Atmospheric Infrared Sounder Research Articles

A Hurricane Initialization Scheme with 4DEnVAR Satellite Ozone and Bogus Data Assimilation (SOBDA) and Its Application: Case Study

The aim of this study is to joint assimilate the ozone product from the satellite Atmospheric Infrared Sounder (AIRS) and bogus data using the four-dimensional ensemble-variational (4DEnVar) method, and demonstrate the potential benefits of this initialization technique in improving hurricane forecasting through a case study. Firstly, the quality control scheme is employed to enhance the ozone product quality from the satellite AIRS; a bogus sea level pressure (SLP) at the hurricane center is constructed simultaneously based on Fujita’s mathematical model for subsequent assimilation. Secondly, a 4DEnVar satellite ozone and bogus data assimilation (SOBDA) model is established, incorporating an observation operator of satellite ozone that utilizes the relationship between satellite ozone and potential vorticity (PV) from the lower level of 400 hPa to the upper level of 50 hPa. Finally, several comparative experiments are performed to assess the influence of assimilating satellite ozone and/or bogus data, the 4DEnVAR method and four-dimensional variational (4D-Var) method, and ensemble size on hurricane prediction. It is found that assimilating satellite ozone and bogus data with the 4DEnVar method concurrently brings about significant alterations to the initial conditions (ICs) of the hurricane vortex, resulting in a more homogeneous and deeper vortex with a larger, warmer, and more humid core as opposed to assimilating only one type of data. As the duration of integration increases, the initial perturbations in the upper levels gradually propagate downwards, giving rise to significant disparities in the hurricane prediction when satellite ozone and/or bogus information is incorporated. The results demonstrate that utilizing the 4DEnVar approach to assimilate both satellite ozone and bogus data leads to the maximum enhancement in reducing track error and central SLP error of hurricane simulation throughout the entire 72 h forecasting period, compared to assimilating a single dataset. Furthermore, comparative experiments have indicated that the performance of 4DEnVar SOBDA in hurricane forecasting is influenced by the ensemble size. Generally, selecting an appropriate number of ensemble members can not only effectively improve the accuracy of hurricane prediction but can also significantly reduce the demand for computational resources relative to the 4D-Var method. This study can also serve as an advantageous technical reference for numerical applications of ozone products from other satellites and hurricane initialization.

The role of continental evapotranspiration on water vapour isotopic variability in the troposphere

ABSTRACT Rainforests play an important role in hydrological and carbon cycles, both at regional and global scales. They pump large quantities of moisture from the soil to the atmosphere and are major rainfall hotspots of the world. Satellite-observed stable water isotope ratios have played an essential role in determining sources of moisture in the atmosphere. Satellites provide information about the processes involving vapour transport in different zones of the world, identifying sources of rainfall and distinguishing moisture transport in monsoonal systems. This paper focuses on major rainforests of the world (Southern Amazon, Congo and Northeast India) to understand the role of continental evapotranspiration in influencing tropospheric water vapour. We have used satellite measurements of 1H2H16O/1H2 16O from Atmospheric InfraRed Sounder (AIRS), evapotranspiration (ET), solar-induced fluorescence (SIF), precipitation (P), atmospheric reanalysis-derived moisture flux convergence (MFC) and wind to discern the role of ET in influencing water vapour isotopes. A global map of the correlation between δ 2Hv and ET-P flux indicates that densely vegetated regions in the tropics show the highest positive correlation (r > 0.5). Using mixing models and observations of specific humidity and isotopic ratio over these forested regions, we discern the source of moisture in pre-wet and wet seasons.

Global Evaluation of the Fidelity of Clouds in the ECMWF Integrated Forecast System

AbstractWeather forecasting centers mainly assimilate infrared sounder data in clear‐conditions or in channels with their main sensitivity to the atmosphere well above the cloud tops. Sometimes channels with stronger cloud sensitivity are used in overcast conditions, but currently no cloud information is used from infrared sounders, and all‐sky assimilation approaches are still under development. However, cloudy radiances could already be used for validating the quality of clouds in forecasts. We illustrate this by comparing the brightness temperatures observed (obs) with AIRS (Atmospheric Infrared Sounder) to those calculated (cal) based on the clouds specified in the ECMWF (European Centre for Medium Range Weather Forecasting) Integrated Forecast System (IFS). Our analysis is based on a 12 hr ingest of AIRS data into the ECMWF assimilation system. We show that the standard deviation of (obs‐cal) using the 1,231 cm−1 atmospheric window channel is a metric of the fidelity of the clouds in the IFS. The global standard deviation of 5 K after accounting for likely space/time interpolation errors, appears to be dominated by clouds in the IFS which are not seen in the AIRS data, and vice versa. Our metric capitalizes on the unique sensitivity of infrared sounders to clouds for the routine monitoring of the fidelity of clouds in weather forecasts.

