Geostationary Satellite Observations Research Articles

Characteristics of hail hazard in South Africa based on satellite detection of convective storms

Abstract. Accurate estimates of hail risk to exposed assets, such as crops, infrastructure, and vehicles, are required for both insurance pricing and preventive measures. Here we present an event catalog to describe the hail hazard in South Africa guided by 14 years of geostationary satellite observations of convective storms. Overshooting cloud tops have been detected, grouped, and tracked to describe the spatiotemporal extent of potential hail events. It is found that hail events concentrate mainly in the southeast of the country, along the Highveld, and around the eastern slopes. Events are most frequent from mid-November through February and peak in the afternoon, between 13:00 and 17:00 UTC. Multivariate stochastic modeling of event properties yields an event catalog spanning 25 000 years, aiming to estimate, in combination with vulnerability and exposure data, hail risk for return periods of 200 years.

Improving ozone simulations in Asia via multisource data assimilation: results from an observing system simulation experiment with GEMS geostationary satellite observations

Abstract. The applications of geostationary (GEO) satellite measurements at an unprecedented spatial and temporal resolution from the Geostationary Environment Monitoring Spectrometer (GEMS) for monitoring and forecasting the alarming ozone pollution in Asia through data assimilation remain at the early stage. Here we investigate the benefit of multiple ozone observations from GEMS geostationary satellite, low Earth orbit (LEO) satellite, and surface networks on summertime ozone simulations through individual or joint data assimilation, built on our previous observing system simulation experiment (OSSE) framework (Shu et al., 2022). We find that data assimilation improves the monitoring of exceedance, spatial patterns, and diurnal variations of surface ozone, with a regional mean negative bias reduction from 2.1 to 0.2–1.2 ppbv in ozone simulations as well as significant improvements of a root-mean-square error (RMSE) of by 5 %–69 % in most Asian countries. Furthermore, the joint assimilation of GEMS and surface observations performs the best. GEMS also brings direct added value for better reproducing ozone vertical distributions, especially in the middle to upper troposphere at low latitudes, but may mask the added value of LEO measurements, which are crucial to constrain surface and upper tropospheric ozone simulations when observations from other platforms are inadequate. Our study provides a valuable reference for ozone data assimilation as multisource observations become gradually available in the era of GEO satellites.

A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations

Abstract. Automated methods for the detection and tracking of deep convective clouds in geostationary satellite imagery have a vital role in both the forecasting of severe storms and research into their behaviour. Studying the interactions and feedbacks between multiple deep convective clouds (DCC), however, poses a challenge for existing algorithms due to the necessary compromise between false detection and missed detection errors. We utilise an optical flow method to determine the motion of deep convective clouds in GOES-16 ABI imagery in order to construct a semi-Lagrangian framework for the motion of the cloud field, independently of the detection and tracking of cloud objects. The semi-Lagrangian framework allows severe storms to be simultaneously detected and tracked in both spatial and temporal dimensions. For the purpose of this framework we have developed a novel Lagrangian convolution method and a number of novel implementations of morphological image operations that account for the motion of observed objects. These novel methods allow the accurate extension of computer vision techniques to the temporal domain for moving objects such as DCCs. By combining this framework with existing methods for detecting DCCs (including detection of growing cores through cloud top cooling and detection of anvil clouds using brightness temperature), we show that the novel framework enables reductions in errors due to both false and missed detections compared to any of the individual methods, reducing the need to compromise when compared with existing frameworks. The novel framework enables the continuous tracking of anvil clouds associated with detected deep convection after convective activity has stopped, enabling the study of the entire life cycle of DCCs and their associated anvils. Furthermore, we expect this framework to be applicable to a wide range of cases including the detection and tracking of low-level clouds and other atmospheric phenomena. In addition, this framework may be used to combine observations from multiple sources, including satellite observations, weather radar and reanalysis model data.

