United States historical climatology network (HCN) serial temperature and precipitation data /

Twenty-eight microfiches (11 x 15 cm.) in pocket mounted on cover p. [3]. Header title: Historical climate network--temperature and precipitation data plots.

Gewitter und Gewitterschäden im südlichen hessischen Berg- und Beckenland und im Rhein-Main-Tiefland 1881 bis 1980

Basierend auf schriftlichen Quellen, hauptsächlich von 1881-1980 (ohne 1941-50), werden die raum-zeitlichen Eigenschaften von Gewittern mit Schäden im südlichen hessischen Berg- und Beckenland und im Rhein-Main-Tiefland (= UG) herausgestellt. Nach Auswertung des Materials in einer Datenbank erfolgt die Intensitätskategorisierung der Schadensvorgänge für gewitterbedingte Starkregen erstmals anhand der Ähnlichkeitsmerkmale von Folgeerscheinungen, für Blitzschlag erstmals nach Schadensaspekten sowie für Hagelschlag und Sturm mittels existierender und modifizierter Klassifizierungen. Diese Kategorisierungen können für alle Beschreibungen von Gewitterschadensereignissen in schriftlichen Quellen angewandt werden. Die einzelnen Phänomene zeigen deutlich voneinander abweichende Eigenschaften. So treten z.B. Starkregenschäden verstärkt im Frühsommer auf, Sturmschäden überwiegend erst im Hoch- und Spätsommer. Generell lassen sich zwei Jahresmaxima in der Gewitterschadenstätigkeit nachweisen (erste Junidekade, zweite Julihälfte). Schwergewitter mit Sturm (auch Hagel) aus SW-W verursachen überwiegend Schäden im südlichen und westlichen UG, aus W-N meist im nördlichen und östlichen UG. Gewitter mit Starkregenschäden dominieren im östlichen UG. Nach der hier vorgestellten Methode der Schadensschwerpunkte können für Schwergewitter aus dem westlichen Sektor (SW-W, W-N) Schwerpunktzugbahnen im UG gedeutet werden. Es wird die Möglichkeit aufgezeigt, die Schadensdatenauswertung auf historische Szenarien zu übertragen. Für Interpretation und Rekonstruktion dieser für die Belange der Historischen Klimatologie wichtigen Vorgänge wird Datenmaterial aus dem zurückreichenden Zeitraum bis 1700 verwendet.

Development of record-breaking statistics for climatological time-series analysis

The number of record-breaking events expected to occur in a strictly stationary time-series depends only on the number of values in the time-series, regardless of distribution. This holds whether the events are record-breaking highs or lows and whether we count from past to present or present to past. However, these symmetries are broken in distinct ways by trends in the mean and variance. We define indices that capture this information and use them to detect weak trends from multiple time-series. Here, we use these methods to answer the following questions: (1) Is there a variability trend among globally distributed surface temperature time-series? We find a significant decreasing variability over the past century for the Global Historical Climatology Network (GHCN). This corresponds to about a 10% change in the standard deviation of inter-annual monthly mean temperature distributions. (2) How are record-breaking high and low surface temperatures in the United States affected by time period? We investigate the United States Historical Climatology Network (USHCN) and find that the ratio of record-breaking highs to lows in 2006 increases as the time-series extend further into the past. When we consider the ratio as it evolves with respect to a fixed start year, we find it is strongly correlated with the ensemble mean. We also compare the ratios for USHCN and GHCN (minus USHCN stations). We find the ratios grow monotonically in the GHCN data set, but not in the USHCN data set. (3) Do we detect either mean or variance trends in annual precipitation within the United States? We find that the total annual and monthly precipitation in the United States (USHCN) has increased over the past century. Evidence for a trend in variance is inconclusive.

Math 412 - Topology with Applications

Highlights of Data Expedition: • Students explored daily observations of local climate data spanning the past 35 years. • Topological Data Analysis, or TDA for short, provides cutting-edge tools for studying the geometry of data in arbitrarily high dimensions. • Using TDA tools, students discovered intrinsic dynamical features of the data and learned how to quantify periodic phenomenon in a time-series. • Since nature invariably produces noisy data which rarely has exact periodicity, students also considered the theoretical basis of almost-periodicity and even invented and tested new mathematical definitions of almost-periodic functions. Summary The dataset we used for this data expedition comes from the Global Historical Climatology Network. “GHCN (Global Historical Climatology Network)-Daily is an integrated database of daily climate summaries from land surface stations across the globe.” Source: https://www.ncdc.noaa.gov/oa/climate/ghcn-daily/ We focused on the daily maximum and minimum temperatures from January 1, 1980 to April 1, 2015 collected from RDU International Airport. Through a guided series of exercises designed to be performed in Matlab, students explore these time-series, initially by direct visualization and basic statistical techniques. Then students are guided through a special sliding-window construction which transforms a time-series into a high-dimensional geometric curve. These high-dimensional curves can be visualized by projecting down to lower dimensions as in the figure below (Figure 1), however, our focus here was to use persistent homology to directly study the high-dimensional embedding. The shape of these curves has meaningful information but how one describes the “shape” of data depends on which scale the data is being considered. However, choosing the appropriate scale is rarely an obvious choice. Persistent homology overcomes this obstacle by allowing us to quantitatively study geometric features of the data across multiple-scales. Through this data expedition, students are introduced to numerically computing persistent homology using the rips collapse algorithm and interpreting the results. In the specific context of sliding-window constructions, 1-dimensional persistent homology can reveal the nature of periodic structure in the original data. I created a special technique to study how these high-dimensional sliding-window curves form loops in order to quantify the periodicity. Students are guided through this construction and learn how to visualize and interpret this information. Climate data is extremely complex (as anyone who has suffered from a bad weather prediction can attest) and numerous variables play a role in determining our daily weather and temperatures. This complexity coupled with imperfections of measuring devices results in very noisy data. This causes the annual seasonal periodicity to be far from exact. To this end, I have students explore existing theoretical notions of almost-periodicity and test it on the data. They find that some existing definitions are also inadequate in this context. Hence I challenged them to invent new mathematics by proposing and testing their own definition. These students rose to the challenge and suggested a number of creative definitions. While autocorrelation and spectral methods based on Fourier analysis are often used to explore periodicity, the construction here provides an alternative paradigm to quantify periodic structure in almost-periodic signals using tools from topological data analysis.

El clima intuido en el Madrid medieval a través de sus documentos y del Libro de la Montería (siglos XIV-XVI)

El presente trabajo pretende analizar cuáles eran las características climáticas que se sucedían en Madrid y su Tierra a lo largo del año, fundamentalmente entre los siglos XIV y XVI. Se intentará comprobar, dentro de las limitaciones que impone la documentación de la época, si en aquel tiempo se daba también un clima mediterráneo, o no, y si este ha variado mucho desde entonces. Igualmente se podrá comprobar algo que tal vez podría, aunque no debería, sorprendernos: el hombre medieval tenía muy claros muchos conceptos meteorológicos y climatológicos, aunque siempre aprehendidos con una finalidad práctica, para aplicarlos en su vida cotidiana tan cercana y dependiente de la naturaleza.