Cloud Top Brightness Temperature Research Articles

Mesoscale Convective Systems Represented in High Resolution E3SMv2 and Impact of New Cloud and Convection Parameterizations

AbstractIn this study, we evaluate mesoscale convective system (MCS) simulations in the second version of U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv2). E3SMv2 atmosphere model (EAMv2) is run at the uniform 0.25° horizontal resolution. We track MCSs consistently in the model and observations using PyFLEXTRKR algorithm, which defines MCSs based on both cloud top brightness temperature (Tb) and surface precipitation. Results from using only Tb to define MCSs are also discussed to understand the impact of different MCS tracking algorithms on MCS evaluation and provide additional insights into model errors in simulating MCSs. Our results show that EAMv2 simulated MCS precipitation is largely underestimated in tropical and extratropical regions. This is mainly attributed to the underestimated MCS genesis and underestimated precipitation intensity in EAMv2. Comparing the two MCS tracking methods, simulated MCS precipitation is increased if MCSs are defined with only cloud top Tb. The Tb‐based MCS tracking method, however, includes cloud systems with very weak precipitation. This illustrates the model issues in simulating heavy precipitation even though the convective cloud shield is overall well simulated from the moist convective processes. Furthermore, sensitivity experiments are performed to examine the impact of new cloud and convection parameterizations developed for EAMv3 on simulated MCSs. The new physics parameterizations help increase the relative contribution of convective precipitation to total precipitation in the tropics, but the simulated MCS properties are not significantly improved. This suggests that simulating MCSs still remain a challenge for the next version of E3SM.

The Development of a Hailstone Disdrometer and Its Preliminary Observation in Aksu, Xinjiang

Hailfall is a severe local weather event that can cause great economic losses as well as the loss of people’s property; however, it is still difficult for domestic meteorological stations to comprehensively observe hail, and domestic independently developed hail observation instruments are still scarce. To help enable better automatic hail observations, a new independently developed hailstone disdrometer based on the acoustic principle, which can be used to measure the hailstone number and particle size and to calculate the corresponding equivalent liquid precipitation of hailstones, is proposed in this paper. The characteristics of hailstones were preliminarily analyzed using observation data from two hailstone disdrometers installed in Aksu, Xinjiang, where three hail events were observed via the hailstone disdrometer in the summer of 2023. By analyzing the development of deep convection clouds using the Fengyun 4A satellite-based cloud-top brightness temperature, and synoptic conditions based on the fifth-generation global climate reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (the ECMWF ERA5 dataset), the hail formation mechanism was investigated in detail for one hailfall event. Accurate hail observations are an important basis for understanding spatiotemporal hail variation. The hailstone disdrometer proposed in this study offers a useful approach for domestic hail observation to provide first-hand hail information for the inspection of weather modification effects and disaster prevention and reduction.

A CatBoost-Based Model for the Intensity Detection of Tropical Cyclones over the Western North Pacific Based on Satellite Cloud Images

A CatBoost-based intelligent tropical cyclone (TC) intensity-detecting model was built to quantify the intensity of TCs over the Western North Pacific (WNP) with the cloud-top brightness temperature (CTBT) data of Fengyun-2F (FY-2F) and Fengyun-2G (FY-2G) and the best-track data of the China Meteorological Administration (CMA-BST) in recent years (2015–2018). The CatBoost-based model was featured with the greedy strategy of combination, the ordering principle in optimizing the possible gradient bias and prediction shift problems, and the oblivious tree in fast scoring. Compared with the previous studies based on the pure convolutional neural network (CNN) models, the CatBoost-based model exhibited better skills in detecting the TC intensity with the root mean square error (RMSE) of 3.74 m s−1. In addition to the three mentioned model features, there are also two reasons for the model’s design. On one hand, the CatBoost-based model used the method of introducing prior physical factors (e.g., the structure and shape of the cloud, deep convections, and background fields) into its training process. On the other hand, the CatBoost-based model expanded the dataset size from 2342 to 13,471 samples through hourly interpolations of the original dataset. Furthermore, this paper investigated the errors of this model in detecting the different categories of TC intensity. The results showed that the deep learning-based TC intensity-detecting model proposed in this paper has systematic biases, namely, the overestimation (underestimation) of intensities in TCs which were weaker (stronger) than at the typhoon level, and the errors of the model in detecting weaker (stronger) TCs were smaller (larger). This implies that more factors than the CTBT should be included to further reduce the errors in detecting strong TCs.

