The importance of choosing precipitation datasets

Simpson, Micheal J.; Hirsch, Adam T.; Grempler, Kevin; Lupo, Anthony R.

doi:10.1002/hyp.11381

Cited by 7 publications

(5 citation statements)

References 49 publications

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“…Although both the meteorological stations are observed with the manually standard process in the standard field with grass land cover, the local factors such as terrain and wind are still different to considered and corrected. Many different studies [62][63][64], among many, have concluded that automated gauges have many errors associated with them, among which some errors as high as 80% under extremely detrimental conditions.…”

Section: Reasons For Different Performance Of Ree Productsmentioning

confidence: 99%

Performance of Three Reanalysis Precipitation Datasets over the Qinling-Daba Mountains, Eastern Fringe of Tibetan Plateau, China

Wang

Zhang

2019

Advances in Meteorology

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Evaluation of different reanalysis precipitation datasets is of great importance to understanding the hydrological processes and water resource management practice in the Qinling-Daba Mountains (QDM), located at the eastern fringe of the Tibetan Plateau. Although the evaluation of satellite precipitation data in this region has been performed, another kind of popular precipitation product-reanalysis dataset has not been assessed in depth. Three popular reanalysis precipitation datasets, including ERA-Interim Reanalysis of European Centre for Medium Forecasts (ERA-Interim), Japanese 55-year Reanalysis (JRA-55), and National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis-1 (NCEP/NCAR-1) were evaluated against rain gauge data over the Qinling-Daba Mountains from 2000 to 2014 on monthly, seasonal, and annual scales. Different statistical measures based on the Correlation Coefficient (CC), relative BIAS (BIAS), Root-Mean-Square Error (RMSE), and Mean Absolute Error (MAE) were adopted to determine the performance of the above reanalysis datasets. Results show that ERA-Interim and JRA-55 have good performance on a monthly scale and annual scale. However, the NCEP/NCAR-1 has the least BIAS with the observed precipitation in annual scale in QDM. All reanalysis datasets performed better in spring, summer, and autumn than in winter. The advantages of involving more precipitation observation stations was probably the main reason of the different performance of three precipitation reanalysis products, and the benefit of a four-dimensional variational analysis model over a three-dimensional variational analysis model may be another reason. The evaluation suggested that ERA-Interim is more suitable for study the precipitation and water cycles in the QDM.

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Section: Reasons For Different Performance Of Ree Productsmentioning

confidence: 99%

Performance of Three Reanalysis Precipitation Datasets over the Qinling-Daba Mountains, Eastern Fringe of Tibetan Plateau, China

Wang

Zhang

2019

Advances in Meteorology

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“…Furthermore, results demonstrate the issues with analyzing QPE from a single gauge, explaining why the Community Collaborative Rain, Hail, and Snow Network (Kelsch, 1998;Cifelli et al, 2005;Reges et al, 2016) or other densely gauged networks (e.g., the Hydrometeorological Automated Data System, HADS; and the Meteorological Assimilation Data Ingest System, MADIS) tend to be more utilized since results have shown that measurements or qualitycontrolled techniques made by these organizations, especially CoCoRaHS (Community Collaborative Rain, Hail and Snow Network), are significantly more accurate than rain gauges (Simpson et al, 2017), especially for convective events (Moon et al, 2009). …”

Section: Discussionmentioning

confidence: 99%

Dual-polarized quantitative precipitation estimation as a function of range

Simpson

Fox

2018

Hydrol. Earth Syst. Sci.

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Abstract. Since the advent of dual-polarization radar technology, many studies have been conducted to determine the extent to which the differential reflectivity (ZDR) and specific differential phase shift (KDP) add benefits to estimating rain rates (R) compared to reflectivity (Z) alone. It has been previously noted that this new technology provides significant improvement to rain-rate estimation, primarily for ranges within 125 km of the radar. Beyond this range, it is unclear as to whether the National Weather Service (NWS) conventional R(Z)-convective algorithm is superior, as little research has investigated radar precipitation estimate performance at larger ranges. The current study investigates the performance of three radars -St. Louis (KLSX), Kansas City (KEAX), and Springfield (KSGF), MO -with 15 tipping bucket gauges serving as ground truth to the radars. With over 300 h of precipitation data being analyzed for the current study, it was found that, in general, performance degraded with range beyond, approximately, 150 km from each of the radars. Probability of detection (PoD) in addition to bias values decreased, while the false alarm rates increased as range increased. Bright-band contamination was observed to play a potential role as large increases in the absolute bias and overall error values near 120 km for the cool season and 150 km in the warm season. Furthermore, upwards of 60 % of the total error was due to precipitation being falsely estimated, while 20 % of the total error was due to missed precipitation. Correlation coefficient values increased by as much as 0.4 when these instances were removed from the analyses (i.e., hits only). Overall, due to the lowest normalized standard error (NSE) of less than 1.0, a National Severe Storms Laboratory (NSSL) R(Z,ZDR) equation was determined to be the most robust, while a R(ZDR,KDP) algorithm recorded NSE values as high as 5. The addition of dual-polarized technology was shown to estimate quantitative precipitation estimates (QPEs) better than the conventional equation. The analyses further our understanding of the strengths and limitations of the Next Generation Radar (NEXRAD) system overall and from a seasonal perspective.

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“…Therefore, it is a primary input when developing different climate and weather numerical models. Long enough records of this variable are necessary to carry out a wide range of studies in many water-related disciplines, such as water resources management, extreme events analysis, climate change, among others (Simpson et al, 2017;Tabari, 2020).…”

Section: Introductionmentioning

confidence: 99%

Validation of chirps gauge – satellite based rainfall dataset over Nicaragua, 2011 - 2021

Rivera Solís,

Ojeda Olivares,

Chamorro Blandón

2023

Nexo Revista Científica

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Rainfall is a key input for many weather and climate numerical models. Therefore the strong need to have a dense enough monitoring network for this parameter. Satellite-based rainfall products have emerged in recent decades as an alternative to the more expensive gauge stations. However, a proper validation of such satellite-based products against gauge data must be performed before using their data. This study presents a validation of CHIRPS dataset against gauge data for 17 stations across Nicaragua. The performance of the product was validated at different temporal scales (daily, pentadal, monthly and annual) by different error metrics. A total of six quantitative error metrics was assessed: Bias Percentage (PBIAS), Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Pearson’s r and Nash Stucliffe Efficiency (NSE). A total three categorical indices were assessed at daily time scale: Probability of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI). The results showed that CHIRPS dataset have better performance at monthly and annual time scales, while it is not capable of adequately represent the daily variability.

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The importance of choosing precipitation datasets

Cited by 7 publications

References 49 publications

Performance of Three Reanalysis Precipitation Datasets over the Qinling-Daba Mountains, Eastern Fringe of Tibetan Plateau, China

Performance of Three Reanalysis Precipitation Datasets over the Qinling-Daba Mountains, Eastern Fringe of Tibetan Plateau, China

Dual-polarized quantitative precipitation estimation as a function of range

Validation of chirps gauge – satellite based rainfall dataset over Nicaragua, 2011 - 2021

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