Multi-functional Transport Satellite Research Articles

The occurrence of the typical mesoscale convective system with a flood-producing storm in the wet season over the Greater Jakarta area

A complex convective cloud with a horizontal scale area of more than 100 km, known as the mesoscale convective system (MCS), is important to the study as it brings heavy rainfall from its activity. The analysis of MCS with the flood-producing storm on the Indonesian Maritime Continent (IMC) receives less attention. The purpose of this study is to identify and evaluate the temporal variability of the MCS to the frequency of flood-producing storms in Greater Jakarta (GJ) during the 2013–2015 wet season. The image data of Multi-functional Transport Satellite (MTSAT) -1R, which represents an equivalent blackbody temperature (TBB), and the tracking algorithm “Grab ‘em Tag ‘em Graph ‘em” (GTG) were used to detect the events of MCS. We also used a rainfall graph in this analysis to measure rainfall threshold values in order to classify flood-producing storms. The results show that MCS around GJ has typical characteristics of tropical belt regions. There is a small TBB scale (maximum size ≥13,000 km2) distinguished by a deep cloud up to 14 km in height. Through the active effects of monsoon and ITCZ, the land-breeze and/or sea-breeze circulations that contribute to MCS growth are triggered. However, about 32 percent of the MCS contributed to the flood-producing storm around the GJ region.

Space-based drought analysis to support agricultural insurance facing climate change

The long drought experienced in Indonesia in the past was identified as one of the main factors in the failure of rice production. The application of rice crop insurance was introduced since 2015 to help rice farmers who suffered from farm damage. The claim was increased year by year, with the highest in 2018-2019 (12,253 ha). The main problem associated with the insurance claim is the delay of inspection of the drought-affected land. Meanwhile, various satellite data can provide relevant information more quickly for those affected areas. The purpose of this study is to examine the potential use of satellite data to detect drought to support the rice crop insurance program. Daily and monthly rainfall data derived from the Tropical Rainfall Measuring Mission (TRMM), Global Satellite Mapping of Precipitation (GSMaP) and Multifunctional Transport Satellites (MTSAT) were analyzed to identify meteorological drought. Agricultural drought is studied through the character of several indices from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Preliminary results showed that up to 89.45% of the analysis comformed with field conditions. Satellite data has its potential to be utilized in the identification of claims on agricultural insurance which requires information on land damage due to drought.

Evaluation of Satellite Based Rainfall Estimation

Rainfall is one major source of water supply needed for most human activities encompassing agricultural activities to heavy industries. Therefore, a reliable and up to date approach shall always be required to accurately quantify rainfall precipitation. In Malaysia, there are three basic systems for providing rainfall measurements; which are conventional ground-based rain gauges, weather radar precipitation, and satellite-based precipitation. This study focused on data from Multi-Functional Transport Satellite (MTSAT-1R) and Terminal Doppler Weather Radar product for rainfall estimation in Malaysia. Both data were verified with the ground rain gauge data. Comparative analysis towards the data was taken based on daily and hourly approaches. Daily rainfall data were taken at Bukit Rajah, Batang Kali, Connaught Bridge, Petaling Jaya and Subang Jaya while hourly rainfall datasets were taken at Petaling Jaya and Subang Jaya. The satellite and radar data is processed using MTSAT HRIT and IRIS software respectively with the aid from Malaysian Meteorological Department (MMD). The correlation coefficient is used in this study to investigate the relationship between the satellite and radar with ground rain gauge data.

