Unveiling the buffering impacts of temperate forests on enhancing grain yields through regional biogeophysical climate modification

Lingxue Yu , Zhuoran Yan , Tingxiang Liu , Xuan Li , Jiaxuan Li , Kun Bu , Wen J. Wang

Geography and Sustainability ›› 2025, Vol. 6 ›› Issue (5) : 100332

PDF
Geography and Sustainability ›› 2025, Vol. 6 ›› Issue (5) :100332 DOI: 10.1016/j.geosus.2025.100332
Research Article
review-article

Unveiling the buffering impacts of temperate forests on enhancing grain yields through regional biogeophysical climate modification

Author information +
History +
PDF

Abstract

Temperate forests exert significant biogeophysical influences on local and regional climates through modulating the energy and moisture exchanges between the land surface and the atmosphere, thereby serving as crucial barriers with significant buffering impacts on the productivity of adjacent agricultural ecosystems. However, the extent and underlying mechanisms of these biogeophysical and buffering effects of temperate forest barriers remains insufficiently understood. In this study, we integrated the dynamic crop model Noah-MP-Crop with the Weather Research and Forecasting (WRF) model to investigate the biogeophysical climate regulation of temperate forests and its buffering effects on crop yields in adjacent agricultural lands across Northeast China. Our findings revealed that temperate forest barriers induced significant local climate effects by cooling air and surface temperatures and reducing wind speeds within forested areas during the growing season, while also regulating non-local climate, particularly by altering regional precipitation patterns, 2 m water vapor mixing ratio (Q2), and soil moisture, predominantly in adjacent cropland areas. Furthermore, these forest barriers were found to modulate climate extremes, through affecting maximum temperature and wind speed on a local scale, as well as both maximum and minimum Q2 in non-local croplands. Our study also observed that temperate forest barriers, through biogeophysical climate regulation, enhanced GPP, NPP, and grain yields across most cropland areas. This productivity boost was especially pronounced, with yield increases up to 20 % in certain regions during the extreme drought conditions of 2017, underscoring the critical role of temperate forest barriers in sustaining and enhancing crop yields under severe climatic stress. Our findings underscore the significant buffering effects of temperate forest barriers on regional agricultural production, having important implications for climate adaptation strategies aimed at bolstering agricultural resilience in the face of increasing climate variability and extremes.

Keywords

Temperate forests / Regional climate / Buffering impact / Grain yields / WRF model / Extreme drought

Cite this article

Download citation ▾
Lingxue Yu, Zhuoran Yan, Tingxiang Liu, Xuan Li, Jiaxuan Li, Kun Bu, Wen J. Wang. Unveiling the buffering impacts of temperate forests on enhancing grain yields through regional biogeophysical climate modification. Geography and Sustainability, 2025, 6(5): 100332 DOI:10.1016/j.geosus.2025.100332

登录浏览全文

4963

注册一个新账户 忘记密码

CRediT authorship contribution statement

Lingxue Yu: Methodology, Writing – original draft, Conceptualization. Zhuoran Yan: Writing – original draft, Data curation, Methodology. Tingxiang Liu: Methodology, Conceptualization, Writing – review & editing. Xuan Li: . Jiaxuan Li: Visualization, Data curation. Kun Bu: Methodology, Resources. Wen J. Wang: Resources, Writing – review & editing, Conceptualization.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The numerical calculations in this study were carried out on the ORISE Supercomputer. This study was supported by National Key R&D Program of China (Grant No. 2024YFD1501600), the National Natural Science Foundation of China (Grants No. 42071025, 42371075), and the Youth Innovation Promotion Association of Chinese Academy of Sciences (Grant No. 2023240).

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2025.100332.

References

[1]

Alkama, R., Cescatti, A., 2016. Biophysical climate impacts of recent changes in global forest cover. Science, 351 (2016), pp. 600-604. doi: 10.1126/science.aac8083.

