Observation of mountain lee waves with MODIS NIR column water vapor

Lyapustin, A.; Alexander, M. Joan; Ott, Lesley; Molod, Andrea; Holben, B. N.; Susskind, Joel; Wang, Y.

doi:10.1002/2013gl058770

Cited by 21 publications

(27 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A 10% systematic negative bias is observed for Aqua retrievals for CWV > 2.0 cm (Figure b ii ), while Terra shows none trend regardless the concentration (Figure a ii ). Given the different sensitivity of NIR channels [ Gao and Kaufman , ], the algorithm computes the average of water vapor based on weighting functions stored in the look‐up table, which depends on atmosphere condition (“dry” or “humid”) (see more details in Lyapustin et al []). Thus, described negative bias should be related to calibration of the least absorbing channel (B17) of MODIS Aqua which has the highest weight in humid conditions.…”

Section: Resultsmentioning

confidence: 99%

Validation of high‐resolution MAIAC aerosol product over South America

et al. 2017

Self Cite

View full text Add to dashboard Cite

Multiangle Implementation of Atmospheric Correction (MAIAC) is a new Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm that combines time series approach and image processing to derive surface reflectance and atmosphere products, such as aerosol optical depth (AOD) and columnar water vapor (CWV). The quality assessment of MAIAC AOD at 1 km resolution is still lacking across South America. In the present study, critical assessment of MAIAC AOD550 was performed using ground‐truth data from 19 Aerosol Robotic Network (AERONET) sites over South America. Additionally, we validated the MAIAC CWV retrievals using the same AERONET sites. In general, MAIAC AOD Terra/Aqua retrievals show high agreement with ground‐based measurements, with a correlation coefficient (R) close to unity (RTerra:0.956 and RAqua: 0.949). MAIAC accuracy depends on the surface properties and comparisons revealed high confidence retrievals over cropland, forest, savanna, and grassland covers, where more than 2/3 (~66%) of retrievals are within the expected error (EE = ±(0.05 + 0.05 × AOD)) and R exceeding 0.86. However, AOD retrievals over bright surfaces show lower correlation than those over vegetated areas. Both MAIAC Terra and Aqua retrievals are similarly comparable to AERONET AOD over the MODIS lifetime (small bias offset ~0.006). Additionally, MAIAC CWV presents quantitative information with R ~ 0.97 and more than 70% of retrievals within error (±15%). Nonetheless, the time series validation shows an upward bias trend in CWV Terra retrievals and systematic negative bias for CWV Aqua. These results contribute to a comprehensive evaluation of MAIAC AOD retrievals as a new atmospheric product for future aerosol studies over South America.

show abstract

Section: Resultsmentioning

confidence: 99%

Validation of high‐resolution MAIAC aerosol product over South America

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…Once the MODIS reflectance is gridded and split into both 600 × 600 km tiles and 25 × 25 km blocks, they are placed in a queue of 4–16 days. Water vapor is first derived from MODIS near‐IR bands [ Lyapustin et al ., ] by using a modification of the algorithm described in Gao and Kaufman []. An internal cloud mask uses spectral reflectance and brightness temperature tests similar to the operational MODIS cloud mask algorithm [ Frey et al ., ], along with the reference clear‐sky image developed by using a covariance‐based algorithm.…”

Section: Datamentioning

confidence: 99%

Evaluation of the multi‐angle implementation of atmospheric correction (MAIAC) aerosol algorithm through intercomparison with VIIRS aerosol products and AERONET

2017

View full text Add to dashboard Cite

The multi‐angle implementation of atmospheric correction (MAIAC) algorithm is under evaluation for use in conjunction with the Geostationary Coastal and Air Pollution Events mission. Column aerosol optical thickness (AOT) data from MAIAC are compared against corresponding data from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument over North America during 2013. Product coverage and retrieval strategy, along with regional variations in AOT through comparison of both matched and unmatched seasonally gridded data, are reviewed. MAIAC shows extended coverage over parts of the continent when compared to VIIRS, owing to its pixel selection process and ability to retrieve aerosol information over brighter surfaces. To estimate data accuracy, both products are compared with Aerosol Robotic Network level 2 measurements to determine the amount of error present and discover if there is any dependency on viewing geometry and/or surface characteristics. Results suggest that MAIAC performs well over this region with a relatively small bias of −0.01; however, there is a tendency for greater negative biases over bright surfaces and at larger scattering angles. Additional analysis over an expanded area and longer time period are likely needed to determine a comprehensive assessment of the products' capability over the Western Hemisphere.

show abstract

“…This is consistent with Nieto et al (2010) who also found poorer prediction of e a when modelled on a daily-time-step, due to the variability of e a in the atmosphere during the day. Additionally, e a performance may have been affected by a lower accuracy of the MOD05 product over parts of Australia, which is reportedly due to iron-rich soils affecting spectral reflectance (Lyapustin et al, 2014). Given this, the strategy of modelling D based Table 3 Validation of MODIS meteorological variables against corresponding on-ground observations measured over one year at each of the flux tower sites.…”

Section: Performance Of Remotely Sensed Vapour Pressure Deficit Modelsmentioning

confidence: 99%

Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data

Nolan

Dios

Boer

et al. 2016

Remote Sensing of Environment

View full text Add to dashboard Cite

Spatially explicit predictions of fuel moisture content are crucial for quantifying fire danger indices and as inputs to fire behaviour models. Remotely sensed predictions of fuel moisture have typically focused on live fuels; but regional estimates of dead fuel moisture have been less common. Here we develop and test the spatial application of a recently developed dead fuel moisture model, which is based on the exponential decline of fine fuel moisture with increasing vapour pressure deficit (D). We first compare the performance of two existing approaches to predict D from satellite observations. We then use remotely sensed D, as well as D estimated from gridded daily weather observations, to predict dead fuel moisture. We calibrate and test the model at a woodland site in South East Australia, and then test the model at a range of sites in South East Australia and Southern California that vary in vegetation type, mean annual precipitation (129-1404 mm year ) and leaf area index (0.1-5.7). We found that D modelled from remotely sensed land surface temperature performed slightly better than a model which also included total precipitable water (MAE b 1.16 kPa and 1.62 kPa respectively). D calculated with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite was under-predicted in areas with low leaf area index. Both D from remotely sensed data and gridded weather station data were good predictors of the moisture content of dead suspended fuels at validation sites, with mean absolute errors less than 3.9% and 6.0% respectively. The occurrence of data gaps in remotely sensed time series presents an obstacle to this approach, and assimilated or extrapolated meteorological observations may offer better continuity.

show abstract

Observation of mountain lee waves with MODIS NIR column water vapor

Cited by 21 publications

References 44 publications

Validation of high‐resolution MAIAC aerosol product over South America

Validation of high‐resolution MAIAC aerosol product over South America

Evaluation of the multi‐angle implementation of atmospheric correction (MAIAC) aerosol algorithm through intercomparison with VIIRS aerosol products and AERONET

Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data

Contact Info

Product

Resources

About