Diagnostic Alarm of Dew Point Temperature for the Occurrence of Middle Eastern Dust Storms
Abstract The sudden occurrence of dust storms results in significant economic damage, with additional negative impacts on public health and welfare. This study investigates one of the most vulnerable areas of the world to dust storms (Ahvaz, Iran) to determine whether there are any meteorological pa...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Goudarzi, Gholamreza [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Pure and applied geophysics - Basel : Birkhäuser, 1939, 179(2022), 12 vom: Dez., Seite 4657-4670 |
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| Übergeordnetes Werk: |
volume:179 ; year:2022 ; number:12 ; month:12 ; pages:4657-4670 |
| Links: |
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| DOI / URN: |
10.1007/s00024-022-03182-x |
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| Katalog-ID: |
SPR048912131 |
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| 520 | |a Abstract The sudden occurrence of dust storms results in significant economic damage, with additional negative impacts on public health and welfare. This study investigates one of the most vulnerable areas of the world to dust storms (Ahvaz, Iran) to determine whether there are any meteorological parameters with predictive skill through which weather forecasters can confidently warn the public about the likelihood of an impending dust storm the following day. To this end, this study focuses on data including meteorological parameters, visibility and particulate matter mass concentrations for both dust event days and preceding days for the period between 2008 and 2016. Data were obtained for four monitoring stations (Naderi, Havashenasi, Edareh Kol and Behdasht) from the Iran Meteorological Administration and Khuzestan Environmental Protection Organization. Pearson correlation coefficients were used to identify influential parameters for dust storm prediction, and an artificial neural network (ANN) approach was applied to predict the maximum dust concentration. Minimum dew point temperature 1 day prior to dust occurrences showed a significant correlation (p-value < 0.01) with the maximum 3-h mean $ PM_{10} $ concentration during dusty days. A less significant relationship (p-value = 0.045) was found when using the minimum dew point temperature from 2 days before dust occurrences. Using the minimum dew point temperature from 1 day before dust events with ANN resulted in strong forecasting skill for the maximum 3-h mean $ PM_{10} $ concentration during dusty days (R2 = 0.71). Therefore, dew point temperature may provide predictive skill for the next day’s dust events. | ||
| 650 | 4 | |a Dew point temperature |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a ANN |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a dust prediction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a PM |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Sorooshian, Armin |4 aut | |
| 700 | 1 | |a Alam, Khan |4 aut | |
| 700 | 1 | |a Weckwerth, Tammy M. |4 aut | |
| 700 | 1 | |a Hamid, Vafa |4 aut | |
| 700 | 1 | |a Maleki, Heidar |4 aut | |
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10.1007/s00024-022-03182-x doi (DE-627)SPR048912131 (SPR)s00024-022-03182-x-e DE-627 ger DE-627 rakwb eng Goudarzi, Gholamreza verfasserin aut Diagnostic Alarm of Dew Point Temperature for the Occurrence of Middle Eastern Dust Storms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The sudden occurrence of dust storms results in significant economic damage, with additional negative impacts on public health and welfare. This study investigates one of the most vulnerable areas of the world to dust storms (Ahvaz, Iran) to determine whether there are any meteorological parameters with predictive skill through which weather forecasters can confidently warn the public about the likelihood of an impending dust storm the following day. To this end, this study focuses on data including meteorological parameters, visibility and particulate matter mass concentrations for both dust event days and preceding days for the period between 2008 and 2016. Data were obtained for four monitoring stations (Naderi, Havashenasi, Edareh Kol and Behdasht) from the Iran Meteorological Administration and Khuzestan Environmental Protection Organization. Pearson correlation coefficients were used to identify influential parameters for dust storm prediction, and an artificial neural network (ANN) approach was applied to predict the maximum dust concentration. Minimum dew point temperature 1 day prior to dust occurrences showed a significant correlation (p-value < 0.01) with the maximum 3-h mean $ PM_{10} $ concentration during dusty days. A less significant relationship (p-value = 0.045) was found when using the minimum dew point temperature from 2 days before dust occurrences. Using