Lina Cordero

Calibration of the 1064 nm lidar channel using water phase and cirrus clouds

Calibration is essential to derive aerosol backscatter coefficients from elastic scattering lidar. Unlike the visible UV wavelengths where calibration is based on a molecular reference, calibration of the 1064 nm lidar channel requires other approaches, which depend on various assumptions. In this paper, we analyze two independent calibration methods which use (i) low-altitude water phase clouds and (ii) high cirrus clouds. In particular, we show that to achieve optimal performance, aerosol attenuation below the cloud base and cloud multiple scattering must be accounted for. When all important processes are considered, we find that these two independent methods can provide a consistent calibration constant with relative differences less than 15%. We apply these calibration techniques to demonstrate the stability of our lidar on a monthly scale, along with a natural reduction of the lidar efficiency on an annual scale. Furthermore, our calibration procedure allows us to derive consistent aerosol backscatter coefficients and angstrom coefficient profiles (532-1064 nm) along with column extinction-to-backscatter ratios which are in good agreement with sky radiometer inversions.

PBL-height derivation from the CALIOP/CALIPSO and comparing with the radiosonde and ground-based lidar measurements

The planetary boundary layer (PBL) heights are derived from the CALIOP/CALIPSO level-1B attenuated backscatter profile using the wavelet transform technique. The results are compared to those by the radiosonde and ground-based lidar coincident measurements. The comparison generally indicates the good agreement and the correlation coefficient is greater than 0.7. In addition, we found the good consistence between the CALIOP-derived PBL height and the selected aerosol-layer-top of CALIPSO level-2 aerosol-layer products (5-km average). Finally, the spatial distribution of PBL heights and their seasonal differences are initially illustrated over the US continent.

Assessing surface PM2.5 estimates using data fusion of active and passive remote sensing methods.

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”.

Assessing satellite AOD based and WRF/CMAQ output PM2.5 estimators

Fine particulate matter measurements (PM2.5) are essential for air quality monitoring and related public health; however, the shortage of reliable measurmennts constrains researchers to use other means for obtaining reliable estimates over large scales. In particular, model forecasters and satellite community use their respective products to develop ground particulate matter estimations but few experiments have explored how the remote sensing approaches compare to the high resolution models. . In this paper we focus on studying the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Geostationary Operational Environmental Satellites (GOES) regression based estimates in comparison to more direct bias corrected outputs from the Community Multiscale Air Quality (CMAQ) model, We use a two-year dataset (2005-2006) and apply urban, season and hour filters to illustrate the agreement between estimated and in-situ measured fine particulate matter from the New York State Department of Environmental Conservation (NYSDEC). We first begin by analyzing the correspondence between ground aerosol optical depth (AOD) measurements from an AERONET (AErosol RObotic NETwork) Cimel sun/sky radiometer with both satellite and model products in one urban location; we show that satellite readings perform better than model outputs, especially during the summer (RMODIS>=0.65, RCMAQ>=0.37). This is a clear symptom of the difficulty in the models to properly model realistic optical properties. We then turn to a direct assessment of PM2.5 presenting individual comparisons between ground PM2.5 measurements with satellite/model predictions and demonstrate the higher accuracy from model estimations (RurbanMODIS ≥ 0.74, RurbanCMAQ ≥ 0.77; Rnon-urbanMODIS ≥ 0.48, Rnon-urbanCMAQ ≥ 0.78). In general, we find that the bias corrected CMAQ estimates are superior to satellite based estimators except at very high resolution. Finally, we show that when using both model and satellite approximations as separate estimators merged optimally, our product (PM2.5 average) becomes closer to real measurements with improved correlations (RAVE ~ 0.86) in urban areas during the summer.

Assessment of long scale plume transport to the US East coast using coordinated CREST lidar network and synergistic AERONET and satellite measurements

The vertical stratification and optical characteristics of aloft aerosol plumes are critical to evaluate their influences on climate radiation and air quality. In this study, we demonstrate the synergistic measurements of aloft aerosol plumes by a ground-based NOAA-CREST lidar network (CLN) along the US East Coast, the AERONET-sun/sky radiometer network at lidar sites, and satellite observations. During the plume intrusion period on March 6, 2012, the CLN and AERONET measurements were consistent in illustrating the onset of dust aerosol plumes. We observed two-layers of aerosol located at 1.0 ~ 8.0 km altitude. The column-average volume size distributions show increasing concentration of both fine- and coarse-modes aerosols, but are dominated by the coarse-mode. Direct lidar inversions illustrate that the aerosol plume layers contributed up to 70% of the total AOD. NOAA-HYSPLIT back-trajectories and CALIPSO observations indicate the trans-Pacific transport of Asian-dust at 3 - 8 km altitude to the US East Coast. Meanwhile, the NOAA-HMS fire and smoke products illustrate the transport and possible mixture of dust with fine-mode smoke particles from the middle and southwestern US. The small Angstrom exponents of MODIS/Aqua in the US East Coast imply the dominance of coarse-mode particles. Accordingly, the upper layer of coarse mode aerosols is most likely transported from the East Asia, while the lower layer at 1-3 km altitude probably consists of continental dust particles from the western US mixed with fine-mode smoke particles. In addition, the transport and vertical structure of aerosol are investigated with the NAAPS global aerosol transport model.

