scholarly journals Underestimation of deep convective cloud tops by thermal imagery

2004 ◽  
Vol 31 (11) ◽  
pp. n/a-n/a ◽  
Author(s):  
Steven C. Sherwood ◽  
Jung-Hyo Chae ◽  
Patrick Minnis ◽  
Matthew McGill
2012 ◽  
Vol 25 (21) ◽  
pp. 7313-7327 ◽  
Author(s):  
Derek J. Posselt ◽  
Andrew R. Jongeward ◽  
Chuan-Yuan Hsu ◽  
Gerald L. Potter

The Modern-Era Retrospective Analysis for Research and Application (MERRA) is a reanalysis designed to produce an improved representation of the Earth’s hydrologic cycle. This study examines the representation of deep convective clouds in MERRA, comparing analyzed liquid and ice clouds with deep convective cloud objects observed by instruments on the Tropical Rainfall Measuring Mission satellite. Results show that MERRA contains deep convective cloud in 98.1% of the observed cases. MERRA-derived probability density functions (PDFs) of cloud properties have a similar form as the observed PDFs and exhibit a similar trend with changes in object size. Total water path, optical depth, and outgoing shortwave radiation (OSR) in MERRA are found to match the cloud object observations quite well; however, there appears to be a bias toward higher-than-observed cloud tops in the MERRA. The reanalysis fits the observations most closely for the largest class of convective systems, with performance generally decreasing with a transition to smaller convective systems. Comparisons of simulated total water path, optical depth, and OSR are found to be highly sensitive to the assumed subgrid distribution of condensate and indicate the need for caution when interpreting model-data comparisons that require disaggregation of grid-scale cloud to satellite pixel scales.


2018 ◽  
Vol 36 (2) ◽  
pp. 189-205 ◽  
Author(s):  
Yi-Xuan Shou ◽  
Feng Lu ◽  
Hui Liu ◽  
Peng Cui ◽  
Shaowen Shou ◽  
...  

Radio Science ◽  
2005 ◽  
Vol 40 (4) ◽  
pp. n/a-n/a ◽  
Author(s):  
Gang Hong ◽  
Georg Heygster ◽  
Jungang Miao ◽  
Klaus Kunzi

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1859
Author(s):  
Bo Zhong ◽  
Yingbo Ma ◽  
Aixia Yang ◽  
Junjun Wu

Fengyun-4A (FY-4A) is the first satellite of the Chinese second-generation geostationary orbit meteorological satellites (FY-4). The Advanced Geostationary Radiation Imager (AGRI), onboard FY-4A does not load with high-precision calibration facility in visible and near infrared (VNIR) channel. As a consequence, it is necessary to comprehensively evaluate its radiometric performance and quantitatively describe the attenuation while using its VNIR data. In this paper, the radiometric performance at VNIR channels of FY-4A/AGRI is evaluated based on Aqua/MODIS data using the deep convective cloud (DCC) target. In order to reduce the influence of view angle and spectral response difference, the bi-directional reflectance distribution function (BRDF) correction and spectral matching have been performed. The evaluation result shows the radiometric performance of FY-4A/AGRI: (1) is less stable and with obvious fluctuations; (2) has a lower radiation level because of 24.99% lower compared with Aqua/MODIS; 3) has a high attenuation with 9.11% total attenuation over 2 years and 4.0% average annual attenuation rate. After the evaluation, relative radiometric normalization between AGRI and MODIS in VNIR channel is performed and the procedure is proved effective. This paper proposed a more reliable reference for the quantitative applications of FY-4A data.


2017 ◽  
Vol 17 (15) ◽  
pp. 9585-9598 ◽  
Author(s):  
Qian Chen ◽  
Ilan Koren ◽  
Orit Altaratz ◽  
Reuven H. Heiblum ◽  
Guy Dagan ◽  
...  

