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Origin of the data: Experimental spectroscopic measurements Data Type: experimental measurements, open access supporting information The data are in CSV, DSW and FBSW format. Supporting information are supplied in PDF format. Data generated by instruments: Varian Cary 5E-UV-Vis-NIR spectrophotometer for UV-Vis measurements, Varian Cary Eclipse fluorescence spectrophotomer for fluorescence quenching measurements. Analytical and procedural information: Stern-Volmer fluorescence quenching experiments, UV-Vis measurements and Fluorescent Quantum Yield determination via ferrioxalate actinometry. Definition of variables: Wavelength, Absorbance, Concentration Units of measurement: nanometers (nm), moles-per-litre (mol/l) Abbreviations: File names and data headers use the following abbreviations: FQY refers to Fluorescence Quantum Yield determination experiments Light refers to irradiated samples in the actinometry experiment, as detailed in the procedure in the supporting information. Dark refers to non-irradiated samples in the actinometry experiment, as detailed in the procedure in the supporting information. SVQuench refers to Stern-Volmer quenching experiments RAxx refer to measurements related to allylbenzene. Xx is the amount of quencher in mol/l (05 should be intended as 0.5 mol/l and so on). RTxx refer to measurements related to S-(4-methylphenyl) 4-methylbenzenethiosulfonate. Xx is the amount of quencher in mol/l as above. RExx refer to measurements related to 1,2-dimethoxy-4-(prop-2-en-1-yl)benzene. Xx is the amount of quencher in mol/l as above. RSxx refer to measurements related to styrene. Xx is the amount of quencher in mol/l. RTFxx refer to measurements related to S-(4-fluorophenyl) 4-fluorobenzenethiosulfonate. Xx is the amount of quencher in mol/l as above. MesAcrMe Xx refers to data related to catalyst 9-mesityl-10-methylacridinium. Xx is the amount of catalyst in mol/l as above. DMC for measurements employing dimethylcarbonate as solvent. ACN for measurements employing acetonitrile as solvent. FBSW and DSW data are used by the proprietary software of the Varian spectrometers (CARY WinUV and Cary Eclipse). Information can be found at https://www.agilent.com/en/product/molecular-spectroscopy/uv-vis-uv-vis-nir-spectroscopy/uv-vis-uv-vis-nir-software/cary-winuv-software and https://www.agilent.com/en/product/molecular-spectroscopy/fluorescence-spectroscopy/fluorescence-software/cary-eclipse-software

This file contains the AiNU data used for the article entitled by Physics-based material parameters extraction from perovskite experiments via Bayesian optimization (https://arxiv.org/abs/2402.11101).

As part of global efforts to reduce dependence on carbon-based energy sources there has been a rapid increase in the installation of renewable energy devices. The installation and operation of these devices can result in conflicts with wildlife. In the marine environment, mammals may avoid wind farms that are under construction or operating. Such avoidance may lead to more time spent travelling or displacement from key habitats. A paucity of data on at-sea movements of marine mammals around wind farms limits our understanding of the nature of their potential impacts. Here, we present the results of a telemetry study on harbour seals Phoca vitulina in The Wash, south-east England, an area where wind farms are being constructed using impact pile driving. We investigated whether seals avoid wind farms during operation, construction in its entirety, or during piling activity. The study was carried out using historical telemetry data collected prior to any wind farm development and telemetry data collected in 2012 during the construction of one wind farm and the operation of another. Within an operational wind farm, there was a close-to-significant increase in seal usage compared to prior to wind farm development. However, the wind farm was at the edge of a large area of increased usage, so the presence of the wind farm was unlikely to be the cause. There was no significant displacement during construction as a whole. However, during piling, seal usage (abundance) was significantly reduced up to 25 km from the piling activity; within 25 km of the centre of the wind farm, there was a 19 to 83% (95% confidence intervals) decrease in usage compared to during breaks in piling, equating to a mean estimated displacement of 440 individuals. This amounts to significant displacement starting from predicted received levels of between 166 and 178 dB re 1 μPa(p-p). Displacement was limited to piling activity; within 2 h of cessation of pile driving, seals were distributed as per the non-piling scenario. Synthesis and applications. Our spatial and temporal quantification of avoidance of wind farms by harbour seals is critical to reduce uncertainty and increase robustness in environmental impact assessments of future developments. Specifically, the results will allow policymakers to produce industry guidance on the likelihood of displacement of seals in response to pile driving; the relationship between sound levels and avoidance rates; and the duration of any avoidance, thus allowing far more accurate environmental assessments to be carried out during the consenting process. Further, our results can be used to inform mitigation strategies in terms of both the sound levels likely to cause displacement and what temporal patterns of piling would minimize the magnitude of the energetic impacts of displacement. Wash_diagWash_diag.xlsx is the historic location data (pre windfarm construction) for the 19 individuals used in the analysis described in Russell et al.