[2]

An, Z., Bork, E. W., Duan, X., Gross, C. D., Carlyle, C. N., Chang, S. X., 2022. Quantifying past, current, and future forest carbon stocks within agroforestry systems in central Alberta, Canada. GCB Bioenergy, 14 (6), pp. 669-680. doi: 10.1111/gcbb.12934.

[3]

Arora, V. K., Montenegro, A., 2011. Small temperature benefits provided by realistic afforestation efforts. Nat. Geosci., 4 (8), pp. 514-518. doi: 10.1038/Ngeo1182.

[4]

Bala, G., Caldeira, K., Wickett, M., Phillips, T. J., Lobell, D. B., Delire, C., Mirin, A., 2007. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl. Acad. Sci. U.S.A., 104 (23), pp. 6550-6555. doi: 10.1073/pnas.0704096104.

[5]

Bastin, J. F., Finegold, Y., Garcia, C., Mollicone, D., Rezende, M., Routh, D., Zohner, C. M., Crowther, T. W., 2019. The global tree restoration potential. Science, 365 (2019), pp. 76-79. doi: 10.1126/science.aax0848.

[6]

Betts, R. A., 2011. Climate Science: afforestation cools more or less. Nat. Geosci., 4 (8), pp. 504-505. doi: 10.1038/Ngeo1223.

[7]

Betts, R. A., Falloon, P. D., Goldewijk, K. K., Ramankutty, N., 2007. Biogeophysical effects of land use on climate: model simulations of radiative forcing and large-scale temperature change. Agric. For. Meteorol., 142 (2–4), pp. 216-233. doi: 10.1016/j.agrformet.2006.08.021.

[8]

Bao, L., Yu, L., Yu, E., Li, R., Cai, Z., Yu, J., Li, X., 2025. Improving the simulation of maize growth using WRF-Crop model based on data assimilation and local maize characteristics. Agric. For. Meteorol., 365, Article 110478. doi: 10.1016/j.agrformet.2025.110478.

[9]

Bonan, G. B., 2008. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science, 320 (2008), pp. 1444-1449. doi: 10.1126/science.1155121.

[10]

Bright, R. M., Zhao, K. G., Jackson, R. B., Cherubini, F., 2015. Quantifying surface albedo and other direct biogeophysical climate forcings of forestry activities. Glob. Change Biol., 21 (9), pp. 3246-3266. doi: 10.1111/gcb.12951.

[11]

Cerasoli, S., Yin, J., Porporato, A., 2021. Cloud cooling effects of afforestation and reforestation at midlatitudes. Proc. Natl. Acad. Sci. U.S.A. 118 (33), e2026241118. doi: 10.1073/pnas.2026241118.

[12]

Cohn, A. S., Bhattarai, N., Campolo, J., Crompton, O., Dralle, D., Duncan, J., Thompson, S., 2019. Forest loss in Brazil increases maximum temperatures within 50 km. Environ. Res. Lett., 14 (8), Article 084047. doi: 10.1088/1748-9326/ab31fb.

[13]

Collins, W. D., 2004. Description of the NCAR Community Atmosphere Model (CAM 3.0).

[14]

Davin, E. L., de Noblet-Ducoudré, N., 2010. Climatic impact of global-scale deforestation: radiative versus nonradiative processes. J. Clim., 23 (1), pp. 97-112. doi: 10.1175/2009jcli3102.1.

[15]

Devaraju, N., Bala, G., Nemani, R., 2015. 38 (9), pp. 1931-1946. doi: 10.1111/pce.12488.

[16]

Ellison, D., Futter, M. N., Bishop, K., 2012. On the forest cover-water yield debate: from demand- to supply-side thinking. Glob. Change Biol., 18 (3), pp. 806-820. doi: 10.1111/j.1365-2486.2011.02589.x.

[17]

ESA 2017. Land Cover CCI Product User Guide Version 2. Technical reports

[18]

Feddema, J. J., Oleson, K. W., Bonan, G. B., Mearns, L. O., Buja, L. E., Meehl, G. A., Washington, W. M., 2005. The importance of land-cover change in simulating future climates. Science, 310 (2005), pp. 1674-1678. doi: 10.1126/science.1118160.

