Modern-Era Retrospective Analysis For Research And Applications Version 2 Research Articles

Using Hybrid Deep Learning Models to Predict Dust Storm Pathways with Enhanced Accuracy

As a potential consequence of climate change, the intensity and frequency of dust storms are increasing. A dust storm arises when strong winds blow loose dust from a dry surface, transporting soil particles from one place to another. The environmental and human health impacts of dust storms are substantial. Accordingly, studying the monitoring of this phenomenon and predicting its pathways for early decision making and warning are vital. This study employs deep learning methods to predict dust storm pathways. Specifically, hybrid CNN-LSTM and ConvLSTM models have been proposed for the 24 h-ahead prediction of dust storms in the region under study. The Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) product that includes the dust particles and the meteorological information, such as surface wind speed and direction, relative humidity, surface air temperature, and skin temperature, is used to train the proposed models. These contextual features are selected utilizing the random forest feature importance method. The results indicate an improvement in the performance of both models by considering the contextual information. Moreover, a 0.2 increase in the Kappa coefficient criterion across all forecast hours indicates the CNN-LSTM model outperforms the ConvLSTM model when contextual information is considered.

Recent Trends in Extreme Temperature Events Across the Contiguous United States

ABSTRACTExtreme heat events (EHEs) are becoming prevalent across the globe and are a major factor in terms of temperature‐related mortality in the United States (US). In this study, we compare trends in extreme temperature events (ETEs) across the Contiguous US, from 3 reanalysis products, namely: European Centre for Medium‐Range Weather Forecasts Reanalysis Version 5 (ERA5), Modern‐Era Retrospective Analysis for Research and Applications Version 2 (MERRA2) and North American Regional Reanalysis (NARR). We focused on the trends (1980–2022) in absolute extreme heat and cold events (ECE) as well as seasonally relative extreme heat and cold events (REHE and RECE). ETEs are defined based on a duration‐intensity metric calculated from excess apparent temperature factors, based on the exceedance of apparent temperature beyond local percentile thresholds while incorporating an acclimatisation factor. Our results show that the reanalysis data sets generally produced consistent climatology of ETEs, though with some inconsistencies in their number and spatial distribution. ETE trends in the study region are spatially heterogeneous and were more consistent between MERRA2 and ERA5. Nonetheless, all data sets agree that the frequency of EHEs is significantly increasing in the western parts of the US, whereas REHEs are significantly increasing in the southern parts. The highest increase in the frequency of EHEs occurs in southern California and Nevada, while REHE trends are maximal in Florida. RECEs are significantly decreasing more in spatial scale and magnitude than ECEs, especially towards the coastal regions. The highest decrease in RECEs is in Florida peninsula, southern California and Nevada. The data sets show inconsistency in ECE trends. Trends in excess temperature factors further indicated that extreme cold conditions are decreasing faster compared to the increasing trends of extreme heat conditions. Our findings highlight the need for improving the monitoring of ETEs across the US and for policies that mitigate the impact of ETEs on biological systems.

A Reanalysis Precipitation Integration Method Utilizing the Generalized Three-Cornered Hat Approach and High-Resolution, Gauge-Based Datasets

The development of high-precision, long-term, hourly-scale precipitation data is essential for understanding extreme precipitation events. Reanalysis systems are particularly promising for this type of research due to their long-term observations and wide spatial coverage. This study aims to construct a more robust precipitation dataset by integrating three widely-used reanalysis precipitation estimates: Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA2), Climate Forecast System Reanalysis (CFSR), and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). A novel integration method based on the generalized three-cornered hat (TCH) approach is employed to quantify uncertainties in these products. To enhance accuracy, the high-density daily precipitation data from the Asian Precipitation-Highly-Resolved Observation Data Integration Towards Evaluation (APHRODITE) dataset is used for correction. Results show that the TCH method effectively identifies seasonal and spatial uncertainties across the products. The TCH-weighted product (TW), calculated using signal-to-noise ratio weighting, outperforms the original reanalysis datasets across various watersheds and seasons. After correction with APHRODITE data, the enhanced integrated product (ATW) significantly improves accuracy, making it more suitable for extreme precipitation event analysis. Quantile mapping was applied to assess the ability of TW and ATW to represent extreme precipitation. Both products showed improved accuracy in regional average precipitation, with ATW demonstrating superior improvement. This integration method provides a robust approach for refining reanalysis precipitation datasets, contributing to more reliable hydrological and climate studies.

