Christman Field Latest Observations
Date Time
MST
Temp
°F
RH
%
DewPt
°F
Wind
mph
Dir
°
Gust
mph
Dir
°
Press
in Hg
Solar
W/m^2
Prec
in
2019-09-16 14:30 89.2 12.1 30.1 5.1 150 8.7 149 24.728 725.2 0.00
2019-09-16 14:25 89.9 12.0 30.6 6.2 146 9.2 139 24.748 772.6 0.00
2019-09-16 14:20 89.8 12.1 30.6 7.0 122 11.3 134 24.740 770.1 0.00
2019-09-16 14:15 89.8 12.8 32.1 5.5 72 9.3 121 24.763 802.0 0.00
2019-09-16 14:10 90.7 12.3 31.7 7.0 109 11.9 118 24.757 857.0 0.00
2019-09-16 14:05 91.5 11.6 31.0 6.0 121 17.9 179 24.745 887.0 0.00
2019-09-16 14:00 89.5 11.8 29.8 2.4 176 6.1 230 24.738 905.0 0.00
2019-09-16 13:55 88.4 11.8 29.1 6.1 244 8.2 220 24.723 839.0 0.00
2019-09-16 13:50 87.9 12.5 30.1 6.8 264 10.4 233 24.750 679.2 0.00
2019-09-16 13:45 88.4 12.5 30.4 4.6 302 6.7 213 24.752 834.0 0.00
2019-09-16 13:40 88.1 12.5 30.1 4.7 241 8.3 191 24.760 847.0 0.00
2019-09-16 13:35 88.9 12.6 30.9 6.1 135 10.6 141 24.764 883.0 0.00
2019-09-16 13:30 88.8 12.6 30.9 5.5 235 9.6 202 24.766 906.0 0.00
2019-09-16 13:25 89.1 12.1 30.2 4.8 135 7.5 151 24.766 907.0 0.00
2019-09-16 13:20 87.3 12.7 29.9 3.4 151 8.1 136 24.767 790.1 0.00
2019-09-16 13:15 86.4 13.1 30.0 4.5 141 7.0 137 24.768 453.6 0.00
2019-09-16 13:10 85.9 14.2 31.5 3.4 144 7.7 116 24.769 385.8 0.00
2019-09-16 13:05 85.9 12.9 29.2 3.5 78 7.7 93 24.770 329.3 0.00
2019-09-16 13:00 86.2 13.4 30.5 5.3 93 8.7 125 24.771 325.0 0.00
2019-09-16 12:55 87.3 13.2 30.9 5.6 115 8.3 81 24.771 386.8 0.00
2019-09-16 12:50 88.0 12.9 30.8 4.9 82 7.3 82 24.773 818.0 0.00
2019-09-16 12:45 87.2 13.2 30.7 6.1 138 9.1 154 24.774 832.0 0.00
2019-09-16 12:40 86.2 13.6 30.8 6.2 92 10.0 142 24.775 556.3 0.00
2019-09-16 12:35 85.4 13.6 30.1 6.8 124 10.8 126 24.774 452.3 0.00
2019-09-16 12:30 85.4 14.5 31.7 7.3 141 12.1 133 24.773 431.1 0.00
2019-09-16 12:25 85.6 14.2 31.3 8.1 152 11.0 149 24.771 415.6 0.00
2019-09-16 12:20 85.7 13.1 29.3 5.8 119 9.6 127 24.771 494.2 0.00
2019-09-16 12:15 86.6 12.7 29.3 5.6 156 9.8 127 24.775 456.1 0.00
2019-09-16 12:10 87.6 12.9 30.6 7.2 131 10.6 130 24.776 888.0 0.00
2019-09-16 12:05 87.1 13.4 31.1 6.5 151 10.7 164 24.776 875.0 0.00
CIRA

Cooperative Institute for Research in the Atmosphere

John Forsythe

Job Title:
Sr. Research Associate
Phone Number:

970-491-8589

Fax Number:

970-491-8241

Mailing Addresss:
John Forsythe
Cooperative Institute for Research in the Atmosphere
Colorado State University
1375 Campus Delivery
Fort Collins, CO 80523-1375
Office Location:
ACRC 103
About Me:

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.

Past Work

Satellites provide the best means to track the evolution of atmospheric moisture over large regions...

Thursday, March 13, 2014

Satellites provide the best means to track the evolution of atmospheric moisture over large regions...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.

    Publications

    Using the multisensor advected layer precipitable water product in the operational forecast environment

    Published Date: 2018
    Published By: Journal Article

    Blended Multisensor Satellite Products for Forecasting Heavy Precipitation

    Published Date: 2017
    Published By: Conference

    New GOES-R Risk Reduction Activities at CIRA

    Published Date: 2017
    Published By: Conference

    The Newly Operational VIIRS Cloud Cover/Layers and Base

    Published Date: 2017
    Published By: Conference

    Improving Cloud Layer Boundaries from GOES-16

    Published Date: 2018
    Published By: Conference

    Tracking Water Vapor with Multisensor Blended Products for Forecasters

    Published Date: 2018
    Published By: Conference

    How MiRS Retrievals Enable a Layered Water Vapor Product for Forecasters

    Published Date: 2017
    Published By: Conference

    Cloud-Base Height Estimation from VIIRS. Part II: A Statistical Algorithm Based on A-Train Satellite Data

    Published Date: 2017
    Published By: Journal of Atmospheric and Oceanic Technology

    A Dynamic Enhancement With Background Reduction Algorithm: Overview and Application to Satellite‐Based Dust Storm Detection

    Published Date: 2017
    Published By: Journal of Geophysical Research

    Comparison of Gaussian, logarithmic transform and mixed Gaussian–log‐normal distribution based 1DVAR microwave temperature–water‐vapour mixing ratio retrievals

    Published Date: 2017
    Published By: Royal Meteorological Society

    Estimating Three-Dimensional Cloud Structure via Statistically Blended Satellite Observations

    Published Date: 2014
    Published By: American Meteorological Society

    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.