the minimum dew point temperature from 1 day before dust events with ANN resulted in strong forecasting skill for the maximum 3-h mean $ PM_{10} $ concentration during dusty days (R2 = 0.71). Therefore, dew point temperature may provide predictive skill for the next day’s dust events. Dew point temperature (dpeaa)DE-He213 ANN (dpeaa)DE-He213 dust prediction (dpeaa)DE-He213 PM (dpeaa)DE-He213 Sorooshian, Armin aut Alam, Khan aut Weckwerth, Tammy M. aut Hamid, Vafa aut Maleki, Heidar aut Enthalten in Pure and applied geophysics Basel : Birkhäuser, 1939 179(2022), 12 vom: Dez., Seite 4657-4670 (DE-627)265506743 (DE-600)1464028-4 1420-9136 nnns volume:179 year:2022 number:12 month:12 pages:4657-4670 https://dx.doi.org/10.1007/s00024-022-03182-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 179 2022 12 12 4657-4670 |
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10.1007/s00024-022-03182-x doi (DE-627)SPR048912131 (SPR)s00024-022-03182-x-e DE-627 ger DE-627 rakwb eng Goudarzi, Gholamreza verfasserin aut Diagnostic Alarm of Dew Point Temperature for the Occurrence of Middle Eastern Dust Storms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The sudden occurrence of dust storms results in significant economic damage, with additional negative impacts on public health and welfare. This study investigates one of the most vulnerable areas of the world to dust storms (Ahvaz, Iran) to determine whether there are any meteorological parameters with predictive skill through which weather forecasters can confidently warn the public about the likelihood of an impending dust storm the following day. To this end, this study focuses on data including meteorological parameters, visibility and particulate matter mass concentrations for both dust event days and preceding days for the period between 2008 and 2016. Data were obtained for four monitoring stations (Naderi, Havashenasi, Edareh Kol and Behdasht) from the Iran Meteorological Administration and Khuzestan Environmental Protection Organization. Pearson correlation coefficients were used to identify influential parameters for dust storm prediction, and an artificial neural network (ANN) approach was applied to predict the maximum dust concentration. Minimum dew point temperature 1 day prior to dust occurrences showed a significant correlation (p-value < 0.01) with the maximum 3-h mean $ PM_{10} $ concentration during dusty days. A less significant relationship (p-value = 0.045) was found when using the minimum dew point temperature from 2 days before dust occurrences. Using the minimum dew point temperature from 1 day before dust events with ANN resulted in strong forecasting skill for the maximum 3-h mean $ PM_{10} $ concentration during dusty days (R2 = 0.71). Therefore, dew point temperature may provide predictive skill for the next day’s dust events. Dew point temperature (dpeaa)DE-He213 ANN (dpeaa)DE-He213 dust prediction (dpeaa)DE-He213 PM (dpeaa)DE-He213 Sorooshian, Armin aut Alam, Khan aut Weckwerth, Tammy M. aut Hamid, Vafa aut Maleki, Heidar aut Enthalten in Pure and applied geophysics Basel : Birkhäuser, 1939 179(2022), 12 vom: Dez., Seite 4657-4670 (DE-627)265506743 (DE-600)1464028-4 1420-9136 nnns volume:179 year:2022 number:12 month:12 pages:4657-4670 https://dx.doi.org/10.1007/s00024-022-03182-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 179 2022 12 12 4657-4670 |
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10.1007/s00024-022-03182-x doi (DE-627)SPR048912131 (SPR)s00024-022-03182-x-e DE-627 ger DE-627 rakwb eng Goudarzi, Gholamreza verfasserin aut Diagnostic Alarm of Dew Point Temperature for the Occurrence of Middle Eastern Dust Storms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The sudden occurrence of dust storms results in significant economic damage, with additional negative impacts on public health and welfare. This study investigates one of the most vulnerable areas of the world to dust storms (Ahvaz, Iran) to determine whether there are any meteorological parameters with predictive skill through which weather forecasters can confidently warn the public about the likelihood of an impending dust storm the following day. To this end, this study focuses on data including meteorological parameters, visibility and particulate matter mass concentrations for both dust event days and preceding days for the period between 2008 and 2016. Data were obtained for four monitoring stations (Naderi, Havashenasi, Edareh Kol and Behdasht) from the Iran Meteorological Administration and Khuzestan Environmental Protection Organization. Pearson correlation coefficients were used to identify influential parameters for dust storm prediction, and an artificial neural network (ANN) approach was applied to predict the maximum dust concentration. Minimum dew point temperature 1 day prior to dust occurrences showed a significant correlation (p-value < 