Evaluation of GPM IMERG rainfall estimates in Singapore and assessing spatial sampling errors in ground reference

We evaluated the Integrated Multi-satellite Retrievals for GPM (IMERG) V06B Early and Final Run products using data from a dense gauge network in Singapore as ground reference (GR). The evaluation is carried out at monthly, daily, and hourly scales, and conditioned on different seasons and rainfa...

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Main Authors: Mandapaka, Pradeep V., Lo, Edmond Yat-Man
其他作者: School of Civil and Environmental Engineering
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語言:English
出版: 2021
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spelling sg-ntu-dr.10356-1460992021-01-26T07:58:28Z Evaluation of GPM IMERG rainfall estimates in Singapore and assessing spatial sampling errors in ground reference Mandapaka, Pradeep V. Lo, Edmond Yat-Man School of Civil and Environmental Engineering Institute of Catastrophe Risk Management (ICRM) Engineering::Civil engineering Rainfall Estimation Remote Sensing We evaluated the Integrated Multi-satellite Retrievals for GPM (IMERG) V06B Early and Final Run products using data from a dense gauge network in Singapore as ground reference (GR). The evaluation is carried out at monthly, daily, and hourly scales, and conditioned on different seasons and rainfall intensities. Further, different spatial configurations and densities of the gauge networks (3-17 gauges per IMERG cell) used here allowed us to examine spatial sampling errors (SSE) in the GR. The results revealed a probability of detection of 0.95 (0.65), critical success index of 0.69 (0.35), and a correlation of 0.60 (0.41) for the daily (hourly) scale. Results also indicate an overestimation of rainy days (hours) by IMERG compared to GR, leading to a false alarm ratio of 0.29 (0.57) at daily (hourly) scales. Analysis of probability distributions and conditional error metrics showed overestimation of lighter (0.2-4 mm/d) and moderate (4-8 mm/d) rainfall by IMERG, but better performance for heavier rainfall (≥32 mm/d). The seasonal analysis showed improved performance of IMERG during November-February compared to June-September months. The hourly analysis further revealed large discrepancies in diurnal cycles during June-September. The SSE are studied in a Monte Carlo framework consisting of several synthetic networks with varying spatial configurations and densities. The effect of SSE on IMERG evaluation results is characterized following the error variance separation approach. For the gauge networks studied here, the contribution of SSE variance to IMERG daily error variance ranges from 4-24% depending on gauge spatial configuration, and is as large as 36% during inter-monsoon months when rainfall is highly convective in nature. National Research Foundation (NRF) Accepted version The authors appreciate the partial support from the Singapore ETH Centre Future Resilience Systems project 2021-01-26T07:56:52Z 2021-01-26T07:56:52Z 2020 Journal Article Mandapaka, P. V., & Lo, E. Y. M. (2020). Evaluation of GPM IMERG rainfall estimates in Singapore and assessing spatial sampling errors in ground reference. Journal of Hydrometeorology, 21(12), 2963-2977. doi:10.1175/JHM-D-20-0135.1 1525-7541 https://hdl.handle.net/10356/146099 10.1175/JHM-D-20-0135.1 12 21 2963 2977 en Journal of Hydrometeorology © 2020 American Meteorological Society (AMS). All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Civil engineering
Rainfall Estimation
Remote Sensing
spellingShingle Engineering::Civil engineering
Rainfall Estimation
Remote Sensing
Mandapaka, Pradeep V.
Lo, Edmond Yat-Man
Evaluation of GPM IMERG rainfall estimates in Singapore and assessing spatial sampling errors in ground reference
description We evaluated the Integrated Multi-satellite Retrievals for GPM (IMERG) V06B Early and Final Run products using data from a dense gauge network in Singapore as ground reference (GR). The evaluation is carried out at monthly, daily, and hourly scales, and conditioned on different seasons and rainfall intensities. Further, different spatial configurations and densities of the gauge networks (3-17 gauges per IMERG cell) used here allowed us to examine spatial sampling errors (SSE) in the GR. The results revealed a probability of detection of 0.95 (0.65), critical success index of 0.69 (0.35), and a correlation of 0.60 (0.41) for the daily (hourly) scale. Results also indicate an overestimation of rainy days (hours) by IMERG compared to GR, leading to a false alarm ratio of 0.29 (0.57) at daily (hourly) scales. Analysis of probability distributions and conditional error metrics showed overestimation of lighter (0.2-4 mm/d) and moderate (4-8 mm/d) rainfall by IMERG, but better performance for heavier rainfall (≥32 mm/d). The seasonal analysis showed improved performance of IMERG during November-February compared to June-September months. The hourly analysis further revealed large discrepancies in diurnal cycles during June-September. The SSE are studied in a Monte Carlo framework consisting of several synthetic networks with varying spatial configurations and densities. The effect of SSE on IMERG evaluation results is characterized following the error variance separation approach. For the gauge networks studied here, the contribution of SSE variance to IMERG daily error variance ranges from 4-24% depending on gauge spatial configuration, and is as large as 36% during inter-monsoon months when rainfall is highly convective in nature.
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Mandapaka, Pradeep V.
Lo, Edmond Yat-Man
format Article
author Mandapaka, Pradeep V.
Lo, Edmond Yat-Man
author_sort Mandapaka, Pradeep V.
title Evaluation of GPM IMERG rainfall estimates in Singapore and assessing spatial sampling errors in ground reference
title_short Evaluation of GPM IMERG rainfall estimates in Singapore and assessing spatial sampling errors in ground reference
title_full Evaluation of GPM IMERG rainfall estimates in Singapore and assessing spatial sampling errors in ground reference
title_fullStr Evaluation of GPM IMERG rainfall estimates in Singapore and assessing spatial sampling errors in ground reference
title_full_unstemmed Evaluation of GPM IMERG rainfall estimates in Singapore and assessing spatial sampling errors in ground reference
title_sort evaluation of gpm imerg rainfall estimates in singapore and assessing spatial sampling errors in ground reference
publishDate 2021
url https://hdl.handle.net/10356/146099
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