Impact of anthropogenic and natural constituents on particulate matter in oasis cities on the southern margin of the Taklimakan Desert based on MERRA-2 and multi-site ground observation

Dust storms are associated with large amounts of particulate matter (PM) that can have adverse effects on health and the environment. The contribution of natural dust to atmospheric PM levels represents a scientific challenge, especially in areas with close proximity to dust sources. To improve our knowledge in this area, we collected 300 PM filter-samples across five oasis cities on the southern edge of the Taklimakan Desert in 2016, and applied the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) data to quantify the contribution of natural dust to PM on sand dust and non-sand dust days. Research has shown that the particle size distribution of mineral dust in Taklimakan dust aerosols was relatively uniform. On sand dust days, CaSO4 and Na2SO4 were directly emitted from the surface, leading to higher sulfate concentrations in PM. While the increase in the proportions of organic carbon - OC1 and OC2 was attributed to the mixed anthropogenic emissions. On non-sand dust days, SO42− concentration was significantly affected by emissions from anthropogenic sources. The automotive composite emissions and coal combustion were the main sources of elemental carbon - EC1. Based on the results of MERRA-2 reanalysis data, natural dust constituted 53 % and 70 % of the total PM2.5 and PM10, respectively. In this study, we have quantified the contribution of natural dust under different weather conditions and identifying potential sources of PM in oasis cities. This study provides support for the assessment of natural dust and PM prevention in oasis cities.

Tropical tropospheric ozone distribution and trends from in situ and satellite data

Abstract. Tropical tropospheric ozone (TTO) is important for the global radiation budget because the longwave radiative effect of tropospheric ozone is higher in the tropics than midlatitudes. In recent decades the TTO burden has increased, partly due to the ongoing shift of ozone precursor emissions from midlatitude regions toward the Equator. In this study, we assess the distribution and trends of TTO using ozone profiles measured by high-quality in situ instruments from the IAGOS (In-Service Aircraft for a Global Observing System) commercial aircraft, the SHADOZ (Southern Hemisphere ADditional OZonesondes) network, and the ATom (Atmospheric Tomographic Mission) aircraft campaign, as well as six satellite records reporting tropical tropospheric column ozone (TTCO): TROPOspheric Monitoring Instrument (TROPOMI), Ozone Monitoring Instrument (OMI), OMI/Microwave Limb Sounder (MLS), Ozone Mapping Profiler Suite (OMPS)/Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), Cross-track Infrared Sounder (CrIS), and Infrared Atmospheric Sounding Interferometer (IASI)/Global Ozone Monitoring Experiment 2 (GOME2). With greater availability of ozone profiles across the tropics we can now demonstrate that tropical India is among the most polluted regions (e.g., western Africa, tropical South Atlantic, Southeast Asia, Malaysia and Indonesia), with present-day 95th percentile ozone values reaching 80 nmol mol−1 in the lower free troposphere, comparable to midlatitude regions such as northeastern China and Korea. In situ observations show that TTO increased between 1994 and 2019, with the largest mid- and upper-tropospheric increases above India, Southeast Asia, and Malaysia and Indonesia (from 3.4 ± 0.8 to 6.8 ± 1.8 nmol mol−1 decade−1), reaching 11 ± 2.4 and 8 ± 0.8 nmol mol−1 decade−1 close to the surface (India and Malaysia–Indonesia, respectively). The longest continuous satellite records only span 2004–2019 but also show increasing ozone across the tropics when their full sampling is considered, with maximum trends over Southeast Asia of 2.31 ± 1.34 nmol mol−1 decade−1 (OMI) and 1.69 ± 0.89 nmol mol−1 decade−1 (OMI/MLS). In general, the sparsely sampled aircraft and ozonesonde records do not detect the 2004–2019 ozone increase, which could be due to the genuine trends on this timescale being masked by the additional uncertainty resulting from sparse sampling. The fact that the sign of the trends detected with satellite records changes above three IAGOS regions, when their sampling frequency is limited to that of the in situ observations, demonstrates the limitations of sparse in situ sampling strategies. This study exposes the need to maintain and develop high-frequency continuous observations (in situ and remote sensing) above the tropical Pacific Ocean, the Indian Ocean, western Africa, and South Asia in order to estimate accurate and precise ozone trends for these regions. In contrast, Southeast Asia and Malaysia–Indonesia are regions with such strong increases in ozone that the current in situ sampling frequency is adequate to detect the trends on a relatively short 15-year timescale.

