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

Inglés. 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.

Inglés. 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.