Abstract
The use of numerical weather prediction (NWP) models for solar resource evaluation is examined. The theory behind NWP models is described highlighting relevant components for solar energy applications as well as how to use NWP models for mapping the solar resource at the regional scale. Future perspectives are briefly outlined.
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References
Alapaty K, Herwehe JA, Otte TL, Nolte CG, Bullock OR, Mallard MS, Dudhia J (2012) Introducing subgrid-scale cloud feedbacks to radiation for regional meteorological and climate modeling. Geophys Res Lett. https://doi.org/10.1029/2012GL054031
Anderson JL (2001) An ensemble adjustment Kalman Filter for data assimilation. Mon Weather Rev. https://doi.org/10.1175/1520-0493(2001)129%3c2884:AEAKFF%3e2.0.CO;2
Anderson J, Hoar T, Raeder K, Liu H, Collins N, Torn R, Avellano A (2009) The data assimilation research testbed a community facility. Bull Am Meteorol Soc. https://doi.org/10.1175/2009BAMS2618.1
Anthes RA, Warner TT (1974) Prediction of mesoscale flows over complex terrain. Retrieved from http://www.dtic.mil/docs/citations/AD0776333
Anthes RA, Warner TT (1978) Development of hydrodynamic models suitable for air pollution and other mesometerological studies. Mon Weather Rev. https://doi.org/10.1175/1520-0493(1978)106%3c1045:DOHMSF%3e2.0.CO;2
Armstrong MA (2000) Comparison of MM5 forecast shortwave radiation with data obtained from the atmospheric radiation measurement program. University of Maryland. Retrieved from https://www.atmos.umd.edu/~bobe/downloads/armstrong.pdf
Auligné T, Auligné T (2014a) Multivariate minimum residual method for cloud retrieval. Part I: theoretical aspects and simulated observation experiments. Mon Weather Rev 142(12):4383–4398 https://doi.org/10.1175/MWR-D-13-00172.1
Auligné T, Auligné T (2014b) Multivariate minimum residual method for cloud retrieval. Part II: real observations experiments. Mon Weather Rev 142(12):4399–4415 https://doi.org/10.1175/MWR-D-13-00173.1
Bannister RN (2017) A review of operational methods of variational and ensemble-variational data assimilation. Q J R Meteorol Soc. https://doi.org/10.1002/qj.2982
Bayler GM, Aune RM, Raymond WH, Bayler GM, Aune RM, Raymond WH (2000) NWP cloud initialization using GOES sounder data and improved modeling of nonprecipitating clouds. Mon Weather Rev 128(11):3911–3920. https://doi.org/10.1175/1520-0493(2001)129%3c3911:NCIUGS%3e2.0.CO;2
Beljaars A, Bechtold P, Köhler M, Morcrette J-J, Tompkins AM, Viterbo P, Wedi N (2004) The numerics of physical parameterization. In: Seminar on recent developments in numerical methods for atmospheric and ocean modelling, 6–10 September 2004. ECMWF, Shinfield Park, Reading, pp 113–134
Berg LK, Gustafson WI, Kassianov EI, Deng L (2013) Evaluation of a modified scheme for shallow convection: implementation of CuP and case studies. Mon Weather Rev. https://doi.org/10.1175/MWR-D-12-00136.1
Bishop CH, Etherton BJ, Majumdar SJ (2001) Adaptive sampling with the ensemble transform Kalman Filter. Part I: theoretical aspects. Mon Weather Rev. https://doi.org/10.1175/1520-0493(2001)129%3c0420:ASWTET%3e2.0.CO;2
Blanc P, Gschwind B, Lefèvre M, Wald L (2011) The HelioClim project: surface solar irradiance data for climate applications. Remote Sens. https://doi.org/10.3390/rs3020343
Brdar S, Baldauf M, Dedner A, Klöfkorn R (2013) Comparison of dynamical cores for NWP models: comparison of COSMO and Dune. Theor Comput Fluid Dyn. https://doi.org/10.1007/s00162-012-0264-z
Caya A, Laprise R, Zwack P, Caya A, Laprise R, Zwack P (1998) Consequences of using the splitting method for implementing physical forcings in a semi-implicit semi-Lagrangian model. Mon Weather Rev 126(6):1707–1713. https://doi.org/10.1175/1520-0493(1998)126%3c1707:COUTSM%3e2.0.CO;2
