Satellite Retrievals Research Articles

Chlorophyll dynamics from Sentinel-3 using an optimized algorithm for enhanced ecological monitoring in complex urban estuarine waters

Urban estuaries are dynamic environments that hold high ecological and economic value. Yet, their optical complexity hinders accurate satellite retrievals of important biogeochemical variables, such as chlorophyll-a (Chl-a) biomass. Approaches based on a limited number of satellite spectral bands often fail to capture seasonal transitions and sharp spatial gradients in estuarine Chl-a concentrations, inhibiting integration of satellite data into water quality monitoring and conservation programs. We propose a novel approach that utilizes the wide range of spectral information captured by the Ocean and Land Color Instrument (OLCI) to retrieve estuarine Chl-a. To validate our approach, we used measurements in Long Island Sound (LIS), a highly urbanized estuary increasingly susceptible to anthropogenic stressors and climate change. Hyperspectral remote sensing reflectance (Rrs) and Chl-a data representing the spatiotemporal diversity of LIS were used to assess the ideal atmospheric correction approach for OLCI and develop a multi-spectral multiple linear regression (MS-MLR) Chl-a algorithm. POLYMER derived Rrs proved to be the preferred atmospheric correction approach. Evaluation of MS-MLR performance in retrieving Chl-a with in situ Rrs showed good agreement with field measurements. Application to OLCI-retrieved Rrs showed significant improvement (20%-30%) in common error metrics relative to other algorithms assessed. The MS-MLR approach successfully captured seasonal cycles and spatial gradients in Chl-a concentration. Application of this method to urban estuaries and coasts enables accurate, high resolution Chl-a observations at the ecosystem scale and across a range of conditions, as needed for conservation and ecosystem management efforts.

Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers

The newest and upcoming geostationary passive imagers have thermal infrared channels comparable to those of more established instruments, but their spectral response functions still differ significantly. Therefore, retrievals developed for a certain type of radiometer cannot simply be applied to another imager. Here, a set of spectral band adjustment factors is determined for MSG/SEVIRI, Himawari-8/AHI, and MTG1/FCI from a training dataset based on MetOp/IASI hyperspectral observations. These correction functions allow to turn the observation of one sensor into an analogue observation of another sensor. This way, the same satellite retrieval—that has been usually developed for a specific instrument with a specific spectral response function—can be applied to produce long time series that go beyond one single satellite/satellite series or to cover the entire geostationary ring in a consistent way. It is shown that the mean uncorrected brightness temperature differences between corresponding channels of two imagers can be >1 K, in particular for the channels centered around 13.4 μm in the carbon dioxide absorption band and even when comparing different imager realizations of the same series, such as the four SEVIRI sensors aboard MSG1 to MSG4. The spectral band adjustment factors can remove the bias and even reduce the standard deviation in the brightness temperature difference by more than 80%, with the effect being dependent on the spectral channel and the complexity of the correction function. Further tests include the application of the spectral band adjustment factors in combination with (a) a volcanic ash cloud retrieval to Himawari-8/AHI observations of the Raikoke eruption 2019 and a comparison to an ICON-ART model simulation, and (b) an ice cloud retrieval to simulated MTG1/FCI test data with the outcome compared to the retrieval results using real MSG3/SEVIRI measurements for the same scene.

Tropospheric NO2 vertical profiles over South Korea and their relation to oxidant chemistry: implications for geostationary satellite retrievals and the observation of NO2 diurnal variation from space

