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- Author or Editor: Emily B. Berndt x
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Abstract
Hyperspectral infrared satellite sounding retrievals are used to examine thermodynamic changes in the tropical cyclone (TC) environment associated with the diurnal cycle of radiation. Vertical profiles of temperature and moisture are retrieved from the Suomi National Polar-Orbiting Partnership (SNPP) satellite system, National Oceanic and Atmospheric Administration-20 (NOAA-20), and the Meteorological Operational (MetOp-A/B) satellite system, leveraging both infrared and microwave sounding technologies. Vertical profiles are binned radially based on distance from the storm center and composited at 4-h intervals to reveal the evolution of the diurnal cycle. For the three cases examined—Hurricane Dorian (2019), Hurricane Florence (2018), and Hurricane Irma (2017)—a marked diurnal signal is evident that extends through a deep layer of the troposphere. Statistically significant differences at the 95% level are observed in temperature, moisture, and lapse rate profiles, indicating a moistening and destabilization of the mid- to upper troposphere that is more pronounced near the inner core of the TC at night. Observations support a favorable environment for the formation of deep convection caused by diurnal differences in radiative heating tendencies, which could partially explain why new diurnal pulses tend to form around sunset. These findings demonstrate that the diurnal cycle of radiation affects TC thermodynamics through a deep layer of the troposphere, and suggest that hyperspectral infrared satellite sounding retrievals are valuable assets in detecting thermodynamic variations in TCs.
Abstract
Hyperspectral infrared satellite sounding retrievals are used to examine thermodynamic changes in the tropical cyclone (TC) environment associated with the diurnal cycle of radiation. Vertical profiles of temperature and moisture are retrieved from the Suomi National Polar-Orbiting Partnership (SNPP) satellite system, National Oceanic and Atmospheric Administration-20 (NOAA-20), and the Meteorological Operational (MetOp-A/B) satellite system, leveraging both infrared and microwave sounding technologies. Vertical profiles are binned radially based on distance from the storm center and composited at 4-h intervals to reveal the evolution of the diurnal cycle. For the three cases examined—Hurricane Dorian (2019), Hurricane Florence (2018), and Hurricane Irma (2017)—a marked diurnal signal is evident that extends through a deep layer of the troposphere. Statistically significant differences at the 95% level are observed in temperature, moisture, and lapse rate profiles, indicating a moistening and destabilization of the mid- to upper troposphere that is more pronounced near the inner core of the TC at night. Observations support a favorable environment for the formation of deep convection caused by diurnal differences in radiative heating tendencies, which could partially explain why new diurnal pulses tend to form around sunset. These findings demonstrate that the diurnal cycle of radiation affects TC thermodynamics through a deep layer of the troposphere, and suggest that hyperspectral infrared satellite sounding retrievals are valuable assets in detecting thermodynamic variations in TCs.
Abstract
It has been theorized that thundersnow (TSSN) occurs in conjunction with heavy snowfall rates and in geographical regions where heavy-banded snow occurs more frequently. This study aims to objectively and quantitatively identify characteristics associated with TSSN to improve the situational awareness of heavy snowfall and associated hazards. The Geostationary Lightning Mapper (GLM), National Environmental Satellite Data and Information Services (NESDIS) merged Snowfall Rate (mSFR) product, and surface observations were utilized to characterize snowfall accumulation, snow-to-liquid ratio (SLR) values, and radar characteristics of heavy snowfall events from a GLM perspective. When at least 2 in. of snowfall accumulation occurred, areas with TSSN flashes identified by the thundersnow detection algorithm (TDA) were likely to receive, on average, a total of 24.5 cm (9.6 in.) of snowfall. TSSN was more likely to occur in snowfall rates less than 2.54 cm h−1 (1 in. h−1) and be associated with snow-to-liquid ratio (SLR) values between 8:1 and 10:1. It was determined that TSSN flashes observed by GLM occurred in isothermal reflectivity values less than 30 dBZ and average spatial offsets of 131 km between the lightning flash location and the heaviest snowfall rates were observed. GLM flashes in proximity of National Lightning Detection Network cloud-to-ground flashes and tall structures were found to be statistically different (p < 0.05) regarding snowfall rates, SLR values, and various Multi-Radar Multi-Sensor variables compared to other TSSN flashes. It was inferred that tower TSSN flashes, on median, were more likely to initiate within light-to-moderately rimed snowfall. Last, a heavy snowfall event was analyzed to demonstrate the capability of these products in identifying storm characteristics associated with TSSN.
