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SEVIRI rainfall retrieval and validation using weather radar observations

Identifieur interne : 000328 ( PascalFrancis/Curation ); précédent : 000327; suivant : 000329

SEVIRI rainfall retrieval and validation using weather radar observations

Auteurs : R. A. Roebeling [Pays-Bas] ; I. Holleman [Pays-Bas]

Source :

RBID : Pascal:10-0027521

Descripteurs français

English descriptors

Abstract

[1] This paper presents and validates a new algorithm to detect precipitating clouds and estimate rain rates from cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The precipitation properties (PP) algorithm uses information on cloud condensed water path (CWP), particle effective radius, and cloud thermodynamic phase to detect precipitating clouds, while information on CWP and cloud top height is used to estimate rain rates. An independent data set of weather radar data is used to determine the optimum settings of the PP algorithm and calibrated it. For a 2-month period, the ability of SEVIRI to discriminate precipitating from nonprecipitating clouds is evaluated using weather radar over the Netherlands. In addition, weather radar and rain gauge observations are used to validate the SEVIRI retrievals of rain rate and accumulated rainfall across the entire study area and period. During the observation period, the spatial extents of precipitation over the study area from SEVIRI and weather radar are highly correlated (correlation ≃ 0.90), while weaker correlations (correlation ≃ 0.63) are found between the spatially mean rain rate retrievals from these instruments. The combined use of information on CWP, cloud thermodynamic phase, and particle size for the detection of precipitation results in an increase in explained variance (∼10%) and decrease in false alarms (∼15%), as compared to detection methods that are solely based on a threshold CWP. At a pixel level, the SEVIRI retrievals have an acceptable accuracy (bias) of about 0.1 mm h-1 and a precision (standard error) of about 0.8 mm h-1. It is argued that parts of the differences are caused by collocation errors and parallax shifts in the SEVIRI data and by irregularities in the weather radar data. In future studies we intend to exploit the observations of the European weather radar network Operational Programme for the Exchange of Weather Radar Information (OPERA) and extend this study to the entirety of Europe.
pA  
A01 01  1    @0 0148-0227
A03   1    @0 J. geophys. res.
A05       @2 114
A06       @2 D21
A08 01  1  ENG  @1 SEVIRI rainfall retrieval and validation using weather radar observations
A11 01  1    @1 ROEBELING (R. A.)
A11 02  1    @1 HOLLEMAN (I.)
A14 01      @1 Royal Netherlands Meteorological Institute @2 De Bilt @3 NLD @Z 1 aut. @Z 2 aut.
A20       @2 D21202.1-D21202.13
A21       @1 2009
A23 01      @0 ENG
A43 01      @1 INIST @2 3144 @5 354000171711750070
A44       @0 0000 @1 © 2010 INIST-CNRS. All rights reserved.
A45       @0 3/4 p.
A47 01  1    @0 10-0027521
A60       @1 P
A61       @0 A
A64 01  1    @0 Journal of geophysical research
A66 01      @0 USA
C01 01    ENG  @0 [1] This paper presents and validates a new algorithm to detect precipitating clouds and estimate rain rates from cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The precipitation properties (PP) algorithm uses information on cloud condensed water path (CWP), particle effective radius, and cloud thermodynamic phase to detect precipitating clouds, while information on CWP and cloud top height is used to estimate rain rates. An independent data set of weather radar data is used to determine the optimum settings of the PP algorithm and calibrated it. For a 2-month period, the ability of SEVIRI to discriminate precipitating from nonprecipitating clouds is evaluated using weather radar over the Netherlands. In addition, weather radar and rain gauge observations are used to validate the SEVIRI retrievals of rain rate and accumulated rainfall across the entire study area and period. During the observation period, the spatial extents of precipitation over the study area from SEVIRI and