Suomi National Polar-orbiting Partnership Research Articles

IMPACT OF FOREST FIRE EMISSIONS ON AIR QUALITY OVER WESTERN HIMALAYA REGION

Abstract. Emissions from forest fires give out huge amounts of greenhouse gases into the atmosphere degrading the surrounding air quality. Climate change results in prolonged summer days and less precipitation thus increasing fuel accumulation facilitating recurring forest fires in the Western Himalayan regions. Most of the forest fire cases are human ignited that damaging the forest irreparably. This study attempts to analyse the impact of the forest fire events on the tropospheric concentrations of five major gaseous pollutants aerosols, Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2) and Formaldehyde (HCHO). A clear increase in daily average of the air pollutants considered in the study was observed as the number of forest fires were also increasing. The gases responded inversely with precipitation. Precipitation washed away majority of the air pollutants. The high-resolution air quality data were retrieved from the Google Earth Engine platform using Sentinel 5 Precursor datasets, daily active fire points from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument of Suomi National Polar-orbiting Partnership (SNPP) and daily precipitation data from CHIRPS Daily Version 2.0 Final. Four out of five gases i.e., CO, aerosols, NO2, HCHO pollutants gave moderate to high correlation values with the forest fire incidences. Air pollutants responded inversely with the precipitation pattern.

DEEP LEARNING-BASED METHOD TO EXTEND THE TIME SERIES OF GLOBAL ANNUAL VIIRS-LIKE NIGHTTIME LIGHT DATA

Abstract. The nighttime light (NTL) remote sensed imagery has been applied in monitoring human activities from many perspectives. As the two most widely used NTL satellites, the Defense Meteorological Satellite Program (DMSP) Operational Linescan System and the Suomi National Polar-orbiting Partnership (NPP)-Visible Infrared Imaging Radiometer Suite (VIIRS) have different spatial and radiometric resolutions. Thus, some long-time series analysis cannot be conducted without effective and accurate cross-calibration of these two datasets. In this study, we proposed a deep-learning based model to simulate VIIRS-liked DMSP NTL data by integrating the enhanced vegetation index (EVI) data product from MODIS. By evaluating the spatial pattern of the results, the modified Self-Supervised Sparse-to-Dense networks delivered satisfying results of spatial resolution downscaling. The quantitative analysing of the simulated VIIRS-liked DMSP NTL with original VIIRS NTL showed a good consistency at the pixel level of four selected sub datasets with R2 ranging from 0.64 to 0.76, and RMSE ranging from 3.96-9.55. Our method presents that the deep learning model can learn from relatively raw data instead of fine processed data based on expert knowledge to cross-sensor calibration and simulation NTL data.

Impact of Stratosphere on Cold Air Outbreak: Observed Evidence by CrIS on SNPP and Its Comparison with Models

A cold air outbreak (CAO) is an extreme weather phenomenon that has significant social and economic impacts over a large region of the midlatitudes. However, the dynamical mechanism of the occurrence and evolution of CAO events, particularly the role of the stratosphere, is not well understood. Through an analysis of one extreme CAO episode that occurred on 27–31 January 2019 across much of the US Midwest, this study examined its thermodynamic structure and the impact of stratospheric downward transport using the single-field-view (SFOV) satellite products (with a spatial resolution of ~14 km at nadir) from the Cross-track Infrared Sounder (CrIS) onboard Suomi National Polar-Orbiting Partnership (SNPP) in conjunction with MERRA-2 and ERA-5 reanalysis products. It is found that along the path of cold air transport, particularly near the coldest surface center, there exists a large enhancement of O3, deep tropopause folding, significant downward transport of stratospheric dry air, and a warm center above the tropopause. The upper warm center can be observed directly using the brightness temperature (BT) of CrIS stratospheric sounding channels. While similar large-scale patterns of temperature (T), relative humidity (RH), and ozone (O3) are captured from CrIS, MERRA-2, and ERA-5 products, it is found that, in the regions impacted by CAO, MERRA-2 has a thicker dry layer under the tropopause (with the difference of RH up to ~10%) and the total column ozone (TCO) from ERA-5 has a relatively large positive bias of 2.8 ± 2.8% compared to that measured by Ozone Mapping and Profiler Suite (OMPS). This study provides some observational evidence from CrIS that confirm the impact of the stratosphere on CAO through downward transport and demonstrates the value of the SFOV retrieval products for CAO dynamic transport study and model evaluation.