Integrating NASA Aqua AIRS in a Real‐Time NUCAPS Science‐To‐Applications System to Support Severe Weather Forecasting

AbstractIn recent years, National Oceanic and Atmospheric Administration (NOAA) Unique Combined Atmospheric Processing System (NUCAPS) hyperspectral infrared satellite sounding retrievals derived from Joint Polar Satellite System (JPSS) polar‐orbiting satellites have been documented as observations that add value to weather forecasting applications. NUCAPS is currently the operational algorithm delivering JPSS satellite sounding retrievals to the NOAA National Weather Service (NWS) and is based on the heritage Atmospheric Infrared Sounder (AIRS) Science Team algorithm for processing vertical temperature, moisture, and trace gas retrievals. For the Special Collection on “Twenty Years of Observations from AIRS,” we highlight the methodology we implemented to develop a prototype science‐to‐applications system to enable real‐time processing of AIRS satellite sounding retrievals through the NUCAPS algorithm (i.e., NUCAPS‐Aqua) to support weather forecasting applications. The addition of NUCAPS‐Aqua to experimental real‐time pathways alongside operational JPSS NUCAPS soundings, facilitated assessment of NUCAPS‐Aqua at the 2022 Hazardous Weather Testbed (HWT) Spring Experiment. Development of NUCAPS‐Aqua described in this technical report includes preservation of microwave observations and calculation of a‐priori regression coefficients. Additionally, the real‐time processing and challenges with implementing a science‐to‐applications system are discussed. Two illustrative pre‐convective forecasting examples analyzed by NWS forecasters during the 2022 HWT Spring Experiment are highlighted to demonstrate the benefit of NUCAPS‐Aqua as (a) special afternoon soundings, (b) an additional observation to assess temporal trends using multiple satellites, and (c) a complement to observational and model analysis.

Detail Enhancement of AIRS/AMSU Temperature and Moisture Profiles Using a 3D Deep Neural Network

Abstract In recent decades, spaceborne microwave and hyperspectral infrared sounding instruments have significantly benefited weather forecasting and climate science. However, existing retrievals of lower-troposphere temperature and humidity profiles have limitations in vertical resolution, and often cannot accurately represent key features such as the mixed-layer thermodynamic structure and the inversion at the planetary boundary layer (PBL) top. Because of the existing limitations in PBL remote sensing from space, there is a compelling need to improve routine, global observations of the PBL and enable advances in scientific understanding and weather and climate prediction. To address this, we have developed a new 3D deep neural network (DNN) that enhances detail and reduces noise in level 2 granules of temperature and humidity profiles from the Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit (AMSU) sounder instruments aboard NASA’s Aqua spacecraft. We show that the enhancement improves accuracy and detail including key features such as capping inversions at the top of the PBL over land, resulting in improved accuracy in estimations of PBL height.

Remote sensing of nighttime air quality over the megacity of Lahore, Pakistan

In recent years, air quality is deteriorating which causes severe impacts on the climate system, and human health, particularly in the mega-cities of Pakistan. As we enter an era of more health related issues, poor air quality and rapid climate change, therefore the implications for nighttime air quality need to be better understood for air quality improvement. In this paper, for the first time, the nighttime air quality of Lahore was assessed using data from Aerosol Robotic Network (AERONET), Atmospheric Infrared Sounder (AIRS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) over Lahore during the period April 2021–August 2022. The highest mean monthly Aerosol Optical Depth (AOD) and Water Vapour Content (WVC) of 1.29 and 5.3 cm were observed in July 2022, respectively, whereas the highest Angstrom Exponent (AE) value of 1.5 occurred in August 2022. Moreover, the nighttime aerosol classification using the association between AOD and AE reveals the dominance of Background Conditions (BC), Urban Industrial/Biomass Burning aerosols (UI/BB), Desert Dust (DD) and Mixed Type (MT) aerosols. Over a couple of previous years, Lahore has witnessed worse air quality mainly due to biomass burning, dust aerosols, industrial pollutants and fuel combustion. Therefore, our main focus was on DD and UI/BB aerosols along with their source regions. We have also used the HYSPLIT model to understand the transport of nighttime aerosols. Moreover, nighttime high concentrations of mean monthly Carbon Monoxide (CO) and Ozone (O3) were observed in March and May 2022, respectively.

