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P Tabary - One of the best experts on this subject based on the ideXlab platform.

  • the new french Operational polarimetric Radar rainfall rate product
    Journal of Applied Meteorology and Climatology, 2013
    Co-Authors: Jordi Figueras I Ventura, P Tabary
    Abstract:

    AbstractIn 2012 the Meteo 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...

  • Evaluation of the new French Operational weather Radar product for the field of urban hydrology
    Atmospheric Research, 2012
    Co-Authors: Isabelle Emmanuel, Hervé Andrieu, P Tabary
    Abstract:

    The main objective of this paper is to evaluate, at the urban scale, the accuracy of the new French Operational Radar processing chain deployed within the French Operational weather Radar network. Such an evaluation is conducted by comparing Radar data resulting from this processing chain (with a 1-km² resolution) to rain gauge data at four different time scales, i.e. 5,15, 30 and 60 min. These data are supplied by the Trappes Radar Station, located 30 km southwest of Paris. A total of 69 rain gauges installed within a radius of 80 km from the weather Radar have provided the ground reference data. The dataset comprises 50 varied rainy days. The influence of distance, quality code of the Radar data and type of rainfall, as well as the adjustment factor, is analyzed. The present analysis moreover seeks to evaluate the error specific to reference data by considering instrumental and representativeness errors, for the purpose of taking reference data accuracy into account in the Radar/rain gauge comparison. This study shows that Radar data obtained by means of this new Operational Radar processing chain are not yet reliable enough for direct use in quantitative applications within the field of urban hydrology.

  • the new french Operational Radar rainfall product part ii validation
    Weather and Forecasting, 2007
    Co-Authors: P Tabary, J Desplats, Do K Khac, F Eideliman, C Gueguen, J C Heinrich
    Abstract:

    Abstract A new Operational Radar-based rainfall product has been developed at Meteo-France and is currently being deployed within the French Operational network. The new quantitative precipitation estimation (QPE) product is based entirely on Radar data and includes a series of modules aimed at correcting for ground clutter, partial beam blocking, and vertical profile of reflectivity (VPR) effects, as well as the nonsimultaneity of Radar measurements. The surface rainfall estimation is computed as a weighted mean of the corrected tilts. In addition to the final QPE, a map of quality indexes is systematically generated. This paper is devoted to the validation of the new Radar QPE. The VPR identification module has been specifically validated by analyzing 489 precipitation events observed over 1 yr by a representative eight-Radar subset of the network. The conceptual model of VPR used in the QPE processing chain is shown to be relevant. A climatology of the three shape parameters of the conceptual VPR (brig...

  • the new french Operational Radar rainfall product part i methodology
    Weather and Forecasting, 2007
    Co-Authors: P Tabary
    Abstract:

    Abstract A new Radar-based rainfall product has been developed at Meteo-France and is currently being deployed within the French Operational Application Radar a la Meteorologie Infra-Synoptique (ARAMIS) Radar network. The rainfall product is based entirely on Radar data and comprises the following successive processing steps: 1) dynamic identification of ground clutter based on the pulse-to-pulse fluctuation of the Radar signal, 2) reflectivity-to-rain-rate conversion using the Marshall–Palmer Z–R relationship, 3) correction for partial beam blocking using numerical simulations of the interaction between the Radar wave and the terrain, 4) correction for vertical profile of reflectivity (VPR) effects based on ratio curves and a priori climatology-based VPR candidates, 5) correction for nonsimultaneity of Radar measurements by making use of a cross-correlation advection field, 6) weighted linear combination of the corrected reflectivity measurements gathered at the various elevation angles of the volume cov...

Alexis Berne - One of the best experts on this subject based on the ideXlab platform.

  • rainfall nowcasting by combining Radars microwave links and rain gauges
    arXiv: Atmospheric and Oceanic Physics, 2018
    Co-Authors: B Bianchi, Peter Jan Van Leeuwen, Robin J Hogan, Alexis Berne
    Abstract:

    The objective of this work is to provide high-resolution rain rate maps at short lead-time forecasts (nowcasts) necessary to anticipate flooding and properly manage sewage systems in urban areas by combining Radars, rain gauges, and Operational microwave links, and taking into account their respective uncertainties. A variational approach (3D-Var) is used to find the best estimate for the rain rate, and its error covariance, from the different rain sensors. Short-term rain rate forecasts are then produced by assuming Lagrangian persistence. A velocity field is obtained from the Operational Radar-derived rain fields, and the rain rate field is advected using the Total Variance Diminishing (TVD) scheme. The error covariance associated to the estimated rain rate is also propagated, and we use these two in the 3D-Var at the next observation time step. This approach can be seen as a Variational Kalman Filter (VKF), in which the covariance of the prior is not constant but dependent on time. The proposed approach has been tested using data from 14 rain gauges, 14 microwave links and the Operational Radar rain product from MeteoSwiss in the area of Zurich (Switzerland). During the applications the assumption of the Lagrangian persistence appears to be valid up to 20 min (a bit longer for stratiform events). During convective events, the algorithm is less powerful and shorter lead times should be considered (i.e., 15 min). Although such lead times are short, they are still useful to various hydrological and outdoor applications.

