Aim: The Okavango Delta at Seronga is fragmented into different land uses ranging from grasslands to woodland (Ximenia and mopane), often punctuated with cropped and fallow fields. The influence of land uses on surface (A1 horizon) soil physico-characteristics, nitrogen, sulphur, carbon, microbial population and biomass were studied to understand soil variability in order to devise conservation strategies for the area. Methodology: Total soil nitrogen (N) was analysed using a Leco N analyser, total carbon and sulphur by CS800 Carbon–Sulphur analyser. NH4+-N, NO3- and NO2- were extracted with KCl and determined using the indophenol blue method and by Griess-Ilosvay colorimetric method respectively. Microbial populations were determined by plate count method. Biomass carbon and flush of nitrogen were determined by fumigation and re- inoculation technique. Results: All the soils had a high sand content (> 85 %). Total soil N was generally very low, 0.017% in grasslands closest to the channel, 0.013% in cropped fields, 0.007% in fallow and lowest in woodlands (0.002%). Grasslands showed higher NH4+-N indicating low nitrification potential. Even if mopane woodlands had low total N, they had higher NH4+-N (0.067 ppm) and low NO2- compared to other land uses, this could be attributed to their inherent nitrification inhibition ability. No NO3--N was detected in these soils, probably due to the low nitrification ability and high leaching capacity of sandy soils. Microbial biomass C and population were highest in the grasslands and cultivated soils, while the woodlands had lower levels. Conclusion: Seronga soils have very low N, with the least in the woodlands furthest from floodplains. Grasslands closest to the channel basin had significantly higher total N, C and microbial biomass C but low S as opposed to the woodlands further from the channel. Cultivated areas had increased N and C levels and microbial biomass C compared to the woodland probably due to incorporation of crop residues and animal manure. The paucity of nitrifiers and undetectable NO3--N indicate a low nitrification potential and a high leaching ability of the soils. Fallowing of fields resulted in a decline in nutrient status.
An increasing number of satellite sensors operating in the optical and microwave spectral bands represent an opportunity for utilizing multi-sensor fusion and data assimilation techniques for improving the estimation of snowpack properties using remote sensing. In this paper, the strength of a synergistic approach of leveraging optical, active and passive microwave remote sensing measurements to estimate snowpack characteristics is discussed and examples from recent work are given. Observations with each type of sensor have specific technical constraints and limitations. Optical sensor data has high spatial resolution but is limited to cloud free days, whereas passive microwave sensors have coarse spatial resolution and are sensitive to multiple snowpack properties. Multi-source and multi-temporal remote sensing data therefore hold great promise for moving the monitoring and analysis of snow toward estimates of a suite of snow properties at high spatial and temporal resolution.
We present first retrievals of the Lidar-Radiometer Inversion Code (LIRIC), applied on combined lidar and sunphotometer data during a Saharan dust episode over Athens, Greece, on July 20, 2011. A full lidar dataset in terms of backscatter signals at 355, 532 and 1064 nm, as well as depolarization at 532 nm was acquired from the European Aerosol Research Network (EARLINET) station of Athens and combined with Aerosol Robotic Network (AERONET) data, in order to retrieve the concentration and extinction coefficient profiles of dust. The lidar measurements showed a free tropospheric layer between 1-5 km above Athens, with low Ångström exponent of ~0.5 and high particle depolarization ratio, ~25-30%, both values characteristic of dust particles. The application of LIRIC revealed high concentration profiles of non-spherical coarse particles in the layer, in the range of 0.04-0.07 ppb and a smaller fine particle component with concentrations of ~0.01 ppb. The extinction coefficients at 532 nm ranged between 50 and 90 Mm-1 for coarse non-spherical particles and between 25 and 50Mm-1 for fine particles. The retrievals were compared with modeled dust concentration and extinction coefficient profiles from the Dust Regional Atmospheric Modeling (BSC-DREAM8b), showing good agreement, especially for the coarse mode.