Evaluation and Analysis of Remotely Sensed Water Vapor from the NASA VIIRS/SNPP Product in Mainland China Using GPS Data

Precipitable water vapor (PWV) is a vitally important factor in atmospheric circulation. PWV is significant for forecasting extreme weather and understanding the dynamics of climate change. Comprehensively evaluating the performance of newly proposed remotely sensed water vapor products is crucial for guaranteeing their suitability for futural PWV applications. In this study, the accuracy of the recently established remotely sensed water vapor product from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensor on the Suomi National Polar-orbiting Partnership (SNPP) (VIIRS-PWV) platform within various regions of mainland China was evaluated via the PWV from Global Positioning System (GPS) observations. The GPS-derived PWV from 231 stations of the Crustal Movement Observation Network of China (CMONOC) from 2012 to 2018 was obtained through precise point positioning (PPP) techniques. The results showed that the mean value of the correlation coefficient (CC), the mean bias (MB), and the root-mean-square error (RMSE) between the VIIRS-PWV and the GPS-PWV were 0.92, −1.6 mm, and 4.7 mm, respectively. These values were comparable with the results of the PWV data derived from the Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) products. This indicates that the VIIRS product could provide PWV data with satisfactory accuracy for large-area scientific applications. Moreover, the MB and RMSE of the differences between the GPS-PWV and VIIRS-PWV showed obvious seasonal variations. The VIIRS-PWV generally performed better in winter (with the MB and RMSE values of 0.1 mm and 2.3 mm) than in summer (with the MB and RMSE values of −4.4 and 7.0 mm). Analysis among different regions revealed that the Central South (CS) region of China attained the largest mean RMSE value of 6.3 mm, and the North West (NW) region attained the smallest mean RMSE value of 3.8 mm. In addition, the southern region of China obtained a mean RMSE value of 5.6 mm, while that for the northern region of China was 3.9 mm. This indicates that the VIIRS-PWV has better accuracy within the northern region of China than within the southern region.

Estimating the Impact of Assimilating Cirrus Cloud–Contaminated Hyperspectral Infrared Radiances for Numerical Weather Prediction

Abstract The assimilation of hyperspectral infrared sounders (HIS) observations aboard Earth-observing satellites has become vital to numerical weather prediction, yet this assimilation is predicated on the assumption of clear-sky observations. Using collocated assimilated observations from the Atmospheric Infrared Sounder (AIRS) and the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), it is found that nearly 7.7% of HIS observations assimilated by the Naval Research Laboratory Variational Data Assimilation System–Accelerated Representer (NAVDAS-AR) are contaminated by cirrus clouds. These contaminating clouds primarily exhibit visible cloud optical depths at 532 nm (COD532nm) below 0.10 and cloud-top temperatures between 240 and 185 K as expected for cirrus clouds. These contamination statistics are consistent with simulations from the Radiative Transfer for TOVS (RTTOV) model showing a cirrus cloud with a COD532nm of 0.10 imparts brightness temperature differences below typical innovation thresholds used by NAVDAS-AR. Using a one-dimensional variational (1DVar) assimilation system coupled with RTTOV for forward and gradient radiative transfer, the analysis temperature and moisture impact of assimilating cirrus-contaminated HIS observations is estimated. Large differences of 2.5 K in temperature and 11 K in dewpoint are possible for a cloud with COD532nm of 0.10 and cloud-top temperature of 210 K. When normalized by the contamination statistics, global differences of nearly 0.11 K in temperature and 0.34 K in dewpoint are possible, with temperature and dewpoint tropospheric root-mean-squared errors (RMSDs) as large as 0.06 and 0.11 K, respectively. While in isolation these global estimates are not particularly concerning, differences are likely much larger in regions with high cirrus frequency.

