the location within the Alps and also on the integration time (integrated rainfall maps being less variable than instantaneous ones). For a 576-km2 basin and 40-min average rain, the fractional error of the basin precipitation estimated by a gauge measurement ranges from 11% (variogram of stratiform autumn rain) to 65% (variogram of a mesoscale convective system). Next steps will extend the variogram analyses to a larger space–time domain toward a climatic description of spatial continuity of Alpine precipitation.

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Jürg Joss and Armin N. Aufdermaur

Abstract

The radar cross sections of particles grown in a hail tunnel (ice spheres with a spongy ice shell) and snow spheres dipped in water (spongy ice throughout) were measured at wavelengths of 3.21 cm, 4.67 cm, and 10 cm. The results were comparable and did not show obvious systematic differences either for the three wavelengths or for the two kinds of particles.

The average normalized cross section versus α(α = πd/λ, where d is the particle diameter and λ the wave-length) is given in the range of 0.4 < α < for liquid water contents of 0 per cent (frozen particles), 5, 10, 20, and 30 per cent. For α > 1, a water content of more than 10 per cent was sufficient to produce a mean cross section equivalent to that of an all-water sphere.

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Martin Löffler-Mang and Jürg Joss

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The characteristics of a prototype optical disdrometer are presented. Particles are detectable in the diameter range from 0.3 to 30 mm having velocities of up to 20 m s−1. Advantages of the new system are (i) it is easy to handle, robust, and low cost, allowing a cluster of instruments to investigate the spatial and temporal fine-scale structure of precipitation; (ii) it provides reliable detection of the range of small drops; and (iii) it allows the possibility of snow measurements. Results of rain measurements are compared with data from a Joss–Waldvogel disdrometer and a Hellmann rain gauge. Furthermore, some snow measurements are presented and compared with results of a research spectrometer. The overall agreement is good. The repeatability of particle size estimation was checked in the diameter range between 1.4 and 8.0 mm and yielded a standard deviation of less than 5%. For drop velocities the standard deviation varies between 25% (0.3-mm drops) and 10% (5-mm drops). The optical disdrometer can also serve as a present weather sensor, detecting and differentiating among rain, snow, drizzle, graupel, hail, and the absence of precipitation.

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Jürg Joss and Enrico G. Gori

Abstract

The “instant” shape of raindrop size distributions (measured during 1 min or less) usually differs from the exponential, generally in the direction of monodispersity. Experimental results are presented for both widespread and thunderstorm rain. It is shown that the measured shape depends significantly on the sample size, and that adding many “instant” distributions from different conditions leads to an exponential distribution such as proposed by Marshall and Palmer. This transition is examined, as well as the sample size needed for a well-defined shape.

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Paul L. Smith, Zhong Liu, and Jurg Joss

Abstract

Because of the randomness associated with sampling from a population of raindrops, variations in the data reflect some undetermined mixture of sampling variability and inhomogeneity in the precipitation. Better understanding of the effects of sampling variability can aid in interpreting drop size observations. This study begins with a Monte Carlo simulation of the sampling process and then evaluates the resulting estimates of the characteristics of the underlying drop population. The characteristics considered include the liquid water concentration and the reflectivity factor; the maximum particle size in each sample is also determined. The results show that skewness in the sampling distributions when the samples are small (which is the usual case in practice) produces a propensity to underestimate all of the characteristic quantities. In particular, the distribution of the sample maximum drop sizes suggests that it may be futile to try to infer an upper truncation point for the size distribution on the basis of the maximum observed particle size.

Resulting paired values, for example, of Z and W for repeated sampling, were plotted on the usual type of log–log scatterplots. This yielded quite plausible-looking Z–R and Z–W relationships even though the parent drop population (and, hence, the actual values of the quantities) was unchanging; the “relationships” arose entirely from the sampling variability. Moreover, if the sample size is small, the sample points are shown to be necessarily displaced from the point corresponding to the actual population values of the variables. Consequently, any assessment of the “accuracy” of a Z–R relationship based on drop size data should include some consideration of the numbers of drops involved in the samples making up the scatterplot.

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Hans Richner, Jürg Joss, and Paul Ruppert

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A boiling-point barometer—commonly called hypsometer—has been developed for use on meteorological radiosondes. In this hypsometer, water is heated electrically, and its boiling temperature is measured with a thermocouple. Once the boiling temperature is known, pressure is determined via the water vapor saturation pressure curve.

The pressure range required is 1050–10 hPa, that is, slightly more than two orders of magnitude. In order to achieve an accuracy of 0.05% in pressure (0.5 hPa at 1000 hPa), boiling temperature must be measured to about 0.01 K. This formidable requirement calls for very accurate calibration procedures that are novel in thermocouple thermometry. However, once the thermocouple is calibrated, individual hypsometers utilizing thermocouples made of the same batch of material do not require calibration. For computing pressure from boiling temperature, the Goff-Gratch reference function is suggested; if approximations cannot be avoided, they must be specially selected. When using another liquid (e.g., fluorochlorohydrocarbons) in the hypsometer, the accuracy required for the temperature measurement would be reduced, however, water was chosen because it is environmentally harmless.

Apart from the fact that the hypsometer does not require individual calibration, its advantage over other pressure sensors is the fact that a given uncertainty in boiling temperature leads to a practically constant relative pressure error dp/p over the entire pressure range. Consequently, heights computed for the hypsometer sondes are more accurate than those obtained from sondes employing other pressure sensors (e.g., aneroids), as was confirmed in an intercomparison. The hypsometer is operationally used in the SRS radiosonde by the Swiss Meteorological Institute; so far nearly 3500 successful flights have been made.

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Bertrand Vignal, Gianmario Galli, Jürg Joss, and Urs Germann

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The vertical variability of radar reflectivity reduces the reliability of precipitation estimation by radar, especially in complex orography. This important source of error can, at least partially, be corrected for, if the vertical profile of radar reflectivity (VPR) is known. This work addresses three ways to determine VPR from volumetric radar data for correcting precipitation estimates. The first way uses a climatological profile. The second method, operational in Switzerland, takes the actual weather conditions into account: a mean profile is estimated directly from volumetric radar data collected close to the radar. The third way determines the identified profile, taking the variability of the VPRs in space into account. This approach yields local estimates of the profile (on areas of about 20 km × 20 km) based on an inverse method. Two cases, a convective event and a stratiform event, are used to illustrate the three ways for determining the VPR, and the resulting improvement, verified with rain gauges. An enlarged dataset of nine cases shows that a correction based on a climatological profile already improves the accuracy of rain estimates by radar significantly: the fractional standard error (FSE) is reduced from the noncorrected 44% to 31%. By correcting with a single, mean profile (averaged over 1 h using real-time data), the FSE is further reduced from 31% to 25%. Last, the use of 70 locally identified profiles leads to best results (FSE = 23%). A higher improvement (lower FSE) is obtained for the stratiform rain event than for the convective case.

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