  • hydrometeor classification through statistical clustering of polarimetric Radar measurements a semi supervised approach
    Atmospheric Measurement Techniques, 2016
    Co-Authors: Nikola Besic, Jacopo Grazioli, Marco Gabella, Urs Germann, Jordi Figueras I Ventura, Alexis Berne
    Abstract:

    Abstract. Polarimetric Radar-based hydrometeor classification is the procedure of identifying different types of hydrometeors by exploiting polarimetric Radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behaviour of different hydrometeor types. Namely, the results of the classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it lacks the constraints related to the hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach, performed offline, which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of each hydrometeor class. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various hydrometeor types are obtained for each of the studied representative datasets, i.e. for each Radar system of interest. These are depicted by sets of centroids which are then employed in Operational labelling of different hydrometeors. The method has been applied on three C-band datasets, each acquired by different Operational Radar from the MeteoSwiss Rad4Alp network, as well as on two X-band datasets acquired by two research mobile Radars. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, emphasising the Operational potential of the proposed method.

  • a variational approach to retrieve rain rate by combining information from rain gauges Radars and microwave links
    Journal of Hydrometeorology, 2013
    Co-Authors: B Bianchi, Peter Jan Van Leeuwen, Robin J Hogan, Alexis Berne
    Abstract:

    Accurate and reliable rain rate estimates are important for various hydrometeorological applications. Consequently, rain sensors of different types have been deployed in many regions. In this work, measurements from different instruments, namely, rain gauge, weather Radar, and microwave link, are combined for the first time to estimate with greater accuracy the spatial distribution and intensity of rainfall. The objective is to retrieve the rain rate that is consistent with all these measurements while incorporating the uncertainty associated with the different sources of information. Assuming the problem is not strongly nonlinear, a variational approach is implemented and the Gauss–Newton method is used to minimize the cost function containing proper error estimates from all sensors. Furthermore, the method can be flexibly adapted to additional data sources. The proposed approach is tested using data from 14 rain gauges and 14 Operational microwave links located in the Zurich area (Switzerland) to correct the prior rain rate provided by the Operational Radar rain product from the Swiss meteorological service (MeteoSwiss). A cross-validation approach demonstrates the improvement of rain rate estimates when assimilating rain gauge and microwave link information.

R Uijlenhoet - One of the best experts on this subject based on the ideXlab platform.

  • a climatological benchmark for Operational Radar rainfall bias reduction
    Hydrology and Earth System Sciences, 2021
    Co-Authors: Ruben Imhoff, R Uijlenhoet, C C Brauer, Klaas Jan Van Heeringen, Hidde Leijnse, Aart Overeem, Albrecht Weerts
    Abstract:

    The presence of significant biases in real-time Radar quantitative precipitation estimations (QPEs) limits its use in hydrometeorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an Operational Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5ĝ€¯min Radar and reference rainfall data for the Netherlands. CARROTS is both Operationally available and independent of real-time rain gauge availability and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for 12 Dutch catchments and polders. We validated the results against the Operational mean field bias (MFB)-adjusted rainfall estimates and a reference dataset. This reference consists of the Radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 32 automatic and 319 manual rain gauges. Only the automatic gauges of this network are available in real time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher away from the Radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. The MFB-adjusted QPE outperforms the CARROTS-corrected QPE when the country-average rainfall estimates are compared to the reference. However, annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB-adjusted rainfall estimates for catchments away from the Radars, where the MFB-adjusted QPE generally underestimates the rainfall amounts. This difference is absent for catchments closer to the Radars. QPE underestimations are amplified when used in the hydrological model simulations. Discharge simulations using the QPE from CARROTS outperform those with the MFB-adjusted product for all but one basin. Moreover, the proposed factor derivation method is robust. It is hardly sensitive to leaving individual years out of the historical set and to the moving window length, given window sizes of more than a week.

