scholarly journals Exploiting Aeolus Level-2B Winds to Better Characterize Atmospheric Motion Vector Bias and Uncertainty

2021 ◽  
Author(s):  
Katherine E. Lukens ◽  
Kayo Ide ◽  
Kevin Garrett ◽  
Hui Liu ◽  
David Santek ◽  
...  

Abstract. The need for highly accurate atmospheric wind observations is a high priority in the science community, and in particular numerical weather prediction (NWP). To address this requirement, this study leverages Aeolus wind LIDAR Level-2B data provided by the European Space Agency (ESA) to better characterize atmospheric motion vector (AMV) bias and uncertainty, with the eventual goal of potentially improving AMV algorithms. AMV products from geostationary (GEO) and low-Earth polar orbiting (LEO) satellites are compared with reprocessed Aeolus horizontal line-of-sight (HLOS) global winds observed in August and September 2019. Winds from two of the four Aeolus observing modes are utilized for comparison with AMVs: Rayleigh-clear (derived from the molecular scattering signal) and Mie-cloudy (derived from particle scattering). For the most direct comparison, quality controlled (QC’d) Aeolus winds are collocated with quality controlled AMVs in space and time, and the AMVs are projected onto the Aeolus HLOS direction. Mean collocation differences (MCD) and standard deviation (SD) of those differences (SDCD) are determined from comparisons based on a number of conditions, and their relation to known AMV bias and uncertainty estimates is discussed. GOES-16 and LEO AMV characterizations based on Aeolus winds are described in more detail. Overall, QC’d AMVs correspond well with QC’d Aeolus HLOS wind velocities (HLOSV) for both Rayleigh-clear and Mie-cloudy observing modes, despite remaining biases in Aeolus winds after reprocessing. Comparisons with Aeolus HLOSV are consistent with known AMV bias and uncertainty in the tropics, NH extratropics, and in the Arctic, and at mid- to upper-levels in both clear and cloudy scenes. SH comparisons generally exhibit larger than expected SDCD, which could be attributed to height assignment errors in regions of high winds and enhanced vertical wind shear. GOES-16 water vapor clear-sky AMVs perform best relative to Rayleigh-clear winds, with small MCD (-0.6 m s-1 to 0.1 m s-1) and SDCD (5.4–5.6 m s-1) in the NH and tropics that fall within the accepted range of AMV error values relative to radiosonde winds. Compared to Mie-cloudy winds, AMVs exhibit similar MCD and smaller SDCD (~4.4–4.8 m s-1) throughout the troposphere. In polar regions, Mie-cloudy comparisons have smaller SDCD (5.2 m s-1 in the Arctic, 6.7 m s-1 in the Antarctic) relative to Rayleigh-clear comparisons, which are larger by 1–2 m s-1. The level of agreement between AMVs and Aeolus winds varies per combination of conditions including the Aeolus observing mode coupled with AMV derivation method, geographic region, and height of the collocated winds. It is advised that these stratifications be considered in future comparison studies and impact assessments involving 3D winds. Additional bias corrections to the Aeolus dataset are anticipated to further refine the results.

2021 ◽  
Author(s):  
Chih-Chun Chou ◽  
Paul J. Kushner ◽  
Stéphane Laroche ◽  
Zen Mariani ◽  
Peter Rodriguez ◽  
...  