weather radar are highly correlated (correlation ≃ 0.90), while weaker correlations (correlation ≃ 0.63) are found between the spatially mean rain rate retrievals from these instruments. The combined use of information on CWP, cloud thermodynamic phase, and particle size for the detection of precipitation results in an increase in explained variance (∼10%) and decrease in false alarms (∼15%), as compared to detection methods that are solely based on a threshold CWP. At a pixel level, the SEVIRI retrievals have an acceptable accuracy (bias) of about 0.1 mm h-1 and a precision (standard error) of about 0.8 mm h-1. It is argued that parts of the differences are caused by collocation errors and parallax shifts in the SEVIRI data and by irregularities in the weather radar data. In future studies we intend to exploit the observations of the European weather radar network Operational Programme for the Exchange of Weather Radar Information (OPERA) and extend this study to the entirety of Europe.
C02 01  3    @0 001E
C02 02  2    @0 001E01
C02 03  2    @0 220
C03 01  2  FRE  @0 Pluie @5 01
C03 01  2  ENG  @0 rainfall @5 01
C03 01  2  SPA  @0 Lluvia @5 01
C03 02  X  FRE  @0 Validation @5 02
C03 02  X  ENG  @0 Validation @5 02
C03 02  X  SPA  @0 Validación @5 02
C03 03  3  FRE  @0 Radar météorologique @5 03
C03 03  3  ENG  @0 Meteorological radar @5 03
C03 04  X  FRE  @0 Observation radar @5 04
C03 04  X  ENG  @0 Radar observation @5 04
C03 04  X  SPA  @0 Observación radar @5 04
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C03 06  2  FRE  @0 Nuage @5 06
C03 06  2  ENG  @0 clouds @5 06
C03 06  2  SPA  @0 Nube @5 06
C03 07  X  FRE  @0 Intensité pluie @5 07
C03 07  X  ENG  @0 Rainfall rate @5 07
C03 07  X  SPA  @0 Indice pluviosidad @5 07
C03 08  2  FRE  @0 Propriété physique @5 08
C03 08  2  ENG  @0 physical properties @5 08
C03 08  2  SPA  @0 Propiedad física @5 08
C03 09  2  FRE  @0 Précipitation atmosphérique @5 09
C03 09  2  ENG  @0 atmospheric precipitation @5 09
C03 09  2  SPA  @0 Precipitación atmosférica @5 09
C03 10  2  FRE  @0 Particule @5 10
C03 10  2  ENG  @0 particles @5 10
C03 11  X  FRE  @0 Rayon effectif @5 11
C03 11  X  ENG  @0 Effective radius @5 11
C03 11  X  SPA  @0 Radio efectivo @5 11
C03 12  2  FRE  @0 Thermodynamique @5 12
C03 12  2  ENG  @0 thermodynamics @5 12
C03 12  2  SPA  @0 Termodinámica @5 12
C03 13  X  FRE  @0 Sommet nuage @5 13
C03 13  X  ENG  @0 Cloud top @5 13
C03 13  X  SPA  @0 Cima nube @5 13
C03 14  X  FRE  @0 Hauteur @5 14
C03 14  X  ENG  @0 Height @5 14
C03 14  X  SPA  @0 Altura @5 14
C03 15  X  FRE  @0 Pluviomètre @5 15
C03 15  X  ENG  @0 Rain gauge @5 15
C03 15  X  SPA  @0 Pluviómetro @5 15
C03 16  2  FRE  @0 Corrélation @5 16
C03 16  2  ENG  @0 correlation @5 16
C03 16  2  SPA  @0 Correlación @5 16
C03 17  2  FRE  @0 Instrumentation @5 17
C03 17  2  ENG  @0 instruments @5 17
C03 17  2  SPA  @0 Instrumentación @5 17
C03 18  X  FRE  @0 Dimension particule @5 18
C03 18  X  ENG  @0 Particle size @5 18
C03 18  X  SPA  @0 Dimensión partícula @5 18
C03 19  2  FRE  @0 Détection @5 19
C03 19  2  ENG  @0 detection @5 19
C03 20  X  FRE  @0 Variance @5 20
C03 20  X  ENG  @0 Variance @5 20
C03 20  X  SPA  @0 Variancia @5 20
C03 21  2  FRE  @0 Pixel @5 21
C03 21  2  ENG  @0 Pixel @5 21
C03 21  2  SPA  @0 Pixel @5 21
C03 22  2  FRE  @0 Précision @5 22
C03 22  2  ENG  @0 accuracy @5 22
C03 22  2  SPA  @0 Precisión @5 22
C03 23  X  FRE  @0 Erreur systématique @5 23
C03 23  X  ENG  @0 Bias @5 23
C03 23  X  SPA  @0 Error sistemático @5 23
C03 24  2  FRE  @0 Echantillon référence @5 24
C03 24  2  ENG  @0 standard samples @5 24
C03 24  2  SPA  @0 Roca patrón @5 24
C03 25  3  FRE  @0 Réseau radar météorologique @5 25
C03 25  3  ENG  @0 Wheather radar network @5 25
C03 26  2  FRE  @0 Pays Bas @2 NG @5 61
C03 26  2  ENG  @0 Netherlands @2 NG @5 61
C03 26  2  SPA  @0 Holanda @2 NG @5 61
C07 01  2  FRE  @0 Europe Ouest @2 NG
C07 01  2  ENG  @0 Western Europe @2 NG
C07 01  2  SPA  @0 Europa del Oeste @2 NG
C07 02  2  FRE  @0 Europe @2 564
C07 02  2  ENG  @0 Europe @2 564
C07 02  2  SPA  @0 Europa @2 564
N21       @1 018
N44 01      @1 OTO
N82       @1 OTO