Hourly 5-km surface total and diffuse solar radiation in China, 2007\u20132018

Surface solar radiation is an indispensable parameter for numerical models, and the diffuse component contributes to the carbon uptake in ecosystems. We generated a 12-year (2007–2018) hourly dataset from Multi-functional Transport Satellite (MTSAT) satellite observations, including surface total solar radiation (Rs) and diffuse radiation (Rdif), with 5-km spatial resolution through deep learning techniques. The used deep network tacks the integration of spatial pattern and the simulation of complex radiation transfer by combining convolutional neural network and multi-layer perceptron. Validation against ground measurements shows the correlation coefficient, mean bias error and root mean square error are 0.94, 2.48 W/m2 and 89.75 W/m2 for hourly Rs and 0.85, 8.63 W/m2 and 66.14 W/m2 for hourly Rdif, respectively. The correlation coefficient of Rs and Rdif increases to 0.94 (0.96) and 0.89 (0.92) at daily (monthly) scales, respectively. The spatially continuous hourly maps accurately reflect regional differences and restore the diurnal cycles of solar radiation at fine resolution. This dataset can be valuable for studies on regional climate changes, terrestrial ecosystem simulations and photovoltaic applications.

IDENTIFIKASI MESOSCALE CONVECTIVE COMPLEX (MCC) DAN DAMPAKNYA TERHADAP CURAH HUJAN DI BENUA MARITIM INDONESIA (BMI) SEPANJANG TAHUN 2018

One year observation of Mesoscale Convective Complexes (MCCs) over the Indonesian Maritime Continent (BMI) and its impact to the rainfall are analyzed. This study is focused on the area of BMI where the MCCs formed and then the criteria are identified based on the size, initiate, duration, shape, and the eccentricity. In term of rainfall, this research also confirm that MCCs will have an impact to the rainfall distribution around the MCCs area. The first MCCs was developed on March, 15 and covered 348,410 km2 of the area. The most significant of MCCs was contributed to a 108 mm of rainfall occurs on December, 28 with 11 hours of duration and covered 771,448 km2 of area. The lag-time between rainfall and the mature stage of MCCs could be 1-3 hours. Furthermore, all initiations of the MCC occurred at night with a duration of between 8-15 hours. Throughout the MCCs event, top cloud of temperature derived by the Multi-functional Transport Satellite (MTSAT)-IR imagery could reached the temperature < -85 °C. Keyword s : MCCs, BMI, rainfall, cloud

Monitoring agricultural drought in Australia using MTSAT-2 land surface temperature retrievals

Drought indices based on thermal remote sensing have been developed and have merit for effective early warning of agricultural droughts, but approaches so far are relatively complex or sensitive to land surface temperature (LST) estimation uncertainties. Here, we propose the temperature rise index (TRI), a drought index that is comparatively robust and easy to calculate, as the anomaly of the intrinsic morning rise of LST. The underlying principle is that the rate of LST rise between 1.5 and 3.5 h after the sunrise is approximately linear and occurs more rapidly under dry conditions than under wet conditions over vegetated surfaces as a consequence of stomatal control. TRI during the growing seasons of 2010–2014 was calculated over the Australian wheatbelt from LST retrievals from the geostationary Multifunction Transport Satellite-2 (MTSAT-2) instrument. The calculated TRI was compared with indices based on precipitation integrated over 1-, 3- and 6-month time scales, on Soil Moisture and Ocean Salinity (SMOS) soil moisture derived from passive microwave remote sensing, and on vegetation condition (normalized difference vegetation index, NDVI) derived from optical remote sensing. The various indices were also compared to annual wheat yield over large areas. The correlation coefficient between TRI and precipitation anomaly that serves as an operational drought index in Australia was above 0.6 in general with 3-month integrative time scale for precipitation. TRI produced spatiotemporal dryness patterns that were very similar to those in soil moisture, but with more detail due to its finer resolution. A time lag of >1 month was found between TRI and observed vegetation condition, supporting the use of TRI in early warning. Among the compared drought indices, TRI explained the largest fraction (35%) of wheat yield variations. TRI correlations with wheat yields peaked higher and earlier by almost one month in comparison to other indices. We conclude that the thermal drought index proposed here shows considerable potential for use in drought early warning as an effective complement.