Abstract. In August 2018, the European Space Agency launched the Aeolus satellite, whose Atmospheric LAser Doppler INstrument (ALADIN) is the first spaceborne Doppler wind lidar to regularly measure vertical profiles of horizontal line-of-sight (HLOS) winds with global sampling. This mission is intended to assess improvement to numerical weather prediction provided by wind observations in regions poorly constrained by atmospheric mass, such as the tropics, but also, potentially, in polar regions such as the Arctic where direct wind observations are especially sparse. There remain gaps in the evaluation of the Aeolus products over the Arctic region, which is the focus of this contribution. Here, an assessment of the Aeolus Level-2B wind product is carried out from measurement stations in Canada’s north, to the pan-Arctic, with Aeolus data being compared to Ka-band radar measurements at Iqaluit, Nunavut; to radiosonde measurements over Northern Canada; to Environment and Climate Change Canada (ECCC)’s short-range forecast; and to the reanalysis product, ERA5, from the European Centre for Medium-Range Weather Forecasts (ECMWF). Periods covered include the early phase during the first laser nominal flight model (FM-A; 2018-09 to 2018-10), the early phase during the second flight laser (FM-B; 2019-08 to 2019-09), and the mid-FM-B periods (2019-12 to 2020-01). The adjusted r-square between Aeolus and other local datasets are around 0.9, except for somewhat lower values in comparison with the ground-based radar, presumably due to limited sampling. This consistency degraded by about 10 % for the Rayleigh winds in the summer, presumably due to scattering from the solar background. Over the pan-Arctic, consistency, with correlation greater than 0.8, is found in the Mie channel from the planetary boundary layer to the lower stratosphere (near surface to 16 km a.g.l.) and in the Rayleigh channel from the troposphere to the stratosphere (2 km to 25 km a.g.l.). Zonal and meridional projections of the HLOS winds are separated to account for the systematic changes in HLOS winds arising from sampling wind components from different viewing orientations in the ascending and descending phases. In all cases, Aeolus standard deviations are found to be 20 % greater than those from ECCC-B and ERA5. We found that L2B estimated error product for Aeolus is coherent with the differences between Aeolus and the other datasets, and can be used as a guide for expected consistency. Thus, our work confirms the quality of the Aeolus dataset over the Arctic and shows that the new Aeolus L2B wind product provides a valuable addition to current wind products in regions such as the Arctic Ocean region where few direct wind observations have been available to date.


2014 ◽  
Vol 53 (2) ◽  
pp. 534-547 ◽  
Author(s):  
Matthew A. Lazzara ◽  
Richard Dworak ◽  
David A. Santek ◽  
Brett T. Hoover ◽  
Christopher S. Velden ◽  
...  

AbstractAtmospheric motion vectors (AMVs) are derived from satellite-observed motions of clouds and water vapor features. They provide crucial information in regions void of conventional observations and contribute to forecaster diagnostics of meteorological conditions, as well as numerical weather prediction. AMVs derived from geostationary (GEO) satellite observations over the middle latitudes and tropics have been utilized operationally since the 1980s; AMVs over the polar regions derived from low‐earth (polar)‐orbiting (LEO) satellites have been utilized since the early 2000s. There still exists a gap in AMV coverage between these two sources in the latitude band poleward of 60° and equatorward of 70° (both hemispheres). To address this AMV gap, the use of a novel approach to create image sequences that consist of composites derived from a combination of LEO and GEO observations that extend into the deep middle latitudes is explored. Experiments are performed to determine whether the satellite composite images can be employed to generate AMVs over the gap regions. The derived AMVs are validated over both the Southern Ocean/Antarctic and the Arctic gap regions over a multiyear period using rawinsonde wind observations. In addition, a two-season numerical model impact study using the Global Forecast System indicates that the assimilation of these AMVs can improve upon the control (operational) forecasts, particularly during lower-skill (dropout) events.


2018 ◽  
Vol 11 (8) ◽  
pp. 3347-3368 ◽  
Author(s):  
Yurii Batrak ◽  
Ekaterina Kourzeneva ◽  
Mariken Homleid

Abstract. Sea ice is an important factor affecting weather regimes, especially in polar regions. A lack of its representation in numerical weather prediction (NWP) systems leads to large errors. For example, in the HARMONIE–AROME model configuration of the ALADIN–HIRLAM NWP system, the mean absolute error in 2 m temperature reaches 1.5 ∘C after 15 forecast hours for Svalbard. A possible reason for this is that the sea ice properties are not reproduced correctly (there is no prognostic sea ice temperature in the model). Here, we develop a new simple sea ice scheme (SICE) and implement it in the ALADIN–HIRLAM NWP system in order to improve the forecast quality in areas influenced by sea ice. The new parameterization is evaluated using HARMONIE–AROME experiments covering the Svalbard and Gulf of Bothnia areas for a selected period in March–April 2013. It is found that using the SICE scheme improves the forecast, decreasing the value of the 2 m temperature mean absolute error on average by 0.5 ∘C in areas that are influenced by sea ice. The new scheme is sensitive to the representation of the form drag. The 10 m wind speed bias increases on average by 0.4 m s−1 when the form drag is not taken into account. Also, the performance of SICE in March–April 2013 and December 2015–December 2016 was studied by comparing modelling results with the sea ice surface temperature products from MODIS and VIIRS. The warm bias (of approximately 5 ∘C) of the new scheme is indicated for areas of thick ice in the Arctic. Impacts of the SICE scheme on the modelling results and possibilities for future improvement of sea ice representation in the ALADIN–HIRLAM NWP system are discussed.


