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Publications Using SRB Data



Present Year

Ma, Y., Z. Hu, Q. Xie, X. Meng, L. Zhao, and W. Dong, 2022: Convection-permitting modeling over the Tibetan Plateau improves the simulation of Meiyu Rainfall during the 2011 Yangtze Plain flood. Atmospheric Research, 265, 105907, https://doi.org/10.1016/j.atmosres.2021.105907.

Qiao, X., J. Liu, S. Wang, J. Wang, H. Ji, X. Chen, H. Liu, and F. Lu, 2021: Lead-lag correlations between snow cover and meteorological factors at multi-time scales in the Tibetan Plateau under climate warming. Theor Appl Climatol, 146, 1459–1477, https://doi.org/10.1007/s00704-021-03802-x.

Jin, H., X. Chen, R. Zhong, P. Wu, and D. Li, 2021: Spatio-temporal changes of precipitation in the Hanjiang River Basin under climate change. Theor Appl Climatol, 146, 1441–1458, https://doi.org/10.1007/s00704-021-03801-y.

Peng, L., Z. Wei, Z. Zeng, P. Lin, E. F. Wood, and J. Sheffield, 2021: Reducing Solar Radiation Forcing Uncertainty and Its Impact on Surface Energy and Water Fluxes. Journal of Hydrometeorology, 22, 813–829, https://doi.org/10.1175/JHM-D-20-0052.1.

Goswami, T., P. Mukhopadhyay, M. Ganai, R. P. M. Krishna, M. Mahakur, and J.-Y. Han, 2020a: How changing cloud water to rain conversion profile impacts on radiation and its linkage to a better Indian summer monsoon rainfall simulation. Theor Appl Climatol, 141, 947–958, https://doi.org/10.1007/s00704-020-03222-3.

Feng, F., and K. Wang, 2021: Merging High-Resolution Satellite Surface Radiation Data with Meteorological Sunshine Duration Observations over China from 1983 to 2017. Remote Sensing, 13, 602, https://doi.org/10.3390/rs13040602.

Wei, Y., and Coauthors, 2021: Trends and Variability of Atmospheric Downward Longwave Radiation Over China From 1958 to 2015. Earth Space Sci, 8, https://doi.org/10.1029/2020EA001370.

2020

Ji, P., X. Yuan, and D. Li, 2020: Atmospheric Radiative Processes Accelerate Ground Surface Warming over the Southeastern Tibetan Plateau during 1998–2013. Journal of Climate, 33, 1881–1895, https://doi.org/10.1175/JCLI-D-19-0410.1.

He, J., K. Yang, W. Tang, H. Lu, J. Qin, Y. Chen, and X. Li, 2020: The first high-resolution meteorological forcing dataset for land process studies over China. Sci Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y.

Zhang, B., Z. Guo, L. Zhang, T. Zhou, and T. Hayasaya, 2020: Cloud Characteristics and Radiation Forcing in the Global Land Monsoon Region From Multisource Satellite Data Sets. Earth and Space Science, 7, https://doi.org/10.1029/2019EA001027.

Srivastava, A. K., A. Ceglar, W. Zeng, T. Gaiser, C. M. Mboh, and F. Ewert, 2020: The Implication of Different Sets of Climate Variables on Regional Maize Yield Simulations. Atmosphere, 11, 180, https://doi.org/10.3390/atmos11020180.

Zhou, Z., A. Lin, L. Wang, W. Qin, Y. zhong, and L. He, 2020: Trends in downward surface shortwave radiation from multi‐source data over China during 1984–2015. Int J Climatol, 40, 3467–3485, https://doi.org/10.1002/joc.6408.

2019

Fernandez, J. P. R., S. H. Franchito, and V. B. Rao, 2019: Future Changes in the Aridity of South America from Regional Climate Model Projections. Pure Appl. Geophys., 176, 2719–2728, https://doi.org/10.1007/s00024-019-02108-4.

Wang, M., J. Wang, A. Duan, J. Yang, and Y. Liu, 2019: Quasi-biweekly impact of the atmospheric heat source over the Tibetan Plateau on summer rainfall in Eastern China. Clim Dyn, 53, 4489–4504, https://doi.org/10.1007/s00382-019-04798-x.

Zhang, X., and Coauthors, 2019: An Operational Approach for Generating the Global Land Surface Downward Shortwave Radiation Product From MODIS Data. IEEE Trans. Geosci. Remote Sensing, 57, 4636–4650, https://doi.org/10.1109/TGRS.2019.2891945.

Xie, Z., and B. Wang, 2019: Summer Atmospheric Heat Sources over the Western–Central Tibetan Plateau: An Integrated Analysis of Multiple Reanalysis and Satellite Datasets. J. Climate, 32, 1181–1202, https://doi.org/10.1175/JCLI-D-18-0176.1.

Tang, C., B. Morel, M. Wild, B. Pohl, B. Abiodun, and M. Bessafi, 2019: Numerical simulation of surface solar radiation over Southern Africa. Part 1: Evaluation of regional and global climate models. Clim Dyn, 52, 457–477, https://doi.org/10.1007/s00382-018-4143-1.

