Operational Near-real Time Drought Monitoring Using Global Satellite Precipitation Estimates

2020 ◽  
Author(s):  
Olivier Prat ◽  
Alec Courtright ◽  
Ronald Leeper ◽  
Brian Nelson ◽  
Rocky Bilotta ◽  
...  
Keyword(s):  
United States ◽  
Real Time ◽  
The United States ◽  
Global Scale ◽  
Drought Monitoring ◽  
Drought Indices ◽  
In Situ Data ◽  
Drought Conditions ◽  
Precipitation Estimates ◽  
Monitoring Information

<p>We present an operational near-real time drought monitoring framework on a global scale that uses satellite quantitative precipitation estimates from the NOAA/CDR program (CMORPH-CDR, PERSIANN-CDR). Monthly and daily Standardized Precipitation Indexes (SPI) are computed for various time scales over the entire period of record of the respective datasets. The near-real time availability of CMORPH-CDR permits for a daily update of the global drought conditions starting in 1998, while the longer period of record of PERSIANN-CDR allows to compute global drought conditions since 1983. The SPI sensitivity to different precipitation datasets and to various lengths of record is quantified. Results indicated that both monthly and daily SPIs computed with both CDRs presented the same timing and area for the major droughts episodes over the continental United States as well as for selected drought events around the globe. Furthermore, the difference resulting from the use of the two-parameter Gamma distribution (McKee et al. 1993) and the three-parameter Pearson III distribution (Guttman 1999) is evaluated. The global mapping of the different distribution parameters (2 and 3 parameters respectively for the Gamma and Pearson III distributions) informs us on how to optimally compute the SPI in areas experiencing too much or too little rainfall. Both CMORPH-CDR and PERSIANN-CDR SPIs are evaluated primarily over CONUS where long-term drought monitoring products based on in-situ data exists such as the United States Drought Monitor (USDM) and the nClimGrid derived SPI. A publicly available interactive visualization tool that provides access to global drought information is also presented. The tools is intended to fill some of the drought monitoring information gaps around the globe. A variety of visualization techniques are used to aid in the interpretation of global drought indices while interactive functionality allows users to focus on a specific region and time-scale of interest. Additional information for region specific drought monitoring resources is also provided to help users access regional drought monitoring information.</p>

scholarly journals DroughtCast: A Machine Learning Forecast of the United States Drought Monitor

2021 ◽  
Vol 4 ◽  
Author(s):  
Colin Brust ◽  
John S. Kimball ◽  
Marco P. Maneta ◽  
Kelsey Jencso ◽  
Rolf H. Reichle
Keyword(s):  
United States ◽  
Machine Learning ◽  
Soil Moisture ◽  
Local Governments ◽  
Warning System ◽  
The United States ◽  
Drought Indices ◽  
Drought Event ◽  
Mitigation And Adaptation ◽  
Drought Conditions

Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods to forecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.

What's the Buzz? The Neuroscience and the Treatment of Tinnitus

2021 ◽  
Author(s):  
Amanda Henton ◽  
Thanos Tzounopoulos
Keyword(s):  
United States ◽  
The United States ◽  
Global Scale ◽  
Current Treatment ◽  
Public Health Issue ◽  
Current State ◽  
Acquired Hearing Loss ◽  
Neurophysiological Mechanisms ◽  
Necessary And Sufficient

Tinnitus is a pervasive public health issue that affects approximately 15% of the United States population. Similar estimates have also been shown on a global scale, with similar prevalence found in Europe, Asia, and Africa. The severity of tinnitus is heterogeneous, ranging from mildly bothersome to extremely disruptive. In the United States, approximately 10-20% of individuals who experience tinnitus report symptoms that severely reduce their quality of life. Due to the huge personal and societal burden, in the last twenty years a concerted effort on basic and clinical research has significantly advanced our understanding and treatment of this disorder. Yet, neither full understanding, nor cure exists. We know that tinnitus is the persistent involuntary phantom percept of internally-generated non-verbal noises and tones, which in most cases is initiated, by acquired hearing loss and maintained only when this loss is coupled with distinct neuronal changes in auditory and extra-auditory brain networks. Yet, the exact mechanisms and patterns of neural activity that are necessary and sufficient for the perceptual generation and maintenance of tinnitus remain incompletely understood. Combinations of animal model and human research will be essential in filling these gaps. Nevertheless, the existing progress in investigating the neurophysiological mechanisms has improved current treatment and highlighted novel targets for drug development and clinical trials. The aim of this review is to thoroughly discuss the current state of human and animal tinnitus research, outline current challenges, and highlight new and exciting research opportunities.

scholarly journals A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States

2019 ◽  
Vol 116 (8) ◽  
pp. 3146-3154 ◽  
Author(s):  
Nicholas G. Reich ◽  
Logan C. Brooks ◽  
Spencer J. Fox ◽  
Sasikiran Kandula ◽  
Craig J. McGowan ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Real Time ◽  
Forecast Accuracy ◽  
The United States ◽  
Public Health Response ◽  
Public Health Decision ◽  
Health Decision Making ◽  
Health Response ◽  
Health Decision

