False Events Research Articles

Dual Geostationary Lightning Mapper Observations

AbstractTwo Geostationary Lightning Mappers (GLMs) now observe spatial and temporal lightning distributions over a vast region. The GOES-16 GLM covers most land areas in the Western Hemisphere, and detects ~4 times as much lightning as the GOES-17 GLM. Although the continents dominate the lightning distributions year-round, each season exhibits widespread lightning over parts of the Atlantic Ocean and within three broad regions over the Pacific. These oceanic regions demonstrate the key role convective organization plays in producing larger, longer-lasting, and more energetic flashes observed by both GLMs over the oceans. Texture within the flash densities reveals a close relationship with the underlying topography, underscored by the complex diurnal cycles observed along coastlines and in mountainous regions. GLM information beyond flash frequency provides additional insights into storm mode and evolution. For example, over the Sierra Madre Occidental, time series reveal initially small, short-duration GLM flashes growing larger and longer as storms grow upscale. These mesoscale convective systems often transition offshore, contributing to an average flash area maximum over the eastern Pacific. Data quality improves during the study period with tuning of the ground system software. GLM artifacts due to solar intrusion and sun glint greatly diminish following the blooming filter installation, and the second-level threshold filter reduces false events along particular subarray boundaries (i.e., bar artifacts). Analysis of the overlap region reveals a pronounced north–south line near 103°W, with the GOES-16 (GOES-17) GLM detecting more flashes to the east (west) of this line.

Rich false memories of autobiographical events can be reversed

False memories of autobiographical events can create enormous problems in forensic settings (e.g., false accusations). While multiple studies succeeded in inducing false memories in interview settings, we present research trying to reverse this effect (and thereby reduce the potential damage) by means of two ecologically valid strategies. We first successfully implanted false memories for two plausible autobiographical events (suggested by the students' parents, alongside two true events). Over three repeated interviews, participants developed false memories (measured by state-of-the-art coding) of the suggested events under minimally suggestive conditions (27%) and even more so using massive suggestion (56%). We then used two techniques to reduce false memory endorsement, source sensitization (alerting interviewees to possible external sources of the memories, e.g., family narratives) and false memory sensitization (raising the possibility of false memories being inadvertently created in memory interviews, delivered by a new interviewer). This reversed the false memory build-up over the first three interviews, returning false memory rates in both suggestion conditions to the baseline levels of the first interview (i.e., to ∼15% and ∼25%, respectively). By comparison, true event memories were endorsed at a higher level overall and less affected by either the repeated interviews or the sensitization techniques. In a 1-y follow-up (after the original interviews and debriefing), false memory rates further dropped to 5%, and participants overwhelmingly rejected the false events. One strong practical implication is that false memories can be substantially reduced by easy-to-implement techniques without causing collateral damage to true memories.

Detection of gene fusions using targeted next-generation sequencing: a comparative evaluation

BackgroundGene fusions represent promising targets for cancer therapy in lung cancer. Reliable detection of multiple gene fusions is therefore essential.MethodsFive commercially available parallel sequencing assays were evaluated for their ability to detect gene fusions in eight cell lines and 18 FFPE tissue samples carrying a variety of known gene fusions. Four RNA-based assays and one DNA-based assay were compared; two were hybrid capture-based, TruSight Tumor 170 Assay (Illumina) and SureSelect XT HS Custom Panel (Agilent), and three were amplicon-based, Archer FusionPlex Lung Panel (ArcherDX), QIAseq RNAscan Custom Panel (Qiagen) and Oncomine Focus Assay (Thermo Fisher Scientific).ResultsThe Illumina assay detected all tested fusions and showed the smallest number of false positive results. Both, the ArcherDX and Qiagen panels missed only one fusion event. Among the RNA-based assays, the Qiagen panel had the highest number of false positive events. The Oncomine Focus Assay (Thermo Fisher Scientific) was the least adequate assay for our purposes, seven fusions were not covered by the assay and two fusions were classified as uncertain. The DNA-based SureSelect XT HS Custom Panel (Agilent) missed three fusions and nine fusions were only called by one software version. Additionally, many false positive fusions were observed.ConclusionsIn summary, especially RNA-based parallel sequencing approaches are potent tools for reliable detection of targetable gene fusions in clinical diagnostics.

Induction of false beliefs and false memories in laboratory studies-A systematic review.

