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 06:40 10.9 90.4 8.6 2.9 141 4.3 144 24.992 3.4 0.00
2019-11-12 06:35 10.1 91.6 8.1 2.8 133 4.6 137 24.995 1.9 0.00
2019-11-12 06:30 10.3 90.3 8.0 3.2 114 4.2 133 24.996 0.8 0.00
2019-11-12 06:25 11.0 89.2 8.4 2.7 144 4.2 151 24.998 0.3 0.00
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
CIRA

Cooperative Institute for Research in the Atmosphere

SeungHyun Son

SeungHyun Son

Job Title:
Research Scientist/Scholar II
    Publications

    Satellite-measured net primary production in the Chesapeake Bay

    Published Date: 2014
    Published By: Science Direct
    The regional daily-integrated net primary production (NPP) model for the Chesapeake Bay, Chesapeake Bay Production Model (CBPM), has been improved for use with ocean color products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua. A polynomial regression formula for the photosynthetic parameter (i.e., optimal carbon fixation rate, PoptB) as a function of sea surface temperature (SST) was derived for the Chesapeake Bay. Results show that the CBPM-derived NPP using the new model for PoptB are improved for the Chesapeake Bay. Comparisons of MODIS-Aqua-derived and in situ-measured NPP show that the satellite-derived data correspond reasonably well to in situ measurements, although MODIS-Aqua-derived NPP values may be slightly overestimated for the upper Bay, primarily due to uncertainties in the bio-optical algorithm for satellite ocean color products for that region. We also generated MODIS-Aqua-derived NPP maps using the improved CBPM for the period of 2002 to 2011 to characterize NPP in the Chesapeake Bay. Spatial distributions of MODIS-Aqua-derived NPP products show that higher NPP values are generally found in the southern upper Bay and northern middle Bay (regions around 38.3°N–39.0°N), including the Potomac River, while relatively low NPP values were found in the northern upper Bay, the eastern area of middle Bay, and lower Bay. The temporal pattern of MODIS-Aqua-derived NPP showed lowest values in winter (December to February) over the entire Bay, while high NPP values were in late spring to summer (May to August), depending on location. Furthermore, there is a strong interannual variability in NPP for the Chesapeake Bay, and an apparent increasing trend from 2003 to 2011.

    Diffuse attenuation coefficient of the photosynthetically available radiation Kd(PAR) for global open ocean and coastal waters

    Published Date: 2015
    Published By: Science Direct

    Satellite-based observations of the diffuse attenuation coefficient for the downwelling spectral irradiance at the wavelength of 490 nm, Kd(490) and the diffuse attenuation coefficient for the downwelling photosynthetically available radiation (PAR), Kd(PAR) in the ocean can play important roles for ocean–atmospheric circulation, biogeochemical, and ecosystem models. Since existing Kd(PAR) models for the satellite ocean color data have wide regional variations, we need to improve the Kd(PAR) algorithm for global ocean applications. In this study, we propose a new blended Kd(PAR) model for both open oceans and turbid coastal waters. The new method has been assessed using in situ optical measurements from the NASA Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-Optical Archive and Storage System (SeaBASS) database. Next, the new method is applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) to derive Kd(PAR) products, and is compared with in situ measurements. Results show that there are significant improvements in model-derived Kd(PAR) values using the new approach compared to those from some existing Kd(PAR) algorithms. In addition, matchup comparisons between MODIS-derived and in situ-measured Kd(PAR) data for the global ocean show a good agreement with mean and median ratios of 1.109 and 1.035, respectively. Synoptic maps of MODIS- and VIIRS-derived Kd(PAR) data generated using the new method provide very similar and consistent spatial patterns in the U.S. East Coast region, although there are some slight differences between two satellite-derived Kd(PAR) images (~ 1–5% higher in VIIRS Kd(PAR) compared with those from MODIS-Aqua in the shallow water region), which are possibly due to differences in spectral bands and sensor performance (e.g., calibrations). Monthly maps of VIIRS-derived Kd(PAR) data for the global ocean are also generated using the new Kd(PAR) model, and provide spatial and temporal Kd(PAR) distributions that show consistent results with those from previous studies. Thus, results show that satellite-derived Kd(PAR) data using the new Kd(PAR) model, e.g., from MODIS and VIIRS, can provide more accurate Kd(PAR) data to science communities, in particular, as an important input for ocean–atmospheric circulation, biogeochemical, and ecosystem models.