South Dakota Diversity of Temperature: Pictures from Statistical Analysis

The regional diversity of monthly temperature was analyzed based on long-term data obtained for South Dakota (SD) from the High Plains Regional Climate Center. Multidimensional statistical methods were used and the principal results presented as a sequence of 2- and 3-dimensional scatterplot pictures depicting the quantitative results. 
System hierarchical model of landscape was used for research tasks formulation. Initial matrixes for three research tasks were compiled for the state. The first set of initial matrices of time series {Xt*n}, where t = number of years and n = number of meteorological stations, contains two matrixes: X1(67*29) and X2(33*94). The second set -{Xt*m}, where t = number of years and m = number of months in a year: X3(113*12), X4(110*12), and X5(102*12). The third set - {Xn*m}, where n = number of meteorological stations and m = number of months in a year, contains two matrixes: X6(29*12) and X7(94*12). 
Statistical analysis allowed us to differentiate weather stations by temporal trends and spatial distribution for the time interval 1932-1998. The most variable stations (Brookings, Camp Crook, and Highmore) were determined; their seasonality was described (the most variable months and correlation among months during the year) and their seasonal regime determined. The average annual and monthly temperature distributions were presented for South Dakota based on 29 and 94 stations for the time intervals 1932-1998 and 1963-1995.
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Precipitation in Aberdeen, SD: data analysis approach

The daily, monthly and annual sum of precipitation was analyzed for station Aberdeen-Airport, South Dakota (NOAA COOP #390020) with traditional normative statistical descriptions of precipitation variability and with methods of data analysis. The goal for research was formulated as finding a way to forecast extremes, such as the flooding of Aberdeen in May 2007 after a 30-hour, 8.42 inch rain event. Daily data were available, analyzed and cannot be considered as having any use for the forecast. The observations of 1423 monthly sums of precipitation have the following characteristics: mean = 1.802 in.; geometric mean = 0.856 in.; median = 1.26 in.; minimum = 0.0; maximum = 12.39 in. Simplified Fourier analysis was performed for monthly data, and 31 cyclic components were determined and used in a model to represent the data with periods from 4 to 379 months. The biggest amplitude from those components belongs to period of 12 months (1.396), and the next largest amplitude (0.261) had a period length of 6 months. The lowest amplitude (0.033) has a period of 75 months; the longest period 379 months has an amplitude of approximately twice that height (0.066). Fourteen periods have amplitudes higher that 0.1. The same analysis was performed on data obtained from the High Plains Regional Climate Center that was collected between January 1932 and April 2006. The model has mean equal 1.623 in. and the same two periods: 12 and 6 months, - with the biggest amplitudes: 1.315 and 0.258, and the model showed the increase in precipitation during May 2007 (3.494 in) that bigger that in April (2.965 in), June (3.115 in) 2007, and May (2.246 in) 2006. Mean annual precipitation for the time interval 1891- 2007 was 21.627 in. Simplified Fourier analysis presented a model of annual variability with eight cyclic components. The biggest amplitude (2.26) has the longest period of 46 years; the smallest (0.336) has a period of 31 years. Five components (46, 3, 10, 24, and 8 years) have amplitudes larger that 1.0 The same analysis for annual sum for time interval 1932-2006 presents mean = 19.514, and a model with five components of 3, 7, 14, 18, and 26 years with sequential altitudes 1.525, 1.451, 1.084, 1.066, and 1.451. The model shows that 2007 annual precipitation is moderately greater than the long-term mean value. During 118 hydrological years, the mean of annual sum for hydrologic year is 21.617 in., the minimum is equal 9.07 and maximum is equal 41.44. The highest mean of 3.704 in. for monthly sum during this time interval has June, the second of 2.948 in. has May; but May has the highest maximum equal 12.39 and June only has 10.91. The factor analysis on the matrix of monthly sum of precipitations {P118*12,13} shows that the annual sum mostly associated with May and August; the seasonal variability may be described by five factors (cumulative variance reflected by the model is equal 57.4%). The seasonal regime may be well represented by the factor scores; four factors charts (for factors 1, 3, 4, and 5) show different degrees of decrease in sum of precipitations for analyzed interval. In contrast, for factor 2 slightly increased. The assumption of possible forecast for precipitation and connected flooding was considered based on the obtained results. The highest daily sum of precipitation may be expected in month with biggest monthly sums in May and June. The time series of annual sum and monthly sums of precipitations have cycles with periods of 24, 31 and 46 years, and 286 and 379 months; traditional tools of characterization variability have to be applied to the longest as it is possible time series