Psychological interventions often use guided discovery and other techniques for diagnostic exploration and intervention planning. This way, memories may arise in the person, which may be true or false. False memories of earlier events can be harmful and result in real suffering, similar to actual traumatic memories. Based on cognitive psychological and psycho-traumatological findings, there is pronounced dissent in the academic disciplines regarding the conceptualization, relevance and research of false memories. This review contributes to the basic question of how often false beliefs and false memories may be induced within the frame of different interactional techniques. A systematic review has been conducted of 59 articles from (quasi-)experimental studies and two qualitative sources from 30 data bases. Three main methods of memory induction provide the basis for reporting: imagination inflation, false feedback, and memory implantation. Due to the conceptual and methodological diversity of the studies, the results appear to be heterogeneous. Free and guided imagery, as well as suggestive statements, could induce false beliefs or false memories in, on average, 20%-50% of the participants who underwent experimental manipulation concerning false past events. A false belief induction may occur after dream interpretation or hypnosis in more than 50% of participants. Personalized suggestion is more effective in inducing memory than the general plausibility of the suggested events. Further research questions are which therapeutic actions seem appropriate in cases of harmful false memories. This depends not only on whether there are veridical elements in the false memory but also on the quality and meaning of the memory for the person's life and ability to cope with burdens.

Reverse Transcription Recombinase Polymerase Amplification Coupled with CRISPR-Cas12a for Facile and Highly Sensitive Colorimetric SARS-CoV-2 Detection.

The outbreak of the pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) calls for an urgent unmet need for developing a facial and cost-effective detection method. The requirement of well-trained personnel and sophisticated instrument of current primary mean (reverse transcription polymerase chain reaction, RT-PCR) may hinder the practical application worldwide. In this regard, a reverse transcription recombinase polymerase amplification (RT-RPA) coupled with CRISPR-Cas12a colorimetric assay is proposed for the SARS-CoV-2 detection. The methodology we have described herein utilizes DNA-modified gold nanoparticles (AuNPs) as a universal colorimetric readout and can specifically target ORF1ab and N regions of the SARS-CoV-2 genome. After the virus genome is amplified through RT-RPA, the resulting abundant dsDNA will bind and activate Cas12a. Under trans-cleavage degradation, the capped DNA substrate will be hydrolyzed gradually from AuNPs, demonstrating a change in the surface plasmon resonance (SPR), which can be facially monitored by UV–vis absorbance spectroscopy and naked eye observation. The high amplification efficiency from RT-RPA and Cas12a trans-cleavage process bring the sensitivity of our method to 1 copy of viral genome sequence per test. Notably, under the dual variations inspecting from the isothermal amplification and Cas12a activation process, the false positive events from other beta coronavirus members can be effectively avoided and thus significantly improve the specificity. Furthermore, the reliability of this colorimetric assay is validated by standard clinical samples from the hospital laboratory department. Through integration of the inherently high sensitivity and specificity from an RPA-coupled Cas12a system with the intrinsic simplicity of AuNP-based colorimetric assay, our method increases the practical testing availability of SARS-CoV-2.

Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson\u2019s Disease patients

BackgroundIdentification of individual gait events is essential for clinical gait analysis, because it can be used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson’s disease. Previous research has shown that gait events can be detected from a shank-mounted inertial measurement unit (IMU), however detection performance was often evaluated only from straight-line walking. For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well as in single-task and dual-task conditions.MethodsParticipants (older adults, people with Parkinson’s disease, or people who had suffered from a stroke) performed three different walking trials: (1) straight-line walking, (2) slalom walking, (3) Stroop-and-walk trial. An optical motion capture system was used a reference system. Markers were attached to the heel and toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of the shank IMUs was used to detect instances of initial foot contact (IC) and final foot contact (FC), which were compared to reference values obtained from the marker trajectories.ResultsThe detection method showed high recall, precision and F1 scores in different populations for both initial contacts and final contacts during straight-line walking (IC: recall = 100%, precision = 100%, F1 score = 100%; FC: recall = 100%, precision = 100%, F1 score = 100%), slalom walking (IC: recall = 100%, precision ge 99%, F1 score =100%; FC: recall = 100%, precision ge 99%, F1 score =100%), and turning (IC: recall ge 85%, precision ge 95%, F1 score ge91%; FC: recall ge 84%, precision ge 95%, F1 score ge89%).ConclusionsShank-mounted IMUs can be used to detect gait events during straight-line walking, slalom walking and turning. However, more false events were observed during turning and more events were missed during turning. For use in daily life we recommend identifying turning before extracting temporal gait parameters from identified gait events.

