Effect of radar beam geometry on radar rainfall estimation
Identifieur interne :
000F46 ( PascalFrancis/Curation );
précédent :
000F45;
suivant :
000F47
Effect of radar beam geometry on radar rainfall estimation
Auteurs : Siriluk Chumchean [
Australie] ;
Alan Seed [
Australie] ;
Ashish Sharma [
Australie]
Source :
-
IAHS-AISH publication [ 0144-7815 ] ; 2003.
RBID : Pascal:03-0459142
Descripteurs français
- Pascal (Inist)
- Australie,
Géométrie,
Pluie,
Fréquence,
Précision,
Goutte pluie,
Distribution dimension,
Résolution spatiale,
Erreur,
Transformation,
Algorithme,
Méthode radar,
Modèle hydrologique,
Prévision,
Etalonnage,
Tempête.
- Wicri :
English descriptors
- KwdEn :
- Australia,
accuracy,
algorithms,
calibration,
errors,
frequency,
geometry,
hydrological modeling,
prediction,
radar methods,
raindrops,
rainfall,
size distribution,
spatial resolution,
storms,
transformations.
Abstract
The frequency, accuracy and resolution of hydrological records is a major limitation in the accurate modelling of hydrological events. The weather radar provides real-time spatially continuous measurements covering a large area at short time intervals. However, considerable uncertainty remains in the procedures used to estimate the rainfall from weather radar observations. This uncertainty may be caused by the variability of raindrop size distribution, the variation of reflectivity with height and with range, and the temporal and spatial resolutions adopted for sampling the radar reflectivity. This paper accounts for the effect of the radar beam geometry as a function of distance. The conical shape of the radar beam causes the observed volume to increase with range from the radar, leading to both bias and an increase in the standard error associated with the measured reflectivity as a function of range. To remove the bias caused by the radar beam spreading, a simple-scaling transformation is proposed. The results show that the transformed reflectivity becomes relatively free from range dependent bias, which leads to more accurate relations with the measured ground rainfall than what is obtained otherwise. A 6-month long rainfall-reflectivity record from the Kurnell radar at Sydney, Australia is used to illustrate the efficiency and applicability of the reflectivity scaling transformation, compared to radar rainfall algorithms used conventionally.
| pA |
| A01 | 01 | 1 | | @0 0144-7815 |
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| A06 | | | | @2 282 |
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| A08 | 01 | 1 | ENG | @1 Effect of radar beam geometry on radar rainfall estimation |
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| A09 | 01 | 1 | ENG | @1 Weather radar information and distributed hydrological modelling : Sapporo, 30 June - 11 July 2003 |
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| A11 | 01 | 1 | | @1 CHUMCHEAN (Siriluk) |
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| A11 | 02 | 1 | | @1 SEED (Alan) |
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| A11 | 03 | 1 | | @1 SHARMA (Ashish) |
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| A12 | 01 | 1 | | @1 TACHIKAWA (Yasuto) @9 ed. |
|---|
| A12 | 02 | 1 | | @1 VIEUX (Baxter E.) @9 ed. |
|---|
| A12 | 03 | 1 | | @1 GEORGAKAKOS (Konstantine P.) @9 ed. |
|---|
| A12 | 04 | 1 | | @1 NAKAKITA (Eiichi) @9 ed. |
|---|
| A14 | 01 | | | @1 School of Civil and Environmental Engineering, The University of New South Wales @2 Sydney 2052, New South Wales @3 AUS @Z 1 aut. @Z 3 aut. |
|---|
| A14 | 02 | | | @1 Cooperative Research Centre for Catchment Hydrology, Bureau of Meteorology @2 Melbourne, Victoria @3 AUS @Z 2 aut. |
|---|
| A18 | 01 | 1 | | @1 International Association of Hydrological Sciences. International Commission on Surface Water @2 Paris @3 FRA @9 patr. |
|---|
| A20 | | | | @1 3-10 @7 2 |
|---|
| A21 | | | | @1 2003 |
|---|
| A23 | 01 | | | @0 ENG |
|---|
| A26 | 01 | | | @0 1-901502-37-6 |
|---|
| A43 | 01 | | | @1 INIST @2 8967 @5 354000117354390010 |
|---|
