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arxiv: 1907.10462 · v1 · pith:E6VV7P5Znew · submitted 2019-07-24 · 📡 eess.SP

The NEFOCAST System for Detection and Estimation of Rainfall Fields by the Opportunistic Use of Broadcast Satellite Signals

Filippo Giannetti , Marco Moretti , Ruggero Reggiannini , Attilio Vaccaro This is my paper

Pith reviewed 2026-05-24 16:50 UTC · model grok-4.3

classification 📡 eess.SP
keywords rainfall estimationsatellite signal attenuationKalman filtersDVB downlinkopportunistic sensingrain field mappingexcess attenuation
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The pith

Two Kalman filters separate slow orbital drifts from fast rain fades to estimate rainfall from ordinary satellite TV signals.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that rain-induced extra loss in a commercial DVB satellite downlink can be turned into rainfall maps by running two Kalman filters on the received signal-to-noise ratio. One filter follows the slow variations caused by satellite orbit wobbles and seasonal changes in rain height; the other follows the rapid drops produced by rain. This time-scale split is claimed to keep the rain signal from being mistaken for atmospheric turbulence or orbital corrections. If the split holds, existing broadcast receivers become rain sensors without added hardware. The authors present the method as a practical way to measure rainfall fields over the satellite coverage area.

Core claim

By employing two differentially-configured Kalman filters designed to track slow and fast changes of the received signal-to-noise ratio, rain events can be reliably detected and the relevant rainfall rate estimated from the rain-induced excess attenuation in DVB satellite signals, after counteracting orbital perturbations, refractive-index irregularities, and melting-layer fluctuations.

What carries the argument

two differentially-configured Kalman filters that track slow and fast changes in received signal-to-noise ratio

If this is right

  • Rain events are distinguished from non-rain signal variations by time-scale separation.
  • Rainfall rate is estimated directly from the excess attenuation once the filters isolate the rain component.
  • Daily and seasonal random fluctuations in rain height are handled without dedicated sensors.
  • Perturbations from lunar-solar gravity and Earth-mass inhomogeneity are removed before rain estimation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • A dense network of ordinary satellite receivers could produce rainfall maps at higher spatial resolution than sparse rain-gauge networks.
  • The same time-scale approach might be tested on signals from other satellite constellations or frequency bands.
  • Integration with existing weather models could supply real-time rain-field inputs in regions without radar coverage.

Load-bearing premise

Rain-induced excess attenuation can be separated from orbital perturbations, refractive-index fluctuations, and melting-layer variations solely by their different time scales using the two Kalman filters.

What would settle it

A recorded rain event whose attenuation time scale matches an orbital correction or melting-layer shift, so that the filters assign the change to the wrong category.

Figures

Figures reproduced from arXiv: 1907.10462 by Attilio Vaccaro, Filippo Giannetti, Marco Moretti, Ruggero Reggiannini.

