Valid data are required to make climate assessments and to make climate-related decisions. The objective of this paper is threefold: to introduce an explicit treatment of Type I and Type II errors in evaluating the performance of quality assurance procedures, to illustrate a quality control approach that allows tailoring to regions and subregions, and to introduce a new spatial regression test. Threshold testing, step change, persistence, and spatial regression were included in a test of three decades of temperature and precipitation data at six weather stations representing different climate regimes. The magnitude of thresholds was addressed in terms of the climatic variability, and multiple thresholds were tested to determine the number of Type I errors generated. In a separate test, random errors were seeded into the data and the performance of the tests was such that most Type II errors were made in the range of Ϯ1ЊC for temperature, not too different from the sensor field accuracy. The study underscores the fact that precipitation is more difficult to quality control than temperature. The new spatial regression test presented in this document outperformed all the other tests, which together identified only a few errors beyond those identified by the spatial regression test.

During early 2019, a series of events set the stage for devastating floods in eastern Nebraska, western Iowa, and southeastern South Dakota. When the floodwaters hit, dams and levees failed, cutting off towns while destroying roads, bridges, and rail lines, further exacerbating the crisis. Lives were lost and thousands of cattle were stranded. Estimates indicate that the cost of the flooding has topped $3 billion as of August 2019, with this number expected to rise. After a warm and wet start to winter, eastern Nebraska, western Iowa, and southeastern South Dakota endured anomalously low temperatures and record-breaking snowfall. By March 2019, rivers were frozen, frost depths were 60–90 cm, and the water equivalent of the snowpack was 30–100 mm. With these conditions in place, a record-breaking surface cyclone rapidly developed in Colorado and moved eastward, producing heavy rain toward the east and blizzard conditions toward the west. In areas of eastern Nebraska, western Iowa, and southeastern South Dakota, rapid melting of the snowpack due to this rain-on-snow event quickly led to excessive runoff that overwhelmed rivers and streams. These conditions brought the region to a standstill. In this paper, we provide an analysis of the antecedent conditions in eastern Nebraska, western Iowa, and southeastern South Dakota and the development of the surface cyclone that triggered the historic flooding, along with a look into the forecast and communication of flood impacts prior to the flood. The study used multiple datasets, including in situ observations and reanalysis data. Understanding the events that led to the flooding could aid in future forecasting efforts.

Since 2003, the High Plains Regional Climate Center (HPRCC) has been producing the Applied Climate Information System (ACIS) Climate Summary Maps for users all across the country. The maps allow users to quickly and easily assess climate conditions for various time scales that range from weeks to months to years, as well as spatial scales varying from state to regional and national levels. Although popular among the climate and drought monitoring community, the maps are utilized by a number of sectors, including academia, agriculture, government, resource management, and utilities. Over the years, the HPRCC has received a number of requests from users looking to customize and enhance the ACIS Climate Summary Maps. With funding provided by the National Integrated Drought Information System (NIDIS), the Center is now able to produce and distribute GIS versions of the ACIS Climate Summary Maps. Maps in GIS formats help to meet user needs by allowing them the opportunity to create custom color scales, choose specific regions, and combine information from various sources with the data available in the mapped products. The GIS data are available via a GIS Portal and GeoServer, which are accessible from the HPRCC website.