Trajectory enhancement of low-earth orbiter thermodynamic retrievals to predict convection: a simulation experiment
Abstract. 3-D fields of temperature (T) and specific humidity (q) retrieved by instruments such as the Atmospheric Infrared Sounder (AIRS) are predictive of convection, but convection often triggers during the multi-hour gaps between satellite overpasses. Here we fill the hours after AIRS overpasses by treating AIRS retrievals as air parcels which are moved adiabatically along Numerical Weather Prediction (NWP) wind trajectories. The approach is tested in a simulation experiment that samples 3-D European Reanalysis-5 (ERA5) T and q following the real-world AIRS time-space sampling from March–November 2019 over much of the Continental U.S. Our time-resolved product is named ERA5-FCST, in correspondence to the AIRS forecast product we are using it to test, named AIRS-FCST. ERA5-FCST errors may arise since processes such as radiative heating and NWP sub-grid convection are ignored. For bulk atmospheric layers, ERA5-FCST captures 59–94 % of local hourly variation in T and q. We then consider the relationship between convective available potential energy (CAPE), convective inhibition (CIN), and ERA5 precipitation. The 1° latitude-longitude ERA5-FCST grid cells in our highest CAPE and lowest CIN bin are more than 50 times as likely to develop heavy precipitation (> 4 mm hr−1), compared with the baseline probability from randomly selecting a location. This is a substantial improvement compared with using the original CAPE and CIN values at overpass time. The results support development of similar FCST products for operational atmospheric sounders to provide time-resolved thermodynamics in rapidly changing pre-convective atmospheres.
Mark T. Richardson et al.
Status: final response (author comments only)
- RC1: 'Comment on egusphere-2023-97', Anonymous Referee #1, 21 Feb 2023
- RC2: 'Comment on egusphere-2023-97', Anonymous Referee #2, 22 Feb 2023
Mark T. Richardson et al.
Mark T. Richardson et al.
Viewed (geographical distribution)
This paper assesses the usefulness of satellite-measured atmospheric temperature and humidity for convection prediction. The assessment is based on surrogate data as opposed to real measurements. Specifically, global reanalysis data are sampled according to the sampling pattern of a polar orbiting satellite, to mimic the retrievals of an infrared hyperspectral sensor, AIRS. One interesting aspect of this investigation is the use of a trajectory model, which was introduced in an earlier work (Kalmus 2019), to increase the spatiotemporal representativeness of the satellite measurements. The paper is logically organized and well written, providing sufficient technical information and clear descriptions of the results. I do have some concerns, as detailed below, on several aspects of the paper, including the design of the research, the method, and the interpretation of some results. I think this paper could add excellent contribution to the literature after these comments are addressed.
L30-35. Two points are provided as the motivation of this work: weather and climatology. These starting points probably need to be reflected on or revised. For the objective of improving weather forecast, since the trajectory relies on NWP-model modelled winds, how could this approach have any advantage over the data assimilation approach? For the objective of studying convection climatology, why not simply use the reanalysis data without reducing the sampling to match AIRS?
L82. An important claim is made here about AIRS being advantageous for studying climate trends compared to reanalyses. This point needs to be better discussed, as one can easily come up with counterarguments. For example, given that the conventional retrievals typically take prior information including first guesses from analysis, it is not obvious to me that the retrieval products aren't subject to the same issues as reanalyses. A general comment is that I think the paper can provide better reasoning or more references to establish suitability of AIRS for studying climate trends. For instance, do you think the radiometric stability of AIRS together with its spectral information may facilitate detecting convection regime changes, taking advantage of their spectral signatures (e.g., Huang and Ramaswamy 2008, https://doi.org/10.1029/2008GL034859; Kahn et al. 2016, https://doi.org/10.1002/2016GL070263)? Or, may methods particularly designed for climate trending, such as the average-then-retrieve approach (e.g., Huang et al. 2010, https://doi.org/10.1029/2009JD012766; Kato et al. 2014, https://doi.org/10.1175/JCLI-D-13-00566.1) be of relevance here?
L129. A critical methodological question here is whether ERA5 profiles can appropriately represent the vertical resolution of AIRS retrieval or its ability in measuring such quantities as CAPE. I'm surprised that this important consideration is completely neglected. The paper would benefit from a proper discussion of this issue or an assessment of the impacts, for instance, by using the AIRS averaging kernels.
L155/L319. What "neglected processes" are referred to here?
L205. A relevant question of interest is how much the 1:30am/pm overpass times of AIRS limit the convection prediction. Or, what different times would be more useful? Can this study provide some insights?
L292. The poor prediction of the temperature of the upper layers (fig. 7c) is surprising. Why?
L321. Is this really surprising since diabatic heating tends to be balanced by adiabatic motion at grid (or large) scales? And, again, clarify what's "neglected". Another philosophical question here is that there is equal amount (50%) of unexplained variance – this raises many questions:
how does this limit the usefulness of the prediction, and in what situations - for example, what regions or weather systems are missed?
L447. Following this reasoning, shouldn't the analysis and comparison be limited to times prior to convection?