| Date Time MST |
Temp °F |
RH % |
DewPt °F |
Wind mph |
Dir ° |
Gust mph |
Dir ° |
Press in Hg |
Solar W/m^2 |
Prec in |
|---|---|---|---|---|---|---|---|---|---|---|
| 2019-11-12 05:45 | 13.2 | 88.8 | 10.5 | 0.5 | 352 | 1.5 | 352 | 24.994 | 0.0 | 0.00 |
| 2019-11-12 05:40 | 13.0 | 92.0 | 11.1 | 0.5 | 352 | 1.6 | 351 | 24.995 | 0.0 | 0.00 |
| 2019-11-12 05:35 | 11.9 | 92.8 | 10.2 | 1.1 | 351 | 2.5 | 352 | 24.995 | 0.0 | 0.00 |
| 2019-11-12 05:30 | 9.8 | 91.6 | 7.9 | 3.1 | 352 | 4.5 | 340 | 24.996 | 0.0 | 0.00 |
| 2019-11-12 05:25 | 8.8 | 89.4 | 6.3 | 3.2 | 340 | 3.8 | 348 | 24.996 | 0.0 | 0.00 |
| 2019-11-12 05:20 | 11.5 | 84.2 | 7.6 | 3.2 | 5 | 4.2 | 5 | 24.999 | 0.0 | 0.00 |
| 2019-11-12 05:15 | 12.1 | 92.6 | 10.3 | 0.3 | 323 | 2.1 | 322 | 24.999 | 0.0 | 0.00 |
| 2019-11-12 05:10 | 10.8 | 93.8 | 9.4 | 0.2 | 236 | 1.8 | 236 | 25.002 | 0.0 | 0.00 |
| 2019-11-12 05:05 | 10.5 | 93.0 | 8.9 | 0.5 | 290 | 1.1 | 290 | 25.006 | 0.0 | 0.00 |
| 2019-11-12 05:00 | 9.2 | 91.8 | 7.3 | 1.2 | 290 | 2.0 | 290 | 25.012 | 0.0 | 0.00 |
| 2019-11-12 04:55 | 8.9 | 90.4 | 6.6 | 2.2 | 290 | 4.6 | 296 | 25.015 | 0.0 | 0.00 |
| 2019-11-12 04:50 | 8.2 | 92.4 | 6.5 | 1.3 | 295 | 4.9 | 299 | 25.017 | 0.0 | 0.00 |
| 2019-11-12 04:45 | 6.3 | 89.2 | 3.8 | 0.5 | 168 | 1.7 | 168 | 25.020 | 0.0 | 0.00 |
| 2019-11-12 04:40 | 7.5 | 87.4 | 4.5 | 1.1 | 168 | 2.0 | 168 | 25.021 | 0.0 | 0.00 |
| 2019-11-12 04:35 | 8.1 | 88.4 | 5.3 | 1.9 | 105 | 3.5 | 25 | 25.023 | 0.0 | 0.00 |
| 2019-11-12 04:30 | 9.4 | 87.4 | 6.4 | 1.2 | 25 | 3.0 | 25 | 25.024 | 0.0 | 0.00 |
| 2019-11-12 04:25 | 10.2 | 91.8 | 8.3 | 0.7 | 171 | 1.6 | 171 | 25.021 | 0.0 | 0.00 |
| 2019-11-12 04:20 | 9.2 | 90.9 | 7.1 | 0.5 | 171 | 1.6 | 171 | 25.023 | 0.0 | 0.00 |
| 2019-11-12 04:15 | 9.3 | 90.9 | 7.2 | 1.2 | 170 | 3.4 | 170 | 25.032 | 0.0 | 0.00 |
| 2019-11-12 04:10 | 7.8 | 92.7 | 6.1 | 1.3 | 231 | 3.2 | 231 | 25.037 | 0.0 | 0.00 |
| 2019-11-12 04:05 | 7.2 | 88.6 | 4.6 | 1.2 | 231 | 3.2 | 231 | 25.040 | 0.0 | 0.00 |
| 2019-11-12 04:00 | 7.9 | 91.1 | 5.8 | 0.4 | 22 | 1.5 | 22 | 25.032 | 0.0 | 0.00 |
| 2019-11-12 03:55 | 7.9 | 90.0 | 5.5 | 1.5 | 22 | 2.1 | 22 | 25.032 | 0.0 | 0.00 |
| 2019-11-12 03:50 | 8.9 | 89.2 | 6.4 | 1.9 | 22 | 2.7 | 22 | 25.036 | 0.0 | 0.00 |
| 2019-11-12 03:45 | 9.4 | 91.5 | 7.4 | 1.4 | 357 | 3.1 | 357 | 25.048 | 0.0 | 0.00 |
| 2019-11-12 03:40 | 9.8 | 92.4 | 8.0 | 0.0 | 339 | 0.0 | 339 | 25.052 | 0.0 | 0.00 |
| 2019-11-12 03:35 | 8.9 | 92.5 | 7.1 | 0.6 | 339 | 1.5 | 340 | 25.056 | 0.0 | 0.00 |
| 2019-11-12 03:30 | 8.0 | 91.9 | 6.1 | 0.8 | 339 | 1.9 | 339 | 25.062 | 0.0 | 0.00 |
| 2019-11-12 03:25 | 8.2 | 91.1 | 6.1 | 2.6 | 339 | 3.5 | 329 | 25.064 | 0.0 | 0.00 |
| 2019-11-12 03:20 | 7.1 | 92.1 | 5.3 | 3.4 | 329 | 4.1 | 321 | 25.065 | 0.0 | 0.00 |
970-491-8589
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John Forsythe received his BSc in Geology from the University of Maryland (1987), and his M.S. in Atmospheric Science (1993) from Colorado State University. His thesis topic was on the first satellite detection of a warm core in a polar low, an intense sometimes hurricane-like type of storm which forms in the Arctic. Extracting new information from satellite for weather analysis and forecasting continues to be a top research interest. He specializes in using satellite data to improve our