Product Description
Satellites can detect the temperature of the atmosphere at various altitudes as well as sea and land surface temperatures using radiometric measurements.
Satellite retrieval of air temperature over ocean is a challenging task that requires sophisticated algorithms and models to account for the effects of clouds, water vapor, sea surface temperature and other factors. Some examples of methods and products that have been developed for this purpose:
- Near-Surface Air Temperature Retrieval Derived from AMSU-A is a method that uses microwave radiometer data from the Advanced Microwave Sounding Unit-A (AMSU-A) to estimate the near-surface air temperature over ocean, with corrections for atmospheric and surface emissivity effects. It has been shown to have less bias and smaller errors than previous methods. The Advanced Microwave Sounding Unit-A (AMSU-A) is a microwave instrument on board several satellites, such as NOAA-15, NOAA-16, NOAA-17, NOAA-18, NOAA-19, Aqua, and MetOp-A. AMSU-A has 15 channels that measure atmospheric temperature profiles from the surface to the stratosphere. AMSU-A data are available at hourlyresolution.
- AIRS V7 Level 2 & 3 are products that use infrared and microwave data from the Atmospheric Infrared Sounder (AIRS) on board of Aqua satellite to retrieve various atmospheric variables, including mid-tropospheric carbon dioxide, water vapor and temperature profiles over ocean and land, under cloudy conditions (Wikipedia, 2023c)
For Arctic/Baltic marine and coastal structures, such as ships and platforms, besides the need to withstand anticipated ice conditions, the ability to withstand the risk of low temperature is equally important. The designs of ships and infrastructures in the Arctic and Baltic low temperature regions need to meet requirements of engineering design criteria and standards.
The long-term time series of historical temperature is needed to satisfy requirements of these criteria and standards. In the past, estimating the minimum design temperature distributions in large-scale areas by only using the historical temperature data of very few ground meteorological stations in the corresponding areas was difficult to meet the accuracy requirements of ocean engineering applications. Satellite observations are good means to obtain large-scale temporal and spatial meteorological data in the Arctic and Baltic Ocean (Xiu et al., 2019).
Reanalysis datasets, such as MERRA and ERA, provide global atmospheric data. MERRA stands for Modern-Era Retrospective analysis for Research and Applications and was introduced in 2008 by NASA ¹. MERRA-2 is the latest version of MERRA and provides data beginning in 1980. ERA stands for ECMWF Re-Analysis and was introduced in 1991 by the European Centre for Medium-Range Weather Forecasts (ECMWF).
MERRA-2 incorporates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme to provide an advanced product suite suitable for weather and climate applications.
ERA5 is the latest version of ERA and provides data beginning in 1950. ERA5 is a global atmospheric reanalysis dataset produced by ECMWF that uses a 4D-Var data assimilation system.
While both datasets provide similar atmospheric data, they differ in terms of the data assimilation system used, the observation types incorporated, and the time period covered (Jourdier, 2020; NASA, n.d.-c, n.d.-d; National Center for Atmospheric Research Staff, 2022).
GISS Surface Temperature Analysis (GISTEMP v4) is an estimate of global surface temperature change based on current data files from NOAA GHCN v4 (meteorological stations) and ERSST v5 (ocean areas), combined as described in NASA publications (Lenssen et al., 2019; NASA Goddard Institute for Space Studies, 2024).
These sources may have different accuracies, biases, uncertainties and trends depending on the region, period and variable of interest (Luo et al., 2020).
