Localized linear regression methods for estimating monthly precipitation grids using elevation, rain gauge, and TRMM data

Taheri, Mercedeh; Dolatabadi, Neda; Nasseri, Mohsen; Zahraie, Banafsheh; Amini, Yasaman; Schoups, Gerrit

doi:10.1007/s00704-020-03320-2

Cited by 14 publications

(3 citation statements)

References 66 publications

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“…Different climatic and topographic conditions may lead to different spatial and temporal distributions of precipitation in Jinan City [46]. Previous studies have established the influence of elevation on satellite precipitation estimates [58]. To explore the spatial differences, the performance of IMERG products in Jinan City as a function of elevation was also evaluated (Figure 8).…”

Section: Discussionmentioning

confidence: 99%

Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

Ren

et al. 2021

Remote Sensing

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In this study, a comprehensive assessment on precipitation estimation from the latest Version 06 release of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) algorithm is conducted by using 24 rain gauge observations at daily scale from 2001 to 2016. The IMERG V06 dataset fuses Tropical Rainfall Measuring Mission (TRMM) satellite data (2000–2015) and Global Precipitation Measurement (GPM) satellite data (2014–present), enabling the use of IMERG data for long-term study. Correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were used to assess the accuracy of satellite-derived precipitation estimation and measure the correspondence between satellite-derived and observed occurrence of precipitation events. The probability density distributions of precipitation intensity and influence of elevation on precipitation estimation were also examined. Results showed that, with high CC and low RMSE and RB, the IMERG Final Run product (IMERG-F) performs better than two other IMERG products at daily, monthly, and yearly scales. At daily scale, the ability of satellite products to detect general precipitation is clearly superior to the ability to detect heavy and extreme precipitation. In addition, CC and RMSE of IMERG products are high in Southeastern Jinan City, while RMSE is relatively low in Southwestern Jinan City. Considering the fact that the IMERG estimation of extreme precipitation indices showed an acceptable level of accuracy, IMERG products can be used to derive extreme precipitation indices in areas without gauged data. At all elevations, IMERG-F exhibits a better performance than the other two IMERG products. However, POD and FAR decrease and CSI increase with the increase of elevation, indicating the need for improvement. This study will provide valuable information for the application of IMERG products at the scale of a large city.

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Section: Discussionmentioning

confidence: 99%

Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

Ren

et al. 2021

Remote Sensing

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“…They revealed that the performance of multivariate geostatistical methods using PP data is superior to OK, a gauge-based interpolation method, only under the circumstance of an insubstantial amount of ground-based data. In other studies, fine-scale DEM and a coarse-scale PP were used as exploratory variables in a moving least square framework and resulted in simultaneous downscaling and calibration of PPs (Taheri et al, 2020;Amini and Nasseri, 2021). Lu et al (2019) introduced a two-stage downscaling merging framework, where a universal kriging algorithm was employed to resample the monthly coarse pixels of PPs, and the resampled pixels along with various topographical features, NDVI, and gauge observations were merged through a GWR and stepwise regression model.…”

Section: Introductionmentioning

confidence: 99%

“…They revealed that the performance of multivariate geostatistical methods using PP data is superior to OK, a gauge‐based interpolation method, only under the circumstance of an insubstantial amount of ground‐based data. In other studies, fine‐scale DEM and a coarse‐scale PP were used as exploratory variables in a moving least square framework and resulted in simultaneous downscaling and calibration of PPs (Taheri et al ., 2020; Amini and Nasseri, 2021). Lu et al .…”

Section: Introductionmentioning

confidence: 99%

A locally weighted linear ridge regression framework for spatial interpolation of monthly precipitation over an orographically complex area

