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Imke Durre

In atmospheric science, information is often communicated in visual form. Maps, radar images, and satellite imagery are widely used to display daily weather forecasts and current conditions. Textbooks, professional journals, and conference presentations are rich in figures that illustrate concepts and research findings. Given this preponderance of visual material, students with visual impairments may be tempted to assume that atmospheric science is not a suitable field for them. In fact, however, thanks to the widespread use of the computer and the availability of assistive technology, many atmospheric science careers are well suited to students with visual impairments who have acquired the necessary skills. Both personal experience and literature suggest that for people with visual impairments, success in science hinges upon the use of effective modes of communication between them and their sighted instructors and colleagues. With these considerations in mind, the author discusses relevant assistive technology and adaptive strategies, presents techniques for ensuring the accessibility of materials and programs to auditory and tactile learners, and suggests a collaborative approach to implementing reasonable accommodations. Together, these strategies create an environment in which the visually impaired student or employee can be expected to perform at the same level as everyone else.

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Imke Durre and John M. Wallace

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In light of numerous studies documenting a decline in the diurnal temperature range (DTR) over much of the globe, some authors have in recent years examined the annual march of the DTR in an effort to understand better the factors that influence the DTR’s seasonal variations. These papers show that, over the southern two-thirds of the eastern United States, the DTR’s climatology features peaks in spring and autumn and minima in winter and mid-to-late summer. However, the factors responsible for these characteristics remain uncertain. In this study, the DTR climatology of the eastern United States is analyzed in detail using daily surface and 850-mb data, with emphasis on possible relationships to seasonal changes in vegetation. It is shown that the warm season dip in the DTR deepens and widens from north to south across the study area, in accordance with a lengthening of the growing season. Furthermore, the dip is particularly prominent in the annual march of the DTR on mostly sunny days, indicating that seasonal variations in cloudiness are not responsible for this feature. The climatologies of daily maximum and daily minimum temperatures are found to be very different from each other: the former flattens out after the springtime peak in the DTR whereas the latter exhibits a pronounced mid-to-late summer maximum. These findings suggest that, by inhibiting daytime surface heating, evapotranspiration from vegetation contributes significantly to the dip in the DTR during the warm season in the eastern United States.

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Imke Durre and John M. Wallace

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This study examines the contributions of sunshine duration, snow cover extent, and the atmospheric circulation to variations of the cold-season diurnal temperature range (DTR) in eight regions of the contiguous United States. The goal of the research is to facilitate the interpretation of long-term changes in the DTR in light of the possible anthropogenic role in these trends. For the cold seasons (Nov–Mar) between 1958/59 and 1994/95, daily surface observations at more than 200 stations from the First Summary of the Day (FSOD) dataset as well as selected daily fields from the NCEP–NCAR 40-Year Reanalysis Project are analyzed using compositing, correlation, and regression techniques. For each region, a sea level pressure anomaly pattern is identified that is linearly related to daily variations in the DTR. It is found that the presence of positive sea level pressure anomalies over a region, clear skies, and the absence of snow on the ground all favor high values of the regionally averaged DTR. The strength of these associations varies geographically because of the effects of nonlinear relationships, the frequency of snow cover, and the complexity of local dynamics.

The cold-season trends of several variables for the period 1965/66–1994/95 are also analyzed. During the 30-yr period of record, the central and southern United States experienced a decrease in the DTR, while the northeast, Pacific coast, and portions of the interior west experienced an increase. Variations in the DTR-related sea level pressure patterns and sunshine duration explain significant fractions of the DTR increase in the coastal Northwest and the DTR decrease in the south-central states. The DTR trends over the rest of the country are largely unrelated to linear trends in sunshine duration, snow cover, or the sea level pressure field. The spatial pattern of DTR trends is reproduced when homogeneity-adjusted data from the Global Historical Climatology Network are used in lieu of FSOD data. Hence, it appears that the geographical pattern of trends is not a result of inhomogeneities in the FSOD data. The findings presented here suggest that many of the observed cold-season trends in the DTR are not induced by linearly related changes in the atmospheric circulation and, therefore, are attributable either to internal nonlinear relationships in the climate system or to anthropogenic factors such as urbanization and increasing concentrations of greenhouse gases and tropospheric aerosols.

