scholarly journals Comparison of Near-Surface Air Temperatures and MODIS Ice-Surface Temperatures at Summit, Greenland (2008–13)

2014 ◽  
Vol 53 (9) ◽  
pp. 2171-2180 ◽  
Author(s):  
Christopher A. Shuman ◽  
Dorothy K. Hall ◽  
Nicolo E. DiGirolamo ◽  
Thomas K. Mefford ◽  
Michael J. Schnaubelt
Keyword(s):  
Surface Temperature ◽  
Temperature Data ◽  
Quality Data ◽  
High Temporal Resolution ◽  
Near Surface ◽  
Annual Cycles ◽  
In Situ Data ◽  
Ice Surface ◽  
Surface Temperatures

AbstractThe stability of the Moderate Resolution Imaging Spectroradiometer (MODIS) ice-surface temperature (IST) product from Terra was investigated for use as a climate-quality data record. The availability of climate-quality air temperature data TA from a NOAA observatory at Greenland’s Summit Station has enabled this high-temporal-resolution study of MODIS ISTs. During a >5-yr period (July 2008–August 2013), more than 2500 IST values were compared with ±3-min-average TA values from NOAA’s primary 2-m temperature sensor. This enabled an expected small offset between air and ice-sheet surface temperatures (TA > IST) to be investigated over multiple annual cycles. The principal findings of this study show 1) that IST values are slightly colder than the TA values near freezing but that this offset increases as temperature decreases and 2) that there is a pattern in IST–TA differences as the solar zenith angle (SoZA) varies annually. This latter result largely explains the progressive offset from the in situ data at colder temperatures but also indicates that the MODIS cloud mask is less accurate approaching and during the polar night. The consistency of the results over each year in this study indicates that MODIS provides a platform for remotely deriving surface temperature data, with the resulting IST data being most compatible with in situ TA data when the sky is clear and the SoZA is less than ~85°. The ongoing development of the IST dataset should benefit from improved cloud filtering as well as algorithm modifications to account for the progressive offset from TA at colder temperatures.

The limitations of in situ data for validating satellite-derived spatio-temporal ocean products.

2021 ◽  
Author(s):  
Rory Scarrott ◽  
Fiona Cawkwell ◽  
Mark Jessopp ◽  
Caroline Cusack
Keyword(s):  
Surface Temperature ◽  
Surface Heterogeneity ◽  
Ocean Surface ◽  
Bias Reduction ◽  
Data Validation ◽  
Near Surface ◽  
In Situ Data ◽  
Spatio Temporal ◽  
Image Pixels

<p>The Ocean-surface Heterogeneity MApping (OHMA) algorithm is an objective, replicable approach to map the spatio-temporal heterogeneity of ocean surface waters. It is used to processes hypertemporal, satellite-derived data and produces a single-image surface heterogeneity (SH) dataset for the selected parameter of interest. The product separates regions of dissimilar temporal characteristics. Data validation is challenging because it requires In-situ observations at spatial and temporal resolutions comparable to the hyper-temporal inputs. Validating this spatio-temporal product highlighted the need to optimise existing vessel-based data collection efforts, to maximise exploitation of the rapidly-growing hyper-temporal data resource.</p><p>For this study, the SH was created using hyper-temporal 1km resolution satellite derived Sea Surface Temperature (SST) data acquired in 2011. Underway ship observations of near surface temperature collected on multiple scientific surveys off the Irish coast in 2011 were used to validate the results. The most suitable underway ship SST data were identified in ocean areas sampled multiple times and with representative measurements across all seasons.</p><p>A 3-stage bias reduction approach was applied to identify suitable ocean areas. The first bias reduction addressed temporal bias, i.e., the temporal spread of available In-situ ship transect data across each satellite image pixel. Only pixels for which In-situ data were obtained at least once in each season were selected; resulting in 14 SH image pixels deemed suitable out of a total of 3,677 SH image pixels with available In-situ data. The second bias reduction addressed spatial bias, to reduce the influence of over-sampled areas in an image pixel with a sub-pixel approach. Statistical dispersion measures and statistical shape measures were calculated for each of the sets of sub-pixel values. This gave heterogeneity estimates for each cruise transit of a pixel area. The third bias reduction addressed bias of temporally oversampled seasons. For each transit-derived heterogeneity measure, the values within each season were averaged, before the annual average value was derived across all four seasons in 2011.</p><p>Significant associations were identified between satellite SST-derived SH values, and In-situ heterogeneity measures related to shape; absolute skewness (Spearman’s Rank, n=14, ρ[12]= +0.5755, P<0.05), and kurtosis (Spearman’s Rank, n=14, ρ[12] = 0.5446, P < 0.05). These are a consequence of (i) locally-extreme measurements, and/or (ii) increased presence of sharp transitions detected spatially by In-situ data. However, the number and location of suitable In-situ validation sites precluded a robust validation of the SH dataset (14 pixels located in Irish waters, for a dataset spanning the North Atlantic). This requires more targeted data. The approach would have benefited from more samples over the winter season, which would have enabled more offshore validation sites to be incorporated into the analysis. This is a challenge that faces satellite product developers, who want to deliver spatio-temporal information to new markets. There is a significant opportunity for dedicated, transit-measured (e.g. Ferry box data), validation sites to be established. These could potentially synergise with key nodes in global shipping routes to maximise data collected by vessels of opportunity, and ensure consistent data are collected over the same area at regular intervals.</p>