Use of passive and active ground and satellite remote sensing to monitor fine particulate pollutants on regional scales

This paper explores the performance of current remote sensing methods for estimation of fine particulate matter (PM2.5, diameter < 2.5μm) in the New York City area (40.821°N, 73.949°W) during 2010. We analyze the relationship between surface PM2.5 mass concentration and column aerosol optical depth (AOD) at 500-nm by using the synergy measurements of surface in-situ, AERONET-sunphotometer, lidar and NOAA-GOES satellite. The regression slopes and correlation coefficients between PM2.5 and AOD show the good performance in summer and indicate dramatic monthly variation which are associated with the seasonal differences of PBL-heights, fine-mode contribution to the total AOD and aerosol volume-to-extinction ratio. Additionally, the relationship of PM2.5 and fine-mode AOD shows higher correlations than the PM2.5 and total AOD (R2 total = 0.5011, R2 fine = 0.6132, R2 coarse = -0.0235). Also, when considering the lidar-derived PBL-heights in the different months and removing aloft layer and cloudy cases, the PM2.5 estimations using AOD show improvements during the cold months; furthermore, the correction on aerosol volume-to-extinction ratio results in better estimations of fine particulate matter concentrations and therefore confirms the importance of including these parameters into air quality models. Moreover, the AOD data from NOAA-Geostationary Operational Environmental Satellites (GOES) are initially evaluated by comparing with AERONET-AOD, and further illustrate the good correlation with PM2.5 concentration.

Assessing satellite based PM2.5 estimates against CMAQ model forecasts

In this work, we focus on estimations of fine particulate matter using MODIS AOD as part of a neural network scheme and compare this to both simple linear regressions and GEOS-CHEM products. In making this comparison, it is well known the seasonal and geographical dependences observed in the PM2.5-AOD relationship; thus, to enhance our predictions, we apply WRF PBL information to our neural network method and assess its performance. As part of our analysis, we first explore the baseline effectiveness of AOD and PBL as strong factors in estimating PM2.5 in a local experiment using data collected at one site in New York City. Then, we expand our analysis to a regional domain where daily estimations are obtained based on site location and season. In our local test, we find the high efficiency of the neural network estimations when AOD, PBL and seasonality are primarily assessed (R~0.94 in summer). Later, we test our regional network and compare it with the GEOS-CHEM PM2.5 product. From this, we see better estimations from our experiment using urban/non-urban stations and applying different spatial schemes for training the neural network (RNN~0.80, RGEOS-CHEM~0.57 in an urban station with a distance radius of 0.1 degree; RNN~0.74, RGEOSCHEM~0.69 in a non-urban station with a distance radius of 0.3 degree). Finally, we create regional daily PM2.5 maps and compare them to GEOS-CHEM outputs, evaluating the corresponding estimations using ground readings.

Application of a multifilter shadowband radiometer and microwave radiometer for ground based evaluation of aerosol-cloud interactions

The quantification of the first direct aerosol cloud interaction mechanism requires simultaneous observations of cloud water drop properties as well as aerosol properties below the cloud. The simultaneous measurement of both these properties is very difficult from space borne systems and efforts to develop ground remote sensing measurements are critical. To measure the cloud properties, we make use of an approach which combines a Microwave radiometer and a MFRSR radiometer for simultaneous Cloud Optical Depth (COD) and Liquid Water Path (LWP). From these measurements, effective droplet diameter can be estimated assuming the homogeneity of the cloud. In using the diffuse flux, we confirm that for COD > 2 and solar zenith angles < 60, the standard MFRSR correction can be applied with errors < 1%. In addition, we develop a method whereby regional retrieval of Microphysical properties from multispectral extinction measurements can be made based on NN based methods trained on full sky scans. Also, we discuss the uncertainty in the inferred COD due to various input parameters in the formation of Look-Up-Tables and present preliminary data sets for evaluation. Finally, we discuss methods to extract useful aerosol information during partly cloudy conditions that can be used to better define the state of the aerosol prior to cloud interaction.

Retrieval of aerosol and cloud properties using multiwavelength elastic-Raman lidar

The aerosol-cloud interaction is a complex and critical process in assessing the climate radiative effects of aerosol and cloud. Lidar can simultaneously measure the range-resolved distribution of aerosol-cloud with the high spatial-temporal resolution, and hence provides the opportunity to explore the cloud-aerosol optical properties and their interaction. Their interactions have been indicated by the significant variation of optical properties and droplet size of aerosol and cloud at the cloud vicinity or edges. But due to dramatic non-linear or irregular variation of lidar returns by the cloud, the evaluation of lidar algorithm deriving cloud extinction coefficient becomes quite important especially at the edges because the common algorithms may result in the artificial influence on the retrievals of cloud extinction and extinctionto- backscatter ratio (e.g. lidar ratio or S-ratio). In particular, the relationships of water cloud optical properties with the droplet size are simulated which include lidar ratio, color ratio and extinction ratio are used and general trends with measurements are demonstrated. To obtain color ratios (355/1064), a good calibration procedure for the 1064nm channel is required and we show that calibration errors using low water drop clouds allow absolute calibration < 10%. Preliminary results seem to indicate that small pre-nucleated droplets form at the aerosol - cloud boundary which is consistent with aerosol uptake into clouds. In addition, we also explore the increase in aerosol lidar-ratio below cloud indicative of hygroscopic growth.

Potential investigation of cloud-aerosol interaction with a multiple-wavelength Raman-elastic lidar

Measurements of low-altitude cloud and its interaction with aerosol are analyzed with a multiple-wavelength elastic- Raman scattering lidar. Using the numerical experiment approach, we first evaluate the retrieval accuracy of cloud extinction from the Raman-lidar algorithms, in particular at the cloud edges. For the low-level water-phase cloud, the simulation also shows the dramatic variation of lidar-ratio, color-ratio and extinction-ratio with the small droplets and their correlation. Then, the measurement examples by CCNY elastic-Raman lidar illustrate that the small droplets probably appear at the cloud edges, which might imply the new particle formation or the cloudaerosol interaction.