Charabi Y, Gastli A, Al-Yahyai S (2016) Production of solar radiation bankable datasets from high-resolution solar irradiance derived with dynamical downscaling Numerical Weather prediction model. Energy Rep. https://doi.org/10.1016/j.egyr.2016.05.001
Chen Y, Wang H, Min J, Huang X-Y, Minnis P, Zhang R, Palikonda R (2015) Variational assimilation of cloud liquid/ice water path and its impact on NWP. J Appl Meteorol Climatol 54(8):1809–1825. https://doi.org/10.1175/JAMC-D-14-0243.1
Chin M, Rood RB, Lin SJ, Müller JF and Thompson AM (2000) Atmospheric sulfur cycle simulated in the global model GOCART: model description and global properties. J Geophys Res Atmos. https://doi.org/10.1029/2000JD900384
Clifton A, Hodge B-M, Draxl C, Badger J, Habte A (2018) Wind and solar resource data sets. Wiley Interdisc Rev Energy Environ 7(2):e276. https://doi.org/10.1002/wene.276
Cressman GP (1959) An operational objective analysis system. Mon Weather Rev. https://doi.org/10.1175/1520-0493(1959)087%3c0367:AOOAS%3e2.0.CO;2
de Haan S, van der Veen SH, de Haan S, van der Veen SH (2014) Cloud initialization in the rapid update cycle of HIRLAM. Weather Forecast 29(5):1120–1133. https://doi.org/10.1175/WAF-D-13-00071.1
Deng A, Stauffer DR, Dudhia J, Otte TL, Hunter GK (2007) Update on analysis nudging Fdda in Wrf-arw. National Center for Atmospheric Research, Boulder, CO. 4.8: 8th WRF users’ workshop. Retrieved from http://www2.mmm.ucar.edu/wrf/users/workshops/WS2007/abstracts/4-8_Deng.pdf
Deng A, Stauffer D, Gaudet B, Dudhia J, Bruyere C, Wu W, Bourgeois A (2009) Update on WRF-ARW end-to-end multi-scale FDDA system. National Center for Atmospheric Research, Boulder, CO. 1.9: 10th WRF users’ workshop. Retrieved from http://www2.mmm.ucar.edu/wrf/users/workshops/WS2009/WorkshopPapers.php
Deng A, Gaudet BJ, Dudhia J, Alapaty K (2014) Implementation and evaluation of a new shallow convection scheme in WRF. In: 26th Conference on weather analysis and forecasting/22nd conference numerical weather prediction. American Society, Atlanta, GA, p 25. Retrieved from https://ams.confex.com/ams/94Annual/webprogram/Paper236925.html
Descombes G, Auligné T, Lin H-C, Xu D, Schwartz C, Vandenberghe F (2014) NCAR technical notes multi-sensor advection diffusion nowCast (MADCast) for cloud analysis and short-term prediction. Retrieved from http://library.ucar.edu/research/publish-technote
Diagne M, David M, Boland J, Schmutz N, Lauret P (2014) Post-processing of solar irradiance forecasts from WRF model at Reunion Island. Sol Energy 105(0):99–108. https://doi.org/10.1016/j.solener.2014.03.016
Dubal M, Wood N, Staniforth A (2004) Analysis of parallel versus sequential splittings for time-stepping physical parameterizations. Mon Weather Rev. https://doi.org/10.1175/1520-0493(2004)131%3c0121:AOPVSS%3e2.0.CO;2
Dubal M, Wood N, Staniforth A (2006) Some numerical properties of approaches to physics-dynamics coupling for NWP. Q J R Meteorol Soc. https://doi.org/10.1256/qj.05.49
Dutton JA, James RP, Ross JD (2018) Probabilistic forecasts for energy: weeks to a century or more. In: Troccoli A (ed) Weather & climate services for the energy industry, UK Palgrave Macmillan, Cham, London, pp 161–177. https://doi.org/10.1007/978-3-319-68418-5_12
Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res. https://doi.org/10.1029/94JC00572
Fast JD, Gustafson WI, Easter RC, Zaveri RA, Barnard JC, Chapman EG, Peckham SE (2006) Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model. J Geophys Res Atmos. https://doi.org/10.1029/2005JD006721