[19]

Ge, J., Pitman, A. J., Guo, W. D., Zan, B. L., Fu, C. B., 2020. Impact of revegetation of the Loess Plateau of China on the regional growing season water balance. Hydrol. Earth Syst. Sci., 24 (2), pp. 515-533. doi: 10.5194/hess-24-515-2020.

[20]

Ge, J., Qiu, B., Chu, B. W., Li, D. Z. T., Jiang, L. L., Zhou, W. D., Tang, J. P., Guo, W. D., 2021. Evaluation of coupled regional climate models in representing the local biophysical effects of afforestation over continental China. J. Clim., 34 (24), pp. 9879-9898. doi: 10.1175/Jcli-D-21-0462.1.

[21]

Griscom, B. W., Adams, J., Ellis, P. W., Houghton, R. A., Lomax, G., Miteva, D. A., Schlesinger, W. H., Shoch, D., Siikamaki, J. V., Smith, P., Woodbury, P., Zganjar, C., Blackman, A., Campari, J., Conant, R. T., Delgado, C., Elias, P., Gopalakrishna, T., Hamsik, M. R., Herrero, M., Kiesecker, J., Landis, E., Laestadius, L., Leavitt, S. M., Minnemeyer, S., Polasky, S., Potapov, P., Putz, F. E., Sanderman, J., Silvius, M., Wollenberg, E., Fargione, J., 2017. Natural climate solutions. Proc. Natl. Acad. Sci. U.S.A., 114 (44), pp. 11645-11650. doi: 10.1073/pnas.1710465114.

[22]

He, Q., Wang, M., Liu, K., Li, K., Jiang, Z., 2022. GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning. Earth Syst. Sci. Data., 14 (7), pp. 3273-3292. doi: 10.5194/essd-14-3273-2022.

[23]

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Thépaut, J., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Sarah Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S, J-Thépaut, N., 2020. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc., 146(730), 1999-2049.

[24]

Hong, S. Y., Dudhia, J., Chen, S. H., 2004. A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Weather Rev., 132 (1), pp. 103-120. doi: 10.1175/1520-0493(2004)132<0103:Aratim>2.0.Co;2.

[25]

Hong, S. Y., Pan, H. L., 1996. Nonlocal boundary layer vertical diffusion in a Medium-Range Forecast Model. Mon. Weather Rev., 124 (10), pp. 2322-2339. doi: 10.1175/1520-0493(1996)124<2322:Nblvdi>2.0.Co;2.

[26]

Houspanossian, J., Nosetto, M., Jobbagy, E. G., 2013. Radiation budget changes with dry forest clearing in temperate Argentina. Glob. Change Biol., 19 (4), pp. 1211-1222. doi: 10.1111/gcb.12121.

[27]

Huang, L., Zhai, J., Liu, J. Y., Sun, C. Y., 2018. The moderating or amplifying biophysical effects of afforestation on CO2-induced cooling depend on the local background climate regimes in China. Agric. For. Meteorol., 260, pp. 193-203. doi: 10.1016/j.agrformet.2018.05.020.

[28]

Jackson, R. B., Randerson, J. T., Canadell, J. G., Anderson, R. G., Avissar, R., Baldocchi, D. D., Bonan, G. B., Caldeira, K., Diffenbaugh, N. S., Field, C. B., Hungate, B. A., Jobbagy, E. G., Kueppers, L. M., Nosetto, M. D., Pataki, D. E., 2008. Diffenbaugh, C.B. Field, B.A. Hungate, E.G. Jobbagy, L.M. Kueppers, M.D. Nosetto, D.E. Pataki. Protecting climate with forests. Environ. Res. Lett., 3 (4), Article 044006. doi: 10.1088/1748-9326/3/4/044006.

[29]

Jiao, Y., Bu, K., Yang, J. C., Li, G. S., Shen, L. D., Liu, T. X., Yu, L. X., Zhang, S. W., Zhang, H. Q., 2021. Biophysical effects of temperate forests in regulating regional temperature and precipitation pattern across Northeast China. Remote Sens., 13 (23), p. 4767. doi: 10.3390/rs13234767.

