h i g h l i g h t sVIIRS Day/Night Band (DNB) is much more sensitive to aerosols than to water vapor Modeling of outdoor light transfer in nighttime atmosphere for VIIRS DNB DNB potential for estimating surface PM 2.5 is shown qualitatively and quantitatively PM 2.5 at VIIRS night overpass time is much closer to daily-mean PM 2.5 than at daytime Strategies for future DNB remote sensing of aerosols are elaborated a r t i c l e i n f o A pilot study is conducted to illustrate the potential of using radiance data collected by the Day/Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polarorbiting Partnership (S-NPP) satellite for particulate matter (PM) air quality monitoring at night. The study focuses on the moonless and cloudless nights in Atlanta, Georgia during AugusteOctober 2012. We show with radiative transfer calculations that DNB at night is sensitive to the change of aerosols and much less sensitive to the change of water vapor in the atmosphere illuminated by common outdoor light bulbs at the surface. We further show both qualitatively that the contrast of DNB images can indicate the change of air quality at the urban scale, and quantitatively that change of light intensity during the night (as characterized by VIIRS DNB) reflects the change of surface PM 2.5 . Compared to four meteorological variables (u and v components of surface wind speed, surface pressure, and columnar water vapor amount) that can be obtained from surface measurements, the DNB light intensity is the only variable that shows either the largest or second largest correlation with surface PM 2.5 measured at 5 different sites. A simple multivariate regression model with consideration of the change of DNB light intensity can yield improved estimate of surface PM 2.5 as compared to the model with consideration of meteorological variables only. Cross validation of this DNB-based regression model shows that the estimated surface PM 2.5 concentration has nearly no bias and a linear correlation coefficient (R) of 0.67 with respect to the corresponding hourly observed surface PM 2.5 concentration. Furthermore, groundbased observations support that surface PM 2.5 concentration at the VIIRS night overpass (~1:00 am local) time is representative of daily-mean PM 2.5 air quality (R ¼ 0.82 and mean bias of À0.1 mg m À3 ).While the potential appears promising, mapping surface PM 2.5 from space with visible light at night still face various challenges and the strategies to address some of these challenges are elaborated for future studies.
In the summer of 2012, the central plains of the United States experienced one of its most severe droughts on record. This study examines the meteorological impacts of irrigation during this drought through observations and model simulations using the Community Land Model coupled to the Weather Research and Forecasting (WRF) Model. A simple parameterization of irrigation processes is added into the WRF Model. In addition to keeping soil moisture in irrigated areas at a minimum of 50% of soil moisture hold capacity, this irrigation scheme has the following new features: 1) accurate representation of the spatial distribution of irrigation area in the study domain by using a MODIS-based land surface classification with 250-m pixel size and 2) improved representation of the time series of leaf area index (LAI) values derived from crop modeling and satellite observations in both irrigated and nonirrigated areas. Several numerical sensitivity experiments are conducted. The WRF-simulated temperature field when including soil moisture and LAI modification within the model is shown to be most consistent with ground and satellite observations, all indicating a temperature decrease of 2–3 K in irrigated areas relative to the control run. Modification of LAI in irrigated and dryland areas led to smaller changes, with a 0.2-K temperature decrease in irrigated areas and up to a 0.5-K temperature increase in dryland areas. Furthermore, the increased soil moisture and modified LAI are shown to lead to statistically significant increases in surface divergence and surface pressure and to decreases in planetary boundary layer height over irrigated areas.
In summer of 2012, the Central Plains of the United States experienced its most severe drought since the ground-based data record began in the late 1900s. By using comprehensive satellite data from MODIS (Moderate Resolution Imaging Spectroradiometer) and TRMM (Tropical Rainfall Measuring Mission), along with in-situ observations, this study documents the geophysical parameters associated with this drought, and thereby providing, for the first time, a large-scale observation-based view of the extent to which the land surface temperature and vegetation can likely be affected by both the severe drought and the agricultural response (irrigation) to the drought. Over non-irrigated area, 2012 summer daytime land surface temperature (LST), and Normalized Difference Vegetation Index (NDVI) monthly anomalies (with respect to climate in 2002-2011) are often respectively greater than 5 K and negative, with some extreme values of 10 K and-0.2 (i.e., no green vegetation). In contrast, much smaller anomalies (< 2 K) of LST and nearly the same NDVI are found over irrigated areas. Precipitation received was an average of 5.2 cm less, while both fire counts and fire radiative power were doubled, thus contributing in part to a nearly 100% increase of aerosol optical depth in many forested areas (close to intermountain west). Water vapor amount, while decreased over the southern part, indeed slightly increased in the northern part of Central Plains. As expected, cloud fraction anomaly is negative in the entire Central Plains; however, the greatest reduction of cloud fraction is found over the irrigated areas, which is in contrast to past modeling studies showing that more irrigation, because of its impact on LST, may lead to increase of cloud fraction.
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