Analysis of the Triggering and Maintenance Mechanisms of a Record-Breaking Warm-Sector Extreme-Rainfall Process in Front of an Upper-Level Trough in Tianjin, China

A short-time rainstorm exceeding the extreme historical rainfall occurred in the Jinnan District of Tianjin, China, on 3 July 2022. Due to the concentrated time period of precipitation, it caused serious water accumulation in the Jinnan District. The purpose of this paper is to study the weather mechanism of this extreme rainstorm in the Jinan District of Tianjin. By analyzing the fine observation facts, we can obtain the mesoscale weather characteristics and environmental conditions of the process. The results provide a reference for similar weather forecasting and warning in the future. Based on the 1 min interval precipitation observation data, the ERA5 reanalysis data, the CINRAD-SA radar reflectivity data of Tanggu, the cloud-top brightness temperature data of the Fengyun-4A satellite, and the Variational Doppler Radar Analysis System data, we comprehensively analyzed a record-breaking extreme rainfall process in Tianjin on 3 July 2022. The results show that the extreme rainfall process presents prominent mesoscale weather characteristics, with high precipitation intensity in a short-term period. This process is influenced by multi-scale weather systems, including the 500 hPa upper-level trough and the long-distance water vapor transport by Typhoon Chaba. The rainstorm event is caused by the combined actions of cold pool outflow produced by the upstream precipitation, the easterly disturbance in the boundary layer, the mesoscale temperature front, and the ground convergence line. Specifically, the ground convergence line is formed by the northerly wind of the cold pool outflow and the warm and moist southerly airflow from the ocean, and the temperature front is caused by the horizontal thermal difference of the underlying surface. Both the ground convergence line and temperature front contribute considerably to the triggering of mesoscale convection. The mesoscale secondary circulation is formed in the meridional direction by the meso-γ-scale convergence and its interaction with strong velocity in front of the trough, contributing to the development and maintenance of vertical motion in the Jinnan region of Tianjin and thereby leading to the occurrence and development of this extreme heavy rainfall process.

Identification of Supercooled Cloud Water by FY-4A Satellite and Validation by CALIPSO and Airborne Detection

Cold clouds are the main operation target of artificial precipitation enhancement, and its key is to find a supercooled cloud water area where the catalyst can be seeded to promote the formation of precipitation particles and increase precipitation to the ground. Based on the multi-spectral characteristics of the Fengyun-4A (FY-4A) satellite, a methodology for identifying supercooled cloud water is developed. Superimposed by a cloud top brightness temperature of 10.8 µm, a combination of 0.46 µm, 1.6 µm, and 2.2 µm red–green–blue (RGB) composites are used to identify the cloud phase and to obtain the real-time supercooled cloud water distribution every 5 min and in a 2 km resolution for the whole coverage of China. Based on the RGB composition, the supervised machine learning method K-mean clustering was applied to classify the cloud top phase. The results were validated extensively with Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). It is worthwhile to highlight that the corresponding hit rate reached 87% over the full disk domain for both the summer and winter seasons. Furthermore, on 29 November 2019, microphysical properties were measured, and the data of supercooled cloud droplets and ice crystals were obtained using YUN-12 transport aircraft in Taiyuan. After simultaneously matching the satellite with an airborne track, the cloud particle image data were obtained near the cloud top and within the clouds during the climb and descending stages of the flight. The phase obtained from the microphysical properties of supercooled cloud droplets and ice crystals was compared with cloud phase results identified by FY-4A and Moderate Resolution Imaging Spectroradiometer (MODIS) cloud phase products. The case study and comparison show that (1) the supercooled water clouds and ice particles identified by FY-4A are in good agreement with those from the airborne measurement at the cloud top and within the cloud and (2) the positions and shapes of water clouds and ice clouds identified by FY-4A correspond well with MODIS cloud phase products. However, there is a small deviation in the extent of ice clouds, which is mainly located in the transition area between ice clouds and water clouds. The extent of ice clouds identified by FY-4A is slightly larger than that of MODIS products. Combined with airborne detection, the comparison shows that the ice clouds identified by the FY-4A satellite are consistent with aircraft detection. The supercooled cloud water identified by FY-4A can meet the needs of the operational precipitation enhancement of cold clouds, improve operational effectiveness, and promote the application of satellite technology for weather modification.