Extended range forecasting and the ENSO effects in the Corn Belt

Contingency tables are used to categorize Midwest monthly precipitation and maximum temperature to study persistence here. The percentage of times each category of the 2 x 2 contingency table persists for single month and several month periods is quantified climatologically using 102 years of data. Persistences reached as high as 50% in the warm months for cool-wet conditions. Winter persistence using this method is less accurate. Warm-dry or cool-wet conditions were the most dominant and persistent except for the southeast part of the region in winter. Here warm-wet or cool-dry conditions were most common. Temperature persistence, staying below or above the mean, reached near 70% for below mean temperatures during the summer and 65% for above mean temperatures during the winter. Precipitation persistence rarely reached the 60% level. Skill scores, as an improvement upon a climatological forecast, were commonly 10-20% for each cell and sometimes higher. Persistence skill is improved by classifying persistence based on the original climatic state;The same monthly climatological data are compared to the monthly Southern Oscillation Index (SOI) to measure effects of the El Nino/Southern Oscillation in the Corn Belt. Monthly extreme phase means are compared to the 102 year temperature and precipitation averages via Student\u27s t-test. Warm anomalies are found in the winter in the northern states and cool anomalies to the south during low phase of the SOI. Summer anomalies produced cooler and wetter conditions. Precipitation was increased to the west and decreased to the east. During the high phase of the SOI, opposite effects were seen in most areas. Significance varied, but many stations had values significant at the 0.05 and 0.01 level. Extreme ENSO phase months categorized via the contingency table displayed differences in the percentage of occurrence of ENSO events to the long-term averages. The percentage of occurrence of temperature and precipitation anomalies agreed with the t-test results. Same sign anomaly persistences (i.e. positive precipitation) were compared to long-term persistences. In Iowa cool conditions persist slightly more than average during El Nino events and dry conditions persist better than average during La Nina events

Quality Control of Soil Water Data in ACIS – A Case Study in Nebraska

Soil moisture is the key state variable from both climate and hydrologic cycle assessment perspectives. Automated measurements of soil moisture were not possible in the past decades. Sensors deployed in the field with real-time monitoring networks such as the Automated Weather Data Network (AWDN) in Nebraska have not only become affordable but enhanced the monitoring capability of the network with valuable soil moisture data added to the existing stream of hourly and daily weather data for precipitation, air temperature, humidity, solar radiation, wind speed, and soil temperature. However, to assure the quality of the data, quality control (QC) tools are needed. Earlier studies lacked the QC of soil water data in general as they were not part of a network that routinely collected soil water measurements. This paper presents a systematic QC analysis and methodology to evaluate the performance of candidate QC techniques using spatiallyextenstive soil water dataset available from the AWDN network. The six tests included are based on the general behavior of soil moisture, the statistical characteristics of the measurements, the soil properties, and the precipitation measurements. The threshold, step change, and spatial regression test proved most effective in identifying data problems. The results demonstrate that these methods will lead to early identification of potential instrument failures and other disturbances to the soil water measurements

Best Management Practices for Corn Production in South Dakota: Seasonal Hazards—Frost, Hail, Drought, and Flooding

Conclusion: Weather conditions such as frost, hail, flood, or drought can severely reduce yields. Effects from these events are manageable to a certain extent, but loss can be expected when these events occur. The degree of loss depends on the severity of the event. Crop insurance has become a common component of corn production in the U.S.; the insurance provides the producer economic protection for uncontrollable events. Producers should consider crop insurance based on the consequences of crop loss

Climate Indicators for Agriculture

The Climate Indicators for Agriculture report presents 20 indicators of climate change, carefully selected across multiple agricultural production types and food system elements in the United States. Together, they represent an overall view of how climate change is influencing U.S. agriculture and food systems. Individually, they provide useful information to support management decisions for a variety of crop and livestock production systems. The report includes multiple categories of indicators, including physical indicators (e.g., temperature, precipitation), crop and livestock (e.g., animal heat stress), biological indicators (e.g., pests), phenological indicators (e.g. seasonality), and socioeconomic indicators (e.g., total factor productivity)

Midwest

The Midwest is home to over 60 million people, and its active economy represents 18% of the U.S. gross domestic product. The region is probably best known for agricultural production. Increases in growingseason temperature in the Midwest are projected to be the largest contributing factor to declines in the productivity of U.S. agriculture. Increases in humidity in spring through mid-century are expected to increase rainfall, which will increase the potential for soil erosion and further reduce planting-season workdays due to waterlogged soil

From Too Much to Too Little: How the central U.S. drought of 2012 evolved out of one of the most devastating floods on record in 2011

Table of Contents Section 1: Introduction....................................................................... 1 Section 2: Regional Drought Perspective................................. 2 Section 3: State Drought Perspectives........................................ 3 Section 3.1: Colorado........................................................................... 20 Section 3.2: Illinois.................................................................. 25 Section 3.3: Indiana................................................. 29 Section 3.4: Iowa...................... 36 Section 3.5: Kansas............................................................... 42 Section 3.6: Kentucky............................................................................ 46 Section 3.7: Michigan.............................. 52 Section 3.8: Minnesota............................................................ 58 Section 3.9: Missouri..................................................... 63 Section 3.10: Nebraska................................................. 67 Section 3.11: North Dakota............................................ 73 Section 3.12: Ohio................................................... 79 Section 3.13: South Dakota..................................... 85 Section 3.14: Wyoming........................................... 96 Section 4: Conclusions.............................................................. 9