scholarly journals Monthly precipitation isoscapes (δ18O) of the United States: Connections with surface temperatures, moisture source conditions, and air mass trajectories

2010 ◽  
Vol 115 (D21) ◽  
Author(s):  
R. W. Vachon ◽  
J. M. Welker ◽  
J. W. C. White ◽  
B. H. Vaughn
Keyword(s):  
United States ◽  
The United States ◽  
Monthly Precipitation ◽  
Air Mass ◽  
Moisture Source ◽  
Air Mass Trajectories ◽  
Surface Temperatures

Preliminary Results from the Air Mass Transformation Experiment (AMTEX)

1976 ◽  
Vol 57 (11) ◽  
pp. 1346-1355 ◽  
Author(s):  
D. H. Lenschow ◽  
E. M. Agee
Keyword(s):  
United States ◽  
Experimental Period ◽  
The United States ◽  
Published Data ◽  
Data Set ◽  
Air Mass ◽  
Preliminary Results ◽  
Cold Air Outbreak ◽  
Transformation Experiment ◽  
Transformation Processes

The field phases of AMTEX, a GARP subprogram on air-sea interaction implemented by Japan, were conducted over the East China Sea in the environs of Okinawa, Japan, during the last two weeks of February in 1974 and 1975. Investigators from Australia, Canada, and the United States also participated in this experiment. The weather was generally very favorable for this study of air mass transformation processes in 1975 because of an extensive cold air outbreak during most of the experimental period. A basic synoptic data set was obtained from 6 h soundings from an array of aerological stations centered at Okinawa. In addition, satellite, hourly surface and surface marine, oceanographic, boundary layer, radiation, radar, cloud physics, and aircraft data were obtained and have been or will be available in published data reports or on magnetic tape. Preliminary results from 1974 and 1975 reported at the Fourth AMTEX Study Conference and joint United States–Japan Cooperative Science Program Seminar, “Air Mass Transformation Processes over the Kuroshio in Winter,” held in Tokyo, 26–30 September 1975, are presented and discussed.

A climatology of cloud chemistry for the eastern United States derived from the mountain cloud chemistry project

1993 ◽  
Vol 1 (1) ◽  
pp. 38-54 ◽  
Author(s):  
Volker A. Mohnen ◽  
Richard J. Vong
Keyword(s):  
United States ◽  
Chemical Composition ◽  
Chemical Analysis ◽  
Cloud Water ◽  
Cloud Base ◽  
Eastern United States ◽  
Cloud Chemistry ◽  
Air Mass ◽  
Ion Concentrations ◽  
Air Mass Trajectories

The chemical composition of clouds collected in the eastern United States has been intensely monitored over a 4-year period as part of the Mountain Cloud Chemistry Project. On the basis of these measurements we prepared a climatology for cloud chemistry, using simple statistical analyses tools and incorporating meteorological and cloud physical and chemical information. Five mountain stations have been established for cloud collection covering the northern and southern Appalachian Mountain range: Whiteface Mountain, New York; Mount Moosilauke, New Hampshire; Shenandoah Mountain, Virginia; Whitetop Mountain, Virginia; and Mount Mitchell, North Carolina. This review presents the major result from this 4-year measurement program. Cloud cover and cloud base over the eastern United States were deduced from the global real-time nephanalysis archives produced by the U.S. Air Force, augmented by local observations. Both active and passive cloud collectors were deployed to sample cloud water on an hourly basis, i.e., with sufficient time resolution to resolve synoptic scale phenomena. Chemical analysis of cloud water was performed by a central analytical laboratory with occasional on-site analysis to satisfy quality control procedures. Reliable methods now exist for collecting cloud-water samples in sufficient quantities for detailed chemical analysis. The chemical composition of cloud water varied significantly between sites. However, the differences in cloud-water ion concentration do not necessarily establish a geographic gradient between the sites but rather reflect differences in air-mass trajectories associated with the synoptic air-flow pattern and differences in sample location above cloud base. The dependence of cloud-water ion concentrations on synoptic weather type and observed differences in relative frequencies of warm sector, marine flow, and post-cold frontal synoptic types between northern and southern sites suggest that the north–south differences in cloud-water ion concentrations are related to cloud climatology at the northern sites. When air-mass trajectories shift from southwest to northwest, the concentrations of H+, SO42−, NO3− and NH4+ normally decrease but the southern sites continue to receive high concentrations under northwest flow. The height of cloud-water sample collection above cloud base was found to be an additional source of variability in both cloud-water chemistry and liquid-water content. Seasonal variation in cloud-water chemical composition was investigated at one site only. Sulfate levels were found to be significantly lower in supercooled clouds (i.e., during the 'cold' season) than in 'warm' clouds, but nitrate levels remained about the same.Key words: cloud chemistry, cloud frequency, air-mass trajectories, ANOVA.