Investigation of the possible relationship between the occurrences of atmospheric gravity waves and equatorial Spread-F

Equatorial Spread F (ESF), is a manifestation linked to Atmospheric Gravity Waves (AGW). There have not been many studies to ascertain the extensive relationship between the occurrence of AGW and the occurrence of ESF. To evaluate the extent of their relationship, this study used data obtained with the aid of a satellite-borne Atmospheric Infrared Sounder (AIRS) and ionograms obtained using a Digisonde Portable Sounder (DPS-4) located at Jicamarca (geog. Lat. 11.950 ᵒS, long. 76.867 ᵒW and geomagnetic Lat. 2.27 ᵒS, Long. 4.15 ᵒW; Dip 2 ᵒN) during the year 2016. The results also suggest that whenever AGW, under the control of tidal winds, propagates into F-region heights such that both AGW and tidal wind structures act together, it could have great potential at influencing plasma instability growth rate. The result further shows that when AGW has a long enough wavelength to reach the F-region, it tends to influence the factors responsible for the occurrence of the Mixed Spread-F (MSF) type of ESF. MSF is observed to be predominant in occurrence whenever AGW and ESF occur simultaneously compared to the other two types of ESF. The coefficient of correlation between AGW and MSF ranged between 0.1 and 0.5, while for Range Spread-F and Frequency Spread-F, it ranged between ± 0.2. These levels of correlations show that AGW does not directly trigger ESF. The range of the correlation coefficient between AGW and MSF however tends to support the suggestion that AGW occurrence is capable of influencing the factors responsible for a type of ESF occurrence rather than triggering ESF occurrence altogether.

Assessment of AIRS Version 7 Temperature Profiles and Low-Level Inversions with GRUAN Radiosonde Observations in the Arctic

The extensive global climate observing system (GCOS) reference upper-air network (GRUAN) datasets provide a chance to validate newly released Atmospheric Infrared Sounder (AIRS) version 7 (v7) products over the Arctic. This manuscript reports on the analysis performed to evaluate errors from AIRS version 6 (v6) and v7 temperature profiles and to characterize the derived low-level temperature inversion (LLI) representativeness in the Arctic region. The AIRS averaging kernel, representing the AIRS measurement sensitivity, is applied to reduce the vertical resolution of the radiosonde profiles for comparison. Due to improved retrieval algorithms, v7 produces smaller biases in the troposphere and suppresses the cold bias in v6. Nevertheless, the profile-averaged root mean square error (RMSE) increased by over 30% in v7, particularly in the winter half-year when v7 showed a larger RMSE below 800 hPa. The AIRS temperature retrieval accuracy is primarily sensitive to surface type and cloud fraction. Compared to v6, v7 has less bias over frozen land and sea ice in different cloud fraction conditions. However, the RMSEs of v7 are more sensitive to the effective cloud fraction (ECF) and are highly influenced by a more significant contribution from nonfrozen land samples. Compared to the kernel-averaged radiosonde profiles, more than 80% of the temperature profiles from v6 and v7 accurately detect LLIs. The discreteness of the AIRS’s predefined pressure level results is consistent with the radiosondes only 65% of the time for LLI depth calculation. In contrast, the AIRS can obtain LLI intensity with a relatively high correlation (>0.9). With the AIRS temperature retrieval in the boundary layer further improved, it has the potential to be used as an independent LLI detector in the Arctic region.

Performance of AIRS ozone retrieval over the central Himalayas: use of ozonesonde and other satellite datasets