| A44 | | | | @0 0000 @1 © 2003 INIST-CNRS. All rights reserved. |
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| A45 | | | | @0 5 ref. |
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| A47 | 01 | 1 | | @0 03-0459142 |
|---|
| A60 | | | | @1 P @2 C |
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| A61 | | | | @0 A |
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| A64 | 01 | 1 | | @0 IAHS-AISH publication |
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| A66 | 01 | | | @0 GBR |
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| C01 | 01 | | ENG | @0 The frequency, accuracy and resolution of hydrological records is a major limitation in the accurate modelling of hydrological events. The weather radar provides real-time spatially continuous measurements covering a large area at short time intervals. However, considerable uncertainty remains in the procedures used to estimate the rainfall from weather radar observations. This uncertainty may be caused by the variability of raindrop size distribution, the variation of reflectivity with height and with range, and the temporal and spatial resolutions adopted for sampling the radar reflectivity. This paper accounts for the effect of the radar beam geometry as a function of distance. The conical shape of the radar beam causes the observed volume to increase with range from the radar, leading to both bias and an increase in the standard error associated with the measured reflectivity as a function of range. To remove the bias caused by the radar beam spreading, a simple-scaling transformation is proposed. The results show that the transformed reflectivity becomes relatively free from range dependent bias, which leads to more accurate relations with the measured ground rainfall than what is obtained otherwise. A 6-month long rainfall-reflectivity record from the Kurnell radar at Sydney, Australia is used to illustrate the efficiency and applicability of the reflectivity scaling transformation, compared to radar rainfall algorithms used conventionally. |
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| C02 | 01 | 2 | | @0 226A01 |
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| C02 | 02 | 2 | | @0 225B04 |
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| C02 | 03 | X | | @0 001E01N01 |
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| C02 | 04 | X | | @0 001E01M04 |
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| C03 | 01 | 2 | FRE | @0 Australie @2 NG @5 01 |
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| C03 | 01 | 2 | ENG | @0 Australia @2 NG @5 01 |
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| C03 | 01 | 2 | SPA | @0 Australia @2 NG @5 01 |
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| C03 | 02 | 2 | FRE | @0 Géométrie @5 06 |
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| C03 | 02 | 2 | ENG | @0 geometry @5 06 |
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| C03 | 02 | 2 | SPA | @0 Geometría @5 06 |
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| C03 | 03 | 2 | FRE | @0 Pluie @5 07 |
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| C03 | 03 | 2 | ENG | @0 rainfall @5 07 |
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| C03 | 03 | 2 | SPA | @0 Lluvia @5 07 |
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| C03 | 04 | 2 | FRE | @0 Fréquence @5 08 |
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| C03 | 04 | 2 | ENG | @0 frequency @5 08 |
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| C03 | 04 | 2 | SPA | @0 Frecuencia @5 08 |
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| C03 | 05 | 2 | FRE | @0 Précision @5 09 |
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| C03 | 05 | 2 | ENG | @0 accuracy @5 09 |
|---|
| C03 | 05 | 2 | SPA | @0 Precisión @5 09 |
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| C03 | 06 | 2 | FRE | @0 Goutte pluie @5 10 |
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| C03 | 06 | 2 | ENG | @0 raindrops @5 10 |