Using satellite images of nighttime lights to predict the economic impact of COVID-19 in India

The outbreak of COVID-19 in early 2020 heralded a deep global recession not seen since the Second World War. With entire nations in lockdown, burgeoning economies of countries like India plunged into a downward spiral. The conventional instruments of estimating the short-term economic impact of a pandemic is limited, and as a result, it is challenging to implement timely monetary policies to mitigate the financial impact of such unforeseen events. This study investigates the promise of using nighttime images of lights on Earth, also known as nightlight (NTL), captured by the Visible Infrared Imaging Radiometer Suite (VIIRS) instrumentation onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) satellite mission to measure the economic cost of the pandemic in India. First, a novel data processing framework was developed for a recently released radiance dataset known as VNP46A1, part of NASA’s Black Marble suite of NTL products. Second, the elasticity of nightlight to India’s National Gross Domestic Product (GDP) was estimated using panel regression followed by machine learning to predict the Year-over-Year (YoY) change in GDP during Fiscal Year (FY) 2020Q1 (Apr-Jun, 2020). Electricity consumption, known to closely track economic output and precipitation were included as additional features to improve model performance. A strong relationship between both electricity usage and nightlight to GDP was observed. The model predicted a YoY contraction of 24% in FY2020Q1, almost identical to the official GDP decline of 23.9% later announced by the Indian Government. Based on the findings, the study concludes that nightlight along with electricity usage can be invaluable proxies for estimating the cost of short-term supply–demand shocks such as COVID-19, and should be explored further.

Combined UV and IR ozone profile retrieval from TROPOMI and CrIS measurements

Abstract. Vertical ozone profiles from combined spectral measurements in the ultraviolet and infrared spectral range were retrieved by using data from the TROPOspheric Monitoring Instrument on the Sentinel-5 Precursor (TROPOMI/S5P) and the Cross-track Infrared Sounder on the Suomi National Polar-orbiting Partnership (CrIS/Suomi-NPP), which are flying in loose formation 3 min apart in the same orbit. A previous study of ozone profiles retrieved exclusively from TROPOMI UV spectra showed that the vertical resolution in the troposphere is clearly limited (Mettig et al., 2021). The vertical resolution and the vertical extent of the ozone profiles is improved by combining both wavelength ranges compared to retrievals limited to UV or IR spectral data only. The combined retrieval particularly improves the accuracy of the retrieved tropospheric ozone and to a lesser degree stratospheric ozone up to 30 km. An increase in the degrees of freedom (DOF) by 1 DOF was found in the UV + IR retrieval compared to the UV-only retrieval. Compared to previous publications, which investigated combinations of UV and IR observations from the Ozone Monitoring Instrument and Tropospheric Emission Spectrometer (OMI and TES) and Global Ozone Monitoring Experiment version 2 and Infrared Atmospheric Sounding Interferometer (GOME-2 and IASI) pairs, the degree of freedom is lower, which is attributed to the reduced spectral resolution of CrIS compared to TES or IASI. Tropospheric lidar and ozonesondes were used to validate the ozone profiles and tropospheric ozone content (TOC). In their comparison with tropospheric lidars, both ozone profiles and TOCs show smaller biases for the retrieved data from the combined UV + IR observation than from the UV observations alone. For the ozone profiles below 10 km, the mean differences are around ±10 % and the mean TOC varies around ±3 DU. We show that TOCs from the combined retrieval agree better with ozonesonde results at northern latitudes than the UV-only and IR-only retrievals and also have lower scatter. In the tropics, the IR-only retrieval shows the best agrement with TOCs derived from ozonesondes. While in general the TOCs show good agreement with ozonesonde data, the profiles have a positive bias of around 30 % between 10 and 15 km. The reason is probably a positive stratospheric bias from the IR retrieval. The comparison of the UV + IR and UV ozone profiles up to 30 km with the Microwave Limb Sounder (MLS) demonstrates the improvement of the UV + IR profile in the stratosphere above 18 km. In comparison to the UV-only approach the retrieval shows improvements of up to 10 % depending on latitude but can also show worse results in some regions and latitudes.

