Effects of reflectivity gradients on rainfall estimates based on specific differential phase measurements

The rainfall estimates RDP based on the specific differential phase shift KDP are unaffected by errors in radar calibration and attenuation along the path. However, due to the signal fluctuations the estimates RDP can be very noisy at low and moderate rain rates. In order to improve the accuracy of the rainfall estimates KDP is to be estimated over a long path. In this way an error due to the reflectivity gradients, which could occur along a long path, can be introduced. In this paper we have analyzed two cases of reflectivity gradients along the path filled with rain; the first one, where the reflectivity varies linearly on dB scale that can be used to approximate regions with a steady increase or decrease of dBZ, and the other corresponding to sharp reflectivity gradient within the measurement cell, where an intense rainshaft is located adjacent in range to weak-echo regions. In both cases the error structure is discussed and the sensitivity of the normalized bias in KDP-based rainfall estimates is evaluated from a theoretical viewpoint and by simulation

Operational monitoring of rainfall over the Arno River basin using dual-polarized radar and rain gauges

Includes bibliographical references (page 1230).Reflectivity (ZH) and differential reflectivity (ZDR) measurements collected by Polar 55C over the Amo River basin in Italy are presented. The applicability of dual-polarization (ZDR)-based rainfall algorithms at C band in an operational setting is studied in conjunction with a network of rain gauges. Conventional pointwise comparison of radar and rain gauge estimates of rainfall, as well as statistical comparison of dual-polarization radar and rain gauge data via probability matching procedure, are presented. Error structure of reflectivity rainfall Z-R relation, as well as ZDR-based algorithms, is evaluated as a function of spatial and temporal averaging. Pointwise comparison, as well as statistical evaluation based on cumulative distribution function (CDF) matching, are used to show that in an operational environment with excessive ground-clutter contamination and attenuation problems the dual-polarization-based rainfall algorithm performs better than any arbitrary Z-R relation. In addition, it is shown that a dual-polarization (ZDR) algorithm obtained matching the CDFs performs better than the best possible Z-R relation

Comparison of radar rainfall estimates and raingage measurements over the Central Apennines

Radar measurement of rainfall over mountainous regions is a difficult task due to the requirements of avoiding beam blockage as well as contamination by the melting layer. In this paper the raingage measurements and radar estimates of rainfall over two distinct locations in the central Apennines are compared to study the effect of beam blocking on radar measurements. A simple procedure is developed to estimate the percentage of beam blockage by the mountain ridges and, correspondingly, to correct the radar estimates of rainfall

Procedure to calibrate multiparameter weather radar using properties of the rain medium, A

Includes bibliographical references (page 276).The joint distribution characteristics of size and shape of raindrops directly translate into features of polarization diversity measurements in rainfall. Theoretical calculations as well as radar observations indicate that the three polarization diversity measurements, namely, reflectivity, differential reflectivity, and specific differential propagation phase, lie in a constrained space that can be approximated by a three-dimensional (3-D) surface. This feature as well as the vertical-looking observation of raindrops are used to determine biases in calibration of the radar system. A simple procedure is developed to obtain the bias in the absolute calibration from polarization diversity observation in rainfall. Simulation study as well as data analysis indicate that calibration errors can be estimated to an accuracy of 1 dB

A technique to obtain a multiparameter radar rainfall algorithm using the probability matching procedure

The natural cumulative distributions of rainfall observed by a network of rain gauges and a multiparameter radar are matched to derive multiparameter radar algorithms for rainfall estimation. Conventional usage of multiparameter radar measurements for rainfall estimation has been associated with tracking the variability of the raindrop size distribution. The use of multiparameter radar measurements in a statistical framework to estimate rainfall is presented in this paper. The techniques developed in this paper are applied to the radar and rain gauge measurement of rainfall observed in central Florida and central Italy. Conventional pointwise estimates of rainfall are also compared. The probability matching procedure, when applied to the radar and surface measurements, shows that multiparameter radar algorithms can match the probability distribution function better than the reflectivity-based algorithms, thereby indicating the potential of multiparameter radar measurements for statistical approach to rainfall estimation. It is also shown that the multiparameter radar algorithm derived matching the cumulative distribution function of rainfall provides more accurate estimates of rainfall on the ground in comparison to any conventional reflectivity-based algorithm