A new method to estimate cloud effective radius using Meteosat Second Generation SEVIRI over Middle East

The cloud particle size distribution varies with height and the phase may change from water to mixed phase to ice through the vertical profile of the cloud, giving rise to different radiative characteristics. Researchers suggested an empirical equation for an approximation of the relation between cloud-particle effective radius and reflectance of channel IR3.7 for Advanced Very High Resolution Radiometer (AVHRR) and Multi-Functional Transport Satellite (MTSAT)-1R satellites. However, when we examined this empirical equation for MSG satellites, there was no correlation between the cloud particle effective radius and those from the Terra and Aqua/MODIS (Moderate Resolution Imaging Spectrometers) products. Given the fact that this relationship is empirical and not applicable for Meteosat satellites with 3.9 µm wavelength cloud properties, we need to introduce a new nonlinear equation that is independent from other cloud properties, to retrieve the cloud effective radius from MSG satellite over Middle East. Thus, in this study, a development of the mentioned method, based on a nonlinear regression model, was introduced to estimate the water/ice-cloud particle effective radius from the 3.9 µm wavelength reflectivity of the Meteosat Second Generation Indian Ocean Data Coverage (MSG-1(IODC)) satellite over the Middle East region. For this purpose, the LibRadTran radiative transfer model was used. This approach is almost independent from other cloud properties, which makes this relationship more efficient for retrieving a cloud’s effective radius. To evaluate this approach, the results have been compared to the effective-radius product of the MODIS on board the Terra and Aqua satellites, and cloud effective radius parameter from the MSG-1 satellite’s optimal cloud analysis (OCA) data. The average of correlation coefficient, standard deviation, and RMSE (root mean square error) of this retrieved algorithm method for 29 randomly selected case studies, in comparison to the corresponding MODIS product, are 0.93, 3.093, and 3.639, and compared with OCA product, 0.88, 4.015, and 4.51, respectively. Therefore, the results of analysis in the Middle East region show that the retrieved effective particle radius from Meteosat satellites corresponds strongly with MODIS data from the Terra and Aqua satellites, and also with the OCA products of the MSG-1. Furthermore, using the algorithm that is presented in this paper, a nonlinear regression relationship can be made for retrieving cloud effective radius in the intended place.

Atmospheric Motion Vectors Derived from an Infrared Window Channel of a Geostationary Satellite Using Particle Image Velocimetry

AbstractAs the new-generation geostationary satellite Himawari-8 provides a greater frequency and more observation channels than its predecessor, the Multifunctional Transport Satellite series (e.g., MTSAT-2), an opportunity arises to generate atmospheric motion vectors (AMVs) with an increased accuracy and extensive distribution over eastern Asia. In this work AMVs were derived from consecutive images of an infrared-window channel (IR1) of the Himawari-8 satellite using particle image velocimetry (PIV) based on the theory of cross-correlation schemes. A multipass scheme and an adaptive interrogation scheme were also employed to increase spatial resolution and accuracy. For height assignment, an infrared-window method was applied for opaque cloud, while an H2O-intercept method was employed for semitransparent cloud. Validation was conducted by comparing the PIV-derived AMVs with wind fields obtained from NWP analysis, radiosonde observations, and the operational system from the Meteorological Satellite Center (MSC) of the Japan Meteorological Agency (JMA) or JMA/MSC. The comparison of wind velocity maps with the NWP data shows that the PIV-derived AMVs are capable of quantitatively depicting full-field wind field maps and strong jets in atmospheric circulation. Through comparisons with radiosonde observations, the root-mean-square error and wind speed bias (4.29 and −1.05 m s−1) of the PIV-derived AMVs are comparable to, although slightly greater than, that of the NWP data (3.88 and −0.26 m s−1). Based on comparison between the PIV-derived AMVs and wind fields obtained from the JMA/MSC operational system, the PIV-derived AMVs are again comparable, producing a slightly lower error but a larger wind speed bias (−1.05 vs 0.20 m s−1). This also implies that a better height assignment algorithm is necessary.

