Articles | Volume 12, issue 4
https://doi.org/10.5194/essd-12-2381-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-2381-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
02 Oct 2020
Data description paper |
| 02 Oct 2020
| 02 Oct 2020
SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018
University of Saskatchewan Coldwater Lab, Canmore, Alberta, Canada
Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Martyn P. Clark
University of Saskatchewan Coldwater Lab, Canmore, Alberta, Canada
Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Andrew J. Newman
National Center for Atmospheric Research, Boulder, CO, USA
Andrew W. Wood
National Center for Atmospheric Research, Boulder, CO, USA
Simon Michael Papalexiou
Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Department of Civil, Geological and Environmental Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Vincent Vionnet
Environmental Numerical Research Prediction, Environment and Climate Change Canada, Dorval, Quebec, Canada
Paul H. Whitfield
University of Saskatchewan Coldwater Lab, Canmore, Alberta, Canada
Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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Guoqiang Tang, Martyn P. Clark, Simon Michael Papalexiou, Andrew J. Newman, Andrew W. Wood, Dominique Brunet, and Paul H. Whitfield
Earth Syst. Sci. Data, 13, 3337–3362, https://doi.org/10.5194/essd-13-3337-2021, https://doi.org/10.5194/essd-13-3337-2021, 2021
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Manuela I. Brunner, Eric Gilleland, and Andrew W. Wood
Earth Syst. Dynam., 12, 621–634, https://doi.org/10.5194/esd-12-621-2021, https://doi.org/10.5194/esd-12-621-2021, 2021
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Compound hot and dry events can lead to severe impacts whose severity may depend on their timescale and spatial extent. Here, we show that the spatial extent and timescale of compound hot–dry events are strongly related, spatial compound event extents are largest at
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Paul H. Whitfield, Philip D. A. Kraaijenbrink, Kevin R. Shook, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 25, 2513–2541, https://doi.org/10.5194/hess-25-2513-2021, https://doi.org/10.5194/hess-25-2513-2021, 2021
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Mountain snow cover provides critical supplies of fresh water to downstream users. Its accurate prediction requires inclusion of often-ignored processes. A multi-scale modelling strategy is presented that efficiently accounts for snow redistribution. Model accuracy is assessed via airborne lidar and optical satellite imagery. With redistribution the model captures the elevation–snow depth relation. Redistribution processes are required to reproduce spatial variability, such as around ridges.
Manuela I. Brunner, Lieke A. Melsen, Andrew W. Wood, Oldrich Rakovec, Naoki Mizukami, Wouter J. M. Knoben, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 105–119, https://doi.org/10.5194/hess-25-105-2021, https://doi.org/10.5194/hess-25-105-2021, 2021
Short summary
Short summary
Assessments of current, local, and regional flood hazards and their future changes often involve the use of hydrologic models. A reliable model ideally reproduces both local flood characteristics and regional aspects of flooding. In this paper we investigate how such characteristics are represented by hydrologic models. Our results show that both the modeling of local and regional flood characteristics are challenging, especially under changing climate conditions.
Shervan Gharari, Martyn P. Clark, Naoki Mizukami, Wouter J. M. Knoben, Jefferson S. Wong, and Alain Pietroniro
Hydrol. Earth Syst. Sci., 24, 5953–5971, https://doi.org/10.5194/hess-24-5953-2020, https://doi.org/10.5194/hess-24-5953-2020, 2020
Short summary
Short summary
This work explores the trade-off between the accuracy of the representation of geospatial data, such as land cover, soil type, and elevation zones, in a land (surface) model and its performance in the context of modeling. We used a vector-based setup instead of the commonly used grid-based setup to identify this trade-off. We also assessed the often neglected parameter uncertainty and its impact on the land model simulations.
