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Michael R. Weber
and
C. Bruce Baker

Abstract

No abstract available.

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Nathaniel B. Guttman
and
C. Bruce Baker

The Automated Surface Observing System is currently replacing conventional observations at the National Weather Service, the Federal Aviation Administration, and other stations that report hourly observations. From a climatological viewpoint, it is necessary to compare the data from the old and new measuring systems in order to gain an understanding of their differences. These differences may become important when using time series for applications such as the computation of climatic normals, the development of homogeneous datasets for long periods of record for the investigation of climatic change, the placing of events into historical perspective, or the analysis of extreme weather events. This exploratory study of temperature data was undertaken to determine first whether there is a data continuity problem between the two observing systems and second, if there is a problem, to identify the magnitude of the problem. The most important conclusion from this study is that differences in site characteristics, even at the same airport, play as much, if not more, of a role in assessing the comparability of measurements from the two observing systems as does the instrument system bias. The instrument bias at most stations is on the order of a few tenths of a degree Fahrenheit, but the siting differences can lead to biases on the order of a couple of degrees. Not only is there a difference in the magnitude of the biases, but there is also a difference in the direction; the instrument bias is usually negative, but the siting biases can be either positive or negative.

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C. Bruce Baker
,
William R. Kuhn
, and
Edward Ryznar

Abstract

Direct normal and diffuse solar irradiances and 500 nm aerosol optical depths measured at the University of Michigan departed far from normal on 26 October 1982, when it is concluded that the main stratospheric cloud from the El Chichon volcanic eruption arrived at the 42°N latitude of the radiation measurement facility. For clear-sky data analyzed through 19 January 1983, direct solar is about 25% less than normal and diffuse solar is about 85% greater. For the same aerosol optical depths and solar zenith angles, the ratio of diffuse to direct is about 30% greater for about 0.3 cm of precipitable water but nearly the same for 0.9 cm. Aerosol optical depths are nearly three times greater for wind directions that naturally advect the cleanest air. The effect of circumsolar irradiance on the methods used to measure direct normal and diffuse irradiances cause the former to be overestimated and the latter to be underestimated.

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Temple R. Lee
,
Michael Buban
,
Edward Dumas
, and
C. Bruce Baker

Abstract

Upscaling point measurements from micrometeorological towers is a challenging task that is important for a variety of applications, for example, in process studies of convection initiation, carbon and energy budget studies, and the improvement of model parameterizations. In the present study, a technique was developed to determine the horizontal variability in sensible heat flux H surrounding micrometeorological towers. The technique was evaluated using 15-min flux observations, as well as measurements of land surface temperature and air temperature obtained from small unmanned aircraft systems (sUAS) conducted during a one-day measurement campaign. The computed H was found to be comparable to the micrometeorological measurements to within 5–10 W m−2. Furthermore, when comparing H computed using this technique with H determined using large-eddy simulations (LES), differences of <10 W m−2 were typically found. Thus, implementing this technique using observations from sUAS will help determine sensible heat flux variability at horizontal spatial scales larger than can be provided from flux tower measurements alone.

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Michael S. Buban
,
Temple R. Lee
, and
C. Bruce Baker

Abstract

Since drought and excessive rainfall can have significant socioeconomic impacts, it is important to have accurate high-resolution gridded datasets that can help improve analysis and forecasting of these conditions. One such widely used dataset is the Parameter-Elevation Regressions on Independent Slopes Model (PRISM). PRISM uses a digital elevation model (DEM) to obtain gridded elevation analyses and then uses a regression analysis along with approximately 15 000 surface precipitation measurements to produce a 4-km resolution daily precipitation product over the conterminous United States. The U.S. Climate Reference Network (USCRN) consists of 114 stations that take highly accurate meteorological measurements across all regions of the United States. A comparison between the USCRN and PRISM was performed using data from 2006 to 2018. There were good comparisons between the two datasets across nearly all seasons and regions; most mean daily differences were <1 mm, with most absolute daily differences ~5 mm. The most general characteristics were for a net dry bias in the PRISM data in the Southwest and a net moist bias in the southern United States. Verifying the PRISM dataset provides us with confidence it can be used with estimates of evapotranspiration, high-resolution gridded soil properties, and vegetation datasets to produce a daily gridded soil moisture product for operational use in the analyses and prediction of drought and excessive soil moisture conditions.

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Temple R. Lee
,
Michael Buban
,
David D. Turner
,
Tilden P. Meyers
, and
C. Bruce Baker

Abstract

The High-Resolution Rapid Refresh (HRRR) model became operational at the National Centers for Environmental Prediction (NCEP) in 2014 but the HRRR’s performance over certain regions of the coterminous United States has not been well studied. In the present study, we evaluated how well version 2 of the HRRR, which became operational at NCEP in August 2016, simulates the near-surface meteorological fields and the surface energy balance at two locations in northern Alabama. We evaluated the 1-, 3-, 6-, 12-, and 18-h HRRR forecasts, as well as the HRRR’s initial conditions (i.e., the 0-h initial fields) using meteorological and flux observations obtained from two 10-m micrometeorological towers installed near Belle Mina and Cullman, Alabama. During the 8-month model evaluation period, from 1 September 2016 to 30 April 2017, we found that the HRRR accurately simulated the observations of near-surface air and dewpoint temperature (R 2 > 0.95). When comparing the HRRR output with the observed sensible, latent, and ground heat flux at both sites, we found that the agreement was weaker (R 2 ≈ 0.7), and the root-mean-square errors were much larger than those found for the near-surface meteorological variables. These findings help motivate the need for additional work to improve the representation of surface fluxes and their coupling to the atmosphere in future versions of the HRRR to be more physically realistic.

