The relationship between lightning flash rate and ice-related volume derived from dual-polarization radar

Hayashi, Syugo; Umehara, Akihito; Nagumo, Nobuhiro; Ushio, Tomoo

doi:10.1016/j.atmosres.2020.105166

Cited by 11 publications

(5 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors developed a successful scheme that predicts flash rate based on radar-derived mixed-phase 35 dBZ echo volume, which indicates the amount of ice necessary to sustain frequent lightning discharges. Results agreed with recent findings by Hayashi et al [41] for 10 isolated thunderstorm cases over the Kanto Plain, Japan.…”

Section: Introductionsupporting

confidence: 93%

“…It was determined from the values of the 2a25 TRMM product, which determines the freezing level height, by adopting the average value of approximately 4.8 km. The second limit, which separates the mixed and glacial phases, was set to −40 • C [24,41,84]. The corresponding altitude was determined from the Tropical Standard Atmosphere developed by the International Civil Aviation Organization-ICAO [87][88][89], by adopting the average value of approximately 8.8 km.…”

Section: Vertical Reflectivity Profiles (Z)mentioning

confidence: 99%

See 1 more Smart Citation

Clouds’ Microphysical Properties and Their Relationship with Lightning Activity in Northeast Brazil

et al. 2021

View full text Add to dashboard Cite

The Northeast region of Brazil (NEB) has a high rate of deaths from lightning strikes (18% of the country’s total). The region has states, such as Piauí, with high mortality rates (1.8 deaths per million), much higher than the national rate (0.8) and the NEB rate (0.5). In this sense, the present work analyzes the microphysical characteristics of clouds with and without the occurrence of total lightning. For this purpose, data from the Lightning Imaging Sensor (LIS), TRMM Microwave Imager (TMI) and Precipitation Radar (PR), aboard the Tropical Rainfall Measuring Mission (TRMM) satellite from 1998 to 2013 were used. The TRMM data were analyzed to establish a relationship between the occurrence of lightning and the clouds’ microphysical characteristics, comparing them as a function of lightning occurrence classes, spatial location and atmospheric profiles. A higher lightning occurrence is associated with higher values of ice water path (>38.9 kg m−2), rain water path (>2 kg m−2), convective precipitation (>5 mm h−1) and surface precipitation (>7 mm h−1), in addition to slightly higher freezing level height values. Reflectivity observations (>36 dBZ) demonstrated typical convective profile curves, with higher values associated with classes with higher lightning densities (class with more than 6.8 flash km−2 year−1).

show abstract

Section: Introductionsupporting

confidence: 93%

Section: Vertical Reflectivity Profiles (Z)mentioning

confidence: 99%

Clouds’ Microphysical Properties and Their Relationship with Lightning Activity in Northeast Brazil

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Among the five hydrometeors, graupel showed the highest correlation with the GLM observations, yet the other hydrometeors were also highly correlated, probably because developed convection with much graupel also contains a large amount of the other hydrometeors. In addition, a recent observational study by Hayashi et al (2021) reported that the lightning activity showed a higher correlation with the ice-related volume that includes ice, snow, graupel, and hail compared to that with the graupel and hail volume. In contrast, the IR radiances have no clear correlations with any hydrometeors below the cloud top.…”

Section: Ensemble-based Correlations With Various Observation Typesmentioning

confidence: 97%

“…In addition, a recent observational study by Hayashi et al. (2021) reported that the lightning activity showed a higher correlation with the ice‐related volume that includes ice, snow, graupel, and hail compared to that with the graupel and hail volume. In contrast, the IR radiances have no clear correlations with any hydrometeors below the cloud top.…”

Section: Ensemble‐based Correlations With Various Observation Typesmentioning

confidence: 99%

Potential Impacts of Lightning Flash Observations on Numerical Weather Prediction With Explicit Lightning Processes

2021

View full text Add to dashboard Cite

Although various lightning observation platforms exist, lightning flash observations have not been fully utilized for numerical weather prediction (NWP), mainly due to complications related to modeling lightning processes. Herein, we investigated the potential impacts of lightning observations using an ensemble Kalman filter (EnKF) with an NWP model, including explicit lightning processes. The EnKF included flow‐dependent error covariances between lightning flashes and the atmospheric state. Observing system simulation experiments were performed for an idealized convective case. Ensemble‐based correlations with various observations indicated that lightning flashes provide valuable information below cloud tops where satellite infrared radiances cannot observe well due to the presence of anvil clouds. Moreover, a single‐step data assimilation experiment showed that lightning flashes positively impact the analysis and forecast of wind, hydrometeors, and electric charge. The results revealed that lightning flashes are potentially useful for an EnKF with an NWP model including explicit lightning processes.

show abstract

“…Since an LPO is not a conventional model variable that cannot be applied to model initialization directly, it is necessary to convert LPO to a model variable or related diagnostic variable using empirical or semiempirical relationships. Part of the previous studies analyzed the relationship between lightning and related variables [12,13] or compared the related variables with and without lightning [14] but did not give a retrieval relationship between them. Some studies found a retrieval relationship between lightning and related variables in order to assimilate the lightning data [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

The Retrieval Relationship between Lightning and Maximum Proxy Reflectivity Based on Random Forest

Yin,

Tian,

Zhou

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Using the SWAN (Severe Weather Automatic Nowcasting) maximum reflectivity mosaic product and the lightning positioning observations (LPOs) from the ADTD (Advanced Direction and Time of Arrival Detection) system obtained during the 2018–2020 warm season (May to September), adding multi-characteristic LPO parameters in addition to lightning density, the retrieval relationship between lightning and maximum proxy reflectivity, deemed FRST, is constructed by using random forest. The FRST is compared with two empirical relationships from the GSI (Gridpoint Statistical Interpolation) assimilation system, and the results show that the FRST retrieved result better reflects the frequency distribution structure and peak interval of maximum reflectivity. The correlation coefficient between the FRST retrieved result and the observed maximum reflectivity is 0.7037, which is 3.38 (3.12) times greater than that of empirical GSI relationships. The root mean square error and the mean absolute error are 50.85% (28.05%) and 57.15% (35.19%) lower than those for the empirical GSI relationships, respectively. The equitable threat score (ETS) and bias score (BIAS) for FRST are better than those of the empirical GSI relationships in all three maximum reflectivity intervals.

show abstract

The relationship between lightning flash rate and ice-related volume derived from dual-polarization radar

Cited by 11 publications

References 64 publications

Clouds’ Microphysical Properties and Their Relationship with Lightning Activity in Northeast Brazil

Clouds’ Microphysical Properties and Their Relationship with Lightning Activity in Northeast Brazil

Potential Impacts of Lightning Flash Observations on Numerical Weather Prediction With Explicit Lightning Processes

The Retrieval Relationship between Lightning and Maximum Proxy Reflectivity Based on Random Forest

Contact Info

Product

Resources

About