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Homogenized mean monthly temperature time series (HClim), station Hom_HCLIM_Bremervoerde, Germany

Instrumental meteorological observations are essential for analysing past climate and reconstructing climate variability. However, many of the long instrumental climate series, some extending back to 1658, have been affected by inhomogeneities (artificial shifts) caused by changes in measurement conditions such as station relocations, instrumentation changes, and environmental modifications. To address this problem, homogenization procedures have been developed to detect and adjust such inhomogeneities. In this work, the records undergo homogenization analysis, during which these inhomogeneities are identified and corrected. The Standard Normal Homogeneity Test (SNHT), developed by Hans Alexandersson, is applied as the statistical method, comparing candidate series with neighbouring reference stations to assess relative homogeneity. The article presents homogenization analyses using three different tools (CLIMATOL, BART, and PHA) applied to the published global multivariable monthly instrumental climate database HCLIM (doi:10.1594/PANGAEA.940724). The resulting database includes the best-performing homogenized series - those produced by BART - comprising 2,892 homogenized temperature time series covering the period 1757–2020.

Homogenized mean monthly temperature time series (HClim), station Hom_HCLIM_Emden-Nesserland, Germany

Instrumental meteorological observations are essential for analysing past climate and reconstructing climate variability. However, many of the long instrumental climate series, some extending back to 1658, have been affected by inhomogeneities (artificial shifts) caused by changes in measurement conditions such as station relocations, instrumentation changes, and environmental modifications. To address this problem, homogenization procedures have been developed to detect and adjust such inhomogeneities. In this work, the records undergo homogenization analysis, during which these inhomogeneities are identified and corrected. The Standard Normal Homogeneity Test (SNHT), developed by Hans Alexandersson, is applied as the statistical method, comparing candidate series with neighbouring reference stations to assess relative homogeneity. The article presents homogenization analyses using three different tools (CLIMATOL, BART, and PHA) applied to the published global multivariable monthly instrumental climate database HCLIM (doi:10.1594/PANGAEA.940724). The resulting database includes the best-performing homogenized series - those produced by BART - comprising 2,892 homogenized temperature time series covering the period 1757–2020.

Homogenized mean monthly temperature time series (HClim), station Hom_HCLIM_MergentheimBad-Neunkirchen, Germany

Instrumental meteorological observations are essential for analysing past climate and reconstructing climate variability. However, many of the long instrumental climate series, some extending back to 1658, have been affected by inhomogeneities (artificial shifts) caused by changes in measurement conditions such as station relocations, instrumentation changes, and environmental modifications. To address this problem, homogenization procedures have been developed to detect and adjust such inhomogeneities. In this work, the records undergo homogenization analysis, during which these inhomogeneities are identified and corrected. The Standard Normal Homogeneity Test (SNHT), developed by Hans Alexandersson, is applied as the statistical method, comparing candidate series with neighbouring reference stations to assess relative homogeneity. The article presents homogenization analyses using three different tools (CLIMATOL, BART, and PHA) applied to the published global multivariable monthly instrumental climate database HCLIM (doi:10.1594/PANGAEA.940724). The resulting database includes the best-performing homogenized series - those produced by BART - comprising 2,892 homogenized temperature time series covering the period 1757–2020.

Homogenized mean monthly temperature time series (HClim), station Hom_HCLIM_Bremen, Germany

Instrumental meteorological observations are essential for analysing past climate and reconstructing climate variability. However, many of the long instrumental climate series, some extending back to 1658, have been affected by inhomogeneities (artificial shifts) caused by changes in measurement conditions such as station relocations, instrumentation changes, and environmental modifications. To address this problem, homogenization procedures have been developed to detect and adjust such inhomogeneities. In this work, the records undergo homogenization analysis, during which these inhomogeneities are identified and corrected. The Standard Normal Homogeneity Test (SNHT), developed by Hans Alexandersson, is applied as the statistical method, comparing candidate series with neighbouring reference stations to assess relative homogeneity. The article presents homogenization analyses using three different tools (CLIMATOL, BART, and PHA) applied to the published global multivariable monthly instrumental climate database HCLIM (doi:10.1594/PANGAEA.940724). The resulting database includes the best-performing homogenized series - those produced by BART - comprising 2,892 homogenized temperature time series covering the period 1757–2020.

Homogenized mean monthly temperature time series (HClim), station Hom_HCLIM_Bamberg, Germany

Instrumental meteorological observations are essential for analysing past climate and reconstructing climate variability. However, many of the long instrumental climate series, some extending back to 1658, have been affected by inhomogeneities (artificial shifts) caused by changes in measurement conditions such as station relocations, instrumentation changes, and environmental modifications. To address this problem, homogenization procedures have been developed to detect and adjust such inhomogeneities. In this work, the records undergo homogenization analysis, during which these inhomogeneities are identified and corrected. The Standard Normal Homogeneity Test (SNHT), developed by Hans Alexandersson, is applied as the statistical method, comparing candidate series with neighbouring reference stations to assess relative homogeneity. The article presents homogenization analyses using three different tools (CLIMATOL, BART, and PHA) applied to the published global multivariable monthly instrumental climate database HCLIM (doi:10.1594/PANGAEA.940724). The resulting database includes the best-performing homogenized series - those produced by BART - comprising 2,892 homogenized temperature time series covering the period 1757–2020.