A 17 year climatology of the macrophysical properties of convection in Darwin

Abstract. The validation of convective processes in global climate models (GCMs) could benefit from the use of large datasets that provide long-term climatologies of the spatial statistics of convection. To that regard, echo top heights (ETHs), convective areas, and frequencies of mesoscale convective systems (MCSs) from 17 years of data from a C-band polarization (CPOL) radar are analyzed in varying phases of the Madden–Julian Oscillation (MJO) and northern Australian monsoon in order to provide ample validation statistics for GCM validation. The ETHs calculated using velocity texture and reflectivity provide similar results, showing that the ETHs are insensitive to various techniques that can be used. Retrieved ETHs are correlated with those from cloud top heights retrieved by Multifunctional Transport Satellites (MTSATs), showing that the ETHs capture the relative variability in cloud top heights over seasonal scales. Bimodal distributions of ETH, likely attributable to the cumulus congestus clouds and mature stages of convection, are more commonly observed when the active phase of the MJO is over Australia due to greater mid-level moisture during the active phase of the MJO. The presence of a convectively stable layer at around 5 km altitude over Darwin inhibiting convection past this level can explain the position of the modes at around 2–4 km and 7–9 km. Larger cells were observed during break conditions compared to monsoon conditions, but only during the inactive phase of the MJO. The spatial distributions show that Hector, a deep convective system that occurs almost daily during the wet season over the Tiwi Islands, and sea-breeze convergence lines are likely more common in break conditions. Oceanic MCSs are more common during the night over Darwin. Convective areas were generally smaller and MCSs more frequent during active monsoon conditions. In general, the MJO is a greater control on the ETHs in the deep convective mode observed over Darwin, with higher distributions of ETH when the MJO is active over Darwin.

Consideration of Radiometric Quantization Error in Satellite Sensor Cross-Calibration

The radiometric resolution of a satellite sensor refers to the smallest increment in the spectral radiance that can be detected by the imaging sensor. The fewer bits that are used for signal discretization, the larger the quantization error in the measured radiance. In satellite inter-calibration, a difference in radiometric resolution between a reference and a target sensor can induce a calibration bias, if not properly accounted for. The effect is greater for satellites with a quadratic count response, such as the Geostationary Meteorological Satellite-5 (GMS-5) visible imager, where the quantization difference can introduce non-linearity in the inter-comparison datasets, thereby affecting the cross-calibration slope and offset. This paper describes a simulation approach to highlight the importance of considering the radiometric quantization in cross-calibration and presents a correction method for mitigating its impact. The method, when applied to the cross-calibration of GMS-5 and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, improved the absolute calibration accuracy of the GMS-5 imager. This was validated via radiometric inter-comparison of GMS-5 and Multifunction Transport Satellite-2 (MTSAT-2) imager top-of-atmosphere (TOA) measurements over deep convective clouds (DCC) and Badain Desert invariant targets. The radiometric bias between GMS-5 and MTSAT-2 was reduced from 1.9% to 0.5% for DCC, and from 7.7% to 2.3% for Badain using the proposed correction method.

Monitoring the Agung (Indonesia) Ash Plume of November 2017 by Means of Infrared Himawari 8 Data

The Agung volcano (Bali; Indonesia) erupted in later November 2017 after several years of quiescence. Because of ash emissions, hundreds of flights were cancelled, causing an important air traffic disruption in Indonesia. We investigate those ash emissions from space by applying the RSTASH algorithm for the first time to Himawari-8 data and using an ad hoc implementation scheme to reduce the time of the elaboration processes. Himawari-8 is a new generation Japanese geostationary meteorological satellite, whose AHI (Advanced Himawari Imager) sensor offers improved features, in terms of spectral, spatial and temporal resolution, in comparison with the previous imagers of the MTSAT (Multi-Functional Transport Satellite) series. Those features should guarantee further improvements in monitoring rapidly evolving weather/environmental phenomena. Results of this work show that RSTASH was capable of successfully detecting and tracking the Agung ash plume, despite some limitations (e.g., underestimation of ash coverage under certain conditions; generation of residual artefacts). Moreover, estimates of ash cloud-top height indicate that the monitored plume extended up to an altitude of about 9.3 km above sea level during the period 25 November at 21:10 UTC–26 November at 00:50 UTC. The study demonstrates that RSTASH may give a useful contribution for the operational monitoring of ash clouds over East Asia and the Western Pacific region, well exploiting the 10 min temporal resolution and the spectral features of the Himawari-8 data.

