Microphysics and Radiation Effect of Dust on the Saharan Air Layer — An HS3 Case Study

• Study uses HS3 observations andsimulations to examine SAL structure and theimpact of dust on environmental structure• Case: 24-25 August 2013 SAL flight• Model: NASA Unified WRF

• SAL layer produces vertical motions withopposite sense of Hadley circulation (upperright)• Dust reinforces dust-layer circulation,reduces rising motion in ITCZ (center right)• Radiative impact of dust leads to warmingwithin, cooling above dust layer (middle left)• Drying near base and within and above thesouthern portion of dust layer, moisteningnear top and above northern part of dustlayer (lower panels), consistent with changesin vertical motion• Microphysical and radiative effects impactITCZ convection, with net reduction of totalhydrometeors

Impact of dust on zonal mean fields

Name: Zhining Tao, NASA/GSFC, Code 614

E-mail: zhining.tao@nasa.gov

Phone: 301-614-5324

Publication: “Microphysics and radiation effect of dust on Saharan Air Layer – A HS3 case study ” by Zhining Tao, S. A. Braun, J. J. Shi, M. Chin, D. Kim, T. Matsui, C. Peters-Lidard. Monthly Weather Review, 146, 1813-1835.

Data sources: Data from the 24-25 August 2013 HS3 Saharan Air Layer (SAL) flight, including data from the AVAPS dropsonde system and Cloud Physics Lidar. MODIS and AERONET data also used. Simulations use the NASA Unified Weather Research and Forecasting (NUWRF) model, including simulations with fully interactive dust (designated AMR), no interactive dust (NoAMR), radiative effects of dust only (AR), and microphysical effects of dust only (AM).

Technical Description of Figures:Figure: Vertical cross section of the zonal-mean temperature (top left) and and vertical motion (top right). Other panels show changes in temperature (middle left), vertical motion (middle right), specific humidity (lower left) and relative humidity (lower right) associated with the radiative and microphysical impacts of dust aerosols, with the impact measured as the difference between the simulationwith radiative and microphysical effects (AMR) and the simulation without radiative and microphysical interactions (NoAMR). The figure shows how temperatures in the SAL (lower part of figure, middle right panel) are warmed by dust, primarily through theradiative interaction, while microphysical effects are small (not shown). Changes in vertical circulation are in the same sense as the mean flow within the dust layer, reinforcing the circulation, while to the south of the dust layer, the radiative and microphysical impacts of dust reduce upward motion in the Intertropical Convergence Zone (ITCZ). Changes in humidity are generally consistent with drying by reduced upward/increased downward motion and moistening by increased upward/decreased downward air motions. Red contours indicate zonal mean total dust mixing ratios at 30 mg m-3 intervals from the NoAMR simulation. Transparent arrows indicate the sense of the vertical motions or changes in vertical motion. Data are only shown were differences between the AMR and NoAMRsimulations are statistically significant.

Scientific significance, societal relevance, relation to future missions: The Global Hawk provides a valuable capability for mapping out large regions of the SAL and its environment. Observations of this SAL event are discussed in the paper and are used to validate the numerical simulation. While HS3 was focused on tropical cyclones, this study addresses one of its secondary objectives, to characterize the structure and impacts of the SAL. Using the NUWRF model with interactive dust, we assess the impact of Saharan dust on the thermodynamic and kinematic structure of the SAL and its environment. These effects can have implications for the interaction of the SAL with African Easterly Wave disturbances that are often the precursors for Atlantic hurricanes.

Earth Sciences Division - Atmospheres

Extending EOS-era aerosol records backward and forward in time

with AVHRR and VIIRS

A. M. Sayer (613/616, USRA), N. C. Hsu (613), J. Lee (613/UMD), W. V. Kim (613/UMD)

This Figure shows time series of aerosol optical depth (AOD), a measure of aerosol (e.g. smoke, mineral dust, industrial haze) amount in the atmosphere at two long-term (1993 onwards) ground sites within the NASA Aerosol Robotic Network (AERONET). EOS-era satellite instruments such as MODIS and SeaWiFS provide a data record of AOD from 1997 to the present day. However, SeaWiFS ceased operation in 2010, and the two MODIS sensors are not expected to operate beyond the next several years. With AVHRR and VIIRS observations, we can use the Deep Blue algorithm family to extend the global AOD time series backward to 1981 and forward into the post-EOS future.