Resilient dataset of rain clusters with life cycle evolution during April to June 2016–2020 over eastern Asia based on observations from the GPM DPR and Himawari-8 AHI

Abstract. Our knowledge of the properties of precipitation and clouds over their life cycles has progressed with the rapid development of satellite observations. However, previous studies have focused on the life cycle evolution of the macroscale features of precipitation and clouds, whereas the evolution of the microphysical properties of precipitation and clouds over their life cycles is yet to be determined. One of the reasons for this lack of knowledge is the fact that there is no single dataset providing both the three-dimensional structure of precipitation and the relevant life cycle properties. We identified initial rain clusters (RCs) from the Global Precipitation Measurement (GPM) 2ADPR dataset and mesoscale convective systems (MCSs) from the Himawari-8 Advanced Himawari Image (AHI) gridded product. Based on the contours of the initial RCs and MCSs, we then carried out a series of resilient processes, including filtration, segmentation, and consolidation, to obtain the final RCs. The final RCs had a one-to-one correspondence with the relevant MCS. We extracted the RC area, central location, average radar reflectivity profile, average droplet size distribution profile, and other precipitation information from the contours of the final RCs and GPM 2ADPR dataset. The life cycle evolution of the MCS area, location, and cloud-top brightness temperature were retrieved from the corresponding MCSs and their tracks from Himawari-8 observations. The final dataset provides both three-dimensional precipitation information and life cycle information of precipitating clouds during April to June 2016–2020 over eastern Asia. This dataset facilitates studies of the life cycle evolution of precipitation and provides a good foundation for convection parameterizations in precipitation simulations. The dataset used in this paper is freely available at https://doi.org/10.5281/zenodo.6198716 (Zhang et al., 2022).

VELOX – a new thermal infrared imager for airborne remote sensing of cloud and surface properties

Abstract. The new airborne thermal infrared (TIR) imager VELOX (Video airbornE Longwave Observations within siX channels) is introduced. VELOX is a commercially available TIR camera system that has been adapted extensively for atmospheric applications, which are introduced in this paper. The system covers six spectral bands with centre wavelengths between 7.7 and 12 µm. Currently, VELOX is installed on board the German High Altitude and Long Range Research Aircraft (HALO) to observe cloud and surface properties. It provides observations of two-dimensional (2D) fields of upward terrestrial spectral radiance with a horizontal resolution of approximately 10 m×10 m at a target distance of 10 km. Atmospheric temperature values are rather low compared to the originally intended commercial applications of VELOX and range close to the detection limit of the sensor. This challenge requires additional calibration efforts to enable atmospheric applications of VELOX. Therefore, required sophisticated calibration and correction procedures, including radiometric calibrations, non-uniformity corrections, bad-pixel replacements, and window corrections, are presented. Furthermore, first observations of cloud properties acquired by VELOX during the EUREC4A (ElUcidating the RolE of Cloud-Circulation Coupling in ClimAte) campaign are discussed, including an analysis of the cloud top brightness temperature, cloud mask/fraction, and cloud top altitude data. The data reveal the potential of VELOX to resolve the cloud top temperature with a resolution of better than 0.1 K, which translates into a resolution of approximately 40 m in cloud top altitude.