Abstract
It has been theorized that thundersnow (TSSN) occurs in conjunction with heavy snowfall rates and in geographical regions where heavy-banded snow occurs more frequently. This study aims to objectively and quantitatively identify characteristics associated with TSSN to improve the situational awareness of heavy snowfall and associated hazards. The Geostationary Lightning Mapper (GLM), National Environmental Satellite Data and Information Services (NESDIS) merged Snowfall Rate (mSFR) product, and surface observations were utilized to characterize snowfall accumulation, snow-to-liquid ratio (SLR) values, and radar characteristics of heavy snowfall events from a GLM perspective. When at least 2 in. of snowfall accumulation occurred, areas with TSSN flashes identified by the thundersnow detection algorithm (TDA) were likely to receive, on average, a total of 24.5 cm (9.6 in.) of snowfall. TSSN was more likely to occur in snowfall rates less than 2.54 cm h−1 (1 in. h−1) and be associated with snow-to-liquid ratio (SLR) values between 8:1 and 10:1. It was determined that TSSN flashes observed by GLM occurred in isothermal reflectivity values less than 30 dBZ and average spatial offsets of 131 km between the lightning flash location and the heaviest snowfall rates were observed. GLM flashes in proximity of National Lightning Detection Network cloud-to-ground flashes and tall structures were found to be statistically different (p < 0.05) regarding snowfall rates, SLR values, and various Multi-Radar Multi-Sensor variables compared to other TSSN flashes. It was inferred that tower TSSN flashes, on median, were more likely to initiate within light-to-moderately rimed snowfall. Last, a heavy snowfall event was analyzed to demonstrate the capability of these products in identifying storm characteristics associated with TSSN.
Abstract
This study examines characteristics of lightning in snowfall events (i.e., thundersnow, TSSN) from the perspective of the Geostationary Lightning Mapper (GLM) and the National Environmental Satellite Data and Information Service (NESDIS) merged Snowfall Rate (mSFR) product. A thundersnow detection algorithm (TDA) was derived from the GLM and mSFR that resulted in a probability of detection (POD) of 66.7% when compared to the aviation routine weather report (METAR) reports of TSSN. However, using the TDA an additional 2175 lightning flashes within detected snowfall were identified that were not observed by the METAR reports, indicating that TSSN has been under reported in previous literature. TSSN flashes observed by GLM have mean flash areas, durations, and total optical energy outputs of 754 km2, 402 ms, and 1342 fJ, which are between the 50th and 99th percentile values for all flashes within the GLM field of view. A comparison with data from the National Lightning Detection Network (NLDN) indicated that the NLDN had at least one cloud or ground flash detection in 1709 of the 2214 flashes observed by GLM in snowfall. An average of 5.85 NLDN flashes was assigned to a single GLM flash when the NLDN flash data were constrained by the GLM flash duration and spatial footprint. Statistically significant (p < 0.01) differences in flash area and flash energy were found between flashes that were observed by the NLDN and those that were not. Additionally, when GLM was combined with the NLDN, at least 11.1% of flashes involved a tall human-made object like an antenna or wind turbine.