Evaluating the consistency and continuity of pixel-scale cloud property data records from Aqua and SNPP (Suomi National Polar-orbiting Partnership)

Abstract. The Aqua, SNPP (Suomi National Polar-orbiting Partnership), and JPSS (Joint Polar Satellite System) satellites carry a combination of hyperspectral infrared sounders (AIRS, Atmospheric Infrared Sounder, and CrIS, Cross-track Infrared Sounder) and high-spatial-resolution narrowband imagers (MODIS, Moderate Resolution Imaging Spectroradiometer, and VIIRS, Visible Infrared Imaging Radiometer Suite). They provide an opportunity to acquire high-quality, long-term cloud data records and are a key component of the existing Program of Record of cloud observations. By matching observations from sounders and imagers across different platforms at the pixel scale, this study evaluates the self-consistency and continuity of cloud retrievals from Aqua and SNPP by multiple algorithms, including the AIRS version 7 retrieval algorithm and the Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) version 2 for sounders and the standard Aqua MODIS collection 6.1 and the NASA MODIS–VIIRS continuity cloud products for imagers. Metrics describing detailed statistical distributions at the sounder field of view (FOV) and the joint histograms of cloud properties are evaluated. These products are found to be highly consistent despite their retrieval from different sensors using different algorithms. Differences between the two sounder cloud products are mainly due to cloud clearing and the treatment of clouds in scenes with unsuccessful atmospheric profile retrievals. The sounder subpixel cloud heterogeneity evaluated using the standard deviation of imager retrievals at the sounder FOV shows good agreement between the standard and continuity products from different satellites. However, the impact of algorithm and instrument differences between MODIS and VIIRS is revealed in cloud top pressure retrievals and in the imager cloud distribution skewness. Our study presents a unique aspect to examine NASA's progress toward building a continuous cloud data record with sufficient quality to investigate clouds' role in global environmental change.

Impact of Power on Uneven Development: Evaluating Built-Up Area Changes in Chengdu Based on NPP-VIIRS Images (2015–2019)

In the context of uneven development studies of China, urban built-up area changes are the index of the impact of power, as the local government is the only party that is able to acquire agricultural land and convert it to construction urban land. Existing studies generally use statistical data to describe the built-up area changes and struggle to meet the requirement of an updated and inexpensive monitoring of uneven development, especially for western cities with tight budgets. Open access NPP-VIIRS (Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite), NDVI (Normalized Difference Vegetation Index), and nighttime LST (Land Surface Temperature) data ranging from 2015 to 2019 were analyzed with a stratified SVM (Support Vector Machine) method in this study to track urban built-up area changes in Chengdu, one of the biggest cities in Western China. The SDE (Standard Deviation Ellipse) and Moran’s I were then applied to evaluate the spatial variations of the built-up area changes. It was revealed that the spatial evolution of built-up area change in Chengdu over the period 2015–2019 demonstrated a “northwest-southeast” spatial expansion pattern, and the change distance in the center of gravity in 2018 and 2019 was greater than that from 2015 to 2017, which reflected the faster uneven development in 2018 and 2019 in Chengdu. The results were verified with finer resolution Landsat-8 OLI images; the high OA (all larger than 92%) and KAPPA (all larger than 0.6) values showed the accuracy of the method. The methodology proposed in this study offers a practical way for cities with tight budgets to monitor uneven development, and this study suggests a further adaption using higher-resolution remote sensing images and field experiments.

