Polar-orbiting Satellite Data Research Articles

Studies on regional ozone formation sensitivities and transport with higher spatiotemporal resolutions in a stereoscopic dimension: GEMS and vertical observations

Currently, ozone pollution is one of the key environmental concerns, and to provide more effective strategies for ozone control, this paper proposes the use of the Geostationary Environmental Monitoring Spectrometer (GEMS) in conjunction with MAX-DOAS for observations and ozone sensitivity analysis. Compared to traditional polar-orbiting satellites, GEMS, launched by South Korea in 2019, offers superior spatiotemporal resolution. However, there is a lack of ground validation for GEMS data related to ozone and its precursors. In this study, we conducted validation in the Jing-Jin-Ji (Beijing-Tianjin-Hebei) region using two typical MAX-DOAS sites and the China National Environmental Monitoring Center (CNEMC) sites, confirming the accuracy of GEMS data. Analysis of the seasonal horizontal distribution results from GEMS and vertical distribution results from MAX-DOAS in 2022 reveals that the concentrations of NO2, HCHO, O3, and ozone sensitivity not only display seasonality and regional variations but also exhibit distinct characteristics at different altitudes and times, influenced by local climatic conditions, human activities, and topography. When formulating ozone control policies, it becomes imperative to consider factors such as time, regional characteristics, and seasonality. Comparisons between daily results from TROPOMI and GEMS indicate that polar-orbiting satellites may overlook high-pollution periods within a single day, and their measurements may not provide a comprehensive representation of an entire day's data. Consequently, relying solely on polar-orbiting satellite data for shaping ozone prevention and control policies may inadvertently encourage ozone formation during specific time periods, rendering these policies ineffective. Therefore, leveraging GEMS is essential for the development of comprehensive, around-the-clock ozone control policies. Finally, we used monitoring data from Beijing on October 6, 2022, to illustrate how the combination of GEMS and MAX-DOAS enables three-dimensional monitoring of pollutant transport processes on an hourly scale.

Public Health Benefits From Improved Identification of Severe Air Pollution Events With Geostationary Satellite Data.

Despite improvements in ambient air quality in the US in recent decades, many people still experience unhealthy levels of pollution. At present, national-level alert-day identification relies predominately on surface monitor networks and forecasters. Satellite-based estimates of surface air quality have rapidly advanced and have the capability to inform exposure-reducing actions to protect public health. At present, we lack a robust framework to quantify public health benefits of these advances in applications of satellite-based atmospheric composition data. Here, we assess possible health benefits of using geostationary satellite data, over polar orbiting satellite data, for identifying particulate air quality alert days (24hr PM2.5>35μgm-3) in 2020. We find the more extensive spatiotemporal coverage of geostationary satellite data leads to a 60% increase in identification of person-alerts (alert days×population) in 2020 over polar-orbiting satellite data. We apply pre-existing estimates of PM2.5 exposure reduction by individual behavior modification and find these additional person-alerts may lead to 1,200 (800-1,500) or 54% more averted PM2.5-attributable premature deaths per year, if geostationary, instead of polar orbiting, satellite data alone are used to identify alert days. These health benefits have an associated economic value of 13 (8.8-17) billion dollars ($2019) per year. Our results highlight one of many potential applications of atmospheric composition data from geostationary satellites for improving public health. Identifying these applications has important implications for guiding use of current satellite data and planning future geostationary satellite missions.

Retrieval of oceanic chlorophyll concentration from GOES-R Advanced Baseline Imager using deep learning

