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Entwicklung der Fehlerschätzungsmethode für Datenassimilation für allgemeine Ozean-Zirkulationsmodelle

Die 4D-Var Datenassimilation (4D-var DA) ist eine spezielle Methode, die zur Initialisierung von Klima- und Wettervorsagen durch die Schätzung von Klimamodellparametern benutzt wird, in dem Modelle an beobachtende Daten angepasst werden. Aus verschiedenen Gründen führen DA unvermeidliche methodische Fehler ein, die sich auf die Genauigkeit der Modellvorhersagen auswirken. Aktuelle Methoden zur Fehlerkorrektur brauchen erhebliche Computerressourcen. Dies ist ein Grund, warum die Verwendung dieser Methoden in der Klimamodellierung begrenzt ist und sie nur in vereinfachten Versionen angewandt werden. Die Entwicklung einer konzeptuell neuartigen, robusten und effizienten, nichtlinear-variationellen Fehlerschätzungsmethode (NOVFEM) ist Ziel dieses Projekts. Diese Methode wird Fehler von DA Methoden schätzen und die notwendigen Korrekturen bestimmen. Im Besonderen ist es geplant, VOVFEM im Rahmen einer Anwendung in Klimavorhersagesystemen zu entwickeln. Der Vorteil der vorgeschlagenen Methode ist, dass der Algorithmus auf einer abstrakten mathematischen Formulierung basiert und deshalb in vielen geophysikalischen Bereichen angewandt werden kann. Eine weitere Innovation dieses Projekts ist die Entwicklung einer Methode zur schnellen und einfachen Berechnung von inversen Kovarianzmatrizen, die z. B. Anwendung in DA finden. Die vorgeschlagenen Methode ist im Vergleich mit existieren Methoden effizienter. Es wird erwartet, dass die theoretischen Ergebnisse dieses Projekt national und international veröffentlicht werden und ein freier Zugang zur NOVFEM Software wird bereitgestellt werden.

Physical oceanography during RV SENCKENBERG cruise SE202203-1

Data presented here were collected during the cruise SE202203-1 with RV Senckenberg from Neuharlingersiel, Germany to Neuharlingersiel, Germany (March, 14th, 2022 to March 18th, 2022). In total, 33 vertical deep CTD-hauls were conducted. The CTD system used was a Sea-Bird Electronics Inc. SBE 19plus V2 probe (SN 7245). The CTD was attached to a SBE 55 Carousel Water Sampler (SN 5571979-0100) containing 6 4-liter Ocean Test Equipment Inc. bottles. The system was equipped with additonally an altimeter (Benthos, SN 4711), and a double chlorophyll fluorometer (SCUFA Turner, SN 0773). The sensors were pre-calibrated by the manufacturers. Data were recorded with the Seasave V 7.26.7.107 software and processed using the SeaBird SBE Data Processing. Data were converted, filtered, loop edited and bin averaged (size 0.25 m) and also visually checked. The ship position was derived from a trimble DGPS-system linked to the CTD data. The time zone is given in UTC. For more details on post-processing see the CTD processing report attached. Raw data on request.

Continuous optical chlorophyll-a and turbidity data along RV MARIA S. MERIAN cruise MSM129/1

Underway optical chlorophyll-a and turbidity data were collected along the cruise track with Sea-Bird Scientific ECO FLNTU sensors installed within two autonomous measurement containers, as part of the "Reinseewassersystem" (RSWS). The containers measure alternatingly. While one container is measuring, the other one is being cleaned. The boxes switched generally every 12 hours. The water inlet for the RSWS is at about 6.5 m below sea surface. Observed chlorophyll-a and turbidity data were both quality controlled. Analysis of the chlorophyll-a and turbidity data during parallel operation of the sensors in the two boxes showed significant differences between the sensors. The sensors were aligned resulting in consistent chlorophyll-a and turbidity time series. The corrected chlorophyll-a data were calibrated based on chlorophyll-a values from discrete water samples taken from a RSWS water outlet in the hangar. Samples were frozen and measured fluorometrically in the lab. The time series was separated into two sections, coastal and open ocean, which were calibrated independently. The turbidity time series was also compared to suspended particulate matter from water samples, however, correlation was low and therefore the comparison not used for calibrating turbidity. The calibrated chlorophyll-a time series and corrected turbidity time series were compared against Globcolour CHL1 and TSM products, respectively. Details on all quality control steps, the calibration, and the comparison with satellite data can be found in the data processing report. The data set user should keep in mind that some parts of the time series are likely affected by non-photochemical quenching, see data processing report. It was out of the scope of the quality control to flag or correct non-photochemical quenching. The resulting data set contains the original data and corresponding quality flags achieved by the quality control algorithm as well as the calibrated chlorophyll-a and corrected turbidity data with corresponding quality flags. The data source is given through the name of the active container. The data set contains data during transit time and station work. We recommend to use ship's speed to filter for only transit data.