Abstract. Data from 242 ozonesondes launched from ARIES, Nainital (29.40∘ N, 79.50∘ E; 1793 m elevation), are used to evaluate the Atmospheric Infrared Sounder (AIRS) version 6 ozone profiles and total column ozone during the period 2011–2017 over the central Himalayas. The AIRS ozone products are analysed in terms of retrieval sensitivity, retrieval biases/errors, and ability to retrieve the natural variability in columnar ozone, which has not been done so far from the Himalayan region, having complex topography. For a direct comparison, averaging kernel information is used to account for the sensitivity difference between the AIRS and ozonesonde data. We show that AIRS has more minor differences from ozonesondes in the lower and middle troposphere and stratosphere with nominal underestimations of less than 20 %. However, in the upper troposphere and lower stratosphere (UTLS), we observe a considerable overestimation of the magnitude, as high as 102 %. The weighted statistical error analysis of AIRS ozone shows a higher positive bias and standard deviation in the upper troposphere of about 65 % and 25 %, respectively. Similarly to AIRS, the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-track Infrared Sounder (CrIS) are also able to produce ozone peak altitudes and gradients successfully. However, the statistical errors are again higher in the UTLS region, which are likely related to larger variability in ozone, lower ozone partial pressure, and inadequate retrieval information on the surface parameters. Furthermore, AIRS fails to capture the monthly variation in the total column ozone, with a strong bimodal variation, unlike unimodal variation seen in ozonesondes and the Ozone Monitoring Instrument (OMI). In contrast, the UTLS and the tropospheric ozone columns are in reasonable agreement. Increases in the ozone values of 5 %–20 % after biomass burning and during events of downward transport are captured well by AIRS. Ozone radiative forcing (RF) derived from total column ozone using ozonesonde data (4.86 mW m−2) matches well with OMI (4.04 mW m−2), while significant RF underestimation is seen in AIRS (2.96 mW m−2). The fragile and complex landscapes of the Himalayas are more sensitive to global climate change, and establishing such biases and error analysis of space-borne sensors will help us study the long-term trends and estimate accurate radiative budgets.

Analysis of spatiotemporal distribution of methane column concentration and emission source in permafrost area of Northeast China based on AIRS data

Northeast China has the largest high-latitude permafrost distribution zone in China. With the intensification of global warming, the carbon stored in the permafrost will gradually thaw and release in the form of methane gas to air, thus increasing the methane column concentration in the near-surface troposphere. However, at present, the spatiotemporal distribution and emission source of methane column concentration in the near-surface troposphere are not clear. In this paper, using the AIRS (Atmospheric Infrared Sounder) remote sensing data from Aqua satellite, we analyzed the distribution and change trend of the methane column concentration in the near-surface troposphere in Northeast China from 2003 to 2021, and combined with the national meteorological and field monitoring data, we studied the emission mechanism and emission source of surface methane in the permafrost area of Northeast China. Study results show that: the permafrost zone in Northeast China has a high methane emission capacity in four seasons, and the methane column concentration shows a significant double-peak seasonal variation. The first peak appears in summer (June to August) and the maximum appears in August, the second peak appears in winter (December to February) and the maximum appears in December. However, the maximum average growth rate of near-surface methane column concentration in Northeast China appeared in spring (5.378ppbv/a), the reason is that the carbon stored under the permafrost is gradually exposed and released in the form of methane. The emission sources include microbial action, methane transported by wetland groundwater, geological methane (metastable methane hydrate, steady-state methane hydrate and thermogenic methane produced in the deep underground or coal seams) stored in frozen layer. The study provide data and technical support for the estimation of carbon emissions in permafrost areas in Northeast China.

Climatic and Vegetation Response Patterns over South Africa during the 2010/2011 and 2015/2016 Strong ENSO Phases

El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon on Earth due to its ability to change the global atmospheric circulation which influences temperature and precipitation across the globe. In this study, we investigate the responses of climatic and vegetation parameters due to two strong ENSO phases, i.e., La Niña (2010/2011) and El Niño (2015/2016) in South Africa. The study aims to understand the influence of strong seasonal ENSO events on climatic and vegetation parameters over South Africa. Remote sensing data from the Global Precipitation Measurement (GPM), Moderate Resolution Imaging Spectroradiometer (MODIS), Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and Atmospheric Infrared Sounder (AIRS) was used. The relationship between precipitation, temperature, and Normalized Difference Vegetation Index (NDVI) were studied using Pearson’s correlation. Comparison between the La Niña, neutral year, and El Niño periods showed two interesting results: (1) higher precipitation from the south coast to the east coast of South Africa, with some low precipitation in the interior during the La Niña and El Niño periods, and (2) a drop in precipitation by ~46.6% was observed in the southwestern parts of South Africa during the La Niña and El Niño events. The study further showed that wind speed and wind direction were not impacted by strong ENSO events during the MAM, JJA and SON seasons, but the DJF season showed varying wind speeds, especially on the west coast, during both ENSO events. Overall, the Pearson’s correlation results clearly showed that the relationship between climatic parameters such as precipitation, temperature, and vegetation parameters such a NDVI is highly correlated while other parameters, such as wind speed and direction, are not. This study has provided new insights into the relationship between temperature, precipitation, and NDVI in South Africa; however, future work will include other climatic and vegetation parameters such as relative humidity and net longwave radiation.