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Pascal:10-0027521

Le document en format XML

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<div type="abstract" xml:lang="en">[1] This paper presents and validates a new algorithm to detect precipitating clouds and estimate rain rates from cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The precipitation properties (PP) algorithm uses information on cloud condensed water path (CWP), particle effective radius, and cloud thermodynamic phase to detect precipitating clouds, while information on CWP and cloud top height is used to estimate rain rates. An independent data set of weather radar data is used to determine the optimum settings of the PP algorithm and calibrated it. For a 2-month period, the ability of SEVIRI to discriminate precipitating from nonprecipitating clouds is evaluated using weather radar over the Netherlands. In addition, weather radar and rain gauge observations are used to validate the SEVIRI retrievals of rain rate and accumulated rainfall across the entire study area and period. During the observation period, the spatial extents of precipitation over the study area from SEVIRI and weather radar are highly correlated (correlation ≃ 0.90), while weaker correlations (correlation ≃ 0.63) are found between the spatially mean rain rate retrievals from these instruments. The combined use of information on CWP, cloud thermodynamic phase, and particle size for the detection of precipitation results in an increase in explained variance (∼10%) and decrease in false alarms (∼15%), as compared to detection methods that are solely based on a threshold CWP. At a pixel level, the SEVIRI retrievals have an acceptable accuracy (bias) of about 0.1 mm h
<sup>-1</sup>
and a precision (standard error) of about 0.8 mm h
<sup>-1</sup>
. It is argued that parts of the differences are caused by collocation errors and parallax shifts in the SEVIRI data and by irregularities in the weather radar data. In future studies we intend to exploit the observations of the European weather radar network Operational Programme for the Exchange of Weather Radar Information (OPERA) and extend this study to the entirety of Europe.</div>
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<sup>-1</sup>
and a precision (standard error) of about 0.8 mm h
<sup>-1</sup>
. It is argued that parts of the differences are caused by collocation errors and parallax shifts in the SEVIRI data and by irregularities in the weather radar data. In future studies we intend to exploit the observations of the European weather radar network Operational Programme for the Exchange of Weather Radar Information (OPERA) and extend this study to the entirety of Europe.</s0>
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</fC03>
<fC03 i1="09" i2="2" l="FRE">
<s0>Précipitation atmosphérique</s0>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="2" l="ENG">
<s0>atmospheric precipitation</s0>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="2" l="SPA">
<s0>Precipitación atmosférica</s0>
<s5>09</s5>
</fC03>
<fC03 i1="10" i2="2" l="FRE">
<s0>Particule</s0>
<s5>10</s5>
</fC03>
<fC03 i1="10" i2="2" l="ENG">
<s0>particles</s0>
<s5>10</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE">
<s0>Rayon effectif</s0>
<s5>11</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG">
<s0>Effective radius</s0>
<s5>11</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA">
<s0>Radio efectivo</s0>
<s5>11</s5>
</fC03>
<fC03 i1="12" i2="2" l="FRE">
<s0>Thermodynamique</s0>
<s5>12</s5>
</fC03>
<fC03 i1="12" i2="2" l="ENG">
<s0>thermodynamics</s0>
<s5>12</s5>
</fC03>
<fC03 i1="12" i2="2" l="SPA">
<s0>Termodinámica</s0>
