Jinsheng You

Development of the Soil Moisture Index to Quantify Agricultural Drought and Its “User Friendliness” in Severity-Area-Duration Assessment

Abstract This paper examines the role of soil moisture in quantifying drought through the development of a drought index using observed and modeled soil moisture. In Nebraska, rainfall is received primarily during the crop-growing season and the supply of moisture from the Gulf of Mexico determines if the impending crop year is either normal or anomalous and any deficit of rain leads to a lack of soil moisture storage. Using observed soil moisture from the Automated Weather Data Network (AWDN), the actual available water content for plants is calculated as the difference between observed or modeled soil moisture and wilting point, which is subsequently normalized with the site-specific, soil property–based, idealistic available water for plants that is calculated as the difference between field capacity and wilting point to derive the soil moisture index (SMI). This index is categorized into five classes from no drought to extreme drought to quantitatively assess drought in both space and time. Additional...

Sensitivity Analysis of Quality Assurance Using the Spatial Regression Approach—A Case Study of the Maximum/Minimum Air Temperature

Abstract Both the spatial regression test (SRT) and inverse distance weighting (IDW) methods have been applied to provide estimates for the maximum air temperature (Tmax) and the minimum air temperature (Tmin) in the Applied Climate Information System (ACIS). This is critical to the processes of estimating missing data and identifying suspect data and is undertaken here to ensure quality data in ACIS. The SRT method was previously found to be superior to the IDW method; however, the sensitivity of the performance of both methods to input parameters has not been evaluated. A set of analyses is presented for both methods whereby the sensitivity to the radius of inclusion, the regression time window, the regression time offset, and the number of stations used to make the estimates are examined. Comparisons were also conducted between the SRT and the IDW methods. The performance of the SRT method stabilized when 10 or more stations were applied in the estimates. The optimal number of stations for the IDW meth...

Quality Control of Weather Data during Extreme Events

Abstract Quality assurance (QA) procedures have been automated to reduce the time and labor necessary to discover outliers in weather data. Measurements from neighboring stations are used in this study in a spatial regression test to provide preliminary estimates of the measured data points. The new method does not assign the largest weight to the nearest estimate but, instead, assigns the weights according to the standard error of estimate. In this paper, the spatial test was employed to study patterns in flagged data in the following extreme events: the 1993 Midwest floods, the 2002 drought, Hurricane Andrew (1992), and a series of cold fronts during October 1990. The location of flagged records and the influence zones for such events relative to QA were compared. The behavior of the spatial test in these events provides important information on the probability of making a type I error in the assignment of the quality control flag. Simple pattern recognition tools that identify zones wherein frequent fl...

An Improved QC Process for Temperature in the Daily Cooperative Weather Observations

Abstract TempVal is a spatial component of data quality assurance algorithms applied by the National Climatic Data Center (NCDC), and it has been used operationally for about 4 yr. A spatial regression test (SRT) approach was developed at the regional climate centers for climate data quality assurance and was found to be superior to currently used quality control (QC) procedures for the daily maximum and minimum air temperature. The performance of the spatial quality assessment procedures has been evaluated by assessing the rate with which seeded errors are identified. A complete dataset with seeded errors for the year 2003 for the contiguous United States was examined for both the maximum and minimum air temperature. The spatial regression quality assessment component (SRT), originating in the Automated Climate Information System (ACIS), and TempVal, originating in the NCDC database, were applied separately and evaluated through the ratio of identified seeded errors to the total number of seeds. The spat...

Performance of Quality Assurance Procedures on Daily Precipitation

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 ...

Some Concerns when Using Data from the Cooperative Weather Station Networks: A Nebraska Case Study

Abstract In this study, daily temperature and precipitation amounts that are observed by the Cooperative Observer Program (COOP) were compared among geographically close stations. Hourly observations from nearby Automatic Weather Data Network (AWDN) stations were utilized to resolve the discrepancies between the observations during the same period. The statistics of maximum differences in temperature and precipitation between COOP stations were summarized. In addition, the quantitative measures of the deviations between COOP and AWDN stations were expressed by root-mean-square error, mean absolute error, and an index of agreement. The results indicated that significant discrepancies exist among the daily observations between some paired stations because of varying observation times, observation error, sensor error, and differences in microclimate exposure. The purpose of this note is to bring attention to the problem and offer guidance on the use of daily observations in the comparison and creation of wea...