[30]

Keys, P. W., Wang-Erlandsson, L., Gordon, L. J., 2016. Revealing invisible water: moisture recycling as an ecosystem service. PLoS One, 11 (3), Article e0151993. doi: 10.1371/journal.pone.0151993.

[31]

Lee, X., Goulden, M. L., Hollinger, D. Y., Barr, A., Black, T. A., Bohrer, G., Bracho, R., Drake, B., Goldstein, A., Gu, L. H., Katul, G., Kolb, T., Law, B. E., Margolis, H., Meyers, T., Monson, R., Munger, W., Oren, R., Kyaw, T. P. U., Richardson, A. D., Schmid, H. P., Staebler, R., Wofsy, S., Zhao, L., 2011. Observed increase in local cooling effect of deforestation at higher latitudes. Nature, 479 (2011), pp. 384-387. doi: 10.1038/nature10588.

[32]

Levis, S., Bonan, G. B., Kluzek, E., Thornton, P. E., Jones, A., Sacks, W. J., Kucharik, C. J., 2012. Interactive crop management in the Community Earth System Model (CESM1): seasonal influences on land-atmosphere fluxes. J. Clim., 25 (14), pp. 4839-4859. doi: 10.1175/Jcli-D-11-00446.1.

[33]

Li, Y., Piao, S. L., Chen, A. P., Wang, X. H., Ciais, P., Li, L. Z. X., 2020. Local and teleconnected temperature effects of afforestation and vegetation greening in China. Nat. Sci. Rev., 7 (5), pp. 897-912. doi: 10.1093/nsr/nwz132.

[34]

Li, Y., Zhao, M., Motesharrei, S., Mu, Q., Kalnay, E., Li, S., 2015. Local cooling and warming effects of forests based on satellite observations. Nat. Commun., 6 (1), p. 6603. doi: 10.1038/ncomms7603.

[35]

Liang, S. L., Zhao, X., Liu, S. H., Yuan, W. P., Cheng, X. L., Xiao, Z. Q., Zhang, X. T., Liu, Q., Cheng, J., Tang, H., 2013. A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies. Int. J. Digit. Earth, 6 (sup1), pp. 5-33. doi: 10.1080/17538947.2013.805262.

[36]

Liu, T., Yu, L., Yan, Z., Li, X., Bu, K., Yang, J., 2025. Enhanced Climate Mitigation Feedbacks by Wetland Vegetation in Semi-Arid Compared to Humid Regions. Geophys. Res. Let., 52 (9), Article e2025GL115242. doi: 10.1029/2025GL115242.

[37]

Liu, X., Chen, F., Barlage, M., Zhou, G. S., Niyogi, D., 2016. Noah-MP-Crop: introducing dynamic crop growth in the Noah-MP land surface model. J. Geophys. Res.-Atmos., 121 (23), pp. 13953-13972. doi: 10.1002/2016jd025597.

[38]

Lundberg, S., Lee, S.-I., 2017. A unified approach to interpreting model predictions. arXiv e-print. doi:10.48550/arXiv.1705.07874

[39]

Mahmood, R., Pielke, R. A., Hubbard, K. G., Niyogi, D., Dirmeyer, P. A., McAlpine, C., Carleton, A. M., Hale, R., Gameda, S., Beltran-Przekurat, A., Baker, B., McNider, R., Legates, D. R., Shepherd, M., Du, J. Y., Blanken, P. D., Frauenfeld, O. W., Nair, U. S., Fall, S., 2014. Land cover changes and their biogeophysical effects on climate. Int. J. Climatol., 34 (4), pp. 929-953. doi: 10.1002/Joc.3736.

[40]

Muñoz Sabater, J., 2019. ERA5-Land monthly averaged data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)

[41]

Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., Xia, Y. L., 2011. The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res.-Atmos., 116, Article D12110. doi: 10.1029/2010jd015139.