Here, we investigate how stochasticity and age-dependence in energy dynamics influence maternal allocation in iteroparous females. We develop a state-dependent model to calculate the optimal maternal allocation strategy with respect to maternal age and energy reserves, focusing on allocation in a single offspring at a time. We introduce stochasticity in energetic costs– in terms of the amount of energy required to forage successfully and individual differences in metabolism – and in feeding success. We systematically assess how allocation is influenced by age-dependence in energetic costs, feeding success, energy intake per successful feeding attempt, and environmentally-driven mortality. First, using stochastic dynamic programming, we calculate the optimal amount of reserves M that mothers allocate to each offspring depending on their own reserves R and age A. The optimal life history strategy is then the set of allocation decisions M(R, A) over the whole lifespan which maximizes the total reproductive success of distant descendants. Second, we simulated the life histories of 1000 mothers following the optimisation strategy and the reserves at the start of adulthood R1, the distribution of which was determined, the distribution of which was determined using an iterative procedure as described . For each individual, we calculated maternal allocation Mt, maternal reserves Rt, and relative allocation Mt⁄Rt at each time period t. The relative allocation helps us to understand how resources are partitioned between mother and offspring. Third, we consider how the optimal strategy varies when there is age-dependence in resource acquisition, energetic costs and survival. Specifically, we include varying scenarios with an age-dependent increase or a decrease with age in energetic costs (c_t), feeding success (q_t), energy intake per successful feeding attempt (y_t), and environmentally-driven extrinsic mortality rate (d_t) (Table 2). We consider the age-dependence of parameters one at a time or in pairs, altering the slope, intercept, or asymptote of the age-dependence (linear or asymptotic function). Our aim is to identify whether the observed reproductive senescence can arise from optimal maternal allocation. As such, we do not impose a decline in selection in later life as all offspring are equally valuable at all ages (for a given maternal allocation), and there are no mutations. For each scenario, we run the backward iteration process with these age-dependent functions, obtain the allocation strategy, and simulate the life history of 1000 individuals based on the novel strategy. We then fit quadratic and linear models to the reproduction of these 1000 individuals using the lme function, nlme package in R. For these models, the response variable is the maternal allocation Mt and explanatory variables are the time period t and t2 (for the quadratic fit only), with individual identity as a random term. We use likelihood ratio tests to compare linear and quadratic models using the anova function (package nlme) with the maximum-likelihood method. If the comparison is significant (p-value <0.05), we considered the quadratic model to have a better fit, otherwise the linear model is considered more parsimonious. We were particularly interested in identifying scenarios where the fit was quadratic with a negative quadratic term. For each scenario, the pseudo R2 conditional value (proportion of variance explained by the fixed and random terms, accounting for individual identity) is calculated to assess the goodness-of-fit of the lme model, on a scale from 0 to 1, using the “r.squared” function, package gabtool. All calculations and coding are done in R. Iteroparous parents face a trade-off between allocating current resources to reproduction versus maximizing survival to produce further offspring. Optimal allocation varies across age, and follows a hump-shaped pattern across diverse taxa, including mammals, birds and invertebrates. This non-linear allocation pattern lacks a general theoretical explanation, potentially because most studies focus on offspring number rather than quality and do not incorporate uncertainty or age-dependence in energy intake or costs. Here, we develop a life history model of maternal allocation in iteroparous animals. We identify the optimal allocation strategy in response to stochasticity when energetic costs, feeding success, energy intake, and environmentally-driven mortality risk are age-dependent. As a case study, we use tsetse, a viviparous insect that produces one offspring per reproductive attempt and relies on an uncertain food supply of vertebrate blood. Diverse scenarios generate a hump-shaped allocation: when energetic costs and energy intake increase with age; and also when energy intake decreases, and energetic costs increase or decrease. Feeding success and mortality risk have little influence on age-dependence in allocation. We conclude that ubiquitous evidence for age-dependence in these influential traits can explain the prevalence of non-linear maternal allocation across diverse taxonomic groups.