Homogenized mean monthly temperature time series (HClim), station Hom_HCLIM_Brandenburg-Gorden, Germany

Instrumental meteorological observations are essential for analysing past climate and reconstructing climate variability. However, many of the long instrumental climate series, some extending back to 1658, have been affected by inhomogeneities (artificial shifts) caused by changes in measurement conditions such as station relocations, instrumentation changes, and environmental modifications. To address this problem, homogenization procedures have been developed to detect and adjust such inhomogeneities. In this work, the records undergo homogenization analysis, during which these inhomogeneities are identified and corrected. The Standard Normal Homogeneity Test (SNHT), developed by Hans Alexandersson, is applied as the statistical method, comparing candidate series with neighbouring reference stations to assess relative homogeneity. The article presents homogenization analyses using three different tools (CLIMATOL, BART, and PHA) applied to the published global multivariable monthly instrumental climate database HCLIM (doi:10.1594/PANGAEA.940724). The resulting database includes the best-performing homogenized series - those produced by BART - comprising 2,892 homogenized temperature time series covering the period 1757–2020.

Homogenisierung des globalen Radiosondendatensatzes

Reanalyseprojekte und Klimadatenassimilationssysteme sollten globale, zeitlich homogene, gegitterte dreidimensionale Datensätze von Temperatur, Feuchte und Wind erzeugen, die sich für Untersuchungen von Klimatrends und Klimavariabilität eignen. Solche Datensätze nennt man Reanalysen. Frühere Reanalysen haben diesen Anforderungen nur teilweise entsprochen, weil sich die atmosphärischen Beobachtungssysteme in den letzten 50 Jahren häufig geändert haben. Das globale Radiosondennetz ist praktisch das einzige Beobachtungssystem für höhere Atmosphärenschichten bis 1973 und beeinflusst auch in der Satelliten-Aera die Qualität der Reanalysen. Zeitserien und daraus abgeleitete Trends praktisch aller Radiosondenstationen sind durch Sprünge beeinträchtigt, die durch die Einführung verbesserte Instrumentierung verursacht sind. Die Korrektur dieser Brüche nennt man Homogenisierung.In diesem Projekt wird versucht, sie durch Vergleich mit Zeitserien aus 6-stündigen Vorhersagen, die im Rahmen des ERA-40 Projektes des Europäischen Zentrums für mittelfristige Wettervorhersagen (ECMWF) erstellt wurden, zu korrigieren. Diese Zeitserien sind zwar nicht perfekt, können aber als Referenz zur Korrektur der meisten Radiosondenzeitreihen verwendet werden. Während eines einjährigen Aufenthalts des Projektleiters am ECMWF wurde eine automatische Korrekturmethode entwickelt, die auf dem Vergleich dieser Zeitserien der Differenz zwischen ERA-40 Vorhersagen und Radiosondenbeobachungen (bg-obs) basiert. Die Methode liefert zwar vielversprechende Ergebnisse, muss aber verbessert werden, bevor die korrigierten Daten ausreichend abgesichert sind. In diesem Projekt sollen (i) die verwendeten statistischen Werkzeuge erweitert und verbessert werden, (ii) die statistisch bestimmten Korrekturen mit unabhängig bestimmten Korrekturen an speziellen Stationen verglichen werden. Es sollen nicht nur ERA-40 bg-obs Differenzen verwendet werden, sondern auch bg-obs Differenzen aus anderen Reanalysen. Ziel ist es, auf diese Weise einen 60-jährigen globalen homogenisierten Radiosondentemperaturdatensatz zu erstellen, der sich als Eingangsdatensatz für künftige Reanalysen eignet. Dieses Ziel wird in Kooperation mit dem ECMWF und dem englischen Wetterdienst verfolgt, und soll innerhalb von drei Jahren erreicht werden.