2019 ◽  
Vol 11 (19) ◽  
pp. 2240 ◽  
Author(s):  
David Santek ◽  
Richard Dworak ◽  
Sharon Nebuda ◽  
Steve Wanzong ◽  
Régis Borde ◽  
...  

Atmospheric Motion Vectors (AMVs) calculated by six different institutions (Brazil Center for Weather Prediction and Climate Studies/CPTEC/INPE, European Organization for the Exploitation of Meteorological Satellites/EUMETSAT, Japan Meteorological Agency/JMA, Korea Meteorological Administration/KMA, Unites States National Oceanic and Atmospheric Administration/NOAA, and the Satellite Application Facility on Support to Nowcasting and Very short range forecasting/NWCSAF) with JMA’s Himawari-8 satellite data and other common input data are here compared. The comparison is based on two different AMV input datasets, calculated with two different image triplets for 21 July 2016, and the use of a prescribed and a specific configuration. The main results of the study are summarized as follows: (1) the differences in the AMV datasets depend very much on the ‘AMV height assignment’ used and much less on the use of a prescribed or specific configuration; (2) the use of the ‘Common Quality Indicator (CQI)’ has a quantified skill in filtering collocated AMVs for an improved statistical agreement between centers; (3) Among the six AMV operational algorithms verified by this AMV Intercomparison, JMA AMV algorithm has the best overall performance considering all validation metrics, mainly due to its new height assignment method: ‘Optimal estimation method considering the observed infrared radiances, the vertical profile of the Numerical Weather Prediction wind, and the estimated brightness temperature using a radiative transfer model’.


2020 ◽  
Vol 12 (14) ◽  
pp. 2255
Author(s):  
Axel Barleben ◽  
Stéphane Haussler ◽  
Richard Müller ◽  
Matthias Jerg

The predictability of aviation turbulence is influenced by energy-intensive flow patterns that are significantly smaller than the horizontal grid scale of current numerical weather prediction (NWP) models. The parameterization of these subgrid scale (SGS) processes is possible by means of an additional prognostic equation for the temporal change of turbulence kinetic energy (TKE), whereby scale transfer terms are used. This turbulence scheme has been applied operationally for 5 years in the NWP model ICON (Icosahedral Nonhydrostatic). The most important of the source terms parameterizes the Kelvin–Helmholtz instability, better known as clear air turbulence. This shear term was subjected to a nowcasting technique, is calculated with satellite data, and shifted forward in time using motion based on optical flow estimates and atmospheric motion vector (AMV). The nowcasts include turbulence altitude as determined by an adapted height assignment scheme presented here. The case studies illustrate that the novel approach for satellite-based turbulence nowcasting is a supplement to the NWP models.


2019 ◽  
Vol 11 (17) ◽  
pp. 2054 ◽  
Author(s):  
Soo Min Oh ◽  
Régis Borde ◽  
Manuel Carranza ◽  
In-Chul Shin

