Geostationary Satellite Observations Research Articles

Characteristic Scales of Tropical Convection Based on the Japanese Advanced Himawari-8 Imager Observations

Convective activities play an important role in tropical weather systems. To investigate the characteristic scales of convection, a method combining a principal component (PC) analysis with Fourier decomposition is applied to brightness temperature observations from Advanced Himawari-8 Imager (AHI). Characteristic scales of different modes in tropical convective systems are obtained. The explained variance reduces rapidly from the first to the 60th PC mode by two magnitudes; the horizontal scale decreases from over 2000 km to about 100 km, and the timescale changes from more than 4 days to around 5 h. By a detailed comparison of the first, 20th, 40th, and 60th PC modes, it is found that large-scale (over 2000 km in wavelength) convective activities usually have significant nocturnal enhancement, whereas meso-scale (about 100 km in wavelength) convective activities feature short period and fast change with more intense development in regions of active large-scale convection. This study may be of some importance for the choice of AHI data assimilation cycling interval, the horizontal resolution as well as data thinning for geostationary satellite observations.

Research on the ionospheric diurnal Double-Maxima patterns in Asia-Australian area based on the VTEC observations of BDS geostationary satellites

Ionospheric diurnal double-maxima (DDM) pattern is a special phenomenon of the ionosphere, which shows that the ionospheric electron density/total electron content (TEC) presents a two-peak structure during the daytime. This paper uses the Beidou geostationary (GEO) vertical total electron content (VTEC) data from the MGEX tracking stations in the Asian-Australian area from 2019 to 2020 to study the ionospheric DDM phenomenon. The results show that the occurrence rate of ionospheric DDM is roughly symmetrical about the magnetic equator, with a “W”-shaped distribution. With decreasing latitude, the occurrence of DDM first decreases from 58%/73% to 7%/17% and then increases in the equatorial region to 30%. Minimum occurrence occurs near ± 20°. The occurrence rate of DDM in different latitude regions varies with longitude. There is no significant variation at low-latitudes (magnetic latitude 15°N ∼ 15°S), while in the northern hemisphere mid-latitudes (regions north of magnetic latitude 15°N), the occurrence of DDM first decreases and then increases with longitude in the area of observation. The southern hemisphere’s mid-latitudes (regions south of magnetic latitude 15°S) east of 90°E show an increasing trend with longitude. The two peaks and one valley of DDM mainly occur in 10:00–13:00 LT, 14:00–18:00 LT, and 12:00–15:00 LT, respectively. As the latitude decreases, the occurrence time of the DDM structures tends to be delayed, but there is no significant variation with an increase in longitude. The DDM phenomenon in the southern hemisphere usually occurs earlier than in the northern hemisphere. The occurrence time of the DDM structures in different months shows an inverted “U” shape in the mid-latitudes of the northern hemisphere and a “U” shape in the mid-latitudes of the southern hemisphere. DDM structures appear the latest in the local summer and the earliest in the winter. There is no significant variation in the low-latitude regions.

A climatology of open and closed mesoscale cellular convection over the Southern Ocean derived from Himawari-8 observations

Abstract. Marine atmospheric boundary layer clouds cover vast areas of the Southern Ocean (SO), where they are commonly organized into mesoscale cellular convection (MCC). Using 3 years of Himawari-8 geostationary satellite observations, open and closed MCC structures are identified using a hybrid convolutional neural network. The results of the climatology show that open MCC clouds are roughly uniformly distributed over the SO storm track across midlatitudes, while closed MCC clouds are most predominant in the southeast Indian Ocean, with a second maximum along the storm track. The ocean polar front, derived from ECMWF-ERA5 sea surface temperature gradients, is found to be aligned with the southern boundaries for both MCC types. Along the storm track, both closed and open MCCs are commonly located in post-frontal, cold air masses. The hourly classification of closed MCC reveals a pronounced daily cycle, with a peak occurring late night/early morning. Seasonally, the diurnal cycle of closed MCC is most intense during the summer months (December–February; DJF). Conversely, almost no diurnal cycle is evident for open MCC.

