An ozonesonde evaluation of spaceborne observations in the Andean tropics

Satellite observations of ozone in the tropics have feedback from in situ measurements at sea level stations, but the tropical Andes is a region that is yet to be included in systematic validations. In this work, ozonesondes launched from the equatorial Andes were used to evaluate total column ozone (TCO) measured by spaceborne sensors TROPOMI/S5P (2018–2021), GOME-2/MetOp-B, OMI/Aura, and OMPS/Suomi NPP (2014–2021). Likewise, we evaluated tropospheric column ozone (TrCO) measured by the first two. Additionally, we evaluated TCO and TrCO from reanalysis products MERRA-2 and CAMS-EAC4. Results indicate that TCO observations by OMPS/Suomi NPP produce the closest comparison to ozonesondes (− 0.2% mean difference) followed by OMI/Aura (+ 1.2% mean difference). Thus, they outperform the sensor with the highest spatial resolution of current satellite measurements, namely TROPOMI/S5P (+ 3.7% mean difference). This overprediction is similar to the one encountered for GOME-2/MetOp-B (+ 3.2% mean difference). A positive bias with respect to soundings was also identified in TrCO measured by TROPOMI/S5P (+ 32.5% mean difference). It was found that the climatology used by TROPOMI overpredicts ozone in the troposphere when compared with the mean of Andes measurements, while both data sets are essentially the same in the stratosphere. Regarding reanalysis products, MERRA-2 compares better to ozonesondes than CAMS, both for TCO and TrCO (mean differences are 1.9% vs. 3.3%, and 11.5% vs. 22.9%, respectively). Identifying spaceborne ozone measurements that currently perform the best over the region is relevant given the present conditions of rapidly changing atmospheric composition. At the same time, ozonesonde data in this work offer an opportunity to improve satellite observations in the Andean tropics, a challenging region for space measurements.

Comprehensive Characteristics of Summer Deep Convection over Tibetan Plateau and Its South Slope from the Global Precipitation Measurement Core Observatory

Observational data from the Global Precipitation Measurement (GPM) Core Observatory during four summers (2014–2017) has been used to investigate deep convection systems (DCSs) over the Tibetan Plateau (TP) and its south slope (SS). The frequency, geographical distribution diurnal variation, and vertical structure of DCSs over the TP and SS are compared among these two regions. The frequency of DCSs over the SS (0.98%) was far higher than over the TP (0.15%), suggesting that stronger DCSs occur to the east and south of the TP. The maximum number of DCS occurred in July and August. A clear diurnal variation in DCS was found over the whole region, DCSs over the TP and SS both have a greatest amplitude in the afternoon. The probability of DCSs from 1200 to 1800 local time (LT) was 76.3% and 44.1% over TP and SS respectively, whereas the probability of DCSs being generated from 2200 (LT) to 0600 on the next day LT was 0.03% and 33.1% over the TP and SS respectively. There was a very low frequency of DCSs over the TP during the night. Five special echo top heights were used to investigate the vertical structure of DCSs. DCSs over the TP were both weaker and smaller than those over the SS.

The Impact of Cloud Radiative Effects on the Tropical Tropopause Layer Temperatures

A single-column radiative-convective model (RCM) is a useful tool to investigate the physical processes that determine the tropical tropopause layer (TTL) temperature structures. Previous studies on the TTL using the RCMs, however, omitted the cloud radiative effects. In this study, we examine the impact of cloud radiative effects on the simulated TTL temperatures using an RCM. We derive the cloud radiative effects based on satellite observations, which show heating rates in the troposphere but cooling rates in the stratosphere. We find that the cloud radiative effect warms the TTL by as much as 2 K but cools the lower stratosphere by as much as −1.5 K, resulting in a thicker TTL. With (without) considering cloud radiative effects, we obtain a convection top of ≈167 hPa (≈150 hPa) with a temperature of ≈213 K (≈209 K), and a cold point at ≈87 hPa (≈94 hPa) with a temperature of ≈204 K (≈204 K). Therefore, the cloud radiative effects widen the TTL by both lowering the convection-top height and enhancing the cold-point height. We also examine the impact of TTL cirrus radiative effects on the RCM-simulated temperatures. We find that the TTL cirrus warms the TTL with a maximum temperature increase of ≈1.3 K near 110 hPa.

First Reprocessing of Southern Hemisphere ADditional OZonesondes (SHADOZ) Profile Records: 3. Uncertainty in Ozone Profile and Total Column

Reprocessed ozonesonde data from eight SHADOZ (Southern Hemisphere ADditional OZonesondes) sites have been used to derive the first analysis of uncertainty estimates for both profile and total column ozone (TCO). The ozone uncertainty is a composite of the uncertainties of the individual terms in the ozone partial pressure (PO3) equation, those being the ozone sensor current, background current, internal pump temperature, pump efficiency factors, conversion efficiency, and flow-rate. Overall, PO3 uncertainties (ΔPO3) are within 15% and peak around the tropopause (15±3km) where ozone is a minimum and ΔPO3 approaches the measured signal. The uncertainty in the background and sensor currents dominate the overall ΔPO3 in the troposphere including the tropopause region, while the uncertainties in the conversion efficiency and flow-rate dominate in the stratosphere. Seasonally, ΔPO3 is generally a maximum in the March-May, with the exception of SHADOZ sites in Asia, for which the highest ΔPO3 occurs in September-February. As a first approach, we calculate sonde TCO uncertainty (ΔTCO) by integrating the profile ΔPO3 and adding the ozone residual uncertainty, derived from the McPeters and Labow [2012] 1-σ ozone mixing ratios. Overall, ΔTCO are within ±15 DU, representing ~5-6% of the TCO. TOMS and OMI satellite overpasses are generally within the sonde ΔTCO. However, there is a discontinuity between TOMS v8.6 (1998-2004/09) and OMI (2004/10-2016) TCO on the order of 10DU that accounts for the significant 16DU overall difference observed between sonde and TOMS. By comparison, the sonde-OMI absolute difference for the eight stations is only ~4DU.