Herbivory assessments were made at the plant community and species levels. We focused on three plant species with a widespread occurrence across the temperature gradient: cuckooflower (Cardamine pratensis, Linnaeus), common mouse-ear (Cerastium fontanum, Baumgerten), and marsh violet (Viola palustris, Linnaeus). For assessments of invertebrate herbivory at the species level, thirty individuals per species of C. pratensis, C. fontanum, and V. palustris were marked in each of ten plots, using a stratified random sampling method where individuals were randomly selected, but the full range of within-plot soil temperatures was represented. For assessments of invertebrate herbivory at the community level, five 50 × 50 cm quadrats were marked at random points in eight of the plots that best captured the full temperature gradient. The community-level herbivory assessment was conducted on 19th June. The number of damaged plants was recorded out of 100 random individuals, selected using a 10 × 10 grid within each 50 × 50 cm quadrat. For the species-level herbivory assessment, individual marked plants were surveyed for signs of invertebrate herbivory every two weeks from 30th May to 2nd July, generating three time-points per species. At each survey, all marked individuals for each species were assessed within a 48-hour period. Plants were recorded as damaged or not damaged by invertebrate herbivores at each time-point. Further details of how phenological stage of development, vegetation community composition, soil temperature, moisture, pH, nitrate, ammonium, and phosphate were recorded are provided in the supporting documentation. This is a dataset of environmental data, vegetation cover, and community- and species-level invertebrate herbivory, sampled at 14 experimental soil plots in the Hengill geothermal valley, Iceland, from May to July 2017. The plots span a temperature gradient of 5-35 °C on average over the sampling period, yet they occur within 1 km of each other and have similar soil moisture, pH, nitrate, ammonium, and phosphate.

We performed systematic mapping of EPC data applications by time, geographical spread, data features & auxiliary data used, problem domains addressed and complexity of employed data analysis. This mapping work was intended to answer the following questions: Q1. Which research studies have used EPC data (hereafter “applications”)? Q2. What input data were used by the EPC data applications? Q3. Which problem domains were addressed by the EPC data applications? Q4. How did the EPC data applications change within the study period? Purpose: To understand how the energy performance certificates (EPC) data has been used since introduction of the first national EPC registers. Kartläggning av tillämpningar av EPC data. En mer detaljerad beskrivning är tillgängligt på den engelska katalogsidan: https://snd.gu.se/en/catalogue/study/SND1066