scholarly journals Benchmarking the performance of pairwise homogenization of surface temperatures in the United States

2012 ◽  
Vol 117 (D5) ◽  
pp. n/a-n/a ◽  
Author(s):  
Claude N. Williams ◽  
Matthew J. Menne ◽  
Peter W. Thorne
Keyword(s):  
United States ◽  
The United States ◽  
Surface Temperatures

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 Probabilistic Projections of Climate Change for the Mid-Atlantic Region of the United States: Validation of Precipitation Downscaling during the Historical Era*

2012 ◽  
Vol 25 (2) ◽  
pp. 509-526 ◽  
Author(s):  
Liang Ning ◽  
Michael E. Mann ◽  
Robert Crane ◽  
Thorsten Wagener
Keyword(s):  
United States ◽  
General Circulation ◽  
Large Scale ◽  
Daily Precipitation ◽  
Circulation Model ◽  
The United States ◽  
Monthly Precipitation ◽  
Atlantic Region ◽  
Daily Precipitation Data ◽  
Training Interval

Abstract This study uses a statistical downscaling method based on self-organizing maps (SOMs) to produce high-resolution, downscaled precipitation estimates over the state of Pennsylvania in the mid-Atlantic region of the United States. The SOMs approach derives a transfer function between large-scale mean atmospheric states and local meteorological variables (daily point precipitation values) of interest. First, the SOM was trained using seven coarsely resolved atmospheric variables from the National Centers for Environmental Prediction (NCEP) reanalysis dataset to model observed daily precipitation data from 17 stations across Pennsylvania for the period 1979–2005. Employing the same coarsely resolved variables from nine general circulation model (GCM) simulations taken from the historical analysis of the Coupled Model Intercomparison Project, phase 3 (CMIP3), the trained SOM was subsequently applied to simulate daily precipitation at the same 17 sites for the period 1961–2000. The SOM analysis indicates that the nine model simulations exhibit similar synoptic-scale features to the (NCEP) observations over the 1979–2007 training interval. An analysis of the sea level pressure signatures and the precipitation distribution corresponding to the trained SOM shows that it is effective in differentiating characteristic synoptic circulation patterns and associated precipitation. The downscaling approach provides a faithful reproduction of the observed probability distributions and temporal characteristics of precipitation on both daily and monthly time scales. The downscaled precipitation field shows significant improvement over the raw GCM precipitation fields with regard to observed average monthly precipitation amounts, average monthly number of rainy days, and standard deviations of monthly precipitation amounts, although certain caveats are noted.

Characterizing Predictability of Precipitation Means and Extremes over the Conterminous United States, 1949–2010*

2016 ◽  
Vol 29 (7) ◽  
pp. 2621-2633 ◽  
Author(s):  
Mingkai Jiang ◽  
Benjamin S. Felzer ◽  
Dork Sahagian
Keyword(s):  
United States ◽  
Pacific Northwest ◽  
West Coast ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
The United States ◽  
Monthly Precipitation ◽  
The West ◽  
Precipitation Seasonality

Abstract The proper understanding of precipitation variability, seasonality, and predictability are important for effective environmental management. Precipitation and its associated extremes vary in magnitude and duration both spatially and temporally, making it one of the most challenging climate parameters to predict on the basis of global and regional climate models. Using information theory, an improved understanding of precipitation predictability in the conterminous United States over the period of 1949–2010 is sought based on a gridded monthly precipitation dataset. Predictability is defined as the recurrent likelihood of patterns described by the metrics of magnitude variability and seasonality. It is shown that monthly mean precipitation and duration-based dry and wet extremes are generally highly variable in the east compared to those in the west, while the reversed spatial pattern is observed for intensity-based wetness indices except along the Pacific Northwest coast. It is thus inferred that, over much of the U.S. landscape, variations of monthly mean precipitation are driven by the variations in precipitation occurrences rather than the intensity of infrequent heavy rainfall. It is further demonstrated that precipitation seasonality for means and extremes is homogeneously invariant within the United States, with the exceptions of the West Coast, Florida, and parts of the Midwest, where stronger seasonality is identified. A proportionally higher role of variability in regulating precipitation predictability is demonstrated. Seasonality surpasses variability only in parts of the West Coast. The quantified patterns of predictability for precipitation means and extremes have direct applications to those phenomena influenced by climate periodicity, such as biodiversity and ecosystem management.

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