Evaluating the potential of MODIS-LST for monitoring ground surface temperatures in the Maritime Antarctic (Barton Peninsula, King George Island, Antarctic). 

2021 ◽  
Author(s):  
Alejandro Corbea-Pérez ◽  
Gonçalo Vieira ◽  
Carmen Recondo ◽  
Joana Baptista ◽  
Javier F.Calleja ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Ground Temperature ◽  
King George Island ◽  
Maritime Antarctic ◽  
In Situ Data ◽  
Surface Temperatures ◽  
Barton Peninsula

<p>Land surface temperature is an important factor for permafrost modelling as well as for understanding the dynamics of Antarctic terrestrial ecosystems (Bockheim et al. 2008). In the South Shetland Islands the distribution of permafrost is complex (Vieira et al. 2010) and the use of remote sensing data is essential since the installation and maintenance of an extensive network of ground-based stations are impossible. Therefore, it is important to evaluate the applicability of satellites and sensors by comparing data with in-situ observations. In this work, we present the results from the analysis of land surface temperatures from Barton Peninsula, an ice-free area in King George Island (South Shetlands). We have studied the period from March 1, 2019 to January 31, 2020 using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and in-situ data from 6 ground temperature loggers. MOD11A1 and MYD11A1 products, from TERRA and AQUA satellites, respectively, were used, following the application of MODIS quality filters. Given the scarce number of high-quality data as defined by MODIS, all average LST with error ≤ 2K were included. Dates with surface temperature below -20ºC, which are rare in the study area, and dates when the difference between MODIS and in-situ data exceeded 10ºC were also examined. In both cases, those days on which MOD09GA/MYD09GA products showed cloud cover were eliminated. Eight in-situ ground temperature measurements per day were available, from which the one nearest to the time of satellite overpass was selected for comparison with MODIS-LST. The results obtained show a better correlation with daytime data than with nighttime data. Specifically, the best results are obtained with daytime data from AQUA (R<sup>2</sup> between 0.55 and 0.81). With daytime data, correlation between MODIS-LST and in-situ data was verified with relative humidity (RH) values provided by King Sejong weather station, located in the study area. When RH is lower, the correlation between LST and in-situ data improves: we obtained correlation coefficients between 0.6 - 0.7 for TERRA data and 0.8 - 0.9 for AQUA data with RH values lower than 80%. The results suggest that MODIS can be used for temperature estimation in the ice-free areas of the Maritime Antarctic.</p><p>References:</p><p>Bockheim, J. G., Campbell, I. B., Guglielmin, M., and López- Martınez, J.: Distribution of permafrost types and buried ice in ice free areas of Antarctica, in: 9th International Conference on Permafrost, 28 June–3 July 2008, Proceedings, University of Alaska Press, Fairbanks, USA, 2008, 125–130.</p><p>Vieira, G.; Bockheim, J.; Guglielmin, M.; Balks, M.; Abramov, A. A.; Boelhouwers, J.; Cannone, N.; Ganzert, L.; Gilichinsky, D. A.; Goryachkin, S.; López-Martínez, J.; Meiklejohn, I.; Raffi, R.; Ramos, M.; Schaefer, C.; Serrano, E.; Simas, F.; Sletten, R.; Wagner, D. Thermal State of Permafrost and Active-layer Monitoring in the Antarctic: Advances During the International Polar Year 2007-2009. Permafr. Periglac. Process. 2010, 21, 182–197.</p><p> </p><p>Acknowledgements</p><p>This work was made possible by an internship at the IGOT, University of Lisbon, Portugal, funded by the Principality of Asturias (code EB20-16).</p><p> </p>

scholarly journals Arctic surface temperatures from Metop AVHRR compared to in situ ocean and land data

