Maksym Petrenko

Long-term statistical assessment of Aqua-MODIS aerosol optical depth over coastal regions: bias characteristics and uncertainty sources

Coastal regions around the globe represent a major source for anthropogenic aerosols in the atmosphere, but the surface characteristics may not be optimal for the Moderate Resolution Imaging Spectroradiometer (MODIS) algorithms designed for aerosol retrievals over dark land or ocean surfaces. Using data collected from 62 coastal stations worldwide by the Aerosol Robotic Network (AERONET) in 2002–2011, statistical assessments of uncertainties are conducted for coastal aerosol optical depth (AOD) retrieved from MODIS measurements aboard the Aqua satellite (i.e., the Collection 5.1 MYD04 data product generated by the MODIS atmosphere group). It is found that coastal AODs (at 550 nm) characterised respectively by the Dark Land algorithm and the Dark Ocean algorithm all exhibit a log-normal distribution, which contrasts to the near-normal distribution of their corresponding biases. After data filtering using quality flags, the MODIS AODs from both the Dark Land and Dark Ocean algorithms over coastal regions are highly correlated with AERONET AODs (R 2≈0.8), but both have larger uncertainties than their counterparts (of MODIS AODs) over land and open ocean. Overall, the Dark Ocean algorithm overestimates the AERONET coastal AOD by 0.021 for AOD < 0.25 and underestimates it by 0.029 for AOD > 0.25. This dichotomy is shown to be related to the ocean-surface wind speed and cloud-contamination effects on the MODIS aerosol retrievals. Consequently, an empirical correction scheme is formulated that uses cloud fraction and sea-surface wind speed from Modern Era Retrospective-Analysis for Research and Applications (MERRA) to correct the AOD bias from the Dark Ocean algorithm, and it is shown to be effective over the majority of the coastal AERONET stations to (a) simultaneously reduce both the mean and the spread of the bias and (b) improve the trend analysis of AOD. Further correlation analysis performed after such an empirical bias correction shows that the MODIS AOD is also likely impacted by the concentration of suspended particulate matter in coastal waters, which is not taken into account during the MODIS AOD retrievals. While mathematically the MODIS AODs over the global coastal AERONET sites show statistically significant discrepancies (p<1%) from their respective AERONET-measured counterparts in terms of mean and frequency, different applications of MODIS AODs in climate and air-quality studies often have their own tolerances of uncertainties. Nevertheless, it is recommended that an improved treatment of varying sea-surface wind and sediment over coastal waters be an integral part in the continuous evolution of the MODIS AOD retrieval algorithms.

Concept location using program dependencies and information retrieval (DepIR)

ContextThe functionality of a software system is most often expressed in terms of concepts from its problem or solution domains. The process of finding where these concepts are implemented in the source code is known as concept location and it is a prerequisite of software change. ObjectiveWe investigate a static approach to concept location named DepIR that combines program dependency search (DepS) with information retrieval-based search (IR). In this approach, programmers explore the static program dependencies of the source code components retrieved by the IR search engine. MethodThe paper presents an empirical study that compares DepIR with its constituent techniques. The evaluation is based on an empirical method of reenactment that emulates the steps of concept location for 50 past changes mined from software repositories of five software systems. ResultsThe results of the study indicate that DepIR significantly outperforms both DepS and IR. ConclusionDepIR allows developers to perform concept location efficiently. It allows finding concepts even with queries that do not rank the relevant software components highly. Since formulating a good query is not always easy, this tolerance of lower-quality queries significantly broadens the usability of DepIR compared to the traditional IR.

Global bias adjustment for MODIS aerosol optical thickness using neural network

Abstract Large uncertainties in calculating radiative forcings from aerosols due to their location, loading, and types pose a great challenge to global climate modeling. Trying to improve retrievals in a statistical manner normally requires detailed knowledge of uncertainty statistics and bias due to possible error sources such as different measurement viewing geometries, instrument calibration, and dynamically changing atmospheric and earth surface conditions. However, a priori estimates of these error sources are not, in general, available. The use of a neural network (NN) approach to compensate for biases and systematic errors of aerosol optical thickness (AOT) from the Moderate Resolution Imaging Spectrometer (MODIS) operational retrieval algorithm is explored. By utilizing the NN as an estimator, we can compensate against unknown sources of errors, nonlinearity in the data sets, and the presence of non-normal distributions. In this study, the highly accurate ground-based Aerosol Robotic Network (AERONET) measurements are used as the ground truth (GT). Our results show that the adjusted AOT with NN has decreased root mean square errors, improved correlations with GT data by 4% to 6%, and increased the number of NN-adjusted data falling within the published expected error envelope by ∼ 10 % .

Using Concept Maps to Assist Program Comprehension and Concept Location: An Empirical Study

Program comprehension is an integral part of the evolution and maintenance of large software systems. As it is increasingly difficult to comprehend these systems completely, programmers have to rely on a partial and as-needed comprehension. We study partial comprehension and programmer learning with the use of concept maps as a tool for capturing programmer knowledge during concept location, which is one of the tasks of software evolution and maintenance, and it is a prerequisite of a software change. We conduct a user study to measure the performance of programmers using concept maps to assist with locating concepts. The results demonstrate that programmer learning occurs during concept location and that concept maps assisted programmers with capturing programmer learning and successful concept location.

Maritime Aerosol Network optical depth measurements and comparison with satellite retrievals from various different sensors

The paper reports on the current status of the Maritime Aerosol Network (MAN) which is a component of the Aerosol Robotic Network (AERONET). A public domain web-based data archive dedicated to MAN activity can be found at https://aeronet.gsfc.nasa.gov/new_web/maritime_aerosol_network.html . Since 2006 over 450 cruises were completed and the data archive consists of more than 6000 measurement days. In this work, we present MAN observations collocated with MODIS Terra, MODIS Aqua, MISR, POLDER, SeaWIFS, OMI, and CALIOP spaceborne aerosol products using a modified version of the Multi-Sensor Aerosol Products Sampling System (MAPSS) framework. Because of different spatio-temporal characteristics of the analyzed products, the number of MAN data points collocated with spaceborne retrievals varied between ~1500 matchups for MODIS to 39 for CALIOP (as of August 2016). Despite these unavoidable sampling biases, latitudinal dependencies of AOD differences for all satellite sensors, except for SeaWIFS and POLDER, showed positive biases against ground truth (i.e. MAN) in the southern latitudes (<50° S), and substantial scatter in the Northern Atlantic “dust belt” (5°-15° N). Our analysis did not intend to determine whether satellite retrievals are within claimed uncertainty boundaries, but rather show where bias exists and corrections are needed.