Infrared Brightness Temperature Research Articles

Impact of Microphysics Parameterization Schemes on the Assimilation of GOES‐16 ABI All‐Sky Infrared Radiances for a Bow Echo Analysis and Prediction

AbstractThis study aims to investigate how assimilating GOES‐16 ABI infrared brightness temperature (BT) with different microphysics schemes affects the convection‐allowing analysis and prediction of the 3 May 2020 bow echo case. The Gridpoint Statistical Interpolation‐based Ensemble Kalman Filter (GSI‐EnKF) system and Weather Research and Forecasting (WRF) model are utilized to conduct data assimilation (DA) experiments using Thompson, WDM6, NSSL, and Morrison microphysics schemes. Correlation structures between BT and model state variables indicate that assimilating infrared BT can adjust bowing MCS dynamics via latent cooling and the rear inflow jet. Such corrections during DA cycling enhance the rear inflow jet and bow echo size, primarily for microphysics schemes featuring faster hydrometeor fall velocity and stronger latent cooling. The improved analyses lead to better forecasts of the bow echo's shape, size, timing of the bowing process, and wind speeds. Substituting a larger microphysics‐dependent effective radius for a constant default value increases prior BT, the magnitude of BT innovations, and accumulated impact on the rear inflow jet, especially for the WDM6 and Morrison schemes. In the subsequent forecasts, incorporating microphysics‐dependent effective radius further improves the experiment using the Morrison scheme but degrades it when using the WDM6 scheme.

Thermal infrared shadow-hiding in GOES-R ABI imagery: snow and forest temperature observations from the SnowEx 2020 Grand Mesa field campaign

Abstract. The high temporal resolution of thermal infrared imagery from the Geostationary Operational Environmental Satellites R-series (GOES-R) presents an opportunity to observe mountain snow and forest temperatures over the full diurnal cycle. However, the off-nadir views of these imagers may impact or bias temperature observations, especially when viewing a surface composed of both snow and forests. We used GOES-16 and -17 thermal infrared brightness temperature observations of a flat snow- and forest-covered study site at Grand Mesa, Colorado, USA, to characterize how forest coverage and view angle impact these observations. These two geostationary satellites provided views of the study area from the southeast (134.1° azimuth, 33.5° elevation) and southwest (221.2° azimuth, 35.9° elevation), respectively. As part of the NASA SnowEx field campaign in February 2020, coincident brightness temperature observations from ground-based and airborne IR sensors were collected to compare with those from the geostationary satellites. Observations over the course of 2 cloud-free days spanned the entire study site. The brightness temperature observations from each dataset were compared to find their relative differences and how those differences may have varied over time and/or as a function of varying forest cover across the study area. GOES-16 and -17 brightness temperatures were found to match the diurnal cycle and temperature range within ∼ 1 h and ± 3 K of ground-based observations. GOES-16 and -17 were both biased warmer than nadir-looking airborne IR and ASTER observations. The warm biases were higher at times when the sun–satellite phase angle was near its daily minimum. The phase angle, the angle between the direction of incoming solar illumination and the direction from which the satellite is viewing, reached daily minimums in the morning for GOES-16 and afternoon for GOES-17. In morning observations, warm biases in GOES-16 brightness temperature were greater for pixels that contained more forest coverage. The observations suggest that a “thermal infrared shadow-hiding” effect may be occurring, where the geostationary satellites are preferentially seeing the warmer sunlit sides of trees at different times of day. These biases are important to understand for applications using GOES-R brightness temperatures or derived land surface temperatures (LSTs) over areas with surface roughness features, such as forests, that could exhibit a thermal infrared shadow-hiding effect.