2018

Doering, K., and S. Steinschneider, 2018: Summer Covariability of Surface Climate for Renewable Energy across the Contiguous United States: Role of the North Atlantic Subtropical High. Journal of Applied Meteorology and Climatology, 57, 2749–2768, https://doi.org/10.1175/JAMC-D-18-0088.1.

Careto, J. A. M., R. M. Cardoso, P. M. M. Soares, and R. M. Trigo, 2018: Land-Atmosphere Coupling in CORDEX-Africa: Hindcast Regional Climate Simulations. J. Geophys. Res. Atmos., 123, 11,048-11,067, https://doi.org/10.1029/2018JD028378.

Zheng, Y., L. Zhang, J. Xiao, W. Yuan, M. Yan, T. Li, and Z. Zhang, 2018: Sources of uncertainty in gross primary productivity simulated by light use efficiency models: Model structure, parameters, input data, and spatial resolution. Agricultural and Forest Meteorology, 263, 242–257, https://doi.org/10.1016/j.agrformet.2018.08.003.

Nojarov, P., 2019: Factors affecting air temperature in Bulgaria. Theor Appl Climatol, 137, 571–586, https://doi.org/10.1007/s00704-018-2622-2.

Duan, A., S. Liu, Y. Zhao, K. Gao, and W. Hu, 2018: Atmospheric heat source/sink dataset over the Tibetan Plateau based on satellite and routine meteorological observations. Big Earth Data, 2, 179–189, https://doi.org/10.1080/20964471.2018.1514143.

Yang, L., X. Zhang, S. Liang, Y. Yao, K. Jia, and A. Jia, 2018: Estimating Surface Downward Shortwave Radiation over China Based on the Gradient Boosting Decision Tree Method. Remote Sensing, 10, 185, https://doi.org/10.3390/rs10020185.

Yao, Y., and Coauthors, 2018: Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years. Glob Change Biol, 24, 184–196, https://doi.org/10.1111/gcb.13830.

Qin, W., L. Wang, A. Lin, M. Zhang, and M. Bilal, 2018: Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network. Remote Sensing, 10, 1022, https://doi.org/10.3390/rs10071022.

Sun, D., C. Ji, W. Sun, Y. Yang, and H. Wang, 2018: Accuracy assessment of three remote sensing shortwave radiation products in the Arctic. Atmospheric Research, 212, 296–308, https://doi.org/10.1016/j.atmosres.2018.01.003.

Halder, S., P. A. Dirmeyer, L. Marx, and J. L. Kinter, 2018: Impact of Land Surface Initialization and Land-Atmosphere Coupling on the Prediction of the Indian Summer Monsoon with the CFSv2. Front. Environ. Sci., 5, 92, https://doi.org/10.3389/fenvs.2017.00092.

2017

Thiery, W., E. L. Davin, D. M. Lawrence, A. L. Hirsch, M. Hauser, and S. I. Seneviratne, 2017: Present‐day irrigation mitigates heat extremes. J. Geophys. Res. Atmos., 122, 1403–1422, https://doi.org/10.1002/2016JD025740.

Goswami, T., S. A. Rao, A. Hazra, H. S. Chaudhari, A. Dhakate, K. Salunke, and S. Mahapatra, 2017: Assessment of simulation of radiation in NCEP Climate Forecasting System (CFS V2). Atmospheric Research, 193, 94–106, https://doi.org/10.1016/j.atmosres.2017.04.013.

Ma, N., G.-Y. Niu, Y. Xia, X. Cai, Y. Zhang, Y. Ma, and Y. Fang, 2017: A Systematic Evaluation of Noah-MP in Simulating Land-Atmosphere Energy, Water, and Carbon Exchanges Over the Continental United States: Noah-MP Evaluation in CONUS. J. Geophys. Res. Atmos., 122, 12,245-12,268, https://doi.org/10.1002/2017JD027597.

Duan, A., R. Sun, and J. He, 2017: Impact of surface sensible heating over the Tibetan Plateau on the western Pacific subtropical high: A land–air–sea interaction perspective. Adv. Atmos. Sci., 34, 157–168, https://doi.org/10.1007/s00376-016-6008-z.

Riihelä, A., J. R. Key, J. F. Meirink, P. Kuipers Munneke, T. Palo, and K.-G. Karlsson, 2017: An intercomparison and validation of satellite-based surface radiative energy flux estimates over the Arctic: ARCTIC RADIATIVE ENERGY FLUXES. J. Geophys. Res. Atmos., 122, 4829–4848, https://doi.org/10.1002/2016JD026443.

Tei, S., A. Sugimoto, H. Yonenobu, Y. Matsuura, A. Osawa, H. Sato, J. Fujinuma, and T. Maximov, 2017: Tree-ring analysis and modeling approaches yield contrary response of circumboreal forest productivity to climate change. Glob Change Biol, 23, 5179–5188, https://doi.org/10.1111/gcb.13780.

Cheng, J., S. Liang, W. Wang, and Y. Guo, 2017: An efficient hybrid method for estimating clear-sky surface downward longwave radiation from MODIS data: A Hybrid Method for Estimating LWDN. J. Geophys. Res. Atmos., 122, 2616–2630, https://doi.org/10.1002/2016JD026250.

Levine, X. J., and W. R. Boos, 2017: Land surface albedo bias in climate models and its association with tropical rainfall. Geophys. Res. Lett., 44, 6363–6372, https://doi.org/10.1002/2017GL072510.