Data of chlorophyll (Chl-a) concentration obtained from polar-orbiting ocean color satellite sensors are subject to major spatial and temporal gaps in data coverage owing to the intrinsically low sampling frequency that coincides with adverse conditions such as cloud obstruction. Such gaps can be minimized with geostationary satellites owing to their higher scanning frequency over a given region, allowing more sampling opportunities as clouds move. However, geostationary ocean color missions have not been available for most regions of the globe. The Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite R series (GOES-R) is a potential alternative for the U.S. owing to improved signal-to-noise ratio compared with its predecessors. Nonetheless, retrieving [Chl-a] from ABI measurements is challenging; the number, placement, and width of its spectral bands are suboptimal and an “ocean-grade” atmospheric correction scheme is lacking. Here we describe the construction of a deep learning-based algorithm to derive [Chl-a] from GOES-16 ABI L1B radiance and ancillary data without an explicit atmospheric correction algorithm. A large (∼22 million examples) training dataset of paired ABI observations and [Chl-a] estimates derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) were used to train, test and validate a 10-layer deep neural network model to retrieve [Chl-a] from ABI data. Preliminary results show that without atmospheric correction but with the aid of deep learning, it is possible to detect open ocean features with strong contrast such as across eddies and fronts, but there are still significant issues in coastal waters and occasionally in the open oceans. In general, the ABI-derived [Chl-a] agrees reasonably well with VIIRS-derived values in the open ocean during 3–5 h of the day. The location of oceanographic features such as fronts and eddies in the ABI-derived [Chl-a] maps are also coincident with those obtained from traditional ocean color and sea surface temperature retrievals. The findings of this study demonstrate that deep learning is a powerful tool for capturing intricate patterns that are too subtle for traditional algorithms developed by humans, Furthermore, the utilization of GOES-R ABI has the potential to complement polar-orbiting satellite data and mitigate data gaps.

Global hourly, 5 km, all-sky land surface temperature data from 2011 to 2021 based on integrating geostationary and polar-orbiting satellite data

Abstract. Land surface temperature (LST) plays a dominant role in the surface energy budget (SEB) and hydrological cycling. Thermal infrared (TIR) remote sensing is the primary method of estimating LST globally. However, cloud cover leaves numerous data gaps in satellite LST products, which seriously restricts their applications. Efforts have been made to produce gap-free LST products from polar-orbiting satellites (e.g., Terra and Aqua); however, satellite data from limited overpasses are not suitable for characterizing the diurnal temperature cycle (DTC), which is directly related to heat waves, plant water stress, and soil moisture. Considering the high temporal variability in LST and the importance of the DTC, we refined the SEB-based cloudy-sky LST recovery method by improving its feasibility and efficiency and produced a global hourly, 5 km, all-sky land surface temperature (GHA-LST) dataset from 2011 to 2021. The GHA-LST product was generated using TIR LST products from geostationary and polar-orbiting satellite data from the Copernicus Global Land Service (CGLS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). Based on ground measurements at the 201 global sites from the Surface Radiation Budget (SURFRAD), Baseline Surface Radiation Network (BSRN), Fluxnet, AmeriFlux, Heihe River basin (HRB), and Tibetan Plateau (TP) networks, the overall root-mean-square error (RMSE) of the hourly GHA-LST product was 3.31 K, with a bias of −0.57 K and R2 of 0.95. Thus, this product was more accurate than the clear-sky CGLS and MODIS MYD21C1 LST samples. The RMSE value of the daily mean LST was 1.76 K. Validation results at individual sites indicate that the GHA-LST dataset has relatively larger RMSEs for high-elevation regions, which can be attributed to high surface heterogeneity and input data uncertainty. Temporal and spatial analyses suggested that GHA-LST has satisfactory spatiotemporal continuity and reasonable variation and matches the reference data well at hourly and daily scales. Furthermore, the regional comparison of GHA-LST with other gap-free hourly datasets (ERA5 and Global Land Data Assimilation System, GLDAS) demonstrated that GHA-LST can provide more spatial texture information. The monthly anomaly analysis suggests that GHA-LST couples well with global surface air temperature datasets and other LST datasets at daily mean and minimum temperature scales, whereas the maximum temperature and diurnal temperature range of LST and air temperature (AT) have different anomalous magnitudes. The GHA-LST dataset is the first global gap-free LST dataset at an hourly, 5 km scale with high accuracy, and it can be used to estimate global evapotranspiration, monitor extreme weather, and advance meteorological forecasting models. GHA-LST is freely available at https://doi.org/10.5281/zenodo.7487284 (Jia et al., 2022b) and http://glass.umd.edu/allsky_LST/GHA-LST (last access: 10 February 2023; Jia et al., 2022c).

