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Climate change is escalating the threat of heat stress to global public health, with the majority of humans today facing increasingly severe and prolonged heat waves. Accurate weather data reflecting the complexity of measuring heat stress is crucial for reducing the impact of extreme heat on health worldwide. Previous studies have employed Heat Index (HI) and Wet Bulb Globe Temperature (WBGT) metrics to understand extreme heat exposure, forming the basis for heat stress guidelines. However, systematic comparisons of meteorological and climate data sets used for these metrics and the related parameters, like air temperature, humidity, wind speed, and solar radiation crucial for human thermoregulation, are lacking. We compared three heat measures (HImax, WBGTBernard, and WBGTLiljegren) approximated from gridded weather data sets (ERA5‐Land, PRISM, Daymet) with ground‐based data, revealing strong agreement from HI and WBGTBernard (R2 0.76–0.95, RMSE 1.69–6.64°C). Discrepancies varied by Köppen‐Geiger climates (e.g., Adjusted R2 HImax 0.88–0.95, WBGTBernard 0.79–0.97, and WBGTLiljegren 0.80–0.96), and metrological input variables (Adjusted R2Tmax 0.86–0.94, Tmin 0.91–0.94, Wind 0.33, Solarmax 0.38, Solaravg 0.38, relative humidity 0.51–0.74). Gridded data sets can offer reliable heat exposure assessment, but further research and local networks are vital to reduce measurement errors to fully enhance our understanding of how heat stress measures link to health outcomes. Plain Language Summary: Extreme heat threatens human health. Rising intensity and duration of heat days expose more to hot environments. To understand how extreme heat affects human health, it is important to use accurate weather information and measures that reflect people's actual experience of the heat. Heat Index (HI) and Wet Bulb Globe Temperature (WBGT) are commonly used heat stress metrics that are widely used to set exposure guidelines and policies. However, there have been limited comparisons between daily heat measures and weather variables. In this study, we compared three heat measures (HI, WBGTBernard, and WBGTLiljegren) derived from three widely used gridded weather data sets (ERA5‐Land, PRISM, and Daymet) with ground‐based weather observations. The heat measures calculated from both the gridded weather data and the station data showed a reasonably strong agreement. However, the differences varied depending on the climate types. Gridded weather data sets can provide a reliable approach to assessing heat exposure and impacts based on meteorological variables to produce heat measures. However, further research and the establishment of local ground station networks are necessary to reduce measurement errors in exposure and improve accuracy. This will help us better understand the relationship between heat measures and their impact on health outcomes. Key Points: Compared heat stress measures using gridded data sets and in situ data from 17 institutions across 42 states in the contiguous USObserved variations in heat measures and weather variables align with Köppen‐Geiger climate classificationsInstalling more Wet Bulb Globe Temperature‐measuring stations in diverse microclimates is key to better heat exposure prevention strategies [ABSTRACT FROM AUTHOR]

ABSTRACT This article introduces a web-based climate information resource: an El Niño Southern Oscillation ENSO-based monthly climatology for the United States, available on . Climate variability affects land managers and decision makers in a variety of ways. The ENSO is one of the leading drivers of seasonal and year-to-year climate variability. Understanding the historical rainfall and temperature anomalies associated with ENSO can be an important way for decision makers to learn how they might anticipate climate variability. We used the ENSO record from 1950 to 2013 to develop an ENSO-specific climatology. This allows decision makers to pinpoint the combinations of location and month in which there are substantial ENSO-associated rainfall or temperature anomalies. Learning about the timing of historical ENSO-driven climate impacts can be an important first step for decision makers to develop management adjustments that could make the most positive outcomes based on the available climate information. [ABSTRACT FROM AUTHOR]

ABSTRACT Regions of strong land-atmosphere coupling are often depicted as static in time. However, the mechanisms through which the land surface impacts atmospheric conditions vary on sub-daily to multi-decadal timescales. Therefore, characterizing the long-term variability of land-atmosphere interactions may provide a means of predicting when surface-induced extreme temperature events will be more or less likely to occur. We evaluate the coupling strength between soil moisture, as represented by in situ observations and 1-month Standardized Precipitation Index ( SPI), and subsequent monthly maximum temperature ( TMAX) over the contiguous United States. We find that the utility of SPI as a proxy for soil moisture anomalies is limited to the summer, as the correlations between SPI and TMAX are not significantly related in the other seasons. We examine the variability in summer SPI-TMAX coupling in four regions of the United States. In general, we find the strongest relationships between SPI and TMAX in the Southern Great Plains. However, our results demonstrate that the coupling strength varies considerably over time in most regions of the United States. The long-term variability in SPI-TMAX coupling strength is strongly related to the Pacific Decadal Oscillation in the northwest and midwest United States, and the Atlantic Multidecadal Oscillation in the southeast United States The results of this study suggest that land-atmosphere coupling in the contiguous United States is modulated by multi-decadal oscillations in Pacific and Atlantic sea surface temperatures. [ABSTRACT FROM AUTHOR]

