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Effective rain rate estimation using satellite-based measurement is imperative for many hydro-meteorological applications. With the recent advancement in satellite products and retrieving algorithm...

This contribution describes a realistic rain model that considers a cluster of axis-symmetric but flattened raindrops with a specified shape–size relation, a raindrop size distribution, and a distribution of orientation angle of the symmetry axis. The backscattering cross section and specific attenuation due to the rain medium are determined by the extended boundary condition method. The model is applied to data from two X-band radars to estimate reflectivity and rainfall rate, considering attenuation effects during the two-way propagation between the radar and each sampled rain volume. The results are then compared with corresponding ones from well-established attenuation-correction methods. One of these methods, which relies on unattenuated reflectivities measured by an S-band radar, has been carefully validated by a previous study. The reasonable agreement between the two methods indicates that the proposed model may be a suitable option to estimate reflectivity and rainfall rate fields based on X-band radar data, particularly when no such references are available.

Method for rainfall rate estimation with satellite based microwave radiometer data is proposed. A method to consider the geometric relationship of the observed ice particles and microwave radiometer in the estimation of precipitation is shown, and its validity is shown by comparing it with precipitation radar data on the ground. Observations at high altitudes, such as ice particles, differ greatly in the location of the observation point projected on the ground surface and in the upper troposphere where the observations exist. This effect was insignificant when the precipitation was small because ice particles were often absent, but it was found that the effect was large when the precipitation was large. In other words, the proposed method is effective and effective for Advanced Microwave Scanning Radiometer (AMSR) data in Houston, which was shown as an example of a highly developed convective rain cloud with an In the case of Kwajalein, the effect is insignificant. In addition, the proposed method requires an assumption of ice particle height, and it is necessary to make assumptions based on climatic values. In addition, microwaves in the 89 GHz band, which are considered to be sensitive to ice particles, are not only sensitive to ice particles, so it must be taken into account that they are also affected by the presence of non-ice particles.

Reliable, cheap and accurate measurements of rainfall rate are growing to be more and more important in many sectors as: meteorology, agriculture, flood warning, and weather forecasting. Recently, indeed, the development of novel competitive techniques has been pushed in order to improve accuracy and reliability performance, such as commercial microwave links and broadcast satellite links. The aim of the current paper is to extend previous works of the literature based on land wireless links only. The basic idea consists in synergically employing both land and satellite based approaches together, by collecting and properly fusing the corresponding measurements. To this end, an iterative optimization procedure has been developed. As shown by numerical results, the proposed procedure gives the estimated rainfall map with a considerable accuracy and improved performance respect to the conventional algorithm based on terrestrial link only.

The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite series R (GOES-R) will greatly expand the ability to observe the earth from geostationary orbit compared to the current-generation GOES, with more than 3 times as many spectral bands and a 75% reduction in footprint size. These enhanced capabilities are beneficial to rainfall rate estimation since they provide sensitivity to cloud-top properties such as phase and particle size that cannot be achieved using the limited channel selection of current GOES. The GOES-R rainfall rate algorithm, which is an infrared-based algorithm calibrated in real time against passive microwave rain rates, has been previously described in an algorithm theoretical basis document (ATBD); this paper describes modifications since the release of the ATBD, including a correction for evaporation of precipitation in dry regions and improved calibration updates. These improvements have been evaluated using a simplified version applicable to current-generation GOES to take advantage of the high-resolution ground validation data routinely available over the conterminous United States. Correcting for subcloud evaporation using relative humidity from a numerical model reduced false alarm rainfall by half and reduced the overall error by 35% for hourly accumulations validated against the National Centers for Environmental Prediction stage IV radar–gauge field; however, the number of missed events did increase slightly. Reducing the size of the calibration regions and increasing the training data requirements improved the consistency of the retrieved rates in time and space and reduced the overall error by an additional 4%.

187 p. Due to the need for an understanding of the rain structure and the effect of rainfall on the high frequency wireless communication links, several issues related to the characters of rainfall rates and rain attenuation in Singapore are studied. This will enable the development of a comprehensive rain attenuation model. MASTER OF ENGINEERING (EEE)

The prediction of rainfall rate characteristics at small space-time scales is currently an important topic, particularly within the context of the planning and design of satellite network systems. A new comprehensive interpolation approach is presented in this paper to deal with such an issue. There are three novelties in the proposed approach: (1) the proposed interpolation approach is not directly applied to measured rain precipitation (either radar or raingauge-derived data) but focuses on the coefficients of the fitted statistical distributions and/or computed rain characteristics at each location; (2) the parameter databases are provided and the contour maps of coefficients spanning Western Europe have been created. It conveniently and efficiently provides the rain parameter for any location within the studied map; (3) more speculatively, the 3D space-time interpolation approach can extrapolate to rain parameters at space-time resolutions shorter than those in the NIMROD databases.