Toward the Estimation of All-Weather Daytime Downward Longwave Radiation over the Tibetan Plateau

Downward longwave radiation (DLR) is a critical parameter for radiation balance, energy budget, and water cycle studies at regional and global scales. Accurate estimation of the all-weather DLR with a high temporal resolution is important for the estimation of the surface net radiation and evapotranspiration. However, most DLR products involve instantaneous DLR estimates based on polar orbiting satellite data under clear-sky conditions. To obtain an in-depth understanding of the performances of different models in the estimation of DLR over the Tibetan Plateau, which is a focus area of climate change study, this study tests eight methods for clear-sky conditions and six methods for cloudy conditions based on ground-measured data. It is found that the Dilley and O’Brien model and the Lhomme model are most suitable for clear-sky conditions and cloudy conditions, respectively. For the Dilley and O’Brien model, the average root mean square error (RMSE) of DLR under clear-sky conditions is approximately 22.5 W/m2 for nine ground sites; for the Lhomme model, the average RMSE is approximately 23.2 W/m2. Based on the estimated cloud fraction and meteorological data provided by the China Land Surface Data Assimilation System (CLDAS), hourly all-weather daytime DLR with a 0.0625° resolution over the Tibetan Plateau is estimated. Results demonstrate that the average RMSE of the estimated hourly all-weather DLR is approximately 26.4 W/m2. With the combined all-weather DLR model, the hourly all-weather daytime DLR dataset with a 0.0625° resolution from 2008 to 2016 over the Tibetan Plateau is generated. This dataset can contribute to studies associated with the radiation balance and energy budget, water cycle, and climate change over the Tibetan Plateau.

Cloudy-sky land surface temperature from VIIRS and MODIS satellite data using a surface energy balance-based method

Land surface temperature (LST) has been effectively retrieved from thermal infrared (TIR) satellite measurements under clear-sky conditions. However, TIR satellite data are often severely contaminated by clouds, which cause spatiotemporal discontinuities and low retrieval accuracy in the LST products. Several solutions have been proposed to fill the “gaps”; however, a majority of these possess constraints. For example, fusion methods with microwave data suffer from coarse spatial resolution and diverse land cover types while spatial-temporal interpolation methods neglect cloudy cooling effects. We developed a novel method to estimate cloudy-sky LST from polar-orbiting satellite data based on the surface energy balance (SEB) principle. First, the hypothetical clear-sky LST of missing or likely cloud-contaminated pixels was reconstructed by assimilating high-quality satellite retrievals into a time-evolving model built from reanalysis data using a Kalman filter data assimilation algorithm. Second, clear-sky LST was hypothetically corrected by accounting for cloud cooling based on SEB theory. The proposed method was applied to Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, and further validated using ground measurements of fourteen sites from SURFRAD, BSRN, and AmeriFlux in 2013. VIIRS LST recovered from cloud gaps exhibited a root mean square error (RMSE) of 3.54 K, a bias of −0.36 K, R2 of 0.94, and sample size (N) of 2411, comparable to the accuracy of clear-sky LST products and cloudy-sky LST estimation from MODIS (RMSE of 3.69 K, bias of −0.45 K, R2 of 0.93, and N of 2398). Thus, the proposed method performs well across different sensors, seasons, and land cover types. The abnormal retrieval values caused by cloud contamination were also corrected in the proposed method. The overall accuracy was better than the downscaled cloudy-sky LST retrieved from passive microwave (PMW) observations and former SEB-based cloudy-sky LST estimation methods. Validation using time-series measurements showed that the all-sky LST time series, including both clear- and cloudy-sky retrievals, can capture realistic variability without sudden abruptions or discontinuities. RMSE values for the all-sky LST varied from 2.54 to 4.15 K at the fourteen sites. Spatially continuous LST maps over the Contiguous United States were compared with corresponding maps from PMW data in the winter and summer of 2018, exhibiting similar spatial patterns but with additional spatial details. Moreover, sensitivity analysis suggested that the reconstruction of clear-sky LST dominantly impacts the accuracy of cloudy-sky LST estimation. The proposed method can be potentially implemented in similar satellite sensors for global real-time production.

