Simulations of Infrared Radiances over a Deep Convective Cloud System Observed during TC4: Potential for Enhancing Nocturnal Ice Cloud Retrievals

Minnis, Patrick; Hong, Gang; Ayers, J. Kirk; Smith, William L.; Yost, Christopher R.; Heymsfield, Andrew J.; Heymsfield, Gerald M.; Hlavka, Dennis L.; King, Michael D.; Korn, Errol; McGill, Matthew J.; Selkirk, Henry B.; Thompson, A. M.; Tian, Lin; Yang, Ping

doi:10.3390/rs4103022

Cited by 9 publications

(13 citation statements)

References 69 publications

(114 reference statements)

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“…Nearly constant annual cycles can be observed in the M8-M11 DCC mode and mean time series, which is different from the fact that no obvious annual cycles exist in the VIS/NIR DCC mode time series. DCC reflectance is mainly influenced by cloud particle type/size in SWIR spectrum [19,27]. For the area of interest of this study, Wang and Cao [14] found that DCCs mostly occur over the land during winter and over ocean during summer.…”

Section: Calibration Stability For Swir M-bandsmentioning

confidence: 86%

Monitoring the NOAA Operational VIIRS RSB and DNB Calibration Stability Using Monthly and Semi-Monthly Deep Convective Clouds Time Series

Wang¹,

Cao

2016

Remote Sensing

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Abstract:The Visible and Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)/Suomi National Polar-Orbiting Partnership (SNPP) satellite provide sensor data records for the retrievals of many environment data records. It is critical to monitor the VIIRS long-term calibration stability to ensure quality EDR retrieval. This study investigates the radiometric calibration stability of the NOAA operational SNPP VIIRS Reflective Solar Bands (RSB) and Day-Night-Band (DNB) using Deep Convective Clouds (DCC). Monthly and semi-monthly DCC time series for 10 moderate resolution bands (M-bands, M1-M5 and M7-M11, March 2013-September 2015), DNB (March 2013-September 2015, low gain stage), and three imagery resolution bands (I-bands, I1-I3, January 2014-September 2015) were developed and analyzed for long-term radiometric calibration stability monitoring. Monthly DCC time series show that M5 and M7 are generally stable, with a stability of 0.4%. DNB has also been stable since May 2013, after its relative response function update, with a stability of 0.5%. The stabilities of M1-M4 are 0.6%-0.8%. Large fluctuations in M1-M4 DCC reflectance were observed since early 2014, correlated with F-factor (calibration coefficients) trend changes during the same period. The stabilities of M8-M11 are from 1.0% to 3.1%, comparable to the natural DCC variability at the shortwave infrared spectrum. DCC mean band ratio time series show that the calibration stabilities of I1-I3 follow closely with M5, M7, and M10. Relative calibration changes were observed in M1/M4 and M5/M7 DCC mean band ratio time series. The DCC time series are generally consistent with results from the VIIRS validation sites and VIIRS/MODIS (the Moderate-resolution Imaging Spectroradiometer) simultaneous nadir overpass time series. Semi-monthly DCC time series for RSB M-bands and DNB were compared with monthly DCC time series. The results indicate that semi-monthly DCC time series are useful for stability monitoring at higher temporal resolution.

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Section: Calibration Stability For Swir M-bandsmentioning

confidence: 86%

Monitoring the NOAA Operational VIIRS RSB and DNB Calibration Stability Using Monthly and Semi-Monthly Deep Convective Clouds Time Series

Wang¹,

Cao

2016

Remote Sensing

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“…large ice water paths (Minnis et al, 2012). Figure 14a shows a time series of N i and the air temperature during the longer segment in the cloud shown in Fig.…”

Section: Implications For Flight Operationsmentioning

confidence: 99%

Properties of small cirrus ice crystals from commercial aircraft measurements and implications for flight operations

