The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
A Meteorological Distribution System for High-Resolution Terrestrial Modeling (MicroMet)
Open accessAbstract
An intermediate-complexity, quasi–physically based, meteorological model (MicroMet) has been developed to produce high-resolution (e.g., 30-m to 1-km horizontal grid increment) atmospheric forcings required to run spatially distributed terrestrial models over a wide variety of landscapes. The following eight variables, required to run most terrestrial models, are distributed: air temperature, relative humidity, wind speed, wind direction, incoming solar radiation, incoming longwave radiation, surface pressure, and precipitation. To produce these distributions, MicroMet assumes that at least one value of each of the following meteorological variables are available for each time step, somewhere within, or near, the simulation domain: air temperature, relative humidity, wind speed, wind direction, and precipitation. These variables are collected at most meteorological stations. For the incoming solar and longwave radiation, and surface pressure, either MicroMet can use its submodels to generate these fields, or it can create the distributions from observations as part of a data assimilation procedure. MicroMet includes a preprocessor component that analyzes meteorological data, then identifies and corrects potential deficiencies. Since providing temporally and spatially continuous atmospheric forcing data for terrestrial models is a core objective of MicroMet, the preprocessor also fills in any missing data segments with realistic values. Data filling is achieved by employing a variety of procedures, including an autoregressive integrated moving average calculation for diurnally varying variables (e.g., air temperature). To create the distributed atmospheric fields, spatial interpolations are performed using the Barnes objective analysis scheme, and subsequent corrections are made to the interpolated fields using known temperature–elevation, wind–topography, humidity–cloudiness, and radiation–cloud–topography relationships.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
River flow forecasting through conceptual models part I — A discussion of principles
Nash, J. E., & Sutcliffe, J. V. (1970) · Journal of Hydrology · doi:10.1016/0022-1694(70)90255-6 · Cited by 23,674
Time Series Analysis: Forecasting and Control
Geurts, M., Box, G. E. P., & Jenkins, G. M. (1977) · Journal of Marketing Research · doi:10.2307/3150485 · Cited by 19,186
Time Series Analysis: Forecasting and Control
Kleiner, B. (1977) · Technometrics · doi:10.1080/00401706.1977.10489562 · Cited by 6,446
Principles of geographical information systems
BURROUGH, P. A., & McDonnell, R. A. (1998) · Cited by 4,082
Atmospheric science: an introductory survey
(2007) · Choice Reviews Online · doi:10.5860/choice.44-3925 · Cited by 1,859
New Equations for Computing Vapor Pressure and Enhancement Factor
Buck, A. L. (1981) · Journal of Applied Meteorology · doi:10.1175/1520-0450(1981)020<1527:nefcvp>2.0.co;2 · Cited by 1,592
Generating surfaces of daily meteorological variables over large regions of complex terrain
Thornton, P. E., Running, S. W., & White, M. A. (1997) · Journal of Hydrology · doi:10.1016/s0022-1694(96)03128-9 · Cited by 1,384
Mitchell, K. E., Lohmann, D., Houser, P. R., Wood, E. F., Schaake, J. C., Robock, A., Cosgrove, B. A., Sheffield, J., Duan, Q., Luo, L., et al. (2004) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2003jd003823 · Cited by 1,297
Forecast Verification: A Practitioner's Guide in Atmospheric Science
(2011) · Wiley eBooks · doi:10.1002/9781119960003 · Cited by 1,267
A Technique for Maximizing Details in Numerical Weather Map Analysis
Barnes, S. L. (1964) · Journal of Applied Meteorology · doi:10.1175/1520-0450(1964)003<0396:atfmdi>2.0.co;2 · Cited by 1,087
Cited by
Bennie, J., Huntley, B., Wiltshire, A., Hill, M. O., & Baxter, R. (2008) · Ecological Modelling · doi:10.1016/j.ecolmodel.2008.04.010 · Cited by 580
Elevation correction of ERA-Interim temperature data in complex terrain
Gao, L., Bernhardt, M., & Schulz, K. (2012) · Hydrology and Earth System Sciences · doi:10.5194/hess-16-4661-2012 · Cited by 142
Zhang, L., Nan, Z., Yu, W., & Ge, Y. (2015) · Water Resources Management · doi:10.1007/s11269-015-1085-9 · Cited by 108
Metadata
- Authors
- Liston, G. E., & Elder, K.
- Venue
- Journal of Hydrometeorology
- Publisher
- American Meteorological Society
- Published
- 2006-04-01
- Volume
- 7
- Issue
- 2
- Pages
- 217-234
- ISSN
- 1525-7541 (ISSN-L)
- 1525-755X
- OpenAlex
- W2162906470
- Cited by
- 629
- References
- 29
- FWCI
- 9.59
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Meteorological Distribution System for High-Resolution Terrestrial Modeling (MicroMet). https://app.cochat.ai/papers/10.1175/jhm486.1
- The paper
- Liston, G. E., & Elder, K. (2006). A Meteorological Distribution System for High-Resolution Terrestrial Modeling (MicroMet). Journal of Hydrometeorology. https://doi.org/10.1175/jhm486.1
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.