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Ground validation and bias correction of GPM-IMERG V6 satellite precipitation product over Sweden

Moravej, Mojtaba LU (2020) In TVVR20/5020 VVRM01 20191
Division of Water Resources Engineering
Abstract
This study attempts to assess and correct the performance of Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM-IMERG) final run daily precipitation product across Sweden. The performance of GPM-IMERG version 6 final run was evaluated against 677 rain gauges in a period from 12 March 2014 to 31 May 2019. Continuous and categorical performance measures were used to characterise different attributes of performance that included correlation coefficient (CC), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), relative bias (rBias), modified relative bias (rBias_ε), false alarm detection (FAR), probability of detection (POD), critical success index (CSI), Heidke skill score (HSS), Kling-Gupta efficiency... (More)
This study attempts to assess and correct the performance of Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM-IMERG) final run daily precipitation product across Sweden. The performance of GPM-IMERG version 6 final run was evaluated against 677 rain gauges in a period from 12 March 2014 to 31 May 2019. Continuous and categorical performance measures were used to characterise different attributes of performance that included correlation coefficient (CC), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), relative bias (rBias), modified relative bias (rBias_ε), false alarm detection (FAR), probability of detection (POD), critical success index (CSI), Heidke skill score (HSS), Kling-Gupta efficiency (KGE), and Willmott index of agreement (WIA). In addition to general evaluations, the impacts of temporal and spatial variability, elevation, and precipitation intensity on satellite performance were also studied.
The results showed that CC, RMSE, and rBias is 0.70, 3.65 mm, and +13.65% (overestimation), respectively, with the best performance observed in spring and autumn followed by summer and winter. The performance of GPM-IMERG spatially varies: it overestimates precipitation in regions below 60°N, close to coastlines, and lowlands. The performance is also not consistent for different precipitation intensities. Precipitation events over 20 mm/day are substantially underestimated while light precipitation (< 1 mm) is overestimated.
A framework for bias correction was developed that uses the results of ground-validation for recognising the driving factors on satellite bias and a Monte Carlo Cross Validation approach for calibrating bias correction models. Using the framework, bias correction models for each month and each precipitation intensity categories were developed to correct GPM-IMERG product. The corrected dataset shows overall improvement over entire domain, confirming the utility of the developed framework.
This study could contribute to both practical use and further development of GPM-IMERG by providing an insight about the performance and the improved data over Sweden. (Less)
Popular Abstract
Precipitation is a scientific term for any forms of water that falls on the ground, for example rain, snow, or hail. We can measure precipitation from space using satellites. The latest cutting-edge technology for doing this is called GPM-IMERG. In this research, I looked at how accurate is GPM-IMERG over Sweden? I analysed its accuracy from a range of statistical perspectives, for example, how accurate it is to tell us if it rains in a certain day and how much it rains. I found that GPM-IMERG has 3.65 mm error on average which is a relatively accurate enough for measuring precipitation from space. However, it tends to guess the amount of precipitation more than it actually is. That is why we need to correct this error that scientists call... (More)
Precipitation is a scientific term for any forms of water that falls on the ground, for example rain, snow, or hail. We can measure precipitation from space using satellites. The latest cutting-edge technology for doing this is called GPM-IMERG. In this research, I looked at how accurate is GPM-IMERG over Sweden? I analysed its accuracy from a range of statistical perspectives, for example, how accurate it is to tell us if it rains in a certain day and how much it rains. I found that GPM-IMERG has 3.65 mm error on average which is a relatively accurate enough for measuring precipitation from space. However, it tends to guess the amount of precipitation more than it actually is. That is why we need to correct this error that scientists call ‘bias’.
I analysed bias and found out it varies for different months. It also depends on elevation, for example, bias is bigger for areas with low elevation. It also varies depending on how intense precipitation is, for example, GPM-IMERG guesses the amount of drizzles higher than reality; but for big storms, its guess is very small. Based on this knowledge, I came up with a new method to correct bias. The method showed useful over Sweden and it helps us to more accurately measure precipitation from space using satellites. (Less)
Please use this url to cite or link to this publication:
author
Moravej, Mojtaba LU
supervisor
organization
alternative title
Markvalidering och biaskorrektion av GPM-IMERG V6 satellitutfällningsprodukt över Sverige
course
VVRM01 20191
year
type
H2 - Master's Degree (Two Years)
subject
keywords
satellite precipitation product, bias correction, GPM-IMERG, Monte Carlo, evaluation, rain
publication/series
TVVR20/5020
report number
20/5020
ISSN
1101-9824
language
English
additional info
Examiner: Magnus Persson
id
9032564
date added to LUP
2020-12-07 13:26:04
date last changed
2020-12-07 13:26:04
@misc{9032564,
  abstract     = {{This study attempts to assess and correct the performance of Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM-IMERG) final run daily precipitation product across Sweden. The performance of GPM-IMERG version 6 final run was evaluated against 677 rain gauges in a period from 12 March 2014 to 31 May 2019. Continuous and categorical performance measures were used to characterise different attributes of performance that included correlation coefficient (CC), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), relative bias (rBias), modified relative bias (rBias_ε), false alarm detection (FAR), probability of detection (POD), critical success index (CSI), Heidke skill score (HSS), Kling-Gupta efficiency (KGE), and Willmott index of agreement (WIA). In addition to general evaluations, the impacts of temporal and spatial variability, elevation, and precipitation intensity on satellite performance were also studied.
The results showed that CC, RMSE, and rBias is 0.70, 3.65 mm, and +13.65% (overestimation), respectively, with the best performance observed in spring and autumn followed by summer and winter. The performance of GPM-IMERG spatially varies: it overestimates precipitation in regions below 60°N, close to coastlines, and lowlands. The performance is also not consistent for different precipitation intensities. Precipitation events over 20 mm/day are substantially underestimated while light precipitation (< 1 mm) is overestimated. 
A framework for bias correction was developed that uses the results of ground-validation for recognising the driving factors on satellite bias and a Monte Carlo Cross Validation approach for calibrating bias correction models. Using the framework, bias correction models for each month and each precipitation intensity categories were developed to correct GPM-IMERG product. The corrected dataset shows overall improvement over entire domain, confirming the utility of the developed framework.
This study could contribute to both practical use and further development of GPM-IMERG by providing an insight about the performance and the improved data over Sweden.}},
  author       = {{Moravej, Mojtaba}},
  issn         = {{1101-9824}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{TVVR20/5020}},
  title        = {{Ground validation and bias correction of GPM-IMERG V6 satellite precipitation product over Sweden}},
  year         = {{2020}},
}