0.01) with the maximum 3-h mean $ PM_{10} $ concentration during dusty days. A less significant relationship (p-value = 0.045) was found when using the minimum dew point temperature from 2 days before dust occurrences. Using the minimum dew point temperature from 1 day before dust events with ANN resulted in strong forecasting skill for the maximum 3-h mean $ PM_{10} $ concentration during dusty days (R2 = 0.71). Therefore, dew point temperature may provide predictive skill for the next day’s dust events. Dew point temperature (dpeaa)DE-He213 ANN (dpeaa)DE-He213 dust prediction (dpeaa)DE-He213 PM (dpeaa)DE-He213 Sorooshian, Armin aut Alam, Khan aut Weckwerth, Tammy M. aut Hamid, Vafa aut Maleki, Heidar aut Enthalten in Pure and applied geophysics Basel : Birkhäuser, 1939 179(2022), 12 vom: Dez., Seite 4657-4670 (DE-627)265506743 (DE-600)1464028-4 1420-9136 nnns volume:179 year:2022 number:12 month:12 pages:4657-4670 https://dx.doi.org/10.1007/s00024-022-03182-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 179 2022 12 12 4657-4670 |
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10.1007/s00024-022-03182-x doi (DE-627)SPR048912131 (SPR)s00024-022-03182-x-e DE-627 ger DE-627 rakwb eng Goudarzi, Gholamreza verfasserin aut Diagnostic Alarm of Dew Point Temperature for the Occurrence of Middle Eastern Dust Storms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The sudden occurrence of dust storms results in significant economic damage, with additional negative impacts on public health and welfare. This study investigates one of the most vulnerable areas of the world to dust storms (Ahvaz, Iran) to determine whether there are any meteorological parameters with predictive skill through which weather forecasters can confidently warn the public about the likelihood of an impending dust storm the following day. To this end, this study focuses on data including meteorological parameters, visibility and particulate matter mass concentrations for both dust event days and preceding days for the period between 2008 and 2016. Data were obtained for four monitoring stations (Naderi, Havashenasi, Edareh Kol and Behdasht) from the Iran Meteorological Administration and Khuzestan Environmental Protection Organization. Pearson correlation coefficients were used to identify influential parameters for dust storm prediction, and an artificial neural network (ANN) approach was applied to predict the maximum dust concentration. Minimum dew point temperature 1 day prior to dust occurrences showed a significant correlation (p-value < 0.01) with the maximum 3-h mean $ PM_{10} $ concentration during dusty days. A less significant relationship (p-value = 0.045) was found when using the minimum dew point temperature from 2 days before dust occurrences. Using the minimum dew point temperature from 1 day before dust events with ANN resulted in strong forecasting skill for the maximum 3-h mean $ PM_{10} $ concentration during dusty days (R2 = 0.71). Therefore, dew point temperature may provide predictive skill for the next day’s dust events. Dew point temperature (dpeaa)DE-He213 ANN (dpeaa)DE-He213 dust prediction (dpeaa)DE-He213 PM (dpeaa)DE-He213 Sorooshian, Armin aut Alam, Khan aut Weckwerth, Tammy M. aut Hamid, Vafa aut Maleki, Heidar aut Enthalten in Pure and applied geophysics Basel : Birkhäuser, 1939 179(2022), 12 vom: Dez., Seite 4657-4670 (DE-627)265506743 (DE-600)1464028-4 1420-9136 nnns volume:179 year:2022 number:12 month:12 pages:4657-4670 https://dx.doi.org/10.1007/s00024-022-03182-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 179 2022 12 12 4657-4670 |
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10.1007/s00024-022-03182-x doi (DE-627)SPR048912131 (SPR)s00024-022-03182-x-e DE-627 ger DE-627 rakwb eng Goudarzi, Gholamreza verfasserin aut Diagnostic Alarm of Dew Point Temperature for the Occurrence of Middle Eastern Dust Storms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The sudden occurrence of dust storms results in significant economic damage, with additional negative impacts on public health and welfare. This study investigates one of the most vulnerable areas of the world to dust storms (Ahvaz, Iran) to determine whether there are any meteorological parameters with predictive skill through which weather forecasters can confidently warn the public about the likelihood of an impending dust storm the following day. To this end, this study focuses on data including meteorological parameters, visibility and particulate matter mass concentrations for both dust event days and preceding days for the period between 2008 and 2016. Data were obtained for four monitoring stations (Naderi, Havashenasi, Edareh Kol and Behdasht) from the Iran Meteorological Administration and Khuzestan Environmental Protection Organization. Pearson correlation coefficients were used to identify influential parameters for dust storm prediction, and