Extraordinary blowing snow transport events in East Antarctica

In the convergence slope/coastal areas of Antarctica, a large fraction of snow is continuously eroded and exported by wind to the atmosphere and into the ocean. Snow transport observations from instruments and satellite images were acquired at the wind convergence zone of Terra Nova Bay (East Antarctica) throughout 2006 and 2007. Snow transport features are well-distinguished in satellite images and can extend vertically up to 200 m as first-order quantitatively estimated by driftometer sensor FlowCapt™. Maximum snow transportation occurs in the fall and winter seasons. Snow transportation (drift/blowing) was recorded for ~80% of the time, and 20% of time recorded, the flux is >10-2 kg m-2 s-1 with particle density increasing with height. Cumulative snow transportation is ~4 orders of magnitude higher than snow precipitation at the site. An increase in wind speed and transportation (~30%) was observed in 2007, which is in agreement with a reduction in observed snow accumulation. Extensive presence of ablation surface (blue ice and wind crust) upwind and downwind of the measurement site suggest that the combine processes of blowing snow sublimation and snow transport remove up to 50% of the precipitation in the coastal and slope convergence area. These phenomena represent a major negative effect on the snow accumulation, and they are not sufficiently taken into account in studies of surface mass balance. The observed wind-driven ablation explains the inconsistency between atmospheric model precipitation and measured snow accumulation value. © 2009 The Author(s)

Attenuation compensation technique and rainfall rate estimation using C-band dual polarization radar

The effectiveness of an attenuation correction procedure on the error structure of a C-band radar rainfall estimation is studied theoretically and by computer simulation. The iterative procedure to correct the radar observables affected by attenuation is based on the best-fit relationships between the absolute and differential reflectivity and the specific absolute and differential attenuation. This paper evaluates this attenuation correction procedure by a computer simulation to value the rainfall rate estimation errors

Comparison between multiparameter radar rainfall estimates and raingauge measurements during convective storms over Po valley

In this paper radar rainfall estimates obtained from C-band Doppler polarimetric weather radar GPM 500C are compared with rain gauge measurements collected by three rain gauge networks during a two months period from September 1 to October 30, 1996 when many convective thunderstorms developed over the Po valley area. In order to verify the capability and the accuracy of radar rainfall estimates two different techniques of comparison with the rain gauges have been analyzed: the first one is based on pointwise comparison of conventional and/or multiparameter radar estimates with the rain gauges measurements, the second utilizes the matching of the cumulative distribution function observed by the two sensors. The results are discussed considering two different areas, where the rain gauges are at a distance less than 40 km and at a distance ranging between 40 and 80 km, respectively

Model-based recursive partitioning to estimate unfair health inequalities in the United Kingdom Household Longitudinal Study

We measure unfair health inequality in the UK using a novel data- driven empirical approach. We explain health variability as the result of circumstances beyond individual control and health-related behaviours. We do this using model-based recursive partitioning, a supervised machine learning algorithm. Unlike usual tree-based algorithms, model-based recursive partitioning does identify social groups with different expected levels of health but also unveils the heterogeneity of the relationship linking behaviors and health outcomes across groups. The empirical application is conducted using the UK Household Longitudinal Study. We show that unfair inequality is a substantial fraction of the total explained health variability. This finding holds no matter which exact definition of fairness is adopted: using both the fairness gap and direct unfairness measures, each evaluated at different reference values for circumstances or effort

Rainfall estimation from polarimetric radar measurements: composite algorithms immune to variability in raindrop shape-size relation

Includes bibliographical references (pages 1785-1786).Polarization diversity radar measurements such as reflectivity factor, differential reflectivity, and differential propagation phase are extensively used in rainfall estimation. Algorithms to estimate rainfall from polarimetric radar measurements are based on a model for the raindrop shape as a function of drop diameter. Most of the current algorithms use an equilibrium shape-size model for raindrops. Variation of the prevailing mean raindrop shapes from an assumed model has a direct impact on the accuracy of radar rainfall estimates. This paper develops composite algorithms to estimate rainfall from polarimetric radar data without an a priori assumption about the specific form of mean raindrop shape-size model such as equilibrium shape model. The accuracy of rainfall estimates is evaluated in the presence of random measurement errors as well as systematic bias errors. The composite algorithms, independent of a prespecified raindrop shape model, were applied to radar parameters simulated from disdrometer data collected over 3 months, and the corresponding rainfall estimates were found to be in good agreement with disdrometer estimates. The composite algorithms were also tested with Colorado State University CHILL radar observations of the 28 July 1997 Fort Collins (Colorado) flood event. The storm total precipitation estimates based on the composite algorithms developed in this paper were in much better agreement with rain gauge estimates in comparison with conventional algorithms