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Matteo Colli
,
Roy Rasmussen
,
Julie M. Thériault
,
Luca G. Lanza
,
C. Bruce Baker
, and
John Kochendorfer

Abstract

Recent studies have used numerical models to estimate the collection efficiency of solid precipitation gauges when exposed to the wind in both shielded and unshielded configurations. The models used computational fluid dynamics (CFD) simulations of the airflow pattern generated by the aerodynamic response to the gauge–shield geometry. These are used as initial conditions to perform Lagrangian tracking of solid precipitation particles. Validation of the results against field observations yielded similarities in the overall behavior, but the model output only approximately reproduced the dependence of the experimental collection efficiency on wind speed. This paper presents an improved snowflake trajectory modeling scheme due to the inclusion of a dynamically determined drag coefficient. The drag coefficient was estimated using the local Reynolds number as derived from CFD simulations within a time-independent Reynolds-averaged Navier–Stokes approach. The proposed dynamic model greatly improves the consistency of results with the field observations recently obtained at the Marshall Field winter precipitation test bed in Boulder, Colorado.

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Bomin Sun
,
C. Bruce Baker
,
Thomas R. Karl
, and
Malcolm D. Gifford

Abstract

Temperature measurements from the U.S. Climate Reference Network (USCRN) instrument system were compared to the Automated Surface Observing System (ASOS) ambient air temperature measurements and were examined under different regimes of wind speed and solar radiation. Influences due to observing practice differences and the effects of siting differences were discussed.

This analysis indicated that the average difference between the ASOS and USCRN temperatures is on the order of 0.1°C. However, problems were noticed that were possibly related to the ASOS shield effectiveness, including a solar radiation warm effect under calm conditions and the dependence of ASOS minus USCRN temperature on wind speed.

The ASOS and USCRN time of observation difference was on the order of ∼0.05°C, with a warmer ASOS daily T max and a cooler ASOS daily T min. The local effect complicates the bias analysis because it depends not only on local heating/cooling, but it can be strongly modified by cloudiness, wind, and solar radiation.

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Jon K. Eischeid
,
C. Bruce Baker
,
Thomas R. Karl
, and
Henry F. Diaz

Abstract

One of the major concerns with detecting global climate change is the quality of the data. Climate data are extremely sensitive to errant values and outliers. Prior to analysis of these time series, it is important to remove outliers in a methodical manner.

This study provides statistically derived bounds for the uncertainty associated with surface temperature and precipitation measurements and yields a baseline dataset for validation of climate models as well as for a variety of other climatological uses. A two-step procedure using objective analysis was used to identify outliers. The first step was a temporal check that determines if a particular monthly value is consistent with other monthly values for the same station. The second step utilizes six different spatial interpolation techniques to estimate each monthly time series. Each of the methods is ranked according to its respective correlation coefficients with the actual time series, and the technique with the highest correlation coefficient is chosen as the best estimator. For both temperature and precipitation, a multiple regression scheme was found to be the best estimator for the majority of records. Results from the two steps are merged, and a combined set of quality control flags are generated.

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Howard J. Diamond
,
Thomas R. Karl
,
Michael A. Palecki
,
C. Bruce Baker
,
Jesse E. Bell
,
Ronald D. Leeper
,
David R. Easterling
,
Jay H. Lawrimore
,
Tilden P. Meyers
,
Michael R. Helfert
,
Grant Goodge
, and
Peter W. Thorne

The year 2012 marks a decade of observations undertaken by the U.S. Climate Reference Network (USCRN) under the auspices of NOAA's National Climatic Data Center and Atmospheric Turbulence and Diffusion Division. The network consists of 114 sites across the conterminous 48 states, with additional sites in Alaska and Hawaii. Stations are installed in open (where possible), rural sites very likely to have stable land-cover/use conditions for several decades to come. At each site a suite of meteorological parameters are monitored, including triple redundancy for the primary air temperature and precipitation variables and for soil moisture/temperature. Instrumentation is regularly calibrated to National Institute for Standards and Technology (NIST) standards and maintained by a staff of expert engineers. This attention to detail in USCRN is intended to ensure the creation of an unimpeachable record of changes in surface climate over the United States for decades to come. Data are made available without restriction for all public, private, and government use. This article describes the rationale for the USCRN, its implementation, and some of the highlights of the first decade of operations. One critical use of these observations is as an independent data source to verify the existing U.S. temperature record derived from networks corrected for nonhomogenous histories. Future directions for the network are also discussed, including the applicability of USCRN approaches for networks monitoring climate at scales from regional to global. Constructive feedback from end users will allow for continued improvement of USCRN in the future and ensure that it continues to meet stakeholder requirements for precise climate measurements.

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