Dynamical drivers of free-tropospheric ozone increases over equatorial Southeast Asia

Abstract. Positive trends in tropical free-tropospheric (FT) ozone are frequently ascribed to emissions growth, but less is known about the effects of changing dynamics. Extending a prior study (Thompson et al., 2021; https://doi.org/10.1029/2021JD034691; “T21”), we re-examine Southern Hemisphere Additional Ozonesondes (SHADOZ) ozone trends over equatorial Southeast Asia (ESEA), one of Earth's most convectively active regions, using 25 years (1998–2022) of ozone soundings. T21 posited that early-year positive FT ozone trends at equatorial SHADOZ stations are related to decreasing convection. The 25-year analysis of Kuala Lumpur and Watukosek SHADOZ records finds that FT ozone trends of +5 % to +15 % (+2 to +6 nmol mol−1) per decade from ∼ February–April coincide with large increases in satellite infrared brightness temperatures and outgoing longwave radiation, indicators of declining convective activity. MERRA-2 reanalyses exhibit increases in upper-tropospheric velocity potential and decreases in precipitable water, also indicating diminished convection. In contrast, trends in ozone and convective indicators are weak the rest of the year. These results suggest that decreases in convective intensity and frequency are primary drivers of FT ozone build-up over ESEA early in the year; i.e., waning convection suppresses lofting and dilution of ozone. Decreasing convection promotes accumulation of biomass burning emissions typical of boreal spring even though satellite FT carbon monoxide trends (2002–2022) over ESEA follow a global decrease pattern. Finally, our results demonstrate the advantages of monthly or seasonally resolved analyses over annual means for robust attribution of observed ozone trends, challenging models to reproduce these detailed features in simulations of the past 25 years.

Hue Channel Mesoscale Convective System Identification Techniques using Kalpana Geostationary Satellite Observations

Climate change is leading to sudden extreme weather events like floods and heavy rain to occur. One of the most significant rain-bearing systems, Mesoscale Convective Systems (MCSs), is in charge of catastrophic rainfall and flood events that may cause loss of life and property. MCSs are one of the vital components of the climate system on Earth. Many studies contributed to two significant steps, MCS identification and tracking. There are a variety of algorithms focused on tracking whereas for MCS identification only limited methods are present. They contribute to global as well as regional climate patterns by transporting heat and moisture. It is necessary to identify MCS properly to track MCS occurrences over time and comprehend their typical lifecycle to get early warnings of their existence. Three MCS identification techniques based on the Hue channel of the Hue Saturation Value (HSV) color model are implemented in this research, and their effectiveness is assessed. These techniques execute segmentation based on Hue channel Thresholding (HT), K means clustering combined with Hue channel Thresholding (KMCHT) and the modified Source Apportionment Technique combined with Hue channel Thresholding (SATHT). Image pixel values are used to depict infrared brightness temperature data obtained from the Indian geostationary satellite Kalpana-1. The generated ground truth images and performance measurements are used to assess the effectiveness of the methods. The proposed SATHT method for multiple cloud segmentation results in superior performance metrics than the HT and KMCHT approaches.

FY‐4A/AGRI Infrared Brightness Temperature Estimation of Precipitation Based on Multi‐Model Ensemble Learning

AbstractSatellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data.

Cross-Calibration of HY-1D/COCTS Thermal Emissive Bands in the South China Sea

Haiyang-1D (HY-1D) is the second operational satellite in China’s Haiyang-1 series of satellites, carrying the Chinese Ocean Color and Temperature Scanner (COCTS) to provide ocean color and temperature observations. The radiometric calibration is a prerequisite to guarantee the quality of the satellite observations and the derived products, and the radiometric calibration of the thermal emissive bands of HY-1D/COCTS can effectively improve the accuracy of sea surface temperature (SST) derived from the thermal infrared data. In this paper, a study on the regional cross-calibration of the COCTS thermal emissive bands is conducted for high-accuracy SST observations in the South China Sea. The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the NOAA-20 satellite launched by the National Oceanic and Atmospheric Administration (NOAA) is selected as the calibration reference sensor, and a double-difference cross-calibration method is used for HY-1D/COCTS thermal infrared brightness temperature (BT) evaluation. The results show that the bias of the 11 µm and 12 µm thermal emissive bands of COCTS and VIIRS in the South China Sea are 0.101 K and 0.892 K, respectively, and the differences in BTs between the two sensors show temperature dependence. The cross-calibration coefficients are obtained and used to correct the BT of the COCTS thermal emissive bands. The bias of the BT of the 11 µm and 12 µm bands of COCTS are about 0.01 K after cross-calibration. To further validate the results, COCTS post-calibration data were examined using the NOAA-20 Cross-track Infrared Sounder (CrIS) data as a third-party source. The BT is calculated with the spectral response functions of the COCTS thermal emissive bands using the convolution calculation of the CrIS hyperspectral region observations. The comparison shows a small bias between the post-calibration COCTS thermal emissive band observations and CrIS, which is consistent with the comparison between VIIRS and CrIS. The accuracy of the post-calibration COCTS thermal emissive band BT data in the South China Sea has been significantly improved.