Data assimilation of volcanic aerosol observations using FALL3D+PDAF

Abstract. Modelling atmospheric dispersal of volcanic ash and aerosols is becoming increasingly valuable for assessing the potential impacts of explosive volcanic eruptions on buildings, air quality, and aviation. Management of volcanic risk and reduction of aviation impacts can strongly benefit from quantitative forecasting of volcanic ash. However, an accurate prediction of volcanic aerosol concentrations using numerical modelling relies on proper estimations of multiple model parameters which are prone to errors. Uncertainties in key parameters such as eruption column height and physical properties of particles or meteorological fields represent a major source of error affecting the forecast quality. The availability of near-real-time geostationary satellite observations with high spatial and temporal resolutions provides the opportunity to improve forecasts in an operational context by incorporating observations into numerical models. Specifically, ensemble-based filters aim at converting a prior ensemble of system states into an analysis ensemble by assimilating a set of noisy observations. Previous studies dealing with volcanic ash transport have demonstrated that a significant improvement of forecast skill can be achieved by this approach. In this work, we present a new implementation of an ensemble-based data assimilation (DA) method coupling the FALL3D dispersal model and the Parallel Data Assimilation Framework (PDAF). The FALL3D+PDAF system runs in parallel, supports online-coupled DA, and can be efficiently integrated into operational workflows by exploiting high-performance computing (HPC) resources. Two numerical experiments are considered: (i) a twin experiment using an incomplete dataset of synthetic observations of volcanic ash and (ii) an experiment based on the 2019 Raikoke eruption using real observations of SO2 mass loading. An ensemble-based Kalman filtering technique based on the local ensemble transform Kalman filter (LETKF) is used to assimilate satellite-retrieved data of column mass loading. We show that this procedure may lead to nonphysical solutions and, consequently, conclude that LETKF is not the best approach for the assimilation of volcanic aerosols. However, we find that a truncated state constructed from the LETKF solution approaches the real solution after a few assimilation cycles, yielding a dramatic improvement of forecast quality when compared to simulations without assimilation.

Iterative assimilation of geostationary satellite observations in retrospective meteorological modeling for air quality studies

Clouds impact many aspects of both the physical and chemical atmosphere at many different spatial and temporal scales. Because of this, their accurate representation within numerical weather prediction (NWP) models is vital. In this study, a previously developed assimilation technique for assimilating Geostationary Operational Environmental Satellite (GOES) derived cloud fields into the Weather Research and Forecasting (WRF) meteorological model was tested over the August–September 2013 time period, on a 12-km domain covering the contiguous United States (CONUS). At the same time, additional improvements to the assimilation technique were introduced to account for the model cloud time tendency. The improvements resulted in more consistent cloud fields, while also improving the model surface statistics when compared to the original technique. The results indicate that both implementations of the assimilation technique improve the agreement between the model-predicted and GOES-derived cloud fields, but the additional refinements significantly improve the overall model performance. The daily average percentage increase in the cloud agreement was 6.53% for the original technique, compared to 11.27% for the refined technique. With the improvement in the model cloud fields, the average error in the predicted solar irradiance across the CONUS domain was reduced by 4.4 W m−2 and 24.6 W m−2 for the original and revised assimilation techniques, respectively. The revised assimilation technique also reduced the slight degradation in the surface statistics of wind speed, temperature, and mixing ratio that was created with the original technique. This resulted in surface error statistics that were nearly the same as a control simulation, but with improved model cloud performance.

A Physical-Based Framework for Estimating the Hourly All-Weather Land Surface Temperature by Synchronizing Geostationary Satellite Observations and Land Surface Model Simulations