Subseasonal Forecast Skill of Evaporative Demand, Soil Moisture, and Flash Drought Onset from Two Dynamic Models over the Contiguous United States

Abstract Flash droughts are rapidly developing subseasonal climate extreme events that are manifested as suddenly decreased soil moisture, driven by increased evaporative demand and/or sustained precipitation deficits. Over each climate region in the contiguous United States (CONUS), we evaluated the forecast skill of weekly root-zone soil moisture (RZSM), evaporative demand (ETo), and relevant flash drought (FD) indices derived from two dynamic models [Goddard Earth Observing System model V2p1 (GEOS-V2p1) and Global Ensemble Forecast System version 12 (GEFSv12)] in the Subseasonal Experiment (SubX) project between years 2000 and 2019 against three reference datasets: Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), North American Land Data Assimilation System, phase 2 (NLDAS-2), and GEFSv12 reanalysis. The ETo and its forcing variables at lead week 1 have moderate-to-high anomaly correlation coefficient (ACC) skill (∼0.70–0.95) except downwelling shortwave radiation, and by weeks 3–4, predictability was low for all forcing variables (ACC < 0.5). RZSM (0–100 cm) for model GEFSv12 showed high skill at lead week 1 (∼0.7–0.85 ACC) in the High Plains, West, Midwest, and South CONUS regions when evaluated against GEFSv12 reanalysis but lower skill against MERRA-2 and NLDAS-2 and ACC skill are still close to 0.5 for lead weeks 3–4, better than ETo forecasts. GEFSv12 analysis has not been evaluated against in situ observations and has substantial RZSM anomaly differences when compared to NLDAS-2, and our analysis identified GEFSv12 reforecast prediction limit, which can maximally achieve ACC ∼0.6 for RZSM forecasts between lead weeks 3 and 4. Analysis of major FD events reveals that GEFSv12 reforecast inconsistently captured the correct location of atmospheric and RZSM anomalies contributing to FD onset, suggesting the needs for improving the dynamic models’ assimilation and initialization procedures to improve subseasonal FD predictability. Significance Statement Flash droughts are rapidly developing climate extremes which reduce soil moisture through enhanced evaporative demand and precipitation deficits, and these events can have large impacts on the ecosystem and crop health. We evaluated the subseasonal forecast skill of soil moisture and evaporative demand against three reanalysis datasets and found that evaporative demand skill was similar between forecasts and reanalyses while soil moisture skill is dependent on the reference dataset. Skill of evaporative demand decreases rapidly after week 1, while soil moisture skill declines more slowly after week 1. Case studies for the 2012, 2017, and 2019 United States flash droughts identified that forecasts could capture rapid decreases in soil moisture in some regions but not consistently, implying that long-lead forecasts still need improvements before being used in early warning systems. Improvements in flash drought predictability at longer lead times will require less biased initial conditions, better model parameterizations, and improved representations of large-scale teleconnections.

Assessing the accuracy and reliability of satellite-derived precipitation products in the Kosi River basin (India).