Cited articles
Alexander, L. V., Zhang, X., Peterson, T. C., Caesar, J., Gleason, B., Tank,
A. M. G. K., Haylock, M., Collins, D., Trewin, B., Rahimzadeh, F., Tagipour, A., Kumar, K. R.,
Revadekar, J., Griffiths, G., Vincent, L., Stephenson, D. B., Burn, J., Aguilar, E., Brunet, M.,
Taylor, M., New, M., Zhai, P., Rusticucci, M., and Vazquez-Aguirre, J. L.: Global observed changes
in daily climate extremes of temperature and precipitation, J. Geophys. Res.-Atmos., 111, D05109, https://doi.org/10.1029/2005JD006290, 2006.
Anderson, B. T., Wang, J., Salvucci, G., Gopal, S., and Islam, S.: Observed Trends in
Summertime Precipitation over the Southwestern United States, J. Climate, 23, 1937–1944,
https://doi.org/10.1175/2009JCLI3317.1, 2009.
Beaulieu, C., Seidou, O., Ouarda, T. B. M. J., Zhang, X., Boulet, G., and Yagouti, A.:
Intercomparison of homogenization techniques for precipitation data, Water Resour. Res., 44, W02425, https://doi.org/10.1029/2006WR005615, 2008.
Beck, H. E., van Dijk, A. I. J. M., Levizzani, V., Schellekens, J., Miralles, D. G.,
Martens, B., and de Roo, A.: MSWEP: 3-hourly 0.25∘ global gridded precipitation
(1979–2015) by merging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci., 21,
589–615, https://doi.org/10.5194/hess-21-589-2017, 2017.
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., van Dijk,
A. I. J. M., McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-Hourly 0.1∘
Precipitation: Methodology and Quantitative Assessment, B. Am. Meteorol. Soc., 100, 473–500,
https://doi.org/10.1175/BAMS-D-17-0138.1, 2019.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324,
2001.
Cannon, A. J., Sobie, S. R., and Murdock, T. Q.: Bias Correction of GCM Precipitation by
Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?, J. Climate, 28,
6938–6959, https://doi.org/10.1175/JCLI-D-14-00754.1, 2015.
Che Ghani, N. Z., Abu Hasan, Z., and Tze Liang, L.: Estimation of Missing Rainfall Data
Using GEP: Case Study of Raja River, Alor Setar, Kedah, Adv. Artif. Intell., 2014, 716398,
https://doi.org/10.1155/2014/716398, 2014.
Copernicus Climate Change Service (C3S): ERA5: Fifth generation of ECMWF atmospheric
reanalyses of the global climate, Copernic. Clim. Change Serv. Clim. Data Store CDS Access Date
July 2019, available at: https://cds.climate.copernicus.eu/cdsapp{#}!/home (last access: 19 October 2019), 2017.
Coulibaly, P. and Evora, N. D.: Comparison of neural network methods for infilling
missing daily weather records, J. Hydrol., 341, 27–41, https://doi.org/10.1016/j.jhydrol.2007.04.020, 2007.
Dastorani, M. T., Moghadamnia, A., Piri, J., and Rico-Ramirez, M.: Application of ANN
and ANFIS models for reconstructing missing flow data, Environ. Monit. Assess., 166, 421–434,
https://doi.org/10.1007/s10661-009-1012-8, 2010.
Devi, U., Shekhar, M. S., Singh, G. P., Rao, N. N., and Bhatt, U. S.: Methodological
application of quantile mapping to generate precipitation data over Northwest Himalaya,
Int. J. Climatol., 39, 3160–3170, https://doi.org/10.1002/joc.6008, 2019.
Devine, K. A. and Mekis, E.: Field accuracy of Canadian rain measurements,
Atmos.-Ocean, 46, 213–227, 2008.
Di Luzio, M., Johnson, G. L., Daly, C., Eischeid, J. K., and Arnold, J. G.:
Constructing Retrospective Gridded Daily Precipitation and Temperature Datasets for the
Conterminous United States, J. Appl. Meteorol. Climatol., 47, 475–497,
https://doi.org/10.1175/2007JAMC1356.1, 2008.
Di Piazza, A., Conti, F. L., Noto, L. V., Viola, F., and La Loggia, G.: Comparative
analysis of different techniques for spatial interpolation of rainfall data to create a serially
complete monthly time series of precipitation for Sicily, Italy, Int. J. Appl. Earth Obs. Geoinf.,
13, 396–408, https://doi.org/10.1016/j.jag.2011.01.005, 2011.