A Study on the Retrieval of Ozone Profiles Using FY-3D/HIRAS Infrared Hyperspectral Data

Atmospheric ozone is a pollutant gas that has an important influence on the process of atmospheric radiation transmission and climate change. The Fengyun-3D (FY-3D) satellite Hyperspectral Infrared Atmospheric Sounder (HIRAS) has better spectral performance than other remote sensing payloads. Its observation radiation data contains abundant atmospheric vertical information, which can be used for ozone retrieval, but there are no ozone profile business products being generated at present. Therefore, for the mainland of Hong Kong, based on HIRAS infrared hyperspectral observation data, we used the traditional one-dimensional variational (1D-VAR) physical retrieval algorithm, combined with the radiative transfer model for TOVS (RTTOV), and selected the spectrum channel according to the optimal sensitive profile algorithm. The artificial neural network (ANN) algorithm was used to optimize the prior profiles, and the atmospheric ozone profile retrieval system was established. Finally, a set of ozone profile retrieval schemes suitable for FY-3D/HIRAS were summarized. We used ERA5 reanalysis data and World Ozone and Ultraviolet Radiation Data Centre (WOUDC) data to determine true values. The retrieval results were compared with Global Forecast System (GFS) forecast data, Ozone Mapping and Profile Suite (OMPS) ozone products, and Atmospheric Infrared Sounder (AIRS) ozone products. The results show that our ozone profile retrieval scheme makes up for the shortcomings of the conventional physical methods in some atmospheric pressure levels. The overall root-mean-square error (RMSE) of the ozone from the ground to the top of the stratosphere is within 30% on average, which was better than that for the GFS forecast data; the retrieval accuracy RMSE (%) was less than 20% in the pressure layer with the highest ozone concentration (15–25 hPa), which is better than that of OMPS ozone products and AIRS ozone products. The retrieval results prove that FY3D/HIRAS observation data allow ozone profile retrieval. This paper provides a reference for generating independent HIRAS ozone profile product data sets in business, and provides support for the subsequent application of Fengyun-3 series meteorological satellites in atmospheric parameter remote sensing.

Secondary Gravity Waves From the Stratospheric Polar Vortex Over ALOMAR Observatory on 12–14 January 2016: Observations and Modeling

AbstractWe analyze the gravity waves (GWs) observed by a Rayleigh lidar at the Arctic Lidar Observatory for Middle Atmosphere Research (ALOMAR) (16.08°E, 69.38°N) in Norway at z ∼ 20–85 km on 12–14 January 2016. These GWs propagate upward and downward away from zknee = 57 and 64 km at a horizontally‐displaced location with periods τr ∼ 5–10 hr and vertical wavelengths λz ∼ 9–20 km. Because the hodographs are distorted, we introduce an alternative method to determine the GW parameters. We find that these GWs are medium to large‐scale, and propagate north/northwestward with intrinsic horizontal phase speeds of ∼35–65 m/s. Since the GW parameters are similar above and below zknee, these are secondary GWs created by local body forces (LBFs) south/southeast of ALOMAR. We use the nudged HIAMCM (HIgh Altitude Mechanistic general Circulation Model) to model these events. Remarkably, the model reproduces similar GW structures over ALOMAR, with zknee = 58 and 66 km. The event #1 GWs are created by a LBF at ∼35°E, ∼60°N, and z ∼ 58 km. This LBF is created by the breaking and dissipation of primary GWs generated and amplified by the imbalance of the polar night jet below the wind maximum; the primary GWs for this event are created at z ∼ 25–35 km at 49–53°N. We also find that the HIAMCM GWs agree well with those observed by the Atmospheric InfraRed Sounder (AIRS) satellite, and that those AIRS GWs south and north of ∼50°N over Europe are mainly mountain waves and GWs from the polar vortex, respectively.