Continuous thermosalinograph oceanography along RV ALKOR cruise track AL644

Underway temperature and salinity data was collected along the cruise track with a SBE21 thermosalinograph (TSG) together with a SBE38 Thermometer. Both systems worked throughout the cruise. While temperature is taken at the water inlet in about 4 m depth, salinity is calculated within the interior TSG from conductivity and interior temperature. No temperature validation was performed. Salinity was validated with independent water samples taken at the water inlet. For details to all processing steps see Data Processing Report.

Continuous current observations near DynaCom experimental islands in the back-barrier tidal flat, Spiekeroog, Germany, 2019-11 to 2023-09

Data presented here were collected between November 2019 to September 2023 within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) involving the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were established in the back-barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog (Germany). A recording current meter (RCM; SEAGUARD® Recording Current Meter, Aanderaa Data Instruments AS, Bergen/Norway) was installed in the back-barrier tidal flat near the experimental islands. The sensor was bottom-mounted in a shallow tidal creek (0.59 m NHN) using a steel girder buried in the sediment, which caused the sensor to be exposed during low tide. All low-tide data have been removed from the dataset. The system was equipped with a ZPulse Doppler Current Sensor (DCS), a conductivity sensor, an oxygen optode, and two analogue sensors for chlorophyll-a and turbidity (16445). All sensors were pre-calibrated by the manufacturer. Recorded data were internally logged until readout with the SeaGuard Studio software (V1.5.23). Salinity was derived in the SeaGuard Studio software using temperature-dependent, nonlinear seawater conductivity compensation following the Practical Salinity Scale (PSS-78). Subsequent data processing was done using MATLAB (R2024b). Turbidity and chlorophyll-a data were excluded from the final dataset, as the recorded signals show implausible values and did not pass quality-control criteria. Post-processing and quality control included (a) the removal of low tide data, data covering maintenance activities, and data affected by biofouling, (b) the removal of implausible values, c) an outlier detection using the Hampel filter method, and (d) visual checks. Identified outlier were removed and synchronously removed across all associated parameters of the respective sensor.

High resolution aircraft measurements of wind and temperature during the MOSAIC-ACA campaign in 2020

During the MOSAiC-ACA campaign conducted in August/September 2020 in Svalbard meteorological data (temperature, 3 wind components, air pressure) have been measured in high temporal resolution (100 Hz) using instrumentation that was installed at the nosebooms of Polar 5. For each flight the data are given as functions of time and position (including height above ground) along the flight tracks. All flights started and ended in Longyearbyen, Svalbard. Each file represents an entire flight starting well before the first movement of the plane and ending after the final parking position has been reached after landing. The wind measurement is only valid during flight and the full accuracy is only achieved during straight level flight sections. The absolute accuracy of the wind components is 0.2m/s for straight and level flights sections and the relative accuracy of the vertical wind speed is about 0.05m/s for straight and level flight sections. For these sections, which can be obtained on the basis of the given roll and pitch angles of the aircraft, the 100 Hz data can be used to derive turbulent fluxes of momentum and sensible heat. For further informations on the data processing and accuracy of the turbulence measurement refer to Hartmann et al. (2018, doi:10.5194/amt-11-4567-2018).