Phytoplankton biomass dynamics in the Arabian Sea from VIIRS observations

Using chlorophyll-a (Chl-a) as an indicator for phytoplankton biomass, observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) between 2012 and 2019 in combination with wind, various ocean hydrographic data, and nutrient data are used to conduct a comprehensive study to characterize and quantify the phytoplankton biomass dynamics in the Arabian Sea, and assess the mechanisms that drive the summer and winter phytoplankton blooms. Phytoplankton biomass dynamics in the Arabian Sea are driven by the seasonal reversal of the monsoon wind. The summer phytoplankton bloom located mainly in the western and central Arabian Sea is stronger than the winter phytoplankton bloom in the northern Arabian Sea in terms of both Chl - a values and the extent of phytoplankton bloom coverage. Interannual variability of Chl - a is less significant in comparison to the seasonal variability. Chl-a data measured by the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) from 1997 to 2010 are also used. The comparison of Ch-a climatology data between SeaWiFS and VIIRS shows no long-term trend of Chl-a change in the Arabian Sea. This study provides an improved understanding (with new knowledge) of (1) phytoplankton biomass dynamics, (2) physical, biological, and biogeochemical processes, and (3) nutrient dynamics in the Arabian Sea. The hydrographic and nutrient data reveal two different driving mechanisms for phytoplankton blooms in the summer and winter monsoons. During the summer monsoon, the cold high-nutrient waters are brought to the surface from the thermocline depth through strong coastal upwelling and Ekman pumping, leading to increased Chl-a at the surface-layer. The vertical velocity reaches ~1 m/day at 70 m depth. In comparison, the winter phytoplankton bloom is driven by the moderate boost of the nutrient level due to the mixed-layer deepening and strong vertical mixing caused by the surface cooling in the northern Arabian Sea. The nutrient dynamics in the Arabian Sea also suggests that the nitrate concentration is the major nutrient driver for its seasonal phytoplankton biomass dynamics. • The phytoplankton biomass dynamics was characterized and quantified. • There is no long-term trend of Chl-a change in the Arabian Sea in the last two decades. • Two different driving mechanisms for phytoplankton blooms in the summer and winter

Global daily gap-free ocean color products from multi-satellite measurements

Gap-free global chlorophyll-a (Chl-a) concentration images derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 are now available online in near-real-time for the ocean color community. With the data fusion of Chl-a from the two satellite sources, missing pixels caused by cloud, sun glint contamination, high satellite viewing angles, and other unfavorable conditions are completely filled using the Data Interpolating Empirical Orthogonal Function (DINEOF) method. Our further studies show that adding more data from additional satellite sensors can remarkably improve the spatial coverage of the merged ocean color products, provide more ocean features over coastal and inland waters, and enhance the accuracy of the gap-free data. Since the launch of the Sentinel-3A and Sentinel-3B satellites, the onboard Ocean and Land Colour Instrument (OLCI) sensors have been providing global ocean color product data. In this work, we recruit the OLCI-Sentinel-3A to join the two VIIRS sensors to produce a three-sensor merged ocean color dataset, and further derive global gap-free images using the DINEOF method. It is found that adding OLCI data as the input source significantly enhances the spatial features of high Chl-a by ∼10–20% in productive coastal regions and inland lakes. Additionally, the first-ever dataset of global gap-free suspended particulate matter (SPM) and water diffuse attenuation coefficient at 490 nm (Kd(490)) is also generated using the three-sensor merged data. Results show that Kd(490) and SPM have similar spatial patterns as that of Chl-a in the major ocean basins, and that SPM can provide more details of the spatial variations than those of Kd(490) in the center of the ocean basin and coastal oceans. Global gap-free Chl-a, Kd(490), and SPM products provide important and useful water quality information particularly over global productive ocean/water regions.

A Global Assessment of Night Lights as an Indicator for Shipping Activity in Anchorage Areas

Accurate information on port shipping activities is critical for monitoring global and local traffic flows and assessing the state of development of the maritime industry. Such information is necessary for managers and analysts to make strategic decisions and monitor the maritime industry in achieving management goals. In this study, we used monthly night light (NTL) images of the Suomi National Polar-Orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band, between 2012 and 2020, to study the night lights emitted by ships in ports’ anchorage areas, as an indicator for shipping activity in anchorage areas and ports. Using a dataset covering 601 anchorage areas from 97 countries, we found a strong correspondence between NTL data and shipping metrics at the country level (n = 97), such as container port throughput (Rs = 0.84, p < 0.01) and maximum cargo carried by ships (Rs = 0.66, p < 0.01), as well as a strong correlation between the number of anchorage points and the NTL values in anchorage areas across the world (Rs = 0.69, p < 0.01; n = 601). The high correspondence levels of the VIIRS NTL data with various shipping indicators show the potential of using NTL data to analyze the spatio-temporal dynamic changes of the shipping activity in anchorage areas, providing convenient open access and a normalized assessment method for shipping industry parameters that are often lacking.