A case study on the impact of severe convective storms on the water vapor mixing ratio in the lower mid-latitude stratosphere observed in 2019 over Europe

Abstract. Extreme convective events in the troposphere not only have immediate impacts on the surface, but they can also influence the dynamics and composition of the lower stratosphere (LS). One major impact is the moistening of the LS by overshooting convection. This effect plays a crucial role in climate feedback, as small changes of water vapor in the upper troposphere and lower stratosphere (UTLS) have a large impact on the radiative budget of the atmosphere. In this case study, we investigate water vapor injections into the LS by two consecutive convective events in the European mid-latitudes within the framework of the MOSES (Modular Observation Solutions for Earth Systems) measurement campaign during the early summer of 2019. Using balloon-borne instruments, measurements of convective water vapor injection into the stratosphere were performed. Such measurements with a high vertical resolution are rare. The magnitude of the stratospheric water vapor reached up to 12.1 ppmv (parts per million by volume), with an estimated background value of 5 ppmv. Hence, the water vapor enhancement reported here is of the same order of magnitude as earlier reports of water vapor injection by convective overshooting over North America. However, the overshooting took place in the extratropical stratosphere over Europe and has a stronger impact on long-term water vapor mixing ratios in the stratosphere compared to the monsoon-influenced region in North America. At the altitude of the measured injection, a sharp drop in a local ozone enhancement peak makes the observed composition of air very unique with high ozone up to 650 ppbv (parts per billion by volume) and high water vapor up to 12.1 ppmv. ERA-Interim does not show any signal of the convective overshoot, the water vapor values measured by the Microwave Limb Sounder (MLS) in the LS are lower than the in situ observations, and the ERA5 overestimated water vapor mixing ratios. Backward trajectories of the measured injected air masses reveal that the moistening of the LS took place several hours before the balloon launch. This is in good agreement with the reanalyses, which shows a strong change in the structure of isotherms and a sudden and short-lived increase in potential vorticity at the altitude and location of the trajectory. Similarly, satellite data show low cloud-top brightness temperatures during the overshooting event, which indicates an elevated cloud top height.

Simulating Tropical Cyclone Passive Microwave Rainfall Imagery Using Infrared Imagery via Generative Adversarial Networks

Tropical cyclones (TCs) generally carry large amounts of water vapor and can cause large-scale extreme rainfall. Passive microwave (PMW) rainfall (PMR) estimation of TC with high spatial and temporal resolution is crucial for disaster warning of TC, but remains a challenging problem due to low temporal resolution of microwave sensors. This study attempts to solve this problem by directly predicting PMW rainfall images (PMRIs) from satellite infrared (IR) images of TC. We develop a generative adversarial network (GAN) to simulate PMRI using IR images and establish the mapping relationship between TC cloud-top brightness temperature and PMR, and the algorithm is named tropical cyclone rainfall (TCR)-GAN. Meanwhile, a new dataset that is available as a benchmark, Dataset of TC IR-to-Rainfall Prediction (TCIRRP), was established, which is expected to advance the development of artificial intelligence in this direction. The experimental results show that the algorithm can effectively extract key features from IR. The end-to-end deep learning approach shows potential as a technique that can be applied globally and provides a new perspective TC precipitation prediction via satellite, which is expected to provide important insights for real-time visualization of TC rainfall globally in operations.

FY4QPE-MSA: An All-Day Near-Real-Time Quantitative Precipitation Estimation Framework Based on Multispectral Analysis From AGRI Onboard Chinese FY-4 Series Satellites