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| C03 | 06 | 2 | SPA | @0 Gota de lluvia @5 10 |
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| C03 | 07 | 2 | FRE | @0 Distribution dimension @5 11 |
|---|
| C03 | 07 | 2 | ENG | @0 size distribution @5 11 |
|---|
| C03 | 08 | 2 | FRE | @0 Résolution spatiale @5 12 |
|---|
| C03 | 08 | 2 | ENG | @0 spatial resolution @5 12 |
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| C03 | 09 | 2 | FRE | @0 Erreur @5 14 |
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| C03 | 09 | 2 | ENG | @0 errors @5 14 |
|---|
| C03 | 09 | 2 | SPA | @0 Error @5 14 |
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| C03 | 10 | 2 | FRE | @0 Transformation @5 15 |
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| C03 | 10 | 2 | ENG | @0 transformations @5 15 |
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| C03 | 10 | 2 | SPA | @0 Transformación @5 15 |
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| C03 | 11 | 2 | FRE | @0 Algorithme @5 17 |
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| C03 | 11 | 2 | ENG | @0 algorithms @5 17 |
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| C03 | 11 | 2 | SPA | @0 Algoritmo @5 17 |
|---|
| C03 | 12 | 2 | FRE | @0 Méthode radar @5 18 |
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| C03 | 12 | 2 | ENG | @0 radar methods @5 18 |
|---|
| C03 | 12 | 2 | SPA | @0 Radar @5 18 |
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| C03 | 13 | 2 | FRE | @0 Modèle hydrologique @5 19 |
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| C03 | 13 | 2 | ENG | @0 hydrological modeling @5 19 |
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| C03 | 14 | 2 | FRE | @0 Prévision @5 20 |
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| C03 | 14 | 2 | ENG | @0 prediction @5 20 |
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| C03 | 14 | 2 | SPA | @0 Previsión @5 20 |
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| C03 | 15 | 2 | FRE | @0 Etalonnage @5 21 |
|---|
| C03 | 15 | 2 | ENG | @0 calibration @5 21 |
|---|
| C03 | 15 | 2 | SPA | @0 Contraste @5 21 |
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| C03 | 16 | 2 | FRE | @0 Tempête @5 22 |
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| C03 | 16 | 2 | ENG | @0 storms @5 22 |
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| C03 | 16 | 2 | SPA | @0 Tempestad @5 22 |
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| C06 | | | | @0 ILS @0 TAS |
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| C07 | 01 | 2 | FRE | @0 Australasie |
|---|
| C07 | 01 | 2 | ENG | @0 Australasia |
|---|
| C07 | 01 | 2 | SPA | @0 Australasia |
|---|
| N21 | | | | @1 315 |
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| N82 | | | | @1 PSI |
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|
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| pR |
| A30 | 01 | 1 | ENG | @1 Weather radar information and distributed hydrological modelling. International symposium @3 Sapporo JPN @4 2003-06-30 |
|---|
|
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Le document en format XML
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<term>accuracy</term>
<term>algorithms</term>
<term>calibration</term>
<term>errors</term>
<term>frequency</term>
<term>geometry</term>
<term>hydrological modeling</term>
<term>prediction</term>
<term>radar methods</term>
<term>raindrops</term>
<term>rainfall</term>
<term>size distribution</term>
<term>spatial resolution</term>
<term>storms</term>
<term>transformations</term>
</keywords><keywords scheme="Pascal" xml:lang="fr"><term>Australie</term>
<term>Géométrie</term>
<term>Pluie</term>
<term>Fréquence</term>
<term>Précision</term>
<term>Goutte pluie</term>
<term>Distribution dimension</term>
<term>Résolution spatiale</term>
<term>Erreur</term>
<term>Transformation</term>
<term>Algorithme</term>
<term>Méthode radar</term>
<term>Modèle hydrologique</term>
<term>Prévision</term>
<term>Etalonnage</term>
<term>Tempête</term>
</keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Australie</term>
