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.
2020Ji, 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.
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.
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.
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.
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
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
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
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