Figure 1
Figure 1. Figure 1: NEFOCAST experimental network for real-time wide-area high-spatial resolution rain-rate measurement. II. THE NEFOCAST PROJECT NEFOCAST is a research project, funded by the government of the Tuscany Region (Italy), aimed at assessing the feasibility of a satellite-based system for real-time con￾struction of rain maps over the regional territory [13]-[15]. The main idea is to make opportunistic use of broadc… view at source ↗
Figure 2
Figure 2. Figure 2: Left: SmartLNB device. Right: 75 cm-parabolic antenna of the NEFOCAST station at the University of Pisa (NEFOCAST-ITA-PI-003X), with a SmartLNB [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Top: satellite map of the metropolitan area of Florence around CNR-IBIMET/LAMMA (ground station NEFOCAST-ITA-FI-001S, 43.8187oN, 11.2018oE) portraying the locations on the 15 km × 10 km grid where the various sensors have been deployed (circular marks). Bottom: positions on the grid of the various type of sensors. The NEFOCAST project has been organized in two different phases: 1) a research phase, where S… view at source ↗
Figure 4
Figure 4. Figure 4: Left: downlink geometry with stratiform rain. Right: rain rate (mm/h) vs. vertical coordinate (km) [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Record of received dry (dry) s0 [( / )( )] E N t (dB) measured by the SmartLNB of station NEFOCAST-ITA-PI-003X (Pisa, 43.7203o N, 10.3836o E), showing 24 h-periodic fluctuations due to GEO satellite orbit perturbations and a series of deep notches from 5 to 16 October 2017, caused by sun transit behind the satellite (Eutelsat 10A at 10o E) around local noon (about 11:00 UTC). (dB). (8) From (8) it is seen … view at source ↗
Figure 6
Figure 6. Figure 6: Record of received dry SNR (dB) measured by the SmartLNB of station NEFOCAST-ITA-PI￾003X (Pisa, 43.7203o N, 10.3836o E) with 1 min sampling interval and 0.1 dB amplitude resolution. Top left: large-scale (4 days) measurement showing the 24 h-periodic fluctuations due to GEO satellite orbit perturbations. Top right: small scale (2 hours) measurement showing the fast fluctuations due to tropospheric scintill… view at source ↗
Figure 7
Figure 7. Figure 7: Block diagram of the NEFOCAST rain rate estimation algorithm based on a double Kalman filter. Given a measured rain attenuation rain dB Lt( ) | , inversion of (12) yields an esti￾mate of LL Rt(). However, due to the transcendental nature of (12), this inversion calls for an iterative procedure or a tabulation. Summarizing the above procedure, at any sampling instant k t kT  , from the current measurement … view at source ↗
Figure 8
Figure 8. Figure 8: Histogram of the probability density function of the difference between ST and FT outputs (in dB) and the relevant Gaussian fitting (dashed line); mean 0.0049 dB, standard deviation 0.0852 dB. The effects of some of these impairments are illustrated in [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Top curves: example showing the smoothing and tracking of the received Es /N0 samples (dB) using the NEFOCAST architecture with two Kalman filters in the presence of a rain event. Bottom curve: estimated rain rate (mm/h). In order to solve the issues above, we note that SNR fluctuations during dry and wet conditions have a quite different behavior: specifically, during rain events, wet samples (wet) s0 [( … view at source ↗
Figure 10
Figure 10. Figure 10: Estimate of the rain rate obtained from SmartLNB measurements (solid line) made by station NEFOCAST-ITA-MS-001X (Massa, 44.0344o N, 10.1399o E), compared with conventional rain gauge measurements (dashed lines) [PITH_FULL_IMAGE:figures/full_fig_p023_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Estimate of the rain rate obtained from SmartLNB measurements (solid line) made by station NEFOCAST-ITA-MS-001X (Massa, 44.0344o N, 10.1399o E), compared with conventional rain gauge measurements (dashed lines). IX. NUMERICAL RESULTS In order to clarify the operation of the double-Kalman-based algorithm outlined above, let us first consider the application example in [PITH_FULL_IMAGE:figures/full_fig_p02… view at source ↗
Figure 12
Figure 12. Figure 12: Characteristics of the rain rate estimator, plotted for the typical values of the parameters of the station NEFOCAST-ITA-PI-003X (Pisa, 43.7203o N, 10.3836o E), and three different values of the 0 oC isotherm height. To validate the accuracy of the NEFOCAST rain retrieval algorithm, [PITH_FULL_IMAGE:figures/full_fig_p026_12.png] view at source ↗
read the original abstract

In this paper we present results from the NEFOCAST project, funded by the Tuscany Region, aiming at detecting and estimating rainfall fields from the opportunistic use of the rain-induced excess attenuation incurred in the downlink channel by a commercial DVB satellite signal. The attenuation is estimated by reverse-engineering the effects of the various propagation phenomena affecting the received signal, among which, in first place, the perturbations factors affecting geostationary orbits, such as the gravitational attraction from the moon and the sun and the inhomogeneity in Earth mass distribution and, secondly, the small-scale irregularities in the atmospheric refractive index, causing rapid fluctuations in signal amplitude. The latter impairments, in particular, even if periodically counteracted by correction maneuvers, may give rise to significant departures of the actual satellite position from the nominal orbit. A further problem to deal with is the daily and seasonal random fluctuation of the rain height and altitude/size of the associated melting layer. All of the above issues lead to non-negligible random deviations from the dry nominal downlink attenuation, that can be misinterpreted as rain events. In this paper we show how to counteract these issues by employing two differentially-configured Kalman filters designed to track slow and fast changes of the received signal-to-noise ratio, so that the rain events can be reliably detected and the relevant rainfall rate estimated.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript presents the NEFOCAST system for detecting and estimating rainfall fields via opportunistic measurement of rain-induced excess attenuation on commercial DVB satellite downlink signals. It identifies confounders including orbital perturbations from lunar/solar gravity and Earth mass inhomogeneity, refractive-index fluctuations causing scintillation, and daily/seasonal variations in rain height and melting layer. The central approach uses two differentially-configured Kalman filters to track slow and fast changes in received SNR, thereby isolating rain events for reliable detection and rainfall-rate estimation.