<s5>12</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE">
<s0>Sommet nuage</s0>
<s5>13</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG">
<s0>Cloud top</s0>
<s5>13</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA">
<s0>Cima nube</s0>
<s5>13</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE">
<s0>Hauteur</s0>
<s5>14</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG">
<s0>Height</s0>
<s5>14</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA">
<s0>Altura</s0>
<s5>14</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE">
<s0>Pluviomètre</s0>
<s5>15</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG">
<s0>Rain gauge</s0>
<s5>15</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA">
<s0>Pluviómetro</s0>
<s5>15</s5>
</fC03>
<fC03 i1="16" i2="2" l="FRE">
<s0>Corrélation</s0>
<s5>16</s5>
</fC03>
<fC03 i1="16" i2="2" l="ENG">
<s0>correlation</s0>
<s5>16</s5>
</fC03>
<fC03 i1="16" i2="2" l="SPA">
<s0>Correlación</s0>
<s5>16</s5>
</fC03>
<fC03 i1="17" i2="2" l="FRE">
<s0>Instrumentation</s0>
<s5>17</s5>
</fC03>
<fC03 i1="17" i2="2" l="ENG">
<s0>instruments</s0>
<s5>17</s5>
</fC03>
<fC03 i1="17" i2="2" l="SPA">
<s0>Instrumentación</s0>
<s5>17</s5>
</fC03>
<fC03 i1="18" i2="X" l="FRE">
<s0>Dimension particule</s0>
<s5>18</s5>
</fC03>
<fC03 i1="18" i2="X" l="ENG">
<s0>Particle size</s0>
<s5>18</s5>
</fC03>
<fC03 i1="18" i2="X" l="SPA">
<s0>Dimensión partícula</s0>
<s5>18</s5>
</fC03>
<fC03 i1="19" i2="2" l="FRE">
<s0>Détection</s0>
<s5>19</s5>
</fC03>
<fC03 i1="19" i2="2" l="ENG">
<s0>detection</s0>
<s5>19</s5>
</fC03>
<fC03 i1="20" i2="X" l="FRE">
<s0>Variance</s0>
<s5>20</s5>
</fC03>
<fC03 i1="20" i2="X" l="ENG">
<s0>Variance</s0>
<s5>20</s5>
</fC03>
<fC03 i1="20" i2="X" l="SPA">
<s0>Variancia</s0>
<s5>20</s5>
</fC03>
<fC03 i1="21" i2="2" l="FRE">
<s0>Pixel</s0>
<s5>21</s5>
</fC03>
<fC03 i1="21" i2="2" l="ENG">
<s0>Pixel</s0>
<s5>21</s5>
</fC03>
<fC03 i1="21" i2="2" l="SPA">
<s0>Pixel</s0>
<s5>21</s5>
</fC03>
<fC03 i1="22" i2="2" l="FRE">
<s0>Précision</s0>
<s5>22</s5>
</fC03>
<fC03 i1="22" i2="2" l="ENG">
<s0>accuracy</s0>
<s5>22</s5>
</fC03>
<fC03 i1="22" i2="2" l="SPA">
<s0>Precisión</s0>
<s5>22</s5>
</fC03>
<fC03 i1="23" i2="X" l="FRE">
<s0>Erreur systématique</s0>
<s5>23</s5>
</fC03>
<fC03 i1="23" i2="X" l="ENG">
<s0>Bias</s0>
<s5>23</s5>
</fC03>
<fC03 i1="23" i2="X" l="SPA">
<s0>Error sistemático</s0>
<s5>23</s5>
</fC03>
<fC03 i1="24" i2="2" l="FRE">
<s0>Echantillon référence</s0>
<s5>24</s5>
</fC03>
<fC03 i1="24" i2="2" l="ENG">
<s0>standard samples</s0>
<s5>24</s5>
</fC03>
<fC03 i1="24" i2="2" l="SPA">
<s0>Roca patrón</s0>
<s5>24</s5>
</fC03>
<fC03 i1="25" i2="3" l="FRE">
<s0>Réseau radar météorologique</s0>
<s5>25</s5>
</fC03>
<fC03 i1="25" i2="3" l="ENG">
<s0>Wheather radar network</s0>
<s5>25</s5>
</fC03>
<fC03 i1="26" i2="2" l="FRE">
<s0>Pays Bas</s0>
<s2>NG</s2>
<s5>61</s5>
</fC03>
<fC03 i1="26" i2="2" l="ENG">
<s0>Netherlands</s0>
<s2>NG</s2>
<s5>61</s5>
</fC03>
<fC03 i1="26" i2="2" l="SPA">
<s0>Holanda</s0>
<s2>NG</s2>
<s5>61</s5>
</fC03>
<fC07 i1="01" i2="2" l="FRE">
<s0>Europe Ouest</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="01" i2="2" l="ENG">
<s0>Western Europe</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="01" i2="2" l="SPA">
<s0>Europa del Oeste</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="02" i2="2" l="FRE">
<s0>Europe</s0>
<s2>564</s2>
</fC07>
<fC07 i1="02" i2="2" l="ENG">
<s0>Europe</s0>
<s2>564</s2>
</fC07>
<fC07 i1="02" i2="2" l="SPA">
<s0>Europa</s0>
<s2>564</s2>
</fC07>
<fN21>
<s1>018</s1>
</fN21>
<fN44 i1="01">
<s1>OTO</s1>
</fN44>
<fN82>
<s1>OTO</s1>
</fN82>
</pA>
</standard>
</inist>
</record>

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