Fountoukis C, Martín-pomares L, Perez-astudillo D, Bachour D, Gladich I (2018) Simulating global horizontal irradiance in the Arabian Peninsula: sensitivity to explicit treatment of aerosols. Sol Energy 163:347–355. https://doi.org/10.1016/j.solener.2018.02.001
Gelaro R, McCarty W, Suárez MJ, Todling R, Molod A, Takacs L, Zhao B (2017) The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J Clim. https://doi.org/10.1175/JCLI-D-16-0758.1
Glahn HR, Lowry DA (1972) The use of model output statistics (MOS) in objective weather forecasting. J Appl Meteorol 11:1203–1211
GOES-R (2018) Instruments: advanced baseline imager (ABI). Retrieved 14 April 2018, from https://www.goes-r.gov/spacesegment/abi.html
Grell GA, Peckham SE, Schmitz R, McKeen SA, Frost G, Skamarock WC, Eder B (2005) Fully coupled “online” chemistry within the WRF model. Atmos Environ. https://doi.org/10.1016/j.atmosenv.2005.04.027
Gross M, Malardel S, Jablonowski C, Wood N, Gross M, Malardel S, Wood N (2016) Bridging the (knowledge) gap between physics and dynamics. Bull Am Meteor Soc 97(1):137–142. https://doi.org/10.1175/BAMS-D-15-00103.1
Gross M, Wan H, Rasch PJ, Caldwell PM, Williamson DL, Klocke D, Leung R (2017) Recent progress and review of issues related to physics dynamics coupling in geophysical models. https://doi.org/10.1175/MWR-D-17-0345.1
Habte A, Sengupta M, Lopez A (2017) Evaluation of the National Solar Radiation Database (NSRDB): 1998–2015. Technical Report TP-5D00-67722 (April), pp 1–38. Retrieved from http://www.nrel.gov/docs/fy17osti/67722.pdf
Haupt SE, Copeland J, Cheng WYY, Zhang Y, Ammann C, Sullivan P, Sullivan P (2016a) A method to assess the wind and solar resource and to quantify interannual variability over the united states under current and projected future climate. J Appl Meteorol Climatol 55(2):345–363. https://doi.org/10.1175/JAMC-D-15-0011.1
Haupt SE, Kosovic B, Jensen L, Lee J, Jimenez Munoz P, Lazo K, Hinkleman L (2016b) The Sun4Cast® solar power forecasting system: the result of the public-private-academic partnership to advance solar power forecasting. https://doi.org/10.5065/D6N58JR2
Haupt SE, Jiménez PA, Lee JA, Kosović B (2017) Principles of meteorology and numerical weather prediction. In: Kariniotakis G (ed) Renewable energy forecasting: from models to applications, Woodhead Publishing (Elsevier), Cambridge, MA, pp 3–28. https://doi.org/10.1016/B978-0-08-100504-0.00001-9
Haupt SE, Kosović B, Jensen T, Lazo JK, Lee JA, Jiménez PA, Heiser J (2018) Building the Sun4Cast system: improvements in solar power forecasting. Bull Amer Meteorol Soc 99:121–135. https://doi.org/10.1175/BAMS-D-16-0221.1
Haupt SE, Kosović B, Lee JA, Jiménez PA (2018b) Mesoscale modeling of the atmosphere. In: Veers P (ed) Wind power modelling: atmosphere and wind plant flow, IET Publishing, Stevenage, UK
Herwehe JA, Alapaty K, Spero TL, Nolte CG (2014) Increasing the credibility of regional climate simulations by introducing subgrid-scale cloud-radiation interactions. J Geophys Res. https://doi.org/10.1002/2014JD021504
Hoke JE, Anthes RA (1976) The initialization of numerical models by a dynamic-initialization technique. Mon Weather Rev. https://doi.org/10.1175/1520-0493(1976)104%3c1551:TIONMB%3e2.0.CO;2
Houtekamer PL, Zhang F (2016) Review of the ensemble Kalman Filter for atmospheric data assimilation. Mon Weather Rev. https://doi.org/10.1175/MWR-D-15-0440.1
Jiménez PA, Alessandrini S, Haupt SE, Deng A, Kosovic B, Lee JA, Delle Monache L (2016a) The role of unresolved clouds on short-range global horizontal irradiance predictability. Mon Weather Rev. https://doi.org/10.1175/MWR-D-16-0104.1
Jiménez PA, Hacker JP, Dudhia J, Haupt SE, Ruiz-Arias JA, Gueymard CA, Deng A (2016b) WRF-SOLAR: description and clear-sky assessment of an augmented NWP model for solar power prediction. Bull Am Meteorol Soc. https://doi.org/10.1175/BAMS-D-14-00279.1