Developing a near Real-Time Cloud Cover Retrieval Algorithm Using Geostationary Satellite Observations for Photovoltaic Plants

Clouds can block solar radiation from reaching the surface, so timely and effective cloud cover test and forecasting is critical to the operation and economic efficiency of photovoltaic (PV) plants. Traditional cloud cover algorithms based on meteorological satellite observation require many auxiliary data and computing resources, which are hard to implement or transplant for applications at PV plants. In this study, a portable and fast cloud mask algorithm (FCMA) is developed to provide near real-time (NRT) spatial-temporally matched cloud cover products for PV plants. The geostationary satellite imager data from the Advanced Himawari Imager aboard Himawari-8 and the related operational cloud mask algorithm (OCMA) are employed as benchmarks for comparison and validation. Furthermore, the ground-based manually observed cloud cover data at seven quintessential stations at 08:00 and 14:00 BJT (Beijing Time) in 2017 are employed to verify the accuracy of cloud cover data derived from FCMA and OCMA. The results show a high consistency with the ground-based data, and the average correlation coefficient (R) is close to 0.85. Remarkably, the detection accuracy of FCMA is slightly higher than that of OCMA, demonstrating the feasibility of FCMA for providing NRT cloud cover at PV plants.

Long-term variations of cloud top patterns associated with heavy rainfall over the Korean peninsula

Study regionKorean peninsula Study focusAlthough heavy rainfall systems over Korea were known to be characterized by warm-type heavy rainfall associated with a relatively low cloud top, this feature can vary under climate change. This study primarily investigated the mean climatology and linear trends of heavy rainfall and cloud top distributions over the Korean peninsula. The rainy seasons from June to September for the period of 1983–2021 were analyzed in this study. To understand the climatology and changes of heavy rainfall events, thermodynamic variables and synoptic forcings were analyzed. New hydrological insights for the regionLong-term consistent geostationary satellite data provides a unique opportunity to understand the global cloud variations associated with climate change. This study examined the mean climatology and decadal trends of cloud tops inferred from geostationary satellite observations, along with heavy rainfall events over the Korean Peninsula. Rainfall and cloud top heights exhibited a decreasing trend in June, whereas an increase in vigorous deep convection was observed in August/September, mainly in southern Korea. The decadal trends in August and September were associated with large-scale circulation changes, while the trend in September was attributed to increasing typhoon visits over the Korean Peninsula. The cloud top changes, with lowered cloud tops in June and elevated cloud tops in August, suggested a shift of deep convective systems toward the late summer over Korea.

Carbonaceous aerosols remote sensing from geostationary satellite observation, Part I: Algorithm development using critical reflectance

Current aerosol remote sensing without using multi-view satellite sensors still fail to retrieve its components directly, such as black carbon (BC) and organic carbon (OC) simultaneously due to the limitations on available observations. In this study, A new general carbonaceous aerosol retrieval strategy for geostationary single-view satellite observations is implemented based on a method of critical reflectance, to retrieve the parameters of BC and extra OC concentration from measured radiance without prior quantification of Aerosol Optical Thickness (AOT). The method is based on measuring the change in upward reflectance between clear and hazy days over a varying surface reflectance, with the aim of finding a balance between brightening due to scattering and darkening due to absorption. Since aerosols in the proposed algorithm are considered as a mixture of BC, OC and clustered shell aerosols, this balance can be used to retrieve concentration parameters of these components via radiative transfer models. Sensitivity tests indicate that different scenarios of BC and OC volume fractions can introduce certain sensitivity pattern on critical reflectance. The retrieval is achieved by minimizing the differences between measured critical reflectance and a set of look-up tables (LUTs). Random sampling consensus (RANSAC) is also used in the strategy to reduce the influence of clouds and anomalous pixels on the retrievals. Since critical reflectance should be calculated by comparing geometrically consistent clear and hazy images, the algorithm is more suitable for geostationary satellites with constant viewing geometries. An initial validation and application applied to Himawari images over the North China Plain (NCP) shows that the carbonaceous aerosol retrievals are highly consistent with the expectations in terms of spatial and temporal patterns. The retrievals of BC and OC concentration follow the daily fluctuations of the Aethalometers observations and feature small differences in mean values throughout the month. Additionally, the AOTs at 0.55 μm can be also satisfactorily reproduced based on the carbonaceous aerosol retrievals, resulting in a mean absolute error of 0.089 and a correlation coefficient of 0.870. The main errors in the method arise from shell model assumptions, RANSAC fitting bias, inconsistent clear reference AOT in LUT, and geometric inconsistency between the clear reference and hazy images. This work indicates that the BC and OC concentration in high resolution can be acquired through the geostationary single-view remote sensing for the further air quality and climate studies.