Abstract. Understanding aerosol effects on deep convective clouds and the derived effects on the radiation budget and rain patterns can largely contribute to estimations of climate uncertainties. The challenge is difficult in part because key microphysical processes in the mixed and cold phases are still not well understood. For deep convective clouds with a warm base, understanding aerosol effects on the warm processes is extremely important as they set the initial and boundary conditions for the cold processes. Therefore, the focus of this study is the warm phase, which can be better resolved. The main question is: How do aerosol-derived changes in the warm phase affect the properties of deep convective cloud systems? To explore this question, we used a weather research and forecasting (WRF) model with spectral bin microphysics to simulate a deep convective cloud system over the Marshall Islands during the Kwajalein Experiment (KWAJEX). The model results were validated against observations, showing similarities in the vertical profile of radar reflectivity and the surface rain rate. Simulations with larger aerosol loading resulted in a larger total cloud mass, a larger cloud fraction in the upper levels, and a larger frequency of strong updrafts and rain rates. Enlarged mass both below and above the zero temperature level (ZTL) contributed to the increase in cloud total mass (water and ice) in the polluted runs. Increased condensation efficiency of cloud droplets governed the gain in mass below the ZTL, while both enhanced condensational and depositional growth led to increased mass above it. The enhanced mass loading above the ZTL acted to reduce the cloud buoyancy, while the thermal buoyancy (driven by the enhanced latent heat release) increased in the polluted runs. The overall effect showed an increased upward transport (across the ZTL) of liquid water driven by both larger updrafts and larger droplet mobility. These aerosol effects were reflected in the larger ratio between the masses located above and below the ZTL in the polluted runs. When comparing the net mass flux crossing the ZTL in the clean and polluted runs, the difference was small. However, when comparing the upward and downward fluxes separately, the increase in aerosol concentration was seen to dramatically increase the fluxes in both directions, indicating the aerosol amplification effect of the convection and the affected cloud system properties, such as cloud fraction and rain rate.


2016 ◽  
Vol 55 (2) ◽  
pp. 479-491 ◽  
Author(s):  
Sarah M. Griffin ◽  
Kristopher M. Bedka ◽  
Christopher S. Velden

AbstractAssigning accurate heights to convective cloud tops that penetrate into the upper troposphere–lower stratosphere (UTLS) region using infrared (IR) satellite imagery has been an unresolved issue for the satellite research community. The height assignment for the tops of optically thick clouds is typically accomplished by matching the observed IR brightness temperature (BT) with a collocated rawinsonde or numerical weather prediction (NWP) profile. However, “overshooting tops” (OTs) are typically colder (in BT) than any vertical level in the associated profile, leaving the height of these tops undetermined using this standard approach. A new method is described here for calculating the heights of convectively driven OTs using the characteristic temperature lapse rate of the cloud top as it ascends into the UTLS region. Using 108 MODIS-identified OT events that are directly observed by the CloudSat Cloud Profiling Radar (CPR), the MODIS-derived brightness temperature difference (BTD) between the OT and anvil regions can be defined. This BTD is combined with the CPR- and NWP-derived height difference between these two regions to determine the mean lapse rate, −7.34 K km−1, for the 108 events. The anvil height is typically well known, and an automated OT detection algorithm is used to derive BTD, so the lapse rate allows a height to be calculated for any detected OT. An empirical fit between MODIS and geostationary imager IR BT for OTs and anvil regions was performed to enable application of this method to coarser-spatial-resolution geostationary data. Validation indicates that ~75% (65%) of MODIS (geostationary) OT heights are within ±500 m of the coincident CPR-estimated heights.


2010 ◽  
Author(s):  
David R. Doelling ◽  
Gang Hong ◽  
Dan Morstad ◽  
Rajendra Bhatt ◽  
Arun Gopalan ◽  
...  

Author(s):  
Xinlei Han ◽  
Bin Zhao ◽  
Yun Lin ◽  
Qixiang Chen ◽  
Hongrong Shi ◽  
...  

2016 ◽  
Author(s):  
Qiaozhen Mu ◽  
Aisheng Wu ◽  
Tiejun Chang ◽  
Amit Angal ◽  
Daniel Link ◽  
...  

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