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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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. |
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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 |
_version_ |
1690658483760791552 |