Mesoscale numerical predication of Colorado snowfall and winds
| dc.contributor.author | Beitler, Brian A., author | |
| dc.date.accessioned | 2022-03-28T19:54:21Z | |
| dc.date.available | 2022-03-28T19:54:21Z | |
| dc.date.issued | 1994-05-24 | |
| dc.description | May 24, 1994. | |
| dc.description.abstract | The prediction of spring snow run-off in the Colorado Rocky Mountains has lasting effects throughout the year. The climatology of the snowfall in the high terrain directly influences the water supply for Colorado and neighboring states and dictates any water restrictions for the remainder of the year. Existing methods of predicting snow amounts include using data from SNOTEL sites and interpolating these measurements for the entire region. The advantages of using a real-time mesoscale model is the spatial regularity of information and expanded coverage a model can allow. Using the Regional Atmospheric Modeling System (RAMS) developed at Colorado State University (CSU), calculations of seasonal precipitation were made, employing a precipitation efficiency scheme based on cloud top temperature. Overall, this scheme under-forecast precipitation; however, a correlation existed between this simple scheme and topography. It was found that the best results are obtained by using a bulk microphysics scheme. The trade-off of using this more physically-based approach is the extended computing time required to attain results. The accuracy in precipitation forecasting in individual events when using the bulk microphysics is seen when simulating the winter storm of 8 February 1994. Not only are the amounts of precipitation forecast at particular sites improved over the efficiency scheme results, but the regions of maximum snowfall are also better predicted. The Colorado Front Range downslope windstorm of 3 July 1993 was also simulated using RAMS. The model was run using different levels of microphysical complexity. For this type of mesoscale phenomenon, increased resolution along the Front Range produced windstorm-scale velocities in the foothills. The use of the bulk microphysics scheme greatly increased the forecast skill of the simulations. | |
| dc.description.sponsorship | Sponsored by Air Force Office of Scientific Research AFOSR-91-0269; the National Oceanic & Atmospheric Administration NA90RAH-00077; and the Colorado Agricultural Experiment Station COL00692. | |
| dc.format.medium | reports | |
| dc.identifier.uri | https://hdl.handle.net/10217/234581 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation | Catalog record number (MMS ID): 991023419129703361 | |
| dc.relation | QC852 .C6 no. 556 | |
| dc.relation.ispartof | Atmospheric Science Papers (Blue Books) | |
| dc.relation.ispartof | Atmospheric science paper, no. 556 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | Snow -- Colorado -- Forecasting | |
| dc.subject | Water-supply -- West (U.S.) | |
| dc.subject | Snow -- Colorado $xMeasurement | |
| dc.title | Mesoscale numerical predication of Colorado snowfall and winds | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). |
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