The present research endeavors to examine the effectiveness of four gridded precipitation datasets, namely Integrated Multi-satellite Retrievals for GPM (IMERG), Tropical Precipitation Measuring Mission (TRMM), Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), with the observed rainfall data of eight rain gauge stations of India Meteorological Department (IMD) from 2001 to 2019 in Kosi River basin, India. Various statistical metrics, contingency tests, trend analysis, and rainfall anomaly index were utilized at daily, monthly, seasonal, and annual time scales. The categorical metrics namely probability of detection (POD) and false alarm ratio (FAR) indicate that MERRA-2 and IMERG datasets have the highest level of concurrence with the observed daily data. Statistical analysis of gridded datasets with observed dataset of IMD showed that the performance of the IMERG dataset is better than MERRA-2, PERSIANN, and TRMM datasets with "very good" coefficient of determination (R2) and Nash-Sutcliffe Efficiency values for monthly data. Trend analysis of gridded seasonal data of IMERG showed similar trends of observed seasonal data whereas other dataset differs. IMERG also performed well in identifying wet and dry years based on annual data. Discrepancies of the satellite sensor in capturing the precipitation have also been discussed. Thus, the IMERG dataset can be effectively used for hydro-meteorological and climatological investigations in cases of lack of observed datasets.

Exploring the factors responsible for aerosol asymmetric trends over Indo-Gangetic Plain using remote sensing observations

The present study investigates the influencing factors responsible for the asymmetry in aerosol optical depth (AOD) trends using long-term datasets (2003–2019) over western and eastern Indo-Gangetic Plain (IGP) regions during the pre-monsoon season. Analysis from Modern-Era Retrospective Analysis for Research and Applications Version-2 (MERRA-2) for different aerosols illustrates that dust aerosols dominate over the western IGP (W-IGP), while sulphate and carbonaceous aerosols (black carbon (BC) and organic carbon (OC)) majorly contributed to the total AOD over the eastern IGP (E-IGP). Our study reveals a significant decline in AOD over the W-IGP, while a rising trend over E-IGP from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. A dipole pattern in AOD trends over IGP indicates the aerosol loading from combined effects of various natural and anthropogenic emissions under favourable meteorological conditions over the W-IGP and E-IGP, respectively. Furthermore, the declining AOD trend over W-IGP is mainly attributed to increased pre-monsoonal rainfall, which supports the wet deposition, increases soil moisture, thus reducing soil erodibility, and correlates strongly with meteorological factors. The rising AOD trend over the E-IGP appears to be influenced by increased anthropogenic emissions (i.e., BC, OC, and sulphate) from the industrialization of the region, decreased rainfall, and enhanced westerly-induced advection of aerosols from W-IGP. Our study indicates that the regional meteorological variables and anthropogenic sources influence changes in the AOD trends over the IGP region.

Monitoring biomass burning aerosol transport using CALIOP observations and reanalysis models: a Canadian wildfire event in 2019

Abstract. In May–June 2019, smoke plumes from wildfires in Alberta, Canada, were advected all the way to Europe. To analyze the evolution of the plumes and to estimate the amount of smoke aerosols transported to Europe, retrievals from the spaceborne lidar CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization) were used. The plumes were located with the help of a trajectory analysis, and the masses of smoke aerosols were retrieved from the CALIOP observations. The accuracy of the CALIOP mass retrievals was compared with the accuracy of ground-based lidars/ceilometer near the source in North America and after the long-range transport in Europe. Overall, CALIOP and the ground-based lidars/ceilometer produced comparable results. Over North America the CALIOP layer mean mass was 30 % smaller than the ground-based estimates, whereas over southern Europe that difference varied between 12 % and 43 %. Finally, the CALIOP mass retrievals were compared with simulated aerosol concentrations from two reanalysis models: MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) and CAMS (Copernicus Atmospheric Monitoring System). The simulated total column aerosol optical depths (AODs) and the total column mass concentration of smoke agreed quite well with CALIOP observations, but the comparison of the layer mass concentration of smoke showed significant discrepancies. The amount of smoke aerosols in the model simulations was consistently smaller than in the CALIOP retrievals. These results highlight the limitations of such models and more specifically their limitation to reproduce properly the smoke vertical distribution. They indicate that CALIOP is a useful tool monitoring smoke plumes over secluded areas, whereas reanalysis models have difficulties in representing the aerosol mass in these plumes. This study shows the advantages of spaceborne aerosol lidars, e.g., being of paramount importance to monitor smoke plumes, and reveals the urgent need of future lidar missions in space.