We derived an atmospheric motion vector (AMV) algorithm for the Geostationary Korea Multipurpose Satellite (GEO-KOMPSAT-2A; GK-2A) launched on 4 December 2018, using the Advanced Himawari Imager (AHI) onboard Himawari-8, which is very similar to the Advanced Meteorological Imager onboard GK-2A. This study clearly describes the main steps in our algorithm and optimizes it for the target box size and height assignment methods by comparing AMVs with numerical weather prediction (NWP) and rawinsonde profiles for July 2016 and January 2017. Target box size sensitivity tests were performed from 8 × 8 to 48 × 48 pixels for three infrared channels and from 16 × 16 to 96 × 96 pixels for one visible channel. The results show that the smaller box increases the speed, whereas the larger one slows the speed without quality control. The best target box sizes were found to be 16 × 16 for CH07, 08, and 13, and 48 × 48 pixels for CH03. Height assignment sensitivity tests were performed for several methods, such as the cross-correlation coefficient (CCC), equivalent blackbody temperature (EBBT), infrared/water vapor (IR/WV) intercept, and CO2 slicing methods for a cloudy target as well as normalized total contribution (NTC) and normalized total cumulative contribution (NTCC) for a clear-air target. For a cloudy target, the CCC method is influenced by the quality of the cloud’s top pressure. Better results were found when using EBBT and IR/WV intercept methods together rather than individually. Furthermore, CO2 slicing had the best statistics. For a clear-air target, the combined use of NTC and NTCC had the best statistics. Additionally, the mean vector difference, root-mean-square (RMS) vector difference, bias, and RMS error (RMSE) between GK-2A AMVs and NWP or rawinsonde were smaller by approximately 18.2% on average than in the case of the Communication, Ocean and Meteorology Satellite (COMS) AMVs. In addition, we verified the similarity between GK-2A and Meteosat Third Generation (MTG) AMVs using the AHI of Himawari-8 from 21 July 2016. This similarity can provide evidence that the GK-2A algorithm works properly because the GK-2A AMV algorithm borrows many methods of the MTG AMV algorithm for geostationary data and inversion layer corrections. The Pearson correlation coefficients in the speed, direction, and height of the prescribed GK-2A and MTG AMVs were larger than 0.97, and the corresponding bias/RMSE were0.07/2.19 m/s, 0.21/14.8°, and 2.61/62.9 hPa, respectively, considering common quality indicator with forecast (CQIF) > 80.


2009 ◽  
Vol 48 (3) ◽  
pp. 450-463 ◽  
Author(s):  
Christopher S. Velden ◽  
Kristopher M. Bedka

Abstract This study investigates the assignment of pressure heights to satellite-derived atmospheric motion vectors (AMVs), commonly known as cloud-drift and water vapor–motion winds. Large volumes of multispectral AMV datasets are compared with collocated rawinsonde wind profiles collected by the U.S. Department of Energy Atmospheric Radiation Measurement Program at three geographically disparate sites: the southern Great Plains, the North Slope of Alaska, and the tropical western Pacific Ocean. From a careful analysis of these comparisons, the authors estimate that mean AMV observation errors are ∼5–5.5 m s−1 and that vector height assignment is the dominant factor in AMV uncertainty, contributing up to 70% of the error. These comparisons also reveal that in most cases the RMS differences between matched AMVs and rawinsonde wind values are minimized if the rawinsonde values are averaged over specified layers. In other words, on average, the AMV values better correlate to a motion over a mean tropospheric layer rather than to a traditionally assigned discrete level. The height assignment behavioral characteristics are specifically identified according to AMV height (high cloud vs low cloud), type (spectral bands; clear vs cloudy), geolocation, height assignment method, and amount of environmental vertical wind shear present. The findings have potentially important implications for data assimilation of AMVs, and these are discussed.


2020 ◽  
Author(s):  
Ole Jakob Hegelund ◽  
Alistair Everett ◽  
Ted Cheeseman ◽  
Penelope Wagner ◽  
Nick Hughes ◽  
...  

