Retrieval of rainfall fields in urban areas using attenuation measurements from mobile phone networks: a modeling feasibility study

Abstract. Rainfall monitoring is an important global issue in urban hydrological applications, such as flood warning and water resources management systems. Until the present time, rain gauges and weather radars have been widely used as sensors to provide rainfall information with a detailed resolution; most cities in the world however are inadequately equipped. Recently, commercial microwave links (MWL) have been proposed as a new means of monitoring space-time rainfall. A transmitted signal along such links is known to be attenuated by rainfall, hence the measurement of this signal attenuation could serve to estimate path-averaged rainfall intensity. The density of commercial MWL is typically high in most cities today, which raises new questions over the possibility of retrieving rainfall using signal attenuation data from multiple links. The objective of this article is to assess the feasibility of retrieving rainfall fields in urban areas using rain attenuation data from commercial MWL that are mainly operated by mobile phone companies. This work is based on a simulation framework applied to a real case study. The study area is the city of Nantes, France. Rainfall datasets containing 207 weather radar images recorded by the Météo-France Agency's C-band at high spatial (250 m × 250 m) and temporal (5 min) resolutions are first used to generate rain attenuation data over the existing mobile phone network, which combines 256 microwave links operating at 18, 23 and 38 GHz. The rain attenuation data generated are used as a real signal dataset. A novel retrieval algorithm is then proposed to convert the rain-induced attenuation data into a rainfall map. A priori knowledge introduced to initialize the algorithm heavily influences retrieval performance if the problem to be solved is under-determined, as is the case herein. The capabilities as well as limitations of the retrieval algorithm, as regards capturing different rainfall variability, are evaluated. A detailed sensitivity analysis, carried out with respect to various parameters including a priori knowledge, decorrelation distance, and the retrieval performance of the algorithm depending on the density level of the MWL network is also evaluated in a light rain, a shower and amidst storm events. The conclusion, based on 200+ retrieval tests, states that the proposed algorithm is capable of capturing high rainfall variability in the presence of large measurement error sources according to the adopted methodology.

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Joint VHR - LIDAR classification framework in urban areas using a priori knowledge and post processing shape optimization

2011 Joint Urban Remote Sensing Event ◽

10.1109/jurse.2011.5764727 ◽

2011 ◽

Author(s):

Paolo Gamba ◽

Gianni Lisini ◽

Livia Tomas ◽

Claudia Almeida ◽

Leila Fonseca

Keyword(s):

Shape Optimization ◽

Urban Areas ◽

A Priori ◽

A Priori Knowledge ◽

Post Processing ◽

Classification Framework ◽

Priori Knowledge

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Seeing, Knowing, and Doing

10.1093/oso/9780197503508.001.0001 ◽

2020 ◽

Author(s):

Robert Audi

Keyword(s):

Practical Reasoning ◽

Practical Knowledge ◽

A Priori ◽

Human Action ◽

A Priori Knowledge ◽

Rational Action ◽

Reasons For Action ◽

Discriminative Response ◽

Rich Information ◽

Priori Knowledge

This book provides an overall theory of perception and an account of knowledge and justification concerning the physical, the abstract, and the normative. It has the rigor appropriate for professionals but explains its main points using concrete examples. It accounts for two important aspects of perception on which philosophers have said too little: its relevance to a priori knowledge—traditionally conceived as independent of perception—and its role in human action. Overall, the book provides a full-scale account of perception, presents a theory of the a priori, and explains how perception guides action. It also clarifies the relation between action and practical reasoning; the notion of rational action; and the relation between propositional and practical knowledge. Part One develops a theory of perception as experiential, representational, and causally connected with its objects: as a discriminative response to those objects, embodying phenomenally distinctive elements; and as yielding rich information that underlies human knowledge. Part Two presents a theory of self-evidence and the a priori. The theory is perceptualist in explicating the apprehension of a priori truths by articulating its parallels to perception. The theory unifies empirical and a priori knowledge by clarifying their reliable connections with their objects—connections many have thought impossible for a priori knowledge as about the abstract. Part Three explores how perception guides action; the relation between knowing how and knowing that; the nature of reasons for action; the role of inference in determining action; and the overall conditions for rational action.

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How Reality is Reasonable

10.1093/oso/9780198810384.003.0006 ◽

2018 ◽

Author(s):

Donald C. Williams

Keyword(s):

A Priori ◽

A Priori Knowledge ◽

Dimensional Manifold ◽

The World ◽

Being There ◽

Ways Of Being ◽

Fundamental Entity ◽

Spatiotemporal Relations ◽

Priori Knowledge

This chapter begins with a systematic presentation of the doctrine of actualism. According to actualism, all that exists is actual, determinate, and of one way of being. There are no possible objects, nor is there any indeterminacy in the world. In addition, there are no ways of being. It is proposed that actual entities stand in three fundamental relations: mereological, spatiotemporal, and resemblance relations. These relations govern the fundamental entities. Each fundamental entity stands in parthood relations, spatiotemporal relations, and resemblance relations to other entities. The resulting picture is one that represents the world as a four-dimensional manifold of actual ‘qualitied contents’—upon which all else supervenes. It is then explained how actualism accounts for classes, quantity, number, causation, laws, a priori knowledge, necessity, and induction.

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How Do We Know that We’re Not Brains in Vats?

10.1093/oso/9780199564477.003.0007 ◽

2018 ◽

Author(s):

Keith DeRose

Keyword(s):

Epistemic Justification ◽

A Priori ◽

A Priori Knowledge ◽

Conservative Approach ◽

Contingent Fact ◽

Brains In Vats ◽

Priori Knowledge

In this chapter the contextualist Moorean account of how we know by ordinary standards that we are not brains in vats (BIVs) utilized in Chapter 1 is developed and defended, and the picture of knowledge and justification that emerges is explained. The account (a) is based on a double-safety picture of knowledge; (b) has it that our knowledge that we’re not BIVs is in an important way a priori; and (c) is knowledge that is easily obtained, without any need for fancy philosophical arguments to the effect that we’re not BIVs; and the account is one that (d) utilizes a conservative approach to epistemic justification. Special attention is devoted to defending the claim that we have a priori knowledge of the deeply contingent fact that we’re not BIVs, and to distinguishing this a prioritist account of this knowledge from the kind of “dogmatist” account prominently championed by James Pryor.

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