A Method for Estimating Daytime Average Evapotranspiration From Diurnal Land Surface Temperature Measurements

Evapotranspiration (ET) is an essential parameter in the water cycle and surface energy balance. Accurate estimation of daytime or daily ET is of great significance for many activities in human economic and social development. This study aims to propose a novel method for estimating daytime average ET by using temporal measurements of land surface temperature (LST) and net surface shortwave radiation (NSSR), following a previous developed elliptical relationship between diurnal cycles of LST and NSSR under cloud-free days. The method was primarily developed from the simulated data of a physics-based Atmosphere–Land Exchange (ALEX) model under different underlying surfaces and atmospheric conditions. Based on the simulated data, the proposed method showed considerable accuracy with the overall coefficient of determination ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of 0.958 and the root mean square error (RMSE) of 25.3 Wm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> . In addition, ground ET measurements at four Ameriflux sites (US-ARM, US-SRM, US-Whs, and US-Wkg) during the 2018 growing season were collected to assess the estimated daytime average ET. Results show an overall RMSE of 64.7 Wm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for the estimated ET at the four sites, and the US-Whs site reveals a best accuracy ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.825, RMSE=44.4 Wm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> ). These results indicated a potential for generating daytime ET with geostationary satellite observations at regional scales in future development.

On the Scattering-Angle Dependence of the Spectral Consistency of Ice Cloud Optical Thickness Retrievals Based on Geostationary Satellite Observations

On the Scattering-Angle Dependence of the Spectral Consistency of Ice Cloud Optical Thickness Retrievals Based on Geostationary Satellite Observations

Hypothetical Ground Radar-Like Rain Rate Generation of Geostationary Weather Satellite Using Data-to-Data Translation

This study proposes a deep-learning-based data-to-data (D2D) translation framework to simulate a radar-like retrieval of rainfall rates using the advantages of spatial coverage and temporal resolution of geostationary (GEO) satellite observation. The D2D method comprises normalization and denormalization in pre- and post-processing and an adversarial learning structure for an inter-domain conversion between physical values of data such as albedo and brightness temperature (BT) unlike the image-to-image translation using digital number values in image data. The GEO-KOMPSAT-2A (GK2A) and radar hybrid surface rainfall (HSR) datasets over the Korean Peninsula from September 2019 to September 2021 were used as the source and target domains for training and testing the D2D model. The constructed D2D model for ground radar-like rainfall generation was validated using the ground radar-observed rainfall data and compared to the GK2A rainfall rate (RR), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), and Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) rainfall products. The D2D model exhibited excellent performance for various rain types in the study area compared to the GK2A RR, PERSIANN-CCS, and IMERG data. Consequently, the D2D model can provide valuable and accurate radar-like rainfall intensity and distribution data with a high temporal resolution and complementary rainfall information over lands and oceans without radar observation.

DETERMINATION DEGRADATION OF SATELLITE SURFACE OPTICAL FEATURES BY PHOTOMETRIC METHOD

A method is described for using photometric data to estimate the reflective properties of a satellite's surface material under conditions of its long stay in space. The technique of multicolour photometric B,V,R observations of several geostationary satellites built on different types of tires is shown, and the results obtained over a period of 3 to 9 years. These are geostationary satellites Astra 2E (on the Eurostar E3000 bus), Azerspace 2/Intelsat 38 (on the SSL-1300 bus), Sicral 2 (on the Spacebus-4000B2 platform) and Blagovest 11L (the Ekspress-2000 bus). It has been revealed that a pattern of change in the reflectivity of different geostationary satellites is dissimilar. Spacecraft materials used for the surfaces of geostationary satellites manufactured in the second decade of the 21 st century are more resistant to the harsh space environment than those used for the satellites built in the late 20 th century. We have proposed several ways of improving the method for identifying the spacecraft material type based on multicolour photometric observations. The presented results show that the proposed photometric method works and can be used for detecting spacecraft surface degradation.