  • automatic prediction of high resolution daily rainfall fields for multiple extents the potential of Operational Radar
    Journal of Hydrometeorology, 2007
    Co-Authors: J M Schuurmans, M F P Bierkens, Edzer Pebesma, R Uijlenhoet
    Abstract:

    Abstract This study investigates the added value of Operational Radar with respect to rain gauges in obtaining high-resolution daily rainfall fields as required in distributed hydrological modeling. To this end data from the Netherlands Operational national rain gauge network (330 gauges nationwide) is combined with an experimental network (30 gauges within 225 km2). Based on 74 selected rainfall events (March–October 2004) the spatial variability of daily rainfall is investigated at three spatial extents: small (225 km2), medium (10 000 km2), and large (82 875 km2). From this analysis it is shown that semivariograms show no clear dependence on season. Predictions of point rainfall are performed for all three extents using three different geostatistical methods: (i) ordinary kriging (OK; rain gauge data only), (ii) kriging with external drift (KED), and (iii) ordinary collocated cokriging (OCCK), with the latter two using both rain gauge data and range-corrected daily Radar composites—a standard operation...

Jordi Figueras I Ventura - One of the best experts on this subject based on the ideXlab platform.

  • hydrometeor classification through statistical clustering of polarimetric Radar measurements a semi supervised approach
    Atmospheric Measurement Techniques, 2016
    Co-Authors: Nikola Besic, Jacopo Grazioli, Marco Gabella, Urs Germann, Jordi Figueras I Ventura, Alexis Berne
    Abstract:

    Abstract. Polarimetric Radar-based hydrometeor classification is the procedure of identifying different types of hydrometeors by exploiting polarimetric Radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behaviour of different hydrometeor types. Namely, the results of the classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it lacks the constraints related to the hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach, performed offline, which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of each hydrometeor class. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various hydrometeor types are obtained for each of the studied representative datasets, i.e. for each Radar system of interest. These are depicted by sets of centroids which are then employed in Operational labelling of different hydrometeors. The method has been applied on three C-band datasets, each acquired by different Operational Radar from the MeteoSwiss Rad4Alp network, as well as on two X-band datasets acquired by two research mobile Radars. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, emphasising the Operational potential of the proposed method.

  • the new french Operational polarimetric Radar rainfall rate product
    Journal of Applied Meteorology and Climatology, 2013
    Co-Authors: Jordi Figueras I Ventura, P Tabary
    Abstract:

    AbstractIn 2012 the Meteo 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...

Sergey Y Matrosov - One of the best experts on this subject based on the ideXlab platform.

  • Radar rain rate estimators and their variability due to rainfall type an assessment based on hydrometeorology testbed data from the southeastern united states
    Journal of Applied Meteorology and Climatology, 2016
    Co-Authors: Sergey Y Matrosov, Robert Cifelli, Paul J Neiman, Allen B White
    Abstract:

    AbstractS-band profiling (S-PROF) Radar measurements from different southeastern U.S. Hydrometeorology Testbed sites indicated a frequent occurrence of rain that did not exhibit Radar bright band (BB) and was observed outside the periods of deep-convective precipitation. This common nonbrightband (NBB) rain contributes ~15%–20% of total accumulation and is not considered as a separate rain type by current precipitation-segregation Operational Radar-based schemes, which separate rain into stratiform, convective, and, sometimes, tropical types. Collocated with S-PROF, disdrometer measurements showed that drop size distributions (DSDs) of NBB rain have much larger relative fractions of smaller drops when compared with those of BB and convective rains. Data from a year of combined DSD and rain-type observations were used to derive S-band-Radar estimators of rain rate R, including those based on traditional reflectivity Ze and ones that also use differential reflectivity ZDR and specific differential phase KDP...

  • the use of cloudsat data to evaluate retrievals of total ice content in precipitating cloud systems from ground based Operational Radar measurements
    Journal of Applied Meteorology and Climatology, 2015
    Co-Authors: Sergey Y Matrosov
    Abstract:

    AbstractAn approach is described to retrieve the total amount of ice in a vertical atmospheric column in precipitating clouds observed by the Operational Weather Surveillance Radar-1988 Doppler (WSR-88D) systems. This amount expressed as ice water path (IWP) is retrieved using measurements obtained during standard WSR-88D scanning procedures performed when observing precipitation. WSR-88D-based IWP estimates are evaluated using dedicated cloud microphysical retrievals available from the CloudSat and auxiliary spaceborne measurements. The evaluation is performed using measurements obtained in extensive predominantly stratiform precipitation systems containing both ice hydrometeors aloft and rain near the ground. The analysis is based on retrievals of IWP from satellite and the ground-based KWGX and KSHV WSR-88D that are closely collocated in time and space. The comparison results indicate a relatively high correlation between satellite and WSR-88D IWP retrievals, with corresponding correlation coefficients...