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

Grasso, Louie

Dr. Louie Grasso

Job Title:
Research Scientist/Scholar III
Phone Number:

970-491-8380

Fax Number:

970-491-8241

Mailing Addresss:
Dr. Louie Grasso
Cooperative Institute for Research in the Atmosphere
Colorado State University
1375 Campus Delivery
Fort Collins, CO 80523-1375
Office Location:
CIRA Room 35
About Me:

Louie Grasso received his BS in Meteorology from Lyndon State College in 1985. Both his MS (1993) and PhD (1996) in Atmospheric Science were obtained from Colorado State University. His areas of interest include numerical modeling, satellite meteorology, and severe thunderstorms. Since 1997 he has work at CIRA where his initial focus was on the numerical modeling of severe storms (Grasso 2000a) and soil moisture impacts on dryline development (Grasso 2000b). His dryline work led to a contribution in the Encyclopedia of Atmospheric Sciences (Grasso 2003). His interests moved into the area of producing synthetic satellite imagery from numerical model output (Grasso and Greenwald, 2004). For the past few years, he has been focused primarily on generating synthetic NPOESS VIIRS and GOES-R ABI imagery for a variety of weather and environmental events: Severe storms, lake effect snow, hurricanes, and wild fires (Lindsey et al. 2006, Grasso et al. 2008).

Past Work

Selected synthetic imagery from three different weather events.

Wednesday, March 12, 2014

Selected synthetic imagery from three different weather events.Selected synthetic imagery from three different weather events. The top row shows synthetic 4 km GOES-12 at 10.7 µm, 2 km GOES-R at 10.35 µm, and 400 m NPOESS VIIRS at 11.02 µm for hurricane Lili. Similar synthetic scenes are displayed for a lake effect snow event, shown in the middle row, and a severe thunderstorm case, shown in the bottom row.

    Publications

    Using the GOES-16 Split Window Difference to Detect a Boundary Prior to Cloud Formation

    Published Date: 2018
    Published By: American Meteorological Society

    New GOES-R Risk Reduction Activities at CIRA

    Published Date: 2017
    Published By: Conference

    Advanced Imagery Applications Development for GOES-16 ABI

    Published Date: 2018
    Published By: Conference

    Disappearing Dust Plumes: Exploring the Roles of Water Vapor and Dust Properties in Detection from Satellite Observations

    Published Date: 2018
    Published By: Conference

    GOES-16 ABI channel differencing used to reveal cloud-free zones of ‘precursors of convective initiation’

    Published Date: 2018
    Published By: Conference

    GOES-16 ABI Observations of Lower Tropospheric Structures of Water Vapor at 1.38 µm

    Published Date: 2018
    Published By: Conference

    Motion of Diffraction Pattern on VIIRS Detectors

    Published Date: 2017
    Published By: Conference

    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

    Hybrid variational-ensemble assimilation of lightning observations in a mesoscale mode

    Published Date: 2014
    Published By: Geoscientific Model Development

    Evaluation of and Suggested Improvements to the WSM6 Microphysics in WRF-ARW Using Synthetic and Observed GOES-13 Imagery

    Published Date: 2014
    Published By: American Meteorological Society
    Synthetic satellite imagery can be employed to evaluate simulated cloud fields. Past studies have revealed that the Weather Research and Forecasting (WRF) single-moment 6-class (WSM6) microphysics scheme in the Advanced Research WRF (WRF-ARW) produces less upper-level ice clouds within synthetic images compared to observations. Synthetic Geostationary Operational Environmental Satellite-13 (GOES-13) imagery at 10.7 μm of simulated cloud fields from the 4-km National Severe Storms Laboratory (NSSL) WRF-ARW is compared to observed GOES-13 imagery. Histograms suggest that too few points contain upper-level simulated ice clouds. In particular, side-by-side examples are shown of synthetic and observed anvils. Such images illustrate the lack of anvil cloud associated with convection produced by the 4-km NSSL WRF-ARW. A vertical profile of simulated hydrometeors suggests that too much cloud water mass may be converted into graupel mass, effectively reducing the main source of ice mass in a simulated anvil. Further, excessive accretion of ice by snow removes ice from an anvil by precipitation settling. Idealized sensitivity tests reveal that a 50% reduction of the accretion rate of ice by snow results in a significant increase in anvil ice of a simulated storm. Such results provide guidance as to which conversions could be reformulated, in a more physical manner, to increase simulated ice mass in the upper troposphere.