Product Specifications
BUSINESS PROCESS | SD, SC, SCE, IN, SO |
DESCRIPTION
| Air temperature is referred to the temperature at some distance above the surface. |
EO INFORMATION OF INTEREST | Air temperature long term statistics (min 10 years) and forecast at 2 m of altitude. |
MAIN PROCESS STEPS | 1. Near-Surface Air Temperature Retrieval Derived from AMSU-A is a method that uses microwave radiometer data from the Advanced Microwave Sounding Unit-A (AMSU-A) to estimate the near-surface air temperature over ocean, with corrections for atmospheric and surface emissivity effects. It has been shown to have less bias and smaller errors than previous methods. 2. AIRS V7 Level 2 & 3 are products that use infrared and microwave data from the Atmospheric Infrared Sounder (AIRS) to retrieve various atmospheric variables, including mid-tropospheric carbon dioxide, water vapor and temperature profiles over ocean and land, under cloudy conditions. 3. GISS surface temp analysis (GISTEMPT V4) consists of several datasets. It has mean temperatures on the northern hemisphere from1880 to today on and another dataset detailing temperature anomalies from 2002 to today which are based on AIRS data vs. 2007-2016. 4. In ERA5 dataset from the C3S in the Copernicus Climate Data Store (CDS) Air Temperature is defined as the “Average air temperature valid for a grid column 2m above the surface.” The air temperature is an average of the temperatures computed for 4 surface types such as sea, inland water, natural land and urban. Temperatures are computed from the temperature at the surface, at the lowest model level, the surface roughness variable and atmospheric stability. ERA5 is a continuous climate reanalysis dataset, while the C3S Arctic Regional Reanalysis (CARRA) provides 3-hourly analyses and hourly short-term forecasts of the average air temperature 2m above the surface. 5. MERRA-2 is a global reanalysis product that provides data on the atmosphere, land, and oceans, based on observations and a model. MERRA-2 data are gridded datasets that cover various variables, such as temperature, wind, and aerosols. MERRA-2 data are available at different resolutions and span from 1980 to present. MERRA-2 processing steps involve assimilating observations from different sources, using the GEOS-5 model to produce forecasts, and merging the observations and forecasts in a statistical way. |
INPUT DATA SOURCE | Models assimilate a variety of observations from different sources, such as satellites, radiosondes, aircrafts, ships, buoys, and ground stations, such as radiances, winds, temperatures, humidities, pressures, ozone, aerosols, and sea ice Satellite: Aqua – AIRS, AMSU and HSB data |
SPATIAL RESOLUTION AND COVERAGE | 1. AMSU-A 48 km from 1998-2015 2. AIRS resampled into a 32 km/pixel visualization.) 3. GISSTEMP V4 – 250 km and 1200km from 1880 - ongoing 4. ERA5 - 2.5km x 2.5km from 1991-ongoing 5. MERRA 50 km x 50km from 1980- ongoing |
ACCURACY / CONSTRAINS | The accuracy of temperature data from satellite depends on several factors, such as the type of sensor, the method of retrieval, the calibration and validation process, and the comparison with other sources of data |
TEMPORAL RESOLUTION/ | 1. AMSU-A - Daily resolution 2. AIRS – Daily, 8-day or monthly, depending on the product level 3. GISSTEMP Monthly, seasonal and annual 4. ERA5 Hourly 5. MERRA 3-hourly |
UPDATE FREQUENCY | ERA5, GISTEMP V4 GISTEMP v4 and MERRA2 – Monthly AIRS – daily |
DELIVERY / OUTPUT FORMAT | AMSU-A and AIRS HDF-EOS ERA5 – GRIB2 GISSTEMP v4NetCDF MERRA netCDF AND HDF-EOS |
LIMITATIONS | AMSU-A data have limited vertical resolution, especially in the lower troposphere, where the temperature sounding channels have broad weighting functions. AMSU-A data also have limited horizontal resolution, as the instrument has a swath width of about 2200 km and a nadir footprint of about 48 km https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20150007690.pdf. AIRS data have limited sensitivity to air temperature and specific humidity near and below clouds, as the instrument is an infrared spectrometer. AIRS data also have limited coverage by the presence of optically thick clouds, which block the infrared radiation . GISTEMP v4 data have limited spatial coverage, especially in the polar regions and over the oceans, where there are fewer meteorological stations and ocean area data. GISTEMP v4 data also have uncertainties due to measurement errors, homogenization adjustments, and interpolation methods . ERA5 data have limitations due to the model assumptions, parameterizations, and errors that are inherent in any reanalysis. ERA5 data also have uncertainties due to the quality and availability of the observations that are assimilated into the reanalysis. MERRA data have similar limitations as ERA5, as they are also based on a reanalysis. MERRA data also have discontinuities due to changes in the observing system over time, such as the introduction or removal of satellite instruments (European Space Agency, 2022; Hearty et al., 2014; National Center for Atmospheric Research, n.d.-a, n.d.-b). |
ACCESSIBILITY | AIRS -nasa GES DISC GISSTEMP v4 -NASA GISS ERA5 – Copernicus Service MERRA2 NASA GES DISC Artic hub |
Business Process Challenges
Ship Design (SD) Challenges
Ship Construction (SC) Challenges
Ship Certification (SCE) Challenges
- SCE-1 Defining Operational Limit Temperatures
- SCE-2 Icing Prediction for Vessel Certification
- SCE-5 Monitoring Ship Icing Conditions During Voyage
- SCE-7 Defining Design Parameters for Ship Class Rules
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