scholarly journals Constructing Retrospective Gridded Daily Precipitation and Temperature Datasets for the Conterminous United States

2008 ◽  
Vol 47 (2) ◽  
pp. 475-497 ◽  
Author(s):  
Mauro Di Luzio ◽  
Gregory L. Johnson ◽  
Christopher Daly ◽  
Jon K. Eischeid ◽  
Jeffrey G. Arnold
Keyword(s):  
United States ◽  
Daily Precipitation ◽  
Temporal Distribution ◽  
Climatic Data ◽  
Digital Archives ◽  
Reference Sites ◽  
National Climatic Data Center ◽  
Noticeable Improvement ◽  
Temporal Coverage ◽  
Precipitation And Temperature

Abstract This paper presents and evaluates a method for the construction of long-range and wide-area temporal spatial datasets of daily precipitation and temperature (maximum and minimum). This method combines the interpolation of daily ratios/fractions derived from ground-based meteorological station records and respective fields of monthly estimates. Data sources for the described implementation over the conterminous United States (CONUS) are two independent and quality-controlled inputs: 1) an enhanced compilation of daily observations derived from the National Climatic Data Center digital archives and 2) the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) maps. The results of this study show that this nonconventional interpolation preserves the spatial and temporal distribution of both the PRISM maps (monthly, topography-sensitive patterns) and the original daily observations. Statistics of a preliminary point comparison with the observed values at high-quality and independent reference sites show a reasonable agreement and a noticeable improvement over the nearest station method in orographically sensitive areas. The implemented datasets provide daily precipitation and temperature values at 2.5-min (around 4 km) resolution for 1960–2001. Combining seamless spatial and temporal coverage and topographic sensitivity characteristics, the datasets offer the potential for supporting current and future regional and historical hydrologic assessments over the CONUS.

Production of Temporally Consistent Gridded Precipitation and Temperature Fields for the Continental United States*

2005 ◽  
Vol 6 (3) ◽  
pp. 330-336 ◽  
Author(s):  
Alan F. Hamlet ◽  
Dennis P. Lettenmaier
Keyword(s):  
United States ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Temperature Fields ◽  
Climatic Data ◽  
National Climatic Data Center ◽  
Snow Water ◽  
Long Term Trends ◽  
Precipitation And Temperature

Abstract The availability of long-term gridded datasets of precipitation, temperature, and other surface meteorological variables offers the potential for deriving a range of land surface conditions that have not been directly observed. These include, for instance, soil moisture, snow water equivalent, evapotranspiration, runoff, and subsurface moisture transport. However, gridding procedures can themselves introduce artificial trends due to incorporation of stations with different record lengths and locations. Hence, existing gridded datasets are in general not appropriate for estimation of long-term trends. Methods are described here for adjustment of gridded daily precipitation and temperature maxima and minima over the continental United States based on newly available (in electronic form) U.S. Cooperative Observer station data archived at the National Climatic Data Center from the early 1900s on. The intent is to produce gridded meteorological datasets that can be used, in conjunction with hydrologic modeling, for long-term trend analysis of simulated hydrologic variables.

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 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.

scholarly journals CPLFD-GDPT5: high-resolution gridded daily precipitation and temperature dataset for two largest Polish river basins

2015 ◽  
Vol 8 (2) ◽  
pp. 1021-1060 ◽  
Author(s):  
T. Berezowski ◽  
M. Szcześniak ◽  
I. Kardel ◽  
R. Michałowski ◽  
T. Okruszko ◽  
...  
Keyword(s):  
Assessment Tool ◽  
Daily Precipitation ◽  
Hydrological Modelling ◽  
Indicator Kriging ◽  
Maximum Temperature ◽  
System Modelling ◽  
Climatic Data ◽  
Air Temperatures ◽  
Spatial Coverage ◽  
Precipitation And Temperature

