A comparison of correction factors for the systematic gauge-measurement errors to improve the global land precipitation estimate

Ehsani, Mohammad Reza; Behrangi, Ali

doi:10.1016/j.jhydrol.2022.127884

Cited by 18 publications

(4 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Systematic errors arise when the design or placement of a rain gage results in a consistent over‐ or under‐estimation of precipitation. Systematic negative bias (underestimation) is common in precipitation measurements (e.g., Rosenberry & Hayashi, 2013) and there is a large body of literature devoted to estimating and determining correction factors for these biases (e.g., Ehsani & Behrangi, 2022). Sources of systematic errors in rain gage measurements include evaporation from the open container, water splashing into or out of the gage, obstruction from vegetation, effects of wind, unlevel gage placement, and the occurrence of trace precipitation that is below the recording‐volume threshold.…”

Section: Uncertainty In Precipitationmentioning

confidence: 99%

Uncertainties in measuring and estimating water‐budget components: Current state of the science

et al. 2023

View full text Add to dashboard Cite

Reducing uncertainty in quantifying basin‐scale water‐budget components is one of the most vexing problems for water‐resource managers. Although a vast literature addresses methods and scales of component estimates, uncertainty associated with these estimates is often lacking. Here we review sources of uncertainty and compile reported uncertainty estimates for measurement and estimation methods for six water‐budget components: precipitation, streamflow, evapotranspiration, subsurface discharge, infiltration, and recharge. Quantifying the uncertainty in each water‐budget component data is required before total water‐budget uncertainty can be determined. This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods

show abstract

Section: Uncertainty In Precipitationmentioning

confidence: 99%

Uncertainties in measuring and estimating water‐budget components: Current state of the science

et al. 2023

View full text Add to dashboard Cite

show abstract

“…An alternative, physically realistic, way for to account for violations of mass conservation is to assess whether systematic observational biases might exist in the input data. For example, in the context of a catchment system, the measured precipitation might be subject to systematic biases such as wind‐induced undercatch (Adam & Lettenmaier, 2003), frequency of snowfall (Yang et al., 1999), or other unforeseeable random factors affecting the measurement process (see review in Ehsani & Behrangi, 2022).…”

Section: Resultsmentioning

confidence: 99%

A Mass‐Conserving‐Perceptron for Machine‐Learning‐Based Modeling of Geoscientific Systems

Wang,

Gupta

2024

Water Resources Research

View full text Add to dashboard Cite

Although decades of effort have been devoted to building Physical‐Conceptual (PC) models for predicting the time‐series evolution of geoscientific systems, recent work shows that Machine Learning (ML) based Gated Recurrent Neural Network technology can be used to develop models that are much more accurate. However, the difficulty of extracting physical understanding from ML‐based models complicates their utility for enhancing scientific knowledge regarding system structure and function. Here, we propose a physically interpretable Mass‐Conserving‐Perceptron (MCP) as a way to bridge the gap between PC‐based and ML‐based modeling approaches. The MCP exploits the inherent isomorphism between the directed graph structures underlying both PC models and GRNNs to explicitly represent the mass‐conserving nature of physical processes while enabling the functional nature of such processes to be directly learned (in an interpretable manner) from available data using off‐the‐shelf ML technology. As a proof of concept, we investigate the functional expressivity (capacity) of the MCP, explore its ability to parsimoniously represent the rainfall‐runoff (RR) dynamics of the Leaf River Basin, and demonstrate its utility for scientific hypothesis testing. To conclude, we discuss extensions of the concept to enable ML‐based physical‐conceptual representation of the coupled nature of mass‐energy‐information flows through geoscientific systems.

show abstract

“…Undercatch occurs when precipitation falling in the presence of wind can cause hydrometeors to pass over the gauge top orifice. This effect has been shown to bias reported precipitation quantities by up to 10 % (Ehsani and Behrangi, 2022). We therefore limit the available training dataset to periods when surface wind speeds are < 5 m s −1 , as this restricts the analysis to low-medium wind speed events at each location to maintain a high gauge-catch efficiency (Yang, 2014).…”

Section: Surface Meteorologymentioning

confidence: 99%

DeepPrecip: a deep neural network for precipitation retrievals

et al. 2022

View full text Add to dashboard Cite

Abstract. Remotely-sensed precipitation retrievals are critical for advancing our understanding of global energy and hydrologic cycles in remote regions. Radar reflectivity profiles of the lower atmosphere are commonly linked to precipitation through empirical power laws, but these relationships are tightly coupled to particle microphysical assumptions that do not generalize well to different regional climates. Here, we develop a robust, highly generalized precipitation retrieval algorithm from a deep convolutional neural network (DeepPrecip) to estimate 20 min average surface precipitation accumulation using near-surface radar data inputs. DeepPrecip displays a high retrieval skill and can accurately model total precipitation accumulation, with a mean square error (MSE) 160 % lower, on average, than current methods. DeepPrecip also outperforms a less complex machine learning retrieval algorithm, demonstrating the value of deep learning when applied to precipitation retrievals. Predictor importance analyses suggest that a combination of both near-surface (below 1 km) and higher-altitude (1.5–2 km) radar measurements are the primary features contributing to retrieval accuracy. Further, DeepPrecip closely captures total precipitation accumulation magnitudes and variability across nine distinct locations without requiring any explicit descriptions of particle microphysics or geospatial covariates. This research reveals the important role for deep learning in extracting relevant information about precipitation from atmospheric radar retrievals.

show abstract

A comparison of correction factors for the systematic gauge-measurement errors to improve the global land precipitation estimate

Cited by 18 publications

References 63 publications

Uncertainties in measuring and estimating water‐budget components: Current state of the science

Uncertainties in measuring and estimating water‐budget components: Current state of the science

A Mass‐Conserving‐Perceptron for Machine‐Learning‐Based Modeling of Geoscientific Systems

DeepPrecip: a deep neural network for precipitation retrievals

Contact Info

Product

Resources

About