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- Author or Editor: Kaicun Wang x
- Journal of Applied Meteorology and Climatology x
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Abstract
Changes in surface net radiation Rn control the earth’s climate, the hydrological cycle, and plant photosynthesis. However, Rn is not readily available. This study develops a method to estimate surface daytime Rn from solar shortwave radiation measurements as well as conventional meteorological observations (or satellite retrievals) including daily minimum temperature, daily temperature range, and relative humidity, and vegetation indices from satellite data. Measurements collected at 22 U.S. and 2 Tibetan Plateau, China, sites from 2000 to 2006 are used to develop and validate the method. Land cover types include desert, semidesert, croplands, grasslands, and forest. Site elevations range from 98 to 4700 m. The results show that the method estimates Rn under clear and cloudy conditions accurately over a range of land cover types, elevations, and climates without requiring local calibration. The results show that the method estimates Rn accurately. The bias varies from −7.8 to 9.7 W m−2 (±3% in relative value) for different sites, and the root-mean-square error ranges from 12.8 to 21 W m−2 (from +5% to +9% in relative value) for different sites, with an average of 16.9 W m−2 (+6% relative) for all sites. The correlation coefficient for all sites is about 0.99. The correlation coefficient between the measured and predicted annual anomaly (year average subtracted from multiyear average) in daytime Rn is as high as 0.91, demonstrating that the method accurately estimates long-term variation in Rn .
Abstract
Changes in surface net radiation Rn control the earth’s climate, the hydrological cycle, and plant photosynthesis. However, Rn is not readily available. This study develops a method to estimate surface daytime Rn from solar shortwave radiation measurements as well as conventional meteorological observations (or satellite retrievals) including daily minimum temperature, daily temperature range, and relative humidity, and vegetation indices from satellite data. Measurements collected at 22 U.S. and 2 Tibetan Plateau, China, sites from 2000 to 2006 are used to develop and validate the method. Land cover types include desert, semidesert, croplands, grasslands, and forest. Site elevations range from 98 to 4700 m. The results show that the method estimates Rn under clear and cloudy conditions accurately over a range of land cover types, elevations, and climates without requiring local calibration. The results show that the method estimates Rn accurately. The bias varies from −7.8 to 9.7 W m−2 (±3% in relative value) for different sites, and the root-mean-square error ranges from 12.8 to 21 W m−2 (from +5% to +9% in relative value) for different sites, with an average of 16.9 W m−2 (+6% relative) for all sites. The correlation coefficient for all sites is about 0.99. The correlation coefficient between the measured and predicted annual anomaly (year average subtracted from multiyear average) in daytime Rn is as high as 0.91, demonstrating that the method accurately estimates long-term variation in Rn .
Abstract
Knowledge of the evaporative fraction (EF: the ratio of latent heat flux to the sum of sensible and latent heat fluxes) and its controls is particularly important for accurate estimates of water flux, heat exchange, and ecosystem response to climatic changes. In this study, the biological and environmental controls on monthly EF were evaluated across 81 AmeriFlux sites, mainly in North America, for 2000–12. The land-cover types of these sites include forest, shrubland, grassland, and cropland, and the local climates vary from humid to arid. The results show that vegetation coverage, indicated by the normalized difference vegetation index (NDVI), has the best agreement with EF (site-averaged partial correlation coefficient ρ = 0.53; significance level p < 0.05) because of vegetation transpiration demand. The minimum air temperature is closely related to EF (site-averaged ρ = 0.51; p < 0.05) because of the inhibition of respiratory enzyme activity. Relative humidity, an indicator of surface aridity, shows a significant positive correlation with EF (site-averaged ρ = 0.46; p < 0.05). The impacts of wind speed and diurnal air temperature range on EF depend on land-cover types and are strong over grasslands and cropland. From these findings, empirical methods were established to predict monthly EF using meteorological data and NDVI. Correlation coefficients between EF estimates and observations range from 0.80 to 0.93, with root-mean-square errors varying from 0.09 to 0.12. This study demonstrates the varying controls on EF across different landscapes and enhances understanding of EF and its dynamics under changing climates.