Summary Ground clutter and beam blockage caused by complex terrain deteriorates the accuracy of radar quantitative precipitation estimations (QPE). To improve radar QPE, we have developed a technique for radar rainfall estimation, the Kyungpook National University Hybrid Surface Rainfall (KHSR), based on a two-dimensional hybrid surface consisting of the lowest radar bins that are immune to ground clutter, beam blockage, and non-meteorological echoes. The KHSR map is a composite of a ground echo mask, a beam blockage mask, and a rain echo mask, and it was applied to an operational S-band dual-polarimetric radar that scans six PPIs at a low elevation angle every 2.5 min. By using three rainfall estimators, R ( Z H ), R ( Z H , Z DR ), and R ( Z H , ξ DR ), this technique was compared with an operational Constant Altitude Plan Position Indicator (CAPPI) QPE of the Korea Meteorological Administration during a summer season from June–August 2012. In comparison with CAPPI, KHSR shows improved rainfall estimates for three algorithms, and it was more effective with dual-polarimetric rainfall algorithms than with single polarimetric rainfall algorithms. Error increased with increasing range from radar, but this increase was more rapid using CAPPI than using KHSR. KHSR using the R ( Z H , Z DR ) algorithm was the most accurate long range (>100 km from the radar) estimator.

The potential of rainfall-rate assignment using Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Instrument (SEVIRI) data is investigated. For this purpose, a new conceptual model for precipitation processes in connection with midlatitude cyclones is developed, based on the assumption that high rainfall rates are linked to a high optical thickness and a large effective particle radius, whereas low rainfall rates are linked to a low optical thickness and a small effective particle radius. Reflection values in the 0.56–0.71-μm (VIS0.6) and 1.5–1.78-μm (NIR1.6) channels, which provide information about the optical thickness and the effective radius, are considered in lieu of the optical and microphysical cloud properties. An analysis of the relationship between VIS0.6 and NIR1.6 reflection and the ground-based rainfall rate revealed a high correlation between the sensor signal and the rainfall rate. Based on these findings, a method for rainfall-rate assignment as a function of VIS0.6 and NIR1.6 reflection is proposed. The validation of the proposed technique showed encouraging results, especially for temporal resolutions of 6 and 12 h. This is a significant improvement compared to existing IR retrievals, which obtain comparable results for monthly resolution. The existing relationship between the VIS0.6 and NIR1.6 reflection values and the ground-based rainfall rate is corroborated with the new conceptual model. The good validation results indicate the high potential for rainfall retrieval in the midlatitudes with the high spatial and temporal resolution provided by MSG SEVIRI.

The goal of this paper is to calculate new calibration matrices using Rapid Scan (RSS) Meteosat Second Generation (MSG) measurements and to evaluate their impact on precipitation estimates for a territory of the Czech Republic. The calibration matrices are the most important part of the Convective Rainfall Rate algorithm, which uses the IR 10.8 μm, WV 6.2 μm and VIS 0.6 μm spectral SEVIRI (Spinning Enhanced Visible and Infrared Imager) channels of the MSG to assess satellite precipitation estimates (SPEs). The calibration matrices were calculated using Czech radar data from 21 summer days during which severe convection and heavy precipitation were observed. The resultant matrices were compared with those obtained using conventional 15 min MSG scans. The comparison showed significant differences in the calibration matrices, which resulted in differences in the estimated precipitation. The application of RSS data significantly increased the rain rates and improved the structure of the matrices; however, the matrices were subjectively modified to increase the accuracy of the resulting SPEs. The calibration matrices were also calibrated by shifting the radar data forward 5, 10, 15 and 20 min with respect to the MSG measurement, because some delay between the information obtained by the MSG and the radars was expected. The impact of the matrices was evaluated by verifying the SPEs with the radar-derived precipitation estimates merged with the rain gauge observations as the ground truth. The results showed that the calibration matrices that were based on the RSS data improved the categorical skill scores and reduced the mean error (ME), the mean absolute error (MAE), and root mean square error (RMSE) of SPEs. Subjectively, the estimated precipitation values are more realistic, the estimated maximum is closer to observations and convective cores are more pronounced and easily identifiable. The difference between the matrices calibrated by shifting the radar data in time was quite small, and the impact on the SPEs was not readily apparent. However, the time shifts of 15 and 20 min slightly improved the categorical skill scores and reduced the ME, MAE and RMSE.