Abstract. Nitrogen oxides (NOx≡ NO + NO2) are of central importance for air quality, climate forcing, and nitrogen deposition to ecosystems. The Geostationary Environment Monitoring Spectrometer (GEMS) is now providing hourly NO2 satellite observations over East Asia, offering the first direct measurements of NO2 diurnal variation from space to guide understanding of NOx emissions and chemistry. The NO2 retrieval requires independent vertical profile information from a chemical transport model (CTM) to compute the air mass factor (AMF) that relates the NO2 column measured along the line of sight to the NO2 vertical column. Here, we use aircraft observations from the Korea-United States Air Quality (KORUS-AQ) campaign over the Seoul metropolitan area (SMA) and around the Korean Peninsula in May–June 2016 to better understand the factors controlling the NO2 vertical profile, its diurnal variation, the implications for the AMFs, and the ability of the GEOS-Chem CTM to compute the NO2 vertical profiles used for AMFs. Proper representation of oxidant chemistry is critical for the CTM simulation of NO2 vertical profiles and is achieved in GEOS-Chem through new model developments, including aerosol nitrate photolysis, reduced uptake of hydroperoxy (HO2) radicals by aerosols, and accounting for atmospheric oxidation of volatile chemical products (VCPs). We find that the tropospheric NO2 columns measured from space in the SMA are mainly contributed by the planetary boundary layer (PBL) below 2 km altitude, reflecting the highly polluted conditions. Repeated measurements of NO2 vertical profiles over the SMA at different times of day show that diurnal change in mixing depth affecting the NO2 vertical profile induces a diurnal variation in AMFs of comparable magnitude to the diurnal variation in the NO2 column. GEOS-Chem captures this diurnal variation in AMFs and more generally the variability in the AMFs for the KORUS-AQ NO2 vertical profiles (2.7 % mean bias, 7.6 % precision), with some outliers in the morning due to errors in the timing of mixed-layer growth.

Quantification of Gridded Precipitation Products for the Streamflow Simulation on the Mekong River Basin Using Rainfall Assessment Framework: A Case Study for the Srepok River Subbasin, Central Highland Vietnam

Many fields have identified an increasing need to use global satellite precipitation products for hydrological applications, especially in ungauged basins. In this study, we conduct a comprehensive evaluation of three Satellite-based Precipitation Products (SPPs): Integrated Multi–satellitE Retrievals for GPM (IMERG) Final run V6, Soil Moisture to Rain (SM2RAIN)-Advanced SCATterometer (ASCAT) V1.5, and Multi-Source Weighted-Ensemble Precipitation (MSWEP) V2.2 for a subbasin of the Mekong River Basin (MRB). The study area of the Srepok River basin (SRB) represents the Central Highland sub-climatic zone in Vietnam under the impacts of newly built reservoirs during 2001–2018. In this study, our evaluation was performed using the Rainfall Assessment Framework (RAF) with two separated parts: (1) an intercomparison of rainfall characteristics between rain gauges and SPPs; and (2) a hydrological comparison of simulated streamflow driven by SPPs and rain gauges. Several key findings are: (1) IMERGF-V6 shows the highest performance compared to other SPP products, followed by SM2RAIN-ASCAT V1.5 and MSWEP V2.2 over assessments in the RAF framework; (2) MSWEP V2.2 shows discrepancies during the dry and wet seasons, exhibiting very low correlation compared to rain gauges when the precipitation intensity is greater than 15 mm/day; (3) SM2RAIN–ASCAT V1.5 is ranked as the second best SPP, after IMERGF-V6, and shows good streamflow simulation, but overestimates the wet seasonal rainfall and underestimates the dry seasonal rainfall, especially when the precipitation intensity is greater than 20 mm/day, suggesting the need for a recalibration and validation of its algorithm; (4) SM2RAIN-ASCAT had the lowest bias score during the dry season, indicating the product’s usefulness for trend analysis and drought detection; and (5) RAF shows good performance to evaluate the performance of SPPs under the impacts of reservoirs, indicating a good framework for use in other similar studies. The results of this study are the first to reveal the performance of MSWEP V2.2 and SM2RAIN-ASCAT V1.5. Additionally, this study proposes a new rainfall assessment framework for a Vietnam basin which could support future studies when selecting suitable products for input into hydrological model simulations in similar regions.