Jonassen MO, Ólafsson H, Ágústsson H, Rögnvaldsson Ó, Reuder J (2012) improving high-resolution numerical weather simulations by assimilating data from an unmanned aerial system. Mon Weather Rev. https://doi.org/10.1175/MWR-D-11-00344.1
Jones TA, Stensrud DJ, Minnis P, Palikonda R (2013) Evaluation of a forward operator to assimilate cloud water path into WRF-DART. Mon Weather Rev. https://doi.org/10.1175/MWR-D-12-00238.1
Kalman RE (1960) A new approach to linear filtering and prediction problems. J Basic Eng. https://doi.org/10.1115/1.3662552
Kalman RE, Bucy RS (1961) New results in linear filtering and prediction theory. J Basic Eng. https://doi.org/10.1115/1.3658902
Kalnay E (2003) Atmospheric modeling, data assimilation, and predictability. Ann Phys. https://doi.org/10.1256/00359000360683511
Kay M, MacGill I (2014) Improving NWP forecasts for the wind energy sector. In: Troccoli A, Dubus L, Haupt SE (eds) Weather matters for energy, Springer, New York, NY, pp 413–428. https://doi.org/10.1007/978-1-4614-9221-4_20
Klinker E, Rabier F, Kelly G, Mahfouf JF (2000) The ECMWF operational implementation of four-dimensional variational assimilation. III: experimental results and diagnostics with operational configuration. Q J R Meteorol Soc. https://doi.org/10.1256/smsqj.56416
Köpken C, Kelly G, Thépaut J-N (2004) Assimilation of meteosat radiance data within the 4D-Var system at ECMWF: assimilation experiments and forecast impact. Q J R Meteorol Soc 130(601):2277–2292. https://doi.org/10.1256/qj.02.230
Lander J, Hoskins BJ (1997) Believable scales and parameterizations in a spectral transform model. Mon Weather Rev. https://doi.org/10.1175/1520-0493(1997)125%3c0292:BSAPIA%3e2.0.CO;2
Lara-Fanego V, Ruiz-Arias JA, Pozo-Vázquez D, Santos-Alamillos FJ, Tovar-Pescador J (2012) Evaluation of the WRF model solar irradiance forecasts in Andalusia (Southern Spain). Sol Energy 86(8):2200–2217. https://doi.org/10.1016/j.solener.2011.02.014
Lee JA, Haupt SE, Jiménez PA, Rogers MA, Miller SD, McCandless TC (2017) Solar irradiance nowcasting case studies near sacramento. J Appl Meteorol Climatol. https://doi.org/10.1175/JAMC-D-16-0183.1
Lei L, Stauffer DR, Deng A (2012) A hybrid nudging-ensemble Kalman filter approach to data assimilation in WRF/DART. Q J R Meteorol Soc 138(669):2066–2078. https://doi.org/10.1002/qj.1939
Liu Y, Warner TT, Bowers JF, Carson LP, Chen F, Clough CA, Weingarten DS (2008) The operational mesogamma-scale analysis and forecast system of the U.S. army test and evaluation command. Part I: overview of the modeling system, the forecast products, and how the products are used. J Appl Meteorol Climatol. https://doi.org/10.1175/2007JAMC1653.1
Lorenc AC, Bowler NE, Clayton AM, Pring SR, Fairbairn D (2015) Comparison of hybrid-4DEnVar and hybrid-4DVar data assimilation methods for global NWP. Mon Weather Rev. https://doi.org/10.1175/MWR-D-14-00195.1
Lorenz E, Kuhnert J, Heinemann D (2014) Overview on irradiance and photovoltaic power prediction. In: Troccoli A, Dubus L, Haupt SE (eds) Weather matters for energy, Springer, New York, NY, pp 429–454. https://doi.org/10.1007/978-1-4614-9221-4_21
Mahoney WP, Parks K, Wiener G, Liu Y, Myers WL, Sun J, Haupt SE (2012) A wind power forecasting system to optimize grid integration. In: IEEE transactions on sustain energy. https://doi.org/10.1109/TSTE.2012.2201758
Marquez R, Coimbra CFM (2011) Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database. Solar Energy 85(5):746–756. https://doi.org/10.1016/j.solener.2011.01.007
Martinet P, Fourrié N, Guidard V, Rabier F, Montmerle T, Brunel P (2013) Towards the use of microphysical variables for the assimilation of cloud-affected infrared radiances. Q J R Mseteorol Soc. https://doi.org/10.1002/qj.2046