The presented paper describes an instrument called Aurometer which was designed for both atmospheric visibility and rainfall rate measurement. The principle of operation of Aurometer is based on determining the amount of light scattered by particles in the air that passes through the sample volume. The achieved results show that simultaneous estimation of the atmospheric visibility and rainfall rate is possible.

This paper describes and comments the results obtained applying a data processing method to a joint set of radar and a rain gauge data for estimating the 2-D rainfall field at ground averaged over a given observation time T and over a radar coverage area that includes a rain gauge network. The estimate of the rainfall field is based on the processing of a data set composed by rain gauge and horizontal reflectivity radar data gathered during a rainfall phenomenon. The procedure has been tested on an experimental data set collected in Tuscany in 1999.

A comprehensive system that is used to invert the geophysical products from microwave measurements has recently been developed. This system, known as the Microwave Integrated Retrieval System (MiRS), ensures that the final solution is consistent with the measurements and, when used as input to the forward operator, fits them to within the instrument noise levels. In the presence of precipitation, this variational algorithm retrieves a set of hydrometeor products consisting of cloud liquid water, ice water, and rain water content profiles. This paper presents the development and assessment of the MiRS rainfall rate that is derived based on a predetermined relationship of the rainfall with these hydrometeor products. Since this relationship relies on the geophysical products retrieved by the MiRS as inputs and not on sensor-dependent parameters, the technique is suitable for all microwave sensors to which the MiRS is applied. This precipitation technique has been designed to facilitate its transition from research to operations when applied to current and future satellite-based sensors. Currently, the MiRS rainfall rate technique has been implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) for the NOAA-18, NOAA-19, Metop-A Advanced Microwave Sounding Unit, and Microwave Humidity Sensor, as well as for the Defense Meteorological Satellite Program (DMSP)-F16 and DMSP-F18 Special Sensor Microwave Imager/Sounder microwave satellite sensors. For the validation of the MiRS rainfall rate technique, extensive comparisons with state-of-the-art precipitation products derived from rain gauge, ground-based radar, and satellite-based microwave observations are presented for different regions and seasons, and over land and ocean. The MiRS rainfall rate technique is shown to estimate precipitation, with a skill comparable to other satellite-based microwave precipitation algorithms, including the MSPPS, 3B40RT, and MWCOMB, while showing no discontinuities at coasts. This is a relevant result, considering that the MiRS is a system not merely designed to retrieve the rainfall rate but to consistently estimate a comprehensive set of atmospheric and surface parameters from microwave measurements.

In 2012 the Météo France metropolitan operational radar network consists of 24 radars operating at C and S bands. In addition, a network of four X-band gap-filler radars is being deployed in the French Alps. The network combines polarimetric and nonpolarimetric radars. Consequently, the operational radar rainfall algorithm has been adapted to process both polarimetric and nonpolarimetric data. The polarimetric processing chain is available in two versions. In the first version, now operational, polarimetry is only used to correct for attenuation and filter out clear-air echoes. In the second version there is a more extensive use of polarimetry. In particular, the specific differential phase Kdp is used to estimate rainfall rate in intense rain. The performance of the three versions of radar rainfall algorithms (conventional, polarimetric V1, and polarimetric V2) at different frequency bands (S, C, and X) is evaluated by processing radar data of significant events offline and comparing hourly radar rainfall accumulations with hourly rain gauge data. The results clearly show a superior performance of the polarimetric products with respect to the nonpolarimetric ones at all frequency bands, but particularly at higher frequency. The second version of the polarimetric product, which makes a broader use of polarimetry, provides the best overall results.