Xie, H., and C. Ringler, 2017: Agricultural nutrient loadings to the freshwater environment: the role of climate change and socioeconomic change. Environ. Res. Lett., 12, 104008, https://doi.org/10.1088/1748-9326/aa8148.

Cao, Y., S. Liang, X. Chen, T. He, D. Wang, and X. Cheng, 2017: Enhanced wintertime greenhouse effect reinforcing Arctic amplification and initial sea-ice melting. Sci Rep, 7, 8462, https://doi.org/10.1038/s41598-017-08545-2.


2016

Liu, W., L. Wang, J. Zhou, and et al., 2016: A Worldwide Evaluation Of Basin-scale Evapotranspiration Estimates Against the Water Balance Method. J. Hydrology, 538, 82-95, https://doi.org/10.1016/j.jhydrol.2016.04.006.

Liu, J., and B. Jia, 2016: Ensemble Simulation Of Land Evapotranspiration In China Based On a Multi-forcing and Multi-model Approach. Adv. Atm. Sci., 6, 673-684, doi:10.1007/s0037

McCabe, M., A. Ershadi, C. Jiminez, and et al., 2016: The GEWEX LandFlux Project: Evaluation Of Model Evaporation Using Tower-based and Globaly Gridded Forcing Data. Geosci. Model Dev., 9, 283-305, doi:10.5194/gmd-9-283-2016

Wang, L., X. Li, Y. Chen, and et al., 2016: Validation Of the Global Land Data Assimilation System Based On Measurements Of Soil Temperature Profiles. Agric. For. Meteorol., 218-219, 288-297, doi:10.1016/j.agrformet.2016.01.003

Tang, W., J. Qin, K. Yang, and et al., 2016: Retrieving High-resolution Surface Solar Radiation With Cloud parameters Derived By Combining MODIS and MTSAT Data. Atmos. Chem. Phys., 16, 2543-2557, doi:10.5194/acp-16-2543-2016

Christensen, M., A. Behrangi, T. L’ecuyer, and et al., 2016: Arctic Observation and Reanalysis Integrated System: A New Data Product for Validation and Climate Study. Bull. Amer. Meteor. Soc., 97, 907-915, doi:10.1175/BAMS-D-14-00273.1

Xia, Y., B. Cosgrove, K. Mitchell, and et al., 2016: Basin-scale assessment of the land surface energy budget in the National Centers for Environmental Prediction operational and research NLDAS-2 systems. Journal of Geophysical Research: Atmospheres, 121,1, 196-220, doi:10.1002/2015JD023889

Jiang, X., Y. Li, S. Yang, and J. Chen, 2016: Interannual Variation of Summer Atmospheric Heat Source over the Tibetan Plateau and the Role of Convection around the Western Maritime Continent. Journal of Climate, 29,1, 121-138, doi:10.1175/JCLI-D-15-0181.1

Loew, A., A. Andersson, J. Trentmann, and M. Schrӧder, 2016: Assessing Surface Solar Radiation Fluxes in the CMIP Ensembles. Journal of Climate, 29, 7231–7246, doi:10.1175/JCLI-D-14-00503.1

Hakuba, M., D. Folini, and M. Wild, 2016: On the zonal near constancy of fractional solar absorption in the atmosphere. Journal of Climate, 29, 3423–3440, doi:10.1175/JCLI-D-15-0277.1

Orth, R., E. Dutra, and F. Pappenberger, 2016: Improving weather predictability by including land-surface model parameter uncertainty. Monthly Weather Review, XX, XX, doi:10.1175/MWR-D-15-0283.1

Slater, A., 2016: Surface Solar Radiation in North America: A Comparison of Observations, Reanalyses, Satellite and Derived Products. J. Hydrometeor. , 17, 401-420, doi:10.1175/JHM-D-15-0087.1

2015

Luo, S., Z. Sun, X. Zheng, L. Rikus, and C. Franklin, 2015: Evaluation of ACCESS model cloud properties over the Southern Ocean area using multiple-satellite products. Quarterly J. of the Royal Meteorological Society , NYIP, 36 pp., doi:10.1002/qj.2641

Cattiaux, J., H. Douville, R. Schoetter, S. Parey, and P. Yiou, 2015: Projected increase in diurnal and interdiurnal variations of European summer temperatures. Geophys. Res. Lett. , 42(3), 899-907, doi:10.1002/2014GL062531

Orth, R., and S. Seneviratne, 2015: Introduction of a simple-model-based land surface dataset for Europe. Environmental Res. Lett. , 10(4), 11 pp., doi:10.1088/1748-9326/10/4/044012

Albarelo, T., I. Marie-Joseph, A. Primerose, F. Seyler, L. Wald, and L. Linguet, 2015: Optimizing the Heliosat-II Method for Surface Solar Irradiation Estimation with GOES Images. Canadian Journal of Remote Sensing , 41(2), 86-100, doi:10.1080/07038992.2015.1040876

Gao, S., Q. Wu, Z. Zhang, and X. Xu, 2015: Impact of climatic factors on permafrost of the Qinghai-Xizang Plateau in the time-frequency domain. Quaternary Intl. , 374, 110-117, doi:10.1016/j.quaint.2015.02.036