2012 ◽  
Vol 9 (2) ◽  
pp. 1009-1043 ◽  
Author(s):  
G. Dybkjær ◽  
R. Tonboe ◽  
J. Høyer
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Temperature Data ◽  
Coupled Systems ◽  
The Arctic ◽  
In Situ Observation ◽  
Clear Sky ◽  
Ice Surface ◽  
Ice Surface Temperature

Abstract. The ice surface temperature (IST) is an important boundary condition for both atmospheric and ocean and sea ice models and for coupled systems. An operational ice surface temperature product using satellite Metop AVHRR infra-red data was developed for MyOcean. The IST can be mapped in clear sky regions using a split window algorithm specially tuned for sea ice. Clear sky conditions are prevailing during spring in the Arctic while persistent cloud cover limits data coverage during summer. The cloud covered regions are detected using the EUMETSAT cloud mask. The Metop IST compares to 2 m temperature at the Greenland ice cap Summit within STD error of 3.14 °C and to Arctic drifting buoy temperature data within STD error of 3.69 °C. A case study reveal that the in situ radiometer data versus satellite IST STD error can be much lower (0.73 °C) and that the different in situ measures complicates the validation. Differences and variability between Metop IST and in situ data are analysed and discussed. An inter-comparison of Metop IST, numerical weather prediction temperatures and in situ observation indicates large biases between the different quantities. Because of the scarcity of conventional surface temperature or surface air temperature data in the Arctic the satellite IST data with its relatively good coverage can potentially add valuable information to model analysis for the Arctic atmosphere.

Analyzing Near Surface Temperature Inversions Across the Greenland Ice Sheet Using In-situ, Remote Sensing, and Reanalysis Data

2020 ◽  
Author(s):  
Alden Adolph ◽  
Wesley Brown ◽  
Karina Zikan ◽  
Robert Fausto
Keyword(s):  
Surface Temperature ◽  
Energy Exchange ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Near Surface ◽  
Surface Temperatures ◽  
Air Temperatures ◽  
Exchange Processes ◽  
Temperature Inversions

<p>As Arctic temperatures have increased, the Greenland Ice Sheet has exhibited a negative mass balance, with a substantial and increasing fraction of mass loss due to surface melt. Understanding surface energy exchange processes in Greenland is critical for our ability to predict changes in mass balance. In-situ and remotely sensed surface temperatures are useful for monitoring trends, melt events, and surface energy balance processes, but these observations are complicated by the fact that surface temperatures and near surface air temperatures can significantly differ due to the presence of inversions that exist across the Arctic. Our previous work shows that even in the summer, very near surface inversions are present between the 2m air and surface temperatures a majority of the time at Summit, Greenland. In this study, we expand upon these results and combine a variety of data sources to quantify differences between surface snow/ice temperatures and 2m air temperatures across the Greenland Ice Sheet and investigate controls on the magnitude of these near surface temperature inversions. In-situ temperatures, wind speed, specific humidity, and albedo data are provided from automatic weather stations operated by the Programme for Monitoring of the Greenland Ice Sheet (PROMICE). We use the Clouds and the Earth's Radiant Energy System (CERES) cloud area fraction data to analyze effects of cloud presence on near surface temperature gradients. The in-situ temperatures are compared to Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) and Moderate Resolution Imaging Spectrometer (MODIS) ice surface temperature data to extend findings across the ice sheet. Using PROMICE in-situ data from 2015, we find that these 2m temperature inversions are present 77% of the time, with a median strength of 1.7°C. The data confirm that the presence of clouds weakens inversions. Initial results indicate a RMSE of 3.9°C between MERRA-2 and PROMICE 2m air temperature, and a RMSE of 5.6°C between the two datasets for surface temperature. Improved understanding of controls on near surface inversions is important for use of remotely sensed snow surface temperatures and for modeling of surface mass and energy exchange processes.</p>

scholarly journals Near-surface thermal stratification during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures

2017 ◽  
Author(s):  
Alden C. Adolph ◽  
Mary R. Albert ◽  
Dorothy K. Hall
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Skin Temperature ◽  
Air Temperature ◽  
The Arctic ◽  
Cold Bias ◽  
Near Surface ◽  
Surface Temperatures ◽  
In Situ Ir