Historical footprints and future projections of global dust burden from bias-corrected CMIP6 models

Dust aerosols significantly affect the Earth’s climate, not only as a source of radiation, but also as ice nuclei, cloud condensation nuclei and thus affect CO2 exchange between the atmosphere and the ocean. However, there are large deviations in dust model simulations due to limited observations on a global scale. Based on ten initial Climate Models Intercomparison Project Phase Six (CMIP6) models, the multi-model ensemble (MME) approximately underestimates future changes in global dust mass loading (DML) by 7–21%, under four scenarios of shared socioeconomic pathways (SSPs). Therefore, this study primarily constrains the CMIP6 simulations under various emission scenarios by applying an equidistant cumulative distribution function (EDCDF) method combined with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) datasets based on observation assimilation. We find that the results (19.0–26.1 Tg) for 2000–2014 are closer to MERRA2 (20.0–24.8 Tg) than the initial simulations (4.4–37.5 Tg), with model deviation reduced by up to 75.6%. We emphasize that the DML during 2081–2100 is expected to increase significantly by 0.023 g m–2 in North Africa and the Atlantic region, while decreasing by 0.006 g m–2 in the Middle East and East Asia. In comparison with internal variability and scenario uncertainty, model uncertainty accounts for more than 70% of total uncertainty. When bias correction is applied, model uncertainty significantly decreases by 65% to 90%, resulting in a similar variance contribution to internal variability.

Evaluation of MERRA-2 and MODIS C6.1 aerosol products over Indonesia

Atmospheric aerosols play an essential role in atmospheric chemistry and climate. Amidst the issue of climate change, analysis of at-mospheric aerosols based on satellite and reanalysis is needed to estimate radiative forcing by aerosols, completing the spatial gap obtained from ground-based observation data. This research aims to evaluate the satellite and reanalysis aerosol products including Modern-Era Retrospective analy-sis for Research and Applications Version 2 (MERRA-2) and Moderate Res-olution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) over In-donesia in the recent two decades, 2001 - 2020. The long-term performance of MERRA-2 and MODIS C6.1 is evaluated using the Aerosol Robotic Net-work (AERONET)-derived AOD. In general, the results show that the cor-relation of the two data models is good. The average correlation between AOD data obtained from AERONET to MERRA-2 and MODIS C6.1 was 0.8 and 0.5 at all locations, respectively. Meanwhile, the average RMSE val-ues for MERRA-2 and MODIS C6.1 were 0.24 and 0.26 at all locations, respectively. This study successfully assessed the performance of MERRA-2 and MODIS C6.1 in Indonesia as an initial study. This initial study verifies that MERRA-2 and MODIS C6.1 can be used to complement surface data in Indonesia.

Influence of pollution control measures on the reduction of black carbon in an urban site of megacity, Tianjin, China based on ground-monitored and MERRA-2 reanalysis data

The concentration of particulate matter (PM) has been reduced significantly with the implementation of air pollution control plans in Tianjin. However, as an important component of PM that can lead to global warming and adverse health effects, the influence of pollution control measures (PCM) on black carbon (BC) has been less studied. In this study, ten years of BC concentration satellite-based reanalysis data were collected from MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2), and their reliability was verified using ground-monitored BC data. Using the proposed Kolmogorov-Zurbenko and artificial neural network (KZ-ANN) model, the influences of meteorology and emission measures were separated. The results indicated that the overall meteorological conditions were not conducive to BC diffusion, especially in autumn and winter with low temperature, surface solar radiation, boundary layer height, and high atmospheric pressure, all of which increased the BC concentration. This study also found that although a significant reduction in BC emissions was observed in Tianjin (the total emissions of BC in 2020 dropped by 52 % compared with the level in 2013), the change in emission-influenced BC was relatively low (the concentration of emission-influenced BC in 2022 dropped by only 2.39 % compared to that in 2013). The reduction of emission-influenced BC concentration during the air pollution prevention control and action plan (APPC) was higher than the level during of the three-year action plan for winning the blue sky defense war (abbreviated as the Blue Sky Defense War). In addition, the lockdown measures during the Corona Virus Disease 2019 (COVID-19) did not have beneficial effect on the reduction of emission-influenced BC concentration. This phenomenon can be explained by the long-range transport of BC from surrounding areas, which was also proven by the results of the backward trajectory analysis. Therefore, efforts on emissions reduction in Tianjin were diminished. It is necessary to cooperate with the governments in surrounding areas to implement joint BC control measures, especially in autumn and winter.