Abstract
Knowledge of the evaporative fraction (EF: the ratio of latent heat flux to the sum of sensible and latent heat fluxes) and its controls is particularly important for accurate estimates of water flux, heat exchange, and ecosystem response to climatic changes. In this study, the biological and environmental controls on monthly EF were evaluated across 81 AmeriFlux sites, mainly in North America, for 2000–12. The land-cover types of these sites include forest, shrubland, grassland, and cropland, and the local climates vary from humid to arid. The results show that vegetation coverage, indicated by the normalized difference vegetation index (NDVI), has the best agreement with EF (site-averaged partial correlation coefficient ρ = 0.53; significance level p < 0.05) because of vegetation transpiration demand. The minimum air temperature is closely related to EF (site-averaged ρ = 0.51; p < 0.05) because of the inhibition of respiratory enzyme activity. Relative humidity, an indicator of surface aridity, shows a significant positive correlation with EF (site-averaged ρ = 0.46; p < 0.05). The impacts of wind speed and diurnal air temperature range on EF depend on land-cover types and are strong over grasslands and cropland. From these findings, empirical methods were established to predict monthly EF using meteorological data and NDVI. Correlation coefficients between EF estimates and observations range from 0.80 to 0.93, with root-mean-square errors varying from 0.09 to 0.12. This study demonstrates the varying controls on EF across different landscapes and enhances understanding of EF and its dynamics under changing climates.
Abstract
Clouds determine the amount of solar radiation incident to the surface. Accurately quantifying cloud fraction is of great importance but is difficult to accomplish. Satellite and surface cloud observations have different fields of view (FOVs); the lack of conformity of different FOVs may cause large discrepancies when comparing satellite- and surface-derived cloud fractions. From the viewpoint of surface-incident solar radiation, this paper compares Moderate Resolution Imaging Spectroradiometer (MODIS) level-2 cloud-fraction data with three surface cloud-fraction datasets at five Surface Radiation Network (SURFRAD) sites. The correlation coefficients between MODIS and the surface cloud fractions are in the 0.80–0.91 range and vary at different SURFRAD sites. In a number of cases, MODIS observations show a large cloud-fraction bias when compared with surface data. The variances between MODIS and the surface cloud-fraction datasets are more apparent when small convective or broken clouds exist in the FOVs. The magnitude of the discrepancy between MODIS and surface-derived cloud fractions depends on the satellite’s view zenith angle (VZA). On average, relative to surface cloud-fraction data, MODIS observes a larger cloud fraction at VZA > 40° and a smaller cloud fraction at VZA < 20°. When comparing long-term MODIS averages with surface datasets, Aqua MODIS observes a higher annual mean cloud fraction, likely because convective clouds are better developed in the afternoon when Aqua is observing.
Abstract
Clouds determine the amount of solar radiation incident to the surface. Accurately quantifying cloud fraction is of great importance but is difficult to accomplish. Satellite and surface cloud observations have different fields of view (FOVs); the lack of conformity of different FOVs may cause large discrepancies when comparing satellite- and surface-derived cloud fractions. From the viewpoint of surface-incident solar radiation, this paper compares Moderate Resolution Imaging Spectroradiometer (MODIS) level-2 cloud-fraction data with three surface cloud-fraction datasets at five Surface Radiation Network (SURFRAD) sites. The correlation coefficients between MODIS and the surface cloud fractions are in the 0.80–0.91 range and vary at different SURFRAD sites. In a number of cases, MODIS observations show a large cloud-fraction bias when compared with surface data. The variances between MODIS and the surface cloud-fraction datasets are more apparent when small convective or broken clouds exist in the FOVs. The magnitude of the discrepancy between MODIS and surface-derived cloud fractions depends on the satellite’s view zenith angle (VZA). On average, relative to surface cloud-fraction data, MODIS observes a larger cloud fraction at VZA > 40° and a smaller cloud fraction at VZA < 20°. When comparing long-term MODIS averages with surface datasets, Aqua MODIS observes a higher annual mean cloud fraction, likely because convective clouds are better developed in the afternoon when Aqua is observing.
Abstract
Incident photosynthetically active radiation (PAR) is an important parameter for terrestrial ecosystem models. Because of its high temporal resolution, the Geostationary Operational Environmental Satellite (GOES) observations are very suited to catch the diurnal variation of PAR. In this paper, a new method is developed to derive PAR using GOES data. What makes this new method distinct from the existing method is that it does not need external knowledge of atmospheric conditions. The new method retrieves both atmospheric and surface conditions using only at-sensor radiance through interpolation of time series of observations. Validations against ground measurement are carried out at four “FLUXNET” sites. The values of RMSE of estimated and ground-measured instantaneous PAR at the four sites are 130.71, 131.44, 141.16, and 190.22 μmol m−2 s−1, respectively. At the four validation sites, the RMSE as the percentage of estimated mean PAR value are 9.52%, 13.01%, 13.92%, and 24.09%, respectively; the biases are −101.54, 16.56, 11.09, and 53.64 μmol m−2 s−1, respectively. The independence of external atmospheric information enables this method to be applicable to many situations in which external atmospheric information is not available. In addition, topographic impacts on surface PAR are examined at the 1-km resolution at which PAR is retrieved using the GOES visible band data.