understanding of the atmosphere for weather forecasting and climate research. Remote sensing of clouds and water vapor from passive microwave sensors are one of his principal interests. He is actively involved in the production of the global climate data record of atmospheric water vapor. He has participated in several studies of the global occurrence of clouds and the creation of high resolution satellite cloud climatologies. He enjoys instructing students on satellite meteorology and scientific programming.
Satellites provide the best means to track the evolution of atmospheric moisture over large regions. Panel (A) above shows the CIRA analysis of total precipitable water (TPW) in mm on June 25, 2006. Panel (B) shows the percent of weekly normal product, where blue areas are moister than average and brown regions reflect a dry atmosphere. Notice the abnormally moist plume (roughly 50 mm TPW) flowing from near Hispaniola to the Mid-Atlantic states. This moisture provides the fuel for heavy precipitation, and severe flooding occurred in the Washington D. C. region. CIRA combines several satellite microwave sensors (AMSU and SSM/I) onboard polar orbiters with TPW measurements from GOES and GPS to allow forecasters to visualize the flow of atmospheric moisture. Mr. Forsythe has partnered with colleagues Andy Jones and Stan Kidder of CIRA and Sheldon Kusselson of NESDIS/SAB to develop these products and they are currently in transition to National Weather Service operations. This work lays the foundation for future multisensor, multispectral products from the National Polar-orbiting Operational Environmental Satellite System (NPOESS) spacecraft in the coming years.
The launch of the NASA CloudSat in April 2006 enabled the first satellite-based global observation of vertically resolved cloud information. However, CloudSat’s nonscanning W-band (94 GHz) Cloud Profiling Radar (CPR) provides only a nadir cross section, or “curtain,” of the atmosphere along the satellite ground track, precluding a full three-dimensional (3D) characterization and thus limiting its utility for certain model verification and cloud-process studies. This paper details an algorithm for extending a limited set of vertically resolved cloud observations to form regional 3D cloud structure. Predicated on the assumption that clouds of the same type (e.g., cirrus, cumulus, and stratocumulus) often share geometric and microphysical properties as well, the algorithm identifies cloud-type-dependent correlations and uses them to estimate cloud-base height and liquid/ice water content vertical structure. These estimates, when combined with conventional retrievals of cloud-top height, result in a 3D structure for the topmost cloud layer. The technique was developed on multiyear CloudSat data and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) swath data from the NASA Aquasatellite. Data-exclusion experiments along the CloudSat ground track show improved predictive skill over both climatology and type-independent nearest-neighbor estimates. More important, the statistical methods, which employ a dynamic range-dependent weighting scheme, were also found to outperform type-dependent near-neighbor estimates. Application to the 3D cloud rendering of a tropical cyclone is demonstrated.