How adequately are elevated moist layers represented in reanalysis and satellite observations?

Abstract. We assess the representation of elevated moist layers (EMLs) in ERA5 reanalysis, the Infrared Atmospheric Sounding Interferometer (IASI) L2 retrieval Climate Data Record (CDR) and the Atmospheric Infrared Sounder (AIRS)-based Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS)-Aqua L2 retrieval. EMLs are free-tropospheric moisture anomalies that typically occur in the vicinity of deep convection in the tropics. EMLs significantly affect the spatial structure of radiative heating, which is considered a key driver for meso-scale dynamics, in particular convective aggregation. To our knowledge, the representation of EMLs in the mentioned data products has not been explicitly studied – a gap we start to address in this work. We assess the different datasets' capability of capturing EMLs by collocating them with 2146 radiosondes launched from Manus Island within the western Pacific warm pool, a region where EMLs occur particularly often. We identify and characterise moisture anomalies in the collocated datasets in terms of moisture anomaly strength, vertical thickness and altitude. By comparing the distributions of these characteristics, we deduce what specific EML characteristics the datasets are capturing well and what they are missing. Distributions of ERA5 moisture anomaly characteristics match those of the radiosonde dataset quite well, and remaining biases can be removed by applying a 1 km moving average to the radiosonde profiles. We conclude that ERA5 is a suitable reference dataset for investigating EMLs. We find that the IASI L2 CDR is subject to stronger smoothing than ERA5, with moisture anomalies being on average 13 % weaker and 28 % thicker than collocated ERA5 anomalies. The CLIMCAPS L2 product shows significant biases in its mean vertical humidity structure compared to the other investigated datasets. These biases manifest as an underestimation of mean moist layer height of about 1.3 km compared to the three other datasets that yields a general mid-tropospheric moist bias and an upper-tropospheric dry bias. Aside from these biases, the CLIMCAPS L2 product shows a similar, if not better, capability of capturing EMLs compared to the IASI L2 CDR. More nuanced evaluations of CLIMCAPS' capabilities may be possible once the underlying cause for the identified biases has been found and fixed. Biases found in the all-sky scenes do not change significantly when limiting the analysis to clear-sky scenes. We calculate radiatively driven vertical velocities ωrad derived from longwave heating rates to estimate the dynamical effect of the moist layers. Moist-layer-associated ωrad values derived from Global Climate Observing System Reference Upper-Air Network (GRUAN) soundings range between 2 and 3 hPa h−1, while mean meso-scale pressure velocities from the EUREC4A (Elucidating the Role of Clouds-Circulation Coupling in Climate) field campaign range between 1 and 2 hPa h−1, highlighting the dynamical significance of EMLs. Subtle differences in the representation of moisture and temperature structures in ERA5 and the satellite datasets create large relative errors in ωrad on the order of 40 % to 80 % with reference to GRUAN, indicating limited usefulness of these datasets to assess the dynamical impact of EMLs.

West Africa's moist convective environment as observed by the Atmospheric InfraRed Sounder (AIRS)

AbstractKnowledge of the seasonal positioning of the Intertropical Discontinuity (ITD) is critical to understanding seasonal moist convective processes and associated rainfall over West Africa. This study constitutes a new analysis of the seasonality of moist convection over West Africa, relative to the ITD, based on NASA's Atmospheric Infrared Sounder (AIRS) measurements from 2003 to 2018. Results show that AIRS resolves the seasonal march of the ITD, including its inherent diurnal‐scale variations. AIRS captures the north–south daytime skin temperature dipole around the ITD, with greater relative temperatures to the north, especially during March–August. In the vicinity of the nighttime ITD, AIRS profiles indicate increased instability that is characteristic of nocturnal thunderstorm propagation. For seven Ghana weather stations, we show that AIRS positive moisture and equivalent potential temperature anomalies coincide with observed thunderstorm days. On these thunderstorm days, the mean latitude of the AIRS‐derived ITD is displaced 3°, 0.2°, and 2° north of its DJF, MAM, and SON climatological positions, respectively, and 1.2° south in JJA. Among four common thunderstorm initiation indices considered, the K‐index is determined to be most skillful. The findings of this study contribute to the Global Challenges Research Fund, African Science for Weather Information and Forecasting Techniques project's mission to build local tropical weather forecasting capacity and capabilities in West Africa.