Accurate and near-real-time rain information at fine scales is critical for forecasting local weather and floods. Traditional classical infrared (IR) cloud-top brightness temperature data alone do not contain sufficient precipitation-related information, and the introduction of visible (VIS) observations limits their applications to daytime. Methods for the efficient and comprehensive utilization of multichannel IR observations for accurately retrieving all-day near-real-time rain rates with consistent high quality warrant further exploration. In this study, we propose an all-day near-real-time quantitative precipitation estimation framework based on multispectral analysis (MSA) from the Advanced Geosynchronous Radiation Imager (AGRI) onboard Chinese FY-4 series satellites; the proposed framework is called FY4QPE- MSA. Multiple IR bands are comprehensively and efficiently considered by adopting the principal component analysis technique to reduce the dimensionality to a few independent features while preserving most of the variations. The main conclusions include, but are not limited to, the following aspects: 1) the MSA from IR channels provides valuable information that facilitates the more accurate delineations of the precipitation occurrences; 2) FY4AQPE -MSA outperforms FY4AQPE- Offical [<inline-formula> <tex-math notation="LaTeX">$\sim 20$ </tex-math></inline-formula>&#x0025; gain in the Pearson correlation coefficient (CC), <inline-formula> <tex-math notation="LaTeX">$\sim 25$ </tex-math></inline-formula>&#x0025; gain in the root mean square error (RMSE), and <inline-formula> <tex-math notation="LaTeX">$\sim 15$ </tex-math></inline-formula>&#x0025; gain in the critical success index (CSI)], FY4AQPE-Single (<inline-formula> <tex-math notation="LaTeX">$\sim 10$ </tex-math></inline-formula>&#x0025; gain in CC, <inline-formula> <tex-math notation="LaTeX">$\sim 15$ </tex-math></inline-formula>&#x0025; gain in RMSE, and <inline-formula> <tex-math notation="LaTeX">$\sim 15$ </tex-math></inline-formula>&#x0025; gain in CSI), and Precipitation Estimation from Remotely Sensed Information using artificial neural network-cloud classification system (PERSIANN-CCS) (<inline-formula> <tex-math notation="LaTeX">$\sim 30$ </tex-math></inline-formula>&#x0025; gain in CC, <inline-formula> <tex-math notation="LaTeX">$\sim 20$ </tex-math></inline-formula>&#x0025; gain in RMSE, and <inline-formula> <tex-math notation="LaTeX">$\sim 25$ </tex-math></inline-formula>&#x0025; gain in CSI); and 3) compared with the international IR-based baseline precipitation product, i.e., PERSIANN-CCS, FY4AQPE-MSA demonstrates no spatial gaps over the Tibetan Plateau. In addition, the results of this study suggest that the proposed general framework is promising and applicable for Chinese FY-4 series satellites in generating all-day near-real-time rain-rate information.

Case Studies on Concentric Gravity Waves Source Using Lightning Flash Rate, Brightness Temperature and Backward Ray Tracing at São Martinho da Serra (29.44°S, 53.82°W)

AbstractWe relate the spatial and temporal distribution of lightning flash rates and cloud top brightness temperature (CTBT) to concentric atmospheric gravity wave (CGW) events observed at the Southern Space Observatory (SSO) in São Martinho da Serra (29.44°S, 53.82°W, 488.7 m) in southern Brazil. The selected identified cases from 2017 to 2018 were observed by a hydroxyl (OH) all‐sky imager. Backward ray tracing shows that the time of gravity wave excitation agrees with the highest values of lightning flash rates (indicating lightning jump) as well as the coldest brightness temperatures that indicate the time of convective overshoot. Radiosonde measurements show high convective available potential energy (CAPE), associated with a maximum updraft velocity just prior to the wave events. We find that these possible source locations correspond to the positions and times that convective plumes overshot the tropopause (seen in GOES‐16 CTBT images). We also show that higher spatial lightning density (i.e., number of lightning flashes at a given longitude and latitude) agree with the overshoot locations from the GOES satellite. We also find that the overshoot times from the GOES‐16 satellite agree with the times lightning jumps were observed in the lightning flash rate. Finally, we find that the periodicities in the lightning flash rate agree with the periods of the observed CGWs, which further strengthens the result that the CGWs were excited by the deep convective systems determined from backward ray tracing.

Relationship between Near-Surface Winds Due to Tropical Cyclones and Infrared Brightness Temperature Obtained from Geostationary Satellite

Based on ten-year tropical cyclones (TCs) observations from 2009 to 2018, the black body temperature (TBB, also called cloud-top brightness temperature) data obtained from the infrared channel 1 (with the wavelength of 10.30–11.30 µm) of the FY-2 satellite image, and the wind observation data at the automatic weather stations (AWSs) in Guangdong province, this study explores the relationship between the TBBs and the winds induced by TCs at AWSs. It is found that the wind speeds at AWSs cannot be obtained directly by using TBB value inversion, but the maximum potential wind gust (MPG) and the maximum potential average-wind (MPAW) at AWSs can be estimated when a TBB is known. Influenced by the terrain, the surrounding environment, and detected height, the MPG and the MPAW values of different AWSs may differ for the same TBB. The wind data from ERA5 reanalysis is also used to explore the relationship between the TBBs and the winds over grids area during the TCs’ periods. Similar to the AWSs, there is a capping function between the winds over the grids and the TBBs. The reanalysis data can generally show the average wind conditions of the weather stations inside the grids, and therefore, can be used to supplement the data for the areas where there is no AWS observation available. Such a study could provide references for estimating the potential wind disasters induced by TCs in the study area.