</keywords><front><div type="abstract" xml:lang="en">The frequency, accuracy and resolution of hydrological records is a major limitation in the accurate modelling of hydrological events. The weather radar provides real-time spatially continuous measurements covering a large area at short time intervals. However, considerable uncertainty remains in the procedures used to estimate the rainfall from weather radar observations. This uncertainty may be caused by the variability of raindrop size distribution, the variation of reflectivity with height and with range, and the temporal and spatial resolutions adopted for sampling the radar reflectivity. This paper accounts for the effect of the radar beam geometry as a function of distance. The conical shape of the radar beam causes the observed volume to increase with range from the radar, leading to both bias and an increase in the standard error associated with the measured reflectivity as a function of range. To remove the bias caused by the radar beam spreading, a simple-scaling transformation is proposed. The results show that the transformed reflectivity becomes relatively free from range dependent bias, which leads to more accurate relations with the measured ground rainfall than what is obtained otherwise. A 6-month long rainfall-reflectivity record from the Kurnell radar at Sydney, Australia is used to illustrate the efficiency and applicability of the reflectivity scaling transformation, compared to radar rainfall algorithms used conventionally.</div>
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</fA01><fA06><s2>282</s2>
</fA06><fA08 i1="01" i2="1" l="ENG"><s1>Effect of radar beam geometry on radar rainfall estimation</s1>
</fA08><fA09 i1="01" i2="1" l="ENG"><s1>Weather radar information and distributed hydrological modelling : Sapporo, 30 June - 11 July 2003</s1>
</fA09><fA11 i1="01" i2="1"><s1>CHUMCHEAN (Siriluk)</s1>
</fA11><fA11 i1="02" i2="1"><s1>SEED (Alan)</s1>
</fA11><fA11 i1="03" i2="1"><s1>SHARMA (Ashish)</s1>
</fA11><fA12 i1="01" i2="1"><s1>TACHIKAWA (Yasuto)</s1>
<s9>ed.</s9>
</fA12><fA12 i1="02" i2="1"><s1>VIEUX (Baxter E.)</s1>
<s9>ed.</s9>
</fA12><fA12 i1="03" i2="1"><s1>GEORGAKAKOS (Konstantine P.)</s1>
<s9>ed.</s9>
</fA12><fA12 i1="04" i2="1"><s1>NAKAKITA (Eiichi)</s1>
<s9>ed.</s9>
</fA12><fA14 i1="01"><s1>School of Civil and Environmental Engineering, The University of New South Wales</s1>
<s2>Sydney 2052, New South Wales</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>3 aut.</sZ>
</fA14><fA14 i1="02"><s1>Cooperative Research Centre for Catchment Hydrology, Bureau of Meteorology</s1>
<s2>Melbourne, Victoria</s2>
<s3>AUS</s3>
<sZ>2 aut.</sZ>
</fA14><fA18 i1="01" i2="1"><s1>International Association of Hydrological Sciences. International Commission on Surface Water</s1>
<s2>Paris</s2>
<s3>FRA</s3>
<s9>patr.</s9>
</fA18><fA20><s1>3-10</s1>
<s7>2</s7>
</fA20><fA21><s1>2003</s1>
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</fA66><fC01 i1="01" l="ENG"><s0>The frequency, accuracy and resolution of hydrological records is a major limitation in the accurate modelling of hydrological events. The weather radar provides real-time spatially continuous measurements covering a large area at short time intervals. However, considerable uncertainty remains in the procedures used to estimate the rainfall from weather radar observations. This uncertainty may be caused by the variability of raindrop size distribution, the variation of reflectivity with height and with range, and the temporal and spatial resolutions adopted for sampling the radar reflectivity. This paper accounts for the effect of the radar beam geometry as a function of distance. The conical shape of the radar beam causes the observed volume to increase with range from the radar, leading to both bias and an increase in the standard error associated with the measured reflectivity as a function of range. To remove the bias caused by the radar beam spreading, a simple-scaling transformation is proposed. The results show that the transformed reflectivity becomes relatively free from range dependent bias, which leads to more accurate relations with the measured ground rainfall than what is obtained otherwise. A 6-month long rainfall-reflectivity record from the Kurnell radar at Sydney, Australia is used to illustrate the efficiency and applicability of the reflectivity scaling transformation, compared to radar rainfall algorithms used conventionally.</s0>