The high-frequency all-weather land surface temperature (LST) product generated from the thermal-infrared (TIR) observations of the geostationary meteorological satellite is of great significance to study the diurnal variations in the LST and the land surface energy balance. However, the TIR sensor cannot penetrate the clouds and obtain the desired LST under cloudy conditions. In this study, we developed a physical-based framework for generating high-frequency (hourly) all-weather LST data by synchronizing geostationary satellite TIR observations and simulations of the land surface model (LSM). There are three parts in the developed framework. First, the clear-sky LST was retrieved from the Advanced Himawari Imager (AHI) onboard the geostationary satellite Himawari-8 using our newly developed temperature and emissivity separation algorithm. Second, the Advanced Microwave Scanning Radiometer 2 (AMSR2) observations were assimilated into the Noah land surface model with multiple parameterization options (Noah-MP) model to generate the all-weather LST. Finally, the retrieved clear-sky AHI LST and Noah-MP assimilated LST were fused using the Ensemble Kalman filter (EnKF) algorithm. In situ measurements from three networks were collected to evaluate the Noah-MP assimilated LST and EnKF fused LST. The bias/RMSE of the Noah-MP assimilated LST and EnKF fused LST were –0.16/3.01 K and 0.15/2.68 K, respectively, under all-weather conditions. Compared to the Noah-MP free-run LST, the absolute values of the bias were reduced by 0.64 K and 0.68 K for the Noah-MP assimilated LST and EnKF fused LST, while the RMSEs were reduced by 0.33 K and 0.65 K, respectively. In addition, the spatial distribution of EnKF fused LST was in good agreement with the retrieved clear-sky AHI LST. The proposed framework in this study was demonstrated to be capable of obtaining accurate high-frequency (hourly) all-weather LST data.

Mapping Diurnal Variability of the Wintertime Pearl River Plume Front from Himawari-8 Geostationary Satellite Observations

The spatial pattern of the wintertime Pearl River plume front (PRPF), and its variability on diurnal and spring-neap time scales are characterized from the geostationary meteorological Himawari-8 satellite, taking advantage of the satellite’s unique 10-minutely sea surface temperature sequential images. Our findings suggest that the PRPF in winter consists of three subfronts: the northern one north of 22° N 20′, the southern one south of 21° N 40′, and the middle one between 22° N 20′ and 21° N 40′. The time-varying trend of the frontal intensity generally exhibits a strong-weak-strong pattern, with the weakest plume front occurring at about 06:00 UTC, which is closely associated with net surface heat flux over the region. The comparison in frontal variability between the spring and neap tides shows that the plume front during the spring tide generally tends to be more diffuse for the frontal probability, move further offshore for the frontal position, and be weaker for the frontal intensity than those found during the neap tide. These great differences largely depend on the tidally induced stronger turbulent mixing during the spring tide while the wind stress only plays a secondary role in the process. To best of our knowledge, the distinct diurnal variations in PRPF with wide coverage are observed for the first time. This study demonstrates that the Himawari-8 geostationary satellite has great potential in characterizing high-frequency surface thermal fronts in considerable detail.

Overview of the performance of satellite fire products in China: Uncertainties and challenges

Satellite fire observations provide an essential constraint for the estimation of global biomass burning emissions. In this study, we present a comprehensive insight into the performance of the common satellite fire products in eastern China. Despite consistent spatial patterns, both polar-orbiting and geostationary satellite observations have large omission errors for the agricultural burning fires. Owing to a coarse resolution of 2 km, approximately 90% of the concurrent 375 m Visible infrared Imaging Radiometer (VIIRS) fires are not detected in Himawari-8 products. Nevertheless, the total amount of daily Himawari-8 fires is much higher than those of VIIRS and 1 km Moderate resolution imaging spectroradiometer (MODIS). The peak time of diurnal fire counts in eastern China has obvious seasonal variations, some of which are missed by polar-orbiting satellite detection. Validation by 3 m PlanetScope images shows that VIIRS and MODIS have a very high accuracy in detecting crop straw burning fires. However, Himawari-8 fires have obvious false alarms due largely to their algorithm defects. Also, the coarse resolution of Himawari-8 tends to make fire detection more likely to be obscured by dense smoke. The unmanned aerial vehicle (UAV) supervisions reveal prevalent small agricultural fires (<0.5 × 104 m2) in the extensive croplands that are not detected in the common satellite fires. As control measures get more stringent, spatial-temporal patterns as well as the scales of biomass burning activities in China have undergone dramatic changes. Considering the crucial role of satellite fires in estimating biomass burning emissions, it is necessary to improve satellite fire detection with more advanced observations and retrieval methods.