Signatures of Oceanic Wind Events in Geostationary Cloud Top Temperature and Lightning Data

AbstractA total of 13 ocean-based wind events from 2018, detected by buoys and Coastal-Marine Automated Network (C-MAN) stations, were analyzed using 1-min mesoscale sector Advanced Baseline Imager (ABI) cloud top brightness temperature (CTTB) data, as well as 1-min Geostationary Lightning Mapper (GLM) lightning data. The ABI and GLM instruments are located on the Geostationary Operational Environmental Satellite-16 (GOES-16) satellite. An oceanic wind event was defined as a buoy or C-MAN station-recorded peak wind gust of at least 14 m s−1, associated with a convective storm. The wind gust was required to exceed the wind speed by at least 4 m s−1 at the time of the event, but not exceed the corresponding wind speed by at least 4 m s−1 for more than 30 min. This study hypothesized that prior to a wind event, there should be unique signatures in ABI CTTB and GLM lightning datasets. The presumption was that the minimum CTTB and maximum flash rate should occur near the same time and prior to the event. The minimum CTTB occurred an average of 10.5 min and a median of 7 min prior to events, with a range from 29 min prior to 1 min after the event. Changes in CTTB were often subtle. A maximum flash rate occurred within 5 min of the minimum CTTB for 11 of the 12 events with lightning and did not exceed 11 flashes per minute for 9 of the 12 events with lightning. Operational weather forecasters might use CTTB and lightning trends to help identify storms capable of producing significant oceanic wind events.

Evaluating Convective Initiation in High-Resolution Numerical Weather Prediction Models Using GOES-16 Infrared Brightness Temperatures

AbstractThe evolution of model-based cloud-top brightness temperatures (BT) associated with convective initiation (CI) is assessed for three bulk cloud microphysics schemes in the Weather Research and Forecasting Model. Using a composite-based analysis, cloud objects derived from high-resolution (500 m) model simulations are compared to 5-min GOES-16 imagery for a case study day located near the Alabama–Mississippi border. Observed and simulated cloud characteristics for clouds reaching CI are examined by utilizing infrared BTs commonly used in satellite-based CI nowcasting methods. The results demonstrate the ability of object-based verification methods with satellite observations to evaluate the evolution of model cloud characteristics, and the BT comparison provides insight into a known issue of model simulations producing too many convective cells reaching CI. The timing of CI from the different microphysical schemes is dependent on the production of ice in the upper levels of the cloud, which typically occurs near the time of maximum cloud growth. In particular, large differences in precipitation formation drive differences in the amount of cloud water able to reach upper layers of the cloud, which impacts cloud-top glaciation. Larger cloud mixing ratios are found in clouds with sustained growth leading to more cloud water lofted to the upper levels of the cloud and the formation of ice. Clouds unable to sustain growth lack the necessary cloud water needed to form ice and grow into cumulonimbus. Clouds with slower growth rates display similar BT trends as clouds exhibiting growth, which suggests that forecasting CI using geostationary satellites might require additional information beyond those derived at cloud top.

Cloud top height retrieval over the Arctic Ocean using a cloud-shadow method based on MODIS

Cloud top height (CTH) plays an important role in the Earth's radiation budget and thus in climate change. However, accurate CTH retrieval based on satellite data remains a challenge when using currently widespread methods based on passive remote sensing satellite data, particularly over the Arctic Ocean. It is well known that using geometric correlation between CTH and cloud-shadow distance along the normal direction of sunlight, cloud-edge top height (CETH) can be determined. However, this cloud-shadow method (CSM) exhibited larger CTH errors due to low-resolution weather satellite data of the past. The aim of the paper is to analyze the effect of higher-resolution images (MODIS with 250-m resolution) on CSM accuracy. We present a method in which CETH is first retrieved using the MODIS data, and to calculate CTH in the center of cloud region by combining the calculated CETH and cloud top brightness temperature using an environmental temperature lapse rate method. We validated 14 cases over the Arctic Ocean by the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) CTH lidar product and another two cases by radiosonde data. CSM revealed better performance (mean difference is 0.45 km and standard deviation is 0.92 km) compared to the operational MODIS CTH product (mean difference is −0.78 km and standard deviation is 1.78 km), when validated by the CALIPSO product. When validated by sounding data, CSM also performed better than the MODIS CTH product: the CSM CTH errors are 0.41 and 0.52 km in two sounding cases, while the corresponding MODIS CTH errors are −1.10 and 1.07 km. The CSM technique especially showed better performance for double-deck cloud systems. We conclude that further improvements in CTH accuracy based on CSM can be achieved by mainly using higher-resolution satellite imagery.