Assessment of OLCI-A and OLCI-B radiometric data products across European seas

The Ocean and Land Color Instruments (OLCI) operated onboard the Copernicus Sentinel-3 satellites are providing globally distributed Ocean Color Radiometry (OCR) data products of relevance for environmental and climate applications. This work summarizes results on the assessment of fundamental OCR data from the Operational Baseline 3 Collection OL_L2M.003.01 of OLCI-A and OLCI-B onboard Sentinel-3A and Sentinel-3B, respectively. Evaluated products are the satellite derived normalized water-leaving radiance LWN(λ), aerosol optical depth at 865 nm τa(865) and Ångström exponent α determined in the near-infrared spectral region. The analyses were performed relying on in situ reference data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) from sites representative of diverse water types. The comparison of OLCI-A and OLCI-B with AERONET-OC LWN(λ) for oligotrophic/mesotrophic waters shows cross-mission consistent spectral median percent differences (i.e., biases) varying within ±6% at the blue-green center-wavelengths. The analysis of data from regions characterized by optically complex waters, however, displays systematic negative biases for both OLCI-A and OLCI-B further increasing for waters dominated by chromophoric dissolved organic matter, thus suggesting a dependence of the atmospheric correction on water type. The direct inter-comparison of OLCI-B and OLCI-A LWN(λ) from the Tandem Phase characterized by Sentinel-3B and Sentinel-3A flying 30 s apart on the same orbit, shows spectral median percent differences lower than ±1% in the 412–560 nm interval, of approximately +5% at 620 and 665 nm, and −7% at 400 nm. However, outside the Tandem Phase, the inter-comparison of OLCI-B and OLCI-A data products indicates large and systematic differences explained by a notable dependence on the viewing angle. The evaluation of τa(865) and α across different geographic regions exhibits overestimated values between +48 and + 79% for the former and underestimated values between −28% and − 41% for the latter. A complementary evaluation of OCR data products from the Visible Infrared Imager Radiometer Suite on board the Suomi National Polar-orbiting Partnership (VIIRS-S), proposed as a further indirect term of reference for OLCI-A and OLCI-B data, shows large underestimates of LWN(λ) with respect to the in situ reference data in the various water types at 410 nm. Nevertheless, opposite to OLCI-A and OLCI-B data products, absolute differences between VIIRS-S and in situ reference data do not reveal any large or systematic dependence on water type and satellite viewing angle. Overall results suggest the need for further developing the OLCI-A and OLCI-B atmospheric correction, possibly improving the capability to identify aerosol types and to model scattering processes.

In‐Depth Evaluation of MiRS Total Precipitable Water From NOAA‐20 ATMS Using Multiple Reference Data Sets

AbstractThe National Oceanic and Atmospheric Administration (NOAA) Microwave Integrated Retrieval System (MiRS) algorithm has been producing retrieval products from NOAA‐20 Advanced Technology Microwave Sounder (ATMS) data since shortly after launch in late 2017. Retrieval products from the Suomi National Polar‐orbiting Partnership (SNPP) ATMS data have been similarly available since 2012. Among the products routinely generated is Total Precipitable Water (TPW), based on the vertical integral of the MiRS retrieved water vapor profile. In this study, TPW data from NOAA‐20 ATMS have been the subject of an in‐depth evaluation. The evaluation included characterization of dependence on orbital node (ascending/descending), season, surface type, scan angle, and meridional (zonally averaged) variation. Due to the similarity between NOAA‐20 and SNPP derived TPW, the emphasis here is on the NOAA‐20 evaluation. The globally averaged values of TPW for 2019 are 25.41 and 25.39 mm from NOAA‐20 and SNPP ATMS, respectively, for combined orbits (ascending and descending). For combined orbits, the bias and standard deviation of MiRS NOAA‐20 ATMS TPW with respect to European Centre for Medium‐Range Weather Forecasts/Global Data Assimilation System (ECMWF/GDAS) analyses are 0.79/1.76 mm and 3.65/3.55 mm, respectively, at 10°N where part of annual mean Intertropical Convergence Zone (ITCZ) is located, while at 40°N, corresponding bias and standard deviation values are 0.84/0.67 mm and 3.00/2.55 mm, respectively. The yearly averaged horizontal distribution of MiRS TPW retrieved from NOAA‐20 ATMS is highly correlated with that of ECMWF and GDAS analyses data, with a correlation coefficient of ∼1.

Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data to Evaluate Poverty in Southwestern China

Poverty alleviation is one of the most important tasks facing human social development. It is necessary to make accurate monitoring and evaluations for areas with poverty to improve capability of implementing poverty alleviation policies. Here, this study introduced nighttime light (NTL) data to estimate county-level poverty in southwest China. First, this study used particle swarm optimization-back propagation hybrid algorithm to explore the potential relationship between two NTL data (the Defense Meteorological Satellite Program’s Operational Line Scan System data and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite data). Then, we integrated two NTL data at the pixel level to establish a consistent time-series of NTL dataset from 2000 to 2019. Next, an actual comprehensive poverty index (ACPI) was employed as an indicator of multidimensional poverty at county level based on 11 socioeconomic and natural variables, and which could be the reference to explore the poverty evaluation using NTL data. Based on the correlation between the ACPI and NTL characteristic variables, a poverty evaluation model was developed to evaluate the poverty situation. The result showed the great matching relationship between DMSP-OLS and NPP-VIIRS data (R2 = 0.84). After calibration, the continuity and comparability of DMSP-OLS data were significantly improved. The integrated NTL data also reflected great consistency with socioeconomic development (r = 0.99). The RMSE between ACPI and the estimated comprehensive poverty index (ECPI) based on the integrated NTL data is approximately 0.19 (R2 = 0.96), which revealed the poverty evaluation model was feasible and reliable. According to the ECPI, we found that the magnitude of poverty eradication increased in southwest China until 2011, but slowed down from 2011 to 2019. Regarding the spatial scale, geographic barriers are a key factor for poverty, with high altitude and mountainous areas typically having a high incidence of poverty. Our approach offers an effective model for evaluation poverty based on the NTL data, which can contribute a more reliable and efficient monitoring of poverty dynamic and a better understanding of socioeconomic development.

Satellite observation of stratospheric intrusions and ozone transport using CrIS on SNPP

The Single Field of View (SFOV) ozone (O3) profiles derived from the Cross-track Infrared Sounder (CrIS) onboard Suomi National Polar-Orbiting Partnership (SNPP), with a high horizontal resolution of approximately 14 km at nadir and good sensitivity to O3 in the upper troposphere and lower stratosphere (UTLS), provide a prominent opportunity to examine stratosphere to troposphere (STT) transport. A process-oriented analysis was performed to examine the fine-scale features of a stratospheric intrusion (SI) event on June 11–13, 2017 in the southwestern US using the CrIS SFOV products together with wind and potential velocity (PV) from models. It was found that the location and strength of O3 enhancement correlate well with the PV contours, and the intrusion depth can be characterized using the vertical cross-sections of O3 and relative humidity (RH). In addition, the capability to use total column ozone (TCO) to identify SI events was confirmed through analysis of TCO from CrIS SFOV and other satellite and reanalysis products. The ozone/PV ratio was derived using SFOV O3 and model PV, and the values, ranging from 23.2 to 35.8 ppbv PVU−1 (1 PV unit (PVU) = 10−6 km2 kg−1 s−1), are in the lower end of previous estimations. These results demonstrate the advantages of the SFOV products in monitoring the fine-scale ozone transport and thermodynamic structure of SI events, as well as their potential value for weather and climate study.

