scholarly journals 1981–2010 U.S. Hourly Normals

2012 ◽  
Vol 93 (11) ◽  
pp. 1637-1640 ◽  
Author(s):  
Scott Applequist ◽  
Anthony Arguez ◽  
Imke Durre ◽  
Michael F. Squires ◽  
Russell S. Vose ◽  
...  
Keyword(s):  
Data Center ◽  
The United States ◽  
Descriptive Statistics ◽  
Dew Point ◽  
Heat Index ◽  
Climatic Data ◽  
Sea Level Pressure ◽  
Wind Chill ◽  
National Climatic Data Center ◽  
Heating And Cooling

The 1981–2010 U.S. Climate Normals released by the National Oceanic and Atmospheric Administration's (NOAA) National Climatic Data Center (NCDC) include a suite of descriptive statistics based on hourly observations. For each hour and day of the year, statistics of temperature, dew point, mean sea level pressure, wind, clouds, heat index, wind chill, and heating and cooling degree hours are provided as 30-year averages, frequencies of occurrence, and percentiles. These hourly normals are available for 262 locations, primarily major airports, from across the United States and its Pacific territories. We encourage use of these products specifically for examination of the diurnal cycle of a particular variable, and how that change may shift over the annual cycle.

scholarly journals The Regional Snowfall Index

2014 ◽  
Vol 95 (12) ◽  
pp. 1835-1848 ◽  
Author(s):  
Michael F. Squires ◽  
Jay H. Lawrimore ◽  
Richard R. Heim ◽  
David A. Robinson ◽  
Mathieu R. Gerbush ◽  
...  
Keyword(s):  
United States ◽  
Data Center ◽  
Historical Perspective ◽  
The United States ◽  
Climatic Data ◽  
National Climatic Data Center ◽  
Impact Scale ◽  
Population Information ◽  
Regional Index ◽  
The Impact

This paper describes a new snowfall index that quantifies the impact of snowstorms within six climate regions in the United States. The regional snowfall index (RSI) is based on the spatial extent of snowfall accumulation, the amount of snowfall, and the juxtaposition of these elements with population. Including population information provides a measure of the societal susceptibility for each region. The RSI is an evolution of the Northeast snowfall impact scale (NESIS), which NOAA's National Climatic Data Center began producing operationally in 2006. While NESIS was developed for storms that had a major impact in the Northeast, it includes all snowfall during the lifetime of a storm across the United States and as such can be thought of as a quasi-national index that is calibrated to Northeast snowstorms. By contrast, the RSI is a regional index calibrated to specific regions using only the snow that falls within that region. This paper describes the methodology used to compute the RSI, which requires region-specific parameters and thresholds, and its application within six climate regions in the eastern two-thirds of the nation. The process used to select the region-specific parameters and thresholds is explained. The new index has been calculated for over 580 snowstorms that occurred between 1900 and 2013 providing a century-scale historical perspective for these snowstorms. The RSI is computed for category 1 or greater storms in near–real time, usually a day after the storm has ended.

scholarly journals Global Warming, Cyclone Damages, and the Issue of Sustainable Tourism in Southeast Asia

2016 ◽  
Vol 23 (01) ◽  
pp. 102-120
Author(s):  
Vu Tam Bang ◽  
IM ERIC IKSOON
Keyword(s):  
Southeast Asia ◽  
Data Center ◽  
Sustainable Tourism ◽  
Damage Index ◽  
The United States ◽  
Maximum Speed ◽  
Climatic Data ◽  
National Climatic Data Center ◽  
Policy Suggestions ◽  
Aggregate Effects