Hu, J., and A. Duan, 2015: Relative contributions of the Tibetan Plateau thermal forcing and the Indian Ocean Sea surface temperature basin mode to the interannual variability of the East Asian summer monsoon. Climate Dynamics , 15 pp., doi:10.1007/s00382-015-2503-7

Knox, R.G., M. Longo, A. L. S. Swann, K. Zhang, N. M. Levine, P. R. Moorcroft, and R. L. Bras, 2015: Hydrometeorological effects of historical land-conversion in an ecosystem-atmosphere model of Northern South America . Hydrology and Earth System Sciences , 19(1), 241-273, doi:10.5194/hess-19-241-2015

Tatsumi, K., and Y. Yamashiki, 2015: Effect of irrigation water withdrawals on water and energy balance in the Mekong River Basin using an improved VIC land surface model with fewer calibration parameters. Agricultural Water Management , 159, 92-106, doi:10.1016/j.agwat.2015.05.011

Guillod, B., B. Orlowsky, D. G. Miralles, A. J. Teuling, and S. I. Seneviratne, 2015: Reconciling spatial and temporal soil moisture effects on afternoon rainfall . Nature communications, 6, 6 pp., doi:10.1038/ncomms7443

Ying, Q., S. Liang, Q. Liu, T. He, S. Liu, and X. Li, 2015: Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products . Remote Sensing, 7(1), 990-1020, doi:10.3390/rs70100990

Zhang, J., F. Yao, and X. Shao, 2015: Estimation and Assessment of Drought in North China based on Evapotranspiration Drought Index and Remote Sensing Data . Atlantis-Press, .

García-Díez, M., J. Fernández, and R. Vautard, 2015: An RCM multi-physics ensemble over Europe: multi-variable evaluation to avoid error compensation. Clim Dyn, 16 pp., doi:10.1007/s00382-015-2529-x

Wang, K., Q. Ma, Z. Li, and J. Wang, 2015: Decadal variability of surface incident solar radiation over China: Observations, satellite retrievals, and reanalyses. Journal of Geophysical Research: Atmospheres, 120, 6500-6514, doi:10.1002/2015JD023420

Wang, G., M. Yu, J. S. Pal, R. Mei, G. Bonan, S. Levis, and P. Thorton, 2015: On the development of a coupled regional climate–vegetation model RCM-CLM-CN-DV and its validation in Tropical Africa . Clim Dyn, 26 pp., doi:10.1007/s00382-015-2596-z

Sharma, S., D. K. Gray, J. S. Read, and et al., 2015: A global database of lake surface temperatures collected by in situ and satellite methods from 1985–2009 . Scientific Data, 2, 19 pp., doi:10.1038/sdata.2015.8

Wang, L., T. Li, and T. Zhou, 2015: Effect of high-frequency wind on intraseasonal SST variabilities over the mid-latitude North Pacific region during boreal summer . Clim Dyn, 11 pp., doi:10.1007/s00382-015-2496-2

Turuncoglu, U. U., 2015: Identifying the sensitivity of precipitation of Anatolian peninsula to Mediterranean and Black Sea surface temperature . Clim Dyn, 23 pp., doi:10.1007/s00382-014-2346-7

Niu, X., and R. T. Pinker, 2015: An improved methodology for deriving high-resolution surface shortwave radiative fluxes from MODIS in the Arctic region. J. Geophys. Res. Atmos., 120, 2382-2393, doi:10.1002/2014JD022151

Lapo, K. E., L. M. Hinkelman, M. S. Raleigh, and J. D. Lundquist, 2015: Impact of errors in the downwelling irradiances on simulations of snow water equivalent, snow surface temperature, and the snow energy balance. Water Resour. Res., 51, 1649-1670, doi:10.1002/2014WR016259

Müller, R, U. Pfeifroth, C. Träger-Chatterjee, J. Trentmann, and R. Cremer, 2015: Digging the METEOSAT Treasure—3 Decades of Solar Surface Radiation. Remote Sensing, 7(6), 8067-8101, doi:10.3390/rs70608067

Zeng, Z., A. Chen, P. Ciais, and et al., 2015: Regional air pollution brightening reverses the greenhouse gases induced warming-elevation relationship. Geophys. Res. Lett., 42, 4563-4572, doi:10.1002/2015GL064410

Wang, L. D., D. R. Lü, and Q. He, 2015: The impact of surface properties on downward surface shortwave radiation over the Tibetan Plateau. Adv. Atmos. Sci., 32(6), 759-771, doi:10.1007/s00376-014-4131-2

Jiao, Z., G. Yan, J. Zhao, T. Wang, and L. Chen, 2015: Estimation of surface upward longwave radiation from MODIS and VIIRS clear-sky data in the Tibetan Plateau. Remote Sensing of Environment, 162, 221-237, doi:10.1016/j.rse.2015.02.021

Wu, H., K. Yang, X.L. Niu, and Y.Y. Chen, 2015: The role of cloud height and warming in the decadal weakening of atmospheric heat source over the Tibetan Plateau . Science China: Earth Sciences, 58, 395-403, doi:10.1007/s11430-014-4973-6

Ruane, A. C., R. Goldberg, and J. Chryssanthacopoulos, 2015: Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation. Agricultural and Forest Meteorology, 200, 233-248, doi:10.1016/j.agrformet.2014.09.016