Characteristics of diabatic heating associated with mesoscale convective systems over Indian Ocean from radiosonde and GPS radio occultation

This study aims to investigate the characteristics of diabatic heating profiles associated with appearances of Mesoscale Convective Systems (MCSs) over Sumatra Island (SI) and Indian Ocean (IO) including the transitional offshore zone (TZ). We define the parameters of eccentricity, cloud lifetime, and cloud coverage from Multi-Functional Transport Satellite (MTSAT) infrared imageries to identify the MCSs. We derive the diabatic heating profiles over SI and IO using radiosonde data collected during Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY2011) / Dynamics of the Madden Julian Oscillation (DYNAMO) campaign. We also estimate the diabatic heating profiles from the GPS Radio Occultation incorporated with the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data over TZ, where the radiosonde observations were limited. The results show that heating occurred throughout the entire troposphere during the mature stages of MCSs with more variations in the amplitude and maximum heating up to 20 K/day overland. Furthermore, the altitude of maximum heating over SI is lower than that of IO and TZ. Over SI and TZ, profiles of dissipations (postmature) stage are characterized by cooling in the lower altitude and an upward shift of maximum heating. By analyzing vertical velocity profiles, we confirm that cooling (heating) processes are associated with updraft (downdraft) in the cloud systems. In addition, the presence of cooling that occurs in layers close to the surface could indicate the formation of cold pool during the dissipation phase of the MCSs.

Hazard mitigation with cloud model based rainfall and convective data

Heavy rain in Semarang 15 January 2013 causes flood. It is related to dynamic of weather’s parameter, especially with convection process, clouds and rainfall data. In this case, weather condition analysis uses Weather Research and Forecasting (WRF) model used to analyze. Some weather’s parameters show significant result. Their fluctuations prove there is a strong convection that produces convective cloud (Cumulonimbus). Nesting and 2 domains on WRF model show good output to represent weather’s condition commonly. The results of this study different between output cloud cover rate of observation result and output of model around 6-12 hours is because spinning-up of processing. Satellite Images of MTSAT (Multifunctional Transport Satellite) are used as a verification data to prove the result of WRF. White color of satellite image is Coldest Dark Grey (CDG) that indicates there is cloud’s top. This image consolidates that the output of WRF is good enough to analyze Semarang’s condition when the case happened.

A Long-Term Overshooting Convective Cloud-Top Detection Database over Australia Derived from MTSAT Japanese Advanced Meteorological Imager Observations

ABSTRACTA 10-yr geostationary (GEO) overshooting cloud-top (OT) detection database using Multifunction Transport Satellite (MTSAT) Japanese Advanced Meteorological Imager (JAMI) observations has been developed over the Australian region. GEO satellite imagers collect spatially and temporally detailed observations of deep convection, providing insight into the development and evolution of hazardous storms, particularly where surface observations of hazardous storms and deep convection are sparse and ground-based radar or lightning sensor networks are limited. Hazardous storms often produce one or more OTs that indicate the location of strong updrafts where weather hazards are typically concentrated, which can cause substantial impacts on the ground such as hail, damaging winds, tornadoes, and lightning and to aviation such as turbulence and in-flight icing. The 10-yr OT database produced using an automated OT detection algorithm is demonstrated for analysis of storm frequency, diurnally, spatially, and seasonally relative to known features such as the Australian monsoon, expected regions of hazardous storms along the southeastern coastal regions of southern Queensland and New South Wales, and the preferential extratropical cyclone track along the Indian Ocean and southern Australian coast. A filter based on atmospheric instability, deep-layer wind shear, and freezing level was used to identify OTs that could have produced hail. The filtered OT database is used to generate a hail frequency estimate that identifies a region extending from north of Brisbane to Sydney and the Goldfields–Esperance region of eastern Western Australia as the most hail-prone regions.