Estimation of Land Surface Incident Shortwave Radiation From Geostationary Advanced Himawari Imager and Advanced Baseline Imager Observations Using an Optimization Method

Surface incident shortwave radiation (ISR) is an important component of the surface radiation budget. We refined the optimization method developed for polar-orbiting satellite data [1] and applied it to estimate ISR from the new generation geostationary Advanced Himawari Imager (AHI) onboard the Himawari-8/9 satellite and Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite-R Series. Validation of the AHI ISR estimation at 2-km resolution showed an R² of 0.93, bias of 0.52 W/m², and RMSE of 106.52 W/m² for instantaneous estimates; an R² of 0.95, bias of -0.12 W/m², and RMSE of 22.49 W/m² for daily mean ISR; and a bias of -0.18 W/m² and RMSE of 7.72 W/m² for monthly mean ISR. Validation of the ABI ISR at 2-km spatial resolution showed an R² value of 0.93, bias of 8.71 W/m², and RMSE of 102.30 W/m² for instantaneous estimates; an R² of 0.95, bias of -2.38 W/m², and RMSE of 27.17 W/m² for daily mean ISR; and a bias of 1.40 W/m² and RMSE of 14.75 W/m² for monthly mean ISR. Our study also demonstrated that AHI and ABI observations have realized much better estimations for hourly and diurnal ISR than previous polar-orbiting satellite data because of their higher frequency of sampling on the atmospheric conditions.

Consistent retrieval of multiple parameters from GOES-R top of atmosphere reflectance data

ABSTRACT Current remote sensing products are mainly generated from polar-orbiting satellite data using parameter-specific algorithms. These products lack physical consistency and cannot accurately characterize intra-day variations of parameters, such as the fraction of absorbed photosynthetically active radiation (FAPAR) and surface albedo. In this study, a multi-parameter consistent retrieval method is proposed to simultaneously retrieve aerosol optical depth (AOD), leaf area index (LAI), photosynthetically active radiation (PAR), FAPAR, surface albedo, and incident shortwave radiation (ISR) from top of atmosphere (TOA) reflectance data acquired by the Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite-R series (GOES-R). The retrieved parameter values were evaluated through comparisons with corresponding Moderate Resolution Imaging Spectroradiometer (MODIS), the second version of the Geoland2 (GEOV2), and GOES-R products and ground measurements over five surface radiation budget network (SURFRAD) sites with different vegetation types. The results demonstrate that the retrieved AOD, PAR, ISR, and surface albedo values have consistent intra-day variations with the ground measurements, and the retrieved parameter values achieve good performance against the ground measurements for all the five SURFRAD sites. The root mean square errors of the retrieved AOD, shortwave albedo, ISR, and PAR values against the ground measurements are 0.071, 0.032, 50.943 W m–2, and 27.975 W m–2, respectively.

ОПРЕДЕЛЕНИЕ ПАРАМЕТРОВ ОБЛАЧНОГО ПОКРОВА СИСТЕМАМИ АВТОМАТИЧЕСКОЙ ОБРАБОТКИ СПУТНИКОВЫХ ДАННЫХ

The paper discusses the results of comparing cloud cover properties determined by using polar orbiting satellite data (AVHRR/NOAA and MSU-MR/Meteor-M No. 2) for the European territory of Russia and Western Siberia. The cloud characteristics were computed by two threshold techniques: Complex Threshold Technique (CTT) (developed at the European Centre of the State Research Center ‗Planeta‘) and Cloud Cover Detection Technique (CCDT) (developed at the Siberian Centre of ‗Planeta‘). Pixel-by-pixel comparison was performed for very close in time satellite observations, and it showed that in spite of technical similarity of the two radiometers and little difference between both techniques used for the classifications, the results were not the same. The quality of the MSU-MR classification is significantly worse than that of the two AVHRR classifications. In fact, the MSU-MR derivation of cloud parameters fails in optically thin cirrus and altocumulus clouds, thus underestimating the cloud top height for multilayered clouds. As a result, the cloud top is found to be lower, warmer and less iced in comparison with both AVHRR estimates, regardless of the region and other conditions; on the contrary, the cloud top of low and middle clouds appears to be colder, higher and more iced according to MSU-MR data. The MSU-MR cloud mask is strongly underestimated at night during the cold period of the year. The CTT and CCDT‘s cloud top height, temperature and water phase retrieved by AVHRR data are quite close for both regions.