[42]

O'Connor, J. C., Dekker, S. C., Staal, A., Tuinenburg, O. A., Rebel, K. T., Santos, M. J., 2021. Forests buffer against variations in precipitation. Glob. Change Biol., 27 (19), pp. 4686-4696. doi: 10.1111/gcb.15763.

[43]

Peng, S. S., Piao, S. L., Zeng, Z. Z., Ciais, P., Zhou, L. M., Li, L. Z. X., Myneni, R. B., Yin, Y., Zeng, H., 2014. Afforestation in China cools local land surface temperature. Proc. Natl. Acad. Sci. U.S.A., 111 (8), pp. 2915-2919. doi: 10.1073/pnas.1315126111.

[44]

Piao, S. L., Fang, J. Y., Zhou, L. M., Ciais, P., Zhu, B., 2006. Variations in satellite-derived phenology in China's temperate vegetation. Glob. Change Biol., 12 (4), pp. 672-685. doi: 10.1111/j.1365-2486.2006.01123.x.

[45]

Qu, L., Zhu, Q., Zhu, C., Zhang, J., 2022. Monthly precipitation data set with 1 km resolution in China from 1960 to 2020. Science Data Bank.. doi: 10.11922/sciencedb.01607.

[46]

Santiago, B., Sergio, M. V. S., Fergus, R-.G., Borja Latorre, G., 2023. SPEIbase v.2.9 [Dataset]. DIGITAL.CSIC

[47]

Smith, C., Baker, J. C. A., Spracklen, D. V., 2023. Tropical deforestation causes large reductions in observed precipitation. Nature, 615 (2023), pp. 270-275. doi: 10.1038/s41586-022-05690-1.

[48]

Sumila, T., Pires, G., Cunha Fontes, V., Costa, M., 2017. Sources of water vapor to economically relevant regions in Amazonia and the effect of deforestation. J. Hydrometeorol., 18 (6), pp. 1643-1655. doi: 10.1175/JHM-D-16-0133.1.

[49]

Teuling, A. J., Seneviratne, S. I., Stöckli, R., Reichstein, M., Moors, E., Ciais, P., Luyssaert, S., van den Hurk, B., Ammann, C., Bernhofer, C., Dellwik, E., Gianelle, D., Gielen, B., Grünwald, T., Klumpp, K., Montagnani, L., Moureaux, C., Sottocornola, M., Wohlfahrt, G., 2010. Contrasting response of European forest and grassland energy exchange to heatwaves. Nat. Geosci., 3 (10), pp. 722-727. doi: 10.1038/ngeo950.

[50]

Teuling, A. J., Taylor, C. M., Meirink, J. F., Melsen, L. A., Miralles, D. G., van Heerwaarden, C. C., Vautard, R., Stegehuis, A. I., Nabuurs, G. J., de Arellano, J. V. G., 2017. Observational evidence for cloud cover enhancement over western European forests. Nat. Commun., 8, Article 14065. doi: 10.1038/ncomms14065.

[51]

Wang, J. F., Chagnon, F. J. F., Williams, E. R., Betts, A. K., Renno, N. O., Machado, L. A. T., Bisht, G., Knox, R., Brase, R. L. 2009a Impact of deforestation in the Amazon basin on cloud climatology. Proc. Natl. Acad. Sci. U.S.A., 106 (10) (2009), pp. 3670-3674. doi: 10.1073/pnas.0810156106.-

[52]

Wang, Z. M., Liu, Z. M., Song, K. S., Zhang, B., Zhang, S. M., Liu, D. W., Ren, C. Y., Yang, F. 2009b Land use changes in Northeast China driven by human activities and climatic variation. Chin. Geogr. Sci., 19 (3) (2009), pp. 225-230. doi: 10.1007/s11769-009-0225-7.

[53]

Xue, Y. K., Janjic, Z., Dudhia, J., Vasic, R., De Sales, F., 2014. A review on regional dynamical downscaling in intraseasonal to seasonal simulation/prediction and major factors that affect downscaling ability. Atmos. Res., 147, pp. 68-85. doi: 10.1016/j.atmosres.2014.05.001.