Radar measurements are useful for determining rainfall rates because of their ability to cover large areas. Unfortunately, estimating rainfall rates from radar reflectivity data alone is prone to errors resulting from variations in drop size distributions, precipitation types, and other physics that cannot be represented in a simple, one-dimensional Z–R relationship. However, improving estimates is possible by utilizing additional inputs, thereby increasing the dimensionality of the model. The main purpose of this study is to determine the value of surface observations for improving rainfall-rate estimation. This work carefully designed an artificial neural network to fit a model that would relate radar reflectivity, surface temperature, humidity, pressure, and wind to observed rainfall rates. Observations taken over 13 years from the Oklahoma Mesonet and the KTLX WSR-88D radar near Oklahoma City, Oklahoma, were used for the training dataset. While the artificial neural network underestimated rainfall rates for higher reflectivities, it did have an overall better performance than the best-fit Z–R relation. Most importantly, it is shown that the surface data contributed significant value to an unaugmented radar-based rainfall-rate estimation model.

Satellite broadcasting in the 21-GHz band is expected to transmit large-capacity signals such as ultrahigh-definition TV. However, this band suffers from large amounts of rain attenuation. In this regard, we have been studying rain fading mitigation techniques, in which the radiation power is increased locally in the area of heavy rainfall. To design such a satellite broadcasting system, it is necessary to evaluate service availability when using the locally increased beam technique. The rain attenuation data should be derived from the rainfall rate data. We developed a method to transform rainfall rate into rain attenuation in the 21GHz band. Then, we performed a simulation that applied the method to the analysis of the service availability for an example phased array antenna configuration. The results confirmed the service availability increased with the locally increased beam technique.

The effect of quantization on rainfall rate estimation from the Global Precipitation Measurement dual-frequency radar is studied quantitatively through numerical simulations. The error generated by a deterministic quantization or by a dithered quantization on rainfall rate estimation is combined with the errors from statistical fluctuation of the signal and the receiver noise. The preliminary results for the rain-rate range of 1-10 mmh/sup -1/ are presented.

Polarization radar offers the promise of much more accurate rainfall-rate R estimates than are possible from radar reflectivity factor Z alone, not only by better characterization of the drop size distribution, but also by more reliable correction for attenuation and the identification of hail. However, practical attempts to implement retrieval algorithms have been hampered by the difficulty in coping with the inherent noise in the polarization parameters. In this paper, a variational retrieval scheme is described that overcomes these problems by employing a forward model for differential reflectivity Zdr and differential phase shift ϕdp and iteratively refining the coefficient a in the relationship Z = aRb such that the difference between the forward model and the measurements is minimized in a least squares sense. Two methods are used to ensure that a varies smoothly in both range and azimuth. In range, a is represented by a set of cubic-spline basis functions; in azimuth, the retrieval at one ray is used as a constraint on the next. The result of this smoothing is that the retrieval is tolerant of random errors in Zdr of up to 1 dB and in ϕdp of up to 5°. Correction for attenuation is achieved simply and effectively by including its effects in the forward model. If hail is present then the forward model is unable to match the observations of Zdr and ϕdp simultaneously. This enables a first pass of the retrieval to be used to identify the radar pixels that contain hail, followed by a second pass in which the fraction of the Z in those gates that is due to hail is retrieved, this time with the scheme being able to forward-model both Zdr and ϕdp accurately. The scheme is tested on S-band radar data from southern England in cases of rain, spherical hail, oblate hail, and mixtures of rain and hail. It is found to be robust and stable, even in the presence of differential phase shift on backscatter.

This paper deals with a new algorithm applicable to rainfall rate profiling with a rain-attenuating-frequency radar for vertical incidence from an air or space platform. The proposed algorithm is independent from external radar calibration which is especially difficult to perform in airborne or space-borne applications. In addition to backscattering data from raindrops, path-integrated rainfall rate is used to estimate a rainfall rate profile. The path-integrated rainfall rate may be estimated from surface echo attenuation or from brightness temperature measured with an auxiliary microwave radiometer. The performance of the proposed algorithm is evaluated by a computer simulation experiment assuming backscattering and attenuation models of the radar wave due to rain. Then, airborne radar data are analysed with the algorithm to demonstrate its validity.