Mallick, K., A. Jarvis, G. Wohlfahrt, and et al., 2015: Components of near-surface energy balance derived from satellite soundings – Part 1: Noontime net available energy . Biogeosciences, 12, 433-451, doi:10.5194/bg-12-433-2015, 2015

Zhang, H., X. Xin, L. Li, and Q. Liu, 2015: Estimating global solar radiation using a hybrid parametric model from MODIS data over the Tibetan Plateau . Solar Energy, 112, 373-382, doi:10.1016/j.solener.2014.12.015

Ma, Q., K. Wang, and M. Wild, 2015: Impact of geolocations of validation data on the evaluation of surface incident shortwave radiation from Earth System Models. J. Geophys. Res. Atmos., 120, 6825-6844, doi:10.1002/2014JD022572

Meynadier, R., G. De Coëtlogon, S. Bastin, L. Eymard, and S. Janicot, 2015: Sensitivity testing of WRF parameterizations on air–sea interaction and its impact on water cycle in the Gulf of Guinea. Quarterly Journal of the Royal Meteorological Society, 141(690), 1804-1820, doi:10.1002/qj.2483

Rongqian, Y., M. Ek, and J. Meng, 2015: Surface Water and Energy Budgets for the Mississippi River Basin in Three NCEP Reanalyses. J. Hydrometeor, 16, 857-873, doi:10.1175/JHM-D-14-0056.1

Rutan, D. A., S. Kato, D. R. Doelling, F. G. Rose, L. T. Nguyen, T. E. Caldwell, and N. G. Loeb, 2015: CERES Synoptic Product: Methodology and Validation of Surface Radiant Flux. J. Atmos. Oceanic Technol, 32, 1121-1143, doi:10.1175/JTECH-D-14-00165.1

Qin, J., W. Tang, K. Yang, N. Lu, X. Niu, and S. Liang, 2015: An efficient physically based parameterization to derive surface solar irradiance based on satellite atmospheric products. J. Geophys. Res. Atmos., , 120, 4975-4988, doi:10.1002/2015JD023097

Pan, X., Y. Liu, and X. Fan, 2015: Comparative assessment of satellite-retreived surface net radiation: an examination on CERES and SRB datasets in China. Remote Sensing, 7, 20 pp., doi:10.3390/rs70404899

Stephens, G. L., and T. L’Ecuyer, 2015: The Earth’s energy balance . Atmospheric Research, 166, 195-203, doi:10.1016/j.atmosres.2015.06.024

Zhang, X., S. Liang, M. Wild, and B. Jiang, 2015: Analysis of surface incident shortwave radiation from four satellite products. Remote Sensing of Environment, 165, 186-202, doi:10.1016/j.rse.2015.05.015

Pyrina, M., N. Hatzianastassiou, C. Matsoukas, and et al., 2015: Cloud effects on the solar and thermal radiation budgets of the Mediterranean basin. Atmospheric Research, 152, 14-28, doi:10.1016/j.atmosres.2013.11.009

He, T., S. Liang, D. Wang, Q. Shi, and M. L. Goulden, 2015: Estimation of high-resolution land surface net shortwave radiation from AVIRIS data: Algorithm development and preliminary results. Remote Sens. Environ, 167, 20-30, doi:10.1016/j.rse.2015.03.021

2014

Panitz, H. J., A. Dosio, M. Büchner, D. Lüthi, and K. Keuler, 2014: COSMO-CLM (CCLM) climate simulations over CORDEX-Africa domain: analysis of the ERA-Interim driven simulations at 0.44 and 0.22 resolution. Climate Dynamics, 42(11-12), 3015-3038, doi:10.1007/s00382-013-1834-5

Long, D., L. Longuevergne, and B. R. Scanlon, 2014: Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites. Water Resources Research, 50(2), 1131-1151, doi:10.1002/2013WR014581

Posselt, R., R. Müller, J. Trentmann, R. Stockli, and M. A. Liniger, 2014: A surface radiation climatology across two Meteosat satellite generations. Remote Sensing of Environment, 142, 103-110, doi:10.1016/j.rse.2013.11.007

Cai, W., W. Yuan, S. Liang, and et al., 2014: Improved estimations of gross primary production using satellite‐derived photosynthetically active radiation . Journal of Geophysical Research: Biogeosciences, 119(1), 110-123, doi:10.1002/2013JG002456

Armanios, D. E., and J. B. Fisher, 2014: Measuring water availability with limited ground data: assessing the feasibility of an entirely remote sensing based hydrologic budget of the Rufiji Basin, Tanzania, using TRMM, GRACE, MODIS, SRB, and AIRS. Hydrological Processes, 28(3), 853-867, doi:10.1002/hyp.9611

Nabat, P., S. Somot, M. Mallet, F. Sevault, M. Chiacchio, and M. Wild, 2014: Direct and semi-direct aerosol radiative effect on the Mediterranean climate variability using a coupled regional climate system model . Climate Dynamics, 44(3-4), 1127-1155, doi:10.1007/s00382-014-2205-6