Stellar flares from blended and neighbouring stars in Kepler short cadence observations

ABSTRACT We present the results of a search for stellar flares from stars neighbouring the target sources in the Kepler short cadence data. These flares have been discarded as contaminants in previous surveys and therefore provide an unexplored resource of flare events, in particular high-energy events from faint stars. We have measured M dwarf flare energies up to 1.5 × 1035 erg, pushing the limit for flare energies measured using Kepler data. We have used our sample to study the flaring activity of wide binaries, finding that the lower mass counterpart in a wide binary flares more often at a given energy. Of the 4430 flares detected in our original search, 298 came from a neighbouring star, a rate of 6.7 ± 0.4 per cent for the Kepler short cadence light curves. We have used our sample to estimate a 5.8 ± 0.1 per cent rate of false positive flare events in studies using Transiting Exoplanet Survey Satellite short cadence data.

A Kind of Event Trust Model for VANET Based on Statistical Method

The paper proposed a trust model based on mathematical statistic method to enhance the safety of a VANET. By applying the statistical principles such as significance test, hypothesis test and confidence interval, the model can help the reputation management center to calculate trust values of all vehicle nodes, can help vehicle nodes to judge whether an event message can be trust or not. The simulation experiment results show that the model can effectively identify false event messages, improve the accuracy of data and enhance the information security of a VANET.

Optimizing PWM Control for Efficiency and Reduction of False Turn-On Events in Synchronous Buck GaN Converters

Half-bridge GaN power converters are susceptible to false turn-on events, which can lead to shoot-through and potentially device-damaging currents. There are three main parameters that can be adjusted in PWM schemes to reduce the likelihood of false turn-on events: negative gate bias, gate resistance, and deadtime. However, these PWM parameters also affect converter efficiency in the inverse way, meaning less false turn-on events must be balanced with lowered efficiency. The novelty of this paper is to investigate the trade-off between reducing GaN false turn-on events (by reducing the transient peak of gate to source voltage) and maximizing the power converter efficiency, which has not been done in prior work. This paper investigates this trade-off using a synchronous buck converter over numerous operating points with variation of the three key PWM parameters. Six converter scenarios are considered with input voltage of 200/400V, switching frequency of 50/100kHz, and output power of 500W/1kW. For each scenario, negative gate bias is set to ™4.4V and −5V, gate on-resistance is set to $10\Omega $ and $12.5\Omega $ , and deadtime is varied at 60ns, 80ns, and 110ns. The results are organized into Pareto plots to find optimal points for efficiency and reduction of false turn-on events. The experimental results show that a further negative gate bias (−5V) most significantly reduces the false turn-on voltage peak and still achieves very high efficiency with appropriate selection of gate resistance and deadtime.

A Framework for Anomaly Identification Applied on Fall Detection

Automatic systems to monitor people and subsequently improve people's lives have been emerging in the last few years, and currently, they are capable of identifying many activities of daily living (ADLs). An important field of research in this context is the monitoring of health risks and the identification of falls. It is estimated that every year, one in three persons older than 65 years will fall, and fall events are associated with high mortality rates among the elderly. We propose an anomaly identification framework to detect falls, which incorporates a spatial-temporal convolutional graph network (ST-GCN) as a feature extractor and uses an encoder process to reconstruct ADLs and identify falls as anomalies. As the publicly available fall datasets are few and generally unbalanced, training a reliable model using approaches that need explicit labeling is challenging. Thus, a focus on learning without external supervision is desirable. Treating a fall as an exception of ADLs allows us to recognize falls as anomalies without explicit labels. Given its modular architecture, our framework can robustly represent visual information and use the encoder's reconstruction error to identify falls as anomalies. We assess our framework's ability to recognize falls by training it with only ADLs. We perform three types of experiments: single dataset training and evaluation that consists of separate 90% of the data to train the model 5% to adjust the model, and the rest to the test. A joint dataset experiment, where we combine two datasets to increase the number of samples our model is trained on, and a cross-dataset evaluation, where we train on one dataset and evaluate using another one. Besides presenting state-of-the-art results on our experiments, particularly on the cross-dataset one, the model also presents a low number of false events, which makes it an ideal candidate for real-world application.