Fusing Geostationary Satellite Observations with Harmonized Landsat-8 and Sentinel-2 Time Series for Monitoring Field-Scale Land Surface Phenology

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover.

Analog Ensemble Methods for Improving Satellite-Based Intensity Estimates of Tropical Cyclones

Accurate, reliable estimates of tropical cyclone (TC) intensity are a crucial element in the warning and forecast process worldwide, and for the better part of 50 years, estimates made from geostationary satellite observations have been indispensable to forecasters for this purpose. One such method, the Advanced Dvorak Technique (ADT), was used to develop analog ensemble (AnEn) techniques that provide more precise estimates of TC intensity with instant access to information on the reliability of the estimate. The resulting methods, ADT-AnEn and ADT-based Error Analog Ensemble (ADTE-AnEn), were trained and tested using seventeen years of historical ADT intensity estimates using k-fold cross-validation with 10 folds. Using only two predictors, ADT-estimated current intensity (maximum wind speed) and TC center latitude, both AnEn techniques produced significant reductions in mean absolute error and bias for all TC intensity classes in the North Atlantic and for most intensity classes in the Eastern Pacific. The ADTE-AnEn performed better for extreme intensities in both basins (significantly so in the Eastern Pacific) and will be incorporated in the University of Wisconsin’s Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) workflow for further testing during operations in 2021.

Enhance Low Level Temperature and Moisture Profiles Through Combining NUCAPS, ABI Observations, and RTMA Analysis

AbstractThermodynamic information from low levels in the atmosphere is crucial for operational weather forecasts and meteorological researchers. The NOAA Unique Combined Atmospheric Processing System (NUCAPS) sounding products have been proven beneficial to fill the data gap between synoptic radiosonde observations (RAOBs). However, compared with the upper troposphere, the accuracy of NUCAPS soundings in the low levels still needs improvement. In this study, a deep neural network (DNN) is applied to fuse multiple data sources to enhance the NUCAPS temperature and moisture profiles in the lower atmosphere. The network is developed by combining satellite observations, including NUCAPS sounding retrievals and high resolution geostationary satellite observations from the Advanced Baseline Imager, and surface analysis from the Real‐Time Mesoscale Analysis (RTMA) as inputs, while collocated soundings from ECMWF re‐analysis version 5 are used as the benchmark for the training. The performance of the model is evaluated by using the independent testing data set, data from a different year, as well as collocated RAOBs, showing improvement to the temperature and moisture profiles by reducing the root‐mean‐squared‐error (RMSE) by more than 30% in the lower atmosphere (from 700 hPa to surface) in both clear sky and partially cloudy conditions. A convective event from June 18, 2017 is presented to illustrate the application of the enhanced low level soundings on high impact weather events. The enhanced soundings from fused data capture the large surface‐based convective available potential energy structures in the preconvection environment, which is very useful for severe storm nowcasting and forecasting applications.

Post-processing correction method for surface solar irradiance forecast data from the numerical weather model using geostationary satellite observation data

The numerical weather prediction (NWP) model still produces forecast errors. In this study, a correction method for the data from the NWP model is developed to obtain more skillful forecast data. The proposed method fuses information about surface global horizontal solar irradiance (GHI) from the NWP model and the satellite observations and gives corrected forecast data over the domain of the NWP model. There are three steps in the correction method algorithm. First, grid cells of the NWP model are grouped into clusters based on atmospheric conditions and regions. Second, cumulative distribution functions of the GHI from both datasets are inferred for each cluster separately. Last, correction functions are built using the statistical transformation. The correction effect is evaluated using satellite observation data as the reference. These analyses are performed using data for Japan. The correction function generally improves the forecast quality measured with the mean bias error (MBE), mean absolute error (MAE), and root mean square error (RMSE). When the forecast quality is measured with MBE, the improvement lasts for two forecast days, but when measured with MAE and RMSE, the improvement lasts for only one forecast day. The correction effects have seasonal and regional characteristics. The correction is effective from spring to autumn and the correction effect weakens in winter. Forecast quality at all ground observation stations in Japan, except for those in northern Japan, is improved owing to the correction. In contrast, the forecast quality at stations in northern Japan tends to be degraded.