A Contextual and Multitemporal Active-Fire Detection Algorithm Based on FengYun-2G S-VISSR Data

Wildfires are one of the most destructive disasters on the planet. They also significantly impact the land surface. Satellite data have been widely used to detect the outbreak and monitor the expansion of fire incidents for damage assessment and disaster management. Polar-orbiting satellite data have been used for several decades but data from geostationary satellites, which can provide observations with a high temporal resolution, have received much less attention. This paper utilizes data from FengYun-2G, a Chinese geostationary satellite, to detect wildfires in two selected research regions in January 2016. The detection algorithm systemizes image-based analysis to filter out obvious nonfire pixels and temporal analysis to confirm the true detections. Fire detection is based on comparisons between predicted and observed values. The results show that the proposed method has some advantages compared with the use of polar-orbiting satellite data, including early detection and continuous observation. The validation work is conducted based on the collection 6.1 Global Monthly Fire Location Product generated from fire detections by Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. The average accuracy within the target time is 56%, while the omission error rate is over 78%. In detail, the algorithm has a lower omission error rate in Australia while it fails in detecting most of the fire pixels in India. The dominance of small fire incidents, as well as low spatial resolution greatly limit the detection ability. Many small fires were beyond the ability of Stretched Visible and Infrared Spin Scan Radiometer (S-VISSR) data when no significant fire characteristics could be captured. Future development of the algorithm will focus on improving the results by enhancing the adaption to different regions, as well as, including multisource data sets.

Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China

Satellite-derived aerosol optical depths (AODs) have been widely used to estimate surface fine particulate matter (PM2.5) concentrations over areas that do not have PM2.5 monitoring sites. To date, most studies have focused on estimating daily PM2.5 concentrations using polar-orbiting satellite data (e.g., from the Moderate Resolution Imaging Spectroradiometer), which are inadequate for understanding the evolution of PM2.5 distributions. This study estimates hourly PM2.5 concentrations from Himawari AOD and meteorological parameters using an ensemble learning model. We analyzed the spatial agglomeration patterns of the estimated PM2.5 concentrations over central East China. The estimated PM2.5 concentrations agree well with ground-based data with an overall cross-validated coefficient of determination of 0.86 and a root-mean-square error of 17.3 μg m−3. Satellite-estimated PM2.5 concentrations over central East China display a north-to-south decreasing gradient with the highest concentration in winter and the lowest concentration in summer. Diurnally, concentrations are higher in the morning and lower in the afternoon. PM2.5 concentrations exhibit a significant spatial agglomeration effect in central East China. The errors in AOD do not necessarily affect the retrieval accuracy of PM2.5 proportionally, especially if the error is systematic. High-frequency spatiotemporal PM2.5 variations can improve our understanding of the formation and transportation processes of regional pollution episodes.

Estimation of hourly land surface heat fluxes over the Tibetan Plateau by the combined use of geostationary and polar-orbiting satellites

Abstract. Estimation of land surface heat fluxes is important for energy and water cycle studies, especially on the Tibetan Plateau (TP), where the topography is unique and the land–atmosphere interactions are strong. The land surface heating conditions also directly influence the movement of atmospheric circulation. However, high-temporal-resolution information on the plateau-scale land surface heat fluxes has been lacking for a long time, which significantly limits the understanding of diurnal variations in land–atmosphere interactions. Based on geostationary and polar-orbiting satellite data, the surface energy balance system (SEBS) was used in this paper to derive hourly land surface heat fluxes at a spatial resolution of 10 km. Six stations scattered throughout the TP and equipped for flux tower measurements were used to perform a cross-validation. The results showed good agreement between the derived fluxes and in situ measurements through 3738 validation samples. The root-mean-square errors (RMSEs) for net radiation flux, sensible heat flux, latent heat flux and soil heat flux were 76.63, 60.29, 71.03 and 37.5 W m−2, respectively; the derived results were also found to be superior to the Global Land Data Assimilation System (GLDAS) flux products (with RMSEs for the surface energy balance components of 114.32, 67.77, 75.6 and 40.05 W m−2, respectively). The diurnal and seasonal cycles of the land surface energy balance components were clearly identified, and their spatial distribution was found to be consistent with the heterogeneous land surface conditions and the general hydrometeorological conditions of the TP.