<p>The Ice Watch program coordinates routine visual observations of sea-ice including icebergs and meteorological parameters. The development and use of the Arctic Shipborne Sea Ice Standardization Tool (ASSIST) software has enabled the program to collect over 6 800 records from numerous ship voyages and it is complementary to the Antarctic Sea-ice Processes and Climate (ASPeCt) in the Antarctic. These observations will enhance validation and calibration of data from the Copernicus Sentinel satellites and other Earth Observation missions where the lack of routine spatially and temporally coincident data from the Polar Regions hinders the development of automatic classification products. A critical piece of information for operations and research, photographic records of observations, is often missing. As mobile phones are nearly ubiquitous and feature high-quality cameras, capable of recording accurate ancillary timing and positional information we are developing the IceWatchApp to aid users in supplementing observations with a photographic record.</p><p>The IceWatchApp has been funded by the Citizen Science Earth Observation Lab (CSEOL) programme of the European Space Agency and the Polar Citizen Science Collective, which has successfully implemented similar observation projects within atmospherics, biology and marine geosciences, is collaborating in its development. The image database will aid the training of machine learning algorithms for automatic sea ice type classification and provide a mechanism for crowd-sourcing identification through an “ask a scientist” feedback feature. The app will also have the capability to provide near real-time satellite and Copernicus services products back to the user, thereby educating them on Earth Observation, and giving them an improved understanding of the surrounding environment.</p><p> </p><p><strong>Keywords</strong>: Polar regions, Arctic, Antarctic, data collection, In-Situ measurements, remote sensing, Sea Ice, user engagement, citizen science, Earth Observation.<br><strong>Abstract</strong>: to session 35413</p><p> </p>


2020 ◽  
Vol 101 (11) ◽  
pp. E2005-E2021
Author(s):  
Ad Stoffelen ◽  
Angela Benedetti ◽  
Régis Borde ◽  
Alain Dabas ◽  
Pierre Flamant ◽  
...  

AbstractThe Aeolus mission objectives are to improve numerical weather prediction (NWP) and enhance the understanding and modeling of atmospheric dynamics on global and regional scale. Given the first successes of Aeolus in NWP, it is time to look forward to future vertical wind profiling capability to fulfill the rolling requirements in operational meteorology. Requirements for wind profiles and information on vertical wind shear are constantly evolving. The need for high-quality wind and profile information to capture and initialize small-amplitude, fast-evolving, and mesoscale dynamical structures increases, as the resolution of global NWP improved well into the 3D turbulence regime on horizontal scales smaller than 500 km. In addition, advanced requirements to describe the transport and dispersion of atmospheric constituents and better depict the circulation on climate scales are well recognized. Direct wind profile observations over the oceans, tropics, and Southern Hemisphere are not provided by the current global observing system. Looking to the future, most other wind observation techniques rely on cloud or regions of water vapor and are necessarily restricted in coverage. Therefore, after its full demonstration, an operational Aeolus-like follow-on mission obtaining globally distributed wind profiles in clear air by exploiting molecular scattering remains unique.


2018 ◽  
Vol 59 ◽  
pp. 21.1-21.36 ◽  
Author(s):  
John E. Walsh ◽  
David H. Bromwich ◽  
James. E. Overland ◽  
Mark C. Serreze ◽  
Kevin R. Wood

AbstractThe polar regions present several unique challenges to meteorology, including remoteness and a harsh environment. We summarize the evolution of polar meteorology in both hemispheres, beginning with measurements made during early expeditions and concluding with the recent decades in which polar meteorology has been central to global challenges such as the ozone hole, weather prediction, and climate change. Whereas the 1800s and early 1900s provided data from expeditions and only a few subarctic stations, the past 100 years have seen great advances in the observational network and corresponding understanding of the meteorology of the polar regions. For example, a persistent view in the early twentieth century was of an Arctic Ocean dominated by a permanent high pressure cell, a glacial anticyclone. With increased observations, by the 1950s it became apparent that, while anticyclones are a common feature of the Arctic circulation, cyclones are frequent and may be found anywhere in the Arctic. Technology has benefited polar meteorology through advances in instrumentation, especially autonomously operated instruments. Moreover, satellite remote sensing and computer models revolutionized polar meteorology. We highlight the four International Polar Years and several high-latitude field programs of recent decades. We also note outstanding challenges, which include understanding of the role of the Arctic in variations of midlatitude weather and climate, the ability to model surface energy exchanges over a changing Arctic Ocean, assessments of ongoing and future trends in extreme events in polar regions, and the role of internal variability in multiyear-to-decadal variations of polar climate.


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