Durre, I., Menne, M. J., Gleason, B. E., Houston, T. G., and Vose, R. S.: Comprehensive
Automated Quality Assurance of Daily Surface Observations, J. Appl. Meteorol. Climatol., 49,
1615–1633, https://doi.org/10.1175/2010JAMC2375.1, 2010.
Eischeid, J. K., Bruce Baker, C., Karl, T. R., and Diaz, H. F.: The Quality Control of
Long-Term Climatological Data Using Objective Data Analysis, J. Appl. Meteorol., 34, 2787–2795,
https://doi.org/10.1175/1520-0450(1995)034<2787:TQCOLT>2.0.CO;2, 1995.
Eischeid, J. K., Pasteris, P. A., Diaz, H. F., Plantico, M. S., and Lott, N. J.:
Creating a Serially Complete, National Daily Time Series of Temperature and Precipitation for the
Western United States, J. Appl. Meteorol., 39, 1580–1591,
https://doi.org/10.1175/1520-0450(2000)039<1580:CASCND>2.0.CO;2, 2000.
Gao, L., Bernhardt, M., and Schulz, K.: Elevation correction of ERA-Interim temperature
data in complex terrain, Hydrol. Earth Syst. Sci., 16, 4661–4673,
https://doi.org/10.5194/hess-16-4661-2012, 2012.
Gao, L., Wei, J., Wang, L., Bernhardt, M., Schulz, K., and Chen, X.: A high-resolution
air temperature data set for the Chinese Tian Shan in 1979–2016, Earth Syst. Sci. Data, 10,
2097–2114, https://doi.org/10.5194/essd-10-2097-2018, 2018.
Gardner, A. S., Sharp, M. J., Koerner, R. M., Labine, C., Boon, S., Marshall, S. J.,
Burgess, D. O., and Lewis, D.: Near-Surface Temperature Lapse Rates over Arctic Glaciers and Their
Implications for Temperature Downscaling, J. Climate, 22, 4281–4298, https://doi.org/10.1175/2009jcli2845.1,
2009.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L.,
Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R.,
Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R.,
Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M.,
Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research
and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/jcli-d-16-0758.1,
2017.
Gruber, S.: Derivation and analysis of a high-resolution estimate of global permafrost
zonation, The Cryosphere, 6, 221–233, https://doi.org/10.5194/tc-6-221-2012, 2012.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean
squared error and NSE performance criteria: Implications for improving hydrological modelling,
J. Hydrol., 377, 80–91, 2009.
Hamada, A., Arakawa, O., and Yatagai, A.: An Automated Quality Control Method for Daily
Rain-gauge Data, Glob. Environ. Res., 15, 183–192, 2011.
Hasanpour Kashani, M., and Dinpashoh, Y.: Evaluation of efficiency of different
estimation methods for missing climatological data, Stoch. Environ. Res. Risk Assess., 26,
59–71, https://doi.org/10.1007/s00477-011-0536-y, 2012.
Hubbard, K. G. and You, J.: Sensitivity Analysis of Quality Assurance Using the Spatial
Regression Approach—A Case Study of the Maximum/Minimum Air Temperature, J. Atmospheric
Ocean. Technol., 22, 1520–1530, https://doi.org/10.1175/JTECH1790.1, 2005.
Kanda, N., Negi, H. S., Rishi, M. S., and Shekhar, M. S.: Performance of various
techniques in estimating missing climatological data over snowbound mountainous areas of Karakoram
Himalaya, Meteorol. Appl., 25, 337–349, https://doi.org/10.1002/met.1699, 2018.
Kemp, W. P., Burnell, D. G., Everson, D. O., and Thomson, A. J.: Estimating Missing
Daily Maximum and Minimum Temperatures, J. Clim. Appl. Meteorol., 22, 1587–1593,
https://doi.org/10.1175/1520-0450(1983)022<1587:EMDMAM>2.0.CO;2, 1983.