The possible linkage of satellite based cloud microphysical parameters (CMP) to the Indian Summer Monsoon (ISM) active and break phase

The unpredictability of Indian Summer Monsoon (ISM) remains an area of major challenge for operational forecasters. The distribution of active and break days in a monsoon season, though of utmost importance is very difficult to predict. In this regard, the geostationary satellite observations from Indian National Satellite System-3D (INSAT-3D) satellite over the Indian region provide significant insights into the propagation of ISM. In this manuscript, the correlation of satellite derived cloud microphysical parameters (CMP) to the strength and development of the ISM is studied objectively for the years 2017 and 2018. In particular, the association of the CMP to the active and break spell has also been investigated. It is found that the Cloud Particle Effective radius (CER) and the Cloud Optical Thickness (COT) bears a maximum correlation of 0.67 and 0.65 with the daily average rainfall at 1 day lag. The histogram analysis with active/break conditions show that CER exhibit a bimodal distribution (~27 µm and ~36 µm) during an active spell, while in case of break phase, smaller cloud droplets ranging in size between 15 and 25 µm is prevalent. The findings of the study will contribute towards better understanding and prediction of active and break spells of ISM.

Top-down estimates of anthropogenic VOC emissions in South Korea using formaldehyde vertical column densities from aircraft during the KORUS-AQ campaign

Nonmethane volatile organic compounds (NMVOCs) result in ozone and aerosol production that adversely affects the environment and human health. For modeling purposes, anthropogenic NMVOC emissions have been typically compiled using the “bottom-up” approach. To minimize uncertainties of the bottom-up emission inventory, “top-down” NMVOC emissions can be estimated using formaldehyde (HCHO) observations. In this study, HCHO vertical column densities (VCDs) obtained from the Geostationary Trace gas and Aerosol Sensor Optimization spectrometer during the Korea–United States Air Quality campaign were used to constrain anthropogenic volatile organic compound (AVOC) emissions in South Korea. Estimated top-down AVOC emissions differed from those of the up-to-date bottom-up inventory over major anthropogenic source regions by factors of 1.0 ± 0.4 to 6.9 ± 3.9. Our evaluation using a 3D chemical transport model indicates that simulated HCHO mixing ratios using the top-down estimates were in better agreement with observations onboard the DC-8 aircraft during the campaign relative to those with the bottom-up emission, showing a decrease in model bias from –25% to –13%. The top-down analysis used in this study, however, has some limitations related to the use of HCHO yields, background HCHO columns, and AVOC speciation in the bottom-up inventory, resulting in uncertainties in the AVOC emission estimates. Our attempt to constrain diurnal variations of the AVOC emissions using the aircraft HCHO VCDs was compromised by infrequent aircraft observations over the same source regions. These limitations can be overcome with geostationary satellite observations by providing hourly HCHO VCDs.

Time Evolution of Storms Producing Terrestrial Gamma-Ray Flashes Using ERA5 Reanalysis Data, GPS, Lightning and Geostationary Satellite Observations

In this article, we report the first investigation over time of the atmospheric conditions around terrestrial gamma-ray flash (TGF) occurrences, using GPS sensors in combination with geostationary satellite observations and ERA5 reanalysis data. The goal is to understand which characteristics are favorable to the development of these events and to investigate if any precursor signals can be expected. A total of 9 TGFs, occurring at a distance lower than 45 km from a GPS sensor, were analyzed and two of them are shown here as an example analysis. Moreover, the lightning activity, collected by the World Wide Lightning Location Network (WWLLN), was used in order to identify any links and correlations with TGF occurrence and precipitable water vapor (PWV) trends. The combined use of GPS and the stroke rate trends identified, for all cases, a recurring pattern in which an increase in PWV is observed on a timescale of about two hours before the TGF occurrence that can be placed within the lightning peak. The temporal relation between the PWV trend and TGF occurrence is strictly related to the position of GPS sensors in relation to TGF coordinates. The life cycle of these storms observed by geostationary sensors described TGF-producing clouds as intense with a wide range of extensions and, in all cases, the TGF is located at the edge of the convective cell. Furthermore, the satellite data provide an added value in associating the GPS water vapor trend to the convective cell generating the TGF. The investigation with ERA5 reanalysis data showed that TGFs mainly occur in convective environments with unexceptional values with respect to the monthly average value of parameters measured at the same location. Moreover, the analysis showed the strong potential of the use of GPS data for the troposphere characterization in areas with complex territorial morphologies. This study provides indications on the dynamics of con-vective systems linked to TGFs and will certainly help refine our understanding of their production, as well as highlighting a potential approach through the use of GPS data to explore the lightning activity trend and TGF occurrences.