Assimilating spaceborne lidar dust extinction can improve dust forecasts

Abstract. Atmospheric mineral dust has a rich tri-dimensional spatial and temporal structure that is poorly constrained in forecasts and analyses when only column-integrated aerosol optical depth (AOD) is assimilated. At present, this is the case of most operational global aerosol assimilation products. Aerosol vertical distributions obtained from spaceborne lidars can be assimilated in aerosol models, but questions about the extent of their benefit upon analyses and forecasts along with their consistency with AOD assimilation remain unresolved. Our study thoroughly explores the added value of assimilating spaceborne vertical dust profiles, with and without the joint assimilation of dust optical depth (DOD). We also discuss the consistency in the assimilation of both sources of information and analyse the role of the smaller footprint of the spaceborne lidar profiles in the results. To that end, we have performed data assimilation experiments using dedicated dust observations for a period of 2 months over northern Africa, the Middle East, and Europe. We assimilate DOD derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-Orbiting Partnership (SUOMI-NPP) Deep Blue and for the first time Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP)-based LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) pure-dust extinction coefficient profiles on an aerosol model. The evaluation is performed against independent ground-based DOD derived from AErosol RObotic NETwork (AERONET) Sun photometers and ground-based lidar dust extinction profiles from the Cyprus Clouds Aerosol and Rain Experiment (CyCARE) and PREparatory: does dust TriboElectrification affect our ClimaTe (Pre-TECT) field campaigns. Jointly assimilating LIVAS and Deep Blue data reduces the root mean square error (RMSE) in the DOD by 39 % and in the dust extinction coefficient by 65 % compared to a control simulation that excludes assimilation. We show that the assimilation of dust extinction coefficient profiles provides a strong added value to the analyses and forecasts. When only Deep Blue data are assimilated, the RMSE in the DOD is reduced further, by 42 %. However, when only LIVAS data are assimilated, the RMSE in the dust extinction coefficient decreases by 72 %, the largest improvement across experiments. We also show that the assimilation of dust extinction profiles yields better skill scores than the assimilation of DOD under an equivalent sensor footprint. Our results demonstrate the strong potential of future lidar space missions to improve desert dust forecasts, particularly if they foresee a depolarization lidar channel to allow discrimination of desert dust from other aerosol types.

Near-real-time estimation of hourly open biomass burning emissions in China using multiple satellite retrievals

Open biomass burning (OBB) is an important source of air pollutants and greenhouse gases, but its dynamic emission estimation remains challenging. Existing OBB emission datasets normally provide daily estimates based upon Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals but tend to underestimate the emissions due to the coarse spatial resolution and sparse observation frequency. In this study, we proposed a novel approach to improve OBB emission estimations by fusing multiple active fires detected by MODIS, Visible Infrared Imaging Radiometer onboard the Suomi National Polar-orbiting Partnership (VIIRS S-NPP) and Himawari-8. The fusion of multiple active fires can capture the missing small fires and the large fires take place during the non-overpass time of MODIS observations. Also, regional-based fire radiative power (FRP) cycle reconstruction models and OBB emission coefficients were developed to address the large spatial discrepancies of OBB emission estimations across China and to promote the estimate to an hourly resolution. Using the new approach, hourly gridded OBB emissions in China were developed and can be updated with a lag of 1-day, or even near-real-time when real-time multiple active fires are available. OBB emissions in China based on this approach were more than 3 times of those in previous datasets. Evaluations revealed that the spatial distribution of the estimated PM2.5 emissions from this study was more consistent with the ambient PM2.5 concentrations during several episodes than existing datasets. The hourly OBB emissions provide new insight into its spatiotemporal variations, enhance timely and reliable air quality modeling and forecast, and support the formulation of accurate prevention and control policies of OBB.

Estimating the VIIRS Thermal Emissive Band Response Versus Scan (RVS) and Calibration Offsets Using On-Orbit Pitch Maneuver Data

The Visible Infrared Imaging Radiometer Suite (VIIRS) is onboard the Suomi National Polar-orbiting Partnership (S-NPP) and the National Oceanic and Atmospheric Administration - 20 (NOAA-20) satellites. This study presents a method for estimating VIIRS Thermal Emissive Bands (TEB) Response Versus Scan (RVS) and calibration offsets simultaneously using on-orbit pitch maneuver data (METHOD2021). Raw Earth View (EV) RVS is estimated using an existing method (METHOD2019), with prelaunch RVS and calibration offsets as first guesses. Errors in the calibration offset are derived based on a prelaunch data-based assumption. Moreover, RVS and calibration offsets are optimized iteratively using the updated calibration coefficients until results converge. Compared to the METHOD2019, the METHOD2021 derived RVS is not affected by errors in the calibration offsets. Evaluation results using independent co-located Cross-track Infrared Sounder (CrIS) observations indicate that the METHOD2021 could effectively mitigate NOAA-20 scan angle and scene temperature dependent biases in M15-M16 and I5, as well as the cold bias in S-NPP M15. S-NPP TEB striping at cold scene temperatures can also be significantly reduced. Furthermore, NOAA-20 I5 and M15 measurements become more in family with S-NPP VIIRS and other radiometers at extremely low temperatures. Our analysis indicates that the impacts of the METHOD2021 and METHOD2019 on TEB SDRs are generally comparable. The METHOD2019 is simpler, while the METHOD2021 better explains the root causes of the VIIRS TEB scan angle and scene temperature dependent biases. Both can be used for improving VIIRS TEB on-orbit calibration, especially at cold scenes.