Disclosure of the NSA's PRISM program demonstrated that Internet companies have become prime targets of government surveillance. But what role do companies themselves play in putting users' privacy at risk? By comparing the changes in the privacy policies of ten companies--the nine in PRISM plus Twitter--I seek to understand how users' privacy shifted. Specifically, I study how company practices surrounding the life cycle of user information (e.g. collection, use, sharing, and retention) shifted between the times when companies joined PRISM and when PRISM news broke. A qualitative analysis of the changes in the privacy policies suggests that company disclosure of tracking for advertising purposes increased. I draw on business scholar Shoshana Zuboff's conceptualization of "surveillance capitalism" and legal scholar Joel Reidenberg's "transparent citizen" to explain the implications such changes hold for users' privacy. These findings underscore why public debates about post-Snowden privacy rights cannot ignore the role that companies play in legitimizing surveillance activities under the auspices of creating market value. [ABSTRACT FROM AUTHOR]

Abstract: Heat waves are expected to become more frequent and severe as climate changes, with unknown consequences for biodiversity. We sought to identify ecologically-relevant broad-scale indicators of heat waves based on MODIS land surface temperature (LST) and interpolated air temperature data and assess their associations with avian community structure. Specifically, we asked which data source, time periods, and heat wave indices best predicted changes in avian abundance and species richness. Using mixed effects models, we analyzed associations between these indices and data from the North American Breeding Bird Survey in the central United States between 2000 and 2007 in four ecoregions and five migratory and nesting species groups. We then quantified avian responses to scenarios of severe, but commonly-occurring early, late, and summer-long heat waves. Indices based on MODIS LST data, rather than interpolated air temperatures, were more predictive of avian community structure. Avian communities were more related to 8-day LST exceedances (positive anomalies only); and were generally more sensitive to summer-long heat waves. Across the region, abundance, and to a lesser extent, species richness, declined following heat waves. Among the ecoregions, relationships were most consistently negative in the southern and montane ecoregions, but were positive in a more humid northern ecoregion. Among migratory groups, permanent resident species were the most sensitive, declining in abundance following a summer-long heat wave by 19% and 13% in the montane and southern ecoregions, respectively. Ground-nesting species, which declined in the south by 12% following a late summer heat wave, were more sensitive than avifauna overall. These results demonstrate the value of MODIS LST data for measuring ecologically-relevant heat waves across large regions. Ecologically, these findings highlight the importance of extreme events for avian biodiversity and the considerable variation in response to environmental change associated with different functional groups and geographic regions. The magnitude of the relationships between avian abundance and heat waves reported here raises concerns about the impacts of more frequent and severe heat waves in a warming climate. [Copyright &y& Elsevier]

The article presents a study on the climatological temperature and precipitation in the U.S. It uses the Parameter-elevation Relationships on Independent Slopes Model (PRISM) interpolation method to develop data sets that reflected and the current states of knowledge of spatial climate patterns in the country. PRISM interpolation uncertainties were estimated with cross-validation mean absolute error and the 70 percent prediction interval of the climate-elevation regression function. It demonstrates that using a relatively dense station data set ad the physiographically sensitive PRISM interpolation process resulted in improved climate grids over those of WorldClim and Daymet.

The article studies the characteristics of drought/pluvial regimes, such as intensity, duration and spatial coverage, in U.S. based on a high resolution precipitation dataset by examining the spatial and temporal characteristics of anomalous precipitation throughout the country. Two indexes, the Palmer drought severity index (PDSI) and the standard precipitation index (SPI) from 1895-2003 was constructed using parameter-elevation regression on independent slopes model (PRISM). Findings reveal that the largest yearly drought or pluvial events happen more frequently in central U.S. while extreme values occur more in western U.S.