Climatologie mensuelle de la surface de la mer Chlorophylle-a concentration (en mg.m^-3 (log10) à résolution de 4 km) dérivée du capteur SeaWiFS (Satellite télédétection de la couleur de l’océan): La chlorophylle est un pigment photosynthétique couramment présent dans toutes les espèces de phytoplancton. Il est utilisé comme indicateur de la biomasse du phytoplancton. La concentration de chlorophylle est un produit standard à partir de capteurs optiques satellitaires, habituellement récupérés à partir d’algorithmes empiriques utilisant des rapports de réflectance à deux bandes d’onde ou plus. Μηνιαία κλιματολογική επιφάνεια της θάλασσας χλωροφύλλη — μια συγκέντρωση (σε mg.m^-3 (log10) σε ανάλυση 4 km) που προέρχεται από τον αισθητήρα SeaWiFS (Δορυφορικά δεδομένα χρωμάτων του Ωκεανού με τηλεπισκόπηση): Η χλωροφύλλη είναι μια φωτοσυνθετική χρωστική ουσία που υπάρχει συνήθως σε όλα τα είδη φυτοπλαγκτού. Χρησιμοποιείται ως υποκατάστατο της βιομάζας φυτοπλαγκτού. Η συγκέντρωση χλωροφύλλης είναι ένα πρότυπο προϊόν από δορυφορικούς οπτικούς αισθητήρες, που συνήθως ανακτώνται από εμπειρικούς αλγόριθμους χρησιμοποιώντας αναλογίες ανάκλασης σε δύο ή περισσότερες ζώνες κύματος. Monatliche Klimatologie der Meeresoberfläche Chlorophyll-eine Konzentration (in mg.m^-3 (log10) bei 4 km Auflösung) abgeleitet vom SeaWiFS-Sensor (Satellite Remote Sensing Ocean Color Data): Chlorophyll ist ein photosynthetisches Pigment, das häufig in allen Phytoplanktonarten vorhanden ist. Es wird als Proxy für Phytoplanktonbiomasse verwendet. Chlorophyll-Konzentration ist ein Standardprodukt von satellitenbasierten optischen Sensoren, die normalerweise aus empirischen Algorithmen unter Verwendung von Reflexionsverhältnissen bei zwei oder mehr Wellenbändern abgerufen werden. Kull xahar il-klimatoloġija wiċċ il-baħar Chlorophyll-a konċentrazzjoni (f’mg.m^-3 (log10) b’riżoluzzjoni ta’ 4 km) derivata mis-sensur SeaWiFS (data tal-kulur tal-Oċean tat-telerilevament bis-satellita): Il-klorofilla hija pigment fotosintetiku preżenti b’mod komuni fl-ispeċijiet kollha tal-fitoplankton. Jintuża bħala indikatur għall-bijomassa tal-fitoplankton. Il-konċentrazzjoni tal-klorofilla hija prodott standard minn sensuri ottiċi bbażati fuq is-satellita, normalment miksuba minn algoritmi empiriċi bl-użu ta’ proporzjonijiet ta’ riflessjoni f’żewġ wavebands jew aktar. Tiúchan Clíomeolaíochta míosúil dromchla farraige Chlorophyll-a (i mg.m^-3 (log10) ag taifeach 4 km) a dhíorthaítear ón mbraiteoir SeaWiFS (sonraí maidir le dath an Aigéin chianbhraiteachta satailít): Is lí fótaisintéiseach é clóraifill atá i láthair go coitianta i ngach speiceas fíteaplanctóin. Úsáidtear é mar sheachvótálaí le haghaidh bithmhais fíteaplanctóin. Is táirge caighdeánach é tiúchan clóraifille ó bhraiteoirí optúla bunaithe ar shatailít, a fhaightear ó algartaim eimpíreacha de ghnáth agus cóimheasa frithchaite á n-úsáid ag dhá thonnbhanda nó níos mó. Climatología mensual de Chlorophyll-una concentración (en mg.m^-3 (log10) a una resolución de 4 km) derivada del sensor SeaWiFS (datos de detección remota por satélite del color del océano): La clorofila es un pigmento fotosintético comúnmente presente en todas las especies de fitoplancton. Se utiliza como un indicador de la biomasa de fitoplancton. La concentración de clorofila es un producto estándar de sensores ópticos basados ​​en satélites, generalmente recuperado de algoritmos empíricos utilizando relaciones de reflectancia en dos o más bandas de onda. Щомісячна кліматологія морської поверхні Хлорофіл-а концентрація (в мг.