Kenawy, A. E., López-Moreno, J. I., Stepanek, P., and Vicente-Serrano, S. M.: An
assessment of the role of homogenization protocol in the performance of daily temperature series
and trends: application to northeastern Spain, Int. J. Climatol., 33, 87–108,
https://doi.org/10.1002/joc.3410, 2013.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under
an ensemble of climate change scenarios, J. Hydrol., 424, 264–277, 2012.
Knowles, N., Dettinger, M. D., and Cayan, D. R.: Trends in Snowfall versus Rainfall in
the Western United States, J. Climate, 19, 4545–4559, https://doi.org/10.1175/JCLI3850.1, 2006.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K.,
Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.: The JRA-55 Reanalysis:
General Specifications and Basic Characteristics, J. Meteorol. Soc. Jpn. Ser II, 93, 5–48,
https://doi.org/10.2151/jmsj.2015-001, 2015.
Livneh, B., Bohn, T. J., Pierce, D. W., Munoz-Arriola, F., Nijssen, B., Vose, R.,
Cayan, D. R., and Brekke, L.: A spatially comprehensive, hydrometeorological data set for Mexico,
the U.S., and Southern Canada 1950–2013, Sci. Data, 2, 150042, https://doi.org/10.1038/sdata.2015.42, 2015.
Longman, R. J., Frazier, A. G., Newman, A. J., Giambelluca, T. W., Schanzenbach, D.,
Kagawa-Viviani, A., Needham, H., Arnold, J. R., and Clark, M. P.: High-Resolution Gridded Daily
Rainfall and Temperature for the Hawaiian Islands (1990–2014), J. Hydrometeorol., 20, 489–508,
https://doi.org/10.1175/JHM-D-18-0112.1, 2019.
Longman, R. J., Newman, A. J., Giambelluca, T. W., and Lucas, M.: Characterizing the
Uncertainty and Assessing the Value of Gap-Filled Daily Rainfall Data in Hawaii,
J. Appl. Meteorol. Climatol., 59, 1261–1276, https://doi.org/10.1175/JAMC-D-20-0007.1, 2020.
Ma, C., Fassnacht, S. R., and Kampf, S. K.: How Temperature Sensor Change Affects
Warming Trends and Modeling: An Evaluation Across the State of Colorado, Water Resour. Res., 55,
9748–9764, 2019.
Ma, L., Zhang, T., Li, Q., Frauenfeld, O. W., and Qin, D.: Evaluation of ERA-40,
NCEP-1, and NCEP-2 reanalysis air temperatures with ground-based measurements in China,
J. Geophys. Res., 113, D15115, https://doi.org/10.1029/2007jd009549, 2008.
Ma, Y., Hong, Y., Chen, Y., Yang, Y., Tang, G., Yao, Y., Long, D., Li, C., Han, Z., and
Liu, R.: Performance of Optimally Merged Multisatellite Precipitation Products Using the Dynamic
Bayesian Model Averaging Scheme Over the Tibetan Plateau, J. Geophys. Res.-Atmos., 123, 814–834,
https://doi.org/10.1002/2017jd026648, 2018.
Maraun, D.: Bias Correction, Quantile Mapping, and Downscaling: Revisiting the
Inflation Issue, J. Climate, 26, 2137–2143, https://doi.org/10.1175/JCLI-D-12-00821.1, 2013.
Marshall, S. J., Sharp, M. J., Burgess, D. O., and Anslow, F. S.:
Near-surface-temperature lapse rates on the Prince of Wales Icefield, Ellesmere Island, Canada:
implications for regional downscaling of temperature, Int. J. Climatol., 27, 385–398,
https://doi.org/10.1002/joc.1396, 2007.
Mekis, É. and Brown, R.: Derivation of an adjustment factor map for the estimation
of the water equivalent of snowfall from ruler measurements in Canada, Atmos.-Ocean, 48, 284–293,
https://doi.org/10.3137/AO1104.2010, 2010.