This paper studies the feedback effect between damages caused by cyclones and unsustainable tourism in Southeast Asia. The data are constructed based on the Annual Tropical Cyclone Reports from the United States National Climatic Data Center website for the period of 1995–2013. Establishing a cyclone damage index by combining the maximum speed when each cyclone goes through a region and characteristics of each region affected by cyclones in Southeast Asia, we first attempt to quantify the two-way causality between these cyclones and the proportion of tourist arrivals per capita. We then analyze differences among the affected countries compared to the aggregate effects. Based on the results, policy suggestions for sustainable tourism are provided in order to mitigate the cyclone damages.

scholarly journals Fatalities Caused by Hydrometeorological Disasters in Texas

2018 ◽  
Author(s):  
Srikanto H. Paul ◽  
Hatim O. Sharif ◽  
Abigail M. Crawford
Keyword(s):  
Data Center ◽  
Natural Hazard ◽  
Flash Flood ◽  
Risk Group ◽  
The Elderly ◽  
The United States ◽  
Gender Disparity ◽  
Climatic Data ◽  
National Climatic Data Center ◽  
Transportation Routes

Texas ranks first in number of natural hazard fatalities in the United States (U.S.). Based on data culled from the National Climatic Data Center databases from 1959 to 2016, the number of hydrometeorological fatalities in Texas have increased over the 58-year study period, but the per capita fatalities have significantly decreased. Spatial review found that flooding is the predominant hydrometeorological disaster in a majority of the Texas counties located in “Flash Flood Alley” and accounts for 43% of all hydrometeorological fatalities in the state. Flooding fatalities are highest on “Transportation Routes” followed by heat fatalities in “Permanent Residences”. Seasonal and monthly stratification identifies Spring and Summer as the deadliest seasons, with the month of May registering the highest number of total fatalities dominated by flooding and tornado fatalities. Demographic trends of hydrometeorological disaster fatalities indicated that approximately twice as many male fatalities occurred during the study period than female fatalities, but with decreasing gender disparity over time. Adults are the highest fatality risk group overall, children most at risk to die in flooding, and the elderly at greatest risk of heat-related death.

scholarly journals Evaluating Statewide Climate Extremes for the United States

2012 ◽  
Vol 51 (11) ◽  
pp. 2047-2059 ◽  
Author(s):  
Karsten A. Shein ◽  
Dennis P. Todey ◽  
F. Adnan Akyuz ◽  
James R. Angel ◽  
Timothy M. Kearns ◽  
...  
Keyword(s):  
Data Center ◽  
Climate Extremes ◽  
Record Values ◽  
The United States ◽  
Daily Basis ◽  
Climatic Data ◽  
Climate Record ◽  
National Climatic Data Center ◽  
Temperature And Precipitation ◽  
Meteorological Observations

AbstractThe NOAA National Climatic Data Center maintains tables for temperature and precipitation extremes in each of the U.S. states. Many of these tables were several years out of date, however, and therefore did not include a number of recent record-setting meteorological observations. Furthermore, there was no formal process for ensuring the currency of the tables or evaluating observations that might tie or break a statewide climate record. This paper describes the evaluation and revision of the statewide climate-extremes tables for all-time maximum and minimum temperature, greatest 24-h precipitation and snowfall, and greatest snow depth (the five basic climate elements observed on a daily basis by the NOAA Cooperative Weather Network). The process resulted in the revision of 40% of the values listed in those tables and underscored both the necessity of manual quality-assurance methods and the importance of continued climate-monitoring and data-rescue activities to ensure that potential record values are not overlooked.

scholarly journals Immediate and Delayed Meteorological Effects on COVID-19 Time-Varying Infectiousness in Tropical Cities

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 513
Author(s):  
Xerxes Seposo ◽  
Chris Fook Sheng Ng ◽  
Lina Madaniyazi
Keyword(s):  
Model Fitting ◽  
Additive Model ◽  
The Philippines ◽  
Air Pressure ◽  
Dew Point ◽  
Outcome Variable ◽  
Climatic Data ◽  
Time Varying ◽  
Rt Pcr ◽  
National Climatic Data Center