A Lookup-Table-Based Approach to Estimating Surface Solar Irradiance from Geostationary and Polar-Orbiting Satellite Data

Incoming surface solar irradiance (SSI) is essential for calculating Earth’s surface radiation budget and is a key parameter for terrestrial ecological modeling and climate change research. Remote sensing images from geostationary and polar-orbiting satellites provide an opportunity for SSI estimation through directly retrieving atmospheric and land-surface parameters. This paper presents a new scheme for estimating SSI from the visible and infrared channels of geostationary meteorological and polar-orbiting satellite data. Aerosol optical thickness and cloud microphysical parameters were retrieved from Geostationary Operational Environmental Satellite (GOES) system images by interpolating lookup tables of clear and cloudy skies, respectively. SSI was estimated using pre-calculated offline lookup tables with different atmospheric input data of clear and cloudy skies. The lookup tables were created via the comprehensive radiative transfer model, Santa Barbara Discrete Ordinate Radiative Transfer (SBDART), to balance computational efficiency and accuracy. The atmospheric attenuation effects considered in our approach were water vapor absorption and aerosol extinction for clear skies, while cloud parameters were the only atmospheric input for cloudy-sky SSI estimation. The approach was validated using one-year pyranometer measurements from seven stations in the SURFRAD (SURFace RADiation budget network). The results of the comparison for 2012 showed that the estimated SSI agreed with ground measurements with correlation coefficients of 0.94, 0.69, and 0.89 with a bias of 26.4 W/m2, −5.9 W/m2, and 14.9 W/m2 for clear-sky, cloudy-sky, and all-sky conditions, respectively. The overall root mean square error (RMSE) of instantaneous SSI was 80.0 W/m2 (16.8%), 127.6 W/m2 (55.1%), and 99.5 W/m2 (25.5%) for clear-sky, cloudy-sky (overcast sky and partly cloudy sky), and all-sky (clear-sky and cloudy-sky) conditions, respectively. A comparison with other state-of-the-art studies suggests that our proposed method can successfully estimate SSI with a maximum improvement of an RMSE of 24 W/m2. The clear-sky SSI retrieval was sensitive to aerosol optical thickness, which was largely dependent on the diurnal surface reflectance accuracy. Uncertainty in the pre-defined horizontal visibility for ‘clearest sky’ will eventually lead to considerable SSI retrieval error. Compared to cloud effective radius, the retrieval error of cloud optical thickness was a primary factor that determined the SSI estimation accuracy for cloudy skies. Our proposed method can be used to estimate SSI for clear and one-layer cloud sky, but is not suitable for multi-layer clouds overlap conditions as a lower-level cloud cannot be detected by the optical sensor when a higher-level cloud has a higher optical thickness.

Tracking oceanic nonlinear internal waves in the Indonesian seas from geostationary orbit

Nonlinear Internal Waves (NLIWs) in the ocean are observable from passive satellite radiometers. The surface manifestations of these internally propagating waves are curvilinear bands demarcating relatively smooth and rough surface waters, providing a means for their detection via observations of sunglint. Typical horizontal scales of NLIWs are such that traditionally only high resolution (<1 km pixel) sensors aboard polar orbiting satellites could resolve them. The launch on 28 October 2014 of the Japanese Meteorological Agency's Himawari-8 geostationary satellite and its Advanced Himawari Imager (AHI) opens up possibilities to study the spatio-temporal change of these relatively small scale NLIW features from geostationary orbit. Himawari-8 provides 500-m resolution visible imagery every 10 min over a hemispheric field of regard centered at 140.7° E longitude. These spatial and temporal resolutions allow for not only the identification of NLIWs over the low-latitude regions such as the Indonesian Seas, but also the ability to track the evolution of these waves in real time. Here, we present examples of NLIWs as viewed from Himawari-8/AHI and calculate a few basic wave quantities, including wavelength and propagation speed. These quantities were found to be consistent with previous observations and model simulations of NLIWs, but observations from AHI provide the ability to calculate wave propagation speeds in the same locations over multiple times a day for multiple days, which is impossible with polar-orbiting satellite data. Geostationary observations will augment the existing database of observed NLIWs and help to better quantify their spatial and temporal attributes.

