Spatial variability of surface irradiance measurements at the Manus ARM site

Riihimaki, Laura D.; Long, Charles

doi:10.1002/2013jd021187

Cited by 6 publications

(7 citation statements)

References 44 publications

(45 reference statements)

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“…This occurs primarily under convectively suppressed conditions such as La Nina as documented in McFarlane et al [2005]. Basically there is a strong ENSO influence on Nauru, negligible for Manus, and Darwin [Riihimaki and Long, 2014]. In terms of bias, the performance of the UMD_DX product seems to do better than the MODIS product, while the std is similar.…”

Section: Roadmap To Results Presentationmentioning

confidence: 97%

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The net energy budget at the ocean‐atmosphere interface of the “Cold Tongue” region

et al. 2017

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Pacific “Cold Tongue” (PCT) sea surface temperature (SST) experiences significant (>0.5°C) interannual variations forced by the El‐Nino Southern Oscillations (ENSO) with global impacts on the Earth climate. In this study, we estimate the PCT net heat budget known to be difficult to derive using numerical models. The main goal is to determine how accurately the net heat flux across the surface/atmosphere interface can currently be determined primarily, from satellite observations; these are first evaluated against the nearest available observations inside and outside the PCT of the Tropical Pacific Ocean, using buoy arrays such as the Tropical Atmosphere Ocean/Triangle Trans‐Ocean Buoy Network (TAO/TRITON). It was found that the satellite‐based estimates of both turbulent and radiative fluxes are in better agreement with the observations than similar estimates from leading numerical models. The monthly mean satellite estimates of PCT SW↓ during January/July 2009 were 273.07/170.14, for LW↓, latent heat and sensible heat they were 378.79/365.54, 95.52/130.31, 9.89/20.67, respectively (all in W/m2). The estimated standard deviations for PCT SW↓ were in the range of 7.2–7.8% of the mean and in the range of 2.0–2.5% for LW↓, at daily time scale. Satellite estimates of both PCT LHF and SHF exhibit much higher variability, characterized by standard deviations of 50% from the mean values.

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Section: Roadmap To Results Presentationmentioning

confidence: 97%

“…[]. Basically there is a strong ENSO influence on Nauru, negligible for Manus, and Darwin [ Riihimaki and Long , ]. In terms of bias , the performance of the UMD_DX product seems to do better than the MODIS product, while the std is similar.…”

Section: Resultsmentioning

confidence: 99%

The net energy budget at the ocean‐atmosphere interface of the “Cold Tongue” region

et al. 2017

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“…The spatial representativeness of a surface site greatly depends on the time scale considered—hours, days, months—as temporal averaging strongly reduces the mismatch between point observation and surrounding area mean [e.g., Zhang et al , ; Wang et al , ; Hakuba et al , ]. Spatial variability in SSR on the subgrid scale is mainly caused by variability in cloud cover and cloud type [e.g., Long and Ackermann , ; Barnett et al , ], as well as altitude, local topography, and surface characteristics surrounding the site [e.g., Hay , ; Tovar et al , ; Riihimaki and Long , ]. The use of multiple sites to approximate a grid cell mean substantially enhances the spatial representativeness [e.g., Barnett et al , ; Li et al , ; Journée et al , ; Hakuba et al , ], but is hardly feasible in most parts of the world due to insufficient coverage by surface sites.…”

Section: Introductionmentioning

confidence: 99%

“…Together, this paves the way to a fully global assessment of site-specific spatial representativeness.The spatial representativeness of a surface site greatly depends on the time scale considered-hours, days, months-as temporal averaging strongly reduces the mismatch between point observation and surrounding area mean [e.g., Zhang et al, 2010;Wang et al, 2012;Hakuba et al, 2013a]. Spatial variability in SSR on the subgrid scale is mainly caused by variability in cloud cover and cloud type [e.g., Long and Ackermann, 1995;Barnett et al, 1998], as well as altitude, local topography, and surface characteristics surrounding the site [e.g., Hay, 1984;Tovar et al, 1995;Riihimaki and Long, 2014]. The use of multiple sites HAKUBA ET AL.…”

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confidence: 99%

Spatial representativeness of ground-based solar radiation measurements-Extension to the full Meteosat disk