Martins FR, Pereira EB, Abreu SL (2007) Satellite-derived solar resource maps for Brazil under SWERA project. Sol Energy. https://doi.org/10.1016/j.solener.2006.07.009
Mathiesen P, Kleissl J (2011) Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States. Sol Energy. https://doi.org/10.1016/j.solener.2011.02.013
Miguez-Macho G, Stenchikov GL, Robock A (2004) Spectral nudging to eliminate the effects of domain position and geometry in regional climate model simulations. J Geophys Res D Atmos 109(13):1–15. https://doi.org/10.1029/2003JD004495
Miguez-Macho G, Stenchikov GL, Robock A, Miguez-Macho G, Stenchikov GL, Robock A (2005) Regional climate simulations over North America: interaction of local processes with improved large-scale flow. J Clim 18(8):1227–1246. https://doi.org/10.1175/JCLI3369.1
Miller SD, Rogers MA, Haynes JM, Sengupta M, Heidinger AK (2018) Short-term solar irradiance forecasting via satellite/model coupling. Sol Energy 168:102–117. https://doi.org/10.1016/J.SOLENER.2017.11.049
Munro R, Köpken C, Kelly G, Thépaut JN, Saunders R (2004) Assimilation of Meteosat radiance data within the 4D-Var system at ECMWF: data quality monitoring, bias correction and single-cycle experiments. Q J R Meteorol Soc. https://doi.org/10.1256/qj.02.229
Otte TL, Nolte CG, Otte MJ, Bowden JH (2012) Does nudging squelch the extremes in regional climate modeling? J Clim. https://doi.org/10.1175/JCLI-D-12-00048.1
Pan Y, Zhu K, Xue M, Wang X, Hu M, Benjamin SG, Whitaker JS (2014) A GSI-Based coupled EnSRF–En3DVar hybrid data assimilation system for the operational rapid refresh model: tests at a reduced resolution. Mon Weather Rev 142(10):3756–3780. https://doi.org/10.1175/MWR-D-13-00242.1
Paquin-Ricard D, Jones C, Vaillancourt PA (2010) Using ARM observations to evaluate cloud and clear-sky radiation processes as simulated by the canadian regional climate model GEM. Mon Weather Rev. https://doi.org/10.1175/2009MWR2745.1
Peng Z, Yu D, Huang D, Heiser J, Yoo S, Kalb P (2015) 3D cloud detection and tracking system for solar forecast using multiple sky imagers. Sol Energy 118(0):496–519. https://doi.org/10.1016/j.solener.2015.05.037
Powers JG, Klemp JB, Skamarock WC, Davis CA, Dudhia J, Gill DO, Duda MG (2017) The weather research and forecasting model: overview, system efforts, and future directions. Bull Am Meteorol Soc. https://doi.org/10.1175/BAMS-D-15-00308.1
Pryor SC, Barthelmie RJ, Kjellström E (2005) Potential climate change impact on wind energy resources in northern Europe: analyses using a regional climate model. Clim Dyn. https://doi.org/10.1007/s00382-005-0072-x
Pryor SC, Barthelmie RJ, Young DT, Takle ES, Arritt RW, Flory D, Roads J (2009) Wind speed trends over the contiguous United States. J Geophys Res. https://doi.org/10.1029/2008JD011416
Rogers RE, Deng A, Stauffer DR, Gaudet BJ, Jia Y, Soong ST, Tanrikulu S (2013) Application of the weather research and forecasting model for air quality modeling in the San Francisco bay area. J Appl Meteorol Clim. https://doi.org/10.1175/JAMC-D-12-0280.1
Ruiz-Arias JA, Dudhia J, Gueymard CA (2014) A simple parameterization of the short-wave aerosol optical properties for surface direct and diffuse irradiances assessment in a numerical weather model. Geoscientific Model Dev 7(3):1159–1174. https://doi.org/10.5194/gmd-7-1159-2014
Ruiz-Arias JA, Arbizu-Barrena C, Santos-Alamillos FJ, Tovar-Pescador J, Pozo-Vázquez D (2016) Assessing the surface solar radiation budget in the WRF model: a spatiotemporal analysis of the bias and its causes. Mon Weather Rev. https://doi.org/10.1175/MWR-D-15-0262.1