Zandi

Nasseri

Zahraie

2023

Intl Journal of Climatology

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This article aimed to investigate the potential of using three large‐scale precipitation products (PPs), including TRMM, ERA5‐Land, and MSWEP, and two land surface characteristics, including NDVI and Land Surface Temperature (LST), to improve the accuracy of an elevation‐based spatial non‐stationary, Locally Weighted Linear Regression (LWLR) method. A two‐step approach consisting of downscaling and merging was proposed and assessed across an orographically complex region in Iran to construct monthly precipitation maps for the 2001–2015 period. In the first step, three large‐scale PPs were downscaled to 1 km spatial resolution via the Area To Point Kriging (ATPK) method to address the spatial resolution discrepancy between the coarse pixels of PPs and pointwise gauge data. In the second step, five regression models with different combinations of predictors were developed and compared with the benchmark precipitation‐elevation regression model. The L2 regularization method was adopted to overcome the potential multicollinearity issue. The proposed framework could successfully diagnose the multicollinearity issues and dynamically estimate the regularization parameter in space and time. We utilized the holdout method to validate and compare the developed models, in which 50% of gauges were used for fitting the regression functions, and the remaining gauges were assigned to the validation dataset. The validation results showed that the overall monthly accuracy of the benchmark univariate elevation‐based model was improved where the MAE metric was decreased by 16.5%, and CC and KGE were increased by 3.4 and 5.8%, respectively. Also, the rBias was enhanced from −5.72 to 1.24%. Mean monthly precipitation maps of the 2001–2015 period generated by the model with the best combination of predictors and the benchmark model displayed the same spatial pattern consistent with the topography. Further analysis under different validation scenarios revealed that the contribution of PPs and auxiliary variables is the greatest when the training gauge network is sparse. Also, the developed multivariate model has a higher extrapolation ability and is more robust than the benchmark model.

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A spatiotemporal framework to calibrate high‐resolution global monthly precipitation products: An application to the Urmia Lake Watershed in Iran

Nasseri

Schoups

Taheri

2021

Intl Journal of Climatology

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Improving precipitation accuracy over a watershed is one of the highest priorities in water resources studies and management. Several global precipitation datasets are available for estimating precipitation over any region in the world. However, local or regional application of these datasets should account for and correct potential errors in the original products. This article presents a novel spatiotemporal calibration framework to improve the accuracy (bias and correlation) of global precipitation datasets in regional applications. The proposed methodology consists of two steps. First, gridded global precipitation datasets are regressed pointwise against rain gauge data. This yields downscaled and bias‐corrected precipitation values at the point scale. Second, the resulting point‐scale regression parameters are used to build a geostatistical model that predicts the regression parameters across the region of interest, allowing for bias‐correcting the precipitation datasets at the regional scale. The framework is applied to the Urmia Lake Watershed in northwestern Iran. Eight global high‐resolution monthly precipitation datasets (CHIRPS, ERA‐5, IMERG, PERSIANN, PERSIANN‐CCS, PERSIANN‐CDR, TRMM and Terra) are evaluated and three downscaling approaches including linear, Q‐Q and Linear Scaling (LS) regression methods are used to calibrate the precipitation datasets based on a regional network of rain gauge observations. Ordinary kriging is subsequently used to predict the regression parameters at ungauged locations. Out of all combinations (i.e., eight datasets and three methods), downscaled IMERG using linear and Q‐Q regression methods showed the best performance in estimating the spatiotemporal variations of monthly precipitation across the watershed of interest. The original IMERG dataset overestimated the monthly precipitation by approximately 20% compared to the precipitation from rain gauges. After applying the proposed methodology in this article, the IMERG bias was reduced by 93%, with an additional 26% decrease in the RMSE.

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Localized linear regression methods for estimating monthly precipitation grids using elevation, rain gauge, and TRMM data

Cited by 14 publications

References 66 publications

Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

A locally weighted linear ridge regression framework for spatial interpolation of monthly precipitation over an orographically complex area

A spatiotemporal framework to calibrate high‐resolution global monthly precipitation products: An application to the Urmia Lake Watershed in Iran

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