Characteristics and Mechanisms of Differential Infrared Brightness Temperature Change of Rock Specimen Partly Loaded to Fracturing

Characteristics and Mechanisms of Differential Infrared Brightness Temperature Change of Rock Specimen Partly Loaded to Fracturing

Tracking Mesoscale Convective Systems in IMERG and Regional Variability of Their Properties in the Tropics

AbstractMesoscale convective systems (MCSs) constitute only a fraction of convective systems in the tropics but significantly impact tropical weather and global climate. Early studies used satellite infrared (IR) data to track MCSs and study their properties but only for a short period due to computing limitations. Though valuable, the IR brightness temperature (Tb)‐derived precipitation properties have biases. The recent availability of Integrated Multi‐satEllite Retrieval for Global (IMERG) precipitation mission rainfall data of lesser bias than IR Tb‐based rainfall and access to high‐performance computers motivated us to track MCSs over global tropics for 10 years, using the Forward in Time (FiT) algorithm. Though IMERG is advantageous, it poses challenges to tracking MCSs due to convective systems connected by light rain areas and resolution differences between contributing passive microwave sensors. The precipitation field is smoothed and normalized to overcome these problems; then, the FiT algorithm identifies MCSs and tracks them. Our results show that MCSs contribute ∼70% of annual precipitation, though they are only ∼7% of all tracked systems. MCSs occur more often over the Amazon basin and Maritime Continent than in central Africa, known for high thunderstorm frequency, highlighting the contrasting convective regimes. The large, long‐lived, and intensely precipitating MCSs occur more often over the ocean than land, except for the Amazon basin. Fast‐moving MCSs often occur over West Africa, the Amazon basin, and the western Pacific, whereas slow‐moving MCSs are common over Colombia and the Maritime Continent.

Simulation of the temperatures in the permanently shadowed region of the Moon's south pole and data validation

The permanently shadowed regions (PSRs) at the lunar poles without direct solar illumination maintain low temperatures throughout the year, making them optimal environments for the preservation of water ice. This paper investigates the diurnal and seasonal temperature variations of the PSRs within craters at the lunar south pole. The craters de Gerlache, Shoemaker, Amundsen, and Wiechert E are selected to explore how latitudes and shapes impact PSR temperature. Using the ray tracing approach, the shadow effect of terrain on the scattered sunlight and thermal radiation is calculated. PSR temperatures are simulated with the one-dimensional heat conduction equation. The thermal emissivity of the lunar surface is studied. Simulations with anisotropic emissivity are closer to the Diviner data in PSRs than the simulations with isotropic emissivity. In addition, by comparing the simulations to the Diviner data, it is found that the thermal conductivity at the extremely low temperatures in PSRs might be lower than that at high temperatures outside the PSRs. Simulations of the selected craters are used to study the influence of polar day phenomena occurring at high-latitude craters during the summer and the effect of direct solar illumination at the floors of low-latitude craters on the temperature distribution of PSRs. The influence of crater shape is analyzed as well by investigating the temperature distributions within PSRs of flat-floored and bowl-shaped craters. With the analysis of the heat flux propagation distances, emission angles, and incident angles, this study quantitatively elucidates the spatial and temporal variations in PSR temperatures. To explain why the Diviner data are lower than the simulation results at the solar-illuminated crater walls, an undulating rough surface is generated. The locations of the Sun and the observer on opposite sides of the rough surface may decrease the observed infrared brightness temperature according to the simulations. The PSR peak temperatures simulated with the anisotropic emissivity and thermal conductivity for PSRs are used to search for low-temperature areas suitable for preserving water ice within de Gerlache and Shoemaker.

Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED)

Abstract To study tropical cyclones and generate forecast applications using satellite observations, researchers often consolidate disparate sources of raw and ancillary data. Data consolidation involves obtaining, collocating, and intercalibrating data from different sensors and derived products; calculating environmental diagnostics from a homogeneous source; and standardizing these various products for a straightforward analysis. To alleviate preprocessing issues and provide a long-term, global digital dataset of tropical cyclone satellite observations, we construct the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED). TC PRIMED contains tropical cyclone–centric 1) intercalibrated, multichannel, multisensor microwave brightness temperatures, 2) retrieved rainfall from NASA’s Goddard Profiling Algorithm (GPROF), 3) nearly coincident geostationary satellite infrared brightness temperatures and derived metrics, 4) tropical cyclone position and intensity information, 5) ECMWF fifth-generation reanalysis fields and derived environmental diagnostics, and 6) precipitation radar observations from the TRMM and GPM Core Observatory satellites. TC PRIMED consists of over 176,000 overpasses of 2,101 storms from 1998 to 2019, providing researchers with an analysis-ready dataset to promote and support research into improving our understanding of the relationship between tropical cyclone convective and precipitation structure, intensity, and environment. Here, we briefly describe data sources and processing steps to create TC PRIMED. To demonstrate TC PRIMED’s potential utility for studying important tropical cyclone processes and for application development, we present a shear-relative composite analysis of several multisensor satellite variables relative to the tropical cyclone lifetime maximum intensity. The composite analysis provides a simple example of how TC PRIMED can benefit future studies to advance our understanding of tropical cyclones and improve forecasts.

Experimental Investigation on Fragmentation Identification in Loose Slope Landslides by Infrared Emissivity Variability Features

Infrared radiation (IR) features that are influenced by infrared emissivity ε and physical temperature Td have been successfully applied to the early-warning of landslides. Although the infrared emissivity of a rock is a key parameter to determine its thermal radiation properties, the effect of particle size on the infrared emissivity of rock fragments is unknown. So in this paper, granite, marble, and sandstone were used as examples to conduct infrared imaging experiments on rock fragments. Their equivalent emissivity was used to interpret the detected infrared emission, including that from indoor backgrounds. In addition, the characteristics of changes in equivalent emissivity were discussed with reference to changes in observation direction and zenith angle. Then, a computation model of equivalent emissivity based on multiple observation directions and zenith angles was built to reveal the change in equivalent emissivity with particle sizes. The result indicates that the indoor background radiation has a predominant direction just above the rock fragments. The maximum deviation of infrared brightness temperature (IBT) was 0.260 K, and the maximum deviation of equivalent emissivity among different observation directions and zenith angles was 0.0065. After eliminating the influence of directional and angle effects with the operation of normalization, the general law of equivalent emissivity for all rock fragments that change with particle size is consistent. The maximum equivalent emissivity occurs at particle size 5 mm in the condition of particle size larger than 1 mm, while the equivalent emissivity changes inversely with particle size in the condition of particle size smaller than 1 mm. Above all, this study contributes new cognitions to Remote Sensing Rock Mechanics, and provides valuable evidence for better thermal infrared remote sensing monitoring on loose slope landslides.

Simulation of Thermal Infrared Brightness Temperatures from an Ocean Color and Temperature Scanner Onboard a New Generation Chinese Ocean Color Observation Satellite

Since 2002, China has launched four Haiyang-1 (HY-1) satellites equipped with the Chinese Ocean Color and Temperature Scanner (COCTS), which can observe the sea surface temperature (SST). The planned new generation ocean color observation satellites also carry a sensor for observing the SST represented by the payload in this paper. We analyze the spectral brightness temperature (BT) difference between the payload and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra for the thermal infrared channels (11 and 12 µm) based on atmospheric radiative transfer simulation. The bias and standard deviation (SD) of spectral BT difference for the 11 µm channel are −0.12 K and 0.15 K, respectively, and those for the 12 µm channel are −0.10 K and 0.03 K, respectively. When the total column water vapor (TCWV) decreases from the oceans near the equator to high-latitude oceans, the spectral BT difference of the 11 µm channel varies from a positive deviation to a negative deviation, and that of the 12 µm channel basically remains stable. By correcting the MODIS BT observation using the spectral BT differences, we produce the simulated BT data for the thermal infrared channels of the payload, and then validate it using the Infrared Atmospheric Sounding Interferometer (IASI) carried on METOP-B. The validation results show that the bias of BT difference between the payload and IASI is −0.22 K for the 11 µm channel, while it is −0.05 K for the 12 µm channel. The SD of both channels is 0.13 K. In this study, we provide the simulated BT dataset for the 11 and 12 µm channels of a payload for the retrieval of SST. The simulated BT dataset corrected may be used to develop SST-retrieval algorithms.