Long-Term MERRA-2 Reanalysis Data Indicate Atmospheric Environmental Changes for Three Major Concentrating-Solar-Power-Plant Project Areas in Xinjiang, China

The characteristics, distributions, and trends of the aerosol optical depth (AOD) and dust aerosol optical depth (DAOD) of three major concentrating solar power (CSP)-plant project areas (Hami, Turpan, and Ruoqiang) in Xinjiang, China were investigated and analyzed during 1980–2022 using the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) reanalysis products. The monthly variation, seasonal variation, inter-annual variation, distributions of AOD and DAOD, and proportions of dust in the aerosols in these three CSP-plant project areas were computed and analyzed. Overall, the annual mean AOD at 550 nm in the Turpan project area was the highest (0.20–0.36), while Ruoqiang had the lowest annual mean AOD at 550 nm (0.13–0.30), and the annual mean AOD at 550 nm in Hami was distributed between 0.17 and 0.33. After 2010, the change in the rate of the annual mean AOD showed an overall downward trend in Hami and Ruoqiang, indicating that the atmospheric environmental changes in both areas were more favorable for the operation of CSP plants. In the project areas of Hami, Turpan, and Ruoqiang, more than 90% of the AOD values were mainly in ranges 0.10–0.30, 0.10–0.35, and 0.05–0.30, respectively. As expected, the AOD values in spring and summer were significantly higher than those in autumn and winter in the three study areas. In spring, the dust contents (i.e., ratios of DAOD to AOD) were the highest, accounting for 64% (Hami), 67% (Turpan), and 69% (Ruoqiang) of the total aerosol contents. In all three areas, the proportions of dust in aerosols in spring have shown an increasing trend since 2000, suggesting that the negative impact of the dust on the power generation efficiency in these areas has gradually been increasing. Therefore, it is recommended that the CSP plants in Hami, Turpan, and Ruoqiang develop a strategy for cleaning heliostats, especially in spring, to reduce the impact of dust adhesion on the efficiency of the CSP plants.

JRA55 is the best reanalysis representing observed near-surface wind speeds over India

Reanalyses are often regarded as the most reliable datasets that give a comprehensive picture of meteorological parameters all over the globe. They are the first choice of dataset for researchers interested in analysing meteorological variables. Recognizing their importance, several agencies worldwide produce reanalysis datasets using different methods. This study aims to reduce the uncertainty associated with choosing among these reanalyses by identifying the best reanalysis representing the near-surface wind speeds over India. For this purpose, 10 m wind speed data from six reanalyses are used: National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis (NCEP1), National Centers for Environmental Prediction-Department of Energy reanalysis (NCEP2), Japanese 55-year Reanalysis (JRA55), Indian Monsoon Data Assimilation and Analysis (IMDAA), European Centre for Medium-Range Weather Forecasts reanalysis version 5 (ERA5), and Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2). The evaluation is done by comparing the spatially-averaged reanalyses against the station observations obtained from National Centers for Environmental Information (NCEI) over seven homogenous climate zones of India for the 1980–2020 period. Results demonstrate wide divergence between the reanalyses and observations in terms of monthly mean statistics, inter-annual variability (IAV), and annual mean trend. Despite the differences, JRA55 best represents the trends, IAV, and shows the closest resemblance to observations. On an annual scale, the 10 m wind speed observations over India have a weak correlation with the Nino 3.4 index; however, they have a significant negative correlation with the Dipole Mode index (DMI), implying that SST anomalies over the Indian Ocean have a considerable impact on the weather and climate patterns of the Indian subcontinent. JRA55 closely reflects these relationships with large-scale ocean-atmospheric features as well. Overall, we conclude that JRA55 can be considered a proxy for 10 m wind speed observations over India.

