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All Journal International Journal of Remote Sensing and Earth Sciences (IJReSES) The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Yunus Subagyo Swarinoto
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Earth & Planetary Sciences

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SPATIAL ANALYSIS OF QUANTITATIVE PRECIPITATION FORECAST ACCURACY BASED ON STRUCTURE AMPLITUDE LOCATION (SAL) TECHNIQUE Abdullah Ali; Achmad Rifani; Supriatna Supriatna; Yunus Subagyo Swarinoto; Umi Sa'adah
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 20, No 2 (2023)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3854

Abstract

Quantitative Precipitation Forecast (QPF) is the final product of a short-term forecasting algorithm (nowcasting) based on weather radar data which is widely used in hydrometeorological aspects. The calculation of the accuracy value using point data on a rainfall gauge often causes a double penalty problem because the QPF prediction results are in the form of spatial objects. This study aims to apply object-based spatial verification in analyzing the accuracy of QPF based on the Short Term Ensemble Prediction System (STEPS) algorithm using the SAL technique. The verification process is carried out by calculating the index value of the structure component (S), amplitude (A), and location (L) in the QPF prediction results based on the results of weather radar observations. The index values for components S and A have a range of -2 to 2, and 0 to 1 for component L with a perfect value of 0. The case study used is the occurrence of heavy rains that caused flooding in Bogor Regency in 2020. SAL verification results from 26 case studies used shows the average value of the components S, A, and L, respectively 0.51, 0.38, and 0.21. As many as 75% of all case studies have S and L component values less than 0.5 which indicate the structure and location of the QPF prediction object is close to the structure and location of the object of observation. A positive value in component A indicates that the QPF prediction results based on the STEPS algorithm tend to be overestimated but on a low scale, namely 0.38 out of 2.
SPATIAL ANALYSIS OF QUANTITATIVE PRECIPITATION FORECAST ACCURACY BASED ON STRUCTURE AMPLITUDE LOCATION (SAL) TECHNIQUE Abdullah Ali; Achmad Rifani; Supriatna; Yunus Subagyo Swarinoto; Umi Sa’adah
International Journal of Remote Sensing and Earth Sciences Vol. 20 No. 2 (2023)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3854

Abstract

Quantitative Precipitation Forecast (QPF) is the final product of a short-term forecasting algorithm (nowcasting) based on weather radar data which is widely used in hydrometeorological aspects. The calculation of the accuracy value using point data on a rainfall gauge often causes a double penalty problem because the QPF prediction results are in the form of spatial objects. This study aims to apply object-based spatial verification in analyzing the accuracy of QPF based on the Short Term Ensemble Prediction System (STEPS) algorithm using the SAL technique. The verification process is carried out by calculating the index value of the structure component (S), amplitude (A), and location (L) in the QPF prediction results based on the results of weather radar observations. The index values for components S and A have a range of -2 to 2, and 0 to 1 for component L with a perfect value of 0. The case study used is the occurrence of heavy rains that caused flooding in Bogor Regency in 2020. SAL verification results from 26 case studies used shows the average value of the components S, A, and L, respectively 0.51, 0.38, and 0.21. As many as 75% of all case studies have S and L component values less than 0.5 which indicate the structure and location of the QPF prediction object is close to the structure and location of the object of observation. A positive value in component A indicates that the QPF prediction results based on the STEPS algorithm tend to be overestimated but on a low scale, namely 0.38 out of 2.
Co-Authors Abdullah Ali Abdullah Ali Achmad Rifani Supriatna Supriatna Supriatna Umi Sa'adah Umi Sa’adah