Overshooting tops (OTs) are a well‐known indicator of updrafts capable of transporting air from the troposphere to the stratosphere and generating hazardous weather conditions. Satellites and radars have long been used to identify OTs, but the results have not been entirely consistent due to differences in sensor and measurement characteristics. OT detection approaches based on satellite infrared (IR) imagery have often been validated using human‐expert OT identifications, but such datasets are time‐consuming to compile over broad geographic regions. Despite radar limitations to detect the true physical cloud top, OTs identified within multi‐radar composites can serve as a stable reference for comprehensive satellite OT analysis and detection validation. This study analyzes a large OT data set compiled from Geostationary Operational Environmental Satellites (GOES)‐13/16 geostationary IR data and gridded volumetric Next‐Generation Radar (NEXRAD) reflectivity to better understand radar and IR observations of OTs, quantify agreement between satellite and radar OT detections, and demonstrate how an increased spatial sampling from GOES‐13 to GOES‐16 impacts OT appearance and detection performance. For nearly time‐matched scenes and moderate OT probability, the GOES‐13 detection rate (∼60%) is ∼15% lower than GOES‐16 (∼75%), which is mostly attributed to coarser spatial resolution. NEXRAD column‐maximum reflectivity and tropopause‐relative echo‐top height as a function of GOES OT probability were quite consistent between the two satellites however, indicating that efforts to account for differing resolution were largely successful. GOES false detections are unavoidable because outflow from nearby or recently decayed OTs can be substantially colder than the tropopause and look like an OT to an automated algorithm.
A thermal radiation directionality correction method for the surface upward longwave radiation of geostationary satellite based on a time-evolving kernel-driven model
Thermal radiation directionality (TRD) characterizes the anisotropic signature of most surface targets in the
thermal infrared domain. It causes significant uncertainties in estimating surface upward longwave radiation (SULR) from space observations. In this regard, kernel-driven models (KDMs) are suitable to remove TRD effects from remote sensing dataset as they are computationally efficient. However, KDMs requires simultaneous multiangle observations as inputs to be well calibrated, which yields a difficulty with geostationary satellites as they can only provide a single-angle observation. To overcome this issue, we proposed a six-parameter time-evolving KDM that combines a four-parameter SULR diurnal variation model and a two-parameter TRD amplitude model to correct the TRD effect for single-angle estimated SULR dataset of geostationary satellites. The significant daytime TRD effect when solar zenith angle is within 60cm can be effectively eliminated. The modeling accuracy of the time-evolving KDM is evaluated using a simulated SULR dataset generated by the 3D Discrete Anisotropic Radiative Transfer (DART) model; the TRD correction method based on the new time-evolving KDM is validated using a two-year single-angle estimated SULR dataset derived from data of the Advanced Baseline Imager (ABI) onboard Geostationary Operational Environmental Satellite-16 (GOES-16) against in situ measurements at 20 AmeriFlux sites. Results show that the proposed time-evolving KDM has a high accuracy with an R2 > 0.999 and a small RMSE = 1.5 W/m2; the TRD correction method based on the time-evolving KDM can greatly reduce the GOES-16 SULR uncertainty caused by the TRD effect with an RMSE decrease of 4.5 W/m2 (22.1%) and mean bias error decrease of 7.9 W/m2 (62.7%). Hence, the proposed TRD correction method is practically efficient for the operational TRD correction of SULR products generated from the geostationary satellites (e.g., GOES-16, FY-4A, Himawari-8, MSG).
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- Award ID(s):
- 1655499
- NSF-PAR ID:
- 10510313
- Editor(s):
- Chen, Jing M
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Remote Sensing of Environment
- Volume:
- 294
- Issue:
- C
- ISSN:
- 0034-4257
- Page Range / eLocation ID:
- 113599
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.rse.2023.113599
