On the spatio-temporal representativeness of observations

Schutgens, Nick; Tsyro, Svetlana; Gryspeerdt, Edward; Goto, Daisuke; Weigum, Natalie; Schulz, Mıchael; Stier, Philip

doi:10.5194/acp-2017-149

Cited by 13 publications

(23 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To be able to evaluate and intercompare the remote sensing datasets, they will need to be collocated in time and space to reduce representation errors (Colarco et al, 2014;Schutgens et al, 2016bSchutgens et al, , 2017. This is achieved by only retaining data from multiple datasets if they occur within the same spatio-temporal window.…”

Section: Collocation and Analysis Methodologymentioning

confidence: 99%

“…Intriguingly, the highest product correlations and lowest RMS differences with AERONET are found for a collocation requirement of 3 hours and at least 5 AERONET measurements and not for a tighter constraint of 1 hour. In Schutgens et al (2017) it was shown that point measurements become more spatially representative for a larger area by temporal averaging. It was estimated that a 1 o grid-box was best represented by a point observation if its measurements were averaged over 4 hours.…”

Section: Selection Of Aeronet Sites and Collocation Criteriamentioning

confidence: 99%

“…Note that different averages might be used (e.g. geometric, see also Levy et al (2009); Sayer and Knobelspiesse (2019)) but a modelling study (Schutgens et al, 2017), backed up by limited sensitivity studies using the present datasets, suggest relatively little impact in the intercomparison of datasets.…”

mentioning

confidence: 92%

See 2 more Smart Citations

An AeroCom/AeroSat study: Intercomparison of Satellite AOD Datasets for Aerosol Model Evaluation

Schutgens¹,

Sayer²,

Heckel³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. To better understand current uncertainties in the important observational constraint to climate models of AOD (Aerosol Optical Depth), we evaluate and intercompare fourteen satellite products, representing 9 different retrieval algorithm families using observations from 5 different sensors on 6 different platforms. The satellite products, super-observations consisting of 1° × 1° daily aggregated retrievals drawn from the years 2006, 2008 and 2010, are evaluated with AERONET (AErosol RObotic NETwork) and MAN (Maritime Aerosol Network) data. Results show that different products exhibit different regionally varying biases (both under- and overestimates) that may reach ±50 %, although a typical bias would be 15–25 % (depending on product). In addition to these biases, the products exhibit random errors that can be 1.6 to 3 times as large. Most products show similar performance, although there are a few exceptions with either larger biases or larger random errors. The intercomparison of satellite products extends this analysis and provides spatial context to it. In particular, we show that aggregated satellite AOD agrees much better than the spatial coverage (often driven by cloud masks) within the 1° × 1° grid cells. Up to 50 % of the difference between satellite AOD is attributed to cloud contamination. The diversity in AOD products shows clear spatial patterns and varies from 10 % (parts of the ocean) to 100 % (central Asia and Australia). More importantly, we show that the diversity may be used as an indication of AOD uncertainty, at least for the better performing products. This provides modellers with a global map of expected AOD uncertainty in satellite products, allows assessment of products away from AERONET sites, can provide guidance for future AERONET locations, and offers suggestions for product improvements. We account for statistical and sampling noise in our analyses. Sampling noise, variations due to the evaluation of different subsets of the data, causes important changes in error metrics. The consequences of this noise term for product evaluation are discussed.

show abstract

Section: Collocation and Analysis Methodologymentioning

confidence: 99%

Section: Selection Of Aeronet Sites and Collocation Criteriamentioning

confidence: 99%

See 1 more Smart Citation

An AeroCom/AeroSat study: Intercomparison of Satellite AOD Datasets for Aerosol Model Evaluation

Schutgens¹,

Sayer²,

Heckel³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similarly large biases can occur when care is not taken to ensure temporal synchronicity in data [125]. Furthermore, a lack of consideration of the diurnally biased sampling associated with polar-orbiting satellites can bias results significantly [93,123]. The spatial heterogeneity of observations also needs careful consideration.…”

Section: Ensuring Scale Awareness In Model-observation Intercomparisonsmentioning

confidence: 99%

“…The spatial heterogeneity of observations also needs careful consideration. Data is often discontinuous in space and time, requiring geospatial statistical methods to address data heterogeneity when comparing to regularly gridded model output [123].…”

Section: Ensuring Scale Awareness In Model-observation Intercomparisonsmentioning

confidence: 99%

The Radiative Forcing of Aerosol–Cloud Interactions in Liquid Clouds: Wrestling and Embracing Uncertainty

Mülmenstädt

Feingold

2018

Curr Clim Change Rep

115

101

View full text Add to dashboard Cite

This article discusses some of the challenges that have limited progress in quantifying the radiative forcing associated with aerosol-cloud interactions (ACI) in warm (liquid-water) clouds. It reviews recent progress and suggests new ways of viewing the problem that might accelerate progress. It calls for much greater attention to the scale problem, both in terms of aerosol-cloud process representation in models and comparison with observations. It suggests careful consideration of the balance in detail with which processes are represented, and proposes a merging of detailed process understanding and system-wide behavior. In this spirit, it advocates tackling the problem with models of varying complexity that consider both the depth and breadth of the complex dynamical system. Finally, it considers shifting attention from untangling aerosol and meteorological effects on cloud systems towards understanding the co-variability of key aerosol and meteorological drivers of cloud systems.

show abstract

Evaluating errors due to unresolved scales in convection‐permitting numerical weather prediction

Waller

Dance

Lean

2021

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

In numerical weather prediction (NWP), observations and models are quantitatively compared for the purposes of data assimilation and forecast verification.The spatial and temporal scales represented by the observation and model may differ and this results in a scale mismatch error which may be biased and correlated. The aim of this paper is to investigate the structure of representation error in convection-permitting NWP models for four meteorological variables: temperature, specific humidity, zonal and meridional wind. We use high-resolution data from the experimental Met Office London Model (approximately 300 m grid-length) to simulate perfect observations and lower-resolution model data. The scale mismatch error and its bias, variance and correlation are calculated from the perfect observation and low-resolution model equivalents. Our new results show that the scale mismatch bias is significant in the boundary layer for temperature and specific humidity, whereas the variance is significant in the boundary layer for all analysed variables. Contrary to previous studies using low-resolution (km-scale) data, horizontal correlations are shown to be insignificant. However, all variables exhibit considerable vertical representation error correlation throughout the boundary layer. Our results suggest that significant biases and vertical correlations exist that should be accounted for to give maximum observation impact in data assimilation and for fairness in model verification and validation. K E Y W O R D Sconvection-permitting data assimilation, observation uncertainty, representation error INTRODUCTIONFor numerical weather prediction (NWP), a wide variety of instruments are used to observe the Earth's atmosphere, each capturing different variables at different spatial scales. When the observations are compared with model data for the purposes of data assimilation or forecast verification, there is, inevitably, a difference in the spatio-temporal scales represented by the model and the observations. The difference in spatio-temporal scalesThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

On the spatio-temporal representativeness of observations

Cited by 13 publications

References 11 publications

An AeroCom/AeroSat study: Intercomparison of Satellite AOD Datasets for Aerosol Model Evaluation

An AeroCom/AeroSat study: Intercomparison of Satellite AOD Datasets for Aerosol Model Evaluation

The Radiative Forcing of Aerosol–Cloud Interactions in Liquid Clouds: Wrestling and Embracing Uncertainty

Evaluating errors due to unresolved scales in convection‐permitting numerical weather prediction

Contact Info

Product

Resources

About