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Imke Durre, Russell S. Vose, and David B. Wuertz

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This paper presents a description of the fully automated quality-assurance (QA) procedures that are being applied to temperatures in the Integrated Global Radiosonde Archive (IGRA). Because these data are routinely used for monitoring variations in tropospheric temperature, it is of critical importance that the system be able to detect as many errors as possible without falsely identifying true meteorological events as erroneous. Three steps were taken to achieve such robust performance. First, 14 tests for excessive persistence, climatological outliers, and vertical and temporal inconsistencies were developed and arranged into a deliberate sequence so as to render the system capable of detecting a variety of data errors. Second, manual review of random samples of flagged values was used to set the “thresholds” for each individual check so as to minimize the number of valid values that are mistakenly identified as errors. The performance of the system as a whole was also assessed through manual inspection of random samples of the quality-assured data. As a result of these efforts, the IGRA temperature QA procedures effectively remove the grossest errors while maintaining a false-positive rate of approximately 10%.

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Imke Durre, Matthew J. Menne, and Russell S. Vose

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The evaluation strategies outlined in this paper constitute a set of tools beneficial to the development and documentation of robust automated quality assurance (QA) procedures. Traditionally, thresholds for the QA of climate data have been based on target flag rates or statistical confidence limits. However, these approaches do not necessarily quantify a procedure’s effectiveness at detecting true errors in the data. Rather, as illustrated by way of an “extremes check” for daily precipitation totals, information on the performance of a QA test is best obtained through a systematic manual inspection of samples of flagged values combined with a careful analysis of geographical and seasonal patterns of flagged observations. Such an evaluation process not only helps to document the effectiveness of each individual test, but, when applied repeatedly throughout the development process, it also aids in choosing the optimal combination of QA procedures and associated thresholds. In addition, the approach described here constitutes a mechanism for reassessing system performance whenever revisions are made following initial development.

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Imke Durre, Thomas C. Peterson, and Russell S. Vose

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The effect of the Luers–Eskridge adjustments on the homogeneity of archived radiosonde temperature observations is evaluated. Using unadjusted and adjusted radiosonde data from the Comprehensive Aerological Reference Dataset (CARDS) as well as microwave sounding unit (MSU) version-d monthly temperature anomalies, the discontinuities in differences between radiosonde and MSU temperature anomalies across times of documented changes in radiosonde are computed for the lower to midtroposphere, mid- to upper troposphere, and lower stratosphere. For this purpose, a discontinuity is defined as a statistically significant difference between means of radiosonde–MSU differences for the 30-month periods immediately prior to and following a documented change in radiosonde type. The magnitude and number of discontinuities based on unadjusted and adjusted radiosonde data are then compared. Since the Luers–Eskridge adjustments have been designed to remove radiation and lag errors from radiosonde temperature measurements, the homogeneity of the data should improve whenever these types of errors dominate.

It is found that even though stratospheric radiosonde temperatures appear to be somewhat more homogeneous after the Luers–Eskridge adjustments have been applied, transition-related discontinuities in the troposphere are frequently amplified by the adjustments. Significant discontinuities remain in the adjusted data in all three atmospheric layers. Based on the findings of this study, it appears that the Luers–Eskridge adjustments do not render upper-air temperature records sufficiently homogeneous for climate change analyses. Given that the method was designed to adjust only for radiation and lag errors in radiosonde temperature measurements, its relative ineffectiveness at producing homogeneous time series is likely to be caused by 1) an inaccurate calculation of the radiation or lag errors and/or 2) the presence of other errors in the data that contribute significantly to observed discontinuities in the time series.

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Imke Durre, Russell S. Vose, and David B. Wuertz

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This paper provides a general description of the Integrated Global Radiosonde Archive (IGRA), a new radiosonde dataset from the National Climatic Data Center (NCDC). IGRA consists of radiosonde and pilot balloon observations at more than 1500 globally distributed stations with varying periods of record, many of which extend from the 1960s to present. Observations include pressure, temperature, geopotential height, dewpoint depression, wind direction, and wind speed at standard, surface, tropopause, and significant levels.

IGRA contains quality-assured data from 11 different sources. Rigorous procedures are employed to ensure proper station identification, eliminate duplicate levels within soundings, and select one sounding for every station, date, and time. The quality assurance algorithms check for format problems, physically implausible values, internal inconsistencies among variables, runs of values across soundings and levels, climatological outliers, and temporal and vertical inconsistencies in temperature. The performance of the various checks was evaluated by careful inspection of selected soundings and time series.

In its final form, IGRA is the largest and most comprehensive dataset of quality-assured radiosonde observations freely available. Its temporal and spatial coverage is most complete over the United States, western Europe, Russia, and Australia. The vertical resolution and extent of soundings improve significantly over time, with nearly three-quarters of all soundings reaching up to at least 100 hPa by 2003. IGRA data are updated on a daily basis and are available online from NCDC as both individual soundings and monthly means.