Microseismic event detection in large heterogeneous velocity models using Bayesian multimodal nested sampling

Abstract In passive seismic and microseismic monitoring, identifying and characterizing events in a strong noisy background is a challenging task. Most of the established methods for geophysical inversion are likely to yield many false event detections. The most advanced of these schemes require thousands of computationally demanding forward elastic-wave propagation simulations. Here we train and use an ensemble of Gaussian process surrogate meta-models, or proxy emulators, to accelerate the generation of accurate template seismograms from random microseismic event locations. In the presence of multiple microseismic events occurring at different spatial locations with arbitrary amplitude and origin time, and in the presence of noise, an inference algorithm needs to navigate an objective function or likelihood landscape of highly complex shape, perhaps with multiple modes and narrow curving degeneracies. This is a challenging computational task even for state-of-the-art Bayesian sampling algorithms. In this paper, we propose a novel method for detecting multiple microseismic events in a strong noise background using Bayesian inference, in particular, the Multimodal Nested Sampling (MultiNest) algorithm. The method not only provides the posterior samples for the 5D spatio-temporal-amplitude inference for the real microseismic events, by inverting the seismic traces in multiple surface receivers, but also computes the Bayesian evidence or the marginal likelihood that permits hypothesis testing for discriminating true vs. false event detection.

Machine-learning-based detection of volcano seismicity using the spatial pattern of amplitudes

SUMMARYWe propose a new algorithm, focusing on spatial amplitude patterns, to automatically detect volcano seismic events from continuous waveforms. Candidate seismic events are detected based on signal-to-noise ratios. The algorithm then utilizes supervised machine learning to classify the existing candidate events into true and false categories. The input learning data are the ratios of the number of time samples with amplitudes greater than the background noise level at 1 s intervals (large amplitude ratios) given at every station site, and a manual classification table in which ‘true’ or ‘false’ flags are assigned to candidate events. A two-step approach is implemented in our procedure. First, using the large amplitude ratios at all stations, a neural network model representing a continuous spatial distribution of large amplitude probabilities is investigated at 1 s intervals. Second, several features are extracted from these spatial distributions, and a relation between the features and classification to true and false events is learned by a support vector machine. This two-step approach is essential to account for temporal loss of data, or station installation, movement, or removal. We evaluated the algorithm using data from Mt. Ontake, Japan, during the first ten days of a dense observation trial in the summit region (2017 November 1–10). Results showed a classification accuracy of more than 97 per cent.

Cirrus cloud shape detection by tomographic extinction retrievals from infrared limb emission sounder measurements

Abstract. An improved cloud-index-based method for the detection of clouds in limb sounder data is presented that exploits the spatial overlap of measurements to more precisely detect the location of (optically thin) clouds. A second method based on a tomographic extinction retrieval is also presented. Using CALIPSO data and a generic advanced infrared limb imaging instrument as examples for a synthetic study, the new cloud index method has a better horizontal resolution in comparison to the traditional cloud index and has a reduction of false positive cloud detection events by about 30 %. The results for the extinction retrieval even show an improvement of 60 %. In a second step, the extinction retrieval is applied to real 3-D measurements of the airborne Gimballed Limb Observer for Radiance Imaging in the Atmosphere (GLORIA) taken during the Wave-driven ISentropic Exchange (WISE) campaign to retrieve small-scale cirrus clouds with high spatial accuracy.

Pionium as a source of false events in the K→πνν¯ decays

We suggest that the decay modes of kaons with a pion and a pionium ($\pi^+\pi^-$ atom) in the final state can constitute a not yet considered background to the very rare decay $K\to\pi\nu\bar\nu$. In fact, a part of pioniums may escape the decay region before decaying into two $\pi^0$s (or to $\pi^0\pi^0\gamma$ in the case of excited pionium). To illustrate the importance of this background, we show that it may even explain, under some assumptions, the unexpected $K_L$ decay events that appeared in the KOTO experiment.

