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Stanley A. Changnon and Kenneth E. Kunkel

Abstract

All long historical climate records are based on measurements that experienced shifts in instrumentation, site characteristics, or locations. How such changes affect the quality of past data remains an uncertainty for the thousands of historical records, confounding efforts to assess climate change. Fortunately, one station in Illinois with 118 yr of records has also kept detailed records of all such shifts plus overlapping measurements of temperatures and precipitation, allowing exact measurements of how conditions changed over time. This study examined these data and found varying discontinuities of 0.1°–0.9°C in annual temperatures due to various shifts, but no changes in daily precipitation related to site shifts. However, hourly precipitation amounts from recording rain gauges did undergo a considerable shift due to changes in rain gauge types. Similar studies need to be made of other stations with comparable historical records of station and instrument shifts and with overlapping measurements when shifts were made.

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Jinsheng You, Kenneth G. Hubbard, Saralees Nadarajah, and Kenneth E. Kunkel

Abstract

The search for precipitation quality control (QC) methods has proven difficult. The high spatial and temporal variability associated with precipitation data causes high uncertainty and edge creep when regression-based approaches are applied. Precipitation frequency distributions are generally skewed rather than normally distributed. The commonly assumed normal distribution in QC methods is not a good representation of the actual distribution of precipitation and is inefficient in identifying the outliers. This paper first explores the use of a single gamma distribution, fit to all precipitation data, in a quality assurance test. A second test, the multiple intervals gamma distribution (MIGD) method, is introduced. It assumes that meteorological conditions that produce a certain range in average precipitation at surrounding stations will produce a predictable range at the target station. The MIGD bins the average of precipitation at neighboring stations; then, for the events in a specific bin, an associated gamma distribution is derived by fit to the same events at the target station. The new gamma distributions can then be used to establish the threshold for QC according to the user-selected probability of exceedance. This paper also explores a test (Q test) for precipitation, which uses a metric based on comparisons with neighboring stations. The performance of the three approaches is evaluated by assessing the fraction of “known” errors that can be identified in a seeded error dataset. The single gamma distribution and Q-test approach were found to be relatively efficient at identifying extreme precipitation values as potential outliers. However, the MIGD method outperforms the other two QC methods. This method identifies more seeded errors and results in fewer type I errors than the other methods. It will be adopted in the Applied Climatic Information System (ACIS) for precipitation quality control.

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Eleonora M. C. Demaria, David C. Goodrich, and Kenneth E. Kunkel

Abstract

The detection and attribution of changes in precipitation characteristics relies on dense networks of rain gauges. In the United States, the COOP network is widely used for such studies even though there are reported inconsistencies due to changes in instruments and location, inadequate maintenance, dissimilar observation time, and the fact that measurements are made by a group of dedicated volunteers. Alternately, the Long-Term Agroecosystem Research (LTAR) network has been consistently and professionally measuring precipitation since the early 1930s. The purpose of this study is to compare changes in extreme daily precipitation characteristics during the warm season using paired rain gauges from the LTAR and COOP networks. The comparison, done at 12 LTAR sites located across the United States, shows underestimation and overestimation of daily precipitation totals at the COOP sites compared to the reference LTAR observations. However, the magnitude and direction of the differences are not linked to the underlying precipitation climatology of the sites. Precipitation indices that focus on extreme precipitation characteristics match closely between the two networks at most of the sites. Our results show consistency between the COOP and LTAR networks with precipitation extremes. It also indicates that despite the discrepancies at the daily time steps, the extreme precipitation observed by COOP rain gauges can be reliably used to characterize changes in the hydrologic cycle due to natural and human causes.

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Kenneth E. Kunkel, Michael Palecki, Leslie Ensor, Kenneth G. Hubbard, David Robinson, Kelly Redmond, and David Easterling

Abstract

A quality assessment of daily manual snowfall data has been undertaken for all U.S. long-term stations and their suitability for climate research. The assessment utilized expert judgment on the quality of each station. Through this process, the authors have identified a set of stations believed to be suitable for analysis of trends. Since the 1920s, snowfall has been declining in the West and the mid-Atlantic coast. In some places during recent years the decline has been more precipitous, strongly trending downward along the southern margins of the seasonal snow region, the southern Missouri River basin, and parts of the Northeast. Snowfall has been increasing since the 1920s in the lee of the Rocky Mountains, the Great Lakes–northern Ohio Valley, and parts of the north-central United States. These areas that are in opposition to the overall pattern of declining snowfall seem to be associated with specific dynamical processes, such as upslope snow and lake-effect snow that may be responding to changes in atmospheric circulation.

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Kenneth E. Kunkel, David R. Easterling, Kenneth Hubbard, Kelly Redmond, Karen Andsager, Michael C. Kruk, and Michael L. Spinar

Abstract

A recent comprehensive effort to digitize U.S. daily temperature and precipitation data observed prior to 1948 has resulted in a major enhancement in the computer database of the records of the National Weather Service’s cooperative observer network. Previous digitization efforts had been selective, concentrating on state or regional areas. Special quality control procedures were applied to these data to enhance their value for climatological analysis. The procedures involved a two-step process. In the first step, each individual temperature and precipitation data value was evaluated against a set of objective screening criteria to flag outliers. These criteria included extreme limits and spatial comparisons with nearby stations. The following data were automatically flagged: 1) all precipitation values exceeding 254 mm (10 in.) and 2) all temperature values whose anomaly from the monthly mean for that station exceeded five standard deviations. Additional values were flagged based on differences with nearby stations; in this case, metrics were used to rank outliers so that the limited resources were concentrated on those values most likely to be invalid. In the second step, each outlier was manually assessed by climatologists and assigned one of the four following flags: valid, plausible, questionable, or invalid. In excess of 22 400 values were manually assessed, of which about 48% were judged to be invalid. Although additional manual assessment of outliers might further improve the quality of the database, the procedures applied in this study appear to have been successful in identifying the most flagrant errors.

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Kenneth E. Kunkel, Michael A. Palecki, Kenneth G. Hubbard, David A. Robinson, Kelly T. Redmond, and David R. Easterling

Abstract

There is an increasing interest in examining long-term trends in measures of snow climatology. An examination of the U.S. daily snowfall records for 1900–2004 revealed numerous apparent inconsistencies. For example, long-term snowfall trends among neighboring lake-effect stations differ greatly from insignificant to +100% century−1. Internal inconsistencies in the snow records, such as a lack of upward trends in maximum seasonal snow depth at stations with large upward trends in snowfall, point to inhomogeneities. Nationwide, the frequency of daily observations with a 10:1 snowfall-to-liquid-equivalent ratio declined from 30% in the 1930s to a current value of around 10%, a change that is clearly due to observational practice. There then must be biases in cold-season liquid-equivalent precipitation, or snowfall, or both. An empirical adjustment of snow-event, liquid-equivalent precipitation indicates that the potential biases can be statistically significant.

Examples from this study show that there are nonclimatic issues that complicate the identification of and significantly change the trends in snow variables. Thus, great care should be taken in interpretation of time series of snow-related variables from the Cooperative Observer Program (COOP) network. Furthermore, full documentation of optional practices should be required of network observers so that future users of these data can properly account for such practices.

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