Schlatter TW (2000) Variational assimilation of meteorological observations in the lower atmosphere: a tutorial on how it works. J Atmos Sol Terr Phys. https://doi.org/10.1016/S1364-6826(00)00096-1
Schraff C, Reich H, Rhodin A, Schomburg A, Stephan, K, Periáñez A, Potthast R (2016) Kilometre-scale ensemble data assimilation for the COSMO model (KENDA). Q J R Meteorol Soc. https://doi.org/10.1002/qj.2748
Schwartz CS, Liu Z, Lin H-C, Cetola JD (2014) Assimilating aerosol observations with a “hybrid” variational-ensemble data assimilation system. J Geophys Res Atmos 119(7):4043–4069. https://doi.org/10.1002/2013JD020937
Schwartz CS, Romine GS, Sobash RA, Fossell KR, Weisman ML (2015) NCAR’s experimental real-time convection-allowing ensemble prediction system. Weather Forecast. https://doi.org/10.1175/WAF-D-15-0103.1
Skamarock WC, Klemp JB, Dudhi J, Gill DO, Barker DM, Duda MG, Powers JG (2008) A description of the advanced research WRF version 3. Technical report (June), p 113. https://doi.org/10.5065/D6DZ069T
Staniforth A, Wood N, Côté J (2002a) Analysis of the numerics of physics–dynamics coupling. Q J R Meteorol Soc 128(586):2779–2799. https://doi.org/10.1256/qj.02.25
Staniforth A, Wood N, Côté J, Staniforth A, Wood N, Côté J (2002b) A simple comparison of four physics–dynamics coupling schemes. Mon Weather Rev. https://doi.org/10.1175/1520-0493(2002)130%3c3129:ASCOFP%3e2.0.CO;2
Stauffer DR, Seaman NL (1990) Use of four-dimensional data assimilation in a limited-area mesoscale model. Part I: experiments with synoptic-scale data. Mon Weather Rev
Stauffer DR, Seaman NL (1994) Multiscale four-dimensional data assimilation. J Appl Meteorol. https://doi.org/10.1175/1520-0450(1994)033%3c0416:MFDDA%3e2.0.CO;2
Stauffer DR, Seaman NL, Binkowski FS (1991) Use of four-dimensional data assimilation in a limited-area mesoscale model. Part II: effects of data assimilation within the planetary boundary layer. Mon Weather Rev. https://doi.org/10.1175/1520-0493(1991)119%3c0734:UOFDDA%3e2.0.CO;2
Stengel M, Lindskog M, Undén P, Gustafsson N, Bennartz R (2010) An extended observation operator in HIRLAM 4D-VAR for the assimilation of cloud-affected satellite radiances. Q J R Meteorol Soc. https://doi.org/10.1002/qj.621
Stengel M, Lindskog M, Undén P, Gustafsson N (2013) The impact of cloud-affected IR radiances on forecast accuracy of a limited-area NWP model. Q J R Meteorol Soc. https://doi.org/10.1002/qj.2102
Stensrud DJ (2011). Parameterization schemes: keys to understanding numerical weather prediction models. https://doi.org/10.1017/CBO9780511812590
Sundqvist H, Berge E, Kristjánsson JE (1989) Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model. Mon Weather Rev. https://doi.org/10.1175/1520-0493(1989)117%3c1641:CACPSW%3e2.0.CO;2
Talagrand O (1997) Assimilation of observations, an introduction. J Meteorol Soc Jpn. https://doi.org/10.1256/qj.02.132
Talagrand O, Courtier P (1987) Variational assimilation of meteorological observations with the adjoint vorticity equation. I: theory. Q J R Meteorol Soc. https://doi.org/10.1002/qj.49711347812
Thompson G, Eidhammer T (2014) A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J Atmos Sci. https://doi.org/10.1175/JAS-D-13-0305.1
Thompson G, Tewari M, Ikeda K, Tessendorf S, Weeks C, Otkin J, Kong F (2016) Explicitly-coupled cloud physics and radiation parameterizations and subsequent evaluation in WRF high-resolution convective forecasts. Atmos Res. https://doi.org/10.1016/j.atmosres.2015.09.005
Tuohy A, Zack J, Haupt SE, Sharp J, Ahlstrom M, Dise S, Collier C (2015) Solar forecasting: methods, challenges, and performance. IEEE Power Energy Magaz. https://doi.org/10.1109/MPE.2015.2461351
van der Veen SH, van der Veen SH (2013) Improving NWP model cloud forecasts using meteosat second-generation imagery. Mon Weather Rev 141(5):1545–1557. https://doi.org/10.1175/MWR-D-12-00021.1
