Assimilation of precipitation Estimates from the Integrated Multisatellite Retrievals for GPM (IMERG, early Run) in the Canadian Precipitation Analysis (CaPA)

Boluwade, Alaba; Stadnyk, Tricia A.; Fortin, Vincent; Roy, Guy

doi:10.1016/j.ejrh.2017.10.005

Cited by 27 publications

(21 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the application of modelling approaches such as Cherry et al (2005) can be used to back out solid precipitation estimates that are more consistent with observed SWEM. The Canadian Precipitation Analysis framework (Lespinas, Fortin, Roy, Rasmussen, & Stadnyk, 2015) represents a promising approach for the assimilation of multiple sources of precipitation information such as surface observations, weather radar (Fortin, Roy, Donaldson, & Mahidjiba, 2015), and satellite data (Boluwade, Stadnyk, Fortin, & Roy, 2017). Second, no dataset was able to provide SWEM estimates within the ±15% error range required by Hydro-Québec operations.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Evaluation of snow water equivalent datasets over the Saint‐Maurice river basin region of southern Québec

Brown

Tapsoba

Derksen

2018

Hydrological Processes

View full text Add to dashboard Cite

A 10‐km gridded snow water equivalent (SWE) dataset is developed over the Saint‐Maurice River basin region in southern Québec from kriging of observed snow survey data for evaluation of SWE products. The gridded SWE dataset covers 1980–2014 and is based on manual gravimetric snow surveys carried out on February 1, March 1, March 15, April 1, and April 15 of each snow season, which captures the annual maximum SWE (SWEM) with a mean interpolation error of ±19%. The dataset is used to evaluate SWEM from a range of sources including satellite retrievals, reanalyses, Canadian regional climate models, and the Canadian Meteorological Centre operational snow depth analysis. We also evaluate a number of solid precipitation datasets to determine their contribution to systematic errors in estimated SWEM. None of the evaluated datasets is able to provide estimates of SWEM that are within operational requirements of ±15% error, and insufficient solid precipitation is determined to be one of the main reasons. The Climate System Forecast Reanalysis is the only dataset where snowfall is sufficiently large to generate SWEM values comparable to observations. Inconsistencies in precipitation are also found to have a strong impact on year‐to‐year variability in SWEM dataset performance and spread. Version 3.6.1 of the Canadian Land Surface Scheme land surface scheme driven with ERA‐Interim output downscaled by Version 5.0.1 of the Canadian Regional Climate Model was the best physically based model at explaining the observed spatial and temporal variability in SWEM (root‐mean‐square error [RMSE] = 33%) and has potential for lower error with adjusted precipitation. Operational snow products relying on the real‐time snow depth observing network performed poorly due to a lack of real‐time data and the strong local scale variability of point snow depth observations. The results underscore the need for more effort to be invested in improving solid precipitation estimates for use in snow hydrology applications.

show abstract

Section: Conclusion and Discussionmentioning

confidence: 99%

Evaluation of snow water equivalent datasets over the Saint‐Maurice river basin region of southern Québec

Brown

Tapsoba

Derksen

2018

Hydrological Processes

View full text Add to dashboard Cite

show abstract

“…Numerous comparison studies involving GPM data have been carried out over different spatial domains, e.g., global [14], Canada [15], Singapore [16], Malaysia [17], China [18][19][20], India [21], Iran [22], and Saudi-Arabia [23]; yet, investigations covering European countries are sparse, and no detailed comparison over Germany exists until today. However, the consistent availability at high temporal and spatial resolution and hence lowered uncertainty propagation in the results of hydrological modeling make GPM a viable data source for applications across European catchments of different scales [24].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the GPM IMERG Final Precipitation Product to RADOLAN Weather Radar Data over the Topographically and Climatically Diverse Germany

Ramsauer¹,

Weiß²,

Marzahn³

2018

Remote Sensing

View full text Add to dashboard Cite

Precipitation measurements provide crucial information for hydrometeorological applications. In regions where typical precipitation measurement gauges are sparse, gridded products aim to provide alternative data sources. This study examines the performance of NASA’s Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement Mission (IMERG, GPM) satellite precipitation dataset in capturing the spatio-temporal variability of weather events compared to the German weather radar dataset RADOLAN RW. Besides quantity, also timing of rainfall is of very high importance when modeling or monitoring the hydrologic cycle. Therefore, detection metrics are evaluated along with standard statistical measures to test both datasets. Using indices like “probability of detection” allows a binary evaluation showing the basic categorical accordance of the radar and satellite data. Furthermore, a pixel-by-pixel comparison is performed to assess the ability to represent the spatial variability of rainfall and precipitation quantity. All calculations are additionally carried out for seasonal subsets of the data to assess potentially different behavior due to differences in precipitation schemes. The results indicate significant differences between the datasets. Overall, GPM IMERG overestimates the quantity of precipitation compared to RADOLAN, especially in the winter season. Moreover, shortcomings in detection performance arise in this season with significant erroneously-detected, yet also missed precipitation events compared to the weather radar data. Additionally, along secondary mountain ranges and the Alps, topographically-induced precipitation is not represented in GPM data, which generally shows a lack of spatial variability in rainfall and snowfall estimates due to lower resolution.