Marine Warm Cloud Fraction Decreases Monotonically With Rain Rate for Fixed Vertical and Horizontal Cloud Sizes

AbstractPrecipitation has been hypothesized to play a key role in the determination of cloud fraction (CF). The relationships between CF and rain rate (R) in marine boundary layer clouds were studied based on long‐term satellite retrievals. We find that both CF and R increase with cloud geometric width (CGW) and thickness (CGT). Increased CGW causes increased R even without cloud deepening, but increasing CGW beyond 15 km leads to a slight decrease in R. Coincident change in CF and R with increased CGW causes a spurious positive CF‐R relationship. When fixing CGT and CGW, CF generally decreases monotonically with increasing R, and the dependence generally weakens with warmer sea surface temperature and thinner, larger clouds. This finding supports well the hypothesis that aerosols could increase cloudiness by suppressing precipitation. This study also emphasizes the importance of cloud morphology in marine boundary layer cloud studies connected to precipitation processes.

Surface temperature comparison of the Arctic winter MOSAiC observations, ERA5 reanalysis, and MODIS satellite retrieval

Atmospheric model systems, such as those used for weather forecast and reanalysis production, often have significant and systematic errors in their representation of the Arctic surface energy budget and its components. The newly available observation data of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition (2019/2020) enable a range of model analyses and validation in order to advance our understanding of potential model deficiencies. In the present study, we analyze deficiencies in the surface radiative energy budget over Arctic sea ice in the ERA5 global atmospheric reanalysis by comparing against the winter MOSAiC campaign data, as well as, a pan-Arctic level-2 MODIS ice surface temperature remote sensing product. We find that ERA5 can simulate the timing of radiatively clear periods, though it is not able to distinguish the two observed radiative Arctic winter states, radiatively clear and opaquely cloudy, in the distribution of the net surface radiative budget. The ERA5 surface temperature over Arctic sea ice has a conditional error with a positive bias in radiatively clear conditions and a negative bias in opaquely cloudy conditions. The mean surface temperature error is 4°C for radiatively clear situations at MOSAiC and up to 15°C in some parts of the Arctic. The spatial variability of the surface temperature, given by 4 observation sites at MOSAiC, is not captured by ERA5 due to its spatial resolution but represented in the level-2 satellite product. The sensitivity analysis of possible error sources, using satellite products of snow depth and sea ice thickness, shows that the positive surface temperature errors during radiatively clear events are, to a large extent, caused by insufficient sea ice thickness and snow depth representation in the reanalysis system. A positive bias characterizes regions with ice thickness greater than 1.5 m, while the negative bias for thinner ice is partly compensated by the effect of snow.

Validation of the TROPOMI/S5P aerosol layer height using EARLINET lidars

Abstract. The purpose of this study is to investigate the ability of the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) to derive accurate geometrical features of lofted aerosol layers, selecting the Mediterranean Basin as the study area. Comparisons with ground-based correlative measurements constitute a key component in the validation of passive and active satellite aerosol products. For this purpose, we use ground-based observations from quality-controlled lidar stations reporting to the European Aerosol Research Lidar Network (EARLINET). An optimal methodology for validation purposes has been developed and applied using the EARLINET optical profiles and TROPOMI aerosol products, aiming at the in-depth evaluation of the TROPOMI aerosol layer height (ALH) product for the period 2018 to 2022 over the Mediterranean Basin. Seven EARLINET stations were chosen, taking into consideration their proximity to the sea, which provided 63 coincident aerosol cases for the satellite retrievals. In the following, we present the first validation results for the TROPOMI/S5P ALH using the optimized EARLINET lidar products employing the automated validation chain designed for this purpose. The quantitative validation at pixels over the selected EARLINET stations illustrates that the TROPOMI ALH product is consistent with the EARLINET lidar products, with a high correlation coefficient R=0.82 (R=0.51) and a mean bias of -0.51±0.77 km and -2.27±1.17 km over ocean and land, respectively. Overall, it appears that aerosol layer altitudes retrieved from TROPOMI are systematically lower than altitudes from the lidar retrievals. High-albedo scenes, as well as low-aerosol-load scenes, are the most challenging for the TROPOMI retrieval algorithm, and these results testify to the need to further investigate the underlying cause. This work provides a clear indication that the TROPOMI ALH product can under certain conditions achieve the required threshold accuracy and precision requirements of 1 km, especially when only ocean pixels are included in the comparison analysis. Furthermore, we describe and analyse three case studies in detail, one dust and two smoke episodes, in order to illustrate the strengths and limitations of the TROPOMI ALH product and demonstrate the presented validation methodology. The present analysis provides important additions to the existing validation studies that have been performed so far for the TROPOMI S5P ALH product, which were based only on satellite-to-satellite comparisons.