COMS-Based Retrieval of Daily Actual Evapotranspiration over Korea

Evapotranspiration (ET) from the land surface is an important hydrometeorological factor in the exchange of energy between the atmosphere and land surface. The accurate quantification for management of water resources and understanding of climate change are crucial, requiring continuous temporal and spatial monitoring. The objective of this study is to apply and estimate daily actual ET using semiempirical method, B-method, which is based on surface energy balance over heterogeneous area, Korea. To estimate daily ET, we used geostationary meteorological satellite data (Communication, Ocean and Meteorological Satellite, COMS) and polar-orbiting satellite data (Système Pour ľObservation de la Terre, SPOT). Estimated daily ET using only satellite data was relatively accurate and reflects land surface characteristics. It had high periodicity and spatial resolution over a wide area on clear-sky days. The daily ET was overestimated by about 1 mm/day at the two flux tower measurements sites, but the simulated seasonal variation and pattern were in good agreement with flux tower measurements. In the mixed forest, the root-mean-square error (RMSE) was 0.94 mm/day and the bias was 1.05 mm/day, while, in the rice paddy, RMSE was 1.12 mm/day and bias was 1.21 mm/day.

Macrophysical, Microphysical, and Radiative Properties of Tropical Mesoscale Convective Systems over Their Life Cycle

Abstract Mesoscale convective systems (MCSs) are important drivers of the atmospheric large-scale circulation through their associated diabatic heating profile. Taking advantage of recent tracking techniques, this study investigates the evolution of macrophysical, microphysical, and radiative properties over the MCS life cycle by merging geostationary and polar-orbiting satellite data. These observations are performed in three major convective areas: continental West Africa, the adjacent Atlantic Ocean, and the open Indian Ocean. MCS properties are also investigated according to internal subregions (convective, stratiform, and nonprecipitating anvil). Continental MCSs show a specific life cycle, with more intense convection at the beginning. Larger and denser hydrometeors are thus found at higher altitudes, as well as up to the cirriform subregion. Oceanic MCSs have more constant reflectivity values, suggesting a less intense convective updraft, but more persistent intensity. A layer of small crystals is found in all subregions, but with a depth that varies according to the MCS subregion and life cycle. Radiative properties are also examined. It appears that the evolution of large and dense hydrometeors tends to control the evolution of the cloud albedo and the outgoing longwave radiation. The impact of dense hydrometeors, detrained from the convective towers, is also seen in the radiative heating profiles, in particular in the shortwave domain. A dipole of cooling near the cloud top and heating near the cloud base is found in the longwave; this cooling intensifies near the end of the life cycle.

Assessment of FY-3A and FY-3B MWHS Observations

Abstract The Fengyun-3 series of satellites (FY-3) began in May 2008 with the launch of FY-3A. The onboard Microwave Humidity Sounders (MWHSs) provide vertical information about water vapor, which is important for numerical weather prediction (NWP). The noise equivalent delta temperature (NEDT) of the MWHS is higher than that of the Microwave Humidity Sounder (MHS) instrument (e.g., on board MetOp-B) but lower than that of the older AMSU-B instruments (on board NOAA-15, NOAA-16, and NOAA-17). Assimilation of MWHS observations into the ECMWF Integrated Forecast System (IFS) improved the fit of short-range forecasts to other observations, notably MHS, and also slightly improved the longer-range forecast scores verified against analyses. Also, assimilating the MWHS on board both FY-3A and FY-3B gave a larger impact than either instrument alone. Furthermore, when MWHS and MHS were added separately to a baseline using neither, the impact of MWHS was found to be comparable to that of MHS. Consequently, ECMWF has been assimilating the FY-3B MWHS data in the operational forecasting system since 24 September 2014. This is the first operational use of Chinese polar-orbiting satellite data by an NWP center outside of China.

A method for estimating hourly photosynthetically active radiation (PAR) in China by combining geostationary and polar-orbiting satellite data

Photosynthetically active radiation (PAR) is an important parameter in ecosystem and land surface models. PAR represents the amount of solar radiation in the spectral range of 400–700nm that travels through the atmosphere to the top of the vegetation canopy. In recent years, various methods using different input data to estimate PAR and produce different PAR products have been developed. However, most of the algorithms used in these state-of-the-art studies have not fully compensated for the low spatial and temporal resolution of the data, which affects the accuracy of the PAR estimates. In this study, we have developed a method for estimating hourly PAR based on a combination of geostationary and polar-orbiting satellite data. The Multifunctional Transport Satellite (MTSAT) was selected to retrieve cloud optical depth (COD) with a higher spatial resolution, and the polar orbit satellite data of the Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to derive surface parameters based on multispectral characteristics. A look-up table was established by the Second Simulation of a Satellite Signal in the Solar Spectrum-Vector (6SV) model and the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model consisting the following parameters: solar zenith angle, total water vapor, total ozone column, aerosol optical depth (AOD), COD, surface elevation, surface albedo and PAR. The instantaneous PAR was linearly interpolated from the input data for the selected parameters and the look-up table. The root mean square error (RMSE) between the estimated and observed instantaneous PAR at the Huailai station was 45.72W/m2 for all sky conditions. The RMSE between the estimated and observed daily PAR at the meteorological stations was 17% in the eastern regions of China. The mean bias error (MBE) was between −2.83 and 32.43W/m2 for the Tibetan Plateau. These results indicated that the proposed method can significantly improve the accuracy of PAR estimates and can be used to produce PAR products with high spatial and temporal resolution. However, the method requires further improvement, especially with respect to cloudy conditions.