• A method was developed to extract the most representative weather for watersheds. • A complete real-time dataset (historical and forecast) was developed at HUC-12 level. • An obvious bias was observed in extreme values in the national forecast data (NFD). • The NFD shows a larger bias in predicting maximum temperature than the minimum. Accurate daily weather data are critical for hydrologic models simulating and predicting hydrologic processes. Many researchers have focused on the impacts of precipitation on hydrologic simulations, but few studies integrated both temperature and precipitation data for historical and forecast periods in hydrologic models and evaluated the weather data accuracy at national scale. This study evaluated four extracting methods (mean (MN), median (MD), centroid (CT), and area-weighted (AW) approaches) for summarizing weather data for sub-watersheds defined in a hydrologic model. Firstly, an optimized extracting method was used to develop a real-time HUC-12 (12-digit Hydrologic Unit Code) level dataset for the Conterminous United States. The hydrologic model with the CT weather data performed the best, followed by MN, AW, and then MD. Secondly, per this method, a real-time dataset including historical and forecast data at HUC-12 level across the conterminous United States was created. Last, continuous daily forecast data at national scale displayed that large forecast overestimations were usually observed in large forecast precipitation events over 20 mm. Simultaneously, there were large underestimations in small forecast precipitation events less than 5 mm. Forecast maximum temperature showed a more substantial bias than that minimum temperature, with the largest underestimation for the lower forecast maximum temperature less than 15 °C. With regard to data stability of the historical observed temperature data, provisional and early temperature data from Parameter-elevation Regressions on Independent Slopes Model (PRISM) in the most recent seven months are as reliable as the stable data that is subject to quality control measures before it replaces the provisional and early data. However, forecast data often included bias in the extreme weather conditions, such as heavy precipitation and scorching temperature. Forecast data, which is updated multiple times each day, was frequently subject to an opposite bias (trending toward less extreme values) in the presence of strong frontal systems presumably due to the exact location and speed of the front being difficult to predict. Fully processed weather data from this work will be published online to facilitate hydrologic modeling efforts in the US and inform users about the uncertainty in the forecast data. [ABSTRACT FROM AUTHOR]

Background: Primary care is the most frequently visited clinic type immediately prior to suicidal behavior, with nearly half of suicide decedents visiting a primary care provider within a month of their death. Data supporting the efficacy of suicide risk screening in this setting is lacking, however. Improved suicide risk screening in primary care could lead to earlier intervention and treatment., Purpose: The PRImary care Screening Methods (PRISM) study is designed to develop and evaluate the efficacy of an alert algorithm that can be used by military primary care providers to accurately identify high-risk patients, to improve the identification of high-risk patients who deny suicidal thoughts, and to quantify patient subgroups who are more likely to be missed by existing screening methods (i.e., false negatives)., Methods: The rationale of the PRISM study is discussed, along with ethical and design considerations related to the conduct of suicide prevention research. The PRISM study enrolled 2690 patients from six primary care clinics across the U.S. Patients were enrolled during routine visits to a primary care clinic, and completed a battery of self-report scales in clinic waiting rooms. Follow-up phone interviews are conducted 1, 6, and 12 months after enrollment. The primary outcome is suicide attempt., Conclusions: PRISM is the first study to prospectively examine multiple suicide risk screening methods in "real-world" military primary care clinics. Ethical and design issues were considered to ensure that human participants, especially suicidal patients, were adequately protected while minimizing the potential confounding effect of risk management protocols., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: The Post-Licensure Rapid Immunization Safety Monitoring (PRISM) program is the immunization safety monitoring component of FDA's Mini-Sentinel project, a program to actively monitor the safety of medical products using electronic health information. FDA sought to assess the surveillance capabilities of this large claims-based distributed database for vaccine safety surveillance by characterizing the underlying data., Methods: We characterized data available on vaccine exposures in PRISM, estimated how much additional data was gained by matching with select state and local immunization registries, and compared vaccination coverage estimates based on PRISM data with other available data sources. We generated rates of computerized codes representing potential health outcomes relevant to vaccine safety monitoring. Standardized algorithms including ICD-9 codes, number of codes required, exclusion criteria and location of the encounter were used to obtain the background rates., Results: The majority of the vaccines routinely administered to infants, children, adolescents and adults were well captured by claims data. Immunization registry data in up to seven states comprised between 5% and 9% of data for all vaccine categories with the exception of 10% for hepatitis B and 3% and 4% for rotavirus and zoster respectively. Vaccination coverage estimates based on PRISM's computerized data were similar to but lower than coverage estimates from the National Immunization Survey and Healthcare Effectiveness Data and Information Set. For the 25 health outcomes of interest studied, the rates of potential outcomes based on ICD-9 codes were generally higher than rates described in the literature, which are typically clinically confirmed cases., Conclusion: PRISM program's data on vaccine exposures and health outcomes appear complete enough to support robust safety monitoring., (Copyright © 2013 Elsevier Ltd. All rights reserved.)