Abstract
This study examines characteristics of lightning in snowfall events (i.e., thundersnow, TSSN) from the perspective of the Geostationary Lightning Mapper (GLM) and the National Environmental Satellite Data and Information Service (NESDIS) merged Snowfall Rate (mSFR) product. A thundersnow detection algorithm (TDA) was derived from the GLM and mSFR that resulted in a probability of detection (POD) of 66.7% when compared to the aviation routine weather report (METAR) reports of TSSN. However, using the TDA an additional 2175 lightning flashes within detected snowfall were identified that were not observed by the METAR reports, indicating that TSSN has been under reported in previous literature. TSSN flashes observed by GLM have mean flash areas, durations, and total optical energy outputs of 754 km2, 402 ms, and 1342 fJ, which are between the 50th and 99th percentile values for all flashes within the GLM field of view. A comparison with data from the National Lightning Detection Network (NLDN) indicated that the NLDN had at least one cloud or ground flash detection in 1709 of the 2214 flashes observed by GLM in snowfall. An average of 5.85 NLDN flashes was assigned to a single GLM flash when the NLDN flash data were constrained by the GLM flash duration and spatial footprint. Statistically significant (p < 0.01) differences in flash area and flash energy were found between flashes that were observed by the NLDN and those that were not. Additionally, when GLM was combined with the NLDN, at least 11.1% of flashes involved a tall human-made object like an antenna or wind turbine.
Abstract
Low-Earth-orbiting (LEO) hyperspectral infrared (IR) sounders have significant yet untapped potential for characterizing thermodynamic environments of convective initiation and ongoing convection. While LEO soundings are of value to weather forecasters, the temporal resolution needed to resolve the rapidly evolving thermodynamics of the convective environment is limited. We have developed a novel nowcasting methodology to extend snapshots of LEO soundings forward in time up to 6 h to create a product available within National Weather Service systems for user assessment. Our methodology is based on parcel forward-trajectory calculations from the satellite-observing time to generate future soundings of temperature (T) and specific humidity (q) at regularly gridded intervals in space and time. The soundings are based on NOAA-Unique Combined Atmospheric Processing System (NUCAPS) retrievals from the Suomi National Polar-Orbiting Partnership (Suomi NPP) and NOAA-20 satellite platforms. The tendencies of derived convective available potential energy (CAPE) and convective inhibition (CIN) are evaluated against gridded, hourly accumulated rainfall obtained from the Multi-Radar Multi-Sensor (MRMS) observations for 24 hand-selected cases over the contiguous United States. Areas with forecast increases in CAPE (reduced CIN) are shown to be associated with areas of precipitation. The increases in CAPE and decreases in CIN are largest for areas that have the heaviest precipitation and are statistically significant compared to areas without precipitation. These results imply that adiabatic parcel advection of LEO satellite sounding snapshots forward in time are capable of identifying convective initiation over an expanded temporal scale compared to soundings used only during the LEO satellite overpass time.
Significance Statement
Advection of low-Earth-orbiting (LEO) satellite observations of temperature and specific humidity forward in time exhibits skill in determining where and when convection eventually initiates. This approach provides a foundation for a new nowcasting methodology leveraging thermodynamic soundings derived from hyperspectral infrared (IR) sounders on LEO satellite platforms. This method may be useful for creating time-resolved soundings with the constellation of LEO satellites until hyperspectral infrared soundings are widely available from geostationary platforms.
Abstract
Low-Earth-orbiting (LEO) hyperspectral infrared (IR) sounders have significant yet untapped potential for characterizing thermodynamic environments of convective initiation and ongoing convection. While LEO soundings are of value to weather forecasters, the temporal resolution needed to resolve the rapidly evolving thermodynamics of the convective environment is limited. We have developed a novel nowcasting methodology to extend snapshots of LEO soundings forward in time up to 6 h to create a product available within National Weather Service systems for user assessment. Our methodology is based on parcel forward-trajectory calculations from the satellite-observing time to generate future soundings of temperature (T) and specific humidity (q) at regularly gridded intervals in space and time. The soundings are based on NOAA-Unique Combined Atmospheric Processing System (NUCAPS) retrievals from the Suomi National Polar-Orbiting Partnership (Suomi NPP) and NOAA-20 satellite platforms. The tendencies of derived convective available potential energy (CAPE) and convective inhibition (CIN) are evaluated against gridded, hourly accumulated rainfall obtained from the Multi-Radar Multi-Sensor (MRMS) observations for 24 hand-selected cases over the contiguous United States. Areas with forecast increases in CAPE (reduced CIN) are shown to be associated with areas of precipitation. The increases in CAPE and decreases in CIN are largest for areas that have the heaviest precipitation and are statistically significant compared to areas without precipitation. These results imply that adiabatic parcel advection of LEO satellite sounding snapshots forward in time are capable of identifying convective initiation over an expanded temporal scale compared to soundings used only during the LEO satellite overpass time.