Globale Radiosondendaten für die Klimaforschung

Lange homogene beobachtete Zeitserien von Klimavariablen werden nicht nur an der Erdoberfläche sondern auch in der freien Atmosphäre benötigt, denn Klimaanomalien und Klimaänderungen haben eine dreidimensionale räumliche Struktur. In-situ Beobachtungen der freien Atmosphäre, vor allem Radiosonden- und Ballondaten, sind in der Nordhemisphäre etwa seit den 1930er Jahren verfügbar, und globale Bedeckung ist seit dem internationalen geophysikalischen Jahr (IGY) 1958 gegeben. Um das volle Potential der Daten auszuschöpfen müssen (i) künstliche systematische Fehler und Sprünge aus den Stationszeitreihen entfernt werden und die Daten müssen (ii) mit einem geeigneten dynamischen Datenassimilations-system im Rahmen sogenannter Reanalysen assimiliert werden. Die Entfernung künstlicher Sprünge aus den Beobachtungsreihen nennt man Homogenisierung. Das Fehlern homogener Klimareihen der freien Atmosphäre zurück bis in die 1970er oder sogar 1930er Jahre wurde vom Intergovernmental Panel on Climate Change (IPCC) als großer Unsicherheitsfaktor identifiziert, der unsere Fähigkeit zur Diagnose von Klimaänderungen wesentlich einschränkt. Im Projekt P18120-N10 (Ende im Mai 2009) hat der Antragsteller weltweit führende Homogeni-sierungsmethoden für Radiosondentemperaturen und -winde entwickelt. Die berechneten Korrekturen werden in laufenden Reanalyseprojekten über die Satellitenperiode (1979-) am europäischen Zentrum für mittelfristige Wettervorhersage (EZMW) und an der National Aeronautics and Space Administration (NASA) benutzt. Ein neues Projekt mit folgenden Zielen wird nun beantragt: (i) Entwicklung eines vereinheitlichten Homogenisierungssystems, das Temperatur, Feuchte und Winddaten gemeinsam homogenisiert. Es soll homogene Datensätze dieser Parameter zurück bis 1958 liefern. (ii) die Schätzung systematischer Fehler in Radiosondenmessungen während des Datenassimilationsprozesses ('online bias estimation'). (iii) Untersuchung und wenn möglich Homogenisierung des Radiosondendatensatzes von 1938-1958. Ziel (i) geht auf die Tatsache ein, dass seit 1958 zwar ein globaler Radiosondenfeuchte- und Winddatensatz zur Verfügung steht, aber keine Korrekturen, die umfassend genug sind, um für eine Klimadatenassimulation hilfreich zu sein. Homogenisierte Temperaturreihen der Universität Wien und von anderen Quellen existieren, aber enthalten immer noch bedeutende Inkonsistenzen, die entfernt werden müssen, wie neuere Forschungsergebnisse gezeigt haben. Die gemeinsame Betrachtung aller Parameter ist ein neuer Ansatz, der zu verbesserter Brucherkennung führen sollte, weil Brüche in verschiedenen Parametern oft synchron auftreten aber nicht in allen Zeitserien erkennbar sind. Die Homogenisierung der Zeitreihenin späteren Perioden sollte auch durch überarbeitete Brucherkennungsverfahren und neu rekalibrierte Satellitenradianzen deutlich verbessert werden können. usw.

Homogenized mean monthly temperature time series (HClim), station Hom_HCLIM_Hechingen, Germany

Instrumental meteorological observations are essential for analysing past climate and reconstructing climate variability. However, many of the long instrumental climate series, some extending back to 1658, have been affected by inhomogeneities (artificial shifts) caused by changes in measurement conditions such as station relocations, instrumentation changes, and environmental modifications. To address this problem, homogenization procedures have been developed to detect and adjust such inhomogeneities. In this work, the records undergo homogenization analysis, during which these inhomogeneities are identified and corrected. The Standard Normal Homogeneity Test (SNHT), developed by Hans Alexandersson, is applied as the statistical method, comparing candidate series with neighbouring reference stations to assess relative homogeneity. The article presents homogenization analyses using three different tools (CLIMATOL, BART, and PHA) applied to the published global multivariable monthly instrumental climate database HCLIM (doi:10.1594/PANGAEA.940724). The resulting database includes the best-performing homogenized series - those produced by BART - comprising 2,892 homogenized temperature time series covering the period 1757–2020.

Homogenized mean monthly temperature time series (HClim), station Hom_HCLIM_Weissenburg-Emetzheim, Germany

Instrumental meteorological observations are essential for analysing past climate and reconstructing climate variability. However, many of the long instrumental climate series, some extending back to 1658, have been affected by inhomogeneities (artificial shifts) caused by changes in measurement conditions such as station relocations, instrumentation changes, and environmental modifications. To address this problem, homogenization procedures have been developed to detect and adjust such inhomogeneities. In this work, the records undergo homogenization analysis, during which these inhomogeneities are identified and corrected. The Standard Normal Homogeneity Test (SNHT), developed by Hans Alexandersson, is applied as the statistical method, comparing candidate series with neighbouring reference stations to assess relative homogeneity. The article presents homogenization analyses using three different tools (CLIMATOL, BART, and PHA) applied to the published global multivariable monthly instrumental climate database HCLIM (doi:10.1594/PANGAEA.940724). The resulting database includes the best-performing homogenized series - those produced by BART - comprising 2,892 homogenized temperature time series covering the period 1757–2020.

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