Wang X, Barker DM, Snyder C, Hamill TM (2008a) A hybrid ETKF–3dvar data assimilation scheme for the WRF model. Part I: observing system simulation experiment. Mon Weather Rev. https://doi.org/10.1175/2008MWR2444.1
Wang X, Barker DM, Snyder C, Hamill TM (2008b) A hybrid ETKF–3DVAR data assimilation scheme for the WRF model. Part II: real observation experiments. Mon Weather Rev. https://doi.org/10.1175/2008MWR2445.1
Warner TT (2011) Quality assurance in atmospheric modeling. Bull Am Meteorol Soc. https://doi.org/10.1175/BAMS-D-11-00054.1
Whitaker JS, Hamill TM (2002) Ensemble data assimilation without perturbed observations. Mon Weather Rev. https://doi.org/10.1175/1520-0493(2002)130%3c1913:EDAWPO%3e2.0.CO;2
White AT, Pour-Biazar A, Doty K, Dornblaser B, McNider RT, White AT, McNider RT (2018) Improving cloud simulation for air quality studies through assimilation of geostationary satellite observations in retrospective meteorological modeling. Mon Weather Rev 146(1):29–48. https://doi.org/10.1175/MWR-D-17-0139.1
Williamson DL (2002) Time-split versus process-split coupling of parameterizations and dynamical core. Mon Weather Rev. https://doi.org/10.1175/1520-0493(2002)130%3c2024:TSVPSC%3e2.0.CO;2
Xie Y, Sengupta M, Dudhia J (2016) A fast all-sky radiation model for solar applications (FARMS): algorithm and performance evaluation. Eur Photovolt Sol Energy Conf 135:435–445. https://doi.org/10.1016/j.solener.2016.06.003
Yang L, Lam JC, Liu J (2007) Analysis of typical meteorological years in different climates of China. Energy Convers Manage 48(2):654–668. https://doi.org/10.1016/j.enconman.2006.05.016
Yang S-C, Corazza M, Carrassi A, Kalnay E, Miyoshi T (2009) Comparison of local ensemble transform Kalman Filter, 3DVAR, and 4DVAR in a Quasigeostrophic model. Mon Weather Rev. https://doi.org/10.1175/2008MWR2396.1
Yang H, Kurtz B, Nguyen D, Urquhart B, Chow CW, Ghonima M, Kleissl J (2014) Solar irradiance forecasting using a ground-based sky imager developed at UC San Diego. Sol Energy 103(0):502–524. https://doi.org/10.1016/j.solener.2014.02.044
Yucel I, Shuttleworth WJ, Pinker RT, Lu L, Sorooshian S, Yucel I, Sorooshian S (2002) Impact of ingesting satellite-derived cloud cover into the regional atmospheric modeling system. Mon Weather Rev 130(3):610–628. https://doi.org/10.1175/1520-0493(2002)130%3c0610:IOISDC%3e2.0.CO;2
Yucel I, James Shuttleworth W, Gao X, Sorooshian S (2003) Short-term performance of MM5 with cloud-cover assimilation from satellite observations. Mon Weather Rev. https://doi.org/10.1175//2565.1
Zamora RJ, Dutton EG, Trainer M, McKeen SA, Wilczak JM, Hou Y-T (2005) The accuracy of solar irradiance calculations used in mesoscale numerical weather prediction. Mon Weather Rev. https://doi.org/10.1175/MWR2886.1
Zhang F, Zhang M, Poterjoy J, Zhang F, Zhang M, Poterjoy J (2013) E3DVar: coupling an ensemble Kalman Filter with three-dimensional variational data assimilation in a limited-area weather prediction model and comparison to E4DVar. Mon Weather Rev 141(3):900–917. https://doi.org/10.1175/MWR-D-12-00075.1
Zhang J, Draxl C, Hopson T, Monache LD, Vanvyve E, Hodge BM (2015) Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods. Appl Energy. https://doi.org/10.1016/j.apenergy.2015.07.059
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Jiménez, P.A., Lee, J.A., Kosovic, B., Haupt, S.E. (2019). Solar Resource Evaluation with Numerical Weather Prediction Models. In: Polo, J., Martín-Pomares, L., Sanfilippo, A. (eds) Solar Resources Mapping. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-97484-2_7
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