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<s5>01</s5>
</fC03><fC03 i1="01" i2="2" l="ENG"><s0>Australia</s0>
<s2>NG</s2>
<s5>01</s5>
</fC03><fC03 i1="01" i2="2" l="SPA"><s0>Australia</s0>
<s2>NG</s2>
<s5>01</s5>
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<s5>06</s5>
</fC03><fC03 i1="02" i2="2" l="ENG"><s0>geometry</s0>
<s5>06</s5>
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<s5>06</s5>
</fC03><fC03 i1="03" i2="2" l="FRE"><s0>Pluie</s0>
<s5>07</s5>
</fC03><fC03 i1="03" i2="2" l="ENG"><s0>rainfall</s0>
<s5>07</s5>
</fC03><fC03 i1="03" i2="2" l="SPA"><s0>Lluvia</s0>
<s5>07</s5>
</fC03><fC03 i1="04" i2="2" l="FRE"><s0>Fréquence</s0>
<s5>08</s5>
</fC03><fC03 i1="04" i2="2" l="ENG"><s0>frequency</s0>
<s5>08</s5>
</fC03><fC03 i1="04" i2="2" l="SPA"><s0>Frecuencia</s0>
<s5>08</s5>
</fC03><fC03 i1="05" i2="2" l="FRE"><s0>Précision</s0>
<s5>09</s5>
</fC03><fC03 i1="05" i2="2" l="ENG"><s0>accuracy</s0>
<s5>09</s5>
</fC03><fC03 i1="05" i2="2" l="SPA"><s0>Precisión</s0>
<s5>09</s5>
</fC03><fC03 i1="06" i2="2" l="FRE"><s0>Goutte pluie</s0>
<s5>10</s5>
</fC03><fC03 i1="06" i2="2" l="ENG"><s0>raindrops</s0>
<s5>10</s5>
</fC03><fC03 i1="06" i2="2" l="SPA"><s0>Gota de lluvia</s0>
<s5>10</s5>
</fC03><fC03 i1="07" i2="2" l="FRE"><s0>Distribution dimension</s0>
<s5>11</s5>
</fC03><fC03 i1="07" i2="2" l="ENG"><s0>size distribution</s0>
<s5>11</s5>
</fC03><fC03 i1="08" i2="2" l="FRE"><s0>Résolution spatiale</s0>
<s5>12</s5>
</fC03><fC03 i1="08" i2="2" l="ENG"><s0>spatial resolution</s0>
<s5>12</s5>
</fC03><fC03 i1="09" i2="2" l="FRE"><s0>Erreur</s0>
<s5>14</s5>
</fC03><fC03 i1="09" i2="2" l="ENG"><s0>errors</s0>
<s5>14</s5>
</fC03><fC03 i1="09" i2="2" l="SPA"><s0>Error</s0>
<s5>14</s5>
</fC03><fC03 i1="10" i2="2" l="FRE"><s0>Transformation</s0>
<s5>15</s5>
</fC03><fC03 i1="10" i2="2" l="ENG"><s0>transformations</s0>
<s5>15</s5>
</fC03><fC03 i1="10" i2="2" l="SPA"><s0>Transformación</s0>
<s5>15</s5>
</fC03><fC03 i1="11" i2="2" l="FRE"><s0>Algorithme</s0>
<s5>17</s5>
</fC03><fC03 i1="11" i2="2" l="ENG"><s0>algorithms</s0>
<s5>17</s5>
</fC03><fC03 i1="11" i2="2" l="SPA"><s0>Algoritmo</s0>
<s5>17</s5>
</fC03><fC03 i1="12" i2="2" l="FRE"><s0>Méthode radar</s0>
<s5>18</s5>
</fC03><fC03 i1="12" i2="2" l="ENG"><s0>radar methods</s0>
<s5>18</s5>
</fC03><fC03 i1="12" i2="2" l="SPA"><s0>Radar</s0>
<s5>18</s5>
</fC03><fC03 i1="13" i2="2" l="FRE"><s0>Modèle hydrologique</s0>
<s5>19</s5>
</fC03><fC03 i1="13" i2="2" l="ENG"><s0>hydrological modeling</s0>
<s5>19</s5>
</fC03><fC03 i1="14" i2="2" l="FRE"><s0>Prévision</s0>
<s5>20</s5>
</fC03><fC03 i1="14" i2="2" l="ENG"><s0>prediction</s0>
<s5>20</s5>
</fC03><fC03 i1="14" i2="2" l="SPA"><s0>Previsión</s0>
<s5>20</s5>
</fC03><fC03 i1="15" i2="2" l="FRE"><s0>Etalonnage</s0>
<s5>21</s5>
</fC03><fC03 i1="15" i2="2" l="ENG"><s0>calibration</s0>
<s5>21</s5>
</fC03><fC03 i1="15" i2="2" l="SPA"><s0>Contraste</s0>
<s5>21</s5>
</fC03><fC03 i1="16" i2="2" l="FRE"><s0>Tempête</s0>
<s5>22</s5>
</fC03><fC03 i1="16" i2="2" l="ENG"><s0>storms</s0>
<s5>22</s5>
</fC03><fC03 i1="16" i2="2" l="SPA"><s0>Tempestad</s0>
<s5>22</s5>
</fC03><fC06><s0>ILS</s0>
<s0>TAS</s0>
</fC06><fC07 i1="01" i2="2" l="FRE"><s0>Australasie</s0>
</fC07><fC07 i1="01" i2="2" l="ENG"><s0>Australasia</s0>
</fC07><fC07 i1="01" i2="2" l="SPA"><s0>Australasia</s0>
</fC07><fN21><s1>315</s1>
</fN21><fN82><s1>PSI</s1>
</fN82><pR><fA30 i1="01" i2="1" l="ENG"><s1>Weather radar information and distributed hydrological modelling. International symposium</s1>
<s3>Sapporo JPN</s3>
<s4>2003-06-30</s4>
</fA30>
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