Evaluating climate and soil effects on regional soil moisture spatial variability using EOFs

Soil moisture is an important state variable in terrestrial water cycles; however, only few studies are available on regional soil moisture spatial variability (SMSV), which yielded inconsistent findings about regional controls on SMSV. Here, long-term soil moisture data were obtained from the Automated Weather Data Network and Soil Climate Analysis Network in three regions with different climate regimes across the continental U.S. Comprehensive datasets were compiled to examine regional controls on SMSV using the method of Empirical Orthogonal Function. One dominant spatial structure (EOF1) of soil moisture was found in the study regions, which explained over 75%, 67%, and 86% of the spatial variance in soil moisture in Nebraska, Utah, and the Southeast U.S., respectively. Despite the significant spatial variability in precipitation and potential evapotranspiration in all the study regions, the results showed that meteorological forcings had limited effects on regional SMSV in those regions with different climatic conditions, which differed from the traditional notion that SMSV is mainly controlled by meteorological forcings at the scale from 50 to 400 km. Instead, local factors related to soil (e.g., sand and clay fractions) were found to have significant correlations with EOF1, although the effects of other local factors (e.g., topography and vegetation) were generally negligible. This study provides strong field evidence that soil can exert much stronger impacts on regional SMSV than previously thought, which can override the effects of meteorological forcings. Future studies are still needed to elaborate on the relative roles of climate and soil in affecting regional SMSV.

A Multi-sensor View of the 2012 Central Plains Drought from Space

In summer of 2012, the Central Plains of the United States experienced its most severe drought since the ground-based data record began in late 1900s. By using comprehensive satellite data from MODIS and TRMM, along with in-situ observations, this study documents the geophysical parameters associated with this drought, and thereby providing, for the first time, a large-scale observation-based view of the extent to which the land surface temperature and vegetation can likely be affected by both the severe drought and the agriculture’s response (irrigation) to the drought. Over non-irrigated area, 2012 summer daytime land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) monthly anomalies (with respect to climate in 2002-2011) are often respectively greater than 5 K and negative, with some extreme values of 10 K and -0.2 (i.e., no green vegetation). In contrast, much smaller anomalies (< 2 K) of LST and nearly the same NDVI are found over irrigated areas. Precipitation received an average of 5.2 cm less, while both fire counts and fire radiative power were doubled, thus contributing in part to a nearly 100% increase of aerosol optical depth in many forested areas (close to intermountain west). Water vapor amount, while decreased over the southern part, indeed slightly increased in the northern part of Central Plains. As expected, cloud fraction anomaly is negative in the entire Central Plains; however, the greatest reduction of cloud fraction is found over the irrigated areas, which is in contrast to past modeling studies showing more irrigation, because of its impact on LST, may lead to increase of cloud fraction.

Toward a Better Quality Control of Weather Data

Previous studies have documented various QC tools for use with weather data (26; 4; 6; 25; 9; 3; 10; 16; 18). As a result, there has been good progress in the automated QC ofweather indices, especially the daily maximum/ minimum air temperature. The QC of precipitation is more dif‐ ficult than for temperature; this is due to the fact that the spatial and temporal variability of a variable (2) is related to the confidence in identifying outliers. Another approach to maintain‐ ing quality of data is to conduct intercomparisons of redundant measurements taken at a site. For example, the designers of the United States Climate Reference Network (USCRN) made it possible to compare between redundant measurements by specifying a rain gauge with multi‐ ple vibrating wires in order to avoid a single point of failure in the measurement process. In this case the three vibrating wires can be compared to determine whether or not the outputs are comparable and any outlying values can result in a site visit. CRN also includes three tempera‐ ture sensors at each site for the purpose of comparison.

Spatial Accuracy of Climate Networks: A Case Study in Nebraska

Climate data are increasingly scrutinized for accuracy because of the need for reliable input for climaterelated decision making and assessments of climate change. Over the last 30 years, vast improvements to U.S. instrumentation, data collection, and station siting have created more accurate data. This study explores the spatial accuracy of daily maximum and minimum air temperature data in Nebraska networks, including the U.S. Historical Climatology Network (HCN), the Automated Weather Data Network (AWDN), and the more recent U.S. Climate Reference Network (CRN). The spatial structure of temperature variations at the earth’s surface is compared for timeframes 2005‐09 for CRN and AWDN and 1985‐2005 for AWDN and HCN. Individual root-mean-squareerrorsbetween candidate stationandsurrounding stationswere calculated and used to determine the spatial accuracy of the networks. This study demonstrated that in the 5-yr analysis CRN and AWDN were of high spatial accuracy. For the 21-yr analysis the AWDN proved to have higher spatial accuracy (smaller errors) than the HCN for both maximum and minimum air temperature and for all months. In addition, accuracy was generally higher in summer months and the subhumid area had higher accuracy than did the semiarid area. The findings of this study can be used for Nebraska as an estimate of the uncertainty associated with using a weather station’s data at a decision point some distance from the station.