an artificial neural network (ANN) approach was applied to predict the maximum dust concentration. Minimum dew point temperature 1 day prior to dust occurrences showed a significant correlation (p-value < 0.01) with the maximum 3-h mean $ PM_{10} $ concentration during dusty days. A less significant relationship (p-value = 0.045) was found when using the minimum dew point temperature from 2 days before dust occurrences. Using the minimum dew point temperature from 1 day before dust events with ANN resulted in strong forecasting skill for the maximum 3-h mean $ PM_{10} $ concentration during dusty days (R2 = 0.71). Therefore, dew point temperature may provide predictive skill for the next day’s dust events. Dew point temperature (dpeaa)DE-He213 ANN (dpeaa)DE-He213 dust prediction (dpeaa)DE-He213 PM (dpeaa)DE-He213 Sorooshian, Armin aut Alam, Khan aut Weckwerth, Tammy M. aut Hamid, Vafa aut Maleki, Heidar aut Enthalten in Pure and applied geophysics Basel : Birkhäuser, 1939 179(2022), 12 vom: Dez., Seite 4657-4670 (DE-627)265506743 (DE-600)1464028-4 1420-9136 nnns volume:179 year:2022 number:12 month:12 pages:4657-4670 https://dx.doi.org/10.1007/s00024-022-03182-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 179 2022 12 12 4657-4670 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The sudden occurrence of dust storms results in significant economic damage, with additional negative impacts on public health and welfare. This study investigates one of the most vulnerable areas of the world to dust storms (Ahvaz, Iran) to determine whether there are any meteorological parameters with predictive skill through which weather forecasters can confidently warn the public about the likelihood of an impending dust storm the following day. To this end, this study focuses on data including meteorological parameters, visibility and particulate matter mass concentrations for both dust event days and preceding days for the period between 2008 and 2016. Data were obtained for four monitoring stations (Naderi, Havashenasi, Edareh Kol and Behdasht) from the Iran Meteorological Administration and Khuzestan Environmental Protection Organization. Pearson correlation coefficients were used to identify influential parameters for dust storm prediction, and an artificial neural network (ANN) approach was applied to predict the maximum dust concentration. Minimum dew point temperature 1 day prior to dust occurrences showed a significant correlation (p-value < 0.01) with the maximum 3-h mean $ PM_{10} $ concentration during dusty days. A less significant relationship (p-value = 0.045) was found when using the minimum dew point temperature from 2 days before dust occurrences. Using the minimum dew point temperature from 1 day before dust events with ANN resulted in strong forecasting skill for the maximum 3-h mean $ PM_{10} $ concentration during dusty days (R2 = 0.71). 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Goudarzi, Gholamreza |
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Goudarzi, Gholamreza misc Dew point temperature misc ANN misc dust prediction misc PM Diagnostic Alarm of Dew Point Temperature for the Occurrence of Middle Eastern Dust Storms |
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Diagnostic Alarm of Dew Point Temperature for the Occurrence of Middle Eastern Dust Storms Dew point temperature (dpeaa)DE-He213 ANN (dpeaa)DE-He213 dust prediction (dpeaa)DE-He213 PM (dpeaa)DE-He213 |
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Goudarzi, Gholamreza Sorooshian, Armin Alam, Khan Weckwerth, Tammy M. Hamid, Vafa Maleki, Heidar |
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diagnostic alarm of dew point temperature for the occurrence of middle eastern dust storms |
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Diagnostic Alarm of Dew Point Temperature for the Occurrence of Middle Eastern Dust Storms |
| abstract |
Abstract The sudden occurrence of dust storms results in significant economic damage, with additional negative impacts on public health and welfare. This study investigates one of the most vulnerable areas of the world to dust storms (Ahvaz, Iran) to determine whether there are any meteorological parameters with predictive skill through which weather forecasters can confidently warn the public about the likelihood of an impending dust storm the following day. To this end, this study focuses on data including meteorological parameters, visibility and particulate matter mass concentrations for both dust event days and preceding days for the period between 2008 and 2016. Data were obtained for four monitoring stations (Naderi, Havashenasi, Edareh Kol and Behdasht) from the Iran Meteorological Administration and Khuzestan Environmental Protection Organization. Pearson correlation coefficients were used to identify influential parameters for dust storm prediction, and an artificial neural network (ANN) approach was applied to predict the maximum dust concentration. Minimum dew point temperature 1 day prior to dust occurrences showed a significant correlation (p-value < 0.01) with the maximum 3-h mean $ PM_{10} $ concentration during dusty days. A less significant relationship (p-value = 0.045) was found when using the minimum dew point temperature from 2 days before dust occurrences. Using the minimum dew point temperature from 1 day before dust events with ANN resulted in strong forecasting skill for the maximum 3-h mean $ PM_{10} $ concentration during dusty days (R2 = 0.71). Therefore, dew point temperature may provide predictive skill for the next day’s dust events. