Motivated by the lack of research on land cover and dust activity in the Middle East, this study seeks to increase the understanding of the sensitivity of dust centers to climatic and surface conditions in this specific region. In this regard, we explore vegetation cover and dust emission interactions using 16-day long-term Normalized Difference Vegetation Index (NDVI) data and daily Aerosol Optical Depth (AOD) data from Moderate Resolution Imaging Spectroradiometer (MODIS) and conduct spatiotemporal and statistical analyses. Eight major dust hotspots were identified based on long-term AOD data (2000–2019). Despite the relatively uniform climate conditions prevailing throughout the region during the study period, there is considerable spatial variability in interannual relationships between AOD and NDVI. Three subsets of periods (2000–2006, 2007–2013, 2014–2019) were examined to assess periodic spatiotemporal changes. In the second period (2007–2013), AOD increased significantly (6% to 32%) across the studied hotspots, simultaneously with a decrease in NDVI (−0.9% to −14.3%) except in Yemen−Oman. Interannual changes over 20 years showed a strong relationship between reduced vegetation cover and increased dust intensity. The correlation between NDVI and AOD (−0.63) for the cumulative region confirms the significant effect of vegetation canopy on annual dust fluctuations. According to the results, changes in vegetation cover have an essential role in dust storm fluctuations. Therefore, this factor must be regarded along with wind speed and other climate factors in Middle East dust hotspots related to research and management efforts.

Airborne measurements of wind speed and direction, temperature, and relative humidity are critical due to their importance for atmospheric processes. Field campaigns with multiple coordinated aircraft present challenges when combining data from each platform due to atmospheric heterogeneity. To confront this issue, this work intercompares for the first time in situ measurements from the Turbulent Air Motion Measurement System (TAMMS) of horizontal winds and temperature and a diode laser hygrometer (relative humidity) deployed on an HU-25 Falcon flying mostly within the marine boundary layer to an independent set of measurements from dropsondes launched from a higher-flying King Air. Leveraging data from 162 joint flights over the northwest Atlantic from these two spatially coordinated aircraft during the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) campaign in winter and summer seasons between 2020–2022, a total of 555 pairs of Falcon–dropsonde data points are identified within 30 km horizontal separation, with minimal vertical separation (usually < 1 m), and within 15 min. This analysis is based on the following range of conditions experienced: altitude = ∼ 0.1–5 km, temperature = −19–27 °C, relative humidity = 1 %–100 %, and wind speed = 0.2–42 m s⁻¹. Based on scatterplots, correlation coefficients, and mean (in situ–dropsonde) error (ME), intercomparisons reveal good agreement for wind speed (r = 0.95, ME = 0.21 ± 1.68 m s⁻¹), $u/v$ wind components (r ∼ 0.96–0.97, ME ∼ 0.03–0.16 (± 1.62–1.67) m s⁻¹), wind direction (r = 0.94, ME = 0.00 ± 0.22 based on cosine of direction angles), temperature (r = 0.99, ME = 0.00 ± 0.71 °C), and relative humidity (r = 0.91, ME = −3.86 ± 10.74 %). Sensitivity analysis shows that binning data into categories of horizontal separation distance, clear versus cloud, winter versus summer, altitude range, and terciles of the values for examined variables did not yield major changes except for relative humidity where there was more deviation, especially above 70 %. The effect of statistics was examined by relaxing the vertical separation distance criteria to expand the number of pairs to over 360 000, without much difference in intercomparison metrics. The effect of averaging more points for each instrument in the final 555 pairs was also shown to lead to minimal change in agreement. Overall, these results provide confidence in both the performance of the measurement techniques compared and combining dropsonde data with in situ data from a separate coordinated aircraft for ACTIVATE, which has relevance to other campaigns with multiple coordinated aircraft conducting similar types of measurements.