Assessment of High-Resolution Surface Soil Moisture Products over the Qinghai–Tibet Plateau for 2009–2017

The surface soil moisture over the Qinghai–Tibet Plateau (QTP) has an important impact on the weather and climate of East Asia. Under climate warming, the imbalance of solid–liquid water of QTP has become a research hotspot, but the surface soil moisture dynamics over QTP are not clear owing to the lack of precise measurements over a large scale. In this paper, the quality of gridded surface soil moisture products including CSSPv2 high-resolution (6 km) simulation, ESA CCI satellite retrieval, ERA5 land-atmosphere coupled reanalysis, and GLDAS2.1 land reanalysis products (Noah, Catchment, VIC) is analyzed over QTP by comparison with the in situ measurements at 140 stations during 2009–2017. We find that the CSSPv2 product shows a higher correlation than the global satellite and reanalysis products, with correlation increased by 7.7%–115.6%. The root mean squared error of the CSSPv2 product is lower than that of other products, with the error decreased by 13.4%–46.3%. The triple collocation analysis using high-resolution simulation, global reanalysis, and satellite retrieval products over the entire plateau shows that the error of CSSPv2 is the lowest, followed by ESA CCI, while ERA5 is the highest. The soil moisture products of ESA CCI, ERA5, and CSSPv2 all show an increasing trend from April to September of 2009 to 2017, with wetting in the west and drying in the east. This study indicates that the CSSPv2 high-resolution surface soil moisture product has better performance over QTP than other global products, and the global satellite and reanalysis products may overestimate the surface soil moisture dynamics.

Sea Ice Production in the 2016 and 2017 Maud Rise Polynyas

AbstractSea ice production within polynyas, an outcome of the atmosphere‐ice‐ocean interaction, is a major source of dense water and hence key to the global overturning circulation, but is poorly quantified over open‐ocean polynyas. Using the two recent extensive open‐ocean polynyas within the wider Maud Rise region of the Weddell Sea in 2016 and 2017, we here explore the sea ice energy budget and estimate their sea ice production based on satellite retrievals, in‐situ hydrographic observations and the Japanese 55‐year Reanalysis. We find that the oceanic heat flux amounts to 36.1 and 30.7 W m−2 within the 2016 and 2017 polynyas, respectively. Especially the 2017 open‐ocean polynya produced nearly 200 km3 of new sea ice, which is comparable to the production in the largest Antarctic coastal polynyas. Finally, we determine that ice production is highly correlated with and sensitive to skin temperature and wind speed, which affect the turbulent fluxes. It is also strongly sensitive to uncertainties in the sea ice concentration and 1,000 hPa temperature, which all urgently need to be better monitored at high latitudes. Lastly, more process‐oriented campaigns are required to further elucidate the role of open‐ocean polynya on the local and global ocean circulations.

Impact of SO2 Flux Estimation in the Modeling of the Plume of Mount Etna Christmas 2018 Eruption and Comparison against Multiple Satellite Sensors