Spatial Distribution of Extreme Precipitation Events and Its Trend in Nepal

The mountainous country with complex topography and climatic conditions over Nepal, the Tropical Rainfall Measuring Mission (TRMM-3B42), were evaluated for monitoring extreme precipitation events using 142-gauge observation from January 1998 to December 2018. Several extreme precipitation indices based on daily timescale data were selected for evaluation. Detection skills, spatial distribution, and trends in extreme precipitation were also investigated. TRMM product moderately capture (POD>70%) the true precipitation events (Probability of Detection) at most of the station. Although, TRMM product shows higher (FAR>40%) false precipitation events (False Alarm Ratio) however, it accurately captures (ACC>80%) the precipitation and no-precipitation events (Accuracy) over the country. Based on five different extreme precipitation indices; heavy precipitation events (R10mm), extreme precipitation events (R25mm), 7 Consecutive Dry Days (CDD), 7 Consecutive Wet Days (CWD), and one-day maximum precipitation (Rx1day), it was observed that the TRMM product can reproduce the spatial distribution of heavy and extreme precipitation events however, it tends to underestimate (overestimate) the frequency of R25mm and CDD (R10mm and CWD spells). Further, increasing (decreasing) trend of dry (wet) spells are observed in both datasets (observed and TRMM) during the study period. The highest Rx1day (about 500 mm/day) was observed during 2014 and 2017; differently, TRMM product shows during 2008 with the highest Rx1day about 300 mm/day. This study provides accuracy of TRMM product to represent the spatio-temporal distribution of extreme precipitation events and its recent trends in Nepal, which is of great significance to hydro-meteorological application. In general, the TRMM product is a good alternative to monitoring extreme precipitation events in Nepal; however, there is still space for further improvement in rainfall retrieval algorithms, especially in high-elevation areas.

Doppler ultrasound cardiac gating of intracranial flow at 7T

BackgroundUltra-high field magnetic resonance imaging (MR) may be used to improve intracranial blood flow measurements. However, standard cardiac synchronization methods tend to fail at ultra-high field MR. Therefore, this study aims to investigate an alternative synchronization technique using Doppler ultrasound.MethodsHealthy subjects (n = 9) were examined with 7T MR. Flow was measured in the M1-branch of the middle cerebral artery (MCA) and in the cerebral aqueduct (CA) using through-plane phase contrast (2D flow). Flow in the circle of Willis was measured with three-dimensional, three-directional phase contrast (4D flow). Scans were gated with Doppler ultrasound (DUS) and electrocardiogram (ECG), and pulse oximetry data (POX) was collected simultaneously. False negative and false positive trigger events were counted for ECG, DUS and POX, and quantitative flow measures were compared.ResultsThere were fewer false positive triggers for DUS compared to ECG (5.3 ± 11 vs. 25 ± 31, p = 0.031), while no other measured parameters differed significantly. Net blood flow in M1 was similar between DUS and ECG for 2D flow (1.5 ± 0.39 vs. 1.6 ± 0.41, bias ± 1.96SD: − 0.021 ± 0.36) and 4D flow (1.8 ± 0.48 vs. 9 ± 0.59, bias ± 1.96SD: − 0.086 ± 0.57 ml). Net CSF flow per heart beat in the CA was also similar for DUS and ECG (3.6 ± 2.1 vs. 3.0 ± 5.8, bias ± 1.96SD: 0.61 ± 13.6 μl).ConclusionGating with DUS produced fewer false trigger events than using ECG, with similar quantitative flow values. DUS gating is a promising technique for cardiac synchronization at 7T.

Towards better heartbeat segmentation with deep learning classification

The confidence of medical equipment is intimately related to false alarms. The higher the number of false events occurs, the less truthful is the equipment. In this sense, reducing (or suppressing) false positive alarms is hugely desirable. In this work, we propose a feasible and real-time approach that works as a validation method for a heartbeat segmentation third-party algorithm. The approach is based on convolutional neural networks (CNNs), which may be embedded in dedicated hardware. Our proposal aims to detect the pattern of a single heartbeat and classifies them into two classes: a heartbeat and not a heartbeat. For this, a seven-layer convolution network is employed for both data representation and classification. We evaluate our approach in two well-settled databases in the literature on the raw heartbeat signal. The first database is a conventional on-the-person database called MIT-BIH, and the second is one less uncontrolled off-the-person type database known as CYBHi. To evaluate the feasibility and the performance of the proposed approach, we use as a baseline the Pam-Tompkins algorithm, which is a well-known method in the literature and still used in the industry. We compare the baseline against the proposed approach: a CNN model validating the heartbeats detected by a third-party algorithm. In this work, the third-party algorithm is the same as the baseline for comparison purposes. The results support the feasibility of our approach showing that our method can enhance the positive prediction of the Pan-Tompkins algorithm from 97.84%/90.28% to 100.00%/96.77% by slightly decreasing the sensitivity from 95.79%/96.95% to 92.98%/95.71% on the MIT-BIH/CYBHi databases.