show abstract

“…As the spatiotemporal resolution and coverage of GPM have been extended beyond the TRMM resolution and coverage, the performance of the GPM IMERG products needs to be evaluated and validated globally.Several studies have compared the GPM IMERG and TRMM products with ground-based measurements, i.e., rain gauge and weather radar [4,6,10,[15][16][17][18][19], considering their hydrological applications [14,[20][21][22]. Also, different GPM IMERG products regarding temporal resolutions have been evaluated considering various climatic and topographic conditions using various statistical measures across the world [5,[23][24][25][26][27][28][29][30]. Although most of these studies confirmed the improvement of the IMERG products relative to those of the TRMM Multi-satellite Precipitation Analysis (TMPA), a more comprehensive investigation is still essential to better understand the IMERG performance in various regions of the world taking into consideration different products' versions and temporal resolution.…”

mentioning

confidence: 99%

Ground Validation of GPM IMERG Precipitation Products over Iran

et al. 2019

View full text Add to dashboard Cite

Accurate estimation of precipitation is crucial for fundamental input to various hydrometeorological applications. Ground-based precipitation data suffer limitations associated with spatial resolution and coverage; hence, satellite precipitation products can be used to complement traditional rain gauge systems. However, the satellite precipitation data need to be validated before extensive use in the applications. Hence, we conducted a thorough validation of the Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) product for all of Iran. The study focused on investigating the performance of daily and monthly GPM IMERG (early, late, final, and monthly) products by comparing them with ground-based precipitation data at synoptic stations throughout the country (2014)(2015)(2016)(2017). The spatial and temporal performance of the GPM IMERG was evaluated using eight statistical criteria considering the rainfall index at the country level. The rainfall detection ability index (POD) showed that the best IMERG product's performance is for the spring season while the false alarm ratio (FAR) index indicated the inferior performance of the IMERG products for the summer season. The performance of the products generally increased from IMERG-Early to -Final according to the relative bias (rBIAS) results while, based on the quantile-quantile (Q-Q) plots, the IMERG-Final could not be suggested for the applications relying on extreme rainfall estimates compared to IMERG-Early and -Late. The results in this paper improve the understanding of IMERG product's performance and open a door to future studies regarding hydrometeorological applications of these products in Iran.Remote Sens. 2020, 12, 48 2 of 23 subject to different errors and uncertainties, such as ground clutter, anomalous propagation, signal attenuation, beam blockage, and bright band contamination [6].Rain gauges are limited in describing the spatial distribution of precipitation depending on the arrangement and density of the rain gauge network [7,8]. In order to spatially characterize precipitation, gauge measurements are transformed to a gridded precipitation dataset. This is carried out through interpolation of rain gauge measurements, using spatial interpolation and geo-statistical methods [9]. These may be prone to missing values, wind effects, insufficient numbers of rain gauges, and a sparse network, especially in less accessible mountainous and oceanic areas [4].In view of the above, the spatial limitations, resolution, and coverage of ground-based measurements highlight the importance of satellite-based precipitation estimates at both the regional and global scale. Satellite-based precipitation estimates are also subject to uncertainties through cloud top reflectance, thermal radiance, infrequent satellite overpasses, and retrieval algorithm related to the nature of indirect measurement [10]. Therefore, a thorough validation of satellite precipitation data in any given area is necessary to achieve insight regarding is accuracy...

show abstract

Assimilation of precipitation Estimates from the Integrated Multisatellite Retrievals for GPM (IMERG, early Run) in the Canadian Precipitation Analysis (CaPA)

Cited by 27 publications

References 20 publications

Evaluation of snow water equivalent datasets over the Saint‐Maurice river basin region of southern Québec

Evaluation of snow water equivalent datasets over the Saint‐Maurice river basin region of southern Québec

Comparison of the GPM IMERG Final Precipitation Product to RADOLAN Weather Radar Data over the Topographically and Climatically Diverse Germany

Ground Validation of GPM IMERG Precipitation Products over Iran

Contact Info

Product

Resources

About