Abstract. The CHASE-PL Forcing Data-Gridded Daily Precipitation and Temperature Dataset-5 km (CPLFD-GDPT5) consists of 1951–2013 daily minimum and maximum air temperatures and precipitation totals interpolated onto a 5 km grid based on daily meteorological observations from Institute of Meteorology and Water Management (IMGW-PIB; Polish stations), Deutscher Wetterdienst (DWD, German and Czech stations), ECAD and NOAA-NCDC (Slovak, Ukrainian and Belarus stations). The main purpose for constructing this product was the need for long-term aerial precipitation and temperature data for earth-system modelling, especially hydrological modelling. The spatial coverage is the union of Vistula and Odra basin and Polish territory. The number of available meteorological stations for precipitation and temperature varies in time from about 100 for temperature and 300 for precipitation in 1950 up to about 180 for temperature and 700 for precipitation in 1990. The precipitation dataset was corrected for snowfall and rainfall under-catch with the Richter method. The interpolation methods were: kriging with elevation as external drift for temperatures and indicator kriging combined with universal kriging for precipitation. The kriging cross-validation revealed low root mean squared errors expressed as a fraction of standard deviation (SD): 0.54 and 0.47 for minimum and maximum temperature, respectively and 0.79 for precipitation. The correlation scores were 0.84 for minimum temperatures, 0.88 for maximum temperatures and 0.65 for precipitation. The CPLFD-GDPT5 product is consistent with 1971–2000 climatic data published by IMGW-PIB. We also confirm good skill of the product for hydrological modelling by performing an application using the Soil and Water Assessment Tool (SWAT) in the Vistula and Odra basins. Link to the dataset: http://data.3tu.nl/repository/uuid:e939aec0-bdd1-440f-bd1e-c49ff10d0a07

scholarly journals Effects of Daily Temperature Highs on Development of Phakopsora pachyrhizi on Soybean

2012 ◽  
Vol 102 (8) ◽  
pp. 761-768 ◽  
Author(s):  
M. R. Bonde ◽  
S. E. Nester ◽  
D. K. Berner
Keyword(s):  
United States ◽  
Southeastern United States ◽  
The United States ◽  
Daily Temperature ◽  
Soybean Rust ◽  
Limiting Factor ◽  
Climatic Data ◽  
Phakopsora Pachyrhizi ◽  
Diurnal Temperature ◽  
National Climatic Data Center

Although considerable information exists regarding the importance of moisture in the development of soybean rust, little is known about the influence of temperature. The purpose of our study was to determine whether temperature might be a significant limiting factor in the development of soybean rust in the southeastern United States. Soybean plants infected with Phakopsora pachyrhizi were incubated in temperature-controlled growth chambers simulating day and night diurnal temperature patterns representative of the southeastern United States during the growing season. At 3-day intervals beginning 12 days after inoculation, urediniospores were collected from each plant and counted. The highest numbers of urediniospores were produced when day temperatures peaked at 21 or 25°C and night temperatures dipped to 8 or 12°C. When day temperatures peaked at 29, 33, or 37°C for a minimum of 1 h/day, urediniospore production was reduced to 36, 19, and 0%, respectively, compared with urediniospore production at the optimum diurnal temperature conditions. Essentially, no lesions developed when the daily temperature high was 37°C or above. Temperature data obtained from the National Climatic Data Center showed that temperature highs during July and August in several southeastern states were too high for significant urediniospore production on 55 to 77% of days. The inhibition of temperature highs on soybean rust development in southeastern states not only limits disease locally but also has implications pertaining to spread of soybean rust into and development of disease in the major soybean-producing regions of the Midwestern and northern states. We concluded from our results that temperature highs common to southeastern states are a factor in the delay or absence of soybean rust in much of the United States.

Modeling Subalpine and Upper Montane Forest-Climate Interactions in Colorado

2014 ◽  
Vol 5 (4) ◽  
pp. 21-34
Author(s):  
Steven Jennings ◽  
Eric Billmeyer
Keyword(s):  
Tree Species ◽  
Pinus Contorta ◽  
Plant Distribution ◽  
Temperature Fields ◽  
Climatic Data ◽  
Distribution Maps ◽  
National Climatic Data Center ◽  
Temperature And Precipitation ◽  
Pinus Aristata ◽  
Precipitation And Temperature

The correlation of the distribution of five subalpine and montane tree species with precipitation and temperature were modeled using GIS. The results were compared with data presented by Thompson et al. (2000). Distributions of subalpine fir (Abies concolor), Engelmann spruce (Picea engelmannii), lodgepole pine (Pinus contorta), limber pine (Pinus flexilis) and bristlecone pine (Pinus aristata) were compared to estimated precipitation and temperature fields that had been constructed from the Parameter-elevation Regressions on Independent Slopes Model (PRISM), National Climatic Data Center (NCDC) station data, and Snowpack Telemetry (SNOTEL) system data. Plant distribution maps from Little (1971) and CoGAP (2001) were used to determine the temperature and precipitation associated with the selected tree species. The estimates from this study were compared to those of Thompson, Anderson & Bartlein (2000). In many cases precipitation and temperatures values were higher than those of Thompson, Anderson & Bartlein (2000). Suggestions are made to improve the predictive power of GIS analysis for mapping climate and plant variability.