Improvement of the Aerosol Forecast and Analysis Over East Asia With Joint Assimilation of Two Geostationary Satellite Observations

AbstractHere we present the first simultaneous assimilation of aerosol optical thicknesses (AOTs) over East Asia retrieved from two next‐generation geostationary satellites named Fengyun‐4A (FY‐4A) and Himawari‐8 with an advanced data assimilation system. A new stringent data‐control method is proposed to assimilate only the high quality aerosol products of FY‐4A. The AOTs retrieved from the two geostationary satellites are both comparable to the ground‐based observations over East Asia, and the FY‐4A provides twice the number of the Himawari‐8 AOT retrievals. Validations with independent observations demonstrate the joint assimilation significantly improve the aerosol analysis and forecast skill over East Asia especially in South China, reducing the model biases of 70% and 40%. The aerosol model performances at 95% independent 61 ground‐based sites are more benefited from joint assimilation than single‐sensor assimilation. This demonstrates the high impact and innovative application of joint assimilation technique in the air quality modeling.

Diverse Responses of Multiple Satellite‐Derived Vegetation Greenup Onsets to Dry Periods in the Amazon

AbstractSatellite‐derived vegetation greenness and seasonal dynamics in the Amazon have generated considerable academic debate over the past two decades. Despite this, the phenological timing of Amazon forests and, in particular their responses to dry periods, remain poorly understood. Here we explicitly identify the diverse timing of vegetation canopy greenup onsets from 10‐min geostationary satellite observations, and compute the timing of both the start and end of dry periods from daily precipitation data. We, for the first time, reveal that the Amazon vegetation canopy regularly experiences two cycles of greenup onsets during a year. The occurrence of greenup onset varies diversely from the start to end of the dry periods, but demonstrates regular shifts in local areas, although irregular shifts across the region. The multiple greenup onsets show complex spatial shifts, which closely follow the spatial movement of dry periods. The results provide a new insight into our understanding of the complexity of Amazonian vegetation canopy dynamics during dry periods, which could significantly improve the simulation of carbon and water cycles.

Exploring the Role of Deep Moist Convection in the Wavenumber Spectra of Atmospheric Kinetic Energy and Brightness Temperature

Abstract Through a series of global convection-permitting simulations and geostationary satellite observations, this study investigates the role of deep moist convection in atmospheric kinetic energy (KE) and brightness temperature (BT) spectra in a realistic framework. The control simulation was produced on a quasi-uniform 3-km global mesh, which allowed the explicit representation of deep convection. To assess the impact of deep moist convection, a fake-dry simulation was performed with latent heating–cooling feedback in the microphysics removed for comparison. The impacts of deep moist convection on mesoscale KE spectrum are concentrated on energizing the mesoscale at the upper troposphere and the lower stratosphere through buoyancy production. BT spectra for the control simulation have a similar shallow slope in the mesoscale as that for the observations. The greater spectral power of BT for the control simulation compared to the observed is attributed to the dislocation and higher intensity of simulated convection. The observed BT spectra exhibit a large diurnal variability due to the diurnal variation of the intensity of convection. The simulated BT spectrum is dependent on convective systems at different scales. Deep convection in the intertropical convergence zone (ITCZ) and shallow convection in the North Pacific storm-track region play an important role in energizing the convective scale of the BT spectrum. In the mesoscale, the BT spectrum is mainly energized by mesoscale convective systems (MCSs) in the ITCZ. Tropical equatorial waves and baroclinic waves in the southern midlatitudes are critical in producing the shallow slope near −5/3 and providing energy in the BT spectrum at the synoptic scale. Significance Statement We further explore the role of deep moist convection in kinetic energy and brightness temperature spectra through high-resolution radiance observations and convection-permitting simulations. Moist processes can energize the mesoscale of kinetic energy. Brightness temperature spectra show dependence on convective systems at different scales. These results point the way toward a new approach to evaluate the predictability of convective systems, and future development of model dynamics and parameterization.