Continuous water level observations at station BEFmate_I4low, 2020-01 to 2023-04

Data presented here were collected between 2020-01 and 2023-04 at station BEFmate_I4low within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) involving the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were established in the back-barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog (Germany). Groundwater levels at different elevation zones were measured using pressure loggers deployed in dip wells within the experimental islands as well as in the saltmarsh enclosed plots. Measurements were obtained using a DEFI-D Miniature Pressure Recorder (JFE Advantech Co., Ltd., Tokyo; DEFI-D). All devices were pre-calibrated by the manufacturer. Logged data were retrieved in the field using a Hobo Underwater Shuttle (U-DTW-1) and were read out with the DEFI Series software (V1.02), depending on the instrument. Subsequent data processing was done using MATLAB (R2024b). Atmospheric pressure correction for water-level calculations was applied using data from a nearby weather station. Post-processing and quality control included (a) the removal of data covering maintenance activities, (b) an outlier detection, and (c) visual checks. Outliers in water level and temperature time series were detected using a moving-median filter and a 3-sigma criterion, with additional cross-checking against a reference sensor. Identified outliers were removed, and height-corrected water level series were produced to ensure consistency across sensors and years.

Continuous water level observations at station BEFmate_I3low, 2020-01 to 2022-05

Data presented here were collected between 2020-01 and 2022-05 at station BEFmate_I3low within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) involving the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were established in the back-barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog (Germany). Groundwater levels at different elevation zones were measured using pressure loggers deployed in dip wells within the experimental islands as well as in the saltmarsh enclosed plots. Measurements were obtained using a Hobo U20L Water Level Logger (Onset Computer Corporation, Bourne, MA/USA) that was pre-calibrated by the manufacturer. Logged data were retrieved in the field using a Hobo Underwater Shuttle (U-DTW-1) and were read out with the HOBOware Pro (V3.7.28) software. Subsequent data processing was done using MATLAB (R2024b). Atmospheric pressure correction for water-level calculations was applied using data from a nearby weather station. Post-processing and quality control included (a) the removal of data covering maintenance activities, (b) an outlier detection, and (c) visual checks. Outliers in water level and temperature time series were detected using a moving-median filter and a 3-sigma criterion, with additional cross-checking against a reference sensor. Identified outliers were removed, and height-corrected water level series were produced to ensure consistency across sensors and years.

Continuous water level observations at station BEFmate_S10pio, 2020-01 to 2023-09

Data presented here were collected between 2020-01 and 2023-09 at station BEFmate_S10pio within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) involving the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were established in the back-barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog (Germany). Groundwater levels at different elevation zones were measured using pressure loggers deployed in dip wells within the experimental islands as well as in the saltmarsh enclosed plots. Measurements were obtained using Hobo U20L Water Level Loggers (Onset Computer Corporation, Bourne, MA/USA). All devices were pre-calibrated by the manufacturer. Logged data were retrieved in the field using a Hobo Underwater Shuttle (U-DTW-1) and were read out with the HOBOware Pro (V3.7.28) software, Subsequent data processing was done using MATLAB (R2024b). Atmospheric pressure correction for water-level calculations was applied using data from a nearby weather station. Post-processing and quality control included (a) the removal of data covering maintenance activities, (b) an outlier detection, and (c) visual checks. Outliers in water level and temperature time series were detected using a moving-median filter and a 3-sigma criterion, with additional cross-checking against a reference sensor. Identified outliers were removed, and height-corrected water level series were produced to ensure consistency across sensors and years.

Continuous water level observations at station BEFmate_I10upp, 2020-01 to 2023-09

Data presented here were collected between 2020-01 and 2023-09 at station BEFmate_I10upp within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) involving the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were established in the back-barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog (Germany). Groundwater levels at different elevation zones were measured using pressure loggers deployed in dip wells within the experimental islands as well as in the saltmarsh enclosed plots. Measurements were obtained using Hobo U20L Water Level Loggers (Onset Computer Corporation, Bourne, MA/USA). All devices were pre-calibrated by the manufacturer. Logged data were retrieved in the field using a Hobo Underwater Shuttle (U-DTW-1) and were read out with the HOBOware Pro (V3.7.28) software, Subsequent data processing was done using MATLAB (R2024b). Atmospheric pressure correction for water-level calculations was applied using data from a nearby weather station. Post-processing and quality control included (a) the removal of data covering maintenance activities, (b) an outlier detection, and (c) visual checks. Outliers in water level and temperature time series were detected using a moving-median filter and a 3-sigma criterion, with additional cross-checking against a reference sensor. Identified outliers were removed, and height-corrected water level series were produced to ensure consistency across sensors and years.

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