Abstract
Incident photosynthetically active radiation (PAR) is an important parameter for terrestrial ecosystem models. Because of its high temporal resolution, the Geostationary Operational Environmental Satellite (GOES) observations are very suited to catch the diurnal variation of PAR. In this paper, a new method is developed to derive PAR using GOES data. What makes this new method distinct from the existing method is that it does not need external knowledge of atmospheric conditions. The new method retrieves both atmospheric and surface conditions using only at-sensor radiance through interpolation of time series of observations. Validations against ground measurement are carried out at four “FLUXNET” sites. The values of RMSE of estimated and ground-measured instantaneous PAR at the four sites are 130.71, 131.44, 141.16, and 190.22 μmol m−2 s−1, respectively. At the four validation sites, the RMSE as the percentage of estimated mean PAR value are 9.52%, 13.01%, 13.92%, and 24.09%, respectively; the biases are −101.54, 16.56, 11.09, and 53.64 μmol m−2 s−1, respectively. The independence of external atmospheric information enables this method to be applicable to many situations in which external atmospheric information is not available. In addition, topographic impacts on surface PAR are examined at the 1-km resolution at which PAR is retrieved using the GOES visible band data.
Abstract
Monitoring land surface drought using remote sensing data is a challenge, although a few methods are available. Evapotranspiration (ET) is a valuable indicator linked to land drought status and plays an important role in surface drought detection at continental and global scales. In this study, the evaporative drought index (EDI), based on the estimated actual ET and potential ET (PET), is described to characterize the surface drought conditions. Daily actual ET at 4-km resolution for April–September 2003–05 across the continental United States is estimated using a simple improved ET model with input solar radiation acquired by Moderate-Resolution Imaging Spectroradiometer (MODIS) at a spatial resolution of 4 km and input meteorological parameters from NCEP Reanalysis-2 data at a spatial resolution of 32 km. The PET is also calculated using some of these data. The estimated actual ET has been rigorously validated with ground-measured ET at six Enhanced Facility sites in the Southern Great Plains (SGP) of the Atmosphere Radiation Measurement Program (ARM) and four AmeriFlux sites. The validation results show that the bias varies from −11.35 to 27.62 W m−2 and the correlation coefficient varies from 0.65 to 0.86. The monthly composites of EDI at 4-km resolution during April–September 2003–05 are found to be in good agreement with the Palmer Z index anomalies, but the advantage of EDI is its finer spatial resolution. The EDI described in this paper incorporates information about energy fluxes in response to soil moisture stress without requiring too many meteorological input parameters, and performs well in assessing drought at continental scales.
Abstract
Monitoring land surface drought using remote sensing data is a challenge, although a few methods are available. Evapotranspiration (ET) is a valuable indicator linked to land drought status and plays an important role in surface drought detection at continental and global scales. In this study, the evaporative drought index (EDI), based on the estimated actual ET and potential ET (PET), is described to characterize the surface drought conditions. Daily actual ET at 4-km resolution for April–September 2003–05 across the continental United States is estimated using a simple improved ET model with input solar radiation acquired by Moderate-Resolution Imaging Spectroradiometer (MODIS) at a spatial resolution of 4 km and input meteorological parameters from NCEP Reanalysis-2 data at a spatial resolution of 32 km. The PET is also calculated using some of these data. The estimated actual ET has been rigorously validated with ground-measured ET at six Enhanced Facility sites in the Southern Great Plains (SGP) of the Atmosphere Radiation Measurement Program (ARM) and four AmeriFlux sites. The validation results show that the bias varies from −11.35 to 27.62 W m−2 and the correlation coefficient varies from 0.65 to 0.86. The monthly composites of EDI at 4-km resolution during April–September 2003–05 are found to be in good agreement with the Palmer Z index anomalies, but the advantage of EDI is its finer spatial resolution. The EDI described in this paper incorporates information about energy fluxes in response to soil moisture stress without requiring too many meteorological input parameters, and performs well in assessing drought at continental scales.