Precipitation plays a crucial role to the global water and energy cycle that governs the weather, climate, and ecological systems. Thorough understanding and accurate forecasting of precipitation is essential to the affairs of humans. The Tropical Rainfall Measuring Mission (TRMM), launched in 1997, is a joint space mission between NASA and the Japan Aerospace Exploration Agency (JAXA) designed to monitor and study tropical rainfall. The Precipitation Radar (PR) on board the TRMM satellite is the first space borne instrument, capable of providing high-resolution vertical profile of precipitation on a global scale. TRMM-PR operates at a single frequency of Ku- (13.6 GHz) band. The microphysical retrieval algorithms for TRMM-PR rely on the surface-reference technique (SRT) to estimate path attenuation and correct the measured Ku-band reflectivity. With the attenuation-corrected reflectivities, a modified Hitschfeld-Bordan method [1] is then used to retrieve limited drop size distribution (DSD) information, and the rainfall rate [2]. One disadvantage of single-frequency space borne radar such as TRMM-PR is that it is not easy to retrieve the DSD parameters completely. Therefore, k-Z and Z-R relationships, with their inherent assumptions, are used to estimate rainfall rate which is not sufficient to capture the variability of precipitation and has large uncertainty. Global Precipitation Measurement (GPM) mission is poised to be the next generation observations from space after the TRMM mission. GPM is a science mission with integrated applications goals for advancing the knowledge of the global water/energy cycle variability as well as improving weather, climate, and hydrological prediction capabilities through more accurate and frequent measurements of global precipitation. The GPM core satellite will be equipped with a dual-frequency precipitation radar (DPR) operating at Ku- (13.6 GHz) and Ka- (35.5 GHz) band [3]. Taking two independent sets of observation, DPR on aboard the GPM is expected to improve our knowledge of precipitation processes relative to the single-frequency (Ku- band) radar used in TRMM by providing greater dynamic range, more detailed information on microphysics. Two parameters of DSDs can be retrieved from dual-frequency observations and better accuracies in rainfall estimation can be achieved. Theoretically, rainfall rate is a function of rain drop size distribution and rain drop terminal velocity, R=0.67π∗10−3∫ v(D)D3N(D)dD. The most critical component in rainfall rate estimation is the time-space variation of drop size distribution. Le and Chandrasekar (2014) [4] developed a hybrid method to retrieve drop size distribution parameters for GPM-DPR. The hybrid method is a profile-based optimization algorithm with the philosophy to combine the attributes of forward method and linear constraints of DSDs in rain. Two of the gamma distribution parameters [5], Do and Nw, at surface are optimized when the deviation between estimates and observations are minimized. The hybrid method can be used to estimate DSDs at each space and temporal resolution of GPM-DPR observation. In this paper, rainfall rate is calculated using DSDs retrieved through the hybrid method [4] based on assumptions of particle terminal velocity. Data collected by GPM-DPR is capability to cover ±65° latitude of the earth with every 2–4 hours. Thus, a global rainfall map can be generated. In polarimetric radar system, rainfall rate can be estimated through dual-polarized radar parameter such as Zdr [6][7]. Zdr is called differential reflectivity and it is a function of particle characteristics itself. Similar of Zdr to the dual-polarization radar retrieval, there is a parameter called dual-frequency ratio (DFR) that plays an important role in the dual-frequency radar retrievals. DFR is defined as the difference of the radar reflectivity at two frequencies in decibels which carries information of single particle characteristics. In this study, we investigate potential relation between rainfall rate, reflectivity at Ku- band and DFR using theoretical simulation [8] and curve fitting. Figure 1 shows a scattergram of rainfall rate estimation from R(Zh, DFR) versus true rainfall rate. Both Zku and DFR are in linear form in this equation. This provides a direct estimation of rainfall rate without prior knowledge of drop size distribution. Since both reflectivity and dual frequency ratio are intrinsic values, attenuation correction is needed before rain rate can be estimated through R(Zh, DFR) equation. The hybrid method [4] described in previous paragraph could be one of the algorithms to perform attenuation correction. The global rainfall map generated through R(Zh, DFR) approach will be cross-compared with DSD approach described in the previous paragraph, as well as other approaches such as attenuation based rainfall rate. The comprehensive analysis of the three techniques are presented.

This paper proposes a method for estimating rainfall rate from ground-based radar observations in eastern Tibet. Firstly, the radar reflectivity-rain rate (Z-R) relationship is derived by regression analysis using the hourly data of a single rain gauge located near the radar and the corresponding radar reflectivity over a period of more than two months. Two different relationships are obtained for stratiformand convective rain types. Secondly, an automated convective-stratiform rain classification methodis introduced that uses the horizontal radar echo structure to identify convective regions and classifies the remainder as stratiform areas. The classification algorithm is verified against visual inspections of horizontal and vertical radar echo structures. Finally, the rain rate is estimated from the radar reflectivity for the rain gauge site using the derived Z-R relationships and the rain type information produced by the classification algorithm. The comparison of the estimation and the gauge data suggests good accuracy.