Remotely sensed soil temperatures beneath snow-free skin-surface using thermal observations from tandem polar-orbiting satellites: An analytical three-time-scale model

Subsurface soil temperature is a key variable of land surface processes and not only responds to but also modulates the interactions of energy fluxes at the Earth's surface. Thermal remote sensing has traditionally been regarded as incapable of detecting the soil temperature beneath the skin-surface. This study shows that thermal remote sensing can be used to estimate soil temperatures. Our results provide insights into thermal observations collected with tandem polar-orbiting satellites when used toward obtaining soil temperatures under clear-sky conditions without the use of any ground-based information or field-measured soil properties.We designed an analytical three-time-scale (3-scale, for short) model, dividing the annual cycle of soil temperatures into three subcycles: the annual temperature cycle (ATC), which represents the daily-averaged temperature; the diurnal temperature cycle (DTC), which represents the instantaneous temperature; and the weather-change temperature cycle (WTC), which is divided into two parts to represent both the daily-averaged (WTCavg) and the instantaneous temperature (WTCinst). The DTC and WTCinst were further parameterized into four undetermined variables, including the daily-averaged temperature, thermal inertia, upward surface flux factor, and day-to-day change rate. Thus, under clear-sky conditions, the four thermal measurements in a diurnal cycle recorded with tandem polar-orbiting satellites are sufficient for reconstructing the DTC of both land surface and soil temperatures. Polar-orbiting satellite data from MODIS are used to show the model's capability. The results demonstrate that soil temperatures with a spatial resolution of 1km under snow-free conditions can be generated at any time of a clear-sky day. Validation is performed by using a comparison between the MODIS-inverted and ground-based soil temperatures. The comparison shows that the accuracy of inverted soil temperatures lies between 0.3 and 2.5K with an average of approximately 1.5K. These results open a new frontier in the application of thermal remote sensing wherein soil temperatures with high spatial and temporal resolutions can be remotely estimated.

Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters

Total suspended particulate matter (TSM) in coastal waters is often characterized by high concentration and significant diurnal dynamics. Insufficient spatial and temporal resolution limits both cruise sampling and polar-orbiting satellite remote sensing in the mapping of TSM diurnal dynamics in coastal regions. However, the in-orbit operation of the world's first geostationary satellite ocean color sensor, GOCI, provides hourly observations of the covered area. In this study, we proposed a practical atmospheric correction algorithm for GOCI data in turbid waters. The validation results showed that the GOCI-retrieved normalized water-leaving radiances matched the in situ values well in both quantity and spectral shapes. We also developed a regional empirical TSM algorithm for GOCI data that is applicable in extremely turbid waters. Based on these atmospheric correction and regional TSM algorithms, we generated hourly TSM maps from GOCI Level-1B data. The diurnal variations derived by GOCI were a good match to the buoy data. The hourly GOCI observations revealed that various regions and tidal phases had different diurnal variation magnitudes, with a maximum of up to 5000mg/l in central Hangzhou Bay. Strong wind events, such as typhoons, can significantly increase TSM in the bay; however, both the GOCI observations and buoy measurements indicated that this increase was episodic, had a short duration, and returned to normal within a day after the passage of a typhoon. Our results suggest that GOCI can successfully map the diurnal dynamics of TSM in turbid coastal waters. Moreover, the significant diurnal dynamics revealed in the hourly GOCI observations implied that caution should be taken in mapping TSM in coastal waters using cruise sampling and conventional polar-orbiting satellite data, as the temporal resolution is insufficient for catching diurnal variations.