In this paper, we focus on estimations of fine particulate matter by combining MODIS satellite Aerosol Optical Depth (AOD) with Weather Research Forecast (WRF) PBL information using a neural network approach and assess its performance. As part of our analysis, we first explore the baseline effectiveness of AOD and PBL as relevant factors in estimating PM2.5 in passive radiometer and active lidar data at CCNY and demonstrate that the PBL height is the most critical additional parameter for accurate PM2.5. Furthermore, active measurements from both ground and satellite based lidar are used to show that summer WRF model PBL heights are most accurate. We then expand our analysis to a regional domain where daily estimations are obtained and compared with operational GEOS-CHEM PM2.5 product. Using our approach, we also create regional daily PM2.5 maps and compare against GEOS-CHEM outputs. Finally, we also consider additional improvements, where multiple satellite observations are used as regressors to predict PM2.5. These results illustrate the significant improvement we obtain within this framework in comparison to a “one size fits all continental scale approach”.
Aims: The objective of this study is to demonstrate the integrated use of passive and active remote sensing instruments to quantify the rate of NOx emissions, and investigate the Ox production rates from an urban area. Place and Duration of Study: A research flight on June 15, 2010 was conducted over Bakersfield, CA and nearby areas with oil and natural gas production. Methodology: Three remote sensing instruments, namely the University of Colorado AMAX-DOAS, NOAA TOPAZ lidar, and NCAS Doppler lidar were deployed aboard the NOAA Twin Otter during summer 2010. Production rates of nitrogen dioxide (NO2) and Ox‘(background corrected O3 + NO2) were quantified using the horizontal flux divergence approach by flying closed loops near Bakersfield, CA. By making concurrent measurements of the trace gases as well as the wind fields, we have reduced the uncertainty due to wind field in production rates. Results: We find that the entire region is a source for both NO2 and Ox’. NO2 production is highest over the city (1.35 kg hr-1 km-2 NO2), and about 30 times lower at background sites (0.04 kg hr-1 km-2 NO2). NOx emissions as represented in the CARB 2010 emission inventory agreewell with our measurements over Bakersfield city (within 30%). However, emissions upwind of the city are significantly underestimated. The Ox’ production is less variable, found ubiquitous, and accounts for 7.4 kg hr-1 km-2 Ox’ at background sites. Interestingly, the maximum of 17.1 kg hr-1 km-2 Ox’production was observed upwind of the city. A plausible explanation for the efficient Ox’ production upwind of Bakersfield, CA are favorable volatile organic compound (VOC) to NOx ratios for Ox’ production, that are affected by emissions from large oil and natural gas operations in that area. Conclusion: The NO2 and O3 source fluxes vary significantly, and allow us to separate and map NOx emissions and Ox production rates in the Central Valley. The data is probed over spatial scales that link closely with those predicted by atmospheric models, and provide innovative means to test and improve atmospheric models that are used to manage air resources. Emissions from oil and natural gas operations are a source for O3 air pollution, and deserve further study to better characterize effects on public health.
Aims: To develop a new satellite-based mixed-phase cloud retrieval algorithm for improving mixed-phase cloud liquid water path (LWP) retrievals by combining Moderate Resolution Imaging Spectro radiometer (MODIS), CloudSat, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements. Study Design: Algorithm development and evaluation by using collocated NASA A-Train and the Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) measurements at the North Slope Alaska (NSA) site. Place and Duration of Study: Collocated MODIS and ground-based measurements at NSA site from March 2000 to October 2004, MODIS measurements and retrievals during July 2006 over Eastern Pacific, and MODIS, CloudSat and CALIPSO measurements on April 04, 2007 over the Arctic Region. Methodology: The stratiform mixed-phase clouds were treated as two adjunct water and ice layers for radiative calculations with the Discrete Ordinate Radiative Transfer (DISORT) model. The ice-phase properties were provided with the 2C-ICE product, which is produced from CloudSat radar and CALIPSO lidar measurements, and they were used as inputs in DISORT for the calculations. Then, the calculated mixed-phase cloud reflectances at selected wavelengths were compared with MODIS reflectances to retrieve liquid-phase cloud properties. Results: A new algorithm was developed to retrieve LWP in stratiform mixed-phase clouds by using MODIS radiances and ice cloud properties fromactive sensor measurements. The algorithm was validated separately by using Operational MODIS retrievals of warm marine stratiform clouds and collocated surface measurements of Arctic stratiform mixed-phase clouds. The results show that the new algorithm reduced the positive LWP biases in the Operational MODIS LWP retrievals for stratiform mixed-phase clouds from 35 and 68% to 10 and 22% in the temperature ranges of -5 to -10ºC and -10 to -20ºC, respectively. Conclusion: The combined A-Train active and MODIS measurements can be used to improve global mixed-phase cloud property retrievals.