The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation

Burgard, Clara; Notz, Dirk; Pedersen, Leif Toudal; Tonboe, Rasmus

doi:10.5194/tc-14-2387-2020

Cited by 7 publications

(2 citation statements)

References 58 publications

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“…Sea ice is a very complex medium and physically-based emissivity models are still challenging to apply for large scale simulations, at multiple frequencies and polarizations (Burgard et al, 2020;Tonboe, 2010). Sea ice emissivities from observations can be derived if the required coincident information can be made available (Mathew et al, 2009).…”

Section: Sea Ice Emissivity Parameterizationmentioning

confidence: 99%

Ocean and Sea Ice Retrievals From an End‐To‐End Simulation of the Copernicus Imaging Microwave Radiometer (CIMR) 1.4–36.5 GHz Measurements

et al. 2021

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The Copernicus Imaging Microwave Radiometer (CIMR) is currently being implemented by the European Space Agency (ESA) as a Copernicus Expansion Mission primarily designed to observe the Polar Regions in support of the Integrated European Policy for the Arctic. It is a conically scanning microwave radiometer with polarized channels centered at 1.414, 6.925, 10.65, 18.7, and 36.5 GHz and channel NEΔT between 0.2 and 0.7 K. A large rotating deployable mesh reflector will provide real‐aperture resolutions ranging from < $< $60 (1.4 GHz) to < $< $5 km (36.5 GHz). To evaluate CIMR retrieval performance, a simplified end‐to‐end simulation of the mission has been carried out. The simulation includes important processes and input parameters, such as test geophysical datasets, forward models, an instrument simulator, and retrieval algorithms to derive the key mission geophysical products. The forward modeling is tested by producing Brightness Temperatures (TBs) from 4 global scenes. A comparison with current observations of the open ocean and sea ice at similar frequencies confirmed the realism of the simulations. The produced top‐of‐atmosphere TBs are converted to Antenna brightness Temperatures (TAs), taking into account the instrument design, and are then inverted to retrieve Sea Ice Concentration (SIC), Sea Surface Temperature (SST), and Sea Surface Salinity (SSS). Evaluating the retrieval performance showed that the simulated CIMR instrument can provide SST, SSS, and SIC measurements with precisions and spatial resolutions conforming with the mission requirements. The evaluation also highlighted the challenges of observing the Arctic environment and put in perspective CIMR capabilities compared with current instruments.

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Section: Sea Ice Emissivity Parameterizationmentioning

confidence: 99%

Ocean and Sea Ice Retrievals From an End‐To‐End Simulation of the Copernicus Imaging Microwave Radiometer (CIMR) 1.4–36.5 GHz Measurements

et al. 2021

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“…We force SAMSIM with 2 m air temperature, surface downward longwave radiation, surface downward shortwave radiation, and precipitation from the ERA-Interim reanalysis (Dee et al, 2011) in the time period from July 2005 to December 2009. This gives us insight into 4.5 annual cycles, so that we can assess the interannual variability of the growth and melt of sea ice and the evolution of its properties.…”

Section: Samsimmentioning

confidence: 99%

The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea ice brightness temperatures at 6.9 GHz from climate model output

et al. 2020

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Abstract. We explore the feasibility of an observation operator producing passive microwave brightness temperatures for sea ice at a frequency of 6.9 GHz. We investigate the influence of simplifying assumptions for the representation of sea ice vertical properties on the simulation of microwave brightness temperatures. We do so in a one-dimensional setup, using a complex 1D thermodynamic sea ice model and a 1D microwave emission model. We find that realistic brightness temperatures can be simulated in cold conditions from a simplified linear temperature profile and a simplified salinity profile as a function of depth in the ice. These realistic brightness temperatures can be obtained based on profiles interpolated to as few as five layers. Most of the uncertainty resulting from the simplifications is introduced by the simplification of the salinity profiles. In warm conditions, the simplified salinity profiles lead to brine volume fractions that are too high in the subsurface layer. To overcome this limitation, we suggest using a constant brightness temperature for the ice during warm conditions and treating melt ponds as water surfaces. Finally, in our setup, we cannot assess the effect of wet snow properties. As periods of snow with intermediate moisture content, typically occurring in spring and fall, locally last for less than a month, our approach allows one to estimate realistic brightness temperatures at 6.9 GHz from climate model output for most of the year.

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Understanding sources of Northern Hemisphere uncertainty and forecast error in a medium‐range coupled ensemble sea‐ice prediction system

Peterson

Smith

Lemieux

et al. 2022

Quart J Royal Meteoro Soc

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The Global Ensemble Prediction System (GEPS) of Environment and Climate Change Canada was recently upgraded to a coupled atmosphere, ocean, and sea‐ice version from an uncoupled atmosphere‐only system. This has been operational since July 2019, with over a year of forecasts now available to evaluate the system throughout all seasons. Using metrics that score the forecast error in ice‐edge position, the spatial probability score and the integrated ice‐edge error, we investigate the spread–error relationship in probabilistic Arctic sea‐ice forecasts from the system and compare this with the skill of the system relative to persistence and a companion Global Deterministic Prediction System (GDPS). Within this ensemble framework, we explore the advantages of having a probabilistic forecast and probe its usefulness in addressing the errors in the system. Both the ensemble GEPS and the deterministic GDPS systems show enhanced sea‐ice prediction over persistence in all months except May and June, when significant biases exist in the systems in shallow‐sea and shelf regions. We attribute a significant portion of these biases to problems modelling landfast ice, but other sources of bias, including significant uncertainties in initializing and verifying sea‐ice analysis, also contribute. The lowest errors in the systems are found during September and continue at reasonably low levels through much of the boreal winter. The minimum and maximum extent periods, along with the early freeze‐up period, are shown to be periods for which the ensemble system offers enhanced benefits over a single deterministic forecast. For these periods, the errors are low and strongly correlated spatially with the ensemble spread. Nevertheless, we find that the ensemble system would likely still benefit from further improvement of the spread/error relationship in the system, currently hampered due to ensemble perturbations that are produced solely in the atmospheric component.

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The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation

Cited by 7 publications

References 58 publications

Ocean and Sea Ice Retrievals From an End‐To‐End Simulation of the Copernicus Imaging Microwave Radiometer (CIMR) 1.4–36.5 GHz Measurements

Ocean and Sea Ice Retrievals From an End‐To‐End Simulation of the Copernicus Imaging Microwave Radiometer (CIMR) 1.4–36.5 GHz Measurements

The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea ice brightness temperatures at 6.9 GHz from climate model output

Understanding sources of Northern Hemisphere uncertainty and forecast error in a medium‐range coupled ensemble sea‐ice prediction system

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