Gianotti, R. L., , and E. A. Eltahir, 2014: Regional climate modeling over the Maritime Continent. Part I: New parameterization for convective cloud fraction. Remote Sensing of Environment, 27(4), 1488-1503, doi:10.1175/JCLI-D-13-00127.1

Zhang, X., S. Liang, G. Zhou, H. Wu, and X. Zhao, 2014: Generating Global LAnd Surface Satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data . Remote Sensing of Environment, 152, 318-332, doi:10.1016/j.rse.2014.07.003

Yao, Y, S. Liang, S. Zhao, and et al., 2014: Validation and application of the modified satellite-based Priestley-Taylor algorithm for mapping terrestrial evapotranspiration. Remote Sens., 6(1), 880-904, doi:10.3390/rs6010880

Güttler, I., Č. Branković, L. Srnec, and M. Patarčić, 2014: The impact of boundary forcing on RegCM4.2 surface energy budget. Climatic change, 125(1), 67-78, doi:10.1007/s10584-013-0995-x

Pessacg, N. L., S. Solman, P. Samuelsson, and et al., 2014: The surface radiation budget over South America in a set of regional climate models from the CLARIS-LPB project . Climate Dynamics, 43(5-6), 1221-1239, doi:10.1007/s00382-013-1916-4

He, T., S. Liang, and D. X. Song, 2014: Analysis of global land surface albedo climatology and spatial‐temporal variation during 1981–2010 from multiple satellite products. Journal of Geophysical Research: Atmospheres, 119(17), 10-281, doi:10.1002/2014JD021667

Wong, S., T. S. L’Ecuyer,, W. S. Olson, X. Jiang, and E. J. Fetzer, 2014: Local balance and variability of atmospheric heat budget over oceans: Observation and reanalysis-based estimates. Journal of Climate, 27(2), 893-913, doi:10.1175/JCLI-D-13-00143.1

Lange, S., , B. Rockel, J. Volkholz, and B. Bookhagen, 2014: Regional climate model sensitivities to parametrizations of convection and non-precipitating subgrid-scale clouds over South America. Climate Dynamics, 44(9), 2839-2857, doi:10.1007/s00382-014-2199-0

Shi, Q., , and S. Liang, 2014: Surface-sensible and latent heat fluxes over the Tibetan Plateau from ground measurements, reanalysis, and satellite data. Atmospheric Chemistry and Physics, 14(11), 5659-5677, doi:10.5194/acp-14-5659-2014

Zhang, Y, and S. Liang, 2014: Surface radiative forcing of forest disturbances over northeastern China. Environmental Research Letters, 9, 7 pp., doi:10.1088/1748-9326/9/2/024002

Jin, Y., and M. Goulden, 2014: Ecological consequences of variation in precipitation: separating short‐versus long‐term effects using satellite data . Global Ecology and Biogeography, 23(3), 358-370, doi:10.1111/geb.12135

Kothe, S., D. Lüthi, and B. Ahrens, 2014: Analysis of the West African Monsoon system in the regional climate model COSMO‐CLM. Intl. Journal of Climatology, 34(2), 481-493, doi:10.1002/joc.3702

Ueyama, M., K. Ichii, H. Iwata, and et al., 2014: Change in surface energy balance in Alaska due to fire and spring warming, based on upscaling eddy covariance measurements. Journal of Geophysical Research: Biogeosciences, 119(10), 1947-1969, doi:10.1002/2014JG002717

Lee, H., J. Kim, D. E. Waliser, and et al., 2014: Using joint probability distribution functions to evaluate simulations of precipitation, cloud fraction and insolation in the North America Regional Climate Change Assessment Program (NARCCAP). . Climate Dynamics, 45(1-2), 309-323, doi:10.1007/s00382-014-2253-y

Yao, Y., S. Zhao, Y. Zhang, K. Jia, and M. Liu, 2014: Spatial and decadal variations in potential evapotranspiration of China based on reanalysis datasets during 1982–2010. Atmosphere, 5(4), 737-754, doi:10.3390/atmos5040737

Yang, K., H. Wu, Y. Chen, J. Qin, and L. Wang, 2014: Toward a satellite‐based observation of atmospheric heat source over land. Journal of Geophysical Research: Atmospheres, 119(6), 3124-3133, doi:10.1002/2013JD021091

Liang, S., X. Zhang, Z. Xiao, and et al., 2014: Incident Shortwave Radiation . Springer International Publishing, Global LAnd Surface Satellite (GLASS) Products, 123-142, doi:10.1007/978-3-319-02588-9_5

Newton, B., S. Cowie, D. Rijks, and et al., 2014: Solar cooking in the Sahel . Bulletin of the American Meteorological Society, 95(9), 1325-1328, doi:10.1175/BAMS-D-13-00182.1

Wang, J., B. H. Tang, X. Y. Zhang, H. Wu, and Z. L. Li, 2014: Estimation of Surface Longwave Radiation over the Tibetan Plateau Region Using MODIS Data for Cloud-Free Skies. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 7(9), 3695-3703, doi:10.1109/JSTARS.2014.2320585

Franchito, S. H., , J. P. R. Fernandez, and D. Pareja, 2014: Surrogate Climate Change Scenario and Projections with a Regional Climate Model: Impact on the Aridity in South America . American Journal of Climate Change, 3(05), 474-489, doi:10.4236/ajcc.2014.35041