м^-3 (log10) при роздільній здатності 4 км), отримана з датчика SeaWiFS (дані кольору супутникового дистанційного зондування океану): Хлорофіл - це фотосинтетичний пігмент, який зазвичай присутній у всіх видах фітопланктону. Використовується як проксі для біомаси фітопланктону. Концентрація хлорофілу є стандартним продуктом з супутникових оптичних датчиків, зазвичай отриманих з емпіричних алгоритмів з використанням коефіцієнтів відбиття на двох або більше хвильових діапазонах. Месечна климатология на морската повърхност Хлорофил — концентрация (в mg.m^-3 (log10) при резолюция 4 km), получена от сензора SeaWiFS (Satellite дистанционно наблюдение на данните за цвета на океана): Хлорофилът е фотосинтетичен пигмент, обикновено присъстващ във всички видове фитопланктон. Използва се като прокси за фитопланктоновата биомаса. Концентрацията на хлорофил е стандартен продукт от сателитни оптични сензори, обикновено извличани от емпирични алгоритми, използващи съотношения на отразяване при две или повече ленти на вълната. Miesięczny klimatologia powierzchni morza Chlorophyll – stężenie (w mg.m^-3 (log10) w rozdzielczości 4 km) pochodzące z czujnika SeaWiFS (dane satelitarnej teledetekcji barwy oceanu): Chlorofil jest pigmentem fotosyntetycznym powszechnie występującym we wszystkich gatunkach fitoplanktonu. Jest stosowany jako zastępca biomasy fitoplanktonu. Stężenie chlorofilu jest standardowym produktem z satelitarnych czujników optycznych, zwykle pobieranych z algorytmów empirycznych przy użyciu współczynników odbicia w dwóch lub więcej pasmach fal. Climatologie lunară la suprafața mării clorofilă – o concentrație (în mg.m^-3 (log10) la o rezoluție de 4 km) derivată din senzorul SeaWiFS (Satelit teledetecție date de culoare ocean): Clorofila este un pigment fotosintetic prezent frecvent în toate speciile de fitoplancton. Este folosit ca un indicator pentru biomasa fitoplanctonă. Concentrația de clorofilă este un produs standard al senzorilor optici bazați pe sateliți, de obicei extrași din algoritmi empirici folosind raporturi de reflexie la două sau mai multe benzi de undă.

Productividad del agua (producción primaria, pp en gCarbon.m-2.day-1 a 4 km de resolución): La producción primaria representa la cantidad de carbono orgánico producido a través de la fotosíntesis del fitoplancton. Es un elemento crítico del presupuesto de carbono de la Tierra y de la red alimentaria marina. La producción primaria integrada en profundidad se modela a partir de la concentración de biomasa de fitoplancton basada en satélites y el PAR. Waterproductiviteit (primaire productie, pp in gCarbon.m-2.day-1 bij een resolutie van 4 km): De primaire productie vertegenwoordigt de hoeveelheid organische koolstof die wordt geproduceerd door middel van fytoplanktonfotosynthese. Het is een cruciaal element van het koolstofbudget van de aarde en het mariene voedselweb. De diepgeïntegreerde primaire productie wordt gemodelleerd van de satellietgebaseerde fytoplanktonbiomassaconcentratie en PAR. Il-produttività tal-ilma (produzzjoni primarja, pp f’gCarbon.m-2.day-1 b’riżoluzzjoni ta’ 4 km): Il-produzzjoni primarja tirrappreżenta l-ammont ta’ karbonju organiku prodott permezz tal-fotosinteżi