Name: Andrew Sayer, USRA/GESTAR and NASA/GSFC Codes 613 and 616 E-mail: andrew.sayer@nasa.govPhone: 301-614-6211

References:This Highlight spans work from three recent papers:• Hsu, N. C., J. Lee, A. M. Sayer, N. Carletta, S.‐H. Chen, C. J. Tucker, B. N. Holben, and S.‐C. Tsay (2017), Retrieving near‐global aerosol loading over land and ocean from AVHRR, Journal of Geophysical Research - Atmospheres, 122, 9968–9989, https://doi.org/10.1002/2017JD026932• Sayer, A. M., N. C. Hsu, J. Lee, N. Carletta, S.‐H. Chen, and A. Smirnov (2017), Evaluation of NASA Deep Blue/SOAR aerosol retrieval algorithms applied to AVHRR measurements, Journal of Geophysical Research - Atmospheres, 122, 9945–9967, https://doi.org/10.1002/2017JD026934• Sayer, A. M., Hsu, N. C., Lee, J., Bettenhausen, C., Kim, W. V., & Smirnov, A. (2018). Satellite Ocean Aerosol Retrieval (SOAR) algorithm extension to S‐NPP VIIRS as part of the “Deep Blue” aerosol project. Journal of Geophysical Research - Atmospheres, 123, 380–400. https://doi.org/10.1002/2017JD027412

Data Sources: AVHRR, SeaWiFS, MODIS Terra/Aqua, and S-NPP VIIRS satellite data products (NOAA/NASA source input files; resulting aerosol data products created and distributed by NASA). NASA AERONET ground-based data.

Technical Description of Figures:This Figure shows monthly time series of midvisible aerosol optical depth (AOD), a measure of aerosol (e.g. smoke, mineral dust, industrial haze) amount in the atmosphere at two locations which have been monitored long-term (1993 onwards) as part of the NASA Aerosol Robotic Network (AERONET, https://aeronet.gsfc.nasa.gov). AERONET (grey lines) is the main resource for satellite-based AOD validation. Also shown are the standard EOS-era Deep Blue aerosol data records from SeaWiFS (green, 1997-2010) and MODIS Aqua (blue, 2002 onwards). These have been extended in time forward using Deep Blue applied to VIIRS retrievals (red, 2012 onwards). We have also processed data from three AVHRR sensors (NOAA11 from 1989-1990, NOAA14 from 1995-1999, NOAA18 from 2006-2011) in black. The Deep Blue family consists of two main algorithms: Deep Blue over land, and the Satellite Ocean Aerosol Retrieval (SOAR) over water. For simplicity, these two are collectively referred to as the Deep Blue algorithms.The main message is that Deep Blue reproduces the seasonal and interannual variation in AOD seen by AERONET reference data at these long-term sites, with minimal offsets between the different satellite records. Interannual variability at Alta Floresta (top panel) is linked to deforestation and drought, which control smoke from burning in August-October. At GSFC (bottom panel), the gradual improvement in air quality over the years is seen by the decrease in the summertime AOD maximum. This long-term monitoring is not possible without consistently-produced long-term satellite records.

Scientific significance, societal relevance, and relationships to future missions:Routine, long-term aerosol monitoring from space is important for many scientific and societal applications, including studies of the Earth’s climate, air quality and human health, and hazard (e.g. volcanic eruption, dust storm) monitoring and navigation avoidance. Obtaining a consistent long-term record is crucial to understand how the global burden of aerosols is changing. High-quality routine monitoring of aerosols from space began in the EOS era with the SeaWiFS (launched 1997) and MODIS (launched 2000 and 2002) instruments. The Deep Blue algorithm has been applied to both of these sensors to monitor aerosols. However, SeaWiFS ceased operations in 2010, and the two MODIS sensors are beyond their design lives.This work consists oftwo parts. First, we have demonstrated the applicability of the same algorithm approaches to VIIRS data; the first VIIRS sensor was launched in late 2011, and future VIIRS sensors on the JPSS platforms will continue the record for the coming several decades. This ensures the future continuity of the Deep Blue aerosol record for both real-time and climate applications. Second, we have demonstrated the potential to extend these time series back in time to potentially 1981 by using the AVHRR sensor series. Near-global aerosol retrievals over land have not been previously achieved using AVHRR measurements. This approximately doubles the potential length of the Deep Blue data record, which will greatly enhance trend studies. This is particularly true for Asian regions, which experienced rapid industrialisation in the 1990s, and parts of Europe and North America, where regulations began to decrease aerosol and precursor emissions in the 1990s, both prior to the currently-available data record from EOS-era sensors.