Mazurek, G. , 2014: Estimation of Solar Irradiation on Inclined Surface Based on Web Databases. International Journal of Electronics and Telecommunications, 60(4), 315-320, doi:10.2478/eletel-2014-0041

Sevault, F., S. Somot, A. Alias, and et al., 2014: A fully coupled Mediterranean regional climate system model: design and evaluation of the ocean component for the 1980-2012 period . Tellus A , 66(2014), 32 pp., doi:10.3402/tellusa.v66.23967

Gianotti, R. L., and E. A. Elthair, 2014: Regional climate modeling over the Maritime Continent. Part II: New parameterization for autoconversion of convective rainfall . Journal of Climate, 27(4), 1504-1523, doi:10.1175/JCLI-D-13-00171.1

Kleidon, A., M. Renner, and P. Porada, 2014: Estimates of the climatological land surface energy and water balance derived from maximum convective power. Hydrology and Earth System Sciences, 18, 2201-2218, doi:10.5194/hess-18-2201-2014

Chaney, N. W., J. Sheffield, G. Villarini, and E. F. Wood, 2014: Development of a high-resolution gridded daily meteorological dataset over sub-saharan Africa: Spatial analysis of trends in climate extremes . Journal of Climate, 27(15), 5815-5835, doi:10.1175/JCLI-D-13-00423.1

Xie, H., L. You, B. Wielgosz, and C. Ringler, 2014: Estimating the potential for expanding smallholder irrigation in Sub-Saharan Africa. Agricultural Water Management, 131, 183-193, doi:10.1016/j.agwat.2013.08.011

Jackson, D. L., and G. A. Wick, 2014: Propagation of uncertainty analysis of CO2 transfer velocities derived from the COARE gas transfer model using satellite inputs. Journal of Geophysical Research: Oceans, 119(3), 1828-1842, doi:10.1002/2013JC009271

Robertson, F. R, M. G. Bosilovich, J. B. Roberts, and et al., 2014: Consistency of estimated global water cycle variations over the satellite era. Journal of Climate, 27(16), 6135-6154, doi:10.1175/JCLI-D-13-00384.1

Guillod, B. P., B. Orlowsky, D. Miralles, and et al., 2014: Land-surface controls on afternoon precipitation diagnosed from observational data: uncertainties and confounding factors. Earth and Environmental Engineering, 14, 8343-8367, doi:10.7916/D8FT8JM2

Minobe, S., and S. Takebayashi, 2014: Diurnal precipitation and high cloud frequency variability over the Gulf Stream and over the Kuroshio. Climate Dynamics, 44(7-8), 2079-2095, doi:10.1007/s00382-014-2245-y

  • CAPABLE/CRAVE Full Site Photo from left to right site enclosures: 1196A NASA LaRC, MPLnet, Virginia DEQ
    CAPABLE/CRAVE Full Site Photo from left to right site enclosures: 1196A NASA LaRC, MPLnet, Virginia DEQ

  • NASA LaRC NAST-I and HU ASSIST side-by-side for intercomparison
    NASA LaRC NAST-I and HU ASSIST side-by-side for intercomparison

  • Virginia DEQ, NASA and Penn State-NATIVE Enclosures (from right to left)
    Virginia DEQ, NASA and Penn State-NATIVE Enclosures (from right to left)

  • Ozone-sonde away.
    Ozone-sonde away.
  • About to lift.
    About to lift.
PurpleAir PA-II-SD Air Quality Sensor
Laser Particle Counters
Type (2) PMS5003
Range of measurement 0.3, 0.5, 1.0, 2.5, 5.0, & 10 μm
Counting efficiency 50% at 0.3μm & 98% at ≥0.5μm
Effective range
(PM2.5 standard)*
0 to 500 μg/m³
Maximum range (PM2.5 standard)* ≥1000 μg/m³
Maximum consistency error (PM2.5 standard) ±10% at 100 to 500μg/m³ & ±10μg/m³ at 0 to 100μg/m³
Standard Volume 0.1 Litre
Single response time ≤1 second
Total response time ≤10 seconds
Pressure, Temperature, & Humidity Sensor
Type BME280
Temperature range -40°F to 185°F (-40°C to 85°C)
Pressure range 300 to 1100 hPa
Humidity Response time (τ63%): 1 s
Accuracy tolerance: ±3% RH
Hysteresis: ≤2% RH


Pandora capabilities

Instrument

Response

Parameter

Precision

Uncertainty

Range

Resolution

Pandora

~2min

Total Column O3, NO2, HCHO, SO2, H2O, BrO

0.01 DU

0.1 DU

 

 

Virginia Department of Environment Quality in-situ instrumentation

Instrument

Response

Parameter

Precision

Uncertainty

Thermo Scientific 42C (Molybdenum converter)
(VADEQ)

60 s

NO and NOx

50 pptv

3%

Teledyne API 200EU w/ photolytic converter
(EPA) PI-Szykman

20 s

NO2

50 pptv

 

Thermo Scientific 49C (VADEQ)

20 s

O3

1 ppbv

4%

Thermo Scientific 48i (VADEQ)

60 s

CO

40 ppbv

5%

Thermo Scientific 43i (VADEQ)

80 s

SO2

0.2 ppbv

5%

Thermo Scientific 1400AB TEOM (VADEQ)

600 s

PM2.5 (continuous)

µg/m3

1 3%

Thermo Scientific Partisol Plus 2025 (VADEQ)

24 hr

PM2.5 (filter-based FRM)- 1/3 days

 

 

BSRN-LRC-49
Large area view.
Latitude: 37.1038
Longitude: -76.3872
Elevation: 3 m Above sea level
Scenes: urban, marsh, bay, river and farm.