In this study, we focus on the eruption of Mount Etna on Christmas 2018, which emitted great amounts of SO2 from 24th to 30th December into the free troposphere. Simulations based on two different estimations of SO2 emission fluxes are conducted with the chemistry-transport model MOCAGE in order to study the impact of these estimations on the volcanic plume modeling. The two flux emissions used are retrieved (1) from the ground-based network FLAME, located on the flank of the volcano, and (2) from the spaceborne instrument SEVIRI onboard the geostationary satellite MSG. Multiple spaceborne observations, in the infrared and ultraviolet bands, are used to evaluate the model results. Overall, the model results match well with the plume location over the period of the eruption showing the good transport of the volcanic plume by the model, which is linked to the use of a realistic estimation of the altitude of injection of the emissions. However, there are some discrepancies in the plume concentrations of SO2 between the two simulations, which are due to the differences between the two emission flux estimations used that are large on some of the days. These differences are linked to uncertainties in the retrieval methods and observations used to derive SO2 volcanic fluxes. We find that the uncertainties in the satellite-retrieved column of SO2 used for the evaluation of the simulations, linked to the instrument sensitivity and/or the retrieval algorithm, are sometimes nearly as large as the differences between the two simulations. This shows a limitation of the use of satellite retrievals of SO2 concentrations to quantitatively validate modeled volcanic plumes. In the paper, we also discuss approaches to improve the simulation of SO2 concentrations in volcanic plumes through model improvements and also via more advanced methods to more effectively use satellite-derived products.

How aerosol size matters in aerosol optical depth (AOD) assimilation and the optimization using the Ångström exponent

Abstract. Satellite-based aerosol optical depth (AOD) has gained popularity as a powerful data source for calibrating aerosol models and correcting model errors through data assimilation. However, simulated airborne particle mass concentrations are not directly comparable to satellite-based AODs. For this, an AOD operator needs to be developed that can convert the simulated mass concentrations into model AODs. The AOD operator is most sensitive to the input of the particle size and chemical composition of aerosols. Furthermore, assumptions regarding particle size vary significantly amongst model AOD operators. More importantly, satellite retrieval algorithms rely on different size assumptions. Consequently, the differences between the simulations and observations do not always reflect the actual difference in aerosol amount. In this study, the sensitivity of the AOD operator to aerosol properties has been explored. We conclude that, to avoid inconsistencies between the AOD operator and retrieved properties, a common understanding of the particle size is required. Accordingly, we designed a hybrid assimilation methodology (hybrid AOD assimilation) that includes two sequentially conducted procedures. First, aerosol size in the model operator has been brought closer to the assumption of the satellite retrieval algorithm via assimilation of Ångström exponents. This ensures that the model AOD operator is more consistent with the AOD retrieval. The second step in the methodology concerns optimization of aerosol mass concentrations through direct assimilation of AOD (standard AOD assimilation). The hybrid assimilation method is tested over the European domain using Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue products. The corrections made to the model aerosol size information are validated through a comparison with the ground-based Aerosol Robotic Network (AERONET) optical product. The increments in surface aerosol mass concentration that occur due to either the standard AOD assimilation analysis or the hybrid AOD assimilation analysis are evaluated against independent ground PM2.5 observations. The standard analysis always results in relatively accurate posterior AOD distributions; however, the corrections are hardly transferred into better aerosol mass concentrations due to the uncertainty in the AOD operator. In contrast, the model AOD and mass concentration states are considerably more accurate when using the hybrid methodology.

Summertime ozone pollution in China affected by stratospheric quasi-biennial oscillation

Abstract. In recent years, the near-surface ozone (O3) level has been rising fast in China, with increasing damage to human health and ecosystems. In this study, the impact of stratospheric quasi-biennial oscillation (QBO) on interannual variations in summertime tropospheric O3 over China is investigated based on GEOS-Chem model simulations and satellite retrievals. QBO has a significant positive correlation with near-surface O3 concentrations over central China (92.5–112.5∘ E, 26–38∘ N) when the sea surface temperature (SST) over the eastern tropical Pacific is warmer than normal, with a correlation coefficient of 0.53, but QBO has no significant effect on O3 under the cold SST anomaly. Compared to the easterly phase of QBO, the near-surface O3 concentrations have an increase of up to 3 ppb (5 % relative to the average) over central China during its westerly phase under the warm SST anomaly. O3 also increases above the surface and up to the upper troposphere, with a maximum increase of 2–3 ppb (3 %–5 %) in 850–500 hPa over central China when comparing westerly phase to easterly phase. Process-based analysis and sensitivity simulations suggest that the O3 increase over central China is mainly attributed to the anomalous downward transport of O3 during the westerly phase of QBO when a warm SST anomaly occurs in the eastern tropical Pacific, while the local chemical reactions and horizontal transport processes partly offset the O3 increase. This work suggests a potentially important role of QBO and the related vertical transport process in affecting near-surface O3 air quality, with an indication for O3 pollution prediction and prevention.