tal-fitoplankton. Huwa element kritiku tal-baġit tal-karbonju tad-Dinja u tax-xibka tal-ikel tal-baħar. Il-produzzjoni primarja integrata fil-fond hija mmudellata mill-konċentrazzjoni tal-bijomassa tal-fitoplankton ibbażata fuq is-satellita u PAR. Productivité de l’eau (production primaire, pp dans gCarbon.m-2.day-1 à une résolution de 4 km): La production primaire représente la quantité de carbone organique produite par la photosynthèse phytoplancton. C’est un élément essentiel du budget carbone de la Terre et du réseau alimentaire marin. La production primaire intégrée en profondeur est modélisée à partir de la concentration de biomasse du phytoplancton par satellite et du PAR. Производителност на водата (първично производство, pp в gCarbon.m-2.day-1 при разделителна способност 4 km): Първичното производство представлява количеството органичен въглерод, произведен чрез фотосинтеза на фитопланктона. Това е критичен елемент от въглеродния бюджет на Земята и морската хранителна мрежа. Дълбочинно интегрирано първично производство е моделирано от сателитната концентрация на фитопланктоновата биомаса и PAR. Παραγωγικότητα του νερού (πρωτογενής παραγωγή, pp σε gCarbon.m-2.ημέρα-1 σε ανάλυση 4 km): Η πρωτογενής παραγωγή αντιπροσωπεύει την ποσότητα οργανικού άνθρακα που παράγεται μέσω φωτοσύνθεσης φυτοπλαγκτού. Είναι ένα κρίσιμο στοιχείο του προϋπολογισμού άνθρακα της Γης και του θαλάσσιου ιστού τροφίμων. Η ενσωματωμένη σε βάθος πρωτογενής παραγωγή διαμορφώνεται με βάση τη συγκέντρωση βιομάζας φυτοπλαγκτού μέσω δορυφόρου και την PAR. Produttività dell'acqua (produzione primaria, pp in gCarbon.m-2.day-1 a risoluzione 4 km): La produzione primaria rappresenta la quantità di carbonio organico prodotto attraverso la fotosintesi del fitoplancton. È un elemento critico del bilancio del carbonio della Terra e della rete alimentare marina. La produzione primaria integrata in profondità è modellata dalla concentrazione satellitare di biomassa di fitoplancton e PAR. Wasserproduktivität (Primärproduktion, pp in gCarbon.m-2.day-1 bei 4 km Auflösung): Die Primärproduktion repräsentiert die Menge an organischem Kohlenstoff, der durch Phytoplankton-Photosynthese erzeugt wird. Es ist ein kritisches Element des CO2-Budgets der Erde und des marinen Nahrungsnetzes. Die tiefenintegrierte Primärproduktion wird aus der satellitengestützten Phytoplankton-Biomasse-Konzentration und PAR modelliert. Produtividade da água (produção primária, pp em gCarbon.m-2.day-1 com resolução de 4 km): A produção primária representa a quantidade de carbono orgânico produzido através da fotossíntese de fitoplâncton. É um elemento crítico do orçamento de carbono da Terra e da rede alimentar marinha. A produção primária integrada em profundidade é modelada a partir da concentração de biomassa de fitoplâncton por satélite e PAR. Продуктивність води (первинне виробництво, pp в gCarbon.m-2.day-1 при роздільній здатності 4км): Первинне виробництво являє собою кількість органічного вуглецю, отриманого шляхом фотосинтезу фітопланктону. Це важливий елемент вуглецевого бюджету Землі і морської продовольчої мережі. Глибина інтегрованого первинного виробництва моделюється з концентрації біомаси на основі супутника фітопланктону та PAR.

