Estimating surface long‐wave radiative fluxes at global scale

Nussbaumer, E.; Pinker, R. T.

doi:10.1002/qj.974

Cited by 13 publications

(6 citation statements)

References 31 publications

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“…We also relied on two radiation datasets that we did not bias-correct. Although these data are best in class and were validated against observations [22,23], they may have biases that affect our net radiation results. Arguing against this is the observed global dimming between 1960-1990, which continued into the 2000s in developing regions (Asia) as a result of increasing aerosol pollution [49,50].…”

Section: Discussionmentioning

confidence: 99%

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Changing water availability during the African maize-growing season, 1979–2010

Estes

Chaney

Herrera-Estrada

et al. 2014

Environ. Res. Lett.

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Understanding how global change is impacting African agriculture requires a full physical accounting of water supply and demand, but accurate, gridded data on key drivers (e.g., humidity) are generally unavailable. We used a new bias-corrected meteorological dataset to analyze changes in precipitation (supply), potential evapotranspiration (E p , demand), and water availability (expressed as the ratio P/E p ) in 20 countries (focusing on their maize-growing regions and seasons), between 1979 and 2010, and the factors driving changes in E p . Maizegrowing areas in Southern Africa, particularly South Africa, benefitted from increased water availability due in large part to demand declines driven primarily by declining net radiation, increasing vapor pressure, and falling temperatures (with no effect from changing windspeed), with smaller increases in supply. Sahelian zone countries in West Africa, as well as Ethiopia in East Africa, had strong increases in availability driven primarily by rainfall rebounding from the long-term Sahelian droughts, with little change or small reductions in demand. However, intraseasonal supply variability generally increased in West and East Africa. Across all three regions, declining net radiation contributed downwards pressure on demand, generally over-riding upwards pressure caused by increasing temperatures, the regional effects of which were largest in East Africa. A small number of countries, mostly in or near East Africa (Tanzania and Malawi) experienced declines in water availability primarily due to decreased rainfall, but exacerbated by increasing demand. Much of the reduced water availability in East Africa occurred during the more sensitive middle part of the maize-growing season, suggesting negative consequences for maize production.

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Section: Discussionmentioning

confidence: 99%

“…We obtained updated short-and longwave radiation datasets from the University of Maryland [22,23], at 0.5°and 1.0°resolution, respectively, and downscaled these to 0.25°u sing bilinear interpolation, correcting longwave radiation for elevation [13].…”

Section: A Bias-corrected Gridded Meteorological Datasetmentioning

confidence: 99%

Changing water availability during the African maize-growing season, 1979–2010

Estes

Chaney

Herrera-Estrada

et al. 2014

Environ. Res. Lett.

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“…These error terms may be compared with those of direct measurements of E l↓, which have been assessed at 7.5–10 W m −2 (Dong et al , ; Colbo and Weller, ), or 5% when expressed as a percent (WMO, ). They may also be compared with the RMSE of 17 W m −2 given for daily estimates of E l↓ derived from satellite measurements (Nussbaumer and Pinker, ).…”

Section: Discussionmentioning

confidence: 99%

Estimating long‐wave radiation at the Earth's surface from measurements of specific humidity

Rosa

Stanhill

2013

Intl Journal of Climatology

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ABSTRACT:The relationship between specific humidity (q) and down-welling long-wave radiation (E l↓ ) was investigated on a daily, monthly and annual basis for 39 widely distributed sites of the Baseline Surface Radiation Network (BSRN) and validated using two additional BSRN sites. A power relationship between E l↓ and q was fitted to the data for each site; the constant did not change significantly with time and geographic location. Neither the mean value of q nor the residual value of E l↓ changed significantly between 1998 and 2011, suggesting the applicability of the relationship over periods without large changes in the Earth's temperature and/or concentration of H 2 O. The root mean square error for both the calibration and validation sites was comparable to that reported for both direct measurements and satellite-derived estimates for daily, monthly and annual periods. The power relationships were weaker for three tropical sites located in the Pacific Ocean, where the seasonal variation in q was smaller than at the other sites. For the remaining stations, a common power relationship, E l↓ = 212.64 q 0.22 , enabled monthly values of E l↓ to be accurately estimated from widely available measurements of air temperature, humidity and pressure at the Earth's surface.KEY WORDS surface long-wave radiation from specific humidity; temporal and spatial variations

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“…There is an excellent agreement between the clear sky artificial neural network and the RRTM calculations with a correlation of 1.00, a rmse of 2.31 W m −2 , and a bias of 0.00 W m −2 for 2007, demonstrating the ability of the neural network to emulate a radiative transfer model with the advantage of computational efficiency. The clear sky artificial neural network is an improvement over the previous DSLW/UMD v1 clear sky parameterization [ Nussbaumer and Pinker , 2012] which was based on total column water vapor and screen level temperature. Because of the large number of data points used in the comparison ∼42 million, Figure 3 shows the log 10 density of the data points.…”

Section: Discussionmentioning

confidence: 99%

Estimating surface longwave radiative fluxes from satellites utilizing artificial neural networks

Nussbaumer¹,

Pinker²

2012

J. Geophys. Res.

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[1] A novel approach for calculating downwelling surface longwave (DSLW) radiation under all sky conditions is presented. The DSLW model (hereafter, DSLW/UMD v2) similarly to its predecessor, DSLW/UMD v1, is driven with a combination of Moderate Resolution Imaging Spectroradiometer (MODIS) level-3 cloud parameters and information from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim model. To compute the clear sky component of DSLW a two layer feed-forward artificial neural network with sigmoid hidden neurons and linear output neurons is implemented; it is trained with simulations derived from runs of the Rapid Radiative Transfer Model (RRTM). When computing the cloud contribution to DSLW, the cloud base temperature is estimated by using an independent artificial neural network approach of similar architecture as previously mentioned, and parameterizations. The cloud base temperature neural network is trained using spatially and temporally co-

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Estimating surface long‐wave radiative fluxes at global scale

Cited by 13 publications

References 31 publications

Changing water availability during the African maize-growing season, 1979–2010

Changing water availability during the African maize-growing season, 1979–2010

Estimating long‐wave radiation at the Earth's surface from measurements of specific humidity

Estimating surface longwave radiative fluxes from satellites utilizing artificial neural networks

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