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract The sudden occurrence of dust storms results in significant economic damage, with additional negative impacts on public health and welfare. This study investigates one of the most vulnerable areas of the world to dust storms (Ahvaz, Iran) to determine whether there are any meteorological parameters with predictive skill through which weather forecasters can confidently warn the public about the likelihood of an impending dust storm the following day. To this end, this study focuses on data including meteorological parameters, visibility and particulate matter mass concentrations for both dust event days and preceding days for the period between 2008 and 2016. Data were obtained for four monitoring stations (Naderi, Havashenasi, Edareh Kol and Behdasht) from the Iran Meteorological Administration and Khuzestan Environmental Protection Organization. Pearson correlation coefficients were used to identify influential parameters for dust storm prediction, and an artificial neural network (ANN) approach was applied to predict the maximum dust concentration. Minimum dew point temperature 1 day prior to dust occurrences showed a significant correlation (p-value < 0.01) with the maximum 3-h mean $ PM_{10} $ concentration during dusty days. A less significant relationship (p-value = 0.045) was found when using the minimum dew point temperature from 2 days before dust occurrences. Using the minimum dew point temperature from 1 day before dust events with ANN resulted in strong forecasting skill for the maximum 3-h mean $ PM_{10} $ concentration during dusty days (R2 = 0.71). Therefore, dew point temperature may provide predictive skill for the next day’s dust events. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The sudden occurrence of dust storms results in significant economic damage, with additional negative impacts on public health and welfare. This study investigates one of the most vulnerable areas of the world to dust storms (Ahvaz, Iran) to determine whether there are any meteorological parameters with predictive skill through which weather forecasters can confidently warn the public about the likelihood of an impending dust storm the following day. To this end, this study focuses on data including meteorological parameters, visibility and particulate matter mass concentrations for both dust event days and preceding days for the period between 2008 and 2016. Data were obtained for four monitoring stations (Naderi, Havashenasi, Edareh Kol and Behdasht) from the Iran Meteorological Administration and Khuzestan Environmental Protection Organization. Pearson correlation coefficients were used to identify influential parameters for dust storm prediction, and an artificial neural network (ANN) approach was applied to predict the maximum dust concentration. Minimum dew point temperature 1 day prior to dust occurrences showed a significant correlation (p-value < 0.01) with the maximum 3-h mean $ PM_{10} $ concentration during dusty days. A less significant relationship (p-value = 0.045) was found when using the minimum dew point temperature from 2 days before dust occurrences. Using the minimum dew point temperature from 1 day before dust events with ANN resulted in strong forecasting skill for the maximum 3-h mean $ PM_{10} $ concentration during dusty days (R2 = 0.71). Therefore, dew point temperature may provide predictive skill for the next day’s dust events. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
Diagnostic Alarm of Dew Point Temperature for the Occurrence of Middle Eastern Dust Storms |
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https://dx.doi.org/10.1007/s00024-022-03182-x |
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Sorooshian, Armin Alam, Khan Weckwerth, Tammy M. Hamid, Vafa Maleki, Heidar |
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Sorooshian, Armin Alam, Khan Weckwerth, Tammy M. Hamid, Vafa Maleki, Heidar |
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10.1007/s00024-022-03182-x |
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2024-07-03T22:13:13.694Z |
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| score |
7.4009743 |