High-resolution satellite-based cloud detection for the analysis of land surface effects on boundary layer clouds

Abstract. The observation of boundary layer clouds with high-resolution satellite data can provide comprehensive insights into spatiotemporal patterns of land-surface-driven modification of cloud occurrence, such as the diurnal variation of the occurrence of fog holes and cloud enhancements attributed to the impact of the urban heat island. High-resolution satellite-based cloud-masking approaches are often based on locally optimised thresholds that can be affected by the local surface reflectance, and they therefore introduce spatial biases in the detected cloud cover. In this study, geostationary satellite observations are used to develop and validate two high-resolution cloud-masking approaches for the region of Paris to show and improve applicability for analyses of urban effects on clouds. Firstly, the Local Empirical Cloud Detection Approach (LECDA) uses an optimised threshold to separate the distribution of visible reflectances into cloudy and clear sky for each individual pixel accounting for its locally specific brightness. Secondly, the Regional Empirical Cloud Detection Approach (RECDA) uses visible reflectance thresholds that are independent of surface reflection at the observed location. Validation against in-situ cloud fractions reveals that both approaches perform similarly, with a probability of detection (POD) of 0.77 and 0.69 for LECDA and RECDA, respectively. Results show that with the application of RECDA a decrease of cloud cover during typical fog or low-stratus conditions over the urban area of Paris for the month of November is likely a result of urban effects on cloud dissipation. While LECDA is representative for the widespread usage of locally optimised approaches, comparison against RECDA reveals that the cloud masks obtained from LECDA result in regional biases of ±5 % that are most likely caused by the differences in surface reflectance in and around the urban areas of Paris. This makes the regional approach, RECDA, a more appropriate choice for the high-resolution satellite-based analysis of cloud cover modifications over different surface types and the interpretation of locally induced cloud processes. Further, this approach is potentially transferable to other regions and temporal scales for analysing long-term natural and anthropogenic impacts of land cover changes on clouds.

A Dataset of Overshooting Cloud Top from 12-Year CloudSat/CALIOP Joint Observations

A strong convective storm is a disastrous weather system with a small spatio-temporal scale. It often occurs suddenly and can cause huge disasters. Thus, it is necessary to improve the forecast accuracy of strong convective storms. Overshooting cloud top (OT) is the product of strong updrafts in convective storms, which can penetrate the tropopause and enter the lower stratosphere. OT is closely related to severe weather and can influence water vapor transport and the material exchange between the troposphere and stratosphere. Therefore, the timely detection of OT can help improve the accuracy of forecasting. In this study, we develop a new objective OT detection algorithm based on geostationary satellite observations from 2006 to 2017. The accuracy of the new algorithm in identifying OT is verified by manually comparing it with the radar echo images and the cloud images of MODIS 250 m. Then, the OT is statistically analyzed in a long time series. It is found that OT events are mainly concentrated in equatorial and low latitude regions, with higher frequency in summer. There are obvious differences between OT events on land and sea. Additionally, this dataset also reveals the close connection between the seasonal shift of OT and the seasonal average precipitation distribution around the globe. This study provides a scientific basis for determining the geographical characteristics of OT frequency and explores the application of this OT objective detection algorithm in the operational forecast of strong convective weather. We hope this study can benefit OT monitoring in operational weather forecasting.

Hourly rolling correction of precipitation forecast via convolutional and long short‐term memory networks

AbstractIn order to improve precipitation forecast from GRAPES_Meso V4.0 in China, we propose a 1–6‐h rolling correction solution, based on infrared (IR) channels from geostationary meteorological satellite and surface observation data. In particular, we design a deep learning extrapolation model to predict the evolution of cloud clusters based on convolutional neural networks and long short‐term memory networks. The predicted cloud clusters, together with the relationship between the rainfall area and the cloud position, are applied to correct the 1–6‐h precipitation forecast. We conduct comprehensive experiments to evaluate the proposed solution over China. Experimental results show that the deep learning model can successfully capture spatial characteristics and temporal variations between the sequences, and achieve reliable predictions of cloud clusters. The analysis further indicates that the rolling correction solution via the predicted cloud clusters has improved the precipitation forecast in China. The distribution of corrected precipitation forecast is more consistent with the observed precipitation compared to GRAPES_Meso forecast. In particular, the rolling correction model could enhance the forecast on “rain/no‐rain” events, light rain, and moderate rain according to TS, ETS, BIAS, and FAR metrics.

4D-Var Inversion of European NH3 Emissions Using CrIS NH3 Measurements and GEOS-Chem Adjoint With Bi-Directional and Uni-Directional Flux Schemes.