A new calibration correction method for INSAT-3D/3DR brightness temperatures to improve rainfall estimation

ABSTRACT Importance of continuous monitoring of rainfall from Indian geostationary satellites INSAT-3D/3DR over India and surrounding oceans is not only necessary to monitor the performance of the monsoon, but is extremely useful in disaster mitigation support system as well. It has been observed that rain products from INSAT-3D/3DR often show overestimation in certain areas with unrealistic instances of intense rain, which need a closer examination. With this objective, the present study is carried out, which suggests that a non-linear calibration equation for thermal infrared brightness temperatures (Tb) of INSAT-3D/3DR is more appropriate than routinely applied linear equation for rainfall estimation. The study elaborates a new calibration correction method and addresses the requirement of such calibration. To evaluate the performance of proposed method, Hydro-Estimator (HE) operational rainfall products from INSAT-3D/3DR are re-estimated from the newly calibrated Tbs during Indian summer monsoon (June-September), 2020. The re-estimated HE rain not only substantially reduces the instances of overestimation, but also show remarkable improvement in capturing rainfall distribution over India and surrounding ocean. Improvement in RMSE from 8.56 mm/hr to 3.23 mm/hr, when compared with the concurrent IMERG (Integrated Multi-satellitE Retrieval for Global Precipitation Mission (GPM)) rain, shows reliability of the new method. Furthermore, the re-estimated HE rain compares better with in-situ rain gauges observations than the operational HE product with RMSE (bias) reduced from 85.43 mm/day (30.23 mm/day) to 40.02 mm/day (17.07 mm/day), and correlation improved from 0.28 to 0.32. The significance of the new calibration method is also recognized in the rain monitoring over complex hilly terrain and coastal regions of India.

Tropical Cyclone Fullness and Outer Region Size Growth: The Role of Spatial Distribution of Very Deep Convection

AbstractThis paper investigates the relationship between the growth of the 34‐kt wind radius (R34) of tropical cyclones (TCs) and their fullness using best‐track data from 2001 to 2020. The storms were categorized into four groups based on a fullness scale: FS1 (fullness ≤ 0.4), FS2 (0.4 < fullness ≤ 0.6), FS3 (0.6 < fullness ≤ 0.8), and FS4 (fullness > 0.8). These groups exhibit unique spatial patterns of very deep convective clouds with infrared brightness temperatures <208 K. The mean R34 growth rates in 24 hr decrease from FS1 to FS4, which is linked to the higher coverage of very deep convection around R34 in storms with lower fullness. This study demonstrates that TC fullness can characterize the spatial distribution of deep convection in storms and serve as a representation of the growth of TC outer region size. These results have implications for understanding the mechanisms behind TC outer size growth.

Diagnosing Factors Leading to an Incorrect Supercell Thunderstorm Forecast

Abstract On 28 April 2019, hourly forecasts from the operational High-Resolution Rapid Refresh (HRRR) model consistently predicted an isolated supercell storm late in the day near Dodge City, Kansas, that subsequently was not observed. Two convection-allowing model (CAM) ensemble runs are created to explore the reasons for this forecast error and implications for severe weather forecasting. The 40-member CAM ensembles are run using the HRRR configuration of the WRF-ARW Model at 3-km horizontal grid spacing. The Gridpoint Statistical Interpolation (GSI)-based ensemble Kalman filter is used to assimilate observations every 15 min from 1500 to 1900 UTC, with resulting ensemble forecasts run out to 0000 UTC. One ensemble only assimilates conventional observations, and its forecasts strongly resemble the operational HRRR with all ensemble members predicting a supercell storm near Dodge City. In the second ensemble, conventional observations plus observations of WSR-88D radar clear-air radial velocities, WSR-88D diagnosed convective boundary layer height, and GOES-16 all-sky infrared brightness temperatures are assimilated to improve forecasts of the preconvective environment, and its forecasts have half of the members predicting supercells. Results further show that the magnitude of the low-level meridional water vapor flux in the moist tongue largely separates members with and without supercells, with water vapor flux differences of 12% leading to these different outcomes. Additional experiments that assimilate only radar or satellite observations show that both are important to predictions of the meridional water vapor flux. This analysis suggests that mesoscale environmental uncertainty remains a challenge that is difficult to overcome. Significance Statement Forecasts from operational numerical models are the foundation of weather forecasting. There are times when these models make forecasts that do not come true, such as 28 April 2019 when successive forecasts from the operational High-Resolution Rapid Refresh (HRRR) model predicted a supercell storm late in the day near Dodge City, Kansas, that subsequently was not observed. Reasons for this forecast error are explored using numerical experiments. Results suggest that relatively small changes to the prestorm environment led to large differences in the evolution of storms on this day. This result emphasizes the challenges to operational severe weather forecasting and the continued need for improved use of all available observations to better define the atmospheric state given to forecast models.

