The Earth‐Observing Aqua Satellite Mission: 20 Years and Counting

Parkinson, Claire L.

doi:10.1029/2022ea002481

Cited by 11 publications

(5 citation statements)

References 114 publications

(136 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The components of the HF radar data processing chain have been mapped onto the data levels presented in the NASA Earth Science Handbook (Parkinson et al, 2006). There are a total of 5 layers with Level 0 representing the unprocessed instrument data at full resolution and Level 4 signifying derived products.…”

Section: Methodsmentioning

confidence: 99%

Real-time quality assurance and quality control for a high frequency radar network

Roarty,

Updyke,

Nazzaro

et al. 2024

Front. Mar. Sci.

View full text Add to dashboard Cite

This paper recommends end to end quality assurance methods and quality control tests for High Frequency Radar Networks. We focus on the network that is operated by the Mid Atlantic Regional Association Coastal Ocean Observing System (MARACOOS). The network currently consists of 38 radars making real-time measurements of the surface currents over the continental shelf for a variety of applications including search and rescue planning, oil spill trajectory modelling and providing a transport context for marine biodiversity observing networks. MARACOOS has been delivering surface current measurements to the United States Coast Guard (USCG) since May 2009. Data quality is important for all applications; however, since the USCG uses this surface current information to plan life-saving missions, delivery of the best quality data is crucial. We have mapped the components of the HF radar data processing chain onto the data levels presented in the NASA Earth Science Reference Handbook and have applied quality assurance and quality control techniques at each data level to achieve the highest quality data. There are approximately 400 High Frequency radars (HFRs) deployed globally and the presented techniques can provide a foundation for data quality checks and standardization of the data collected by the large number of systems operating today.

show abstract

Section: Methodsmentioning

confidence: 99%

Real-time quality assurance and quality control for a high frequency radar network

Roarty,

Updyke,

Nazzaro

et al. 2024

Front. Mar. Sci.

View full text Add to dashboard Cite

show abstract

“…The AIRS/Advanced Microwave Sounding Unit (AMSU) instrument pair was launched on the Aqua satellite on 4 May 2002 (Parkinson, 2022). The observations are taken from 705 km altitude with an inclination of 98.2° at 0130 LT (descending, or southward moving) and 1330 LT (ascending, or northward moving), forming a 1,650-km-wide swath with scan angles that range between ±49° in the cross-track direction.…”

Section: Airs/advanced Microwave Sounding Unitmentioning

confidence: 99%

Two Decades of Temperature and Specific Humidity Variance Scaling With the Atmospheric Infrared Sounder

Kahn,

Fetzer,

Teixeira

et al. 2023

JGR Atmospheres

View full text Add to dashboard Cite

A two‐decade climatology of height‐resolved horizontal variance scaling exponents (𝛼) for temperature (T) and specific humidity (q) is described using Aqua Atmospheric Infrared Sounder (AIRS) sounding profiles. The AIRS Team Version 6 (V6), Version 7 (V7), and Community Long‐term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) retrieval algorithms are compared to European Centre for Medium‐Range Weather Forecasts Reanalysis v5 (ERA5) and Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2) reanalyses. Large‐scale T exponents derived between 600 and 1,200 km (𝛼L) show close agreement between V6, V7, and CLIMCAPS algorithms. However, small‐scale q exponents derived between 150 and 400 km (𝛼S) are in poor agreement, including an unrealistically steep 𝛼S in the planetary boundary layer (PBL) in the V7 and CLIMCAPS algorithms that is caused by a combination of algorithm damping and overconstraint by the first guess fields. ERA5 and MERRA‐2 reanalyses have large values of 𝛼L and 𝛼S for both T and q that indicate reduced small‐scale variability in the reanalysis fields. Differences in 𝛼S between free‐running MERRA‐2 AMIP and MERRA‐2 are negligible, implying that suppressed small‐scale variability in reanalyses is imposed by the background model and not caused by the data assimilation process. AIRS has positively skewed T distributions in the tropical‐free troposphere that is consistent with positively buoyant air parcels in convection, and negative skewness in the PBL that is related to the existence of cold pools, behavior that is mostly absent in ERA5 and MERRA‐2. AIRS provides a global view of scale‐dependent variance and skewness that is useful for subgrid parameterization development and validation of weather and climate prediction models.

show abstract

“…In situ observations are local and sparse in the Arctic, thus, to obtain a general overview of the quality of ice surface temperature in CARRA, remote-sensing products are employed as the main source of observational data. In the present study we use near real time (NRT) level-2 (Parkinson et al, 2006) ice surface temperature products based on data from the MODIS instrument onboard the Terra and Aqua satellites (Hall and Riggs, 2015a, b). The MODIS sea ice surface temperature product is provided in 5 minute swathes, which have a nominal resolution of 1 km.…”

Section: Modis Ice Surface Temperature Productsmentioning

confidence: 99%

Sea ice cover in the Copernicus Arctic Regional Reanalysis

Batrak

Cheng

Kallio‐Myers

2023

Preprint

View full text Add to dashboard Cite

Abstract. The Copernicus Arctic Regional Reanalysis (CARRA) is a novel regional high-resolution atmospheric reanalysis product that covers a considerable part of the European Arctic including substantial amounts of ice-covered areas. Sea ice in CARRA is modelled by means of a one-dimensional thermodynamic sea ice parameterisation scheme, which also explicitly resolves the evolution of the snow layer over sea ice. In the present study we assess the representation of sea ice cover in the CARRA product and validate it against a wide set of satellite products and observations from ice mass balance buoys. We show that sea ice cover in CARRA adequately represents general interannual trends towards thinner and warmer ice in the Arctic. Compared to ERA5, sea ice in CARRA shows a reduced warm bias in the ice surface temperature. The strongest improvement was observed for winter months over the Central Arctic, and the Greenland and Barents seas where a 4.91 °C median ice surface temperature error of ERA5 is reduced to 1.88 °C in CARRA on average. Over the Baffin Bay, intercomparisons suggest the presence of a cold winter-time ice surface temperature bias in CARRA. No improvement over ERA5 was found in the ice surface albedo with spring-time errors in CARRA being up to 8 % higher on average than those in ERA5 when computed against the CLARA-A2 satellite retrieval product. Summer-time ice surface albedos are comparable in CARRA and ERA5. Sea ice thickness and snow depth in CARRA adequately resolve the annual cycle of sea ice cover in the Arctic and bring added value compared to ERA5. However, limitations of CARRA indicate potential benefits of utilising more advanced approaches for representing sea ice cover in next generation reanalyses.

show abstract

The Earth‐Observing Aqua Satellite Mission: 20 Years and Counting

Cited by 11 publications

References 114 publications

Real-time quality assurance and quality control for a high frequency radar network

Real-time quality assurance and quality control for a high frequency radar network

Two Decades of Temperature and Specific Humidity Variance Scaling With the Atmospheric Infrared Sounder

Sea ice cover in the Copernicus Arctic Regional Reanalysis

Contact Info

Product

Resources

About