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 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.

scholarly journals A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions

2013 ◽  
Vol 26 (23) ◽  
pp. 9384-9392 ◽  
Author(s):  
Ben Livneh ◽  
Eric A. Rosenberg ◽  
Chiyu Lin ◽  
Bart Nijssen ◽  
Vimal Mishra ◽  
...  
Keyword(s):  
United States ◽  
Spatial Resolution ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Infiltration Capacity ◽  
Surface Heat Fluxes

This paper describes a publicly available, long-term (1915–2011), hydrologically consistent dataset for the conterminous United States, intended to aid in studies of water and energy exchanges at the land surface. These data are gridded at a spatial resolution of [Formula: see text] latitude/longitude and are derived from daily temperature and precipitation observations from approximately 20 000 NOAA Cooperative Observer (COOP) stations. The available meteorological data include temperature, precipitation, and wind, as well as derived humidity and downwelling solar and infrared radiation estimated via algorithms that index these quantities to the daily mean temperature, temperature range, and precipitation, and disaggregate them to 3-hourly time steps. Furthermore, the authors employ the variable infiltration capacity (VIC) model to produce 3-hourly estimates of soil moisture, snow water equivalent, discharge, and surface heat fluxes. Relative to an earlier similar dataset by Maurer and others, the improved dataset has 1) extended the period of analysis (1915–2011 versus 1950–2000), 2) increased the spatial resolution from ⅛° to [Formula: see text], and 3) used an updated version of VIC. The previous dataset has been widely used in water and energy budget studies, climate change assessments, drought reconstructions, and for many other purposes. It is anticipated that the spatial refinement and temporal extension will be of interest to a wide cross section of the scientific community.

scholarly journals Has spring snowpack declined in the Washington Cascades?

2008 ◽  
Vol 12 (1) ◽  
pp. 193-206 ◽  
Author(s):  
P. Mote ◽  
A. Hamlet ◽  
E. Salathé
Keyword(s):  
Snow Water Equivalent ◽  
Spatial And Temporal Patterns ◽  
Regional Warming ◽  
Temperature And Precipitation ◽  
The Pacific ◽  
Snow Water ◽  
Long Term Trends ◽  
Natural Variations ◽  
High Elevations

Abstract. Our best estimates of 1 April snow water equivalent (SWE) in the Cascade Mountains of Washington State indicate a substantial (roughly 15–35%) decline from mid-century to 2006, with larger declines at low elevations and smaller declines or increases at high elevations. This range of values includes estimates from observations and hydrologic modeling, reflects a range of starting points between about 1930 and 1970 and also reflects uncertainties about sampling. The most important sampling issue springs from the fact that half the 1 April SWE in the Cascades is found below about 1240 m, altitudes at which sampling was poor before 1945. Separating the influences of temperature and precipitation on 1 April SWE in several ways, it is clear that long-term trends are dominated by trends in temperature, whereas variability in precipitation adds "noise" to the time series. Consideration of spatial and temporal patterns of change rules out natural variations like the Pacific Decadal Oscillation as the sole cause of the decline. Regional warming has clearly played a role, but it is not yet possible to quantify how much of that regional warming is related to greenhouse gas emissions.

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 Has spring snowpack declined in the Washington Cascades?

2007 ◽  
Vol 4 (4) ◽  
pp. 2073-2110 ◽  
Author(s):  
P. Mote ◽  
A. Hamlet ◽  
E. Salathé
Keyword(s):  
Snow Water Equivalent ◽  
Spatial And Temporal Patterns ◽  
Regional Warming ◽  
Temperature And Precipitation ◽  
The Pacific ◽  
Snow Water ◽  
Long Term Trends ◽  
Natural Variations ◽  
High Elevations

Abstract. Our best estimates of 1 April snow water equivalent (SWE) in the Cascade Mountains of Washington State indicate a substantial (roughly 15–35%) decline from mid-century to 2006, with larger declines at low elevations and smaller declines or increases at high elevations. This range of values includes estimates from observations and hydrologic modeling, reflects a range of starting points between about 1930 and 1970 and also reflects uncertainties about sampling. The most important sampling issue springs from the fact that half the 1 April SWE in the Cascades is found below about 1000 m, where sampling was poor before 1945. Separating the influences of temperature and precipitation on 1 April SWE in several ways, it is clear that long-term trends are dominated by trends in temperature, whereas variability in precipitation adds "noise" to the time series. Consideration of spatial and temporal patterns of change rules out natural variations like the Pacific Decadal Oscillation as the sole cause of the decline. Regional warming has clearly played a role, but it is not yet possible to quantify how much of that regional warming is related to greenhouse gas emissions.

scholarly journals Noah LSM Snow Model Diagnostics and Enhancements

2010 ◽  
Vol 11 (3) ◽  
pp. 721-738 ◽  
Author(s):  
Ben Livneh ◽  
Youlong Xia ◽  
Kenneth E. Mitchell ◽  
Michael B. Ek ◽  
Dennis P. Lettenmaier
Keyword(s):  
United States ◽  
Liquid Water ◽  
Land Surface ◽  
Prediction Models ◽  
Snow Water Equivalent ◽  
Mapping System ◽  
Snow Water ◽  
Snow Model ◽  
Resources Conservation ◽  
Surface Scheme