The Quagmire of Arrested Development in Tropical Cyclones

Abstract The 48-h intensity forecasts for Hurricane Pamela (2021) from numerical weather prediction models, statistical–dynamical aids, and forecasters were a major forecast bust with Pamela making landfall as a minor rather than major hurricane. From the satellite presentation, Pamela exhibited a symmetric pattern referred to as central cold cover (CCC) in the subjective Dvorak intensity technique. Per the technique, the CCC pattern is accompanied by arrested development in intensity despite the seemingly favorable convective signature. To understand forecast uncertainty during occurrences, central cold cover frequency from 2011 to 2021 is documented. From these cases, composites of longwave infrared brightness temperatures from geostationary satellites for CCC cases are presented, and the surrounding tropical cyclone large-scale environment is quantified and compared with other tropical cyclones at similar latitudes and intensities. These composites show that central cold cover has a consistent presentation, but varies in the preceding hours for storms that eventually intensify or weaken. And, the synoptic-scale environment surrounding the tropical cyclone thermodynamically supports the vigorous deep convection associated with CCC. Finally, intensity forecast errors from numerical weather prediction models and statistical–dynamical aids are examined in comparison to similar tropical cyclones. This work shows that guidance struggles during CCC cases with intensity errors from these models being in the lowest percentiles of performance, particularly for 24- and 36-h forecasts. Significance Statement The appearance of symmetric cold clouds near the center of developing tropical cyclones is most often associated with future intensification. This simple relationship is widely used by statistical tropical cyclone intensity forecast models. Here, we reexamine and confirm that one subjectively determined nighttime cold cyclone cloud pattern termed the “central cold cover” pattern in Vern Dvorak’s seminal technique for estimating tropical cyclone intensity from infrared satellite images is indeed related to slow or arrested development, and represents a failure mode for these simple forecast models.

Diurnal variations of tropical cyclone outer region size growth

AbstractVarious aspects of tropical cyclones (TCs) fluctuate with the diurnal cycle. TC size is critical to the extent of its damage, but diurnal variations in the growth of the outer region size have largely remained unexplored. This paper examines the diurnal variations in the growth of the radius of gale‐force winds (34 kt; R34) by analyzing best‐track data from 2001 to 2019 for both North Atlantic TCs and global TCs outside of the North Atlantic region. Statistically significant diurnal variations was found for R34 growth, with the maximum 6‐h growth rate occurring at 2100–0300 local solar time (LST) for North Atlantic TCs, and at 0300–0900 LST for global TCs excluding the North Atlantic which experienced rapid intensification (≥30 knots within 24 h). The higher R34 6‐h growth rates during the night were linked to a larger extent of very deep convective clouds with infrared brightness temperatures <208 K during this time.

Statistics of Isolated Deep Convection Initiation and Its Relation to Topography in the North China Area