Legend

  • The inner red circle is a 20km CERES foot print centered on the BSRN-LRC site.
  • The pink circle represents a possible tangential 20km foot print.
  • The middle red circle represents the area in which a 20km foot print could fall and still see the site.
  • Yellow is a sample 40 deg off nadir foot print.
  • The outer red circle is the region which would be seen by a possible 40 deg off nadir foot print.
The BSRN-LRC sun tracker at the NASA Langley Research Center on a snowy day (02/20/2015) The BSRN-LRC sun tracker at the NASA Langley Research Center on a snowy day (02/20/2015)
CAPABLE-BSRN Google Site Location Image

Team Satellite Sensor G/L Dates Number of obs Phase angle range (°)
CMA FY-3C MERSI LEO 2013-2014 9 [43 57]
CMA FY-2D VISSR GEO 2007-2014
CMA FY-2E VISSR GEO 2010-2014
CMA FY-2F VISSR GEO 2012-2014
JMA MTSAT-2 IMAGER GEO 2010-2013 62 [-138,147]
JMA GMS5 VISSR GEO 1995-2003 50 [-94,96]
JMA Himawari-8 AHI GEO 2014- -
EUMETSAT MSG1 SEVIRI GEO 2003-2014 380/43 [-150,152]
EUMETSAT MSG2 SEVIRI GEO 2006-2014 312/54 [-147,150]
EUMETSAT MSG3 SEVIRI GEO 2013-2014 45/7 [-144,143]
EUMETSAT MET7 MVIRI GEO 1998-2014 128 [-147,144]
CNES Pleiades-1A PHR LEO 2012 10 [+/-40]
CNES Pleiades-1B PHR LEO 2013-2014 10 [+/-40]
NASA-MODIS Terra MODIS LEO 2000-2014 136 [54,56]
NASA-MODIS Aqua MODIS LEO 2002-2014 117 [-54,-56]
NASA-VIIRS NPP VIIRS LEO 2012-2014 20 [50,52]
NASA-OBPG SeaStar SeaWiFS LEO 1997-2010 204 (<10, [27-66])
NASA/USGS Landsat-8 OLI LEO 2013-2014 3 [-7]
NASA OCO-2 OCO LEO 2014
NOAA-STAR NPP VIIRS LEO 2011-2014 19 [-52,-50]
NOAA GOES-10 IMAGER GEO 1998-2006 33 [-66, 81]
NOAA GOES-11 IMAGER GEO 2006-2007 10 [-62, 57]
NOAA GOES-12 IMAGER GEO 2003-2010 49 [-83, 66]
NOAA GOES-13 IMAGER GEO 2006 11
NOAA GOES-15 IMAGER GEO 2012-2013 28 [-52, 69]
VITO Proba-V VGT-P LEO 2013-2014 25 [-7]
KMA COMS MI GEO 2010-2014 60
AIST Terra ASTER LEO 1999-2014 1 -27.7
ISRO OceanSat2 OCM-2 LEO 2009-2014 2
ISRO INSAT-3D IMAGER GEO 2013-2014 2

The NASA Prediction Of Worldwide Energy Resources (POWER) Project improves the accessibility and usage NASA Earth Observations (EO) supporting community research in three focus areas: 1) renewable energy development, 2) building energy efficiency, and 3) agroclimatology applications. The latest POWER version enhances its distribution systems to provide the latest NASA EO source data, be more resilient, support users more effectively, and provide data more efficiently. The update will include hourly-based source Analysis Ready Data (ARD), in addition to enhanced daily, monthly, annual, and climatology ARD. The daily time-series now spans 40 years for meteorology available from 1981 and solar-based parameters start in 1984. The hourly source data are from Clouds and the Earth's Radiant Energy System (CERES) and Global Modeling and Assimilation Office (GMAO), spanning 20 years from 2001.

The newly available hourly data will provide users the ARD needed to model the energy performance of building systems, providing information directly amenable to decision support tools introducing the industry standard EPW (EnergyPlus Weather file). One of POWER’s partners, Natural Resource Canada’s RETScreen™, will be simultaneously releasing a new version of its software, which will have integrated POWER hourly and daily ARD products. For our agroclimatology users, the ICASA (International Consortium for Agricultural Systems Applications standards) format for the crop modelers has been modernized.

POWER is releasing new user-defined analytic capabilities, including custom climatologies and climatological-based reports for parameter anomalies, ASHRAE® compatible climate design condition statistics, and building climate zones. The ARD and climate analytics will be readily accessible through POWER's integrated services suite, including the Data Access Viewer (DAV). The DAV has been improved to incorporate updated parameter groupings, new analytical capabilities, and the new data formats. Updated methodology documentation and usage tutorials, as well as application developer specific pages, allow users to access to POWER Data efficiently.

+Visit the POWER Program Site to Learn More.