We focus on two rainfall measurement algorithms which will be used with a spaceborne radar for the Tropical Rainfall Measuring Mission. One is the conventional rainfall measurement algorithm (Z method), which uses the radar reflectivity factor, and the other is the surface reference technique (SRT method), which uses rain attenuation. Non-uniform beam filling (NUBF) is one source of error in the above methods. This paper investigates the error caused by NUBF using a computer simulation that employs rain data measured by a ground-based radar. The simulation shows that the Z method always overestimates the rainfall rate, and the SRT method almost always underestimates the rainfall rate. The simulation takes into consideration the effect of the signal fluctuation. The results show that the effect of the signal fluctuation is larger than that of the NUBF, in both the Z method and the SRT method. In particular, the signal fluctuation causes serious errors in rainfall rate estimation by the SRT method in light rain.

The Communications Research Laboratory (CRL) has developed a simultaneous reception Ku-band dual-polarization radar. Simultaneous reception can give high accuracy of the differential reflectivity (ZDR) measurement from fewer averaged samples because of a good correlation between simultaneously sampled horizontal and vertical reflectivity. This paper pursues the possibility of estimating the rainfall rate using ZDR alone by comparison with a conventional polarimetric radar algorithm using ZDR and ZH on the assumption of the data handling of a CRL's dual-polarization radar. A computer simulation based on a physical raindrop model is used to pursue this possibility. The results show that estimating the rainfall rate using ZDR alone is not a good method.

Recently, we proposed a novel remote sensing method for rainfall rate estimation in real time by means of tomographic processing applied to power attenuation measurements made across the microwave links defined by radiobase station networks for mobile communication systems. In this paper we analyze the results of a new tomographic algorithm that has been specifically developed for the typical topology of the urban radiobase station networks. Such algorithm has been tested on simulated specific attenuation (K) maps based on true polarimetric weather radar data. We considered 18, 23, and 38 GHz for the carrier frequencies of the radiobase network and the weather data gathered by the POLAR 55C polarimetric radar located in Rome. (C) 2011 Elsevier B.V. All rights reserved.

Various associations between radar reflectivity (Z) and rainfall rate (R), identified as Z-R relationships have been derived to facilitate the rainfall rate estimation by weather radar. Most of the previously proposed Z-R relationships were implied as too generalized and only applicable within the region that has identical climate characteristics or behavior. Based on such notion, the most accurate Z-R relationship applicable for Malaysian weather radar therefore needs to be investigated and identified. In this paper, two renowned models will be evaluated on how well they fit to Malaysian weather radar data. Preliminary comparisons between the previously proposed Z-R relationships using newly acquired radar data in Malaysia and ground-based rain gauge measurements are also presented. The use of inaccurate relationship can cause error in radar rainfall estimates. This might pose unacceptable risk for the aviation industry where accurate, dependable weather information deduced by radar system is expected at all times.

This research presents a step by step description of the procedure to correct high resolution satellite rainfall estimation for parallax error and orographic effects, and shows how these adjustments can be performed on any satellite rainfall estimation technique. The procedure is tested on the experimental automatic rainfall estimation technique currently in use by the National Environment Satellite Data, and Information Service (NESDIS), at the National and Oceanic Administration (NOAA)

The Inverse Gaussian (IG) distribution is examined for modeling the rainfall rate and slant path and terrestrial link rain attenuation. The long-term statistics of rain rate and rain attenuation are modeled using the IG distribution. The method is validated using the recommendation of International Telecommunication Union (ITU) recommendation ITU-R. P. 837 and rain rate data from the ITU Study Group 3 database (DBSG3) database for the case of rain rate. For the modeling of rain attenuation, data which are derived from two databases of DBSG3, these of Earth-space links and line-of-sight terrestrial links are used for validating the model. The results are compared to the one using the lognormal distribution. It has been shown that IG distribution could be more appropriate for modeling rainfall rate and slant path and terrestrial link rain attenuation. Finally, some useful conclusions are derived and presented in this paper.

In this work it is shown that for frequencies from 3 to 13 GHz, the ratio of the specific propagation differential phase shift phi(sub DP) to the rainfall rate can be specified essentially independently of the form of the drop size distribution by a function only of the mass-weighted mean drop size D(sub m). This significantly reduces one source of substantial bias errors common to most other techniques for measuring rain by radar. For frequencies 9 GHz and greater, the coefficient can be well estimated from the ratio of the specific differential attenuation to phi(sub DP), while at nonattenuating frequencies such as 3 GHz, the coefficient can be well estimated using the differential reflectivity. In practice it appears that this approach yields better estimates of the rainfall rate than any other current technique. The best results are most likely at 13.80 GHz, followed by those at 2.80 GHz. An optimum radar system for measuring rain should probably include components at a both frequencies so that when signals at 13.8 GHz are lost because of attenuation, good measurements are still possible at the lower frequency.

