@article{huangModelingMonthlyNearsurface2008,
  title = {Modeling Monthly Near-Surface Air Temperature from Solar Radiation and Lapse Rate: Application over Complex Terrain in {{Yellowstone}} National Park},
  author = {Huang, Shengli and Rich, Paul M. and Crabtree, Robert L. and Potter, Christopher S. and Fu, Pinde},
  date = {2008-03},
  journaltitle = {Physical Geography},
  volume = {29},
  pages = {158--178},
  issn = {0272-3646},
  doi = {10.2747/0272-3646.29.2.158},
  url = {http://mfkp.org/INRMM/article/14073925},
  abstract = {Spatially distributed surface temperature over complex topography is important to many ecological processes, but it varies spatially and temporally in complex ways and is difficult to measure at landscape scales at tens of meters resolution. Our goal is to develop a methodology that accurately predicts surface temperature in mountain ecosystems. First, we modeled monthly incoming solar radiation (insolation) based on topography and observed variation in atmospheric conditions, and accounting for site latitude, elevation, and surface orientation (slope and aspect), daily and seasonal shifts in sun angle, and effects of shadows cast by surrounding topography. Then we investigated the ability to predict monthly average temperature from lapse rates together with energy input from insolation. Monthly lapse rates are not constant and have a seasonal variation.

[] Monthly temperature is not highly correlated with the corresponding monthly insolation; however, it is highly correlated with a one-month-lag insolation, enabling calculation of a lag-corrected linear regression. We used this regression to calculate 30 m resolution surface temperature maps for mountain areas at Yellowstone National Park. The comparison between our modeled temperature and observed temperature shows the mean average errors range from 0.90°C to 1.49°C. Our approach, with consideration of influences of topography and variable atmospheric conditions on energy from insolation, and with appropriate time lags, greatly improves the ability to calculate spatially distributed temperature regimes in complex terrain.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14073925,~to-add-doi-URL,complexity,delay,elevation,lapse-rate,regression,solar-radiation,temperature,time-series,united-states},
  number = {2}
}

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S.","Fu, P."],"bibdata":{"bibtype":"article","type":"article","title":"Modeling Monthly Near-Surface Air Temperature from Solar Radiation and Lapse Rate: Application over Complex Terrain in Yellowstone National Park","author":[{"propositions":[],"lastnames":["Huang"],"firstnames":["Shengli"],"suffixes":[]},{"propositions":[],"lastnames":["Rich"],"firstnames":["Paul","M."],"suffixes":[]},{"propositions":[],"lastnames":["Crabtree"],"firstnames":["Robert","L."],"suffixes":[]},{"propositions":[],"lastnames":["Potter"],"firstnames":["Christopher","S."],"suffixes":[]},{"propositions":[],"lastnames":["Fu"],"firstnames":["Pinde"],"suffixes":[]}],"date":"2008-03","journaltitle":"Physical Geography","volume":"29","pages":"158–178","issn":"0272-3646","doi":"10.2747/0272-3646.29.2.158","url":"http://mfkp.org/INRMM/article/14073925","abstract":"Spatially distributed surface temperature over complex topography is important to many ecological processes, but it varies spatially and temporally in complex ways and is difficult to measure at landscape scales at tens of meters resolution. Our goal is to develop a methodology that accurately predicts surface temperature in mountain ecosystems. First, we modeled monthly incoming solar radiation (insolation) based on topography and observed variation in atmospheric conditions, and accounting for site latitude, elevation, and surface orientation (slope and aspect), daily and seasonal shifts in sun angle, and effects of shadows cast by surrounding topography. Then we investigated the ability to predict monthly average temperature from lapse rates together with energy input from insolation. Monthly lapse rates are not constant and have a seasonal variation. [] Monthly temperature is not highly correlated with the corresponding monthly insolation; however, it is highly correlated with a one-month-lag insolation, enabling calculation of a lag-corrected linear regression. We used this regression to calculate 30 m resolution surface temperature maps for mountain areas at Yellowstone National Park. The comparison between our modeled temperature and observed temperature shows the mean average errors range from 0.90°C to 1.49°C. Our approach, with consideration of influences of topography and variable atmospheric conditions on energy from insolation, and with appropriate time lags, greatly improves the ability to calculate spatially distributed temperature regimes in complex terrain.","keywords":"*imported-from-citeulike-INRMM,~INRMM-MiD:c-14073925,~to-add-doi-URL,complexity,delay,elevation,lapse-rate,regression,solar-radiation,temperature,time-series,united-states","number":"2","bibtex":"@article{huangModelingMonthlyNearsurface2008,\n title = {Modeling Monthly Near-Surface Air Temperature from Solar Radiation and Lapse Rate: Application over Complex Terrain in {{Yellowstone}} National Park},\n author = {Huang, Shengli and Rich, Paul M. and Crabtree, Robert L. and Potter, Christopher S. and Fu, Pinde},\n date = {2008-03},\n journaltitle = {Physical Geography},\n volume = {29},\n pages = {158--178},\n issn = {0272-3646},\n doi = {10.2747/0272-3646.29.2.158},\n url = {http://mfkp.org/INRMM/article/14073925},\n abstract = {Spatially distributed surface temperature over complex topography is important to many ecological processes, but it varies spatially and temporally in complex ways and is difficult to measure at landscape scales at tens of meters resolution. Our goal is to develop a methodology that accurately predicts surface temperature in mountain ecosystems. First, we modeled monthly incoming solar radiation (insolation) based on topography and observed variation in atmospheric conditions, and accounting for site latitude, elevation, and surface orientation (slope and aspect), daily and seasonal shifts in sun angle, and effects of shadows cast by surrounding topography. Then we investigated the ability to predict monthly average temperature from lapse rates together with energy input from insolation. Monthly lapse rates are not constant and have a seasonal variation.\n\n[] Monthly temperature is not highly correlated with the corresponding monthly insolation; however, it is highly correlated with a one-month-lag insolation, enabling calculation of a lag-corrected linear regression. We used this regression to calculate 30 m resolution surface temperature maps for mountain areas at Yellowstone National Park. 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