Abstract A negative snow water equivalent (SWE) bias in the snow model of the Noah land surface scheme used in the NCEP suite of numerical weather and climate prediction models has been noted by several investigators. This bias motivated a series of offline tests of model extensions and improvements intended to reduce or eliminate the bias. These improvements consist of changes to the model’s albedo formulation that include a parameterization for snowpack aging, changes to how pack temperature is computed, and inclusion of a provision for refreeze of liquid water in the pack. Less extensive testing was done on the performance of model extensions with alternate areal depletion parameterizations. Model improvements were evaluated through comparisons of point simulations with National Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) SWE data for deep-mountain snowpacks at selected stations in the western United States, as well as simulations of snow areal extent over the conterminous United States (CONUS) domain, compared with observational data from the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS). The combination of snow-albedo decay and liquid-water refreeze results in substantial improvements in the magnitude and timing of peak SWE, as well as increased snow-covered extent at large scales. Modifications to areal snow depletion thresholds yielded more realistic snow-covered albedos at large scales.

scholarly journals American Engineering Doctoral Enrollments

2015 ◽  
pp. 23-24
Author(s):  
Richard Skinner
Keyword(s):  
Higher Education ◽  
United States ◽  
International Education ◽  
Doctoral Programs ◽  
The United States ◽  
American Higher Education ◽  
Long Term Trends

International education has deep historical roots and has spurred relationships that persist for decades. In the case of the United States and the field of engineering, American dependence since the mid-1960s on other countries' students – especially Indian ones – for enrollments and graduates of engineering doctoral programs has been, is and will likely continue to be significant. But long-term trends portend a time when the appeal of American higher education may be less than has been the case.

Characteristics and Predictability of Midwestern United States Drought

2021 ◽  
Author(s):  
Andrew Hoell ◽  
Trent W. Ford ◽  
Molly Woloszyn ◽  
Jason A. Otkin ◽  
Jon Eischeid
Keyword(s):  
United States ◽  
Great Plains ◽  
Land Surface ◽  
Clustering Algorithm ◽  
Snow Water Equivalent ◽  
Southern Oscillation ◽  
Northern Great Plains ◽  
Ohio Valley ◽  
Midwestern United States ◽  
Wave Trains

AbstractCharacteristics and predictability of drought in the Midwestern United States, spanning the Great Plains to the Ohio Valley, at local and regional scales are examined during 1916-2015. Given vast differences in hydroclimatic variability across the Midwest, drought is evaluated in four regions identified using a hierarchical clustering algorithm applied to an integrated drought index based on soil moisture, snow water equivalent, and three-month runoff from land surface models forced by observed analyses. Highlighting the regions containing the Ohio Valley (OV) and Northern Great Plains (NGP), the OV demonstrates a preference for sub-annual droughts, the timing of which can lead to prevalent dry epochs, while the NGP demonstrates a preference for annual-to-multi-annual droughts. Regional drought variations are closely related to precipitation, resulting in a higher likelihood of drought onset or demise during wet seasons: March-November in the NGP and all year in the OV, with a preference for March-May and September-November. Due to the distinct dry season in the NGP, there is a higher likelihood of longer drought persistence, as the NGP is four times more likely to experience drought lasting at least one year compared to the OV. While drought variability in all regions and seasons are related to atmospheric wave trains spanning the Pacific-North American sector, longer-lead predictability is limited to the OV in December-February because it is the only region/season related to slow-varying sea surface temperatures consistent with El Niño-Southern Oscillation. The wave trains in all other regions appear to be generated in the atmosphere, highlighting the importance of internal atmospheric variability in shaping Midwestern drought.

scholarly journals Examining the Climatology of Shortwave Radiation in the Northeastern United States

2017 ◽  
Vol 56 (10) ◽  
pp. 2869-2881
Author(s):  
Janel Hanrahan ◽  
Alexandria Maynard ◽  
Sarah Y. Murphy ◽  
Colton Zercher ◽  
Allison Fitzpatrick
Keyword(s):  
Climate Change ◽  
United States ◽  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Predictor Variable ◽  
Shortwave Radiation ◽  
Northeastern United States ◽  
Weather Patterns ◽  
Long Term Trends

AbstractAs demand for renewable energy grows, so does the need for an improved understanding of renewable energy sources. Paradoxically, the climate change mitigation strategy of fossil fuel divestment is in itself subject to shifts in weather patterns resulting from climate change. This is particularly true with solar power, which depends on local cloud cover. However, because observed shortwave radiation data usually span a decade or less, persistent long-term trends may not be identified. A simple linear regression model is created here using diurnal temperature range (DTR) during 2002–15 as a predictor variable to estimate long-term shortwave radiation (SR) values in the northeastern United States. Using an extended DTR dataset, SR values are computed for 1956–2015. Statistically significant decreases in shortwave radiation are identified that are dominated by changes during the summer months. Because this coincides with the season of greatest insolation and the highest potential for energy production, financial implications may be large for the solar energy industry if such trends persist into the future.

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 17 (7) ◽  
pp. 2781-2796 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Data Set ◽  
Model Based ◽  
Snow Water

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

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