Significance Statement
Advection of low-Earth-orbiting (LEO) satellite observations of temperature and specific humidity forward in time exhibits skill in determining where and when convection eventually initiates. This approach provides a foundation for a new nowcasting methodology leveraging thermodynamic soundings derived from hyperspectral infrared (IR) sounders on LEO satellite platforms. This method may be useful for creating time-resolved soundings with the constellation of LEO satellites until hyperspectral infrared soundings are widely available from geostationary platforms.
Abstract
Using gridded and interpolated derived motion winds from the Advanced Baseline Imager (ABI), a Lagrangian cloud-feature tracking technique was developed to create and document trajectories associated with electrified snowfall and changes in cloud characteristics leading up to the initiation of lightning, respectively. This study implemented a thundersnow detection algorithm and defined thundersnow initiation (TSI) as the first group in a flash detected by the Geostationary Lightning Mapper when snow was occurring. A total of 10 ABI channels and four multispectral [e.g., red–green–blue (RGB)] composites were analyzed to investigate characteristics that lead up to TSI for 16 644 thundersnow (TSSN) flashes. From the 10.3-μm channel, TSI trajectories were associated with a median decrease of 12.2 K in brightness temperature (TB ) 1 h prior to TSI. Decreases in the reflectance component of the 3.9-μm channel indicated that TSI trajectories were associated with ice crystal collisions and/or particle settling at cloud top. The nighttime microphysics, day cloud phase distinction, differential water vapor, and airmass RGBs were examined to evaluate the microphysical and environmental changes prior to TSI. For daytime TSI trajectories, the predominant colors associated with the day cloud phase distinction RGB transitioned from cyan to yellow/green, physically representing cloud growth and glaciation at cloud top. Gold/orange hues in the differential water vapor RGB indicated that some trajectories were associated with dry upper-level air masses prior to TSI. The analysis of ABI characteristics prior to TSI, and subsequently relating those characteristics to physical processes, inherently increases the fundamental understanding and ability to forecast TSI; thus, providing additional lead time into changes in surface conditions (i.e., snowfall rates).
Abstract
Using gridded and interpolated derived motion winds from the Advanced Baseline Imager (ABI), a Lagrangian cloud-feature tracking technique was developed to create and document trajectories associated with electrified snowfall and changes in cloud characteristics leading up to the initiation of lightning, respectively. This study implemented a thundersnow detection algorithm and defined thundersnow initiation (TSI) as the first group in a flash detected by the Geostationary Lightning Mapper when snow was occurring. A total of 10 ABI channels and four multispectral [e.g., red–green–blue (RGB)] composites were analyzed to investigate characteristics that lead up to TSI for 16 644 thundersnow (TSSN) flashes. From the 10.3-μm channel, TSI trajectories were associated with a median decrease of 12.2 K in brightness temperature (TB ) 1 h prior to TSI. Decreases in the reflectance component of the 3.9-μm channel indicated that TSI trajectories were associated with ice crystal collisions and/or particle settling at cloud top. The nighttime microphysics, day cloud phase distinction, differential water vapor, and airmass RGBs were examined to evaluate the microphysical and environmental changes prior to TSI. For daytime TSI trajectories, the predominant colors associated with the day cloud phase distinction RGB transitioned from cyan to yellow/green, physically representing cloud growth and glaciation at cloud top. Gold/orange hues in the differential water vapor RGB indicated that some trajectories were associated with dry upper-level air masses prior to TSI. The analysis of ABI characteristics prior to TSI, and subsequently relating those characteristics to physical processes, inherently increases the fundamental understanding and ability to forecast TSI; thus, providing additional lead time into changes in surface conditions (i.e., snowfall rates).