scholarly journals CPLFD-GDPT5: High-resolution gridded daily precipitation and temperature data set for two largest Polish river basins

2016 ◽  
Vol 8 (1) ◽  
pp. 127-139 ◽  
Author(s):  
Tomasz Berezowski ◽  
Mateusz Szcześniak ◽  
Ignacy Kardel ◽  
Robert Michałowski ◽  
Tomasz Okruszko ◽  
...  
Keyword(s):  
Assessment Tool ◽  
Daily Precipitation ◽  
Hydrological Modelling ◽  
Temperature Data ◽  
Maximum Temperature ◽  
System Modelling ◽  
Climatic Data ◽  
Data Set ◽  
Spatial Coverage ◽  
Precipitation And Temperature

Abstract. The CHASE-PL (Climate change impact assessment for selected sectors in Poland) Forcing Data–Gridded Daily Precipitation & Temperature Dataset–5 km (CPLFD-GDPT5) consists of 1951–2013 daily minimum and maximum air temperatures and precipitation totals interpolated onto a 5 km grid based on daily meteorological observations from the Institute of Meteorology and Water Management (IMGW-PIB; Polish stations), Deutscher Wetterdienst (DWD, German and Czech stations), and European Climate Assessment and Dataset (ECAD) and National Oceanic and Atmosphere Administration–National Climatic Data Center (NOAA-NCDC) (Slovak, Ukrainian, and Belarusian stations). The main purpose for constructing this product was the need for long-term aerial precipitation and temperature data for earth-system modelling, especially hydrological modelling. The spatial coverage is the union of the Vistula and Oder basins and Polish territory. The number of available meteorological stations for precipitation and temperature varies in time from about 100 for temperature and 300 for precipitation in the 1950s up to about 180 for temperature and 700 for precipitation in the 1990s. The precipitation data set was corrected for snowfall and rainfall under-catch with the Richter method. The interpolation methods were kriging with elevation as external drift for temperatures and indicator kriging combined with universal kriging for precipitation. The kriging cross validation revealed low root-mean-squared errors expressed as a fraction of standard deviation (SD): 0.54 and 0.47 for minimum and maximum temperature, respectively, and 0.79 for precipitation. The correlation scores were 0.84 for minimum temperatures, 0.88 for maximum temperatures, and 0.65 for precipitation. The CPLFD-GDPT5 product is consistent with 1971–2000 climatic data published by IMGW-PIB. We also confirm good skill of the product for hydrological modelling by performing an application using the Soil and Water Assessment Tool (SWAT) in the Vistula and Oder basins. Link to the data set: doi:10.4121/uuid:e939aec0-bdd1-440f-bd1e-c49ff10d0a07.

Measured Severe Convective Wind Climatology and Associated Convective Modes of Thunderstorms in the Contiguous United States, 2003–09

2013 ◽  
Vol 28 (1) ◽  
pp. 229-236 ◽  
Author(s):  
Bryan T. Smith ◽  
Tomas E. Castellanos ◽  
Andrew C. Winters ◽  
Corey M. Mead ◽  
Andrew R. Dean ◽  
...  
Keyword(s):  
United States ◽  
Radar Data ◽  
High Plains ◽  
Climatic Data ◽  
Wind Gust ◽  
Severe Thunderstorm ◽  
Convective Systems ◽  
National Climatic Data Center ◽  
Wind Gusts ◽  
Convective Mode

Abstract A severe thunderstorm wind gust climatology spanning 2003–09 for the contiguous United States is developed using measured Automated Surface Observing System (ASOS) and Automated Weather Observing System (AWOS) wind gusts. Archived severe report information from the National Climatic Data Center publication Storm Data and single-site volumetric radar data are used to identify severe wind gust observations [≥50 kt (25.7 m s−1)] associated with thunderstorms and to classify the convective mode of the storms. The measured severe wind gust distribution, comprising only 2% of all severe gusts, is examined with respect to radar-based convective modes. The convective mode scheme presented herein focuses on three primary radar-based storm categories: supercell, quasi-linear convective systems (QLCSs), and disorganized. Measured severe gust frequency revealed distinct spatial patterns, where the high plains received the greatest number of gusts and occurred most often in the late spring and summer months. Severe wind gusts produced by supercells were most frequent over the plains, while those from QLCS gusts were most frequent in the plains and Midwest. Meanwhile, disorganized storms produced most of their severe gusts in the plains and Intermountain West. A reverse spatial distribution signal exists in the location between the maximum measured severe wind gust corridor located over the high plains and the maximum in all severe thunderstorm wind reports from Storm Data, located near and west of the southern Appalachians.

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.

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