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An overview
of the global historical climatology network-daily database, J. Atmospheric Ocean. Technol.,
https://doi.org/10.1175/JTECH-D-11-00103.1, 2012.
Metcalfe, J. R., Routledge, B., and Devine, K.: Rainfall measurement in Canada:
changing observational methodsand archive adjustment procedures, J. Climate, 10, 92–101, 1997.
Minder, J. R., Mote, P. W., and Lundquist, J. D.: Surface temperature lapse rates over
complex terrain: Lessons from the Cascade Mountains, J. Geophys. Res.-Atmos., 115, D14122.
https://doi.org/10.1029/2009JD013493, 2010.
Newman, A. J., Clark, M. P., Craig, J., Nijssen, B., Wood, A., Gutmann, E., Mizukami,
N., Brekke, L., and Arnold, J. R.: Gridded Ensemble Precipitation and Temperature Estimates for
the Contiguous United States, J. Hydrometeorol., 16, 2481–2500, https://doi.org/10.1175/JHM-D-15-0026.1,
2015.
Newman, A. J., Clark, M. P., Longman, R. J., Gilleland, E., Giambelluca, T. W., and
Arnold, J. R.: Use of Daily Station Observations to Produce High-Resolution Gridded Probabilistic
Precipitation and Temperature Time Series for the Hawaiian Islands, J. Hydrometeorol., 20,
509–529, https://doi.org/10.1175/JHM-D-18-0113.1, 2019.
Papalexiou, S. M. and Montanari, A.: Global and regional increase of precipitation
extremes under global warming, Water Resour. Res., 55, 4901–4914, 2019.
Papalexiou, S. M., AghaKouchak, A., Trenberth, K. E., and Foufoula-Georgiou, E.:
Global, regional, and megacity trends in the highest temperature of the year: Diagnostics and
evidence for accelerating trends, Earths Future, 6, 71–79, 2018.
Pappas, C., Papalexiou, S. M., and Koutsoyiannis, D.: A quick gap filling of missing
hydrometeorological data, J. Geophys. Res.-Atmos., 119, 9290–9300, 2014.
Paulhus, J. L. H. and Kohler, M. A.: Interpolation of missing precipitation records,
Mon. Weather Rev., 80, 129–133,
https://doi.org/10.1175/1520-0493(1952)080<0129:IOMPR>2.0.CO;2, 1952.
Pielke Sr, R., Nielsen-Gammon, J., Davey, C., Angel, J., Bliss, O., Doesken, N., Cai,
M., Fall, S., Niyogi, D., and Gallo, K.: Documentation of uncertainties and biases associated with
surface temperature measurement sites for climate change assessment, B. Am. Meteorol. Soc., 88,
913–928, 2007.
Ramos-Calzado, P., Gómez-Camacho, J., Pérez-Bernal, F., and Pita-López,
M. F.: A novel approach to precipitation series completion in climatological datasets: application
to Andalusia, Int. J. Climatol., 28, 1525–1534, https://doi.org/10.1002/joc.1657, 2008.
Reeves, J., Chen, J., Wang, X. L., Lund, R., and Lu, Q. Q.: A Review and Comparison of
Changepoint Detection Techniques for Climate Data, J. Appl. Meteorol. Climatol., 46, 900–915,
https://doi.org/10.1175/JAM2493.1, 2007.
Rubin, D. B.: Inference and missing data, Biometrika, 63, 581–592,
https://doi.org/10.1093/biomet/63.3.581, 1976.
Santos, L., Thirel, G., and Perrin, C.: Technical note: Pitfalls in using
log-transformed flows within the KGE criterion, Hydrol. Earth Syst. Sci., 22, 4583–4591,
https://doi.org/10.5194/hess-22-4583-2018, 2018.
Sattari, M.-T., Rezazadeh-Joudi, A., and Kusiak, A.: Assessment of different methods
for estimation of missing data in precipitation studies, Hydrol. Res., 48, 1032–1044,
https://doi.org/10.2166/nh.2016.364, 2017.