[54]

You, N. S., Dong, J. W., Huang, J. X., Du, G. M., Zhang, G. L., He, Y. L., Yang, T., Di, Y. Y., Xiao, X. M., 2021. The 10-m crop type maps in Northeast China during 2017–2019. Sci. Data, 8 (1), p. 41. doi: 10.1038/s41597-021-00827-9.

[55]

Yu, L., Li, X., Bu, K., Yan, F., Zhang, S., Liu, T., 2023. Increased background precipitation masks the moisture deficit caused by crop greening in Northeast China. J. Hydrol., 623, Article 129857. doi: 10.1016/j.jhydrol.2023.129857.

[56]

Yu, L., Liu, Y., Liu, T., Yu, E., Bu, K., Jia, Q., Shen, L., Zheng, X., Zhang, S., 2022. Coupling localized Noah-MP-Crop model with the WRF model improved dynamic crop growth simulation across Northeast China. Comput. Electron. Agric., 201, Article 107323. doi: 10.1016/j.compag.2022.107323.

[57]

Yu, L., Liu, Y., Shen, M., Yu, Z., Li, X., Liu, H., Lyne, V., Jiang, M., Wu, C. 2025a. Extreme hydroclimates amplify the biophysical effects of advanced green-up in temperate China. Agric. For. Meteorol., 363 (2025), Article 110421. doi: 10.1016/j.agrformet.2025.110421.

[58]

Yu, L., Liu, Y., Yan, F., Lu, L., Li, X., Zhang, S., Yang, J. 2025b. Phenological control of vegetation biophysical feedbacks to the regional climate. Geogr. Sustain., 6 (1) (2025), Article 100202. doi: 10.1016/j.geosus.2024.05.005.

[59]

Yu, L., Yan, Z., Zhang, S., 2020a. Forest phenology shifts in response to climate change over China–Mongolia–Rus

[60]

Yu, L. X., Liu, T. X., Bu, K., Yan, F. Q., Yang, J. C., Chang, L. P., Zhang, S. W., 2017. Monitoring the long term vegetation phenology change in Northeast China from 1982 to 2015. Sci. Rep., 7, p. 14770. doi: 10.1038/s41598-017-14918-4.

[61]

Yu, L. X., Liu, Y., Liu, T. X., Yan, F. Q. 2020b. Impact of recent vegetation greening on temperature and precipitation over China. Agric. For. Meteorol., 295 (2020), Article 108197. doi: 10.1016/j.agrformet.2020.108197.

[62]

Yu, L. X., Xue, Y. K., Diallo, I., 2021. Vegetation greening in China and its effect on summer regional climate. Sci. Bull., 66 (1), pp. 13-17. doi: 10.1016/j.scib.2020.09.003.

[63]

Zeng, Z. Z., Wang, D. S., Yang, L., Wu, J., Ziegler, A. D., Liu, M. F., Ciais, P., Searchinger, T. D., Yang, Z. L., Chen, D. L., Chen, A. P., Li, L. Z. X., Piao, S. L., Taylor, D., Cai, X. T., Pan, M., Peng, L. Q., Lin, P. R., Gower, D., Feng, Y., Zheng, C. M., Guan, K. Y., Lian, X., Wang, T., Wang, L., Jeong, S. J., Wei, Z. W., Sheffield, J., Caylor, K., Wood, E. F., 2021. Deforestation-induced warming over tropical mountain regions regulated by elevation. Nat. Geosci., 14 (1), pp. 23-29. doi: 10.1038/s41561-020-00666-0.

[64]

Zheng, Y., Alapaty, K., Herwehe, J. A., Del Genio, A. D., Niyogi, D., 2016. Improving high-resolution weather forecasts using the Weather Research and Forecasting (WRF) model with an updated Kain-Fritsch scheme. Mon. Weather Rev., 144 (3), pp. 833-860. doi: 10.1175/MWR-D-15-0005.1.

PDF

132

Accesses

0

Citation

Detail

Sections
Recommended

/