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

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.