The capabilities of the adjoint of GEOS-Chem model to support HEMCO emission inventories and MERRA-2 meteorological data

Abstract. The adjoint of the GEOS-Chem (Goddard Earth Observing System with Chemistry) model has been widely used to constrain the sources of atmospheric compositions. Here, we designed a new framework to facilitate emission inventory updates in the adjoint of the GEOS-Chem model. The major advantage of this new framework is good readability and extensibility, which allows us to support Harmonized Emissions Component (HEMCO) emission inventories conveniently and to easily add more emission inventories following future updates in GEOS-Chem forward simulations. Furthermore, we developed new modules to support MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) meteorological data, which allows us to perform long-term analyses with consistent meteorological data for the period 1979–present. The performances of the developed capabilities were evaluated with the following steps: (1) diagnostic outputs of carbon monoxide (CO) sources and sinks to ensure the correct reading and use of emission inventories, (2) forward simulations to compare the modeled surface and column CO concentrations among various model versions, (3) backward simulations to compare adjoint gradients of global CO concentrations to CO emissions with finite-difference gradients, and (4) observing system simulation experiments (OSSEs) to evaluate the model performance in 4D variational (4D-Var) assimilations. Finally, an example application of 4D-Var assimilation was presented to constrain anthropogenic CO emissions in 2015 by assimilating Measurement of Pollution in the Troposphere (MOPITT) CO observations. The capabilities developed in this work are important for better applications of the adjoint of the GEOS-Chem model in the future. These capabilities will be submitted to the standard GEOS-Chem adjoint code base for better development of the community of the adjoint of the GEOS-Chem model.

Impact of regional climate patterns on the biomass burning emissions and transport over Peninsular Southeast Asia, 2000-2019

The regional climate variability in peninsular Southeast Asia (PSEA) can influence springtime biomass burning (BB) aerosol emissions and associated transport patterns. To comprehend the interannual variation of regional climate and its impact on PSEA BB, a diagnostic analysis based on the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) dataset and Moderate Resolution Imaging Spectroradiometer (MODIS) observations from 2000 to 2019 has presented. Employing principal component, composite, and correlation analyses, this study identified four climatic factors governing the emission and transport of PSEA BB aerosols: (i) a low-level anticyclone (suppressed monsoon trough) in the Bay of Bengal, (ii) the relative strength of the anticyclone over the South China Sea, (iii) the Pacific subtropical high, and (iv) low-level westerlies from PSEA to Taiwan. Additionally, BB emissions and transport significantly correlate with the El Niño-Southern Oscillation (ENSO). In the El Niño year, increased anticyclones in the Bay of Bengal and South China Sea accompanied the stronger westerlies, which enhanced BB aerosol emission and transport. The diagnostic results of this study can contribute to a better understanding and improved model simulations of aerosol-climate interactions in South and Southeast Asian monsoon regions.

Validation of MERRA-2 AOT Modeling Data over China Using SIAVNET Measurement

The Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) Aerosol Optical Thickness (AOT) dataset is a consistent and comprehensive dataset combining observations from various satellite instruments and other sources with a numerical model, supporting climate studies, atmospheric modeling, air quality monitoring, and environmental research. Due to the uneven and sparse distribution of the Aerosol Robotic Network (AERONET) in China, the validation for the MERRA-2 AOT dataset over China is inadequate. The construction of the National Civil Space Infrastructure Satellite Aerosol Product Validation Network (SIAVNET) is helpful to compensate for MERRA-2 AOT dataset validation over China. The validation results show that the accuracy of the MERRA-2 AOT goes down along with the aerosol loading in the atmosphere increase. In general, when the AOT is less than 1.0, the slope can reach 0.712 with R2 = 0.584. The percentage of data pairs that fall within the GCOS minimum requirement is less than 60%. Research also shows that MERRA-2 has a lower simulation quality of AOT at high altitudes than at low altitudes in China. Additionally, MERRA-2’s AOT simulation quality varies by season. Simulated quality is worst in spring, improving in subsequent seasons. During the winter season, simulations are of the highest quality.