In this paper, we propose a novel remote sensing method that is adequate for rainfall rate measurements in real time by means of tomographic processing applied to power attenuation measurements made across the microwave links defined by radio base station networks for mobile communication systems. A description of the tomographic algorithms we developed is presented together with some simulation results. These concern rainfall rate estimation applying such algorithms to the current radio base station arrangement over the city of Florence, Italy, while the rainfall field is simulated through weather radar data.

Among the many ways investigated for radar estimation of rainfall, polarimetric methods are the most promising. However most polarimetric algorithms are degraded by attenuation by precipitation and clouds and by calibration error. A new method was recently proposed in which the differential polarimetric attenuation is used to perform an accurate rain rate measurement. The method is independent of the radar calibration and of the attenuation by undetected clouds. This algorithm is also usable as a qualitative hail detector, as well as a detector of anomalous propagation. The goal of the paper is to describe the results of the first experimental implementation of this method using the 35 GHz RABELAIS radar, as attenuated radar, and the 3 GHz CAMRa radar as a reference. We show that the proposed algorithm is stable and enables us to retrieve the actual rain rate even from an observed signal attenuated by more than 30 dB. The results are insensitive to the value used for the power coefficient of the Z(R) relation.

A semi-empirical method for the prediction of rain attenuation in terrestrial links is presented in this paper. The proposed method is simple to apply and uses the concept of an effective rainfall rate and the full rainfall rate cumulative probability distribution to predict the attenuation distribution. Test results indicate that the proposed method provides significant improvement over the current ITU-R method and other methods available in the literature.

Wireless access networks based in millimetre wavelength technologies are mainly impaired by rain. To evaluate the rain effects over a communication system, it is essential to know the temporal and spatial evolution of rainfall rate. For this reason it is necessary to develop an experimental network which provides the adequate data to study, prevent and compensate the rain fade. In this paper, an experimental rain gauge network is presented. This network comprises weather stations capable of measuring rainfall rate, temperature and humidity. The paper describes first the experimental network for automatic data acquisition as a system based in a distributed process. The design of the experimental network is explained in detail and finally the interest for millimetre wavelength applications is pointed out.

This paper investigates the performances achievable in the retrieval of rain-rate profile from a spaceborne radar operating at attenuated frequencies. Results obtained from three radar systems using relevant range-profiling algorithms to estimate rainfall rate are numerically simulated and compared. The three considered systems are a single-beam-single-frequency (SBSF) radar, a single-beam-dual-frequency (SBDF) radar, and a dual-beam-single-frequency (DBSF) radar or “stereoradar.” In each case, the sampling of a typical model rain cell is performed and the data are analyzed according to the selected algorithm for rainfall retrieval. Three possible frequencies for the SRSF and DBSF radars (13.8, 24, and 35 GHz) and two frequency pairs for the SBDF radar (13.8–35 GHz and 13.8–24 GHz) are used. For obtaining objective comparisons, the three instruments are assumed to operate with an identical detection threshold, spatial resolutions, and power measurement accuracy. The main aspects investigated are ...

Polarimetric radars provide measurements that describe the shape and dimensions of hydrometeors and are unaffected by calibration, attenuation, and the presence of ice. These measurements can potentially lead to a more detailed description of hydrometeors and to an improvement in quantitative rainfall rate estimation. The authors present an algorithm that exploits polarimetric measures for rain-rate estimation and investigate its application in a real-time framework by using measurements from the C-band polarimetric radar at Monte Settepani in Savona, Italy. It is based on a flowchart decision tree that allows the use of the best rain-rate retrieval algorithm, depending on the value of polarimetric variables. The methodology was applied to a real-time framework for more than a year, and the results were presented for all the significant events observed during the test period. To evaluate the performance of the algorithm, a comparison was made with rain gauge observation from a dense regional network. The performances of the algorithm were compared with those obtained by standard operational Z–R formulations to evaluate the benefit of this approach for operational applications.