Abstract. As rapid warming of the Arctic occurs, it is imperative that climate indicators such as temperature be monitored over large areas to understand and predict the effects of climate changes. Temperatures are traditionally tracked using in situ 2 m air temperatures, but in remote locations where few ground-based measurements exist, such as on the Greenland Ice Sheet, temperatures over large areas are assessed using remote sensing techniques. Because of the presence of surface-based temperature inversions in ice-covered areas, differences between 2 m air temperature and the temperature of the actual snow surface (referred to as skin temperature) can be significant and are particularly relevant when considering validation and application of remote sensing temperature data. We present results from a field campaign extending from 8 June through 18 July 2015, near Summit Station in Greenland to study surface temperature using the following measurements: skin temperature measured by an infrared (IR) sensor, thermochrons, and thermocouples; 2 m air temperature measured by a NOAA meteorological station; and a MODerate-resolution Imaging Spectroradiometer (MODIS) surface temperature product. Our data indicate that 2 m air temperature is often significantly higher than snow skin temperature measured in-situ, and this finding may account for apparent biases in previous surface temperature studies of MODIS products that used 2 m air temperature for validation. This inversion is present during summer months when incoming solar radiation and wind speed are both low. As compared to our in-situ IR skin temperature measurements, after additional cloud masking, the MOD/MYD11 Collection 6 surface-temperature standard product has an RMSE of 1.0 °C, spanning a range of temperatures from −35 °C to −5 °C. For our study area and time series, MODIS surface temperature products agree with skin surface temperatures better than previous studies indicated, especially at temperatures below −20 °C where other studies found a significant cold bias. The apparent cold bias present in others’ comparison of 2 m air temperature and MODIS surface temperature is perhaps a result of the near-surface temperature inversion that our data demonstrate. Further investigation of how in-situ IR skin temperatures compare to MODIS surface temperature at lower temperatures (below −35 °C) is warranted to determine if this cold bias does indeed exist.

scholarly journals Near-surface diurnal warming simulations: validation with high resolution profile measurements

2012 ◽  
Vol 9 (6) ◽  
pp. 3851-3878
Author(s):  
B. Scanlon ◽  
G. A. Wick ◽  
B. Ward
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Wave Breaking ◽  
Solar Absorption ◽  
Near Surface ◽  
Annual Cycles ◽  
Diurnal Warming ◽  
In Situ Methods ◽  
Critical Requirement

Abstract. Sea surface temperature (SST) is an important property for governing the exchange of energy between the ocean and the atmosphere. Common in-situ methods of measuring SST often require a cool-skin and warm-layer adjustment in the presence of diurnal warming effects. A critical requirement for an ocean sub-model is that it can simulate the change in SST over diurnal, seasonal, and annual cycles. In this paper we use high-resolution near-surface profiles of SST to validate simulated near-surface temperature profiles from a modified version of the Kantha and Clayson 1-D mixed layer model. Additional model enhancements such as the incorporation of a parameterisation of turbulence generated by wave breaking and a solar absorption model are also validated. The model simulations show a strong variability in highly stratified conditions, with different models providing the best results depending on the specific criteria and conditions. In general, the models with enhanced wave breaking effects tended to underestimate the temperature profile measurements while the more coarse baseline and blended approaches produced the most accurate comparisons with the in-situ SST data.

scholarly journals A Nowcast/Forecast System for Japan’s Coasts Using Daily Assimilation of Remote Sensing and In Situ Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2431
Author(s):  
Yasumasa Miyazawa ◽  
Sergey M. Varlamov ◽  
Toru Miyama ◽  
Yukio Kurihara ◽  
Hiroshi Murakami ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ocean Model ◽  
Temperature Data ◽  
Sea Surface ◽  
Forecast System ◽  
In Situ Data

