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
2019-11-12 06:20 11.0 91.4 9.0 4.6 151 6.0 173 24.995 0.0 0.00
2019-11-12 06:15 10.5 90.1 8.2 5.3 173 6.3 173 24.992 0.0 0.00
2019-11-12 06:10 10.7 91.2 8.6 5.2 164 6.5 161 24.993 0.0 0.00
2019-11-12 06:05 10.0 90.4 7.7 3.8 158 5.9 160 24.991 0.0 0.00
2019-11-12 06:00 9.8 88.6 7.2 0.7 166 3.2 166 24.991 0.0 0.00
2019-11-12 05:55 10.1 89.3 7.6 0.3 240 1.7 333 24.992 0.0 0.00
2019-11-12 05:50 11.6 86.3 8.3 0.7 352 2.1 352 24.993 0.0 0.00
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
CIRA

Cooperative Institute for Research in the Atmosphere

Jiang, Lide

Lide Jiang

Job Title:
Research Scientist/Scholar II
Mailing Addresss:
1375 Campus Delivery
Fort Collins, CO 80523-1375
    Publications

    VIIRS ocean color products over global open oceans and turbid coastal/inland waters

    Published Date: 2017
    Published By: Conference

    VIIRS ocean color products over global open oceans and turbid coastal/inland waters

    Published Date: 2018
    Published By: Conference

    VIIRS Mission-long ocean color data reprocessing and demonstration of global ocean color data monitoring tool

    Published Date: 2017
    Published By: Conference

    RSB calibration of SNPP VIIRS using solar diffuser illuminated by scattered light

    Published Date: 2017
    Published By: Conference

    Evaluation of VIIRS ocean color products in open ocean and coastal/inland waters

    Published Date: 2017
    Published By: Conference

    Comparison of GOCI and VIIRS ocean color products in the Western Pacific Region

    Published Date: 2018
    Published By: Conference

    Comparison of GOCI and VIIRS ocean color products in the Western Pacific region

    Published Date: 2017
    Published By: Conference

    Ocean color remote sensing of oceanic respone to passage of tropical cyclones

    Published Date: 2018
    Published By: Conference

    VIIRS-derived ocean color product using imaging bands

    Published Date: 2018
    Published By: Remote Sensing

    An Efficient Approach for VIIRS RDR to SDR Data Processing

    Published Date: 2014
    Published By: IEEE Xplore Digital Library
    The Visible Infrared Imaging Radiometer Suite (VIIRS) Raw Data Records (or Level-0 data) are processed using the current standard Algorithm Development Library (ADL) to produce Sensor Data Records (SDR; or Level-1B data). The ocean color Environmental Data Records (EDR), one of the most important product sets derived from VIIRS, are processed from the SDR of the visible and near-infrared moderate resolution (M) bands. As the ocean color EDR are highly sensitive to the quality of the SDR, the bands from which the EDR data arise must be accurately calibrated. These bands are calibrated on-orbit using the onboard Solar Diffuser, and the derived calibration coefficients are called F-factors. The F-factors used in the forward operational process may have large uncertainty due to various reasons, and thus, to obtain high-quality ocean color EDR, the SDR needs to be regularly reprocessed with improved F-factors. The SDR reprocessing, however, requires tremendous computational power and storage space, which is about 27 TB for one year of ocean-color-related SDR data. In this letter, we present an efficient and robust method for reduction of the computational demand and storage requirement. The method is developed based on the linear relationship between the SDR radiance/reflectance and the F-factors. With this linear relationship, the new SDR radiance/reflectance can be calculated from the original SDR radiance/reflectance and the ratio of the updated and the original F-factors at approximately 100th or less of the original central processing unit requirement. The produced SDR with this new approach fully agrees with those generated using the ADL package. This new approach can also be implemented to directly update the SDR in the EDR data processing, which eliminates the hassle of a huge data storage requirement as well as that of intensive computational demand. This approach may also be applied to other remote sensors for data reprocessing from raw instrument data to science data.