New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests

Assessing the seasonal patterns of the Amazon rainforests has been difficult because of the paucity of ground observations and persistent cloud cover over these forests obscuring optical remote sensing observations. Here, we use data from a new generation of geostationary satellites that carry the Advanced Baseline Imager (ABI) to study the Amazon canopy. ABI is similar to the widely used polar orbiting sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), but provides observations every 10–15 min. Our analysis of NDVI data collected over the Amazon during 2018–19 shows that ABI provides 21–35 times more cloud-free observations in a month than MODIS. The analyses show statistically significant changes in seasonality over 85% of Amazon forest pixels, an area about three times greater than previously reported using MODIS data. Though additional work is needed in converting the observed changes in seasonality into meaningful changes in canopy dynamics, our results highlight the potential of the new generation geostationary satellites to help us better understand tropical ecosystems, which has been a challenge with only polar orbiting satellites.

Estimation of Field-Level NOx Emissions from Crop Residue Burning Using Remote Sensing Data: A Case Study in Hubei, China

Crop residue burning is the major biomass burning activity in China, strongly influencing the regional air quality and climate. As the cultivation pattern in China is rather scattered and intricate, it is a challenge to derive an accurate emission inventory for crop residue burning. In this study, we proposed a remote sensing-based method to estimate nitrogen oxide (NOx) emissions related to crop residue burning at the field level over Hubei, China. The new method considers differences in emission factors and the spatial distribution for different crop types. Fire radiative power (FRP) derived from moderate-resolution imaging spectroradiometer (MODIS) was used to quantify NOx emissions related to agricultural biomass combustion. The spatial distribution of different crops classified by multisource remote sensing data was used as an a priori constraint. We derived a new NOx emission database for Hubei from 2014 to 2016 with spatial resolution of 1 × 1 km. Significant seasonal patterns were observed from the NOx emission database. Peak NOx emission occurring in October was related to the residue burning in late autumn harvesting. Another peak was observed between January and April, which was due to the frequent burning of stubble before spring sowing. Our results were validated by comparing our emission inventory with geostationary satellite observations, previous studies, global fire emission database (GFED), NO2 vertical column densities (VCDs) from ozone monitoring instrument (OMI) satellite observations, and measurements from environmental monitoring stations. The comparisons showed NOx emission from GFED database was 47% lower than ours, while the evaluations from most of the statistical studies were significantly higher than our results. The discrepancies were likely related to the differences of methodology and data sources. The spatiotemporal variations of NOx emission in this study showed strong correlations with NO2 VCDs, which agreed well with geostationary satellite observations. A reasonable correlation between in situ NO2 observations and our results in agricultural regions demonstrated that our method is reliable. We believe that the new NOx emission database for crop residue burning derived in this study can potentially improve the understanding of pollution sources and can provide additional information for the design of pollution control measures.

Variational Based Estimation of Sea Surface Temperature from Split-Window Observations of INSAT-3D/3DR Imager