Beswick

Baumgardner

Gallagher

et al. 2015

Tellus B: Chemical and Physical Meteorology

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A B S T R A C T Measurements of cloud ice crystal size distributions have been made by a backscatter cloud probe (BCP) mounted on five commercial airliners flying international routes that cross five continents. Bulk cloud parameters were also derived from the size distributions. As of 31 December 2014, a total of 4399 flights had accumulated data from 665 hours in more than 19 000 cirrus clouds larger than 5 km in length. The BCP measures the equivalent optical diameter (EOD) of individual crystals in the 5Á90 mm range from which size distributions are derived and recorded every 4 seconds. The cirrus cloud property database, an ongoing development stemming from these measurements, registers the total crystal number and mass concentration, effective and median volume diameters and extinction coefficients derived from the size distribution. This information is accompanied by the environmental temperature, pressure, aircraft position, date and time of each sample. The seasonal variations of the cirrus cloud properties measured from 2012 to 2014 are determined for six geographic regions in the tropics and extratropics. Number concentrations range from a few per litre for thin cirrus to several hundreds of thousands for heavy cirrus. Temperatures range from 205 to 250 K and effective radii from 12 to 20 mm. A comparison of the regional and seasonal number and mass size distributions, and the bulk microphysical properties derived from them, demonstrates that cirrus properties cannot be easily parameterised by temperature or by latitude. The seasonal changes in the size distributions from the extratropical Atlantic and Eurasian air routes are distinctly different, showing shifts from monomodal to bi-modal spectra out of phase with one another. This phase difference may be linked to the timing of deep convection and cold fronts that lead to the cirrus formation. Likewise, the size spectra of cirrus over the tropical Atlantic and Eastern Brazil differ from each other although they were measured in adjoining regions. The cirrus crystals in the maritime continental tropical region over Malaysia form tri-modal spectra that are not found in any of the other regions measured by the IAGOS aircraft so far, a feature that is possibly linked to biomass burning or dust. Frequent measurements of ice crystal concentrations greater than 1)10 5 L (1 , often accompanied by anomalously warm temperature and erratic airspeed readings, suggest that aircraft often experience conditions that affect their sensors. This new instrument, if used operationally, has the potential of providing real-time and valuable information to assist in flight operations as well as providing real-time information for along-track nowcasting.

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“…Polar orbiting satellites employing infrared channels have been widely used to detect convective clouds since the 1990s [11][12][13][14][15][16][17][18] using brightness temperature (TB) difference depending on cloud types. Setvak and Doswell III [17] used an infrared channel of Advanced Very High Resolution Radiometer (AVHRR) to detect convective storms by assuming that the tops of convective storms are composed of ice cloud particles only and thus they can be characterized as blackbodies.…”

Section: Introductionmentioning

confidence: 99%

Detection of Convective Initiation Using Meteorological Imager Onboard Communication, Ocean, and Meteorological Satellite Based on Machine Learning Approaches

Han

Lee

et al. 2015

Remote Sensing

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As convective clouds in Northeast Asia are accompanied by various hazards related with heavy rainfall and thunderstorms, it is very important to detect convective initiation (CI) in the region in order to mitigate damage by such hazards. In this study, a novel approach for CI detection using images from Meteorological Imager (MI), a payload of the Communication, Ocean, and Meteorological Satellite (COMS), was developed by improving the criteria of the interest fields of Rapidly Developing Cumulus Areas (RDCA) derivation algorithm, an official CI detection algorithm for Multi-functional Transport SATellite-2 (MTSAT-2), based on three machine learning approaches-decision trees (DT), random forest (RF), and support vector machines (SVM). CI was defined as clouds within a 16 × 16 km window with the first detection of lightning occurrence at the center. A total of nine interest fields derived from visible, water vapor, and two thermal infrared images of MI obtained 15-75 min before the lightning occurrence were used as input variables for CI OPEN ACCESSRemote Sens. 2015, 7 9185 detection. RF produced slightly higher performance (probability of detection (POD) of 75.5% and false alarm rate (FAR) of 46.2%) than DT (POD of 70.7% and FAR of 46.6%) for detection of CI caused by migrating frontal cyclones and unstable atmosphere. SVM resulted in relatively poor performance with very high FAR ~83.3%. The averaged lead times of CI detection based on the DT and RF models were 36.8 and 37.7 min, respectively. This implies that CI over Northeast Asia can be forecasted ~30-45 min in advance using COMS MI data.

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Simulations of Infrared Radiances over a Deep Convective Cloud System Observed during TC4: Potential for Enhancing Nocturnal Ice Cloud Retrievals

Cited by 9 publications

References 69 publications

Monitoring the NOAA Operational VIIRS RSB and DNB Calibration Stability Using Monthly and Semi-Monthly Deep Convective Clouds Time Series

Monitoring the NOAA Operational VIIRS RSB and DNB Calibration Stability Using Monthly and Semi-Monthly Deep Convective Clouds Time Series

Properties of small cirrus ice crystals from commercial aircraft measurements and implications for flight operations

Detection of Convective Initiation Using Meteorological Imager Onboard Communication, Ocean, and Meteorological Satellite Based on Machine Learning Approaches

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