We have developed an ocean state nowcast/forecast system (JCOPE-T DA) that targets the coastal waters around Japan and assimilates daily remote sensing and in situ data. The ocean model component is developed based on the Princeton Ocean Model with a generalized sigma coordinate and calculates oceanic conditions with a 1/36-degree (2–3 km) resolution and an hourly result output interval. To effectively represent oceanic phenomena with a spatial scale smaller than 100 km, we adopted a data assimilation scheme that explicitly separates larger and smaller horizontal scales from satellite sea surface temperature data. Our model is updated daily through data assimilation using the latest available remote-sensing data. Here we validate the data assimilation products of JCOPE-T DA using various kinds of in situ observational data. This validation proves that the JCOPE-T DA model output outperforms those of a previous version of JCOPE-T, which is based on nudging the values of temperature and salinity toward those provided by a different coarse grid data-assimilated model JCOPE2M. Parameter sensitivity experiments show that the selection of horizontal scale separation parameters considerably affects the representation of sea surface temperature. Additional experiments demonstrate that the assimilation of daily-updated satellite sea surface temperature data actually improves the model’s efficiency in representing typhoon-induced disturbances of sea surface temperature on a time scale of a few days. Assimilation of additional in situ data, such as temperature/salinity/ocean current information, further improves the model’s ability to represent the ocean currents near the coast accurately.

scholarly journals Arctic surface temperatures from Metop AVHRR compared to in situ ocean and land data

Ocean Science ◽  
2012 ◽  
Vol 8 (6) ◽  
pp. 959-970 ◽  
Author(s):  
G. Dybkjær ◽  
R. Tonboe ◽  
J. L. Høyer
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Temperature Data ◽  
Coupled Systems ◽  
The Arctic ◽  
In Situ Observation ◽  
Clear Sky ◽  
Ice Surface ◽  
Ice Surface Temperature

Abstract. The ice surface temperature (IST) is an important boundary condition for both atmospheric and ocean and sea ice models and for coupled systems. An operational ice surface temperature product using satellite Metop AVHRR infra-red data was developed for MyOcean. The IST can be mapped in clear sky regions using a split window algorithm specially tuned for sea ice. Clear sky conditions prevail during spring in the Arctic, while persistent cloud cover limits data coverage during summer. The cloud covered regions are detected using the EUMETSAT cloud mask. The Metop IST compares to 2 m temperature at the Greenland ice cap Summit within STD error of 3.14 °C and to Arctic drifting buoy temperature data within STD error of 3.69 °C. A case study reveals that the in situ radiometer data versus satellite IST STD error can be much lower (0.73 °C) and that the different in situ measurements complicate the validation. Differences and variability between Metop IST and in situ data are analysed and discussed. An inter-comparison of Metop IST, numerical weather prediction temperatures and in situ observation indicates large biases between the different quantities. Because of the scarcity of conventional surface temperature or surface air temperature data in the Arctic, the satellite IST data with its relatively good coverage can potentially add valuable information to model analysis for the Arctic atmosphere.

scholarly journals Ground thermal and geomechanical conditions in a permafrost-affected high-latitude rockslide site (Polvartinden, Northern Norway)

2016 ◽  
Author(s):  
Regula Frauenfelder ◽  
Ketil Isaksen ◽  
Jeannette Nötzli ◽  
Matthew J. Lato
Keyword(s):  
Temperature Regime ◽  
Laser Scanning ◽  
Ground Surface ◽  
Failure Zone ◽  
Climate Data ◽  
Conduction Model ◽  
Near Surface ◽  
Northern Norway ◽  
Surface Temperatures

Abstract. In June 2008, a rockslide detached in the northeast facing slope of Polvartinden, a high-alpine mountain in Signaldalen, Northern Norway. Here, we report on the observed and modelled past and present near-surface temperature regime close to the failure zone, as well as on a subsequent simulation of the subsurface temperature regime, and on initial geomechanical mapping based on laser scanning. The volume of the rockslide was estimated to be approximately 500 000 m3. The depth to the actual failure surface was found to range from 40 m at the back of the failure zone to 0 m at its toe. Visible in-situ ice was observed in the failure zone just after the rockslide. Between September 2009 and August 2013 ground surface temperatures were measured with miniature temperature data loggers at fourteen different localities close to the original failure zone along the northern ridge of Polvartinden, and in the valley floor. The results from these measurements and from a basic three-dimensional heat conduction model suggest that the lower altitudinal limit of permafrost at present is at 600–650 m a.s.l., which corresponds to the upper limit of the failure zone. A coupling of our in-situ data with regional climate data since 1958 suggests a general gradual warming and that a period with highest mean near surface temperatures on record ended four months before the Signaldalen rockslide detached. A comparison with a transient permafrost model run at 10 m depth, representative for areas where snow accumulates, strengthen this findings, which are also in congruence with measurements in nearby permafrost boreholes. It is likely that permafrost in and near the failure zone is presently subject to degradation. This degradation, in combination with the extreme warm year antecedent to the rock failure, is seen to have played an important role in the detaching of the Signaldalen rockslide.

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