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Imke Durre, John M. Wallace, and Dennis P. Lettenmaier

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The paper presents an analysis of the dependence of summertime daily maximum temperature on antecedent soil moisture using daily surface observations from a selection of stations in the contiguous United States and daily time series of soil moisture computed with a simple local water balance model. The computed soil moisture time series are offered as an alternative to Palmer’s soil moisture anomaly (Z) index, the Palmer Drought Severity Index (PDSI), and other such time series. In contrast to other water balance models that have been designed for the computation of soil moisture time series, the model herein is driven by daily rather than monthly data, uses the Priestley–Taylor method in lieu of Thornthwaite’s method to calculate potential evapotranspiration, allows for runoff during dry periods as well as when soil moisture is not at field capacity, includes a crude scheme for taking into account the effects of snowmelt on the water balance, and permits geographical variations in soil water capacity. The Priestley–Taylor method is considered to yield more realistic estimates of evapotranspiration than Thornthwaite’s method since it accounts for net radiation and represents a special case of the widely used Penman–Monteith method. Total runoff is parameterized according to the Variable Infiltration Capacity model. Based on a comparison with soil moisture measurements at Peoria, Illinois, the model appears to simulate the variability of soil moisture anomalies (W′) reasonably well.

Analysis of the relationship between W′ and daily maximum temperatures (T max) shows that in the central and eastern United States during the summer, the entire frequency distribution of standardized T max is shifted toward higher values following a “low-W′” day (i.e., a day on which W′ falls into the bottom quartile of its frequency distribution). The shift is most pronounced at the high end of the temperature distribution, indicating that as the soil gets drier, hot days tend to get hotter to a greater degree than cool days get warmer. Over the southeastern United States, where local evapotranspiration contributes a significant portion of the moisture available for precipitation, the temperature signal is particularly prominent and persists for up to several weeks after the soil moisture anomaly is observed. The relationship between temperature and daily precipitation is found to be much weaker and less persistent than the T maxW′ association. Thus, the frequency of record and near-record high temperatures is shown to be sensitive to soil moisture conditions, particularly on timescales shorter than one month.

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Matthew J. Menne, Imke Durre, Russell S. Vose, Byron E. Gleason, and Tamara G. Houston

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A database is described that has been designed to fulfill the need for daily climate data over global land areas. The dataset, known as Global Historical Climatology Network (GHCN)-Daily, was developed for a wide variety of potential applications, including climate analysis and monitoring studies that require data at a daily time resolution (e.g., assessments of the frequency of heavy rainfall, heat wave duration, etc.). The dataset contains records from over 80 000 stations in 180 countries and territories, and its processing system produces the official archive for U.S. daily data. Variables commonly include maximum and minimum temperature, total daily precipitation, snowfall, and snow depth; however, about two-thirds of the stations report precipitation only. Quality assurance checks are routinely applied to the full dataset, but the data are not homogenized to account for artifacts associated with the various eras in reporting practice at any particular station (i.e., for changes in systematic bias).

Daily updates are provided for many of the station records in GHCN-Daily. The dataset is also regularly reconstructed, usually once per week, from its 20+ data source components, ensuring that the dataset is broadly synchronized with its growing list of constituent sources. The daily updates and weekly reprocessed versions of GHCN-Daily are assigned a unique version number, and the most recent dataset version is provided on the GHCN-Daily website for free public access. Each version of the dataset is also archived at the NOAA/National Climatic Data Center in perpetuity for future retrieval.

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Imke Durre, Matthew J. Menne, Byron E. Gleason, Tamara G. Houston, and Russell S. Vose

Abstract

This paper describes a comprehensive set of fully automated quality assurance (QA) procedures for observations of daily surface temperature, precipitation, snowfall, and snow depth. The QA procedures are being applied operationally to the Global Historical Climatology Network (GHCN)-Daily dataset. Since these data are used for analyzing and monitoring variations in extremes, the QA system is designed to detect as many errors as possible while maintaining a low probability of falsely identifying true meteorological events as erroneous. The system consists of 19 carefully evaluated tests that detect duplicate data, climatological outliers, and various inconsistencies (internal, temporal, and spatial). Manual review of random samples of the values flagged as errors is used to set the threshold for each procedure such that its false-positive rate, or fraction of valid values identified as errors, is minimized. In addition, the tests are arranged in a deliberate sequence in which the performance of the later checks is enhanced by the error detection capabilities of the earlier tests. Based on an assessment of each individual check and a final evaluation for each element, the system identifies 3.6 million (0.24%) of the more than 1.5 billion maximum/minimum temperature, precipitation, snowfall, and snow depth values in GHCN-Daily as errors, has a false-positive rate of 1%−2%, and is effective at detecting both the grossest errors as well as more subtle inconsistencies among elements.

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