Multiscale Evaluation of Satellite Precipitation Products: Effective Resolution of IMERG

Guilloteau, Clément; Foufoula‐Georgiou, Efi

doi:10.1007/978-3-030-35798-6_5

Cited by 12 publications

(10 citation statements)

References 44 publications

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“…The thresholds can be easily changed in the MOAAP algorithm to optimize it to various input data sets. IMERG precipitation does not cover high latitudes. Although data is provided on a 0.1° grid the effective resolution of IMERG precipitation can be up to 100 km (Guilloteau & Foufoula‐Georgiou, 2020) meaning that hourly rainfall rates (particularly extremes) are smoothed. This can result in erroneous detection of extreme hourly rainfall events compared to station data and might bias our analysis toward phenomena that create large precipitation footprints (e.g., cyclones, MCSs) compared to for example, smaller thunderstorms, that are not well captured.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…IMERG merges satellite microwave precipitation estimates with satellite infrared observations and precipitation gauge records. Although IMERG has a fairly high spatiotemporal spacing, its effective resolution is several times coarser than its grid spacing (Guilloteau & Foufoula‐Georgiou, 2020). We also acknowledge that gridded precipitation data sets may under‐represent the most extreme precipitation recorded by gauges.…”

Section: Methodsmentioning

confidence: 99%

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The Multi‐Scale Interactions of Atmospheric Phenomenon in Mean and Extreme Precipitation

Prein,

Mooney,

Done

2023

Earth's Future

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Climate change increases the frequency and intensity of extreme precipitation, which in combination with rising population enhances exposure to major floods. An improved understanding of the atmospheric processes that cause extreme precipitation events would help to advance predictions and projections of such events. To date, such analyses have typically been performed rather unsystematically and over limited areas (e.g., the U.S.) which has resulted in contradictory findings. Here we present the Multi‐Object Analysis of Atmospheric Phenomenon algorithm that uses a set of 12 common atmospheric variables to identify and track tropical and extra‐tropical cyclones, cut‐off lows, frontal zones, anticyclones, atmospheric rivers (ARs), jets, mesoscale convective systems (MCSs), and equatorial waves. We apply the algorithm to global historical data between 2001–2020 and associate phenomena with hourly and daily satellite‐derived extreme precipitation estimates in major climate regions. We find that MCSs produce the vast majority of extreme precipitation in the tropics and some mid‐latitude land regions, while extreme precipitation in mid and high‐latitude ocean and coastal regions are dominated by cyclones and ARs. Importantly, most extreme precipitation events are associated with phenomena interacting across scales that intensify precipitation. These interactions are a function of the intensity (i.e., rarity) of extreme events. The presented methodology and results could have wide‐ranging applications including training of machine learning methods, Lagrangian‐based evaluation of climate models, and process‐based understanding of extreme precipitation in a changing climate.

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Section: Summary and Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The Multi‐Scale Interactions of Atmospheric Phenomenon in Mean and Extreme Precipitation

Prein,

Mooney,

Done

2023

Earth's Future

View full text Add to dashboard Cite

show abstract

“…This is explained by its coarse resolution and smoothing by spatial averaging in complex topography. Guilloteau & Foufoula-Georgiou (2020) suggested that the effective resolution of IMERG data could be as low as 80 km in contrast with the nominal 10 km resolution.…”

Section: Hydrologic Modeling Gray Zonementioning

confidence: 99%

Toward Optimal Rainfall for Flood Prediction in Headwater Basins - Orographic QPE error modeling using machine learning

Liao

Barros

2023

Preprint

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Quantitative Precipitation Estimates (QPE) from merged rain gauge and radar measurements have become widely available in the last two decades. The errors associated with these products are yet to be fully understood, especially in complex terrain where ground clutter and overshooting artifacts are significant and vary in space and time depending on the storm and underlying synoptic conditions. The location and timing of precipitation in addition to rainfall intensity and duration are critical to the simulation of flood response in headwater basins. This work proposes a generalizable Physics-guided Artificial Intelligence (PAI) framework for QPE error modeling. First, QPE error climatology derived from the hydrologic Inverse Rainfall Correction (Liao & Barros, 2022) to historical floods in selected headwater basins is analyzed to identify dominant precipitation regimes. Second, for each precipitation regime, a Multilayer Perceptron (MLP) error prediction model is trained using event-specific precipitation metrics at hourly scale as input, and subsequently used to predict estimation errors for various QPE products. The corrected QPE can then be used for hydrologic simulations and flood nowcasting. The PAI framework is demonstrated in the Southern Appalachian Mountains using the 57 largest floods over 2008-2017. The Probability Distribution Function of predicted precipitation errors follows a Gaussian-like distribution but varies significantly between cold and warm season events, while the spatial distribution is inextricably connected to basin geomorphology. On average, large improvements on hourly KGE from -0.5 to 0.4 are achieved, and the peak flood error is reduced by 70%, with distinctively better results for cold season events.

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“…This is explained by its coarse resolution and smoothing by spatial averaging in complex topography. Guilloteau and Foufoula-Georgiou (2020) suggested that the effective resolution of IMERG data could be as low as 80 km in contrast with the nominal 10 km resolution.…”

Section: Hydrologic Modeling Gray Zonementioning

confidence: 99%

Toward Optimal Rainfall for Flood Prediction in Headwater Basins—Orographic QPE Error Modeling Using Machine Learning

Liao,

Barros

2023

Water Resources Research

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Quantitative Precipitation Estimates (QPE) from merged rain gauge and radar measurements have become widely available in the last two decades. The errors associated with these products are yet to be fully understood, especially in complex terrain where ground clutter and overshooting artifacts are significant and vary in space and time depending on the storm and underlying synoptic conditions. The location and timing of precipitation in addition to rainfall intensity and duration are critical to the simulation of flood response in headwater basins. This work proposes a generalizable Physics‐guided Artificial Intelligence (PAI) framework for QPE error modeling. First, QPE error climatology derived from the hydrologic Inverse Rainfall Correction (Liao & Barros, 2022, https://doi.org/10.1016/j.rse.2022.113107) to historical floods in selected headwater basins is analyzed to identify dominant precipitation regimes. Second, for each precipitation regime, a Multilayer Perceptron error prediction model is trained using event‐specific precipitation metrics at hourly scale as input, and subsequently used to predict estimation errors for various QPE products. The corrected QPE can then be used for hydrologic simulations and flood nowcasting. The PAI framework is demonstrated in the Southern Appalachian Mountains using the 57 largest floods over 2008–2017. The Probability Distribution Function of predicted precipitation errors follows a Gaussian‐like distribution but varies significantly between cold and warm season events, while the spatial distribution is inextricably connected to basin geomorphology. On average, large improvements on hourly Kling‐Gupta Efficiency from −0.5 to 0.4 are achieved, and the peak flood error is reduced by 70%, with distinctively better results for cold season events.

show abstract

Multiscale Evaluation of Satellite Precipitation Products: Effective Resolution of IMERG

Cited by 12 publications

References 44 publications

The Multi‐Scale Interactions of Atmospheric Phenomenon in Mean and Extreme Precipitation

The Multi‐Scale Interactions of Atmospheric Phenomenon in Mean and Extreme Precipitation

Toward Optimal Rainfall for Flood Prediction in Headwater Basins - Orographic QPE error modeling using machine learning

Toward Optimal Rainfall for Flood Prediction in Headwater Basins—Orographic QPE Error Modeling Using Machine Learning

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