Scaff, L., Yang, D., Li, Y., and Mekis, E.: Inconsistency in precipitation measurements
across the Alaska–Yukon border, The Cryosphere, 9, 2417–2428,
https://doi.org/10.5194/tc-9-2417-2015, 2015.
Serrano-Notivoli, R., Beguería, S., and de Luis, M.: STEAD: a high-resolution
daily gridded temperature dataset for Spain, Earth Syst. Sci. Data, 11, 1171–1188,
https://doi.org/10.5194/essd-11-1171-2019, 2019.
Shepard, D.: A two-dimensional interpolation function for irregularly-spaced data, in:
Proceedings of the 1968 23rd ACM national conference, pp. 517–524, ACM, Association for Computing Machinery, New York, NY, United States, 1968.
Simolo, C., Brunetti, M., Maugeri, M., and Nanni, T.: Improving estimation of missing
values in daily precipitation series by a probability density function-preserving approach,
Int. J. Climatol., 30, 1564–1576, https://doi.org/10.1002/joc.1992, 2010.
Stooksbury, D. E., Idso, C. D., and Hubbard, K. G.: The Effects of Data Gaps on the
Calculated Monthly Mean Maximum and Minimum Temperatures in the Continental United States: A
Spatial and Temporal Study, J. Climate, 12, 1524–1533,
https://doi.org/10.1175/1520-0442(1999)012<1524:TEODGO>2.0.CO;2, 1999.
Tang, G., Behrangi, A., Long, D., Li, C., and Hong, Y.: Accounting for spatiotemporal
errors of gauges: A critical step to evaluate gridded precipitation products, J. Hydrol., 559,
294–306, https://doi.org/10.1016/j.jhydrol.2018.02.057, 2018a.
Tang, G., Behrangi, A., Ma, Z., Long, D., and Hong, Y.: Downscaling of ERA-Interim
Temperature in the Contiguous United States and Its Implications for Rain–Snow Partitioning,
J. Hydrometeorol., 19, 1215–1233, https://doi.org/10.1175/jhm-d-18-0041.1, 2018b.
Tang, G., Clark, M. P., Newman, A. J., Wood, A. W., Papalexiou, S. M., Vionnet, V.,
Whitfield, P. H.: SCDNA: a serially complete precipitation and temperature dataset for North
America from 1979 to 2018, Zenodo, https://doi.org/10.5281/zenodo.3735533, 2020.
Tang, Q., Wood, A. W., and Lettenmaier, D. P.: Real-Time Precipitation Estimation Based
on Index Station Percentiles, J. Hydrometeorol., 10, 266–277, https://doi.org/10.1175/2008JHM1017.1, 2009.
Teegavarapu, R. S. V. and Chandramouli, V.: Improved weighting methods, deterministic
and stochastic data-driven models for estimation of missing precipitation records, J. Hydrol.,
312, 191–206, https://doi.org/10.1016/j.jhydrol.2005.02.015, 2005.
Ustaoglu, B., Cigizoglu, H. K., and Karaca, M.: Forecast of daily mean, maximum and
minimum temperature time series by three artificial neural network methods, Meteorol. Appl., 15,
431–445, https://doi.org/10.1002/met.83, 2008.
Venema, V. K. C., Mestre, O., Aguilar, E., Auer, I., Guijarro, J. A., Domonkos, P.,
Vertacnik, G., Szentimrey, T., Stepanek, P., Zahradnicek, P., Viarre, J., Müller-Westermeier,
G., Lakatos, M., Williams, C. N., Menne, M. J., Lindau, R., Rasol, D., Rustemeier, E., Kolokythas,
K., Marinova, T., Andresen, L., Acquaotta, F., Fratianni, S., Cheval, S., Klancar, M., Brunetti,
M., Gruber, C., Prohom Duran, M., Likso, T., Esteban, P., and Brandsma, T.: Benchmarking
homogenization algorithms for monthly data, Clim. Past, 8, 89–115,
https://doi.org/10.5194/cp-8-89-2012, 2012.
Vicente-Serrano, S. M., Saz-Sanchez, M. A., and Cuadrat, J. M.: Comparative analysis of
interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and
temperature, Clim. Res., 24, 161–180, https://doi.org/10.3354/cr024161, 2003.