EasyQuake: Putting Machine Learning to Work for Your Regional Seismic Network or Local Earthquake Study

AbstractWe developed a Python package—easyQuake—that consists of a flexible set of tools for detecting and locating earthquakes from International Federation of Digital Seismograph Networks-collected or field-collected seismograms. The package leverages a machine-learning driven phase picker, coupled with an associator, to produce a Quake Markup Language (QuakeML) style catalog complete with magnitudes and P-wave polarity determinations. We describe how nightly computations on day-long seismograms identify lower-magnitude candidate events that were otherwise missed due to cultural noise and how those events are incorporated into the Oklahoma Geological Survey statewide network upon analyst manual review. We discuss applications for the package, including earthquake detection for regional networks and microseismicity studies in arbitrary user-defined regions. Because the fundamentals of the package are scale invariant, it has wide application to seismological earthquake analysis from regional to local arrays and has great potential for identifying early aftershocks that are otherwise missed. The package is fast and reliable; the computations are relatively efficient across a range of hardware, and we have encountered very few (∼1%) false positive event detections for the Oklahoma case study. The utility and novelty of the package is the turnkey earthquake analysis with QuakeML file output, which can be dropped directly into existing real-time earthquake analysis systems. We have designed the functions to be quite modular so that a user could replace the provided picker or associator with one of their choosing. The Python package is open source and development continues.

Desert mixed seismic noise suppression by using multiple forward models and a supervised deep-learning method

Desert seismic data pose a particular problem due to its low signal-to-noise ratio and severe spectral overlapping. Most of the existing denoising methods have negative suppression effect on the desert mixed seismic noise, which mainly includes random noise and surface waves. The convolutional neural network (CNN) is a classical supervised deep-learning method that has proven to be an effective tool for noise suppression. The drawback is that CNN usually needs a large amount of ideal noise-free data to complete network training, which largely limits its applications to seismic data denoising. In this paper, we combine forward modelling with CNN to overcome this technical problem. Specifically, we construct multiple forward models from different physical parameters to obtain a noise-free seismic dataset by finite difference method. After adding noise data taken from a real desert noise record, we use the above data as the input for CNN training. Then, the MSE loss function allows us to obtain an optimal denoising model for the mixed noise suppression of desert seismic data. Continuing with this optimal model, we can predict mixed noise, which, once subtracted from the desert original seismic data, provide the denoised desert seismic data. Experiments with synthetic and real data demonstrate that this CNN-based denoising method can suppress the desert mixed seismic noise successfully. Several tests using artificially generated models with a different number of signal patches prove that the forward modelling can significantly improve the continuity of events and prevent the appearance of false events. In addition, analysis of the effect of network depth supports the effectiveness of our method through a trade-off among higher signal-to-noise ratio, lower root mean square error and shorter training time.

Streamflow Variability Indicated by False Rings in Bald Cypress (Taxodium distichum (L.) Rich.)

Despite growing in wet lowland and riparian settings, Taxodium distichum (L.) Rich. (bald cypress) has a strong response to hydroclimate variability, and tree ring chronologies derived from bald cypress have been used extensively to reconstruct drought, precipitation and streamflow. Previous studies have also demonstrated that false rings in bald cypress appear to be the result of variations in water availability during the growing season. In this study 28 trees from two sites located adjacent to the Choctawhatchee River in Northwestern Florida, USA were used to develop a false ring record extending from 1881 to 2014. Twenty false ring events were recorded during the available instrumental era (1931–2014). This record was compared with daily and monthly streamflow data from a nearby gage. All 20 of the false-ring events recorded during the instrumental period occurred during years in which greatly increased streamflow occurred late in the growing season. Many of these wet events appear to be the result of rainfall resulting from landfalling tropical cyclones. We also found that the intra-annual position of false rings within growth rings reflects streamflow variability and combining the false-ring record with tree ring width chronologies improves the estimation of overall summer streamflow by 14%. Future work using these and other quantitative approaches for the identification and measurement of false ring variables in tree rings may improve tree-ring reconstructions of streamflow and potentially the record of tropical cyclone rainfall events.