Observations of Aerosol, Cloud, Turbulence, and Radiation Properties at the Top of the Marine Boundary Layer over the Eastern North Atlantic Ocean: The ACORES Campaign

AbstractWe report on the Azores Stratocumulus Measurements of Radiation, Turbulence and Aerosols (ACORES) campaign, which took place around Graciosa and Pico Islands/Azores in July 2017. The main objective was to investigate the vertical distribution of aerosol particles, stratocumulus microphysical and radiative properties, and turbulence parameters in the eastern North Atlantic. The vertical exchange of mass, momentum, and energy between the free troposphere (FT) and the cloudy marine boundary layer (MBL) was explored over a range of scales from submeters to kilometers. To cover these spatial scales with appropriate measurements, helicopter-borne observations with unprecedented high resolution were realized using the Airborne Cloud Turbulence Observation System (ACTOS) and Spectral Modular Airborne Radiation Measurement System–Helicopter-Borne Observations (SMART-HELIOS) instrumental payloads. The helicopter-borne observations were combined with ground-based aerosol measurements collected at two continuously running field stations on Pico Mountain (2,225 m above sea level, in the FT), and at the Atmospheric Radiation Measurement (ARM) station on Graciosa (at sea level). First findings from the ACORES observations we are discussing in the paper are as follows: (i) we have observed a high variability of the turbulent cloud-top structure on horizontal scales below 100 m with local temperature gradients of up to 4 K over less than 1 m vertical distance, (ii) we have collected strictly collocated radiation measurements supporting the relevance of small-scale processes by revealing significant inhomogeneities in cloud-top brightness temperature to scales well below 100 m, and (iii) we have concluded that aerosol properties are completely different in the MBL and FT with often-complex stratification and frequently observed burst-like new particle formation.

Analysis of a Gigantic Jet in Southern China: Morphology, Meteorology, Storm Evolution, Lightning, and Narrow Bipolar Events

AbstractAt about 22:43:30 BJT (Beijing Time = UTC + 8) on 13 August 2016, two amateur astronomers in Shikengkong, Guangdong province, and Jiahe County, Hunan province, respectively, fortuitously captured a gigantic jet (GJ) event simultaneously, and the GJ exact location could be triangulated. The parent thunderstorm was in a very humid environment (Precipitable Water [PWAT] in excess of 60 mm), featuring high convective available potential energy (CAPE of 2,428 J/kg). The GJ occurred in the region with the coldest cloud top brightness temperature of −64 °C, suggesting the GJ was associated with strong vertical development of the thunderstorm. The vertical cross sections of radar reflectivity also show that the GJ occurred near the thunderstorm strong convection region (overshooting top). The negative cloud‐to‐ground flashes dominated during the thunderstorm evolution. Three positive narrow bipolar events (NBEs) were detected within 30 s before and after the GJ. It indicates that the NBEs were occurred in the upper and middle layers of the thunderstorm (altitude of 11–13 km) with radar reflectivity of 30–35 dBZ.

PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation

AbstractPrecipitation measurements with high spatiotemporal resolution are a vital input for hydrometeorological and water resources studies; decision-making in disaster management; and weather, climate, and hydrological forecasting. Moreover, real-time precipitation estimation with high precision is pivotal for the monitoring and managing of catastrophic hydroclimate disasters such as flash floods, which frequently transpire after extreme rainfall. While algorithms that exclusively use satellite infrared data as input are attractive owing to their rich spatiotemporal resolution and near-instantaneous availability, their sole reliance on cloud-top brightness temperature (Tb) readings causes underestimates in wet regions and overestimates in dry regions—this is especially evident over the western contiguous United States (CONUS). We introduce an algorithm, the Precipitation Estimations from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain rate model (PDIR), which utilizes climatological data to construct a dynamic (i.e., laterally shifting) Tb–rain rate relationship that has several notable advantages over other quantitative precipitation-estimation algorithms and noteworthy skill over the western CONUS. Validation of PDIR over the western CONUS shows a promising degree of skill, notably at the annual scale, where it performs well in comparison to other satellite-based products. Analysis of two extreme landfalling atmospheric rivers show that solely IR-based PDIR performs reasonably well compared to other IR- and PMW-based satellite rainfall products, marking its potential to be effective in real-time monitoring of extreme storms. This research suggests that IR-based algorithms that contain the spatiotemporal richness and near-instantaneous availability needed for rapid natural hazards response may soon contain the skill needed for hydrologic and water resource applications.

Quantification and Exploration of Diurnal Oscillations in Tropical Cyclones

AbstractDiurnal oscillations of infrared cloud-top brightness temperatures (Tbs) in tropical cyclones (TCs) as inferred from storm-centered, direction-relative longwave infrared (~11 μm) imagery are quantified for Northern Hemisphere TCs (2005–15) using statistical methods. These methods show that 45%, 54%, and 61% of at least tropical storm-, hurricane-, and major hurricane-strength TC cases have moderate or strong diurnal signals. Principal component analysis–based average behavior of all TCs with intensities of 34 kt (17.5 m s−1) or greater is shown to have a nearly symmetric diurnal signal where Tbs oscillate from warm to cold and cold to warm within and outside of a radius of approximately 220 km, with maximum central cooling occurring in the early morning (0300–0800 local standard time), and a nearly simultaneous maximum warming occurring near the 500-km radius—a radial standing wave with a node near 220-km radius. Amplitude and phase of these diurnal oscillations are quantified for individual 24-h periods (or cases) relative to the mean oscillation. Details of the diurnal behavior of TCs are used to examine preferred storm and environmental characteristics using a combination of spatial, composite, and regression analyses. Results suggest that diurnal, cloud-top Tb oscillations in TCs are strongest and most regular when storm characteristics (e.g., intensity and motion) and environmental conditions (e.g., vertical wind shear and low-level temperature advection) support azimuthally symmetric storm structures and when surrounding mid- and upper-level relative humidity values are greater. Finally, it is hypothesized that larger mid- and upper-level relative humidity values are necessary ingredients for robust, large-amplitude, and regular diurnal oscillations of Tbs in TCs.

Analysis of the first positive polarity gigantic jet recorded near the Yellow Sea in mainland China

At 12:16:22 UTC on 12 August 2010, a gigantic jet (GJ) was recorded over a thunderstorm near the Yellow Sea in China. The extremely low-frequency (ELF) magnetic field recorded at the Onagawa Observatory indicates that this GJ transferred positive charge from the thundercloud to the ionosphere (namely a +GJ event), which is the first observation of a +GJ reported in mainland China. The top altitude of this GJ was estimated to be about 89 km. The parent thunderstorm formed in a very moist environment (precipitable water of 75.4 mm) with moderate convective available potential energy (1294 J/kg) and lifted index (−3.19), and strong 0–6 km wind speed shear (16.3 m/s). The meteorological parameters are not much different from typical summer thunderstorms. The area of cloud top brightness temperature colder than −60 °C (altitudes above 14 km) increased significantly after 10:00 UTC and reached the maximum at 12:00 UTC (16 min before the GJ), suggesting the occurrence of the GJ was related to the strong vertical development of the thunderstorm. Around the time of the GJ, the south cell of the storm featured overshooting top as indicated by radar data. Additionally, the storm was dominated by negative cloud-to-ground (-CG) flashes with an increase in -CG flash rate around the time of the GJ occurrence, indicating the storm appeared to be of normal polarity (the main positive charge region located above the main negative charge region). The + GJ was probably produced by a normally electrified thunderstorm, and a possible explanation for this unexpected behavior and different lightning activity of GJ-producing storms were discussed.