The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback

AbstractA supervised neural network algorithm is used to categorize near‐global satellite retrievals into three mesoscale cellular convective (MCC) cloud morphology patterns. At constant cloud amount, morphology patterns differ in brightness associated with the amount of optically thin cloud features. Environmentally driven transitions from closed MCC to other morphology patterns, typically accompanied by more optically thin cloud features, are used as a framework to quantify the morphology contribution to the optical depth component of the shortwave cloud feedback. A marine heat wave is used as an out‐of‐sample test of closed MCC occurrence predictions. Morphology shifts in optical depth between 65°S and 65°N under projected environmental changes (i.e., from an abrupt quadrupling of CO2) assuming constant cloud cover contributes between 0.04 and 0.07 W m−2 K−1 (aggregate of 0.06) to the global mean cloud feedback.

Impacts of biomass burning in Southeast Asia on aerosols over the low-latitude plateau in China: An analysis of a typical pollution event

From March to April, widespread forest fires and agro-residue burning frequently occur in Southeast Asia, which release large amounts of gas species and aerosols and impact air quality over the wide source and downwind regions. In this study, we investigated the impact of biomass burning (BB) over Southeast Asia on particulate matter concentrations and aerosol properties in downwind areas of the low-latitude plateau from 1 March to 30 April 2019, with a focus on a typical pollution event in Kunming (KM), the capital of Yunnan Province, by using a wide variety of observations from the Chenggong ground monitoring station in Yunnan University, an air quality network in China, satellite retrievals and ERA-5 reanalysis data and numerical simulation. A regional pollution event contributed by BB pollutants from Southeast Asia and the India-Myanmar trough occurred in Yunnan Province on 31 March to 1 April 2019, which was the only typical pollution event that pollution transmission ran through central Yunnan Province from south to north since 2013, when the Airborne Pollution Action Plan was unveiled by China government. The daily mean PM2.5, PM1, and black carbon concentrations increased by 73.3 μg m−3(78%), 70.5 μg m−3 (80%), and 7.7 μg m−3 (83%), respectively, and the scattering and absorbing coefficients increased by 471.6 Mm−1 and 63.5 Mm−1, respectively, at the Chenggong station. The southwest winds exceeding 2 km vertically thick appeared in front of the India-Myanmar trough over the fire regions, pushing BB plumes northward into Yunnan Province. The model results show that 59.5% of PM2.5 mass produced by BB in Yunnan Province was sourced from the Myanmar-Thailand border, and 29.3% was from western Myanmar at a lower altitude (<4.9 km), which indicated that BB in the Myanmar-Thailand border was the dominant contributor.

Improved soil evaporation remote sensing retrieval algorithms and associated uncertainty analysis on the Tibetan Plateau