An Improved Method for VIIRS Radiance Limit Verification and Saturation Rollover Flagging

This article presents an improved radiance limit verification and saturation rollover flagging method for the Visible Infrared Imaging Radiometer Suite (VIIRS) reflective solar band (RSB) and thermal emissive band (TEB) sensor data records (SDRs). Platform (satellite)-dependent radiance limits are introduced to account for the different radiometric characteristics of the VIIRS onboard the Suomi National Polar-Orbiting Partnership (S-NPP) and the National Oceanic and Atmospheric Administration-20 (NOAA-20) satellites. Two reference band-based tests are added for better saturation rollover flagging. Evaluation results using NOAA-20 and S-NPP reprocessed and on-orbit SDRs indicate that saturation rollover flagging can be significantly improved. To date, the improved scheme of saturation rollover flagging is applied only to NOAA-20 and S-NPP M6. The application of this scheme to other RSB and TEB bands can be achieved by updating the Quality Assurance lookup table. Moreover, the issue of radiance and brightness temperature mismatch for NOAA-20 TEBs is also resolved. A VIIRS SDR algorithm code change for the improved method has been implemented in the NOAA operational processing for NOAA-20 and S-NPP since March 25, 2019. It can also be applied to the VIIRS onboard the future Joint Polar Satellite System satellites (J2–J4).

ATMS Radiance Data Products’ Calibration and Evaluation

The Advanced Technology Microwave Sounder (ATMS) is a passive microwave radiometer for the current generation of polar-orbiting meteorological satellites operated by the National Oceanic and Atmospheric Administration (NOAA). The first two ATMS instruments are manifested onboard the Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites. Several critical changes have been made to ATMS operational calibration algorithm since March 2017. The calibration processing has been revised from a Rayleigh–Jeans approximated algorithm to a full radiance algorithm in order to reduce the error introduced by the approximation over cold radiances in the higher frequency channels. In addition, based on the lessons learned from S-NPP and NOAA-20 postlaunch calibration/validation tests, some major improvements have been made in the updated operational algorithm. These include reflector emission and antenna pattern corrections. Details of the radiance-based ATMS on-orbit calibration are documented in this report, and results of prelaunch calibration error budget analysis and postlaunch calibration accuracy evaluation are also presented for reference.

Deconvolution of SNPP VIIRS Solar Diffuser Bidirectional Reflectance Distribution Function On-Orbit Change Factor

The earth-observing visible infrared imaging radiometer suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) satellite regularly calibrates its reflective solar bands (RSBs), primarily through observing an onboard sunlit solar diffuser (SD). The on-orbit change of the value of the SD bidirectional reflectance distribution function (BRDF) is quantified by a numerical factor, called the H-factor, and is determined by the onboard SD stability monitor (SDSM). Because the spectral response function of an SDSM detector spreads in wavelength, the directly measured H-factor is the true H-factor convolved with the spectral response function. To find the true H-factor, we use the traditional direct method and an innovative iterative approach to separately deconvolve the measured H-factor. Our iterative approach relies on two properties of the SDSM detector spectral response function: the central peak width is narrow enough so that the H-factor does not change much over the peak width, and the dominance of the spectral response function’s integral with respect to the wavelength over the width. The iterative approach is more accurate, of a smaller noise impact, much more flexible in terms of interpolation and extrapolation of functional values, and faster. We have used deconvolved H-factors to calibrate the NASA SNPP VIIRS RSB Collections 1 and 2 Level-1B products.