Hakuba

Folini

Sánchez-Lorenzo

et al. 2014

J. Geophys. Res. Atmos.

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The spatial representativeness of a point measurement of surface solar radiation (SSR) of its larger-scale surrounding, e.g., collocated grid cell, is a potential source of uncertainty in the validation of climate models and satellite products. Here we expand our previous study over Europe to the entire Meteosat disk, covering additional climate zones in Africa, the Middle east, and South America between −70• to 70• East and −70• to 70• North. Using a high-resolution (0.03 [2001][2002][2003][2004][2005], we quantify the spatial subgrid variability in grids of 1 • and 3• resolution and the spatial representativeness of 887 surface sites with respect to site-centered surroundings of variable size. In the multiannual mean the subgrid variability is the largest in some mountainous and coastal regions but varies seasonally due to changes in the Intertropical Convergence Zone location. The absolute mean representation errors at the surface sites with respect to surroundings of 1 • and 3• are on average 1-2% (3 W m −2 ) and 2-3% (4 W m −2 ), respectively. The majority of sites are found to be representative within the in situ measurement accuracy. We show that their site-specific representativeness can be reliably approximated by the subgrid variability in a fixed grid (1 • ). The subgrid variability in turn is only moderately reduced when computed from coarser grid data, typically the only data available in areas not covered by the 0.03• resolved Meteosat disk. Together, this paves the way to a fully global assessment of site-specific spatial representativeness.

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“…Unprecedented long-term time series of cloud and precipitation observations have been collected at these sites. These long-term ARM observations are widely used for a variety of purposes, such as characterizing cloud microphysics and their radiative impacts, improving process-level understanding of cloud life cycles, and model evaluations (Xie et al 2004;Ovtchinnikov et al 2006;Henderson and Pincus 2009;Riihimaki and Long 2014).…”

Section: Introductionmentioning

confidence: 99%

Stratiform and Convective Precipitation Observed by Multiple Radars during the DYNAMO/AMIE Experiment

Deng

Kollias

Feng

et al. 2014

Journal of Applied Meteorology and Climatology

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In this study, methods of convective/stratiform precipitation classification and surface rain-rate estimation based on the Atmospheric Radiation Measurement Program (ARM) cloud radar measurements were developed and evaluated. Simultaneous and collocated observations of the Ka-band ARM zenith radar (KAZR), two scanning precipitation radars [NCAR S-band/Ka-band Dual Polarization, Dual Wavelength Doppler Radar (S-PolKa) and Texas A&M University Shared Mobile Atmospheric Research and Teaching Radar (SMART-R)], and surface precipitation during the Dynamics of the Madden-Julian Oscillation/ARM MJO Investigation Experiment (DYNAMO/AMIE) field campaign were used. The motivation of this study is to apply the unique long-term ARM cloud radar observations without accompanying precipitation radars to the study of cloud life cycle and precipitation features under different weather and climate regimes. The resulting convective/stratiform classification from KAZR was evaluated against precipitation radars. Precipitation occurrence and classified convective/stratiform rain fractions from KAZR compared favorably to the collocated SMART-R and S-PolKa observations. Both KAZR and S-PolKa radars observed about 5% precipitation occurrence. The convective (stratiform) precipitation fraction is about 18% (82%). Collocated disdrometer observations of two days showed an increased number concentration of small and large raindrops in convective rain relative to dominant small raindrops in stratiform rain. The composite distributions of KAZR reflectivity and Doppler velocity also showed distinct structures for convective and stratiform rain. These evidences indicate that the method produces physically consistent results for the two types of rain. A new KAZR-based, two-parameter [the gradient of accumulative radar reflectivity Z e (GAZ) below 1 km and near-surface Z e ] rain-rate estimation procedure was developed for both convective and stratiform rain. This estimate was compared with the exponential Z-R (reflectivity-rain rate) relation. The relative difference between the estimated and surface-measured rainfall rates showed that the two-parameter relation can improve rainfall estimation relative to the Z-R relation.

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Spatial variability of surface irradiance measurements at the Manus ARM site

Cited by 6 publications

References 44 publications

The net energy budget at the ocean‐atmosphere interface of the “Cold Tongue” region

The net energy budget at the ocean‐atmosphere interface of the “Cold Tongue” region

Spatial representativeness of ground-based solar radiation measurements-Extension to the full Meteosat disk

Stratiform and Convective Precipitation Observed by Multiple Radars during the DYNAMO/AMIE Experiment

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