Infrared (IR) radiometers from geostationary (GEO) satellites have an advantage over low-earth orbiting (LEO) satellites as they provide continuous observations to monitor the diurnal variations in the sea surface temperature (SST), typically better than 30-minute interval. However, GEO satellite observations suffer from significant diurnal and seasonal biases arising due to varying sun-earth-satellite geometry, leading to biases in SST estimates from conventional non-linear regression-based algorithms (NLSST). The midnight calibration issue occurring in GEO sensors poses a different challenge altogether. To mitigate these issues, we propose SST estimation from split-window IR observations of INSAT-3D and 3DR Imagers using One-Dimensional Variational (1DVAR) scheme. Prior to SST estimation, the bias correction in Imager observations is carried out using cumulative density function (CDF) matching. Then NLSST and 1DVAR algorithms were applied on six months of INSAT-3D/3DR observations to retrieve the SST. For the assessment of the developed algorithms, the retrieved SST was validated against in-situ SST measurements available from in-situ SST Quality Monitor (iQuam) for the study period. The quantitative assessment confirms the superiority of the 1DVAR technique over the NLSST algorithm. However, both the schemes under-estimate the SST as compared to in-situ SST, which may be primarily due to the differences in the retrieved skin SST versus bulk in-situ SST. The 1DVAR scheme gives similar accuracy of SST for both INSAT-3D and 3DR with a bias of −0.36 K and standard deviation (Std) of 0.63 K. However, the NLSST algorithm provides slightly less accurate SST with bias (Std) of −0.18 K (0.87 K) for INSAT-3DR and −0.27 K (0.95 K) for INSAT-3D. Both the NLSST and 1DVAR algorithms are capable of producing the accurate thermal gradients from the retrieved SST as compared to the gradients calculated from daily Multiscale Ultrahigh Resolution (MUR) level-4 analysis SST acquired from Group for High-Resolution Sea Surface Temperature (GHRSST). Based on these spatial gradients, thermal fronts can be generated that are very useful for predicting potential fishery zones (PFZ), which is available from GEO satellites, INSAT-3D/3DR, in near real-time at 15-minute intervals. Results from the proposed 1DVAR and NLSST algorithms suggest a marked improvement in the SST estimates with reduced diurnal/seasonal biases as compared to the operational NLSST algorithm.

Prediction of Leaf Wetness Duration Using Geostationary Satellite Observations and Machine Learning Algorithms

Leaf wetness duration (LWD) and plant diseases are strongly associated with each other. Therefore, LWD is a critical ecological variable for plant disease risk assessment. However, LWD is rarely used in the analysis of plant disease epidemiology and risk assessment because it is a non-standard meteorological variable. The application of satellite observations may facilitate the prediction of LWD as they may represent important related parameters and are particularly useful for meteorologically ungauged locations. In this study, the applicability of geostationary satellite observations for LWD prediction was investigated. GEO-KOMPSAT-2A satellite observations were used as inputs and six machine learning (ML) algorithms were employed to arrive at hourly LW predictions. The performances of these models were compared with that of a physical model through systematic evaluation. Results indicated that the LWD could be predicted using satellite observations and ML. A random forest model exhibited larger accuracy (0.82) than that of the physical model (0.79) in leaf wetness prediction. The performance of the proposed approach was comparable to that of the physical model in predicting LWD. Overall, the artificial intelligence (AI) models exhibited good performances in predicting LWD in South Korea.

Inverse modeling of fire emissions constrained by smoke plume transport using HYSPLIT dispersion model and geostationary satellite observations

Abstract. Smoke forecasts have been challenged by high uncertainty in fire emission estimates. We develop an inverse modeling system, the HYSPLIT-based Emissions Inverse Modeling System for wildfires (or HEIMS-fire), that estimates wildfire emissions from the transport and dispersion of smoke plumes as measured by satellite observations. A cost function quantifies the differences between model predictions and satellite measurements, weighted by their uncertainties. The system then minimizes this cost function by adjusting smoke sources until wildfire smoke emission estimates agree well with satellite observations. Based on HYSPLIT and Geostationary Operational Environmental Satellite (GOES) Aerosol/Smoke Product (GASP), the system resolves smoke source strength as a function of time and vertical level. Using a wildfire event that took place in the southeastern United States during November 2016, we tested the system's performance and its sensitivity to varying configurations of modeling options, including vertical allocation of emissions and spatial and temporal coverage of constraining satellite observations. Compared with currently operational BlueSky emission predictions, emission estimates from this inverse modeling system outperform in both reanalysis (21 out of 21 d; −27 % average root-mean-square-error change) and hindcast modes (29 out of 38 d; −6 % average root-mean-square-error change) compared with satellite observed smoke mass loadings.