Influenza infects an estimated 9–35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making.

scholarly journals Impact of the 2012 extreme drought conditions on private well owners in the United States, a qualitative analysis

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Michelle Murti ◽  
Ellen Yard ◽  
Rachel Kramer ◽  
Dirk Haselow ◽  
Mike Mettler ◽  
...  
Keyword(s):  
United States ◽  
Qualitative Analysis ◽  
The United States ◽  
Extreme Drought ◽  
Drought Conditions

Emerging Synthetic Cannabinoids: Development and Validation of a Novel Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry Assay for Real-Time Detection

2019 ◽  
Vol 44 (3) ◽  
pp. 207-217 ◽  
Author(s):  
Alex J Krotulski ◽  
Amanda L A Mohr ◽  
Barry K Logan
Keyword(s):  
United States ◽  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Real Time ◽  
Synthetic Cannabinoids ◽  
Time Of Flight ◽  
Forensic Toxicology ◽  
The United States ◽  
Synthetic Cannabinoid ◽  
Flight Mass Spectrometry

Abstract Synthetic cannabinoids pose significant threats to public health and safety, as their implications in overdose and adverse events continue to arise in United States and around the world. Synthetic cannabinoids have seen several generations of chemically diverse structural elements, impacting potency and effects. These factors create new analytical challenges for forensic laboratories. This report describes an efficient liquid chromatography/quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) assay for the identification of synthetic cannabinoid parent compounds and metabolites, including real-time identification of emergent compounds, using a SCIEX TripleTOF® 5600+ with non-targeted SWATH® acquisition. Method validation evaluated precision/accuracy, limits of detection, interferences, processed sample stability and carryover, for which 19 parent compounds and 19 metabolites were tested. To demonstrate feasibility, de-identified blood sample extracts were acquired from a large forensic toxicology laboratory and analyzed using the validated LC-QTOF-MS assay. In mid-2018, 200 blood extracts were analyzed, demonstrating a 19% positivity rate with > 94% agreement rate with original testing. In addition, three newly discovered synthetic cannabinoids were identified, including 5F-MDMB-PICA, 4-cyano CUMYL-BUTINACA and 5F-EDMB-PINACA. These synthetic cannabinoids were previously unreported in forensic toxicology casework in the United States. 5F-MDMB-PICA has become the most prevalent synthetic cannabinoid in United States, as of early 2019. These results demonstrate the effectiveness of this assay and workflow in the identification and characterization of synthetic cannabinoids, as well as the usefulness of sample-mining using non-targeted mass acquisition by LC-QTOF-MS for the discovery of NPS. High resolution mass spectrometry should be considered when developing new or novel assays for synthetic cannabinoids.

scholarly journals Analysing the Relationship between Multiple-Timescale SPI and GRACE Terrestrial Water Storage in the Framework of Drought Monitoring

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1672 ◽  
Author(s):  
Carmelo Cammalleri ◽  
Paulo Barbosa ◽  
Jürgen V. Vogt
Keyword(s):  
Real Time ◽  
Extreme Values ◽  
Water Storage ◽  
Global Scale ◽  
Drought Monitoring ◽  
Limited Area ◽  
Standardised Precipitation Index ◽  
Wide Range ◽  
Multiple Timescale

The operational monitoring of long-term hydrological droughts is often based on the standardised precipitation index (SPI) for long accumulation periods (i.e., 12 months or longer) as a proxy indicator. This is mainly due to the current lack of near-real-time observations of relevant hydrological quantities, such as groundwater levels or total water storage (TWS). In this study, the correlation between multiple-timescale SPIs (between 1 and 48 months) and GRACE-derived TWS is investigated, with the goals of: (i) evaluating the benefit of including TWS data in a drought monitoring system, and (ii) testing the potential use of SPI as a robust proxy for TWS in the absence of near-real-time measurements of the latter. The main outcomes of this study highlight the good correlation between TWS anomalies (TWSA) and long-term SPI (12, 24 and 48 months), with SPI-12 representing a global-average optimal solution (R = 0.350 ± 0.250). Unfortunately, the spatial variability of the local-optimal SPI underlines the difficulty in reliably capturing the dynamics of TWSA using a single meteorological drought index, at least at the global scale. On the contrary, over a limited area, such as Europe, the SPI-12 is able to capture most of the key traits of TWSA that are relevant for drought studies, including the occurrence of dry extreme values. In the absence of actual TWS observations, the SPI-12 seems to represent a good proxy of long-term hydrological drought over Europe, whereas the wide range of meteorological conditions and complex hydrological processes involved in the transformation of precipitation into TWS seems to limit the possibility of extending this result to the global scale.

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