Średnia miesięczna temperatura powierzchni morza (w stopniach C przy rozdzielczości 4 km) pochodząca z czujnika PATHFINDER (dane satelitarnej teledetekcji barwy oceanu): Temperatura powierzchni morza to temperatura wody blisko powierzchni morza. SST jest standardowym produktem z satelitarnych czujników termicznych na podczerwień oraz czujników optycznych uzupełnionych pasmami podczerwieni. Średnia miesięczna temperatura powierzchni morza (w stopniach C przy rozdzielczości 4 km) pochodząca z czujnika PATHFINDER (dane satelitarnej teledetekcji barwy oceanu): Temperatura powierzchni morza to temperatura wody blisko powierzchni morza. SST jest standardowym produktem z satelitarnych czujników termicznych na podczerwień oraz czujników optycznych uzupełnionych pasmami podczerwieni. Température moyenne mensuelle de la surface de la mer (en degrés C à une résolution de 4 km) dérivée du capteur PATHFINDER (Télédétection satellite de la couleur de l’océan): La température de la surface de la mer est la température de l’eau près de la surface de la mer. SST est un produit standard à partir de capteurs infrarouges thermiques satellitaires, et de capteurs optiques complétés par des bandes infrarouges. Μηνιαία μέση θερμοκρασία επιφάνειας της θάλασσας (σε βαθμό-C σε ανάλυση 4 km) προερχόμενη από τον αισθητήρα PATHFINDER (Δορυφορικά δεδομένα χρώματος Ωκεανού τηλεπισκόπησης): Η θερμοκρασία επιφάνειας της θάλασσας είναι η θερμοκρασία του νερού κοντά στην επιφάνεια της θάλασσας. Το SST είναι ένα πρότυπο προϊόν από δορυφορικούς θερμικούς υπέρυθρους αισθητήρες και οπτικούς αισθητήρες που συμπληρώνονται με υπέρυθρες ζώνες. Temperatura media mensual de la superficie del mar (en grado C a una resolución de 4 km) derivada del sensor PATHFINDER (datos de color del océano de teledetección por satélite): La temperatura de la superficie del mar es la temperatura del agua cerca de la superficie del mar. SST es un producto estándar de sensores infrarrojos térmicos basados en satélites, y sensores ópticos complementados con bandas infrarrojas. Месечна средна температура на морската повърхност (в градус-C при разделителна способност 4 km), получена от сензора PATHFINDER (Сателитна дистанционна сензорна информация за цветовете на океана): Температурата на морската повърхност е температурата на водата в близост до морската повърхност. SST е стандартен продукт от сателитни термоинфрачервени сензори и оптични сензори, допълнени с инфрачервени ленти. Meánteocht mhíosúil dhromchla na farraige (i gcéim-C ag taifeach 4 km) a dhíorthaítear ón mbraiteoir PATHFINDER (sonraí cianbhraiteachta satailíte maidir le dath an Aigéin): Is é teocht dhromchla na farraige teocht an uisce gar do dhromchla na farraige. Is táirge caighdeánach é SST ó bhraiteoirí infridhearg teirmeacha atá bunaithe ar shatailítí, agus braiteoirí optúla arna gcomhlánú le bandaí infridhearg. Mėnesio vidutinė jūros paviršiaus temperatūra (C laipsniais esant 4 km skyrai), gauta iš PATHFINDER jutiklio (palydovinio nuotolinio stebėjimo vandenyno spalvų duomenys): Jūros paviršiaus temperatūra yra vandens temperatūra arti jūros paviršiaus. SST yra standartinis produktas iš palydovinių šiluminių infraraudonųjų spindulių jutiklių ir optinių jutiklių, papildytų infraraudonųjų spindulių juostomis. It-temperatura medja fix-xahar tas-superfiċje tal-baħar (fi grad-C b’riżoluzzjoni ta’ 4 km) derivata mis-sensur PATHFINDER (data tal-kulur tal-Oċean tat-telerilevament bis-satellita): It-temperatura tal-wiċċ tal-baħar hija t-temperatura tal-ilma qrib wiċċ il-baħar. L-SST huwa prodott standard minn sensuri infraħomor termali bbażati fuq is-satellita, u sensuri ottiċi kumplimentati b’faxex infra-aħmar. Temperatura media mensile della superficie del mare (in grado C a risoluzione di 4 km) derivata dal sensore PATHFINDER (Satellite telerilevamento dati del colore dell'oceano): La temperatura superficiale del mare è la temperatura dell'acqua vicino alla superficie del mare. SST è un prodotto standard da sensori termici a infrarossi satellitari e sensori ottici integrati con bande a infrarossi. Monatliche mittlere Meeresoberflächentemperatur (in Grad-C bei 4 km Auflösung), abgeleitet vom PATHFINDER-Sensor (Satellitenfernerkundung Ozeanfarbdaten): Meeresoberflächentemperatur ist die Temperatur des Wassers in der Nähe der Meeresoberfläche. SST ist ein Standardprodukt von satellitengestützten thermischen Infrarotsensoren und optischen Sensoren, die durch Infrarotbänder ergänzt werden.