Relationship between Tropical Cyclone Intensification and Cloud-Top Outflow Revealed by Upper-Tropospheric Atmospheric Motion Vectors

AbstractThe temporally dense geostationary satellite observations made possible by recent technological advances enable atmospheric motion vectors (AMVs) to be derived that are suitable for capturing atmospheric flows even of mesoscale phenomena, for which in situ data are scarce. Tropical cyclone (TC) outflows around the cloud top, reflecting TC secondary circulation, were computed by using AMVs derived from successive Multifunctional Transport Satellite (MTSAT) imagery, and the relationship between TC intensification rate (defined as the change of the best-track maximum sustained wind in the previous 24 h) and the outflow was investigated for 44 TCs occurring during 2011–14. During the TC intensification phase, temporal changes in the outflow were generally synchronous with changes in the cloud-top temperature of TC inner-core convective clouds detected by MTSAT infrared band. It was noteworthy that the intensification rates of 66% of the TCs peaked 0–36 h after outflow maximization and that the intensification rate for TCs with a maximum rate of &gt;15 m s−1 day−1 peaked after the outflow maximum. Furthermore, TCs with a large intensification rate and latent-heat release around the midlevel tended to have a large outflow during the intensification phase. A comparison of TCs with and without convective bursts (CBs) revealed that the correlation between outflow and the TC intensification rate was higher for TCs accompanied by CBs than for those without CBs, implying that a rapid deepening of inner-core convection is important for intensification of a TC’s secondary circulation. The outflow tended to be most correlated with the TC intensification rate 0–6 h earlier.

Quantifying the inflation factors of observation error variance for COMS and MTSAT Atmospheric Motion Vectors considering spatial observation error correlation

In this study, the spatial error correlations in the Atmospheric Motion Vectors (AMVs) derived from the geostationary satellite imagery over East Asia are investigated. Good characterization of systematic errors in observations is essential in order to extract the maximum amount of information from the observations during the assimilation process. The spatial structure of AMV error correlations was identified based on datasets of AMVs collocated with sonde observations for July 2015. Results of AMVs from water vapour (WV; 6.7 µm) and infrared (IR; 10.8 µm) channels of the Korean geostationary Communication, Ocean and Meteorological Satellite (COMS) and the Multifunction Transport Satellite‐2 (MTSAT) are presented. For both channels, the length scales of the spatial error correlations in the MTSAT AMVs are longer than those of the COMS AMVs. For MTSAT (COMS) AMVs, a longer (shorter) length‐scale yields more (less) inflated observation error variance, and a larger (smaller) inflation factor for the diagonal form of the observation error covariance matrix is required to obtain the best quality of analysis.

Formation of Winter Supertyphoons Haiyan (2013) and Hagupit (2014) Through Interactions With Cold Fronts as Observed by Multifunctional Transport Satellite

This paper incorporates remote sensing imagery and image processing techniques to analyze typhoons’ evolution into supertyphoons through interactions with cold fronts in the Western Pacific Ocean. The purpose is to enhance understanding and predictability of the tracks and profiles of supertyphoons. Evolutions of typhoons Haiyan (2013) and Hagupit (2014) into supertyphoons were studied. The 3-D profiles of weather systems were reconstructed using multifunctional transport satellite IR cloud images. When interactions between typhoon and cold front happened, enhancements of typhoons were observed causing typhoons Haiyan and Hagupit to strengthen power and evolve into supertyphoons. The origins of typhoons Haiyan and Hagupit are closely located at 152°50E/05°12N and 151°30E/04°19N, respectively. Both typhoons went through The Philippine Sea in the zone of 112°52E–146°11E. The lowest values of central pressures of typhoons Haiyan and Hagupit occurred along their paths at the positions 128°41E/10°16N and 131°06E/11°00N, respectively. Their distances from cold front and average speed of their movement were dominating parameters for the formation of winter supertyphoons. The supertyphoon Haiyan showed higher intensity than the supertyphoon Hagupit. Each of the two typhoons roughly consisted of two paths with the first path moving westerly and the second path northerly. The two typhoons evolved into supertyphoons at the turning points of the two paths. The first paths were about 2330 and 2050 km for typhoons Haiyan and Hagupit, respectively.