Monitoring and Quantification of Carbon Dioxide Emissions and Impact of Sea Surface Temperature on Marine Ecosystems as Climate Change Indicators in the Niger Delta Using Geospatial Technology

The Niger Delta marine environment has experienced a series of environmental disasters since the inception of oil and gas exploration, which can be attributed to climate change. Carbon dioxide (CO2) emissions and sea surface temperature (T) ties associated with burning fossil fuels, such as gas flaring, vehicular traffic, and marine vessel movement along the sea, are increasing. Using data extracted from the NASA Giovanni satellite’s Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS), this study mapped the carbon footprint and T along the coastline into the deep sea from 2003 to 2011, using ArcGIS software. The spatial distribution of CO2 and T concentrations determined by the inverse distance weighting (IDW) method reveals variations in the study area. The results show an increase in the quantity of the mean tropospheric CO2 from July 2003 to December 2011, from 374.5129 ppm to 390.7831 ppm annual CO2 emissions, which also reflects a continuous increase. The average Monthly sea surface temperature had a general increasing trend from 25.79 °C in July 2003 to 27.8 °C in December, with the Pearson correlation coefficient between CO2 and T indicating 50% strongly positive, 20% strongly negative, 20% weakly positive,and 10% weakly negative. CO2 levels, like temperature, follow a seasonal cycle, with a decrease during the wet season due to precipitation dissolving and plant uptake during the growing season, and then a rise during the dry season. Carbon capture and storage technologies must be implemented to benefit the marine ecosystem and human well-being.

A comparative assessment of air quality spatial distribution in Alagoas and Asia

The state of Alagoas in the Brazilian Northeast (NEB) has not yet been assessed for pollutant spatial distribution through satellite remote sensing and reanalysis data. This research aimed to analyse the state’s spatial distribution throughout 2022 by collecting and processing data from Atmospheric Infrared Sounder (AIRS) and Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2). The data were then interpolated and provided as a state map distribution by city or municipality for all the 102 towns in Alagoas. Additionally, the state is divided into three mesoregions (Coastal – “litoral”, Hinterland – “agreste”, and Arid – “sertão”), each with its specific climatology and characteristics. The pollutants chosen were Ozone (O3) (ozone), Carbon Monoxide (CO), Methane (CH4), and Particulate Matter 2.5 nm (PM2.5) and were displayed in a gradient state map from coastal to arid. The results were also compared to other case studies in Asia. The findings show that pollutants O3 and CH4 form a gradient from the coast to the state’s interior, which is associated with local effects and mesoscale circulation. In contrast, CO and PM2.5 directly relate to high concentrations of vehicles, industries, and agricultural production.

The Mission Support System (MSS v7.0.4) and its use in planning for the SouthTRAC aircraft campaign

Abstract. The Mission Support System (MSS) is an open source software package that has been used for planning flight tracks of scientific aircraft in multiple measurement campaigns during the last decade. It consists of three major components: a web map server located close to the model data storage site that is capable of producing a variety of 2-D figures from 4-D meteorological data; a client application capable of displaying the figures in combination with the planned flight track and an assortment of additional information; and a new collaboration server component that enables real-time collaboration of multiple remote parties. During the last decade, these components were constantly improved towards being simple to set up and use and being standard compliant. Here, we describe the use of MSS during the Southern Hemisphere Transport, Dynamics, and Chemistry–Gravity Waves (SouthTRAC-GW) measurement campaign in 2019. This campaign, based in Rio Grande, Argentina, used the German research aircraft HALO to investigate several scientific objectives related to the Southern Hemisphere chemistry and dynamics. We present the diverse data products offered by the MSS web map server dedicated to the campaign, which were derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) forecast data, Chemical Lagrangian Model of the Stratosphere (CLaMS) simulations, and Atmospheric Infrared Sounder (AIRS) near-real time brightness temperature measurements. As an example for how the MSS software is used in conjunction with the different data sets, we describe the planning of a single flight, which eventually took place on 12 September 2019, probing orographic gravity waves propagating up into the lower mesosphere.