The novel coronavirus, which was first reported in Wuhan, China in December 2019, has been spreading globally at an unprecedented rate, leading to the virus being declared a global pandemic by the WHO on 12 March 2020. The clinical disease, COVID-19, associated with the pandemic is caused by the pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Aside from the inherent transmission dynamics, environmental factors were found to be associated with COVID-19. However, most of the evidence documenting the association was from temperate locations. In this study, we examined the association between meteorological factors and the time-varying infectiousness of COVID-19 in the Philippines. We obtained the daily time series from 3 April 2020 to 2 September 2020 of COVID-19 confirmed cases from three major cities in the Philippines, namely Manila, Quezon, and Cebu. Same period city-specific daily average temperature (degrees Celsius; °C), dew point (degrees Celsius; °C), relative humidity (percent; %), air pressure (kilopascal; kPa), windspeed (meters per second; m/s) and visibility (kilometer; km) data were obtained from the National Oceanic and Atmospheric Administration—National Climatic Data Center. City-specific COVID-19-related detection and intervention measures such as reverse transcriptase polymerase chain reaction (RT-PCR) testing and community quarantine measures were extracted from online public resources. We estimated the time-varying reproduction number (Rt) using the serial interval information sourced from the literature. The estimated Rt was used as an outcome variable for model fitting via a generalized additive model, while adjusting for relevant covariates. Results indicated that a same-day and the prior week’s air pressure was positively associated with an increase in Rt by 2.59 (95% CI: 1.25 to 3.94) and 2.26 (95% CI: 1.02 to 3.50), respectively. Same-day RT-PCR was associated with an increase in Rt, while the imposition of community quarantine measures resulted in a decrease in Rt. Our findings suggest that air pressure plays a role in the infectiousness of COVID-19. The determination of the association of air pressure on infectiousness, aside from the testing frequency and community quarantine measures, may aide the current health systems in controlling the COVID-19 infectiousness by integrating such information into an early warning platform.

scholarly journals NOAA’s 1981–2010 U.S. Climate Normals: Monthly Precipitation, Snowfall, and Snow Depth

2013 ◽  
Vol 52 (11) ◽  
pp. 2377-2395 ◽  
Author(s):  
Imke Durre ◽  
Michael F. Squires ◽  
Russell S. Vose ◽  
Xungang Yin ◽  
Anthony Arguez ◽  
...  
Keyword(s):  
United States ◽  
Pacific Northwest ◽  
Great Plains ◽  
Snow Depth ◽  
State Of The Art ◽  
The United States ◽  
Climatic Data ◽  
Monthly Precipitation ◽  
National Climatic Data Center ◽  
The Caribbean

AbstractThe 1981–2010 “U.S. Climate Normals” released by the National Oceanic and Atmospheric Administration’s (NOAA) National Climatic Data Center include a suite of monthly, seasonal, and annual statistics that are based on precipitation, snowfall, and snow-depth measurements. This paper describes the procedures used to calculate the average totals, frequencies of occurrence, and percentiles that constitute these normals. All parameters were calculated from a single, state-of-the-art dataset of daily observations, taking care to produce normals that were as representative as possible of the full 1981–2010 period, even when the underlying data records were incomplete. In the resulting product, average precipitation totals are available at approximately 9300 stations across the United States and parts of the Caribbean Sea and Pacific Ocean islands. Snowfall and snow-depth statistics are provided for approximately 5300 of those stations, as compared with several hundred stations in the 1971–2000 normals. The 1981–2010 statistics exhibit the familiar climatological patterns across the contiguous United States. When compared with the same calculations for 1971–2000, the later period is characterized by a smaller number of days with snow on the ground and less total annual snowfall across much of the contiguous United States; wetter conditions over much of the Great Plains, Midwest, and northern California; and drier conditions over much of the Southeast and Pacific Northwest. These differences are a reflection of the removal of the 1970s and the addition of the 2000s to the 30-yr-normals period as part of this latest revision of the normals.

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