Abstract. Actual evapotranspiration (ET) is the key link between water and energy cycles. However, accurate evaporation estimation in alpine barren areas remains understudied. In this study, we aimed to improve the satellite-driven Process-based Land Surface ET/Heat fluxes algorithm (P-LSH) for better satellite retrieval of ET on the Tibetan Plateau by introducing two effective soil moisture constraint schemes in which normalized surface soil moisture and the ratio of cumulative antecedent precipitation to cumulative antecedent equilibrium evaporation are used to represent soil water stress, respectively, based on the intercomparison and knowledge-learning of the existing schemes. We first conducted intercomparison of six existing soil evaporation algorithms and sorted out the two most effective soil moisture constraint schemes. We then introduced the modified versions of the two constraint schemes into the P-LSH algorithm and further optimized the parameters using the differential evolution method. As a result, it formed two improved P-LSH algorithms. We systematically assessed the performances of the two improved P-LSH algorithms and six existing remote sensing ET retrieval algorithms on two barren-dominated basins of the Tibetan Plateau using reconstructed ET estimates derived from the terrestrial water balance method as a benchmark. The two moisture constraint schemes largely improved the performance of the P-LSH algorithm and showed better performance in both basins (root mean square error (RMSE) = 7.36 and 7.76 mm per month; R2=0.86 and 0.87), resulting in a higher simulation accuracy than all six existing algorithms. We used five soil moisture datasets and five precipitation datasets to further investigate the impact of moisture constraint uncertainty on the improved P-LSH algorithm. The ET estimates of the improved P-LSH algorithm, driven by the GLDAS_Noah soil moisture, performed best compared with those driven by other soil moisture and precipitation datasets, while ET estimates driven by various precipitation datasets generally showed a high and stable accuracy. These results suggest that high-quality soil moisture can optimally express moisture supply to ET, and that more accessible precipitation data can serve as a substitute for soil moisture as an indicator of moisture status for its robust performance in barren evaporation.

The electrical activity of a thunderstorm under high dust circumstances over Beijing metropolis region

A rare dust-thunderstorm affected the Beijing area on April 15, 2021, generating frequent lightning, dirt precipitation, and gusting. Based on comprehensive data from satellite retrieval, in-situ observation, weather radar, and reanalysis data, this study investigated the electrical activity of this dust-thunderstorm. The results showed that dust aerosols from Mongolia were involved in the growth process of the thunderstorm. During the evolution of the thunderstorm, the ground electrical field always changed positively with the highest value of 9 kV, and PM2.5 and PM10 increased rapidly with the highest values of 1500 μg/m3 and 250 μg/m3. The positive cloud-to-ground (+CG) lightning accounted for a high percentage with an average ratio of >50%. The dust aerosols acting as effective ice nuclei (IN) and cloud condensation nuclei (CCN) invading the thunderstorm were likely to increase the content of ice-phase particles and supercooled water, resulting in a high proportion of +CG lightning. It is deduced that the dust-thunderstorm possibly presented an inverted charge structure.

UAV hyperspectral imaging for multiscale assessment of Landsat 9 snow grain size and albedo

Snow albedo, a measure of the amount of solar radiation that is reflected at the snow surface, plays a critical role in Earth’s climate and in regional hydrology because it is a primary driver of snowmelt timing. Satellite multi-spectral remote sensing provides a multi-decade record of land surface reflectance, from which snow albedo can be retrieved. However, this observational record is challenging to assess because discrete in situ observations are not well suited for validation of snow properties at the spatial resolution of satellites (tens to hundreds of meters). For example, snow grain size, a primary driver of snow albedo, can vary at the sub-meter scale driven by changes in aspect, elevation, and vegetation. Here, we present a new uncrewed aerial vehicle hyperspectral imaging (UAV-HSI) method for mapping snow surface properties at high resolution (20 cm). A Resonon near-infrared HSI was flown on a DJI Matrice 600 Pro over the meadow encompassing Swamp Angel Study Plot in Senator Beck Basin, Colorado. Using a radiative transfer forward modeling approach, effective snow grain size and albedo maps were produced from measured surface reflectance. Coincident ground observations were used for validation; relative to retrievals from a field spectrometer the mean grain size difference was 2 μm, with an RMSE of 12 μm, and the mean broadband albedo was within 1% of that measured near the center of the flight area. Even though the snow surface was visually homogenous, the maps showed spatial variability and coherent patterns in the freshly fallen snow. To demonstrate the potential for UAV-HSI to be used to improve validation of satellite retrievals, the high-resolution maps were used to assess grain size and albedo retrievals, and subpixel variability, across 17 Landsat 9 OLI pixels from a satellite overpass with similar conditions two days following the flight. Although Landsat 9 did not capture the same range of values and spatial variability as the UAV-HSI, on average the comparison showed good agreement, with a mean grain size difference of 9 μm and the same broadband albedo (86%).