The large number of millimetre and microwave terrestrial links that are in operation has lead to the belief that it may be possible to estimate two-dimensional rainfall rate fields. This paper outlines a reconstruction algorithm that can be used to estimate the rainfall field from a number of measurements of path attenuation on terrestrial links. In this paper we adopt a different approach to the regular-ization of this inverse problem. The main focus of the paper is a description of the retrieval algorithm which is demonstrated through simulation. The approach has been shown to be robust and relatively insensitive to errors and quantisation in the link measurements.

A range-profiling algorithm for rainfall rate retrieval from a single-frequency downward-looking spaceborne radar is presented. The algorithm is based on a linear reformulation of the radar equation. The path-integrated attenuation given by the surface echo measurement is used as a constraint for normalizing the range-gated rain echoes. The expected performances are studied analytically and compared with those of the approach of W. Hitschfeld and J. Bordan (1954). The stability and accuracy of the rainfall profile retrieval in the presence of various types of errors are pointed out and illustrated by means of numerical simulations of data processing from a spaceborne rain radar operating basically at Ku band. >

This paper extends some first steps given in merging radar and rain gauge data for a better understanding of rainfall rate spatial structure. The study has been performed analyzing spatial correlation between sites within a densely meshed rain gauge network and also with their corresponding pixels in weather radar images.

Peaks in number density observed by many authors using Joss-Waldvogel disdrometers are almost certainly due to the manner in which a best-fit curve was chosen to represent the calibration data. The extreme sensitivity to small changes in the calibration curve was demonstrated with extensive observations of Malaysian tropical rain, which for best-fit calibrations showed multiple peaks; however, when a linear interpolation between calibration points was applied, only single peaks were seen in this first, recalibrated dataset. Hence, field evidence for multiple peak equilibrium distributions obtained in numerical models should be reconsidered. On the other hand, the rainfall and radar reflectivity, calculated from best fits, differ by less than 4% from that based on linear interpolation between calibration points.

The performance (stability and accuracy) of a range profiling algorithm (kZS), using the measurement of surface echo as a constraint on total path integrated attenuation, for rainfall retrieval from a spaceborne radar was presented and discussed by C. Matzler (1987). Omitting other causes of errors previously examined, the present authors focus specifically on possible bias due to nonuniform beam filling effects. This problem is studied analytically to identify the physical origin of the biasing terms. In addition, bias in rainfall profile estimates is quantified through simulations of Ku-band spaceborne radar data processing, featuring the intercept of a raincell model by the downward looking radar beam. >

Spaceborne rain radars may be operated with a fairly low S/N (signal to noise ratio) condition, because of several hardware limitations and the large distance between radar and target. The rain radar does not directly measure the rain echo power, instead, it measures the total received power which is the sum of the rain echo power and the receiver noise power. The rain echo power is obtained by taking the difference between the total received power and the receiver noise power. The experimentally obtained rain echo power is sometimes estimated to be the negative value on the low S/N condition. The rainfall rate can not be directly estimated from this negative data. Three data processings, which are the truncation, the zero rain and the negative rain method, are applied to the rainfall rate estimation from the negative data. The zero rain method is effective for reducing the errors of the rainfall rate and its statistic. >

Radar methods and data processing techniques for improving rainfall rate estimates over such surfaces are gaining increasing interest. Algorithms proposed for rainfall rate retrieval exploit backscatter or attenuation. A number of single frequency algorithms have been surveyed by Marzoug and Amayenc (see Journ. of Atmosph. Ocean. Rem. Sensing, vol.11, p.1480-1506, 1994), each aiming to minimize the effects of errors. They showed that the surface-referenced algorithm referred to as kZS is potentially the most effective and stable, since it is not sensitive to calibration errors and to errors related to path integrated attenuation (PIA). It is reasonably expected that a well-grounded prediction of the backscattering behaviour of the sea surface when it is perturbed jointly by wind and rainfall can be usefully exploited to improve the kZS algorithm. To fulfil such prediction function, EM models are needed that suitably represent effects of rainfall on the NRCS (normalised radar cross section) of the sea surface. We first show that kZS performs at its best when uncertainty related to the sea surface NRCS is limited. Then, we suggest a possible kZS upgrade, based on the prediction of the NRCS of the water surface roughened by both wind and rainfall. Such prediction relies on the full wave model of Bahar (1987), which incidentally highlights the relevance of rainfall induced corrugation at the considered frequency (13.75 GHz). Results of numerical simulations are finally presented, that confirm the potential of the upgraded algorithm.