AbstractThis study investigates the statistical characteristics of isolated deep convection initiation (DCI) and its relation to topography in the North China area during the summer season of 2017–2019. A total of 2,534 isolated DCI events are identified using the infrared brightness temperature data from Himawari‐8 satellite with a resolution of 0.05° × 0.05°. The spatial distribution of DCI shows clustering over mountains and hills, indicating that the local topography may be critical for DCI. Topography is described with elevation and relief amplitude in this study. Results show that normalized DCI counts increase with elevation and relief amplitude. For the regime characterized by fewer morning clouds (regime one), DCI strongly depends on elevation and relief amplitude, while for the regime with more morning clouds (regime two), topography only moderately impacts DCI. Linear regression analyses suggest the existence of significant linear relationships between DCI and relief amplitude when elevation is relatively high. Topography is also categorized into four types: plain/sea areas, plateaus, hilly areas, and mountains. Normalized DCI counts show that mountains and hilly areas are more favorable for initiating isolated deep convection, especially for regime one. Moreover, DCI counts in regime one show a stronger diurnal variation and a peak occurring 2 hr earlier than that of regime two. We suggest that special attention should be paid to elevation and relief amplitude, as well as morning cloud cover condition when forecasting DCI in the North China area. Further study is necessary to understand this DCI occurrence preference on specific topography.

Making a Superbolt: Reconciling Observations of the Optically Brightest Lightning on Earth From Different Satellites

AbstractWe previously documented geographic distributions of the optically brightest lightning on Earth—known as “superbolts”—using two space‐based instruments: the photodiode detector (PDD) on the Fast On‐orbit Recording of Transient Events (FORTE) satellite and the Geostationary Lightning Mapper (GLM) on NOAA's Geostationary Operational Environmental Satellites. In this study, we further examine the superbolts identified by the PDD and GLM to reconcile the differences between their geographic distributions. We find that both the physical extent of the parent flash and the development speed of its leaders are important for making a superbolt. The oceanic PDD superbolts tend to occur early in flashes that rapidly expand laterally into long horizontal “megaflashes.” The top GLM superbolts occur over land at later times in particularly large megaflashes. These land‐based flashes grow more slowly until they extend over multiple hundreds of kilometers. The FORTE PDD missed these delayed superbolts due to limitations in its triggering. Coincident Tropical Rainfall Measuring Mission measurements show that the warm season megaflash superbolts detected by Lightning Imaging Sensor/GLM and wintertime oceanic superbolts observed by the PDD occur in otherwise similar thunderstorm environments. Both are marked by: low storm heights (<10 km), widespread precipitation near the surface, small infrared brightness temperature gradients, and low flash rates. We suggest that the vertically compact, stratiform nature of these clouds provides favorable conditions for superbolt production.

Improving Tropical Cyclogenesis Forecasts of Hurricane Irma (2017) through the Assimilation of All-Sky Infrared Brightness Temperatures

Abstract The assimilation of satellite all-sky infrared (IR) brightness temperatures (BTs) has been shown in previous studies to improve intensity forecasts of tropical cyclones. In this study, we examine whether assimilating all-sky IR BTs can also potentially improve tropical cyclogenesis forecasts by improving the pregenesis cloud and moisture fields. By using an ensemble-based data assimilation system, we show that the assimilation of upper-tropospheric water vapor channel BTs observed by the Meteosat-10 SEVIRI instrument two days before the formation of a tropical depression improves the genesis forecast of Hurricane Irma (2017), a classic Cape Verde storm, by up to 24 h while also capturing its later rapid intensification in deterministic forecasts. In an experiment that withholds the assimilation of all-sky IR BTs, the assimilation of conventional observations from the Global Telecommunications System (GTS) leads to the premature genesis of Hurricane Irma by at least 24 h. This premature genesis is shown to result from an overestimation of the spatial coverage of deep convection within the African easterly wave (AEW) from which Irma eventually forms. The gross overestimation of deep convection without all-sky IR BTs is accompanied by higher column saturation fraction, stronger low-level convergence, and the earlier spinup of a low-level meso-β-scale vortex within the AEW that ultimately becomes Hurricane Irma. Through its adjustment to the initial moisture and cloud conditions, the assimilation of all-sky IR BTs leads to a more realistic convective evolution in forecasts and ultimately a more realistic timing of genesis. Significance Statement Every year hurricanes impact the lives of thousands of people living along the eastern coast of the United States. Many of these storms originate from tropical disturbances that exit the west coast of Africa. To give the public more warning time ahead of these storms, it is important to improve the forecasts of their formation. This study uses a system developed at The Pennsylvania State University to incorporate satellite observations into forecasts of a classic Cape Verde storm, Hurricane Irma (2017), two days before it formed. By using satellite-collected radiances, we improve the timing of its formation by up to 24 h due to a better representation of the mesoscale tropical disturbance from which it originated.