First results of Antarctic sea ice type retrieval from active and passive microwave remote sensing data

Abstract. Polar sea ice is one of the Earth's climate components that has been significantly affected by the recent trend of global warming. While the sea ice area in the Arctic has been decreasing at a rate of about 4 % per decade, the multi-year ice (MYI), also called perennial ice, is decreasing at a faster rate of 10 %–15 % per decade. On the other hand, the sea ice area in the Antarctic region was slowly increasing at a rate of about 1.5 % per decade until 2014, and since then it has fluctuated without a clear trend. However, no data about ice type areas are available from that region, particularly for MYI. Due to differences in the physical and crystalline structural properties of sea ice and snow between the two polar regions, it has become difficult to identify ice types in the Antarctic. Until recently,no satellite retrieval scheme was ready to monitor the distribution and temporal development of Antarctic ice types, particularly MYI, throughout the freezing season and on timescales of several years. In this study, we have adapted a method for retrieving Arctic sea ice types and partial concentrations using microwave satellite observations to fit the Antarctic sea ice conditions. The core of the retrieval method is a mathematical scheme that needs empirical distributions of the microwave brightness temperature and backscatter input parameters for the different ice types. The first circumpolar, long-term time series of Antarctic sea ice types (MYI, first-year ice, and young ice) is being established, and so far covers the years 2013–2021. Qualitative comparison with (a) synthetic aperture radar data, (b) charts of the development stage of the sea ice, and (c) the Antarctic polynya distribution data show that the retrieved ice types, in particular the MYI, are reasonable. Although there are still some shortcomings, the new retrieval allows insight into the interannual evolution and dynamics of Antarctic sea ice types for the first time. The current time series can in principle be extended backwards to start in the year 2002 and can be continued with current and future sensors.

The total mass and spatial–temporal variability of aerial cryosphere over the Tibetan Plateau from 2003 to 2020

Changes in snow, ice, and ecological system over the Tibetan Plateau (TP) are extremely sensitive to local precipitation and radiation budget, which are largely modulated by the atmospheric ice. However, how much ice is there in the atmosphere over the TP and how it is distributed are still unclear. The total mass, spatial distributions, and long-term trends of atmospheric ice over the TP were evaluated by using four sets of satellite retrieval data (Aqua, Terra, the Suomi National Polar-orbiting Partnership (Suomi NPP), and NOAA-20) and ERA5 reanalysis data from 2003 to 2020. Based on the estimations using multiple satellite datasets, we concluded that the total mass of atmospheric ice could be up to 0.26±0.03 Gt over the TP from 2013 to 2020. The spatial distributions of atmospheric ice derived from various datasets were highly consistent. In general, the southwest and northeast areas of the TP were the low-concentration areas (0.05 kg/m2 in average), while the southeast area was the high-concentration area (0.09 kg/m2 in average), and this spatial pattern was most evident in summer. The high values around (0.15 kg/m2) were centered over Linzhi and its surrounding areas. The plentiful water vapor transported by southwest summer monsoon and steep topography jointly led to rapid growth of atmospheric ice in Southeast Tibet, which was the dominant reason for the higher ice concentration in this area.

Assessment of FY-3D SST Data on Typhoon In-Fa Simulation

Sea surface temperature (SST) plays an important role in the typhoon forecast. By comparison, satellite retrieval products can provide more accurate SST data than reanalysis data in typhoon simulations. However, the effect of SST data from Chinese Fengyun-3 (FY-3) satellites on typhoon simulation hasn’t been evaluated yet. In this paper, the impact of FY-3D SST retrieval data with ascending and descending orbits on the forecast of typhoon In-Fa, 2021, is investigated with the Weather Research Forecast (WRF) model. Compared to the control experiments with SST data from Global Data Assimilation System (GDAS) data, the replacement experiments with FY-3D SST data significantly improve the forecast of typhoon central sea level pressure, track, and precipitation. Especially, the SST from the descending orbit satellite data provides the optimal track and intensity forecast, which are verified against the best track data from the Japan Meteorological Agency.