Significance. If the time-scale separation and resulting estimates prove accurate, the work could enable low-cost, wide-area rainfall monitoring by repurposing existing satellite broadcast infrastructure without dedicated sensors. However, the abstract supplies no error statistics, detection probabilities, rate-estimation accuracy, or comparisons against rain gauges, so the practical significance cannot yet be evaluated. The method applies standard Kalman tracking to an external physical quantity rather than introducing new derivations or parameter-free results.

major comments (2)
  1. [Abstract] Abstract (final sentence): the claim that the two Kalman filters enable 'reliable' detection and rainfall-rate estimation is load-bearing for the entire contribution, yet the provided text contains no quantitative validation, no error metrics, no ground-truth comparisons, and no description of how the process/measurement noise covariances were selected or tuned.
  2. [Abstract] Abstract: the separation of rain-induced excess attenuation from orbital perturbations, refractive-index fluctuations, and melting-layer variations rests on the assumption that these effects occupy sufficiently disjoint time scales. No spectral analysis, transfer-function characterization of the filters, or quantitative bounds on the frequency content of each confounder are supplied to demonstrate that the slow/fast split avoids misattribution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed review and constructive comments on our manuscript. We address each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract (final sentence): the claim that the two Kalman filters enable 'reliable' detection and rainfall-rate estimation is load-bearing for the entire contribution, yet the provided text contains no quantitative validation, no error metrics, no ground-truth comparisons, and no description of how the process/measurement noise covariances were selected or tuned.

    Authors: The abstract is a concise summary; the full manuscript provides quantitative validation through ground-truth comparisons with rain gauges, detection probabilities, rate-estimation RMSE, and details on covariance tuning via empirical clear-sky and rain-event data in Sections III and IV. We will revise the abstract to incorporate key performance metrics for completeness. revision: yes

  2. Referee: [Abstract] Abstract: the separation of rain-induced excess attenuation from orbital perturbations, refractive-index fluctuations, and melting-layer variations rests on the assumption that these effects occupy sufficiently disjoint time scales. No spectral analysis, transfer-function characterization of the filters, or quantitative bounds on the frequency content of each confounder are supplied to demonstrate that the slow/fast split avoids misattribution.

    Authors: The separation relies on well-established physical time scales (orbital and rain-height effects over hours to days; scintillation over seconds to minutes). The differentially configured Kalman filters use distinct process-noise settings to track these regimes. While the manuscript does not include explicit spectral analysis, we can add a brief discussion or supplementary material with frequency-content bounds to further substantiate the approach. revision: partial

Circularity Check

0 steps flagged

No circularity: standard Kalman application to external SNR data with no self-referential reduction.

full rationale

The paper's central method applies two differentially-configured Kalman filters to track slow and fast SNR changes for rain detection and rate estimation. The provided abstract and description contain no equations, derivations, or self-citations that reduce the claimed rainfall estimate to a fitted parameter defined by the same data or to a self-citation chain. The time-scale separation is presented as an application of standard filtering to an external physical quantity (rain-induced attenuation), not as a quantity defined in terms of itself. No load-bearing step reduces by construction to the inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the premise that temporal-scale separation via Kalman filters isolates rain attenuation; this depends on domain assumptions about the statistical properties of the competing impairments and on the existence of suitable filter parameters.

free parameters (1)
  • Kalman filter process and measurement noise covariances
    The differential configuration requires tuned noise parameters that determine what counts as slow versus fast; these are not stated in the abstract.
axioms (1)
  • domain assumption Rain attenuation exhibits a distinct temporal signature separable from orbital and refractive perturbations by linear filtering
    Invoked in the final sentence of the abstract as the basis for reliable detection.

pith-pipeline@v0.9.0 · 5783 in / 1286 out tokens · 26140 ms · 2026-05-24T16:50:19.157787+00:00 · methodology

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Reference graph

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