Earth Sciences Division - Atmospheres

Crop Production, Residue Fires, and Air Quality over Northern India:

An Intriguing LinkHiren Jethva, Code 614-USRA/GESTAR, Omar Torres, Code 614, NASA GSFC

Post-monsoon rice crop production in northwestern India has increased by 0.18 million tons/year (2002-2016). A-train satellite measurements show a consistent increase in NDVI (~0.007/year), residue fires (~500/year), and UVaerosol index/optical depth (~0.03/year).

Fig. 1 Inter-annual variations and trends in a) MODIS NDVI and crop production amounts, and b) MODIS fire counts and fire radiative power over northwestern India

Fig. 2 Spatial patterns of trends in fire counts (left), Aqua-MODIS/MAIAC AOD470 (middle), and Aura-OMI UV Aerosol Index (right) over the Indian subcontinent.

Fire counts Aqua-MODIS MAIAC AOD (470 nm) Aura-OMI UV Aerosol Index

Name: Hiren Jethva & Omar Torres, NASA/GSFC, Code 614E-mail: hiren.t.jethva@nasa.govPhone: 301-614-5225

References:Hiren Jethva, Duli Chand, Omar Torres, Pawan Gupta, Alexei Lyapustin, Falguni Patadia, “Agricultural Burning and Air Quality over Northern India: A Synergistic Analysis using NASA’s A-train Satellite Data and Ground Measurements”, Aerosols and Air Quality Research, 18: 1756-1773, 2018, doi: 10.4209/aaqr.2017.12.0583

Adam Voiland and Hiren Jethva, “Using Satellites to Size Up the Severity of Crop Fires in Northern India”, NASA Earth Observatory, February 8th, 2017, URL: https://earthobservatory.nasa.gov/blogs/earthmatters/2017/02/08/the-crop-residue-fires-in-northern-india-were-the-most-severe-in-more-than-a-decade/

Data Sources: MODIS Level-2 Thermal anomaly/Fires 1-km product (https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms) MODIS Level-2 monthly gridded NDVI product (https://e4ftl01.cr.usgs.gov/MOLA/)Crop production amounts data from Ministry of Agriculture and Farmers Welfare, Govt. of India (http://eands.dacnet.nic.in/)Aura-OMI Level-2 OMAERUV aerosol product (https://disc.gsfc.nasa.gov/) Aqua-MODIS MAIAC 1-km aerosol product (https://www.nccs.nasa.gov/)

Technical Description of Figures:Fig 1 Time-series: Inter-annual evolution of rice crop production and concurrent monthly mean NDVI values derived from Aqua/MODIS over the northwestern Indian state of Punjab show an overall increase of 0.18 million tons and 0.0074 per/year, respectively during the Aqua record. Coherent temporal changes in both quantities further signify the effectiveness of NDVI as a proxy of the crop production amounts as demonstrated in various studies. Total fire counts and associated fire radiative power detected by Aqua/MODIS during post-harvest season (October and November) over Punjab reveals an upward trend (~500/year) during the Aqua record. The post-monsoon season of year 2016 has been the most anomalous in highest rice production (12.6 million tons), NDVI (0.71), and fire counts (~18,000).

Fig 2 Maps: Spatial distribution of trends in satellite detected fire counts and resulting aerosol loading (UV Aerosol Index and aerosol optical depth from OMI and MODIS) reveals coherent upward trends with latter spread over the entire Indo-Gangetic Plain signifying the role of transport and impact of agricultural fires on air quality at regional scale.

Scientific significance, societal relevance, and relationships to future missions: The traditional practice of crop residue burning post-harvest over northwestern India causes hazardous levels air pollution over the populous northern India. In addition to its climatic impacts, extreme levels of particulate matter and trace gases emitted from crop fires during post-monsoon poses a serious threat to the human health of millions living in the region. While the increasing amounts of crop production ensure nation’s food security, the lack of an effective crop residue management system has led farmers resorting to burning the waste that has played a major role in deteriorating regional air quality during post-monsoon. Willingness and partnership between the government and the agricultural sector is crucial for the adoption and enforcement of the viable alternatives to burning. Owing to its long-term record, NASA’s A-train satellites have helped in tracking the temporal evolution of fires and resulting aerosol amounts over the region making possible to quantify the trends and spatial patterns. Currently in-orbit VIIRS instrument on board NASA-NOAA joint satellite mission Suomi-NPP will continue the record of fires and aerosol detection at higher spatial resolution.

Earth Sciences Division - Atmospheres