Nonstationarity in Intermittent Rainfall: The “Dry Drift”

Schleiss, Marc; Chamoun, Sabine; Berne, Alexis

doi:10.1175/jhm-d-13-095.1

Cited by 26 publications

(50 citation statements)

References 31 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that, with λ = 0.5, transformation (2) maps all dry measurements to -2. Methods have been proposed to deal with intermittency explicitly in the spatial modeling of precipitation (e.g., Fuentes et al, 2008;Schleiss et al, 2014;Seo, 1998). These were not considered in our application.…”

Section: Masson and C Frei: Exploring Methods With Multi-scale Tomentioning

confidence: 99%

Spatial analysis of precipitation in a high-mountain region: exploring methods with multi-scale topographic predictors and circulation types

Masson

Frei

2014

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Statistical models of the relationship between precipitation and topography are key elements for the spatial interpolation of rain-gauge measurements in high-mountain regions. This study investigates several extensions of the classical precipitation-height model in a direct comparison and within two popular interpolation frameworks, namely linear regression and kriging with external drift. The models studied include predictors of topographic height and slope at several spatial scales, a stratification by types of a circulation classification, and a predictor for wind-aligned topographic gradients. The benefit of the modeling components is investigated for the interpolation of seasonal mean and daily precipitation using leave-one-out cross-validation. The study domain is a north-south cross section of the European Alps (154 km × 187 km) that is inclined towards dense rain-gauge measurements (approx. 440 stations, 1971-2008).The significance of the topographic predictors was found to strongly depend on the interpolation framework. In linear regression, predictors of slope and at multiple scales reduce interpolation errors substantially. But with as many as nine predictors, the resulting interpolation still poorly replicates the across-ridge variation of climatological mean precipitation. Kriging with external drift (KED) leads to much smaller interpolation errors than linear regression, but this is achieved with a single predictor (local topographic height), whereas the incorporation of more extended predictor sets brings only marginal further improvement. Furthermore, the stratification by circulation types and the wind-aligned gradient predictor do not improve over the single predictor KED model. As for daily precipitation, interpolation accuracy improves considerably with KED and the use of a single predictor field (the distribution of seasonal mean precipitation) as compared to ordinary kriging (i.e., without any predictor). Nonetheless, information from circulation types did not improve interpolation accuracy.Our results confirm that the consideration of topography effects is important for spatial interpolation of precipitation in high-mountain regions. But a single predictor may be sufficient and taking appropriate account of the spatial autocorrelation (by kriging) can be more effective than the development of elaborate predictor sets within a regression model. Our results also question a popular practice of using linear regression for predictor selection in spatial interpolation; however they support the common practice of using a climatological mean field as a background in the interpolation of daily precipitation.

show abstract

Section: Masson and C Frei: Exploring Methods With Multi-scale Tomentioning

confidence: 99%

Spatial analysis of precipitation in a high-mountain region: exploring methods with multi-scale topographic predictors and circulation types

Masson

Frei

2014

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Interestingly, we note that this problem also appears in the study of rainfall prediction: large areas of the space may be dry, while areas experiencing nonzero rainfall are somehow well-related. Schleiss et al propose a sort of masking technique, in which areas not experiencing the phenomenon are treated separately from those that are [18]. Integrating such an approach with a mobile sensor may further improve the results reported in this work.…”

Section: Discussionmentioning

confidence: 89%

Environmental field estimation with hybrid-mobility sensor networks

Evans

Dias

Roelofsen

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

Abstract-The remarkable accessibility of modern flying robots makes them an attractive platform for environmental sensing. However, low cost and ease of use are currently incompatible with large payloads, severely limiting the choice of sensor and ultimately modality. This paper describes the design of a system for using a small infrared thermometer to estimate the surface temperature over an area that is large compared to the area measured by the sensor, by mounting it on a flying robot. We leverage a priori knowledge about the spatial statistics of the phenomena under measure in order to plan an informative sampling path, fusing observations by Gaussian process regression. Our approach is designed to be evaluated in an indoor testbed, in which a quadrotor, in cooperation with simulated static sensing nodes, estimates the spatial distribution of surface temperature over a controlled thermal gradient. We perform extensive systematic experimentation both in simulation and our real-world testbed environment, with our algorithm estimating surface temperature to an accuracy of up to 2.1 C over a 16 m 2 area ranging in value from 25-65 C.

show abstract

“…H-SAF, on the other hand, seems to struggle less with this. One possible solution could be to integrate as additional predictor a variable characterizing the "dry drift", i.e., the distance from the edge of the closest surrounding dry area (e.g., [71]). …”

Section: Discussionmentioning

confidence: 99%

Satellite-Based Rainfall Retrieval: From Generalized Linear Models to Artificial Neural Networks

et al. 2018

View full text Add to dashboard Cite

Abstract:In this study, we develop and compare satellite rainfall retrievals based on generalized linear models and artificial neural networks. Both approaches are used in classification mode in a first step to identify the precipitating areas (precipitation detection) and in regression mode in a second step to estimate the rainfall intensity at the ground (rain rate). The input predictors are geostationary satellite infrared (IR) brightness temperatures and Satellite Application Facility (SAF) nowcasting products which consist of cloud properties, such as cloud top height and cloud type. Additionally, a set of auxiliary location-describing input variables is employed. The output predictand is the ground-based instantaneous rain rate provided by the European-scale radar composite OPERA, that was additionally quality-controlled. We compare our results to a precipitation product which uses a single infrared (IR) channel for the rainfall retrieval. Specifically, we choose the operational PR-OBS-3 hydrology SAF product as a representative example for this type of approach.With generalized linear models, we show that we are able to substantially improve in terms of hits by considering more IR channels and cloud property predictors. Furthermore, we demonstrate the added value of using artificial neural networks to further improve prediction skill by additionally reducing false alarms. In the rain rate estimation, the indirect relationship between surface rain rates and the cloud properties measurable with geostationary satellites limit the skill of all models, which leads to smooth predictions close to the mean rainfall intensity. Probability matching is explored as a tool to recover higher order statistics to obtain a more realistic rain rate distribution.

show abstract

Nonstationarity in Intermittent Rainfall: The “Dry Drift”

Cited by 26 publications

References 31 publications

Spatial analysis of precipitation in a high-mountain region: exploring methods with multi-scale topographic predictors and circulation types

Spatial analysis of precipitation in a high-mountain region: exploring methods with multi-scale topographic predictors and circulation types

Environmental field estimation with hybrid-mobility sensor networks

Satellite-Based Rainfall Retrieval: From Generalized Linear Models to Artificial Neural Networks

Contact Info

Product

Resources

About