We conduct the first 4D‐Var inversion of NH3 accounting for NH3 bi‐directional flux, using CrIS satellite NH3 observations over Europe in 2016. We find posterior NH3 emissions peak more in springtime than prior emissions at continental to national scales, and annually they are generally smaller than the prior emissions over central Europe, but larger over most of the rest of Europe. Annual posterior anthropogenic NH3 emissions for 25 European Union members (EU25) are 25% higher than the prior emissions and very close (<2% difference) to other inventories. Our posterior annual anthropogenic emissions for EU25, the UK, the Netherlands, and Switzerland are generally 10%–20% smaller than when treating NH3 fluxes as uni‐directional emissions, while the monthly regional difference can be up to 34% (Switzerland in July). Compared to monthly mean in‐situ observations, our posterior NH3 emissions from both schemes generally improve the magnitude and seasonality of simulated surface NH3 and bulk NH x wet deposition throughout most of Europe, whereas evaluation against hourly measurements at a background site shows the bi‐directional scheme better captures observed diurnal variability of surface NH3. This contrast highlights the need for accurately simulating diurnal variability of NH3 in assimilation of sun‐synchronous observations and also the potential value of future geostationary satellite observations. Overall, our top‐down ammonia emissions can help to examine the effectiveness of air pollution control policies to facilitate future air pollution management, as well as helping us understand the uncertainty in top‐down NH3 emissions estimates associated with treatment of NH3 surface exchange.

Distinct changes of cloud microphysical properties and height development by dust aerosols from a case study over Inner-Mongolia region

Dust aerosols, by serving as cloud condensation nuclei and/or ice nuclei, can impact the properties and development of clouds. Selecting the dust event occurred in May 2016 as a case, this study investigates the potential impacts of dust aerosols on cloud system by examining the differences in cloud properties between the dust polluted and unpolluted parts using the geostationary satellite (Himawari-8) observation data. It is found that the cloud particles effective radius (re) is significantly smaller in the dust polluted region than that in unpolluted region. With the increase of dust aerosol optical depth (AOD), the cloud particle re decreases, showing a first indirect effect (IE) value (defined as −dlnredlndustAODCWP) of 0.31. From the beginning of the formation of ice crystals to the complete transformation into ice crystals, compared to the polluted region, cloud particle re had a more rapid increase as the cloud top height increased in unpolluted region, which might be associated with the formation of large supercooled droplets with less ice nuclei along with the higher possibility of collision-coalescence. It is also found that the increase in dust AOD likely causes the release of latent heat and the promotion of convection, which makes the cloud develop stronger to a higher position. This study reveals distinct impacts of dust aerosol on clouds from a microphysical perspective along with the potential mechanism behind the findings, which can provide observational support for improvement of cloud parameterization in numerical models

Satellite Observations of Fire Activity in Relation to Biophysical Forcing Effect of Land Surface Temperature in Mediterranean Climate

The present work is aimed at gaining more knowledge on the nature of the relation between land surface temperature (LST) as a biophysical parameter, which is related to the coupled effect of the energy and water cycles, and fire activity over Bulgaria, in the Eastern Mediterranean. In the ecosystems of this area, prolonged droughts and heat waves create preconditions in the land surface state that increase the frequency and intensity of landscape fires. The relationships between the spatial–temporal variability of LST and fire activity modulated by land cover types and Soil Moisture Availability (SMA) are quantified. Long-term (2007–2018) datasets derived from geostationary MSG satellite observations are used: LST retrieved by the LSASAF LST product; fire activity assessed by the LSASAF FRP-Pixel product. All fires in the period of July–September occur in days associated with positive LST anomalies. Exponential regression models fit the link between LST monthly means, LST positive anomalies, LST-T2 (as a first proxy of sensible heat exchange with atmosphere), and FRP fire characteristics (number of detections; released energy FRP, MW) at high correlations. The values of biophysical drivers, at which the maximum FRP (MW) might be expected at the corresponding probability level, are identified. Results suggest that the biophysical index LST is sensitive to the changes in the dynamics of vegetation fire occurrence and severity. Dependences are found for forest, shrubs, and cultivated LCs, which indicate that satellite IR retrievals of radiative temperature is a reliable source of information for vegetation dryness and fire activity.

Estimation of the Accuracy of Geostationary Satellite Observations

Estimation of the Accuracy of Geostationary Satellite Observations