Favorable environments for the occurrence of overshooting tops in tropical cyclones

Based on Multifunctional Transport Satellite data and the infrared window-texture detection algorithm, the level of overshooting top (OT) activity within a tropical cyclone (TC), which is defined as the hourly mean number of OT occurrence, was statistically investigated in the western North Pacific basin for the period 2005–12. Based on the level of OT activity, the samples were divided into OT and non-OT cases or high-activity-OT (HA-OT) and low-activity-OT (LA-OT) cases. The differences in large-scale environmental variables between OT (HA-OT) and non-OT (LA-OT) cases were examined 12 hours prior to the OT occurrence. Statistical analysis showed that environmental differences did exist between the OT and non-OT cases. The OTs were more skewed towards the early stage of the TC life cycle, and mostly concentrated in low latitudes. Meanwhile, a sufficiently deep warm-water layer, large temperature difference between the upper- and lower-level troposphere, large humidity at the middle and upper levels, and large atmospheric instability, were favorable for OT occurrence. The differences in large-scale environmental characteristics between HA-OTs and LA-OTs were not as significant as those between OTs and non-OTs, but the HA-OT samples tended to occur when the vertical shear was weak and the TC intensity was low. Finally, statistical models were designed to predict the OT and HA-OT. When at least three OT (HA-OT) predictor thresholds were satisfied, the Peirce skill score reached a maximum value of 0.49 (0.30).

Deep convective cloud characterizations from both broadband imager and hyperspectral infrared sounder measurements

AbstractDeep convective storms have contributed to airplane accidents, making them a threat to aviation safety. The most common method to identify deep convective clouds (DCCs) is using the brightness temperature difference (BTD) between the atmospheric infrared (IR) window band and the water vapor (WV) absorption band. The effectiveness of the BTD method for DCC detection is highly related to the spectral resolution and signal‐to‐noise ratio (SNR) of the WV band. In order to understand the sensitivity of BTD to spectral resolution and SNR for DCC detection, a BTD to noise ratio method using the difference between the WV and IR window radiances is developed to assess the uncertainty of DCC identification for different instruments. We examined the case of AirAsia Flight QZ8501. The brightness temperatures (Tbs) over DCCs from this case are simulated for BTD sensitivity studies by a fast forward radiative transfer model with an opaque cloud assumption for both broadband imager (e.g., Multifunction Transport Satellite imager, MTSAT‐2 imager) and hyperspectral IR sounder (e.g., Atmospheric Infrared Sounder) instruments; we also examined the relationship between the simulated Tb and the cloud top height. Results show that despite the coarser spatial resolution, BTDs measured by a hyperspectral IR sounder are much more sensitive to high cloud tops than broadband BTDs. As demonstrated in this study, a hyperspectral IR sounder can identify DCCs with better accuracy.

Propagation of convective complex systems triggering potential flooding rainfall of Greater Jakarta using satellite data

A convective cloud system that large, long lived, and exhibits a quasi-circular cloud shield could be called a mesoscale convective complexes (MCC) system. These systems produce a wide variety of severe convective weather such as heavy rainfall. On January 15th, 2013, Jakarta experienced an extraordinary heavy rainfall event. In this study, we examined the propagation of the convective complex system on that date that was triggering heavy rainfall occurred in the Greater Jakarta area, using observations from the Multi-functional Transport Satellite (MTSAT)-IR and the synoptic data. The convective complex system developed from midnight of January 14th, 2013 until the morning of January 15th, 2013 and it was intensified by the influence of low-level westerly winds. The convective complex systems were generated during the nighttime of January 14th, 2013 of over Sumatera, and propagate to the Southeast through Java Sea until the northern coast of the Great Jakarta in the morning of January 15th, 2013. These convective complex systems give heavy rainfall up to 63 mm/h on average over the Greater Jakarta area at 04.00 LT. This heavy rainfall had triggered flash flood in Jakarta January 2013.