Multiparameter study for a new ground-based telescope in Egypt

ABSTRACT A multiparameter analysis was conducted to evaluate the impact of meteorological parameters, night sky brightness and seismic hazards on proposed sites for the new optical/infrared Egyptian astronomical telescope. The European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data set is used to obtain the following meteorological parameters: total cloud coverage fraction, precipitable water vapour, relative humidity, wind speed and direction, and air temperature. To estimate the aerosol optical depth, we used the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). Light pollution over the candidate sites was measured using the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band. In order to assess the seismic hazards for the candidate sites, the seismic input in terms of maximum acceleration and response spectra was computed using a physics-based ground motion approach to assess the seismic hazards and, consequently, the designation of a seismic-resistant structure for the proposed sites. Of the seven nominated sites, two sites are found to have the best measurements and might be considered as future sites for the new Egyptian astronomical telescope. The first site is located in the south of the Sinai peninsula, while the second site is located in the Red Sea mountains region.

Vortex preconditioning of the 2021 sudden stratospheric warming: barotropic–baroclinic instability associated with the double westerly jets

Abstract. This study explores the abrupt split of the polar vortex in the upper stratosphere prior to a recent sudden stratospheric warming event on 5 January 2021 (SSW21) and the mechanisms of vortex preconditioning by using the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) global reanalysis data. SSW21 is preceded by the highly distorted polar vortex that was initially displaced from the pole but eventually split at the onset date. Vortex splitting is most significant in the upper stratosphere (1 hPa altitude) accompanied by the anomalous growth of westward-propagating planetary waves (PWs) of zonal wavenumber (ZWN) 2 (WPW2). While previous studies have suggested the East Asian trough as a potential source for the abnormal WPW2 growth, the prominent westward-propagating nature cannot be explained satisfactorily by the upward propagation of the quasi-stationary ZWN2 fluxes in the troposphere. More importantly, WPW2 exhibits an obvious in situ excitation signature within the barotropically and baroclinically destabilized stratosphere, dominated by the easterlies descending from the stratopause containing the WPW2 critical levels. This suggests that the vortex split is attributed to the WPW2 generated in situ within the stratosphere via instability. Vortex destabilization is achieved as the double-jet structure consisting of a subtropical mesospheric core and a polar stratospheric core develops into SSW21 by encouraging the anomalous dissipation of the upward-propagating tropospheric ZWN1 PWs. This double-jet configuration is likely a favorable precursor for SSW onset, not only for the SSW21 but generally for most SSWs, through promoting the anomalous growth of unstable PWs as well as the enhancement of the tropospheric PW dissipation.

Comparisons of reanalysis and measured lower‐troposphere winds over a portion of equatorial Africa

AbstractDespite lower‐troposphere wind flow being a major control of rainfall in equatorial Africa, no studies have systematically compared winds from multiple reanalyses nor have attempted to validate the wind directions. Therefore, the objectives of this study are to assess differences in wind directions among multiple reanalysis products and compare reanalysis winds with wind measurements made at weather stations. The study region is western Uganda, part of the transition region between western equatorial Africa (WEA) and eastern equatorial Africa (EEA). Four‐times daily (i.e., 0Z, 6Z, 12Z and 18Z) 10‐m and 850‐hPa winds from 1980 to 2021 are obtained for ECMWF Reanalysis v5 (ERA5), Japan Meteorological Agency 55‐year Reanalysis (JRA55), Modern‐Era Retrospective analysis for Research and Applications Version 2 (MERRA2), NCEP‐NCAR Reanalysis 1 (R1) and NCEP/DOE Reanalysis II (R2). Wind measurements at 10 m and 850 hPa are obtained for six weather stations and two weather stations, respectively. Agreements between pairs of products and between measurements and reanalysis estimates are determined. In addition, differences between reanalyses and measurements with respect to wind vectors are calculated. Results show that the majority of reanalyses have western Uganda within the prevailing easterly flow over EEA and east of the prevailing westerly flow over WEA. Ten‐meter wind measurements also show easterly flow being prevalent throughout western Uganda. R1 is unique among the products due to a relatively large number of westerly days. However, much of the westerly flow is likely artificial, based on station data. MERRA2 has large easterly biases. JRA55 is much more accurate than the other products at reproducing the intra‐annual frequencies of wind directions. JRA55 and ERA5 are the least biased products based on the magnitudes of difference vectors. Therefore, it is recommended that JRA55 and ERA5 continue to be used in examinations of winds in western Uganda.