Vincent, L. A., Zhang, X., Bonsal, B. R., and Hogg, W. D.: Homogenization of Daily
Temperatures over Canada, J. Climate, 15, 1322–1334,
https://doi.org/10.1175/1520-0442(2002)015<1322:HODTOC>2.0.CO;2, 2002.
Vincent, L. A., Milewska, E. J., Hopkinson, R., and Malone, L.: Bias in Minimum
Temperature Introduced by a Redefinition of the Climatological Day at the Canadian Synoptic
Stations, J. Appl. Meteorol. Climatol., 48, 2160–2168, https://doi.org/10.1175/2009JAMC2191.1, 2009.
Vincent, L. A., Wang, X. L., Milewska, E. J., Wan, H., Yang, F., and Swail, V.: A
second generation of homogenized Canadian monthly surface air temperature for climate trend
analysis, J. Geophys. Res.-Atmos., 117, D18110, https://doi.org/10.1029/2012JD017859, 2012.
Wambua, R. M., Mutua, B. M., and Raude, J. M.: Prediction of Missing
Hydro-Meteorological Data Series Using Artificial Neural Networks (ANN) for Upper Tana River
Basin, Kenya, Am. J. Water Resour., 4, 35–43, , 2016.
Wang, X. L., Xu, H., Qian, B., Feng, Y., and Mekis, E.: Adjusted daily rainfall and
snowfall data for Canada, Atmos.-Ocean, 55, 155–168, 2017.
Werner, A. T., Schnorbus, M. A., Shrestha, R. R., Cannon, A. J., Zwiers, F. W., Dayon,
G., and Anslow, F.: A long-term, temporally consistent, gridded daily meteorological dataset for
northwestern North America, Sci. Data, 6, 1–16, https://doi.org/10.1038/sdata.2018.299, 2019.
Whitfield, P. H.: Climate Station Analysis and Fitness for Purpose Assessment of
3053600 Kananaskis, Alberta, Atmos.-Ocean, 52, 363–383, https://doi.org/10.1080/07055900.2014.946388, 2014.
Woldesenbet, T. A., Elagib, N. A., Ribbe, L., and Heinrich, J.: Gap filling and
homogenization of climatological datasets in the headwater region of the Upper Blue Nile Basin,
Ethiopia, Int. J. Climatol., 37, 2122–2140, https://doi.org/10.1002/joc.4839, 2017.
Wood, A. W.: The University of Washington Surface Water Monitor: An experimental platform for national hydrologic assessment and prediction, in: Proceedings of the AMS 22nd Conference on Hydrology, New Orleans, Louisiana, USA, 2008.
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C.,
Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of global terrain elevations,
Geophys. Res. Lett., 44, 5844–5853, https://doi.org/10.1002/2017GL072874, 2017.
Yang, D., Kane, D., Zhang, Z., Legates, D., and Goodison, B.: Bias corrections of
long-term (1973-2004) daily precipitation data over the northern regions, Geophys. Res. Lett., 32,
L19501, https://doi.org/10.1029/2005gl024057, 2005.
Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., and Kitoh, A.:
APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense
Network of Rain Gauges, B. Am. Meteorol. Soc., 93, 1401–1415, https://doi.org/10.1175/bams-d-11-00122.1,
2012.
Young, K. C.: A Three-Way Model for Interpolating for Monthly Precipitation Values,
Mon. Weather Rev., 120, 2561–2569,
https://doi.org/10.1175/1520-0493(1992)120<2561:ATWMFI>2.0.CO;2, 1992.
Short summary
Station observations are critical for hydrological and meteorological studies, but they often contain missing values and have short measurement periods. This study developed a serially complete dataset for North America (SCDNA) from 1979 to 2018 for 27 276 precipitation and temperature stations. SCDNA is built on multiple data sources and infilling/reconstruction strategies to achieve high-quality estimates which can be used for a variety of applications.
Station observations are critical for hydrological and meteorological studies, but they often...
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